diff --git a/.gitignore b/.gitignore
index 4a0faa5..2a50671 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,15 +1,30 @@
-# Editor
+.idea
+cmake-build-debug/*
+3rdparty/opencv/*
+3rdparty/ncnn-bak/*
+3rdparty/MNN-bak/*
+build_android/*
+*.zip
+3rdparty/*
+full_models/*
+images/tmp/*
+.DS_Store
+build/
+install/
+build/
+.xmake
+tools/pack/pack_elixir_mb01/
+examples/*
+doxygen/
+build/
+.vscode/
+3rdparty_hyper_inspire_op
+*.so
+__pycache__
+build/
+dist/
+hyperlpr3.egg-info/
+venv/
+*.pyc
 *~
 *.swp
-.vscode
-
-# Build files
-Prj-Linux/lpr/TEST_*
-Prj-Linux/*build*/
-*.pyc
-/Prj-PHP/build
-
-/Prj-ROS/build
-/Prj-ROS/devel
-/Prj-ROS/logs
-/Prj-ROS/.catkin_tools
\ No newline at end of file
diff --git a/.idea/HyperLPR.iml b/.idea/HyperLPR.iml
deleted file mode 100644
index 6711606..0000000
--- a/.idea/HyperLPR.iml
+++ /dev/null
@@ -1,11 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<module type="PYTHON_MODULE" version="4">
-  <component name="NewModuleRootManager">
-    <content url="file://$MODULE_DIR$" />
-    <orderEntry type="inheritedJdk" />
-    <orderEntry type="sourceFolder" forTests="false" />
-  </component>
-  <component name="TestRunnerService">
-    <option name="PROJECT_TEST_RUNNER" value="Unittests" />
-  </component>
-</module>
\ No newline at end of file
diff --git a/.idea/misc.xml b/.idea/misc.xml
index 6c993b7..669dc1b 100644
--- a/.idea/misc.xml
+++ b/.idea/misc.xml
@@ -1,4 +1,8 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
-  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.5" project-jdk-type="Python SDK" />
+  <component name="CMakeWorkspace" PROJECT_DIR="$PROJECT_DIR$" />
+  <component name="XMakeProjectSettings">
+    <option name="currentArchitecture" value="x86_64" />
+    <option name="workingDirectory" value="$PROJECT_DIR$" />
+  </component>
 </project>
\ No newline at end of file
diff --git a/.idea/modules.xml b/.idea/modules.xml
index bb3c4fd..e19e225 100644
--- a/.idea/modules.xml
+++ b/.idea/modules.xml
@@ -2,7 +2,7 @@
 <project version="4">
   <component name="ProjectModuleManager">
     <modules>
-      <module fileurl="file://$PROJECT_DIR$/.idea/HyperLPR.iml" filepath="$PROJECT_DIR$/.idea/HyperLPR.iml" />
+      <module fileurl="file://$PROJECT_DIR$/.idea/ZephyrLPR.iml" filepath="$PROJECT_DIR$/.idea/ZephyrLPR.iml" />
     </modules>
   </component>
 </project>
\ No newline at end of file
diff --git a/.idea/workspace.xml b/.idea/workspace.xml
index 53ab48d..07669b9 100644
--- a/.idea/workspace.xml
+++ b/.idea/workspace.xml
@@ -1,43 +1,43 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
+  <component name="CMakeRunConfigurationManager" shouldGenerate="true" shouldDeleteObsolete="true">
+    <generated>
+      <config projectName="HyperLPR3-Source" targetName="hyperlpr3" />
+    </generated>
+  </component>
+  <component name="CMakeSettings">
+    <configurations>
+      <configuration PROFILE_NAME="Debug" ENABLED="true" CONFIG_NAME="Debug" />
+    </configurations>
+  </component>
   <component name="ChangeListManager">
-    <list default="true" id="c514fefb-e579-4297-b908-55de31256ade" name="Default" comment="">
-      <change beforePath="$PROJECT_DIR$/benchmark.py" afterPath="" />
-      <change beforePath="$PROJECT_DIR$/config.json" afterPath="" />
-      <change beforePath="$PROJECT_DIR$/hyperlpr/pipline.py" afterPath="$PROJECT_DIR$/hyperlpr/pipline.py" />
-      <change beforePath="$PROJECT_DIR$/hyperlpr/recognizer.py" afterPath="$PROJECT_DIR$/hyperlpr/recognizer.py" />
-      <change beforePath="$PROJECT_DIR$/hyperlpr/segmentation.py" afterPath="$PROJECT_DIR$/hyperlpr/segmentation.py" />
-      <change beforePath="$PROJECT_DIR$/hyperlpr/typeDistinguish.py" afterPath="$PROJECT_DIR$/hyperlpr/typeDistinguish.py" />
-      <change beforePath="$PROJECT_DIR$/hyperlpr_py3/pipline.py" afterPath="$PROJECT_DIR$/hyperlpr_py3/pipline.py" />
-      <change beforePath="$PROJECT_DIR$/hyperlpr_py3/recognizer.py" afterPath="$PROJECT_DIR$/hyperlpr_py3/recognizer.py" />
-      <change beforePath="$PROJECT_DIR$/hyperlpr_py3/segmentation.py" afterPath="$PROJECT_DIR$/hyperlpr_py3/segmentation.py" />
-      <change beforePath="$PROJECT_DIR$/hyperlpr_py3/typeDistinguish.py" afterPath="$PROJECT_DIR$/hyperlpr_py3/typeDistinguish.py" />
-      <change beforePath="$PROJECT_DIR$/model/plate_type.h5" afterPath="$PROJECT_DIR$/model/plate_type.h5" />
+    <list default="true" id="4be586f9-7a61-4585-994f-eaa32375497d" name="Changes" comment="">
+      <change beforePath="$PROJECT_DIR$/CMakeLists.txt" beforeDir="false" afterPath="$PROJECT_DIR$/CMakeLists.txt" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/Prj-Android/app/build.gradle" beforeDir="false" afterPath="$PROJECT_DIR$/Prj-Android/app/build.gradle" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/Prj-Android/app/release/app-release.apk" beforeDir="false" />
+      <change beforePath="$PROJECT_DIR$/Prj-Android/app/release/output-metadata.json" beforeDir="false" />
+      <change beforePath="$PROJECT_DIR$/Prj-Android/app/src/main/java/com/hyperai/hyperlpr_sdk_demo/MainActivity.java" beforeDir="false" afterPath="$PROJECT_DIR$/Prj-Android/app/src/main/java/com/hyperai/hyperlpr_sdk_demo/MainActivity.java" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/Prj-Android/build.gradle" beforeDir="false" afterPath="$PROJECT_DIR$/Prj-Android/build.gradle" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/Prj-Python/hyperlpr3/config/settings.py" beforeDir="false" afterPath="$PROJECT_DIR$/Prj-Python/hyperlpr3/config/settings.py" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/Prj-Python/hyperlpr3/inference/multitask_detect.py" beforeDir="false" afterPath="$PROJECT_DIR$/Prj-Python/hyperlpr3/inference/multitask_detect.py" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/Prj-Python/hyperlpr3/inference/pipeline.py" beforeDir="false" afterPath="$PROJECT_DIR$/Prj-Python/hyperlpr3/inference/pipeline.py" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/Prj-Python/hyperlpr3/inference/recognition.py" beforeDir="false" afterPath="$PROJECT_DIR$/Prj-Python/hyperlpr3/inference/recognition.py" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/cpp/src/context_module/hyper_lpr_context.cpp" beforeDir="false" afterPath="$PROJECT_DIR$/cpp/src/context_module/hyper_lpr_context.cpp" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/resource/models/r2_mobile/rpv3_mdict_160_r3.mnn" beforeDir="false" afterPath="$PROJECT_DIR$/resource/models/r2_mobile/rpv3_mdict_160_r3.mnn" afterDir="false" />
     </list>
-    <option name="EXCLUDED_CONVERTED_TO_IGNORED" value="true" />
-    <option name="TRACKING_ENABLED" value="true" />
     <option name="SHOW_DIALOG" value="false" />
     <option name="HIGHLIGHT_CONFLICTS" value="true" />
     <option name="HIGHLIGHT_NON_ACTIVE_CHANGELIST" value="false" />
     <option name="LAST_RESOLUTION" value="IGNORE" />
   </component>
-  <component name="FileEditorManager">
-    <leaf>
-      <file leaf-file-name="pipline.py" pinned="false" current-in-tab="true">
-        <entry file="file://$PROJECT_DIR$/hyperlpr_py3/pipline.py">
-          <provider selected="true" editor-type-id="text-editor">
-            <state relative-caret-position="90">
-              <caret line="153" column="0" lean-forward="false" selection-start-line="153" selection-start-column="0" selection-end-line="153" selection-end-column="0" />
-              <folding />
-            </state>
-          </provider>
-        </entry>
-      </file>
-    </leaf>
+  <component name="ClangdSettings">
+    <option name="formatViaClangd" value="false" />
   </component>
+  <component name="ExecutionTargetManager" SELECTED_TARGET="CMakeBuildProfile:Debug" />
   <component name="FileTemplateManagerImpl">
     <option name="RECENT_TEMPLATES">
       <list>
+        <option value="CMakeLists.txt" />
         <option value="Python Script" />
       </list>
     </option>
@@ -45,127 +45,108 @@
   <component name="Git.Settings">
     <option name="RECENT_GIT_ROOT_PATH" value="$PROJECT_DIR$" />
   </component>
-  <component name="IdeDocumentHistory">
-    <option name="CHANGED_PATHS">
-      <list>
-        <option value="$PROJECT_DIR$/demo_py3.py" />
-      </list>
-    </option>
+  <component name="GitSEFilterConfiguration">
+    <file-type-list>
+      <filtered-out-file-type name="LOCAL_BRANCH" />
+      <filtered-out-file-type name="REMOTE_BRANCH" />
+      <filtered-out-file-type name="TAG" />
+      <filtered-out-file-type name="COMMIT_BY_MESSAGE" />
+    </file-type-list>
   </component>
-  <component name="ProjectFrameBounds" extendedState="6">
-    <option name="x" value="493" />
-    <option name="y" value="84" />
-    <option name="width" value="1085" />
-    <option name="height" value="704" />
+  <component name="OCResolveContextSettings">
+    <option name="configuration" value="0-Debug-CAPISample" />
   </component>
-  <component name="ProjectView">
-    <navigator currentView="ProjectPane" proportions="" version="1">
-      <flattenPackages />
-      <showMembers />
-      <showModules />
-      <showLibraryContents />
-      <hideEmptyPackages />
-      <abbreviatePackageNames />
-      <autoscrollToSource />
-      <autoscrollFromSource />
-      <sortByType />
-      <manualOrder />
-      <foldersAlwaysOnTop value="true" />
-    </navigator>
-    <panes>
-      <pane id="Scratches" />
-      <pane id="Scope" />
-      <pane id="ProjectPane">
-        <subPane>
-          <expand>
-            <path>
-              <item name="HyperLPR" type="b2602c69:ProjectViewProjectNode" />
-              <item name="HyperLPR" type="462c0819:PsiDirectoryNode" />
-            </path>
-            <path>
-              <item name="HyperLPR" type="b2602c69:ProjectViewProjectNode" />
-              <item name="HyperLPR" type="462c0819:PsiDirectoryNode" />
-              <item name="hyperlpr_py3" type="462c0819:PsiDirectoryNode" />
-            </path>
-          </expand>
-          <select />
-        </subPane>
-      </pane>
-    </panes>
+  <component name="ProjectId" id="2JZnjSwoRDsATe87YQzqJ7YkiGg" />
+  <component name="ProjectLevelVcsManager" settingsEditedManually="true" />
+  <component name="ProjectViewState">
+    <option name="hideEmptyMiddlePackages" value="true" />
+    <option name="showLibraryContents" value="true" />
   </component>
-  <component name="RunDashboard">
-    <option name="ruleStates">
-      <list>
-        <RuleState>
-          <option name="name" value="ConfigurationTypeDashboardGroupingRule" />
-        </RuleState>
-        <RuleState>
-          <option name="name" value="StatusDashboardGroupingRule" />
-        </RuleState>
-      </list>
-    </option>
+  <component name="PropertiesComponent">
+    <property name="RunOnceActivity.OpenProjectViewOnStart" value="true" />
+    <property name="RunOnceActivity.ShowReadmeOnStart" value="true" />
+    <property name="RunOnceActivity.cidr.known.project.marker" value="true" />
+    <property name="WebServerToolWindowFactoryState" value="false" />
+    <property name="cf.first.check.clang-format" value="false" />
+    <property name="cidr.known.project.marker" value="true" />
+    <property name="cmake.loaded.for.project" value="true" />
+    <property name="last_opened_file_path" value="$PROJECT_DIR$/resource/models/r2_mobile" />
+    <property name="nodejs_package_manager_path" value="npm" />
+    <property name="settings.editor.selected.configurable" value="com.jetbrains.python.configuration.PyActiveSdkModuleConfigurable" />
   </component>
-  <component name="ShelveChangesManager" show_recycled="false">
-    <option name="remove_strategy" value="false" />
+  <component name="RecentsManager">
+    <key name="CopyFile.RECENT_KEYS">
+      <recent name="$PROJECT_DIR$/resource/models/r2_mobile" />
+      <recent name="$PROJECT_DIR$/command" />
+      <recent name="$PROJECT_DIR$/cpp/samples" />
+      <recent name="$PROJECT_DIR$/tools/rknn_export_repo" />
+      <recent name="$PROJECT_DIR$/resource/images" />
+    </key>
+    <key name="MoveFile.RECENT_KEYS">
+      <recent name="$PROJECT_DIR$/Prj-Python" />
+      <recent name="$PROJECT_DIR$/cpp/src/nn_implementation_module/classification" />
+      <recent name="$PROJECT_DIR$/cpp/src/model_load" />
+      <recent name="$PROJECT_DIR$/resource/images" />
+      <recent name="$PROJECT_DIR$/cpp/src" />
+    </key>
   </component>
-  <component name="SvnConfiguration">
-    <configuration />
+  <component name="RunManager">
+    <configuration default="true" type="CLionExternalRunConfiguration" factoryName="Application" REDIRECT_INPUT="false" ELEVATE="false" USE_EXTERNAL_CONSOLE="false" PASS_PARENT_ENVS_2="true">
+      <method v="2">
+        <option name="CLION.EXTERNAL.BUILD" enabled="true" />
+      </method>
+    </configuration>
+    <configuration default="true" type="CMakeRunConfiguration" factoryName="Application" REDIRECT_INPUT="false" ELEVATE="false" USE_EXTERNAL_CONSOLE="false" PASS_PARENT_ENVS_2="true">
+      <method v="2">
+        <option name="com.jetbrains.cidr.execution.CidrBuildBeforeRunTaskProvider$BuildBeforeRunTask" enabled="true" />
+      </method>
+    </configuration>
+    <configuration name="hyperlpr3" type="CMakeRunConfiguration" factoryName="Application" REDIRECT_INPUT="false" ELEVATE="false" USE_EXTERNAL_CONSOLE="false" PASS_PARENT_ENVS_2="true" PROJECT_NAME="HyperLPR3-Source" TARGET_NAME="hyperlpr3" CONFIG_NAME="Debug">
+      <method v="2">
+        <option name="com.jetbrains.cidr.execution.CidrBuildBeforeRunTaskProvider$BuildBeforeRunTask" enabled="true" />
+      </method>
+    </configuration>
   </component>
+  <component name="SpellCheckerSettings" RuntimeDictionaries="0" Folders="0" CustomDictionaries="0" DefaultDictionary="application-level" UseSingleDictionary="true" transferred="true" />
   <component name="TaskManager">
     <task active="true" id="Default" summary="Default task">
-      <changelist id="c514fefb-e579-4297-b908-55de31256ade" name="Default" comment="" />
-      <created>1591067075688</created>
+      <changelist id="4be586f9-7a61-4585-994f-eaa32375497d" name="Changes" comment="" />
+      <created>1672294609542</created>
       <option name="number" value="Default" />
       <option name="presentableId" value="Default" />
-      <updated>1591067075688</updated>
+      <updated>1672294609542</updated>
+      <workItem from="1672294615702" duration="31662000" />
+      <workItem from="1672907618661" duration="1860000" />
+      <workItem from="1675042522538" duration="41206000" />
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+      <workItem from="1676533389966" duration="46803000" />
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+      <workItem from="1677043986150" duration="4211000" />
+      <workItem from="1677050704713" duration="8926000" />
+      <workItem from="1677111778645" duration="5714000" />
+      <workItem from="1677139138915" duration="6553000" />
+      <workItem from="1677218101830" duration="1207000" />
+      <workItem from="1677220611638" duration="33000" />
+      <workItem from="1677457111481" duration="10241000" />
     </task>
     <servers />
   </component>
-  <component name="ToolWindowManager">
-    <frame x="65" y="-4" width="1855" height="940" extended-state="6" />
-    <layout>
-      <window_info id="Project" active="false" anchor="left" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="true" show_stripe_button="true" weight="0.24959569" sideWeight="0.5" order="0" side_tool="false" content_ui="combo" />
-      <window_info id="TODO" active="false" anchor="bottom" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.33" sideWeight="0.5" order="6" side_tool="false" content_ui="tabs" />
-      <window_info id="Event Log" active="false" anchor="bottom" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.33" sideWeight="0.5" order="-1" side_tool="true" content_ui="tabs" />
-      <window_info id="Run" active="false" anchor="bottom" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.33" sideWeight="0.5" order="2" side_tool="false" content_ui="tabs" />
-      <window_info id="Version Control" active="false" anchor="bottom" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.33" sideWeight="0.5" order="-1" side_tool="false" content_ui="tabs" />
-      <window_info id="Python Console" active="false" anchor="bottom" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.33" sideWeight="0.5" order="-1" side_tool="false" content_ui="tabs" />
-      <window_info id="Structure" active="false" anchor="left" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.25" sideWeight="0.5" order="1" side_tool="false" content_ui="tabs" />
-      <window_info id="Terminal" active="false" anchor="bottom" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.33" sideWeight="0.5" order="-1" side_tool="false" content_ui="tabs" />
-      <window_info id="Debug" active="false" anchor="bottom" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.4" sideWeight="0.5" order="3" side_tool="false" content_ui="tabs" />
-      <window_info id="Favorites" active="false" anchor="left" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.33" sideWeight="0.5" order="-1" side_tool="true" content_ui="tabs" />
-      <window_info id="Cvs" active="false" anchor="bottom" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.25" sideWeight="0.5" order="4" side_tool="false" content_ui="tabs" />
-      <window_info id="Hierarchy" active="false" anchor="right" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.25" sideWeight="0.5" order="2" side_tool="false" content_ui="combo" />
-      <window_info id="Message" active="false" anchor="bottom" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.33" sideWeight="0.5" order="0" side_tool="false" content_ui="tabs" />
-      <window_info id="Commander" active="false" anchor="right" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.4" sideWeight="0.5" order="0" side_tool="false" content_ui="tabs" />
-      <window_info id="Find" active="false" anchor="bottom" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.33" sideWeight="0.5" order="1" side_tool="false" content_ui="tabs" />
-      <window_info id="Inspection" active="false" anchor="bottom" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.4" sideWeight="0.5" order="5" side_tool="false" content_ui="tabs" />
-      <window_info id="Ant Build" active="false" anchor="right" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.25" sideWeight="0.5" order="1" side_tool="false" content_ui="tabs" />
-    </layout>
-  </component>
-  <component name="VcsContentAnnotationSettings">
-    <option name="myLimit" value="2678400000" />
+  <component name="TypeScriptGeneratedFilesManager">
+    <option name="version" value="3" />
   </component>
-  <component name="XDebuggerManager">
-    <breakpoint-manager />
-    <watches-manager />
-  </component>
-  <component name="editorHistoryManager">
-    <entry file="file://$PROJECT_DIR$/demo_py3.py">
-      <provider selected="true" editor-type-id="text-editor">
-        <state relative-caret-position="90">
-          <caret line="5" column="39" lean-forward="false" selection-start-line="5" selection-start-column="39" selection-end-line="5" selection-end-column="39" />
-          <folding />
-        </state>
-      </provider>
-    </entry>
-    <entry file="file://$PROJECT_DIR$/hyperlpr_py3/pipline.py">
-      <provider selected="true" editor-type-id="text-editor">
-        <state relative-caret-position="90">
-          <caret line="153" column="0" lean-forward="false" selection-start-line="153" selection-start-column="0" selection-end-line="153" selection-end-column="0" />
-          <folding />
-        </state>
-      </provider>
-    </entry>
+  <component name="Vcs.Log.Tabs.Properties">
+    <option name="TAB_STATES">
+      <map>
+        <entry key="MAIN">
+          <value>
+            <State />
+          </value>
+        </entry>
+      </map>
+    </option>
+    <option name="oldMeFiltersMigrated" value="true" />
   </component>
 </project>
\ No newline at end of file
diff --git a/CMakeLists.txt b/CMakeLists.txt
new file mode 100644
index 0000000..19b6e39
--- /dev/null
+++ b/CMakeLists.txt
@@ -0,0 +1,161 @@
+cmake_minimum_required(VERSION 3.10.2)
+project(HyperLPR3-Source)
+
+set(CMAKE_CXX_STANDARD 11)
+
+set(LIBRARY_NAME hyperlpr3)
+
+option( BUILD_SHARE             "Build shared libs"                 ON )
+option( BUILD_SAMPLES           "Build samples demo"                OFF )
+option( BUILD_TEST              "Build unit-test exec"              OFF )
+
+set(PATH_3RDPARTY ${CMAKE_CURRENT_SOURCE_DIR}/3rdparty_hyper_inspire_op)
+
+# find all cpp file
+file(GLOB_RECURSE SRC_BUFFER_MODULE_FILES ${CMAKE_CURRENT_SOURCE_DIR}/cpp/src/buffer_module/*.cpp)
+file(GLOB_RECURSE SRC_CONTEXT_MODULE_FILES ${CMAKE_CURRENT_SOURCE_DIR}/cpp/src/context_module/*.cpp)
+file(GLOB_RECURSE SRC_NN_MODULE_FILES ${CMAKE_CURRENT_SOURCE_DIR}/cpp/src/nn_module/*.cpp)
+file(GLOB_RECURSE SRC_NN_IMPL_MODULE_FILES ${CMAKE_CURRENT_SOURCE_DIR}/cpp/src/nn_implementation_module/*.cpp)
+file(GLOB SRC_FILES ${CMAKE_CURRENT_SOURCE_DIR}/cpp/src/*.cpp)
+# local files
+set(SRC_INFERENCE_HELPER_LOCAL_FILES ${CMAKE_CURRENT_SOURCE_DIR}/cpp/src/inference_helper_module/inference_helper.cpp cpp/src/inference_helper_module/inference_helper_mnn.cpp)
+# include src header
+include_directories(${CMAKE_CURRENT_SOURCE_DIR}/cpp/src)
+
+
+set(SRC_C_CPP_FILES ${SRC_BUFFER_MODULE_FILES}
+        ${SRC_CONTEXT_MODULE_FILES}
+        ${SRC_LOADER_MODULE_FILES}
+        ${SRC_NN_MODULE_FILES}
+        ${SRC_NN_IMPL_MODULE_FILES}
+        ${SRC_SLOG_MODULE_FILES}
+        ${SRC_FILES}
+        ${SRC_INFERENCE_HELPER_LOCAL_FILES})
+
+# find all c file for c_api
+file(GLOB_RECURSE CAPI_CC_FILES ${CMAKE_CURRENT_SOURCE_DIR}/cpp/c_api/*.cc)
+include_directories(${CMAKE_CURRENT_SOURCE_DIR}/cpp/c_api)
+
+include_directories(${CMAKE_CURRENT_SOURCE_DIR}/cpp/platform)
+
+# must use mnn
+add_definitions("-DINFERENCE_HELPER_ENABLE_MNN")
+
+set(LINK_THIRD_LIBS pthread MNN)
+
+if (ANDROID)
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11")
+    set(CMAKE_C_FLAGS "${CMAKE_CXX_FLAGS}")
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}")
+    file(GLOB_RECURSE NATIVE_CPP_FILES ${CMAKE_CURRENT_SOURCE_DIR}/cpp/platform/jni/android/*.cpp)
+
+    find_package(OpenCV REQUIRED)
+    include_directories(${MNN_INCLUDE_DIRS})
+    link_directories(${MNN_LIBS})
+
+    add_library(${LIBRARY_NAME} SHARED ${NATIVE_CPP_FILES} ${SRC_C_CPP_FILES} ${CAPI_CC_FILES})
+    target_link_libraries(${LIBRARY_NAME} ${OpenCV_LIBS} jnigraphics log -Wl,--whole-archive MNN -Wl,--no-whole-archive)
+
+elseif(IOS)
+    # TODO: Not implement
+else ()
+    # Build Linux or MacOS
+    if (BUILD_CUDA)
+        # TODO: Not implement
+    elseif (BUILD_LINUX_ARM7)
+        message("[BUILD]Linux Armv7")
+        add_definitions("-DLINUX_ARM7")
+        set(PLAT linux-arm32)
+        # MNN Third party dependence
+        set(MNN_INCLUDE_DIRS ${PATH_3RDPARTY}/MNN-2.2.0/${PLAT}-static/include)
+        set(MNN_LIBS ${PATH_3RDPARTY}/MNN-2.2.0/${PLAT}-static/lib)
+        # OpenCV Third party dependence
+        set(OpenCV_DIR ${PATH_3RDPARTY}/opencv/opencv-linux-armhf/share/OpenCV)
+        set(OpenCV_STATIC_INCLUDE_DIR ${PATH_3RDPARTY}/opencv/opencv-linux-armhf/include/)
+        find_package(OpenCV REQUIRED)
+
+    elseif (BUILD_LINUX_ARM64)
+        # TODO: Not implement
+    else()
+        # Local Build
+        message("[BUILD]Local")
+        if (APPLE)
+            set(PLAT darwin)
+        else()
+            set(PLAT linux)
+        endif ()
+        # MNN Third party dependence
+        set(MNN_INCLUDE_DIRS ${PATH_3RDPARTY}/MNN-2.2.0/${PLAT}-static/include)
+        set(MNN_LIBS ${PATH_3RDPARTY}/MNN-2.2.0/${PLAT}-static/lib)
+        # OpenCV Third party dependence
+        set(OpenCV_DIR ${PATH_3RDPARTY}/opencv-4.5.1/${PLAT}/lib/cmake/opencv4)
+        set(OpenCV_STATIC_INCLUDE_DIR ${PATH_3RDPARTY}/opencv-4.5.1/${PLAT}/include/opencv4)
+        find_package(OpenCV REQUIRED)
+
+    endif()
+endif()
+
+if (NOT ANDROID)
+    # mnn
+    message(MNN_INCLUDE_DIRS=${MNN_INCLUDE_DIRS})
+    message(MNN_LIBS=${MNN_LIBS})
+    include_directories(${MNN_INCLUDE_DIRS})
+    link_directories(${MNN_LIBS})
+    # opencv
+    message(OpenCV_Version: ${OpenCV_VERSION})
+    message(libraries: ${OpenCV_LIBS})
+    message(libraries path: ${OpenCV_DIR})
+    message(OpenCV_INCLUDE_DIRS=${OpenCV_STATIC_INCLUDE_DIR})
+    include_directories(${OpenCV_STATIC_INCLUDE_DIR})
+
+    if (BUILD_SAMPLES)
+        # built samples exec
+        add_executable(ContextSample ${CMAKE_CURRENT_SOURCE_DIR}/cpp/samples/sample_context.cpp ${SRC_C_CPP_FILES})
+        target_link_libraries(ContextSample ${OpenCV_LIBS} ${LINK_THIRD_LIBS} )
+
+        add_executable(SplitDetSample ${CMAKE_CURRENT_SOURCE_DIR}/cpp/samples/sample_split_model.cpp ${SRC_C_CPP_FILES})
+        target_link_libraries(SplitDetSample ${OpenCV_LIBS} ${LINK_THIRD_LIBS} )
+
+        add_executable(CAPISample ${CMAKE_CURRENT_SOURCE_DIR}/cpp/samples/sample_capi.cpp ${SRC_C_CPP_FILES} ${CAPI_CC_FILES})
+        target_link_libraries(CAPISample ${OpenCV_LIBS} ${LINK_THIRD_LIBS} )
+
+    endif()
+
+    if (BUILD_TEST)
+        if (ENABLE_BENCHMARK_TEST)
+            message([Test]Open Benchmark Test)
+            add_definitions(-DENABLE_BENCHMARK_TEST)
+        endif ()
+#        # catch2
+        include_directories(${PATH_3RDPARTY}/catch2)
+        file(GLOB_RECURSE TEST_C_CPP_FILES ${CMAKE_CURRENT_SOURCE_DIR}/cpp/test/*.cpp)
+        add_executable(UnitTest ${TEST_C_CPP_FILES} ${SRC_C_CPP_FILES} ${CAPI_CC_FILES})
+        target_link_libraries(UnitTest ${OpenCV_LIBS} ${LINK_THIRD_LIBS})
+    endif()
+
+    if (BUILD_SHARE)
+        # build dynamic library
+        add_library(${LIBRARY_NAME} SHARED ${SRC_C_CPP_FILES} ${CAPI_CC_FILES})
+        target_link_libraries(${LIBRARY_NAME} ${OpenCV_LIBS} ${LINK_THIRD_LIBS} )
+        set(SRC_RKNN_RELATED ${CMAKE_CURRENT_SOURCE_DIR}/cpp/src/inference_helper_rknn.cpp)
+        set(SRC_C_CPP_FILES ${SRC_C_CPP_FILES} )
+
+    endif()
+
+endif()
+
+set(CMAKE_INSTALL_PREFIX ${PROJECT_BINARY_DIR}/install/hyperlpr3)
+message(CMAKE_INSTALL_PREFIX=${CMAKE_INSTALL_PREFIX})
+install(DIRECTORY resource DESTINATION ./)
+if (BUILD_SAMPLES)
+    install(TARGETS ContextSample DESTINATION ./bin)
+    install(TARGETS CAPISample DESTINATION ./bin)
+    install(TARGETS SplitDetSample DESTINATION ./bin)
+endif()
+if (BUILD_SHARE)
+    install(TARGETS ${LIBRARY_NAME} DESTINATION ./lib)
+    install(FILES ${CMAKE_CURRENT_SOURCE_DIR}/cpp/c_api/hyper_lpr_sdk.h DESTINATION ./include)
+endif ()
+if (BUILD_TEST)
+    install(TARGETS UnitTest DESTINATION test)
+endif ()
\ No newline at end of file
diff --git a/Dockerfile b/Dockerfile
deleted file mode 100644
index b82ed91..0000000
--- a/Dockerfile
+++ /dev/null
@@ -1,11 +0,0 @@
-#基于的基础镜像
-FROM python:3.6
-#代码添加到code文件夹，后面可以通过进入容器中看的
-ADD ./ /code
-# 设置code文件夹是工作目录
-WORKDIR /code
-# 安装支持
-RUN pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
-#当容器启动时，使用python3执行指定路径的py脚本
-CMD ["python", "/code/WebAPI.py"]
-
diff --git a/Font/platech.ttf b/Font/platech.ttf
deleted file mode 100644
index d66a970..0000000
Binary files a/Font/platech.ttf and /dev/null differ
diff --git a/HyperLPRLite.py b/HyperLPRLite.py
deleted file mode 100644
index 36b9d3c..0000000
--- a/HyperLPRLite.py
+++ /dev/null
@@ -1,155 +0,0 @@
-#coding=utf-8
-import cv2
-import numpy as np
-from keras import backend as K
-from keras.models import *
-from keras.layers import *
-
-chars = [u"京", u"沪", u"津", u"渝", u"冀", u"晋", u"蒙", u"辽", u"吉", u"黑", u"苏", u"浙", u"皖", u"闽", u"赣", u"鲁", u"豫", u"鄂", u"湘", u"粤", u"桂",
-             u"琼", u"川", u"贵", u"云", u"藏", u"陕", u"甘", u"青", u"宁", u"新", u"0", u"1", u"2", u"3", u"4", u"5", u"6", u"7", u"8", u"9", u"A",
-             u"B", u"C", u"D", u"E", u"F", u"G", u"H", u"J", u"K", u"L", u"M", u"N", u"P", u"Q", u"R", u"S", u"T", u"U", u"V", u"W", u"X",
-             u"Y", u"Z",u"港",u"学",u"使",u"警",u"澳",u"挂",u"军",u"北",u"南",u"广",u"沈",u"兰",u"成",u"济",u"海",u"民",u"航",u"空"
-             ]
-
-class LPR():
-    def __init__(self,model_detection,model_finemapping,model_seq_rec):
-        self.watch_cascade = cv2.CascadeClassifier(model_detection)
-        self.modelFineMapping = self.model_finemapping()
-        self.modelFineMapping.load_weights(model_finemapping)
-        self.modelSeqRec = self.model_seq_rec(model_seq_rec)
-
-    def computeSafeRegion(self,shape,bounding_rect):
-        top = bounding_rect[1] # y
-        bottom  = bounding_rect[1] + bounding_rect[3] # y +  h
-        left = bounding_rect[0] # x
-        right =   bounding_rect[0] + bounding_rect[2] # x +  w
-        min_top = 0
-        max_bottom = shape[0]
-        min_left = 0
-        max_right = shape[1]
-        if top < min_top:
-            top = min_top
-        if left < min_left:
-            left = min_left
-        if bottom > max_bottom:
-            bottom = max_bottom
-        if right > max_right:
-            right = max_right
-        return [left,top,right-left,bottom-top]
-
-    def cropImage(self,image,rect):
-        x, y, w, h = self.computeSafeRegion(image.shape,rect)
-        return image[y:y+h,x:x+w]
-
-    def detectPlateRough(self,image_gray,resize_h = 720,en_scale =1.08 ,top_bottom_padding_rate = 0.05):
-        if top_bottom_padding_rate>0.2:
-            print("error:top_bottom_padding_rate > 0.2:",top_bottom_padding_rate)
-            exit(1)
-        height = image_gray.shape[0]
-        padding =    int(height*top_bottom_padding_rate)
-        scale = image_gray.shape[1]/float(image_gray.shape[0])
-        image = cv2.resize(image_gray, (int(scale*resize_h), resize_h))
-        image_color_cropped = image[padding:resize_h-padding,0:image_gray.shape[1]]
-        image_gray = cv2.cvtColor(image_color_cropped,cv2.COLOR_RGB2GRAY)
-        watches = self.watch_cascade.detectMultiScale(image_gray, en_scale, 2, minSize=(36, 9),maxSize=(36*40, 9*40))
-        cropped_images = []
-        for (x, y, w, h) in watches:
-            x -= w * 0.14
-            w += w * 0.28
-            y -= h * 0.15
-            h += h * 0.3
-            cropped = self.cropImage(image_color_cropped, (int(x), int(y), int(w), int(h)))
-            cropped_images.append([cropped,[x, y+padding, w, h]])
-        return cropped_images
-
-    def fastdecode(self,y_pred):
-        results = ""
-        confidence = 0.0
-        table_pred = y_pred.reshape(-1, len(chars)+1)
-        res = table_pred.argmax(axis=1)
-        for i,one in enumerate(res):
-            if one<len(chars) and (i==0 or (one!=res[i-1])):
-                results+= chars[one]
-                confidence+=table_pred[i][one]
-        confidence/= len(results)
-        return results,confidence
-
-    def model_seq_rec(self,model_path):
-        width, height, n_len, n_class = 164, 48, 7, len(chars)+ 1
-        rnn_size = 256
-        input_tensor = Input((164, 48, 3))
-        x = input_tensor
-        base_conv = 32
-        for i in range(3):
-            x = Conv2D(base_conv * (2 ** (i)), (3, 3))(x)
-            x = BatchNormalization()(x)
-            x = Activation('relu')(x)
-            x = MaxPooling2D(pool_size=(2, 2))(x)
-        conv_shape = x.get_shape()
-        x = Reshape(target_shape=(int(conv_shape[1]), int(conv_shape[2] * conv_shape[3])))(x)
-        x = Dense(32)(x)
-        x = BatchNormalization()(x)
-        x = Activation('relu')(x)
-        gru_1 = GRU(rnn_size, return_sequences=True, kernel_initializer='he_normal', name='gru1')(x)
-        gru_1b = GRU(rnn_size, return_sequences=True, go_backwards=True, kernel_initializer='he_normal', name='gru1_b')(x)
-        gru1_merged = add([gru_1, gru_1b])
-        gru_2 = GRU(rnn_size, return_sequences=True, kernel_initializer='he_normal', name='gru2')(gru1_merged)
-        gru_2b = GRU(rnn_size, return_sequences=True, go_backwards=True, kernel_initializer='he_normal', name='gru2_b')(gru1_merged)
-        x = concatenate([gru_2, gru_2b])
-        x = Dropout(0.25)(x)
-        x = Dense(n_class, kernel_initializer='he_normal', activation='softmax')(x)
-        base_model = Model(inputs=input_tensor, outputs=x)
-        base_model.load_weights(model_path)
-        return base_model
-
-    def model_finemapping(self):
-        input = Input(shape=[16, 66, 3])  # change this shape to [None,None,3] to enable arbitraty shape input
-        x = Conv2D(10, (3, 3), strides=1, padding='valid', name='conv1')(input)
-        x = Activation("relu", name='relu1')(x)
-        x = MaxPool2D(pool_size=2)(x)
-        x = Conv2D(16, (3, 3), strides=1, padding='valid', name='conv2')(x)
-        x = Activation("relu", name='relu2')(x)
-        x = Conv2D(32, (3, 3), strides=1, padding='valid', name='conv3')(x)
-        x = Activation("relu", name='relu3')(x)
-        x = Flatten()(x)
-        output = Dense(2,name = "dense")(x)
-        output = Activation("relu", name='relu4')(output)
-        model = Model([input], [output])
-        return model
-
-    def finemappingVertical(self,image,rect):
-        resized = cv2.resize(image,(66,16))
-        resized = resized.astype(np.float)/255
-        res_raw= self.modelFineMapping.predict(np.array([resized]))[0]
-        res  =res_raw*image.shape[1]
-        res = res.astype(np.int)
-        H,T = res
-        H-=3
-        if H<0:
-            H=0
-        T+=2;
-        if T>= image.shape[1]-1:
-            T= image.shape[1]-1
-        rect[2] -=  rect[2]*(1-res_raw[1] + res_raw[0])
-        rect[0]+=res[0]
-        image = image[:,H:T+2]
-        image = cv2.resize(image, (int(136), int(36)))
-        return image,rect
-
-    def recognizeOne(self,src):
-        x_tempx = src
-        x_temp = cv2.resize(x_tempx,( 164,48))
-        x_temp = x_temp.transpose(1, 0, 2)
-        y_pred = self.modelSeqRec.predict(np.array([x_temp]))
-        y_pred = y_pred[:,2:,:]
-        return self.fastdecode(y_pred)
-
-    def SimpleRecognizePlateByE2E(self,image):
-        images = self.detectPlateRough(image,image.shape[0],top_bottom_padding_rate=0.1)
-        res_set = []
-        for j,plate in enumerate(images):
-            plate, rect  =plate
-            image_rgb,rect_refine = self.finemappingVertical(plate,rect)
-            res,confidence = self.recognizeOne(image_rgb)
-            res_set.append([res,confidence,rect_refine])
-        return res_set
diff --git a/HyperLprGUI.py b/HyperLprGUI.py
deleted file mode 100644
index f77756d..0000000
--- a/HyperLprGUI.py
+++ /dev/null
@@ -1,794 +0,0 @@
-"""
-Author: youngorsu
-Email : zhiyongsu@qq.com
-Last edited: 2018.1.29
-"""
-# coding=utf-8
-
-
-import sys
-import os
-from PyQt5.QtWidgets import (
-    QMainWindow,
-    QLabel,
-    QLineEdit,
-    QPushButton,
-    QHBoxLayout,
-    QVBoxLayout,
-    QGridLayout,
-    QTableWidget,
-    QWidget,
-    QAbstractItemView,
-    QHeaderView,
-    QGraphicsView,
-    QGraphicsScene,
-    QGraphicsPixmapItem,
-    QSplitter,
-    QFileDialog,
-    QTableWidgetItem,
-    QGraphicsRectItem,
-    QCheckBox,
-    QMessageBox,
-    QGroupBox,
-    QGraphicsSimpleTextItem,
-    qApp,
-    QAction,
-    QApplication)
-from PyQt5.QtGui import QIcon, QColor, QPainter, QImage, QPixmap, QPen, QBrush, QFont, QPalette, QKeySequence
-from PyQt5.QtCore import Qt, QDir, QSize, QEventLoop, QThread, pyqtSignal
-
-from hyperlpr_py3 import pipline as pp
-
-import cv2
-
-import numpy as np
-
-import time
-
-import shutil
-
-draw_plate_in_image_enable = 1
-
-plateTypeName = ["蓝", "黄", "绿", "白", "黑 "]
-
-
-def SimpleRecognizePlateWithGui(image):
-    t0 = time.time()
-
-    images = pp.detect.detectPlateRough(
-        image, image.shape[0], top_bottom_padding_rate=0.1)
-
-    res_set = []
-    y_offset = 32
-    for j, plate in enumerate(images):
-        plate, rect, origin_plate = plate
-
-        plate = cv2.resize(plate, (136, 36 * 2))
-        t1 = time.time()
-
-        plate_type = pp.td.SimplePredict(plate)
-        plate_color = plateTypeName[plate_type]
-
-        if (plate_type > 0) and (plate_type < 5):
-            plate = cv2.bitwise_not(plate)
-
-        if draw_plate_in_image_enable == 1:
-            image[y_offset:y_offset + plate.shape[0], 0:plate.shape[1]] = plate
-            y_offset = y_offset + plate.shape[0] + 4
-
-        image_rgb = pp.fm.findContoursAndDrawBoundingBox(plate)
-
-        if draw_plate_in_image_enable == 1:
-            image[y_offset:y_offset + image_rgb.shape[0],
-                  0:image_rgb.shape[1]] = image_rgb
-            y_offset = y_offset + image_rgb.shape[0] + 4
-
-        image_rgb = pp.fv.finemappingVertical(image_rgb)
-
-        if draw_plate_in_image_enable == 1:
-            image[y_offset:y_offset + image_rgb.shape[0],
-                  0:image_rgb.shape[1]] = image_rgb
-            y_offset = y_offset + image_rgb.shape[0] + 4
-
-        pp.cache.verticalMappingToFolder(image_rgb)
-
-        if draw_plate_in_image_enable == 1:
-            image[y_offset:y_offset + image_rgb.shape[0],
-                  0:image_rgb.shape[1]] = image_rgb
-            y_offset = y_offset + image_rgb.shape[0] + 4
-
-        e2e_plate, e2e_confidence = pp.e2e.recognizeOne(image_rgb)
-        print("e2e:", e2e_plate, e2e_confidence)
-
-        image_gray = cv2.cvtColor(image_rgb, cv2.COLOR_RGB2GRAY)
-
-        #print("校正", time.time() - t1, "s")
-
-        t2 = time.time()
-        val = pp.segmentation.slidingWindowsEval(image_gray)
-        # print val
-        #print("分割和识别", time.time() - t2, "s")
-
-        res=""
-        confidence = 0
-        if len(val) == 3:
-            blocks, res, confidence = val
-            if confidence / 7 > 0.7:
-
-                if draw_plate_in_image_enable == 1:
-                    image = pp.drawRectBox(image, rect, res)
-                    for i, block in enumerate(blocks):
-                        block_ = cv2.resize(block, (24, 24))
-                        block_ = cv2.cvtColor(block_, cv2.COLOR_GRAY2BGR)
-                        image[j * 24:(j * 24) + 24, i *
-                              24:(i * 24) + 24] = block_
-                        if image[j * 24:(j * 24) + 24,
-                                 i * 24:(i * 24) + 24].shape == block_.shape:
-                            pass
-
-        res_set.append([res,
-                        confidence / 7,
-                        rect,
-                        plate_color,
-                        e2e_plate,
-                        e2e_confidence,
-                        len(blocks)])
-        print("seg:",res,confidence/7)
-    #print(time.time() - t0, "s")
-
-    print("---------------------------------")
-    return image, res_set
-
-
-class LicenseRecognizationThread(QThread):
-
-    recognization_done_signal = pyqtSignal(list)
-
-    def __init__(self, parent=None):
-        super().__init__(parent)
-        self.hyperlpr_dir_path = ""
-        self.filenames = []
-
-    def set_parameter(self, filename_list, path):
-        self.hyperlpr_dir_path = path
-        self.filenames = filename_list
-
-    def run(self):
-        while True:
-            time.sleep(1)
-            if len(self.hyperlpr_dir_path) > 0:
-                for i in range(0, len(self.filenames)):
-                    path = os.path.join(
-                        self.hyperlpr_dir_path, self.filenames[i])
-                    image = cv2.imdecode(np.fromfile(path, dtype=np.uint8), -1)
-                    image, res_set = SimpleRecognizePlateWithGui(image)
-                    self.recognization_done_signal.emit([i, res_set])
-
-                self.hyperlpr_dir_path = ""
-
-
-class HyperLprImageView(QGraphicsView):
-
-    def __init__(self):
-
-        super().__init__()
-
-        self.init_ui()
-
-    def init_ui(self):
-
-        scene = QGraphicsScene()
-        scene.setBackgroundBrush(QColor(100, 100, 100))
-        scene.setItemIndexMethod(QGraphicsScene.BspTreeIndex)
-
-        scene.setSceneRect(scene.itemsBoundingRect())
-
-        self.setDragMode(QGraphicsView.RubberBandDrag)
-        self.setViewportUpdateMode(QGraphicsView.FullViewportUpdate)
-        self.setRenderHints(QPainter.Antialiasing | QPainter.TextAntialiasing)
-
-        self.frame_item = QGraphicsPixmapItem()
-
-        self.text_item_offset = 0
-        self.rect_item_array = []
-        self.text_item_array = []
-        for i in range(0, 5):
-            rect_item = QGraphicsRectItem()
-            rect_item.setVisible(False)
-            rect_item.setZValue(20.0)
-            rect_item.setPen(QPen(Qt.red, 5))
-            rect_item.setRect(20, 20, 20, 20)
-            scene.addItem(rect_item)
-            self.rect_item_array.append(rect_item)
-            text_item = QGraphicsSimpleTextItem("")
-            text_item.setBrush(QBrush(Qt.red))
-            text_item.setZValue(20.0)
-            text_item.setPos(10, 50)
-            text_item.setFont(QFont("黑体", 24))
-            text_item.setVisible(False)
-            scene.addItem(text_item)
-            self.text_item_array.append(text_item)
-
-        scene.addItem(self.frame_item)
-
-        self.curr_factor = 1.0
-
-        self.setScene(scene)
-
-    def resetRectText(self, res_set):
-        max_no = len(res_set)
-
-        if max_no > 5:
-            max_no = 5
-
-        for i in range(0, 5):
-            if i < max_no:
-                curr_rect = res_set[i][2]
-                self.rect_item_array[i].setRect(int(curr_rect[0]), int(
-                    curr_rect[1]), int(curr_rect[2]), int(curr_rect[3]))
-                self.rect_item_array[i].setVisible(True)
-
-                self.text_item_array[i].setText(
-                    res_set[i][4] + " " + res_set[i][3])
-                self.text_item_array[i].setPos(
-                    int(curr_rect[0]), int(curr_rect[1]) - 48)
-                self.text_item_array[i].setVisible(True)
-            else:
-                self.text_item_array[i].setVisible(False)
-                self.rect_item_array[i].setVisible(False)
-
-    def wheelEvent(self, event):
-        factor = event.angleDelta().y() / 120.0
-        if event.angleDelta().y() / 120.0 > 0:
-            factor = 1.08
-        else:
-            factor = 0.92
-
-        if self.curr_factor > 0.1 and self.curr_factor < 10:
-            self.curr_factor = self.curr_factor * factor
-            self.scale(factor, factor)
-
-    def resetPixmap(self, image):
-
-        self.frame_item.setPixmap(QPixmap.fromImage(image))
-
-
-class HyperLprWindow(QMainWindow):
-
-    start_init_signal = pyqtSignal()
-
-    def __init__(self):
-
-        super().__init__()
-
-        self.initUI()
-
-    def initUI(self):
-
-        self.statusBar().showMessage('Ready')
-
-        self.left_action = QAction('上一个', self)
-        self.left_action.setShortcut(QKeySequence.MoveToPreviousChar)
-        self.left_action.triggered.connect(self.analyze_last_one_image)
-
-        self.right_action = QAction('下一个', self)
-        self.right_action.setShortcut(QKeySequence.MoveToNextChar)
-        self.right_action.triggered.connect(self.analyze_next_one_image)
-
-        self.rename_image_action = QAction('保存e2e文件名', self)
-        self.rename_image_action.setShortcut(QKeySequence.MoveToPreviousLine)
-        self.rename_image_action.triggered.connect(self.rename_current_image_with_info)
-
-        self.statusBar()
-
-        menubar = self.menuBar()
-        fileMenu = menubar.addMenu('&Function')
-        fileMenu.addAction(self.left_action)
-        fileMenu.addAction(self.right_action)
-        fileMenu.addAction(self.rename_image_action)
-
-        self.image_window_view = HyperLprImageView()
-
-        table_widget_header_labels = [
-            "文件名",
-            "分割识别",
-            "置信度",
-            "颜色",
-            "E2E识别",
-            "E2E置信度"]
-
-        self.hyperlpr_tableview = QTableWidget(
-            0, len(table_widget_header_labels))
-        self.hyperlpr_tableview.setHorizontalHeaderLabels(
-            table_widget_header_labels)
-
-        self.hyperlpr_tableview.setSelectionBehavior(
-            QAbstractItemView.SelectItems)
-        self.hyperlpr_tableview.setSelectionMode(
-            QAbstractItemView.SingleSelection)
-        self.hyperlpr_tableview.setEditTriggers(
-            QAbstractItemView.NoEditTriggers)
-        self.hyperlpr_tableview.horizontalHeader().setSectionResizeMode(
-            QHeaderView.ResizeToContents)
-        self.hyperlpr_tableview.setEditTriggers(
-            QAbstractItemView.NoEditTriggers)
-
-        self.hyperlpr_tableview.cellClicked.connect(
-            self.recognize_one_license_plate)
-
-        self.left_button = QPushButton("<")
-        self.left_button.setFixedWidth(60)
-        self.right_button = QPushButton(">")
-        self.right_button.setFixedWidth(60)
-        self.left_button.setEnabled(False)
-        self.right_button.setEnabled(False)
-        self.left_button.clicked.connect(self.analyze_last_one_image)
-        self.right_button.clicked.connect(self.analyze_next_one_image)
-        left_right_layout = QHBoxLayout()
-        left_right_layout.addStretch()
-        left_right_layout.addWidget(self.left_button)
-        left_right_layout.addStretch()
-        left_right_layout.addWidget(self.right_button)
-        left_right_layout.addStretch()
-
-        self.location_label = QLabel("车牌目录", self)
-        self.location_text = QLineEdit(self)
-        self.location_text.setEnabled(False)
-        #self.location_text.setFixedWidth(300)
-        self.location_button = QPushButton("...")
-        self.location_button.clicked.connect(self.select_new_dir)
-
-        self.location_layout = QHBoxLayout()
-        self.location_layout.addWidget(self.location_label)
-        self.location_layout.addWidget(self.location_text)
-        self.location_layout.addWidget(self.location_button)
-        self.location_layout.addStretch()
-
-        self.check_box = QCheckBox("与文件名比较车牌")
-        self.check_box.setChecked(True)
-
-        self.update_file_path_button = QPushButton('批量识别')
-        self.update_file_path_button.clicked.connect(
-            self.batch_recognize_all_images)
-
-        self.update_file_path_layout = QHBoxLayout()
-        self.update_file_path_layout.addWidget(self.check_box)
-        self.update_file_path_layout.addWidget(self.update_file_path_button)
-        self.update_file_path_layout.addStretch()
-
-        self.save_as_e2e_filename_button = QPushButton("保存e2e文件名")
-        self.save_as_e2e_filename_button.setEnabled(False)
-        self.save_as_e2e_filename_button.clicked.connect(self.rename_current_image_with_info)
-        self.save_layout = QHBoxLayout()
-        self.save_layout.addWidget(self.save_as_e2e_filename_button)
-        self.save_layout.addStretch()
-
-        self.top_layout = QVBoxLayout()
-        self.top_layout.addLayout(left_right_layout)
-        self.top_layout.addLayout(self.location_layout)
-        self.top_layout.addLayout(self.update_file_path_layout)
-        self.top_layout.addLayout(self.save_layout)
-
-        function_groupbox = QGroupBox("功能区")
-        function_groupbox.setLayout(self.top_layout)
-
-        license_plate_image_label = QLabel("车牌图")
-        self.license_plate_widget = QLabel("")
-
-        block_image_label = QLabel("分割图")
-        self.block_plate_widget = QLabel("")
-
-        filename_label = QLabel("文件名：")
-        self.filename_edit = QLineEdit()
-
-        segmentation_recognition_label = QLabel("分割识别：")
-        self.segmentation_recognition_edit = QLineEdit()
-        self.segmentation_recognition_edit.setFont(QFont("黑体", 24, QFont.Bold))
-        # self.segmentation_recognition_edit.setStyleSheet("color:red")
-
-        confidence_label = QLabel("分割识别\n置信度")
-        self.confidence_edit = QLineEdit()
-        #self.confidence_edit.setFont(QFont("黑体", 24, QFont.Bold))
-        # self.confidence_edit.setStyleSheet("color:red")
-
-        plate_color_label = QLabel("车牌颜色")
-        self.plate_color_edit = QLineEdit()
-        self.plate_color_edit.setFont(QFont("黑体", 24, QFont.Bold))
-        # self.plate_color_edit.setStyleSheet("color:red")
-
-        e2e_recognization_label = QLabel("e2e识别：")
-        self.e2e_recognization_edit = QLineEdit()
-        self.e2e_recognization_edit.setFont(QFont("黑体", 24, QFont.Bold))
-        # self.e2e_recognization_edit.setStyleSheet("color:red")
-
-        e2e_confidence_label = QLabel("e2e置信度")
-        self.e2e_confidence_edit = QLineEdit()
-        #self.e2e_confidence_edit.setFont(QFont("黑体", 24, QFont.Bold))
-        # self.e2e_confidence_edit.setStyleSheet("color:red")
-
-        info_gridlayout = QGridLayout()
-        line_index = 0
-        info_gridlayout.addWidget(filename_label, line_index, 0)
-        info_gridlayout.addWidget(self.filename_edit, line_index, 1)
-        line_index += 1
-        info_gridlayout.addWidget(license_plate_image_label, line_index, 0)
-        info_gridlayout.addWidget(self.license_plate_widget, line_index, 1)
-        line_index += 1
-        info_gridlayout.addWidget(e2e_recognization_label, line_index, 0)
-        info_gridlayout.addWidget(self.e2e_recognization_edit, line_index, 1)
-        line_index += 1
-        info_gridlayout.addWidget(
-            segmentation_recognition_label, line_index, 0)
-        info_gridlayout.addWidget(
-            self.segmentation_recognition_edit, line_index, 1)
-        line_index += 1
-        info_gridlayout.addWidget(plate_color_label, line_index, 0)
-        info_gridlayout.addWidget(self.plate_color_edit, line_index, 1)
-        line_index += 1
-        info_gridlayout.addWidget(block_image_label, line_index, 0)
-        info_gridlayout.addWidget(self.block_plate_widget, line_index, 1)
-        line_index += 1
-        info_gridlayout.addWidget(confidence_label, line_index, 0)
-        info_gridlayout.addWidget(self.confidence_edit, line_index, 1)
-        line_index += 1
-        info_gridlayout.addWidget(e2e_confidence_label, line_index, 0)
-        info_gridlayout.addWidget(self.e2e_confidence_edit, line_index, 1)
-
-        info_widget = QGroupBox("分割识别&e2e")
-
-        info_widget.setLayout(info_gridlayout)
-
-        right_splitter = QSplitter(Qt.Vertical)
-        right_splitter.addWidget(self.hyperlpr_tableview)
-        right_splitter.addWidget(function_groupbox)
-        right_splitter.addWidget(info_widget)
-        right_splitter.setStretchFactor(0, 2)
-        right_splitter.setStretchFactor(2, 1)
-
-        main_splitter = QSplitter(Qt.Horizontal)
-        main_splitter.addWidget(self.image_window_view)
-        main_splitter.addWidget(right_splitter)
-        main_splitter.setStretchFactor(0, 1)
-
-        self.image_filename_list = []
-        self.hyperlpr_dir_path = ""
-        self.segmentation_recognition_correct_number = 0
-        self.color_correct_number = 0
-        self.e2e_recognization_correct_number = 0
-        self.current_row = 0
-
-        self.batch_recognization_thread = LicenseRecognizationThread()
-        self.batch_recognization_thread.recognization_done_signal.connect(
-            self.recognization_done_slot)
-        self.batch_recognization_thread.start()
-
-        self.start_init_signal.connect(self.read_path_and_show_one_image)
-
-        self.setCentralWidget(main_splitter)
-
-        self.setWindowTitle("HyperLPR车牌识别软件v1.0")
-
-        self.start_init_signal.emit()
-
-    def read_path_and_show_one_image(self):
-
-        hyperlpr_dir_info_filepath = QDir.homePath() + "/hyperlpr_dir_file"
-        if os.path.exists(hyperlpr_dir_info_filepath):
-            with open(hyperlpr_dir_info_filepath, 'r') as f:
-                self.hyperlpr_dir_path = f.read()
-
-        if len(self.hyperlpr_dir_path) > 0:
-            self.reset_info_gui()
-
-        if len(self.image_filename_list) > 0:
-            self.recognize_and_show_one_image(self.image_filename_list[0], 0)
-
-    def select_new_dir(self):
-
-        self.hyperlpr_dir_path = QFileDialog.getExistingDirectory(
-            self, "读取文件夹", QDir.currentPath())
-
-        if len(self.hyperlpr_dir_path) > 0:
-            hyperlpr_dir_info_filepath = QDir.homePath() + "/hyperlpr_dir_file"
-            with open(hyperlpr_dir_info_filepath, 'w') as f:
-                f.write(self.hyperlpr_dir_path)
-            self.reset_info_gui()
-
-    def rename_current_image_with_info(self):
-        if len(self.hyperlpr_dir_path) > 0:
-            target_dir_path = self.hyperlpr_dir_path + "/result"
-            if not os.path.exists(target_dir_path):
-                os.makedirs(target_dir_path)
-            if len(self.plate_color_edit.text())>0 and len(self.e2e_recognization_edit.text())>0:
-                orign_path = os.path.join(self.hyperlpr_dir_path, self.filename_edit.text())
-                target_path = os.path.join(target_dir_path,self.plate_color_edit.text()+"-"+self.e2e_recognization_edit.text()+".jpg")
-                shutil.copyfile(orign_path, target_path)
-
-    def reset_info_gui(self):
-
-        self.location_text.setText(self.hyperlpr_dir_path)
-        self.scan_files_with_new_dir(self.hyperlpr_dir_path)
-        self.fill_table_with_new_info()
-
-    def scan_files_with_new_dir(self, path):
-
-        name_list = os.listdir(path)  # 列出文件夹下所有的目录与文件
-        self.image_filename_list.clear()
-        for i in range(0, len(name_list)):
-            if name_list[i].endswith(
-                    ".jpg") or name_list[i].endswith(".png"):
-                self.image_filename_list.append(name_list[i])
-
-    def fill_table_with_new_info(self):
-        self.hyperlpr_tableview.clearContents()
-        row_count = self.hyperlpr_tableview.rowCount()
-        for i in range(row_count, -1, -1):
-            self.hyperlpr_tableview.removeRow(i)
-
-        for i in range(0, len(self.image_filename_list)):
-            row = self.hyperlpr_tableview.rowCount()
-            self.hyperlpr_tableview.insertRow(row)
-
-            item0 = QTableWidgetItem()
-            item0.setTextAlignment(Qt.AlignCenter)
-            self.hyperlpr_tableview.setItem(row, 0, item0)
-            self.hyperlpr_tableview.item(
-                row, 0).setText(
-                self.image_filename_list[i])
-
-            item1 = QTableWidgetItem()
-            item1.setTextAlignment(Qt.AlignCenter)
-            self.hyperlpr_tableview.setItem(row, 1, item1)
-
-            item2 = QTableWidgetItem()
-            item2.setTextAlignment(Qt.AlignCenter)
-            self.hyperlpr_tableview.setItem(row, 2, item2)
-
-            item3 = QTableWidgetItem()
-            item3.setTextAlignment(Qt.AlignCenter)
-            self.hyperlpr_tableview.setItem(row, 3, item3)
-
-            item4 = QTableWidgetItem()
-            item4.setTextAlignment(Qt.AlignCenter)
-            self.hyperlpr_tableview.setItem(row, 4, item4)
-
-            item5 = QTableWidgetItem()
-            item5.setTextAlignment(Qt.AlignCenter)
-            self.hyperlpr_tableview.setItem(row, 5, item5)
-
-        if len(self.image_filename_list) > 0:
-            self.left_button.setEnabled(True)
-            self.right_button.setEnabled(True)
-            self.save_as_e2e_filename_button.setEnabled(True)
-
-    def analyze_last_one_image(self):
-        if self.current_row > 0:
-            self.recognize_one_license_plate(self.current_row-1, 0)
-
-    def analyze_next_one_image(self):
-        if self.current_row < (len(self.image_filename_list)-1):
-            self.recognize_one_license_plate(self.current_row + 1, 0)
-
-    def recognize_one_license_plate(self, row, col):
-        if col == 0 and row < len(self.image_filename_list):
-            self.current_row = row
-            self.recognize_and_show_one_image(
-                self.image_filename_list[row], row)
-
-    def recognize_and_show_one_image(self, image_filename_text, row):
-
-        if image_filename_text.endswith(".jpg"):
-
-            print(image_filename_text)
-            path = os.path.join(self.hyperlpr_dir_path, image_filename_text)
-            image = cv2.imdecode(np.fromfile(path, dtype=np.uint8), -1)
-            image, res_set = SimpleRecognizePlateWithGui(image)
-            img = QImage(
-                image.data,
-                image.shape[1],
-                image.shape[0],
-                image.shape[1] * image.shape[2],
-                QImage.Format_RGB888)
-            self.image_window_view.resetPixmap(img.rgbSwapped())
-            self.image_window_view.resetRectText(res_set)
-
-            if len(res_set) > 0:
-                curr_rect = res_set[0][2]
-                image_crop = image[int(curr_rect[1]):int(
-                    curr_rect[1] + curr_rect[3]), int(curr_rect[0]):int(curr_rect[0] + curr_rect[2])]
-                curr_plate = cv2.resize(image_crop, (204, 108))
-                plate_img = QImage(
-                    curr_plate.data,
-                    curr_plate.shape[1],
-                    curr_plate.shape[0],
-                    curr_plate.shape[1] *
-                    curr_plate.shape[2],
-                    QImage.Format_RGB888)
-                self.license_plate_widget.setPixmap(
-                    QPixmap.fromImage(plate_img.rgbSwapped()))
-
-                # print(res_set[0][6])
-                block_crop = image[0:24, 0:(24 * int(res_set[0][6]))]
-                curr_block = cv2.resize(
-                    block_crop, (24 * int(res_set[0][6]), 24))
-                block_image = QImage(
-                    curr_block.data,
-                    curr_block.shape[1],
-                    curr_block.shape[0],
-                    curr_block.shape[1] *
-                    curr_block.shape[2],
-                    QImage.Format_RGB888)
-                self.block_plate_widget.setPixmap(
-                    QPixmap.fromImage(block_image.rgbSwapped()))
-
-                self.segmentation_recognition_edit.setText(res_set[0][0])
-                if res_set[0][0] in image_filename_text:
-                    self.segmentation_recognition_edit.setStyleSheet("color:black")
-                else:
-                    self.segmentation_recognition_edit.setStyleSheet("color:red")
-
-
-                self.filename_edit.setText(image_filename_text)
-                self.confidence_edit.setText("%.3f" % (float(res_set[0][1])))
-
-                self.plate_color_edit.setText(res_set[0][3])
-                if res_set[0][3] in image_filename_text:
-                    self.plate_color_edit.setStyleSheet("color:black")
-                else:
-                    self.plate_color_edit.setStyleSheet("color:red")
-
-                self.e2e_recognization_edit.setText(res_set[0][4])
-                if res_set[0][4] in image_filename_text:
-                    self.e2e_recognization_edit.setStyleSheet("color:black")
-                else:
-                    self.e2e_recognization_edit.setStyleSheet("color:red")
-
-                self.e2e_confidence_edit.setText(
-                    "%.3f" % (float(res_set[0][5])))
-            else:
-                self.license_plate_widget.clear()
-                self.block_plate_widget.clear()
-                self.segmentation_recognition_edit.setText("")
-                self.filename_edit.setText(image_filename_text)
-                self.confidence_edit.setText("")
-                self.plate_color_edit.setText("")
-                self.e2e_recognization_edit.setText("")
-                self.e2e_confidence_edit.setText("")
-
-            self.fill_table_widget_with_res_info(res_set, row)
-
-    def batch_recognize_all_images(self):
-        self.segmentation_recognition_correct_number = 0
-        self.color_correct_number = 0
-        self.e2e_recognization_correct_number = 0
-        self.batch_recognization_thread.set_parameter(
-            self.image_filename_list, self.hyperlpr_dir_path)
-
-    def recognization_done_slot(self, result_list):
-        row = result_list[0]
-        res_set = result_list[1]
-        self.fill_table_widget_with_res_info(res_set, row)
-
-        if row == len(self.image_filename_list) - 1:
-            total_number = len(self.image_filename_list)
-
-            row_count = self.hyperlpr_tableview.rowCount()
-            if row_count > total_number:
-                self.hyperlpr_tableview.removeRow(total_number)
-
-            self.hyperlpr_tableview.insertRow(total_number)
-
-            item0 = QTableWidgetItem()
-            item0.setTextAlignment(Qt.AlignCenter)
-            self.hyperlpr_tableview.setItem(total_number, 0, item0)
-            self.hyperlpr_tableview.item(
-                total_number, 0).setText(
-                "统计结果")
-
-            item1 = QTableWidgetItem()
-            item1.setTextAlignment(Qt.AlignCenter)
-            self.hyperlpr_tableview.setItem(total_number, 1, item1)
-            self.hyperlpr_tableview.item(
-                total_number,
-                1).setText(
-                "{0} / {1} = {2: .3f}".format(
-                    self.segmentation_recognition_correct_number,
-                    total_number,
-                    self.segmentation_recognition_correct_number /
-                    total_number))
-
-            item2 = QTableWidgetItem()
-            item2.setTextAlignment(Qt.AlignCenter)
-            self.hyperlpr_tableview.setItem(total_number, 2, item2)
-
-            item3 = QTableWidgetItem()
-            item3.setTextAlignment(Qt.AlignCenter)
-            self.hyperlpr_tableview.setItem(total_number, 3, item3)
-            self.hyperlpr_tableview.item(
-                total_number, 3).setText(
-                "{0} / {1} = {2: .3f}".format(self.e2e_recognization_correct_number, total_number,
-                                              self.e2e_recognization_correct_number / total_number))
-
-            item4 = QTableWidgetItem()
-            item4.setTextAlignment(Qt.AlignCenter)
-            self.hyperlpr_tableview.setItem(total_number, 4, item4)
-            self.hyperlpr_tableview.item(
-                total_number, 4).setText(
-                "{0} / {1} = {2: .3f}".format(self.color_correct_number, total_number,
-                                              self.color_correct_number / total_number))
-
-            item5 = QTableWidgetItem()
-            item5.setTextAlignment(Qt.AlignCenter)
-            self.hyperlpr_tableview.setItem(total_number, 5, item5)
-
-    def fill_table_widget_with_res_info(self, res_set, row):
-        image_filename_text = self.image_filename_list[row]
-        if len(res_set) > 0:
-
-            self.hyperlpr_tableview.item(row, 1).setText(res_set[0][0])
-            if res_set[0][0] in image_filename_text:
-                self.hyperlpr_tableview.item(
-                    row, 1).setForeground(
-                    QBrush(
-                        QColor(
-                            0, 0, 255)))
-                self.segmentation_recognition_correct_number += 1
-            else:
-                self.hyperlpr_tableview.item(
-                    row, 1).setForeground(
-                    QBrush(
-                        QColor(
-                            255, 0, 0)))
-
-            self.hyperlpr_tableview.item(
-                row, 2).setText(
-                "%.3f" %
-                (float(
-                    res_set[0][1])))
-
-            self.hyperlpr_tableview.item(row, 3).setText(res_set[0][3])
-            if res_set[0][3] in image_filename_text:
-                self.hyperlpr_tableview.item(
-                    row, 3).setForeground(
-                    QBrush(
-                        QColor(
-                            0, 0, 255)))
-                self.color_correct_number += 1
-            else:
-                self.hyperlpr_tableview.item(
-                    row, 3).setForeground(
-                    QBrush(
-                        QColor(
-                            255, 0, 0)))
-
-            self.hyperlpr_tableview.item(row, 4).setText(res_set[0][4])
-            if res_set[0][4] in image_filename_text:
-                self.hyperlpr_tableview.item(
-                    row, 4).setForeground(
-                    QBrush(
-                        QColor(
-                            0, 0, 255)))
-                self.e2e_recognization_correct_number += 1
-            else:
-                self.hyperlpr_tableview.item(
-                    row, 4).setForeground(
-                    QBrush(
-                        QColor(
-                            255, 0, 0)))
-
-            self.hyperlpr_tableview.item(
-                row, 5).setText(
-                "%.3f" %
-                (float(
-                    res_set[0][5])))
-
-
-if __name__ == '__main__':
-
-    app = QApplication(sys.argv)
-
-    hyper_lpr_widow = HyperLprWindow()
-
-    hyper_lpr_widow.showMaximized()
-
-    sys.exit(app.exec_())
diff --git a/LICENSE b/LICENSE
deleted file mode 100644
index 8dada3e..0000000
--- a/LICENSE
+++ /dev/null
@@ -1,201 +0,0 @@
-                                 Apache License
-                           Version 2.0, January 2004
-                        http://www.apache.org/licenses/
-
-   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
-
-   1. Definitions.
-
-      "License" shall mean the terms and conditions for use, reproduction,
-      and distribution as defined by Sections 1 through 9 of this document.
-
-      "Licensor" shall mean the copyright owner or entity authorized by
-      the copyright owner that is granting the License.
-
-      "Legal Entity" shall mean the union of the acting entity and all
-      other entities that control, are controlled by, or are under common
-      control with that entity. For the purposes of this definition,
-      "control" means (i) the power, direct or indirect, to cause the
-      direction or management of such entity, whether by contract or
-      otherwise, or (ii) ownership of fifty percent (50%) or more of the
-      outstanding shares, or (iii) beneficial ownership of such entity.
-
-      "You" (or "Your") shall mean an individual or Legal Entity
-      exercising permissions granted by this License.
-
-      "Source" form shall mean the preferred form for making modifications,
-      including but not limited to software source code, documentation
-      source, and configuration files.
-
-      "Object" form shall mean any form resulting from mechanical
-      transformation or translation of a Source form, including but
-      not limited to compiled object code, generated documentation,
-      and conversions to other media types.
-
-      "Work" shall mean the work of authorship, whether in Source or
-      Object form, made available under the License, as indicated by a
-      copyright notice that is included in or attached to the work
-      (an example is provided in the Appendix below).
-
-      "Derivative Works" shall mean any work, whether in Source or Object
-      form, that is based on (or derived from) the Work and for which the
-      editorial revisions, annotations, elaborations, or other modifications
-      represent, as a whole, an original work of authorship. For the purposes
-      of this License, Derivative Works shall not include works that remain
-      separable from, or merely link (or bind by name) to the interfaces of,
-      the Work and Derivative Works thereof.
-
-      "Contribution" shall mean any work of authorship, including
-      the original version of the Work and any modifications or additions
-      to that Work or Derivative Works thereof, that is intentionally
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-      to the Licensor or its representatives, including but not limited to
-      communication on electronic mailing lists, source code control systems,
-      and issue tracking systems that are managed by, or on behalf of, the
-      Licensor for the purpose of discussing and improving the Work, but
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-      designated in writing by the copyright owner as "Not a Contribution."
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-      on behalf of whom a Contribution has been received by Licensor and
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-
-   2. Grant of Copyright License. Subject to the terms and conditions of
-      this License, each Contributor hereby grants to You a perpetual,
-      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
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-   4. Redistribution. You may reproduce and distribute copies of the
-      Work or Derivative Works thereof in any medium, with or without
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-      (c) You must retain, in the Source form of any Derivative Works
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-          notices within Derivative Works that You distribute, alongside
-          or as an addendum to the NOTICE text from the Work, provided
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-
-      You may add Your own copyright statement to Your modifications and
-      may provide additional or different license terms and conditions
-      for use, reproduction, or distribution of Your modifications, or
-      for any such Derivative Works as a whole, provided Your use,
-      reproduction, and distribution of the Work otherwise complies with
-      the conditions stated in this License.
-
-   5. Submission of Contributions. Unless You explicitly state otherwise,
-      any Contribution intentionally submitted for inclusion in the Work
-      by You to the Licensor shall be under the terms and conditions of
-      this License, without any additional terms or conditions.
-      Notwithstanding the above, nothing herein shall supersede or modify
-      the terms of any separate license agreement you may have executed
-      with Licensor regarding such Contributions.
-
-   6. Trademarks. This License does not grant permission to use the trade
-      names, trademarks, service marks, or product names of the Licensor,
-      except as required for reasonable and customary use in describing the
-      origin of the Work and reproducing the content of the NOTICE file.
-
-   7. Disclaimer of Warranty. Unless required by applicable law or
-      agreed to in writing, Licensor provides the Work (and each
-      Contributor provides its Contributions) on an "AS IS" BASIS,
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-   8. Limitation of Liability. In no event and under no legal theory,
-      whether in tort (including negligence), contract, or otherwise,
-      unless required by applicable law (such as deliberate and grossly
-      negligent acts) or agreed to in writing, shall any Contributor be
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-      other commercial damages or losses), even if such Contributor
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-   9. Accepting Warranty or Additional Liability. While redistributing
-      the Work or Derivative Works thereof, You may choose to offer,
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-      License. However, in accepting such obligations, You may act only
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-      incurred by, or claims asserted against, such Contributor by reason
-      of your accepting any such warranty or additional liability.
-
-   END OF TERMS AND CONDITIONS
-
-   APPENDIX: How to apply the Apache License to your work.
-
-      To apply the Apache License to your work, attach the following
-      boilerplate notice, with the fields enclosed by brackets "{}"
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-   Copyright {yyyy} {name of copyright owner}
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-   Licensed under the Apache License, Version 2.0 (the "License");
-   you may not use this file except in compliance with the License.
-   You may obtain a copy of the License at
-
-       http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing, software
-   distributed under the License is distributed on an "AS IS" BASIS,
-   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   See the License for the specific language governing permissions and
-   limitations under the License.
diff --git a/Prj-Android/.gitignore b/Prj-Android/.gitignore
index b29764e..aa724b7 100644
--- a/Prj-Android/.gitignore
+++ b/Prj-Android/.gitignore
@@ -1,9 +1,15 @@
 *.iml
 .gradle
 /local.properties
-/.idea
+/.idea/caches
+/.idea/libraries
+/.idea/modules.xml
+/.idea/workspace.xml
+/.idea/navEditor.xml
+/.idea/assetWizardSettings.xml
 .DS_Store
 /build
 /captures
 .externalNativeBuild
-.src/main/cpp/CMakeFiles
+.cxx
+local.properties
diff --git a/Prj-Android/README.md b/Prj-Android/README.md
deleted file mode 100644
index 6563c02..0000000
--- a/Prj-Android/README.md
+++ /dev/null
@@ -1,17 +0,0 @@
-HyperLPR Android Demo集成指南
-
-Prj-Android 是 HyperLPR的一部分，集成时需要引入HyperLPR中的依赖文件以及环境
-
-主要依赖如下  a、模型文件  b、NDK
-
-集成方法如下：
-
-a、引入模型文件：请到HyperLRP中的Linux项目下拷贝模型model目录下的模型文件（https://github.com/zeusees/HyperLPR/tree/master/Prj-Linux/lpr/model），
-   到Android项目下的asset/pr目录中（如果没有pr，请自行建立，如果拷贝不正确，可能会出现模型文件找不到的问题。
-     
-   
-b、NDK，项目依赖的NDK版本为ndk16、请开发者自行修改为自己的NDK路径
-   
-
-   
- 
diff --git a/Prj-Android/app/.gitignore b/Prj-Android/app/.gitignore
old mode 100755
new mode 100644
index e072d26..42afabf
--- a/Prj-Android/app/.gitignore
+++ b/Prj-Android/app/.gitignore
@@ -1,12 +1 @@
-*.iml
-app.iml
-.gradle
-/local.properties
-/.idea/libraries
-/.idea/modules.xml
-/.idea/workspace.xml
-.DS_Store
-/build
-/captures
-.externalNativeBuild
-.src/main/cpp/CMakeFiles
\ No newline at end of file
+/build
\ No newline at end of file
diff --git a/Prj-Android/app/CMakeLists.txt b/Prj-Android/app/CMakeLists.txt
deleted file mode 100755
index 76ab34a..0000000
--- a/Prj-Android/app/CMakeLists.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-# For more information about using CMake with Android Studio, read the
-# documentation: https://d.android.com/studio/projects/add-native-code.html
-
-# Sets the minimum version of CMake required to build the native library.
-
-cmake_minimum_required(VERSION 3.4.1)
-add_subdirectory(src/main/cpp)
diff --git a/Prj-Android/app/app.iml b/Prj-Android/app/app.iml
deleted file mode 100755
index 82d9eee..0000000
--- a/Prj-Android/app/app.iml
+++ /dev/null
@@ -1,164 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<module external.linked.project.id=":app" external.linked.project.path="$MODULE_DIR$" external.root.project.path="$MODULE_DIR$/.." external.system.id="GRADLE" type="JAVA_MODULE" version="4">
-  <component name="FacetManager">
-    <facet type="android-gradle" name="Android-Gradle">
-      <configuration>
-        <option name="GRADLE_PROJECT_PATH" value=":app" />
-      </configuration>
-    </facet>
-    <facet type="native-android-gradle" name="Native-Android-Gradle">
-      <configuration>
-        <option name="SELECTED_BUILD_VARIANT" value="debug-armeabi-v7a" />
-      </configuration>
-    </facet>
-    <facet type="android" name="Android">
-      <configuration>
-        <option name="SELECTED_BUILD_VARIANT" value="debug" />
-        <option name="ASSEMBLE_TASK_NAME" value="assembleDebug" />
-        <option name="COMPILE_JAVA_TASK_NAME" value="compileDebugSources" />
-        <afterSyncTasks>
-          <task>generateDebugSources</task>
-        </afterSyncTasks>
-        <option name="ALLOW_USER_CONFIGURATION" value="false" />
-        <option name="MANIFEST_FILE_RELATIVE_PATH" value="/src/main/AndroidManifest.xml" />
-        <option name="RES_FOLDER_RELATIVE_PATH" value="/src/main/res" />
-        <option name="RES_FOLDERS_RELATIVE_PATH" value="file://$MODULE_DIR$/src/main/res;file://$MODULE_DIR$/build/generated/res/rs/debug;file://$MODULE_DIR$/build/generated/res/resValues/debug" />
-        <option name="TEST_RES_FOLDERS_RELATIVE_PATH" value="" />
-        <option name="ASSETS_FOLDER_RELATIVE_PATH" value="/src/main/assets" />
-      </configuration>
-    </facet>
-  </component>
-  <component name="NewModuleRootManager" LANGUAGE_LEVEL="JDK_1_7">
-    <output url="file://$MODULE_DIR$/build/intermediates/classes/debug" />
-    <output-test url="file://$MODULE_DIR$/build/intermediates/classes/test/debug" />
-    <exclude-output />
-    <content url="file://$MODULE_DIR$">
-      <sourceFolder url="file://$MODULE_DIR$/src/main/cpp" isTestSource="false" />
-      <sourceFolder url="file://$MODULE_DIR$/src/main/cpp/src" isTestSource="false" />
-      <sourceFolder url="file://$MODULE_DIR$/build/generated/source/apt/debug" isTestSource="false" generated="true" />
-      <sourceFolder url="file://$MODULE_DIR$/build/generated/source/aidl/debug" isTestSource="false" generated="true" />
-      <sourceFolder url="file://$MODULE_DIR$/build/generated/source/buildConfig/debug" isTestSource="false" generated="true" />
-      <sourceFolder url="file://$MODULE_DIR$/build/generated/source/rs/debug" isTestSource="false" generated="true" />
-      <sourceFolder url="file://$MODULE_DIR$/build/generated/res/rs/debug" type="java-resource" />
-      <sourceFolder url="file://$MODULE_DIR$/build/generated/res/resValues/debug" type="java-resource" />
-      <sourceFolder url="file://$MODULE_DIR$/build/generated/source/apt/androidTest/debug" isTestSource="true" generated="true" />
-      <sourceFolder url="file://$MODULE_DIR$/build/generated/source/aidl/androidTest/debug" isTestSource="true" generated="true" />
-      <sourceFolder url="file://$MODULE_DIR$/build/generated/source/buildConfig/androidTest/debug" isTestSource="true" generated="true" />
-      <sourceFolder url="file://$MODULE_DIR$/build/generated/source/rs/androidTest/debug" isTestSource="true" generated="true" />
-      <sourceFolder url="file://$MODULE_DIR$/build/generated/res/rs/androidTest/debug" type="java-test-resource" />
-      <sourceFolder url="file://$MODULE_DIR$/build/generated/res/resValues/androidTest/debug" type="java-test-resource" />
-      <sourceFolder url="file://$MODULE_DIR$/build/generated/source/apt/test/debug" isTestSource="true" generated="true" />
-      <sourceFolder url="file://$MODULE_DIR$/src/debug/res" type="java-resource" />
-      <sourceFolder url="file://$MODULE_DIR$/src/debug/resources" type="java-resource" />
-      <sourceFolder url="file://$MODULE_DIR$/src/debug/assets" type="java-resource" />
-      <sourceFolder url="file://$MODULE_DIR$/src/debug/aidl" isTestSource="false" />
-      <sourceFolder url="file://$MODULE_DIR$/src/debug/java" isTestSource="false" />
-      <sourceFolder url="file://$MODULE_DIR$/src/debug/jni" isTestSource="false" />
-      <sourceFolder url="file://$MODULE_DIR$/src/debug/rs" isTestSource="false" />
-      <sourceFolder url="file://$MODULE_DIR$/src/debug/shaders" isTestSource="false" />
-      <sourceFolder url="file://$MODULE_DIR$/src/androidTestDebug/res" type="java-test-resource" />
-      <sourceFolder url="file://$MODULE_DIR$/src/androidTestDebug/resources" type="java-test-resource" />
-      <sourceFolder url="file://$MODULE_DIR$/src/androidTestDebug/assets" type="java-test-resource" />
-      <sourceFolder url="file://$MODULE_DIR$/src/androidTestDebug/aidl" isTestSource="true" />
-      <sourceFolder url="file://$MODULE_DIR$/src/androidTestDebug/java" isTestSource="true" />
-      <sourceFolder url="file://$MODULE_DIR$/src/androidTestDebug/jni" isTestSource="true" />
-      <sourceFolder url="file://$MODULE_DIR$/src/androidTestDebug/rs" isTestSource="true" />
-      <sourceFolder url="file://$MODULE_DIR$/src/androidTestDebug/shaders" isTestSource="true" />
-      <sourceFolder url="file://$MODULE_DIR$/src/testDebug/res" type="java-test-resource" />
-      <sourceFolder url="file://$MODULE_DIR$/src/testDebug/resources" type="java-test-resource" />
-      <sourceFolder url="file://$MODULE_DIR$/src/testDebug/assets" type="java-test-resource" />
-      <sourceFolder url="file://$MODULE_DIR$/src/testDebug/aidl" isTestSource="true" />
-      <sourceFolder url="file://$MODULE_DIR$/src/testDebug/java" isTestSource="true" />
-      <sourceFolder url="file://$MODULE_DIR$/src/testDebug/jni" isTestSource="true" />
-      <sourceFolder url="file://$MODULE_DIR$/src/testDebug/rs" isTestSource="true" />
-      <sourceFolder url="file://$MODULE_DIR$/src/testDebug/shaders" isTestSource="true" />
-      <sourceFolder url="file://$MODULE_DIR$/src/main/res" type="java-resource" />
-      <sourceFolder url="file://$MODULE_DIR$/src/main/resources" type="java-resource" />
-      <sourceFolder url="file://$MODULE_DIR$/src/main/assets" type="java-resource" />
-      <sourceFolder url="file://$MODULE_DIR$/src/main/aidl" isTestSource="false" />
-      <sourceFolder url="file://$MODULE_DIR$/src/main/java" isTestSource="false" />
-      <sourceFolder url="file://$MODULE_DIR$/src/main/rs" isTestSource="false" />
-      <sourceFolder url="file://$MODULE_DIR$/src/main/shaders" isTestSource="false" />
-      <sourceFolder url="file://$MODULE_DIR$/src/test/res" type="java-test-resource" />
-      <sourceFolder url="file://$MODULE_DIR$/src/test/resources" type="java-test-resource" />
-      <sourceFolder url="file://$MODULE_DIR$/src/test/assets" type="java-test-resource" />
-      <sourceFolder url="file://$MODULE_DIR$/src/test/aidl" isTestSource="true" />
-      <sourceFolder url="file://$MODULE_DIR$/src/test/java" isTestSource="true" />
-      <sourceFolder url="file://$MODULE_DIR$/src/test/jni" isTestSource="true" />
-      <sourceFolder url="file://$MODULE_DIR$/src/test/rs" isTestSource="true" />
-      <sourceFolder url="file://$MODULE_DIR$/src/test/shaders" isTestSource="true" />
-      <sourceFolder url="file://$MODULE_DIR$/src/androidTest/res" type="java-test-resource" />
-      <sourceFolder url="file://$MODULE_DIR$/src/androidTest/resources" type="java-test-resource" />
-      <sourceFolder url="file://$MODULE_DIR$/src/androidTest/assets" type="java-test-resource" />
-      <sourceFolder url="file://$MODULE_DIR$/src/androidTest/aidl" isTestSource="true" />
-      <sourceFolder url="file://$MODULE_DIR$/src/androidTest/java" isTestSource="true" />
-      <sourceFolder url="file://$MODULE_DIR$/src/androidTest/jni" isTestSource="true" />
-      <sourceFolder url="file://$MODULE_DIR$/src/androidTest/rs" isTestSource="true" />
-      <sourceFolder url="file://$MODULE_DIR$/src/androidTest/shaders" isTestSource="true" />
-      <excludeFolder url="file://$MODULE_DIR$/.externalNativeBuild" />
-      <excludeFolder url="file://$MODULE_DIR$/build/generated/not_namespaced_r_class_sources" />
-      <excludeFolder url="file://$MODULE_DIR$/build/generated/source/r" />
-      <excludeFolder url="file://$MODULE_DIR$/build/intermediates/assets" />
-      <excludeFolder url="file://$MODULE_DIR$/build/intermediates/blame" />
-      <excludeFolder url="file://$MODULE_DIR$/build/intermediates/build-info" />
-      <excludeFolder url="file://$MODULE_DIR$/build/intermediates/check-manifest" />
-      <excludeFolder url="file://$MODULE_DIR$/build/intermediates/classes" />
-      <excludeFolder url="file://$MODULE_DIR$/build/intermediates/cmake" />
-      <excludeFolder url="file://$MODULE_DIR$/build/intermediates/incremental" />
-      <excludeFolder url="file://$MODULE_DIR$/build/intermediates/incremental-classes" />
-      <excludeFolder url="file://$MODULE_DIR$/build/intermediates/incremental-runtime-classes" />
-      <excludeFolder url="file://$MODULE_DIR$/build/intermediates/incremental-verifier" />
-      <excludeFolder url="file://$MODULE_DIR$/build/intermediates/instant-run-apk" />
-      <excludeFolder url="file://$MODULE_DIR$/build/intermediates/instant-run-resources" />
-      <excludeFolder url="file://$MODULE_DIR$/build/intermediates/javaPrecompile" />
-      <excludeFolder url="file://$MODULE_DIR$/build/intermediates/jniLibs" />
-      <excludeFolder url="file://$MODULE_DIR$/build/intermediates/manifest-checker" />
-      <excludeFolder url="file://$MODULE_DIR$/build/intermediates/manifests" />
-      <excludeFolder url="file://$MODULE_DIR$/build/intermediates/prebuild" />
-      <excludeFolder url="file://$MODULE_DIR$/build/intermediates/reload-dex" />
-      <excludeFolder url="file://$MODULE_DIR$/build/intermediates/res" />
-      <excludeFolder url="file://$MODULE_DIR$/build/intermediates/resources" />
-      <excludeFolder url="file://$MODULE_DIR$/build/intermediates/rs" />
-      <excludeFolder url="file://$MODULE_DIR$/build/intermediates/shaders" />
-      <excludeFolder url="file://$MODULE_DIR$/build/intermediates/split-apk" />
-      <excludeFolder url="file://$MODULE_DIR$/build/intermediates/splits-support" />
-      <excludeFolder url="file://$MODULE_DIR$/build/intermediates/symbols" />
-      <excludeFolder url="file://$MODULE_DIR$/build/intermediates/transforms" />
-      <excludeFolder url="file://$MODULE_DIR$/build/outputs" />
-      <excludeFolder url="file://$MODULE_DIR$/build/tmp" />
-    </content>
-    <orderEntry type="jdk" jdkName="Android API 27 Platform" jdkType="Android SDK" />
-    <orderEntry type="sourceFolder" forTests="false" />
-    <orderEntry type="library" name="Gradle: com.android.support:support-vector-drawable:27.1.1@aar" level="project" />
-    <orderEntry type="library" name="Gradle: com.android.support:support-core-utils:27.1.1@aar" level="project" />
-    <orderEntry type="library" name="Gradle: android.arch.lifecycle:livedata-core:1.1.0@aar" level="project" />
-    <orderEntry type="library" name="Gradle: android.arch.lifecycle:runtime:1.1.0@aar" level="project" />
-    <orderEntry type="library" name="Gradle: android.arch.lifecycle:common:1.1.0@jar" level="project" />
-    <orderEntry type="library" name="Gradle: com.android.support:support-annotations:27.1.1@jar" level="project" />
-    <orderEntry type="library" name="Gradle: android.arch.lifecycle:viewmodel:1.1.0@aar" level="project" />
-    <orderEntry type="library" name="Gradle: com.android.support.constraint:constraint-layout:1.1.2@aar" level="project" />
-    <orderEntry type="library" scope="TEST" name="Gradle: com.android.support.test.espresso:espresso-idling-resource:3.0.2@aar" level="project" />
-    <orderEntry type="library" scope="TEST" name="Gradle: com.android.support.test:runner:1.0.2@aar" level="project" />
-    <orderEntry type="library" name="Gradle: com.github.bumptech.glide:glide:3.8.0@jar" level="project" />
-    <orderEntry type="library" name="Gradle: android.arch.core:runtime:1.1.0@aar" level="project" />
-    <orderEntry type="library" scope="TEST" name="Gradle: com.squareup:javawriter:2.1.1@jar" level="project" />
-    <orderEntry type="library" name="Gradle: com.android.support:animated-vector-drawable:27.1.1@aar" level="project" />
-    <orderEntry type="library" scope="TEST" name="Gradle: com.android.support.test.espresso:espresso-core:3.0.2@aar" level="project" />
-    <orderEntry type="library" scope="TEST" name="Gradle: com.google.code.findbugs:jsr305:2.0.1@jar" level="project" />
-    <orderEntry type="library" scope="TEST" name="Gradle: javax.inject:javax.inject:1@jar" level="project" />
-    <orderEntry type="library" scope="TEST" name="Gradle: junit:junit:4.12@jar" level="project" />
-    <orderEntry type="library" name="Gradle: com.android.support:support-core-ui:27.1.1@aar" level="project" />
-    <orderEntry type="library" scope="TEST" name="Gradle: org.hamcrest:hamcrest-core:1.3@jar" level="project" />
-    <orderEntry type="library" name="Gradle: com.android.support:support-compat:27.1.1@aar" level="project" />
-    <orderEntry type="library" name="Gradle: org.greenrobot:eventbus:3.0.0@jar" level="project" />
-    <orderEntry type="library" name="Gradle: com.android.support.constraint:constraint-layout-solver:1.1.2@jar" level="project" />
-    <orderEntry type="library" name="Gradle: com.android.support:support-fragment:27.1.1@aar" level="project" />
-    <orderEntry type="library" scope="TEST" name="Gradle: com.android.support.test:monitor:1.0.2@aar" level="project" />
-    <orderEntry type="library" scope="TEST" name="Gradle: org.hamcrest:hamcrest-library:1.3@jar" level="project" />
-    <orderEntry type="library" scope="TEST" name="Gradle: org.hamcrest:hamcrest-integration:1.3@jar" level="project" />
-    <orderEntry type="library" name="Gradle: com.android.support:appcompat-v7:27.1.1@aar" level="project" />
-    <orderEntry type="library" name="Gradle: android.arch.core:common:1.1.0@jar" level="project" />
-    <orderEntry type="library" scope="TEST" name="Gradle: net.sf.kxml:kxml2:2.3.0@jar" level="project" />
-    <orderEntry type="module" module-name="openCVLibrary342" />
-  </component>
-</module>
\ No newline at end of file
diff --git a/Prj-Android/app/build.gradle b/Prj-Android/app/build.gradle
old mode 100755
new mode 100644
index 59b7e2f..0e2a3a7
--- a/Prj-Android/app/build.gradle
+++ b/Prj-Android/app/build.gradle
@@ -1,65 +1,44 @@
-apply plugin: 'com.android.application'
+plugins {
+    id 'com.android.application'
+}
 
 android {
-    compileSdkVersion 27
-    buildToolsVersion '27.0.3'
+    compileSdk 28
+
     defaultConfig {
-        applicationId "pr.platerecognization"
-        minSdkVersion 19
-        targetSdkVersion 22
+        applicationId "com.hyperai.hyperlpr_sdk_demo"
+        minSdk 22
+        targetSdk 28
         versionCode 1
         versionName "1.0"
-        testInstrumentationRunner "android.support.test.runner.AndroidJUnitRunner"
-        externalNativeBuild {
-            cmake {
-                cppFlags "-std=c++11"
-//                cppFlags "-std=c++11 -frtti -fexceptions"
-//                abiFilters 'armeabi-v7a'
-                arguments '-DANDROID_STL=gnustl_static'
-//                arguments '-DANDROID_STL=gnustl_shared'
-//                        '-DANDROID_ABI=armeabi-v7a'
-            }
-        }
-        ndk {
-            // Specifies the ABI configurations of your native
-            // libraries Gradle should build and package with your APK.
-//           abiFilters 'x86', 'x86_64', 'armeabi', 'armeabi-v7a', 'arm64-v8a', 'mips', 'mips64'
-            abiFilters 'armeabi-v7a'
-        }
 
+        testInstrumentationRunner "androidx.test.runner.AndroidJUnitRunner"
     }
+
     buildTypes {
         release {
             minifyEnabled false
-            proguardFiles getDefaultProguardFile('proguard-android.txt'), 'proguard-rules.pro'
+            proguardFiles getDefaultProguardFile('proguard-android-optimize.txt'), 'proguard-rules.pro'
         }
     }
-
-    sourceSets.main {
-        jni.srcDirs = [] // This prevents the auto generation of Android.mk
-        jniLibs.srcDirs = ['libs','src/main/cpp/opencv342/sdk/native/libs']
-        //jniLibs.srcDirs =['src/main/libs','src/main/jniLibs']
-    }
-  /*  externalNativeBuild {
-        cmake {
-            path 'src/main/cpp/CMakeLists.txt'
-        }
-    }*/
-    externalNativeBuild {
-        cmake {
-            path 'src/main/cpp/CMakeLists.txt'
-        }
+    compileOptions {
+        sourceCompatibility JavaVersion.VERSION_1_8
+        targetCompatibility JavaVersion.VERSION_1_8
     }
 }
 
 dependencies {
-    implementation fileTree(include: ['*.jar'], dir: 'libs')
-    implementation 'com.android.support:appcompat-v7:27.1.1'
-    implementation 'com.android.support.constraint:constraint-layout:1.1.2'
-    testImplementation 'junit:junit:4.12'
-    androidTestImplementation 'com.android.support.test:runner:1.0.2'
-    androidTestImplementation 'com.android.support.test.espresso:espresso-core:3.0.2'
-    api 'com.github.bumptech.glide:glide:3.8.0'
+
     implementation 'org.greenrobot:eventbus:3.0.0'
-    implementation project(':openCVLibrary342')
+    implementation 'com.android.support:appcompat-v7:28.0.0'
+    implementation 'com.android.support.constraint:constraint-layout:2.0.4'
+
+    implementation 'androidx.appcompat:appcompat:1.2.0'
+    implementation 'com.google.android.material:material:1.3.0'
+    implementation 'androidx.constraintlayout:constraintlayout:2.0.4'
+    implementation project(path: ':hyperlpr3')
+    testImplementation 'junit:junit:4.+'
+    androidTestImplementation 'androidx.test.ext:junit:1.1.2'
+    androidTestImplementation 'androidx.test.espresso:espresso-core:3.3.0'
+    implementation 'com.github.smuyyh:ImageSelector:3.0'
 }
\ No newline at end of file
diff --git a/Prj-Android/app/proguard-rules.pro b/Prj-Android/app/proguard-rules.pro
old mode 100755
new mode 100644
index 2a84321..481bb43
--- a/Prj-Android/app/proguard-rules.pro
+++ b/Prj-Android/app/proguard-rules.pro
@@ -1,14 +1,10 @@
 # Add project specific ProGuard rules here.
-# By default, the flags in this file are appended to flags specified
-# in /Users/yujinke/Library/Android/sdk/tools/proguard/proguard-android.txt
-# You can edit the include path and order by changing the proguardFiles
-# directive in build.gradle.
+# You can control the set of applied configuration files using the
+# proguardFiles setting in build.gradle.
 #
 # For more details, see
 #   http://developer.android.com/guide/developing/tools/proguard.html
 
-# Add any project specific keep options here:
-
 # If your project uses WebView with JS, uncomment the following
 # and specify the fully qualified class name to the JavaScript interface
 # class:
@@ -22,4 +18,4 @@
 
 # If you keep the line number information, uncomment this to
 # hide the original source file name.
-#-renamesourcefileattribute SourceFile
+#-renamesourcefileattribute SourceFile
\ No newline at end of file
diff --git a/Prj-Android/app/src/androidTest/java/com/hyperai/hyperlpr_sdk_demo/ExampleInstrumentedTest.java b/Prj-Android/app/src/androidTest/java/com/hyperai/hyperlpr_sdk_demo/ExampleInstrumentedTest.java
new file mode 100644
index 0000000..08c1862
--- /dev/null
+++ b/Prj-Android/app/src/androidTest/java/com/hyperai/hyperlpr_sdk_demo/ExampleInstrumentedTest.java
@@ -0,0 +1,26 @@
+package com.hyperai.hyperlpr_sdk_demo;
+
+import android.content.Context;
+
+import androidx.test.platform.app.InstrumentationRegistry;
+import androidx.test.ext.junit.runners.AndroidJUnit4;
+
+import org.junit.Test;
+import org.junit.runner.RunWith;
+
+import static org.junit.Assert.*;
+
+/**
+ * Instrumented test, which will execute on an Android device.
+ *
+ * @see <a href="http://d.android.com/tools/testing">Testing documentation</a>
+ */
+@RunWith(AndroidJUnit4.class)
+public class ExampleInstrumentedTest {
+    @Test
+    public void useAppContext() {
+        // Context of the app under test.
+        Context appContext = InstrumentationRegistry.getInstrumentation().getTargetContext();
+        assertEquals("com.hyperai.hyperlpr_sdk_demo", appContext.getPackageName());
+    }
+}
\ No newline at end of file
diff --git a/Prj-Android/app/src/main/AndroidManifest.xml b/Prj-Android/app/src/main/AndroidManifest.xml
old mode 100755
new mode 100644
index 819ca6b..0843522
--- a/Prj-Android/app/src/main/AndroidManifest.xml
+++ b/Prj-Android/app/src/main/AndroidManifest.xml
@@ -1,59 +1,59 @@
 <?xml version="1.0" encoding="utf-8"?>
 <manifest xmlns:android="http://schemas.android.com/apk/res/android"
-    package="pr.platerecognization">
-
+    xmlns:tools="http://schemas.android.com/tools"
+    package="com.hyperai.hyperlpr_sdk_demo">
 
     <uses-feature android:name="android.hardware.camera" />
     <uses-feature android:name="android.hardware.camera.autofocus" />
     <!-- 4.2以上的手机需要的权限 -->
-    <uses-permission android:name="android.permission.INTERACT_ACROSS_USERS_FULL" />
+    <uses-permission android:name="android.permission.INTERACT_ACROSS_USERS_FULL"
+        tools:ignore="ProtectedPermissions" />
     <uses-permission android:name="android.permission.RESTART_PACKAGES" />
     <!-- 访问INTERNET的权限 -->
-    <uses-permission android:name="android.permission.INTERNET" >
-    </uses-permission>
-    <uses-permission android:name="android.permission.ACCESS_NETWORK_STATE" >
-    </uses-permission>
-    <uses-permission android:name="android.permission.CHANGE_WIFI_STATE" >
-    </uses-permission>
-    <uses-permission android:name="android.permission.ACCESS_WIFI_STATE" >
-    </uses-permission>
+    <uses-permission android:name="android.permission.INTERNET"></uses-permission>
+    <uses-permission android:name="android.permission.ACCESS_NETWORK_STATE"></uses-permission>
+    <uses-permission android:name="android.permission.CHANGE_WIFI_STATE"></uses-permission>
+    <uses-permission android:name="android.permission.ACCESS_WIFI_STATE"></uses-permission>
     <!-- 手机信息 -->
     <uses-permission android:name="android.permission.READ_PHONE_STATE" />
     <!-- ************************************* -->
     <uses-permission android:name="android.permission.WRITE_EXTERNAL_STORAGE" />
     <!-- 在SD卡中创建文件与删除文件权限 -->
-    <uses-permission android:name="android.permission.MOUNT_UNMOUNT_FILESYSTEMS" />
+    <uses-permission android:name="android.permission.MOUNT_UNMOUNT_FILESYSTEMS"
+        tools:ignore="ProtectedPermissions" />
     <!-- 传感器 -->
     <uses-permission android:name="android.permission.VIBRATE" />
     <!-- 摄像头权限 -->
-    <uses-permission android:name="android.permission.CAMERA" >
-    </uses-permission>
+    <uses-permission android:name="android.permission.CAMERA"></uses-permission>
     <uses-permission android:name="android.permission.FLASHLIGHT" />
-
     <uses-permission android:name="com.meilapp.meila.permission.MIPUSH_RECEIVE" />
     <uses-permission android:name="android.permission.READ_EXTERNAL_STORAGE" />
 
     <!-- 开启闪光灯权限 -->
-    <uses-permission android:name="android.permission.FLASHLIGHT"/>
+    <uses-permission android:name="android.permission.FLASHLIGHT" />
 
-    <!--for android 10-->
-    <!---android:requestLegacyExternalStorage="true" -->
     <application
         android:allowBackup="true"
         android:icon="@mipmap/ic_launcher"
         android:label="@string/app_name"
         android:roundIcon="@mipmap/ic_launcher_round"
         android:supportsRtl="true"
-        android:theme="@style/AppTheme">
-        <activity android:name="pr.platerecognization.MainActivity">
+        android:theme="@style/Theme.HyperLPR_SDK_Demo">
+        <activity
+            android:name=".MainActivity"
+            android:exported="true">
             <intent-filter>
                 <action android:name="android.intent.action.MAIN" />
 
                 <category android:name="android.intent.category.LAUNCHER" />
             </intent-filter>
         </activity>
-        <activity android:name=".CameraActivity"
-            />
+        <activity android:name=".CameraActivity" />
+<!--        <activity-->
+<!--            android:name=".CameraPlate"-->
+<!--            android:screenOrientation="landscape"-->
+<!--            >-->
+<!--        </activity>-->
     </application>
 
-</manifest>
+</manifest>
\ No newline at end of file
diff --git a/Prj-Android/app/src/main/assets/pr/cascade.xml b/Prj-Android/app/src/main/assets/pr/cascade.xml
deleted file mode 100755
index 277775b..0000000
--- a/Prj-Android/app/src/main/assets/pr/cascade.xml
+++ /dev/null
@@ -1,12117 +0,0 @@
-<?xml version="1.0"?>
-<opencv_storage>
-<cascade>
-  <stageType>BOOST</stageType>
-  <featureType>HAAR</featureType>
-  <height>13</height>
-  <width>51</width>
-  <stageParams>
-    <boostType>GAB</boostType>
-    <minHitRate>9.9900001287460327e-001</minHitRate>
-    <maxFalseAlarm>5.0000000000000000e-001</maxFalseAlarm>
-    <weightTrimRate>9.4999999999999996e-001</weightTrimRate>
-    <maxDepth>1</maxDepth>
-    <maxWeakCount>100</maxWeakCount></stageParams>
-  <featureParams>
-    <maxCatCount>0</maxCatCount>
-    <featSize>1</featSize>
-    <mode>ALL</mode></featureParams>
-  <stageNum>20</stageNum>
-  <stages>
-    <!-- stage 0 -->
-    <_>
-      <maxWeakCount>8</maxWeakCount>
-      <stageThreshold>-1.9158077239990234e+000</stageThreshold>
-      <weakClassifiers>
-        <_>
-          <internalNodes>
-            0 -1 344 -8.1478752195835114e-002</internalNodes>
-          <leafValues>
-            6.2639594078063965e-001 -8.1564724445343018e-001</leafValues></_>
-        <_>
-          <internalNodes>
-            0 -1 701 -1.2957378290593624e-002</internalNodes>
-          <leafValues>
-            7.7114331722259521e-001 -4.9504086375236511e-001</leafValues></_>
-        <_>
-          <internalNodes>
-            0 -1 687 -9.2470366507768631e-003</internalNodes>
-          <leafValues>
-            8.1202191114425659e-001 -2.8070560097694397e-001</leafValues></_>
-        <_>
-          <internalNodes>
-            0 -1 614 1.2374955229461193e-002</internalNodes>
-          <leafValues>
-            -2.5367051362991333e-001 7.3795551061630249e-001</leafValues></_>
-        <_>
-          <internalNodes>
-            0 -1 299 -4.7858944162726402e-003</internalNodes>
-          <leafValues>
-            7.1150565147399902e-001 -3.0462509393692017e-001</leafValues></_>
-        <_>
-          <internalNodes>
-            0 -1 297 3.8920845836400986e-003</internalNodes>
-          <leafValues>
-            -2.8375166654586792e-001 7.3174893856048584e-001</leafValues></_>
-        <_>
-          <internalNodes>
-            0 -1 814 -8.8258963078260422e-003</internalNodes>
-          <leafValues>
-            7.5333666801452637e-001 -1.9880458712577820e-001</leafValues></_>
-        <_>
-          <internalNodes>
-            0 -1 845 -6.7375516518950462e-003</internalNodes>
-          <leafValues>
-            7.5299704074859619e-001 -2.3570337891578674e-001</leafValues></_></weakClassifiers></_>
-    <!-- stage 1 -->
-    <_>
-      <maxWeakCount>12</maxWeakCount>
-      <stageThreshold>-2.2599112987518311e+000</stageThreshold>
-      <weakClassifiers>
-        <_>
-          <internalNodes>
-            0 -1 577 -1.4449171721935272e-002</internalNodes>
-          <leafValues>
-            5.5890566110610962e-001 -7.6307392120361328e-001</leafValues></_>
-        <_>
-          <internalNodes>
-            0 -1 364 -3.8289055228233337e-002</internalNodes>
-          <leafValues>
-            5.1702296733856201e-001 -5.7946079969406128e-001</leafValues></_>
-        <_>
-          <internalNodes>
-            0 -1 124 -1.4895259402692318e-002</internalNodes>
-          <leafValues>
-            6.1277741193771362e-001 -3.2827928662300110e-001</leafValues></_>
-        <_>
-          <internalNodes>
-            0 -1 579 8.3044255152344704e-003</internalNodes>
-          <leafValues>
-            -3.3253005146980286e-001 7.2171914577484131e-001</leafValues></_>
-        <_>
-          <internalNodes>
-            0 -1 314 6.0594235546886921e-003</internalNodes>
-          <leafValues>
-            -3.2760250568389893e-001 4.9508789181709290e-001</leafValues></_>
-        <_>
-          <internalNodes>
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-          <leafValues>
-            6.6238498687744141e-001 -2.4112002551555634e-001</leafValues></_>
-        <_>
-          <internalNodes>
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-          <leafValues>
-            7.1757602691650391e-001 -2.0989039540290833e-001</leafValues></_>
-        <_>
-          <internalNodes>
-            0 -1 767 1.3139605522155762e-002</internalNodes>
-          <leafValues>
-            -1.8845251202583313e-001 6.7252415418624878e-001</leafValues></_>
-        <_>
-          <internalNodes>
-            0 -1 689 -6.1739999800920486e-003</internalNodes>
-          <leafValues>
-            6.3675141334533691e-001 -2.4877758324146271e-001</leafValues></_>
-        <_>
-          <internalNodes>
-            0 -1 230 -9.2421043664216995e-003</internalNodes>
-          <leafValues>
-            6.5140277147293091e-001 -2.1352872252464294e-001</leafValues></_>
-        <_>
-          <internalNodes>
-            0 -1 48 -1.0526084899902344e-001</internalNodes>
-          <leafValues>
-            -9.0194213390350342e-001 1.8239501118659973e-001</leafValues></_>
-        <_>
-          <internalNodes>
-            0 -1 281 1.2764739990234375e-001</internalNodes>
-          <leafValues>
-            1.6771897673606873e-001 -7.7668786048889160e-001</leafValues></_></weakClassifiers></_>
-    <!-- stage 2 -->
-    <_>
-      <maxWeakCount>14</maxWeakCount>
-      <stageThreshold>-2.6526770591735840e+000</stageThreshold>
-      <weakClassifiers>
-        <_>
-          <internalNodes>
-            0 -1 829 -1.3842798769474030e-002</internalNodes>
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-        <_>
-          <internalNodes>
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-          <leafValues>
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-        <_>
-          <internalNodes>
-            0 -1 21 3.1671046745032072e-003</internalNodes>
-          <leafValues>
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-        <_>
-          <internalNodes>
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-        <_>
-          <internalNodes>
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-          <leafValues>
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-        <_>
-          <internalNodes>
-            0 -1 518 1.5905253589153290e-002</internalNodes>
-          <leafValues>
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-        <_>
-          <internalNodes>
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-          <leafValues>
-            6.3098442554473877e-001 -2.2128470242023468e-001</leafValues></_>
-        <_>
-          <internalNodes>
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-          <leafValues>
-            -1.6205528378486633e-001 7.1688497066497803e-001</leafValues></_>
-        <_>
-          <internalNodes>
-            0 -1 373 -1.4500595629215240e-002</internalNodes>
-          <leafValues>
-            5.2634650468826294e-001 -2.5339555740356445e-001</leafValues></_>
-        <_>
-          <internalNodes>
-            0 -1 720 8.4515195339918137e-003</internalNodes>
-          <leafValues>
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-        <_>
-          <internalNodes>
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-          <leafValues>
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-        <_>
-          <internalNodes>
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-          <leafValues>
-            -1.9179263710975647e-001 6.1396795511245728e-001</leafValues></_></weakClassifiers></_>
-    <!-- stage 3 -->
-    <_>
-      <maxWeakCount>16</maxWeakCount>
-      <stageThreshold>-2.2411971092224121e+000</stageThreshold>
-      <weakClassifiers>
-        <_>
-          <internalNodes>
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-          <leafValues>
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diff --git a/Prj-Android/app/src/main/cpp/CMakeLists.txt b/Prj-Android/app/src/main/cpp/CMakeLists.txt
deleted file mode 100755
index 46848ed..0000000
--- a/Prj-Android/app/src/main/cpp/CMakeLists.txt
+++ /dev/null
@@ -1,65 +0,0 @@
-# For more information about using CMake with Android Studio, read the
-# documentation: https://d.android.com/studio/projects/add-native-code.html
-
-# Sets the minimum version of CMake required to build the native library.
-
-cmake_minimum_required(VERSION 3.4.1)
-#set(OpenCV_DIR /Users/mac02/Downloads/OpenCV-android/sdk/native/jni)
-set(OpenCV_DIR ${CMAKE_CURRENT_SOURCE_DIR}/opencv342/sdk/native/jni)
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diff --git a/Prj-Android/app/src/main/cpp/include/CNNRecognizer.h b/Prj-Android/app/src/main/cpp/include/CNNRecognizer.h
deleted file mode 100755
index ad491a0..0000000
--- a/Prj-Android/app/src/main/cpp/include/CNNRecognizer.h
+++ /dev/null
@@ -1,24 +0,0 @@
-//
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-//
-
-#ifndef SWIFTPR_CNNRECOGNIZER_H
-#define SWIFTPR_CNNRECOGNIZER_H
-
-#include "Recognizer.h"
-namespace pr{
-    class CNNRecognizer: public GeneralRecognizer{
-    public:
-        const int CHAR_INPUT_W = 14;
-        const int CHAR_INPUT_H = 30;
-
-        CNNRecognizer(std::string prototxt,std::string caffemodel);
-        label recognizeCharacter(cv::Mat character);
-    private:
-        cv::dnn::Net net;
-
-    };
-
-}
-
-#endif //SWIFTPR_CNNRECOGNIZER_H
diff --git a/Prj-Android/app/src/main/cpp/include/FastDeskew.h b/Prj-Android/app/src/main/cpp/include/FastDeskew.h
deleted file mode 100755
index 08359e5..0000000
--- a/Prj-Android/app/src/main/cpp/include/FastDeskew.h
+++ /dev/null
@@ -1,18 +0,0 @@
-//
-// Created by 庾金科 on 22/09/2017.
-//
-
-#ifndef SWIFTPR_FASTDESKEW_H
-#define SWIFTPR_FASTDESKEW_H
-
-#include <math.h>
-#include <opencv2/opencv.hpp>
-namespace pr{
-
-    cv::Mat fastdeskew(cv::Mat skewImage,int blockSize);
-//    cv::Mat spatialTransformer(cv::Mat skewImage);
-
-}//namepace pr
-
-
-#endif //SWIFTPR_FASTDESKEW_H
diff --git a/Prj-Android/app/src/main/cpp/include/FineMapping.h b/Prj-Android/app/src/main/cpp/include/FineMapping.h
deleted file mode 100755
index 352202e..0000000
--- a/Prj-Android/app/src/main/cpp/include/FineMapping.h
+++ /dev/null
@@ -1,32 +0,0 @@
-//
-// Created by 庾金科 on 22/09/2017.
-//
-
-#ifndef SWIFTPR_FINEMAPPING_H
-#define SWIFTPR_FINEMAPPING_H
-
-#include <opencv2/opencv.hpp>
-#include <opencv2/dnn.hpp>
-
-#include <string>
-namespace pr{
-    class FineMapping{
-    public:
-        FineMapping();
-
-
-        FineMapping(std::string prototxt,std::string caffemodel);
-        static cv::Mat FineMappingVertical(cv::Mat InputProposal,int sliceNum=15,int upper=0,int lower=-50,int windows_size=17);
-        cv::Mat FineMappingHorizon(cv::Mat FinedVertical,int leftPadding,int rightPadding);
-
-
-    private:
-        cv::dnn::Net net;
-
-    };
-
-
-
-
-}
-#endif //SWIFTPR_FINEMAPPING_H
diff --git a/Prj-Android/app/src/main/cpp/include/Pipeline.h b/Prj-Android/app/src/main/cpp/include/Pipeline.h
deleted file mode 100755
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+++ /dev/null
@@ -1,48 +0,0 @@
-//
-// Created by 庾金科 on 22/10/2017.
-//
-
-#ifndef SWIFTPR_PIPLINE_H
-#define SWIFTPR_PIPLINE_H
-
-#include "PlateDetection.h"
-#include "PlateSegmentation.h"
-#include "CNNRecognizer.h"
-#include "PlateInfo.h"
-#include "FastDeskew.h"
-#include "FineMapping.h"
-#include "Recognizer.h"
-#include "SegmentationFreeRecognizer.h"
-
-namespace pr{
-
-    const std::vector<std::string> CH_PLATE_CODE{"京", "沪", "津", "渝", "冀", "晋", "蒙", "辽", "吉", "黑", "苏", "浙", "皖", "闽", "赣", "鲁", "豫", "鄂", "湘", "粤", "桂",
-                                                 "琼", "川", "贵", "云", "藏", "陕", "甘", "青", "宁", "新", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "A",
-                                                 "B", "C", "D", "E", "F", "G", "H", "J", "K", "L", "M", "N", "P", "Q", "R", "S", "T", "U", "V", "W", "X",
-                                                 "Y", "Z","港","学","使","警","澳","挂","军","北","南","广","沈","兰","成","济","海","民","航","空"};
-
-    const int SEGMENTATION_FREE_METHOD = 0;
-    const int SEGMENTATION_BASED_METHOD = 1;
-
-    class PipelinePR{
-    public:
-        GeneralRecognizer *generalRecognizer;
-        PlateDetection *plateDetection;
-        PlateSegmentation *plateSegmentation;
-        FineMapping *fineMapping;
-        SegmentationFreeRecognizer *segmentationFreeRecognizer;
-
-        PipelinePR(std::string detector_filename,
-                   std::string finemapping_prototxt,std::string finemapping_caffemodel,
-                   std::string segmentation_prototxt,std::string segmentation_caffemodel,
-                   std::string charRecognization_proto,std::string charRecognization_caffemodel,
-                   std::string segmentationfree_proto,std::string segmentationfree_caffemodel
-        );
-        ~PipelinePR();
-
-        std::vector<std::string> plateRes;
-        std::vector<PlateInfo> RunPiplineAsImage(cv::Mat plateImage,int method);
-
-    };
-}
-#endif //SWIFTPR_PIPLINE_H
diff --git a/Prj-Android/app/src/main/cpp/include/PlateDetection.h b/Prj-Android/app/src/main/cpp/include/PlateDetection.h
deleted file mode 100755
index ded7ef9..0000000
--- a/Prj-Android/app/src/main/cpp/include/PlateDetection.h
+++ /dev/null
@@ -1,34 +0,0 @@
-//
-// Created by 庾金科 on 20/09/2017.
-//
-
-#ifndef SWIFTPR_PLATEDETECTION_H
-#define SWIFTPR_PLATEDETECTION_H
-
-#include <opencv2/opencv.hpp>
-#include <vector>
-#include "PlateInfo.h"
-
-namespace pr{
-    class PlateDetection{
-    public:
-        PlateDetection(std::string filename_cascade);
-        PlateDetection();
-        void LoadModel(std::string filename_cascade);
-        void plateDetectionRough(cv::Mat InputImage,std::vector<pr::PlateInfo>  &plateInfos,int min_w=36,int max_w=800);
-//        std::vector<pr::PlateInfo> plateDetectionRough(cv::Mat InputImage,int min_w= 60,int max_h = 400);
-
-
-//        std::vector<pr::PlateInfo> plateDetectionRoughByMultiScaleEdge(cv::Mat InputImage);
-
-
-
-    public:
-        cv::CascadeClassifier cascade;
-
-
-    };
-
-}// namespace pr
-
-#endif //SWIFTPR_PLATEDETECTION_H
diff --git a/Prj-Android/app/src/main/cpp/include/PlateInfo.h b/Prj-Android/app/src/main/cpp/include/PlateInfo.h
deleted file mode 100755
index f500bb5..0000000
--- a/Prj-Android/app/src/main/cpp/include/PlateInfo.h
+++ /dev/null
@@ -1,126 +0,0 @@
-//
-// Created by 庾金科 on 20/09/2017.
-//
-
-#ifndef SWIFTPR_PLATEINFO_H
-#define SWIFTPR_PLATEINFO_H
-#include <opencv2/opencv.hpp>
-namespace pr {
-
-    typedef std::vector<cv::Mat> Character;
-
-    enum PlateColor { BLUE, YELLOW, WHITE, GREEN, BLACK,UNKNOWN};
-    enum CharType {CHINESE,LETTER,LETTER_NUMS,INVALID};
-
-
-    class PlateInfo {
-        public:
-            std::vector<std::pair<CharType,cv::Mat>> plateChars;
-            std::vector<std::pair<CharType,cv::Mat>> plateCoding;
-            float confidence = 0;
-            PlateInfo(const cv::Mat &plateData, std::string plateName, cv::Rect plateRect, PlateColor plateType) {
-                    licensePlate = plateData;
-                    name = plateName;
-                    ROI = plateRect;
-                    Type = plateType;
-                }
-            PlateInfo(const cv::Mat &plateData, cv::Rect plateRect, PlateColor plateType) {
-                licensePlate = plateData;
-                ROI = plateRect;
-                Type = plateType;
-            }
-            PlateInfo(const cv::Mat &plateData, cv::Rect plateRect) {
-                licensePlate = plateData;
-                ROI = plateRect;
-            }
-            PlateInfo() {
-
-            }
-
-            cv::Mat getPlateImage() {
-                return licensePlate;
-            }
-
-            void setPlateImage(cv::Mat plateImage){
-                licensePlate = plateImage;
-            }
-
-            cv::Rect getPlateRect() {
-                return ROI;
-            }
-
-            void setPlateRect(cv::Rect plateRect)   {
-                ROI = plateRect;
-            }
-            cv::String getPlateName() {
-                return name;
-
-            }
-            void setPlateName(cv::String plateName) {
-                name = plateName;
-            }
-            int getPlateType() {
-                return Type;
-            }
-
-            void appendPlateChar(const std::pair<CharType,cv::Mat> &plateChar)
-            {
-                plateChars.push_back(plateChar);
-            }
-
-            void appendPlateCoding(const std::pair<CharType,cv::Mat> &charProb){
-                plateCoding.push_back(charProb);
-            }
-
-    //        cv::Mat getPlateChars(int id) {
-    //            if(id<PlateChars.size())
-    //                return PlateChars[id];
-    //        }
-            std::string decodePlateNormal(std::vector<std::string> mappingTable) {
-                std::string decode;
-                for(auto plate:plateCoding) {
-                    float *prob = (float *)plate.second.data;
-                    if(plate.first == CHINESE) {
-
-                        decode += mappingTable[std::max_element(prob,prob+31) - prob];
-                        confidence+=*std::max_element(prob,prob+31);
-
-
-//                        std::cout<<*std::max_element(prob,prob+31)<<std::endl;
-
-                    }
-
-                    else if(plate.first == LETTER) {
-                        decode += mappingTable[std::max_element(prob+41,prob+65)- prob];
-                        confidence+=*std::max_element(prob+41,prob+65);
-                    }
-
-                    else if(plate.first == LETTER_NUMS) {
-                        decode += mappingTable[std::max_element(prob+31,prob+65)- prob];
-                        confidence+=*std::max_element(prob+31,prob+65);
-//                        std::cout<<*std::max_element(prob+31,prob+65)<<std::endl;
-
-                    }
-                    else if(plate.first == INVALID)
-                    {
-                        decode+='*';
-                    }
-
-                }
-                name = decode;
-
-                confidence/=7;
-
-                return decode;
-            }
-
-    private:
-        cv::Mat licensePlate;
-        cv::Rect ROI;
-        std::string name ;
-        PlateColor Type;
-    };
-}
-
-
-#endif //SWIFTPR_PLATEINFO_H
diff --git a/Prj-Android/app/src/main/cpp/include/PlateSegmentation.h b/Prj-Android/app/src/main/cpp/include/PlateSegmentation.h
deleted file mode 100755
index c5767be..0000000
--- a/Prj-Android/app/src/main/cpp/include/PlateSegmentation.h
+++ /dev/null
@@ -1,39 +0,0 @@
-//
-// Created by 庾金科 on 16/10/2017.
-//
-
-#ifndef SWIFTPR_PLATESEGMENTATION_H
-#define SWIFTPR_PLATESEGMENTATION_H
-
-#include "opencv2/opencv.hpp"
-#include "opencv2/dnn.hpp"
-#include "PlateInfo.h"
-
-namespace pr{
-
-
-    class PlateSegmentation{
-    public:
-        const int PLATE_NORMAL = 6;
-        const int PLATE_NORMAL_GREEN = 7;
-        const int DEFAULT_WIDTH = 20;
-        PlateSegmentation(std::string phototxt,std::string caffemodel);
-        PlateSegmentation(){}
-        void segmentPlatePipline(PlateInfo &plateInfo,int stride,std::vector<cv::Rect> &Char_rects);
-
-        void segmentPlateBySlidingWindows(cv::Mat &plateImage,int windowsWidth,int stride,cv::Mat &respones);
-        void templateMatchFinding(const cv::Mat &respones,int windowsWidth,std::pair<float,std::vector<int>> &candidatePts);
-        void refineRegion(cv::Mat &plateImage,const std::vector<int> &candidatePts,const int padding,std::vector<cv::Rect> &rects);
-        void ExtractRegions(PlateInfo &plateInfo,std::vector<cv::Rect> &rects);
-        cv::Mat classifyResponse(const cv::Mat &cropped);
-    private:
-        cv::dnn::Net net;
-
-
-//        RefineRegion()
-
-    };
-
-}//namespace pr
-
-#endif //SWIFTPR_PLATESEGMENTATION_H
diff --git a/Prj-Android/app/src/main/cpp/include/Recognizer.h b/Prj-Android/app/src/main/cpp/include/Recognizer.h
deleted file mode 100755
index 556e42e..0000000
--- a/Prj-Android/app/src/main/cpp/include/Recognizer.h
+++ /dev/null
@@ -1,23 +0,0 @@
-//
-// Created by 庾金科 on 20/10/2017.
-//
-
-
-#ifndef SWIFTPR_RECOGNIZER_H
-#define SWIFTPR_RECOGNIZER_H
-
-#include "PlateInfo.h"
-#include "opencv2/dnn.hpp"
-namespace pr{
-    typedef cv::Mat label;
-    class GeneralRecognizer{
-        public:
-            virtual label recognizeCharacter(cv::Mat character) = 0;
-//            virtual cv::Mat SegmentationFreeForSinglePlate(cv::Mat plate) = 0;
-            void SegmentBasedSequenceRecognition(PlateInfo &plateinfo);
-            void SegmentationFreeSequenceRecognition(PlateInfo &plateInfo);
-
-    };
-
-}
-#endif //SWIFTPR_RECOGNIZER_H
diff --git a/Prj-Android/app/src/main/cpp/include/SegmentationFreeRecognizer.h b/Prj-Android/app/src/main/cpp/include/SegmentationFreeRecognizer.h
deleted file mode 100755
index 583899e..0000000
--- a/Prj-Android/app/src/main/cpp/include/SegmentationFreeRecognizer.h
+++ /dev/null
@@ -1,28 +0,0 @@
-//
-// Created by 庾金科 on 28/11/2017.
-//
-
-#ifndef SWIFTPR_SEGMENTATIONFREERECOGNIZER_H
-#define SWIFTPR_SEGMENTATIONFREERECOGNIZER_H
-
-#include "Recognizer.h"
-namespace pr{
-
-
-    class SegmentationFreeRecognizer{
-    public:
-        const int CHAR_INPUT_W = 14;
-        const int CHAR_INPUT_H = 30;
-        const int CHAR_LEN = 84;
-
-        SegmentationFreeRecognizer(std::string prototxt,std::string caffemodel);
-        std::pair<std::string,float> SegmentationFreeForSinglePlate(cv::Mat plate,std::vector<std::string> mapping_table);
-
-
-    private:
-        cv::dnn::Net net;
-
-    };
-
-}
-#endif //SWIFTPR_SEGMENTATIONFREERECOGNIZER_H
diff --git a/Prj-Android/app/src/main/cpp/include/niBlackThreshold.h b/Prj-Android/app/src/main/cpp/include/niBlackThreshold.h
deleted file mode 100755
index 5ad7e14..0000000
--- a/Prj-Android/app/src/main/cpp/include/niBlackThreshold.h
+++ /dev/null
@@ -1,107 +0,0 @@
-//
-// Created by 庾金科 on 26/10/2017.
-//
-
-#ifndef SWIFTPR_NIBLACKTHRESHOLD_H
-#define SWIFTPR_NIBLACKTHRESHOLD_H
-
-
-#include <opencv2/opencv.hpp>
-using namespace cv;
-
-enum LocalBinarizationMethods{
-    BINARIZATION_NIBLACK = 0, //!< Classic Niblack binarization. See @cite Niblack1985 .
-    BINARIZATION_SAUVOLA = 1, //!< Sauvola's technique. See @cite Sauvola1997 .
-    BINARIZATION_WOLF = 2,    //!< Wolf's technique. See @cite Wolf2004 .
-    BINARIZATION_NICK = 3     //!< NICK technique. See @cite Khurshid2009 .
-};
-
-
-void niBlackThreshold( InputArray _src, OutputArray _dst, double maxValue,
-                       int type, int blockSize, double k, int binarizationMethod )
-{
-    // Input grayscale image
-    Mat src = _src.getMat();
-    CV_Assert(src.channels() == 1);
-    CV_Assert(blockSize % 2 == 1 && blockSize > 1);
-    if (binarizationMethod == BINARIZATION_SAUVOLA) {
-        CV_Assert(src.depth() == CV_8U);
-    }
-    type &= THRESH_MASK;
-    // Compute local threshold (T = mean + k * stddev)
-    // using mean and standard deviation in the neighborhood of each pixel
-    // (intermediate calculations are done with floating-point precision)
-    Mat test;
-    Mat thresh;
-    {
-        // note that: Var[X] = E[X^2] - E[X]^2
-        Mat mean, sqmean, variance, stddev, sqrtVarianceMeanSum;
-        double srcMin, stddevMax;
-        boxFilter(src, mean, CV_32F, Size(blockSize, blockSize),
-                  Point(-1,-1), true, BORDER_REPLICATE);
-        sqrBoxFilter(src, sqmean, CV_32F, Size(blockSize, blockSize),
-                     Point(-1,-1), true, BORDER_REPLICATE);
-        variance = sqmean - mean.mul(mean);
-        sqrt(variance, stddev);
-        switch (binarizationMethod)
-        {
-            case BINARIZATION_NIBLACK:
-                thresh = mean + stddev * static_cast<float>(k);
-
-                break;
-            case BINARIZATION_SAUVOLA:
-                thresh = mean.mul(1. + static_cast<float>(k) * (stddev / 128.0 - 1.));
-                break;
-            case BINARIZATION_WOLF:
-                minMaxIdx(src, &srcMin,NULL);
-                minMaxIdx(stddev, NULL, &stddevMax);
-                thresh = mean - static_cast<float>(k) * (mean - srcMin - stddev.mul(mean - srcMin) / stddevMax);
-                break;
-            case BINARIZATION_NICK:
-                sqrt(variance + sqmean, sqrtVarianceMeanSum);
-                thresh = mean + static_cast<float>(k) * sqrtVarianceMeanSum;
-                break;
-            default:
-                CV_Error( CV_StsBadArg, "Unknown binarization method" );
-                break;
-        }
-        thresh.convertTo(thresh, src.depth());
-
-        thresh.convertTo(test, src.depth());
-//
-//        cv::imshow("imagex",test);
-//        cv::waitKey(0);
-
-    }
-    // Prepare output image
-    _dst.create(src.size(), src.type());
-    Mat dst = _dst.getMat();
-    CV_Assert(src.data != dst.data);  // no inplace processing
-    // Apply thresholding: ( pixel > threshold ) ? foreground : background
-    Mat mask;
-    switch (type)
-    {
-        case THRESH_BINARY:      // dst = (src > thresh) ? maxval : 0
-        case THRESH_BINARY_INV:  // dst = (src > thresh) ? 0 : maxval
-            compare(src, thresh, mask, (type == THRESH_BINARY ? CMP_GT : CMP_LE));
-            dst.setTo(0);
-            dst.setTo(maxValue, mask);
-            break;
-        case THRESH_TRUNC:       // dst = (src > thresh) ? thresh : src
-            compare(src, thresh, mask, CMP_GT);
-            src.copyTo(dst);
-            thresh.copyTo(dst, mask);
-            break;
-        case THRESH_TOZERO:      // dst = (src > thresh) ? src : 0
-        case THRESH_TOZERO_INV:  // dst = (src > thresh) ? 0 : src
-            compare(src, thresh, mask, (type == THRESH_TOZERO ? CMP_GT : CMP_LE));
-            dst.setTo(0);
-            src.copyTo(dst, mask);
-            break;
-        default:
-            CV_Error( CV_StsBadArg, "Unknown threshold type" );
-            break;
-    }
-}
-
-#endif //SWIFTPR_NIBLACKTHRESHOLD_H
diff --git a/Prj-Android/app/src/main/cpp/javaWarpper.cpp b/Prj-Android/app/src/main/cpp/javaWarpper.cpp
deleted file mode 100755
index 24f81fd..0000000
--- a/Prj-Android/app/src/main/cpp/javaWarpper.cpp
+++ /dev/null
@@ -1,222 +0,0 @@
-#include <jni.h>
-#include <string>
-#include "include/Pipeline.h"
-
-#include <android/log.h>
-#include <android/bitmap.h>
-
-#include <opencv2/opencv.hpp>
-
-using namespace cv;
-#define LOG_TAG "System.out"
-#define  LOGI(...)  __android_log_print(ANDROID_LOG_INFO,LOG_TAG,__VA_ARGS__)
-#define  LOGD(...)  __android_log_print(ANDROID_LOG_DEBUG,LOG_TAG,__VA_ARGS__)
-#define  LOGE(...)  __android_log_print(ANDROID_LOG_ERROR,LOG_TAG,__VA_ARGS__)
-jobject mat_to_bitmap(JNIEnv * env, Mat & src, bool needPremultiplyAlpha, jobject bitmap_config){
-
-    jclass java_bitmap_class = (jclass)env->FindClass("android/graphics/Bitmap");
-    jmethodID mid = env->GetStaticMethodID(java_bitmap_class,
-                                           "createBitmap", "(IILandroid/graphics/Bitmap$Config;)Landroid/graphics/Bitmap;");
-
-    jobject bitmap = env->CallStaticObjectMethod(java_bitmap_class,
-                                                 mid, src.size().width, src.size().height, bitmap_config);
-    AndroidBitmapInfo  info;
-    void* pixels = 0;
-
-    try {
-        //validate
-        CV_Assert(AndroidBitmap_getInfo(env, bitmap, &info) >= 0);
-        CV_Assert(src.type() == CV_8UC1 || src.type() == CV_8UC3 || src.type() == CV_8UC4);
-        CV_Assert(AndroidBitmap_lockPixels(env, bitmap, &pixels) >= 0);
-        CV_Assert(pixels);
-
-        //type mat
-        if(info.format == ANDROID_BITMAP_FORMAT_RGBA_8888){
-            Mat tmp(info.height, info.width, CV_8UC4, pixels);
-            if(src.type() == CV_8UC1){
-                cvtColor(src, tmp, CV_GRAY2RGBA);
-            } else if(src.type() == CV_8UC3){
-                cvtColor(src, tmp, CV_RGB2RGBA);
-            } else if(src.type() == CV_8UC4){
-                if(needPremultiplyAlpha){
-                    cvtColor(src, tmp, COLOR_RGBA2mRGBA);
-                }else{
-                    src.copyTo(tmp);
-                }
-            }
-
-        } else{
-            Mat tmp(info.height, info.width, CV_8UC2, pixels);
-            if(src.type() == CV_8UC1){
-                cvtColor(src, tmp, CV_GRAY2BGR565);
-            } else if(src.type() == CV_8UC3){
-                cvtColor(src, tmp, CV_RGB2BGR565);
-            } else if(src.type() == CV_8UC4){
-                cvtColor(src, tmp, CV_RGBA2BGR565);
-            }
-        }
-        AndroidBitmap_unlockPixels(env, bitmap);
-        return bitmap;
-    } catch(cv::Exception e){
-        AndroidBitmap_unlockPixels(env, bitmap);
-        jclass je = env->FindClass("org/opencv/core/CvException");
-        if(!je) je = env->FindClass("java/lang/Exception");
-        env->ThrowNew(je, e.what());
-        return bitmap;
-    } catch (...){
-        AndroidBitmap_unlockPixels(env, bitmap);
-        jclass je = env->FindClass("java/lang/Exception");
-        env->ThrowNew(je, "Unknown exception in JNI code {nMatToBitmap}");
-        return bitmap;
-    }
-}
-
-std::string jstring2str(JNIEnv* env, jstring jstr)
-{
-    char*   rtn   =   NULL;
-    jclass   clsstring   =   env->FindClass("java/lang/String");
-    jstring   strencode   =   env->NewStringUTF("GB2312");
-    jmethodID   mid   =   env->GetMethodID(clsstring,   "getBytes",   "(Ljava/lang/String;)[B");
-    jbyteArray   barr=   (jbyteArray)env->CallObjectMethod(jstr,mid,strencode);
-    jsize   alen   =   env->GetArrayLength(barr);
-    jbyte*   ba   =   env->GetByteArrayElements(barr,JNI_FALSE);
-    if(alen   >   0)
-    {
-        rtn   =   (char*)malloc(alen+1);
-        memcpy(rtn,ba,alen);
-        rtn[alen]=0;
-    }
-    env->ReleaseByteArrayElements(barr,ba,0);
-    std::string stemp(rtn);
-    free(rtn);
-    return   stemp;
-}
-
-
-
-
-
-extern "C" {
-JNIEXPORT jlong JNICALL
-Java_pr_platerecognization_PlateRecognition_InitPlateRecognizer(
-        JNIEnv *env, jobject obj,
-        jstring detector_filename,
-        jstring finemapping_prototxt, jstring finemapping_caffemodel,
-        jstring segmentation_prototxt, jstring segmentation_caffemodel,
-        jstring charRecognization_proto, jstring charRecognization_caffemodel,
-        jstring segmentationfree_proto, jstring segmentationfree_caffemodel) {
-
-    std::string detector_path = jstring2str(env, detector_filename);
-    std::string finemapping_prototxt_path = jstring2str(env, finemapping_prototxt);
-    std::string finemapping_caffemodel_path = jstring2str(env, finemapping_caffemodel);
-    std::string segmentation_prototxt_path = jstring2str(env, segmentation_prototxt);
-    std::string segmentation_caffemodel_path = jstring2str(env, segmentation_caffemodel);
-    std::string charRecognization_proto_path = jstring2str(env, charRecognization_proto);
-    std::string charRecognization_caffemodel_path = jstring2str(env, charRecognization_caffemodel);
-    std::string segmentationfree_proto_path = jstring2str(env, segmentationfree_proto);
-    std::string segmentationfree_caffemodel_path = jstring2str(env, segmentationfree_caffemodel);
-
-
-    pr::PipelinePR *PR = new pr::PipelinePR(detector_path,
-                                            finemapping_prototxt_path, finemapping_caffemodel_path,
-                                            segmentation_prototxt_path, segmentation_caffemodel_path,
-                                            charRecognization_proto_path, charRecognization_caffemodel_path,
-                                            segmentationfree_proto_path, segmentationfree_caffemodel_path);
-
-    return (jlong) PR;
-}
-
-
-JNIEXPORT jstring JNICALL
-Java_pr_platerecognization_PlateRecognition_SimpleRecognization(
-        JNIEnv *env, jobject obj,
-        jlong matPtr, jlong object_pr) {
-    pr::PipelinePR *PR = (pr::PipelinePR *) object_pr;
-    cv::Mat &mRgb = *(cv::Mat *) matPtr;
-    cv::Mat rgb;
-    cv::cvtColor(mRgb,rgb,cv::COLOR_RGBA2BGR);
-
-
-    //1表示SEGMENTATION_BASED_METHOD在方法里有说明
-    std::vector<pr::PlateInfo> list_res= PR->RunPiplineAsImage(rgb,pr::SEGMENTATION_FREE_METHOD);
-//    std::vector<pr::PlateInfo> list_res= PR->RunPiplineAsImage(rgb,1);
-    std::string concat_results;
-    for(auto one:list_res)
-    {
-        //可信度
-        if (one.confidence>0.7)
-            concat_results+=one.getPlateName()+",";
-    }
-
-    concat_results = concat_results.substr(0,concat_results.size()-1);
-
-    return env->NewStringUTF(concat_results.c_str());
-
-}
-
-/**
- * 车牌号的详细信息
- * @param env
- * @param obj
- * @param matPtr
- * @param object_pr
- * @return
- */
-JNIEXPORT jobject JNICALL
-Java_pr_platerecognization_PlateRecognition_PlateInfoRecognization(
-        JNIEnv *env, jobject obj,
-        jlong matPtr, jlong object_pr) {
-    jclass plateInfo_class = env -> FindClass("pr/platerecognization/PlateInfo");
-    jmethodID mid = env->GetMethodID(plateInfo_class,"<init>","()V");
-    jobject plateInfoObj   = env->NewObject(plateInfo_class,mid);
-
-    pr::PipelinePR *PR = (pr::PipelinePR *) object_pr;
-    cv::Mat &mRgb = *(cv::Mat *) matPtr;
-    cv::Mat rgb;
-    cv::cvtColor(mRgb,rgb,cv::COLOR_RGBA2BGR);
-
-    //1表示SEGMENTATION_BASED_METHOD在方法里有说明
-    std::vector<pr::PlateInfo> list_res= PR->RunPiplineAsImage(rgb,pr::SEGMENTATION_FREE_METHOD);
-    std::string concat_results;
-    pr::PlateInfo plateInfo;
-    for(auto one:list_res)
-    {
-        //可信度
-        if (one.confidence>0.7) {
-            plateInfo = one;
-            //车牌号
-            jfieldID fid_plate_name  = env->GetFieldID(plateInfo_class,"plateName","Ljava/lang/String;");
-            env->SetObjectField(plateInfoObj,fid_plate_name,env->NewStringUTF(plateInfo.getPlateName().c_str()));
-
-            //识别区域
-            Mat src = plateInfo.getPlateImage();
-
-            jclass java_bitmap_class = (jclass)env->FindClass("android/graphics/Bitmap$Config");
-            jmethodID bitmap_mid = env->GetStaticMethodID(java_bitmap_class,
-                                                          "nativeToConfig", "(I)Landroid/graphics/Bitmap$Config;");
-            jobject bitmap_config = env->CallStaticObjectMethod(java_bitmap_class, bitmap_mid, 5);
-
-            jfieldID fid_bitmap = env->GetFieldID(plateInfo_class, "bitmap","Landroid/graphics/Bitmap;");
-            jobject _bitmap = mat_to_bitmap(env, src, false, bitmap_config);
-            env->SetObjectField(plateInfoObj,fid_bitmap, _bitmap);
-            return plateInfoObj;
-        }
-    }
-    return plateInfoObj;
-
-}
-
-
-JNIEXPORT void JNICALL
-Java_pr_platerecognization_PlateRecognition_ReleasePlateRecognizer(
-        JNIEnv *env, jobject obj,
-        jlong object_re) {
-//    std::string hello = "Hello from C++";
-    pr::PipelinePR *PR = (pr::PipelinePR *) object_re;
-    delete PR;
-
-}
-}
-
-
-
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/3rdparty/libs/armeabi-v7a/libIlmImf.a b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/3rdparty/libs/armeabi-v7a/libIlmImf.a
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diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/3rdparty/libs/armeabi-v7a/liblibprotobuf.a b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/3rdparty/libs/armeabi-v7a/liblibprotobuf.a
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diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/3rdparty/libs/armeabi-v7a/liblibtiff.a b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/3rdparty/libs/armeabi-v7a/liblibtiff.a
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diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/3rdparty/libs/armeabi-v7a/liblibwebp.a b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/3rdparty/libs/armeabi-v7a/liblibwebp.a
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diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/3rdparty/libs/armeabi-v7a/libtbb.a b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/3rdparty/libs/armeabi-v7a/libtbb.a
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diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/3rdparty/libs/armeabi-v7a/libtegra_hal.a b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/3rdparty/libs/armeabi-v7a/libtegra_hal.a
deleted file mode 100755
index a5fd2e6..0000000
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diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/OpenCV-armeabi-v7a.mk b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/OpenCV-armeabi-v7a.mk
deleted file mode 100755
index 776bfaf..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/OpenCV-armeabi-v7a.mk
+++ /dev/null
@@ -1,2 +0,0 @@
-OPENCV_3RDPARTY_COMPONENTS:=libjpeg-turbo libwebp libpng libtiff libjasper IlmImf libprotobuf tbb cpufeatures tegra_hal
-OPENCV_EXTRA_COMPONENTS:=z dl m log
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/OpenCV.mk b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/OpenCV.mk
deleted file mode 100755
index 502641f..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/OpenCV.mk
+++ /dev/null
@@ -1,120 +0,0 @@
-# In order to compile your application under cygwin
-# you might need to define NDK_USE_CYGPATH=1 before calling the ndk-build
-
-USER_LOCAL_PATH:=$(LOCAL_PATH)
-
-USER_LOCAL_C_INCLUDES:=$(LOCAL_C_INCLUDES)
-USER_LOCAL_CFLAGS:=$(LOCAL_CFLAGS)
-USER_LOCAL_STATIC_LIBRARIES:=$(LOCAL_STATIC_LIBRARIES)
-USER_LOCAL_SHARED_LIBRARIES:=$(LOCAL_SHARED_LIBRARIES)
-USER_LOCAL_LDLIBS:=$(LOCAL_LDLIBS)
-
-LOCAL_PATH:=$(subst ?,,$(firstword ?$(subst \, ,$(subst /, ,$(call my-dir)))))
-
-OPENCV_TARGET_ARCH_ABI:=$(TARGET_ARCH_ABI)
-OPENCV_THIS_DIR:=$(patsubst $(LOCAL_PATH)\\%,%,$(patsubst $(LOCAL_PATH)/%,%,$(call my-dir)))
-OPENCV_MK_DIR:=$(dir $(lastword $(MAKEFILE_LIST)))
-OPENCV_3RDPARTY_LIBS_DIR:=$(OPENCV_THIS_DIR)/../3rdparty/libs/$(OPENCV_TARGET_ARCH_ABI)
-OPENCV_BASEDIR:=
-OPENCV_LOCAL_C_INCLUDES:="$(LOCAL_PATH)/$(OPENCV_THIS_DIR)/include/opencv" "$(LOCAL_PATH)/$(OPENCV_THIS_DIR)/include"
-OPENCV_MODULES:=shape ml dnn objdetect superres stitching videostab calib3d features2d highgui videoio imgcodecs video photo imgproc flann core
-OPENCV_SUB_MK:=$(call my-dir)/OpenCV-$(TARGET_ARCH_ABI).mk
-
-ifeq ($(OPENCV_LIB_TYPE),)
-    OPENCV_LIB_TYPE:=SHARED
-endif
-
-ifeq ($(OPENCV_LIB_TYPE),SHARED)
-    OPENCV_LIBS:=java3
-    OPENCV_LIB_TYPE:=SHARED
-else
-    OPENCV_LIBS:=$(OPENCV_MODULES)
-    OPENCV_LIB_TYPE:=STATIC
-endif
-
-ifeq ($(OPENCV_LIB_TYPE),SHARED)
-    OPENCV_3RDPARTY_COMPONENTS:=
-    OPENCV_EXTRA_COMPONENTS:=
-else
-    include $(OPENCV_SUB_MK)
-endif
-
-ifeq ($(OPENCV_LIB_TYPE),SHARED)
-    OPENCV_LIBS_DIR:=$(OPENCV_THIS_DIR)/../libs/$(OPENCV_TARGET_ARCH_ABI)
-    OPENCV_LIB_SUFFIX:=so
-else
-    OPENCV_LIBS_DIR:=$(OPENCV_THIS_DIR)/../staticlibs/$(OPENCV_TARGET_ARCH_ABI)
-    OPENCV_LIB_SUFFIX:=a
-    OPENCV_INSTALL_MODULES:=on
-endif
-
-define add_opencv_module
-    include $(CLEAR_VARS)
-    LOCAL_MODULE:=opencv_$1
-    LOCAL_SRC_FILES:=$(OPENCV_LIBS_DIR)/libopencv_$1.$(OPENCV_LIB_SUFFIX)
-    include $(PREBUILT_$(OPENCV_LIB_TYPE)_LIBRARY)
-endef
-
-define add_opencv_3rdparty_component
-    include $(CLEAR_VARS)
-    LOCAL_MODULE:=$1
-    LOCAL_SRC_FILES:=$(OPENCV_3RDPARTY_LIBS_DIR)/lib$1.a
-    include $(PREBUILT_STATIC_LIBRARY)
-endef
-
-ifeq ($(OPENCV_MK_$(OPENCV_TARGET_ARCH_ABI)_ALREADY_INCLUDED),)
-    ifeq ($(OPENCV_INSTALL_MODULES),on)
-        $(foreach module,$(OPENCV_LIBS),$(eval $(call add_opencv_module,$(module))))
-    endif
-
-    $(foreach module,$(OPENCV_3RDPARTY_COMPONENTS),$(eval $(call add_opencv_3rdparty_component,$(module))))
-
-    ifneq ($(OPENCV_BASEDIR),)
-        OPENCV_LOCAL_C_INCLUDES += $(foreach mod, $(OPENCV_MODULES), $(OPENCV_BASEDIR)/modules/$(mod)/include)
-    endif
-
-    #turn off module installation to prevent their redefinition
-    OPENCV_MK_$(OPENCV_TARGET_ARCH_ABI)_ALREADY_INCLUDED:=on
-endif
-
-ifeq ($(OPENCV_LOCAL_CFLAGS),)
-    OPENCV_LOCAL_CFLAGS := -fPIC -DANDROID -fsigned-char
-endif
-
-include $(CLEAR_VARS)
-
-LOCAL_C_INCLUDES:=$(USER_LOCAL_C_INCLUDES)
-LOCAL_CFLAGS:=$(USER_LOCAL_CFLAGS)
-LOCAL_STATIC_LIBRARIES:=$(USER_LOCAL_STATIC_LIBRARIES)
-LOCAL_SHARED_LIBRARIES:=$(USER_LOCAL_SHARED_LIBRARIES)
-LOCAL_LDLIBS:=$(USER_LOCAL_LDLIBS)
-
-# Details: #10229
-ifeq ($(OPENCV_SKIP_ANDROID_IPP_FIX_1),)
-  LOCAL_LDFLAGS += -Wl,--exclude-libs,libippicv.a
-  LOCAL_LDFLAGS += -Wl,--exclude-libs,libippiw.a
-else
-  ifeq ($(OPENCV_SKIP_ANDROID_IPP_FIX_2),)
-    LOCAL_LDFLAGS += -Wl,-Bsymbolic
-  endif
-endif
-
-LOCAL_C_INCLUDES += $(OPENCV_LOCAL_C_INCLUDES)
-LOCAL_CFLAGS     += $(OPENCV_LOCAL_CFLAGS)
-
-ifeq ($(OPENCV_INSTALL_MODULES),on)
-    LOCAL_$(OPENCV_LIB_TYPE)_LIBRARIES += $(foreach mod, $(OPENCV_LIBS), opencv_$(mod))
-else
-    $(call __ndk_info,OpenCV: You should ignore warning about 'non-system libraries in linker flags' and 'opencv_java' library.)
-    $(call __ndk_info,        'OPENCV_INSTALL_MODULES:=on' can be used to build APK with included OpenCV binaries)
-    LOCAL_LDLIBS += -L$(call host-path,$(LOCAL_PATH)/$(OPENCV_LIBS_DIR)) $(foreach lib, $(OPENCV_LIBS), -lopencv_$(lib))
-endif
-
-ifeq ($(OPENCV_LIB_TYPE),STATIC)
-    LOCAL_STATIC_LIBRARIES += $(OPENCV_3RDPARTY_COMPONENTS)
-endif
-
-LOCAL_LDLIBS += $(foreach lib,$(OPENCV_EXTRA_COMPONENTS), -l$(lib))
-
-#restore the LOCAL_PATH
-LOCAL_PATH:=$(USER_LOCAL_PATH)
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/OpenCVConfig-version.cmake b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/OpenCVConfig-version.cmake
deleted file mode 100755
index a02d494..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/OpenCVConfig-version.cmake
+++ /dev/null
@@ -1,15 +0,0 @@
-set(OpenCV_VERSION 3.4.2)
-set(PACKAGE_VERSION ${OpenCV_VERSION})
-
-set(PACKAGE_VERSION_EXACT False)
-set(PACKAGE_VERSION_COMPATIBLE False)
-
-if(PACKAGE_FIND_VERSION VERSION_EQUAL PACKAGE_VERSION)
-  set(PACKAGE_VERSION_EXACT True)
-  set(PACKAGE_VERSION_COMPATIBLE True)
-endif()
-
-if(PACKAGE_FIND_VERSION_MAJOR EQUAL 3
-   AND PACKAGE_FIND_VERSION VERSION_LESS PACKAGE_VERSION)
-  set(PACKAGE_VERSION_COMPATIBLE True)
-endif()
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/OpenCVConfig.cmake b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/OpenCVConfig.cmake
deleted file mode 100755
index 43f2d96..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/OpenCVConfig.cmake
+++ /dev/null
@@ -1,50 +0,0 @@
-# ===================================================================================
-#  The OpenCV CMake configuration file
-#
-#             ** File generated automatically, do not modify **
-#
-#  Usage from an external project:
-#    In your CMakeLists.txt, add these lines:
-#
-#    find_package(OpenCV REQUIRED)
-#    include_directories(${OpenCV_INCLUDE_DIRS}) # Not needed for CMake >= 2.8.11
-#    target_link_libraries(MY_TARGET_NAME ${OpenCV_LIBS})
-#
-#    Or you can search for specific OpenCV modules:
-#
-#    find_package(OpenCV REQUIRED core videoio)
-#
-#    If the module is found then OPENCV_<MODULE>_FOUND is set to TRUE.
-#
-#    This file will define the following variables:
-#      - OpenCV_LIBS                     : The list of all imported targets for OpenCV modules.
-#      - OpenCV_INCLUDE_DIRS             : The OpenCV include directories.
-#      - OpenCV_ANDROID_NATIVE_API_LEVEL : Minimum required level of Android API.
-#      - OpenCV_VERSION                  : The version of this OpenCV build: "3.4.2"
-#      - OpenCV_VERSION_MAJOR            : Major version part of OpenCV_VERSION: "3"
-#      - OpenCV_VERSION_MINOR            : Minor version part of OpenCV_VERSION: "4"
-#      - OpenCV_VERSION_PATCH            : Patch version part of OpenCV_VERSION: "2"
-#      - OpenCV_VERSION_STATUS           : Development status of this build: ""
-#
-# ===================================================================================
-
-# Extract directory name from full path of the file currently being processed.
-# Note that CMake 2.8.3 introduced CMAKE_CURRENT_LIST_DIR. We reimplement it
-# for older versions of CMake to support these as well.
-if(CMAKE_VERSION VERSION_LESS "2.8.3")
-  get_filename_component(CMAKE_CURRENT_LIST_DIR "${CMAKE_CURRENT_LIST_FILE}" PATH)
-endif()
-
-if(NOT DEFINED OpenCV_CONFIG_SUBDIR)
-  set(OpenCV_CONFIG_SUBDIR "/abi-${ANDROID_NDK_ABI_NAME}")
-endif()
-
-set(OpenCV_CONFIG_PATH "${CMAKE_CURRENT_LIST_DIR}${OpenCV_CONFIG_SUBDIR}")
-if(EXISTS "${OpenCV_CONFIG_PATH}/OpenCVConfig.cmake")
-  include("${OpenCV_CONFIG_PATH}/OpenCVConfig.cmake")
-else()
-  if(NOT OpenCV_FIND_QUIETLY)
-    message(WARNING "Found OpenCV Android Pack but it has no binaries compatible with your ABI (can't find: ${OpenCV_CONFIG_SUBDIR})")
-  endif()
-  set(OpenCV_FOUND FALSE)
-endif()
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/abi-armeabi-v7a/OpenCVConfig-version.cmake b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/abi-armeabi-v7a/OpenCVConfig-version.cmake
deleted file mode 100755
index a02d494..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/abi-armeabi-v7a/OpenCVConfig-version.cmake
+++ /dev/null
@@ -1,15 +0,0 @@
-set(OpenCV_VERSION 3.4.2)
-set(PACKAGE_VERSION ${OpenCV_VERSION})
-
-set(PACKAGE_VERSION_EXACT False)
-set(PACKAGE_VERSION_COMPATIBLE False)
-
-if(PACKAGE_FIND_VERSION VERSION_EQUAL PACKAGE_VERSION)
-  set(PACKAGE_VERSION_EXACT True)
-  set(PACKAGE_VERSION_COMPATIBLE True)
-endif()
-
-if(PACKAGE_FIND_VERSION_MAJOR EQUAL 3
-   AND PACKAGE_FIND_VERSION VERSION_LESS PACKAGE_VERSION)
-  set(PACKAGE_VERSION_COMPATIBLE True)
-endif()
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/abi-armeabi-v7a/OpenCVConfig.cmake b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/abi-armeabi-v7a/OpenCVConfig.cmake
deleted file mode 100755
index da30bde..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/abi-armeabi-v7a/OpenCVConfig.cmake
+++ /dev/null
@@ -1,328 +0,0 @@
-# ===================================================================================
-#  The OpenCV CMake configuration file
-#
-#             ** File generated automatically, do not modify **
-#
-#  Usage from an external project:
-#    In your CMakeLists.txt, add these lines:
-#
-#    find_package(OpenCV REQUIRED)
-#    include_directories(${OpenCV_INCLUDE_DIRS}) # Not needed for CMake >= 2.8.11
-#    target_link_libraries(MY_TARGET_NAME ${OpenCV_LIBS})
-#
-#    Or you can search for specific OpenCV modules:
-#
-#    find_package(OpenCV REQUIRED core videoio)
-#
-#    You can also mark OpenCV components as optional:
-
-#    find_package(OpenCV REQUIRED core OPTIONAL_COMPONENTS viz)
-#
-#    If the module is found then OPENCV_<MODULE>_FOUND is set to TRUE.
-#
-#    This file will define the following variables:
-#      - OpenCV_LIBS                     : The list of all imported targets for OpenCV modules.
-#      - OpenCV_INCLUDE_DIRS             : The OpenCV include directories.
-#      - OpenCV_COMPUTE_CAPABILITIES     : The version of compute capability.
-#      - OpenCV_ANDROID_NATIVE_API_LEVEL : Minimum required level of Android API.
-#      - OpenCV_VERSION                  : The version of this OpenCV build: "3.4.2"
-#      - OpenCV_VERSION_MAJOR            : Major version part of OpenCV_VERSION: "3"
-#      - OpenCV_VERSION_MINOR            : Minor version part of OpenCV_VERSION: "4"
-#      - OpenCV_VERSION_PATCH            : Patch version part of OpenCV_VERSION: "2"
-#      - OpenCV_VERSION_STATUS           : Development status of this build: ""
-#
-#    Advanced variables:
-#      - OpenCV_SHARED                   : Use OpenCV as shared library
-#      - OpenCV_INSTALL_PATH             : OpenCV location
-#      - OpenCV_LIB_COMPONENTS           : Present OpenCV modules list
-#      - OpenCV_USE_MANGLED_PATHS        : Mangled OpenCV path flag
-#
-#    Deprecated variables:
-#      - OpenCV_VERSION_TWEAK            : Always "0"
-#
-# ===================================================================================
-
-# ======================================================
-#  Version variables:
-# ======================================================
-SET(OpenCV_VERSION 3.4.2)
-SET(OpenCV_VERSION_MAJOR  3)
-SET(OpenCV_VERSION_MINOR  4)
-SET(OpenCV_VERSION_PATCH  2)
-SET(OpenCV_VERSION_TWEAK  0)
-SET(OpenCV_VERSION_STATUS "")
-
-include(FindPackageHandleStandardArgs)
-
-if(NOT CMAKE_VERSION VERSION_LESS 2.8.8
-    AND OpenCV_FIND_COMPONENTS  # prevent excessive output
-)
-  # HANDLE_COMPONENTS was introduced in CMake 2.8.8
-  list(APPEND _OpenCV_FPHSA_ARGS HANDLE_COMPONENTS)
-  # The missing components will be handled by the FindPackageHandleStandardArgs
-  # module.
-  set(_OpenCV_HANDLE_COMPONENTS_MANUALLY FALSE)
-else()
-  # The missing components will be handled by this config.
-  set(_OpenCV_HANDLE_COMPONENTS_MANUALLY TRUE)
-endif()
-
-# Extract directory name from full path of the file currently being processed.
-# Note that CMake 2.8.3 introduced CMAKE_CURRENT_LIST_DIR. We reimplement it
-# for older versions of CMake to support these as well.
-if(CMAKE_VERSION VERSION_LESS "2.8.3")
-  get_filename_component(CMAKE_CURRENT_LIST_DIR "${CMAKE_CURRENT_LIST_FILE}" PATH)
-endif()
-
-# Extract the directory where *this* file has been installed (determined at cmake run-time)
-# Get the absolute path with no ../.. relative marks, to eliminate implicit linker warnings
-set(OpenCV_CONFIG_PATH "${CMAKE_CURRENT_LIST_DIR}")
-get_filename_component(OpenCV_INSTALL_PATH "${OpenCV_CONFIG_PATH}/../../../../" REALPATH)
-
-# Search packages for host system instead of packages for target system.
-# in case of cross compilation this macro should be defined by toolchain file
-if(NOT COMMAND find_host_package)
-    macro(find_host_package)
-        find_package(${ARGN})
-    endmacro()
-endif()
-if(NOT COMMAND find_host_program)
-    macro(find_host_program)
-        find_program(${ARGN})
-    endmacro()
-endif()
-
-
-
-# Android API level from which OpenCV has been compiled is remembered
-set(OpenCV_ANDROID_NATIVE_API_LEVEL "9")
-
-# ==============================================================
-#  Check OpenCV availability
-# ==============================================================
-if(OpenCV_ANDROID_NATIVE_API_LEVEL GREATER ANDROID_NATIVE_API_LEVEL)
-  if(NOT OpenCV_FIND_QUIETLY)
-    message(WARNING "Minimum required by OpenCV API level is android-${OpenCV_ANDROID_NATIVE_API_LEVEL}")
-  endif()
-  set(OpenCV_FOUND 0)
-  return()
-endif()
-
-
-
-
-
-# Some additional settings are required if OpenCV is built as static libs
-set(OpenCV_SHARED OFF)
-
-# Enables mangled install paths, that help with side by side installs
-set(OpenCV_USE_MANGLED_PATHS FALSE)
-
-set(OpenCV_LIB_COMPONENTS opencv_highgui;opencv_features2d;opencv_shape;opencv_imgcodecs;opencv_ml;opencv_videoio;opencv_dnn;opencv_flann;opencv_objdetect;opencv_core;opencv_calib3d;opencv_video;opencv_superres;opencv_photo;opencv_imgproc;opencv_stitching;opencv_videostab;opencv_java)
-set(OpenCV_INCLUDE_DIRS "${OpenCV_INSTALL_PATH}/sdk/native/jni/include" "${OpenCV_INSTALL_PATH}/sdk/native/jni/include/opencv")
-
-if(NOT TARGET opencv_core)
-  include(${CMAKE_CURRENT_LIST_DIR}/OpenCVModules${OpenCV_MODULES_SUFFIX}.cmake)
-endif()
-
-if(NOT CMAKE_VERSION VERSION_LESS "2.8.11")
-  # Target property INTERFACE_INCLUDE_DIRECTORIES available since 2.8.11:
-  # * http://www.cmake.org/cmake/help/v2.8.11/cmake.html#prop_tgt:INTERFACE_INCLUDE_DIRECTORIES
-  foreach(__component ${OpenCV_LIB_COMPONENTS})
-    if(TARGET ${__component})
-      set_target_properties(
-          ${__component}
-          PROPERTIES
-          INTERFACE_INCLUDE_DIRECTORIES "${OpenCV_INCLUDE_DIRS}"
-      )
-    endif()
-  endforeach()
-endif()
-
-
-if(NOT DEFINED OPENCV_MAP_IMPORTED_CONFIG)
-  if(CMAKE_GENERATOR MATCHES "Visual Studio" OR MSVC)
-    # OpenCV supports Debug and Release builds only.
-    # But MSVS has 'RelWithDebInfo' 'MinSizeRel' configurations for applications.
-    # By default CMake maps these configuration on the first available (Debug) which is wrong.
-    # Non-Debug build of Application can't be used with OpenCV Debug build (ABI mismatch problem)
-    # Add mapping of RelWithDebInfo and MinSizeRel to Release here
-    set(OPENCV_MAP_IMPORTED_CONFIG "RELWITHDEBINFO=!Release;MINSIZEREL=!Release")
-  endif()
-endif()
-set(__remap_warnings "")
-macro(ocv_map_imported_config target)
-  if(DEFINED OPENCV_MAP_IMPORTED_CONFIG) # list, "RELWITHDEBINFO=Release;MINSIZEREL=Release"
-    get_target_property(__available_configurations ${target} IMPORTED_CONFIGURATIONS)
-    foreach(remap ${OPENCV_MAP_IMPORTED_CONFIG})
-      if(remap MATCHES "^(.+)=(!?)([^!]+)$")
-        set(__remap_config "${CMAKE_MATCH_1}")
-        set(__final_config "${CMAKE_MATCH_3}")
-        set(__force_flag "${CMAKE_MATCH_2}")
-        string(TOUPPER "${__final_config}" __final_config_upper)
-        string(TOUPPER "${__remap_config}" __remap_config_upper)
-        if(";${__available_configurations};" MATCHES ";${__remap_config_upper};" AND NOT "${__force_flag}" STREQUAL "!")
-          # configuration already exists, skip remap
-          set(__remap_warnings "${__remap_warnings}... Configuration already exists ${__remap_config} (skip mapping ${__remap_config} => ${__final_config}) (available configurations: ${__available_configurations})\n")
-          continue()
-        endif()
-        if(__available_configurations AND NOT ";${__available_configurations};" MATCHES ";${__final_config_upper};")
-          # skip, configuration is not available
-          if(NOT "${__force_flag}" STREQUAL "!")
-            set(__remap_warnings "${__remap_warnings}... Configuration is not available '${__final_config}' for ${target}, build may fail (available configurations: ${__available_configurations})\n")
-          endif()
-        endif()
-        set_target_properties(${target} PROPERTIES
-            MAP_IMPORTED_CONFIG_${__remap_config} "${__final_config}"
-        )
-      else()
-        message(WARNING "Invalid entry of OPENCV_MAP_IMPORTED_CONFIG: '${remap}' (${OPENCV_MAP_IMPORTED_CONFIG})")
-      endif()
-    endforeach()
-  endif()
-endmacro()
-
-
-# ==============================================================
-#  Form list of modules (components) to find
-# ==============================================================
-if(NOT OpenCV_FIND_COMPONENTS)
-  set(OpenCV_FIND_COMPONENTS ${OpenCV_LIB_COMPONENTS})
-  list(REMOVE_ITEM OpenCV_FIND_COMPONENTS opencv_java)
-  if(GTest_FOUND OR GTEST_FOUND)
-    list(REMOVE_ITEM OpenCV_FIND_COMPONENTS opencv_ts)
-  endif()
-endif()
-
-set(OpenCV_WORLD_COMPONENTS )
-
-# expand short module names and see if requested components exist
-foreach(__cvcomponent ${OpenCV_FIND_COMPONENTS})
-  # Store the name of the original component so we can set the
-  # OpenCV_<component>_FOUND variable which can be checked by the user.
-  set (__original_cvcomponent ${__cvcomponent})
-  if(NOT __cvcomponent MATCHES "^opencv_")
-    set(__cvcomponent opencv_${__cvcomponent})
-  endif()
-  list(FIND OpenCV_LIB_COMPONENTS ${__cvcomponent} __cvcomponentIdx)
-  if(__cvcomponentIdx LESS 0)
-    if(_OpenCV_HANDLE_COMPONENTS_MANUALLY)
-      # Either the component is required or the user did not set any components at
-      # all. In the latter case, the OpenCV_FIND_REQUIRED_<component> variable
-      # will not be defined since it is not set by this config. So let's assume
-      # the implicitly set components are always required.
-      if(NOT DEFINED OpenCV_FIND_REQUIRED_${__original_cvcomponent} OR
-          OpenCV_FIND_REQUIRED_${__original_cvcomponent})
-        message(FATAL_ERROR "${__cvcomponent} is required but was not found")
-      elseif(NOT OpenCV_FIND_QUIETLY)
-        # The component was marked as optional using OPTIONAL_COMPONENTS
-        message(WARNING "Optional component ${__cvcomponent} was not found")
-      endif()
-    endif(_OpenCV_HANDLE_COMPONENTS_MANUALLY)
-    #indicate that module is NOT found
-    string(TOUPPER "${__cvcomponent}" __cvcomponentUP)
-    set(${__cvcomponentUP}_FOUND "${__cvcomponentUP}_FOUND-NOTFOUND")
-    set(OpenCV_${__original_cvcomponent}_FOUND FALSE)
-  else()
-    # Not using list(APPEND) here, because OpenCV_LIBS may not exist yet.
-    # Also not clearing OpenCV_LIBS anywhere, so that multiple calls
-    # to find_package(OpenCV) with different component lists add up.
-    set(OpenCV_LIBS ${OpenCV_LIBS} "${__cvcomponent}")
-    #indicate that module is found
-    string(TOUPPER "${__cvcomponent}" __cvcomponentUP)
-    set(${__cvcomponentUP}_FOUND 1)
-    set(OpenCV_${__original_cvcomponent}_FOUND TRUE)
-  endif()
-  if(OpenCV_SHARED AND ";${OpenCV_WORLD_COMPONENTS};" MATCHES ";${__cvcomponent};" AND NOT TARGET ${__cvcomponent})
-    get_target_property(__implib_dbg opencv_world IMPORTED_IMPLIB_DEBUG)
-    get_target_property(__implib_release opencv_world  IMPORTED_IMPLIB_RELEASE)
-    get_target_property(__location_dbg opencv_world IMPORTED_LOCATION_DEBUG)
-    get_target_property(__location_release opencv_world  IMPORTED_LOCATION_RELEASE)
-    get_target_property(__include_dir opencv_world INTERFACE_INCLUDE_DIRECTORIES)
-    add_library(${__cvcomponent} SHARED IMPORTED)
-    set_target_properties(${__cvcomponent} PROPERTIES INTERFACE_INCLUDE_DIRECTORIES "${__include_dir}")
-    if(__location_dbg)
-      set_property(TARGET ${__cvcomponent} APPEND PROPERTY IMPORTED_CONFIGURATIONS DEBUG)
-      set_target_properties(${__cvcomponent} PROPERTIES
-        IMPORTED_IMPLIB_DEBUG "${__implib_dbg}"
-        IMPORTED_LINK_INTERFACE_LIBRARIES_DEBUG ""
-        IMPORTED_LOCATION_DEBUG "${__location_dbg}"
-      )
-    endif()
-    if(__location_release)
-      set_property(TARGET ${__cvcomponent} APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE)
-      set_target_properties(${__cvcomponent} PROPERTIES
-        IMPORTED_IMPLIB_RELEASE "${__implib_release}"
-        IMPORTED_LINK_INTERFACE_LIBRARIES_RELEASE ""
-        IMPORTED_LOCATION_RELEASE "${__location_release}"
-      )
-    endif()
-  endif()
-  if(TARGET ${__cvcomponent})
-    ocv_map_imported_config(${__cvcomponent})
-  endif()
-endforeach()
-
-if(__remap_warnings AND NOT OpenCV_FIND_QUIETLY)
-  message("OpenCV: configurations remap warnings:\n${__remap_warnings}OpenCV: Check variable OPENCV_MAP_IMPORTED_CONFIG=${OPENCV_MAP_IMPORTED_CONFIG}")
-endif()
-
-# ==============================================================
-# Compatibility stuff
-# ==============================================================
-set(OpenCV_LIBRARIES ${OpenCV_LIBS})
-
-#
-# Some macroses for samples
-#
-macro(ocv_check_dependencies)
-  set(OCV_DEPENDENCIES_FOUND TRUE)
-  foreach(d ${ARGN})
-    if(NOT TARGET ${d})
-      message(WARNING "OpenCV: Can't resolve dependency: ${d}")
-      set(OCV_DEPENDENCIES_FOUND FALSE)
-      break()
-    endif()
-  endforeach()
-endmacro()
-
-# adds include directories in such way that directories from the OpenCV source tree go first
-function(ocv_include_directories)
-  set(__add_before "")
-  file(TO_CMAKE_PATH "${OpenCV_INSTALL_PATH}" __baseDir)
-  foreach(dir ${ARGN})
-    get_filename_component(__abs_dir "${dir}" ABSOLUTE)
-    if("${__abs_dir}" MATCHES "^${__baseDir}")
-      list(APPEND __add_before "${dir}")
-    else()
-      include_directories(AFTER SYSTEM "${dir}")
-    endif()
-  endforeach()
-  include_directories(BEFORE ${__add_before})
-endfunction()
-
-macro(ocv_include_modules)
-  include_directories(BEFORE "${OpenCV_INCLUDE_DIRS}")
-endmacro()
-
-macro(ocv_include_modules_recurse)
-  include_directories(BEFORE "${OpenCV_INCLUDE_DIRS}")
-endmacro()
-
-macro(ocv_target_link_libraries)
-  target_link_libraries(${ARGN})
-endmacro()
-
-# remove all matching elements from the list
-macro(ocv_list_filterout lst regex)
-  foreach(item ${${lst}})
-    if(item MATCHES "${regex}")
-      list(REMOVE_ITEM ${lst} "${item}")
-    endif()
-  endforeach()
-endmacro()
-
-# We do not actually need REQUIRED_VARS to be checked for. Just use the
-# installation directory for the status.
-find_package_handle_standard_args(OpenCV REQUIRED_VARS OpenCV_INSTALL_PATH
-                                  VERSION_VAR OpenCV_VERSION ${_OpenCV_FPHSA_ARGS})
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/abi-armeabi-v7a/OpenCVModules-release.cmake b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/abi-armeabi-v7a/OpenCVModules-release.cmake
deleted file mode 100755
index ee00ef9..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/abi-armeabi-v7a/OpenCVModules-release.cmake
+++ /dev/null
@@ -1,285 +0,0 @@
-#----------------------------------------------------------------
-# Generated CMake target import file for configuration "Release".
-#----------------------------------------------------------------
-
-# Commands may need to know the format version.
-set(CMAKE_IMPORT_FILE_VERSION 1)
-
-# Import target "libcpufeatures" for configuration "Release"
-set_property(TARGET libcpufeatures APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE)
-set_target_properties(libcpufeatures PROPERTIES
-  IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/sdk/native/3rdparty/libs/armeabi-v7a/libcpufeatures.a"
-  )
-
-list(APPEND _IMPORT_CHECK_TARGETS libcpufeatures )
-list(APPEND _IMPORT_CHECK_FILES_FOR_libcpufeatures "${_IMPORT_PREFIX}/sdk/native/3rdparty/libs/armeabi-v7a/libcpufeatures.a" )
-
-# Import target "libjpeg-turbo" for configuration "Release"
-set_property(TARGET libjpeg-turbo APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE)
-set_target_properties(libjpeg-turbo PROPERTIES
-  IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/sdk/native/3rdparty/libs/armeabi-v7a/liblibjpeg-turbo.a"
-  )
-
-list(APPEND _IMPORT_CHECK_TARGETS libjpeg-turbo )
-list(APPEND _IMPORT_CHECK_FILES_FOR_libjpeg-turbo "${_IMPORT_PREFIX}/sdk/native/3rdparty/libs/armeabi-v7a/liblibjpeg-turbo.a" )
-
-# Import target "libtiff" for configuration "Release"
-set_property(TARGET libtiff APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE)
-set_target_properties(libtiff PROPERTIES
-  IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "C;CXX"
-  IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/sdk/native/3rdparty/libs/armeabi-v7a/liblibtiff.a"
-  )
-
-list(APPEND _IMPORT_CHECK_TARGETS libtiff )
-list(APPEND _IMPORT_CHECK_FILES_FOR_libtiff "${_IMPORT_PREFIX}/sdk/native/3rdparty/libs/armeabi-v7a/liblibtiff.a" )
-
-# Import target "libwebp" for configuration "Release"
-set_property(TARGET libwebp APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE)
-set_target_properties(libwebp PROPERTIES
-  IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "C"
-  IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/sdk/native/3rdparty/libs/armeabi-v7a/liblibwebp.a"
-  )
-
-list(APPEND _IMPORT_CHECK_TARGETS libwebp )
-list(APPEND _IMPORT_CHECK_FILES_FOR_libwebp "${_IMPORT_PREFIX}/sdk/native/3rdparty/libs/armeabi-v7a/liblibwebp.a" )
-
-# Import target "libjasper" for configuration "Release"
-set_property(TARGET libjasper APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE)
-set_target_properties(libjasper PROPERTIES
-  IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/sdk/native/3rdparty/libs/armeabi-v7a/liblibjasper.a"
-  )
-
-list(APPEND _IMPORT_CHECK_TARGETS libjasper )
-list(APPEND _IMPORT_CHECK_FILES_FOR_libjasper "${_IMPORT_PREFIX}/sdk/native/3rdparty/libs/armeabi-v7a/liblibjasper.a" )
-
-# Import target "libpng" for configuration "Release"
-set_property(TARGET libpng APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE)
-set_target_properties(libpng PROPERTIES
-  IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "ASM;C"
-  IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/sdk/native/3rdparty/libs/armeabi-v7a/liblibpng.a"
-  )
-
-list(APPEND _IMPORT_CHECK_TARGETS libpng )
-list(APPEND _IMPORT_CHECK_FILES_FOR_libpng "${_IMPORT_PREFIX}/sdk/native/3rdparty/libs/armeabi-v7a/liblibpng.a" )
-
-# Import target "IlmImf" for configuration "Release"
-set_property(TARGET IlmImf APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE)
-set_target_properties(IlmImf PROPERTIES
-  IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "CXX"
-  IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/sdk/native/3rdparty/libs/armeabi-v7a/libIlmImf.a"
-  )
-
-list(APPEND _IMPORT_CHECK_TARGETS IlmImf )
-list(APPEND _IMPORT_CHECK_FILES_FOR_IlmImf "${_IMPORT_PREFIX}/sdk/native/3rdparty/libs/armeabi-v7a/libIlmImf.a" )
-
-# Import target "tbb" for configuration "Release"
-set_property(TARGET tbb APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE)
-set_target_properties(tbb PROPERTIES
-  IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "CXX"
-  IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/sdk/native/3rdparty/libs/armeabi-v7a/libtbb.a"
-  )
-
-list(APPEND _IMPORT_CHECK_TARGETS tbb )
-list(APPEND _IMPORT_CHECK_FILES_FOR_tbb "${_IMPORT_PREFIX}/sdk/native/3rdparty/libs/armeabi-v7a/libtbb.a" )
-
-# Import target "libprotobuf" for configuration "Release"
-set_property(TARGET libprotobuf APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE)
-set_target_properties(libprotobuf PROPERTIES
-  IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/sdk/native/3rdparty/libs/armeabi-v7a/liblibprotobuf.a"
-  )
-
-list(APPEND _IMPORT_CHECK_TARGETS libprotobuf )
-list(APPEND _IMPORT_CHECK_FILES_FOR_libprotobuf "${_IMPORT_PREFIX}/sdk/native/3rdparty/libs/armeabi-v7a/liblibprotobuf.a" )
-
-# Import target "tegra_hal" for configuration "Release"
-set_property(TARGET tegra_hal APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE)
-set_target_properties(tegra_hal PROPERTIES
-  IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "CXX"
-  IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/sdk/native/3rdparty/libs/armeabi-v7a/libtegra_hal.a"
-  )
-
-list(APPEND _IMPORT_CHECK_TARGETS tegra_hal )
-list(APPEND _IMPORT_CHECK_FILES_FOR_tegra_hal "${_IMPORT_PREFIX}/sdk/native/3rdparty/libs/armeabi-v7a/libtegra_hal.a" )
-
-# Import target "opencv_core" for configuration "Release"
-set_property(TARGET opencv_core APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE)
-set_target_properties(opencv_core PROPERTIES
-  IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "CXX"
-  IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/sdk/native/staticlibs/armeabi-v7a/libopencv_core.a"
-  )
-
-list(APPEND _IMPORT_CHECK_TARGETS opencv_core )
-list(APPEND _IMPORT_CHECK_FILES_FOR_opencv_core "${_IMPORT_PREFIX}/sdk/native/staticlibs/armeabi-v7a/libopencv_core.a" )
-
-# Import target "opencv_flann" for configuration "Release"
-set_property(TARGET opencv_flann APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE)
-set_target_properties(opencv_flann PROPERTIES
-  IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "CXX"
-  IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/sdk/native/staticlibs/armeabi-v7a/libopencv_flann.a"
-  )
-
-list(APPEND _IMPORT_CHECK_TARGETS opencv_flann )
-list(APPEND _IMPORT_CHECK_FILES_FOR_opencv_flann "${_IMPORT_PREFIX}/sdk/native/staticlibs/armeabi-v7a/libopencv_flann.a" )
-
-# Import target "opencv_imgproc" for configuration "Release"
-set_property(TARGET opencv_imgproc APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE)
-set_target_properties(opencv_imgproc PROPERTIES
-  IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "CXX"
-  IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/sdk/native/staticlibs/armeabi-v7a/libopencv_imgproc.a"
-  )
-
-list(APPEND _IMPORT_CHECK_TARGETS opencv_imgproc )
-list(APPEND _IMPORT_CHECK_FILES_FOR_opencv_imgproc "${_IMPORT_PREFIX}/sdk/native/staticlibs/armeabi-v7a/libopencv_imgproc.a" )
-
-# Import target "opencv_ml" for configuration "Release"
-set_property(TARGET opencv_ml APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE)
-set_target_properties(opencv_ml PROPERTIES
-  IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "CXX"
-  IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/sdk/native/staticlibs/armeabi-v7a/libopencv_ml.a"
-  )
-
-list(APPEND _IMPORT_CHECK_TARGETS opencv_ml )
-list(APPEND _IMPORT_CHECK_FILES_FOR_opencv_ml "${_IMPORT_PREFIX}/sdk/native/staticlibs/armeabi-v7a/libopencv_ml.a" )
-
-# Import target "opencv_objdetect" for configuration "Release"
-set_property(TARGET opencv_objdetect APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE)
-set_target_properties(opencv_objdetect PROPERTIES
-  IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "CXX"
-  IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/sdk/native/staticlibs/armeabi-v7a/libopencv_objdetect.a"
-  )
-
-list(APPEND _IMPORT_CHECK_TARGETS opencv_objdetect )
-list(APPEND _IMPORT_CHECK_FILES_FOR_opencv_objdetect "${_IMPORT_PREFIX}/sdk/native/staticlibs/armeabi-v7a/libopencv_objdetect.a" )
-
-# Import target "opencv_photo" for configuration "Release"
-set_property(TARGET opencv_photo APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE)
-set_target_properties(opencv_photo PROPERTIES
-  IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "CXX"
-  IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/sdk/native/staticlibs/armeabi-v7a/libopencv_photo.a"
-  )
-
-list(APPEND _IMPORT_CHECK_TARGETS opencv_photo )
-list(APPEND _IMPORT_CHECK_FILES_FOR_opencv_photo "${_IMPORT_PREFIX}/sdk/native/staticlibs/armeabi-v7a/libopencv_photo.a" )
-
-# Import target "opencv_video" for configuration "Release"
-set_property(TARGET opencv_video APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE)
-set_target_properties(opencv_video PROPERTIES
-  IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "CXX"
-  IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/sdk/native/staticlibs/armeabi-v7a/libopencv_video.a"
-  )
-
-list(APPEND _IMPORT_CHECK_TARGETS opencv_video )
-list(APPEND _IMPORT_CHECK_FILES_FOR_opencv_video "${_IMPORT_PREFIX}/sdk/native/staticlibs/armeabi-v7a/libopencv_video.a" )
-
-# Import target "opencv_dnn" for configuration "Release"
-set_property(TARGET opencv_dnn APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE)
-set_target_properties(opencv_dnn PROPERTIES
-  IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "CXX"
-  IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/sdk/native/staticlibs/armeabi-v7a/libopencv_dnn.a"
-  )
-
-list(APPEND _IMPORT_CHECK_TARGETS opencv_dnn )
-list(APPEND _IMPORT_CHECK_FILES_FOR_opencv_dnn "${_IMPORT_PREFIX}/sdk/native/staticlibs/armeabi-v7a/libopencv_dnn.a" )
-
-# Import target "opencv_imgcodecs" for configuration "Release"
-set_property(TARGET opencv_imgcodecs APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE)
-set_target_properties(opencv_imgcodecs PROPERTIES
-  IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "CXX"
-  IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/sdk/native/staticlibs/armeabi-v7a/libopencv_imgcodecs.a"
-  )
-
-list(APPEND _IMPORT_CHECK_TARGETS opencv_imgcodecs )
-list(APPEND _IMPORT_CHECK_FILES_FOR_opencv_imgcodecs "${_IMPORT_PREFIX}/sdk/native/staticlibs/armeabi-v7a/libopencv_imgcodecs.a" )
-
-# Import target "opencv_shape" for configuration "Release"
-set_property(TARGET opencv_shape APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE)
-set_target_properties(opencv_shape PROPERTIES
-  IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "CXX"
-  IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/sdk/native/staticlibs/armeabi-v7a/libopencv_shape.a"
-  )
-
-list(APPEND _IMPORT_CHECK_TARGETS opencv_shape )
-list(APPEND _IMPORT_CHECK_FILES_FOR_opencv_shape "${_IMPORT_PREFIX}/sdk/native/staticlibs/armeabi-v7a/libopencv_shape.a" )
-
-# Import target "opencv_videoio" for configuration "Release"
-set_property(TARGET opencv_videoio APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE)
-set_target_properties(opencv_videoio PROPERTIES
-  IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "CXX"
-  IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/sdk/native/staticlibs/armeabi-v7a/libopencv_videoio.a"
-  )
-
-list(APPEND _IMPORT_CHECK_TARGETS opencv_videoio )
-list(APPEND _IMPORT_CHECK_FILES_FOR_opencv_videoio "${_IMPORT_PREFIX}/sdk/native/staticlibs/armeabi-v7a/libopencv_videoio.a" )
-
-# Import target "opencv_highgui" for configuration "Release"
-set_property(TARGET opencv_highgui APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE)
-set_target_properties(opencv_highgui PROPERTIES
-  IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "CXX"
-  IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/sdk/native/staticlibs/armeabi-v7a/libopencv_highgui.a"
-  )
-
-list(APPEND _IMPORT_CHECK_TARGETS opencv_highgui )
-list(APPEND _IMPORT_CHECK_FILES_FOR_opencv_highgui "${_IMPORT_PREFIX}/sdk/native/staticlibs/armeabi-v7a/libopencv_highgui.a" )
-
-# Import target "opencv_superres" for configuration "Release"
-set_property(TARGET opencv_superres APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE)
-set_target_properties(opencv_superres PROPERTIES
-  IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "CXX"
-  IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/sdk/native/staticlibs/armeabi-v7a/libopencv_superres.a"
-  )
-
-list(APPEND _IMPORT_CHECK_TARGETS opencv_superres )
-list(APPEND _IMPORT_CHECK_FILES_FOR_opencv_superres "${_IMPORT_PREFIX}/sdk/native/staticlibs/armeabi-v7a/libopencv_superres.a" )
-
-# Import target "opencv_features2d" for configuration "Release"
-set_property(TARGET opencv_features2d APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE)
-set_target_properties(opencv_features2d PROPERTIES
-  IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "CXX"
-  IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/sdk/native/staticlibs/armeabi-v7a/libopencv_features2d.a"
-  )
-
-list(APPEND _IMPORT_CHECK_TARGETS opencv_features2d )
-list(APPEND _IMPORT_CHECK_FILES_FOR_opencv_features2d "${_IMPORT_PREFIX}/sdk/native/staticlibs/armeabi-v7a/libopencv_features2d.a" )
-
-# Import target "opencv_calib3d" for configuration "Release"
-set_property(TARGET opencv_calib3d APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE)
-set_target_properties(opencv_calib3d PROPERTIES
-  IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "CXX"
-  IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/sdk/native/staticlibs/armeabi-v7a/libopencv_calib3d.a"
-  )
-
-list(APPEND _IMPORT_CHECK_TARGETS opencv_calib3d )
-list(APPEND _IMPORT_CHECK_FILES_FOR_opencv_calib3d "${_IMPORT_PREFIX}/sdk/native/staticlibs/armeabi-v7a/libopencv_calib3d.a" )
-
-# Import target "opencv_java" for configuration "Release"
-set_property(TARGET opencv_java APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE)
-set_target_properties(opencv_java PROPERTIES
-  IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/sdk/native/libs/armeabi-v7a/libopencv_java3.so"
-  IMPORTED_SONAME_RELEASE "libopencv_java3.so"
-  )
-
-list(APPEND _IMPORT_CHECK_TARGETS opencv_java )
-list(APPEND _IMPORT_CHECK_FILES_FOR_opencv_java "${_IMPORT_PREFIX}/sdk/native/libs/armeabi-v7a/libopencv_java3.so" )
-
-# Import target "opencv_stitching" for configuration "Release"
-set_property(TARGET opencv_stitching APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE)
-set_target_properties(opencv_stitching PROPERTIES
-  IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "CXX"
-  IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/sdk/native/staticlibs/armeabi-v7a/libopencv_stitching.a"
-  )
-
-list(APPEND _IMPORT_CHECK_TARGETS opencv_stitching )
-list(APPEND _IMPORT_CHECK_FILES_FOR_opencv_stitching "${_IMPORT_PREFIX}/sdk/native/staticlibs/armeabi-v7a/libopencv_stitching.a" )
-
-# Import target "opencv_videostab" for configuration "Release"
-set_property(TARGET opencv_videostab APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE)
-set_target_properties(opencv_videostab PROPERTIES
-  IMPORTED_LINK_INTERFACE_LANGUAGES_RELEASE "CXX"
-  IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/sdk/native/staticlibs/armeabi-v7a/libopencv_videostab.a"
-  )
-
-list(APPEND _IMPORT_CHECK_TARGETS opencv_videostab )
-list(APPEND _IMPORT_CHECK_FILES_FOR_opencv_videostab "${_IMPORT_PREFIX}/sdk/native/staticlibs/armeabi-v7a/libopencv_videostab.a" )
-
-# Commands beyond this point should not need to know the version.
-set(CMAKE_IMPORT_FILE_VERSION)
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/abi-armeabi-v7a/OpenCVModules.cmake b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/abi-armeabi-v7a/OpenCVModules.cmake
deleted file mode 100755
index 1fee904..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/abi-armeabi-v7a/OpenCVModules.cmake
+++ /dev/null
@@ -1,263 +0,0 @@
-# Generated by CMake 2.8.12.2
-
-if("${CMAKE_MAJOR_VERSION}.${CMAKE_MINOR_VERSION}" LESS 2.5)
-   message(FATAL_ERROR "CMake >= 2.6.0 required")
-endif()
-cmake_policy(PUSH)
-cmake_policy(VERSION 2.6)
-#----------------------------------------------------------------
-# Generated CMake target import file.
-#----------------------------------------------------------------
-
-# Commands may need to know the format version.
-set(CMAKE_IMPORT_FILE_VERSION 1)
-
-# Protect against multiple inclusion, which would fail when already imported targets are added once more.
-set(_targetsDefined)
-set(_targetsNotDefined)
-set(_expectedTargets)
-foreach(_expectedTarget libcpufeatures libjpeg-turbo libtiff libwebp libjasper libpng IlmImf tbb libprotobuf tegra_hal opencv_core opencv_flann opencv_imgproc opencv_ml opencv_objdetect opencv_photo opencv_video opencv_dnn opencv_imgcodecs opencv_shape opencv_videoio opencv_highgui opencv_superres opencv_features2d opencv_calib3d opencv_java opencv_stitching opencv_videostab)
-  list(APPEND _expectedTargets ${_expectedTarget})
-  if(NOT TARGET ${_expectedTarget})
-    list(APPEND _targetsNotDefined ${_expectedTarget})
-  endif()
-  if(TARGET ${_expectedTarget})
-    list(APPEND _targetsDefined ${_expectedTarget})
-  endif()
-endforeach()
-if("${_targetsDefined}" STREQUAL "${_expectedTargets}")
-  set(CMAKE_IMPORT_FILE_VERSION)
-  cmake_policy(POP)
-  return()
-endif()
-if(NOT "${_targetsDefined}" STREQUAL "")
-  message(FATAL_ERROR "Some (but not all) targets in this export set were already defined.\nTargets Defined: ${_targetsDefined}\nTargets not yet defined: ${_targetsNotDefined}\n")
-endif()
-unset(_targetsDefined)
-unset(_targetsNotDefined)
-unset(_expectedTargets)
-
-
-# Compute the installation prefix relative to this file.
-get_filename_component(_IMPORT_PREFIX "${CMAKE_CURRENT_LIST_FILE}" PATH)
-get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH)
-get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH)
-get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH)
-get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH)
-
-# Create imported target libcpufeatures
-add_library(libcpufeatures STATIC IMPORTED)
-
-# Create imported target libjpeg-turbo
-add_library(libjpeg-turbo STATIC IMPORTED)
-
-# Create imported target libtiff
-add_library(libtiff STATIC IMPORTED)
-
-set_target_properties(libtiff PROPERTIES
-  INTERFACE_LINK_LIBRARIES "z"
-)
-
-# Create imported target libwebp
-add_library(libwebp STATIC IMPORTED)
-
-set_target_properties(libwebp PROPERTIES
-  INTERFACE_LINK_LIBRARIES "libcpufeatures"
-)
-
-# Create imported target libjasper
-add_library(libjasper STATIC IMPORTED)
-
-# Create imported target libpng
-add_library(libpng STATIC IMPORTED)
-
-set_target_properties(libpng PROPERTIES
-  INTERFACE_LINK_LIBRARIES "z"
-)
-
-# Create imported target IlmImf
-add_library(IlmImf STATIC IMPORTED)
-
-set_target_properties(IlmImf PROPERTIES
-  INTERFACE_LINK_LIBRARIES "z"
-)
-
-# Create imported target tbb
-add_library(tbb STATIC IMPORTED)
-
-set_target_properties(tbb PROPERTIES
-  INTERFACE_COMPILE_DEFINITIONS "TBB_USE_GCC_BUILTINS=1;__TBB_GCC_BUILTIN_ATOMICS_PRESENT=1"
-  INTERFACE_LINK_LIBRARIES "c;m;dl"
-)
-
-# Create imported target libprotobuf
-add_library(libprotobuf STATIC IMPORTED)
-
-# Create imported target tegra_hal
-add_library(tegra_hal STATIC IMPORTED)
-
-# Create imported target opencv_core
-add_library(opencv_core STATIC IMPORTED)
-
-set_target_properties(opencv_core PROPERTIES
-  INTERFACE_LINK_LIBRARIES "$<LINK_ONLY:dl>;$<LINK_ONLY:m>;$<LINK_ONLY:log>;$<LINK_ONLY:tegra_hal>;$<LINK_ONLY:tbb>;$<LINK_ONLY:z>;$<LINK_ONLY:libcpufeatures>;$<LINK_ONLY:tegra_hal>"
-)
-
-# Create imported target opencv_flann
-add_library(opencv_flann STATIC IMPORTED)
-
-set_target_properties(opencv_flann PROPERTIES
-  INTERFACE_LINK_LIBRARIES "opencv_core;$<LINK_ONLY:dl>;$<LINK_ONLY:m>;$<LINK_ONLY:log>;$<LINK_ONLY:tegra_hal>"
-)
-
-# Create imported target opencv_imgproc
-add_library(opencv_imgproc STATIC IMPORTED)
-
-set_target_properties(opencv_imgproc PROPERTIES
-  INTERFACE_LINK_LIBRARIES "opencv_core;$<LINK_ONLY:dl>;$<LINK_ONLY:m>;$<LINK_ONLY:log>;$<LINK_ONLY:tegra_hal>"
-)
-
-# Create imported target opencv_ml
-add_library(opencv_ml STATIC IMPORTED)
-
-set_target_properties(opencv_ml PROPERTIES
-  INTERFACE_LINK_LIBRARIES "opencv_core;$<LINK_ONLY:dl>;$<LINK_ONLY:m>;$<LINK_ONLY:log>;$<LINK_ONLY:tegra_hal>"
-)
-
-# Create imported target opencv_objdetect
-add_library(opencv_objdetect STATIC IMPORTED)
-
-set_target_properties(opencv_objdetect PROPERTIES
-  INTERFACE_LINK_LIBRARIES "opencv_core;opencv_imgproc;$<LINK_ONLY:dl>;$<LINK_ONLY:m>;$<LINK_ONLY:log>;$<LINK_ONLY:tegra_hal>"
-)
-
-# Create imported target opencv_photo
-add_library(opencv_photo STATIC IMPORTED)
-
-set_target_properties(opencv_photo PROPERTIES
-  INTERFACE_LINK_LIBRARIES "opencv_core;opencv_imgproc;$<LINK_ONLY:dl>;$<LINK_ONLY:m>;$<LINK_ONLY:log>;$<LINK_ONLY:tegra_hal>"
-)
-
-# Create imported target opencv_video
-add_library(opencv_video STATIC IMPORTED)
-
-set_target_properties(opencv_video PROPERTIES
-  INTERFACE_LINK_LIBRARIES "opencv_core;opencv_imgproc;$<LINK_ONLY:dl>;$<LINK_ONLY:m>;$<LINK_ONLY:log>;$<LINK_ONLY:tegra_hal>"
-)
-
-# Create imported target opencv_dnn
-add_library(opencv_dnn STATIC IMPORTED)
-
-set_target_properties(opencv_dnn PROPERTIES
-  INTERFACE_LINK_LIBRARIES "opencv_core;opencv_imgproc;$<LINK_ONLY:dl>;$<LINK_ONLY:m>;$<LINK_ONLY:log>;$<LINK_ONLY:tegra_hal>;$<LINK_ONLY:libprotobuf>"
-)
-
-# Create imported target opencv_imgcodecs
-add_library(opencv_imgcodecs STATIC IMPORTED)
-
-set_target_properties(opencv_imgcodecs PROPERTIES
-  INTERFACE_LINK_LIBRARIES "opencv_core;opencv_imgproc;$<LINK_ONLY:dl>;$<LINK_ONLY:m>;$<LINK_ONLY:log>;$<LINK_ONLY:tegra_hal>;$<LINK_ONLY:z>;$<LINK_ONLY:libjpeg-turbo>;$<LINK_ONLY:libwebp>;$<LINK_ONLY:libpng>;$<LINK_ONLY:libtiff>;$<LINK_ONLY:libjasper>;$<LINK_ONLY:IlmImf>"
-)
-
-# Create imported target opencv_shape
-add_library(opencv_shape STATIC IMPORTED)
-
-set_target_properties(opencv_shape PROPERTIES
-  INTERFACE_LINK_LIBRARIES "opencv_core;opencv_imgproc;opencv_video;$<LINK_ONLY:dl>;$<LINK_ONLY:m>;$<LINK_ONLY:log>;$<LINK_ONLY:tegra_hal>"
-)
-
-# Create imported target opencv_videoio
-add_library(opencv_videoio STATIC IMPORTED)
-
-set_target_properties(opencv_videoio PROPERTIES
-  INTERFACE_LINK_LIBRARIES "opencv_core;opencv_imgproc;opencv_imgcodecs;$<LINK_ONLY:dl>;$<LINK_ONLY:m>;$<LINK_ONLY:log>;$<LINK_ONLY:tegra_hal>"
-)
-
-# Create imported target opencv_highgui
-add_library(opencv_highgui STATIC IMPORTED)
-
-set_target_properties(opencv_highgui PROPERTIES
-  INTERFACE_LINK_LIBRARIES "opencv_core;opencv_imgproc;opencv_imgcodecs;opencv_videoio;$<LINK_ONLY:dl>;$<LINK_ONLY:m>;$<LINK_ONLY:log>;$<LINK_ONLY:tegra_hal>"
-)
-
-# Create imported target opencv_superres
-add_library(opencv_superres STATIC IMPORTED)
-
-set_target_properties(opencv_superres PROPERTIES
-  INTERFACE_LINK_LIBRARIES "opencv_core;opencv_imgproc;opencv_video;opencv_imgcodecs;opencv_videoio;$<LINK_ONLY:dl>;$<LINK_ONLY:m>;$<LINK_ONLY:log>;$<LINK_ONLY:tegra_hal>"
-)
-
-# Create imported target opencv_features2d
-add_library(opencv_features2d STATIC IMPORTED)
-
-set_target_properties(opencv_features2d PROPERTIES
-  INTERFACE_LINK_LIBRARIES "opencv_core;opencv_flann;opencv_imgproc;opencv_imgcodecs;opencv_videoio;opencv_highgui;$<LINK_ONLY:dl>;$<LINK_ONLY:m>;$<LINK_ONLY:log>;$<LINK_ONLY:tegra_hal>"
-)
-
-# Create imported target opencv_calib3d
-add_library(opencv_calib3d STATIC IMPORTED)
-
-set_target_properties(opencv_calib3d PROPERTIES
-  INTERFACE_LINK_LIBRARIES "opencv_core;opencv_flann;opencv_imgproc;opencv_imgcodecs;opencv_videoio;opencv_highgui;opencv_features2d;$<LINK_ONLY:dl>;$<LINK_ONLY:m>;$<LINK_ONLY:log>;$<LINK_ONLY:tegra_hal>"
-)
-
-# Create imported target opencv_java
-add_library(opencv_java SHARED IMPORTED)
-
-set_target_properties(opencv_java PROPERTIES
-  INTERFACE_LINK_LIBRARIES "jnigraphics;log;dl;z"
-)
-
-# Create imported target opencv_stitching
-add_library(opencv_stitching STATIC IMPORTED)
-
-set_target_properties(opencv_stitching PROPERTIES
-  INTERFACE_LINK_LIBRARIES "opencv_core;opencv_flann;opencv_imgproc;opencv_imgcodecs;opencv_videoio;opencv_highgui;opencv_features2d;opencv_calib3d;$<LINK_ONLY:dl>;$<LINK_ONLY:m>;$<LINK_ONLY:log>;$<LINK_ONLY:tegra_hal>"
-)
-
-# Create imported target opencv_videostab
-add_library(opencv_videostab STATIC IMPORTED)
-
-set_target_properties(opencv_videostab PROPERTIES
-  INTERFACE_LINK_LIBRARIES "opencv_core;opencv_flann;opencv_imgproc;opencv_photo;opencv_video;opencv_imgcodecs;opencv_videoio;opencv_highgui;opencv_features2d;opencv_calib3d;$<LINK_ONLY:dl>;$<LINK_ONLY:m>;$<LINK_ONLY:log>;$<LINK_ONLY:tegra_hal>"
-)
-
-if(CMAKE_VERSION VERSION_LESS 2.8.12)
-  message(FATAL_ERROR "This file relies on consumers using CMake 2.8.12 or greater.")
-endif()
-
-# Load information for each installed configuration.
-get_filename_component(_DIR "${CMAKE_CURRENT_LIST_FILE}" PATH)
-file(GLOB CONFIG_FILES "${_DIR}/OpenCVModules-*.cmake")
-foreach(f ${CONFIG_FILES})
-  include(${f})
-endforeach()
-
-# Cleanup temporary variables.
-set(_IMPORT_PREFIX)
-
-# Loop over all imported files and verify that they actually exist
-foreach(target ${_IMPORT_CHECK_TARGETS} )
-  foreach(file ${_IMPORT_CHECK_FILES_FOR_${target}} )
-    if(NOT EXISTS "${file}" )
-      message(FATAL_ERROR "The imported target \"${target}\" references the file
-   \"${file}\"
-but this file does not exist.  Possible reasons include:
-* The file was deleted, renamed, or moved to another location.
-* An install or uninstall procedure did not complete successfully.
-* The installation package was faulty and contained
-   \"${CMAKE_CURRENT_LIST_FILE}\"
-but not all the files it references.
-")
-    endif()
-  endforeach()
-  unset(_IMPORT_CHECK_FILES_FOR_${target})
-endforeach()
-unset(_IMPORT_CHECK_TARGETS)
-
-# This file does not depend on other imported targets which have
-# been exported from the same project but in a separate export set.
-
-# Commands beyond this point should not need to know the version.
-set(CMAKE_IMPORT_FILE_VERSION)
-cmake_policy(POP)
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/android.toolchain.cmake b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/android.toolchain.cmake
deleted file mode 100755
index b37dea0..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/android.toolchain.cmake
+++ /dev/null
@@ -1,1792 +0,0 @@
-# Copyright (c) 2010-2011, Ethan Rublee
-# Copyright (c) 2011-2014, Andrey Kamaev
-# All rights reserved.
-#
-# Redistribution and use in source and binary forms, with or without
-# modification, are permitted provided that the following conditions are met:
-#
-# 1.  Redistributions of source code must retain the above copyright notice,
-#     this list of conditions and the following disclaimer.
-#
-# 2.  Redistributions in binary form must reproduce the above copyright notice,
-#     this list of conditions and the following disclaimer in the documentation
-#     and/or other materials provided with the distribution.
-#
-# 3.  Neither the name of the copyright holder nor the names of its
-#     contributors may be used to endorse or promote products derived from this
-#     software without specific prior written permission.
-#
-# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
-# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-# POSSIBILITY OF SUCH DAMAGE.
-
-# ------------------------------------------------------------------------------
-#  Android CMake toolchain file, for use with the Android NDK r5-r10d
-#  Requires cmake 2.6.3 or newer (2.8.9 or newer is recommended).
-#  See home page: https://github.com/taka-no-me/android-cmake
-#
-#  Usage Linux:
-#   $ export ANDROID_NDK=/absolute/path/to/the/android-ndk
-#   $ mkdir build && cd build
-#   $ cmake -DCMAKE_TOOLCHAIN_FILE=path/to/the/android.toolchain.cmake ..
-#   $ make -j8
-#
-#  Usage Windows:
-#     You need native port of make to build your project.
-#     Android NDK r7 (and newer) already has make.exe on board.
-#     For older NDK you have to install it separately.
-#     For example, this one: http://gnuwin32.sourceforge.net/packages/make.htm
-#
-#   $ SET ANDROID_NDK=C:\absolute\path\to\the\android-ndk
-#   $ mkdir build && cd build
-#   $ cmake.exe -G"MinGW Makefiles"
-#       -DCMAKE_TOOLCHAIN_FILE=path\to\the\android.toolchain.cmake
-#       -DCMAKE_MAKE_PROGRAM="%ANDROID_NDK%\prebuilt\windows\bin\make.exe" ..
-#   $ cmake.exe --build .
-#
-#
-#  Options (can be set as cmake parameters: -D<option_name>=<value>):
-#    ANDROID_NDK=/opt/android-ndk - path to the NDK root.
-#      Can be set as environment variable. Can be set only at first cmake run.
-#
-#    ANDROID_ABI=armeabi-v7a - specifies the target Application Binary
-#      Interface (ABI). This option nearly matches to the APP_ABI variable
-#      used by ndk-build tool from Android NDK.
-#
-#      Possible targets are:
-#        "armeabi" - ARMv5TE based CPU with software floating point operations
-#        "armeabi-v7a" - ARMv7 based devices with hardware FPU instructions
-#            this ABI target is used by default
-#        "armeabi-v7a-hard with NEON" - ARMv7 based devices with hardware FPU instructions and hardfp
-#        "armeabi-v7a with NEON" - same as armeabi-v7a, but
-#            sets NEON as floating-point unit
-#        "armeabi-v7a with VFPV3" - same as armeabi-v7a, but
-#            sets VFPV3 as floating-point unit (has 32 registers instead of 16)
-#        "armeabi-v6 with VFP" - tuned for ARMv6 processors having VFP
-#        "x86" - IA-32 instruction set
-#        "mips" - MIPS32 instruction set
-#
-#      64-bit ABIs for NDK r10 and newer:
-#        "arm64-v8a" - ARMv8 AArch64 instruction set
-#        "x86_64" - Intel64 instruction set (r1)
-#        "mips64" - MIPS64 instruction set (r6)
-#
-#    ANDROID_NATIVE_API_LEVEL=android-8 - level of Android API compile for.
-#      Option is read-only when standalone toolchain is used.
-#      Note: building for "android-L" requires explicit configuration.
-#
-#    ANDROID_TOOLCHAIN_NAME=arm-linux-androideabi-4.9 - the name of compiler
-#      toolchain to be used. The list of possible values depends on the NDK
-#      version. For NDK r10c the possible values are:
-#
-#        * aarch64-linux-android-4.9
-#        * aarch64-linux-android-clang3.4
-#        * aarch64-linux-android-clang3.5
-#        * arm-linux-androideabi-4.6
-#        * arm-linux-androideabi-4.8
-#        * arm-linux-androideabi-4.9 (default)
-#        * arm-linux-androideabi-clang3.4
-#        * arm-linux-androideabi-clang3.5
-#        * mips64el-linux-android-4.9
-#        * mips64el-linux-android-clang3.4
-#        * mips64el-linux-android-clang3.5
-#        * mipsel-linux-android-4.6
-#        * mipsel-linux-android-4.8
-#        * mipsel-linux-android-4.9
-#        * mipsel-linux-android-clang3.4
-#        * mipsel-linux-android-clang3.5
-#        * x86-4.6
-#        * x86-4.8
-#        * x86-4.9
-#        * x86-clang3.4
-#        * x86-clang3.5
-#        * x86_64-4.9
-#        * x86_64-clang3.4
-#        * x86_64-clang3.5
-#
-#    ANDROID_FORCE_ARM_BUILD=OFF - set ON to generate 32-bit ARM instructions
-#      instead of Thumb. Is not available for "armeabi-v6 with VFP"
-#      (is forced to be ON) ABI.
-#
-#    ANDROID_NO_UNDEFINED=ON - set ON to show all undefined symbols as linker
-#      errors even if they are not used.
-#
-#    ANDROID_SO_UNDEFINED=OFF - set ON to allow undefined symbols in shared
-#      libraries. Automatically turned for NDK r5x and r6x due to GLESv2
-#      problems.
-#
-#    ANDROID_STL=gnustl_static - specify the runtime to use.
-#
-#      Possible values are:
-#        none           -> Do not configure the runtime.
-#        system         -> Use the default minimal system C++ runtime library.
-#                          Implies -fno-rtti -fno-exceptions.
-#                          Is not available for standalone toolchain.
-#        system_re      -> Use the default minimal system C++ runtime library.
-#                          Implies -frtti -fexceptions.
-#                          Is not available for standalone toolchain.
-#        gabi++_static  -> Use the GAbi++ runtime as a static library.
-#                          Implies -frtti -fno-exceptions.
-#                          Available for NDK r7 and newer.
-#                          Is not available for standalone toolchain.
-#        gabi++_shared  -> Use the GAbi++ runtime as a shared library.
-#                          Implies -frtti -fno-exceptions.
-#                          Available for NDK r7 and newer.
-#                          Is not available for standalone toolchain.
-#        stlport_static -> Use the STLport runtime as a static library.
-#                          Implies -fno-rtti -fno-exceptions for NDK before r7.
-#                          Implies -frtti -fno-exceptions for NDK r7 and newer.
-#                          Is not available for standalone toolchain.
-#        stlport_shared -> Use the STLport runtime as a shared library.
-#                          Implies -fno-rtti -fno-exceptions for NDK before r7.
-#                          Implies -frtti -fno-exceptions for NDK r7 and newer.
-#                          Is not available for standalone toolchain.
-#        gnustl_static  -> Use the GNU STL as a static library.
-#                          Implies -frtti -fexceptions.
-#        gnustl_shared  -> Use the GNU STL as a shared library.
-#                          Implies -frtti -fno-exceptions.
-#                          Available for NDK r7b and newer.
-#                          Silently degrades to gnustl_static if not available.
-#        c++_static     -> Use the LLVM libc++ runtime as a static library.
-#                          Implies -frtti -fexceptions.
-#        c++_shared     -> Use the LLVM libc++ runtime as a shared library.
-#                          Implies -frtti -fno-exceptions.
-#
-#    ANDROID_STL_FORCE_FEATURES=ON - turn rtti and exceptions support based on
-#      chosen runtime. If disabled, then the user is responsible for settings
-#      these options.
-#
-#  What?:
-#    android-cmake toolchain searches for NDK/toolchain in the following order:
-#      ANDROID_NDK - cmake parameter
-#      ANDROID_NDK - environment variable
-#      ANDROID_STANDALONE_TOOLCHAIN - cmake parameter
-#      ANDROID_STANDALONE_TOOLCHAIN - environment variable
-#      ANDROID_NDK - default locations
-#      ANDROID_STANDALONE_TOOLCHAIN - default locations
-#
-#    Make sure to do the following in your scripts:
-#      SET( CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${my_cxx_flags}" )
-#      SET( CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${my_cxx_flags}" )
-#    The flags will be prepopulated with critical flags, so don't loose them.
-#    Also be aware that toolchain also sets configuration-specific compiler
-#    flags and linker flags.
-#
-#    ANDROID and BUILD_ANDROID will be set to true, you may test any of these
-#    variables to make necessary Android-specific configuration changes.
-#
-#    Also ARMEABI or ARMEABI_V7A or ARMEABI_V7A_HARD or X86 or MIPS or ARM64_V8A or X86_64 or MIPS64
-#    will be set true, mutually exclusive. NEON option will be set true
-#    if VFP is set to NEON.
-#
-# ------------------------------------------------------------------------------
-
-cmake_minimum_required( VERSION 2.6.3 )
-
-if( DEFINED CMAKE_CROSSCOMPILING )
- # subsequent toolchain loading is not really needed
- return()
-endif()
-
-if( CMAKE_TOOLCHAIN_FILE )
- # touch toolchain variable to suppress "unused variable" warning
-endif()
-
-# inherit settings in recursive loads
-get_property( _CMAKE_IN_TRY_COMPILE GLOBAL PROPERTY IN_TRY_COMPILE )
-if( _CMAKE_IN_TRY_COMPILE )
- include( "${CMAKE_CURRENT_SOURCE_DIR}/../android.toolchain.config.cmake" OPTIONAL )
-endif()
-
-# this one is important
-if( CMAKE_VERSION VERSION_GREATER "3.0.99" )
- set( CMAKE_SYSTEM_NAME Android )
-else()
- set( CMAKE_SYSTEM_NAME Linux )
-endif()
-
-# this one not so much
-set( CMAKE_SYSTEM_VERSION 1 )
-
-# rpath makes low sense for Android
-set( CMAKE_SHARED_LIBRARY_RUNTIME_C_FLAG "" )
-set( CMAKE_SKIP_RPATH TRUE CACHE BOOL "If set, runtime paths are not added when using shared libraries." )
-
-# NDK search paths
-set( ANDROID_SUPPORTED_NDK_VERSIONS ${ANDROID_EXTRA_NDK_VERSIONS} -r10d -r10c -r10b -r10 -r9d -r9c -r9b -r9 -r8e -r8d -r8c -r8b -r8 -r7c -r7b -r7 -r6b -r6 -r5c -r5b -r5 "" )
-if( NOT DEFINED ANDROID_NDK_SEARCH_PATHS )
- if( CMAKE_HOST_WIN32 )
-  file( TO_CMAKE_PATH "$ENV{PROGRAMFILES}" ANDROID_NDK_SEARCH_PATHS )
-  set( ANDROID_NDK_SEARCH_PATHS "${ANDROID_NDK_SEARCH_PATHS}" "$ENV{SystemDrive}/NVPACK" )
- else()
-  file( TO_CMAKE_PATH "$ENV{HOME}" ANDROID_NDK_SEARCH_PATHS )
-  set( ANDROID_NDK_SEARCH_PATHS /opt "${ANDROID_NDK_SEARCH_PATHS}/NVPACK" )
- endif()
-endif()
-if( NOT DEFINED ANDROID_STANDALONE_TOOLCHAIN_SEARCH_PATH )
- set( ANDROID_STANDALONE_TOOLCHAIN_SEARCH_PATH /opt/android-toolchain )
-endif()
-
-# known ABIs
-set( ANDROID_SUPPORTED_ABIS_arm "armeabi-v7a;armeabi;armeabi-v7a with NEON;armeabi-v7a-hard with NEON;armeabi-v7a with VFPV3;armeabi-v6 with VFP" )
-set( ANDROID_SUPPORTED_ABIS_arm64 "arm64-v8a" )
-set( ANDROID_SUPPORTED_ABIS_x86 "x86" )
-set( ANDROID_SUPPORTED_ABIS_x86_64 "x86_64" )
-set( ANDROID_SUPPORTED_ABIS_mips "mips" )
-set( ANDROID_SUPPORTED_ABIS_mips64 "mips64" )
-
-# API level defaults
-set( ANDROID_DEFAULT_NDK_API_LEVEL 9 )
-set( ANDROID_DEFAULT_NDK_API_LEVEL_arm64 21 )
-set( ANDROID_DEFAULT_NDK_API_LEVEL_x86 9 )
-set( ANDROID_DEFAULT_NDK_API_LEVEL_x86_64 21 )
-set( ANDROID_DEFAULT_NDK_API_LEVEL_mips 9 )
-set( ANDROID_DEFAULT_NDK_API_LEVEL_mips64 21 )
-
-
-macro( __LIST_FILTER listvar regex )
-  if( ${listvar} )
-    foreach( __val ${${listvar}} )
-      if( __val MATCHES "${regex}" )
-        list( REMOVE_ITEM ${listvar} "${__val}" )
-      endif()
-    endforeach()
-  endif()
-endmacro()
-
-macro( __INIT_VARIABLE var_name )
-  set( __test_path 0 )
-  foreach( __var ${ARGN} )
-    if( __var STREQUAL "PATH" )
-      set( __test_path 1 )
-      break()
-    endif()
-  endforeach()
-
-  if( __test_path AND NOT EXISTS "${${var_name}}" )
-    unset( ${var_name} CACHE )
-  endif()
-
-  if( " ${${var_name}}" STREQUAL " " )
-    set( __values 0 )
-    foreach( __var ${ARGN} )
-      if( __var STREQUAL "VALUES" )
-        set( __values 1 )
-      elseif( NOT __var STREQUAL "PATH" )
-        if( __var MATCHES "^ENV_.*$" )
-          string( REPLACE "ENV_" "" __var "${__var}" )
-          set( __value "$ENV{${__var}}" )
-        elseif( DEFINED ${__var} )
-          set( __value "${${__var}}" )
-        elseif( __values )
-          set( __value "${__var}" )
-        else()
-          set( __value "" )
-        endif()
-
-        if( NOT " ${__value}" STREQUAL " " AND (NOT __test_path OR EXISTS "${__value}") )
-          set( ${var_name} "${__value}" )
-          break()
-        endif()
-      endif()
-    endforeach()
-    unset( __value )
-    unset( __values )
-  endif()
-
-  if( __test_path )
-    file( TO_CMAKE_PATH "${${var_name}}" ${var_name} )
-  endif()
-  unset( __test_path )
-endmacro()
-
-macro( __DETECT_NATIVE_API_LEVEL _var _path )
-  set( __ndkApiLevelRegex "^[\t ]*#define[\t ]+__ANDROID_API__[\t ]+([0-9]+)[\t ]*.*$" )
-  file( STRINGS ${_path} __apiFileContent REGEX "${__ndkApiLevelRegex}" )
-  if( NOT __apiFileContent )
-    set( __ndkApiLevelRegex "^[\t ]*#define[\t ]+__ANDROID_API__[\t ]+__ANDROID_API_FUTURE__[\t ]*$" )
-    file( STRINGS ${_path} __apiFileContent REGEX "${__ndkApiLevelRegex}" )
-    if( __apiFileContent )
-      set(${_var} 10000)
-    else()
-      message( SEND_ERROR "Could not get Android native API level. Probably you have specified invalid level value, or your copy of NDK/toolchain is broken." )
-    endif()
-  else()
-    string( REGEX REPLACE "${__ndkApiLevelRegex}" "\\1" ${_var} "${__apiFileContent}" )
-  endif()
-  unset( __apiFileContent )
-  unset( __ndkApiLevelRegex )
-endmacro()
-
-macro( __DETECT_TOOLCHAIN_MACHINE_NAME _var _root )
- if( EXISTS "${_root}" )
-    file( GLOB __gccExePath RELATIVE "${_root}/bin/" "${_root}/bin/*-gcc${TOOL_OS_SUFFIX}" )
-    __LIST_FILTER( __gccExePath "^[.].*" )
-    list( LENGTH __gccExePath __gccExePathsCount )
-    if( NOT __gccExePathsCount EQUAL 1  AND NOT _CMAKE_IN_TRY_COMPILE )
-      message( WARNING "Could not determine machine name for compiler from ${_root}" )
-      set( ${_var} "" )
-    else()
-      get_filename_component( __gccExeName "${__gccExePath}" NAME_WE )
-      string( REPLACE "-gcc" "" ${_var} "${__gccExeName}" )
-    endif()
-    unset( __gccExePath )
-    unset( __gccExePathsCount )
-    unset( __gccExeName )
-  else()
-    set( ${_var} "" )
-  endif()
-endmacro()
-
-
-# fight against cygwin
-set( ANDROID_FORBID_SYGWIN TRUE CACHE BOOL "Prevent cmake from working under cygwin and using cygwin tools")
-mark_as_advanced( ANDROID_FORBID_SYGWIN )
-if( ANDROID_FORBID_SYGWIN )
- if( CYGWIN )
-  message( FATAL_ERROR "Android NDK and android-cmake toolchain are not welcome Cygwin. It is unlikely that this cmake toolchain will work under cygwin. But if you want to try then you can set cmake variable ANDROID_FORBID_SYGWIN to FALSE and rerun cmake." )
- endif()
-
- if( CMAKE_HOST_WIN32 )
-  # remove cygwin from PATH
-  set( __new_path "$ENV{PATH}")
-  __LIST_FILTER( __new_path "cygwin" )
-  set(ENV{PATH} "${__new_path}")
-  unset(__new_path)
- endif()
-endif()
-
-
-# detect current host platform
-if( NOT DEFINED ANDROID_NDK_HOST_X64 AND (CMAKE_HOST_SYSTEM_PROCESSOR MATCHES "amd64|x86_64|AMD64" OR CMAKE_HOST_APPLE) )
- set( ANDROID_NDK_HOST_X64 1 CACHE BOOL "Try to use 64-bit compiler toolchain" )
- mark_as_advanced( ANDROID_NDK_HOST_X64 )
-endif()
-
-set( TOOL_OS_SUFFIX "" )
-if( CMAKE_HOST_APPLE )
- set( ANDROID_NDK_HOST_SYSTEM_NAME "darwin-x86_64" )
- set( ANDROID_NDK_HOST_SYSTEM_NAME2 "darwin-x86" )
-elseif( CMAKE_HOST_WIN32 )
- set( ANDROID_NDK_HOST_SYSTEM_NAME "windows-x86_64" )
- set( ANDROID_NDK_HOST_SYSTEM_NAME2 "windows" )
- set( TOOL_OS_SUFFIX ".exe" )
-elseif( CMAKE_HOST_UNIX )
- set( ANDROID_NDK_HOST_SYSTEM_NAME "linux-x86_64" )
- set( ANDROID_NDK_HOST_SYSTEM_NAME2 "linux-x86" )
-else()
- message( FATAL_ERROR "Cross-compilation on your platform is not supported by this cmake toolchain" )
-endif()
-
-if( NOT ANDROID_NDK_HOST_X64 )
- set( ANDROID_NDK_HOST_SYSTEM_NAME ${ANDROID_NDK_HOST_SYSTEM_NAME2} )
-endif()
-
-# see if we have path to Android NDK
-if( NOT ANDROID_NDK AND NOT ANDROID_STANDALONE_TOOLCHAIN )
-  __INIT_VARIABLE( ANDROID_NDK PATH ENV_ANDROID_NDK )
-endif()
-if( NOT ANDROID_NDK )
- # see if we have path to Android standalone toolchain
- __INIT_VARIABLE( ANDROID_STANDALONE_TOOLCHAIN PATH ENV_ANDROID_STANDALONE_TOOLCHAIN )
-
- if( NOT ANDROID_STANDALONE_TOOLCHAIN )
-  #try to find Android NDK in one of the the default locations
-  set( __ndkSearchPaths )
-  foreach( __ndkSearchPath ${ANDROID_NDK_SEARCH_PATHS} )
-   foreach( suffix ${ANDROID_SUPPORTED_NDK_VERSIONS} )
-    list( APPEND __ndkSearchPaths "${__ndkSearchPath}/android-ndk${suffix}" )
-   endforeach()
-  endforeach()
-  __INIT_VARIABLE( ANDROID_NDK PATH VALUES ${__ndkSearchPaths} )
-  unset( __ndkSearchPaths )
-
-  if( ANDROID_NDK )
-   message( STATUS "Using default path for Android NDK: ${ANDROID_NDK}" )
-   message( STATUS "  If you prefer to use a different location, please define a cmake or environment variable: ANDROID_NDK" )
-  else()
-   #try to find Android standalone toolchain in one of the the default locations
-   __INIT_VARIABLE( ANDROID_STANDALONE_TOOLCHAIN PATH ANDROID_STANDALONE_TOOLCHAIN_SEARCH_PATH )
-
-   if( ANDROID_STANDALONE_TOOLCHAIN )
-    message( STATUS "Using default path for standalone toolchain ${ANDROID_STANDALONE_TOOLCHAIN}" )
-    message( STATUS "  If you prefer to use a different location, please define the variable: ANDROID_STANDALONE_TOOLCHAIN" )
-   endif( ANDROID_STANDALONE_TOOLCHAIN )
-  endif( ANDROID_NDK )
- endif( NOT ANDROID_STANDALONE_TOOLCHAIN )
-endif( NOT ANDROID_NDK )
-
-# remember found paths
-if( ANDROID_NDK )
- get_filename_component( ANDROID_NDK "${ANDROID_NDK}" ABSOLUTE )
- set( ANDROID_NDK "${ANDROID_NDK}" CACHE INTERNAL "Path of the Android NDK" FORCE )
- set( BUILD_WITH_ANDROID_NDK True )
- if( EXISTS "${ANDROID_NDK}/RELEASE.TXT" )
-  file( STRINGS "${ANDROID_NDK}/RELEASE.TXT" ANDROID_NDK_RELEASE_FULL LIMIT_COUNT 1 REGEX "r[0-9]+[a-z]?" )
-  string( REGEX MATCH "r([0-9]+)([a-z]?)" ANDROID_NDK_RELEASE "${ANDROID_NDK_RELEASE_FULL}" )
- else()
-  set( ANDROID_NDK_RELEASE "r1x" )
-  set( ANDROID_NDK_RELEASE_FULL "unreleased" )
- endif()
- string( REGEX REPLACE "r([0-9]+)([a-z]?)" "\\1*1000" ANDROID_NDK_RELEASE_NUM "${ANDROID_NDK_RELEASE}" )
- string( FIND " abcdefghijklmnopqastuvwxyz" "${CMAKE_MATCH_2}" __ndkReleaseLetterNum )
- math( EXPR ANDROID_NDK_RELEASE_NUM "${ANDROID_NDK_RELEASE_NUM}+${__ndkReleaseLetterNum}" )
-elseif( ANDROID_STANDALONE_TOOLCHAIN )
- get_filename_component( ANDROID_STANDALONE_TOOLCHAIN "${ANDROID_STANDALONE_TOOLCHAIN}" ABSOLUTE )
- # try to detect change
- if( CMAKE_AR )
-  string( LENGTH "${ANDROID_STANDALONE_TOOLCHAIN}" __length )
-  string( SUBSTRING "${CMAKE_AR}" 0 ${__length} __androidStandaloneToolchainPreviousPath )
-  if( NOT __androidStandaloneToolchainPreviousPath STREQUAL ANDROID_STANDALONE_TOOLCHAIN )
-   message( FATAL_ERROR "It is not possible to change path to the Android standalone toolchain on subsequent run." )
-  endif()
-  unset( __androidStandaloneToolchainPreviousPath )
-  unset( __length )
- endif()
- set( ANDROID_STANDALONE_TOOLCHAIN "${ANDROID_STANDALONE_TOOLCHAIN}" CACHE INTERNAL "Path of the Android standalone toolchain" FORCE )
- set( BUILD_WITH_STANDALONE_TOOLCHAIN True )
-else()
- list(GET ANDROID_NDK_SEARCH_PATHS 0 ANDROID_NDK_SEARCH_PATH)
- message( FATAL_ERROR "Could not find neither Android NDK nor Android standalone toolchain.
-    You should either set an environment variable:
-      export ANDROID_NDK=~/my-android-ndk
-    or
-      export ANDROID_STANDALONE_TOOLCHAIN=~/my-android-toolchain
-    or put the toolchain or NDK in the default path:
-      sudo ln -s ~/my-android-ndk ${ANDROID_NDK_SEARCH_PATH}/android-ndk
-      sudo ln -s ~/my-android-toolchain ${ANDROID_STANDALONE_TOOLCHAIN_SEARCH_PATH}" )
-endif()
-
-# android NDK layout
-if( BUILD_WITH_ANDROID_NDK )
- if( NOT DEFINED ANDROID_NDK_LAYOUT )
-  # try to automatically detect the layout
-  if( EXISTS "${ANDROID_NDK}/RELEASE.TXT")
-   set( ANDROID_NDK_LAYOUT "RELEASE" )
-  elseif( EXISTS "${ANDROID_NDK}/../../linux-x86/toolchain/" )
-   set( ANDROID_NDK_LAYOUT "LINARO" )
-  elseif( EXISTS "${ANDROID_NDK}/../../gcc/" )
-   set( ANDROID_NDK_LAYOUT "ANDROID" )
-  endif()
- endif()
- set( ANDROID_NDK_LAYOUT "${ANDROID_NDK_LAYOUT}" CACHE STRING "The inner layout of NDK" )
- mark_as_advanced( ANDROID_NDK_LAYOUT )
- if( ANDROID_NDK_LAYOUT STREQUAL "LINARO" )
-  set( ANDROID_NDK_HOST_SYSTEM_NAME ${ANDROID_NDK_HOST_SYSTEM_NAME2} ) # only 32-bit at the moment
-  set( ANDROID_NDK_TOOLCHAINS_PATH "${ANDROID_NDK}/../../${ANDROID_NDK_HOST_SYSTEM_NAME}/toolchain" )
-  set( ANDROID_NDK_TOOLCHAINS_SUBPATH  "" )
-  set( ANDROID_NDK_TOOLCHAINS_SUBPATH2 "" )
- elseif( ANDROID_NDK_LAYOUT STREQUAL "ANDROID" )
-  set( ANDROID_NDK_HOST_SYSTEM_NAME ${ANDROID_NDK_HOST_SYSTEM_NAME2} ) # only 32-bit at the moment
-  set( ANDROID_NDK_TOOLCHAINS_PATH "${ANDROID_NDK}/../../gcc/${ANDROID_NDK_HOST_SYSTEM_NAME}/arm" )
-  set( ANDROID_NDK_TOOLCHAINS_SUBPATH  "" )
-  set( ANDROID_NDK_TOOLCHAINS_SUBPATH2 "" )
- else() # ANDROID_NDK_LAYOUT STREQUAL "RELEASE"
-  set( ANDROID_NDK_TOOLCHAINS_PATH "${ANDROID_NDK}/toolchains" )
-  set( ANDROID_NDK_TOOLCHAINS_SUBPATH  "/prebuilt/${ANDROID_NDK_HOST_SYSTEM_NAME}" )
-  set( ANDROID_NDK_TOOLCHAINS_SUBPATH2 "/prebuilt/${ANDROID_NDK_HOST_SYSTEM_NAME2}" )
- endif()
- get_filename_component( ANDROID_NDK_TOOLCHAINS_PATH "${ANDROID_NDK_TOOLCHAINS_PATH}" ABSOLUTE )
-
- # try to detect change of NDK
- if( CMAKE_AR )
-  string( LENGTH "${ANDROID_NDK_TOOLCHAINS_PATH}" __length )
-  string( SUBSTRING "${CMAKE_AR}" 0 ${__length} __androidNdkPreviousPath )
-  if( NOT __androidNdkPreviousPath STREQUAL ANDROID_NDK_TOOLCHAINS_PATH )
-   message( FATAL_ERROR "It is not possible to change the path to the NDK on subsequent CMake run. You must remove all generated files from your build folder first.
-   " )
-  endif()
-  unset( __androidNdkPreviousPath )
-  unset( __length )
- endif()
-endif()
-
-
-# get all the details about standalone toolchain
-if( BUILD_WITH_STANDALONE_TOOLCHAIN )
- __DETECT_NATIVE_API_LEVEL( ANDROID_SUPPORTED_NATIVE_API_LEVELS "${ANDROID_STANDALONE_TOOLCHAIN}/sysroot/usr/include/android/api-level.h" )
- set( ANDROID_STANDALONE_TOOLCHAIN_API_LEVEL ${ANDROID_SUPPORTED_NATIVE_API_LEVELS} )
- set( __availableToolchains "standalone" )
- __DETECT_TOOLCHAIN_MACHINE_NAME( __availableToolchainMachines "${ANDROID_STANDALONE_TOOLCHAIN}" )
- if( NOT __availableToolchainMachines )
-  message( FATAL_ERROR "Could not determine machine name of your toolchain. Probably your Android standalone toolchain is broken." )
- endif()
- if( __availableToolchainMachines MATCHES x86_64 )
-  set( __availableToolchainArchs "x86_64" )
- elseif( __availableToolchainMachines MATCHES i686 )
-  set( __availableToolchainArchs "x86" )
- elseif( __availableToolchainMachines MATCHES aarch64 )
-  set( __availableToolchainArchs "arm64" )
- elseif( __availableToolchainMachines MATCHES arm )
-  set( __availableToolchainArchs "arm" )
- elseif( __availableToolchainMachines MATCHES mips64el )
-  set( __availableToolchainArchs "mips64" )
- elseif( __availableToolchainMachines MATCHES mipsel )
-  set( __availableToolchainArchs "mips" )
- endif()
- execute_process( COMMAND "${ANDROID_STANDALONE_TOOLCHAIN}/bin/${__availableToolchainMachines}-gcc${TOOL_OS_SUFFIX}" -dumpversion
-                  OUTPUT_VARIABLE __availableToolchainCompilerVersions OUTPUT_STRIP_TRAILING_WHITESPACE )
- string( REGEX MATCH "[0-9]+[.][0-9]+([.][0-9]+)?" __availableToolchainCompilerVersions "${__availableToolchainCompilerVersions}" )
- if( EXISTS "${ANDROID_STANDALONE_TOOLCHAIN}/bin/clang${TOOL_OS_SUFFIX}" )
-  list( APPEND __availableToolchains "standalone-clang" )
-  list( APPEND __availableToolchainMachines ${__availableToolchainMachines} )
-  list( APPEND __availableToolchainArchs ${__availableToolchainArchs} )
-  list( APPEND __availableToolchainCompilerVersions ${__availableToolchainCompilerVersions} )
- endif()
-endif()
-
-macro( __GLOB_NDK_TOOLCHAINS __availableToolchainsVar __availableToolchainsLst __toolchain_subpath )
- foreach( __toolchain ${${__availableToolchainsLst}} )
-  if( "${__toolchain}" MATCHES "-clang3[.][0-9]$" AND NOT EXISTS "${ANDROID_NDK_TOOLCHAINS_PATH}/${__toolchain}${__toolchain_subpath}" )
-   SET( __toolchainVersionRegex "^TOOLCHAIN_VERSION[\t ]+:=[\t ]+(.*)$" )
-   FILE( STRINGS "${ANDROID_NDK_TOOLCHAINS_PATH}/${__toolchain}/setup.mk" __toolchainVersionStr REGEX "${__toolchainVersionRegex}" )
-   if( __toolchainVersionStr )
-    string( REGEX REPLACE "${__toolchainVersionRegex}" "\\1" __toolchainVersionStr "${__toolchainVersionStr}" )
-    string( REGEX REPLACE "-clang3[.][0-9]$" "-${__toolchainVersionStr}" __gcc_toolchain "${__toolchain}" )
-   else()
-    string( REGEX REPLACE "-clang3[.][0-9]$" "-4.6" __gcc_toolchain "${__toolchain}" )
-   endif()
-   unset( __toolchainVersionStr )
-   unset( __toolchainVersionRegex )
-  else()
-   set( __gcc_toolchain "${__toolchain}" )
-  endif()
-  __DETECT_TOOLCHAIN_MACHINE_NAME( __machine "${ANDROID_NDK_TOOLCHAINS_PATH}/${__gcc_toolchain}${__toolchain_subpath}" )
-  if( __machine )
-   string( REGEX MATCH "[0-9]+[.][0-9]+([.][0-9x]+)?$" __version "${__gcc_toolchain}" )
-   if( __machine MATCHES x86_64 )
-    set( __arch "x86_64" )
-   elseif( __machine MATCHES i686 )
-    set( __arch "x86" )
-   elseif( __machine MATCHES aarch64 )
-    set( __arch "arm64" )
-   elseif( __machine MATCHES arm )
-    set( __arch "arm" )
-   elseif( __machine MATCHES mips64el )
-    set( __arch "mips64" )
-   elseif( __machine MATCHES mipsel )
-    set( __arch "mips" )
-   else()
-    set( __arch "" )
-   endif()
-   #message("machine: !${__machine}!\narch: !${__arch}!\nversion: !${__version}!\ntoolchain: !${__toolchain}!\n")
-   if (__arch)
-    list( APPEND __availableToolchainMachines "${__machine}" )
-    list( APPEND __availableToolchainArchs "${__arch}" )
-    list( APPEND __availableToolchainCompilerVersions "${__version}" )
-    list( APPEND ${__availableToolchainsVar} "${__toolchain}" )
-   endif()
-  endif()
-  unset( __gcc_toolchain )
- endforeach()
-endmacro()
-
-# get all the details about NDK
-if( BUILD_WITH_ANDROID_NDK )
- file( GLOB ANDROID_SUPPORTED_NATIVE_API_LEVELS RELATIVE "${ANDROID_NDK}/platforms" "${ANDROID_NDK}/platforms/android-*" )
- string( REPLACE "android-" "" ANDROID_SUPPORTED_NATIVE_API_LEVELS "${ANDROID_SUPPORTED_NATIVE_API_LEVELS}" )
- set( __availableToolchains "" )
- set( __availableToolchainMachines "" )
- set( __availableToolchainArchs "" )
- set( __availableToolchainCompilerVersions "" )
- if( ANDROID_TOOLCHAIN_NAME AND EXISTS "${ANDROID_NDK_TOOLCHAINS_PATH}/${ANDROID_TOOLCHAIN_NAME}/" )
-  # do not go through all toolchains if we know the name
-  set( __availableToolchainsLst "${ANDROID_TOOLCHAIN_NAME}" )
-  __GLOB_NDK_TOOLCHAINS( __availableToolchains __availableToolchainsLst "${ANDROID_NDK_TOOLCHAINS_SUBPATH}" )
-  if( NOT __availableToolchains AND NOT ANDROID_NDK_TOOLCHAINS_SUBPATH STREQUAL ANDROID_NDK_TOOLCHAINS_SUBPATH2 )
-   __GLOB_NDK_TOOLCHAINS( __availableToolchains __availableToolchainsLst "${ANDROID_NDK_TOOLCHAINS_SUBPATH2}" )
-   if( __availableToolchains )
-    set( ANDROID_NDK_TOOLCHAINS_SUBPATH ${ANDROID_NDK_TOOLCHAINS_SUBPATH2} )
-   endif()
-  endif()
- endif()
- if( NOT __availableToolchains )
-  file( GLOB __availableToolchainsLst RELATIVE "${ANDROID_NDK_TOOLCHAINS_PATH}" "${ANDROID_NDK_TOOLCHAINS_PATH}/*" )
-  if( __availableToolchainsLst )
-   list(SORT __availableToolchainsLst) # we need clang to go after gcc
-  endif()
-  __LIST_FILTER( __availableToolchainsLst "^[.]" )
-  __LIST_FILTER( __availableToolchainsLst "llvm" )
-  __LIST_FILTER( __availableToolchainsLst "renderscript" )
-  __GLOB_NDK_TOOLCHAINS( __availableToolchains __availableToolchainsLst "${ANDROID_NDK_TOOLCHAINS_SUBPATH}" )
-  if( NOT __availableToolchains AND NOT ANDROID_NDK_TOOLCHAINS_SUBPATH STREQUAL ANDROID_NDK_TOOLCHAINS_SUBPATH2 )
-   __GLOB_NDK_TOOLCHAINS( __availableToolchains __availableToolchainsLst "${ANDROID_NDK_TOOLCHAINS_SUBPATH2}" )
-   if( __availableToolchains )
-    set( ANDROID_NDK_TOOLCHAINS_SUBPATH ${ANDROID_NDK_TOOLCHAINS_SUBPATH2} )
-   endif()
-  endif()
- endif()
- if( NOT __availableToolchains )
-  message( FATAL_ERROR "Could not find any working toolchain in the NDK. Probably your Android NDK is broken." )
- endif()
-endif()
-
-# build list of available ABIs
-set( ANDROID_SUPPORTED_ABIS "" )
-set( __uniqToolchainArchNames ${__availableToolchainArchs} )
-list( REMOVE_DUPLICATES __uniqToolchainArchNames )
-list( SORT __uniqToolchainArchNames )
-foreach( __arch ${__uniqToolchainArchNames} )
- list( APPEND ANDROID_SUPPORTED_ABIS ${ANDROID_SUPPORTED_ABIS_${__arch}} )
-endforeach()
-unset( __uniqToolchainArchNames )
-if( NOT ANDROID_SUPPORTED_ABIS )
- message( FATAL_ERROR "No one of known Android ABIs is supported by this cmake toolchain." )
-endif()
-
-# choose target ABI
-__INIT_VARIABLE( ANDROID_ABI VALUES ${ANDROID_SUPPORTED_ABIS} )
-# verify that target ABI is supported
-list( FIND ANDROID_SUPPORTED_ABIS "${ANDROID_ABI}" __androidAbiIdx )
-if( __androidAbiIdx EQUAL -1 )
- string( REPLACE ";" "\", \"" PRINTABLE_ANDROID_SUPPORTED_ABIS  "${ANDROID_SUPPORTED_ABIS}" )
- message( FATAL_ERROR "Specified ANDROID_ABI = \"${ANDROID_ABI}\" is not supported by this cmake toolchain or your NDK/toolchain.
-   Supported values are: \"${PRINTABLE_ANDROID_SUPPORTED_ABIS}\"
-   " )
-endif()
-unset( __androidAbiIdx )
-
-# set target ABI options
-if( ANDROID_ABI STREQUAL "x86" )
- set( X86 true )
- set( ANDROID_NDK_ABI_NAME "x86" )
- set( ANDROID_ARCH_NAME "x86" )
- set( ANDROID_LLVM_TRIPLE "i686-none-linux-android" )
- set( CMAKE_SYSTEM_PROCESSOR "i686" )
-elseif( ANDROID_ABI STREQUAL "x86_64" )
- set( X86 true )
- set( X86_64 true )
- set( ANDROID_NDK_ABI_NAME "x86_64" )
- set( ANDROID_ARCH_NAME "x86_64" )
- set( CMAKE_SYSTEM_PROCESSOR "x86_64" )
- set( ANDROID_LLVM_TRIPLE "x86_64-none-linux-android" )
-elseif( ANDROID_ABI STREQUAL "mips64" )
- set( MIPS64 true )
- set( ANDROID_NDK_ABI_NAME "mips64" )
- set( ANDROID_ARCH_NAME "mips64" )
- set( ANDROID_LLVM_TRIPLE "mips64el-none-linux-android" )
- set( CMAKE_SYSTEM_PROCESSOR "mips64" )
-elseif( ANDROID_ABI STREQUAL "mips" )
- set( MIPS true )
- set( ANDROID_NDK_ABI_NAME "mips" )
- set( ANDROID_ARCH_NAME "mips" )
- set( ANDROID_LLVM_TRIPLE "mipsel-none-linux-android" )
- set( CMAKE_SYSTEM_PROCESSOR "mips" )
-elseif( ANDROID_ABI STREQUAL "arm64-v8a" )
- set( ARM64_V8A true )
- set( ANDROID_NDK_ABI_NAME "arm64-v8a" )
- set( ANDROID_ARCH_NAME "arm64" )
- set( ANDROID_LLVM_TRIPLE "aarch64-none-linux-android" )
- set( CMAKE_SYSTEM_PROCESSOR "aarch64" )
- set( VFPV3 true )
- set( NEON true )
-elseif( ANDROID_ABI STREQUAL "armeabi" )
- set( ARMEABI true )
- set( ANDROID_NDK_ABI_NAME "armeabi" )
- set( ANDROID_ARCH_NAME "arm" )
- set( ANDROID_LLVM_TRIPLE "armv5te-none-linux-androideabi" )
- set( CMAKE_SYSTEM_PROCESSOR "armv5te" )
-elseif( ANDROID_ABI STREQUAL "armeabi-v6 with VFP" )
- set( ARMEABI_V6 true )
- set( ANDROID_NDK_ABI_NAME "armeabi" )
- set( ANDROID_ARCH_NAME "arm" )
- set( ANDROID_LLVM_TRIPLE "armv5te-none-linux-androideabi" )
- set( CMAKE_SYSTEM_PROCESSOR "armv6" )
- # need always fallback to older platform
- set( ARMEABI true )
-elseif( ANDROID_ABI STREQUAL "armeabi-v7a")
- set( ARMEABI_V7A true )
- set( ANDROID_NDK_ABI_NAME "armeabi-v7a" )
- set( ANDROID_ARCH_NAME "arm" )
- set( ANDROID_LLVM_TRIPLE "armv7-none-linux-androideabi" )
- set( CMAKE_SYSTEM_PROCESSOR "armv7-a" )
-elseif( ANDROID_ABI STREQUAL "armeabi-v7a with VFPV3" )
- set( ARMEABI_V7A true )
- set( ANDROID_NDK_ABI_NAME "armeabi-v7a" )
- set( ANDROID_ARCH_NAME "arm" )
- set( ANDROID_LLVM_TRIPLE "armv7-none-linux-androideabi" )
- set( CMAKE_SYSTEM_PROCESSOR "armv7-a" )
- set( VFPV3 true )
-elseif( ANDROID_ABI STREQUAL "armeabi-v7a with NEON" )
- set( ARMEABI_V7A true )
- set( ANDROID_NDK_ABI_NAME "armeabi-v7a" )
- set( ANDROID_ARCH_NAME "arm" )
- set( ANDROID_LLVM_TRIPLE "armv7-none-linux-androideabi" )
- set( CMAKE_SYSTEM_PROCESSOR "armv7-a" )
- set( VFPV3 true )
- set( NEON true )
-elseif( ANDROID_ABI STREQUAL "armeabi-v7a-hard with NEON" )
- set( ARMEABI_V7A_HARD true )
- set( ANDROID_NDK_ABI_NAME "armeabi-v7a-hard" )
- set( ANDROID_ARCH_NAME "arm" )
- set( ANDROID_LLVM_TRIPLE "armv7-none-linux-androideabi" )
- set( CMAKE_SYSTEM_PROCESSOR "armv7-a" )
- set( VFPV3 true )
- set( NEON true )
-else()
- message( SEND_ERROR "Unknown ANDROID_ABI=\"${ANDROID_ABI}\" is specified." )
-endif()
-
-if( CMAKE_BINARY_DIR AND EXISTS "${CMAKE_BINARY_DIR}${CMAKE_FILES_DIRECTORY}/CMakeSystem.cmake" )
- # really dirty hack
- # it is not possible to change CMAKE_SYSTEM_PROCESSOR after the first run...
- file( APPEND "${CMAKE_BINARY_DIR}${CMAKE_FILES_DIRECTORY}/CMakeSystem.cmake" "SET(CMAKE_SYSTEM_PROCESSOR \"${CMAKE_SYSTEM_PROCESSOR}\")\n" )
-endif()
-
-if( ANDROID_ARCH_NAME STREQUAL "arm" AND NOT ARMEABI_V6 )
- __INIT_VARIABLE( ANDROID_FORCE_ARM_BUILD VALUES OFF )
- set( ANDROID_FORCE_ARM_BUILD ${ANDROID_FORCE_ARM_BUILD} CACHE BOOL "Use 32-bit ARM instructions instead of Thumb-1" FORCE )
- mark_as_advanced( ANDROID_FORCE_ARM_BUILD )
-else()
- unset( ANDROID_FORCE_ARM_BUILD CACHE )
-endif()
-
-# choose toolchain
-if( ANDROID_TOOLCHAIN_NAME )
- list( FIND __availableToolchains "${ANDROID_TOOLCHAIN_NAME}" __toolchainIdx )
- if( __toolchainIdx EQUAL -1 )
-  list( SORT __availableToolchains )
-  string( REPLACE ";" "\n  * " toolchains_list "${__availableToolchains}" )
-  set( toolchains_list "  * ${toolchains_list}")
-  message( FATAL_ERROR "Specified toolchain \"${ANDROID_TOOLCHAIN_NAME}\" is missing in your NDK or broken. Please verify that your NDK is working or select another compiler toolchain.
-To configure the toolchain set CMake variable ANDROID_TOOLCHAIN_NAME to one of the following values:\n${toolchains_list}\n" )
- endif()
- list( GET __availableToolchainArchs ${__toolchainIdx} __toolchainArch )
- if( NOT __toolchainArch STREQUAL ANDROID_ARCH_NAME )
-  message( SEND_ERROR "Selected toolchain \"${ANDROID_TOOLCHAIN_NAME}\" is not able to compile binaries for the \"${ANDROID_ARCH_NAME}\" platform." )
- endif()
-else()
- set( __toolchainIdx -1 )
- set( __applicableToolchains "" )
- set( __toolchainMaxVersion "0.0.0" )
- list( LENGTH __availableToolchains __availableToolchainsCount )
- math( EXPR __availableToolchainsCount "${__availableToolchainsCount}-1" )
- foreach( __idx RANGE ${__availableToolchainsCount} )
-  list( GET __availableToolchainArchs ${__idx} __toolchainArch )
-  if( __toolchainArch STREQUAL ANDROID_ARCH_NAME )
-   list( GET __availableToolchainCompilerVersions ${__idx} __toolchainVersion )
-   string( REPLACE "x" "99" __toolchainVersion "${__toolchainVersion}")
-   if( __toolchainVersion VERSION_GREATER __toolchainMaxVersion )
-    set( __toolchainMaxVersion "${__toolchainVersion}" )
-    set( __toolchainIdx ${__idx} )
-   endif()
-  endif()
- endforeach()
- unset( __availableToolchainsCount )
- unset( __toolchainMaxVersion )
- unset( __toolchainVersion )
-endif()
-unset( __toolchainArch )
-if( __toolchainIdx EQUAL -1 )
- message( FATAL_ERROR "No one of available compiler toolchains is able to compile for ${ANDROID_ARCH_NAME} platform." )
-endif()
-list( GET __availableToolchains ${__toolchainIdx} ANDROID_TOOLCHAIN_NAME )
-list( GET __availableToolchainMachines ${__toolchainIdx} ANDROID_TOOLCHAIN_MACHINE_NAME )
-list( GET __availableToolchainCompilerVersions ${__toolchainIdx} ANDROID_COMPILER_VERSION )
-
-unset( __toolchainIdx )
-unset( __availableToolchains )
-unset( __availableToolchainMachines )
-unset( __availableToolchainArchs )
-unset( __availableToolchainCompilerVersions )
-
-# choose native API level
-__INIT_VARIABLE( ANDROID_NATIVE_API_LEVEL ENV_ANDROID_NATIVE_API_LEVEL ANDROID_API_LEVEL ENV_ANDROID_API_LEVEL ANDROID_STANDALONE_TOOLCHAIN_API_LEVEL ANDROID_DEFAULT_NDK_API_LEVEL_${ANDROID_ARCH_NAME} ANDROID_DEFAULT_NDK_API_LEVEL )
-string( REPLACE "android-" "" ANDROID_NATIVE_API_LEVEL "${ANDROID_NATIVE_API_LEVEL}" )
-string( STRIP "${ANDROID_NATIVE_API_LEVEL}" ANDROID_NATIVE_API_LEVEL )
-# adjust API level
-set( __real_api_level ${ANDROID_DEFAULT_NDK_API_LEVEL_${ANDROID_ARCH_NAME}} )
-foreach( __level ${ANDROID_SUPPORTED_NATIVE_API_LEVELS} )
- if( (__level LESS ANDROID_NATIVE_API_LEVEL OR __level STREQUAL ANDROID_NATIVE_API_LEVEL) AND NOT __level LESS __real_api_level )
-  set( __real_api_level ${__level} )
- endif()
-endforeach()
-if( __real_api_level AND NOT ANDROID_NATIVE_API_LEVEL STREQUAL __real_api_level )
- message( STATUS "Adjusting Android API level 'android-${ANDROID_NATIVE_API_LEVEL}' to 'android-${__real_api_level}'")
- set( ANDROID_NATIVE_API_LEVEL ${__real_api_level} )
-endif()
-unset(__real_api_level)
-# validate
-list( FIND ANDROID_SUPPORTED_NATIVE_API_LEVELS "${ANDROID_NATIVE_API_LEVEL}" __levelIdx )
-if( __levelIdx EQUAL -1 )
- message( SEND_ERROR "Specified Android native API level 'android-${ANDROID_NATIVE_API_LEVEL}' is not supported by your NDK/toolchain.\nSupported values of ANDROID_NATIVE_API_LEVEL: ${ANDROID_SUPPORTED_NATIVE_API_LEVELS}" )
-else()
- if( BUILD_WITH_ANDROID_NDK )
-  if(EXISTS "${ANDROID_NDK}/platforms/android-${ANDROID_NATIVE_API_LEVEL}/arch-${ANDROID_ARCH_NAME}/usr/include/android/api-level.h")
-    __DETECT_NATIVE_API_LEVEL( __realApiLevel "${ANDROID_NDK}/platforms/android-${ANDROID_NATIVE_API_LEVEL}/arch-${ANDROID_ARCH_NAME}/usr/include/android/api-level.h" )
-  else()
-    __DETECT_NATIVE_API_LEVEL( __realApiLevel "${ANDROID_NDK}/sysroot/usr/include/android/api-level.h")
-  endif()
-
-  if( NOT __realApiLevel EQUAL ANDROID_NATIVE_API_LEVEL AND NOT __realApiLevel GREATER 9000 )
-   message( SEND_ERROR "Specified Android API level (${ANDROID_NATIVE_API_LEVEL}) does not match to the level found (${__realApiLevel}). Probably your copy of NDK is broken." )
-  endif()
-  unset( __realApiLevel )
- endif()
- set( ANDROID_NATIVE_API_LEVEL "${ANDROID_NATIVE_API_LEVEL}" CACHE STRING "Android API level for native code" FORCE )
- set( CMAKE_ANDROID_API ${ANDROID_NATIVE_API_LEVEL} )
- if( CMAKE_VERSION VERSION_GREATER "2.8" )
-  list( SORT ANDROID_SUPPORTED_NATIVE_API_LEVELS )
-  set_property( CACHE ANDROID_NATIVE_API_LEVEL PROPERTY STRINGS ${ANDROID_SUPPORTED_NATIVE_API_LEVELS} )
- endif()
-endif()
-unset( __levelIdx )
-
-
-# remember target ABI
-set( ANDROID_ABI "${ANDROID_ABI}" CACHE STRING "The target ABI for Android. If arm, then armeabi-v7a is recommended for hardware floating point." FORCE )
-if( CMAKE_VERSION VERSION_GREATER "2.8" )
- list( SORT ANDROID_SUPPORTED_ABIS_${ANDROID_ARCH_NAME} )
- set_property( CACHE ANDROID_ABI PROPERTY STRINGS ${ANDROID_SUPPORTED_ABIS_${ANDROID_ARCH_NAME}} )
-endif()
-
-
-# runtime choice (STL, rtti, exceptions)
-if( NOT ANDROID_STL )
-  set( ANDROID_STL gnustl_static )
-endif()
-set( ANDROID_STL "${ANDROID_STL}" CACHE STRING "C++ runtime" )
-set( ANDROID_STL_FORCE_FEATURES ON CACHE BOOL "automatically configure rtti and exceptions support based on C++ runtime" )
-mark_as_advanced( ANDROID_STL ANDROID_STL_FORCE_FEATURES )
-
-if( BUILD_WITH_ANDROID_NDK )
- if( NOT "${ANDROID_STL}" MATCHES "^(none|system|system_re|gabi\\+\\+_static|gabi\\+\\+_shared|stlport_static|stlport_shared|gnustl_static|gnustl_shared|c\\+\\+_static|c\\+\\+_shared)$")
-  message( FATAL_ERROR "ANDROID_STL is set to invalid value \"${ANDROID_STL}\".
-The possible values are:
-  none           -> Do not configure the runtime.
-  system         -> Use the default minimal system C++ runtime library.
-  system_re      -> Same as system but with rtti and exceptions.
-  gabi++_static  -> Use the GAbi++ runtime as a static library.
-  gabi++_shared  -> Use the GAbi++ runtime as a shared library.
-  stlport_static -> Use the STLport runtime as a static library.
-  stlport_shared -> Use the STLport runtime as a shared library.
-  gnustl_static  -> (default) Use the GNU STL as a static library.
-  gnustl_shared  -> Use the GNU STL as a shared library.
-  c++_shared     -> Use the LLVM libc++ runtime as a shared library.
-  c++_static     -> Use the LLVM libc++ runtime as a static library.
-" )
- endif()
-elseif( BUILD_WITH_STANDALONE_TOOLCHAIN )
- if( NOT "${ANDROID_STL}" MATCHES "^(none|gnustl_static|gnustl_shared|c\\+\\+_static|c\\+\\+_shared)$")
-  message( FATAL_ERROR "ANDROID_STL is set to invalid value \"${ANDROID_STL}\".
-The possible values are:
-  none           -> Do not configure the runtime.
-  gnustl_static  -> (default) Use the GNU STL as a static library.
-  gnustl_shared  -> Use the GNU STL as a shared library.
-  c++_shared     -> Use the LLVM libc++ runtime as a shared library.
-  c++_static     -> Use the LLVM libc++ runtime as a static library.
-" )
- endif()
-endif()
-
-unset( ANDROID_RTTI )
-unset( ANDROID_EXCEPTIONS )
-unset( ANDROID_STL_INCLUDE_DIRS )
-unset( __libstl )
-unset( __libsupcxx )
-
-if( NOT _CMAKE_IN_TRY_COMPILE AND ANDROID_NDK_RELEASE STREQUAL "r7b" AND ARMEABI_V7A AND NOT VFPV3 AND ANDROID_STL MATCHES "gnustl" )
- message( WARNING  "The GNU STL armeabi-v7a binaries from NDK r7b can crash non-NEON devices. The files provided with NDK r7b were not configured properly, resulting in crashes on Tegra2-based devices and others when trying to use certain floating-point functions (e.g., cosf, sinf, expf).
-You are strongly recommended to switch to another NDK release.
-" )
-endif()
-
-if( NOT _CMAKE_IN_TRY_COMPILE AND X86 AND ANDROID_STL MATCHES "gnustl" AND ANDROID_NDK_RELEASE STREQUAL "r6" )
-  message( WARNING  "The x86 system header file from NDK r6 has incorrect definition for ptrdiff_t. You are recommended to upgrade to a newer NDK release or manually patch the header:
-See https://android.googlesource.com/platform/development.git f907f4f9d4e56ccc8093df6fee54454b8bcab6c2
-  diff --git a/ndk/platforms/android-9/arch-x86/include/machine/_types.h b/ndk/platforms/android-9/arch-x86/include/machine/_types.h
-  index 5e28c64..65892a1 100644
-  --- a/ndk/platforms/android-9/arch-x86/include/machine/_types.h
-  +++ b/ndk/platforms/android-9/arch-x86/include/machine/_types.h
-  @@ -51,7 +51,11 @@ typedef long int       ssize_t;
-   #endif
-   #ifndef _PTRDIFF_T
-   #define _PTRDIFF_T
-  -typedef long           ptrdiff_t;
-  +#  ifdef __ANDROID__
-  +     typedef int            ptrdiff_t;
-  +#  else
-  +     typedef long           ptrdiff_t;
-  +#  endif
-   #endif
-" )
-endif()
-
-
-# setup paths and STL for standalone toolchain
-if( BUILD_WITH_STANDALONE_TOOLCHAIN )
- set( ANDROID_TOOLCHAIN_ROOT "${ANDROID_STANDALONE_TOOLCHAIN}" )
- set( ANDROID_CLANG_TOOLCHAIN_ROOT "${ANDROID_STANDALONE_TOOLCHAIN}" )
- set( ANDROID_SYSROOT "${ANDROID_STANDALONE_TOOLCHAIN}/sysroot" )
- set( ANDROID_SYSROOT_INCLUDE "${ANDROID_STANDALONE_TOOLCHAIN}/sysroot/usr/include" )
-
- if( NOT ANDROID_STL STREQUAL "none" )
-  set( ANDROID_STL_INCLUDE_DIRS "${ANDROID_STANDALONE_TOOLCHAIN}/include/c++/${ANDROID_COMPILER_VERSION}" )
-  if( NOT EXISTS "${ANDROID_STL_INCLUDE_DIRS}" )
-   # old location ( pre r8c )
-   set( ANDROID_STL_INCLUDE_DIRS "${ANDROID_STANDALONE_TOOLCHAIN}/${ANDROID_TOOLCHAIN_MACHINE_NAME}/include/c++/${ANDROID_COMPILER_VERSION}" )
-  endif()
-  if( (ARMEABI_V7A OR ARMEABI_V7A_HARD) AND EXISTS "${ANDROID_STL_INCLUDE_DIRS}/${ANDROID_TOOLCHAIN_MACHINE_NAME}/${CMAKE_SYSTEM_PROCESSOR}/bits" )
-   list( APPEND ANDROID_STL_INCLUDE_DIRS "${ANDROID_STL_INCLUDE_DIRS}/${ANDROID_TOOLCHAIN_MACHINE_NAME}/${CMAKE_SYSTEM_PROCESSOR}" )
-  elseif( ARMEABI AND NOT ANDROID_FORCE_ARM_BUILD AND EXISTS "${ANDROID_STL_INCLUDE_DIRS}/${ANDROID_TOOLCHAIN_MACHINE_NAME}/thumb/bits" )
-   list( APPEND ANDROID_STL_INCLUDE_DIRS "${ANDROID_STL_INCLUDE_DIRS}/${ANDROID_TOOLCHAIN_MACHINE_NAME}/thumb" )
-  else()
-   list( APPEND ANDROID_STL_INCLUDE_DIRS "${ANDROID_STL_INCLUDE_DIRS}/${ANDROID_TOOLCHAIN_MACHINE_NAME}" )
-  endif()
-  # always search static GNU STL to get the location of libsupc++.a
-  if( (ARMEABI_V7A OR ARMEABI_V7A_HARD) AND NOT ANDROID_FORCE_ARM_BUILD AND EXISTS "${ANDROID_STANDALONE_TOOLCHAIN}/${ANDROID_TOOLCHAIN_MACHINE_NAME}/lib/${CMAKE_SYSTEM_PROCESSOR}/thumb/libstdc++.a" )
-   set( __libstl "${ANDROID_STANDALONE_TOOLCHAIN}/${ANDROID_TOOLCHAIN_MACHINE_NAME}/lib/${CMAKE_SYSTEM_PROCESSOR}/thumb" )
-  elseif( (ARMEABI_V7A OR ARMEABI_V7A_HARD) AND EXISTS "${ANDROID_STANDALONE_TOOLCHAIN}/${ANDROID_TOOLCHAIN_MACHINE_NAME}/lib/${CMAKE_SYSTEM_PROCESSOR}/libstdc++.a" )
-   set( __libstl "${ANDROID_STANDALONE_TOOLCHAIN}/${ANDROID_TOOLCHAIN_MACHINE_NAME}/lib/${CMAKE_SYSTEM_PROCESSOR}" )
-  elseif( ARMEABI AND NOT ANDROID_FORCE_ARM_BUILD AND EXISTS "${ANDROID_STANDALONE_TOOLCHAIN}/${ANDROID_TOOLCHAIN_MACHINE_NAME}/lib/thumb/libstdc++.a" )
-   set( __libstl "${ANDROID_STANDALONE_TOOLCHAIN}/${ANDROID_TOOLCHAIN_MACHINE_NAME}/lib/thumb" )
-  elseif( EXISTS "${ANDROID_STANDALONE_TOOLCHAIN}/${ANDROID_TOOLCHAIN_MACHINE_NAME}/lib/libstdc++.a" )
-   set( __libstl "${ANDROID_STANDALONE_TOOLCHAIN}/${ANDROID_TOOLCHAIN_MACHINE_NAME}/lib" )
-  endif()
-  if( __libstl )
-   set( __libsupcxx "${__libstl}/libsupc++.a" )
-   set( __libstl    "${__libstl}/libstdc++.a" )
-  endif()
-  if( NOT EXISTS "${__libsupcxx}" )
-   message( FATAL_ERROR "The required libstdsupc++.a is missing in your standalone toolchain.
- Usually it happens because of bug in make-standalone-toolchain.sh script from NDK r7, r7b and r7c.
- You need to either upgrade to newer NDK or manually copy
-     $ANDROID_NDK/sources/cxx-stl/gnu-libstdc++/libs/${ANDROID_NDK_ABI_NAME}/libsupc++.a
- to
-     ${__libsupcxx}
-   " )
-  endif()
-  if( ANDROID_STL STREQUAL "gnustl_shared" )
-   if( (ARMEABI_V7A OR ARMEABI_V7A_HARD) AND EXISTS "${ANDROID_STANDALONE_TOOLCHAIN}/${ANDROID_TOOLCHAIN_MACHINE_NAME}/lib/${CMAKE_SYSTEM_PROCESSOR}/libgnustl_shared.so" )
-    set( __libstl "${ANDROID_STANDALONE_TOOLCHAIN}/${ANDROID_TOOLCHAIN_MACHINE_NAME}/lib/${CMAKE_SYSTEM_PROCESSOR}/libgnustl_shared.so" )
-   elseif( ARMEABI AND NOT ANDROID_FORCE_ARM_BUILD AND EXISTS "${ANDROID_STANDALONE_TOOLCHAIN}/${ANDROID_TOOLCHAIN_MACHINE_NAME}/lib/thumb/libgnustl_shared.so" )
-    set( __libstl "${ANDROID_STANDALONE_TOOLCHAIN}/${ANDROID_TOOLCHAIN_MACHINE_NAME}/lib/thumb/libgnustl_shared.so" )
-   elseif( EXISTS "${ANDROID_STANDALONE_TOOLCHAIN}/${ANDROID_TOOLCHAIN_MACHINE_NAME}/lib/libgnustl_shared.so" )
-    set( __libstl "${ANDROID_STANDALONE_TOOLCHAIN}/${ANDROID_TOOLCHAIN_MACHINE_NAME}/lib/libgnustl_shared.so" )
-   endif()
-  endif()
- endif()
-endif()
-
-# clang
-if( "${ANDROID_TOOLCHAIN_NAME}" STREQUAL "standalone-clang" )
- set( ANDROID_COMPILER_IS_CLANG 1 )
- execute_process( COMMAND "${ANDROID_CLANG_TOOLCHAIN_ROOT}/bin/clang${TOOL_OS_SUFFIX}" --version OUTPUT_VARIABLE ANDROID_CLANG_VERSION OUTPUT_STRIP_TRAILING_WHITESPACE )
- string( REGEX MATCH "[0-9]+[.][0-9]+" ANDROID_CLANG_VERSION "${ANDROID_CLANG_VERSION}")
-elseif( "${ANDROID_TOOLCHAIN_NAME}" MATCHES "-clang3[.][0-9]?$" )
- string( REGEX MATCH "3[.][0-9]$" ANDROID_CLANG_VERSION "${ANDROID_TOOLCHAIN_NAME}")
- string( REGEX REPLACE "-clang${ANDROID_CLANG_VERSION}$" "-${ANDROID_COMPILER_VERSION}" ANDROID_GCC_TOOLCHAIN_NAME "${ANDROID_TOOLCHAIN_NAME}" )
- if( NOT EXISTS "${ANDROID_NDK_TOOLCHAINS_PATH}/llvm-${ANDROID_CLANG_VERSION}${ANDROID_NDK_TOOLCHAINS_SUBPATH}/bin/clang${TOOL_OS_SUFFIX}" )
-  message( FATAL_ERROR "Could not find the Clang compiler driver" )
- endif()
- set( ANDROID_COMPILER_IS_CLANG 1 )
- set( ANDROID_CLANG_TOOLCHAIN_ROOT "${ANDROID_NDK_TOOLCHAINS_PATH}/llvm-${ANDROID_CLANG_VERSION}${ANDROID_NDK_TOOLCHAINS_SUBPATH}" )
-else()
- set( ANDROID_GCC_TOOLCHAIN_NAME "${ANDROID_TOOLCHAIN_NAME}" )
- unset( ANDROID_COMPILER_IS_CLANG CACHE )
-endif()
-
-string( REPLACE "." "" _clang_name "clang${ANDROID_CLANG_VERSION}" )
-if( NOT EXISTS "${ANDROID_CLANG_TOOLCHAIN_ROOT}/bin/${_clang_name}${TOOL_OS_SUFFIX}" )
- set( _clang_name "clang" )
-endif()
-
-
-# setup paths and STL for NDK
-if( BUILD_WITH_ANDROID_NDK )
- set( ANDROID_TOOLCHAIN_ROOT "${ANDROID_NDK_TOOLCHAINS_PATH}/${ANDROID_GCC_TOOLCHAIN_NAME}${ANDROID_NDK_TOOLCHAINS_SUBPATH}" )
- set( ANDROID_SYSROOT "${ANDROID_NDK}/platforms/android-${ANDROID_NATIVE_API_LEVEL}/arch-${ANDROID_ARCH_NAME}" )
- if( EXISTS "${ANDROID_SYSROOT}/usr/include" )
-   set( ANDROID_SYSROOT_INCLUDE "${ANDROID_SYSROOT}/usr/include" )
- else()
-   set( ANDROID_SYSROOT_INCLUDE "${ANDROID_NDK}/sysroot/usr/include" "${ANDROID_NDK}/sysroot/usr/include/${ANDROID_TOOLCHAIN_MACHINE_NAME}" )
- endif()
-
- if( ANDROID_STL STREQUAL "none" )
-  # do nothing
- elseif( ANDROID_STL STREQUAL "system" )
-  set( ANDROID_RTTI             OFF )
-  set( ANDROID_EXCEPTIONS       OFF )
-  set( ANDROID_STL_INCLUDE_DIRS "${ANDROID_NDK}/sources/cxx-stl/system/include" )
- elseif( ANDROID_STL STREQUAL "system_re" )
-  set( ANDROID_RTTI             ON )
-  set( ANDROID_EXCEPTIONS       ON )
-  set( ANDROID_STL_INCLUDE_DIRS "${ANDROID_NDK}/sources/cxx-stl/system/include" )
- elseif( ANDROID_STL MATCHES "gabi" )
-  if( ANDROID_NDK_RELEASE_NUM LESS 7000 ) # before r7
-   message( FATAL_ERROR "gabi++ is not available in your NDK. You have to upgrade to NDK r7 or newer to use gabi++.")
-  endif()
-  set( ANDROID_RTTI             ON )
-  set( ANDROID_EXCEPTIONS       OFF )
-  set( ANDROID_STL_INCLUDE_DIRS "${ANDROID_NDK}/sources/cxx-stl/gabi++/include" )
-  set( __libstl                 "${ANDROID_NDK}/sources/cxx-stl/gabi++/libs/${ANDROID_NDK_ABI_NAME}/libgabi++_static.a" )
- elseif( ANDROID_STL MATCHES "stlport" )
-  if( NOT ANDROID_NDK_RELEASE_NUM LESS 8004 ) # before r8d
-   set( ANDROID_EXCEPTIONS       ON )
-  else()
-   set( ANDROID_EXCEPTIONS       OFF )
-  endif()
-  if( ANDROID_NDK_RELEASE_NUM LESS 7000 ) # before r7
-   set( ANDROID_RTTI            OFF )
-  else()
-   set( ANDROID_RTTI            ON )
-  endif()
-  set( ANDROID_STL_INCLUDE_DIRS "${ANDROID_NDK}/sources/cxx-stl/stlport/stlport" )
-  set( __libstl                 "${ANDROID_NDK}/sources/cxx-stl/stlport/libs/${ANDROID_NDK_ABI_NAME}/libstlport_static.a" )
- elseif( ANDROID_STL MATCHES "gnustl" )
-  set( ANDROID_EXCEPTIONS       ON )
-  set( ANDROID_RTTI             ON )
-  if( EXISTS "${ANDROID_NDK}/sources/cxx-stl/gnu-libstdc++/${ANDROID_COMPILER_VERSION}" )
-   if( ARMEABI_V7A AND ANDROID_COMPILER_VERSION VERSION_EQUAL "4.7" AND ANDROID_NDK_RELEASE STREQUAL "r8d" )
-    # gnustl binary for 4.7 compiler is buggy :(
-    # TODO: look for right fix
-    set( __libstl                "${ANDROID_NDK}/sources/cxx-stl/gnu-libstdc++/4.6" )
-   else()
-    set( __libstl                "${ANDROID_NDK}/sources/cxx-stl/gnu-libstdc++/${ANDROID_COMPILER_VERSION}" )
-   endif()
-  else()
-   set( __libstl                "${ANDROID_NDK}/sources/cxx-stl/gnu-libstdc++" )
-  endif()
-  set( ANDROID_STL_INCLUDE_DIRS "${__libstl}/include" "${__libstl}/libs/${ANDROID_NDK_ABI_NAME}/include" "${__libstl}/include/backward" )
-  if( EXISTS "${__libstl}/libs/${ANDROID_NDK_ABI_NAME}/libgnustl_static.a" )
-   set( __libstl                "${__libstl}/libs/${ANDROID_NDK_ABI_NAME}/libgnustl_static.a" )
-  else()
-   set( __libstl                "${__libstl}/libs/${ANDROID_NDK_ABI_NAME}/libstdc++.a" )
-  endif()
- elseif( ANDROID_STL MATCHES "c\\+\\+" )
-  set( ANDROID_EXCEPTIONS       ON )
-  set( ANDROID_RTTI             ON )
-  set( __libstl                "${ANDROID_NDK}/sources/cxx-stl/llvm-libc++" )
-  set( __libstl                "${__libstl}/libs/${ANDROID_NDK_ABI_NAME}/libc++_static.a" )
-  set( ANDROID_STL_INCLUDE_DIRS "${ANDROID_NDK}/sources/android/support/include" "${ANDROID_NDK}/sources/cxx-stl/llvm-libc++/libcxx/include" "${ANDROID_NDK}/sources/cxx-stl/llvm-libc++abi/libcxxabi/include" )
- else()
-  message( FATAL_ERROR "Unknown runtime: ${ANDROID_STL}" )
- endif()
-
- # find libsupc++.a - rtti & exceptions
- if( ANDROID_STL STREQUAL "system_re" OR ANDROID_STL MATCHES "gnustl" )
-  set( __libsupcxx "${ANDROID_NDK}/sources/cxx-stl/gnu-libstdc++/${ANDROID_COMPILER_VERSION}/libs/${ANDROID_NDK_ABI_NAME}/libsupc++.a" ) # r8b or newer
-  if( NOT EXISTS "${__libsupcxx}" )
-   set( __libsupcxx "${ANDROID_NDK}/sources/cxx-stl/gnu-libstdc++/libs/${ANDROID_NDK_ABI_NAME}/libsupc++.a" ) # r7-r8
-  endif()
-  if( NOT EXISTS "${__libsupcxx}" ) # before r7
-   if( ARMEABI_V7A )
-    if( ANDROID_FORCE_ARM_BUILD )
-     set( __libsupcxx "${ANDROID_TOOLCHAIN_ROOT}/${ANDROID_TOOLCHAIN_MACHINE_NAME}/lib/${CMAKE_SYSTEM_PROCESSOR}/libsupc++.a" )
-    else()
-     set( __libsupcxx "${ANDROID_TOOLCHAIN_ROOT}/${ANDROID_TOOLCHAIN_MACHINE_NAME}/lib/${CMAKE_SYSTEM_PROCESSOR}/thumb/libsupc++.a" )
-    endif()
-   elseif( ARMEABI AND NOT ANDROID_FORCE_ARM_BUILD )
-    set( __libsupcxx "${ANDROID_TOOLCHAIN_ROOT}/${ANDROID_TOOLCHAIN_MACHINE_NAME}/lib/thumb/libsupc++.a" )
-   else()
-    set( __libsupcxx "${ANDROID_TOOLCHAIN_ROOT}/${ANDROID_TOOLCHAIN_MACHINE_NAME}/lib/libsupc++.a" )
-   endif()
-  endif()
-  if( NOT EXISTS "${__libsupcxx}")
-   message( ERROR "Could not find libsupc++.a for a chosen platform. Either your NDK is not supported or is broken.")
-  endif()
- endif()
-endif()
-
-
-# case of shared STL linkage
-if( ANDROID_STL MATCHES "shared" AND DEFINED __libstl )
- string( REPLACE "_static.a" "_shared.so" __libstl "${__libstl}" )
- if( NOT EXISTS "${__libstl}" )
-   message( FATAL_ERROR "Unable to find shared library ${__libstl}" )
- endif()
-endif()
-
-
-# ccache support
-__INIT_VARIABLE( _ndk_ccache NDK_CCACHE ENV_NDK_CCACHE )
-if( _ndk_ccache )
- if( DEFINED NDK_CCACHE AND NOT EXISTS NDK_CCACHE )
-  unset( NDK_CCACHE CACHE )
- endif()
- find_program( NDK_CCACHE "${_ndk_ccache}" DOC "The path to ccache binary")
-else()
- unset( NDK_CCACHE CACHE )
-endif()
-unset( _ndk_ccache )
-
-
-# setup the cross-compiler
-if( NOT CMAKE_C_COMPILER )
- if( NDK_CCACHE AND NOT ANDROID_SYSROOT MATCHES "[ ;\"]" )
-  set( CMAKE_C_COMPILER   "${NDK_CCACHE}" CACHE PATH "ccache as C compiler" )
-  set( CMAKE_CXX_COMPILER "${NDK_CCACHE}" CACHE PATH "ccache as C++ compiler" )
-  if( ANDROID_COMPILER_IS_CLANG )
-   set( CMAKE_C_COMPILER_ARG1   "${ANDROID_CLANG_TOOLCHAIN_ROOT}/bin/${_clang_name}${TOOL_OS_SUFFIX}"   CACHE PATH "C compiler")
-   set( CMAKE_CXX_COMPILER_ARG1 "${ANDROID_CLANG_TOOLCHAIN_ROOT}/bin/${_clang_name}++${TOOL_OS_SUFFIX}" CACHE PATH "C++ compiler")
-  else()
-   set( CMAKE_C_COMPILER_ARG1   "${ANDROID_TOOLCHAIN_ROOT}/bin/${ANDROID_TOOLCHAIN_MACHINE_NAME}-gcc${TOOL_OS_SUFFIX}" CACHE PATH "C compiler")
-   set( CMAKE_CXX_COMPILER_ARG1 "${ANDROID_TOOLCHAIN_ROOT}/bin/${ANDROID_TOOLCHAIN_MACHINE_NAME}-g++${TOOL_OS_SUFFIX}" CACHE PATH "C++ compiler")
-  endif()
- else()
-  if( ANDROID_COMPILER_IS_CLANG )
-   set( CMAKE_C_COMPILER   "${ANDROID_CLANG_TOOLCHAIN_ROOT}/bin/${_clang_name}${TOOL_OS_SUFFIX}"   CACHE PATH "C compiler")
-   set( CMAKE_CXX_COMPILER "${ANDROID_CLANG_TOOLCHAIN_ROOT}/bin/${_clang_name}++${TOOL_OS_SUFFIX}" CACHE PATH "C++ compiler")
-  else()
-   set( CMAKE_C_COMPILER   "${ANDROID_TOOLCHAIN_ROOT}/bin/${ANDROID_TOOLCHAIN_MACHINE_NAME}-gcc${TOOL_OS_SUFFIX}"    CACHE PATH "C compiler" )
-   set( CMAKE_CXX_COMPILER "${ANDROID_TOOLCHAIN_ROOT}/bin/${ANDROID_TOOLCHAIN_MACHINE_NAME}-g++${TOOL_OS_SUFFIX}"    CACHE PATH "C++ compiler" )
-  endif()
- endif()
- set( CMAKE_ASM_COMPILER "${ANDROID_TOOLCHAIN_ROOT}/bin/${ANDROID_TOOLCHAIN_MACHINE_NAME}-gcc${TOOL_OS_SUFFIX}"     CACHE PATH "assembler" )
- set( CMAKE_STRIP        "${ANDROID_TOOLCHAIN_ROOT}/bin/${ANDROID_TOOLCHAIN_MACHINE_NAME}-strip${TOOL_OS_SUFFIX}"   CACHE PATH "strip" )
- if( EXISTS "${ANDROID_TOOLCHAIN_ROOT}/bin/${ANDROID_TOOLCHAIN_MACHINE_NAME}-gcc-ar${TOOL_OS_SUFFIX}" )
-  # Use gcc-ar if we have it for better LTO support.
-  set( CMAKE_AR           "${ANDROID_TOOLCHAIN_ROOT}/bin/${ANDROID_TOOLCHAIN_MACHINE_NAME}-gcc-ar${TOOL_OS_SUFFIX}"      CACHE PATH "archive" )
- else()
-  set( CMAKE_AR           "${ANDROID_TOOLCHAIN_ROOT}/bin/${ANDROID_TOOLCHAIN_MACHINE_NAME}-ar${TOOL_OS_SUFFIX}"      CACHE PATH "archive" )
- endif()
- set( CMAKE_LINKER       "${ANDROID_TOOLCHAIN_ROOT}/bin/${ANDROID_TOOLCHAIN_MACHINE_NAME}-ld${TOOL_OS_SUFFIX}"      CACHE PATH "linker" )
- set( CMAKE_NM           "${ANDROID_TOOLCHAIN_ROOT}/bin/${ANDROID_TOOLCHAIN_MACHINE_NAME}-nm${TOOL_OS_SUFFIX}"      CACHE PATH "nm" )
- set( CMAKE_OBJCOPY      "${ANDROID_TOOLCHAIN_ROOT}/bin/${ANDROID_TOOLCHAIN_MACHINE_NAME}-objcopy${TOOL_OS_SUFFIX}" CACHE PATH "objcopy" )
- set( CMAKE_OBJDUMP      "${ANDROID_TOOLCHAIN_ROOT}/bin/${ANDROID_TOOLCHAIN_MACHINE_NAME}-objdump${TOOL_OS_SUFFIX}" CACHE PATH "objdump" )
- set( CMAKE_RANLIB       "${ANDROID_TOOLCHAIN_ROOT}/bin/${ANDROID_TOOLCHAIN_MACHINE_NAME}-ranlib${TOOL_OS_SUFFIX}"  CACHE PATH "ranlib" )
-endif()
-
-set( _CMAKE_TOOLCHAIN_PREFIX "${ANDROID_TOOLCHAIN_MACHINE_NAME}-" )
-if( CMAKE_VERSION VERSION_LESS 2.8.5 )
- set( CMAKE_ASM_COMPILER_ARG1 "-c" )
-endif()
-if( APPLE )
- find_program( CMAKE_INSTALL_NAME_TOOL NAMES install_name_tool )
- if( NOT CMAKE_INSTALL_NAME_TOOL )
-  message( FATAL_ERROR "Could not find install_name_tool, please check your installation." )
- endif()
- mark_as_advanced( CMAKE_INSTALL_NAME_TOOL )
-endif()
-
-# Force set compilers because standard identification works badly for us
-if( CMAKE_VERSION VERSION_LESS 3.5.0 )
-  include( CMakeForceCompiler )
-  CMAKE_FORCE_C_COMPILER( "${CMAKE_C_COMPILER}" GNU )
-endif()
-if( ANDROID_COMPILER_IS_CLANG )
- set( CMAKE_C_COMPILER_ID Clang )
-endif()
-set( CMAKE_C_PLATFORM_ID Linux )
-if( X86_64 OR MIPS64 OR ARM64_V8A )
- set( CMAKE_C_SIZEOF_DATA_PTR 8 )
-else()
- set( CMAKE_C_SIZEOF_DATA_PTR 4 )
-endif()
-set( CMAKE_C_HAS_ISYSROOT 1 )
-set( CMAKE_C_COMPILER_ABI ELF )
-if( CMAKE_VERSION VERSION_LESS 3.5.0 )
-  CMAKE_FORCE_CXX_COMPILER( "${CMAKE_CXX_COMPILER}" GNU )
-endif()
-if( ANDROID_COMPILER_IS_CLANG )
- set( CMAKE_CXX_COMPILER_ID Clang)
-endif()
-set( CMAKE_CXX_PLATFORM_ID Linux )
-set( CMAKE_CXX_SIZEOF_DATA_PTR ${CMAKE_C_SIZEOF_DATA_PTR} )
-set( CMAKE_CXX_HAS_ISYSROOT 1 )
-set( CMAKE_CXX_COMPILER_ABI ELF )
-set( CMAKE_CXX_SOURCE_FILE_EXTENSIONS cc cp cxx cpp CPP c++ C )
-# force ASM compiler (required for CMake < 2.8.5)
-set( CMAKE_ASM_COMPILER_ID_RUN TRUE )
-set( CMAKE_ASM_COMPILER_ID GNU )
-set( CMAKE_ASM_COMPILER_WORKS TRUE )
-set( CMAKE_ASM_COMPILER_FORCED TRUE )
-set( CMAKE_COMPILER_IS_GNUASM 1)
-set( CMAKE_ASM_SOURCE_FILE_EXTENSIONS s S asm )
-
-foreach( lang C CXX ASM )
- if( ANDROID_COMPILER_IS_CLANG )
-  set( CMAKE_${lang}_COMPILER_VERSION ${ANDROID_CLANG_VERSION} )
- else()
-  set( CMAKE_${lang}_COMPILER_VERSION ${ANDROID_COMPILER_VERSION} )
- endif()
-endforeach()
-
-# flags and definitions
-remove_definitions( -DANDROID )
-add_definitions( -DANDROID )
-
-if( ANDROID_SYSROOT MATCHES "[ ;\"]" )
- if( CMAKE_HOST_WIN32 )
-  # try to convert path to 8.3 form
-  file( WRITE "${CMAKE_BINARY_DIR}${CMAKE_FILES_DIRECTORY}/cvt83.cmd" "@echo %~s1" )
-  execute_process( COMMAND "$ENV{ComSpec}" /c "${CMAKE_BINARY_DIR}${CMAKE_FILES_DIRECTORY}/cvt83.cmd" "${ANDROID_SYSROOT}"
-                   OUTPUT_VARIABLE __path OUTPUT_STRIP_TRAILING_WHITESPACE
-                   RESULT_VARIABLE __result ERROR_QUIET )
-  if( __result EQUAL 0 )
-   file( TO_CMAKE_PATH "${__path}" ANDROID_SYSROOT )
-   set( ANDROID_CXX_FLAGS "--sysroot=${ANDROID_SYSROOT}" )
-  else()
-   set( ANDROID_CXX_FLAGS "--sysroot=\"${ANDROID_SYSROOT}\"" )
-  endif()
- else()
-  set( ANDROID_CXX_FLAGS "'--sysroot=${ANDROID_SYSROOT}'" )
- endif()
- if( NOT _CMAKE_IN_TRY_COMPILE )
-  # quotes can break try_compile and compiler identification
-  message(WARNING "Path to your Android NDK (or toolchain) has non-alphanumeric symbols.\nThe build might be broken.\n")
- endif()
-else()
- set( ANDROID_CXX_FLAGS "--sysroot=${ANDROID_SYSROOT}" )
-endif()
-
-# NDK flags
-if (ARM64_V8A )
- set( ANDROID_CXX_FLAGS         "${ANDROID_CXX_FLAGS} -funwind-tables" )
- set( ANDROID_CXX_FLAGS_RELEASE "-fomit-frame-pointer -fstrict-aliasing" )
- set( ANDROID_CXX_FLAGS_DEBUG   "-fno-omit-frame-pointer -fno-strict-aliasing" )
- if( NOT ANDROID_COMPILER_IS_CLANG )
-  set( ANDROID_CXX_FLAGS_RELEASE "${ANDROID_CXX_FLAGS_RELEASE} -funswitch-loops -finline-limit=300" )
- endif()
-elseif( ARMEABI OR ARMEABI_V7A OR ARMEABI_V7A_HARD)
- set( ANDROID_CXX_FLAGS "${ANDROID_CXX_FLAGS} -funwind-tables" )
- if( NOT ANDROID_FORCE_ARM_BUILD AND NOT ARMEABI_V6 )
-  set( ANDROID_CXX_FLAGS_RELEASE "-mthumb -fomit-frame-pointer -fno-strict-aliasing" )
-  set( ANDROID_CXX_FLAGS_DEBUG   "-marm -fno-omit-frame-pointer -fno-strict-aliasing" )
-  if( NOT ANDROID_COMPILER_IS_CLANG )
-   set( ANDROID_CXX_FLAGS "${ANDROID_CXX_FLAGS} -finline-limit=64" )
-  endif()
- else()
-  # always compile ARMEABI_V6 in arm mode; otherwise there is no difference from ARMEABI
-  set( ANDROID_CXX_FLAGS_RELEASE "-marm -fomit-frame-pointer -fstrict-aliasing" )
-  set( ANDROID_CXX_FLAGS_DEBUG   "-marm -fno-omit-frame-pointer -fno-strict-aliasing" )
-  if( NOT ANDROID_COMPILER_IS_CLANG )
-   set( ANDROID_CXX_FLAGS "${ANDROID_CXX_FLAGS} -funswitch-loops -finline-limit=300" )
-  endif()
- endif()
-elseif( X86 OR X86_64 )
- set( ANDROID_CXX_FLAGS "${ANDROID_CXX_FLAGS} -funwind-tables" )
- if( NOT ANDROID_COMPILER_IS_CLANG )
-  set( ANDROID_CXX_FLAGS "${ANDROID_CXX_FLAGS} -funswitch-loops -finline-limit=300" )
- endif()
- set( ANDROID_CXX_FLAGS_RELEASE "-fomit-frame-pointer -fstrict-aliasing" )
- set( ANDROID_CXX_FLAGS_DEBUG   "-fno-omit-frame-pointer -fno-strict-aliasing" )
-elseif( MIPS OR MIPS64 )
- set( ANDROID_CXX_FLAGS         "${ANDROID_CXX_FLAGS} -fno-strict-aliasing -finline-functions -funwind-tables -fmessage-length=0" )
- set( ANDROID_CXX_FLAGS_RELEASE "-fomit-frame-pointer" )
- set( ANDROID_CXX_FLAGS_DEBUG   "-fno-omit-frame-pointer" )
- if( NOT ANDROID_COMPILER_IS_CLANG )
-  set( ANDROID_CXX_FLAGS "${ANDROID_CXX_FLAGS} -fno-inline-functions-called-once -fgcse-after-reload -frerun-cse-after-loop -frename-registers" )
-  set( ANDROID_CXX_FLAGS_RELEASE "${ANDROID_CXX_FLAGS_RELEASE} -funswitch-loops -finline-limit=300" )
- endif()
-elseif()
- set( ANDROID_CXX_FLAGS_RELEASE "" )
- set( ANDROID_CXX_FLAGS_DEBUG   "" )
-endif()
-
-set( ANDROID_CXX_FLAGS "${ANDROID_CXX_FLAGS} -fsigned-char" ) # good/necessary when porting desktop libraries
-
-if( NOT X86 AND NOT ANDROID_COMPILER_IS_CLANG )
- set( ANDROID_CXX_FLAGS "-Wno-psabi ${ANDROID_CXX_FLAGS}" )
-endif()
-
-if( NOT ANDROID_COMPILER_VERSION VERSION_LESS "4.6" )
- set( ANDROID_CXX_FLAGS "${ANDROID_CXX_FLAGS} -no-canonical-prefixes" ) # see https://android-review.googlesource.com/#/c/47564/
-endif()
-
-# ABI-specific flags
-if( ARMEABI_V7A_HARD )
- set( ANDROID_CXX_FLAGS "${ANDROID_CXX_FLAGS} -march=armv7-a -mfloat-abi=hard -mhard-float -D_NDK_MATH_NO_SOFTFP=1" )
- if( NEON )
-  set( ANDROID_CXX_FLAGS "${ANDROID_CXX_FLAGS} -mfpu=neon" )
- elseif( VFPV3 )
-  set( ANDROID_CXX_FLAGS "${ANDROID_CXX_FLAGS} -mfpu=vfpv3" )
- else()
-  set( ANDROID_CXX_FLAGS "${ANDROID_CXX_FLAGS} -mfpu=vfpv3-d16" )
- endif()
-elseif( ARMEABI_V7A )
- set( ANDROID_CXX_FLAGS "${ANDROID_CXX_FLAGS} -march=armv7-a -mfloat-abi=softfp" )
- if( NEON )
-  set( ANDROID_CXX_FLAGS "${ANDROID_CXX_FLAGS} -mfpu=neon" )
- elseif( VFPV3 )
-  set( ANDROID_CXX_FLAGS "${ANDROID_CXX_FLAGS} -mfpu=vfpv3" )
- else()
-  set( ANDROID_CXX_FLAGS "${ANDROID_CXX_FLAGS} -mfpu=vfpv3-d16" )
- endif()
-
-elseif( ARMEABI_V6 )
- set( ANDROID_CXX_FLAGS "${ANDROID_CXX_FLAGS} -march=armv6 -mfloat-abi=softfp -mfpu=vfp" ) # vfp == vfpv2
-elseif( ARMEABI )
- set( ANDROID_CXX_FLAGS "${ANDROID_CXX_FLAGS} -march=armv5te -mtune=xscale -msoft-float" )
-endif()
-
-if( ANDROID_STL MATCHES "gnustl" AND (EXISTS "${__libstl}" OR EXISTS "${__libsupcxx}") )
- set( CMAKE_CXX_CREATE_SHARED_LIBRARY "<CMAKE_C_COMPILER> <CMAKE_SHARED_LIBRARY_CXX_FLAGS> <LANGUAGE_COMPILE_FLAGS> <LINK_FLAGS> <CMAKE_SHARED_LIBRARY_CREATE_CXX_FLAGS> <CMAKE_SHARED_LIBRARY_SONAME_CXX_FLAG><TARGET_SONAME> -o <TARGET> <OBJECTS> <LINK_LIBRARIES>" )
- set( CMAKE_CXX_CREATE_SHARED_MODULE  "<CMAKE_C_COMPILER> <CMAKE_SHARED_LIBRARY_CXX_FLAGS> <LANGUAGE_COMPILE_FLAGS> <LINK_FLAGS> <CMAKE_SHARED_LIBRARY_CREATE_CXX_FLAGS> <CMAKE_SHARED_LIBRARY_SONAME_CXX_FLAG><TARGET_SONAME> -o <TARGET> <OBJECTS> <LINK_LIBRARIES>" )
- set( CMAKE_CXX_LINK_EXECUTABLE       "<CMAKE_C_COMPILER> <FLAGS> <CMAKE_CXX_LINK_FLAGS> <LINK_FLAGS> <OBJECTS> -o <TARGET> <LINK_LIBRARIES>" )
-else()
- set( CMAKE_CXX_CREATE_SHARED_LIBRARY "<CMAKE_CXX_COMPILER> <CMAKE_SHARED_LIBRARY_CXX_FLAGS> <LANGUAGE_COMPILE_FLAGS> <LINK_FLAGS> <CMAKE_SHARED_LIBRARY_CREATE_CXX_FLAGS> <CMAKE_SHARED_LIBRARY_SONAME_CXX_FLAG><TARGET_SONAME> -o <TARGET> <OBJECTS> <LINK_LIBRARIES>" )
- set( CMAKE_CXX_CREATE_SHARED_MODULE  "<CMAKE_CXX_COMPILER> <CMAKE_SHARED_LIBRARY_CXX_FLAGS> <LANGUAGE_COMPILE_FLAGS> <LINK_FLAGS> <CMAKE_SHARED_LIBRARY_CREATE_CXX_FLAGS> <CMAKE_SHARED_LIBRARY_SONAME_CXX_FLAG><TARGET_SONAME> -o <TARGET> <OBJECTS> <LINK_LIBRARIES>" )
- set( CMAKE_CXX_LINK_EXECUTABLE       "<CMAKE_CXX_COMPILER> <FLAGS> <CMAKE_CXX_LINK_FLAGS> <LINK_FLAGS> <OBJECTS> -o <TARGET> <LINK_LIBRARIES>" )
-endif()
-
-# STL
-if( EXISTS "${__libstl}" OR EXISTS "${__libsupcxx}" )
- if( EXISTS "${__libstl}" )
-  set( CMAKE_CXX_CREATE_SHARED_LIBRARY "${CMAKE_CXX_CREATE_SHARED_LIBRARY} \"${__libstl}\"" )
-  set( CMAKE_CXX_CREATE_SHARED_MODULE  "${CMAKE_CXX_CREATE_SHARED_MODULE} \"${__libstl}\"" )
-  set( CMAKE_CXX_LINK_EXECUTABLE       "${CMAKE_CXX_LINK_EXECUTABLE} \"${__libstl}\"" )
- endif()
- if( EXISTS "${__libsupcxx}" )
-  set( CMAKE_CXX_CREATE_SHARED_LIBRARY "${CMAKE_CXX_CREATE_SHARED_LIBRARY} \"${__libsupcxx}\"" )
-  set( CMAKE_CXX_CREATE_SHARED_MODULE  "${CMAKE_CXX_CREATE_SHARED_MODULE} \"${__libsupcxx}\"" )
-  set( CMAKE_CXX_LINK_EXECUTABLE       "${CMAKE_CXX_LINK_EXECUTABLE} \"${__libsupcxx}\"" )
-  # C objects:
-  set( CMAKE_C_CREATE_SHARED_LIBRARY "<CMAKE_C_COMPILER> <CMAKE_SHARED_LIBRARY_C_FLAGS> <LANGUAGE_COMPILE_FLAGS> <LINK_FLAGS> <CMAKE_SHARED_LIBRARY_CREATE_C_FLAGS> <CMAKE_SHARED_LIBRARY_SONAME_C_FLAG><TARGET_SONAME> -o <TARGET> <OBJECTS> <LINK_LIBRARIES>" )
-  set( CMAKE_C_CREATE_SHARED_MODULE  "<CMAKE_C_COMPILER> <CMAKE_SHARED_LIBRARY_C_FLAGS> <LANGUAGE_COMPILE_FLAGS> <LINK_FLAGS> <CMAKE_SHARED_LIBRARY_CREATE_C_FLAGS> <CMAKE_SHARED_LIBRARY_SONAME_C_FLAG><TARGET_SONAME> -o <TARGET> <OBJECTS> <LINK_LIBRARIES>" )
-  set( CMAKE_C_LINK_EXECUTABLE       "<CMAKE_C_COMPILER> <FLAGS> <CMAKE_C_LINK_FLAGS> <LINK_FLAGS> <OBJECTS> -o <TARGET> <LINK_LIBRARIES>" )
-  set( CMAKE_C_CREATE_SHARED_LIBRARY "${CMAKE_C_CREATE_SHARED_LIBRARY} \"${__libsupcxx}\"" )
-  set( CMAKE_C_CREATE_SHARED_MODULE  "${CMAKE_C_CREATE_SHARED_MODULE} \"${__libsupcxx}\"" )
-  set( CMAKE_C_LINK_EXECUTABLE       "${CMAKE_C_LINK_EXECUTABLE} \"${__libsupcxx}\"" )
- endif()
- if( ANDROID_STL MATCHES "gnustl" )
-  if( NOT EXISTS "${ANDROID_LIBM_PATH}" )
-   set( ANDROID_LIBM_PATH -lm )
-  endif()
-  set( CMAKE_CXX_CREATE_SHARED_LIBRARY "${CMAKE_CXX_CREATE_SHARED_LIBRARY} ${ANDROID_LIBM_PATH}" )
-  set( CMAKE_CXX_CREATE_SHARED_MODULE  "${CMAKE_CXX_CREATE_SHARED_MODULE} ${ANDROID_LIBM_PATH}" )
-  set( CMAKE_CXX_LINK_EXECUTABLE       "${CMAKE_CXX_LINK_EXECUTABLE} ${ANDROID_LIBM_PATH}" )
- endif()
-endif()
-
-# variables controlling optional build flags
-if( ANDROID_NDK_RELEASE_NUM LESS 7000 ) # before r7
- # libGLESv2.so in NDK's prior to r7 refers to missing external symbols.
- # So this flag option is required for all projects using OpenGL from native.
- __INIT_VARIABLE( ANDROID_SO_UNDEFINED                      VALUES ON )
-else()
- __INIT_VARIABLE( ANDROID_SO_UNDEFINED                      VALUES OFF )
-endif()
-__INIT_VARIABLE( ANDROID_NO_UNDEFINED                       VALUES ON )
-__INIT_VARIABLE( ANDROID_FUNCTION_LEVEL_LINKING             VALUES ON )
-__INIT_VARIABLE( ANDROID_GOLD_LINKER                        VALUES ON )
-__INIT_VARIABLE( ANDROID_NOEXECSTACK                        VALUES ON )
-__INIT_VARIABLE( ANDROID_RELRO                              VALUES ON )
-
-set( ANDROID_NO_UNDEFINED           ${ANDROID_NO_UNDEFINED}           CACHE BOOL "Show all undefined symbols as linker errors" )
-set( ANDROID_SO_UNDEFINED           ${ANDROID_SO_UNDEFINED}           CACHE BOOL "Allows or disallows undefined symbols in shared libraries" )
-set( ANDROID_FUNCTION_LEVEL_LINKING ${ANDROID_FUNCTION_LEVEL_LINKING} CACHE BOOL "Put each function in separate section and enable garbage collection of unused input sections at link time" )
-set( ANDROID_GOLD_LINKER            ${ANDROID_GOLD_LINKER}            CACHE BOOL "Enables gold linker" )
-set( ANDROID_NOEXECSTACK            ${ANDROID_NOEXECSTACK}            CACHE BOOL "Allows or disallows undefined symbols in shared libraries" )
-set( ANDROID_RELRO                  ${ANDROID_RELRO}                  CACHE BOOL "Enables RELRO - a memory corruption mitigation technique" )
-mark_as_advanced( ANDROID_NO_UNDEFINED ANDROID_SO_UNDEFINED ANDROID_FUNCTION_LEVEL_LINKING ANDROID_GOLD_LINKER ANDROID_NOEXECSTACK ANDROID_RELRO )
-
-# linker flags
-set( ANDROID_LINKER_FLAGS "" )
-
-if( ARMEABI_V7A )
- # this is *required* to use the following linker flags that routes around
- # a CPU bug in some Cortex-A8 implementations:
- set( ANDROID_LINKER_FLAGS "${ANDROID_LINKER_FLAGS} -Wl,--fix-cortex-a8" )
-endif()
-
-if( ARMEABI_V7A_HARD )
- set( ANDROID_LINKER_FLAGS "${ANDROID_LINKER_FLAGS} -Wl,--no-warn-mismatch -lm_hard" )
-endif()
-
-if( ANDROID_NO_UNDEFINED )
- if( MIPS )
-  # there is some sysroot-related problem in mips linker...
-  if( NOT ANDROID_SYSROOT MATCHES "[ ;\"]" )
-   set( ANDROID_LINKER_FLAGS "${ANDROID_LINKER_FLAGS} -Wl,--no-undefined -Wl,-rpath-link,${ANDROID_SYSROOT}/usr/lib" )
-  endif()
- else()
-  set( ANDROID_LINKER_FLAGS "${ANDROID_LINKER_FLAGS} -Wl,--no-undefined" )
- endif()
-endif()
-
-if( ANDROID_SO_UNDEFINED )
- set( ANDROID_LINKER_FLAGS "${ANDROID_LINKER_FLAGS} -Wl,-allow-shlib-undefined" )
-endif()
-
-if( ANDROID_FUNCTION_LEVEL_LINKING )
- set( ANDROID_CXX_FLAGS    "${ANDROID_CXX_FLAGS} -fdata-sections -ffunction-sections" )
- set( ANDROID_LINKER_FLAGS "${ANDROID_LINKER_FLAGS} -Wl,--gc-sections" )
-endif()
-
-if( ANDROID_COMPILER_VERSION VERSION_EQUAL "4.6" )
- if( ANDROID_GOLD_LINKER AND (CMAKE_HOST_UNIX OR ANDROID_NDK_RELEASE_NUM GREATER 8002) AND (ARMEABI OR ARMEABI_V7A OR ARMEABI_V7A_HARD OR X86) )
-  set( ANDROID_LINKER_FLAGS "${ANDROID_LINKER_FLAGS} -fuse-ld=gold" )
- elseif( ANDROID_NDK_RELEASE_NUM GREATER 8002 ) # after r8b
-  set( ANDROID_LINKER_FLAGS "${ANDROID_LINKER_FLAGS} -fuse-ld=bfd" )
- elseif( ANDROID_NDK_RELEASE STREQUAL "r8b" AND ARMEABI AND NOT _CMAKE_IN_TRY_COMPILE )
-  message( WARNING "The default bfd linker from arm GCC 4.6 toolchain can fail with 'unresolvable R_ARM_THM_CALL relocation' error message. See https://code.google.com/p/android/issues/detail?id=35342
-  On Linux and OS X host platform you can workaround this problem using gold linker (default).
-  Rerun cmake with -DANDROID_GOLD_LINKER=ON option in case of problems.
-" )
- endif()
-endif() # version 4.6
-
-if( ANDROID_NOEXECSTACK )
- if( ANDROID_COMPILER_IS_CLANG )
-  set( ANDROID_CXX_FLAGS    "${ANDROID_CXX_FLAGS} -Xclang -mnoexecstack" )
- else()
-  set( ANDROID_CXX_FLAGS    "${ANDROID_CXX_FLAGS} -Wa,--noexecstack" )
- endif()
- set( ANDROID_LINKER_FLAGS "${ANDROID_LINKER_FLAGS} -Wl,-z,noexecstack" )
-endif()
-
-if( ANDROID_RELRO )
- set( ANDROID_LINKER_FLAGS "${ANDROID_LINKER_FLAGS} -Wl,-z,relro -Wl,-z,now" )
-endif()
-
-if( ANDROID_COMPILER_IS_CLANG )
- set( ANDROID_CXX_FLAGS "-target ${ANDROID_LLVM_TRIPLE} -Qunused-arguments ${ANDROID_CXX_FLAGS}" )
- if( BUILD_WITH_ANDROID_NDK )
-  set( ANDROID_CXX_FLAGS "-gcc-toolchain ${ANDROID_TOOLCHAIN_ROOT} ${ANDROID_CXX_FLAGS}" )
- endif()
-endif()
-
-# cache flags
-set( CMAKE_CXX_FLAGS           ""                        CACHE STRING "c++ flags" )
-set( CMAKE_C_FLAGS             ""                        CACHE STRING "c flags" )
-set( CMAKE_CXX_FLAGS_RELEASE   "-O3 -DNDEBUG"            CACHE STRING "c++ Release flags" )
-set( CMAKE_C_FLAGS_RELEASE     "-O3 -DNDEBUG"            CACHE STRING "c Release flags" )
-set( CMAKE_CXX_FLAGS_DEBUG     "-O0 -g -DDEBUG -D_DEBUG" CACHE STRING "c++ Debug flags" )
-set( CMAKE_C_FLAGS_DEBUG       "-O0 -g -DDEBUG -D_DEBUG" CACHE STRING "c Debug flags" )
-set( CMAKE_SHARED_LINKER_FLAGS ""                        CACHE STRING "shared linker flags" )
-set( CMAKE_MODULE_LINKER_FLAGS ""                        CACHE STRING "module linker flags" )
-set( CMAKE_EXE_LINKER_FLAGS    "-Wl,-z,nocopyreloc"      CACHE STRING "executable linker flags" )
-
-# put flags to cache (for debug purpose only)
-set( ANDROID_CXX_FLAGS         "${ANDROID_CXX_FLAGS}"         CACHE INTERNAL "Android specific c/c++ flags" )
-set( ANDROID_CXX_FLAGS_RELEASE "${ANDROID_CXX_FLAGS_RELEASE}" CACHE INTERNAL "Android specific c/c++ Release flags" )
-set( ANDROID_CXX_FLAGS_DEBUG   "${ANDROID_CXX_FLAGS_DEBUG}"   CACHE INTERNAL "Android specific c/c++ Debug flags" )
-set( ANDROID_LINKER_FLAGS      "${ANDROID_LINKER_FLAGS}"      CACHE INTERNAL "Android specific c/c++ linker flags" )
-
-# finish flags
-set( CMAKE_CXX_FLAGS           "${ANDROID_CXX_FLAGS} ${CMAKE_CXX_FLAGS}" )
-set( CMAKE_C_FLAGS             "${ANDROID_CXX_FLAGS} ${CMAKE_C_FLAGS}" )
-set( CMAKE_CXX_FLAGS_RELEASE   "${ANDROID_CXX_FLAGS_RELEASE} ${CMAKE_CXX_FLAGS_RELEASE}" )
-set( CMAKE_C_FLAGS_RELEASE     "${ANDROID_CXX_FLAGS_RELEASE} ${CMAKE_C_FLAGS_RELEASE}" )
-set( CMAKE_CXX_FLAGS_DEBUG     "${ANDROID_CXX_FLAGS_DEBUG} ${CMAKE_CXX_FLAGS_DEBUG}" )
-set( CMAKE_C_FLAGS_DEBUG       "${ANDROID_CXX_FLAGS_DEBUG} ${CMAKE_C_FLAGS_DEBUG}" )
-set( CMAKE_SHARED_LINKER_FLAGS "${ANDROID_LINKER_FLAGS} ${CMAKE_SHARED_LINKER_FLAGS}" )
-set( CMAKE_MODULE_LINKER_FLAGS "${ANDROID_LINKER_FLAGS} ${CMAKE_MODULE_LINKER_FLAGS}" )
-set( CMAKE_EXE_LINKER_FLAGS    "${ANDROID_LINKER_FLAGS} ${CMAKE_EXE_LINKER_FLAGS}" )
-
-if( MIPS AND BUILD_WITH_ANDROID_NDK AND ANDROID_NDK_RELEASE STREQUAL "r8" )
- set( CMAKE_SHARED_LINKER_FLAGS "-Wl,-T,${ANDROID_NDK_TOOLCHAINS_PATH}/${ANDROID_GCC_TOOLCHAIN_NAME}/mipself.xsc ${CMAKE_SHARED_LINKER_FLAGS}" )
- set( CMAKE_MODULE_LINKER_FLAGS "-Wl,-T,${ANDROID_NDK_TOOLCHAINS_PATH}/${ANDROID_GCC_TOOLCHAIN_NAME}/mipself.xsc ${CMAKE_MODULE_LINKER_FLAGS}" )
- set( CMAKE_EXE_LINKER_FLAGS    "-Wl,-T,${ANDROID_NDK_TOOLCHAINS_PATH}/${ANDROID_GCC_TOOLCHAIN_NAME}/mipself.x ${CMAKE_EXE_LINKER_FLAGS}" )
-endif()
-
-# pie/pic
-if( NOT (ANDROID_NATIVE_API_LEVEL LESS 16) AND (NOT DEFINED ANDROID_APP_PIE OR ANDROID_APP_PIE) AND (CMAKE_VERSION VERSION_GREATER 2.8.8) )
- set( CMAKE_POSITION_INDEPENDENT_CODE TRUE )
- set( CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} -fPIE -pie")
-else()
- set( CMAKE_POSITION_INDEPENDENT_CODE FALSE )
- set( CMAKE_CXX_FLAGS "-fpic ${CMAKE_CXX_FLAGS}" )
- set( CMAKE_C_FLAGS   "-fpic ${CMAKE_C_FLAGS}" )
-endif()
-
-# configure rtti
-if( DEFINED ANDROID_RTTI AND ANDROID_STL_FORCE_FEATURES )
- if( ANDROID_RTTI )
-  set( CMAKE_CXX_FLAGS "-frtti ${CMAKE_CXX_FLAGS}" )
- else()
-  set( CMAKE_CXX_FLAGS "-fno-rtti ${CMAKE_CXX_FLAGS}" )
- endif()
-endif()
-
-# configure exceptions
-if( DEFINED ANDROID_EXCEPTIONS AND ANDROID_STL_FORCE_FEATURES )
- if( ANDROID_EXCEPTIONS )
-  set( CMAKE_CXX_FLAGS "-fexceptions ${CMAKE_CXX_FLAGS}" )
-  set( CMAKE_C_FLAGS "-fexceptions ${CMAKE_C_FLAGS}" )
- else()
-  set( CMAKE_CXX_FLAGS "-fno-exceptions ${CMAKE_CXX_FLAGS}" )
-  set( CMAKE_C_FLAGS "-fno-exceptions ${CMAKE_C_FLAGS}" )
- endif()
-endif()
-
-# global includes and link directories
-include_directories( SYSTEM "${ANDROID_SYSROOT_INCLUDE}" ${ANDROID_STL_INCLUDE_DIRS} )
-get_filename_component(__android_install_path "${CMAKE_INSTALL_PREFIX}/libs/${ANDROID_NDK_ABI_NAME}" ABSOLUTE) # avoid CMP0015 policy warning
-link_directories( "${__android_install_path}" )
-set( CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DANDROID -D__ANDROID_API__=${ANDROID_NATIVE_API_LEVEL}" )
-set( CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -DANDROID -D__ANDROID_API__=${ANDROID_NATIVE_API_LEVEL}" )
-
-# detect if need link crtbegin_so.o explicitly
-if( NOT DEFINED ANDROID_EXPLICIT_CRT_LINK )
- set( __cmd "${CMAKE_CXX_CREATE_SHARED_LIBRARY}" )
- string( REPLACE "<CMAKE_CXX_COMPILER>" "${CMAKE_CXX_COMPILER} ${CMAKE_CXX_COMPILER_ARG1}" __cmd "${__cmd}" )
- string( REPLACE "<CMAKE_C_COMPILER>"   "${CMAKE_C_COMPILER} ${CMAKE_C_COMPILER_ARG1}"   __cmd "${__cmd}" )
- string( REPLACE "<CMAKE_SHARED_LIBRARY_CXX_FLAGS>" "${CMAKE_CXX_FLAGS}" __cmd "${__cmd}" )
- string( REPLACE "<LANGUAGE_COMPILE_FLAGS>" "" __cmd "${__cmd}" )
- string( REPLACE "<LINK_FLAGS>" "${CMAKE_SHARED_LINKER_FLAGS}" __cmd "${__cmd}" )
- string( REPLACE "<CMAKE_SHARED_LIBRARY_CREATE_CXX_FLAGS>" "-shared" __cmd "${__cmd}" )
- string( REPLACE "<CMAKE_SHARED_LIBRARY_SONAME_CXX_FLAG>" "" __cmd "${__cmd}" )
- string( REPLACE "<TARGET_SONAME>" "" __cmd "${__cmd}" )
- string( REPLACE "<TARGET>" "${CMAKE_BINARY_DIR}${CMAKE_FILES_DIRECTORY}/toolchain_crtlink_test.so" __cmd "${__cmd}" )
- string( REPLACE "<OBJECTS>" "\"${ANDROID_SYSROOT}/usr/lib/crtbegin_so.o\"" __cmd "${__cmd}" )
- string( REPLACE "<LINK_LIBRARIES>" "" __cmd "${__cmd}" )
- separate_arguments( __cmd )
- foreach( __var ANDROID_NDK ANDROID_NDK_TOOLCHAINS_PATH ANDROID_STANDALONE_TOOLCHAIN )
-  if( ${__var} )
-   set( __tmp "${${__var}}" )
-   separate_arguments( __tmp )
-   string( REPLACE "${__tmp}" "${${__var}}" __cmd "${__cmd}")
-  endif()
- endforeach()
- string( REPLACE "'" "" __cmd "${__cmd}" )
- string( REPLACE "\"" "" __cmd "${__cmd}" )
- execute_process( COMMAND ${__cmd} RESULT_VARIABLE __cmd_result OUTPUT_QUIET ERROR_QUIET )
- if( __cmd_result EQUAL 0 )
-  set( ANDROID_EXPLICIT_CRT_LINK ON )
- else()
-  set( ANDROID_EXPLICIT_CRT_LINK OFF )
- endif()
-endif()
-
-if( ANDROID_EXPLICIT_CRT_LINK )
- set( CMAKE_CXX_CREATE_SHARED_LIBRARY "${CMAKE_CXX_CREATE_SHARED_LIBRARY} \"${ANDROID_SYSROOT}/usr/lib/crtbegin_so.o\"" )
- set( CMAKE_CXX_CREATE_SHARED_MODULE  "${CMAKE_CXX_CREATE_SHARED_MODULE} \"${ANDROID_SYSROOT}/usr/lib/crtbegin_so.o\"" )
-endif()
-
-# setup output directories
-set( CMAKE_INSTALL_PREFIX "${ANDROID_TOOLCHAIN_ROOT}/user" CACHE STRING "path for installing" )
-
-if( DEFINED LIBRARY_OUTPUT_PATH_ROOT
-      OR EXISTS "${CMAKE_SOURCE_DIR}/AndroidManifest.xml"
-      OR (EXISTS "${CMAKE_SOURCE_DIR}/../AndroidManifest.xml" AND EXISTS "${CMAKE_SOURCE_DIR}/../jni/") )
-  set( LIBRARY_OUTPUT_PATH_ROOT ${CMAKE_SOURCE_DIR} CACHE PATH "Root for binaries output, set this to change where Android libs are installed to" )
-  if( NOT _CMAKE_IN_TRY_COMPILE )
-    if( EXISTS "${CMAKE_SOURCE_DIR}/jni/CMakeLists.txt" )
-      set( EXECUTABLE_OUTPUT_PATH "${LIBRARY_OUTPUT_PATH_ROOT}/bin/${ANDROID_NDK_ABI_NAME}" CACHE PATH "Output directory for applications" )
-    else()
-      set( EXECUTABLE_OUTPUT_PATH "${LIBRARY_OUTPUT_PATH_ROOT}/bin" CACHE PATH "Output directory for applications" )
-    endif()
-    set( LIBRARY_OUTPUT_PATH "${LIBRARY_OUTPUT_PATH_ROOT}/libs/${ANDROID_NDK_ABI_NAME}" CACHE PATH "Output directory for Android libs" )
-  endif()
-endif()
-
-# copy shaed stl library to build directory
-if( NOT _CMAKE_IN_TRY_COMPILE AND __libstl MATCHES "[.]so$" AND DEFINED LIBRARY_OUTPUT_PATH )
-  get_filename_component( __libstlname "${__libstl}" NAME )
-  execute_process( COMMAND "${CMAKE_COMMAND}" -E copy_if_different "${__libstl}" "${LIBRARY_OUTPUT_PATH}/${__libstlname}" RESULT_VARIABLE __fileCopyProcess )
-  if( NOT __fileCopyProcess EQUAL 0 OR NOT EXISTS "${LIBRARY_OUTPUT_PATH}/${__libstlname}")
-    message( SEND_ERROR "Failed copying of ${__libstl} to the ${LIBRARY_OUTPUT_PATH}/${__libstlname}" )
-  endif()
-  unset( __fileCopyProcess )
-  unset( __libstlname )
-endif()
-
-
-# set these global flags for cmake client scripts to change behavior
-set( ANDROID True )
-set( BUILD_ANDROID True )
-
-# where is the target environment
-set( CMAKE_FIND_ROOT_PATH
-    "${ANDROID_TOOLCHAIN_ROOT}/bin"
-    "${ANDROID_TOOLCHAIN_ROOT}/${ANDROID_TOOLCHAIN_MACHINE_NAME}"
-    "${ANDROID_SYSROOT}"
-    "${ANDROID_NDK}/sysroot"  # NDK16+
-    "${CMAKE_INSTALL_PREFIX}"
-    "${CMAKE_INSTALL_PREFIX}/share" )
-
-# only search for libraries and includes in the ndk toolchain
-if(NOT CMAKE_FIND_ROOT_PATH_MODE_LIBRARY)
-  set( CMAKE_FIND_ROOT_PATH_MODE_LIBRARY ONLY )
-endif()
-
-if(NOT CMAKE_FIND_ROOT_PATH_MODE_INCLUDE)
-  set( CMAKE_FIND_ROOT_PATH_MODE_INCLUDE ONLY )
-endif()
-
-if(NOT CMAKE_FIND_ROOT_PATH_MODE_PACKAGE)
-  set( CMAKE_FIND_ROOT_PATH_MODE_PACKAGE ONLY )
-endif()
-
-if(NOT CMAKE_FIND_ROOT_PATH_MODE_PROGRAM)
-  set( CMAKE_FIND_ROOT_PATH_MODE_PROGRAM NEVER )
-endif()
-
-macro(__cmake_find_root_save_and_reset)
-  foreach(v
-      CMAKE_FIND_ROOT_PATH_MODE_LIBRARY
-      CMAKE_FIND_ROOT_PATH_MODE_INCLUDE
-      CMAKE_FIND_ROOT_PATH_MODE_PACKAGE
-      CMAKE_FIND_ROOT_PATH_MODE_PROGRAM
-  )
-    set(__save_${v} ${${v}})
-    set(${v} NEVER)
-  endforeach()
-endmacro()
-
-macro(__cmake_find_root_restore)
-  foreach(v
-      CMAKE_FIND_ROOT_PATH_MODE_LIBRARY
-      CMAKE_FIND_ROOT_PATH_MODE_INCLUDE
-      CMAKE_FIND_ROOT_PATH_MODE_PACKAGE
-      CMAKE_FIND_ROOT_PATH_MODE_PROGRAM
-  )
-    set(${v} ${__save_${v}})
-    unset(__save_${v})
-  endforeach()
-endmacro()
-
-# macro to find packages on the host OS
-macro( find_host_package )
- __cmake_find_root_save_and_reset()
- if( CMAKE_HOST_WIN32 )
-  SET( WIN32 1 )
-  SET( UNIX )
- elseif( CMAKE_HOST_APPLE )
-  SET( APPLE 1 )
-  SET( UNIX )
- endif()
- find_package( ${ARGN} )
- SET( WIN32 )
- SET( APPLE )
- SET( UNIX 1 )
- __cmake_find_root_restore()
-endmacro()
-
-
-# macro to find programs on the host OS
-macro( find_host_program )
- __cmake_find_root_save_and_reset()
- if( CMAKE_HOST_WIN32 )
-  SET( WIN32 1 )
-  SET( UNIX )
- elseif( CMAKE_HOST_APPLE )
-  SET( APPLE 1 )
-  SET( UNIX )
- endif()
- find_program( ${ARGN} )
- SET( WIN32 )
- SET( APPLE )
- SET( UNIX 1 )
- __cmake_find_root_restore()
-endmacro()
-
-
-# export toolchain settings for the try_compile() command
-if( NOT _CMAKE_IN_TRY_COMPILE )
- set( __toolchain_config "")
- foreach( __var NDK_CCACHE  LIBRARY_OUTPUT_PATH_ROOT  ANDROID_FORBID_SYGWIN
-                ANDROID_NDK_HOST_X64
-                ANDROID_NDK
-                ANDROID_NDK_LAYOUT
-                ANDROID_STANDALONE_TOOLCHAIN
-                ANDROID_TOOLCHAIN_NAME
-                ANDROID_ABI
-                ANDROID_NATIVE_API_LEVEL
-                ANDROID_STL
-                ANDROID_STL_FORCE_FEATURES
-                ANDROID_FORCE_ARM_BUILD
-                ANDROID_NO_UNDEFINED
-                ANDROID_SO_UNDEFINED
-                ANDROID_FUNCTION_LEVEL_LINKING
-                ANDROID_GOLD_LINKER
-                ANDROID_NOEXECSTACK
-                ANDROID_RELRO
-                ANDROID_LIBM_PATH
-                ANDROID_EXPLICIT_CRT_LINK
-                ANDROID_APP_PIE
-                )
-  if( DEFINED ${__var} )
-   if( ${__var} MATCHES " ")
-    set( __toolchain_config "${__toolchain_config}set( ${__var} \"${${__var}}\" CACHE INTERNAL \"\" )\n" )
-   else()
-    set( __toolchain_config "${__toolchain_config}set( ${__var} ${${__var}} CACHE INTERNAL \"\" )\n" )
-   endif()
-  endif()
- endforeach()
- file( WRITE "${CMAKE_BINARY_DIR}${CMAKE_FILES_DIRECTORY}/android.toolchain.config.cmake" "${__toolchain_config}" )
- unset( __toolchain_config )
-endif()
-
-
-# force cmake to produce / instead of \ in build commands for Ninja generator
-if( CMAKE_GENERATOR MATCHES "Ninja" AND CMAKE_HOST_WIN32 )
- # it is a bad hack after all
- # CMake generates Ninja makefiles with UNIX paths only if it thinks that we are going to build with MinGW
- set( CMAKE_COMPILER_IS_MINGW TRUE ) # tell CMake that we are MinGW
- set( CMAKE_CROSSCOMPILING TRUE )    # stop recursion
- enable_language( C )
- enable_language( CXX )
- # unset( CMAKE_COMPILER_IS_MINGW ) # can't unset because CMake does not convert back-slashes in response files without it
- unset( MINGW )
-endif()
-
-
-# Variables controlling behavior or set by cmake toolchain:
-#   ANDROID_ABI : "armeabi-v7a" (default), "armeabi", "armeabi-v7a with NEON", "armeabi-v7a-hard with NEON", "armeabi-v7a with VFPV3", "armeabi-v6 with VFP", "x86", "mips", "arm64-v8a", "x86_64", "mips64"
-#   ANDROID_NATIVE_API_LEVEL : 3,4,5,8,9,14,15,16,17,18,19,21 (depends on NDK version)
-#   ANDROID_STL : gnustl_static/gnustl_shared/stlport_static/stlport_shared/gabi++_static/gabi++_shared/system_re/system/none
-#   ANDROID_FORBID_SYGWIN : ON/OFF
-#   ANDROID_NO_UNDEFINED : ON/OFF
-#   ANDROID_SO_UNDEFINED : OFF/ON  (default depends on NDK version)
-#   ANDROID_FUNCTION_LEVEL_LINKING : ON/OFF
-#   ANDROID_GOLD_LINKER : ON/OFF
-#   ANDROID_NOEXECSTACK : ON/OFF
-#   ANDROID_RELRO : ON/OFF
-#   ANDROID_FORCE_ARM_BUILD : ON/OFF
-#   ANDROID_STL_FORCE_FEATURES : ON/OFF
-#   ANDROID_LIBM_PATH : path to libm.so (set to something like $(TOP)/out/target/product/<product_name>/obj/lib/libm.so) to workaround unresolved `sincos`
-# Can be set only at the first run:
-#   ANDROID_NDK : path to your NDK install
-#   NDK_CCACHE : path to your ccache executable
-#   ANDROID_TOOLCHAIN_NAME : the NDK name of compiler toolchain
-#   ANDROID_NDK_HOST_X64 : try to use x86_64 toolchain (default for x64 host systems)
-#   ANDROID_NDK_LAYOUT : the inner NDK structure (RELEASE, LINARO, ANDROID)
-#   LIBRARY_OUTPUT_PATH_ROOT : <any valid path>
-#   ANDROID_STANDALONE_TOOLCHAIN
-#
-# Primary read-only variables:
-#   ANDROID : always TRUE
-#   ARMEABI : TRUE for arm v6 and older devices
-#   ARMEABI_V6 : TRUE for arm v6
-#   ARMEABI_V7A : TRUE for arm v7a
-#   ARMEABI_V7A_HARD : TRUE for arm v7a with hardfp
-#   ARM64_V8A : TRUE for arm64-v8a
-#   NEON : TRUE if NEON unit is enabled
-#   VFPV3 : TRUE if VFP version 3 is enabled
-#   X86 : TRUE if configured for x86
-#   X86_64 : TRUE if configured for x86_64
-#   MIPS : TRUE if configured for mips
-#   MIPS64 : TRUE if configured for mips64
-#   BUILD_WITH_ANDROID_NDK : TRUE if NDK is used
-#   BUILD_WITH_STANDALONE_TOOLCHAIN : TRUE if standalone toolchain is used
-#   ANDROID_NDK_HOST_SYSTEM_NAME : "windows", "linux-x86" or "darwin-x86" depending on host platform
-#   ANDROID_NDK_ABI_NAME : "armeabi", "armeabi-v7a", "armeabi-v7a-hard", "x86", "mips", "arm64-v8a", "x86_64", "mips64" depending on ANDROID_ABI
-#   ANDROID_NDK_RELEASE : from r5 to r10d; set only for NDK
-#   ANDROID_NDK_RELEASE_NUM : numeric ANDROID_NDK_RELEASE version (1000*major+minor)
-#   ANDROID_ARCH_NAME : "arm", "x86", "mips", "arm64", "x86_64", "mips64" depending on ANDROID_ABI
-#   ANDROID_SYSROOT : path to the compiler sysroot
-#   ANDROID_SYSROOT_INCLUDE : paths to system include paths
-#   TOOL_OS_SUFFIX : "" or ".exe" depending on host platform
-#   ANDROID_COMPILER_IS_CLANG : TRUE if clang compiler is used
-#
-# Secondary (less stable) read-only variables:
-#   ANDROID_COMPILER_VERSION : GCC version used (not Clang version)
-#   ANDROID_CLANG_VERSION : version of clang compiler if clang is used
-#   ANDROID_CXX_FLAGS : C/C++ compiler flags required by Android platform
-#   ANDROID_SUPPORTED_ABIS : list of currently allowed values for ANDROID_ABI
-#   ANDROID_TOOLCHAIN_MACHINE_NAME : "arm-linux-androideabi", "arm-eabi" or "i686-android-linux"
-#   ANDROID_TOOLCHAIN_ROOT : path to the top level of toolchain (standalone or placed inside NDK)
-#   ANDROID_CLANG_TOOLCHAIN_ROOT : path to clang tools
-#   ANDROID_SUPPORTED_NATIVE_API_LEVELS : list of native API levels found inside NDK
-#   ANDROID_STL_INCLUDE_DIRS : stl include paths
-#   ANDROID_RTTI : if rtti is enabled by the runtime
-#   ANDROID_EXCEPTIONS : if exceptions are enabled by the runtime
-#   ANDROID_GCC_TOOLCHAIN_NAME : read-only, differs from ANDROID_TOOLCHAIN_NAME only if clang is used
-#
-# Defaults:
-#   ANDROID_DEFAULT_NDK_API_LEVEL
-#   ANDROID_DEFAULT_NDK_API_LEVEL_${ARCH}
-#   ANDROID_NDK_SEARCH_PATHS
-#   ANDROID_SUPPORTED_ABIS_${ARCH}
-#   ANDROID_SUPPORTED_NDK_VERSIONS
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv/cv.h b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv/cv.h
deleted file mode 100755
index 19a74e2..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv/cv.h
+++ /dev/null
@@ -1,73 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_OLD_CV_H
-#define OPENCV_OLD_CV_H
-
-#if defined(_MSC_VER)
-    #define CV_DO_PRAGMA(x) __pragma(x)
-    #define __CVSTR2__(x) #x
-    #define __CVSTR1__(x) __CVSTR2__(x)
-    #define __CVMSVCLOC__ __FILE__ "("__CVSTR1__(__LINE__)") : "
-    #define CV_MSG_PRAGMA(_msg) CV_DO_PRAGMA(message (__CVMSVCLOC__ _msg))
-#elif defined(__GNUC__)
-    #define CV_DO_PRAGMA(x) _Pragma (#x)
-    #define CV_MSG_PRAGMA(_msg) CV_DO_PRAGMA(message (_msg))
-#else
-    #define CV_DO_PRAGMA(x)
-    #define CV_MSG_PRAGMA(_msg)
-#endif
-#define CV_WARNING(x) CV_MSG_PRAGMA("Warning: " #x)
-
-//CV_WARNING("This is a deprecated opencv header provided for compatibility. Please include a header from a corresponding opencv module")
-
-#include "opencv2/core/core_c.h"
-#include "opencv2/imgproc/imgproc_c.h"
-#include "opencv2/photo/photo_c.h"
-#include "opencv2/video/tracking_c.h"
-#include "opencv2/objdetect/objdetect_c.h"
-
-#if !defined(CV_IMPL)
-#define CV_IMPL extern "C"
-#endif //CV_IMPL
-
-#endif // __OPENCV_OLD_CV_H_
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv/cv.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv/cv.hpp
deleted file mode 100755
index 8673956..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv/cv.hpp
+++ /dev/null
@@ -1,60 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_OLD_CV_HPP
-#define OPENCV_OLD_CV_HPP
-
-//#if defined(__GNUC__)
-//#warning "This is a deprecated opencv header provided for compatibility. Please include a header from a corresponding opencv module"
-//#endif
-
-#include "cv.h"
-#include "opencv2/core.hpp"
-#include "opencv2/imgproc.hpp"
-#include "opencv2/photo.hpp"
-#include "opencv2/video.hpp"
-#include "opencv2/highgui.hpp"
-#include "opencv2/features2d.hpp"
-#include "opencv2/calib3d.hpp"
-#include "opencv2/objdetect.hpp"
-
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv/cvaux.h b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv/cvaux.h
deleted file mode 100755
index c0367cc..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv/cvaux.h
+++ /dev/null
@@ -1,57 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                        Intel License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000, Intel Corporation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of Intel Corporation may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_OLD_AUX_H
-#define OPENCV_OLD_AUX_H
-
-//#if defined(__GNUC__)
-//#warning "This is a deprecated opencv header provided for compatibility. Please include a header from a corresponding opencv module"
-//#endif
-
-#include "opencv2/core/core_c.h"
-#include "opencv2/imgproc/imgproc_c.h"
-#include "opencv2/photo/photo_c.h"
-#include "opencv2/video/tracking_c.h"
-#include "opencv2/objdetect/objdetect_c.h"
-
-#endif
-
-/* End of file. */
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv/cvaux.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv/cvaux.hpp
deleted file mode 100755
index 4888eef..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv/cvaux.hpp
+++ /dev/null
@@ -1,52 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                        Intel License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000, Intel Corporation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of Intel Corporation may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_OLD_AUX_HPP
-#define OPENCV_OLD_AUX_HPP
-
-//#if defined(__GNUC__)
-//#warning "This is a deprecated opencv header provided for compatibility. Please include a header from a corresponding opencv module"
-//#endif
-
-#include "cvaux.h"
-#include "opencv2/core/utility.hpp"
-
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv/cvwimage.h b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv/cvwimage.h
deleted file mode 100755
index ec0ab14..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv/cvwimage.h
+++ /dev/null
@@ -1,46 +0,0 @@
-///////////////////////////////////////////////////////////////////////////////
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to
-//  this license.  If you do not agree to this license, do not download,
-//  install, copy or use the software.
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2008, Google, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are met:
-//
-//  * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//  * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//  * The name of Intel Corporation or contributors may not be used to endorse
-//     or promote products derived from this software without specific
-//     prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is"
-// and any express or implied warranties, including, but not limited to, the
-// implied warranties of merchantability and fitness for a particular purpose
-// are disclaimed. In no event shall the Intel Corporation or contributors be
-// liable for any direct, indirect, incidental, special, exemplary, or
-// consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-
-
-#ifndef OPENCV_OLD_WIMAGE_HPP
-#define OPENCV_OLD_WIMAGE_HPP
-
-#include "opencv2/core/wimage.hpp"
-
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv/cxcore.h b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv/cxcore.h
deleted file mode 100755
index dc070c7..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv/cxcore.h
+++ /dev/null
@@ -1,52 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_OLD_CXCORE_H
-#define OPENCV_OLD_CXCORE_H
-
-//#if defined(__GNUC__)
-//#warning "This is a deprecated opencv header provided for compatibility. Please include a header from a corresponding opencv module"
-//#endif
-
-#include "opencv2/core/core_c.h"
-
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv/cxcore.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv/cxcore.hpp
deleted file mode 100755
index c371677..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv/cxcore.hpp
+++ /dev/null
@@ -1,53 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_OLD_CXCORE_HPP
-#define OPENCV_OLD_CXCORE_HPP
-
-//#if defined(__GNUC__)
-//#warning "This is a deprecated opencv header provided for compatibility. Please include a header from a corresponding opencv module"
-//#endif
-
-#include "cxcore.h"
-#include "opencv2/core.hpp"
-
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv/cxeigen.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv/cxeigen.hpp
deleted file mode 100755
index 1d3df91..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv/cxeigen.hpp
+++ /dev/null
@@ -1,48 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_OLD_EIGEN_HPP
-#define OPENCV_OLD_EIGEN_HPP
-
-#include "opencv2/core/eigen.hpp"
-
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv/cxmisc.h b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv/cxmisc.h
deleted file mode 100755
index 9b9bc82..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv/cxmisc.h
+++ /dev/null
@@ -1,8 +0,0 @@
-#ifndef OPENCV_OLD_CXMISC_H
-#define OPENCV_OLD_CXMISC_H
-
-#ifdef __cplusplus
-#  include "opencv2/core/utility.hpp"
-#endif
-
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv/highgui.h b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv/highgui.h
deleted file mode 100755
index 69b394e..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv/highgui.h
+++ /dev/null
@@ -1,48 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                        Intel License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000, Intel Corporation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of Intel Corporation may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_OLD_HIGHGUI_H
-#define OPENCV_OLD_HIGHGUI_H
-
-#include "opencv2/core/core_c.h"
-#include "opencv2/highgui/highgui_c.h"
-
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv/ml.h b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv/ml.h
deleted file mode 100755
index 0c376ba..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv/ml.h
+++ /dev/null
@@ -1,47 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                        Intel License Agreement
-//
-// Copyright (C) 2000, Intel Corporation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of Intel Corporation may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_OLD_ML_H
-#define OPENCV_OLD_ML_H
-
-#include "opencv2/core/core_c.h"
-#include "opencv2/ml.hpp"
-
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/calib3d.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/calib3d.hpp
deleted file mode 100755
index fcd295d..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/calib3d.hpp
+++ /dev/null
@@ -1,2458 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_CALIB3D_HPP
-#define OPENCV_CALIB3D_HPP
-
-#include "opencv2/core.hpp"
-#include "opencv2/features2d.hpp"
-#include "opencv2/core/affine.hpp"
-
-/**
-  @defgroup calib3d Camera Calibration and 3D Reconstruction
-
-The functions in this section use a so-called pinhole camera model. In this model, a scene view is
-formed by projecting 3D points into the image plane using a perspective transformation.
-
-\f[s  \; m' = A [R|t] M'\f]
-
-or
-
-\f[s  \vecthree{u}{v}{1} = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}
-\begin{bmatrix}
-r_{11} & r_{12} & r_{13} & t_1  \\
-r_{21} & r_{22} & r_{23} & t_2  \\
-r_{31} & r_{32} & r_{33} & t_3
-\end{bmatrix}
-\begin{bmatrix}
-X \\
-Y \\
-Z \\
-1
-\end{bmatrix}\f]
-
-where:
-
--   \f$(X, Y, Z)\f$ are the coordinates of a 3D point in the world coordinate space
--   \f$(u, v)\f$ are the coordinates of the projection point in pixels
--   \f$A\f$ is a camera matrix, or a matrix of intrinsic parameters
--   \f$(cx, cy)\f$ is a principal point that is usually at the image center
--   \f$fx, fy\f$ are the focal lengths expressed in pixel units.
-
-Thus, if an image from the camera is scaled by a factor, all of these parameters should be scaled
-(multiplied/divided, respectively) by the same factor. The matrix of intrinsic parameters does not
-depend on the scene viewed. So, once estimated, it can be re-used as long as the focal length is
-fixed (in case of zoom lens). The joint rotation-translation matrix \f$[R|t]\f$ is called a matrix of
-extrinsic parameters. It is used to describe the camera motion around a static scene, or vice versa,
-rigid motion of an object in front of a still camera. That is, \f$[R|t]\f$ translates coordinates of a
-point \f$(X, Y, Z)\f$ to a coordinate system, fixed with respect to the camera. The transformation above
-is equivalent to the following (when \f$z \ne 0\f$ ):
-
-\f[\begin{array}{l}
-\vecthree{x}{y}{z} = R  \vecthree{X}{Y}{Z} + t \\
-x' = x/z \\
-y' = y/z \\
-u = f_x*x' + c_x \\
-v = f_y*y' + c_y
-\end{array}\f]
-
-The following figure illustrates the pinhole camera model.
-
-![Pinhole camera model](pics/pinhole_camera_model.png)
-
-Real lenses usually have some distortion, mostly radial distortion and slight tangential distortion.
-So, the above model is extended as:
-
-\f[\begin{array}{l}
-\vecthree{x}{y}{z} = R  \vecthree{X}{Y}{Z} + t \\
-x' = x/z \\
-y' = y/z \\
-x'' = x'  \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6} + 2 p_1 x' y' + p_2(r^2 + 2 x'^2) + s_1 r^2 + s_2 r^4 \\
-y'' = y'  \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6} + p_1 (r^2 + 2 y'^2) + 2 p_2 x' y' + s_3 r^2 + s_4 r^4 \\
-\text{where} \quad r^2 = x'^2 + y'^2  \\
-u = f_x*x'' + c_x \\
-v = f_y*y'' + c_y
-\end{array}\f]
-
-\f$k_1\f$, \f$k_2\f$, \f$k_3\f$, \f$k_4\f$, \f$k_5\f$, and \f$k_6\f$ are radial distortion coefficients. \f$p_1\f$ and \f$p_2\f$ are
-tangential distortion coefficients. \f$s_1\f$, \f$s_2\f$, \f$s_3\f$, and \f$s_4\f$, are the thin prism distortion
-coefficients. Higher-order coefficients are not considered in OpenCV.
-
-The next figure shows two common types of radial distortion: barrel distortion (typically \f$ k_1 > 0 \f$ and pincushion distortion (typically \f$ k_1 < 0 \f$).
-
-![](pics/distortion_examples.png)
-
-In some cases the image sensor may be tilted in order to focus an oblique plane in front of the
-camera (Scheimpfug condition). This can be useful for particle image velocimetry (PIV) or
-triangulation with a laser fan. The tilt causes a perspective distortion of \f$x''\f$ and
-\f$y''\f$. This distortion can be modelled in the following way, see e.g. @cite Louhichi07.
-
-\f[\begin{array}{l}
-s\vecthree{x'''}{y'''}{1} =
-\vecthreethree{R_{33}(\tau_x, \tau_y)}{0}{-R_{13}(\tau_x, \tau_y)}
-{0}{R_{33}(\tau_x, \tau_y)}{-R_{23}(\tau_x, \tau_y)}
-{0}{0}{1} R(\tau_x, \tau_y) \vecthree{x''}{y''}{1}\\
-u = f_x*x''' + c_x \\
-v = f_y*y''' + c_y
-\end{array}\f]
-
-where the matrix \f$R(\tau_x, \tau_y)\f$ is defined by two rotations with angular parameter \f$\tau_x\f$
-and \f$\tau_y\f$, respectively,
-
-\f[
-R(\tau_x, \tau_y) =
-\vecthreethree{\cos(\tau_y)}{0}{-\sin(\tau_y)}{0}{1}{0}{\sin(\tau_y)}{0}{\cos(\tau_y)}
-\vecthreethree{1}{0}{0}{0}{\cos(\tau_x)}{\sin(\tau_x)}{0}{-\sin(\tau_x)}{\cos(\tau_x)} =
-\vecthreethree{\cos(\tau_y)}{\sin(\tau_y)\sin(\tau_x)}{-\sin(\tau_y)\cos(\tau_x)}
-{0}{\cos(\tau_x)}{\sin(\tau_x)}
-{\sin(\tau_y)}{-\cos(\tau_y)\sin(\tau_x)}{\cos(\tau_y)\cos(\tau_x)}.
-\f]
-
-In the functions below the coefficients are passed or returned as
-
-\f[(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f]
-
-vector. That is, if the vector contains four elements, it means that \f$k_3=0\f$ . The distortion
-coefficients do not depend on the scene viewed. Thus, they also belong to the intrinsic camera
-parameters. And they remain the same regardless of the captured image resolution. If, for example, a
-camera has been calibrated on images of 320 x 240 resolution, absolutely the same distortion
-coefficients can be used for 640 x 480 images from the same camera while \f$f_x\f$, \f$f_y\f$, \f$c_x\f$, and
-\f$c_y\f$ need to be scaled appropriately.
-
-The functions below use the above model to do the following:
-
--   Project 3D points to the image plane given intrinsic and extrinsic parameters.
--   Compute extrinsic parameters given intrinsic parameters, a few 3D points, and their
-projections.
--   Estimate intrinsic and extrinsic camera parameters from several views of a known calibration
-pattern (every view is described by several 3D-2D point correspondences).
--   Estimate the relative position and orientation of the stereo camera "heads" and compute the
-*rectification* transformation that makes the camera optical axes parallel.
-
-@note
-   -   A calibration sample for 3 cameras in horizontal position can be found at
-        opencv_source_code/samples/cpp/3calibration.cpp
-    -   A calibration sample based on a sequence of images can be found at
-        opencv_source_code/samples/cpp/calibration.cpp
-    -   A calibration sample in order to do 3D reconstruction can be found at
-        opencv_source_code/samples/cpp/build3dmodel.cpp
-    -   A calibration sample of an artificially generated camera and chessboard patterns can be
-        found at opencv_source_code/samples/cpp/calibration_artificial.cpp
-    -   A calibration example on stereo calibration can be found at
-        opencv_source_code/samples/cpp/stereo_calib.cpp
-    -   A calibration example on stereo matching can be found at
-        opencv_source_code/samples/cpp/stereo_match.cpp
-    -   (Python) A camera calibration sample can be found at
-        opencv_source_code/samples/python/calibrate.py
-
-  @{
-    @defgroup calib3d_fisheye Fisheye camera model
-
-    Definitions: Let P be a point in 3D of coordinates X in the world reference frame (stored in the
-    matrix X) The coordinate vector of P in the camera reference frame is:
-
-    \f[Xc = R X + T\f]
-
-    where R is the rotation matrix corresponding to the rotation vector om: R = rodrigues(om); call x, y
-    and z the 3 coordinates of Xc:
-
-    \f[x = Xc_1 \\ y = Xc_2 \\ z = Xc_3\f]
-
-    The pinhole projection coordinates of P is [a; b] where
-
-    \f[a = x / z \ and \ b = y / z \\ r^2 = a^2 + b^2 \\ \theta = atan(r)\f]
-
-    Fisheye distortion:
-
-    \f[\theta_d = \theta (1 + k_1 \theta^2 + k_2 \theta^4 + k_3 \theta^6 + k_4 \theta^8)\f]
-
-    The distorted point coordinates are [x'; y'] where
-
-    \f[x' = (\theta_d / r) a \\ y' = (\theta_d / r) b \f]
-
-    Finally, conversion into pixel coordinates: The final pixel coordinates vector [u; v] where:
-
-    \f[u = f_x (x' + \alpha y') + c_x \\
-    v = f_y y' + c_y\f]
-
-    @defgroup calib3d_c C API
-
-  @}
- */
-
-namespace cv
-{
-
-//! @addtogroup calib3d
-//! @{
-
-//! type of the robust estimation algorithm
-enum { LMEDS  = 4, //!< least-median of squares algorithm
-       RANSAC = 8, //!< RANSAC algorithm
-       RHO    = 16 //!< RHO algorithm
-     };
-
-enum { SOLVEPNP_ITERATIVE = 0,
-       SOLVEPNP_EPNP      = 1, //!< EPnP: Efficient Perspective-n-Point Camera Pose Estimation @cite lepetit2009epnp
-       SOLVEPNP_P3P       = 2, //!< Complete Solution Classification for the Perspective-Three-Point Problem @cite gao2003complete
-       SOLVEPNP_DLS       = 3, //!< A Direct Least-Squares (DLS) Method for PnP  @cite hesch2011direct
-       SOLVEPNP_UPNP      = 4, //!< Exhaustive Linearization for Robust Camera Pose and Focal Length Estimation @cite penate2013exhaustive
-       SOLVEPNP_AP3P      = 5, //!< An Efficient Algebraic Solution to the Perspective-Three-Point Problem @cite Ke17
-       SOLVEPNP_MAX_COUNT      //!< Used for count
-};
-
-enum { CALIB_CB_ADAPTIVE_THRESH = 1,
-       CALIB_CB_NORMALIZE_IMAGE = 2,
-       CALIB_CB_FILTER_QUADS    = 4,
-       CALIB_CB_FAST_CHECK      = 8
-     };
-
-enum { CALIB_CB_SYMMETRIC_GRID  = 1,
-       CALIB_CB_ASYMMETRIC_GRID = 2,
-       CALIB_CB_CLUSTERING      = 4
-     };
-
-enum { CALIB_USE_INTRINSIC_GUESS = 0x00001,
-       CALIB_FIX_ASPECT_RATIO    = 0x00002,
-       CALIB_FIX_PRINCIPAL_POINT = 0x00004,
-       CALIB_ZERO_TANGENT_DIST   = 0x00008,
-       CALIB_FIX_FOCAL_LENGTH    = 0x00010,
-       CALIB_FIX_K1              = 0x00020,
-       CALIB_FIX_K2              = 0x00040,
-       CALIB_FIX_K3              = 0x00080,
-       CALIB_FIX_K4              = 0x00800,
-       CALIB_FIX_K5              = 0x01000,
-       CALIB_FIX_K6              = 0x02000,
-       CALIB_RATIONAL_MODEL      = 0x04000,
-       CALIB_THIN_PRISM_MODEL    = 0x08000,
-       CALIB_FIX_S1_S2_S3_S4     = 0x10000,
-       CALIB_TILTED_MODEL        = 0x40000,
-       CALIB_FIX_TAUX_TAUY       = 0x80000,
-       CALIB_USE_QR              = 0x100000, //!< use QR instead of SVD decomposition for solving. Faster but potentially less precise
-       CALIB_FIX_TANGENT_DIST    = 0x200000,
-       // only for stereo
-       CALIB_FIX_INTRINSIC       = 0x00100,
-       CALIB_SAME_FOCAL_LENGTH   = 0x00200,
-       // for stereo rectification
-       CALIB_ZERO_DISPARITY      = 0x00400,
-       CALIB_USE_LU              = (1 << 17), //!< use LU instead of SVD decomposition for solving. much faster but potentially less precise
-       CALIB_USE_EXTRINSIC_GUESS = (1 << 22), //!< for stereoCalibrate
-     };
-
-//! the algorithm for finding fundamental matrix
-enum { FM_7POINT = 1, //!< 7-point algorithm
-       FM_8POINT = 2, //!< 8-point algorithm
-       FM_LMEDS  = 4, //!< least-median algorithm. 7-point algorithm is used.
-       FM_RANSAC = 8  //!< RANSAC algorithm. It needs at least 15 points. 7-point algorithm is used.
-     };
-
-
-
-/** @brief Converts a rotation matrix to a rotation vector or vice versa.
-
-@param src Input rotation vector (3x1 or 1x3) or rotation matrix (3x3).
-@param dst Output rotation matrix (3x3) or rotation vector (3x1 or 1x3), respectively.
-@param jacobian Optional output Jacobian matrix, 3x9 or 9x3, which is a matrix of partial
-derivatives of the output array components with respect to the input array components.
-
-\f[\begin{array}{l} \theta \leftarrow norm(r) \\ r  \leftarrow r/ \theta \\ R =  \cos{\theta} I + (1- \cos{\theta} ) r r^T +  \sin{\theta} \vecthreethree{0}{-r_z}{r_y}{r_z}{0}{-r_x}{-r_y}{r_x}{0} \end{array}\f]
-
-Inverse transformation can be also done easily, since
-
-\f[\sin ( \theta ) \vecthreethree{0}{-r_z}{r_y}{r_z}{0}{-r_x}{-r_y}{r_x}{0} = \frac{R - R^T}{2}\f]
-
-A rotation vector is a convenient and most compact representation of a rotation matrix (since any
-rotation matrix has just 3 degrees of freedom). The representation is used in the global 3D geometry
-optimization procedures like calibrateCamera, stereoCalibrate, or solvePnP .
- */
-CV_EXPORTS_W void Rodrigues( InputArray src, OutputArray dst, OutputArray jacobian = noArray() );
-
-/** @example pose_from_homography.cpp
-  An example program about pose estimation from coplanar points
-
-  Check @ref tutorial_homography "the corresponding tutorial" for more details
- */
-
-/** @brief Finds a perspective transformation between two planes.
-
-@param srcPoints Coordinates of the points in the original plane, a matrix of the type CV_32FC2
-or vector\<Point2f\> .
-@param dstPoints Coordinates of the points in the target plane, a matrix of the type CV_32FC2 or
-a vector\<Point2f\> .
-@param method Method used to compute a homography matrix. The following methods are possible:
--   **0** - a regular method using all the points, i.e., the least squares method
--   **RANSAC** - RANSAC-based robust method
--   **LMEDS** - Least-Median robust method
--   **RHO** - PROSAC-based robust method
-@param ransacReprojThreshold Maximum allowed reprojection error to treat a point pair as an inlier
-(used in the RANSAC and RHO methods only). That is, if
-\f[\| \texttt{dstPoints} _i -  \texttt{convertPointsHomogeneous} ( \texttt{H} * \texttt{srcPoints} _i) \|_2  >  \texttt{ransacReprojThreshold}\f]
-then the point \f$i\f$ is considered as an outlier. If srcPoints and dstPoints are measured in pixels,
-it usually makes sense to set this parameter somewhere in the range of 1 to 10.
-@param mask Optional output mask set by a robust method ( RANSAC or LMEDS ). Note that the input
-mask values are ignored.
-@param maxIters The maximum number of RANSAC iterations.
-@param confidence Confidence level, between 0 and 1.
-
-The function finds and returns the perspective transformation \f$H\f$ between the source and the
-destination planes:
-
-\f[s_i  \vecthree{x'_i}{y'_i}{1} \sim H  \vecthree{x_i}{y_i}{1}\f]
-
-so that the back-projection error
-
-\f[\sum _i \left ( x'_i- \frac{h_{11} x_i + h_{12} y_i + h_{13}}{h_{31} x_i + h_{32} y_i + h_{33}} \right )^2+ \left ( y'_i- \frac{h_{21} x_i + h_{22} y_i + h_{23}}{h_{31} x_i + h_{32} y_i + h_{33}} \right )^2\f]
-
-is minimized. If the parameter method is set to the default value 0, the function uses all the point
-pairs to compute an initial homography estimate with a simple least-squares scheme.
-
-However, if not all of the point pairs ( \f$srcPoints_i\f$, \f$dstPoints_i\f$ ) fit the rigid perspective
-transformation (that is, there are some outliers), this initial estimate will be poor. In this case,
-you can use one of the three robust methods. The methods RANSAC, LMeDS and RHO try many different
-random subsets of the corresponding point pairs (of four pairs each, collinear pairs are discarded), estimate the homography matrix
-using this subset and a simple least-squares algorithm, and then compute the quality/goodness of the
-computed homography (which is the number of inliers for RANSAC or the least median re-projection error for
-LMeDS). The best subset is then used to produce the initial estimate of the homography matrix and
-the mask of inliers/outliers.
-
-Regardless of the method, robust or not, the computed homography matrix is refined further (using
-inliers only in case of a robust method) with the Levenberg-Marquardt method to reduce the
-re-projection error even more.
-
-The methods RANSAC and RHO can handle practically any ratio of outliers but need a threshold to
-distinguish inliers from outliers. The method LMeDS does not need any threshold but it works
-correctly only when there are more than 50% of inliers. Finally, if there are no outliers and the
-noise is rather small, use the default method (method=0).
-
-The function is used to find initial intrinsic and extrinsic matrices. Homography matrix is
-determined up to a scale. Thus, it is normalized so that \f$h_{33}=1\f$. Note that whenever an \f$H\f$ matrix
-cannot be estimated, an empty one will be returned.
-
-@sa
-getAffineTransform, estimateAffine2D, estimateAffinePartial2D, getPerspectiveTransform, warpPerspective,
-perspectiveTransform
- */
-CV_EXPORTS_W Mat findHomography( InputArray srcPoints, InputArray dstPoints,
-                                 int method = 0, double ransacReprojThreshold = 3,
-                                 OutputArray mask=noArray(), const int maxIters = 2000,
-                                 const double confidence = 0.995);
-
-/** @overload */
-CV_EXPORTS Mat findHomography( InputArray srcPoints, InputArray dstPoints,
-                               OutputArray mask, int method = 0, double ransacReprojThreshold = 3 );
-
-/** @brief Computes an RQ decomposition of 3x3 matrices.
-
-@param src 3x3 input matrix.
-@param mtxR Output 3x3 upper-triangular matrix.
-@param mtxQ Output 3x3 orthogonal matrix.
-@param Qx Optional output 3x3 rotation matrix around x-axis.
-@param Qy Optional output 3x3 rotation matrix around y-axis.
-@param Qz Optional output 3x3 rotation matrix around z-axis.
-
-The function computes a RQ decomposition using the given rotations. This function is used in
-decomposeProjectionMatrix to decompose the left 3x3 submatrix of a projection matrix into a camera
-and a rotation matrix.
-
-It optionally returns three rotation matrices, one for each axis, and the three Euler angles in
-degrees (as the return value) that could be used in OpenGL. Note, there is always more than one
-sequence of rotations about the three principal axes that results in the same orientation of an
-object, e.g. see @cite Slabaugh . Returned tree rotation matrices and corresponding three Euler angles
-are only one of the possible solutions.
- */
-CV_EXPORTS_W Vec3d RQDecomp3x3( InputArray src, OutputArray mtxR, OutputArray mtxQ,
-                                OutputArray Qx = noArray(),
-                                OutputArray Qy = noArray(),
-                                OutputArray Qz = noArray());
-
-/** @brief Decomposes a projection matrix into a rotation matrix and a camera matrix.
-
-@param projMatrix 3x4 input projection matrix P.
-@param cameraMatrix Output 3x3 camera matrix K.
-@param rotMatrix Output 3x3 external rotation matrix R.
-@param transVect Output 4x1 translation vector T.
-@param rotMatrixX Optional 3x3 rotation matrix around x-axis.
-@param rotMatrixY Optional 3x3 rotation matrix around y-axis.
-@param rotMatrixZ Optional 3x3 rotation matrix around z-axis.
-@param eulerAngles Optional three-element vector containing three Euler angles of rotation in
-degrees.
-
-The function computes a decomposition of a projection matrix into a calibration and a rotation
-matrix and the position of a camera.
-
-It optionally returns three rotation matrices, one for each axis, and three Euler angles that could
-be used in OpenGL. Note, there is always more than one sequence of rotations about the three
-principal axes that results in the same orientation of an object, e.g. see @cite Slabaugh . Returned
-tree rotation matrices and corresponding three Euler angles are only one of the possible solutions.
-
-The function is based on RQDecomp3x3 .
- */
-CV_EXPORTS_W void decomposeProjectionMatrix( InputArray projMatrix, OutputArray cameraMatrix,
-                                             OutputArray rotMatrix, OutputArray transVect,
-                                             OutputArray rotMatrixX = noArray(),
-                                             OutputArray rotMatrixY = noArray(),
-                                             OutputArray rotMatrixZ = noArray(),
-                                             OutputArray eulerAngles =noArray() );
-
-/** @brief Computes partial derivatives of the matrix product for each multiplied matrix.
-
-@param A First multiplied matrix.
-@param B Second multiplied matrix.
-@param dABdA First output derivative matrix d(A\*B)/dA of size
-\f$\texttt{A.rows*B.cols} \times {A.rows*A.cols}\f$ .
-@param dABdB Second output derivative matrix d(A\*B)/dB of size
-\f$\texttt{A.rows*B.cols} \times {B.rows*B.cols}\f$ .
-
-The function computes partial derivatives of the elements of the matrix product \f$A*B\f$ with regard to
-the elements of each of the two input matrices. The function is used to compute the Jacobian
-matrices in stereoCalibrate but can also be used in any other similar optimization function.
- */
-CV_EXPORTS_W void matMulDeriv( InputArray A, InputArray B, OutputArray dABdA, OutputArray dABdB );
-
-/** @brief Combines two rotation-and-shift transformations.
-
-@param rvec1 First rotation vector.
-@param tvec1 First translation vector.
-@param rvec2 Second rotation vector.
-@param tvec2 Second translation vector.
-@param rvec3 Output rotation vector of the superposition.
-@param tvec3 Output translation vector of the superposition.
-@param dr3dr1
-@param dr3dt1
-@param dr3dr2
-@param dr3dt2
-@param dt3dr1
-@param dt3dt1
-@param dt3dr2
-@param dt3dt2 Optional output derivatives of rvec3 or tvec3 with regard to rvec1, rvec2, tvec1 and
-tvec2, respectively.
-
-The functions compute:
-
-\f[\begin{array}{l} \texttt{rvec3} =  \mathrm{rodrigues} ^{-1} \left ( \mathrm{rodrigues} ( \texttt{rvec2} )  \cdot \mathrm{rodrigues} ( \texttt{rvec1} ) \right )  \\ \texttt{tvec3} =  \mathrm{rodrigues} ( \texttt{rvec2} )  \cdot \texttt{tvec1} +  \texttt{tvec2} \end{array} ,\f]
-
-where \f$\mathrm{rodrigues}\f$ denotes a rotation vector to a rotation matrix transformation, and
-\f$\mathrm{rodrigues}^{-1}\f$ denotes the inverse transformation. See Rodrigues for details.
-
-Also, the functions can compute the derivatives of the output vectors with regards to the input
-vectors (see matMulDeriv ). The functions are used inside stereoCalibrate but can also be used in
-your own code where Levenberg-Marquardt or another gradient-based solver is used to optimize a
-function that contains a matrix multiplication.
- */
-CV_EXPORTS_W void composeRT( InputArray rvec1, InputArray tvec1,
-                             InputArray rvec2, InputArray tvec2,
-                             OutputArray rvec3, OutputArray tvec3,
-                             OutputArray dr3dr1 = noArray(), OutputArray dr3dt1 = noArray(),
-                             OutputArray dr3dr2 = noArray(), OutputArray dr3dt2 = noArray(),
-                             OutputArray dt3dr1 = noArray(), OutputArray dt3dt1 = noArray(),
-                             OutputArray dt3dr2 = noArray(), OutputArray dt3dt2 = noArray() );
-
-/** @brief Projects 3D points to an image plane.
-
-@param objectPoints Array of object points, 3xN/Nx3 1-channel or 1xN/Nx1 3-channel (or
-vector\<Point3f\> ), where N is the number of points in the view.
-@param rvec Rotation vector. See Rodrigues for details.
-@param tvec Translation vector.
-@param cameraMatrix Camera matrix \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{_1}\f$ .
-@param distCoeffs Input vector of distortion coefficients
-\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ of
-4, 5, 8, 12 or 14 elements. If the vector is empty, the zero distortion coefficients are assumed.
-@param imagePoints Output array of image points, 2xN/Nx2 1-channel or 1xN/Nx1 2-channel, or
-vector\<Point2f\> .
-@param jacobian Optional output 2Nx(10+\<numDistCoeffs\>) jacobian matrix of derivatives of image
-points with respect to components of the rotation vector, translation vector, focal lengths,
-coordinates of the principal point and the distortion coefficients. In the old interface different
-components of the jacobian are returned via different output parameters.
-@param aspectRatio Optional "fixed aspect ratio" parameter. If the parameter is not 0, the
-function assumes that the aspect ratio (*fx/fy*) is fixed and correspondingly adjusts the jacobian
-matrix.
-
-The function computes projections of 3D points to the image plane given intrinsic and extrinsic
-camera parameters. Optionally, the function computes Jacobians - matrices of partial derivatives of
-image points coordinates (as functions of all the input parameters) with respect to the particular
-parameters, intrinsic and/or extrinsic. The Jacobians are used during the global optimization in
-calibrateCamera, solvePnP, and stereoCalibrate . The function itself can also be used to compute a
-re-projection error given the current intrinsic and extrinsic parameters.
-
-@note By setting rvec=tvec=(0,0,0) or by setting cameraMatrix to a 3x3 identity matrix, or by
-passing zero distortion coefficients, you can get various useful partial cases of the function. This
-means that you can compute the distorted coordinates for a sparse set of points or apply a
-perspective transformation (and also compute the derivatives) in the ideal zero-distortion setup.
- */
-CV_EXPORTS_W void projectPoints( InputArray objectPoints,
-                                 InputArray rvec, InputArray tvec,
-                                 InputArray cameraMatrix, InputArray distCoeffs,
-                                 OutputArray imagePoints,
-                                 OutputArray jacobian = noArray(),
-                                 double aspectRatio = 0 );
-
-/** @example homography_from_camera_displacement.cpp
-  An example program about homography from the camera displacement
-
-  Check @ref tutorial_homography "the corresponding tutorial" for more details
- */
-
-/** @brief Finds an object pose from 3D-2D point correspondences.
-
-@param objectPoints Array of object points in the object coordinate space, Nx3 1-channel or
-1xN/Nx1 3-channel, where N is the number of points. vector\<Point3f\> can be also passed here.
-@param imagePoints Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel,
-where N is the number of points. vector\<Point2f\> can be also passed here.
-@param cameraMatrix Input camera matrix \f$A = \vecthreethree{fx}{0}{cx}{0}{fy}{cy}{0}{0}{1}\f$ .
-@param distCoeffs Input vector of distortion coefficients
-\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ of
-4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are
-assumed.
-@param rvec Output rotation vector (see @ref Rodrigues ) that, together with tvec , brings points from
-the model coordinate system to the camera coordinate system.
-@param tvec Output translation vector.
-@param useExtrinsicGuess Parameter used for #SOLVEPNP_ITERATIVE. If true (1), the function uses
-the provided rvec and tvec values as initial approximations of the rotation and translation
-vectors, respectively, and further optimizes them.
-@param flags Method for solving a PnP problem:
--   **SOLVEPNP_ITERATIVE** Iterative method is based on Levenberg-Marquardt optimization. In
-this case the function finds such a pose that minimizes reprojection error, that is the sum
-of squared distances between the observed projections imagePoints and the projected (using
-projectPoints ) objectPoints .
--   **SOLVEPNP_P3P** Method is based on the paper of X.S. Gao, X.-R. Hou, J. Tang, H.-F. Chang
-"Complete Solution Classification for the Perspective-Three-Point Problem" (@cite gao2003complete).
-In this case the function requires exactly four object and image points.
--   **SOLVEPNP_AP3P** Method is based on the paper of T. Ke, S. Roumeliotis
-"An Efficient Algebraic Solution to the Perspective-Three-Point Problem" (@cite Ke17).
-In this case the function requires exactly four object and image points.
--   **SOLVEPNP_EPNP** Method has been introduced by F.Moreno-Noguer, V.Lepetit and P.Fua in the
-paper "EPnP: Efficient Perspective-n-Point Camera Pose Estimation" (@cite lepetit2009epnp).
--   **SOLVEPNP_DLS** Method is based on the paper of Joel A. Hesch and Stergios I. Roumeliotis.
-"A Direct Least-Squares (DLS) Method for PnP" (@cite hesch2011direct).
--   **SOLVEPNP_UPNP** Method is based on the paper of A.Penate-Sanchez, J.Andrade-Cetto,
-F.Moreno-Noguer. "Exhaustive Linearization for Robust Camera Pose and Focal Length
-Estimation" (@cite penate2013exhaustive). In this case the function also estimates the parameters \f$f_x\f$ and \f$f_y\f$
-assuming that both have the same value. Then the cameraMatrix is updated with the estimated
-focal length.
--   **SOLVEPNP_AP3P** Method is based on the paper of Tong Ke and Stergios I. Roumeliotis.
-"An Efficient Algebraic Solution to the Perspective-Three-Point Problem" (@cite Ke17). In this case the
-function requires exactly four object and image points.
-
-The function estimates the object pose given a set of object points, their corresponding image
-projections, as well as the camera matrix and the distortion coefficients, see the figure below
-(more precisely, the X-axis of the camera frame is pointing to the right, the Y-axis downward
-and the Z-axis forward).
-
-![](pnp.jpg)
-
-Points expressed in the world frame \f$ \bf{X}_w \f$ are projected into the image plane \f$ \left[ u, v \right] \f$
-using the perspective projection model \f$ \Pi \f$ and the camera intrinsic parameters matrix \f$ \bf{A} \f$:
-
-\f[
-  \begin{align*}
-  \begin{bmatrix}
-  u \\
-  v \\
-  1
-  \end{bmatrix} &=
-  \bf{A} \hspace{0.1em} \Pi \hspace{0.2em} ^{c}\bf{M}_w
-  \begin{bmatrix}
-  X_{w} \\
-  Y_{w} \\
-  Z_{w} \\
-  1
-  \end{bmatrix} \\
-  \begin{bmatrix}
-  u \\
-  v \\
-  1
-  \end{bmatrix} &=
-  \begin{bmatrix}
-  f_x & 0 & c_x \\
-  0 & f_y & c_y \\
-  0 & 0 & 1
-  \end{bmatrix}
-  \begin{bmatrix}
-  1 & 0 & 0 & 0 \\
-  0 & 1 & 0 & 0 \\
-  0 & 0 & 1 & 0
-  \end{bmatrix}
-  \begin{bmatrix}
-  r_{11} & r_{12} & r_{13} & t_x \\
-  r_{21} & r_{22} & r_{23} & t_y \\
-  r_{31} & r_{32} & r_{33} & t_z \\
-  0 & 0 & 0 & 1
-  \end{bmatrix}
-  \begin{bmatrix}
-  X_{w} \\
-  Y_{w} \\
-  Z_{w} \\
-  1
-  \end{bmatrix}
-  \end{align*}
-\f]
-
-The estimated pose is thus the rotation (`rvec`) and the translation (`tvec`) vectors that allow to transform
-a 3D point expressed in the world frame into the camera frame:
-
-\f[
-  \begin{align*}
-  \begin{bmatrix}
-  X_c \\
-  Y_c \\
-  Z_c \\
-  1
-  \end{bmatrix} &=
-  \hspace{0.2em} ^{c}\bf{M}_w
-  \begin{bmatrix}
-  X_{w} \\
-  Y_{w} \\
-  Z_{w} \\
-  1
-  \end{bmatrix} \\
-  \begin{bmatrix}
-  X_c \\
-  Y_c \\
-  Z_c \\
-  1
-  \end{bmatrix} &=
-  \begin{bmatrix}
-  r_{11} & r_{12} & r_{13} & t_x \\
-  r_{21} & r_{22} & r_{23} & t_y \\
-  r_{31} & r_{32} & r_{33} & t_z \\
-  0 & 0 & 0 & 1
-  \end{bmatrix}
-  \begin{bmatrix}
-  X_{w} \\
-  Y_{w} \\
-  Z_{w} \\
-  1
-  \end{bmatrix}
-  \end{align*}
-\f]
-
-@note
-   -   An example of how to use solvePnP for planar augmented reality can be found at
-        opencv_source_code/samples/python/plane_ar.py
-   -   If you are using Python:
-        - Numpy array slices won't work as input because solvePnP requires contiguous
-        arrays (enforced by the assertion using cv::Mat::checkVector() around line 55 of
-        modules/calib3d/src/solvepnp.cpp version 2.4.9)
-        - The P3P algorithm requires image points to be in an array of shape (N,1,2) due
-        to its calling of cv::undistortPoints (around line 75 of modules/calib3d/src/solvepnp.cpp version 2.4.9)
-        which requires 2-channel information.
-        - Thus, given some data D = np.array(...) where D.shape = (N,M), in order to use a subset of
-        it as, e.g., imagePoints, one must effectively copy it into a new array: imagePoints =
-        np.ascontiguousarray(D[:,:2]).reshape((N,1,2))
-   -   The methods **SOLVEPNP_DLS** and **SOLVEPNP_UPNP** cannot be used as the current implementations are
-       unstable and sometimes give completely wrong results. If you pass one of these two
-       flags, **SOLVEPNP_EPNP** method will be used instead.
-   -   The minimum number of points is 4 in the general case. In the case of **SOLVEPNP_P3P** and **SOLVEPNP_AP3P**
-       methods, it is required to use exactly 4 points (the first 3 points are used to estimate all the solutions
-       of the P3P problem, the last one is used to retain the best solution that minimizes the reprojection error).
-   -   With **SOLVEPNP_ITERATIVE** method and `useExtrinsicGuess=true`, the minimum number of points is 3 (3 points
-       are sufficient to compute a pose but there are up to 4 solutions). The initial solution should be close to the
-       global solution to converge.
- */
-CV_EXPORTS_W bool solvePnP( InputArray objectPoints, InputArray imagePoints,
-                            InputArray cameraMatrix, InputArray distCoeffs,
-                            OutputArray rvec, OutputArray tvec,
-                            bool useExtrinsicGuess = false, int flags = SOLVEPNP_ITERATIVE );
-
-/** @brief Finds an object pose from 3D-2D point correspondences using the RANSAC scheme.
-
-@param objectPoints Array of object points in the object coordinate space, Nx3 1-channel or
-1xN/Nx1 3-channel, where N is the number of points. vector\<Point3f\> can be also passed here.
-@param imagePoints Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel,
-where N is the number of points. vector\<Point2f\> can be also passed here.
-@param cameraMatrix Input camera matrix \f$A = \vecthreethree{fx}{0}{cx}{0}{fy}{cy}{0}{0}{1}\f$ .
-@param distCoeffs Input vector of distortion coefficients
-\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ of
-4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are
-assumed.
-@param rvec Output rotation vector (see Rodrigues ) that, together with tvec , brings points from
-the model coordinate system to the camera coordinate system.
-@param tvec Output translation vector.
-@param useExtrinsicGuess Parameter used for SOLVEPNP_ITERATIVE. If true (1), the function uses
-the provided rvec and tvec values as initial approximations of the rotation and translation
-vectors, respectively, and further optimizes them.
-@param iterationsCount Number of iterations.
-@param reprojectionError Inlier threshold value used by the RANSAC procedure. The parameter value
-is the maximum allowed distance between the observed and computed point projections to consider it
-an inlier.
-@param confidence The probability that the algorithm produces a useful result.
-@param inliers Output vector that contains indices of inliers in objectPoints and imagePoints .
-@param flags Method for solving a PnP problem (see solvePnP ).
-
-The function estimates an object pose given a set of object points, their corresponding image
-projections, as well as the camera matrix and the distortion coefficients. This function finds such
-a pose that minimizes reprojection error, that is, the sum of squared distances between the observed
-projections imagePoints and the projected (using projectPoints ) objectPoints. The use of RANSAC
-makes the function resistant to outliers.
-
-@note
-   -   An example of how to use solvePNPRansac for object detection can be found at
-        opencv_source_code/samples/cpp/tutorial_code/calib3d/real_time_pose_estimation/
-   -   The default method used to estimate the camera pose for the Minimal Sample Sets step
-       is #SOLVEPNP_EPNP. Exceptions are:
-         - if you choose #SOLVEPNP_P3P or #SOLVEPNP_AP3P, these methods will be used.
-         - if the number of input points is equal to 4, #SOLVEPNP_P3P is used.
-   -   The method used to estimate the camera pose using all the inliers is defined by the
-       flags parameters unless it is equal to #SOLVEPNP_P3P or #SOLVEPNP_AP3P. In this case,
-       the method #SOLVEPNP_EPNP will be used instead.
- */
-CV_EXPORTS_W bool solvePnPRansac( InputArray objectPoints, InputArray imagePoints,
-                                  InputArray cameraMatrix, InputArray distCoeffs,
-                                  OutputArray rvec, OutputArray tvec,
-                                  bool useExtrinsicGuess = false, int iterationsCount = 100,
-                                  float reprojectionError = 8.0, double confidence = 0.99,
-                                  OutputArray inliers = noArray(), int flags = SOLVEPNP_ITERATIVE );
-/** @brief Finds an object pose from 3 3D-2D point correspondences.
-
-@param objectPoints Array of object points in the object coordinate space, 3x3 1-channel or
-1x3/3x1 3-channel. vector\<Point3f\> can be also passed here.
-@param imagePoints Array of corresponding image points, 3x2 1-channel or 1x3/3x1 2-channel.
- vector\<Point2f\> can be also passed here.
-@param cameraMatrix Input camera matrix \f$A = \vecthreethree{fx}{0}{cx}{0}{fy}{cy}{0}{0}{1}\f$ .
-@param distCoeffs Input vector of distortion coefficients
-\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ of
-4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are
-assumed.
-@param rvecs Output rotation vectors (see Rodrigues ) that, together with tvecs , brings points from
-the model coordinate system to the camera coordinate system. A P3P problem has up to 4 solutions.
-@param tvecs Output translation vectors.
-@param flags Method for solving a P3P problem:
--   **SOLVEPNP_P3P** Method is based on the paper of X.S. Gao, X.-R. Hou, J. Tang, H.-F. Chang
-"Complete Solution Classification for the Perspective-Three-Point Problem" (@cite gao2003complete).
--   **SOLVEPNP_AP3P** Method is based on the paper of Tong Ke and Stergios I. Roumeliotis.
-"An Efficient Algebraic Solution to the Perspective-Three-Point Problem" (@cite Ke17).
-
-The function estimates the object pose given 3 object points, their corresponding image
-projections, as well as the camera matrix and the distortion coefficients.
- */
-CV_EXPORTS_W int solveP3P( InputArray objectPoints, InputArray imagePoints,
-                           InputArray cameraMatrix, InputArray distCoeffs,
-                           OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs,
-                           int flags );
-
-/** @brief Finds an initial camera matrix from 3D-2D point correspondences.
-
-@param objectPoints Vector of vectors of the calibration pattern points in the calibration pattern
-coordinate space. In the old interface all the per-view vectors are concatenated. See
-calibrateCamera for details.
-@param imagePoints Vector of vectors of the projections of the calibration pattern points. In the
-old interface all the per-view vectors are concatenated.
-@param imageSize Image size in pixels used to initialize the principal point.
-@param aspectRatio If it is zero or negative, both \f$f_x\f$ and \f$f_y\f$ are estimated independently.
-Otherwise, \f$f_x = f_y * \texttt{aspectRatio}\f$ .
-
-The function estimates and returns an initial camera matrix for the camera calibration process.
-Currently, the function only supports planar calibration patterns, which are patterns where each
-object point has z-coordinate =0.
- */
-CV_EXPORTS_W Mat initCameraMatrix2D( InputArrayOfArrays objectPoints,
-                                     InputArrayOfArrays imagePoints,
-                                     Size imageSize, double aspectRatio = 1.0 );
-
-/** @brief Finds the positions of internal corners of the chessboard.
-
-@param image Source chessboard view. It must be an 8-bit grayscale or color image.
-@param patternSize Number of inner corners per a chessboard row and column
-( patternSize = cvSize(points_per_row,points_per_colum) = cvSize(columns,rows) ).
-@param corners Output array of detected corners.
-@param flags Various operation flags that can be zero or a combination of the following values:
--   **CALIB_CB_ADAPTIVE_THRESH** Use adaptive thresholding to convert the image to black
-and white, rather than a fixed threshold level (computed from the average image brightness).
--   **CALIB_CB_NORMALIZE_IMAGE** Normalize the image gamma with equalizeHist before
-applying fixed or adaptive thresholding.
--   **CALIB_CB_FILTER_QUADS** Use additional criteria (like contour area, perimeter,
-square-like shape) to filter out false quads extracted at the contour retrieval stage.
--   **CALIB_CB_FAST_CHECK** Run a fast check on the image that looks for chessboard corners,
-and shortcut the call if none is found. This can drastically speed up the call in the
-degenerate condition when no chessboard is observed.
-
-The function attempts to determine whether the input image is a view of the chessboard pattern and
-locate the internal chessboard corners. The function returns a non-zero value if all of the corners
-are found and they are placed in a certain order (row by row, left to right in every row).
-Otherwise, if the function fails to find all the corners or reorder them, it returns 0. For example,
-a regular chessboard has 8 x 8 squares and 7 x 7 internal corners, that is, points where the black
-squares touch each other. The detected coordinates are approximate, and to determine their positions
-more accurately, the function calls cornerSubPix. You also may use the function cornerSubPix with
-different parameters if returned coordinates are not accurate enough.
-
-Sample usage of detecting and drawing chessboard corners: :
-@code
-    Size patternsize(8,6); //interior number of corners
-    Mat gray = ....; //source image
-    vector<Point2f> corners; //this will be filled by the detected corners
-
-    //CALIB_CB_FAST_CHECK saves a lot of time on images
-    //that do not contain any chessboard corners
-    bool patternfound = findChessboardCorners(gray, patternsize, corners,
-            CALIB_CB_ADAPTIVE_THRESH + CALIB_CB_NORMALIZE_IMAGE
-            + CALIB_CB_FAST_CHECK);
-
-    if(patternfound)
-      cornerSubPix(gray, corners, Size(11, 11), Size(-1, -1),
-        TermCriteria(CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, 30, 0.1));
-
-    drawChessboardCorners(img, patternsize, Mat(corners), patternfound);
-@endcode
-@note The function requires white space (like a square-thick border, the wider the better) around
-the board to make the detection more robust in various environments. Otherwise, if there is no
-border and the background is dark, the outer black squares cannot be segmented properly and so the
-square grouping and ordering algorithm fails.
- */
-CV_EXPORTS_W bool findChessboardCorners( InputArray image, Size patternSize, OutputArray corners,
-                                         int flags = CALIB_CB_ADAPTIVE_THRESH + CALIB_CB_NORMALIZE_IMAGE );
-
-//! finds subpixel-accurate positions of the chessboard corners
-CV_EXPORTS bool find4QuadCornerSubpix( InputArray img, InputOutputArray corners, Size region_size );
-
-/** @brief Renders the detected chessboard corners.
-
-@param image Destination image. It must be an 8-bit color image.
-@param patternSize Number of inner corners per a chessboard row and column
-(patternSize = cv::Size(points_per_row,points_per_column)).
-@param corners Array of detected corners, the output of findChessboardCorners.
-@param patternWasFound Parameter indicating whether the complete board was found or not. The
-return value of findChessboardCorners should be passed here.
-
-The function draws individual chessboard corners detected either as red circles if the board was not
-found, or as colored corners connected with lines if the board was found.
- */
-CV_EXPORTS_W void drawChessboardCorners( InputOutputArray image, Size patternSize,
-                                         InputArray corners, bool patternWasFound );
-
-struct CV_EXPORTS_W_SIMPLE CirclesGridFinderParameters
-{
-    CV_WRAP CirclesGridFinderParameters();
-    CV_PROP_RW cv::Size2f densityNeighborhoodSize;
-    CV_PROP_RW float minDensity;
-    CV_PROP_RW int kmeansAttempts;
-    CV_PROP_RW int minDistanceToAddKeypoint;
-    CV_PROP_RW int keypointScale;
-    CV_PROP_RW float minGraphConfidence;
-    CV_PROP_RW float vertexGain;
-    CV_PROP_RW float vertexPenalty;
-    CV_PROP_RW float existingVertexGain;
-    CV_PROP_RW float edgeGain;
-    CV_PROP_RW float edgePenalty;
-    CV_PROP_RW float convexHullFactor;
-    CV_PROP_RW float minRNGEdgeSwitchDist;
-
-    enum GridType
-    {
-      SYMMETRIC_GRID, ASYMMETRIC_GRID
-    };
-    GridType gridType;
-};
-
-struct CV_EXPORTS_W_SIMPLE CirclesGridFinderParameters2 : public CirclesGridFinderParameters
-{
-    CV_WRAP CirclesGridFinderParameters2();
-
-    CV_PROP_RW float squareSize; //!< Distance between two adjacent points. Used by CALIB_CB_CLUSTERING.
-    CV_PROP_RW float maxRectifiedDistance; //!< Max deviation from predicion. Used by CALIB_CB_CLUSTERING.
-};
-
-/** @brief Finds centers in the grid of circles.
-
-@param image grid view of input circles; it must be an 8-bit grayscale or color image.
-@param patternSize number of circles per row and column
-( patternSize = Size(points_per_row, points_per_colum) ).
-@param centers output array of detected centers.
-@param flags various operation flags that can be one of the following values:
--   **CALIB_CB_SYMMETRIC_GRID** uses symmetric pattern of circles.
--   **CALIB_CB_ASYMMETRIC_GRID** uses asymmetric pattern of circles.
--   **CALIB_CB_CLUSTERING** uses a special algorithm for grid detection. It is more robust to
-perspective distortions but much more sensitive to background clutter.
-@param blobDetector feature detector that finds blobs like dark circles on light background.
-@param parameters struct for finding circles in a grid pattern.
-
-The function attempts to determine whether the input image contains a grid of circles. If it is, the
-function locates centers of the circles. The function returns a non-zero value if all of the centers
-have been found and they have been placed in a certain order (row by row, left to right in every
-row). Otherwise, if the function fails to find all the corners or reorder them, it returns 0.
-
-Sample usage of detecting and drawing the centers of circles: :
-@code
-    Size patternsize(7,7); //number of centers
-    Mat gray = ....; //source image
-    vector<Point2f> centers; //this will be filled by the detected centers
-
-    bool patternfound = findCirclesGrid(gray, patternsize, centers);
-
-    drawChessboardCorners(img, patternsize, Mat(centers), patternfound);
-@endcode
-@note The function requires white space (like a square-thick border, the wider the better) around
-the board to make the detection more robust in various environments.
- */
-CV_EXPORTS_W bool findCirclesGrid( InputArray image, Size patternSize,
-                                   OutputArray centers, int flags,
-                                   const Ptr<FeatureDetector> &blobDetector,
-                                   CirclesGridFinderParameters parameters);
-
-/** @overload */
-CV_EXPORTS_W bool findCirclesGrid2( InputArray image, Size patternSize,
-                                   OutputArray centers, int flags,
-                                   const Ptr<FeatureDetector> &blobDetector,
-                                   CirclesGridFinderParameters2 parameters);
-
-/** @overload */
-CV_EXPORTS_W bool findCirclesGrid( InputArray image, Size patternSize,
-                                   OutputArray centers, int flags = CALIB_CB_SYMMETRIC_GRID,
-                                   const Ptr<FeatureDetector> &blobDetector = SimpleBlobDetector::create());
-
-/** @brief Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern.
-
-@param objectPoints In the new interface it is a vector of vectors of calibration pattern points in
-the calibration pattern coordinate space (e.g. std::vector<std::vector<cv::Vec3f>>). The outer
-vector contains as many elements as the number of the pattern views. If the same calibration pattern
-is shown in each view and it is fully visible, all the vectors will be the same. Although, it is
-possible to use partially occluded patterns, or even different patterns in different views. Then,
-the vectors will be different. The points are 3D, but since they are in a pattern coordinate system,
-then, if the rig is planar, it may make sense to put the model to a XY coordinate plane so that
-Z-coordinate of each input object point is 0.
-In the old interface all the vectors of object points from different views are concatenated
-together.
-@param imagePoints In the new interface it is a vector of vectors of the projections of calibration
-pattern points (e.g. std::vector<std::vector<cv::Vec2f>>). imagePoints.size() and
-objectPoints.size() and imagePoints[i].size() must be equal to objectPoints[i].size() for each i.
-In the old interface all the vectors of object points from different views are concatenated
-together.
-@param imageSize Size of the image used only to initialize the intrinsic camera matrix.
-@param cameraMatrix Output 3x3 floating-point camera matrix
-\f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ . If CV\_CALIB\_USE\_INTRINSIC\_GUESS
-and/or CALIB_FIX_ASPECT_RATIO are specified, some or all of fx, fy, cx, cy must be
-initialized before calling the function.
-@param distCoeffs Output vector of distortion coefficients
-\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ of
-4, 5, 8, 12 or 14 elements.
-@param rvecs Output vector of rotation vectors (see Rodrigues ) estimated for each pattern view
-(e.g. std::vector<cv::Mat>>). That is, each k-th rotation vector together with the corresponding
-k-th translation vector (see the next output parameter description) brings the calibration pattern
-from the model coordinate space (in which object points are specified) to the world coordinate
-space, that is, a real position of the calibration pattern in the k-th pattern view (k=0.. *M* -1).
-@param tvecs Output vector of translation vectors estimated for each pattern view.
-@param stdDeviationsIntrinsics Output vector of standard deviations estimated for intrinsic parameters.
- Order of deviations values:
-\f$(f_x, f_y, c_x, c_y, k_1, k_2, p_1, p_2, k_3, k_4, k_5, k_6 , s_1, s_2, s_3,
- s_4, \tau_x, \tau_y)\f$ If one of parameters is not estimated, it's deviation is equals to zero.
-@param stdDeviationsExtrinsics Output vector of standard deviations estimated for extrinsic parameters.
- Order of deviations values: \f$(R_1, T_1, \dotsc , R_M, T_M)\f$ where M is number of pattern views,
- \f$R_i, T_i\f$ are concatenated 1x3 vectors.
- @param perViewErrors Output vector of the RMS re-projection error estimated for each pattern view.
-@param flags Different flags that may be zero or a combination of the following values:
--   **CALIB_USE_INTRINSIC_GUESS** cameraMatrix contains valid initial values of
-fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image
-center ( imageSize is used), and focal distances are computed in a least-squares fashion.
-Note, that if intrinsic parameters are known, there is no need to use this function just to
-estimate extrinsic parameters. Use solvePnP instead.
--   **CALIB_FIX_PRINCIPAL_POINT** The principal point is not changed during the global
-optimization. It stays at the center or at a different location specified when
-CALIB_USE_INTRINSIC_GUESS is set too.
--   **CALIB_FIX_ASPECT_RATIO** The functions considers only fy as a free parameter. The
-ratio fx/fy stays the same as in the input cameraMatrix . When
-CALIB_USE_INTRINSIC_GUESS is not set, the actual input values of fx and fy are
-ignored, only their ratio is computed and used further.
--   **CALIB_ZERO_TANGENT_DIST** Tangential distortion coefficients \f$(p_1, p_2)\f$ are set
-to zeros and stay zero.
--   **CALIB_FIX_K1,...,CALIB_FIX_K6** The corresponding radial distortion
-coefficient is not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is
-set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0.
--   **CALIB_RATIONAL_MODEL** Coefficients k4, k5, and k6 are enabled. To provide the
-backward compatibility, this extra flag should be explicitly specified to make the
-calibration function use the rational model and return 8 coefficients. If the flag is not
-set, the function computes and returns only 5 distortion coefficients.
--   **CALIB_THIN_PRISM_MODEL** Coefficients s1, s2, s3 and s4 are enabled. To provide the
-backward compatibility, this extra flag should be explicitly specified to make the
-calibration function use the thin prism model and return 12 coefficients. If the flag is not
-set, the function computes and returns only 5 distortion coefficients.
--   **CALIB_FIX_S1_S2_S3_S4** The thin prism distortion coefficients are not changed during
-the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the
-supplied distCoeffs matrix is used. Otherwise, it is set to 0.
--   **CALIB_TILTED_MODEL** Coefficients tauX and tauY are enabled. To provide the
-backward compatibility, this extra flag should be explicitly specified to make the
-calibration function use the tilted sensor model and return 14 coefficients. If the flag is not
-set, the function computes and returns only 5 distortion coefficients.
--   **CALIB_FIX_TAUX_TAUY** The coefficients of the tilted sensor model are not changed during
-the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the
-supplied distCoeffs matrix is used. Otherwise, it is set to 0.
-@param criteria Termination criteria for the iterative optimization algorithm.
-
-@return the overall RMS re-projection error.
-
-The function estimates the intrinsic camera parameters and extrinsic parameters for each of the
-views. The algorithm is based on @cite Zhang2000 and @cite BouguetMCT . The coordinates of 3D object
-points and their corresponding 2D projections in each view must be specified. That may be achieved
-by using an object with a known geometry and easily detectable feature points. Such an object is
-called a calibration rig or calibration pattern, and OpenCV has built-in support for a chessboard as
-a calibration rig (see findChessboardCorners ). Currently, initialization of intrinsic parameters
-(when CALIB_USE_INTRINSIC_GUESS is not set) is only implemented for planar calibration
-patterns (where Z-coordinates of the object points must be all zeros). 3D calibration rigs can also
-be used as long as initial cameraMatrix is provided.
-
-The algorithm performs the following steps:
-
--   Compute the initial intrinsic parameters (the option only available for planar calibration
-    patterns) or read them from the input parameters. The distortion coefficients are all set to
-    zeros initially unless some of CALIB_FIX_K? are specified.
-
--   Estimate the initial camera pose as if the intrinsic parameters have been already known. This is
-    done using solvePnP .
-
--   Run the global Levenberg-Marquardt optimization algorithm to minimize the reprojection error,
-    that is, the total sum of squared distances between the observed feature points imagePoints and
-    the projected (using the current estimates for camera parameters and the poses) object points
-    objectPoints. See projectPoints for details.
-
-@note
-   If you use a non-square (=non-NxN) grid and findChessboardCorners for calibration, and
-    calibrateCamera returns bad values (zero distortion coefficients, an image center very far from
-    (w/2-0.5,h/2-0.5), and/or large differences between \f$f_x\f$ and \f$f_y\f$ (ratios of 10:1 or more)),
-    then you have probably used patternSize=cvSize(rows,cols) instead of using
-    patternSize=cvSize(cols,rows) in findChessboardCorners .
-
-@sa
-   findChessboardCorners, solvePnP, initCameraMatrix2D, stereoCalibrate, undistort
- */
-CV_EXPORTS_AS(calibrateCameraExtended) double calibrateCamera( InputArrayOfArrays objectPoints,
-                                     InputArrayOfArrays imagePoints, Size imageSize,
-                                     InputOutputArray cameraMatrix, InputOutputArray distCoeffs,
-                                     OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs,
-                                     OutputArray stdDeviationsIntrinsics,
-                                     OutputArray stdDeviationsExtrinsics,
-                                     OutputArray perViewErrors,
-                                     int flags = 0, TermCriteria criteria = TermCriteria(
-                                        TermCriteria::COUNT + TermCriteria::EPS, 30, DBL_EPSILON) );
-
-/** @overload double calibrateCamera( InputArrayOfArrays objectPoints,
-                                     InputArrayOfArrays imagePoints, Size imageSize,
-                                     InputOutputArray cameraMatrix, InputOutputArray distCoeffs,
-                                     OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs,
-                                     OutputArray stdDeviations, OutputArray perViewErrors,
-                                     int flags = 0, TermCriteria criteria = TermCriteria(
-                                        TermCriteria::COUNT + TermCriteria::EPS, 30, DBL_EPSILON) )
- */
-CV_EXPORTS_W double calibrateCamera( InputArrayOfArrays objectPoints,
-                                     InputArrayOfArrays imagePoints, Size imageSize,
-                                     InputOutputArray cameraMatrix, InputOutputArray distCoeffs,
-                                     OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs,
-                                     int flags = 0, TermCriteria criteria = TermCriteria(
-                                        TermCriteria::COUNT + TermCriteria::EPS, 30, DBL_EPSILON) );
-
-/** @brief Computes useful camera characteristics from the camera matrix.
-
-@param cameraMatrix Input camera matrix that can be estimated by calibrateCamera or
-stereoCalibrate .
-@param imageSize Input image size in pixels.
-@param apertureWidth Physical width in mm of the sensor.
-@param apertureHeight Physical height in mm of the sensor.
-@param fovx Output field of view in degrees along the horizontal sensor axis.
-@param fovy Output field of view in degrees along the vertical sensor axis.
-@param focalLength Focal length of the lens in mm.
-@param principalPoint Principal point in mm.
-@param aspectRatio \f$f_y/f_x\f$
-
-The function computes various useful camera characteristics from the previously estimated camera
-matrix.
-
-@note
-   Do keep in mind that the unity measure 'mm' stands for whatever unit of measure one chooses for
-    the chessboard pitch (it can thus be any value).
- */
-CV_EXPORTS_W void calibrationMatrixValues( InputArray cameraMatrix, Size imageSize,
-                                           double apertureWidth, double apertureHeight,
-                                           CV_OUT double& fovx, CV_OUT double& fovy,
-                                           CV_OUT double& focalLength, CV_OUT Point2d& principalPoint,
-                                           CV_OUT double& aspectRatio );
-
-/** @brief Calibrates the stereo camera.
-
-@param objectPoints Vector of vectors of the calibration pattern points.
-@param imagePoints1 Vector of vectors of the projections of the calibration pattern points,
-observed by the first camera.
-@param imagePoints2 Vector of vectors of the projections of the calibration pattern points,
-observed by the second camera.
-@param cameraMatrix1 Input/output first camera matrix:
-\f$\vecthreethree{f_x^{(j)}}{0}{c_x^{(j)}}{0}{f_y^{(j)}}{c_y^{(j)}}{0}{0}{1}\f$ , \f$j = 0,\, 1\f$ . If
-any of CALIB_USE_INTRINSIC_GUESS , CALIB_FIX_ASPECT_RATIO ,
-CALIB_FIX_INTRINSIC , or CALIB_FIX_FOCAL_LENGTH are specified, some or all of the
-matrix components must be initialized. See the flags description for details.
-@param distCoeffs1 Input/output vector of distortion coefficients
-\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ of
-4, 5, 8, 12 or 14 elements. The output vector length depends on the flags.
-@param cameraMatrix2 Input/output second camera matrix. The parameter is similar to cameraMatrix1
-@param distCoeffs2 Input/output lens distortion coefficients for the second camera. The parameter
-is similar to distCoeffs1 .
-@param imageSize Size of the image used only to initialize intrinsic camera matrix.
-@param R Output rotation matrix between the 1st and the 2nd camera coordinate systems.
-@param T Output translation vector between the coordinate systems of the cameras.
-@param E Output essential matrix.
-@param F Output fundamental matrix.
-@param perViewErrors Output vector of the RMS re-projection error estimated for each pattern view.
-@param flags Different flags that may be zero or a combination of the following values:
--   **CALIB_FIX_INTRINSIC** Fix cameraMatrix? and distCoeffs? so that only R, T, E , and F
-matrices are estimated.
--   **CALIB_USE_INTRINSIC_GUESS** Optimize some or all of the intrinsic parameters
-according to the specified flags. Initial values are provided by the user.
--   **CALIB_USE_EXTRINSIC_GUESS** R, T contain valid initial values that are optimized further.
-Otherwise R, T are initialized to the median value of the pattern views (each dimension separately).
--   **CALIB_FIX_PRINCIPAL_POINT** Fix the principal points during the optimization.
--   **CALIB_FIX_FOCAL_LENGTH** Fix \f$f^{(j)}_x\f$ and \f$f^{(j)}_y\f$ .
--   **CALIB_FIX_ASPECT_RATIO** Optimize \f$f^{(j)}_y\f$ . Fix the ratio \f$f^{(j)}_x/f^{(j)}_y\f$
-.
--   **CALIB_SAME_FOCAL_LENGTH** Enforce \f$f^{(0)}_x=f^{(1)}_x\f$ and \f$f^{(0)}_y=f^{(1)}_y\f$ .
--   **CALIB_ZERO_TANGENT_DIST** Set tangential distortion coefficients for each camera to
-zeros and fix there.
--   **CALIB_FIX_K1,...,CALIB_FIX_K6** Do not change the corresponding radial
-distortion coefficient during the optimization. If CALIB_USE_INTRINSIC_GUESS is set,
-the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0.
--   **CALIB_RATIONAL_MODEL** Enable coefficients k4, k5, and k6. To provide the backward
-compatibility, this extra flag should be explicitly specified to make the calibration
-function use the rational model and return 8 coefficients. If the flag is not set, the
-function computes and returns only 5 distortion coefficients.
--   **CALIB_THIN_PRISM_MODEL** Coefficients s1, s2, s3 and s4 are enabled. To provide the
-backward compatibility, this extra flag should be explicitly specified to make the
-calibration function use the thin prism model and return 12 coefficients. If the flag is not
-set, the function computes and returns only 5 distortion coefficients.
--   **CALIB_FIX_S1_S2_S3_S4** The thin prism distortion coefficients are not changed during
-the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the
-supplied distCoeffs matrix is used. Otherwise, it is set to 0.
--   **CALIB_TILTED_MODEL** Coefficients tauX and tauY are enabled. To provide the
-backward compatibility, this extra flag should be explicitly specified to make the
-calibration function use the tilted sensor model and return 14 coefficients. If the flag is not
-set, the function computes and returns only 5 distortion coefficients.
--   **CALIB_FIX_TAUX_TAUY** The coefficients of the tilted sensor model are not changed during
-the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the
-supplied distCoeffs matrix is used. Otherwise, it is set to 0.
-@param criteria Termination criteria for the iterative optimization algorithm.
-
-The function estimates transformation between two cameras making a stereo pair. If you have a stereo
-camera where the relative position and orientation of two cameras is fixed, and if you computed
-poses of an object relative to the first camera and to the second camera, (R1, T1) and (R2, T2),
-respectively (this can be done with solvePnP ), then those poses definitely relate to each other.
-This means that, given ( \f$R_1\f$,\f$T_1\f$ ), it should be possible to compute ( \f$R_2\f$,\f$T_2\f$ ). You only
-need to know the position and orientation of the second camera relative to the first camera. This is
-what the described function does. It computes ( \f$R\f$,\f$T\f$ ) so that:
-
-\f[R_2=R*R_1\f]
-\f[T_2=R*T_1 + T,\f]
-
-Optionally, it computes the essential matrix E:
-
-\f[E= \vecthreethree{0}{-T_2}{T_1}{T_2}{0}{-T_0}{-T_1}{T_0}{0} *R\f]
-
-where \f$T_i\f$ are components of the translation vector \f$T\f$ : \f$T=[T_0, T_1, T_2]^T\f$ . And the function
-can also compute the fundamental matrix F:
-
-\f[F = cameraMatrix2^{-T} E cameraMatrix1^{-1}\f]
-
-Besides the stereo-related information, the function can also perform a full calibration of each of
-two cameras. However, due to the high dimensionality of the parameter space and noise in the input
-data, the function can diverge from the correct solution. If the intrinsic parameters can be
-estimated with high accuracy for each of the cameras individually (for example, using
-calibrateCamera ), you are recommended to do so and then pass CALIB_FIX_INTRINSIC flag to the
-function along with the computed intrinsic parameters. Otherwise, if all the parameters are
-estimated at once, it makes sense to restrict some parameters, for example, pass
-CALIB_SAME_FOCAL_LENGTH and CALIB_ZERO_TANGENT_DIST flags, which is usually a
-reasonable assumption.
-
-Similarly to calibrateCamera , the function minimizes the total re-projection error for all the
-points in all the available views from both cameras. The function returns the final value of the
-re-projection error.
- */
-CV_EXPORTS_AS(stereoCalibrateExtended) double stereoCalibrate( InputArrayOfArrays objectPoints,
-                                     InputArrayOfArrays imagePoints1, InputArrayOfArrays imagePoints2,
-                                     InputOutputArray cameraMatrix1, InputOutputArray distCoeffs1,
-                                     InputOutputArray cameraMatrix2, InputOutputArray distCoeffs2,
-                                     Size imageSize, InputOutputArray R,InputOutputArray T, OutputArray E, OutputArray F,
-                                     OutputArray perViewErrors, int flags = CALIB_FIX_INTRINSIC,
-                                     TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 1e-6) );
-
-/// @overload
-CV_EXPORTS_W double stereoCalibrate( InputArrayOfArrays objectPoints,
-                                     InputArrayOfArrays imagePoints1, InputArrayOfArrays imagePoints2,
-                                     InputOutputArray cameraMatrix1, InputOutputArray distCoeffs1,
-                                     InputOutputArray cameraMatrix2, InputOutputArray distCoeffs2,
-                                     Size imageSize, OutputArray R,OutputArray T, OutputArray E, OutputArray F,
-                                     int flags = CALIB_FIX_INTRINSIC,
-                                     TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 1e-6) );
-
-/** @brief Computes rectification transforms for each head of a calibrated stereo camera.
-
-@param cameraMatrix1 First camera matrix.
-@param distCoeffs1 First camera distortion parameters.
-@param cameraMatrix2 Second camera matrix.
-@param distCoeffs2 Second camera distortion parameters.
-@param imageSize Size of the image used for stereo calibration.
-@param R Rotation matrix between the coordinate systems of the first and the second cameras.
-@param T Translation vector between coordinate systems of the cameras.
-@param R1 Output 3x3 rectification transform (rotation matrix) for the first camera.
-@param R2 Output 3x3 rectification transform (rotation matrix) for the second camera.
-@param P1 Output 3x4 projection matrix in the new (rectified) coordinate systems for the first
-camera.
-@param P2 Output 3x4 projection matrix in the new (rectified) coordinate systems for the second
-camera.
-@param Q Output \f$4 \times 4\f$ disparity-to-depth mapping matrix (see reprojectImageTo3D ).
-@param flags Operation flags that may be zero or CALIB_ZERO_DISPARITY . If the flag is set,
-the function makes the principal points of each camera have the same pixel coordinates in the
-rectified views. And if the flag is not set, the function may still shift the images in the
-horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the
-useful image area.
-@param alpha Free scaling parameter. If it is -1 or absent, the function performs the default
-scaling. Otherwise, the parameter should be between 0 and 1. alpha=0 means that the rectified
-images are zoomed and shifted so that only valid pixels are visible (no black areas after
-rectification). alpha=1 means that the rectified image is decimated and shifted so that all the
-pixels from the original images from the cameras are retained in the rectified images (no source
-image pixels are lost). Obviously, any intermediate value yields an intermediate result between
-those two extreme cases.
-@param newImageSize New image resolution after rectification. The same size should be passed to
-initUndistortRectifyMap (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0)
-is passed (default), it is set to the original imageSize . Setting it to larger value can help you
-preserve details in the original image, especially when there is a big radial distortion.
-@param validPixROI1 Optional output rectangles inside the rectified images where all the pixels
-are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller
-(see the picture below).
-@param validPixROI2 Optional output rectangles inside the rectified images where all the pixels
-are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller
-(see the picture below).
-
-The function computes the rotation matrices for each camera that (virtually) make both camera image
-planes the same plane. Consequently, this makes all the epipolar lines parallel and thus simplifies
-the dense stereo correspondence problem. The function takes the matrices computed by stereoCalibrate
-as input. As output, it provides two rotation matrices and also two projection matrices in the new
-coordinates. The function distinguishes the following two cases:
-
--   **Horizontal stereo**: the first and the second camera views are shifted relative to each other
-    mainly along the x axis (with possible small vertical shift). In the rectified images, the
-    corresponding epipolar lines in the left and right cameras are horizontal and have the same
-    y-coordinate. P1 and P2 look like:
-
-    \f[\texttt{P1} = \begin{bmatrix} f & 0 & cx_1 & 0 \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\f]
-
-    \f[\texttt{P2} = \begin{bmatrix} f & 0 & cx_2 & T_x*f \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix} ,\f]
-
-    where \f$T_x\f$ is a horizontal shift between the cameras and \f$cx_1=cx_2\f$ if
-    CALIB_ZERO_DISPARITY is set.
-
--   **Vertical stereo**: the first and the second camera views are shifted relative to each other
-    mainly in vertical direction (and probably a bit in the horizontal direction too). The epipolar
-    lines in the rectified images are vertical and have the same x-coordinate. P1 and P2 look like:
-
-    \f[\texttt{P1} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_1 & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\f]
-
-    \f[\texttt{P2} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_2 & T_y*f \\ 0 & 0 & 1 & 0 \end{bmatrix} ,\f]
-
-    where \f$T_y\f$ is a vertical shift between the cameras and \f$cy_1=cy_2\f$ if CALIB_ZERO_DISPARITY is
-    set.
-
-As you can see, the first three columns of P1 and P2 will effectively be the new "rectified" camera
-matrices. The matrices, together with R1 and R2 , can then be passed to initUndistortRectifyMap to
-initialize the rectification map for each camera.
-
-See below the screenshot from the stereo_calib.cpp sample. Some red horizontal lines pass through
-the corresponding image regions. This means that the images are well rectified, which is what most
-stereo correspondence algorithms rely on. The green rectangles are roi1 and roi2 . You see that
-their interiors are all valid pixels.
-
-![image](pics/stereo_undistort.jpg)
- */
-CV_EXPORTS_W void stereoRectify( InputArray cameraMatrix1, InputArray distCoeffs1,
-                                 InputArray cameraMatrix2, InputArray distCoeffs2,
-                                 Size imageSize, InputArray R, InputArray T,
-                                 OutputArray R1, OutputArray R2,
-                                 OutputArray P1, OutputArray P2,
-                                 OutputArray Q, int flags = CALIB_ZERO_DISPARITY,
-                                 double alpha = -1, Size newImageSize = Size(),
-                                 CV_OUT Rect* validPixROI1 = 0, CV_OUT Rect* validPixROI2 = 0 );
-
-/** @brief Computes a rectification transform for an uncalibrated stereo camera.
-
-@param points1 Array of feature points in the first image.
-@param points2 The corresponding points in the second image. The same formats as in
-findFundamentalMat are supported.
-@param F Input fundamental matrix. It can be computed from the same set of point pairs using
-findFundamentalMat .
-@param imgSize Size of the image.
-@param H1 Output rectification homography matrix for the first image.
-@param H2 Output rectification homography matrix for the second image.
-@param threshold Optional threshold used to filter out the outliers. If the parameter is greater
-than zero, all the point pairs that do not comply with the epipolar geometry (that is, the points
-for which \f$|\texttt{points2[i]}^T*\texttt{F}*\texttt{points1[i]}|>\texttt{threshold}\f$ ) are
-rejected prior to computing the homographies. Otherwise, all the points are considered inliers.
-
-The function computes the rectification transformations without knowing intrinsic parameters of the
-cameras and their relative position in the space, which explains the suffix "uncalibrated". Another
-related difference from stereoRectify is that the function outputs not the rectification
-transformations in the object (3D) space, but the planar perspective transformations encoded by the
-homography matrices H1 and H2 . The function implements the algorithm @cite Hartley99 .
-
-@note
-   While the algorithm does not need to know the intrinsic parameters of the cameras, it heavily
-    depends on the epipolar geometry. Therefore, if the camera lenses have a significant distortion,
-    it would be better to correct it before computing the fundamental matrix and calling this
-    function. For example, distortion coefficients can be estimated for each head of stereo camera
-    separately by using calibrateCamera . Then, the images can be corrected using undistort , or
-    just the point coordinates can be corrected with undistortPoints .
- */
-CV_EXPORTS_W bool stereoRectifyUncalibrated( InputArray points1, InputArray points2,
-                                             InputArray F, Size imgSize,
-                                             OutputArray H1, OutputArray H2,
-                                             double threshold = 5 );
-
-//! computes the rectification transformations for 3-head camera, where all the heads are on the same line.
-CV_EXPORTS_W float rectify3Collinear( InputArray cameraMatrix1, InputArray distCoeffs1,
-                                      InputArray cameraMatrix2, InputArray distCoeffs2,
-                                      InputArray cameraMatrix3, InputArray distCoeffs3,
-                                      InputArrayOfArrays imgpt1, InputArrayOfArrays imgpt3,
-                                      Size imageSize, InputArray R12, InputArray T12,
-                                      InputArray R13, InputArray T13,
-                                      OutputArray R1, OutputArray R2, OutputArray R3,
-                                      OutputArray P1, OutputArray P2, OutputArray P3,
-                                      OutputArray Q, double alpha, Size newImgSize,
-                                      CV_OUT Rect* roi1, CV_OUT Rect* roi2, int flags );
-
-/** @brief Returns the new camera matrix based on the free scaling parameter.
-
-@param cameraMatrix Input camera matrix.
-@param distCoeffs Input vector of distortion coefficients
-\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ of
-4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are
-assumed.
-@param imageSize Original image size.
-@param alpha Free scaling parameter between 0 (when all the pixels in the undistorted image are
-valid) and 1 (when all the source image pixels are retained in the undistorted image). See
-stereoRectify for details.
-@param newImgSize Image size after rectification. By default, it is set to imageSize .
-@param validPixROI Optional output rectangle that outlines all-good-pixels region in the
-undistorted image. See roi1, roi2 description in stereoRectify .
-@param centerPrincipalPoint Optional flag that indicates whether in the new camera matrix the
-principal point should be at the image center or not. By default, the principal point is chosen to
-best fit a subset of the source image (determined by alpha) to the corrected image.
-@return new_camera_matrix Output new camera matrix.
-
-The function computes and returns the optimal new camera matrix based on the free scaling parameter.
-By varying this parameter, you may retrieve only sensible pixels alpha=0 , keep all the original
-image pixels if there is valuable information in the corners alpha=1 , or get something in between.
-When alpha\>0 , the undistorted result is likely to have some black pixels corresponding to
-"virtual" pixels outside of the captured distorted image. The original camera matrix, distortion
-coefficients, the computed new camera matrix, and newImageSize should be passed to
-initUndistortRectifyMap to produce the maps for remap .
- */
-CV_EXPORTS_W Mat getOptimalNewCameraMatrix( InputArray cameraMatrix, InputArray distCoeffs,
-                                            Size imageSize, double alpha, Size newImgSize = Size(),
-                                            CV_OUT Rect* validPixROI = 0,
-                                            bool centerPrincipalPoint = false);
-
-/** @brief Converts points from Euclidean to homogeneous space.
-
-@param src Input vector of N-dimensional points.
-@param dst Output vector of N+1-dimensional points.
-
-The function converts points from Euclidean to homogeneous space by appending 1's to the tuple of
-point coordinates. That is, each point (x1, x2, ..., xn) is converted to (x1, x2, ..., xn, 1).
- */
-CV_EXPORTS_W void convertPointsToHomogeneous( InputArray src, OutputArray dst );
-
-/** @brief Converts points from homogeneous to Euclidean space.
-
-@param src Input vector of N-dimensional points.
-@param dst Output vector of N-1-dimensional points.
-
-The function converts points homogeneous to Euclidean space using perspective projection. That is,
-each point (x1, x2, ... x(n-1), xn) is converted to (x1/xn, x2/xn, ..., x(n-1)/xn). When xn=0, the
-output point coordinates will be (0,0,0,...).
- */
-CV_EXPORTS_W void convertPointsFromHomogeneous( InputArray src, OutputArray dst );
-
-/** @brief Converts points to/from homogeneous coordinates.
-
-@param src Input array or vector of 2D, 3D, or 4D points.
-@param dst Output vector of 2D, 3D, or 4D points.
-
-The function converts 2D or 3D points from/to homogeneous coordinates by calling either
-convertPointsToHomogeneous or convertPointsFromHomogeneous.
-
-@note The function is obsolete. Use one of the previous two functions instead.
- */
-CV_EXPORTS void convertPointsHomogeneous( InputArray src, OutputArray dst );
-
-/** @brief Calculates a fundamental matrix from the corresponding points in two images.
-
-@param points1 Array of N points from the first image. The point coordinates should be
-floating-point (single or double precision).
-@param points2 Array of the second image points of the same size and format as points1 .
-@param method Method for computing a fundamental matrix.
--   **CV_FM_7POINT** for a 7-point algorithm. \f$N = 7\f$
--   **CV_FM_8POINT** for an 8-point algorithm. \f$N \ge 8\f$
--   **CV_FM_RANSAC** for the RANSAC algorithm. \f$N \ge 8\f$
--   **CV_FM_LMEDS** for the LMedS algorithm. \f$N \ge 8\f$
-@param ransacReprojThreshold Parameter used only for RANSAC. It is the maximum distance from a point to an epipolar
-line in pixels, beyond which the point is considered an outlier and is not used for computing the
-final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the
-point localization, image resolution, and the image noise.
-@param confidence Parameter used for the RANSAC and LMedS methods only. It specifies a desirable level
-of confidence (probability) that the estimated matrix is correct.
-@param mask
-
-The epipolar geometry is described by the following equation:
-
-\f[[p_2; 1]^T F [p_1; 1] = 0\f]
-
-where \f$F\f$ is a fundamental matrix, \f$p_1\f$ and \f$p_2\f$ are corresponding points in the first and the
-second images, respectively.
-
-The function calculates the fundamental matrix using one of four methods listed above and returns
-the found fundamental matrix. Normally just one matrix is found. But in case of the 7-point
-algorithm, the function may return up to 3 solutions ( \f$9 \times 3\f$ matrix that stores all 3
-matrices sequentially).
-
-The calculated fundamental matrix may be passed further to computeCorrespondEpilines that finds the
-epipolar lines corresponding to the specified points. It can also be passed to
-stereoRectifyUncalibrated to compute the rectification transformation. :
-@code
-    // Example. Estimation of fundamental matrix using the RANSAC algorithm
-    int point_count = 100;
-    vector<Point2f> points1(point_count);
-    vector<Point2f> points2(point_count);
-
-    // initialize the points here ...
-    for( int i = 0; i < point_count; i++ )
-    {
-        points1[i] = ...;
-        points2[i] = ...;
-    }
-
-    Mat fundamental_matrix =
-     findFundamentalMat(points1, points2, FM_RANSAC, 3, 0.99);
-@endcode
- */
-CV_EXPORTS_W Mat findFundamentalMat( InputArray points1, InputArray points2,
-                                     int method = FM_RANSAC,
-                                     double ransacReprojThreshold = 3., double confidence = 0.99,
-                                     OutputArray mask = noArray() );
-
-/** @overload */
-CV_EXPORTS Mat findFundamentalMat( InputArray points1, InputArray points2,
-                                   OutputArray mask, int method = FM_RANSAC,
-                                   double ransacReprojThreshold = 3., double confidence = 0.99 );
-
-/** @brief Calculates an essential matrix from the corresponding points in two images.
-
-@param points1 Array of N (N \>= 5) 2D points from the first image. The point coordinates should
-be floating-point (single or double precision).
-@param points2 Array of the second image points of the same size and format as points1 .
-@param cameraMatrix Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ .
-Note that this function assumes that points1 and points2 are feature points from cameras with the
-same camera matrix.
-@param method Method for computing an essential matrix.
--   **RANSAC** for the RANSAC algorithm.
--   **LMEDS** for the LMedS algorithm.
-@param prob Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of
-confidence (probability) that the estimated matrix is correct.
-@param threshold Parameter used for RANSAC. It is the maximum distance from a point to an epipolar
-line in pixels, beyond which the point is considered an outlier and is not used for computing the
-final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the
-point localization, image resolution, and the image noise.
-@param mask Output array of N elements, every element of which is set to 0 for outliers and to 1
-for the other points. The array is computed only in the RANSAC and LMedS methods.
-
-This function estimates essential matrix based on the five-point algorithm solver in @cite Nister03 .
-@cite SteweniusCFS is also a related. The epipolar geometry is described by the following equation:
-
-\f[[p_2; 1]^T K^{-T} E K^{-1} [p_1; 1] = 0\f]
-
-where \f$E\f$ is an essential matrix, \f$p_1\f$ and \f$p_2\f$ are corresponding points in the first and the
-second images, respectively. The result of this function may be passed further to
-decomposeEssentialMat or recoverPose to recover the relative pose between cameras.
- */
-CV_EXPORTS_W Mat findEssentialMat( InputArray points1, InputArray points2,
-                                 InputArray cameraMatrix, int method = RANSAC,
-                                 double prob = 0.999, double threshold = 1.0,
-                                 OutputArray mask = noArray() );
-
-/** @overload
-@param points1 Array of N (N \>= 5) 2D points from the first image. The point coordinates should
-be floating-point (single or double precision).
-@param points2 Array of the second image points of the same size and format as points1 .
-@param focal focal length of the camera. Note that this function assumes that points1 and points2
-are feature points from cameras with same focal length and principal point.
-@param pp principal point of the camera.
-@param method Method for computing a fundamental matrix.
--   **RANSAC** for the RANSAC algorithm.
--   **LMEDS** for the LMedS algorithm.
-@param threshold Parameter used for RANSAC. It is the maximum distance from a point to an epipolar
-line in pixels, beyond which the point is considered an outlier and is not used for computing the
-final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the
-point localization, image resolution, and the image noise.
-@param prob Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of
-confidence (probability) that the estimated matrix is correct.
-@param mask Output array of N elements, every element of which is set to 0 for outliers and to 1
-for the other points. The array is computed only in the RANSAC and LMedS methods.
-
-This function differs from the one above that it computes camera matrix from focal length and
-principal point:
-
-\f[K =
-\begin{bmatrix}
-f & 0 & x_{pp}  \\
-0 & f & y_{pp}  \\
-0 & 0 & 1
-\end{bmatrix}\f]
- */
-CV_EXPORTS_W Mat findEssentialMat( InputArray points1, InputArray points2,
-                                 double focal = 1.0, Point2d pp = Point2d(0, 0),
-                                 int method = RANSAC, double prob = 0.999,
-                                 double threshold = 1.0, OutputArray mask = noArray() );
-
-/** @brief Decompose an essential matrix to possible rotations and translation.
-
-@param E The input essential matrix.
-@param R1 One possible rotation matrix.
-@param R2 Another possible rotation matrix.
-@param t One possible translation.
-
-This function decompose an essential matrix E using svd decomposition @cite HartleyZ00 . Generally 4
-possible poses exists for a given E. They are \f$[R_1, t]\f$, \f$[R_1, -t]\f$, \f$[R_2, t]\f$, \f$[R_2, -t]\f$. By
-decomposing E, you can only get the direction of the translation, so the function returns unit t.
- */
-CV_EXPORTS_W void decomposeEssentialMat( InputArray E, OutputArray R1, OutputArray R2, OutputArray t );
-
-/** @brief Recover relative camera rotation and translation from an estimated essential matrix and the
-corresponding points in two images, using cheirality check. Returns the number of inliers which pass
-the check.
-
-@param E The input essential matrix.
-@param points1 Array of N 2D points from the first image. The point coordinates should be
-floating-point (single or double precision).
-@param points2 Array of the second image points of the same size and format as points1 .
-@param cameraMatrix Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ .
-Note that this function assumes that points1 and points2 are feature points from cameras with the
-same camera matrix.
-@param R Recovered relative rotation.
-@param t Recovered relative translation.
-@param mask Input/output mask for inliers in points1 and points2.
-:   If it is not empty, then it marks inliers in points1 and points2 for then given essential
-matrix E. Only these inliers will be used to recover pose. In the output mask only inliers
-which pass the cheirality check.
-This function decomposes an essential matrix using decomposeEssentialMat and then verifies possible
-pose hypotheses by doing cheirality check. The cheirality check basically means that the
-triangulated 3D points should have positive depth. Some details can be found in @cite Nister03 .
-
-This function can be used to process output E and mask from findEssentialMat. In this scenario,
-points1 and points2 are the same input for findEssentialMat. :
-@code
-    // Example. Estimation of fundamental matrix using the RANSAC algorithm
-    int point_count = 100;
-    vector<Point2f> points1(point_count);
-    vector<Point2f> points2(point_count);
-
-    // initialize the points here ...
-    for( int i = 0; i < point_count; i++ )
-    {
-        points1[i] = ...;
-        points2[i] = ...;
-    }
-
-    // cametra matrix with both focal lengths = 1, and principal point = (0, 0)
-    Mat cameraMatrix = Mat::eye(3, 3, CV_64F);
-
-    Mat E, R, t, mask;
-
-    E = findEssentialMat(points1, points2, cameraMatrix, RANSAC, 0.999, 1.0, mask);
-    recoverPose(E, points1, points2, cameraMatrix, R, t, mask);
-@endcode
- */
-CV_EXPORTS_W int recoverPose( InputArray E, InputArray points1, InputArray points2,
-                            InputArray cameraMatrix, OutputArray R, OutputArray t,
-                            InputOutputArray mask = noArray() );
-
-/** @overload
-@param E The input essential matrix.
-@param points1 Array of N 2D points from the first image. The point coordinates should be
-floating-point (single or double precision).
-@param points2 Array of the second image points of the same size and format as points1 .
-@param R Recovered relative rotation.
-@param t Recovered relative translation.
-@param focal Focal length of the camera. Note that this function assumes that points1 and points2
-are feature points from cameras with same focal length and principal point.
-@param pp principal point of the camera.
-@param mask Input/output mask for inliers in points1 and points2.
-:   If it is not empty, then it marks inliers in points1 and points2 for then given essential
-matrix E. Only these inliers will be used to recover pose. In the output mask only inliers
-which pass the cheirality check.
-
-This function differs from the one above that it computes camera matrix from focal length and
-principal point:
-
-\f[K =
-\begin{bmatrix}
-f & 0 & x_{pp}  \\
-0 & f & y_{pp}  \\
-0 & 0 & 1
-\end{bmatrix}\f]
- */
-CV_EXPORTS_W int recoverPose( InputArray E, InputArray points1, InputArray points2,
-                            OutputArray R, OutputArray t,
-                            double focal = 1.0, Point2d pp = Point2d(0, 0),
-                            InputOutputArray mask = noArray() );
-
-/** @overload
-@param E The input essential matrix.
-@param points1 Array of N 2D points from the first image. The point coordinates should be
-floating-point (single or double precision).
-@param points2 Array of the second image points of the same size and format as points1.
-@param cameraMatrix Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ .
-Note that this function assumes that points1 and points2 are feature points from cameras with the
-same camera matrix.
-@param R Recovered relative rotation.
-@param t Recovered relative translation.
-@param distanceThresh threshold distance which is used to filter out far away points (i.e. infinite points).
-@param mask Input/output mask for inliers in points1 and points2.
-:   If it is not empty, then it marks inliers in points1 and points2 for then given essential
-matrix E. Only these inliers will be used to recover pose. In the output mask only inliers
-which pass the cheirality check.
-@param triangulatedPoints 3d points which were reconstructed by triangulation.
- */
-
-CV_EXPORTS_W int recoverPose( InputArray E, InputArray points1, InputArray points2,
-                            InputArray cameraMatrix, OutputArray R, OutputArray t, double distanceThresh, InputOutputArray mask = noArray(),
-                            OutputArray triangulatedPoints = noArray());
-
-/** @brief For points in an image of a stereo pair, computes the corresponding epilines in the other image.
-
-@param points Input points. \f$N \times 1\f$ or \f$1 \times N\f$ matrix of type CV_32FC2 or
-vector\<Point2f\> .
-@param whichImage Index of the image (1 or 2) that contains the points .
-@param F Fundamental matrix that can be estimated using findFundamentalMat or stereoRectify .
-@param lines Output vector of the epipolar lines corresponding to the points in the other image.
-Each line \f$ax + by + c=0\f$ is encoded by 3 numbers \f$(a, b, c)\f$ .
-
-For every point in one of the two images of a stereo pair, the function finds the equation of the
-corresponding epipolar line in the other image.
-
-From the fundamental matrix definition (see findFundamentalMat ), line \f$l^{(2)}_i\f$ in the second
-image for the point \f$p^{(1)}_i\f$ in the first image (when whichImage=1 ) is computed as:
-
-\f[l^{(2)}_i = F p^{(1)}_i\f]
-
-And vice versa, when whichImage=2, \f$l^{(1)}_i\f$ is computed from \f$p^{(2)}_i\f$ as:
-
-\f[l^{(1)}_i = F^T p^{(2)}_i\f]
-
-Line coefficients are defined up to a scale. They are normalized so that \f$a_i^2+b_i^2=1\f$ .
- */
-CV_EXPORTS_W void computeCorrespondEpilines( InputArray points, int whichImage,
-                                             InputArray F, OutputArray lines );
-
-/** @brief Reconstructs points by triangulation.
-
-@param projMatr1 3x4 projection matrix of the first camera.
-@param projMatr2 3x4 projection matrix of the second camera.
-@param projPoints1 2xN array of feature points in the first image. In case of c++ version it can
-be also a vector of feature points or two-channel matrix of size 1xN or Nx1.
-@param projPoints2 2xN array of corresponding points in the second image. In case of c++ version
-it can be also a vector of feature points or two-channel matrix of size 1xN or Nx1.
-@param points4D 4xN array of reconstructed points in homogeneous coordinates.
-
-The function reconstructs 3-dimensional points (in homogeneous coordinates) by using their
-observations with a stereo camera. Projections matrices can be obtained from stereoRectify.
-
-@note
-   Keep in mind that all input data should be of float type in order for this function to work.
-
-@sa
-   reprojectImageTo3D
- */
-CV_EXPORTS_W void triangulatePoints( InputArray projMatr1, InputArray projMatr2,
-                                     InputArray projPoints1, InputArray projPoints2,
-                                     OutputArray points4D );
-
-/** @brief Refines coordinates of corresponding points.
-
-@param F 3x3 fundamental matrix.
-@param points1 1xN array containing the first set of points.
-@param points2 1xN array containing the second set of points.
-@param newPoints1 The optimized points1.
-@param newPoints2 The optimized points2.
-
-The function implements the Optimal Triangulation Method (see Multiple View Geometry for details).
-For each given point correspondence points1[i] \<-\> points2[i], and a fundamental matrix F, it
-computes the corrected correspondences newPoints1[i] \<-\> newPoints2[i] that minimize the geometric
-error \f$d(points1[i], newPoints1[i])^2 + d(points2[i],newPoints2[i])^2\f$ (where \f$d(a,b)\f$ is the
-geometric distance between points \f$a\f$ and \f$b\f$ ) subject to the epipolar constraint
-\f$newPoints2^T * F * newPoints1 = 0\f$ .
- */
-CV_EXPORTS_W void correctMatches( InputArray F, InputArray points1, InputArray points2,
-                                  OutputArray newPoints1, OutputArray newPoints2 );
-
-/** @brief Filters off small noise blobs (speckles) in the disparity map
-
-@param img The input 16-bit signed disparity image
-@param newVal The disparity value used to paint-off the speckles
-@param maxSpeckleSize The maximum speckle size to consider it a speckle. Larger blobs are not
-affected by the algorithm
-@param maxDiff Maximum difference between neighbor disparity pixels to put them into the same
-blob. Note that since StereoBM, StereoSGBM and may be other algorithms return a fixed-point
-disparity map, where disparity values are multiplied by 16, this scale factor should be taken into
-account when specifying this parameter value.
-@param buf The optional temporary buffer to avoid memory allocation within the function.
- */
-CV_EXPORTS_W void filterSpeckles( InputOutputArray img, double newVal,
-                                  int maxSpeckleSize, double maxDiff,
-                                  InputOutputArray buf = noArray() );
-
-//! computes valid disparity ROI from the valid ROIs of the rectified images (that are returned by cv::stereoRectify())
-CV_EXPORTS_W Rect getValidDisparityROI( Rect roi1, Rect roi2,
-                                        int minDisparity, int numberOfDisparities,
-                                        int SADWindowSize );
-
-//! validates disparity using the left-right check. The matrix "cost" should be computed by the stereo correspondence algorithm
-CV_EXPORTS_W void validateDisparity( InputOutputArray disparity, InputArray cost,
-                                     int minDisparity, int numberOfDisparities,
-                                     int disp12MaxDisp = 1 );
-
-/** @brief Reprojects a disparity image to 3D space.
-
-@param disparity Input single-channel 8-bit unsigned, 16-bit signed, 32-bit signed or 32-bit
-floating-point disparity image. If 16-bit signed format is used, the values are assumed to have no
-fractional bits.
-@param _3dImage Output 3-channel floating-point image of the same size as disparity . Each
-element of _3dImage(x,y) contains 3D coordinates of the point (x,y) computed from the disparity
-map.
-@param Q \f$4 \times 4\f$ perspective transformation matrix that can be obtained with stereoRectify.
-@param handleMissingValues Indicates, whether the function should handle missing values (i.e.
-points where the disparity was not computed). If handleMissingValues=true, then pixels with the
-minimal disparity that corresponds to the outliers (see StereoMatcher::compute ) are transformed
-to 3D points with a very large Z value (currently set to 10000).
-@param ddepth The optional output array depth. If it is -1, the output image will have CV_32F
-depth. ddepth can also be set to CV_16S, CV_32S or CV_32F.
-
-The function transforms a single-channel disparity map to a 3-channel image representing a 3D
-surface. That is, for each pixel (x,y) and the corresponding disparity d=disparity(x,y) , it
-computes:
-
-\f[\begin{array}{l} [X \; Y \; Z \; W]^T =  \texttt{Q} *[x \; y \; \texttt{disparity} (x,y) \; 1]^T  \\ \texttt{\_3dImage} (x,y) = (X/W, \; Y/W, \; Z/W) \end{array}\f]
-
-The matrix Q can be an arbitrary \f$4 \times 4\f$ matrix (for example, the one computed by
-stereoRectify). To reproject a sparse set of points {(x,y,d),...} to 3D space, use
-perspectiveTransform .
- */
-CV_EXPORTS_W void reprojectImageTo3D( InputArray disparity,
-                                      OutputArray _3dImage, InputArray Q,
-                                      bool handleMissingValues = false,
-                                      int ddepth = -1 );
-
-/** @brief Calculates the Sampson Distance between two points.
-
-The function cv::sampsonDistance calculates and returns the first order approximation of the geometric error as:
-\f[
-sd( \texttt{pt1} , \texttt{pt2} )=
-\frac{(\texttt{pt2}^t \cdot \texttt{F} \cdot \texttt{pt1})^2}
-{((\texttt{F} \cdot \texttt{pt1})(0))^2 +
-((\texttt{F} \cdot \texttt{pt1})(1))^2 +
-((\texttt{F}^t \cdot \texttt{pt2})(0))^2 +
-((\texttt{F}^t \cdot \texttt{pt2})(1))^2}
-\f]
-The fundamental matrix may be calculated using the cv::findFundamentalMat function. See @cite HartleyZ00 11.4.3 for details.
-@param pt1 first homogeneous 2d point
-@param pt2 second homogeneous 2d point
-@param F fundamental matrix
-@return The computed Sampson distance.
-*/
-CV_EXPORTS_W double sampsonDistance(InputArray pt1, InputArray pt2, InputArray F);
-
-/** @brief Computes an optimal affine transformation between two 3D point sets.
-
-It computes
-\f[
-\begin{bmatrix}
-x\\
-y\\
-z\\
-\end{bmatrix}
-=
-\begin{bmatrix}
-a_{11} & a_{12} & a_{13}\\
-a_{21} & a_{22} & a_{23}\\
-a_{31} & a_{32} & a_{33}\\
-\end{bmatrix}
-\begin{bmatrix}
-X\\
-Y\\
-Z\\
-\end{bmatrix}
-+
-\begin{bmatrix}
-b_1\\
-b_2\\
-b_3\\
-\end{bmatrix}
-\f]
-
-@param src First input 3D point set containing \f$(X,Y,Z)\f$.
-@param dst Second input 3D point set containing \f$(x,y,z)\f$.
-@param out Output 3D affine transformation matrix \f$3 \times 4\f$ of the form
-\f[
-\begin{bmatrix}
-a_{11} & a_{12} & a_{13} & b_1\\
-a_{21} & a_{22} & a_{23} & b_2\\
-a_{31} & a_{32} & a_{33} & b_3\\
-\end{bmatrix}
-\f]
-@param inliers Output vector indicating which points are inliers (1-inlier, 0-outlier).
-@param ransacThreshold Maximum reprojection error in the RANSAC algorithm to consider a point as
-an inlier.
-@param confidence Confidence level, between 0 and 1, for the estimated transformation. Anything
-between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation
-significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation.
-
-The function estimates an optimal 3D affine transformation between two 3D point sets using the
-RANSAC algorithm.
- */
-CV_EXPORTS_W  int estimateAffine3D(InputArray src, InputArray dst,
-                                   OutputArray out, OutputArray inliers,
-                                   double ransacThreshold = 3, double confidence = 0.99);
-
-/** @brief Computes an optimal affine transformation between two 2D point sets.
-
-It computes
-\f[
-\begin{bmatrix}
-x\\
-y\\
-\end{bmatrix}
-=
-\begin{bmatrix}
-a_{11} & a_{12}\\
-a_{21} & a_{22}\\
-\end{bmatrix}
-\begin{bmatrix}
-X\\
-Y\\
-\end{bmatrix}
-+
-\begin{bmatrix}
-b_1\\
-b_2\\
-\end{bmatrix}
-\f]
-
-@param from First input 2D point set containing \f$(X,Y)\f$.
-@param to Second input 2D point set containing \f$(x,y)\f$.
-@param inliers Output vector indicating which points are inliers (1-inlier, 0-outlier).
-@param method Robust method used to compute transformation. The following methods are possible:
--   cv::RANSAC - RANSAC-based robust method
--   cv::LMEDS - Least-Median robust method
-RANSAC is the default method.
-@param ransacReprojThreshold Maximum reprojection error in the RANSAC algorithm to consider
-a point as an inlier. Applies only to RANSAC.
-@param maxIters The maximum number of robust method iterations.
-@param confidence Confidence level, between 0 and 1, for the estimated transformation. Anything
-between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation
-significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation.
-@param refineIters Maximum number of iterations of refining algorithm (Levenberg-Marquardt).
-Passing 0 will disable refining, so the output matrix will be output of robust method.
-
-@return Output 2D affine transformation matrix \f$2 \times 3\f$ or empty matrix if transformation
-could not be estimated. The returned matrix has the following form:
-\f[
-\begin{bmatrix}
-a_{11} & a_{12} & b_1\\
-a_{21} & a_{22} & b_2\\
-\end{bmatrix}
-\f]
-
-The function estimates an optimal 2D affine transformation between two 2D point sets using the
-selected robust algorithm.
-
-The computed transformation is then refined further (using only inliers) with the
-Levenberg-Marquardt method to reduce the re-projection error even more.
-
-@note
-The RANSAC method can handle practically any ratio of outliers but needs a threshold to
-distinguish inliers from outliers. The method LMeDS does not need any threshold but it works
-correctly only when there are more than 50% of inliers.
-
-@sa estimateAffinePartial2D, getAffineTransform
-*/
-CV_EXPORTS_W cv::Mat estimateAffine2D(InputArray from, InputArray to, OutputArray inliers = noArray(),
-                                  int method = RANSAC, double ransacReprojThreshold = 3,
-                                  size_t maxIters = 2000, double confidence = 0.99,
-                                  size_t refineIters = 10);
-
-/** @brief Computes an optimal limited affine transformation with 4 degrees of freedom between
-two 2D point sets.
-
-@param from First input 2D point set.
-@param to Second input 2D point set.
-@param inliers Output vector indicating which points are inliers.
-@param method Robust method used to compute transformation. The following methods are possible:
--   cv::RANSAC - RANSAC-based robust method
--   cv::LMEDS - Least-Median robust method
-RANSAC is the default method.
-@param ransacReprojThreshold Maximum reprojection error in the RANSAC algorithm to consider
-a point as an inlier. Applies only to RANSAC.
-@param maxIters The maximum number of robust method iterations.
-@param confidence Confidence level, between 0 and 1, for the estimated transformation. Anything
-between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation
-significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation.
-@param refineIters Maximum number of iterations of refining algorithm (Levenberg-Marquardt).
-Passing 0 will disable refining, so the output matrix will be output of robust method.
-
-@return Output 2D affine transformation (4 degrees of freedom) matrix \f$2 \times 3\f$ or
-empty matrix if transformation could not be estimated.
-
-The function estimates an optimal 2D affine transformation with 4 degrees of freedom limited to
-combinations of translation, rotation, and uniform scaling. Uses the selected algorithm for robust
-estimation.
-
-The computed transformation is then refined further (using only inliers) with the
-Levenberg-Marquardt method to reduce the re-projection error even more.
-
-Estimated transformation matrix is:
-\f[ \begin{bmatrix} \cos(\theta) \cdot s & -\sin(\theta) \cdot s & t_x \\
-                \sin(\theta) \cdot s & \cos(\theta) \cdot s & t_y
-\end{bmatrix} \f]
-Where \f$ \theta \f$ is the rotation angle, \f$ s \f$ the scaling factor and \f$ t_x, t_y \f$ are
-translations in \f$ x, y \f$ axes respectively.
-
-@note
-The RANSAC method can handle practically any ratio of outliers but need a threshold to
-distinguish inliers from outliers. The method LMeDS does not need any threshold but it works
-correctly only when there are more than 50% of inliers.
-
-@sa estimateAffine2D, getAffineTransform
-*/
-CV_EXPORTS_W cv::Mat estimateAffinePartial2D(InputArray from, InputArray to, OutputArray inliers = noArray(),
-                                  int method = RANSAC, double ransacReprojThreshold = 3,
-                                  size_t maxIters = 2000, double confidence = 0.99,
-                                  size_t refineIters = 10);
-
-/** @example decompose_homography.cpp
-  An example program with homography decomposition.
-
-  Check @ref tutorial_homography "the corresponding tutorial" for more details.
- */
-
-/** @brief Decompose a homography matrix to rotation(s), translation(s) and plane normal(s).
-
-@param H The input homography matrix between two images.
-@param K The input intrinsic camera calibration matrix.
-@param rotations Array of rotation matrices.
-@param translations Array of translation matrices.
-@param normals Array of plane normal matrices.
-
-This function extracts relative camera motion between two views observing a planar object from the
-homography H induced by the plane. The intrinsic camera matrix K must also be provided. The function
-may return up to four mathematical solution sets. At least two of the solutions may further be
-invalidated if point correspondences are available by applying positive depth constraint (all points
-must be in front of the camera). The decomposition method is described in detail in @cite Malis .
- */
-CV_EXPORTS_W int decomposeHomographyMat(InputArray H,
-                                        InputArray K,
-                                        OutputArrayOfArrays rotations,
-                                        OutputArrayOfArrays translations,
-                                        OutputArrayOfArrays normals);
-
-/** @brief Filters homography decompositions based on additional information.
-
-@param rotations Vector of rotation matrices.
-@param normals Vector of plane normal matrices.
-@param beforePoints Vector of (rectified) visible reference points before the homography is applied
-@param afterPoints Vector of (rectified) visible reference points after the homography is applied
-@param possibleSolutions Vector of int indices representing the viable solution set after filtering
-@param pointsMask optional Mat/Vector of 8u type representing the mask for the inliers as given by the findHomography function
-
-This function is intended to filter the output of the decomposeHomographyMat based on additional
-information as described in @cite Malis . The summary of the method: the decomposeHomographyMat function
-returns 2 unique solutions and their "opposites" for a total of 4 solutions. If we have access to the
-sets of points visible in the camera frame before and after the homography transformation is applied,
-we can determine which are the true potential solutions and which are the opposites by verifying which
-homographies are consistent with all visible reference points being in front of the camera. The inputs
-are left unchanged; the filtered solution set is returned as indices into the existing one.
-
-*/
-CV_EXPORTS_W void filterHomographyDecompByVisibleRefpoints(InputArrayOfArrays rotations,
-                                                           InputArrayOfArrays normals,
-                                                           InputArray beforePoints,
-                                                           InputArray afterPoints,
-                                                           OutputArray possibleSolutions,
-                                                           InputArray pointsMask = noArray());
-
-/** @brief The base class for stereo correspondence algorithms.
- */
-class CV_EXPORTS_W StereoMatcher : public Algorithm
-{
-public:
-    enum { DISP_SHIFT = 4,
-           DISP_SCALE = (1 << DISP_SHIFT)
-         };
-
-    /** @brief Computes disparity map for the specified stereo pair
-
-    @param left Left 8-bit single-channel image.
-    @param right Right image of the same size and the same type as the left one.
-    @param disparity Output disparity map. It has the same size as the input images. Some algorithms,
-    like StereoBM or StereoSGBM compute 16-bit fixed-point disparity map (where each disparity value
-    has 4 fractional bits), whereas other algorithms output 32-bit floating-point disparity map.
-     */
-    CV_WRAP virtual void compute( InputArray left, InputArray right,
-                                  OutputArray disparity ) = 0;
-
-    CV_WRAP virtual int getMinDisparity() const = 0;
-    CV_WRAP virtual void setMinDisparity(int minDisparity) = 0;
-
-    CV_WRAP virtual int getNumDisparities() const = 0;
-    CV_WRAP virtual void setNumDisparities(int numDisparities) = 0;
-
-    CV_WRAP virtual int getBlockSize() const = 0;
-    CV_WRAP virtual void setBlockSize(int blockSize) = 0;
-
-    CV_WRAP virtual int getSpeckleWindowSize() const = 0;
-    CV_WRAP virtual void setSpeckleWindowSize(int speckleWindowSize) = 0;
-
-    CV_WRAP virtual int getSpeckleRange() const = 0;
-    CV_WRAP virtual void setSpeckleRange(int speckleRange) = 0;
-
-    CV_WRAP virtual int getDisp12MaxDiff() const = 0;
-    CV_WRAP virtual void setDisp12MaxDiff(int disp12MaxDiff) = 0;
-};
-
-
-/** @brief Class for computing stereo correspondence using the block matching algorithm, introduced and
-contributed to OpenCV by K. Konolige.
- */
-class CV_EXPORTS_W StereoBM : public StereoMatcher
-{
-public:
-    enum { PREFILTER_NORMALIZED_RESPONSE = 0,
-           PREFILTER_XSOBEL              = 1
-         };
-
-    CV_WRAP virtual int getPreFilterType() const = 0;
-    CV_WRAP virtual void setPreFilterType(int preFilterType) = 0;
-
-    CV_WRAP virtual int getPreFilterSize() const = 0;
-    CV_WRAP virtual void setPreFilterSize(int preFilterSize) = 0;
-
-    CV_WRAP virtual int getPreFilterCap() const = 0;
-    CV_WRAP virtual void setPreFilterCap(int preFilterCap) = 0;
-
-    CV_WRAP virtual int getTextureThreshold() const = 0;
-    CV_WRAP virtual void setTextureThreshold(int textureThreshold) = 0;
-
-    CV_WRAP virtual int getUniquenessRatio() const = 0;
-    CV_WRAP virtual void setUniquenessRatio(int uniquenessRatio) = 0;
-
-    CV_WRAP virtual int getSmallerBlockSize() const = 0;
-    CV_WRAP virtual void setSmallerBlockSize(int blockSize) = 0;
-
-    CV_WRAP virtual Rect getROI1() const = 0;
-    CV_WRAP virtual void setROI1(Rect roi1) = 0;
-
-    CV_WRAP virtual Rect getROI2() const = 0;
-    CV_WRAP virtual void setROI2(Rect roi2) = 0;
-
-    /** @brief Creates StereoBM object
-
-    @param numDisparities the disparity search range. For each pixel algorithm will find the best
-    disparity from 0 (default minimum disparity) to numDisparities. The search range can then be
-    shifted by changing the minimum disparity.
-    @param blockSize the linear size of the blocks compared by the algorithm. The size should be odd
-    (as the block is centered at the current pixel). Larger block size implies smoother, though less
-    accurate disparity map. Smaller block size gives more detailed disparity map, but there is higher
-    chance for algorithm to find a wrong correspondence.
-
-    The function create StereoBM object. You can then call StereoBM::compute() to compute disparity for
-    a specific stereo pair.
-     */
-    CV_WRAP static Ptr<StereoBM> create(int numDisparities = 0, int blockSize = 21);
-};
-
-/** @brief The class implements the modified H. Hirschmuller algorithm @cite HH08 that differs from the original
-one as follows:
-
--   By default, the algorithm is single-pass, which means that you consider only 5 directions
-instead of 8. Set mode=StereoSGBM::MODE_HH in createStereoSGBM to run the full variant of the
-algorithm but beware that it may consume a lot of memory.
--   The algorithm matches blocks, not individual pixels. Though, setting blockSize=1 reduces the
-blocks to single pixels.
--   Mutual information cost function is not implemented. Instead, a simpler Birchfield-Tomasi
-sub-pixel metric from @cite BT98 is used. Though, the color images are supported as well.
--   Some pre- and post- processing steps from K. Konolige algorithm StereoBM are included, for
-example: pre-filtering (StereoBM::PREFILTER_XSOBEL type) and post-filtering (uniqueness
-check, quadratic interpolation and speckle filtering).
-
-@note
-   -   (Python) An example illustrating the use of the StereoSGBM matching algorithm can be found
-        at opencv_source_code/samples/python/stereo_match.py
- */
-class CV_EXPORTS_W StereoSGBM : public StereoMatcher
-{
-public:
-    enum
-    {
-        MODE_SGBM = 0,
-        MODE_HH   = 1,
-        MODE_SGBM_3WAY = 2,
-        MODE_HH4  = 3
-    };
-
-    CV_WRAP virtual int getPreFilterCap() const = 0;
-    CV_WRAP virtual void setPreFilterCap(int preFilterCap) = 0;
-
-    CV_WRAP virtual int getUniquenessRatio() const = 0;
-    CV_WRAP virtual void setUniquenessRatio(int uniquenessRatio) = 0;
-
-    CV_WRAP virtual int getP1() const = 0;
-    CV_WRAP virtual void setP1(int P1) = 0;
-
-    CV_WRAP virtual int getP2() const = 0;
-    CV_WRAP virtual void setP2(int P2) = 0;
-
-    CV_WRAP virtual int getMode() const = 0;
-    CV_WRAP virtual void setMode(int mode) = 0;
-
-    /** @brief Creates StereoSGBM object
-
-    @param minDisparity Minimum possible disparity value. Normally, it is zero but sometimes
-    rectification algorithms can shift images, so this parameter needs to be adjusted accordingly.
-    @param numDisparities Maximum disparity minus minimum disparity. The value is always greater than
-    zero. In the current implementation, this parameter must be divisible by 16.
-    @param blockSize Matched block size. It must be an odd number \>=1 . Normally, it should be
-    somewhere in the 3..11 range.
-    @param P1 The first parameter controlling the disparity smoothness. See below.
-    @param P2 The second parameter controlling the disparity smoothness. The larger the values are,
-    the smoother the disparity is. P1 is the penalty on the disparity change by plus or minus 1
-    between neighbor pixels. P2 is the penalty on the disparity change by more than 1 between neighbor
-    pixels. The algorithm requires P2 \> P1 . See stereo_match.cpp sample where some reasonably good
-    P1 and P2 values are shown (like 8\*number_of_image_channels\*SADWindowSize\*SADWindowSize and
-    32\*number_of_image_channels\*SADWindowSize\*SADWindowSize , respectively).
-    @param disp12MaxDiff Maximum allowed difference (in integer pixel units) in the left-right
-    disparity check. Set it to a non-positive value to disable the check.
-    @param preFilterCap Truncation value for the prefiltered image pixels. The algorithm first
-    computes x-derivative at each pixel and clips its value by [-preFilterCap, preFilterCap] interval.
-    The result values are passed to the Birchfield-Tomasi pixel cost function.
-    @param uniquenessRatio Margin in percentage by which the best (minimum) computed cost function
-    value should "win" the second best value to consider the found match correct. Normally, a value
-    within the 5-15 range is good enough.
-    @param speckleWindowSize Maximum size of smooth disparity regions to consider their noise speckles
-    and invalidate. Set it to 0 to disable speckle filtering. Otherwise, set it somewhere in the
-    50-200 range.
-    @param speckleRange Maximum disparity variation within each connected component. If you do speckle
-    filtering, set the parameter to a positive value, it will be implicitly multiplied by 16.
-    Normally, 1 or 2 is good enough.
-    @param mode Set it to StereoSGBM::MODE_HH to run the full-scale two-pass dynamic programming
-    algorithm. It will consume O(W\*H\*numDisparities) bytes, which is large for 640x480 stereo and
-    huge for HD-size pictures. By default, it is set to false .
-
-    The first constructor initializes StereoSGBM with all the default parameters. So, you only have to
-    set StereoSGBM::numDisparities at minimum. The second constructor enables you to set each parameter
-    to a custom value.
-     */
-    CV_WRAP static Ptr<StereoSGBM> create(int minDisparity = 0, int numDisparities = 16, int blockSize = 3,
-                                          int P1 = 0, int P2 = 0, int disp12MaxDiff = 0,
-                                          int preFilterCap = 0, int uniquenessRatio = 0,
-                                          int speckleWindowSize = 0, int speckleRange = 0,
-                                          int mode = StereoSGBM::MODE_SGBM);
-};
-
-//! @} calib3d
-
-/** @brief The methods in this namespace use a so-called fisheye camera model.
-  @ingroup calib3d_fisheye
-*/
-namespace fisheye
-{
-//! @addtogroup calib3d_fisheye
-//! @{
-
-    enum{
-        CALIB_USE_INTRINSIC_GUESS   = 1 << 0,
-        CALIB_RECOMPUTE_EXTRINSIC   = 1 << 1,
-        CALIB_CHECK_COND            = 1 << 2,
-        CALIB_FIX_SKEW              = 1 << 3,
-        CALIB_FIX_K1                = 1 << 4,
-        CALIB_FIX_K2                = 1 << 5,
-        CALIB_FIX_K3                = 1 << 6,
-        CALIB_FIX_K4                = 1 << 7,
-        CALIB_FIX_INTRINSIC         = 1 << 8,
-        CALIB_FIX_PRINCIPAL_POINT   = 1 << 9
-    };
-
-    /** @brief Projects points using fisheye model
-
-    @param objectPoints Array of object points, 1xN/Nx1 3-channel (or vector\<Point3f\> ), where N is
-    the number of points in the view.
-    @param imagePoints Output array of image points, 2xN/Nx2 1-channel or 1xN/Nx1 2-channel, or
-    vector\<Point2f\>.
-    @param affine
-    @param K Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{_1}\f$.
-    @param D Input vector of distortion coefficients \f$(k_1, k_2, k_3, k_4)\f$.
-    @param alpha The skew coefficient.
-    @param jacobian Optional output 2Nx15 jacobian matrix of derivatives of image points with respect
-    to components of the focal lengths, coordinates of the principal point, distortion coefficients,
-    rotation vector, translation vector, and the skew. In the old interface different components of
-    the jacobian are returned via different output parameters.
-
-    The function computes projections of 3D points to the image plane given intrinsic and extrinsic
-    camera parameters. Optionally, the function computes Jacobians - matrices of partial derivatives of
-    image points coordinates (as functions of all the input parameters) with respect to the particular
-    parameters, intrinsic and/or extrinsic.
-     */
-    CV_EXPORTS void projectPoints(InputArray objectPoints, OutputArray imagePoints, const Affine3d& affine,
-        InputArray K, InputArray D, double alpha = 0, OutputArray jacobian = noArray());
-
-    /** @overload */
-    CV_EXPORTS_W void projectPoints(InputArray objectPoints, OutputArray imagePoints, InputArray rvec, InputArray tvec,
-        InputArray K, InputArray D, double alpha = 0, OutputArray jacobian = noArray());
-
-    /** @brief Distorts 2D points using fisheye model.
-
-    @param undistorted Array of object points, 1xN/Nx1 2-channel (or vector\<Point2f\> ), where N is
-    the number of points in the view.
-    @param K Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{_1}\f$.
-    @param D Input vector of distortion coefficients \f$(k_1, k_2, k_3, k_4)\f$.
-    @param alpha The skew coefficient.
-    @param distorted Output array of image points, 1xN/Nx1 2-channel, or vector\<Point2f\> .
-
-    Note that the function assumes the camera matrix of the undistorted points to be identity.
-    This means if you want to transform back points undistorted with undistortPoints() you have to
-    multiply them with \f$P^{-1}\f$.
-     */
-    CV_EXPORTS_W void distortPoints(InputArray undistorted, OutputArray distorted, InputArray K, InputArray D, double alpha = 0);
-
-    /** @brief Undistorts 2D points using fisheye model
-
-    @param distorted Array of object points, 1xN/Nx1 2-channel (or vector\<Point2f\> ), where N is the
-    number of points in the view.
-    @param K Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{_1}\f$.
-    @param D Input vector of distortion coefficients \f$(k_1, k_2, k_3, k_4)\f$.
-    @param R Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3
-    1-channel or 1x1 3-channel
-    @param P New camera matrix (3x3) or new projection matrix (3x4)
-    @param undistorted Output array of image points, 1xN/Nx1 2-channel, or vector\<Point2f\> .
-     */
-    CV_EXPORTS_W void undistortPoints(InputArray distorted, OutputArray undistorted,
-        InputArray K, InputArray D, InputArray R = noArray(), InputArray P  = noArray());
-
-    /** @brief Computes undistortion and rectification maps for image transform by cv::remap(). If D is empty zero
-    distortion is used, if R or P is empty identity matrixes are used.
-
-    @param K Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{_1}\f$.
-    @param D Input vector of distortion coefficients \f$(k_1, k_2, k_3, k_4)\f$.
-    @param R Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3
-    1-channel or 1x1 3-channel
-    @param P New camera matrix (3x3) or new projection matrix (3x4)
-    @param size Undistorted image size.
-    @param m1type Type of the first output map that can be CV_32FC1 or CV_16SC2 . See convertMaps()
-    for details.
-    @param map1 The first output map.
-    @param map2 The second output map.
-     */
-    CV_EXPORTS_W void initUndistortRectifyMap(InputArray K, InputArray D, InputArray R, InputArray P,
-        const cv::Size& size, int m1type, OutputArray map1, OutputArray map2);
-
-    /** @brief Transforms an image to compensate for fisheye lens distortion.
-
-    @param distorted image with fisheye lens distortion.
-    @param undistorted Output image with compensated fisheye lens distortion.
-    @param K Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{_1}\f$.
-    @param D Input vector of distortion coefficients \f$(k_1, k_2, k_3, k_4)\f$.
-    @param Knew Camera matrix of the distorted image. By default, it is the identity matrix but you
-    may additionally scale and shift the result by using a different matrix.
-    @param new_size
-
-    The function transforms an image to compensate radial and tangential lens distortion.
-
-    The function is simply a combination of fisheye::initUndistortRectifyMap (with unity R ) and remap
-    (with bilinear interpolation). See the former function for details of the transformation being
-    performed.
-
-    See below the results of undistortImage.
-       -   a\) result of undistort of perspective camera model (all possible coefficients (k_1, k_2, k_3,
-            k_4, k_5, k_6) of distortion were optimized under calibration)
-        -   b\) result of fisheye::undistortImage of fisheye camera model (all possible coefficients (k_1, k_2,
-            k_3, k_4) of fisheye distortion were optimized under calibration)
-        -   c\) original image was captured with fisheye lens
-
-    Pictures a) and b) almost the same. But if we consider points of image located far from the center
-    of image, we can notice that on image a) these points are distorted.
-
-    ![image](pics/fisheye_undistorted.jpg)
-     */
-    CV_EXPORTS_W void undistortImage(InputArray distorted, OutputArray undistorted,
-        InputArray K, InputArray D, InputArray Knew = cv::noArray(), const Size& new_size = Size());
-
-    /** @brief Estimates new camera matrix for undistortion or rectification.
-
-    @param K Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{_1}\f$.
-    @param image_size
-    @param D Input vector of distortion coefficients \f$(k_1, k_2, k_3, k_4)\f$.
-    @param R Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3
-    1-channel or 1x1 3-channel
-    @param P New camera matrix (3x3) or new projection matrix (3x4)
-    @param balance Sets the new focal length in range between the min focal length and the max focal
-    length. Balance is in range of [0, 1].
-    @param new_size
-    @param fov_scale Divisor for new focal length.
-     */
-    CV_EXPORTS_W void estimateNewCameraMatrixForUndistortRectify(InputArray K, InputArray D, const Size &image_size, InputArray R,
-        OutputArray P, double balance = 0.0, const Size& new_size = Size(), double fov_scale = 1.0);
-
-    /** @brief Performs camera calibaration
-
-    @param objectPoints vector of vectors of calibration pattern points in the calibration pattern
-    coordinate space.
-    @param imagePoints vector of vectors of the projections of calibration pattern points.
-    imagePoints.size() and objectPoints.size() and imagePoints[i].size() must be equal to
-    objectPoints[i].size() for each i.
-    @param image_size Size of the image used only to initialize the intrinsic camera matrix.
-    @param K Output 3x3 floating-point camera matrix
-    \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ . If
-    fisheye::CALIB_USE_INTRINSIC_GUESS/ is specified, some or all of fx, fy, cx, cy must be
-    initialized before calling the function.
-    @param D Output vector of distortion coefficients \f$(k_1, k_2, k_3, k_4)\f$.
-    @param rvecs Output vector of rotation vectors (see Rodrigues ) estimated for each pattern view.
-    That is, each k-th rotation vector together with the corresponding k-th translation vector (see
-    the next output parameter description) brings the calibration pattern from the model coordinate
-    space (in which object points are specified) to the world coordinate space, that is, a real
-    position of the calibration pattern in the k-th pattern view (k=0.. *M* -1).
-    @param tvecs Output vector of translation vectors estimated for each pattern view.
-    @param flags Different flags that may be zero or a combination of the following values:
-    -   **fisheye::CALIB_USE_INTRINSIC_GUESS** cameraMatrix contains valid initial values of
-    fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image
-    center ( imageSize is used), and focal distances are computed in a least-squares fashion.
-    -   **fisheye::CALIB_RECOMPUTE_EXTRINSIC** Extrinsic will be recomputed after each iteration
-    of intrinsic optimization.
-    -   **fisheye::CALIB_CHECK_COND** The functions will check validity of condition number.
-    -   **fisheye::CALIB_FIX_SKEW** Skew coefficient (alpha) is set to zero and stay zero.
-    -   **fisheye::CALIB_FIX_K1..fisheye::CALIB_FIX_K4** Selected distortion coefficients
-    are set to zeros and stay zero.
-    -   **fisheye::CALIB_FIX_PRINCIPAL_POINT** The principal point is not changed during the global
-optimization. It stays at the center or at a different location specified when CALIB_USE_INTRINSIC_GUESS is set too.
-    @param criteria Termination criteria for the iterative optimization algorithm.
-     */
-    CV_EXPORTS_W double calibrate(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints, const Size& image_size,
-        InputOutputArray K, InputOutputArray D, OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs, int flags = 0,
-            TermCriteria criteria = TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 100, DBL_EPSILON));
-
-    /** @brief Stereo rectification for fisheye camera model
-
-    @param K1 First camera matrix.
-    @param D1 First camera distortion parameters.
-    @param K2 Second camera matrix.
-    @param D2 Second camera distortion parameters.
-    @param imageSize Size of the image used for stereo calibration.
-    @param R Rotation matrix between the coordinate systems of the first and the second
-    cameras.
-    @param tvec Translation vector between coordinate systems of the cameras.
-    @param R1 Output 3x3 rectification transform (rotation matrix) for the first camera.
-    @param R2 Output 3x3 rectification transform (rotation matrix) for the second camera.
-    @param P1 Output 3x4 projection matrix in the new (rectified) coordinate systems for the first
-    camera.
-    @param P2 Output 3x4 projection matrix in the new (rectified) coordinate systems for the second
-    camera.
-    @param Q Output \f$4 \times 4\f$ disparity-to-depth mapping matrix (see reprojectImageTo3D ).
-    @param flags Operation flags that may be zero or CALIB_ZERO_DISPARITY . If the flag is set,
-    the function makes the principal points of each camera have the same pixel coordinates in the
-    rectified views. And if the flag is not set, the function may still shift the images in the
-    horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the
-    useful image area.
-    @param newImageSize New image resolution after rectification. The same size should be passed to
-    initUndistortRectifyMap (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0)
-    is passed (default), it is set to the original imageSize . Setting it to larger value can help you
-    preserve details in the original image, especially when there is a big radial distortion.
-    @param balance Sets the new focal length in range between the min focal length and the max focal
-    length. Balance is in range of [0, 1].
-    @param fov_scale Divisor for new focal length.
-     */
-    CV_EXPORTS_W void stereoRectify(InputArray K1, InputArray D1, InputArray K2, InputArray D2, const Size &imageSize, InputArray R, InputArray tvec,
-        OutputArray R1, OutputArray R2, OutputArray P1, OutputArray P2, OutputArray Q, int flags, const Size &newImageSize = Size(),
-        double balance = 0.0, double fov_scale = 1.0);
-
-    /** @brief Performs stereo calibration
-
-    @param objectPoints Vector of vectors of the calibration pattern points.
-    @param imagePoints1 Vector of vectors of the projections of the calibration pattern points,
-    observed by the first camera.
-    @param imagePoints2 Vector of vectors of the projections of the calibration pattern points,
-    observed by the second camera.
-    @param K1 Input/output first camera matrix:
-    \f$\vecthreethree{f_x^{(j)}}{0}{c_x^{(j)}}{0}{f_y^{(j)}}{c_y^{(j)}}{0}{0}{1}\f$ , \f$j = 0,\, 1\f$ . If
-    any of fisheye::CALIB_USE_INTRINSIC_GUESS , fisheye::CALIB_FIX_INTRINSIC are specified,
-    some or all of the matrix components must be initialized.
-    @param D1 Input/output vector of distortion coefficients \f$(k_1, k_2, k_3, k_4)\f$ of 4 elements.
-    @param K2 Input/output second camera matrix. The parameter is similar to K1 .
-    @param D2 Input/output lens distortion coefficients for the second camera. The parameter is
-    similar to D1 .
-    @param imageSize Size of the image used only to initialize intrinsic camera matrix.
-    @param R Output rotation matrix between the 1st and the 2nd camera coordinate systems.
-    @param T Output translation vector between the coordinate systems of the cameras.
-    @param flags Different flags that may be zero or a combination of the following values:
-    -   **fisheye::CALIB_FIX_INTRINSIC** Fix K1, K2? and D1, D2? so that only R, T matrices
-    are estimated.
-    -   **fisheye::CALIB_USE_INTRINSIC_GUESS** K1, K2 contains valid initial values of
-    fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image
-    center (imageSize is used), and focal distances are computed in a least-squares fashion.
-    -   **fisheye::CALIB_RECOMPUTE_EXTRINSIC** Extrinsic will be recomputed after each iteration
-    of intrinsic optimization.
-    -   **fisheye::CALIB_CHECK_COND** The functions will check validity of condition number.
-    -   **fisheye::CALIB_FIX_SKEW** Skew coefficient (alpha) is set to zero and stay zero.
-    -   **fisheye::CALIB_FIX_K1..4** Selected distortion coefficients are set to zeros and stay
-    zero.
-    @param criteria Termination criteria for the iterative optimization algorithm.
-     */
-    CV_EXPORTS_W double stereoCalibrate(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints1, InputArrayOfArrays imagePoints2,
-                                  InputOutputArray K1, InputOutputArray D1, InputOutputArray K2, InputOutputArray D2, Size imageSize,
-                                  OutputArray R, OutputArray T, int flags = fisheye::CALIB_FIX_INTRINSIC,
-                                  TermCriteria criteria = TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 100, DBL_EPSILON));
-
-//! @} calib3d_fisheye
-} // end namespace fisheye
-
-} //end namespace cv
-
-#ifndef DISABLE_OPENCV_24_COMPATIBILITY
-#include "opencv2/calib3d/calib3d_c.h"
-#endif
-
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/calib3d/calib3d.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/calib3d/calib3d.hpp
deleted file mode 100755
index b3da45e..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/calib3d/calib3d.hpp
+++ /dev/null
@@ -1,48 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifdef __OPENCV_BUILD
-#error this is a compatibility header which should not be used inside the OpenCV library
-#endif
-
-#include "opencv2/calib3d.hpp"
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/calib3d/calib3d_c.h b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/calib3d/calib3d_c.h
deleted file mode 100755
index 8ec6390..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/calib3d/calib3d_c.h
+++ /dev/null
@@ -1,427 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_CALIB3D_C_H
-#define OPENCV_CALIB3D_C_H
-
-#include "opencv2/core/core_c.h"
-
-#ifdef __cplusplus
-extern "C" {
-#endif
-
-/** @addtogroup calib3d_c
-  @{
-  */
-
-/****************************************************************************************\
-*                      Camera Calibration, Pose Estimation and Stereo                    *
-\****************************************************************************************/
-
-typedef struct CvPOSITObject CvPOSITObject;
-
-/* Allocates and initializes CvPOSITObject structure before doing cvPOSIT */
-CVAPI(CvPOSITObject*)  cvCreatePOSITObject( CvPoint3D32f* points, int point_count );
-
-
-/* Runs POSIT (POSe from ITeration) algorithm for determining 3d position of
-   an object given its model and projection in a weak-perspective case */
-CVAPI(void)  cvPOSIT(  CvPOSITObject* posit_object, CvPoint2D32f* image_points,
-                       double focal_length, CvTermCriteria criteria,
-                       float* rotation_matrix, float* translation_vector);
-
-/* Releases CvPOSITObject structure */
-CVAPI(void)  cvReleasePOSITObject( CvPOSITObject**  posit_object );
-
-/* updates the number of RANSAC iterations */
-CVAPI(int) cvRANSACUpdateNumIters( double p, double err_prob,
-                                   int model_points, int max_iters );
-
-CVAPI(void) cvConvertPointsHomogeneous( const CvMat* src, CvMat* dst );
-
-/* Calculates fundamental matrix given a set of corresponding points */
-#define CV_FM_7POINT 1
-#define CV_FM_8POINT 2
-
-#define CV_LMEDS 4
-#define CV_RANSAC 8
-
-#define CV_FM_LMEDS_ONLY  CV_LMEDS
-#define CV_FM_RANSAC_ONLY CV_RANSAC
-#define CV_FM_LMEDS CV_LMEDS
-#define CV_FM_RANSAC CV_RANSAC
-
-enum
-{
-    CV_ITERATIVE = 0,
-    CV_EPNP = 1, // F.Moreno-Noguer, V.Lepetit and P.Fua "EPnP: Efficient Perspective-n-Point Camera Pose Estimation"
-    CV_P3P = 2, // X.S. Gao, X.-R. Hou, J. Tang, H.-F. Chang; "Complete Solution Classification for the Perspective-Three-Point Problem"
-    CV_DLS = 3 // Joel A. Hesch and Stergios I. Roumeliotis. "A Direct Least-Squares (DLS) Method for PnP"
-};
-
-CVAPI(int) cvFindFundamentalMat( const CvMat* points1, const CvMat* points2,
-                                 CvMat* fundamental_matrix,
-                                 int method CV_DEFAULT(CV_FM_RANSAC),
-                                 double param1 CV_DEFAULT(3.), double param2 CV_DEFAULT(0.99),
-                                 CvMat* status CV_DEFAULT(NULL) );
-
-/* For each input point on one of images
-   computes parameters of the corresponding
-   epipolar line on the other image */
-CVAPI(void) cvComputeCorrespondEpilines( const CvMat* points,
-                                         int which_image,
-                                         const CvMat* fundamental_matrix,
-                                         CvMat* correspondent_lines );
-
-/* Triangulation functions */
-
-CVAPI(void) cvTriangulatePoints(CvMat* projMatr1, CvMat* projMatr2,
-                                CvMat* projPoints1, CvMat* projPoints2,
-                                CvMat* points4D);
-
-CVAPI(void) cvCorrectMatches(CvMat* F, CvMat* points1, CvMat* points2,
-                             CvMat* new_points1, CvMat* new_points2);
-
-
-/* Computes the optimal new camera matrix according to the free scaling parameter alpha:
-   alpha=0 - only valid pixels will be retained in the undistorted image
-   alpha=1 - all the source image pixels will be retained in the undistorted image
-*/
-CVAPI(void) cvGetOptimalNewCameraMatrix( const CvMat* camera_matrix,
-                                         const CvMat* dist_coeffs,
-                                         CvSize image_size, double alpha,
-                                         CvMat* new_camera_matrix,
-                                         CvSize new_imag_size CV_DEFAULT(cvSize(0,0)),
-                                         CvRect* valid_pixel_ROI CV_DEFAULT(0),
-                                         int center_principal_point CV_DEFAULT(0));
-
-/* Converts rotation vector to rotation matrix or vice versa */
-CVAPI(int) cvRodrigues2( const CvMat* src, CvMat* dst,
-                         CvMat* jacobian CV_DEFAULT(0) );
-
-/* Finds perspective transformation between the object plane and image (view) plane */
-CVAPI(int) cvFindHomography( const CvMat* src_points,
-                             const CvMat* dst_points,
-                             CvMat* homography,
-                             int method CV_DEFAULT(0),
-                             double ransacReprojThreshold CV_DEFAULT(3),
-                             CvMat* mask CV_DEFAULT(0),
-                             int maxIters CV_DEFAULT(2000),
-                             double confidence CV_DEFAULT(0.995));
-
-/* Computes RQ decomposition for 3x3 matrices */
-CVAPI(void) cvRQDecomp3x3( const CvMat *matrixM, CvMat *matrixR, CvMat *matrixQ,
-                           CvMat *matrixQx CV_DEFAULT(NULL),
-                           CvMat *matrixQy CV_DEFAULT(NULL),
-                           CvMat *matrixQz CV_DEFAULT(NULL),
-                           CvPoint3D64f *eulerAngles CV_DEFAULT(NULL));
-
-/* Computes projection matrix decomposition */
-CVAPI(void) cvDecomposeProjectionMatrix( const CvMat *projMatr, CvMat *calibMatr,
-                                         CvMat *rotMatr, CvMat *posVect,
-                                         CvMat *rotMatrX CV_DEFAULT(NULL),
-                                         CvMat *rotMatrY CV_DEFAULT(NULL),
-                                         CvMat *rotMatrZ CV_DEFAULT(NULL),
-                                         CvPoint3D64f *eulerAngles CV_DEFAULT(NULL));
-
-/* Computes d(AB)/dA and d(AB)/dB */
-CVAPI(void) cvCalcMatMulDeriv( const CvMat* A, const CvMat* B, CvMat* dABdA, CvMat* dABdB );
-
-/* Computes r3 = rodrigues(rodrigues(r2)*rodrigues(r1)),
-   t3 = rodrigues(r2)*t1 + t2 and the respective derivatives */
-CVAPI(void) cvComposeRT( const CvMat* _rvec1, const CvMat* _tvec1,
-                         const CvMat* _rvec2, const CvMat* _tvec2,
-                         CvMat* _rvec3, CvMat* _tvec3,
-                         CvMat* dr3dr1 CV_DEFAULT(0), CvMat* dr3dt1 CV_DEFAULT(0),
-                         CvMat* dr3dr2 CV_DEFAULT(0), CvMat* dr3dt2 CV_DEFAULT(0),
-                         CvMat* dt3dr1 CV_DEFAULT(0), CvMat* dt3dt1 CV_DEFAULT(0),
-                         CvMat* dt3dr2 CV_DEFAULT(0), CvMat* dt3dt2 CV_DEFAULT(0) );
-
-/* Projects object points to the view plane using
-   the specified extrinsic and intrinsic camera parameters */
-CVAPI(void) cvProjectPoints2( const CvMat* object_points, const CvMat* rotation_vector,
-                              const CvMat* translation_vector, const CvMat* camera_matrix,
-                              const CvMat* distortion_coeffs, CvMat* image_points,
-                              CvMat* dpdrot CV_DEFAULT(NULL), CvMat* dpdt CV_DEFAULT(NULL),
-                              CvMat* dpdf CV_DEFAULT(NULL), CvMat* dpdc CV_DEFAULT(NULL),
-                              CvMat* dpddist CV_DEFAULT(NULL),
-                              double aspect_ratio CV_DEFAULT(0));
-
-/* Finds extrinsic camera parameters from
-   a few known corresponding point pairs and intrinsic parameters */
-CVAPI(void) cvFindExtrinsicCameraParams2( const CvMat* object_points,
-                                          const CvMat* image_points,
-                                          const CvMat* camera_matrix,
-                                          const CvMat* distortion_coeffs,
-                                          CvMat* rotation_vector,
-                                          CvMat* translation_vector,
-                                          int use_extrinsic_guess CV_DEFAULT(0) );
-
-/* Computes initial estimate of the intrinsic camera parameters
-   in case of planar calibration target (e.g. chessboard) */
-CVAPI(void) cvInitIntrinsicParams2D( const CvMat* object_points,
-                                     const CvMat* image_points,
-                                     const CvMat* npoints, CvSize image_size,
-                                     CvMat* camera_matrix,
-                                     double aspect_ratio CV_DEFAULT(1.) );
-
-#define CV_CALIB_CB_ADAPTIVE_THRESH  1
-#define CV_CALIB_CB_NORMALIZE_IMAGE  2
-#define CV_CALIB_CB_FILTER_QUADS     4
-#define CV_CALIB_CB_FAST_CHECK       8
-
-// Performs a fast check if a chessboard is in the input image. This is a workaround to
-// a problem of cvFindChessboardCorners being slow on images with no chessboard
-// - src: input image
-// - size: chessboard size
-// Returns 1 if a chessboard can be in this image and findChessboardCorners should be called,
-// 0 if there is no chessboard, -1 in case of error
-CVAPI(int) cvCheckChessboard(IplImage* src, CvSize size);
-
-    /* Detects corners on a chessboard calibration pattern */
-CVAPI(int) cvFindChessboardCorners( const void* image, CvSize pattern_size,
-                                    CvPoint2D32f* corners,
-                                    int* corner_count CV_DEFAULT(NULL),
-                                    int flags CV_DEFAULT(CV_CALIB_CB_ADAPTIVE_THRESH+CV_CALIB_CB_NORMALIZE_IMAGE) );
-
-/* Draws individual chessboard corners or the whole chessboard detected */
-CVAPI(void) cvDrawChessboardCorners( CvArr* image, CvSize pattern_size,
-                                     CvPoint2D32f* corners,
-                                     int count, int pattern_was_found );
-
-#define CV_CALIB_USE_INTRINSIC_GUESS  1
-#define CV_CALIB_FIX_ASPECT_RATIO     2
-#define CV_CALIB_FIX_PRINCIPAL_POINT  4
-#define CV_CALIB_ZERO_TANGENT_DIST    8
-#define CV_CALIB_FIX_FOCAL_LENGTH 16
-#define CV_CALIB_FIX_K1  32
-#define CV_CALIB_FIX_K2  64
-#define CV_CALIB_FIX_K3  128
-#define CV_CALIB_FIX_K4  2048
-#define CV_CALIB_FIX_K5  4096
-#define CV_CALIB_FIX_K6  8192
-#define CV_CALIB_RATIONAL_MODEL 16384
-#define CV_CALIB_THIN_PRISM_MODEL 32768
-#define CV_CALIB_FIX_S1_S2_S3_S4  65536
-#define CV_CALIB_TILTED_MODEL  262144
-#define CV_CALIB_FIX_TAUX_TAUY  524288
-#define CV_CALIB_FIX_TANGENT_DIST 2097152
-
-#define CV_CALIB_NINTRINSIC 18
-
-/* Finds intrinsic and extrinsic camera parameters
-   from a few views of known calibration pattern */
-CVAPI(double) cvCalibrateCamera2( const CvMat* object_points,
-                                const CvMat* image_points,
-                                const CvMat* point_counts,
-                                CvSize image_size,
-                                CvMat* camera_matrix,
-                                CvMat* distortion_coeffs,
-                                CvMat* rotation_vectors CV_DEFAULT(NULL),
-                                CvMat* translation_vectors CV_DEFAULT(NULL),
-                                int flags CV_DEFAULT(0),
-                                CvTermCriteria term_crit CV_DEFAULT(cvTermCriteria(
-                                    CV_TERMCRIT_ITER+CV_TERMCRIT_EPS,30,DBL_EPSILON)) );
-
-/* Computes various useful characteristics of the camera from the data computed by
-   cvCalibrateCamera2 */
-CVAPI(void) cvCalibrationMatrixValues( const CvMat *camera_matrix,
-                                CvSize image_size,
-                                double aperture_width CV_DEFAULT(0),
-                                double aperture_height CV_DEFAULT(0),
-                                double *fovx CV_DEFAULT(NULL),
-                                double *fovy CV_DEFAULT(NULL),
-                                double *focal_length CV_DEFAULT(NULL),
-                                CvPoint2D64f *principal_point CV_DEFAULT(NULL),
-                                double *pixel_aspect_ratio CV_DEFAULT(NULL));
-
-#define CV_CALIB_FIX_INTRINSIC  256
-#define CV_CALIB_SAME_FOCAL_LENGTH 512
-
-/* Computes the transformation from one camera coordinate system to another one
-   from a few correspondent views of the same calibration target. Optionally, calibrates
-   both cameras */
-CVAPI(double) cvStereoCalibrate( const CvMat* object_points, const CvMat* image_points1,
-                               const CvMat* image_points2, const CvMat* npoints,
-                               CvMat* camera_matrix1, CvMat* dist_coeffs1,
-                               CvMat* camera_matrix2, CvMat* dist_coeffs2,
-                               CvSize image_size, CvMat* R, CvMat* T,
-                               CvMat* E CV_DEFAULT(0), CvMat* F CV_DEFAULT(0),
-                               int flags CV_DEFAULT(CV_CALIB_FIX_INTRINSIC),
-                               CvTermCriteria term_crit CV_DEFAULT(cvTermCriteria(
-                                   CV_TERMCRIT_ITER+CV_TERMCRIT_EPS,30,1e-6)) );
-
-#define CV_CALIB_ZERO_DISPARITY 1024
-
-/* Computes 3D rotations (+ optional shift) for each camera coordinate system to make both
-   views parallel (=> to make all the epipolar lines horizontal or vertical) */
-CVAPI(void) cvStereoRectify( const CvMat* camera_matrix1, const CvMat* camera_matrix2,
-                             const CvMat* dist_coeffs1, const CvMat* dist_coeffs2,
-                             CvSize image_size, const CvMat* R, const CvMat* T,
-                             CvMat* R1, CvMat* R2, CvMat* P1, CvMat* P2,
-                             CvMat* Q CV_DEFAULT(0),
-                             int flags CV_DEFAULT(CV_CALIB_ZERO_DISPARITY),
-                             double alpha CV_DEFAULT(-1),
-                             CvSize new_image_size CV_DEFAULT(cvSize(0,0)),
-                             CvRect* valid_pix_ROI1 CV_DEFAULT(0),
-                             CvRect* valid_pix_ROI2 CV_DEFAULT(0));
-
-/* Computes rectification transformations for uncalibrated pair of images using a set
-   of point correspondences */
-CVAPI(int) cvStereoRectifyUncalibrated( const CvMat* points1, const CvMat* points2,
-                                        const CvMat* F, CvSize img_size,
-                                        CvMat* H1, CvMat* H2,
-                                        double threshold CV_DEFAULT(5));
-
-
-
-/* stereo correspondence parameters and functions */
-
-#define CV_STEREO_BM_NORMALIZED_RESPONSE  0
-#define CV_STEREO_BM_XSOBEL               1
-
-/* Block matching algorithm structure */
-typedef struct CvStereoBMState
-{
-    // pre-filtering (normalization of input images)
-    int preFilterType; // =CV_STEREO_BM_NORMALIZED_RESPONSE now
-    int preFilterSize; // averaging window size: ~5x5..21x21
-    int preFilterCap; // the output of pre-filtering is clipped by [-preFilterCap,preFilterCap]
-
-    // correspondence using Sum of Absolute Difference (SAD)
-    int SADWindowSize; // ~5x5..21x21
-    int minDisparity;  // minimum disparity (can be negative)
-    int numberOfDisparities; // maximum disparity - minimum disparity (> 0)
-
-    // post-filtering
-    int textureThreshold;  // the disparity is only computed for pixels
-                           // with textured enough neighborhood
-    int uniquenessRatio;   // accept the computed disparity d* only if
-                           // SAD(d) >= SAD(d*)*(1 + uniquenessRatio/100.)
-                           // for any d != d*+/-1 within the search range.
-    int speckleWindowSize; // disparity variation window
-    int speckleRange; // acceptable range of variation in window
-
-    int trySmallerWindows; // if 1, the results may be more accurate,
-                           // at the expense of slower processing
-    CvRect roi1, roi2;
-    int disp12MaxDiff;
-
-    // temporary buffers
-    CvMat* preFilteredImg0;
-    CvMat* preFilteredImg1;
-    CvMat* slidingSumBuf;
-    CvMat* cost;
-    CvMat* disp;
-} CvStereoBMState;
-
-#define CV_STEREO_BM_BASIC 0
-#define CV_STEREO_BM_FISH_EYE 1
-#define CV_STEREO_BM_NARROW 2
-
-CVAPI(CvStereoBMState*) cvCreateStereoBMState(int preset CV_DEFAULT(CV_STEREO_BM_BASIC),
-                                              int numberOfDisparities CV_DEFAULT(0));
-
-CVAPI(void) cvReleaseStereoBMState( CvStereoBMState** state );
-
-CVAPI(void) cvFindStereoCorrespondenceBM( const CvArr* left, const CvArr* right,
-                                          CvArr* disparity, CvStereoBMState* state );
-
-CVAPI(CvRect) cvGetValidDisparityROI( CvRect roi1, CvRect roi2, int minDisparity,
-                                      int numberOfDisparities, int SADWindowSize );
-
-CVAPI(void) cvValidateDisparity( CvArr* disparity, const CvArr* cost,
-                                 int minDisparity, int numberOfDisparities,
-                                 int disp12MaxDiff CV_DEFAULT(1) );
-
-/* Reprojects the computed disparity image to the 3D space using the specified 4x4 matrix */
-CVAPI(void)  cvReprojectImageTo3D( const CvArr* disparityImage,
-                                   CvArr* _3dImage, const CvMat* Q,
-                                   int handleMissingValues CV_DEFAULT(0) );
-
-/** @} calib3d_c */
-
-#ifdef __cplusplus
-} // extern "C"
-
-//////////////////////////////////////////////////////////////////////////////////////////
-class CV_EXPORTS CvLevMarq
-{
-public:
-    CvLevMarq();
-    CvLevMarq( int nparams, int nerrs, CvTermCriteria criteria=
-              cvTermCriteria(CV_TERMCRIT_EPS+CV_TERMCRIT_ITER,30,DBL_EPSILON),
-              bool completeSymmFlag=false );
-    ~CvLevMarq();
-    void init( int nparams, int nerrs, CvTermCriteria criteria=
-              cvTermCriteria(CV_TERMCRIT_EPS+CV_TERMCRIT_ITER,30,DBL_EPSILON),
-              bool completeSymmFlag=false );
-    bool update( const CvMat*& param, CvMat*& J, CvMat*& err );
-    bool updateAlt( const CvMat*& param, CvMat*& JtJ, CvMat*& JtErr, double*& errNorm );
-
-    void clear();
-    void step();
-    enum { DONE=0, STARTED=1, CALC_J=2, CHECK_ERR=3 };
-
-    cv::Ptr<CvMat> mask;
-    cv::Ptr<CvMat> prevParam;
-    cv::Ptr<CvMat> param;
-    cv::Ptr<CvMat> J;
-    cv::Ptr<CvMat> err;
-    cv::Ptr<CvMat> JtJ;
-    cv::Ptr<CvMat> JtJN;
-    cv::Ptr<CvMat> JtErr;
-    cv::Ptr<CvMat> JtJV;
-    cv::Ptr<CvMat> JtJW;
-    double prevErrNorm, errNorm;
-    int lambdaLg10;
-    CvTermCriteria criteria;
-    int state;
-    int iters;
-    bool completeSymmFlag;
-    int solveMethod;
-};
-
-#endif
-
-#endif /* OPENCV_CALIB3D_C_H */
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core.hpp
deleted file mode 100755
index 43da5a9..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core.hpp
+++ /dev/null
@@ -1,3273 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2015, Intel Corporation, all rights reserved.
-// Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved.
-// Copyright (C) 2015, OpenCV Foundation, all rights reserved.
-// Copyright (C) 2015, Itseez Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_CORE_HPP
-#define OPENCV_CORE_HPP
-
-#ifndef __cplusplus
-#  error core.hpp header must be compiled as C++
-#endif
-
-#include "opencv2/core/cvdef.h"
-#include "opencv2/core/version.hpp"
-#include "opencv2/core/base.hpp"
-#include "opencv2/core/cvstd.hpp"
-#include "opencv2/core/traits.hpp"
-#include "opencv2/core/matx.hpp"
-#include "opencv2/core/types.hpp"
-#include "opencv2/core/mat.hpp"
-#include "opencv2/core/persistence.hpp"
-
-/**
-@defgroup core Core functionality
-@{
-    @defgroup core_basic Basic structures
-    @defgroup core_c C structures and operations
-    @{
-        @defgroup core_c_glue Connections with C++
-    @}
-    @defgroup core_array Operations on arrays
-    @defgroup core_xml XML/YAML Persistence
-    @defgroup core_cluster Clustering
-    @defgroup core_utils Utility and system functions and macros
-    @{
-        @defgroup core_utils_sse SSE utilities
-        @defgroup core_utils_neon NEON utilities
-        @defgroup core_utils_softfloat Softfloat support
-    @}
-    @defgroup core_opengl OpenGL interoperability
-    @defgroup core_ipp Intel IPP Asynchronous C/C++ Converters
-    @defgroup core_optim Optimization Algorithms
-    @defgroup core_directx DirectX interoperability
-    @defgroup core_eigen Eigen support
-    @defgroup core_opencl OpenCL support
-    @defgroup core_va_intel Intel VA-API/OpenCL (CL-VA) interoperability
-    @defgroup core_hal Hardware Acceleration Layer
-    @{
-        @defgroup core_hal_functions Functions
-        @defgroup core_hal_interface Interface
-        @defgroup core_hal_intrin Universal intrinsics
-        @{
-            @defgroup core_hal_intrin_impl Private implementation helpers
-        @}
-    @}
-@}
- */
-
-namespace cv {
-
-//! @addtogroup core_utils
-//! @{
-
-/*! @brief Class passed to an error.
-
-This class encapsulates all or almost all necessary
-information about the error happened in the program. The exception is
-usually constructed and thrown implicitly via CV_Error and CV_Error_ macros.
-@see error
- */
-class CV_EXPORTS Exception : public std::exception
-{
-public:
-    /*!
-     Default constructor
-     */
-    Exception();
-    /*!
-     Full constructor. Normally the constructor is not called explicitly.
-     Instead, the macros CV_Error(), CV_Error_() and CV_Assert() are used.
-    */
-    Exception(int _code, const String& _err, const String& _func, const String& _file, int _line);
-    virtual ~Exception() throw();
-
-    /*!
-     \return the error description and the context as a text string.
-    */
-    virtual const char *what() const throw() CV_OVERRIDE;
-    void formatMessage();
-
-    String msg; ///< the formatted error message
-
-    int code; ///< error code @see CVStatus
-    String err; ///< error description
-    String func; ///< function name. Available only when the compiler supports getting it
-    String file; ///< source file name where the error has occurred
-    int line; ///< line number in the source file where the error has occurred
-};
-
-/*! @brief Signals an error and raises the exception.
-
-By default the function prints information about the error to stderr,
-then it either stops if cv::setBreakOnError() had been called before or raises the exception.
-It is possible to alternate error processing by using #redirectError().
-@param exc the exception raisen.
-@deprecated drop this version
- */
-CV_EXPORTS void error( const Exception& exc );
-
-enum SortFlags { SORT_EVERY_ROW    = 0, //!< each matrix row is sorted independently
-                 SORT_EVERY_COLUMN = 1, //!< each matrix column is sorted
-                                        //!< independently; this flag and the previous one are
-                                        //!< mutually exclusive.
-                 SORT_ASCENDING    = 0, //!< each matrix row is sorted in the ascending
-                                        //!< order.
-                 SORT_DESCENDING   = 16 //!< each matrix row is sorted in the
-                                        //!< descending order; this flag and the previous one are also
-                                        //!< mutually exclusive.
-               };
-
-//! @} core_utils
-
-//! @addtogroup core
-//! @{
-
-//! Covariation flags
-enum CovarFlags {
-    /** The output covariance matrix is calculated as:
-       \f[\texttt{scale}   \cdot  [  \texttt{vects}  [0]-  \texttt{mean}  , \texttt{vects}  [1]-  \texttt{mean}  ,...]^T  \cdot  [ \texttt{vects}  [0]- \texttt{mean}  , \texttt{vects}  [1]- \texttt{mean}  ,...],\f]
-       The covariance matrix will be nsamples x nsamples. Such an unusual covariance matrix is used
-       for fast PCA of a set of very large vectors (see, for example, the EigenFaces technique for
-       face recognition). Eigenvalues of this "scrambled" matrix match the eigenvalues of the true
-       covariance matrix. The "true" eigenvectors can be easily calculated from the eigenvectors of
-       the "scrambled" covariance matrix. */
-    COVAR_SCRAMBLED = 0,
-    /**The output covariance matrix is calculated as:
-        \f[\texttt{scale}   \cdot  [  \texttt{vects}  [0]-  \texttt{mean}  , \texttt{vects}  [1]-  \texttt{mean}  ,...]  \cdot  [ \texttt{vects}  [0]- \texttt{mean}  , \texttt{vects}  [1]- \texttt{mean}  ,...]^T,\f]
-        covar will be a square matrix of the same size as the total number of elements in each input
-        vector. One and only one of #COVAR_SCRAMBLED and #COVAR_NORMAL must be specified.*/
-    COVAR_NORMAL    = 1,
-    /** If the flag is specified, the function does not calculate mean from
-        the input vectors but, instead, uses the passed mean vector. This is useful if mean has been
-        pre-calculated or known in advance, or if the covariance matrix is calculated by parts. In
-        this case, mean is not a mean vector of the input sub-set of vectors but rather the mean
-        vector of the whole set.*/
-    COVAR_USE_AVG   = 2,
-    /** If the flag is specified, the covariance matrix is scaled. In the
-        "normal" mode, scale is 1./nsamples . In the "scrambled" mode, scale is the reciprocal of the
-        total number of elements in each input vector. By default (if the flag is not specified), the
-        covariance matrix is not scaled ( scale=1 ).*/
-    COVAR_SCALE     = 4,
-    /** If the flag is
-        specified, all the input vectors are stored as rows of the samples matrix. mean should be a
-        single-row vector in this case.*/
-    COVAR_ROWS      = 8,
-    /** If the flag is
-        specified, all the input vectors are stored as columns of the samples matrix. mean should be a
-        single-column vector in this case.*/
-    COVAR_COLS      = 16
-};
-
-//! k-Means flags
-enum KmeansFlags {
-    /** Select random initial centers in each attempt.*/
-    KMEANS_RANDOM_CENTERS     = 0,
-    /** Use kmeans++ center initialization by Arthur and Vassilvitskii [Arthur2007].*/
-    KMEANS_PP_CENTERS         = 2,
-    /** During the first (and possibly the only) attempt, use the
-        user-supplied labels instead of computing them from the initial centers. For the second and
-        further attempts, use the random or semi-random centers. Use one of KMEANS_\*_CENTERS flag
-        to specify the exact method.*/
-    KMEANS_USE_INITIAL_LABELS = 1
-};
-
-//! type of line
-enum LineTypes {
-    FILLED  = -1,
-    LINE_4  = 4, //!< 4-connected line
-    LINE_8  = 8, //!< 8-connected line
-    LINE_AA = 16 //!< antialiased line
-};
-
-//! Only a subset of Hershey fonts <https://en.wikipedia.org/wiki/Hershey_fonts> are supported
-enum HersheyFonts {
-    FONT_HERSHEY_SIMPLEX        = 0, //!< normal size sans-serif font
-    FONT_HERSHEY_PLAIN          = 1, //!< small size sans-serif font
-    FONT_HERSHEY_DUPLEX         = 2, //!< normal size sans-serif font (more complex than FONT_HERSHEY_SIMPLEX)
-    FONT_HERSHEY_COMPLEX        = 3, //!< normal size serif font
-    FONT_HERSHEY_TRIPLEX        = 4, //!< normal size serif font (more complex than FONT_HERSHEY_COMPLEX)
-    FONT_HERSHEY_COMPLEX_SMALL  = 5, //!< smaller version of FONT_HERSHEY_COMPLEX
-    FONT_HERSHEY_SCRIPT_SIMPLEX = 6, //!< hand-writing style font
-    FONT_HERSHEY_SCRIPT_COMPLEX = 7, //!< more complex variant of FONT_HERSHEY_SCRIPT_SIMPLEX
-    FONT_ITALIC                 = 16 //!< flag for italic font
-};
-
-enum ReduceTypes { REDUCE_SUM = 0, //!< the output is the sum of all rows/columns of the matrix.
-                   REDUCE_AVG = 1, //!< the output is the mean vector of all rows/columns of the matrix.
-                   REDUCE_MAX = 2, //!< the output is the maximum (column/row-wise) of all rows/columns of the matrix.
-                   REDUCE_MIN = 3  //!< the output is the minimum (column/row-wise) of all rows/columns of the matrix.
-                 };
-
-
-/** @brief Swaps two matrices
-*/
-CV_EXPORTS void swap(Mat& a, Mat& b);
-/** @overload */
-CV_EXPORTS void swap( UMat& a, UMat& b );
-
-//! @} core
-
-//! @addtogroup core_array
-//! @{
-
-/** @brief Computes the source location of an extrapolated pixel.
-
-The function computes and returns the coordinate of a donor pixel corresponding to the specified
-extrapolated pixel when using the specified extrapolation border mode. For example, if you use
-cv::BORDER_WRAP mode in the horizontal direction, cv::BORDER_REFLECT_101 in the vertical direction and
-want to compute value of the "virtual" pixel Point(-5, 100) in a floating-point image img , it
-looks like:
-@code{.cpp}
-    float val = img.at<float>(borderInterpolate(100, img.rows, cv::BORDER_REFLECT_101),
-                              borderInterpolate(-5, img.cols, cv::BORDER_WRAP));
-@endcode
-Normally, the function is not called directly. It is used inside filtering functions and also in
-copyMakeBorder.
-@param p 0-based coordinate of the extrapolated pixel along one of the axes, likely \<0 or \>= len
-@param len Length of the array along the corresponding axis.
-@param borderType Border type, one of the #BorderTypes, except for #BORDER_TRANSPARENT and
-#BORDER_ISOLATED . When borderType==#BORDER_CONSTANT , the function always returns -1, regardless
-of p and len.
-
-@sa copyMakeBorder
-*/
-CV_EXPORTS_W int borderInterpolate(int p, int len, int borderType);
-
-/** @example copyMakeBorder_demo.cpp
-An example using copyMakeBorder function
- */
-/** @brief Forms a border around an image.
-
-The function copies the source image into the middle of the destination image. The areas to the
-left, to the right, above and below the copied source image will be filled with extrapolated
-pixels. This is not what filtering functions based on it do (they extrapolate pixels on-fly), but
-what other more complex functions, including your own, may do to simplify image boundary handling.
-
-The function supports the mode when src is already in the middle of dst . In this case, the
-function does not copy src itself but simply constructs the border, for example:
-
-@code{.cpp}
-    // let border be the same in all directions
-    int border=2;
-    // constructs a larger image to fit both the image and the border
-    Mat gray_buf(rgb.rows + border*2, rgb.cols + border*2, rgb.depth());
-    // select the middle part of it w/o copying data
-    Mat gray(gray_canvas, Rect(border, border, rgb.cols, rgb.rows));
-    // convert image from RGB to grayscale
-    cvtColor(rgb, gray, COLOR_RGB2GRAY);
-    // form a border in-place
-    copyMakeBorder(gray, gray_buf, border, border,
-                   border, border, BORDER_REPLICATE);
-    // now do some custom filtering ...
-    ...
-@endcode
-@note When the source image is a part (ROI) of a bigger image, the function will try to use the
-pixels outside of the ROI to form a border. To disable this feature and always do extrapolation, as
-if src was not a ROI, use borderType | #BORDER_ISOLATED.
-
-@param src Source image.
-@param dst Destination image of the same type as src and the size Size(src.cols+left+right,
-src.rows+top+bottom) .
-@param top
-@param bottom
-@param left
-@param right Parameter specifying how many pixels in each direction from the source image rectangle
-to extrapolate. For example, top=1, bottom=1, left=1, right=1 mean that 1 pixel-wide border needs
-to be built.
-@param borderType Border type. See borderInterpolate for details.
-@param value Border value if borderType==BORDER_CONSTANT .
-
-@sa  borderInterpolate
-*/
-CV_EXPORTS_W void copyMakeBorder(InputArray src, OutputArray dst,
-                                 int top, int bottom, int left, int right,
-                                 int borderType, const Scalar& value = Scalar() );
-
-/** @brief Calculates the per-element sum of two arrays or an array and a scalar.
-
-The function add calculates:
-- Sum of two arrays when both input arrays have the same size and the same number of channels:
-\f[\texttt{dst}(I) =  \texttt{saturate} ( \texttt{src1}(I) +  \texttt{src2}(I)) \quad \texttt{if mask}(I) \ne0\f]
-- Sum of an array and a scalar when src2 is constructed from Scalar or has the same number of
-elements as `src1.channels()`:
-\f[\texttt{dst}(I) =  \texttt{saturate} ( \texttt{src1}(I) +  \texttt{src2} ) \quad \texttt{if mask}(I) \ne0\f]
-- Sum of a scalar and an array when src1 is constructed from Scalar or has the same number of
-elements as `src2.channels()`:
-\f[\texttt{dst}(I) =  \texttt{saturate} ( \texttt{src1} +  \texttt{src2}(I) ) \quad \texttt{if mask}(I) \ne0\f]
-where `I` is a multi-dimensional index of array elements. In case of multi-channel arrays, each
-channel is processed independently.
-
-The first function in the list above can be replaced with matrix expressions:
-@code{.cpp}
-    dst = src1 + src2;
-    dst += src1; // equivalent to add(dst, src1, dst);
-@endcode
-The input arrays and the output array can all have the same or different depths. For example, you
-can add a 16-bit unsigned array to a 8-bit signed array and store the sum as a 32-bit
-floating-point array. Depth of the output array is determined by the dtype parameter. In the second
-and third cases above, as well as in the first case, when src1.depth() == src2.depth(), dtype can
-be set to the default -1. In this case, the output array will have the same depth as the input
-array, be it src1, src2 or both.
-@note Saturation is not applied when the output array has the depth CV_32S. You may even get
-result of an incorrect sign in the case of overflow.
-@param src1 first input array or a scalar.
-@param src2 second input array or a scalar.
-@param dst output array that has the same size and number of channels as the input array(s); the
-depth is defined by dtype or src1/src2.
-@param mask optional operation mask - 8-bit single channel array, that specifies elements of the
-output array to be changed.
-@param dtype optional depth of the output array (see the discussion below).
-@sa subtract, addWeighted, scaleAdd, Mat::convertTo
-*/
-CV_EXPORTS_W void add(InputArray src1, InputArray src2, OutputArray dst,
-                      InputArray mask = noArray(), int dtype = -1);
-
-/** @brief Calculates the per-element difference between two arrays or array and a scalar.
-
-The function subtract calculates:
-- Difference between two arrays, when both input arrays have the same size and the same number of
-channels:
-    \f[\texttt{dst}(I) =  \texttt{saturate} ( \texttt{src1}(I) -  \texttt{src2}(I)) \quad \texttt{if mask}(I) \ne0\f]
-- Difference between an array and a scalar, when src2 is constructed from Scalar or has the same
-number of elements as `src1.channels()`:
-    \f[\texttt{dst}(I) =  \texttt{saturate} ( \texttt{src1}(I) -  \texttt{src2} ) \quad \texttt{if mask}(I) \ne0\f]
-- Difference between a scalar and an array, when src1 is constructed from Scalar or has the same
-number of elements as `src2.channels()`:
-    \f[\texttt{dst}(I) =  \texttt{saturate} ( \texttt{src1} -  \texttt{src2}(I) ) \quad \texttt{if mask}(I) \ne0\f]
-- The reverse difference between a scalar and an array in the case of `SubRS`:
-    \f[\texttt{dst}(I) =  \texttt{saturate} ( \texttt{src2} -  \texttt{src1}(I) ) \quad \texttt{if mask}(I) \ne0\f]
-where I is a multi-dimensional index of array elements. In case of multi-channel arrays, each
-channel is processed independently.
-
-The first function in the list above can be replaced with matrix expressions:
-@code{.cpp}
-    dst = src1 - src2;
-    dst -= src1; // equivalent to subtract(dst, src1, dst);
-@endcode
-The input arrays and the output array can all have the same or different depths. For example, you
-can subtract to 8-bit unsigned arrays and store the difference in a 16-bit signed array. Depth of
-the output array is determined by dtype parameter. In the second and third cases above, as well as
-in the first case, when src1.depth() == src2.depth(), dtype can be set to the default -1. In this
-case the output array will have the same depth as the input array, be it src1, src2 or both.
-@note Saturation is not applied when the output array has the depth CV_32S. You may even get
-result of an incorrect sign in the case of overflow.
-@param src1 first input array or a scalar.
-@param src2 second input array or a scalar.
-@param dst output array of the same size and the same number of channels as the input array.
-@param mask optional operation mask; this is an 8-bit single channel array that specifies elements
-of the output array to be changed.
-@param dtype optional depth of the output array
-@sa  add, addWeighted, scaleAdd, Mat::convertTo
-  */
-CV_EXPORTS_W void subtract(InputArray src1, InputArray src2, OutputArray dst,
-                           InputArray mask = noArray(), int dtype = -1);
-
-
-/** @brief Calculates the per-element scaled product of two arrays.
-
-The function multiply calculates the per-element product of two arrays:
-
-\f[\texttt{dst} (I)= \texttt{saturate} ( \texttt{scale} \cdot \texttt{src1} (I)  \cdot \texttt{src2} (I))\f]
-
-There is also a @ref MatrixExpressions -friendly variant of the first function. See Mat::mul .
-
-For a not-per-element matrix product, see gemm .
-
-@note Saturation is not applied when the output array has the depth
-CV_32S. You may even get result of an incorrect sign in the case of
-overflow.
-@param src1 first input array.
-@param src2 second input array of the same size and the same type as src1.
-@param dst output array of the same size and type as src1.
-@param scale optional scale factor.
-@param dtype optional depth of the output array
-@sa add, subtract, divide, scaleAdd, addWeighted, accumulate, accumulateProduct, accumulateSquare,
-Mat::convertTo
-*/
-CV_EXPORTS_W void multiply(InputArray src1, InputArray src2,
-                           OutputArray dst, double scale = 1, int dtype = -1);
-
-/** @brief Performs per-element division of two arrays or a scalar by an array.
-
-The function cv::divide divides one array by another:
-\f[\texttt{dst(I) = saturate(src1(I)*scale/src2(I))}\f]
-or a scalar by an array when there is no src1 :
-\f[\texttt{dst(I) = saturate(scale/src2(I))}\f]
-
-When src2(I) is zero, dst(I) will also be zero. Different channels of
-multi-channel arrays are processed independently.
-
-@note Saturation is not applied when the output array has the depth CV_32S. You may even get
-result of an incorrect sign in the case of overflow.
-@param src1 first input array.
-@param src2 second input array of the same size and type as src1.
-@param scale scalar factor.
-@param dst output array of the same size and type as src2.
-@param dtype optional depth of the output array; if -1, dst will have depth src2.depth(), but in
-case of an array-by-array division, you can only pass -1 when src1.depth()==src2.depth().
-@sa  multiply, add, subtract
-*/
-CV_EXPORTS_W void divide(InputArray src1, InputArray src2, OutputArray dst,
-                         double scale = 1, int dtype = -1);
-
-/** @overload */
-CV_EXPORTS_W void divide(double scale, InputArray src2,
-                         OutputArray dst, int dtype = -1);
-
-/** @brief Calculates the sum of a scaled array and another array.
-
-The function scaleAdd is one of the classical primitive linear algebra operations, known as DAXPY
-or SAXPY in [BLAS](http://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms). It calculates
-the sum of a scaled array and another array:
-\f[\texttt{dst} (I)= \texttt{scale} \cdot \texttt{src1} (I) +  \texttt{src2} (I)\f]
-The function can also be emulated with a matrix expression, for example:
-@code{.cpp}
-    Mat A(3, 3, CV_64F);
-    ...
-    A.row(0) = A.row(1)*2 + A.row(2);
-@endcode
-@param src1 first input array.
-@param alpha scale factor for the first array.
-@param src2 second input array of the same size and type as src1.
-@param dst output array of the same size and type as src1.
-@sa add, addWeighted, subtract, Mat::dot, Mat::convertTo
-*/
-CV_EXPORTS_W void scaleAdd(InputArray src1, double alpha, InputArray src2, OutputArray dst);
-
-/** @example AddingImagesTrackbar.cpp
-
- */
-/** @brief Calculates the weighted sum of two arrays.
-
-The function addWeighted calculates the weighted sum of two arrays as follows:
-\f[\texttt{dst} (I)= \texttt{saturate} ( \texttt{src1} (I)* \texttt{alpha} +  \texttt{src2} (I)* \texttt{beta} +  \texttt{gamma} )\f]
-where I is a multi-dimensional index of array elements. In case of multi-channel arrays, each
-channel is processed independently.
-The function can be replaced with a matrix expression:
-@code{.cpp}
-    dst = src1*alpha + src2*beta + gamma;
-@endcode
-@note Saturation is not applied when the output array has the depth CV_32S. You may even get
-result of an incorrect sign in the case of overflow.
-@param src1 first input array.
-@param alpha weight of the first array elements.
-@param src2 second input array of the same size and channel number as src1.
-@param beta weight of the second array elements.
-@param gamma scalar added to each sum.
-@param dst output array that has the same size and number of channels as the input arrays.
-@param dtype optional depth of the output array; when both input arrays have the same depth, dtype
-can be set to -1, which will be equivalent to src1.depth().
-@sa  add, subtract, scaleAdd, Mat::convertTo
-*/
-CV_EXPORTS_W void addWeighted(InputArray src1, double alpha, InputArray src2,
-                              double beta, double gamma, OutputArray dst, int dtype = -1);
-
-/** @brief Scales, calculates absolute values, and converts the result to 8-bit.
-
-On each element of the input array, the function convertScaleAbs
-performs three operations sequentially: scaling, taking an absolute
-value, conversion to an unsigned 8-bit type:
-\f[\texttt{dst} (I)= \texttt{saturate\_cast<uchar>} (| \texttt{src} (I)* \texttt{alpha} +  \texttt{beta} |)\f]
-In case of multi-channel arrays, the function processes each channel
-independently. When the output is not 8-bit, the operation can be
-emulated by calling the Mat::convertTo method (or by using matrix
-expressions) and then by calculating an absolute value of the result.
-For example:
-@code{.cpp}
-    Mat_<float> A(30,30);
-    randu(A, Scalar(-100), Scalar(100));
-    Mat_<float> B = A*5 + 3;
-    B = abs(B);
-    // Mat_<float> B = abs(A*5+3) will also do the job,
-    // but it will allocate a temporary matrix
-@endcode
-@param src input array.
-@param dst output array.
-@param alpha optional scale factor.
-@param beta optional delta added to the scaled values.
-@sa  Mat::convertTo, cv::abs(const Mat&)
-*/
-CV_EXPORTS_W void convertScaleAbs(InputArray src, OutputArray dst,
-                                  double alpha = 1, double beta = 0);
-
-/** @brief Converts an array to half precision floating number.
-
-This function converts FP32 (single precision floating point) from/to FP16 (half precision floating point). CV_16S format is used to represent FP16 data.
-There are two use modes (src -> dst): CV_32F -> CV_16S and CV_16S -> CV_32F. The input array has to have type of CV_32F or
-CV_16S to represent the bit depth. If the input array is neither of them, the function will raise an error.
-The format of half precision floating point is defined in IEEE 754-2008.
-
-@param src input array.
-@param dst output array.
-*/
-CV_EXPORTS_W void convertFp16(InputArray src, OutputArray dst);
-
-/** @brief Performs a look-up table transform of an array.
-
-The function LUT fills the output array with values from the look-up table. Indices of the entries
-are taken from the input array. That is, the function processes each element of src as follows:
-\f[\texttt{dst} (I)  \leftarrow \texttt{lut(src(I) + d)}\f]
-where
-\f[d =  \fork{0}{if \(\texttt{src}\) has depth \(\texttt{CV_8U}\)}{128}{if \(\texttt{src}\) has depth \(\texttt{CV_8S}\)}\f]
-@param src input array of 8-bit elements.
-@param lut look-up table of 256 elements; in case of multi-channel input array, the table should
-either have a single channel (in this case the same table is used for all channels) or the same
-number of channels as in the input array.
-@param dst output array of the same size and number of channels as src, and the same depth as lut.
-@sa  convertScaleAbs, Mat::convertTo
-*/
-CV_EXPORTS_W void LUT(InputArray src, InputArray lut, OutputArray dst);
-
-/** @brief Calculates the sum of array elements.
-
-The function cv::sum calculates and returns the sum of array elements,
-independently for each channel.
-@param src input array that must have from 1 to 4 channels.
-@sa  countNonZero, mean, meanStdDev, norm, minMaxLoc, reduce
-*/
-CV_EXPORTS_AS(sumElems) Scalar sum(InputArray src);
-
-/** @brief Counts non-zero array elements.
-
-The function returns the number of non-zero elements in src :
-\f[\sum _{I: \; \texttt{src} (I) \ne0 } 1\f]
-@param src single-channel array.
-@sa  mean, meanStdDev, norm, minMaxLoc, calcCovarMatrix
-*/
-CV_EXPORTS_W int countNonZero( InputArray src );
-
-/** @brief Returns the list of locations of non-zero pixels
-
-Given a binary matrix (likely returned from an operation such
-as threshold(), compare(), >, ==, etc, return all of
-the non-zero indices as a cv::Mat or std::vector<cv::Point> (x,y)
-For example:
-@code{.cpp}
-    cv::Mat binaryImage; // input, binary image
-    cv::Mat locations;   // output, locations of non-zero pixels
-    cv::findNonZero(binaryImage, locations);
-
-    // access pixel coordinates
-    Point pnt = locations.at<Point>(i);
-@endcode
-or
-@code{.cpp}
-    cv::Mat binaryImage; // input, binary image
-    vector<Point> locations;   // output, locations of non-zero pixels
-    cv::findNonZero(binaryImage, locations);
-
-    // access pixel coordinates
-    Point pnt = locations[i];
-@endcode
-@param src single-channel array (type CV_8UC1)
-@param idx the output array, type of cv::Mat or std::vector<Point>, corresponding to non-zero indices in the input
-*/
-CV_EXPORTS_W void findNonZero( InputArray src, OutputArray idx );
-
-/** @brief Calculates an average (mean) of array elements.
-
-The function cv::mean calculates the mean value M of array elements,
-independently for each channel, and return it:
-\f[\begin{array}{l} N =  \sum _{I: \; \texttt{mask} (I) \ne 0} 1 \\ M_c =  \left ( \sum _{I: \; \texttt{mask} (I) \ne 0}{ \texttt{mtx} (I)_c} \right )/N \end{array}\f]
-When all the mask elements are 0's, the function returns Scalar::all(0)
-@param src input array that should have from 1 to 4 channels so that the result can be stored in
-Scalar_ .
-@param mask optional operation mask.
-@sa  countNonZero, meanStdDev, norm, minMaxLoc
-*/
-CV_EXPORTS_W Scalar mean(InputArray src, InputArray mask = noArray());
-
-/** Calculates a mean and standard deviation of array elements.
-
-The function cv::meanStdDev calculates the mean and the standard deviation M
-of array elements independently for each channel and returns it via the
-output parameters:
-\f[\begin{array}{l} N =  \sum _{I, \texttt{mask} (I)  \ne 0} 1 \\ \texttt{mean} _c =  \frac{\sum_{ I: \; \texttt{mask}(I) \ne 0} \texttt{src} (I)_c}{N} \\ \texttt{stddev} _c =  \sqrt{\frac{\sum_{ I: \; \texttt{mask}(I) \ne 0} \left ( \texttt{src} (I)_c -  \texttt{mean} _c \right )^2}{N}} \end{array}\f]
-When all the mask elements are 0's, the function returns
-mean=stddev=Scalar::all(0).
-@note The calculated standard deviation is only the diagonal of the
-complete normalized covariance matrix. If the full matrix is needed, you
-can reshape the multi-channel array M x N to the single-channel array
-M\*N x mtx.channels() (only possible when the matrix is continuous) and
-then pass the matrix to calcCovarMatrix .
-@param src input array that should have from 1 to 4 channels so that the results can be stored in
-Scalar_ 's.
-@param mean output parameter: calculated mean value.
-@param stddev output parameter: calculated standard deviation.
-@param mask optional operation mask.
-@sa  countNonZero, mean, norm, minMaxLoc, calcCovarMatrix
-*/
-CV_EXPORTS_W void meanStdDev(InputArray src, OutputArray mean, OutputArray stddev,
-                             InputArray mask=noArray());
-
-/** @brief Calculates the  absolute norm of an array.
-
-This version of #norm calculates the absolute norm of src1. The type of norm to calculate is specified using #NormTypes.
-
-As example for one array consider the function \f$r(x)= \begin{pmatrix} x \\ 1-x \end{pmatrix}, x \in [-1;1]\f$.
-The \f$ L_{1}, L_{2} \f$ and \f$ L_{\infty} \f$ norm for the sample value \f$r(-1) = \begin{pmatrix} -1 \\ 2 \end{pmatrix}\f$
-is calculated as follows
-\f{align*}
-    \| r(-1) \|_{L_1} &= |-1| + |2| = 3 \\
-    \| r(-1) \|_{L_2} &= \sqrt{(-1)^{2} + (2)^{2}} = \sqrt{5} \\
-    \| r(-1) \|_{L_\infty} &= \max(|-1|,|2|) = 2
-\f}
-and for \f$r(0.5) = \begin{pmatrix} 0.5 \\ 0.5 \end{pmatrix}\f$ the calculation is
-\f{align*}
-    \| r(0.5) \|_{L_1} &= |0.5| + |0.5| = 1 \\
-    \| r(0.5) \|_{L_2} &= \sqrt{(0.5)^{2} + (0.5)^{2}} = \sqrt{0.5} \\
-    \| r(0.5) \|_{L_\infty} &= \max(|0.5|,|0.5|) = 0.5.
-\f}
-The following graphic shows all values for the three norm functions \f$\| r(x) \|_{L_1}, \| r(x) \|_{L_2}\f$ and \f$\| r(x) \|_{L_\infty}\f$.
-It is notable that the \f$ L_{1} \f$ norm forms the upper and the \f$ L_{\infty} \f$ norm forms the lower border for the example function \f$ r(x) \f$.
-![Graphs for the different norm functions from the above example](pics/NormTypes_OneArray_1-2-INF.png)
-
-When the mask parameter is specified and it is not empty, the norm is
-
-If normType is not specified, #NORM_L2 is used.
-calculated only over the region specified by the mask.
-
-Multi-channel input arrays are treated as single-channel arrays, that is,
-the results for all channels are combined.
-
-Hamming norms can only be calculated with CV_8U depth arrays.
-
-@param src1 first input array.
-@param normType type of the norm (see #NormTypes).
-@param mask optional operation mask; it must have the same size as src1 and CV_8UC1 type.
-*/
-CV_EXPORTS_W double norm(InputArray src1, int normType = NORM_L2, InputArray mask = noArray());
-
-/** @brief Calculates an absolute difference norm or a relative difference norm.
-
-This version of cv::norm calculates the absolute difference norm
-or the relative difference norm of arrays src1 and src2.
-The type of norm to calculate is specified using #NormTypes.
-
-@param src1 first input array.
-@param src2 second input array of the same size and the same type as src1.
-@param normType type of the norm (see #NormTypes).
-@param mask optional operation mask; it must have the same size as src1 and CV_8UC1 type.
-*/
-CV_EXPORTS_W double norm(InputArray src1, InputArray src2,
-                         int normType = NORM_L2, InputArray mask = noArray());
-/** @overload
-@param src first input array.
-@param normType type of the norm (see #NormTypes).
-*/
-CV_EXPORTS double norm( const SparseMat& src, int normType );
-
-/** @brief Computes the Peak Signal-to-Noise Ratio (PSNR) image quality metric.
-
-This function calculates the Peak Signal-to-Noise Ratio (PSNR) image quality metric in decibels (dB), between two input arrays src1 and src2. Arrays must have depth CV_8U.
-
-The PSNR is calculated as follows:
-
-\f[
-\texttt{PSNR} = 10 \cdot \log_{10}{\left( \frac{R^2}{MSE} \right) }
-\f]
-
-where R is the maximum integer value of depth CV_8U (255) and MSE is the mean squared error between the two arrays.
-
-@param src1 first input array.
-@param src2 second input array of the same size as src1.
-
-  */
-CV_EXPORTS_W double PSNR(InputArray src1, InputArray src2);
-
-/** @brief naive nearest neighbor finder
-
-see http://en.wikipedia.org/wiki/Nearest_neighbor_search
-@todo document
-  */
-CV_EXPORTS_W void batchDistance(InputArray src1, InputArray src2,
-                                OutputArray dist, int dtype, OutputArray nidx,
-                                int normType = NORM_L2, int K = 0,
-                                InputArray mask = noArray(), int update = 0,
-                                bool crosscheck = false);
-
-/** @brief Normalizes the norm or value range of an array.
-
-The function cv::normalize normalizes scale and shift the input array elements so that
-\f[\| \texttt{dst} \| _{L_p}= \texttt{alpha}\f]
-(where p=Inf, 1 or 2) when normType=NORM_INF, NORM_L1, or NORM_L2, respectively; or so that
-\f[\min _I  \texttt{dst} (I)= \texttt{alpha} , \, \, \max _I  \texttt{dst} (I)= \texttt{beta}\f]
-
-when normType=NORM_MINMAX (for dense arrays only). The optional mask specifies a sub-array to be
-normalized. This means that the norm or min-n-max are calculated over the sub-array, and then this
-sub-array is modified to be normalized. If you want to only use the mask to calculate the norm or
-min-max but modify the whole array, you can use norm and Mat::convertTo.
-
-In case of sparse matrices, only the non-zero values are analyzed and transformed. Because of this,
-the range transformation for sparse matrices is not allowed since it can shift the zero level.
-
-Possible usage with some positive example data:
-@code{.cpp}
-    vector<double> positiveData = { 2.0, 8.0, 10.0 };
-    vector<double> normalizedData_l1, normalizedData_l2, normalizedData_inf, normalizedData_minmax;
-
-    // Norm to probability (total count)
-    // sum(numbers) = 20.0
-    // 2.0      0.1     (2.0/20.0)
-    // 8.0      0.4     (8.0/20.0)
-    // 10.0     0.5     (10.0/20.0)
-    normalize(positiveData, normalizedData_l1, 1.0, 0.0, NORM_L1);
-
-    // Norm to unit vector: ||positiveData|| = 1.0
-    // 2.0      0.15
-    // 8.0      0.62
-    // 10.0     0.77
-    normalize(positiveData, normalizedData_l2, 1.0, 0.0, NORM_L2);
-
-    // Norm to max element
-    // 2.0      0.2     (2.0/10.0)
-    // 8.0      0.8     (8.0/10.0)
-    // 10.0     1.0     (10.0/10.0)
-    normalize(positiveData, normalizedData_inf, 1.0, 0.0, NORM_INF);
-
-    // Norm to range [0.0;1.0]
-    // 2.0      0.0     (shift to left border)
-    // 8.0      0.75    (6.0/8.0)
-    // 10.0     1.0     (shift to right border)
-    normalize(positiveData, normalizedData_minmax, 1.0, 0.0, NORM_MINMAX);
-@endcode
-
-@param src input array.
-@param dst output array of the same size as src .
-@param alpha norm value to normalize to or the lower range boundary in case of the range
-normalization.
-@param beta upper range boundary in case of the range normalization; it is not used for the norm
-normalization.
-@param norm_type normalization type (see cv::NormTypes).
-@param dtype when negative, the output array has the same type as src; otherwise, it has the same
-number of channels as src and the depth =CV_MAT_DEPTH(dtype).
-@param mask optional operation mask.
-@sa norm, Mat::convertTo, SparseMat::convertTo
-*/
-CV_EXPORTS_W void normalize( InputArray src, InputOutputArray dst, double alpha = 1, double beta = 0,
-                             int norm_type = NORM_L2, int dtype = -1, InputArray mask = noArray());
-
-/** @overload
-@param src input array.
-@param dst output array of the same size as src .
-@param alpha norm value to normalize to or the lower range boundary in case of the range
-normalization.
-@param normType normalization type (see cv::NormTypes).
-*/
-CV_EXPORTS void normalize( const SparseMat& src, SparseMat& dst, double alpha, int normType );
-
-/** @brief Finds the global minimum and maximum in an array.
-
-The function cv::minMaxLoc finds the minimum and maximum element values and their positions. The
-extremums are searched across the whole array or, if mask is not an empty array, in the specified
-array region.
-
-The function do not work with multi-channel arrays. If you need to find minimum or maximum
-elements across all the channels, use Mat::reshape first to reinterpret the array as
-single-channel. Or you may extract the particular channel using either extractImageCOI , or
-mixChannels , or split .
-@param src input single-channel array.
-@param minVal pointer to the returned minimum value; NULL is used if not required.
-@param maxVal pointer to the returned maximum value; NULL is used if not required.
-@param minLoc pointer to the returned minimum location (in 2D case); NULL is used if not required.
-@param maxLoc pointer to the returned maximum location (in 2D case); NULL is used if not required.
-@param mask optional mask used to select a sub-array.
-@sa max, min, compare, inRange, extractImageCOI, mixChannels, split, Mat::reshape
-*/
-CV_EXPORTS_W void minMaxLoc(InputArray src, CV_OUT double* minVal,
-                            CV_OUT double* maxVal = 0, CV_OUT Point* minLoc = 0,
-                            CV_OUT Point* maxLoc = 0, InputArray mask = noArray());
-
-
-/** @brief Finds the global minimum and maximum in an array
-
-The function cv::minMaxIdx finds the minimum and maximum element values and their positions. The
-extremums are searched across the whole array or, if mask is not an empty array, in the specified
-array region. The function does not work with multi-channel arrays. If you need to find minimum or
-maximum elements across all the channels, use Mat::reshape first to reinterpret the array as
-single-channel. Or you may extract the particular channel using either extractImageCOI , or
-mixChannels , or split . In case of a sparse matrix, the minimum is found among non-zero elements
-only.
-@note When minIdx is not NULL, it must have at least 2 elements (as well as maxIdx), even if src is
-a single-row or single-column matrix. In OpenCV (following MATLAB) each array has at least 2
-dimensions, i.e. single-column matrix is Mx1 matrix (and therefore minIdx/maxIdx will be
-(i1,0)/(i2,0)) and single-row matrix is 1xN matrix (and therefore minIdx/maxIdx will be
-(0,j1)/(0,j2)).
-@param src input single-channel array.
-@param minVal pointer to the returned minimum value; NULL is used if not required.
-@param maxVal pointer to the returned maximum value; NULL is used if not required.
-@param minIdx pointer to the returned minimum location (in nD case); NULL is used if not required;
-Otherwise, it must point to an array of src.dims elements, the coordinates of the minimum element
-in each dimension are stored there sequentially.
-@param maxIdx pointer to the returned maximum location (in nD case). NULL is used if not required.
-@param mask specified array region
-*/
-CV_EXPORTS void minMaxIdx(InputArray src, double* minVal, double* maxVal = 0,
-                          int* minIdx = 0, int* maxIdx = 0, InputArray mask = noArray());
-
-/** @overload
-@param a input single-channel array.
-@param minVal pointer to the returned minimum value; NULL is used if not required.
-@param maxVal pointer to the returned maximum value; NULL is used if not required.
-@param minIdx pointer to the returned minimum location (in nD case); NULL is used if not required;
-Otherwise, it must point to an array of src.dims elements, the coordinates of the minimum element
-in each dimension are stored there sequentially.
-@param maxIdx pointer to the returned maximum location (in nD case). NULL is used if not required.
-*/
-CV_EXPORTS void minMaxLoc(const SparseMat& a, double* minVal,
-                          double* maxVal, int* minIdx = 0, int* maxIdx = 0);
-
-/** @brief Reduces a matrix to a vector.
-
-The function #reduce reduces the matrix to a vector by treating the matrix rows/columns as a set of
-1D vectors and performing the specified operation on the vectors until a single row/column is
-obtained. For example, the function can be used to compute horizontal and vertical projections of a
-raster image. In case of #REDUCE_MAX and #REDUCE_MIN , the output image should have the same type as the source one.
-In case of #REDUCE_SUM and #REDUCE_AVG , the output may have a larger element bit-depth to preserve accuracy.
-And multi-channel arrays are also supported in these two reduction modes.
-
-The following code demonstrates its usage for a single channel matrix.
-@snippet snippets/core_reduce.cpp example
-
-And the following code demonstrates its usage for a two-channel matrix.
-@snippet snippets/core_reduce.cpp example2
-
-@param src input 2D matrix.
-@param dst output vector. Its size and type is defined by dim and dtype parameters.
-@param dim dimension index along which the matrix is reduced. 0 means that the matrix is reduced to
-a single row. 1 means that the matrix is reduced to a single column.
-@param rtype reduction operation that could be one of #ReduceTypes
-@param dtype when negative, the output vector will have the same type as the input matrix,
-otherwise, its type will be CV_MAKE_TYPE(CV_MAT_DEPTH(dtype), src.channels()).
-@sa repeat
-*/
-CV_EXPORTS_W void reduce(InputArray src, OutputArray dst, int dim, int rtype, int dtype = -1);
-
-/** @brief Creates one multi-channel array out of several single-channel ones.
-
-The function cv::merge merges several arrays to make a single multi-channel array. That is, each
-element of the output array will be a concatenation of the elements of the input arrays, where
-elements of i-th input array are treated as mv[i].channels()-element vectors.
-
-The function cv::split does the reverse operation. If you need to shuffle channels in some other
-advanced way, use cv::mixChannels.
-
-The following example shows how to merge 3 single channel matrices into a single 3-channel matrix.
-@snippet snippets/core_merge.cpp example
-
-@param mv input array of matrices to be merged; all the matrices in mv must have the same
-size and the same depth.
-@param count number of input matrices when mv is a plain C array; it must be greater than zero.
-@param dst output array of the same size and the same depth as mv[0]; The number of channels will
-be equal to the parameter count.
-@sa  mixChannels, split, Mat::reshape
-*/
-CV_EXPORTS void merge(const Mat* mv, size_t count, OutputArray dst);
-
-/** @overload
-@param mv input vector of matrices to be merged; all the matrices in mv must have the same
-size and the same depth.
-@param dst output array of the same size and the same depth as mv[0]; The number of channels will
-be the total number of channels in the matrix array.
-  */
-CV_EXPORTS_W void merge(InputArrayOfArrays mv, OutputArray dst);
-
-/** @brief Divides a multi-channel array into several single-channel arrays.
-
-The function cv::split splits a multi-channel array into separate single-channel arrays:
-\f[\texttt{mv} [c](I) =  \texttt{src} (I)_c\f]
-If you need to extract a single channel or do some other sophisticated channel permutation, use
-mixChannels .
-
-The following example demonstrates how to split a 3-channel matrix into 3 single channel matrices.
-@snippet snippets/core_split.cpp example
-
-@param src input multi-channel array.
-@param mvbegin output array; the number of arrays must match src.channels(); the arrays themselves are
-reallocated, if needed.
-@sa merge, mixChannels, cvtColor
-*/
-CV_EXPORTS void split(const Mat& src, Mat* mvbegin);
-
-/** @overload
-@param m input multi-channel array.
-@param mv output vector of arrays; the arrays themselves are reallocated, if needed.
-*/
-CV_EXPORTS_W void split(InputArray m, OutputArrayOfArrays mv);
-
-/** @brief Copies specified channels from input arrays to the specified channels of
-output arrays.
-
-The function cv::mixChannels provides an advanced mechanism for shuffling image channels.
-
-cv::split,cv::merge,cv::extractChannel,cv::insertChannel and some forms of cv::cvtColor are partial cases of cv::mixChannels.
-
-In the example below, the code splits a 4-channel BGRA image into a 3-channel BGR (with B and R
-channels swapped) and a separate alpha-channel image:
-@code{.cpp}
-    Mat bgra( 100, 100, CV_8UC4, Scalar(255,0,0,255) );
-    Mat bgr( bgra.rows, bgra.cols, CV_8UC3 );
-    Mat alpha( bgra.rows, bgra.cols, CV_8UC1 );
-
-    // forming an array of matrices is a quite efficient operation,
-    // because the matrix data is not copied, only the headers
-    Mat out[] = { bgr, alpha };
-    // bgra[0] -> bgr[2], bgra[1] -> bgr[1],
-    // bgra[2] -> bgr[0], bgra[3] -> alpha[0]
-    int from_to[] = { 0,2, 1,1, 2,0, 3,3 };
-    mixChannels( &bgra, 1, out, 2, from_to, 4 );
-@endcode
-@note Unlike many other new-style C++ functions in OpenCV (see the introduction section and
-Mat::create ), cv::mixChannels requires the output arrays to be pre-allocated before calling the
-function.
-@param src input array or vector of matrices; all of the matrices must have the same size and the
-same depth.
-@param nsrcs number of matrices in `src`.
-@param dst output array or vector of matrices; all the matrices **must be allocated**; their size and
-depth must be the same as in `src[0]`.
-@param ndsts number of matrices in `dst`.
-@param fromTo array of index pairs specifying which channels are copied and where; fromTo[k\*2] is
-a 0-based index of the input channel in src, fromTo[k\*2+1] is an index of the output channel in
-dst; the continuous channel numbering is used: the first input image channels are indexed from 0 to
-src[0].channels()-1, the second input image channels are indexed from src[0].channels() to
-src[0].channels() + src[1].channels()-1, and so on, the same scheme is used for the output image
-channels; as a special case, when fromTo[k\*2] is negative, the corresponding output channel is
-filled with zero .
-@param npairs number of index pairs in `fromTo`.
-@sa split, merge, extractChannel, insertChannel, cvtColor
-*/
-CV_EXPORTS void mixChannels(const Mat* src, size_t nsrcs, Mat* dst, size_t ndsts,
-                            const int* fromTo, size_t npairs);
-
-/** @overload
-@param src input array or vector of matrices; all of the matrices must have the same size and the
-same depth.
-@param dst output array or vector of matrices; all the matrices **must be allocated**; their size and
-depth must be the same as in src[0].
-@param fromTo array of index pairs specifying which channels are copied and where; fromTo[k\*2] is
-a 0-based index of the input channel in src, fromTo[k\*2+1] is an index of the output channel in
-dst; the continuous channel numbering is used: the first input image channels are indexed from 0 to
-src[0].channels()-1, the second input image channels are indexed from src[0].channels() to
-src[0].channels() + src[1].channels()-1, and so on, the same scheme is used for the output image
-channels; as a special case, when fromTo[k\*2] is negative, the corresponding output channel is
-filled with zero .
-@param npairs number of index pairs in fromTo.
-*/
-CV_EXPORTS void mixChannels(InputArrayOfArrays src, InputOutputArrayOfArrays dst,
-                            const int* fromTo, size_t npairs);
-
-/** @overload
-@param src input array or vector of matrices; all of the matrices must have the same size and the
-same depth.
-@param dst output array or vector of matrices; all the matrices **must be allocated**; their size and
-depth must be the same as in src[0].
-@param fromTo array of index pairs specifying which channels are copied and where; fromTo[k\*2] is
-a 0-based index of the input channel in src, fromTo[k\*2+1] is an index of the output channel in
-dst; the continuous channel numbering is used: the first input image channels are indexed from 0 to
-src[0].channels()-1, the second input image channels are indexed from src[0].channels() to
-src[0].channels() + src[1].channels()-1, and so on, the same scheme is used for the output image
-channels; as a special case, when fromTo[k\*2] is negative, the corresponding output channel is
-filled with zero .
-*/
-CV_EXPORTS_W void mixChannels(InputArrayOfArrays src, InputOutputArrayOfArrays dst,
-                              const std::vector<int>& fromTo);
-
-/** @brief Extracts a single channel from src (coi is 0-based index)
-@param src input array
-@param dst output array
-@param coi index of channel to extract
-@sa mixChannels, split
-*/
-CV_EXPORTS_W void extractChannel(InputArray src, OutputArray dst, int coi);
-
-/** @brief Inserts a single channel to dst (coi is 0-based index)
-@param src input array
-@param dst output array
-@param coi index of channel for insertion
-@sa mixChannels, merge
-*/
-CV_EXPORTS_W void insertChannel(InputArray src, InputOutputArray dst, int coi);
-
-/** @brief Flips a 2D array around vertical, horizontal, or both axes.
-
-The function cv::flip flips the array in one of three different ways (row
-and column indices are 0-based):
-\f[\texttt{dst} _{ij} =
-\left\{
-\begin{array}{l l}
-\texttt{src} _{\texttt{src.rows}-i-1,j} & if\;  \texttt{flipCode} = 0 \\
-\texttt{src} _{i, \texttt{src.cols} -j-1} & if\;  \texttt{flipCode} > 0 \\
-\texttt{src} _{ \texttt{src.rows} -i-1, \texttt{src.cols} -j-1} & if\; \texttt{flipCode} < 0 \\
-\end{array}
-\right.\f]
-The example scenarios of using the function are the following:
-*   Vertical flipping of the image (flipCode == 0) to switch between
-    top-left and bottom-left image origin. This is a typical operation
-    in video processing on Microsoft Windows\* OS.
-*   Horizontal flipping of the image with the subsequent horizontal
-    shift and absolute difference calculation to check for a
-    vertical-axis symmetry (flipCode \> 0).
-*   Simultaneous horizontal and vertical flipping of the image with
-    the subsequent shift and absolute difference calculation to check
-    for a central symmetry (flipCode \< 0).
-*   Reversing the order of point arrays (flipCode \> 0 or
-    flipCode == 0).
-@param src input array.
-@param dst output array of the same size and type as src.
-@param flipCode a flag to specify how to flip the array; 0 means
-flipping around the x-axis and positive value (for example, 1) means
-flipping around y-axis. Negative value (for example, -1) means flipping
-around both axes.
-@sa transpose , repeat , completeSymm
-*/
-CV_EXPORTS_W void flip(InputArray src, OutputArray dst, int flipCode);
-
-enum RotateFlags {
-    ROTATE_90_CLOCKWISE = 0, //!<Rotate 90 degrees clockwise
-    ROTATE_180 = 1, //!<Rotate 180 degrees clockwise
-    ROTATE_90_COUNTERCLOCKWISE = 2, //!<Rotate 270 degrees clockwise
-};
-/** @brief Rotates a 2D array in multiples of 90 degrees.
-The function cv::rotate rotates the array in one of three different ways:
-*   Rotate by 90 degrees clockwise (rotateCode = ROTATE_90_CLOCKWISE).
-*   Rotate by 180 degrees clockwise (rotateCode = ROTATE_180).
-*   Rotate by 270 degrees clockwise (rotateCode = ROTATE_90_COUNTERCLOCKWISE).
-@param src input array.
-@param dst output array of the same type as src.  The size is the same with ROTATE_180,
-and the rows and cols are switched for ROTATE_90_CLOCKWISE and ROTATE_90_COUNTERCLOCKWISE.
-@param rotateCode an enum to specify how to rotate the array; see the enum #RotateFlags
-@sa transpose , repeat , completeSymm, flip, RotateFlags
-*/
-CV_EXPORTS_W void rotate(InputArray src, OutputArray dst, int rotateCode);
-
-/** @brief Fills the output array with repeated copies of the input array.
-
-The function cv::repeat duplicates the input array one or more times along each of the two axes:
-\f[\texttt{dst} _{ij}= \texttt{src} _{i\mod src.rows, \; j\mod src.cols }\f]
-The second variant of the function is more convenient to use with @ref MatrixExpressions.
-@param src input array to replicate.
-@param ny Flag to specify how many times the `src` is repeated along the
-vertical axis.
-@param nx Flag to specify how many times the `src` is repeated along the
-horizontal axis.
-@param dst output array of the same type as `src`.
-@sa cv::reduce
-*/
-CV_EXPORTS_W void repeat(InputArray src, int ny, int nx, OutputArray dst);
-
-/** @overload
-@param src input array to replicate.
-@param ny Flag to specify how many times the `src` is repeated along the
-vertical axis.
-@param nx Flag to specify how many times the `src` is repeated along the
-horizontal axis.
-  */
-CV_EXPORTS Mat repeat(const Mat& src, int ny, int nx);
-
-/** @brief Applies horizontal concatenation to given matrices.
-
-The function horizontally concatenates two or more cv::Mat matrices (with the same number of rows).
-@code{.cpp}
-    cv::Mat matArray[] = { cv::Mat(4, 1, CV_8UC1, cv::Scalar(1)),
-                           cv::Mat(4, 1, CV_8UC1, cv::Scalar(2)),
-                           cv::Mat(4, 1, CV_8UC1, cv::Scalar(3)),};
-
-    cv::Mat out;
-    cv::hconcat( matArray, 3, out );
-    //out:
-    //[1, 2, 3;
-    // 1, 2, 3;
-    // 1, 2, 3;
-    // 1, 2, 3]
-@endcode
-@param src input array or vector of matrices. all of the matrices must have the same number of rows and the same depth.
-@param nsrc number of matrices in src.
-@param dst output array. It has the same number of rows and depth as the src, and the sum of cols of the src.
-@sa cv::vconcat(const Mat*, size_t, OutputArray), @sa cv::vconcat(InputArrayOfArrays, OutputArray) and @sa cv::vconcat(InputArray, InputArray, OutputArray)
-*/
-CV_EXPORTS void hconcat(const Mat* src, size_t nsrc, OutputArray dst);
-/** @overload
- @code{.cpp}
-    cv::Mat_<float> A = (cv::Mat_<float>(3, 2) << 1, 4,
-                                                  2, 5,
-                                                  3, 6);
-    cv::Mat_<float> B = (cv::Mat_<float>(3, 2) << 7, 10,
-                                                  8, 11,
-                                                  9, 12);
-
-    cv::Mat C;
-    cv::hconcat(A, B, C);
-    //C:
-    //[1, 4, 7, 10;
-    // 2, 5, 8, 11;
-    // 3, 6, 9, 12]
- @endcode
- @param src1 first input array to be considered for horizontal concatenation.
- @param src2 second input array to be considered for horizontal concatenation.
- @param dst output array. It has the same number of rows and depth as the src1 and src2, and the sum of cols of the src1 and src2.
- */
-CV_EXPORTS void hconcat(InputArray src1, InputArray src2, OutputArray dst);
-/** @overload
- @code{.cpp}
-    std::vector<cv::Mat> matrices = { cv::Mat(4, 1, CV_8UC1, cv::Scalar(1)),
-                                      cv::Mat(4, 1, CV_8UC1, cv::Scalar(2)),
-                                      cv::Mat(4, 1, CV_8UC1, cv::Scalar(3)),};
-
-    cv::Mat out;
-    cv::hconcat( matrices, out );
-    //out:
-    //[1, 2, 3;
-    // 1, 2, 3;
-    // 1, 2, 3;
-    // 1, 2, 3]
- @endcode
- @param src input array or vector of matrices. all of the matrices must have the same number of rows and the same depth.
- @param dst output array. It has the same number of rows and depth as the src, and the sum of cols of the src.
-same depth.
- */
-CV_EXPORTS_W void hconcat(InputArrayOfArrays src, OutputArray dst);
-
-/** @brief Applies vertical concatenation to given matrices.
-
-The function vertically concatenates two or more cv::Mat matrices (with the same number of cols).
-@code{.cpp}
-    cv::Mat matArray[] = { cv::Mat(1, 4, CV_8UC1, cv::Scalar(1)),
-                           cv::Mat(1, 4, CV_8UC1, cv::Scalar(2)),
-                           cv::Mat(1, 4, CV_8UC1, cv::Scalar(3)),};
-
-    cv::Mat out;
-    cv::vconcat( matArray, 3, out );
-    //out:
-    //[1,   1,   1,   1;
-    // 2,   2,   2,   2;
-    // 3,   3,   3,   3]
-@endcode
-@param src input array or vector of matrices. all of the matrices must have the same number of cols and the same depth.
-@param nsrc number of matrices in src.
-@param dst output array. It has the same number of cols and depth as the src, and the sum of rows of the src.
-@sa cv::hconcat(const Mat*, size_t, OutputArray), @sa cv::hconcat(InputArrayOfArrays, OutputArray) and @sa cv::hconcat(InputArray, InputArray, OutputArray)
-*/
-CV_EXPORTS void vconcat(const Mat* src, size_t nsrc, OutputArray dst);
-/** @overload
- @code{.cpp}
-    cv::Mat_<float> A = (cv::Mat_<float>(3, 2) << 1, 7,
-                                                  2, 8,
-                                                  3, 9);
-    cv::Mat_<float> B = (cv::Mat_<float>(3, 2) << 4, 10,
-                                                  5, 11,
-                                                  6, 12);
-
-    cv::Mat C;
-    cv::vconcat(A, B, C);
-    //C:
-    //[1, 7;
-    // 2, 8;
-    // 3, 9;
-    // 4, 10;
-    // 5, 11;
-    // 6, 12]
- @endcode
- @param src1 first input array to be considered for vertical concatenation.
- @param src2 second input array to be considered for vertical concatenation.
- @param dst output array. It has the same number of cols and depth as the src1 and src2, and the sum of rows of the src1 and src2.
- */
-CV_EXPORTS void vconcat(InputArray src1, InputArray src2, OutputArray dst);
-/** @overload
- @code{.cpp}
-    std::vector<cv::Mat> matrices = { cv::Mat(1, 4, CV_8UC1, cv::Scalar(1)),
-                                      cv::Mat(1, 4, CV_8UC1, cv::Scalar(2)),
-                                      cv::Mat(1, 4, CV_8UC1, cv::Scalar(3)),};
-
-    cv::Mat out;
-    cv::vconcat( matrices, out );
-    //out:
-    //[1,   1,   1,   1;
-    // 2,   2,   2,   2;
-    // 3,   3,   3,   3]
- @endcode
- @param src input array or vector of matrices. all of the matrices must have the same number of cols and the same depth
- @param dst output array. It has the same number of cols and depth as the src, and the sum of rows of the src.
-same depth.
- */
-CV_EXPORTS_W void vconcat(InputArrayOfArrays src, OutputArray dst);
-
-/** @brief computes bitwise conjunction of the two arrays (dst = src1 & src2)
-Calculates the per-element bit-wise conjunction of two arrays or an
-array and a scalar.
-
-The function cv::bitwise_and calculates the per-element bit-wise logical conjunction for:
-*   Two arrays when src1 and src2 have the same size:
-    \f[\texttt{dst} (I) =  \texttt{src1} (I)  \wedge \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\f]
-*   An array and a scalar when src2 is constructed from Scalar or has
-    the same number of elements as `src1.channels()`:
-    \f[\texttt{dst} (I) =  \texttt{src1} (I)  \wedge \texttt{src2} \quad \texttt{if mask} (I) \ne0\f]
-*   A scalar and an array when src1 is constructed from Scalar or has
-    the same number of elements as `src2.channels()`:
-    \f[\texttt{dst} (I) =  \texttt{src1}  \wedge \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\f]
-In case of floating-point arrays, their machine-specific bit
-representations (usually IEEE754-compliant) are used for the operation.
-In case of multi-channel arrays, each channel is processed
-independently. In the second and third cases above, the scalar is first
-converted to the array type.
-@param src1 first input array or a scalar.
-@param src2 second input array or a scalar.
-@param dst output array that has the same size and type as the input
-arrays.
-@param mask optional operation mask, 8-bit single channel array, that
-specifies elements of the output array to be changed.
-*/
-CV_EXPORTS_W void bitwise_and(InputArray src1, InputArray src2,
-                              OutputArray dst, InputArray mask = noArray());
-
-/** @brief Calculates the per-element bit-wise disjunction of two arrays or an
-array and a scalar.
-
-The function cv::bitwise_or calculates the per-element bit-wise logical disjunction for:
-*   Two arrays when src1 and src2 have the same size:
-    \f[\texttt{dst} (I) =  \texttt{src1} (I)  \vee \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\f]
-*   An array and a scalar when src2 is constructed from Scalar or has
-    the same number of elements as `src1.channels()`:
-    \f[\texttt{dst} (I) =  \texttt{src1} (I)  \vee \texttt{src2} \quad \texttt{if mask} (I) \ne0\f]
-*   A scalar and an array when src1 is constructed from Scalar or has
-    the same number of elements as `src2.channels()`:
-    \f[\texttt{dst} (I) =  \texttt{src1}  \vee \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\f]
-In case of floating-point arrays, their machine-specific bit
-representations (usually IEEE754-compliant) are used for the operation.
-In case of multi-channel arrays, each channel is processed
-independently. In the second and third cases above, the scalar is first
-converted to the array type.
-@param src1 first input array or a scalar.
-@param src2 second input array or a scalar.
-@param dst output array that has the same size and type as the input
-arrays.
-@param mask optional operation mask, 8-bit single channel array, that
-specifies elements of the output array to be changed.
-*/
-CV_EXPORTS_W void bitwise_or(InputArray src1, InputArray src2,
-                             OutputArray dst, InputArray mask = noArray());
-
-/** @brief Calculates the per-element bit-wise "exclusive or" operation on two
-arrays or an array and a scalar.
-
-The function cv::bitwise_xor calculates the per-element bit-wise logical "exclusive-or"
-operation for:
-*   Two arrays when src1 and src2 have the same size:
-    \f[\texttt{dst} (I) =  \texttt{src1} (I)  \oplus \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\f]
-*   An array and a scalar when src2 is constructed from Scalar or has
-    the same number of elements as `src1.channels()`:
-    \f[\texttt{dst} (I) =  \texttt{src1} (I)  \oplus \texttt{src2} \quad \texttt{if mask} (I) \ne0\f]
-*   A scalar and an array when src1 is constructed from Scalar or has
-    the same number of elements as `src2.channels()`:
-    \f[\texttt{dst} (I) =  \texttt{src1}  \oplus \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\f]
-In case of floating-point arrays, their machine-specific bit
-representations (usually IEEE754-compliant) are used for the operation.
-In case of multi-channel arrays, each channel is processed
-independently. In the 2nd and 3rd cases above, the scalar is first
-converted to the array type.
-@param src1 first input array or a scalar.
-@param src2 second input array or a scalar.
-@param dst output array that has the same size and type as the input
-arrays.
-@param mask optional operation mask, 8-bit single channel array, that
-specifies elements of the output array to be changed.
-*/
-CV_EXPORTS_W void bitwise_xor(InputArray src1, InputArray src2,
-                              OutputArray dst, InputArray mask = noArray());
-
-/** @brief  Inverts every bit of an array.
-
-The function cv::bitwise_not calculates per-element bit-wise inversion of the input
-array:
-\f[\texttt{dst} (I) =  \neg \texttt{src} (I)\f]
-In case of a floating-point input array, its machine-specific bit
-representation (usually IEEE754-compliant) is used for the operation. In
-case of multi-channel arrays, each channel is processed independently.
-@param src input array.
-@param dst output array that has the same size and type as the input
-array.
-@param mask optional operation mask, 8-bit single channel array, that
-specifies elements of the output array to be changed.
-*/
-CV_EXPORTS_W void bitwise_not(InputArray src, OutputArray dst,
-                              InputArray mask = noArray());
-
-/** @brief Calculates the per-element absolute difference between two arrays or between an array and a scalar.
-
-The function cv::absdiff calculates:
-*   Absolute difference between two arrays when they have the same
-    size and type:
-    \f[\texttt{dst}(I) =  \texttt{saturate} (| \texttt{src1}(I) -  \texttt{src2}(I)|)\f]
-*   Absolute difference between an array and a scalar when the second
-    array is constructed from Scalar or has as many elements as the
-    number of channels in `src1`:
-    \f[\texttt{dst}(I) =  \texttt{saturate} (| \texttt{src1}(I) -  \texttt{src2} |)\f]
-*   Absolute difference between a scalar and an array when the first
-    array is constructed from Scalar or has as many elements as the
-    number of channels in `src2`:
-    \f[\texttt{dst}(I) =  \texttt{saturate} (| \texttt{src1} -  \texttt{src2}(I) |)\f]
-    where I is a multi-dimensional index of array elements. In case of
-    multi-channel arrays, each channel is processed independently.
-@note Saturation is not applied when the arrays have the depth CV_32S.
-You may even get a negative value in the case of overflow.
-@param src1 first input array or a scalar.
-@param src2 second input array or a scalar.
-@param dst output array that has the same size and type as input arrays.
-@sa cv::abs(const Mat&)
-*/
-CV_EXPORTS_W void absdiff(InputArray src1, InputArray src2, OutputArray dst);
-
-/** @brief  Checks if array elements lie between the elements of two other arrays.
-
-The function checks the range as follows:
--   For every element of a single-channel input array:
-    \f[\texttt{dst} (I)= \texttt{lowerb} (I)_0  \leq \texttt{src} (I)_0 \leq  \texttt{upperb} (I)_0\f]
--   For two-channel arrays:
-    \f[\texttt{dst} (I)= \texttt{lowerb} (I)_0  \leq \texttt{src} (I)_0 \leq  \texttt{upperb} (I)_0  \land \texttt{lowerb} (I)_1  \leq \texttt{src} (I)_1 \leq  \texttt{upperb} (I)_1\f]
--   and so forth.
-
-That is, dst (I) is set to 255 (all 1 -bits) if src (I) is within the
-specified 1D, 2D, 3D, ... box and 0 otherwise.
-
-When the lower and/or upper boundary parameters are scalars, the indexes
-(I) at lowerb and upperb in the above formulas should be omitted.
-@param src first input array.
-@param lowerb inclusive lower boundary array or a scalar.
-@param upperb inclusive upper boundary array or a scalar.
-@param dst output array of the same size as src and CV_8U type.
-*/
-CV_EXPORTS_W void inRange(InputArray src, InputArray lowerb,
-                          InputArray upperb, OutputArray dst);
-
-/** @brief Performs the per-element comparison of two arrays or an array and scalar value.
-
-The function compares:
-*   Elements of two arrays when src1 and src2 have the same size:
-    \f[\texttt{dst} (I) =  \texttt{src1} (I)  \,\texttt{cmpop}\, \texttt{src2} (I)\f]
-*   Elements of src1 with a scalar src2 when src2 is constructed from
-    Scalar or has a single element:
-    \f[\texttt{dst} (I) =  \texttt{src1}(I) \,\texttt{cmpop}\,  \texttt{src2}\f]
-*   src1 with elements of src2 when src1 is constructed from Scalar or
-    has a single element:
-    \f[\texttt{dst} (I) =  \texttt{src1}  \,\texttt{cmpop}\, \texttt{src2} (I)\f]
-When the comparison result is true, the corresponding element of output
-array is set to 255. The comparison operations can be replaced with the
-equivalent matrix expressions:
-@code{.cpp}
-    Mat dst1 = src1 >= src2;
-    Mat dst2 = src1 < 8;
-    ...
-@endcode
-@param src1 first input array or a scalar; when it is an array, it must have a single channel.
-@param src2 second input array or a scalar; when it is an array, it must have a single channel.
-@param dst output array of type ref CV_8U that has the same size and the same number of channels as
-    the input arrays.
-@param cmpop a flag, that specifies correspondence between the arrays (cv::CmpTypes)
-@sa checkRange, min, max, threshold
-*/
-CV_EXPORTS_W void compare(InputArray src1, InputArray src2, OutputArray dst, int cmpop);
-
-/** @brief Calculates per-element minimum of two arrays or an array and a scalar.
-
-The function cv::min calculates the per-element minimum of two arrays:
-\f[\texttt{dst} (I)= \min ( \texttt{src1} (I), \texttt{src2} (I))\f]
-or array and a scalar:
-\f[\texttt{dst} (I)= \min ( \texttt{src1} (I), \texttt{value} )\f]
-@param src1 first input array.
-@param src2 second input array of the same size and type as src1.
-@param dst output array of the same size and type as src1.
-@sa max, compare, inRange, minMaxLoc
-*/
-CV_EXPORTS_W void min(InputArray src1, InputArray src2, OutputArray dst);
-/** @overload
-needed to avoid conflicts with const _Tp& std::min(const _Tp&, const _Tp&, _Compare)
-*/
-CV_EXPORTS void min(const Mat& src1, const Mat& src2, Mat& dst);
-/** @overload
-needed to avoid conflicts with const _Tp& std::min(const _Tp&, const _Tp&, _Compare)
-*/
-CV_EXPORTS void min(const UMat& src1, const UMat& src2, UMat& dst);
-
-/** @brief Calculates per-element maximum of two arrays or an array and a scalar.
-
-The function cv::max calculates the per-element maximum of two arrays:
-\f[\texttt{dst} (I)= \max ( \texttt{src1} (I), \texttt{src2} (I))\f]
-or array and a scalar:
-\f[\texttt{dst} (I)= \max ( \texttt{src1} (I), \texttt{value} )\f]
-@param src1 first input array.
-@param src2 second input array of the same size and type as src1 .
-@param dst output array of the same size and type as src1.
-@sa  min, compare, inRange, minMaxLoc, @ref MatrixExpressions
-*/
-CV_EXPORTS_W void max(InputArray src1, InputArray src2, OutputArray dst);
-/** @overload
-needed to avoid conflicts with const _Tp& std::min(const _Tp&, const _Tp&, _Compare)
-*/
-CV_EXPORTS void max(const Mat& src1, const Mat& src2, Mat& dst);
-/** @overload
-needed to avoid conflicts with const _Tp& std::min(const _Tp&, const _Tp&, _Compare)
-*/
-CV_EXPORTS void max(const UMat& src1, const UMat& src2, UMat& dst);
-
-/** @brief Calculates a square root of array elements.
-
-The function cv::sqrt calculates a square root of each input array element.
-In case of multi-channel arrays, each channel is processed
-independently. The accuracy is approximately the same as of the built-in
-std::sqrt .
-@param src input floating-point array.
-@param dst output array of the same size and type as src.
-*/
-CV_EXPORTS_W void sqrt(InputArray src, OutputArray dst);
-
-/** @brief Raises every array element to a power.
-
-The function cv::pow raises every element of the input array to power :
-\f[\texttt{dst} (I) =  \fork{\texttt{src}(I)^{power}}{if \(\texttt{power}\) is integer}{|\texttt{src}(I)|^{power}}{otherwise}\f]
-
-So, for a non-integer power exponent, the absolute values of input array
-elements are used. However, it is possible to get true values for
-negative values using some extra operations. In the example below,
-computing the 5th root of array src shows:
-@code{.cpp}
-    Mat mask = src < 0;
-    pow(src, 1./5, dst);
-    subtract(Scalar::all(0), dst, dst, mask);
-@endcode
-For some values of power, such as integer values, 0.5 and -0.5,
-specialized faster algorithms are used.
-
-Special values (NaN, Inf) are not handled.
-@param src input array.
-@param power exponent of power.
-@param dst output array of the same size and type as src.
-@sa sqrt, exp, log, cartToPolar, polarToCart
-*/
-CV_EXPORTS_W void pow(InputArray src, double power, OutputArray dst);
-
-/** @brief Calculates the exponent of every array element.
-
-The function cv::exp calculates the exponent of every element of the input
-array:
-\f[\texttt{dst} [I] = e^{ src(I) }\f]
-
-The maximum relative error is about 7e-6 for single-precision input and
-less than 1e-10 for double-precision input. Currently, the function
-converts denormalized values to zeros on output. Special values (NaN,
-Inf) are not handled.
-@param src input array.
-@param dst output array of the same size and type as src.
-@sa log , cartToPolar , polarToCart , phase , pow , sqrt , magnitude
-*/
-CV_EXPORTS_W void exp(InputArray src, OutputArray dst);
-
-/** @brief Calculates the natural logarithm of every array element.
-
-The function cv::log calculates the natural logarithm of every element of the input array:
-\f[\texttt{dst} (I) =  \log (\texttt{src}(I)) \f]
-
-Output on zero, negative and special (NaN, Inf) values is undefined.
-
-@param src input array.
-@param dst output array of the same size and type as src .
-@sa exp, cartToPolar, polarToCart, phase, pow, sqrt, magnitude
-*/
-CV_EXPORTS_W void log(InputArray src, OutputArray dst);
-
-/** @brief Calculates x and y coordinates of 2D vectors from their magnitude and angle.
-
-The function cv::polarToCart calculates the Cartesian coordinates of each 2D
-vector represented by the corresponding elements of magnitude and angle:
-\f[\begin{array}{l} \texttt{x} (I) =  \texttt{magnitude} (I) \cos ( \texttt{angle} (I)) \\ \texttt{y} (I) =  \texttt{magnitude} (I) \sin ( \texttt{angle} (I)) \\ \end{array}\f]
-
-The relative accuracy of the estimated coordinates is about 1e-6.
-@param magnitude input floating-point array of magnitudes of 2D vectors;
-it can be an empty matrix (=Mat()), in this case, the function assumes
-that all the magnitudes are =1; if it is not empty, it must have the
-same size and type as angle.
-@param angle input floating-point array of angles of 2D vectors.
-@param x output array of x-coordinates of 2D vectors; it has the same
-size and type as angle.
-@param y output array of y-coordinates of 2D vectors; it has the same
-size and type as angle.
-@param angleInDegrees when true, the input angles are measured in
-degrees, otherwise, they are measured in radians.
-@sa cartToPolar, magnitude, phase, exp, log, pow, sqrt
-*/
-CV_EXPORTS_W void polarToCart(InputArray magnitude, InputArray angle,
-                              OutputArray x, OutputArray y, bool angleInDegrees = false);
-
-/** @brief Calculates the magnitude and angle of 2D vectors.
-
-The function cv::cartToPolar calculates either the magnitude, angle, or both
-for every 2D vector (x(I),y(I)):
-\f[\begin{array}{l} \texttt{magnitude} (I)= \sqrt{\texttt{x}(I)^2+\texttt{y}(I)^2} , \\ \texttt{angle} (I)= \texttt{atan2} ( \texttt{y} (I), \texttt{x} (I))[ \cdot180 / \pi ] \end{array}\f]
-
-The angles are calculated with accuracy about 0.3 degrees. For the point
-(0,0), the angle is set to 0.
-@param x array of x-coordinates; this must be a single-precision or
-double-precision floating-point array.
-@param y array of y-coordinates, that must have the same size and same type as x.
-@param magnitude output array of magnitudes of the same size and type as x.
-@param angle output array of angles that has the same size and type as
-x; the angles are measured in radians (from 0 to 2\*Pi) or in degrees (0 to 360 degrees).
-@param angleInDegrees a flag, indicating whether the angles are measured
-in radians (which is by default), or in degrees.
-@sa Sobel, Scharr
-*/
-CV_EXPORTS_W void cartToPolar(InputArray x, InputArray y,
-                              OutputArray magnitude, OutputArray angle,
-                              bool angleInDegrees = false);
-
-/** @brief Calculates the rotation angle of 2D vectors.
-
-The function cv::phase calculates the rotation angle of each 2D vector that
-is formed from the corresponding elements of x and y :
-\f[\texttt{angle} (I) =  \texttt{atan2} ( \texttt{y} (I), \texttt{x} (I))\f]
-
-The angle estimation accuracy is about 0.3 degrees. When x(I)=y(I)=0 ,
-the corresponding angle(I) is set to 0.
-@param x input floating-point array of x-coordinates of 2D vectors.
-@param y input array of y-coordinates of 2D vectors; it must have the
-same size and the same type as x.
-@param angle output array of vector angles; it has the same size and
-same type as x .
-@param angleInDegrees when true, the function calculates the angle in
-degrees, otherwise, they are measured in radians.
-*/
-CV_EXPORTS_W void phase(InputArray x, InputArray y, OutputArray angle,
-                        bool angleInDegrees = false);
-
-/** @brief Calculates the magnitude of 2D vectors.
-
-The function cv::magnitude calculates the magnitude of 2D vectors formed
-from the corresponding elements of x and y arrays:
-\f[\texttt{dst} (I) =  \sqrt{\texttt{x}(I)^2 + \texttt{y}(I)^2}\f]
-@param x floating-point array of x-coordinates of the vectors.
-@param y floating-point array of y-coordinates of the vectors; it must
-have the same size as x.
-@param magnitude output array of the same size and type as x.
-@sa cartToPolar, polarToCart, phase, sqrt
-*/
-CV_EXPORTS_W void magnitude(InputArray x, InputArray y, OutputArray magnitude);
-
-/** @brief Checks every element of an input array for invalid values.
-
-The function cv::checkRange checks that every array element is neither NaN nor infinite. When minVal \>
--DBL_MAX and maxVal \< DBL_MAX, the function also checks that each value is between minVal and
-maxVal. In case of multi-channel arrays, each channel is processed independently. If some values
-are out of range, position of the first outlier is stored in pos (when pos != NULL). Then, the
-function either returns false (when quiet=true) or throws an exception.
-@param a input array.
-@param quiet a flag, indicating whether the functions quietly return false when the array elements
-are out of range or they throw an exception.
-@param pos optional output parameter, when not NULL, must be a pointer to array of src.dims
-elements.
-@param minVal inclusive lower boundary of valid values range.
-@param maxVal exclusive upper boundary of valid values range.
-*/
-CV_EXPORTS_W bool checkRange(InputArray a, bool quiet = true, CV_OUT Point* pos = 0,
-                            double minVal = -DBL_MAX, double maxVal = DBL_MAX);
-
-/** @brief converts NaN's to the given number
-*/
-CV_EXPORTS_W void patchNaNs(InputOutputArray a, double val = 0);
-
-/** @brief Performs generalized matrix multiplication.
-
-The function cv::gemm performs generalized matrix multiplication similar to the
-gemm functions in BLAS level 3. For example,
-`gemm(src1, src2, alpha, src3, beta, dst, GEMM_1_T + GEMM_3_T)`
-corresponds to
-\f[\texttt{dst} =  \texttt{alpha} \cdot \texttt{src1} ^T  \cdot \texttt{src2} +  \texttt{beta} \cdot \texttt{src3} ^T\f]
-
-In case of complex (two-channel) data, performed a complex matrix
-multiplication.
-
-The function can be replaced with a matrix expression. For example, the
-above call can be replaced with:
-@code{.cpp}
-    dst = alpha*src1.t()*src2 + beta*src3.t();
-@endcode
-@param src1 first multiplied input matrix that could be real(CV_32FC1,
-CV_64FC1) or complex(CV_32FC2, CV_64FC2).
-@param src2 second multiplied input matrix of the same type as src1.
-@param alpha weight of the matrix product.
-@param src3 third optional delta matrix added to the matrix product; it
-should have the same type as src1 and src2.
-@param beta weight of src3.
-@param dst output matrix; it has the proper size and the same type as
-input matrices.
-@param flags operation flags (cv::GemmFlags)
-@sa mulTransposed , transform
-*/
-CV_EXPORTS_W void gemm(InputArray src1, InputArray src2, double alpha,
-                       InputArray src3, double beta, OutputArray dst, int flags = 0);
-
-/** @brief Calculates the product of a matrix and its transposition.
-
-The function cv::mulTransposed calculates the product of src and its
-transposition:
-\f[\texttt{dst} = \texttt{scale} ( \texttt{src} - \texttt{delta} )^T ( \texttt{src} - \texttt{delta} )\f]
-if aTa=true , and
-\f[\texttt{dst} = \texttt{scale} ( \texttt{src} - \texttt{delta} ) ( \texttt{src} - \texttt{delta} )^T\f]
-otherwise. The function is used to calculate the covariance matrix. With
-zero delta, it can be used as a faster substitute for general matrix
-product A\*B when B=A'
-@param src input single-channel matrix. Note that unlike gemm, the
-function can multiply not only floating-point matrices.
-@param dst output square matrix.
-@param aTa Flag specifying the multiplication ordering. See the
-description below.
-@param delta Optional delta matrix subtracted from src before the
-multiplication. When the matrix is empty ( delta=noArray() ), it is
-assumed to be zero, that is, nothing is subtracted. If it has the same
-size as src , it is simply subtracted. Otherwise, it is "repeated" (see
-repeat ) to cover the full src and then subtracted. Type of the delta
-matrix, when it is not empty, must be the same as the type of created
-output matrix. See the dtype parameter description below.
-@param scale Optional scale factor for the matrix product.
-@param dtype Optional type of the output matrix. When it is negative,
-the output matrix will have the same type as src . Otherwise, it will be
-type=CV_MAT_DEPTH(dtype) that should be either CV_32F or CV_64F .
-@sa calcCovarMatrix, gemm, repeat, reduce
-*/
-CV_EXPORTS_W void mulTransposed( InputArray src, OutputArray dst, bool aTa,
-                                 InputArray delta = noArray(),
-                                 double scale = 1, int dtype = -1 );
-
-/** @brief Transposes a matrix.
-
-The function cv::transpose transposes the matrix src :
-\f[\texttt{dst} (i,j) =  \texttt{src} (j,i)\f]
-@note No complex conjugation is done in case of a complex matrix. It
-should be done separately if needed.
-@param src input array.
-@param dst output array of the same type as src.
-*/
-CV_EXPORTS_W void transpose(InputArray src, OutputArray dst);
-
-/** @brief Performs the matrix transformation of every array element.
-
-The function cv::transform performs the matrix transformation of every
-element of the array src and stores the results in dst :
-\f[\texttt{dst} (I) =  \texttt{m} \cdot \texttt{src} (I)\f]
-(when m.cols=src.channels() ), or
-\f[\texttt{dst} (I) =  \texttt{m} \cdot [ \texttt{src} (I); 1]\f]
-(when m.cols=src.channels()+1 )
-
-Every element of the N -channel array src is interpreted as N -element
-vector that is transformed using the M x N or M x (N+1) matrix m to
-M-element vector - the corresponding element of the output array dst .
-
-The function may be used for geometrical transformation of
-N -dimensional points, arbitrary linear color space transformation (such
-as various kinds of RGB to YUV transforms), shuffling the image
-channels, and so forth.
-@param src input array that must have as many channels (1 to 4) as
-m.cols or m.cols-1.
-@param dst output array of the same size and depth as src; it has as
-many channels as m.rows.
-@param m transformation 2x2 or 2x3 floating-point matrix.
-@sa perspectiveTransform, getAffineTransform, estimateAffine2D, warpAffine, warpPerspective
-*/
-CV_EXPORTS_W void transform(InputArray src, OutputArray dst, InputArray m );
-
-/** @brief Performs the perspective matrix transformation of vectors.
-
-The function cv::perspectiveTransform transforms every element of src by
-treating it as a 2D or 3D vector, in the following way:
-\f[(x, y, z)  \rightarrow (x'/w, y'/w, z'/w)\f]
-where
-\f[(x', y', z', w') =  \texttt{mat} \cdot \begin{bmatrix} x & y & z & 1  \end{bmatrix}\f]
-and
-\f[w =  \fork{w'}{if \(w' \ne 0\)}{\infty}{otherwise}\f]
-
-Here a 3D vector transformation is shown. In case of a 2D vector
-transformation, the z component is omitted.
-
-@note The function transforms a sparse set of 2D or 3D vectors. If you
-want to transform an image using perspective transformation, use
-warpPerspective . If you have an inverse problem, that is, you want to
-compute the most probable perspective transformation out of several
-pairs of corresponding points, you can use getPerspectiveTransform or
-findHomography .
-@param src input two-channel or three-channel floating-point array; each
-element is a 2D/3D vector to be transformed.
-@param dst output array of the same size and type as src.
-@param m 3x3 or 4x4 floating-point transformation matrix.
-@sa  transform, warpPerspective, getPerspectiveTransform, findHomography
-*/
-CV_EXPORTS_W void perspectiveTransform(InputArray src, OutputArray dst, InputArray m );
-
-/** @brief Copies the lower or the upper half of a square matrix to its another half.
-
-The function cv::completeSymm copies the lower or the upper half of a square matrix to
-its another half. The matrix diagonal remains unchanged:
- - \f$\texttt{m}_{ij}=\texttt{m}_{ji}\f$ for \f$i > j\f$ if
-    lowerToUpper=false
- - \f$\texttt{m}_{ij}=\texttt{m}_{ji}\f$ for \f$i < j\f$ if
-    lowerToUpper=true
-
-@param m input-output floating-point square matrix.
-@param lowerToUpper operation flag; if true, the lower half is copied to
-the upper half. Otherwise, the upper half is copied to the lower half.
-@sa flip, transpose
-*/
-CV_EXPORTS_W void completeSymm(InputOutputArray m, bool lowerToUpper = false);
-
-/** @brief Initializes a scaled identity matrix.
-
-The function cv::setIdentity initializes a scaled identity matrix:
-\f[\texttt{mtx} (i,j)= \fork{\texttt{value}}{ if \(i=j\)}{0}{otherwise}\f]
-
-The function can also be emulated using the matrix initializers and the
-matrix expressions:
-@code
-    Mat A = Mat::eye(4, 3, CV_32F)*5;
-    // A will be set to [[5, 0, 0], [0, 5, 0], [0, 0, 5], [0, 0, 0]]
-@endcode
-@param mtx matrix to initialize (not necessarily square).
-@param s value to assign to diagonal elements.
-@sa Mat::zeros, Mat::ones, Mat::setTo, Mat::operator=
-*/
-CV_EXPORTS_W void setIdentity(InputOutputArray mtx, const Scalar& s = Scalar(1));
-
-/** @brief Returns the determinant of a square floating-point matrix.
-
-The function cv::determinant calculates and returns the determinant of the
-specified matrix. For small matrices ( mtx.cols=mtx.rows\<=3 ), the
-direct method is used. For larger matrices, the function uses LU
-factorization with partial pivoting.
-
-For symmetric positively-determined matrices, it is also possible to use
-eigen decomposition to calculate the determinant.
-@param mtx input matrix that must have CV_32FC1 or CV_64FC1 type and
-square size.
-@sa trace, invert, solve, eigen, @ref MatrixExpressions
-*/
-CV_EXPORTS_W double determinant(InputArray mtx);
-
-/** @brief Returns the trace of a matrix.
-
-The function cv::trace returns the sum of the diagonal elements of the
-matrix mtx .
-\f[\mathrm{tr} ( \texttt{mtx} ) =  \sum _i  \texttt{mtx} (i,i)\f]
-@param mtx input matrix.
-*/
-CV_EXPORTS_W Scalar trace(InputArray mtx);
-
-/** @brief Finds the inverse or pseudo-inverse of a matrix.
-
-The function cv::invert inverts the matrix src and stores the result in dst
-. When the matrix src is singular or non-square, the function calculates
-the pseudo-inverse matrix (the dst matrix) so that norm(src\*dst - I) is
-minimal, where I is an identity matrix.
-
-In case of the #DECOMP_LU method, the function returns non-zero value if
-the inverse has been successfully calculated and 0 if src is singular.
-
-In case of the #DECOMP_SVD method, the function returns the inverse
-condition number of src (the ratio of the smallest singular value to the
-largest singular value) and 0 if src is singular. The SVD method
-calculates a pseudo-inverse matrix if src is singular.
-
-Similarly to #DECOMP_LU, the method #DECOMP_CHOLESKY works only with
-non-singular square matrices that should also be symmetrical and
-positively defined. In this case, the function stores the inverted
-matrix in dst and returns non-zero. Otherwise, it returns 0.
-
-@param src input floating-point M x N matrix.
-@param dst output matrix of N x M size and the same type as src.
-@param flags inversion method (cv::DecompTypes)
-@sa solve, SVD
-*/
-CV_EXPORTS_W double invert(InputArray src, OutputArray dst, int flags = DECOMP_LU);
-
-/** @brief Solves one or more linear systems or least-squares problems.
-
-The function cv::solve solves a linear system or least-squares problem (the
-latter is possible with SVD or QR methods, or by specifying the flag
-#DECOMP_NORMAL ):
-\f[\texttt{dst} =  \arg \min _X \| \texttt{src1} \cdot \texttt{X} -  \texttt{src2} \|\f]
-
-If #DECOMP_LU or #DECOMP_CHOLESKY method is used, the function returns 1
-if src1 (or \f$\texttt{src1}^T\texttt{src1}\f$ ) is non-singular. Otherwise,
-it returns 0. In the latter case, dst is not valid. Other methods find a
-pseudo-solution in case of a singular left-hand side part.
-
-@note If you want to find a unity-norm solution of an under-defined
-singular system \f$\texttt{src1}\cdot\texttt{dst}=0\f$ , the function solve
-will not do the work. Use SVD::solveZ instead.
-
-@param src1 input matrix on the left-hand side of the system.
-@param src2 input matrix on the right-hand side of the system.
-@param dst output solution.
-@param flags solution (matrix inversion) method (#DecompTypes)
-@sa invert, SVD, eigen
-*/
-CV_EXPORTS_W bool solve(InputArray src1, InputArray src2,
-                        OutputArray dst, int flags = DECOMP_LU);
-
-/** @brief Sorts each row or each column of a matrix.
-
-The function cv::sort sorts each matrix row or each matrix column in
-ascending or descending order. So you should pass two operation flags to
-get desired behaviour. If you want to sort matrix rows or columns
-lexicographically, you can use STL std::sort generic function with the
-proper comparison predicate.
-
-@param src input single-channel array.
-@param dst output array of the same size and type as src.
-@param flags operation flags, a combination of #SortFlags
-@sa sortIdx, randShuffle
-*/
-CV_EXPORTS_W void sort(InputArray src, OutputArray dst, int flags);
-
-/** @brief Sorts each row or each column of a matrix.
-
-The function cv::sortIdx sorts each matrix row or each matrix column in the
-ascending or descending order. So you should pass two operation flags to
-get desired behaviour. Instead of reordering the elements themselves, it
-stores the indices of sorted elements in the output array. For example:
-@code
-    Mat A = Mat::eye(3,3,CV_32F), B;
-    sortIdx(A, B, SORT_EVERY_ROW + SORT_ASCENDING);
-    // B will probably contain
-    // (because of equal elements in A some permutations are possible):
-    // [[1, 2, 0], [0, 2, 1], [0, 1, 2]]
-@endcode
-@param src input single-channel array.
-@param dst output integer array of the same size as src.
-@param flags operation flags that could be a combination of cv::SortFlags
-@sa sort, randShuffle
-*/
-CV_EXPORTS_W void sortIdx(InputArray src, OutputArray dst, int flags);
-
-/** @brief Finds the real roots of a cubic equation.
-
-The function solveCubic finds the real roots of a cubic equation:
--   if coeffs is a 4-element vector:
-\f[\texttt{coeffs} [0] x^3 +  \texttt{coeffs} [1] x^2 +  \texttt{coeffs} [2] x +  \texttt{coeffs} [3] = 0\f]
--   if coeffs is a 3-element vector:
-\f[x^3 +  \texttt{coeffs} [0] x^2 +  \texttt{coeffs} [1] x +  \texttt{coeffs} [2] = 0\f]
-
-The roots are stored in the roots array.
-@param coeffs equation coefficients, an array of 3 or 4 elements.
-@param roots output array of real roots that has 1 or 3 elements.
-@return number of real roots. It can be 0, 1 or 2.
-*/
-CV_EXPORTS_W int solveCubic(InputArray coeffs, OutputArray roots);
-
-/** @brief Finds the real or complex roots of a polynomial equation.
-
-The function cv::solvePoly finds real and complex roots of a polynomial equation:
-\f[\texttt{coeffs} [n] x^{n} +  \texttt{coeffs} [n-1] x^{n-1} + ... +  \texttt{coeffs} [1] x +  \texttt{coeffs} [0] = 0\f]
-@param coeffs array of polynomial coefficients.
-@param roots output (complex) array of roots.
-@param maxIters maximum number of iterations the algorithm does.
-*/
-CV_EXPORTS_W double solvePoly(InputArray coeffs, OutputArray roots, int maxIters = 300);
-
-/** @brief Calculates eigenvalues and eigenvectors of a symmetric matrix.
-
-The function cv::eigen calculates just eigenvalues, or eigenvalues and eigenvectors of the symmetric
-matrix src:
-@code
-    src*eigenvectors.row(i).t() = eigenvalues.at<srcType>(i)*eigenvectors.row(i).t()
-@endcode
-
-@note Use cv::eigenNonSymmetric for calculation of real eigenvalues and eigenvectors of non-symmetric matrix.
-
-@param src input matrix that must have CV_32FC1 or CV_64FC1 type, square size and be symmetrical
-(src ^T^ == src).
-@param eigenvalues output vector of eigenvalues of the same type as src; the eigenvalues are stored
-in the descending order.
-@param eigenvectors output matrix of eigenvectors; it has the same size and type as src; the
-eigenvectors are stored as subsequent matrix rows, in the same order as the corresponding
-eigenvalues.
-@sa eigenNonSymmetric, completeSymm , PCA
-*/
-CV_EXPORTS_W bool eigen(InputArray src, OutputArray eigenvalues,
-                        OutputArray eigenvectors = noArray());
-
-/** @brief Calculates eigenvalues and eigenvectors of a non-symmetric matrix (real eigenvalues only).
-
-@note Assumes real eigenvalues.
-
-The function calculates eigenvalues and eigenvectors (optional) of the square matrix src:
-@code
-    src*eigenvectors.row(i).t() = eigenvalues.at<srcType>(i)*eigenvectors.row(i).t()
-@endcode
-
-@param src input matrix (CV_32FC1 or CV_64FC1 type).
-@param eigenvalues output vector of eigenvalues (type is the same type as src).
-@param eigenvectors output matrix of eigenvectors (type is the same type as src). The eigenvectors are stored as subsequent matrix rows, in the same order as the corresponding eigenvalues.
-@sa eigen
-*/
-CV_EXPORTS_W void eigenNonSymmetric(InputArray src, OutputArray eigenvalues,
-                                    OutputArray eigenvectors);
-
-/** @brief Calculates the covariance matrix of a set of vectors.
-
-The function cv::calcCovarMatrix calculates the covariance matrix and, optionally, the mean vector of
-the set of input vectors.
-@param samples samples stored as separate matrices
-@param nsamples number of samples
-@param covar output covariance matrix of the type ctype and square size.
-@param mean input or output (depending on the flags) array as the average value of the input vectors.
-@param flags operation flags as a combination of #CovarFlags
-@param ctype type of the matrixl; it equals 'CV_64F' by default.
-@sa PCA, mulTransposed, Mahalanobis
-@todo InputArrayOfArrays
-*/
-CV_EXPORTS void calcCovarMatrix( const Mat* samples, int nsamples, Mat& covar, Mat& mean,
-                                 int flags, int ctype = CV_64F);
-
-/** @overload
-@note use #COVAR_ROWS or #COVAR_COLS flag
-@param samples samples stored as rows/columns of a single matrix.
-@param covar output covariance matrix of the type ctype and square size.
-@param mean input or output (depending on the flags) array as the average value of the input vectors.
-@param flags operation flags as a combination of #CovarFlags
-@param ctype type of the matrixl; it equals 'CV_64F' by default.
-*/
-CV_EXPORTS_W void calcCovarMatrix( InputArray samples, OutputArray covar,
-                                   InputOutputArray mean, int flags, int ctype = CV_64F);
-
-/** wrap PCA::operator() */
-CV_EXPORTS_W void PCACompute(InputArray data, InputOutputArray mean,
-                             OutputArray eigenvectors, int maxComponents = 0);
-
-/** wrap PCA::operator() */
-CV_EXPORTS_W void PCACompute(InputArray data, InputOutputArray mean,
-                             OutputArray eigenvectors, double retainedVariance);
-
-/** wrap PCA::project */
-CV_EXPORTS_W void PCAProject(InputArray data, InputArray mean,
-                             InputArray eigenvectors, OutputArray result);
-
-/** wrap PCA::backProject */
-CV_EXPORTS_W void PCABackProject(InputArray data, InputArray mean,
-                                 InputArray eigenvectors, OutputArray result);
-
-/** wrap SVD::compute */
-CV_EXPORTS_W void SVDecomp( InputArray src, OutputArray w, OutputArray u, OutputArray vt, int flags = 0 );
-
-/** wrap SVD::backSubst */
-CV_EXPORTS_W void SVBackSubst( InputArray w, InputArray u, InputArray vt,
-                               InputArray rhs, OutputArray dst );
-
-/** @brief Calculates the Mahalanobis distance between two vectors.
-
-The function cv::Mahalanobis calculates and returns the weighted distance between two vectors:
-\f[d( \texttt{vec1} , \texttt{vec2} )= \sqrt{\sum_{i,j}{\texttt{icovar(i,j)}\cdot(\texttt{vec1}(I)-\texttt{vec2}(I))\cdot(\texttt{vec1(j)}-\texttt{vec2(j)})} }\f]
-The covariance matrix may be calculated using the #calcCovarMatrix function and then inverted using
-the invert function (preferably using the #DECOMP_SVD method, as the most accurate).
-@param v1 first 1D input vector.
-@param v2 second 1D input vector.
-@param icovar inverse covariance matrix.
-*/
-CV_EXPORTS_W double Mahalanobis(InputArray v1, InputArray v2, InputArray icovar);
-
-/** @brief Performs a forward or inverse Discrete Fourier transform of a 1D or 2D floating-point array.
-
-The function cv::dft performs one of the following:
--   Forward the Fourier transform of a 1D vector of N elements:
-    \f[Y = F^{(N)}  \cdot X,\f]
-    where \f$F^{(N)}_{jk}=\exp(-2\pi i j k/N)\f$ and \f$i=\sqrt{-1}\f$
--   Inverse the Fourier transform of a 1D vector of N elements:
-    \f[\begin{array}{l} X'=  \left (F^{(N)} \right )^{-1}  \cdot Y =  \left (F^{(N)} \right )^*  \cdot y  \\ X = (1/N)  \cdot X, \end{array}\f]
-    where \f$F^*=\left(\textrm{Re}(F^{(N)})-\textrm{Im}(F^{(N)})\right)^T\f$
--   Forward the 2D Fourier transform of a M x N matrix:
-    \f[Y = F^{(M)}  \cdot X  \cdot F^{(N)}\f]
--   Inverse the 2D Fourier transform of a M x N matrix:
-    \f[\begin{array}{l} X'=  \left (F^{(M)} \right )^*  \cdot Y  \cdot \left (F^{(N)} \right )^* \\ X =  \frac{1}{M \cdot N} \cdot X' \end{array}\f]
-
-In case of real (single-channel) data, the output spectrum of the forward Fourier transform or input
-spectrum of the inverse Fourier transform can be represented in a packed format called *CCS*
-(complex-conjugate-symmetrical). It was borrowed from IPL (Intel\* Image Processing Library). Here
-is how 2D *CCS* spectrum looks:
-\f[\begin{bmatrix} Re Y_{0,0} & Re Y_{0,1} & Im Y_{0,1} & Re Y_{0,2} & Im Y_{0,2} &  \cdots & Re Y_{0,N/2-1} & Im Y_{0,N/2-1} & Re Y_{0,N/2}  \\ Re Y_{1,0} & Re Y_{1,1} & Im Y_{1,1} & Re Y_{1,2} & Im Y_{1,2} &  \cdots & Re Y_{1,N/2-1} & Im Y_{1,N/2-1} & Re Y_{1,N/2}  \\ Im Y_{1,0} & Re Y_{2,1} & Im Y_{2,1} & Re Y_{2,2} & Im Y_{2,2} &  \cdots & Re Y_{2,N/2-1} & Im Y_{2,N/2-1} & Im Y_{1,N/2}  \\ \hdotsfor{9} \\ Re Y_{M/2-1,0} &  Re Y_{M-3,1}  & Im Y_{M-3,1} &  \hdotsfor{3} & Re Y_{M-3,N/2-1} & Im Y_{M-3,N/2-1}& Re Y_{M/2-1,N/2}  \\ Im Y_{M/2-1,0} &  Re Y_{M-2,1}  & Im Y_{M-2,1} &  \hdotsfor{3} & Re Y_{M-2,N/2-1} & Im Y_{M-2,N/2-1}& Im Y_{M/2-1,N/2}  \\ Re Y_{M/2,0}  &  Re Y_{M-1,1} &  Im Y_{M-1,1} &  \hdotsfor{3} & Re Y_{M-1,N/2-1} & Im Y_{M-1,N/2-1}& Re Y_{M/2,N/2} \end{bmatrix}\f]
-
-In case of 1D transform of a real vector, the output looks like the first row of the matrix above.
-
-So, the function chooses an operation mode depending on the flags and size of the input array:
--   If #DFT_ROWS is set or the input array has a single row or single column, the function
-    performs a 1D forward or inverse transform of each row of a matrix when #DFT_ROWS is set.
-    Otherwise, it performs a 2D transform.
--   If the input array is real and #DFT_INVERSE is not set, the function performs a forward 1D or
-    2D transform:
-    -   When #DFT_COMPLEX_OUTPUT is set, the output is a complex matrix of the same size as
-        input.
-    -   When #DFT_COMPLEX_OUTPUT is not set, the output is a real matrix of the same size as
-        input. In case of 2D transform, it uses the packed format as shown above. In case of a
-        single 1D transform, it looks like the first row of the matrix above. In case of
-        multiple 1D transforms (when using the #DFT_ROWS flag), each row of the output matrix
-        looks like the first row of the matrix above.
--   If the input array is complex and either #DFT_INVERSE or #DFT_REAL_OUTPUT are not set, the
-    output is a complex array of the same size as input. The function performs a forward or
-    inverse 1D or 2D transform of the whole input array or each row of the input array
-    independently, depending on the flags DFT_INVERSE and DFT_ROWS.
--   When #DFT_INVERSE is set and the input array is real, or it is complex but #DFT_REAL_OUTPUT
-    is set, the output is a real array of the same size as input. The function performs a 1D or 2D
-    inverse transformation of the whole input array or each individual row, depending on the flags
-    #DFT_INVERSE and #DFT_ROWS.
-
-If #DFT_SCALE is set, the scaling is done after the transformation.
-
-Unlike dct , the function supports arrays of arbitrary size. But only those arrays are processed
-efficiently, whose sizes can be factorized in a product of small prime numbers (2, 3, and 5 in the
-current implementation). Such an efficient DFT size can be calculated using the getOptimalDFTSize
-method.
-
-The sample below illustrates how to calculate a DFT-based convolution of two 2D real arrays:
-@code
-    void convolveDFT(InputArray A, InputArray B, OutputArray C)
-    {
-        // reallocate the output array if needed
-        C.create(abs(A.rows - B.rows)+1, abs(A.cols - B.cols)+1, A.type());
-        Size dftSize;
-        // calculate the size of DFT transform
-        dftSize.width = getOptimalDFTSize(A.cols + B.cols - 1);
-        dftSize.height = getOptimalDFTSize(A.rows + B.rows - 1);
-
-        // allocate temporary buffers and initialize them with 0's
-        Mat tempA(dftSize, A.type(), Scalar::all(0));
-        Mat tempB(dftSize, B.type(), Scalar::all(0));
-
-        // copy A and B to the top-left corners of tempA and tempB, respectively
-        Mat roiA(tempA, Rect(0,0,A.cols,A.rows));
-        A.copyTo(roiA);
-        Mat roiB(tempB, Rect(0,0,B.cols,B.rows));
-        B.copyTo(roiB);
-
-        // now transform the padded A & B in-place;
-        // use "nonzeroRows" hint for faster processing
-        dft(tempA, tempA, 0, A.rows);
-        dft(tempB, tempB, 0, B.rows);
-
-        // multiply the spectrums;
-        // the function handles packed spectrum representations well
-        mulSpectrums(tempA, tempB, tempA);
-
-        // transform the product back from the frequency domain.
-        // Even though all the result rows will be non-zero,
-        // you need only the first C.rows of them, and thus you
-        // pass nonzeroRows == C.rows
-        dft(tempA, tempA, DFT_INVERSE + DFT_SCALE, C.rows);
-
-        // now copy the result back to C.
-        tempA(Rect(0, 0, C.cols, C.rows)).copyTo(C);
-
-        // all the temporary buffers will be deallocated automatically
-    }
-@endcode
-To optimize this sample, consider the following approaches:
--   Since nonzeroRows != 0 is passed to the forward transform calls and since A and B are copied to
-    the top-left corners of tempA and tempB, respectively, it is not necessary to clear the whole
-    tempA and tempB. It is only necessary to clear the tempA.cols - A.cols ( tempB.cols - B.cols)
-    rightmost columns of the matrices.
--   This DFT-based convolution does not have to be applied to the whole big arrays, especially if B
-    is significantly smaller than A or vice versa. Instead, you can calculate convolution by parts.
-    To do this, you need to split the output array C into multiple tiles. For each tile, estimate
-    which parts of A and B are required to calculate convolution in this tile. If the tiles in C are
-    too small, the speed will decrease a lot because of repeated work. In the ultimate case, when
-    each tile in C is a single pixel, the algorithm becomes equivalent to the naive convolution
-    algorithm. If the tiles are too big, the temporary arrays tempA and tempB become too big and
-    there is also a slowdown because of bad cache locality. So, there is an optimal tile size
-    somewhere in the middle.
--   If different tiles in C can be calculated in parallel and, thus, the convolution is done by
-    parts, the loop can be threaded.
-
-All of the above improvements have been implemented in #matchTemplate and #filter2D . Therefore, by
-using them, you can get the performance even better than with the above theoretically optimal
-implementation. Though, those two functions actually calculate cross-correlation, not convolution,
-so you need to "flip" the second convolution operand B vertically and horizontally using flip .
-@note
--   An example using the discrete fourier transform can be found at
-    opencv_source_code/samples/cpp/dft.cpp
--   (Python) An example using the dft functionality to perform Wiener deconvolution can be found
-    at opencv_source/samples/python/deconvolution.py
--   (Python) An example rearranging the quadrants of a Fourier image can be found at
-    opencv_source/samples/python/dft.py
-@param src input array that could be real or complex.
-@param dst output array whose size and type depends on the flags .
-@param flags transformation flags, representing a combination of the #DftFlags
-@param nonzeroRows when the parameter is not zero, the function assumes that only the first
-nonzeroRows rows of the input array (#DFT_INVERSE is not set) or only the first nonzeroRows of the
-output array (#DFT_INVERSE is set) contain non-zeros, thus, the function can handle the rest of the
-rows more efficiently and save some time; this technique is very useful for calculating array
-cross-correlation or convolution using DFT.
-@sa dct , getOptimalDFTSize , mulSpectrums, filter2D , matchTemplate , flip , cartToPolar ,
-magnitude , phase
-*/
-CV_EXPORTS_W void dft(InputArray src, OutputArray dst, int flags = 0, int nonzeroRows = 0);
-
-/** @brief Calculates the inverse Discrete Fourier Transform of a 1D or 2D array.
-
-idft(src, dst, flags) is equivalent to dft(src, dst, flags | #DFT_INVERSE) .
-@note None of dft and idft scales the result by default. So, you should pass #DFT_SCALE to one of
-dft or idft explicitly to make these transforms mutually inverse.
-@sa dft, dct, idct, mulSpectrums, getOptimalDFTSize
-@param src input floating-point real or complex array.
-@param dst output array whose size and type depend on the flags.
-@param flags operation flags (see dft and #DftFlags).
-@param nonzeroRows number of dst rows to process; the rest of the rows have undefined content (see
-the convolution sample in dft description.
-*/
-CV_EXPORTS_W void idft(InputArray src, OutputArray dst, int flags = 0, int nonzeroRows = 0);
-
-/** @brief Performs a forward or inverse discrete Cosine transform of 1D or 2D array.
-
-The function cv::dct performs a forward or inverse discrete Cosine transform (DCT) of a 1D or 2D
-floating-point array:
--   Forward Cosine transform of a 1D vector of N elements:
-    \f[Y = C^{(N)}  \cdot X\f]
-    where
-    \f[C^{(N)}_{jk}= \sqrt{\alpha_j/N} \cos \left ( \frac{\pi(2k+1)j}{2N} \right )\f]
-    and
-    \f$\alpha_0=1\f$, \f$\alpha_j=2\f$ for *j \> 0*.
--   Inverse Cosine transform of a 1D vector of N elements:
-    \f[X =  \left (C^{(N)} \right )^{-1}  \cdot Y =  \left (C^{(N)} \right )^T  \cdot Y\f]
-    (since \f$C^{(N)}\f$ is an orthogonal matrix, \f$C^{(N)} \cdot \left(C^{(N)}\right)^T = I\f$ )
--   Forward 2D Cosine transform of M x N matrix:
-    \f[Y = C^{(N)}  \cdot X  \cdot \left (C^{(N)} \right )^T\f]
--   Inverse 2D Cosine transform of M x N matrix:
-    \f[X =  \left (C^{(N)} \right )^T  \cdot X  \cdot C^{(N)}\f]
-
-The function chooses the mode of operation by looking at the flags and size of the input array:
--   If (flags & #DCT_INVERSE) == 0 , the function does a forward 1D or 2D transform. Otherwise, it
-    is an inverse 1D or 2D transform.
--   If (flags & #DCT_ROWS) != 0 , the function performs a 1D transform of each row.
--   If the array is a single column or a single row, the function performs a 1D transform.
--   If none of the above is true, the function performs a 2D transform.
-
-@note Currently dct supports even-size arrays (2, 4, 6 ...). For data analysis and approximation, you
-can pad the array when necessary.
-Also, the function performance depends very much, and not monotonically, on the array size (see
-getOptimalDFTSize ). In the current implementation DCT of a vector of size N is calculated via DFT
-of a vector of size N/2 . Thus, the optimal DCT size N1 \>= N can be calculated as:
-@code
-    size_t getOptimalDCTSize(size_t N) { return 2*getOptimalDFTSize((N+1)/2); }
-    N1 = getOptimalDCTSize(N);
-@endcode
-@param src input floating-point array.
-@param dst output array of the same size and type as src .
-@param flags transformation flags as a combination of cv::DftFlags (DCT_*)
-@sa dft , getOptimalDFTSize , idct
-*/
-CV_EXPORTS_W void dct(InputArray src, OutputArray dst, int flags = 0);
-
-/** @brief Calculates the inverse Discrete Cosine Transform of a 1D or 2D array.
-
-idct(src, dst, flags) is equivalent to dct(src, dst, flags | DCT_INVERSE).
-@param src input floating-point single-channel array.
-@param dst output array of the same size and type as src.
-@param flags operation flags.
-@sa  dct, dft, idft, getOptimalDFTSize
-*/
-CV_EXPORTS_W void idct(InputArray src, OutputArray dst, int flags = 0);
-
-/** @brief Performs the per-element multiplication of two Fourier spectrums.
-
-The function cv::mulSpectrums performs the per-element multiplication of the two CCS-packed or complex
-matrices that are results of a real or complex Fourier transform.
-
-The function, together with dft and idft , may be used to calculate convolution (pass conjB=false )
-or correlation (pass conjB=true ) of two arrays rapidly. When the arrays are complex, they are
-simply multiplied (per element) with an optional conjugation of the second-array elements. When the
-arrays are real, they are assumed to be CCS-packed (see dft for details).
-@param a first input array.
-@param b second input array of the same size and type as src1 .
-@param c output array of the same size and type as src1 .
-@param flags operation flags; currently, the only supported flag is cv::DFT_ROWS, which indicates that
-each row of src1 and src2 is an independent 1D Fourier spectrum. If you do not want to use this flag, then simply add a `0` as value.
-@param conjB optional flag that conjugates the second input array before the multiplication (true)
-or not (false).
-*/
-CV_EXPORTS_W void mulSpectrums(InputArray a, InputArray b, OutputArray c,
-                               int flags, bool conjB = false);
-
-/** @brief Returns the optimal DFT size for a given vector size.
-
-DFT performance is not a monotonic function of a vector size. Therefore, when you calculate
-convolution of two arrays or perform the spectral analysis of an array, it usually makes sense to
-pad the input data with zeros to get a bit larger array that can be transformed much faster than the
-original one. Arrays whose size is a power-of-two (2, 4, 8, 16, 32, ...) are the fastest to process.
-Though, the arrays whose size is a product of 2's, 3's, and 5's (for example, 300 = 5\*5\*3\*2\*2)
-are also processed quite efficiently.
-
-The function cv::getOptimalDFTSize returns the minimum number N that is greater than or equal to vecsize
-so that the DFT of a vector of size N can be processed efficiently. In the current implementation N
-= 2 ^p^ \* 3 ^q^ \* 5 ^r^ for some integer p, q, r.
-
-The function returns a negative number if vecsize is too large (very close to INT_MAX ).
-
-While the function cannot be used directly to estimate the optimal vector size for DCT transform
-(since the current DCT implementation supports only even-size vectors), it can be easily processed
-as getOptimalDFTSize((vecsize+1)/2)\*2.
-@param vecsize vector size.
-@sa dft , dct , idft , idct , mulSpectrums
-*/
-CV_EXPORTS_W int getOptimalDFTSize(int vecsize);
-
-/** @brief Returns the default random number generator.
-
-The function cv::theRNG returns the default random number generator. For each thread, there is a
-separate random number generator, so you can use the function safely in multi-thread environments.
-If you just need to get a single random number using this generator or initialize an array, you can
-use randu or randn instead. But if you are going to generate many random numbers inside a loop, it
-is much faster to use this function to retrieve the generator and then use RNG::operator _Tp() .
-@sa RNG, randu, randn
-*/
-CV_EXPORTS RNG& theRNG();
-
-/** @brief Sets state of default random number generator.
-
-The function cv::setRNGSeed sets state of default random number generator to custom value.
-@param seed new state for default random number generator
-@sa RNG, randu, randn
-*/
-CV_EXPORTS_W void setRNGSeed(int seed);
-
-/** @brief Generates a single uniformly-distributed random number or an array of random numbers.
-
-Non-template variant of the function fills the matrix dst with uniformly-distributed
-random numbers from the specified range:
-\f[\texttt{low} _c  \leq \texttt{dst} (I)_c <  \texttt{high} _c\f]
-@param dst output array of random numbers; the array must be pre-allocated.
-@param low inclusive lower boundary of the generated random numbers.
-@param high exclusive upper boundary of the generated random numbers.
-@sa RNG, randn, theRNG
-*/
-CV_EXPORTS_W void randu(InputOutputArray dst, InputArray low, InputArray high);
-
-/** @brief Fills the array with normally distributed random numbers.
-
-The function cv::randn fills the matrix dst with normally distributed random numbers with the specified
-mean vector and the standard deviation matrix. The generated random numbers are clipped to fit the
-value range of the output array data type.
-@param dst output array of random numbers; the array must be pre-allocated and have 1 to 4 channels.
-@param mean mean value (expectation) of the generated random numbers.
-@param stddev standard deviation of the generated random numbers; it can be either a vector (in
-which case a diagonal standard deviation matrix is assumed) or a square matrix.
-@sa RNG, randu
-*/
-CV_EXPORTS_W void randn(InputOutputArray dst, InputArray mean, InputArray stddev);
-
-/** @brief Shuffles the array elements randomly.
-
-The function cv::randShuffle shuffles the specified 1D array by randomly choosing pairs of elements and
-swapping them. The number of such swap operations will be dst.rows\*dst.cols\*iterFactor .
-@param dst input/output numerical 1D array.
-@param iterFactor scale factor that determines the number of random swap operations (see the details
-below).
-@param rng optional random number generator used for shuffling; if it is zero, theRNG () is used
-instead.
-@sa RNG, sort
-*/
-CV_EXPORTS_W void randShuffle(InputOutputArray dst, double iterFactor = 1., RNG* rng = 0);
-
-/** @brief Principal Component Analysis
-
-The class is used to calculate a special basis for a set of vectors. The
-basis will consist of eigenvectors of the covariance matrix calculated
-from the input set of vectors. The class %PCA can also transform
-vectors to/from the new coordinate space defined by the basis. Usually,
-in this new coordinate system, each vector from the original set (and
-any linear combination of such vectors) can be quite accurately
-approximated by taking its first few components, corresponding to the
-eigenvectors of the largest eigenvalues of the covariance matrix.
-Geometrically it means that you calculate a projection of the vector to
-a subspace formed by a few eigenvectors corresponding to the dominant
-eigenvalues of the covariance matrix. And usually such a projection is
-very close to the original vector. So, you can represent the original
-vector from a high-dimensional space with a much shorter vector
-consisting of the projected vector's coordinates in the subspace. Such a
-transformation is also known as Karhunen-Loeve Transform, or KLT.
-See http://en.wikipedia.org/wiki/Principal_component_analysis
-
-The sample below is the function that takes two matrices. The first
-function stores a set of vectors (a row per vector) that is used to
-calculate PCA. The second function stores another "test" set of vectors
-(a row per vector). First, these vectors are compressed with PCA, then
-reconstructed back, and then the reconstruction error norm is computed
-and printed for each vector. :
-
-@code{.cpp}
-using namespace cv;
-
-PCA compressPCA(const Mat& pcaset, int maxComponents,
-                const Mat& testset, Mat& compressed)
-{
-    PCA pca(pcaset, // pass the data
-            Mat(), // we do not have a pre-computed mean vector,
-                   // so let the PCA engine to compute it
-            PCA::DATA_AS_ROW, // indicate that the vectors
-                                // are stored as matrix rows
-                                // (use PCA::DATA_AS_COL if the vectors are
-                                // the matrix columns)
-            maxComponents // specify, how many principal components to retain
-            );
-    // if there is no test data, just return the computed basis, ready-to-use
-    if( !testset.data )
-        return pca;
-    CV_Assert( testset.cols == pcaset.cols );
-
-    compressed.create(testset.rows, maxComponents, testset.type());
-
-    Mat reconstructed;
-    for( int i = 0; i < testset.rows; i++ )
-    {
-        Mat vec = testset.row(i), coeffs = compressed.row(i), reconstructed;
-        // compress the vector, the result will be stored
-        // in the i-th row of the output matrix
-        pca.project(vec, coeffs);
-        // and then reconstruct it
-        pca.backProject(coeffs, reconstructed);
-        // and measure the error
-        printf("%d. diff = %g\n", i, norm(vec, reconstructed, NORM_L2));
-    }
-    return pca;
-}
-@endcode
-@sa calcCovarMatrix, mulTransposed, SVD, dft, dct
-*/
-class CV_EXPORTS PCA
-{
-public:
-    enum Flags { DATA_AS_ROW = 0, //!< indicates that the input samples are stored as matrix rows
-                 DATA_AS_COL = 1, //!< indicates that the input samples are stored as matrix columns
-                 USE_AVG     = 2  //!
-               };
-
-    /** @brief default constructor
-
-    The default constructor initializes an empty %PCA structure. The other
-    constructors initialize the structure and call PCA::operator()().
-    */
-    PCA();
-
-    /** @overload
-    @param data input samples stored as matrix rows or matrix columns.
-    @param mean optional mean value; if the matrix is empty (@c noArray()),
-    the mean is computed from the data.
-    @param flags operation flags; currently the parameter is only used to
-    specify the data layout (PCA::Flags)
-    @param maxComponents maximum number of components that %PCA should
-    retain; by default, all the components are retained.
-    */
-    PCA(InputArray data, InputArray mean, int flags, int maxComponents = 0);
-
-    /** @overload
-    @param data input samples stored as matrix rows or matrix columns.
-    @param mean optional mean value; if the matrix is empty (noArray()),
-    the mean is computed from the data.
-    @param flags operation flags; currently the parameter is only used to
-    specify the data layout (PCA::Flags)
-    @param retainedVariance Percentage of variance that PCA should retain.
-    Using this parameter will let the PCA decided how many components to
-    retain but it will always keep at least 2.
-    */
-    PCA(InputArray data, InputArray mean, int flags, double retainedVariance);
-
-    /** @brief performs %PCA
-
-    The operator performs %PCA of the supplied dataset. It is safe to reuse
-    the same PCA structure for multiple datasets. That is, if the structure
-    has been previously used with another dataset, the existing internal
-    data is reclaimed and the new @ref eigenvalues, @ref eigenvectors and @ref
-    mean are allocated and computed.
-
-    The computed @ref eigenvalues are sorted from the largest to the smallest and
-    the corresponding @ref eigenvectors are stored as eigenvectors rows.
-
-    @param data input samples stored as the matrix rows or as the matrix
-    columns.
-    @param mean optional mean value; if the matrix is empty (noArray()),
-    the mean is computed from the data.
-    @param flags operation flags; currently the parameter is only used to
-    specify the data layout. (Flags)
-    @param maxComponents maximum number of components that PCA should
-    retain; by default, all the components are retained.
-    */
-    PCA& operator()(InputArray data, InputArray mean, int flags, int maxComponents = 0);
-
-    /** @overload
-    @param data input samples stored as the matrix rows or as the matrix
-    columns.
-    @param mean optional mean value; if the matrix is empty (noArray()),
-    the mean is computed from the data.
-    @param flags operation flags; currently the parameter is only used to
-    specify the data layout. (PCA::Flags)
-    @param retainedVariance Percentage of variance that %PCA should retain.
-    Using this parameter will let the %PCA decided how many components to
-    retain but it will always keep at least 2.
-     */
-    PCA& operator()(InputArray data, InputArray mean, int flags, double retainedVariance);
-
-    /** @brief Projects vector(s) to the principal component subspace.
-
-    The methods project one or more vectors to the principal component
-    subspace, where each vector projection is represented by coefficients in
-    the principal component basis. The first form of the method returns the
-    matrix that the second form writes to the result. So the first form can
-    be used as a part of expression while the second form can be more
-    efficient in a processing loop.
-    @param vec input vector(s); must have the same dimensionality and the
-    same layout as the input data used at %PCA phase, that is, if
-    DATA_AS_ROW are specified, then `vec.cols==data.cols`
-    (vector dimensionality) and `vec.rows` is the number of vectors to
-    project, and the same is true for the PCA::DATA_AS_COL case.
-    */
-    Mat project(InputArray vec) const;
-
-    /** @overload
-    @param vec input vector(s); must have the same dimensionality and the
-    same layout as the input data used at PCA phase, that is, if
-    DATA_AS_ROW are specified, then `vec.cols==data.cols`
-    (vector dimensionality) and `vec.rows` is the number of vectors to
-    project, and the same is true for the PCA::DATA_AS_COL case.
-    @param result output vectors; in case of PCA::DATA_AS_COL, the
-    output matrix has as many columns as the number of input vectors, this
-    means that `result.cols==vec.cols` and the number of rows match the
-    number of principal components (for example, `maxComponents` parameter
-    passed to the constructor).
-     */
-    void project(InputArray vec, OutputArray result) const;
-
-    /** @brief Reconstructs vectors from their PC projections.
-
-    The methods are inverse operations to PCA::project. They take PC
-    coordinates of projected vectors and reconstruct the original vectors.
-    Unless all the principal components have been retained, the
-    reconstructed vectors are different from the originals. But typically,
-    the difference is small if the number of components is large enough (but
-    still much smaller than the original vector dimensionality). As a
-    result, PCA is used.
-    @param vec coordinates of the vectors in the principal component
-    subspace, the layout and size are the same as of PCA::project output
-    vectors.
-     */
-    Mat backProject(InputArray vec) const;
-
-    /** @overload
-    @param vec coordinates of the vectors in the principal component
-    subspace, the layout and size are the same as of PCA::project output
-    vectors.
-    @param result reconstructed vectors; the layout and size are the same as
-    of PCA::project input vectors.
-     */
-    void backProject(InputArray vec, OutputArray result) const;
-
-    /** @brief write PCA objects
-
-    Writes @ref eigenvalues @ref eigenvectors and @ref mean to specified FileStorage
-     */
-    void write(FileStorage& fs) const;
-
-    /** @brief load PCA objects
-
-    Loads @ref eigenvalues @ref eigenvectors and @ref mean from specified FileNode
-     */
-    void read(const FileNode& fn);
-
-    Mat eigenvectors; //!< eigenvectors of the covariation matrix
-    Mat eigenvalues; //!< eigenvalues of the covariation matrix
-    Mat mean; //!< mean value subtracted before the projection and added after the back projection
-};
-
-/** @example pca.cpp
-  An example using %PCA for dimensionality reduction while maintaining an amount of variance
- */
-
-/**
-   @brief Linear Discriminant Analysis
-   @todo document this class
- */
-class CV_EXPORTS LDA
-{
-public:
-    /** @brief constructor
-    Initializes a LDA with num_components (default 0).
-    */
-    explicit LDA(int num_components = 0);
-
-    /** Initializes and performs a Discriminant Analysis with Fisher's
-     Optimization Criterion on given data in src and corresponding labels
-     in labels. If 0 (or less) number of components are given, they are
-     automatically determined for given data in computation.
-    */
-    LDA(InputArrayOfArrays src, InputArray labels, int num_components = 0);
-
-    /** Serializes this object to a given filename.
-      */
-    void save(const String& filename) const;
-
-    /** Deserializes this object from a given filename.
-      */
-    void load(const String& filename);
-
-    /** Serializes this object to a given cv::FileStorage.
-      */
-    void save(FileStorage& fs) const;
-
-    /** Deserializes this object from a given cv::FileStorage.
-      */
-    void load(const FileStorage& node);
-
-    /** destructor
-      */
-    ~LDA();
-
-    /** Compute the discriminants for data in src (row aligned) and labels.
-      */
-    void compute(InputArrayOfArrays src, InputArray labels);
-
-    /** Projects samples into the LDA subspace.
-        src may be one or more row aligned samples.
-      */
-    Mat project(InputArray src);
-
-    /** Reconstructs projections from the LDA subspace.
-        src may be one or more row aligned projections.
-      */
-    Mat reconstruct(InputArray src);
-
-    /** Returns the eigenvectors of this LDA.
-      */
-    Mat eigenvectors() const { return _eigenvectors; }
-
-    /** Returns the eigenvalues of this LDA.
-      */
-    Mat eigenvalues() const { return _eigenvalues; }
-
-    static Mat subspaceProject(InputArray W, InputArray mean, InputArray src);
-    static Mat subspaceReconstruct(InputArray W, InputArray mean, InputArray src);
-
-protected:
-    bool _dataAsRow; // unused, but needed for 3.0 ABI compatibility.
-    int _num_components;
-    Mat _eigenvectors;
-    Mat _eigenvalues;
-    void lda(InputArrayOfArrays src, InputArray labels);
-};
-
-/** @brief Singular Value Decomposition
-
-Class for computing Singular Value Decomposition of a floating-point
-matrix. The Singular Value Decomposition is used to solve least-square
-problems, under-determined linear systems, invert matrices, compute
-condition numbers, and so on.
-
-If you want to compute a condition number of a matrix or an absolute value of
-its determinant, you do not need `u` and `vt`. You can pass
-flags=SVD::NO_UV|... . Another flag SVD::FULL_UV indicates that full-size u
-and vt must be computed, which is not necessary most of the time.
-
-@sa invert, solve, eigen, determinant
-*/
-class CV_EXPORTS SVD
-{
-public:
-    enum Flags {
-        /** allow the algorithm to modify the decomposed matrix; it can save space and speed up
-            processing. currently ignored. */
-        MODIFY_A = 1,
-        /** indicates that only a vector of singular values `w` is to be processed, while u and vt
-            will be set to empty matrices */
-        NO_UV    = 2,
-        /** when the matrix is not square, by default the algorithm produces u and vt matrices of
-            sufficiently large size for the further A reconstruction; if, however, FULL_UV flag is
-            specified, u and vt will be full-size square orthogonal matrices.*/
-        FULL_UV  = 4
-    };
-
-    /** @brief the default constructor
-
-    initializes an empty SVD structure
-      */
-    SVD();
-
-    /** @overload
-    initializes an empty SVD structure and then calls SVD::operator()
-    @param src decomposed matrix. The depth has to be CV_32F or CV_64F.
-    @param flags operation flags (SVD::Flags)
-      */
-    SVD( InputArray src, int flags = 0 );
-
-    /** @brief the operator that performs SVD. The previously allocated u, w and vt are released.
-
-    The operator performs the singular value decomposition of the supplied
-    matrix. The u,`vt` , and the vector of singular values w are stored in
-    the structure. The same SVD structure can be reused many times with
-    different matrices. Each time, if needed, the previous u,`vt` , and w
-    are reclaimed and the new matrices are created, which is all handled by
-    Mat::create.
-    @param src decomposed matrix. The depth has to be CV_32F or CV_64F.
-    @param flags operation flags (SVD::Flags)
-      */
-    SVD& operator ()( InputArray src, int flags = 0 );
-
-    /** @brief decomposes matrix and stores the results to user-provided matrices
-
-    The methods/functions perform SVD of matrix. Unlike SVD::SVD constructor
-    and SVD::operator(), they store the results to the user-provided
-    matrices:
-
-    @code{.cpp}
-    Mat A, w, u, vt;
-    SVD::compute(A, w, u, vt);
-    @endcode
-
-    @param src decomposed matrix. The depth has to be CV_32F or CV_64F.
-    @param w calculated singular values
-    @param u calculated left singular vectors
-    @param vt transposed matrix of right singular vectors
-    @param flags operation flags - see SVD::Flags.
-      */
-    static void compute( InputArray src, OutputArray w,
-                         OutputArray u, OutputArray vt, int flags = 0 );
-
-    /** @overload
-    computes singular values of a matrix
-    @param src decomposed matrix. The depth has to be CV_32F or CV_64F.
-    @param w calculated singular values
-    @param flags operation flags - see SVD::Flags.
-      */
-    static void compute( InputArray src, OutputArray w, int flags = 0 );
-
-    /** @brief performs back substitution
-      */
-    static void backSubst( InputArray w, InputArray u,
-                           InputArray vt, InputArray rhs,
-                           OutputArray dst );
-
-    /** @brief solves an under-determined singular linear system
-
-    The method finds a unit-length solution x of a singular linear system
-    A\*x = 0. Depending on the rank of A, there can be no solutions, a
-    single solution or an infinite number of solutions. In general, the
-    algorithm solves the following problem:
-    \f[dst =  \arg \min _{x:  \| x \| =1}  \| src  \cdot x  \|\f]
-    @param src left-hand-side matrix.
-    @param dst found solution.
-      */
-    static void solveZ( InputArray src, OutputArray dst );
-
-    /** @brief performs a singular value back substitution.
-
-    The method calculates a back substitution for the specified right-hand
-    side:
-
-    \f[\texttt{x} =  \texttt{vt} ^T  \cdot diag( \texttt{w} )^{-1}  \cdot \texttt{u} ^T  \cdot \texttt{rhs} \sim \texttt{A} ^{-1}  \cdot \texttt{rhs}\f]
-
-    Using this technique you can either get a very accurate solution of the
-    convenient linear system, or the best (in the least-squares terms)
-    pseudo-solution of an overdetermined linear system.
-
-    @param rhs right-hand side of a linear system (u\*w\*v')\*dst = rhs to
-    be solved, where A has been previously decomposed.
-
-    @param dst found solution of the system.
-
-    @note Explicit SVD with the further back substitution only makes sense
-    if you need to solve many linear systems with the same left-hand side
-    (for example, src ). If all you need is to solve a single system
-    (possibly with multiple rhs immediately available), simply call solve
-    add pass #DECOMP_SVD there. It does absolutely the same thing.
-      */
-    void backSubst( InputArray rhs, OutputArray dst ) const;
-
-    /** @todo document */
-    template<typename _Tp, int m, int n, int nm> static
-    void compute( const Matx<_Tp, m, n>& a, Matx<_Tp, nm, 1>& w, Matx<_Tp, m, nm>& u, Matx<_Tp, n, nm>& vt );
-
-    /** @todo document */
-    template<typename _Tp, int m, int n, int nm> static
-    void compute( const Matx<_Tp, m, n>& a, Matx<_Tp, nm, 1>& w );
-
-    /** @todo document */
-    template<typename _Tp, int m, int n, int nm, int nb> static
-    void backSubst( const Matx<_Tp, nm, 1>& w, const Matx<_Tp, m, nm>& u, const Matx<_Tp, n, nm>& vt, const Matx<_Tp, m, nb>& rhs, Matx<_Tp, n, nb>& dst );
-
-    Mat u, w, vt;
-};
-
-/** @brief Random Number Generator
-
-Random number generator. It encapsulates the state (currently, a 64-bit
-integer) and has methods to return scalar random values and to fill
-arrays with random values. Currently it supports uniform and Gaussian
-(normal) distributions. The generator uses Multiply-With-Carry
-algorithm, introduced by G. Marsaglia (
-<http://en.wikipedia.org/wiki/Multiply-with-carry> ).
-Gaussian-distribution random numbers are generated using the Ziggurat
-algorithm ( <http://en.wikipedia.org/wiki/Ziggurat_algorithm> ),
-introduced by G. Marsaglia and W. W. Tsang.
-*/
-class CV_EXPORTS RNG
-{
-public:
-    enum { UNIFORM = 0,
-           NORMAL  = 1
-         };
-
-    /** @brief constructor
-
-    These are the RNG constructors. The first form sets the state to some
-    pre-defined value, equal to 2\*\*32-1 in the current implementation. The
-    second form sets the state to the specified value. If you passed state=0
-    , the constructor uses the above default value instead to avoid the
-    singular random number sequence, consisting of all zeros.
-    */
-    RNG();
-    /** @overload
-    @param state 64-bit value used to initialize the RNG.
-    */
-    RNG(uint64 state);
-    /**The method updates the state using the MWC algorithm and returns the
-    next 32-bit random number.*/
-    unsigned next();
-
-    /**Each of the methods updates the state using the MWC algorithm and
-    returns the next random number of the specified type. In case of integer
-    types, the returned number is from the available value range for the
-    specified type. In case of floating-point types, the returned value is
-    from [0,1) range.
-    */
-    operator uchar();
-    /** @overload */
-    operator schar();
-    /** @overload */
-    operator ushort();
-    /** @overload */
-    operator short();
-    /** @overload */
-    operator unsigned();
-    /** @overload */
-    operator int();
-    /** @overload */
-    operator float();
-    /** @overload */
-    operator double();
-
-    /** @brief returns a random integer sampled uniformly from [0, N).
-
-    The methods transform the state using the MWC algorithm and return the
-    next random number. The first form is equivalent to RNG::next . The
-    second form returns the random number modulo N , which means that the
-    result is in the range [0, N) .
-    */
-    unsigned operator ()();
-    /** @overload
-    @param N upper non-inclusive boundary of the returned random number.
-    */
-    unsigned operator ()(unsigned N);
-
-    /** @brief returns uniformly distributed integer random number from [a,b) range
-
-    The methods transform the state using the MWC algorithm and return the
-    next uniformly-distributed random number of the specified type, deduced
-    from the input parameter type, from the range [a, b) . There is a nuance
-    illustrated by the following sample:
-
-    @code{.cpp}
-    RNG rng;
-
-    // always produces 0
-    double a = rng.uniform(0, 1);
-
-    // produces double from [0, 1)
-    double a1 = rng.uniform((double)0, (double)1);
-
-    // produces float from [0, 1)
-    float b = rng.uniform(0.f, 1.f);
-
-    // produces double from [0, 1)
-    double c = rng.uniform(0., 1.);
-
-    // may cause compiler error because of ambiguity:
-    //  RNG::uniform(0, (int)0.999999)? or RNG::uniform((double)0, 0.99999)?
-    double d = rng.uniform(0, 0.999999);
-    @endcode
-
-    The compiler does not take into account the type of the variable to
-    which you assign the result of RNG::uniform . The only thing that
-    matters to the compiler is the type of a and b parameters. So, if you
-    want a floating-point random number, but the range boundaries are
-    integer numbers, either put dots in the end, if they are constants, or
-    use explicit type cast operators, as in the a1 initialization above.
-    @param a lower inclusive boundary of the returned random number.
-    @param b upper non-inclusive boundary of the returned random number.
-      */
-    int uniform(int a, int b);
-    /** @overload */
-    float uniform(float a, float b);
-    /** @overload */
-    double uniform(double a, double b);
-
-    /** @brief Fills arrays with random numbers.
-
-    @param mat 2D or N-dimensional matrix; currently matrices with more than
-    4 channels are not supported by the methods, use Mat::reshape as a
-    possible workaround.
-    @param distType distribution type, RNG::UNIFORM or RNG::NORMAL.
-    @param a first distribution parameter; in case of the uniform
-    distribution, this is an inclusive lower boundary, in case of the normal
-    distribution, this is a mean value.
-    @param b second distribution parameter; in case of the uniform
-    distribution, this is a non-inclusive upper boundary, in case of the
-    normal distribution, this is a standard deviation (diagonal of the
-    standard deviation matrix or the full standard deviation matrix).
-    @param saturateRange pre-saturation flag; for uniform distribution only;
-    if true, the method will first convert a and b to the acceptable value
-    range (according to the mat datatype) and then will generate uniformly
-    distributed random numbers within the range [saturate(a), saturate(b)),
-    if saturateRange=false, the method will generate uniformly distributed
-    random numbers in the original range [a, b) and then will saturate them,
-    it means, for example, that
-    <tt>theRNG().fill(mat_8u, RNG::UNIFORM, -DBL_MAX, DBL_MAX)</tt> will likely
-    produce array mostly filled with 0's and 255's, since the range (0, 255)
-    is significantly smaller than [-DBL_MAX, DBL_MAX).
-
-    Each of the methods fills the matrix with the random values from the
-    specified distribution. As the new numbers are generated, the RNG state
-    is updated accordingly. In case of multiple-channel images, every
-    channel is filled independently, which means that RNG cannot generate
-    samples from the multi-dimensional Gaussian distribution with
-    non-diagonal covariance matrix directly. To do that, the method
-    generates samples from multi-dimensional standard Gaussian distribution
-    with zero mean and identity covariation matrix, and then transforms them
-    using transform to get samples from the specified Gaussian distribution.
-    */
-    void fill( InputOutputArray mat, int distType, InputArray a, InputArray b, bool saturateRange = false );
-
-    /** @brief Returns the next random number sampled from the Gaussian distribution
-    @param sigma standard deviation of the distribution.
-
-    The method transforms the state using the MWC algorithm and returns the
-    next random number from the Gaussian distribution N(0,sigma) . That is,
-    the mean value of the returned random numbers is zero and the standard
-    deviation is the specified sigma .
-    */
-    double gaussian(double sigma);
-
-    uint64 state;
-
-    bool operator ==(const RNG& other) const;
-};
-
-/** @brief Mersenne Twister random number generator
-
-Inspired by http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/MT2002/CODES/mt19937ar.c
-@todo document
- */
-class CV_EXPORTS RNG_MT19937
-{
-public:
-    RNG_MT19937();
-    RNG_MT19937(unsigned s);
-    void seed(unsigned s);
-
-    unsigned next();
-
-    operator int();
-    operator unsigned();
-    operator float();
-    operator double();
-
-    unsigned operator ()(unsigned N);
-    unsigned operator ()();
-
-    /** @brief returns uniformly distributed integer random number from [a,b) range
-
-*/
-    int uniform(int a, int b);
-    /** @brief returns uniformly distributed floating-point random number from [a,b) range
-
-*/
-    float uniform(float a, float b);
-    /** @brief returns uniformly distributed double-precision floating-point random number from [a,b) range
-
-*/
-    double uniform(double a, double b);
-
-private:
-    enum PeriodParameters {N = 624, M = 397};
-    unsigned state[N];
-    int mti;
-};
-
-//! @} core_array
-
-//! @addtogroup core_cluster
-//!  @{
-
-/** @example kmeans.cpp
-  An example on K-means clustering
-*/
-
-/** @brief Finds centers of clusters and groups input samples around the clusters.
-
-The function kmeans implements a k-means algorithm that finds the centers of cluster_count clusters
-and groups the input samples around the clusters. As an output, \f$\texttt{labels}_i\f$ contains a
-0-based cluster index for the sample stored in the \f$i^{th}\f$ row of the samples matrix.
-
-@note
--   (Python) An example on K-means clustering can be found at
-    opencv_source_code/samples/python/kmeans.py
-@param data Data for clustering. An array of N-Dimensional points with float coordinates is needed.
-Examples of this array can be:
--   Mat points(count, 2, CV_32F);
--   Mat points(count, 1, CV_32FC2);
--   Mat points(1, count, CV_32FC2);
--   std::vector\<cv::Point2f\> points(sampleCount);
-@param K Number of clusters to split the set by.
-@param bestLabels Input/output integer array that stores the cluster indices for every sample.
-@param criteria The algorithm termination criteria, that is, the maximum number of iterations and/or
-the desired accuracy. The accuracy is specified as criteria.epsilon. As soon as each of the cluster
-centers moves by less than criteria.epsilon on some iteration, the algorithm stops.
-@param attempts Flag to specify the number of times the algorithm is executed using different
-initial labellings. The algorithm returns the labels that yield the best compactness (see the last
-function parameter).
-@param flags Flag that can take values of cv::KmeansFlags
-@param centers Output matrix of the cluster centers, one row per each cluster center.
-@return The function returns the compactness measure that is computed as
-\f[\sum _i  \| \texttt{samples} _i -  \texttt{centers} _{ \texttt{labels} _i} \| ^2\f]
-after every attempt. The best (minimum) value is chosen and the corresponding labels and the
-compactness value are returned by the function. Basically, you can use only the core of the
-function, set the number of attempts to 1, initialize labels each time using a custom algorithm,
-pass them with the ( flags = #KMEANS_USE_INITIAL_LABELS ) flag, and then choose the best
-(most-compact) clustering.
-*/
-CV_EXPORTS_W double kmeans( InputArray data, int K, InputOutputArray bestLabels,
-                            TermCriteria criteria, int attempts,
-                            int flags, OutputArray centers = noArray() );
-
-//! @} core_cluster
-
-//! @addtogroup core_basic
-//! @{
-
-/////////////////////////////// Formatted output of cv::Mat ///////////////////////////
-
-/** @todo document */
-class CV_EXPORTS Formatted
-{
-public:
-    virtual const char* next() = 0;
-    virtual void reset() = 0;
-    virtual ~Formatted();
-};
-
-/** @todo document */
-class CV_EXPORTS Formatter
-{
-public:
-    enum { FMT_DEFAULT = 0,
-           FMT_MATLAB  = 1,
-           FMT_CSV     = 2,
-           FMT_PYTHON  = 3,
-           FMT_NUMPY   = 4,
-           FMT_C       = 5
-         };
-
-    virtual ~Formatter();
-
-    virtual Ptr<Formatted> format(const Mat& mtx) const = 0;
-
-    virtual void set32fPrecision(int p = 8) = 0;
-    virtual void set64fPrecision(int p = 16) = 0;
-    virtual void setMultiline(bool ml = true) = 0;
-
-    static Ptr<Formatter> get(int fmt = FMT_DEFAULT);
-
-};
-
-static inline
-String& operator << (String& out, Ptr<Formatted> fmtd)
-{
-    fmtd->reset();
-    for(const char* str = fmtd->next(); str; str = fmtd->next())
-        out += cv::String(str);
-    return out;
-}
-
-static inline
-String& operator << (String& out, const Mat& mtx)
-{
-    return out << Formatter::get()->format(mtx);
-}
-
-//////////////////////////////////////// Algorithm ////////////////////////////////////
-
-class CV_EXPORTS Algorithm;
-
-template<typename _Tp> struct ParamType {};
-
-
-/** @brief This is a base class for all more or less complex algorithms in OpenCV
-
-especially for classes of algorithms, for which there can be multiple implementations. The examples
-are stereo correspondence (for which there are algorithms like block matching, semi-global block
-matching, graph-cut etc.), background subtraction (which can be done using mixture-of-gaussians
-models, codebook-based algorithm etc.), optical flow (block matching, Lucas-Kanade, Horn-Schunck
-etc.).
-
-Here is example of SimpleBlobDetector use in your application via Algorithm interface:
-@snippet snippets/core_various.cpp Algorithm
- */
-class CV_EXPORTS_W Algorithm
-{
-public:
-    Algorithm();
-    virtual ~Algorithm();
-
-    /** @brief Clears the algorithm state
-    */
-    CV_WRAP virtual void clear() {}
-
-    /** @brief Stores algorithm parameters in a file storage
-    */
-    virtual void write(FileStorage& fs) const { (void)fs; }
-
-    /** @brief simplified API for language bindings
-     * @overload
-     */
-    CV_WRAP void write(const Ptr<FileStorage>& fs, const String& name = String()) const;
-
-    /** @brief Reads algorithm parameters from a file storage
-    */
-    CV_WRAP virtual void read(const FileNode& fn) { (void)fn; }
-
-    /** @brief Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read
-     */
-    CV_WRAP virtual bool empty() const { return false; }
-
-    /** @brief Reads algorithm from the file node
-
-     This is static template method of Algorithm. It's usage is following (in the case of SVM):
-     @code
-     cv::FileStorage fsRead("example.xml", FileStorage::READ);
-     Ptr<SVM> svm = Algorithm::read<SVM>(fsRead.root());
-     @endcode
-     In order to make this method work, the derived class must overwrite Algorithm::read(const
-     FileNode& fn) and also have static create() method without parameters
-     (or with all the optional parameters)
-     */
-    template<typename _Tp> static Ptr<_Tp> read(const FileNode& fn)
-    {
-        Ptr<_Tp> obj = _Tp::create();
-        obj->read(fn);
-        return !obj->empty() ? obj : Ptr<_Tp>();
-    }
-
-    /** @brief Loads algorithm from the file
-
-     @param filename Name of the file to read.
-     @param objname The optional name of the node to read (if empty, the first top-level node will be used)
-
-     This is static template method of Algorithm. It's usage is following (in the case of SVM):
-     @code
-     Ptr<SVM> svm = Algorithm::load<SVM>("my_svm_model.xml");
-     @endcode
-     In order to make this method work, the derived class must overwrite Algorithm::read(const
-     FileNode& fn).
-     */
-    template<typename _Tp> static Ptr<_Tp> load(const String& filename, const String& objname=String())
-    {
-        FileStorage fs(filename, FileStorage::READ);
-        CV_Assert(fs.isOpened());
-        FileNode fn = objname.empty() ? fs.getFirstTopLevelNode() : fs[objname];
-        if (fn.empty()) return Ptr<_Tp>();
-        Ptr<_Tp> obj = _Tp::create();
-        obj->read(fn);
-        return !obj->empty() ? obj : Ptr<_Tp>();
-    }
-
-    /** @brief Loads algorithm from a String
-
-     @param strModel The string variable containing the model you want to load.
-     @param objname The optional name of the node to read (if empty, the first top-level node will be used)
-
-     This is static template method of Algorithm. It's usage is following (in the case of SVM):
-     @code
-     Ptr<SVM> svm = Algorithm::loadFromString<SVM>(myStringModel);
-     @endcode
-     */
-    template<typename _Tp> static Ptr<_Tp> loadFromString(const String& strModel, const String& objname=String())
-    {
-        FileStorage fs(strModel, FileStorage::READ + FileStorage::MEMORY);
-        FileNode fn = objname.empty() ? fs.getFirstTopLevelNode() : fs[objname];
-        Ptr<_Tp> obj = _Tp::create();
-        obj->read(fn);
-        return !obj->empty() ? obj : Ptr<_Tp>();
-    }
-
-    /** Saves the algorithm to a file.
-     In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs). */
-    CV_WRAP virtual void save(const String& filename) const;
-
-    /** Returns the algorithm string identifier.
-     This string is used as top level xml/yml node tag when the object is saved to a file or string. */
-    CV_WRAP virtual String getDefaultName() const;
-
-protected:
-    void writeFormat(FileStorage& fs) const;
-};
-
-struct Param {
-    enum { INT=0, BOOLEAN=1, REAL=2, STRING=3, MAT=4, MAT_VECTOR=5, ALGORITHM=6, FLOAT=7,
-           UNSIGNED_INT=8, UINT64=9, UCHAR=11, SCALAR=12 };
-};
-
-
-
-template<> struct ParamType<bool>
-{
-    typedef bool const_param_type;
-    typedef bool member_type;
-
-    enum { type = Param::BOOLEAN };
-};
-
-template<> struct ParamType<int>
-{
-    typedef int const_param_type;
-    typedef int member_type;
-
-    enum { type = Param::INT };
-};
-
-template<> struct ParamType<double>
-{
-    typedef double const_param_type;
-    typedef double member_type;
-
-    enum { type = Param::REAL };
-};
-
-template<> struct ParamType<String>
-{
-    typedef const String& const_param_type;
-    typedef String member_type;
-
-    enum { type = Param::STRING };
-};
-
-template<> struct ParamType<Mat>
-{
-    typedef const Mat& const_param_type;
-    typedef Mat member_type;
-
-    enum { type = Param::MAT };
-};
-
-template<> struct ParamType<std::vector<Mat> >
-{
-    typedef const std::vector<Mat>& const_param_type;
-    typedef std::vector<Mat> member_type;
-
-    enum { type = Param::MAT_VECTOR };
-};
-
-template<> struct ParamType<Algorithm>
-{
-    typedef const Ptr<Algorithm>& const_param_type;
-    typedef Ptr<Algorithm> member_type;
-
-    enum { type = Param::ALGORITHM };
-};
-
-template<> struct ParamType<float>
-{
-    typedef float const_param_type;
-    typedef float member_type;
-
-    enum { type = Param::FLOAT };
-};
-
-template<> struct ParamType<unsigned>
-{
-    typedef unsigned const_param_type;
-    typedef unsigned member_type;
-
-    enum { type = Param::UNSIGNED_INT };
-};
-
-template<> struct ParamType<uint64>
-{
-    typedef uint64 const_param_type;
-    typedef uint64 member_type;
-
-    enum { type = Param::UINT64 };
-};
-
-template<> struct ParamType<uchar>
-{
-    typedef uchar const_param_type;
-    typedef uchar member_type;
-
-    enum { type = Param::UCHAR };
-};
-
-template<> struct ParamType<Scalar>
-{
-    typedef const Scalar& const_param_type;
-    typedef Scalar member_type;
-
-    enum { type = Param::SCALAR };
-};
-
-//! @} core_basic
-
-} //namespace cv
-
-#include "opencv2/core/operations.hpp"
-#include "opencv2/core/cvstd.inl.hpp"
-#include "opencv2/core/utility.hpp"
-#include "opencv2/core/optim.hpp"
-#include "opencv2/core/ovx.hpp"
-
-#endif /*OPENCV_CORE_HPP*/
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/PlateInfo.h b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/PlateInfo.h
deleted file mode 100755
index f500bb5..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/PlateInfo.h
+++ /dev/null
@@ -1,126 +0,0 @@
-//
-// Created by 庾金科 on 20/09/2017.
-//
-
-#ifndef SWIFTPR_PLATEINFO_H
-#define SWIFTPR_PLATEINFO_H
-#include <opencv2/opencv.hpp>
-namespace pr {
-
-    typedef std::vector<cv::Mat> Character;
-
-    enum PlateColor { BLUE, YELLOW, WHITE, GREEN, BLACK,UNKNOWN};
-    enum CharType {CHINESE,LETTER,LETTER_NUMS,INVALID};
-
-
-    class PlateInfo {
-        public:
-            std::vector<std::pair<CharType,cv::Mat>> plateChars;
-            std::vector<std::pair<CharType,cv::Mat>> plateCoding;
-            float confidence = 0;
-            PlateInfo(const cv::Mat &plateData, std::string plateName, cv::Rect plateRect, PlateColor plateType) {
-                    licensePlate = plateData;
-                    name = plateName;
-                    ROI = plateRect;
-                    Type = plateType;
-                }
-            PlateInfo(const cv::Mat &plateData, cv::Rect plateRect, PlateColor plateType) {
-                licensePlate = plateData;
-                ROI = plateRect;
-                Type = plateType;
-            }
-            PlateInfo(const cv::Mat &plateData, cv::Rect plateRect) {
-                licensePlate = plateData;
-                ROI = plateRect;
-            }
-            PlateInfo() {
-
-            }
-
-            cv::Mat getPlateImage() {
-                return licensePlate;
-            }
-
-            void setPlateImage(cv::Mat plateImage){
-                licensePlate = plateImage;
-            }
-
-            cv::Rect getPlateRect() {
-                return ROI;
-            }
-
-            void setPlateRect(cv::Rect plateRect)   {
-                ROI = plateRect;
-            }
-            cv::String getPlateName() {
-                return name;
-
-            }
-            void setPlateName(cv::String plateName) {
-                name = plateName;
-            }
-            int getPlateType() {
-                return Type;
-            }
-
-            void appendPlateChar(const std::pair<CharType,cv::Mat> &plateChar)
-            {
-                plateChars.push_back(plateChar);
-            }
-
-            void appendPlateCoding(const std::pair<CharType,cv::Mat> &charProb){
-                plateCoding.push_back(charProb);
-            }
-
-    //        cv::Mat getPlateChars(int id) {
-    //            if(id<PlateChars.size())
-    //                return PlateChars[id];
-    //        }
-            std::string decodePlateNormal(std::vector<std::string> mappingTable) {
-                std::string decode;
-                for(auto plate:plateCoding) {
-                    float *prob = (float *)plate.second.data;
-                    if(plate.first == CHINESE) {
-
-                        decode += mappingTable[std::max_element(prob,prob+31) - prob];
-                        confidence+=*std::max_element(prob,prob+31);
-
-
-//                        std::cout<<*std::max_element(prob,prob+31)<<std::endl;
-
-                    }
-
-                    else if(plate.first == LETTER) {
-                        decode += mappingTable[std::max_element(prob+41,prob+65)- prob];
-                        confidence+=*std::max_element(prob+41,prob+65);
-                    }
-
-                    else if(plate.first == LETTER_NUMS) {
-                        decode += mappingTable[std::max_element(prob+31,prob+65)- prob];
-                        confidence+=*std::max_element(prob+31,prob+65);
-//                        std::cout<<*std::max_element(prob+31,prob+65)<<std::endl;
-
-                    }
-                    else if(plate.first == INVALID)
-                    {
-                        decode+='*';
-                    }
-
-                }
-                name = decode;
-
-                confidence/=7;
-
-                return decode;
-            }
-
-    private:
-        cv::Mat licensePlate;
-        cv::Rect ROI;
-        std::string name ;
-        PlateColor Type;
-    };
-}
-
-
-#endif //SWIFTPR_PLATEINFO_H
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/affine.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/affine.hpp
deleted file mode 100755
index 7e2ed30..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/affine.hpp
+++ /dev/null
@@ -1,678 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_CORE_AFFINE3_HPP
-#define OPENCV_CORE_AFFINE3_HPP
-
-#ifdef __cplusplus
-
-#include <opencv2/core.hpp>
-
-namespace cv
-{
-
-//! @addtogroup core
-//! @{
-
-    /** @brief Affine transform
-     *
-     * It represents a 4x4 homogeneous transformation matrix \f$T\f$
-     *
-     *  \f[T =
-     *  \begin{bmatrix}
-     *  R & t\\
-     *  0 & 1\\
-     *  \end{bmatrix}
-     *  \f]
-     *
-     *  where \f$R\f$ is a 3x3 rotation matrix and \f$t\f$ is a 3x1 translation vector.
-     *
-     *  You can specify \f$R\f$ either by a 3x3 rotation matrix or by a 3x1 rotation vector,
-     *  which is converted to a 3x3 rotation matrix by the Rodrigues formula.
-     *
-     *  To construct a matrix \f$T\f$ representing first rotation around the axis \f$r\f$ with rotation
-     *  angle \f$|r|\f$ in radian (right hand rule) and then translation by the vector \f$t\f$, you can use
-     *
-     *  @code
-     *  cv::Vec3f r, t;
-     *  cv::Affine3f T(r, t);
-     *  @endcode
-     *
-     *  If you already have the rotation matrix \f$R\f$, then you can use
-     *
-     *  @code
-     *  cv::Matx33f R;
-     *  cv::Affine3f T(R, t);
-     *  @endcode
-     *
-     *  To extract the rotation matrix \f$R\f$ from \f$T\f$, use
-     *
-     *  @code
-     *  cv::Matx33f R = T.rotation();
-     *  @endcode
-     *
-     *  To extract the translation vector \f$t\f$ from \f$T\f$, use
-     *
-     *  @code
-     *  cv::Vec3f t = T.translation();
-     *  @endcode
-     *
-     *  To extract the rotation vector \f$r\f$ from \f$T\f$, use
-     *
-     *  @code
-     *  cv::Vec3f r = T.rvec();
-     *  @endcode
-     *
-     *  Note that since the mapping from rotation vectors to rotation matrices
-     *  is many to one. The returned rotation vector is not necessarily the one
-     *  you used before to set the matrix.
-     *
-     *  If you have two transformations \f$T = T_1 * T_2\f$, use
-     *
-     *  @code
-     *  cv::Affine3f T, T1, T2;
-     *  T = T2.concatenate(T1);
-     *  @endcode
-     *
-     *  To get the inverse transform of \f$T\f$, use
-     *
-     *  @code
-     *  cv::Affine3f T, T_inv;
-     *  T_inv = T.inv();
-     *  @endcode
-     *
-     */
-    template<typename T>
-    class Affine3
-    {
-    public:
-        typedef T float_type;
-        typedef Matx<float_type, 3, 3> Mat3;
-        typedef Matx<float_type, 4, 4> Mat4;
-        typedef Vec<float_type, 3> Vec3;
-
-       //! Default constructor. It represents a 4x4 identity matrix.
-        Affine3();
-
-        //! Augmented affine matrix
-        Affine3(const Mat4& affine);
-
-        /**
-         *  The resulting 4x4 matrix is
-         *
-         *  \f[
-         *  \begin{bmatrix}
-         *  R & t\\
-         *  0 & 1\\
-         *  \end{bmatrix}
-         *  \f]
-         *
-         * @param R 3x3 rotation matrix.
-         * @param t 3x1 translation vector.
-         */
-        Affine3(const Mat3& R, const Vec3& t = Vec3::all(0));
-
-        /**
-         * Rodrigues vector.
-         *
-         * The last row of the current matrix is set to [0,0,0,1].
-         *
-         * @param rvec 3x1 rotation vector. Its direction indicates the rotation axis and its length
-         *             indicates the rotation angle in radian (using right hand rule).
-         * @param t 3x1 translation vector.
-         */
-        Affine3(const Vec3& rvec, const Vec3& t = Vec3::all(0));
-
-        /**
-         * Combines all constructors above. Supports 4x4, 3x4, 3x3, 1x3, 3x1 sizes of data matrix.
-         *
-         * The last row of the current matrix is set to [0,0,0,1] when data is not 4x4.
-         *
-         * @param data 1-channel matrix.
-         *             when it is 4x4, it is copied to the current matrix and t is not used.
-         *             When it is 3x4, it is copied to the upper part 3x4 of the current matrix and t is not used.
-         *             When it is 3x3, it is copied to the upper left 3x3 part of the current matrix.
-         *             When it is 3x1 or 1x3, it is treated as a rotation vector and the Rodrigues formula is used
-         *                             to compute a 3x3 rotation matrix.
-         * @param t 3x1 translation vector. It is used only when data is neither 4x4 nor 3x4.
-         */
-        explicit Affine3(const Mat& data, const Vec3& t = Vec3::all(0));
-
-        //! From 16-element array
-        explicit Affine3(const float_type* vals);
-
-        //! Create an 4x4 identity transform
-        static Affine3 Identity();
-
-        /**
-         * Rotation matrix.
-         *
-         * Copy the rotation matrix to the upper left 3x3 part of the current matrix.
-         * The remaining elements of the current matrix are not changed.
-         *
-         * @param R 3x3 rotation matrix.
-         *
-         */
-        void rotation(const Mat3& R);
-
-        /**
-         * Rodrigues vector.
-         *
-         * It sets the upper left 3x3 part of the matrix. The remaining part is unaffected.
-         *
-         * @param rvec 3x1 rotation vector. The direction indicates the rotation axis and
-         *             its length indicates the rotation angle in radian (using the right thumb convention).
-         */
-        void rotation(const Vec3& rvec);
-
-        /**
-         * Combines rotation methods above. Supports 3x3, 1x3, 3x1 sizes of data matrix.
-         *
-         * It sets the upper left 3x3 part of the matrix. The remaining part is unaffected.
-         *
-         * @param data 1-channel matrix.
-         *             When it is a 3x3 matrix, it sets the upper left 3x3 part of the current matrix.
-         *             When it is a 1x3 or 3x1 matrix, it is used as a rotation vector. The Rodrigues formula
-         *             is used to compute the rotation matrix and sets the upper left 3x3 part of the current matrix.
-         */
-        void rotation(const Mat& data);
-
-        /**
-         * Copy the 3x3 matrix L to the upper left part of the current matrix
-         *
-         * It sets the upper left 3x3 part of the matrix. The remaining part is unaffected.
-         *
-         * @param L 3x3 matrix.
-         */
-        void linear(const Mat3& L);
-
-        /**
-         * Copy t to the first three elements of the last column of the current matrix
-         *
-         * It sets the upper right 3x1 part of the matrix. The remaining part is unaffected.
-         *
-         * @param t 3x1 translation vector.
-         */
-        void translation(const Vec3& t);
-
-        //! @return the upper left 3x3 part
-        Mat3 rotation() const;
-
-        //! @return the upper left 3x3 part
-        Mat3 linear() const;
-
-        //! @return the upper right 3x1 part
-        Vec3 translation() const;
-
-        //! Rodrigues vector.
-        //! @return a vector representing the upper left 3x3 rotation matrix of the current matrix.
-        //! @warning  Since the mapping between rotation vectors and rotation matrices is many to one,
-        //!           this function returns only one rotation vector that represents the current rotation matrix,
-        //!           which is not necessarily the same one set by `rotation(const Vec3& rvec)`.
-        Vec3 rvec() const;
-
-        //! @return the inverse of the current matrix.
-        Affine3 inv(int method = cv::DECOMP_SVD) const;
-
-        //! a.rotate(R) is equivalent to Affine(R, 0) * a;
-        Affine3 rotate(const Mat3& R) const;
-
-        //! a.rotate(rvec) is equivalent to Affine(rvec, 0) * a;
-        Affine3 rotate(const Vec3& rvec) const;
-
-        //! a.translate(t) is equivalent to Affine(E, t) * a, where E is an identity matrix
-        Affine3 translate(const Vec3& t) const;
-
-        //! a.concatenate(affine) is equivalent to affine * a;
-        Affine3 concatenate(const Affine3& affine) const;
-
-        template <typename Y> operator Affine3<Y>() const;
-
-        template <typename Y> Affine3<Y> cast() const;
-
-        Mat4 matrix;
-
-#if defined EIGEN_WORLD_VERSION && defined EIGEN_GEOMETRY_MODULE_H
-        Affine3(const Eigen::Transform<T, 3, Eigen::Affine, (Eigen::RowMajor)>& affine);
-        Affine3(const Eigen::Transform<T, 3, Eigen::Affine>& affine);
-        operator Eigen::Transform<T, 3, Eigen::Affine, (Eigen::RowMajor)>() const;
-        operator Eigen::Transform<T, 3, Eigen::Affine>() const;
-#endif
-    };
-
-    template<typename T> static
-    Affine3<T> operator*(const Affine3<T>& affine1, const Affine3<T>& affine2);
-
-    //! V is a 3-element vector with member fields x, y and z
-    template<typename T, typename V> static
-    V operator*(const Affine3<T>& affine, const V& vector);
-
-    typedef Affine3<float> Affine3f;
-    typedef Affine3<double> Affine3d;
-
-    static Vec3f operator*(const Affine3f& affine, const Vec3f& vector);
-    static Vec3d operator*(const Affine3d& affine, const Vec3d& vector);
-
-    template<typename _Tp> class DataType< Affine3<_Tp> >
-    {
-    public:
-        typedef Affine3<_Tp>                               value_type;
-        typedef Affine3<typename DataType<_Tp>::work_type> work_type;
-        typedef _Tp                                        channel_type;
-
-        enum { generic_type = 0,
-               channels     = 16,
-               fmt          = traits::SafeFmt<channel_type>::fmt + ((channels - 1) << 8)
-#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED
-               ,depth        = DataType<channel_type>::depth
-               ,type         = CV_MAKETYPE(depth, channels)
-#endif
-             };
-
-        typedef Vec<channel_type, channels> vec_type;
-    };
-
-    namespace traits {
-    template<typename _Tp>
-    struct Depth< Affine3<_Tp> > { enum { value = Depth<_Tp>::value }; };
-    template<typename _Tp>
-    struct Type< Affine3<_Tp> > { enum { value = CV_MAKETYPE(Depth<_Tp>::value, 16) }; };
-    } // namespace
-
-//! @} core
-
-}
-
-//! @cond IGNORED
-
-///////////////////////////////////////////////////////////////////////////////////
-// Implementation
-
-template<typename T> inline
-cv::Affine3<T>::Affine3()
-    : matrix(Mat4::eye())
-{}
-
-template<typename T> inline
-cv::Affine3<T>::Affine3(const Mat4& affine)
-    : matrix(affine)
-{}
-
-template<typename T> inline
-cv::Affine3<T>::Affine3(const Mat3& R, const Vec3& t)
-{
-    rotation(R);
-    translation(t);
-    matrix.val[12] = matrix.val[13] = matrix.val[14] = 0;
-    matrix.val[15] = 1;
-}
-
-template<typename T> inline
-cv::Affine3<T>::Affine3(const Vec3& _rvec, const Vec3& t)
-{
-    rotation(_rvec);
-    translation(t);
-    matrix.val[12] = matrix.val[13] = matrix.val[14] = 0;
-    matrix.val[15] = 1;
-}
-
-template<typename T> inline
-cv::Affine3<T>::Affine3(const cv::Mat& data, const Vec3& t)
-{
-    CV_Assert(data.type() == cv::traits::Type<T>::value);
-    CV_Assert(data.channels() == 1);
-
-    if (data.cols == 4 && data.rows == 4)
-    {
-        data.copyTo(matrix);
-        return;
-    }
-    else if (data.cols == 4 && data.rows == 3)
-    {
-        rotation(data(Rect(0, 0, 3, 3)));
-        translation(data(Rect(3, 0, 1, 3)));
-    }
-    else
-    {
-        rotation(data);
-        translation(t);
-    }
-
-    matrix.val[12] = matrix.val[13] = matrix.val[14] = 0;
-    matrix.val[15] = 1;
-}
-
-template<typename T> inline
-cv::Affine3<T>::Affine3(const float_type* vals) : matrix(vals)
-{}
-
-template<typename T> inline
-cv::Affine3<T> cv::Affine3<T>::Identity()
-{
-    return Affine3<T>(cv::Affine3<T>::Mat4::eye());
-}
-
-template<typename T> inline
-void cv::Affine3<T>::rotation(const Mat3& R)
-{
-    linear(R);
-}
-
-template<typename T> inline
-void cv::Affine3<T>::rotation(const Vec3& _rvec)
-{
-    double theta = norm(_rvec);
-
-    if (theta < DBL_EPSILON)
-        rotation(Mat3::eye());
-    else
-    {
-        double c = std::cos(theta);
-        double s = std::sin(theta);
-        double c1 = 1. - c;
-        double itheta = (theta != 0) ? 1./theta : 0.;
-
-        Point3_<T> r = _rvec*itheta;
-
-        Mat3 rrt( r.x*r.x, r.x*r.y, r.x*r.z, r.x*r.y, r.y*r.y, r.y*r.z, r.x*r.z, r.y*r.z, r.z*r.z );
-        Mat3 r_x( 0, -r.z, r.y, r.z, 0, -r.x, -r.y, r.x, 0 );
-
-        // R = cos(theta)*I + (1 - cos(theta))*r*rT + sin(theta)*[r_x]
-        // where [r_x] is [0 -rz ry; rz 0 -rx; -ry rx 0]
-        Mat3 R = c*Mat3::eye() + c1*rrt + s*r_x;
-
-        rotation(R);
-    }
-}
-
-//Combines rotation methods above. Supports 3x3, 1x3, 3x1 sizes of data matrix;
-template<typename T> inline
-void cv::Affine3<T>::rotation(const cv::Mat& data)
-{
-    CV_Assert(data.type() == cv::traits::Type<T>::value);
-    CV_Assert(data.channels() == 1);
-
-    if (data.cols == 3 && data.rows == 3)
-    {
-        Mat3 R;
-        data.copyTo(R);
-        rotation(R);
-    }
-    else if ((data.cols == 3 && data.rows == 1) || (data.cols == 1 && data.rows == 3))
-    {
-        Vec3 _rvec;
-        data.reshape(1, 3).copyTo(_rvec);
-        rotation(_rvec);
-    }
-    else
-        CV_Error(Error::StsError, "Input matrix can only be 3x3, 1x3 or 3x1");
-}
-
-template<typename T> inline
-void cv::Affine3<T>::linear(const Mat3& L)
-{
-    matrix.val[0] = L.val[0]; matrix.val[1] = L.val[1];  matrix.val[ 2] = L.val[2];
-    matrix.val[4] = L.val[3]; matrix.val[5] = L.val[4];  matrix.val[ 6] = L.val[5];
-    matrix.val[8] = L.val[6]; matrix.val[9] = L.val[7];  matrix.val[10] = L.val[8];
-}
-
-template<typename T> inline
-void cv::Affine3<T>::translation(const Vec3& t)
-{
-    matrix.val[3] = t[0]; matrix.val[7] = t[1]; matrix.val[11] = t[2];
-}
-
-template<typename T> inline
-typename cv::Affine3<T>::Mat3 cv::Affine3<T>::rotation() const
-{
-    return linear();
-}
-
-template<typename T> inline
-typename cv::Affine3<T>::Mat3 cv::Affine3<T>::linear() const
-{
-    typename cv::Affine3<T>::Mat3 R;
-    R.val[0] = matrix.val[0];  R.val[1] = matrix.val[1];  R.val[2] = matrix.val[ 2];
-    R.val[3] = matrix.val[4];  R.val[4] = matrix.val[5];  R.val[5] = matrix.val[ 6];
-    R.val[6] = matrix.val[8];  R.val[7] = matrix.val[9];  R.val[8] = matrix.val[10];
-    return R;
-}
-
-template<typename T> inline
-typename cv::Affine3<T>::Vec3 cv::Affine3<T>::translation() const
-{
-    return Vec3(matrix.val[3], matrix.val[7], matrix.val[11]);
-}
-
-template<typename T> inline
-typename cv::Affine3<T>::Vec3 cv::Affine3<T>::rvec() const
-{
-    cv::Vec3d w;
-    cv::Matx33d u, vt, R = rotation();
-    cv::SVD::compute(R, w, u, vt, cv::SVD::FULL_UV + cv::SVD::MODIFY_A);
-    R = u * vt;
-
-    double rx = R.val[7] - R.val[5];
-    double ry = R.val[2] - R.val[6];
-    double rz = R.val[3] - R.val[1];
-
-    double s = std::sqrt((rx*rx + ry*ry + rz*rz)*0.25);
-    double c = (R.val[0] + R.val[4] + R.val[8] - 1) * 0.5;
-    c = c > 1.0 ? 1.0 : c < -1.0 ? -1.0 : c;
-    double theta = acos(c);
-
-    if( s < 1e-5 )
-    {
-        if( c > 0 )
-            rx = ry = rz = 0;
-        else
-        {
-            double t;
-            t = (R.val[0] + 1) * 0.5;
-            rx = std::sqrt(std::max(t, 0.0));
-            t = (R.val[4] + 1) * 0.5;
-            ry = std::sqrt(std::max(t, 0.0)) * (R.val[1] < 0 ? -1.0 : 1.0);
-            t = (R.val[8] + 1) * 0.5;
-            rz = std::sqrt(std::max(t, 0.0)) * (R.val[2] < 0 ? -1.0 : 1.0);
-
-            if( fabs(rx) < fabs(ry) && fabs(rx) < fabs(rz) && (R.val[5] > 0) != (ry*rz > 0) )
-                rz = -rz;
-            theta /= std::sqrt(rx*rx + ry*ry + rz*rz);
-            rx *= theta;
-            ry *= theta;
-            rz *= theta;
-        }
-    }
-    else
-    {
-        double vth = 1/(2*s);
-        vth *= theta;
-        rx *= vth; ry *= vth; rz *= vth;
-    }
-
-    return cv::Vec3d(rx, ry, rz);
-}
-
-template<typename T> inline
-cv::Affine3<T> cv::Affine3<T>::inv(int method) const
-{
-    return matrix.inv(method);
-}
-
-template<typename T> inline
-cv::Affine3<T> cv::Affine3<T>::rotate(const Mat3& R) const
-{
-    Mat3 Lc = linear();
-    Vec3 tc = translation();
-    Mat4 result;
-    result.val[12] = result.val[13] = result.val[14] = 0;
-    result.val[15] = 1;
-
-    for(int j = 0; j < 3; ++j)
-    {
-        for(int i = 0; i < 3; ++i)
-        {
-            float_type value = 0;
-            for(int k = 0; k < 3; ++k)
-                value += R(j, k) * Lc(k, i);
-            result(j, i) = value;
-        }
-
-        result(j, 3) = R.row(j).dot(tc.t());
-    }
-    return result;
-}
-
-template<typename T> inline
-cv::Affine3<T> cv::Affine3<T>::rotate(const Vec3& _rvec) const
-{
-    return rotate(Affine3f(_rvec).rotation());
-}
-
-template<typename T> inline
-cv::Affine3<T> cv::Affine3<T>::translate(const Vec3& t) const
-{
-    Mat4 m = matrix;
-    m.val[ 3] += t[0];
-    m.val[ 7] += t[1];
-    m.val[11] += t[2];
-    return m;
-}
-
-template<typename T> inline
-cv::Affine3<T> cv::Affine3<T>::concatenate(const Affine3<T>& affine) const
-{
-    return (*this).rotate(affine.rotation()).translate(affine.translation());
-}
-
-template<typename T> template <typename Y> inline
-cv::Affine3<T>::operator Affine3<Y>() const
-{
-    return Affine3<Y>(matrix);
-}
-
-template<typename T> template <typename Y> inline
-cv::Affine3<Y> cv::Affine3<T>::cast() const
-{
-    return Affine3<Y>(matrix);
-}
-
-template<typename T> inline
-cv::Affine3<T> cv::operator*(const cv::Affine3<T>& affine1, const cv::Affine3<T>& affine2)
-{
-    return affine2.concatenate(affine1);
-}
-
-template<typename T, typename V> inline
-V cv::operator*(const cv::Affine3<T>& affine, const V& v)
-{
-    const typename Affine3<T>::Mat4& m = affine.matrix;
-
-    V r;
-    r.x = m.val[0] * v.x + m.val[1] * v.y + m.val[ 2] * v.z + m.val[ 3];
-    r.y = m.val[4] * v.x + m.val[5] * v.y + m.val[ 6] * v.z + m.val[ 7];
-    r.z = m.val[8] * v.x + m.val[9] * v.y + m.val[10] * v.z + m.val[11];
-    return r;
-}
-
-static inline
-cv::Vec3f cv::operator*(const cv::Affine3f& affine, const cv::Vec3f& v)
-{
-    const cv::Matx44f& m = affine.matrix;
-    cv::Vec3f r;
-    r.val[0] = m.val[0] * v[0] + m.val[1] * v[1] + m.val[ 2] * v[2] + m.val[ 3];
-    r.val[1] = m.val[4] * v[0] + m.val[5] * v[1] + m.val[ 6] * v[2] + m.val[ 7];
-    r.val[2] = m.val[8] * v[0] + m.val[9] * v[1] + m.val[10] * v[2] + m.val[11];
-    return r;
-}
-
-static inline
-cv::Vec3d cv::operator*(const cv::Affine3d& affine, const cv::Vec3d& v)
-{
-    const cv::Matx44d& m = affine.matrix;
-    cv::Vec3d r;
-    r.val[0] = m.val[0] * v[0] + m.val[1] * v[1] + m.val[ 2] * v[2] + m.val[ 3];
-    r.val[1] = m.val[4] * v[0] + m.val[5] * v[1] + m.val[ 6] * v[2] + m.val[ 7];
-    r.val[2] = m.val[8] * v[0] + m.val[9] * v[1] + m.val[10] * v[2] + m.val[11];
-    return r;
-}
-
-
-
-#if defined EIGEN_WORLD_VERSION && defined EIGEN_GEOMETRY_MODULE_H
-
-template<typename T> inline
-cv::Affine3<T>::Affine3(const Eigen::Transform<T, 3, Eigen::Affine, (Eigen::RowMajor)>& affine)
-{
-    cv::Mat(4, 4, cv::traits::Type<T>::value, affine.matrix().data()).copyTo(matrix);
-}
-
-template<typename T> inline
-cv::Affine3<T>::Affine3(const Eigen::Transform<T, 3, Eigen::Affine>& affine)
-{
-    Eigen::Transform<T, 3, Eigen::Affine, (Eigen::RowMajor)> a = affine;
-    cv::Mat(4, 4, cv::traits::Type<T>::value, a.matrix().data()).copyTo(matrix);
-}
-
-template<typename T> inline
-cv::Affine3<T>::operator Eigen::Transform<T, 3, Eigen::Affine, (Eigen::RowMajor)>() const
-{
-    Eigen::Transform<T, 3, Eigen::Affine, (Eigen::RowMajor)> r;
-    cv::Mat hdr(4, 4, cv::traits::Type<T>::value, r.matrix().data());
-    cv::Mat(matrix, false).copyTo(hdr);
-    return r;
-}
-
-template<typename T> inline
-cv::Affine3<T>::operator Eigen::Transform<T, 3, Eigen::Affine>() const
-{
-    return this->operator Eigen::Transform<T, 3, Eigen::Affine, (Eigen::RowMajor)>();
-}
-
-#endif /* defined EIGEN_WORLD_VERSION && defined EIGEN_GEOMETRY_MODULE_H */
-
-//! @endcond
-
-#endif /* __cplusplus */
-
-#endif /* OPENCV_CORE_AFFINE3_HPP */
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/base.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/base.hpp
deleted file mode 100755
index 766e63b..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/base.hpp
+++ /dev/null
@@ -1,772 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
-// Copyright (C) 2014, Itseez Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_CORE_BASE_HPP
-#define OPENCV_CORE_BASE_HPP
-
-#ifndef __cplusplus
-#  error base.hpp header must be compiled as C++
-#endif
-
-#include "opencv2/opencv_modules.hpp"
-
-#include <climits>
-#include <algorithm>
-
-#include "opencv2/core/cvdef.h"
-#include "opencv2/core/cvstd.hpp"
-
-namespace cv
-{
-
-//! @addtogroup core_utils
-//! @{
-
-namespace Error {
-//! error codes
-enum Code {
-    StsOk=                       0,  //!< everything is ok
-    StsBackTrace=               -1,  //!< pseudo error for back trace
-    StsError=                   -2,  //!< unknown /unspecified error
-    StsInternal=                -3,  //!< internal error (bad state)
-    StsNoMem=                   -4,  //!< insufficient memory
-    StsBadArg=                  -5,  //!< function arg/param is bad
-    StsBadFunc=                 -6,  //!< unsupported function
-    StsNoConv=                  -7,  //!< iteration didn't converge
-    StsAutoTrace=               -8,  //!< tracing
-    HeaderIsNull=               -9,  //!< image header is NULL
-    BadImageSize=              -10,  //!< image size is invalid
-    BadOffset=                 -11,  //!< offset is invalid
-    BadDataPtr=                -12,  //!<
-    BadStep=                   -13,  //!< image step is wrong, this may happen for a non-continuous matrix.
-    BadModelOrChSeq=           -14,  //!<
-    BadNumChannels=            -15,  //!< bad number of channels, for example, some functions accept only single channel matrices.
-    BadNumChannel1U=           -16,  //!<
-    BadDepth=                  -17,  //!< input image depth is not supported by the function
-    BadAlphaChannel=           -18,  //!<
-    BadOrder=                  -19,  //!< number of dimensions is out of range
-    BadOrigin=                 -20,  //!< incorrect input origin
-    BadAlign=                  -21,  //!< incorrect input align
-    BadCallBack=               -22,  //!<
-    BadTileSize=               -23,  //!<
-    BadCOI=                    -24,  //!< input COI is not supported
-    BadROISize=                -25,  //!< incorrect input roi
-    MaskIsTiled=               -26,  //!<
-    StsNullPtr=                -27,  //!< null pointer
-    StsVecLengthErr=           -28,  //!< incorrect vector length
-    StsFilterStructContentErr= -29,  //!< incorrect filter structure content
-    StsKernelStructContentErr= -30,  //!< incorrect transform kernel content
-    StsFilterOffsetErr=        -31,  //!< incorrect filter offset value
-    StsBadSize=                -201, //!< the input/output structure size is incorrect
-    StsDivByZero=              -202, //!< division by zero
-    StsInplaceNotSupported=    -203, //!< in-place operation is not supported
-    StsObjectNotFound=         -204, //!< request can't be completed
-    StsUnmatchedFormats=       -205, //!< formats of input/output arrays differ
-    StsBadFlag=                -206, //!< flag is wrong or not supported
-    StsBadPoint=               -207, //!< bad CvPoint
-    StsBadMask=                -208, //!< bad format of mask (neither 8uC1 nor 8sC1)
-    StsUnmatchedSizes=         -209, //!< sizes of input/output structures do not match
-    StsUnsupportedFormat=      -210, //!< the data format/type is not supported by the function
-    StsOutOfRange=             -211, //!< some of parameters are out of range
-    StsParseError=             -212, //!< invalid syntax/structure of the parsed file
-    StsNotImplemented=         -213, //!< the requested function/feature is not implemented
-    StsBadMemBlock=            -214, //!< an allocated block has been corrupted
-    StsAssert=                 -215, //!< assertion failed
-    GpuNotSupported=           -216, //!< no CUDA support
-    GpuApiCallError=           -217, //!< GPU API call error
-    OpenGlNotSupported=        -218, //!< no OpenGL support
-    OpenGlApiCallError=        -219, //!< OpenGL API call error
-    OpenCLApiCallError=        -220, //!< OpenCL API call error
-    OpenCLDoubleNotSupported=  -221,
-    OpenCLInitError=           -222, //!< OpenCL initialization error
-    OpenCLNoAMDBlasFft=        -223
-};
-} //Error
-
-//! @} core_utils
-
-//! @addtogroup core_array
-//! @{
-
-//! matrix decomposition types
-enum DecompTypes {
-    /** Gaussian elimination with the optimal pivot element chosen. */
-    DECOMP_LU       = 0,
-    /** singular value decomposition (SVD) method; the system can be over-defined and/or the matrix
-    src1 can be singular */
-    DECOMP_SVD      = 1,
-    /** eigenvalue decomposition; the matrix src1 must be symmetrical */
-    DECOMP_EIG      = 2,
-    /** Cholesky \f$LL^T\f$ factorization; the matrix src1 must be symmetrical and positively
-    defined */
-    DECOMP_CHOLESKY = 3,
-    /** QR factorization; the system can be over-defined and/or the matrix src1 can be singular */
-    DECOMP_QR       = 4,
-    /** while all the previous flags are mutually exclusive, this flag can be used together with
-    any of the previous; it means that the normal equations
-    \f$\texttt{src1}^T\cdot\texttt{src1}\cdot\texttt{dst}=\texttt{src1}^T\texttt{src2}\f$ are
-    solved instead of the original system
-    \f$\texttt{src1}\cdot\texttt{dst}=\texttt{src2}\f$ */
-    DECOMP_NORMAL   = 16
-};
-
-/** norm types
-
-src1 and src2 denote input arrays.
-*/
-
-enum NormTypes {
-                /**
-                \f[
-                norm =  \forkthree
-                {\|\texttt{src1}\|_{L_{\infty}} =  \max _I | \texttt{src1} (I)|}{if  \(\texttt{normType} = \texttt{NORM_INF}\) }
-                {\|\texttt{src1}-\texttt{src2}\|_{L_{\infty}} =  \max _I | \texttt{src1} (I) -  \texttt{src2} (I)|}{if  \(\texttt{normType} = \texttt{NORM_INF}\) }
-                {\frac{\|\texttt{src1}-\texttt{src2}\|_{L_{\infty}}    }{\|\texttt{src2}\|_{L_{\infty}} }}{if  \(\texttt{normType} = \texttt{NORM_RELATIVE | NORM_INF}\) }
-                \f]
-                */
-                NORM_INF       = 1,
-                /**
-                \f[
-                norm =  \forkthree
-                {\| \texttt{src1} \| _{L_1} =  \sum _I | \texttt{src1} (I)|}{if  \(\texttt{normType} = \texttt{NORM_L1}\)}
-                { \| \texttt{src1} - \texttt{src2} \| _{L_1} =  \sum _I | \texttt{src1} (I) -  \texttt{src2} (I)|}{if  \(\texttt{normType} = \texttt{NORM_L1}\) }
-                { \frac{\|\texttt{src1}-\texttt{src2}\|_{L_1} }{\|\texttt{src2}\|_{L_1}} }{if  \(\texttt{normType} = \texttt{NORM_RELATIVE | NORM_L1}\) }
-                \f]*/
-                 NORM_L1        = 2,
-                 /**
-                 \f[
-                 norm =  \forkthree
-                 { \| \texttt{src1} \| _{L_2} =  \sqrt{\sum_I \texttt{src1}(I)^2} }{if  \(\texttt{normType} = \texttt{NORM_L2}\) }
-                 { \| \texttt{src1} - \texttt{src2} \| _{L_2} =  \sqrt{\sum_I (\texttt{src1}(I) - \texttt{src2}(I))^2} }{if  \(\texttt{normType} = \texttt{NORM_L2}\) }
-                 { \frac{\|\texttt{src1}-\texttt{src2}\|_{L_2} }{\|\texttt{src2}\|_{L_2}} }{if  \(\texttt{normType} = \texttt{NORM_RELATIVE | NORM_L2}\) }
-                 \f]
-                 */
-                 NORM_L2        = 4,
-                 /**
-                 \f[
-                 norm =  \forkthree
-                 { \| \texttt{src1} \| _{L_2} ^{2} = \sum_I \texttt{src1}(I)^2} {if  \(\texttt{normType} = \texttt{NORM_L2SQR}\)}
-                 { \| \texttt{src1} - \texttt{src2} \| _{L_2} ^{2} =  \sum_I (\texttt{src1}(I) - \texttt{src2}(I))^2 }{if  \(\texttt{normType} = \texttt{NORM_L2SQR}\) }
-                 { \left(\frac{\|\texttt{src1}-\texttt{src2}\|_{L_2} }{\|\texttt{src2}\|_{L_2}}\right)^2 }{if  \(\texttt{normType} = \texttt{NORM_RELATIVE | NORM_L2}\) }
-                 \f]
-                 */
-                 NORM_L2SQR     = 5,
-                 /**
-                 In the case of one input array, calculates the Hamming distance of the array from zero,
-                 In the case of two input arrays, calculates the Hamming distance between the arrays.
-                 */
-                 NORM_HAMMING   = 6,
-                 /**
-                 Similar to NORM_HAMMING, but in the calculation, each two bits of the input sequence will
-                 be added and treated as a single bit to be used in the same calculation as NORM_HAMMING.
-                 */
-                 NORM_HAMMING2  = 7,
-                 NORM_TYPE_MASK = 7, //!< bit-mask which can be used to separate norm type from norm flags
-                 NORM_RELATIVE  = 8, //!< flag
-                 NORM_MINMAX    = 32 //!< flag
-               };
-
-//! comparison types
-enum CmpTypes { CMP_EQ = 0, //!< src1 is equal to src2.
-                CMP_GT = 1, //!< src1 is greater than src2.
-                CMP_GE = 2, //!< src1 is greater than or equal to src2.
-                CMP_LT = 3, //!< src1 is less than src2.
-                CMP_LE = 4, //!< src1 is less than or equal to src2.
-                CMP_NE = 5  //!< src1 is unequal to src2.
-              };
-
-//! generalized matrix multiplication flags
-enum GemmFlags { GEMM_1_T = 1, //!< transposes src1
-                 GEMM_2_T = 2, //!< transposes src2
-                 GEMM_3_T = 4 //!< transposes src3
-               };
-
-enum DftFlags {
-    /** performs an inverse 1D or 2D transform instead of the default forward
-        transform. */
-    DFT_INVERSE        = 1,
-    /** scales the result: divide it by the number of array elements. Normally, it is
-        combined with DFT_INVERSE. */
-    DFT_SCALE          = 2,
-    /** performs a forward or inverse transform of every individual row of the input
-        matrix; this flag enables you to transform multiple vectors simultaneously and can be used to
-        decrease the overhead (which is sometimes several times larger than the processing itself) to
-        perform 3D and higher-dimensional transformations and so forth.*/
-    DFT_ROWS           = 4,
-    /** performs a forward transformation of 1D or 2D real array; the result,
-        though being a complex array, has complex-conjugate symmetry (*CCS*, see the function
-        description below for details), and such an array can be packed into a real array of the same
-        size as input, which is the fastest option and which is what the function does by default;
-        however, you may wish to get a full complex array (for simpler spectrum analysis, and so on) -
-        pass the flag to enable the function to produce a full-size complex output array. */
-    DFT_COMPLEX_OUTPUT = 16,
-    /** performs an inverse transformation of a 1D or 2D complex array; the
-        result is normally a complex array of the same size, however, if the input array has
-        conjugate-complex symmetry (for example, it is a result of forward transformation with
-        DFT_COMPLEX_OUTPUT flag), the output is a real array; while the function itself does not
-        check whether the input is symmetrical or not, you can pass the flag and then the function
-        will assume the symmetry and produce the real output array (note that when the input is packed
-        into a real array and inverse transformation is executed, the function treats the input as a
-        packed complex-conjugate symmetrical array, and the output will also be a real array). */
-    DFT_REAL_OUTPUT    = 32,
-    /** specifies that input is complex input. If this flag is set, the input must have 2 channels.
-        On the other hand, for backwards compatibility reason, if input has 2 channels, input is
-        already considered complex. */
-    DFT_COMPLEX_INPUT  = 64,
-    /** performs an inverse 1D or 2D transform instead of the default forward transform. */
-    DCT_INVERSE        = DFT_INVERSE,
-    /** performs a forward or inverse transform of every individual row of the input
-        matrix. This flag enables you to transform multiple vectors simultaneously and can be used to
-        decrease the overhead (which is sometimes several times larger than the processing itself) to
-        perform 3D and higher-dimensional transforms and so forth.*/
-    DCT_ROWS           = DFT_ROWS
-};
-
-//! Various border types, image boundaries are denoted with `|`
-//! @see borderInterpolate, copyMakeBorder
-enum BorderTypes {
-    BORDER_CONSTANT    = 0, //!< `iiiiii|abcdefgh|iiiiiii`  with some specified `i`
-    BORDER_REPLICATE   = 1, //!< `aaaaaa|abcdefgh|hhhhhhh`
-    BORDER_REFLECT     = 2, //!< `fedcba|abcdefgh|hgfedcb`
-    BORDER_WRAP        = 3, //!< `cdefgh|abcdefgh|abcdefg`
-    BORDER_REFLECT_101 = 4, //!< `gfedcb|abcdefgh|gfedcba`
-    BORDER_TRANSPARENT = 5, //!< `uvwxyz|abcdefgh|ijklmno`
-
-    BORDER_REFLECT101  = BORDER_REFLECT_101, //!< same as BORDER_REFLECT_101
-    BORDER_DEFAULT     = BORDER_REFLECT_101, //!< same as BORDER_REFLECT_101
-    BORDER_ISOLATED    = 16 //!< do not look outside of ROI
-};
-
-//! @} core_array
-
-//! @addtogroup core_utils
-//! @{
-
-//! @cond IGNORED
-
-//////////////// static assert /////////////////
-#define CVAUX_CONCAT_EXP(a, b) a##b
-#define CVAUX_CONCAT(a, b) CVAUX_CONCAT_EXP(a,b)
-
-#if defined(__clang__)
-#  ifndef __has_extension
-#    define __has_extension __has_feature /* compatibility, for older versions of clang */
-#  endif
-#  if __has_extension(cxx_static_assert)
-#    define CV_StaticAssert(condition, reason)    static_assert((condition), reason " " #condition)
-#  elif __has_extension(c_static_assert)
-#    define CV_StaticAssert(condition, reason)    _Static_assert((condition), reason " " #condition)
-#  endif
-#elif defined(__GNUC__)
-#  if (defined(__GXX_EXPERIMENTAL_CXX0X__) || __cplusplus >= 201103L)
-#    define CV_StaticAssert(condition, reason)    static_assert((condition), reason " " #condition)
-#  endif
-#elif defined(_MSC_VER)
-#  if _MSC_VER >= 1600 /* MSVC 10 */
-#    define CV_StaticAssert(condition, reason)    static_assert((condition), reason " " #condition)
-#  endif
-#endif
-#ifndef CV_StaticAssert
-#  if !defined(__clang__) && defined(__GNUC__) && (__GNUC__*100 + __GNUC_MINOR__ > 302)
-#    define CV_StaticAssert(condition, reason) ({ extern int __attribute__((error("CV_StaticAssert: " reason " " #condition))) CV_StaticAssert(); ((condition) ? 0 : CV_StaticAssert()); })
-#  else
-     template <bool x> struct CV_StaticAssert_failed;
-     template <> struct CV_StaticAssert_failed<true> { enum { val = 1 }; };
-     template<int x> struct CV_StaticAssert_test {};
-#    define CV_StaticAssert(condition, reason)\
-       typedef cv::CV_StaticAssert_test< sizeof(cv::CV_StaticAssert_failed< static_cast<bool>(condition) >) > CVAUX_CONCAT(CV_StaticAssert_failed_at_, __LINE__)
-#  endif
-#endif
-
-// Suppress warning "-Wdeprecated-declarations" / C4996
-#if defined(_MSC_VER)
-    #define CV_DO_PRAGMA(x) __pragma(x)
-#elif defined(__GNUC__)
-    #define CV_DO_PRAGMA(x) _Pragma (#x)
-#else
-    #define CV_DO_PRAGMA(x)
-#endif
-
-#ifdef _MSC_VER
-#define CV_SUPPRESS_DEPRECATED_START \
-    CV_DO_PRAGMA(warning(push)) \
-    CV_DO_PRAGMA(warning(disable: 4996))
-#define CV_SUPPRESS_DEPRECATED_END CV_DO_PRAGMA(warning(pop))
-#elif defined (__clang__) || ((__GNUC__)  && (__GNUC__*100 + __GNUC_MINOR__ > 405))
-#define CV_SUPPRESS_DEPRECATED_START \
-    CV_DO_PRAGMA(GCC diagnostic push) \
-    CV_DO_PRAGMA(GCC diagnostic ignored "-Wdeprecated-declarations")
-#define CV_SUPPRESS_DEPRECATED_END CV_DO_PRAGMA(GCC diagnostic pop)
-#else
-#define CV_SUPPRESS_DEPRECATED_START
-#define CV_SUPPRESS_DEPRECATED_END
-#endif
-
-#define CV_UNUSED(name) (void)name
-
-#if defined __GNUC__ && !defined __EXCEPTIONS
-#define CV_TRY
-#define CV_CATCH(A, B) for (A B; false; )
-#define CV_CATCH_ALL if (false)
-#define CV_THROW(A) abort()
-#define CV_RETHROW() abort()
-#else
-#define CV_TRY try
-#define CV_CATCH(A, B) catch(const A & B)
-#define CV_CATCH_ALL catch(...)
-#define CV_THROW(A) throw A
-#define CV_RETHROW() throw
-#endif
-
-//! @endcond
-
-/*! @brief Signals an error and raises the exception.
-
-By default the function prints information about the error to stderr,
-then it either stops if setBreakOnError() had been called before or raises the exception.
-It is possible to alternate error processing by using redirectError().
-@param _code - error code (Error::Code)
-@param _err - error description
-@param _func - function name. Available only when the compiler supports getting it
-@param _file - source file name where the error has occurred
-@param _line - line number in the source file where the error has occurred
-@see CV_Error, CV_Error_, CV_Assert, CV_DbgAssert
- */
-CV_EXPORTS void error(int _code, const String& _err, const char* _func, const char* _file, int _line);
-
-#ifdef __GNUC__
-# if defined __clang__ || defined __APPLE__
-#   pragma GCC diagnostic push
-#   pragma GCC diagnostic ignored "-Winvalid-noreturn"
-# endif
-#endif
-
-/** same as cv::error, but does not return */
-CV_INLINE CV_NORETURN void errorNoReturn(int _code, const String& _err, const char* _func, const char* _file, int _line)
-{
-    error(_code, _err, _func, _file, _line);
-#ifdef __GNUC__
-# if !defined __clang__ && !defined __APPLE__
-    // this suppresses this warning: "noreturn" function does return [enabled by default]
-    __builtin_trap();
-    // or use infinite loop: for (;;) {}
-# endif
-#endif
-}
-#ifdef __GNUC__
-# if defined __clang__ || defined __APPLE__
-#   pragma GCC diagnostic pop
-# endif
-#endif
-
-#if defined __GNUC__
-#define CV_Func __func__
-#elif defined _MSC_VER
-#define CV_Func __FUNCTION__
-#else
-#define CV_Func ""
-#endif
-
-#ifdef CV_STATIC_ANALYSIS
-
-// In practice, some macro are not processed correctly (noreturn is not detected).
-// We need to use simplified definition for them.
-#define CV_Error(...) do { abort(); } while (0)
-#define CV_Error_( code, args ) do { cv::format args; abort(); } while (0)
-#define CV_Assert_1( expr ) do { if (!(expr)) abort(); } while (0)
-
-#else // CV_STATIC_ANALYSIS
-
-/** @brief Call the error handler.
-
-Currently, the error handler prints the error code and the error message to the standard
-error stream `stderr`. In the Debug configuration, it then provokes memory access violation, so that
-the execution stack and all the parameters can be analyzed by the debugger. In the Release
-configuration, the exception is thrown.
-
-@param code one of Error::Code
-@param msg error message
-*/
-#define CV_Error( code, msg ) cv::error( code, msg, CV_Func, __FILE__, __LINE__ )
-
-/**  @brief Call the error handler.
-
-This macro can be used to construct an error message on-fly to include some dynamic information,
-for example:
-@code
-    // note the extra parentheses around the formatted text message
-    CV_Error_( CV_StsOutOfRange,
-    ("the value at (%d, %d)=%g is out of range", badPt.x, badPt.y, badValue));
-@endcode
-@param code one of Error::Code
-@param args printf-like formatted error message in parentheses
-*/
-#define CV_Error_( code, args ) cv::error( code, cv::format args, CV_Func, __FILE__, __LINE__ )
-
-#define CV_Assert_1( expr ) if(!!(expr)) ; else cv::error( cv::Error::StsAssert, #expr, CV_Func, __FILE__, __LINE__ )
-
-//! @cond IGNORED
-#define CV__ErrorNoReturn( code, msg ) cv::errorNoReturn( code, msg, CV_Func, __FILE__, __LINE__ )
-#define CV__ErrorNoReturn_( code, args ) cv::errorNoReturn( code, cv::format args, CV_Func, __FILE__, __LINE__ )
-#ifdef __OPENCV_BUILD
-#undef CV_Error
-#define CV_Error CV__ErrorNoReturn
-#undef CV_Error_
-#define CV_Error_ CV__ErrorNoReturn_
-#undef CV_Assert_1
-#define CV_Assert_1( expr ) if(!!(expr)) ; else cv::errorNoReturn( cv::Error::StsAssert, #expr, CV_Func, __FILE__, __LINE__ )
-#else
-// backward compatibility
-#define CV_ErrorNoReturn CV__ErrorNoReturn
-#define CV_ErrorNoReturn_ CV__ErrorNoReturn_
-#endif
-//! @endcond
-
-#endif // CV_STATIC_ANALYSIS
-
-#define CV_Assert_2( expr1, expr2 ) CV_Assert_1(expr1); CV_Assert_1(expr2)
-#define CV_Assert_3( expr1, expr2, expr3 ) CV_Assert_2(expr1, expr2); CV_Assert_1(expr3)
-#define CV_Assert_4( expr1, expr2, expr3, expr4 ) CV_Assert_3(expr1, expr2, expr3); CV_Assert_1(expr4)
-#define CV_Assert_5( expr1, expr2, expr3, expr4, expr5 ) CV_Assert_4(expr1, expr2, expr3, expr4); CV_Assert_1(expr5)
-#define CV_Assert_6( expr1, expr2, expr3, expr4, expr5, expr6 ) CV_Assert_5(expr1, expr2, expr3, expr4, expr5); CV_Assert_1(expr6)
-#define CV_Assert_7( expr1, expr2, expr3, expr4, expr5, expr6, expr7 ) CV_Assert_6(expr1, expr2, expr3, expr4, expr5, expr6 ); CV_Assert_1(expr7)
-#define CV_Assert_8( expr1, expr2, expr3, expr4, expr5, expr6, expr7, expr8 ) CV_Assert_7(expr1, expr2, expr3, expr4, expr5, expr6, expr7 ); CV_Assert_1(expr8)
-#define CV_Assert_9( expr1, expr2, expr3, expr4, expr5, expr6, expr7, expr8, expr9 ) CV_Assert_8(expr1, expr2, expr3, expr4, expr5, expr6, expr7, expr8 ); CV_Assert_1(expr9)
-#define CV_Assert_10( expr1, expr2, expr3, expr4, expr5, expr6, expr7, expr8, expr9, expr10 ) CV_Assert_9(expr1, expr2, expr3, expr4, expr5, expr6, expr7, expr8, expr9 ); CV_Assert_1(expr10)
-
-#define CV_VA_NUM_ARGS_HELPER(_1, _2, _3, _4, _5, _6, _7, _8, _9, _10, N, ...) N
-#define CV_VA_NUM_ARGS(...) CV_VA_NUM_ARGS_HELPER(__VA_ARGS__, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0)
-
-/** @brief Checks a condition at runtime and throws exception if it fails
-
-The macros CV_Assert (and CV_DbgAssert(expr)) evaluate the specified expression. If it is 0, the macros
-raise an error (see cv::error). The macro CV_Assert checks the condition in both Debug and Release
-configurations while CV_DbgAssert is only retained in the Debug configuration.
-*/
-#define CV_Assert(...) do { CVAUX_CONCAT(CV_Assert_, CV_VA_NUM_ARGS(__VA_ARGS__)) (__VA_ARGS__); } while(0)
-
-/** replaced with CV_Assert(expr) in Debug configuration */
-#ifdef _DEBUG
-#  define CV_DbgAssert(expr) CV_Assert(expr)
-#else
-#  define CV_DbgAssert(expr)
-#endif
-
-/*
- * Hamming distance functor - counts the bit differences between two strings - useful for the Brief descriptor
- * bit count of A exclusive XOR'ed with B
- */
-struct CV_EXPORTS Hamming
-{
-    enum { normType = NORM_HAMMING };
-    typedef unsigned char ValueType;
-    typedef int ResultType;
-
-    /** this will count the bits in a ^ b
-     */
-    ResultType operator()( const unsigned char* a, const unsigned char* b, int size ) const;
-};
-
-typedef Hamming HammingLUT;
-
-/////////////////////////////////// inline norms ////////////////////////////////////
-
-template<typename _Tp> inline _Tp cv_abs(_Tp x) { return std::abs(x); }
-inline int cv_abs(uchar x) { return x; }
-inline int cv_abs(schar x) { return std::abs(x); }
-inline int cv_abs(ushort x) { return x; }
-inline int cv_abs(short x) { return std::abs(x); }
-
-template<typename _Tp, typename _AccTp> static inline
-_AccTp normL2Sqr(const _Tp* a, int n)
-{
-    _AccTp s = 0;
-    int i=0;
-#if CV_ENABLE_UNROLLED
-    for( ; i <= n - 4; i += 4 )
-    {
-        _AccTp v0 = a[i], v1 = a[i+1], v2 = a[i+2], v3 = a[i+3];
-        s += v0*v0 + v1*v1 + v2*v2 + v3*v3;
-    }
-#endif
-    for( ; i < n; i++ )
-    {
-        _AccTp v = a[i];
-        s += v*v;
-    }
-    return s;
-}
-
-template<typename _Tp, typename _AccTp> static inline
-_AccTp normL1(const _Tp* a, int n)
-{
-    _AccTp s = 0;
-    int i = 0;
-#if CV_ENABLE_UNROLLED
-    for(; i <= n - 4; i += 4 )
-    {
-        s += (_AccTp)cv_abs(a[i]) + (_AccTp)cv_abs(a[i+1]) +
-            (_AccTp)cv_abs(a[i+2]) + (_AccTp)cv_abs(a[i+3]);
-    }
-#endif
-    for( ; i < n; i++ )
-        s += cv_abs(a[i]);
-    return s;
-}
-
-template<typename _Tp, typename _AccTp> static inline
-_AccTp normInf(const _Tp* a, int n)
-{
-    _AccTp s = 0;
-    for( int i = 0; i < n; i++ )
-        s = std::max(s, (_AccTp)cv_abs(a[i]));
-    return s;
-}
-
-template<typename _Tp, typename _AccTp> static inline
-_AccTp normL2Sqr(const _Tp* a, const _Tp* b, int n)
-{
-    _AccTp s = 0;
-    int i= 0;
-#if CV_ENABLE_UNROLLED
-    for(; i <= n - 4; i += 4 )
-    {
-        _AccTp v0 = _AccTp(a[i] - b[i]), v1 = _AccTp(a[i+1] - b[i+1]), v2 = _AccTp(a[i+2] - b[i+2]), v3 = _AccTp(a[i+3] - b[i+3]);
-        s += v0*v0 + v1*v1 + v2*v2 + v3*v3;
-    }
-#endif
-    for( ; i < n; i++ )
-    {
-        _AccTp v = _AccTp(a[i] - b[i]);
-        s += v*v;
-    }
-    return s;
-}
-
-static inline float normL2Sqr(const float* a, const float* b, int n)
-{
-    float s = 0.f;
-    for( int i = 0; i < n; i++ )
-    {
-        float v = a[i] - b[i];
-        s += v*v;
-    }
-    return s;
-}
-
-template<typename _Tp, typename _AccTp> static inline
-_AccTp normL1(const _Tp* a, const _Tp* b, int n)
-{
-    _AccTp s = 0;
-    int i= 0;
-#if CV_ENABLE_UNROLLED
-    for(; i <= n - 4; i += 4 )
-    {
-        _AccTp v0 = _AccTp(a[i] - b[i]), v1 = _AccTp(a[i+1] - b[i+1]), v2 = _AccTp(a[i+2] - b[i+2]), v3 = _AccTp(a[i+3] - b[i+3]);
-        s += std::abs(v0) + std::abs(v1) + std::abs(v2) + std::abs(v3);
-    }
-#endif
-    for( ; i < n; i++ )
-    {
-        _AccTp v = _AccTp(a[i] - b[i]);
-        s += std::abs(v);
-    }
-    return s;
-}
-
-inline float normL1(const float* a, const float* b, int n)
-{
-    float s = 0.f;
-    for( int i = 0; i < n; i++ )
-    {
-        s += std::abs(a[i] - b[i]);
-    }
-    return s;
-}
-
-inline int normL1(const uchar* a, const uchar* b, int n)
-{
-    int s = 0;
-    for( int i = 0; i < n; i++ )
-    {
-        s += std::abs(a[i] - b[i]);
-    }
-    return s;
-}
-
-template<typename _Tp, typename _AccTp> static inline
-_AccTp normInf(const _Tp* a, const _Tp* b, int n)
-{
-    _AccTp s = 0;
-    for( int i = 0; i < n; i++ )
-    {
-        _AccTp v0 = a[i] - b[i];
-        s = std::max(s, std::abs(v0));
-    }
-    return s;
-}
-
-/** @brief Computes the cube root of an argument.
-
- The function cubeRoot computes \f$\sqrt[3]{\texttt{val}}\f$. Negative arguments are handled correctly.
- NaN and Inf are not handled. The accuracy approaches the maximum possible accuracy for
- single-precision data.
- @param val A function argument.
- */
-CV_EXPORTS_W float cubeRoot(float val);
-
-/** @brief Calculates the angle of a 2D vector in degrees.
-
- The function fastAtan2 calculates the full-range angle of an input 2D vector. The angle is measured
- in degrees and varies from 0 to 360 degrees. The accuracy is about 0.3 degrees.
- @param x x-coordinate of the vector.
- @param y y-coordinate of the vector.
- */
-CV_EXPORTS_W float fastAtan2(float y, float x);
-
-/** proxy for hal::LU */
-CV_EXPORTS int LU(float* A, size_t astep, int m, float* b, size_t bstep, int n);
-/** proxy for hal::LU */
-CV_EXPORTS int LU(double* A, size_t astep, int m, double* b, size_t bstep, int n);
-/** proxy for hal::Cholesky */
-CV_EXPORTS bool Cholesky(float* A, size_t astep, int m, float* b, size_t bstep, int n);
-/** proxy for hal::Cholesky */
-CV_EXPORTS bool Cholesky(double* A, size_t astep, int m, double* b, size_t bstep, int n);
-
-////////////////// forward declarations for important OpenCV types //////////////////
-
-//! @cond IGNORED
-
-template<typename _Tp, int cn> class Vec;
-template<typename _Tp, int m, int n> class Matx;
-
-template<typename _Tp> class Complex;
-template<typename _Tp> class Point_;
-template<typename _Tp> class Point3_;
-template<typename _Tp> class Size_;
-template<typename _Tp> class Rect_;
-template<typename _Tp> class Scalar_;
-
-class CV_EXPORTS RotatedRect;
-class CV_EXPORTS Range;
-class CV_EXPORTS TermCriteria;
-class CV_EXPORTS KeyPoint;
-class CV_EXPORTS DMatch;
-class CV_EXPORTS RNG;
-
-class CV_EXPORTS Mat;
-class CV_EXPORTS MatExpr;
-
-class CV_EXPORTS UMat;
-
-class CV_EXPORTS SparseMat;
-typedef Mat MatND;
-
-template<typename _Tp> class Mat_;
-template<typename _Tp> class SparseMat_;
-
-class CV_EXPORTS MatConstIterator;
-class CV_EXPORTS SparseMatIterator;
-class CV_EXPORTS SparseMatConstIterator;
-template<typename _Tp> class MatIterator_;
-template<typename _Tp> class MatConstIterator_;
-template<typename _Tp> class SparseMatIterator_;
-template<typename _Tp> class SparseMatConstIterator_;
-
-namespace ogl
-{
-    class CV_EXPORTS Buffer;
-    class CV_EXPORTS Texture2D;
-    class CV_EXPORTS Arrays;
-}
-
-namespace cuda
-{
-    class CV_EXPORTS GpuMat;
-    class CV_EXPORTS HostMem;
-    class CV_EXPORTS Stream;
-    class CV_EXPORTS Event;
-}
-
-namespace cudev
-{
-    template <typename _Tp> class GpuMat_;
-}
-
-namespace ipp
-{
-#if OPENCV_ABI_COMPATIBILITY > 300
-CV_EXPORTS   unsigned long long getIppFeatures();
-#else
-CV_EXPORTS   int getIppFeatures();
-#endif
-CV_EXPORTS   void setIppStatus(int status, const char * const funcname = NULL, const char * const filename = NULL,
-                             int line = 0);
-CV_EXPORTS   int getIppStatus();
-CV_EXPORTS   String getIppErrorLocation();
-CV_EXPORTS_W bool   useIPP();
-CV_EXPORTS_W void   setUseIPP(bool flag);
-CV_EXPORTS_W String getIppVersion();
-
-// IPP Not-Exact mode. This function may force use of IPP then both IPP and OpenCV provide proper results
-// but have internal accuracy differences which have to much direct or indirect impact on accuracy tests.
-CV_EXPORTS_W bool useIPP_NE();
-CV_EXPORTS_W void setUseIPP_NE(bool flag);
-
-} // ipp
-
-//! @endcond
-
-//! @} core_utils
-
-
-
-
-} // cv
-
-#include "opencv2/core/neon_utils.hpp"
-#include "opencv2/core/vsx_utils.hpp"
-#include "opencv2/core/check.hpp"
-
-#endif //OPENCV_CORE_BASE_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/bufferpool.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/bufferpool.hpp
deleted file mode 100755
index 4698e5d..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/bufferpool.hpp
+++ /dev/null
@@ -1,40 +0,0 @@
-// This file is part of OpenCV project.
-// It is subject to the license terms in the LICENSE file found in the top-level directory
-// of this distribution and at http://opencv.org/license.html.
-//
-// Copyright (C) 2014, Advanced Micro Devices, Inc., all rights reserved.
-
-#ifndef OPENCV_CORE_BUFFER_POOL_HPP
-#define OPENCV_CORE_BUFFER_POOL_HPP
-
-#ifdef _MSC_VER
-#pragma warning(push)
-#pragma warning(disable: 4265)
-#endif
-
-namespace cv
-{
-
-//! @addtogroup core
-//! @{
-
-class BufferPoolController
-{
-protected:
-    ~BufferPoolController() { }
-public:
-    virtual size_t getReservedSize() const = 0;
-    virtual size_t getMaxReservedSize() const = 0;
-    virtual void setMaxReservedSize(size_t size) = 0;
-    virtual void freeAllReservedBuffers() = 0;
-};
-
-//! @}
-
-}
-
-#ifdef _MSC_VER
-#pragma warning(pop)
-#endif
-
-#endif // OPENCV_CORE_BUFFER_POOL_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/check.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/check.hpp
deleted file mode 100755
index 92d114d..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/check.hpp
+++ /dev/null
@@ -1,135 +0,0 @@
-// This file is part of OpenCV project.
-// It is subject to the license terms in the LICENSE file found in the top-level directory
-// of this distribution and at http://opencv.org/license.html.
-
-#ifndef OPENCV_CORE_CHECK_HPP
-#define OPENCV_CORE_CHECK_HPP
-
-#include <opencv2/core/base.hpp>
-
-namespace cv {
-
-/** Returns string of cv::Mat depth value: CV_8U -> "CV_8U" or "<invalid depth>" */
-CV_EXPORTS const char* depthToString(int depth);
-
-/** Returns string of cv::Mat depth value: CV_8UC3 -> "CV_8UC3" or "<invalid type>" */
-CV_EXPORTS const String typeToString(int type);
-
-
-//! @cond IGNORED
-namespace detail {
-
-/** Returns string of cv::Mat depth value: CV_8U -> "CV_8U" or NULL */
-CV_EXPORTS const char* depthToString_(int depth);
-
-/** Returns string of cv::Mat depth value: CV_8UC3 -> "CV_8UC3" or cv::String() */
-CV_EXPORTS const cv::String typeToString_(int type);
-
-enum TestOp {
-  TEST_CUSTOM = 0,
-  TEST_EQ = 1,
-  TEST_NE = 2,
-  TEST_LE = 3,
-  TEST_LT = 4,
-  TEST_GE = 5,
-  TEST_GT = 6,
-  CV__LAST_TEST_OP
-};
-
-struct CheckContext {
-    const char* func;
-    const char* file;
-    int line;
-    enum TestOp testOp;
-    const char* message;
-    const char* p1_str;
-    const char* p2_str;
-};
-
-#ifndef CV__CHECK_FILENAME
-# define CV__CHECK_FILENAME __FILE__
-#endif
-
-#ifndef CV__CHECK_FUNCTION
-# if defined _MSC_VER
-#   define CV__CHECK_FUNCTION __FUNCSIG__
-# elif defined __GNUC__
-#   define CV__CHECK_FUNCTION __PRETTY_FUNCTION__
-# else
-#   define CV__CHECK_FUNCTION "<unknown>"
-# endif
-#endif
-
-#define CV__CHECK_LOCATION_VARNAME(id) CVAUX_CONCAT(CVAUX_CONCAT(__cv_check_, id), __LINE__)
-#define CV__DEFINE_CHECK_CONTEXT(id, message, testOp, p1_str, p2_str) \
-    static const cv::detail::CheckContext CV__CHECK_LOCATION_VARNAME(id) = \
-            { CV__CHECK_FUNCTION, CV__CHECK_FILENAME, __LINE__, testOp, message, p1_str, p2_str }
-
-CV_EXPORTS void CV_NORETURN check_failed_auto(const int v1, const int v2, const CheckContext& ctx);
-CV_EXPORTS void CV_NORETURN check_failed_auto(const float v1, const float v2, const CheckContext& ctx);
-CV_EXPORTS void CV_NORETURN check_failed_auto(const double v1, const double v2, const CheckContext& ctx);
-CV_EXPORTS void CV_NORETURN check_failed_MatDepth(const int v1, const int v2, const CheckContext& ctx);
-CV_EXPORTS void CV_NORETURN check_failed_MatType(const int v1, const int v2, const CheckContext& ctx);
-CV_EXPORTS void CV_NORETURN check_failed_MatChannels(const int v1, const int v2, const CheckContext& ctx);
-
-CV_EXPORTS void CV_NORETURN check_failed_auto(const int v, const CheckContext& ctx);
-CV_EXPORTS void CV_NORETURN check_failed_auto(const float v, const CheckContext& ctx);
-CV_EXPORTS void CV_NORETURN check_failed_auto(const double v, const CheckContext& ctx);
-CV_EXPORTS void CV_NORETURN check_failed_MatDepth(const int v, const CheckContext& ctx);
-CV_EXPORTS void CV_NORETURN check_failed_MatType(const int v, const CheckContext& ctx);
-CV_EXPORTS void CV_NORETURN check_failed_MatChannels(const int v, const CheckContext& ctx);
-
-
-#define CV__TEST_EQ(v1, v2) ((v1) == (v2))
-#define CV__TEST_NE(v1, v2) ((v1) != (v2))
-#define CV__TEST_LE(v1, v2) ((v1) <= (v2))
-#define CV__TEST_LT(v1, v2) ((v1) < (v2))
-#define CV__TEST_GE(v1, v2) ((v1) >= (v2))
-#define CV__TEST_GT(v1, v2) ((v1) > (v2))
-
-#define CV__CHECK(id, op, type, v1, v2, v1_str, v2_str, msg_str) do { \
-    if(CV__TEST_##op((v1), (v2))) ; else { \
-        CV__DEFINE_CHECK_CONTEXT(id, msg_str, cv::detail::TEST_ ## op, v1_str, v2_str); \
-        cv::detail::check_failed_ ## type((v1), (v2), CV__CHECK_LOCATION_VARNAME(id)); \
-    } \
-} while (0)
-
-#define CV__CHECK_CUSTOM_TEST(id, type, v, test_expr, v_str, test_expr_str, msg_str) do { \
-    if(!!(test_expr)) ; else { \
-        CV__DEFINE_CHECK_CONTEXT(id, msg_str, cv::detail::TEST_CUSTOM, v_str, test_expr_str); \
-        cv::detail::check_failed_ ## type((v), CV__CHECK_LOCATION_VARNAME(id)); \
-    } \
-} while (0)
-
-} // namespace
-//! @endcond
-
-
-/// Supported values of these types: int, float, double
-#define CV_CheckEQ(v1, v2, msg)  CV__CHECK(_, EQ, auto, v1, v2, #v1, #v2, msg)
-#define CV_CheckNE(v1, v2, msg)  CV__CHECK(_, NE, auto, v1, v2, #v1, #v2, msg)
-#define CV_CheckLE(v1, v2, msg)  CV__CHECK(_, LE, auto, v1, v2, #v1, #v2, msg)
-#define CV_CheckLT(v1, v2, msg)  CV__CHECK(_, LT, auto, v1, v2, #v1, #v2, msg)
-#define CV_CheckGE(v1, v2, msg)  CV__CHECK(_, GE, auto, v1, v2, #v1, #v2, msg)
-#define CV_CheckGT(v1, v2, msg)  CV__CHECK(_, GT, auto, v1, v2, #v1, #v2, msg)
-
-/// Check with additional "decoding" of type values in error message
-#define CV_CheckTypeEQ(t1, t2, msg)  CV__CHECK(_, EQ, MatType, t1, t2, #t1, #t2, msg)
-/// Check with additional "decoding" of depth values in error message
-#define CV_CheckDepthEQ(d1, d2, msg)  CV__CHECK(_, EQ, MatDepth, d1, d2, #d1, #d2, msg)
-
-#define CV_CheckChannelsEQ(c1, c2, msg)  CV__CHECK(_, EQ, MatChannels, c1, c2, #c1, #c2, msg)
-
-
-/// Example: type == CV_8UC1 || type == CV_8UC3
-#define CV_CheckType(t, test_expr, msg)  CV__CHECK_CUSTOM_TEST(_, MatType, t, (test_expr), #t, #test_expr, msg)
-
-/// Example: depth == CV_32F || depth == CV_64F
-#define CV_CheckDepth(t, test_expr, msg)  CV__CHECK_CUSTOM_TEST(_, MatDepth, t, (test_expr), #t, #test_expr, msg)
-
-/// Some complex conditions: CV_Check(src2, src2.empty() || (src2.type() == src1.type() && src2.size() == src1.size()), "src2 should have same size/type as src1")
-// TODO define pretty-printers: #define CV_Check(v, test_expr, msg)  CV__CHECK_CUSTOM_TEST(_, auto, v, (test_expr), #v, #test_expr, msg)
-
-} // namespace
-
-#endif // OPENCV_CORE_CHECK_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/core.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/core.hpp
deleted file mode 100755
index 4389183..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/core.hpp
+++ /dev/null
@@ -1,48 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifdef __OPENCV_BUILD
-#error this is a compatibility header which should not be used inside the OpenCV library
-#endif
-
-#include "opencv2/core.hpp"
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/core_c.h b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/core_c.h
deleted file mode 100755
index ce28fa1..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/core_c.h
+++ /dev/null
@@ -1,3175 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-
-#ifndef OPENCV_CORE_C_H
-#define OPENCV_CORE_C_H
-
-#include "opencv2/core/types_c.h"
-
-#ifdef __cplusplus
-#  ifdef _MSC_VER
-/* disable warning C4190: 'function' has C-linkage specified, but returns UDT 'typename'
-                          which is incompatible with C
-
-   It is OK to disable it because we only extend few plain structures with
-   C++ construrtors for simpler interoperability with C++ API of the library
-*/
-#    pragma warning(disable:4190)
-#  elif defined __clang__ && __clang_major__ >= 3
-#    pragma GCC diagnostic ignored "-Wreturn-type-c-linkage"
-#  endif
-#endif
-
-#ifdef __cplusplus
-extern "C" {
-#endif
-
-/** @addtogroup core_c
-    @{
-*/
-
-/****************************************************************************************\
-*          Array allocation, deallocation, initialization and access to elements         *
-\****************************************************************************************/
-
-/** `malloc` wrapper.
-   If there is no enough memory, the function
-   (as well as other OpenCV functions that call cvAlloc)
-   raises an error. */
-CVAPI(void*)  cvAlloc( size_t size );
-
-/** `free` wrapper.
-   Here and further all the memory releasing functions
-   (that all call cvFree) take double pointer in order to
-   to clear pointer to the data after releasing it.
-   Passing pointer to NULL pointer is Ok: nothing happens in this case
-*/
-CVAPI(void)   cvFree_( void* ptr );
-#define cvFree(ptr) (cvFree_(*(ptr)), *(ptr)=0)
-
-/** @brief Creates an image header but does not allocate the image data.
-
-@param size Image width and height
-@param depth Image depth (see cvCreateImage )
-@param channels Number of channels (see cvCreateImage )
- */
-CVAPI(IplImage*)  cvCreateImageHeader( CvSize size, int depth, int channels );
-
-/** @brief Initializes an image header that was previously allocated.
-
-The returned IplImage\* points to the initialized header.
-@param image Image header to initialize
-@param size Image width and height
-@param depth Image depth (see cvCreateImage )
-@param channels Number of channels (see cvCreateImage )
-@param origin Top-left IPL_ORIGIN_TL or bottom-left IPL_ORIGIN_BL
-@param align Alignment for image rows, typically 4 or 8 bytes
- */
-CVAPI(IplImage*) cvInitImageHeader( IplImage* image, CvSize size, int depth,
-                                   int channels, int origin CV_DEFAULT(0),
-                                   int align CV_DEFAULT(4));
-
-/** @brief Creates an image header and allocates the image data.
-
-This function call is equivalent to the following code:
-@code
-    header = cvCreateImageHeader(size, depth, channels);
-    cvCreateData(header);
-@endcode
-@param size Image width and height
-@param depth Bit depth of image elements. See IplImage for valid depths.
-@param channels Number of channels per pixel. See IplImage for details. This function only creates
-images with interleaved channels.
- */
-CVAPI(IplImage*)  cvCreateImage( CvSize size, int depth, int channels );
-
-/** @brief Deallocates an image header.
-
-This call is an analogue of :
-@code
-    if(image )
-    {
-        iplDeallocate(*image, IPL_IMAGE_HEADER | IPL_IMAGE_ROI);
-        *image = 0;
-    }
-@endcode
-but it does not use IPL functions by default (see the CV_TURN_ON_IPL_COMPATIBILITY macro).
-@param image Double pointer to the image header
- */
-CVAPI(void)  cvReleaseImageHeader( IplImage** image );
-
-/** @brief Deallocates the image header and the image data.
-
-This call is a shortened form of :
-@code
-    if(*image )
-    {
-        cvReleaseData(*image);
-        cvReleaseImageHeader(image);
-    }
-@endcode
-@param image Double pointer to the image header
-*/
-CVAPI(void)  cvReleaseImage( IplImage** image );
-
-/** Creates a copy of IPL image (widthStep may differ) */
-CVAPI(IplImage*) cvCloneImage( const IplImage* image );
-
-/** @brief Sets the channel of interest in an IplImage.
-
-If the ROI is set to NULL and the coi is *not* 0, the ROI is allocated. Most OpenCV functions do
-*not* support the COI setting, so to process an individual image/matrix channel one may copy (via
-cvCopy or cvSplit) the channel to a separate image/matrix, process it and then copy the result
-back (via cvCopy or cvMerge) if needed.
-@param image A pointer to the image header
-@param coi The channel of interest. 0 - all channels are selected, 1 - first channel is selected,
-etc. Note that the channel indices become 1-based.
- */
-CVAPI(void)  cvSetImageCOI( IplImage* image, int coi );
-
-/** @brief Returns the index of the channel of interest.
-
-Returns the channel of interest of in an IplImage. Returned values correspond to the coi in
-cvSetImageCOI.
-@param image A pointer to the image header
- */
-CVAPI(int)  cvGetImageCOI( const IplImage* image );
-
-/** @brief Sets an image Region Of Interest (ROI) for a given rectangle.
-
-If the original image ROI was NULL and the rect is not the whole image, the ROI structure is
-allocated.
-
-Most OpenCV functions support the use of ROI and treat the image rectangle as a separate image. For
-example, all of the pixel coordinates are counted from the top-left (or bottom-left) corner of the
-ROI, not the original image.
-@param image A pointer to the image header
-@param rect The ROI rectangle
- */
-CVAPI(void)  cvSetImageROI( IplImage* image, CvRect rect );
-
-/** @brief Resets the image ROI to include the entire image and releases the ROI structure.
-
-This produces a similar result to the following, but in addition it releases the ROI structure. :
-@code
-    cvSetImageROI(image, cvRect(0, 0, image->width, image->height ));
-    cvSetImageCOI(image, 0);
-@endcode
-@param image A pointer to the image header
- */
-CVAPI(void)  cvResetImageROI( IplImage* image );
-
-/** @brief Returns the image ROI.
-
-If there is no ROI set, cvRect(0,0,image-\>width,image-\>height) is returned.
-@param image A pointer to the image header
- */
-CVAPI(CvRect) cvGetImageROI( const IplImage* image );
-
-/** @brief Creates a matrix header but does not allocate the matrix data.
-
-The function allocates a new matrix header and returns a pointer to it. The matrix data can then be
-allocated using cvCreateData or set explicitly to user-allocated data via cvSetData.
-@param rows Number of rows in the matrix
-@param cols Number of columns in the matrix
-@param type Type of the matrix elements, see cvCreateMat
- */
-CVAPI(CvMat*)  cvCreateMatHeader( int rows, int cols, int type );
-
-#define CV_AUTOSTEP  0x7fffffff
-
-/** @brief Initializes a pre-allocated matrix header.
-
-This function is often used to process raw data with OpenCV matrix functions. For example, the
-following code computes the matrix product of two matrices, stored as ordinary arrays:
-@code
-    double a[] = { 1, 2, 3, 4,
-                   5, 6, 7, 8,
-                   9, 10, 11, 12 };
-
-    double b[] = { 1, 5, 9,
-                   2, 6, 10,
-                   3, 7, 11,
-                   4, 8, 12 };
-
-    double c[9];
-    CvMat Ma, Mb, Mc ;
-
-    cvInitMatHeader(&Ma, 3, 4, CV_64FC1, a);
-    cvInitMatHeader(&Mb, 4, 3, CV_64FC1, b);
-    cvInitMatHeader(&Mc, 3, 3, CV_64FC1, c);
-
-    cvMatMulAdd(&Ma, &Mb, 0, &Mc);
-    // the c array now contains the product of a (3x4) and b (4x3)
-@endcode
-@param mat A pointer to the matrix header to be initialized
-@param rows Number of rows in the matrix
-@param cols Number of columns in the matrix
-@param type Type of the matrix elements, see cvCreateMat .
-@param data Optional: data pointer assigned to the matrix header
-@param step Optional: full row width in bytes of the assigned data. By default, the minimal
-possible step is used which assumes there are no gaps between subsequent rows of the matrix.
- */
-CVAPI(CvMat*) cvInitMatHeader( CvMat* mat, int rows, int cols,
-                              int type, void* data CV_DEFAULT(NULL),
-                              int step CV_DEFAULT(CV_AUTOSTEP) );
-
-/** @brief Creates a matrix header and allocates the matrix data.
-
-The function call is equivalent to the following code:
-@code
-    CvMat* mat = cvCreateMatHeader(rows, cols, type);
-    cvCreateData(mat);
-@endcode
-@param rows Number of rows in the matrix
-@param cols Number of columns in the matrix
-@param type The type of the matrix elements in the form
-CV_\<bit depth\>\<S|U|F\>C\<number of channels\> , where S=signed, U=unsigned, F=float. For
-example, CV _ 8UC1 means the elements are 8-bit unsigned and the there is 1 channel, and CV _
-32SC2 means the elements are 32-bit signed and there are 2 channels.
- */
-CVAPI(CvMat*)  cvCreateMat( int rows, int cols, int type );
-
-/** @brief Deallocates a matrix.
-
-The function decrements the matrix data reference counter and deallocates matrix header. If the data
-reference counter is 0, it also deallocates the data. :
-@code
-    if(*mat )
-        cvDecRefData(*mat);
-    cvFree((void**)mat);
-@endcode
-@param mat Double pointer to the matrix
- */
-CVAPI(void)  cvReleaseMat( CvMat** mat );
-
-/** @brief Decrements an array data reference counter.
-
-The function decrements the data reference counter in a CvMat or CvMatND if the reference counter
-
-pointer is not NULL. If the counter reaches zero, the data is deallocated. In the current
-implementation the reference counter is not NULL only if the data was allocated using the
-cvCreateData function. The counter will be NULL in other cases such as: external data was assigned
-to the header using cvSetData, header is part of a larger matrix or image, or the header was
-converted from an image or n-dimensional matrix header.
-@param arr Pointer to an array header
- */
-CV_INLINE  void  cvDecRefData( CvArr* arr )
-{
-    if( CV_IS_MAT( arr ))
-    {
-        CvMat* mat = (CvMat*)arr;
-        mat->data.ptr = NULL;
-        if( mat->refcount != NULL && --*mat->refcount == 0 )
-            cvFree( &mat->refcount );
-        mat->refcount = NULL;
-    }
-    else if( CV_IS_MATND( arr ))
-    {
-        CvMatND* mat = (CvMatND*)arr;
-        mat->data.ptr = NULL;
-        if( mat->refcount != NULL && --*mat->refcount == 0 )
-            cvFree( &mat->refcount );
-        mat->refcount = NULL;
-    }
-}
-
-/** @brief Increments array data reference counter.
-
-The function increments CvMat or CvMatND data reference counter and returns the new counter value if
-the reference counter pointer is not NULL, otherwise it returns zero.
-@param arr Array header
- */
-CV_INLINE  int  cvIncRefData( CvArr* arr )
-{
-    int refcount = 0;
-    if( CV_IS_MAT( arr ))
-    {
-        CvMat* mat = (CvMat*)arr;
-        if( mat->refcount != NULL )
-            refcount = ++*mat->refcount;
-    }
-    else if( CV_IS_MATND( arr ))
-    {
-        CvMatND* mat = (CvMatND*)arr;
-        if( mat->refcount != NULL )
-            refcount = ++*mat->refcount;
-    }
-    return refcount;
-}
-
-
-/** Creates an exact copy of the input matrix (except, may be, step value) */
-CVAPI(CvMat*) cvCloneMat( const CvMat* mat );
-
-
-/** @brief Returns matrix header corresponding to the rectangular sub-array of input image or matrix.
-
-The function returns header, corresponding to a specified rectangle of the input array. In other
-
-words, it allows the user to treat a rectangular part of input array as a stand-alone array. ROI is
-taken into account by the function so the sub-array of ROI is actually extracted.
-@param arr Input array
-@param submat Pointer to the resultant sub-array header
-@param rect Zero-based coordinates of the rectangle of interest
- */
-CVAPI(CvMat*) cvGetSubRect( const CvArr* arr, CvMat* submat, CvRect rect );
-#define cvGetSubArr cvGetSubRect
-
-/** @brief Returns array row or row span.
-
-The function returns the header, corresponding to a specified row/row span of the input array.
-cvGetRow(arr, submat, row) is a shortcut for cvGetRows(arr, submat, row, row+1).
-@param arr Input array
-@param submat Pointer to the resulting sub-array header
-@param start_row Zero-based index of the starting row (inclusive) of the span
-@param end_row Zero-based index of the ending row (exclusive) of the span
-@param delta_row Index step in the row span. That is, the function extracts every delta_row -th
-row from start_row and up to (but not including) end_row .
- */
-CVAPI(CvMat*) cvGetRows( const CvArr* arr, CvMat* submat,
-                        int start_row, int end_row,
-                        int delta_row CV_DEFAULT(1));
-
-/** @overload
-@param arr Input array
-@param submat Pointer to the resulting sub-array header
-@param row Zero-based index of the selected row
-*/
-CV_INLINE  CvMat*  cvGetRow( const CvArr* arr, CvMat* submat, int row )
-{
-    return cvGetRows( arr, submat, row, row + 1, 1 );
-}
-
-
-/** @brief Returns one of more array columns.
-
-The function returns the header, corresponding to a specified column span of the input array. That
-
-is, no data is copied. Therefore, any modifications of the submatrix will affect the original array.
-If you need to copy the columns, use cvCloneMat. cvGetCol(arr, submat, col) is a shortcut for
-cvGetCols(arr, submat, col, col+1).
-@param arr Input array
-@param submat Pointer to the resulting sub-array header
-@param start_col Zero-based index of the starting column (inclusive) of the span
-@param end_col Zero-based index of the ending column (exclusive) of the span
- */
-CVAPI(CvMat*) cvGetCols( const CvArr* arr, CvMat* submat,
-                        int start_col, int end_col );
-
-/** @overload
-@param arr Input array
-@param submat Pointer to the resulting sub-array header
-@param col Zero-based index of the selected column
-*/
-CV_INLINE  CvMat*  cvGetCol( const CvArr* arr, CvMat* submat, int col )
-{
-    return cvGetCols( arr, submat, col, col + 1 );
-}
-
-/** @brief Returns one of array diagonals.
-
-The function returns the header, corresponding to a specified diagonal of the input array.
-@param arr Input array
-@param submat Pointer to the resulting sub-array header
-@param diag Index of the array diagonal. Zero value corresponds to the main diagonal, -1
-corresponds to the diagonal above the main, 1 corresponds to the diagonal below the main, and so
-forth.
- */
-CVAPI(CvMat*) cvGetDiag( const CvArr* arr, CvMat* submat,
-                            int diag CV_DEFAULT(0));
-
-/** low-level scalar <-> raw data conversion functions */
-CVAPI(void) cvScalarToRawData( const CvScalar* scalar, void* data, int type,
-                              int extend_to_12 CV_DEFAULT(0) );
-
-CVAPI(void) cvRawDataToScalar( const void* data, int type, CvScalar* scalar );
-
-/** @brief Creates a new matrix header but does not allocate the matrix data.
-
-The function allocates a header for a multi-dimensional dense array. The array data can further be
-allocated using cvCreateData or set explicitly to user-allocated data via cvSetData.
-@param dims Number of array dimensions
-@param sizes Array of dimension sizes
-@param type Type of array elements, see cvCreateMat
- */
-CVAPI(CvMatND*)  cvCreateMatNDHeader( int dims, const int* sizes, int type );
-
-/** @brief Creates the header and allocates the data for a multi-dimensional dense array.
-
-This function call is equivalent to the following code:
-@code
-    CvMatND* mat = cvCreateMatNDHeader(dims, sizes, type);
-    cvCreateData(mat);
-@endcode
-@param dims Number of array dimensions. This must not exceed CV_MAX_DIM (32 by default, but can be
-changed at build time).
-@param sizes Array of dimension sizes.
-@param type Type of array elements, see cvCreateMat .
- */
-CVAPI(CvMatND*)  cvCreateMatND( int dims, const int* sizes, int type );
-
-/** @brief Initializes a pre-allocated multi-dimensional array header.
-
-@param mat A pointer to the array header to be initialized
-@param dims The number of array dimensions
-@param sizes An array of dimension sizes
-@param type Type of array elements, see cvCreateMat
-@param data Optional data pointer assigned to the matrix header
- */
-CVAPI(CvMatND*)  cvInitMatNDHeader( CvMatND* mat, int dims, const int* sizes,
-                                    int type, void* data CV_DEFAULT(NULL) );
-
-/** @brief Deallocates a multi-dimensional array.
-
-The function decrements the array data reference counter and releases the array header. If the
-reference counter reaches 0, it also deallocates the data. :
-@code
-    if(*mat )
-        cvDecRefData(*mat);
-    cvFree((void**)mat);
-@endcode
-@param mat Double pointer to the array
- */
-CV_INLINE  void  cvReleaseMatND( CvMatND** mat )
-{
-    cvReleaseMat( (CvMat**)mat );
-}
-
-/** Creates a copy of CvMatND (except, may be, steps) */
-CVAPI(CvMatND*) cvCloneMatND( const CvMatND* mat );
-
-/** @brief Creates sparse array.
-
-The function allocates a multi-dimensional sparse array. Initially the array contain no elements,
-that is PtrND and other related functions will return 0 for every index.
-@param dims Number of array dimensions. In contrast to the dense matrix, the number of dimensions is
-practically unlimited (up to \f$2^{16}\f$ ).
-@param sizes Array of dimension sizes
-@param type Type of array elements. The same as for CvMat
- */
-CVAPI(CvSparseMat*)  cvCreateSparseMat( int dims, const int* sizes, int type );
-
-/** @brief Deallocates sparse array.
-
-The function releases the sparse array and clears the array pointer upon exit.
-@param mat Double pointer to the array
- */
-CVAPI(void)  cvReleaseSparseMat( CvSparseMat** mat );
-
-/** Creates a copy of CvSparseMat (except, may be, zero items) */
-CVAPI(CvSparseMat*) cvCloneSparseMat( const CvSparseMat* mat );
-
-/** @brief Initializes sparse array elements iterator.
-
-The function initializes iterator of sparse array elements and returns pointer to the first element,
-or NULL if the array is empty.
-@param mat Input array
-@param mat_iterator Initialized iterator
- */
-CVAPI(CvSparseNode*) cvInitSparseMatIterator( const CvSparseMat* mat,
-                                              CvSparseMatIterator* mat_iterator );
-
-/** @brief Returns the next sparse matrix element
-
-The function moves iterator to the next sparse matrix element and returns pointer to it. In the
-current version there is no any particular order of the elements, because they are stored in the
-hash table. The sample below demonstrates how to iterate through the sparse matrix:
-@code
-    // print all the non-zero sparse matrix elements and compute their sum
-    double sum = 0;
-    int i, dims = cvGetDims(sparsemat);
-    CvSparseMatIterator it;
-    CvSparseNode* node = cvInitSparseMatIterator(sparsemat, &it);
-
-    for(; node != 0; node = cvGetNextSparseNode(&it))
-    {
-        int* idx = CV_NODE_IDX(array, node);
-        float val = *(float*)CV_NODE_VAL(array, node);
-        printf("M");
-        for(i = 0; i < dims; i++ )
-            printf("[%d]", idx[i]);
-        printf("=%g\n", val);
-
-        sum += val;
-    }
-
-    printf("nTotal sum = %g\n", sum);
-@endcode
-@param mat_iterator Sparse array iterator
- */
-CV_INLINE CvSparseNode* cvGetNextSparseNode( CvSparseMatIterator* mat_iterator )
-{
-    if( mat_iterator->node->next )
-        return mat_iterator->node = mat_iterator->node->next;
-    else
-    {
-        int idx;
-        for( idx = ++mat_iterator->curidx; idx < mat_iterator->mat->hashsize; idx++ )
-        {
-            CvSparseNode* node = (CvSparseNode*)mat_iterator->mat->hashtable[idx];
-            if( node )
-            {
-                mat_iterator->curidx = idx;
-                return mat_iterator->node = node;
-            }
-        }
-        return NULL;
-    }
-}
-
-
-#define CV_MAX_ARR 10
-
-/** matrix iterator: used for n-ary operations on dense arrays */
-typedef struct CvNArrayIterator
-{
-    int count; /**< number of arrays */
-    int dims; /**< number of dimensions to iterate */
-    CvSize size; /**< maximal common linear size: { width = size, height = 1 } */
-    uchar* ptr[CV_MAX_ARR]; /**< pointers to the array slices */
-    int stack[CV_MAX_DIM]; /**< for internal use */
-    CvMatND* hdr[CV_MAX_ARR]; /**< pointers to the headers of the
-                                 matrices that are processed */
-}
-CvNArrayIterator;
-
-#define CV_NO_DEPTH_CHECK     1
-#define CV_NO_CN_CHECK        2
-#define CV_NO_SIZE_CHECK      4
-
-/** initializes iterator that traverses through several arrays simulteneously
-   (the function together with cvNextArraySlice is used for
-    N-ari element-wise operations) */
-CVAPI(int) cvInitNArrayIterator( int count, CvArr** arrs,
-                                 const CvArr* mask, CvMatND* stubs,
-                                 CvNArrayIterator* array_iterator,
-                                 int flags CV_DEFAULT(0) );
-
-/** returns zero value if iteration is finished, non-zero (slice length) otherwise */
-CVAPI(int) cvNextNArraySlice( CvNArrayIterator* array_iterator );
-
-
-/** @brief Returns type of array elements.
-
-The function returns type of the array elements. In the case of IplImage the type is converted to
-CvMat-like representation. For example, if the image has been created as:
-@code
-    IplImage* img = cvCreateImage(cvSize(640, 480), IPL_DEPTH_8U, 3);
-@endcode
-The code cvGetElemType(img) will return CV_8UC3.
-@param arr Input array
- */
-CVAPI(int) cvGetElemType( const CvArr* arr );
-
-/** @brief Return number of array dimensions
-
-The function returns the array dimensionality and the array of dimension sizes. In the case of
-IplImage or CvMat it always returns 2 regardless of number of image/matrix rows. For example, the
-following code calculates total number of array elements:
-@code
-    int sizes[CV_MAX_DIM];
-    int i, total = 1;
-    int dims = cvGetDims(arr, size);
-    for(i = 0; i < dims; i++ )
-        total *= sizes[i];
-@endcode
-@param arr Input array
-@param sizes Optional output vector of the array dimension sizes. For 2d arrays the number of rows
-(height) goes first, number of columns (width) next.
- */
-CVAPI(int) cvGetDims( const CvArr* arr, int* sizes CV_DEFAULT(NULL) );
-
-
-/** @brief Returns array size along the specified dimension.
-
-@param arr Input array
-@param index Zero-based dimension index (for matrices 0 means number of rows, 1 means number of
-columns; for images 0 means height, 1 means width)
- */
-CVAPI(int) cvGetDimSize( const CvArr* arr, int index );
-
-
-/** @brief Return pointer to a particular array element.
-
-The functions return a pointer to a specific array element. Number of array dimension should match
-to the number of indices passed to the function except for cvPtr1D function that can be used for
-sequential access to 1D, 2D or nD dense arrays.
-
-The functions can be used for sparse arrays as well - if the requested node does not exist they
-create it and set it to zero.
-
-All these as well as other functions accessing array elements ( cvGetND , cvGetRealND , cvSet
-, cvSetND , cvSetRealND ) raise an error in case if the element index is out of range.
-@param arr Input array
-@param idx0 The first zero-based component of the element index
-@param type Optional output parameter: type of matrix elements
- */
-CVAPI(uchar*) cvPtr1D( const CvArr* arr, int idx0, int* type CV_DEFAULT(NULL));
-/** @overload */
-CVAPI(uchar*) cvPtr2D( const CvArr* arr, int idx0, int idx1, int* type CV_DEFAULT(NULL) );
-/** @overload */
-CVAPI(uchar*) cvPtr3D( const CvArr* arr, int idx0, int idx1, int idx2,
-                      int* type CV_DEFAULT(NULL));
-/** @overload
-@param arr Input array
-@param idx Array of the element indices
-@param type Optional output parameter: type of matrix elements
-@param create_node Optional input parameter for sparse matrices. Non-zero value of the parameter
-means that the requested element is created if it does not exist already.
-@param precalc_hashval Optional input parameter for sparse matrices. If the pointer is not NULL,
-the function does not recalculate the node hash value, but takes it from the specified location.
-It is useful for speeding up pair-wise operations (TODO: provide an example)
-*/
-CVAPI(uchar*) cvPtrND( const CvArr* arr, const int* idx, int* type CV_DEFAULT(NULL),
-                      int create_node CV_DEFAULT(1),
-                      unsigned* precalc_hashval CV_DEFAULT(NULL));
-
-/** @brief Return a specific array element.
-
-The functions return a specific array element. In the case of a sparse array the functions return 0
-if the requested node does not exist (no new node is created by the functions).
-@param arr Input array
-@param idx0 The first zero-based component of the element index
- */
-CVAPI(CvScalar) cvGet1D( const CvArr* arr, int idx0 );
-/** @overload */
-CVAPI(CvScalar) cvGet2D( const CvArr* arr, int idx0, int idx1 );
-/** @overload */
-CVAPI(CvScalar) cvGet3D( const CvArr* arr, int idx0, int idx1, int idx2 );
-/** @overload
-@param arr Input array
-@param idx Array of the element indices
-*/
-CVAPI(CvScalar) cvGetND( const CvArr* arr, const int* idx );
-
-/** @brief Return a specific element of single-channel 1D, 2D, 3D or nD array.
-
-Returns a specific element of a single-channel array. If the array has multiple channels, a runtime
-error is raised. Note that Get?D functions can be used safely for both single-channel and
-multiple-channel arrays though they are a bit slower.
-
-In the case of a sparse array the functions return 0 if the requested node does not exist (no new
-node is created by the functions).
-@param arr Input array. Must have a single channel.
-@param idx0 The first zero-based component of the element index
- */
-CVAPI(double) cvGetReal1D( const CvArr* arr, int idx0 );
-/** @overload */
-CVAPI(double) cvGetReal2D( const CvArr* arr, int idx0, int idx1 );
-/** @overload */
-CVAPI(double) cvGetReal3D( const CvArr* arr, int idx0, int idx1, int idx2 );
-/** @overload
-@param arr Input array. Must have a single channel.
-@param idx Array of the element indices
-*/
-CVAPI(double) cvGetRealND( const CvArr* arr, const int* idx );
-
-/** @brief Change the particular array element.
-
-The functions assign the new value to a particular array element. In the case of a sparse array the
-functions create the node if it does not exist yet.
-@param arr Input array
-@param idx0 The first zero-based component of the element index
-@param value The assigned value
- */
-CVAPI(void) cvSet1D( CvArr* arr, int idx0, CvScalar value );
-/** @overload */
-CVAPI(void) cvSet2D( CvArr* arr, int idx0, int idx1, CvScalar value );
-/** @overload */
-CVAPI(void) cvSet3D( CvArr* arr, int idx0, int idx1, int idx2, CvScalar value );
-/** @overload
-@param arr Input array
-@param idx Array of the element indices
-@param value The assigned value
-*/
-CVAPI(void) cvSetND( CvArr* arr, const int* idx, CvScalar value );
-
-/** @brief Change a specific array element.
-
-The functions assign a new value to a specific element of a single-channel array. If the array has
-multiple channels, a runtime error is raised. Note that the Set\*D function can be used safely for
-both single-channel and multiple-channel arrays, though they are a bit slower.
-
-In the case of a sparse array the functions create the node if it does not yet exist.
-@param arr Input array
-@param idx0 The first zero-based component of the element index
-@param value The assigned value
- */
-CVAPI(void) cvSetReal1D( CvArr* arr, int idx0, double value );
-/** @overload */
-CVAPI(void) cvSetReal2D( CvArr* arr, int idx0, int idx1, double value );
-/** @overload */
-CVAPI(void) cvSetReal3D( CvArr* arr, int idx0,
-                        int idx1, int idx2, double value );
-/** @overload
-@param arr Input array
-@param idx Array of the element indices
-@param value The assigned value
-*/
-CVAPI(void) cvSetRealND( CvArr* arr, const int* idx, double value );
-
-/** clears element of ND dense array,
-   in case of sparse arrays it deletes the specified node */
-CVAPI(void) cvClearND( CvArr* arr, const int* idx );
-
-/** @brief Returns matrix header for arbitrary array.
-
-The function returns a matrix header for the input array that can be a matrix - CvMat, an image -
-IplImage, or a multi-dimensional dense array - CvMatND (the third option is allowed only if
-allowND != 0) . In the case of matrix the function simply returns the input pointer. In the case of
-IplImage\* or CvMatND it initializes the header structure with parameters of the current image ROI
-and returns &header. Because COI is not supported by CvMat, it is returned separately.
-
-The function provides an easy way to handle both types of arrays - IplImage and CvMat using the same
-code. Input array must have non-zero data pointer, otherwise the function will report an error.
-
-@note If the input array is IplImage with planar data layout and COI set, the function returns the
-pointer to the selected plane and COI == 0. This feature allows user to process IplImage structures
-with planar data layout, even though OpenCV does not support such images.
-@param arr Input array
-@param header Pointer to CvMat structure used as a temporary buffer
-@param coi Optional output parameter for storing COI
-@param allowND If non-zero, the function accepts multi-dimensional dense arrays (CvMatND\*) and
-returns 2D matrix (if CvMatND has two dimensions) or 1D matrix (when CvMatND has 1 dimension or
-more than 2 dimensions). The CvMatND array must be continuous.
-@sa cvGetImage, cvarrToMat.
- */
-CVAPI(CvMat*) cvGetMat( const CvArr* arr, CvMat* header,
-                       int* coi CV_DEFAULT(NULL),
-                       int allowND CV_DEFAULT(0));
-
-/** @brief Returns image header for arbitrary array.
-
-The function returns the image header for the input array that can be a matrix (CvMat) or image
-(IplImage). In the case of an image the function simply returns the input pointer. In the case of
-CvMat it initializes an image_header structure with the parameters of the input matrix. Note that
-if we transform IplImage to CvMat using cvGetMat and then transform CvMat back to IplImage using
-this function, we will get different headers if the ROI is set in the original image.
-@param arr Input array
-@param image_header Pointer to IplImage structure used as a temporary buffer
- */
-CVAPI(IplImage*) cvGetImage( const CvArr* arr, IplImage* image_header );
-
-
-/** @brief Changes the shape of a multi-dimensional array without copying the data.
-
-The function is an advanced version of cvReshape that can work with multi-dimensional arrays as
-well (though it can work with ordinary images and matrices) and change the number of dimensions.
-
-Below are the two samples from the cvReshape description rewritten using cvReshapeMatND:
-@code
-    IplImage* color_img = cvCreateImage(cvSize(320,240), IPL_DEPTH_8U, 3);
-    IplImage gray_img_hdr, *gray_img;
-    gray_img = (IplImage*)cvReshapeMatND(color_img, sizeof(gray_img_hdr), &gray_img_hdr, 1, 0, 0);
-    ...
-    int size[] = { 2, 2, 2 };
-    CvMatND* mat = cvCreateMatND(3, size, CV_32F);
-    CvMat row_header, *row;
-    row = (CvMat*)cvReshapeMatND(mat, sizeof(row_header), &row_header, 0, 1, 0);
-@endcode
-In C, the header file for this function includes a convenient macro cvReshapeND that does away with
-the sizeof_header parameter. So, the lines containing the call to cvReshapeMatND in the examples
-may be replaced as follow:
-@code
-    gray_img = (IplImage*)cvReshapeND(color_img, &gray_img_hdr, 1, 0, 0);
-    ...
-    row = (CvMat*)cvReshapeND(mat, &row_header, 0, 1, 0);
-@endcode
-@param arr Input array
-@param sizeof_header Size of output header to distinguish between IplImage, CvMat and CvMatND
-output headers
-@param header Output header to be filled
-@param new_cn New number of channels. new_cn = 0 means that the number of channels remains
-unchanged.
-@param new_dims New number of dimensions. new_dims = 0 means that the number of dimensions
-remains the same.
-@param new_sizes Array of new dimension sizes. Only new_dims-1 values are used, because the
-total number of elements must remain the same. Thus, if new_dims = 1, new_sizes array is not
-used.
- */
-CVAPI(CvArr*) cvReshapeMatND( const CvArr* arr,
-                             int sizeof_header, CvArr* header,
-                             int new_cn, int new_dims, int* new_sizes );
-
-#define cvReshapeND( arr, header, new_cn, new_dims, new_sizes )   \
-      cvReshapeMatND( (arr), sizeof(*(header)), (header),         \
-                      (new_cn), (new_dims), (new_sizes))
-
-/** @brief Changes shape of matrix/image without copying data.
-
-The function initializes the CvMat header so that it points to the same data as the original array
-but has a different shape - different number of channels, different number of rows, or both.
-
-The following example code creates one image buffer and two image headers, the first is for a
-320x240x3 image and the second is for a 960x240x1 image:
-@code
-    IplImage* color_img = cvCreateImage(cvSize(320,240), IPL_DEPTH_8U, 3);
-    CvMat gray_mat_hdr;
-    IplImage gray_img_hdr, *gray_img;
-    cvReshape(color_img, &gray_mat_hdr, 1);
-    gray_img = cvGetImage(&gray_mat_hdr, &gray_img_hdr);
-@endcode
-And the next example converts a 3x3 matrix to a single 1x9 vector:
-@code
-    CvMat* mat = cvCreateMat(3, 3, CV_32F);
-    CvMat row_header, *row;
-    row = cvReshape(mat, &row_header, 0, 1);
-@endcode
-@param arr Input array
-@param header Output header to be filled
-@param new_cn New number of channels. 'new_cn = 0' means that the number of channels remains
-unchanged.
-@param new_rows New number of rows. 'new_rows = 0' means that the number of rows remains
-unchanged unless it needs to be changed according to new_cn value.
-*/
-CVAPI(CvMat*) cvReshape( const CvArr* arr, CvMat* header,
-                        int new_cn, int new_rows CV_DEFAULT(0) );
-
-/** Repeats source 2d array several times in both horizontal and
-   vertical direction to fill destination array */
-CVAPI(void) cvRepeat( const CvArr* src, CvArr* dst );
-
-/** @brief Allocates array data
-
-The function allocates image, matrix or multi-dimensional dense array data. Note that in the case of
-matrix types OpenCV allocation functions are used. In the case of IplImage they are used unless
-CV_TURN_ON_IPL_COMPATIBILITY() has been called before. In the latter case IPL functions are used
-to allocate the data.
-@param arr Array header
- */
-CVAPI(void)  cvCreateData( CvArr* arr );
-
-/** @brief Releases array data.
-
-The function releases the array data. In the case of CvMat or CvMatND it simply calls
-cvDecRefData(), that is the function can not deallocate external data. See also the note to
-cvCreateData .
-@param arr Array header
- */
-CVAPI(void)  cvReleaseData( CvArr* arr );
-
-/** @brief Assigns user data to the array header.
-
-The function assigns user data to the array header. Header should be initialized before using
-cvCreateMatHeader, cvCreateImageHeader, cvCreateMatNDHeader, cvInitMatHeader,
-cvInitImageHeader or cvInitMatNDHeader.
-@param arr Array header
-@param data User data
-@param step Full row length in bytes
- */
-CVAPI(void)  cvSetData( CvArr* arr, void* data, int step );
-
-/** @brief Retrieves low-level information about the array.
-
-The function fills output variables with low-level information about the array data. All output
-
-parameters are optional, so some of the pointers may be set to NULL. If the array is IplImage with
-ROI set, the parameters of ROI are returned.
-
-The following example shows how to get access to array elements. It computes absolute values of the
-array elements :
-@code
-    float* data;
-    int step;
-    CvSize size;
-
-    cvGetRawData(array, (uchar**)&data, &step, &size);
-    step /= sizeof(data[0]);
-
-    for(int y = 0; y < size.height; y++, data += step )
-        for(int x = 0; x < size.width; x++ )
-            data[x] = (float)fabs(data[x]);
-@endcode
-@param arr Array header
-@param data Output pointer to the whole image origin or ROI origin if ROI is set
-@param step Output full row length in bytes
-@param roi_size Output ROI size
- */
-CVAPI(void) cvGetRawData( const CvArr* arr, uchar** data,
-                         int* step CV_DEFAULT(NULL),
-                         CvSize* roi_size CV_DEFAULT(NULL));
-
-/** @brief Returns size of matrix or image ROI.
-
-The function returns number of rows (CvSize::height) and number of columns (CvSize::width) of the
-input matrix or image. In the case of image the size of ROI is returned.
-@param arr array header
- */
-CVAPI(CvSize) cvGetSize( const CvArr* arr );
-
-/** @brief Copies one array to another.
-
-The function copies selected elements from an input array to an output array:
-
-\f[\texttt{dst} (I)= \texttt{src} (I)  \quad \text{if} \quad \texttt{mask} (I)  \ne 0.\f]
-
-If any of the passed arrays is of IplImage type, then its ROI and COI fields are used. Both arrays
-must have the same type, the same number of dimensions, and the same size. The function can also
-copy sparse arrays (mask is not supported in this case).
-@param src The source array
-@param dst The destination array
-@param mask Operation mask, 8-bit single channel array; specifies elements of the destination array
-to be changed
- */
-CVAPI(void)  cvCopy( const CvArr* src, CvArr* dst,
-                     const CvArr* mask CV_DEFAULT(NULL) );
-
-/** @brief Sets every element of an array to a given value.
-
-The function copies the scalar value to every selected element of the destination array:
-\f[\texttt{arr} (I)= \texttt{value} \quad \text{if} \quad \texttt{mask} (I)  \ne 0\f]
-If array arr is of IplImage type, then is ROI used, but COI must not be set.
-@param arr The destination array
-@param value Fill value
-@param mask Operation mask, 8-bit single channel array; specifies elements of the destination
-array to be changed
- */
-CVAPI(void)  cvSet( CvArr* arr, CvScalar value,
-                    const CvArr* mask CV_DEFAULT(NULL) );
-
-/** @brief Clears the array.
-
-The function clears the array. In the case of dense arrays (CvMat, CvMatND or IplImage),
-cvZero(array) is equivalent to cvSet(array,cvScalarAll(0),0). In the case of sparse arrays all the
-elements are removed.
-@param arr Array to be cleared
- */
-CVAPI(void)  cvSetZero( CvArr* arr );
-#define cvZero  cvSetZero
-
-
-/** Splits a multi-channel array into the set of single-channel arrays or
-   extracts particular [color] plane */
-CVAPI(void)  cvSplit( const CvArr* src, CvArr* dst0, CvArr* dst1,
-                      CvArr* dst2, CvArr* dst3 );
-
-/** Merges a set of single-channel arrays into the single multi-channel array
-   or inserts one particular [color] plane to the array */
-CVAPI(void)  cvMerge( const CvArr* src0, const CvArr* src1,
-                      const CvArr* src2, const CvArr* src3,
-                      CvArr* dst );
-
-/** Copies several channels from input arrays to
-   certain channels of output arrays */
-CVAPI(void)  cvMixChannels( const CvArr** src, int src_count,
-                            CvArr** dst, int dst_count,
-                            const int* from_to, int pair_count );
-
-/** @brief Converts one array to another with optional linear transformation.
-
-The function has several different purposes, and thus has several different names. It copies one
-array to another with optional scaling, which is performed first, and/or optional type conversion,
-performed after:
-
-\f[\texttt{dst} (I) =  \texttt{scale} \texttt{src} (I) + ( \texttt{shift} _0, \texttt{shift} _1,...)\f]
-
-All the channels of multi-channel arrays are processed independently.
-
-The type of conversion is done with rounding and saturation, that is if the result of scaling +
-conversion can not be represented exactly by a value of the destination array element type, it is
-set to the nearest representable value on the real axis.
-@param src Source array
-@param dst Destination array
-@param scale Scale factor
-@param shift Value added to the scaled source array elements
- */
-CVAPI(void)  cvConvertScale( const CvArr* src, CvArr* dst,
-                             double scale CV_DEFAULT(1),
-                             double shift CV_DEFAULT(0) );
-#define cvCvtScale cvConvertScale
-#define cvScale  cvConvertScale
-#define cvConvert( src, dst )  cvConvertScale( (src), (dst), 1, 0 )
-
-
-/** Performs linear transformation on every source array element,
-   stores absolute value of the result:
-   dst(x,y,c) = abs(scale*src(x,y,c)+shift).
-   destination array must have 8u type.
-   In other cases one may use cvConvertScale + cvAbsDiffS */
-CVAPI(void)  cvConvertScaleAbs( const CvArr* src, CvArr* dst,
-                                double scale CV_DEFAULT(1),
-                                double shift CV_DEFAULT(0) );
-#define cvCvtScaleAbs  cvConvertScaleAbs
-
-
-/** checks termination criteria validity and
-   sets eps to default_eps (if it is not set),
-   max_iter to default_max_iters (if it is not set)
-*/
-CVAPI(CvTermCriteria) cvCheckTermCriteria( CvTermCriteria criteria,
-                                           double default_eps,
-                                           int default_max_iters );
-
-/****************************************************************************************\
-*                   Arithmetic, logic and comparison operations                          *
-\****************************************************************************************/
-
-/** dst(mask) = src1(mask) + src2(mask) */
-CVAPI(void)  cvAdd( const CvArr* src1, const CvArr* src2, CvArr* dst,
-                    const CvArr* mask CV_DEFAULT(NULL));
-
-/** dst(mask) = src(mask) + value */
-CVAPI(void)  cvAddS( const CvArr* src, CvScalar value, CvArr* dst,
-                     const CvArr* mask CV_DEFAULT(NULL));
-
-/** dst(mask) = src1(mask) - src2(mask) */
-CVAPI(void)  cvSub( const CvArr* src1, const CvArr* src2, CvArr* dst,
-                    const CvArr* mask CV_DEFAULT(NULL));
-
-/** dst(mask) = src(mask) - value = src(mask) + (-value) */
-CV_INLINE  void  cvSubS( const CvArr* src, CvScalar value, CvArr* dst,
-                         const CvArr* mask CV_DEFAULT(NULL))
-{
-    cvAddS( src, cvScalar( -value.val[0], -value.val[1], -value.val[2], -value.val[3]),
-            dst, mask );
-}
-
-/** dst(mask) = value - src(mask) */
-CVAPI(void)  cvSubRS( const CvArr* src, CvScalar value, CvArr* dst,
-                      const CvArr* mask CV_DEFAULT(NULL));
-
-/** dst(idx) = src1(idx) * src2(idx) * scale
-   (scaled element-wise multiplication of 2 arrays) */
-CVAPI(void)  cvMul( const CvArr* src1, const CvArr* src2,
-                    CvArr* dst, double scale CV_DEFAULT(1) );
-
-/** element-wise division/inversion with scaling:
-    dst(idx) = src1(idx) * scale / src2(idx)
-    or dst(idx) = scale / src2(idx) if src1 == 0 */
-CVAPI(void)  cvDiv( const CvArr* src1, const CvArr* src2,
-                    CvArr* dst, double scale CV_DEFAULT(1));
-
-/** dst = src1 * scale + src2 */
-CVAPI(void)  cvScaleAdd( const CvArr* src1, CvScalar scale,
-                         const CvArr* src2, CvArr* dst );
-#define cvAXPY( A, real_scalar, B, C ) cvScaleAdd(A, cvRealScalar(real_scalar), B, C)
-
-/** dst = src1 * alpha + src2 * beta + gamma */
-CVAPI(void)  cvAddWeighted( const CvArr* src1, double alpha,
-                            const CvArr* src2, double beta,
-                            double gamma, CvArr* dst );
-
-/** @brief Calculates the dot product of two arrays in Euclidean metrics.
-
-The function calculates and returns the Euclidean dot product of two arrays.
-
-\f[src1  \bullet src2 =  \sum _I ( \texttt{src1} (I)  \texttt{src2} (I))\f]
-
-In the case of multiple channel arrays, the results for all channels are accumulated. In particular,
-cvDotProduct(a,a) where a is a complex vector, will return \f$||\texttt{a}||^2\f$. The function can
-process multi-dimensional arrays, row by row, layer by layer, and so on.
-@param src1 The first source array
-@param src2 The second source array
- */
-CVAPI(double)  cvDotProduct( const CvArr* src1, const CvArr* src2 );
-
-/** dst(idx) = src1(idx) & src2(idx) */
-CVAPI(void) cvAnd( const CvArr* src1, const CvArr* src2,
-                  CvArr* dst, const CvArr* mask CV_DEFAULT(NULL));
-
-/** dst(idx) = src(idx) & value */
-CVAPI(void) cvAndS( const CvArr* src, CvScalar value,
-                   CvArr* dst, const CvArr* mask CV_DEFAULT(NULL));
-
-/** dst(idx) = src1(idx) | src2(idx) */
-CVAPI(void) cvOr( const CvArr* src1, const CvArr* src2,
-                 CvArr* dst, const CvArr* mask CV_DEFAULT(NULL));
-
-/** dst(idx) = src(idx) | value */
-CVAPI(void) cvOrS( const CvArr* src, CvScalar value,
-                  CvArr* dst, const CvArr* mask CV_DEFAULT(NULL));
-
-/** dst(idx) = src1(idx) ^ src2(idx) */
-CVAPI(void) cvXor( const CvArr* src1, const CvArr* src2,
-                  CvArr* dst, const CvArr* mask CV_DEFAULT(NULL));
-
-/** dst(idx) = src(idx) ^ value */
-CVAPI(void) cvXorS( const CvArr* src, CvScalar value,
-                   CvArr* dst, const CvArr* mask CV_DEFAULT(NULL));
-
-/** dst(idx) = ~src(idx) */
-CVAPI(void) cvNot( const CvArr* src, CvArr* dst );
-
-/** dst(idx) = lower(idx) <= src(idx) < upper(idx) */
-CVAPI(void) cvInRange( const CvArr* src, const CvArr* lower,
-                      const CvArr* upper, CvArr* dst );
-
-/** dst(idx) = lower <= src(idx) < upper */
-CVAPI(void) cvInRangeS( const CvArr* src, CvScalar lower,
-                       CvScalar upper, CvArr* dst );
-
-#define CV_CMP_EQ   0
-#define CV_CMP_GT   1
-#define CV_CMP_GE   2
-#define CV_CMP_LT   3
-#define CV_CMP_LE   4
-#define CV_CMP_NE   5
-
-/** The comparison operation support single-channel arrays only.
-   Destination image should be 8uC1 or 8sC1 */
-
-/** dst(idx) = src1(idx) _cmp_op_ src2(idx) */
-CVAPI(void) cvCmp( const CvArr* src1, const CvArr* src2, CvArr* dst, int cmp_op );
-
-/** dst(idx) = src1(idx) _cmp_op_ value */
-CVAPI(void) cvCmpS( const CvArr* src, double value, CvArr* dst, int cmp_op );
-
-/** dst(idx) = min(src1(idx),src2(idx)) */
-CVAPI(void) cvMin( const CvArr* src1, const CvArr* src2, CvArr* dst );
-
-/** dst(idx) = max(src1(idx),src2(idx)) */
-CVAPI(void) cvMax( const CvArr* src1, const CvArr* src2, CvArr* dst );
-
-/** dst(idx) = min(src(idx),value) */
-CVAPI(void) cvMinS( const CvArr* src, double value, CvArr* dst );
-
-/** dst(idx) = max(src(idx),value) */
-CVAPI(void) cvMaxS( const CvArr* src, double value, CvArr* dst );
-
-/** dst(x,y,c) = abs(src1(x,y,c) - src2(x,y,c)) */
-CVAPI(void) cvAbsDiff( const CvArr* src1, const CvArr* src2, CvArr* dst );
-
-/** dst(x,y,c) = abs(src(x,y,c) - value(c)) */
-CVAPI(void) cvAbsDiffS( const CvArr* src, CvArr* dst, CvScalar value );
-#define cvAbs( src, dst ) cvAbsDiffS( (src), (dst), cvScalarAll(0))
-
-/****************************************************************************************\
-*                                Math operations                                         *
-\****************************************************************************************/
-
-/** Does cartesian->polar coordinates conversion.
-   Either of output components (magnitude or angle) is optional */
-CVAPI(void)  cvCartToPolar( const CvArr* x, const CvArr* y,
-                            CvArr* magnitude, CvArr* angle CV_DEFAULT(NULL),
-                            int angle_in_degrees CV_DEFAULT(0));
-
-/** Does polar->cartesian coordinates conversion.
-   Either of output components (magnitude or angle) is optional.
-   If magnitude is missing it is assumed to be all 1's */
-CVAPI(void)  cvPolarToCart( const CvArr* magnitude, const CvArr* angle,
-                            CvArr* x, CvArr* y,
-                            int angle_in_degrees CV_DEFAULT(0));
-
-/** Does powering: dst(idx) = src(idx)^power */
-CVAPI(void)  cvPow( const CvArr* src, CvArr* dst, double power );
-
-/** Does exponention: dst(idx) = exp(src(idx)).
-   Overflow is not handled yet. Underflow is handled.
-   Maximal relative error is ~7e-6 for single-precision input */
-CVAPI(void)  cvExp( const CvArr* src, CvArr* dst );
-
-/** Calculates natural logarithms: dst(idx) = log(abs(src(idx))).
-   Logarithm of 0 gives large negative number(~-700)
-   Maximal relative error is ~3e-7 for single-precision output
-*/
-CVAPI(void)  cvLog( const CvArr* src, CvArr* dst );
-
-/** Fast arctangent calculation */
-CVAPI(float) cvFastArctan( float y, float x );
-
-/** Fast cubic root calculation */
-CVAPI(float)  cvCbrt( float value );
-
-#define  CV_CHECK_RANGE    1
-#define  CV_CHECK_QUIET    2
-/** Checks array values for NaNs, Infs or simply for too large numbers
-   (if CV_CHECK_RANGE is set). If CV_CHECK_QUIET is set,
-   no runtime errors is raised (function returns zero value in case of "bad" values).
-   Otherwise cvError is called */
-CVAPI(int)  cvCheckArr( const CvArr* arr, int flags CV_DEFAULT(0),
-                        double min_val CV_DEFAULT(0), double max_val CV_DEFAULT(0));
-#define cvCheckArray cvCheckArr
-
-#define CV_RAND_UNI      0
-#define CV_RAND_NORMAL   1
-
-/** @brief Fills an array with random numbers and updates the RNG state.
-
-The function fills the destination array with uniformly or normally distributed random numbers.
-@param rng CvRNG state initialized by cvRNG
-@param arr The destination array
-@param dist_type Distribution type
-> -   **CV_RAND_UNI** uniform distribution
-> -   **CV_RAND_NORMAL** normal or Gaussian distribution
-@param param1 The first parameter of the distribution. In the case of a uniform distribution it is
-the inclusive lower boundary of the random numbers range. In the case of a normal distribution it
-is the mean value of the random numbers.
-@param param2 The second parameter of the distribution. In the case of a uniform distribution it
-is the exclusive upper boundary of the random numbers range. In the case of a normal distribution
-it is the standard deviation of the random numbers.
-@sa randu, randn, RNG::fill.
- */
-CVAPI(void) cvRandArr( CvRNG* rng, CvArr* arr, int dist_type,
-                      CvScalar param1, CvScalar param2 );
-
-CVAPI(void) cvRandShuffle( CvArr* mat, CvRNG* rng,
-                           double iter_factor CV_DEFAULT(1.));
-
-#define CV_SORT_EVERY_ROW 0
-#define CV_SORT_EVERY_COLUMN 1
-#define CV_SORT_ASCENDING 0
-#define CV_SORT_DESCENDING 16
-
-CVAPI(void) cvSort( const CvArr* src, CvArr* dst CV_DEFAULT(NULL),
-                    CvArr* idxmat CV_DEFAULT(NULL),
-                    int flags CV_DEFAULT(0));
-
-/** Finds real roots of a cubic equation */
-CVAPI(int) cvSolveCubic( const CvMat* coeffs, CvMat* roots );
-
-/** Finds all real and complex roots of a polynomial equation */
-CVAPI(void) cvSolvePoly(const CvMat* coeffs, CvMat *roots2,
-      int maxiter CV_DEFAULT(20), int fig CV_DEFAULT(100));
-
-/****************************************************************************************\
-*                                Matrix operations                                       *
-\****************************************************************************************/
-
-/** @brief Calculates the cross product of two 3D vectors.
-
-The function calculates the cross product of two 3D vectors:
-\f[\texttt{dst} =  \texttt{src1} \times \texttt{src2}\f]
-or:
-\f[\begin{array}{l} \texttt{dst} _1 =  \texttt{src1} _2  \texttt{src2} _3 -  \texttt{src1} _3  \texttt{src2} _2 \\ \texttt{dst} _2 =  \texttt{src1} _3  \texttt{src2} _1 -  \texttt{src1} _1  \texttt{src2} _3 \\ \texttt{dst} _3 =  \texttt{src1} _1  \texttt{src2} _2 -  \texttt{src1} _2  \texttt{src2} _1 \end{array}\f]
-@param src1 The first source vector
-@param src2 The second source vector
-@param dst The destination vector
- */
-CVAPI(void)  cvCrossProduct( const CvArr* src1, const CvArr* src2, CvArr* dst );
-
-/** Matrix transform: dst = A*B + C, C is optional */
-#define cvMatMulAdd( src1, src2, src3, dst ) cvGEMM( (src1), (src2), 1., (src3), 1., (dst), 0 )
-#define cvMatMul( src1, src2, dst )  cvMatMulAdd( (src1), (src2), NULL, (dst))
-
-#define CV_GEMM_A_T 1
-#define CV_GEMM_B_T 2
-#define CV_GEMM_C_T 4
-/** Extended matrix transform:
-   dst = alpha*op(A)*op(B) + beta*op(C), where op(X) is X or X^T */
-CVAPI(void)  cvGEMM( const CvArr* src1, const CvArr* src2, double alpha,
-                     const CvArr* src3, double beta, CvArr* dst,
-                     int tABC CV_DEFAULT(0));
-#define cvMatMulAddEx cvGEMM
-
-/** Transforms each element of source array and stores
-   resultant vectors in destination array */
-CVAPI(void)  cvTransform( const CvArr* src, CvArr* dst,
-                          const CvMat* transmat,
-                          const CvMat* shiftvec CV_DEFAULT(NULL));
-#define cvMatMulAddS cvTransform
-
-/** Does perspective transform on every element of input array */
-CVAPI(void)  cvPerspectiveTransform( const CvArr* src, CvArr* dst,
-                                     const CvMat* mat );
-
-/** Calculates (A-delta)*(A-delta)^T (order=0) or (A-delta)^T*(A-delta) (order=1) */
-CVAPI(void) cvMulTransposed( const CvArr* src, CvArr* dst, int order,
-                             const CvArr* delta CV_DEFAULT(NULL),
-                             double scale CV_DEFAULT(1.) );
-
-/** Tranposes matrix. Square matrices can be transposed in-place */
-CVAPI(void)  cvTranspose( const CvArr* src, CvArr* dst );
-#define cvT cvTranspose
-
-/** Completes the symmetric matrix from the lower (LtoR=0) or from the upper (LtoR!=0) part */
-CVAPI(void)  cvCompleteSymm( CvMat* matrix, int LtoR CV_DEFAULT(0) );
-
-/** Mirror array data around horizontal (flip=0),
-   vertical (flip=1) or both(flip=-1) axises:
-   cvFlip(src) flips images vertically and sequences horizontally (inplace) */
-CVAPI(void)  cvFlip( const CvArr* src, CvArr* dst CV_DEFAULT(NULL),
-                     int flip_mode CV_DEFAULT(0));
-#define cvMirror cvFlip
-
-
-#define CV_SVD_MODIFY_A   1
-#define CV_SVD_U_T        2
-#define CV_SVD_V_T        4
-
-/** Performs Singular Value Decomposition of a matrix */
-CVAPI(void)   cvSVD( CvArr* A, CvArr* W, CvArr* U CV_DEFAULT(NULL),
-                     CvArr* V CV_DEFAULT(NULL), int flags CV_DEFAULT(0));
-
-/** Performs Singular Value Back Substitution (solves A*X = B):
-   flags must be the same as in cvSVD */
-CVAPI(void)   cvSVBkSb( const CvArr* W, const CvArr* U,
-                        const CvArr* V, const CvArr* B,
-                        CvArr* X, int flags );
-
-#define CV_LU  0
-#define CV_SVD 1
-#define CV_SVD_SYM 2
-#define CV_CHOLESKY 3
-#define CV_QR  4
-#define CV_NORMAL 16
-
-/** Inverts matrix */
-CVAPI(double)  cvInvert( const CvArr* src, CvArr* dst,
-                         int method CV_DEFAULT(CV_LU));
-#define cvInv cvInvert
-
-/** Solves linear system (src1)*(dst) = (src2)
-   (returns 0 if src1 is a singular and CV_LU method is used) */
-CVAPI(int)  cvSolve( const CvArr* src1, const CvArr* src2, CvArr* dst,
-                     int method CV_DEFAULT(CV_LU));
-
-/** Calculates determinant of input matrix */
-CVAPI(double) cvDet( const CvArr* mat );
-
-/** Calculates trace of the matrix (sum of elements on the main diagonal) */
-CVAPI(CvScalar) cvTrace( const CvArr* mat );
-
-/** Finds eigen values and vectors of a symmetric matrix */
-CVAPI(void)  cvEigenVV( CvArr* mat, CvArr* evects, CvArr* evals,
-                        double eps CV_DEFAULT(0),
-                        int lowindex CV_DEFAULT(-1),
-                        int highindex CV_DEFAULT(-1));
-
-///* Finds selected eigen values and vectors of a symmetric matrix */
-//CVAPI(void)  cvSelectedEigenVV( CvArr* mat, CvArr* evects, CvArr* evals,
-//                                int lowindex, int highindex );
-
-/** Makes an identity matrix (mat_ij = i == j) */
-CVAPI(void)  cvSetIdentity( CvArr* mat, CvScalar value CV_DEFAULT(cvRealScalar(1)) );
-
-/** Fills matrix with given range of numbers */
-CVAPI(CvArr*)  cvRange( CvArr* mat, double start, double end );
-
-/**   @anchor core_c_CovarFlags
-@name Flags for cvCalcCovarMatrix
-@see cvCalcCovarMatrix
-  @{
-*/
-
-/** flag for cvCalcCovarMatrix, transpose([v1-avg, v2-avg,...]) * [v1-avg,v2-avg,...] */
-#define CV_COVAR_SCRAMBLED 0
-
-/** flag for cvCalcCovarMatrix, [v1-avg, v2-avg,...] * transpose([v1-avg,v2-avg,...]) */
-#define CV_COVAR_NORMAL    1
-
-/** flag for cvCalcCovarMatrix, do not calc average (i.e. mean vector) - use the input vector instead
-   (useful for calculating covariance matrix by parts) */
-#define CV_COVAR_USE_AVG   2
-
-/** flag for cvCalcCovarMatrix, scale the covariance matrix coefficients by number of the vectors */
-#define CV_COVAR_SCALE     4
-
-/** flag for cvCalcCovarMatrix, all the input vectors are stored in a single matrix, as its rows */
-#define CV_COVAR_ROWS      8
-
-/** flag for cvCalcCovarMatrix, all the input vectors are stored in a single matrix, as its columns */
-#define CV_COVAR_COLS     16
-
-/** @} */
-
-/** Calculates covariation matrix for a set of vectors
-@see @ref core_c_CovarFlags "flags"
-*/
-CVAPI(void)  cvCalcCovarMatrix( const CvArr** vects, int count,
-                                CvArr* cov_mat, CvArr* avg, int flags );
-
-#define CV_PCA_DATA_AS_ROW 0
-#define CV_PCA_DATA_AS_COL 1
-#define CV_PCA_USE_AVG 2
-CVAPI(void)  cvCalcPCA( const CvArr* data, CvArr* mean,
-                        CvArr* eigenvals, CvArr* eigenvects, int flags );
-
-CVAPI(void)  cvProjectPCA( const CvArr* data, const CvArr* mean,
-                           const CvArr* eigenvects, CvArr* result );
-
-CVAPI(void)  cvBackProjectPCA( const CvArr* proj, const CvArr* mean,
-                               const CvArr* eigenvects, CvArr* result );
-
-/** Calculates Mahalanobis(weighted) distance */
-CVAPI(double)  cvMahalanobis( const CvArr* vec1, const CvArr* vec2, const CvArr* mat );
-#define cvMahalonobis  cvMahalanobis
-
-/****************************************************************************************\
-*                                    Array Statistics                                    *
-\****************************************************************************************/
-
-/** Finds sum of array elements */
-CVAPI(CvScalar)  cvSum( const CvArr* arr );
-
-/** Calculates number of non-zero pixels */
-CVAPI(int)  cvCountNonZero( const CvArr* arr );
-
-/** Calculates mean value of array elements */
-CVAPI(CvScalar)  cvAvg( const CvArr* arr, const CvArr* mask CV_DEFAULT(NULL) );
-
-/** Calculates mean and standard deviation of pixel values */
-CVAPI(void)  cvAvgSdv( const CvArr* arr, CvScalar* mean, CvScalar* std_dev,
-                       const CvArr* mask CV_DEFAULT(NULL) );
-
-/** Finds global minimum, maximum and their positions */
-CVAPI(void)  cvMinMaxLoc( const CvArr* arr, double* min_val, double* max_val,
-                          CvPoint* min_loc CV_DEFAULT(NULL),
-                          CvPoint* max_loc CV_DEFAULT(NULL),
-                          const CvArr* mask CV_DEFAULT(NULL) );
-
-/** @anchor core_c_NormFlags
-  @name Flags for cvNorm and cvNormalize
-  @{
-*/
-#define CV_C            1
-#define CV_L1           2
-#define CV_L2           4
-#define CV_NORM_MASK    7
-#define CV_RELATIVE     8
-#define CV_DIFF         16
-#define CV_MINMAX       32
-
-#define CV_DIFF_C       (CV_DIFF | CV_C)
-#define CV_DIFF_L1      (CV_DIFF | CV_L1)
-#define CV_DIFF_L2      (CV_DIFF | CV_L2)
-#define CV_RELATIVE_C   (CV_RELATIVE | CV_C)
-#define CV_RELATIVE_L1  (CV_RELATIVE | CV_L1)
-#define CV_RELATIVE_L2  (CV_RELATIVE | CV_L2)
-/** @} */
-
-/** Finds norm, difference norm or relative difference norm for an array (or two arrays)
-@see ref core_c_NormFlags "flags"
-*/
-CVAPI(double)  cvNorm( const CvArr* arr1, const CvArr* arr2 CV_DEFAULT(NULL),
-                       int norm_type CV_DEFAULT(CV_L2),
-                       const CvArr* mask CV_DEFAULT(NULL) );
-
-/** @see ref core_c_NormFlags "flags" */
-CVAPI(void)  cvNormalize( const CvArr* src, CvArr* dst,
-                          double a CV_DEFAULT(1.), double b CV_DEFAULT(0.),
-                          int norm_type CV_DEFAULT(CV_L2),
-                          const CvArr* mask CV_DEFAULT(NULL) );
-
-/** @anchor core_c_ReduceFlags
-  @name Flags for cvReduce
-  @{
-*/
-#define CV_REDUCE_SUM 0
-#define CV_REDUCE_AVG 1
-#define CV_REDUCE_MAX 2
-#define CV_REDUCE_MIN 3
-/** @} */
-
-/** @see @ref core_c_ReduceFlags "flags" */
-CVAPI(void)  cvReduce( const CvArr* src, CvArr* dst, int dim CV_DEFAULT(-1),
-                       int op CV_DEFAULT(CV_REDUCE_SUM) );
-
-/****************************************************************************************\
-*                      Discrete Linear Transforms and Related Functions                  *
-\****************************************************************************************/
-
-/** @anchor core_c_DftFlags
-  @name Flags for cvDFT, cvDCT and cvMulSpectrums
-  @{
-  */
-#define CV_DXT_FORWARD  0
-#define CV_DXT_INVERSE  1
-#define CV_DXT_SCALE    2 /**< divide result by size of array */
-#define CV_DXT_INV_SCALE (CV_DXT_INVERSE + CV_DXT_SCALE)
-#define CV_DXT_INVERSE_SCALE CV_DXT_INV_SCALE
-#define CV_DXT_ROWS     4 /**< transform each row individually */
-#define CV_DXT_MUL_CONJ 8 /**< conjugate the second argument of cvMulSpectrums */
-/** @} */
-
-/** Discrete Fourier Transform:
-    complex->complex,
-    real->ccs (forward),
-    ccs->real (inverse)
-@see core_c_DftFlags "flags"
-*/
-CVAPI(void)  cvDFT( const CvArr* src, CvArr* dst, int flags,
-                    int nonzero_rows CV_DEFAULT(0) );
-#define cvFFT cvDFT
-
-/** Multiply results of DFTs: DFT(X)*DFT(Y) or DFT(X)*conj(DFT(Y))
-@see core_c_DftFlags "flags"
-*/
-CVAPI(void)  cvMulSpectrums( const CvArr* src1, const CvArr* src2,
-                             CvArr* dst, int flags );
-
-/** Finds optimal DFT vector size >= size0 */
-CVAPI(int)  cvGetOptimalDFTSize( int size0 );
-
-/** Discrete Cosine Transform
-@see core_c_DftFlags "flags"
-*/
-CVAPI(void)  cvDCT( const CvArr* src, CvArr* dst, int flags );
-
-/****************************************************************************************\
-*                              Dynamic data structures                                   *
-\****************************************************************************************/
-
-/** Calculates length of sequence slice (with support of negative indices). */
-CVAPI(int) cvSliceLength( CvSlice slice, const CvSeq* seq );
-
-
-/** Creates new memory storage.
-   block_size == 0 means that default,
-   somewhat optimal size, is used (currently, it is 64K) */
-CVAPI(CvMemStorage*)  cvCreateMemStorage( int block_size CV_DEFAULT(0));
-
-
-/** Creates a memory storage that will borrow memory blocks from parent storage */
-CVAPI(CvMemStorage*)  cvCreateChildMemStorage( CvMemStorage* parent );
-
-
-/** Releases memory storage. All the children of a parent must be released before
-   the parent. A child storage returns all the blocks to parent when it is released */
-CVAPI(void)  cvReleaseMemStorage( CvMemStorage** storage );
-
-
-/** Clears memory storage. This is the only way(!!!) (besides cvRestoreMemStoragePos)
-   to reuse memory allocated for the storage - cvClearSeq,cvClearSet ...
-   do not free any memory.
-   A child storage returns all the blocks to the parent when it is cleared */
-CVAPI(void)  cvClearMemStorage( CvMemStorage* storage );
-
-/** Remember a storage "free memory" position */
-CVAPI(void)  cvSaveMemStoragePos( const CvMemStorage* storage, CvMemStoragePos* pos );
-
-/** Restore a storage "free memory" position */
-CVAPI(void)  cvRestoreMemStoragePos( CvMemStorage* storage, CvMemStoragePos* pos );
-
-/** Allocates continuous buffer of the specified size in the storage */
-CVAPI(void*) cvMemStorageAlloc( CvMemStorage* storage, size_t size );
-
-/** Allocates string in memory storage */
-CVAPI(CvString) cvMemStorageAllocString( CvMemStorage* storage, const char* ptr,
-                                         int len CV_DEFAULT(-1) );
-
-/** Creates new empty sequence that will reside in the specified storage */
-CVAPI(CvSeq*)  cvCreateSeq( int seq_flags, size_t header_size,
-                            size_t elem_size, CvMemStorage* storage );
-
-/** Changes default size (granularity) of sequence blocks.
-   The default size is ~1Kbyte */
-CVAPI(void)  cvSetSeqBlockSize( CvSeq* seq, int delta_elems );
-
-
-/** Adds new element to the end of sequence. Returns pointer to the element */
-CVAPI(schar*)  cvSeqPush( CvSeq* seq, const void* element CV_DEFAULT(NULL));
-
-
-/** Adds new element to the beginning of sequence. Returns pointer to it */
-CVAPI(schar*)  cvSeqPushFront( CvSeq* seq, const void* element CV_DEFAULT(NULL));
-
-
-/** Removes the last element from sequence and optionally saves it */
-CVAPI(void)  cvSeqPop( CvSeq* seq, void* element CV_DEFAULT(NULL));
-
-
-/** Removes the first element from sequence and optioanally saves it */
-CVAPI(void)  cvSeqPopFront( CvSeq* seq, void* element CV_DEFAULT(NULL));
-
-
-#define CV_FRONT 1
-#define CV_BACK 0
-/** Adds several new elements to the end of sequence */
-CVAPI(void)  cvSeqPushMulti( CvSeq* seq, const void* elements,
-                             int count, int in_front CV_DEFAULT(0) );
-
-/** Removes several elements from the end of sequence and optionally saves them */
-CVAPI(void)  cvSeqPopMulti( CvSeq* seq, void* elements,
-                            int count, int in_front CV_DEFAULT(0) );
-
-/** Inserts a new element in the middle of sequence.
-   cvSeqInsert(seq,0,elem) == cvSeqPushFront(seq,elem) */
-CVAPI(schar*)  cvSeqInsert( CvSeq* seq, int before_index,
-                            const void* element CV_DEFAULT(NULL));
-
-/** Removes specified sequence element */
-CVAPI(void)  cvSeqRemove( CvSeq* seq, int index );
-
-
-/** Removes all the elements from the sequence. The freed memory
-   can be reused later only by the same sequence unless cvClearMemStorage
-   or cvRestoreMemStoragePos is called */
-CVAPI(void)  cvClearSeq( CvSeq* seq );
-
-
-/** Retrieves pointer to specified sequence element.
-   Negative indices are supported and mean counting from the end
-   (e.g -1 means the last sequence element) */
-CVAPI(schar*)  cvGetSeqElem( const CvSeq* seq, int index );
-
-/** Calculates index of the specified sequence element.
-   Returns -1 if element does not belong to the sequence */
-CVAPI(int)  cvSeqElemIdx( const CvSeq* seq, const void* element,
-                         CvSeqBlock** block CV_DEFAULT(NULL) );
-
-/** Initializes sequence writer. The new elements will be added to the end of sequence */
-CVAPI(void)  cvStartAppendToSeq( CvSeq* seq, CvSeqWriter* writer );
-
-
-/** Combination of cvCreateSeq and cvStartAppendToSeq */
-CVAPI(void)  cvStartWriteSeq( int seq_flags, int header_size,
-                              int elem_size, CvMemStorage* storage,
-                              CvSeqWriter* writer );
-
-/** Closes sequence writer, updates sequence header and returns pointer
-   to the resultant sequence
-   (which may be useful if the sequence was created using cvStartWriteSeq))
-*/
-CVAPI(CvSeq*)  cvEndWriteSeq( CvSeqWriter* writer );
-
-
-/** Updates sequence header. May be useful to get access to some of previously
-   written elements via cvGetSeqElem or sequence reader */
-CVAPI(void)   cvFlushSeqWriter( CvSeqWriter* writer );
-
-
-/** Initializes sequence reader.
-   The sequence can be read in forward or backward direction */
-CVAPI(void) cvStartReadSeq( const CvSeq* seq, CvSeqReader* reader,
-                           int reverse CV_DEFAULT(0) );
-
-
-/** Returns current sequence reader position (currently observed sequence element) */
-CVAPI(int)  cvGetSeqReaderPos( CvSeqReader* reader );
-
-
-/** Changes sequence reader position. It may seek to an absolute or
-   to relative to the current position */
-CVAPI(void)   cvSetSeqReaderPos( CvSeqReader* reader, int index,
-                                 int is_relative CV_DEFAULT(0));
-
-/** Copies sequence content to a continuous piece of memory */
-CVAPI(void*)  cvCvtSeqToArray( const CvSeq* seq, void* elements,
-                               CvSlice slice CV_DEFAULT(CV_WHOLE_SEQ) );
-
-/** Creates sequence header for array.
-   After that all the operations on sequences that do not alter the content
-   can be applied to the resultant sequence */
-CVAPI(CvSeq*) cvMakeSeqHeaderForArray( int seq_type, int header_size,
-                                       int elem_size, void* elements, int total,
-                                       CvSeq* seq, CvSeqBlock* block );
-
-/** Extracts sequence slice (with or without copying sequence elements) */
-CVAPI(CvSeq*) cvSeqSlice( const CvSeq* seq, CvSlice slice,
-                         CvMemStorage* storage CV_DEFAULT(NULL),
-                         int copy_data CV_DEFAULT(0));
-
-CV_INLINE CvSeq* cvCloneSeq( const CvSeq* seq, CvMemStorage* storage CV_DEFAULT(NULL))
-{
-    return cvSeqSlice( seq, CV_WHOLE_SEQ, storage, 1 );
-}
-
-/** Removes sequence slice */
-CVAPI(void)  cvSeqRemoveSlice( CvSeq* seq, CvSlice slice );
-
-/** Inserts a sequence or array into another sequence */
-CVAPI(void)  cvSeqInsertSlice( CvSeq* seq, int before_index, const CvArr* from_arr );
-
-/** a < b ? -1 : a > b ? 1 : 0 */
-typedef int (CV_CDECL* CvCmpFunc)(const void* a, const void* b, void* userdata );
-
-/** Sorts sequence in-place given element comparison function */
-CVAPI(void) cvSeqSort( CvSeq* seq, CvCmpFunc func, void* userdata CV_DEFAULT(NULL) );
-
-/** Finds element in a [sorted] sequence */
-CVAPI(schar*) cvSeqSearch( CvSeq* seq, const void* elem, CvCmpFunc func,
-                           int is_sorted, int* elem_idx,
-                           void* userdata CV_DEFAULT(NULL) );
-
-/** Reverses order of sequence elements in-place */
-CVAPI(void) cvSeqInvert( CvSeq* seq );
-
-/** Splits sequence into one or more equivalence classes using the specified criteria */
-CVAPI(int)  cvSeqPartition( const CvSeq* seq, CvMemStorage* storage,
-                            CvSeq** labels, CvCmpFunc is_equal, void* userdata );
-
-/************ Internal sequence functions ************/
-CVAPI(void)  cvChangeSeqBlock( void* reader, int direction );
-CVAPI(void)  cvCreateSeqBlock( CvSeqWriter* writer );
-
-
-/** Creates a new set */
-CVAPI(CvSet*)  cvCreateSet( int set_flags, int header_size,
-                            int elem_size, CvMemStorage* storage );
-
-/** Adds new element to the set and returns pointer to it */
-CVAPI(int)  cvSetAdd( CvSet* set_header, CvSetElem* elem CV_DEFAULT(NULL),
-                      CvSetElem** inserted_elem CV_DEFAULT(NULL) );
-
-/** Fast variant of cvSetAdd */
-CV_INLINE  CvSetElem* cvSetNew( CvSet* set_header )
-{
-    CvSetElem* elem = set_header->free_elems;
-    if( elem )
-    {
-        set_header->free_elems = elem->next_free;
-        elem->flags = elem->flags & CV_SET_ELEM_IDX_MASK;
-        set_header->active_count++;
-    }
-    else
-        cvSetAdd( set_header, NULL, &elem );
-    return elem;
-}
-
-/** Removes set element given its pointer */
-CV_INLINE  void cvSetRemoveByPtr( CvSet* set_header, void* elem )
-{
-    CvSetElem* _elem = (CvSetElem*)elem;
-    assert( _elem->flags >= 0 /*&& (elem->flags & CV_SET_ELEM_IDX_MASK) < set_header->total*/ );
-    _elem->next_free = set_header->free_elems;
-    _elem->flags = (_elem->flags & CV_SET_ELEM_IDX_MASK) | CV_SET_ELEM_FREE_FLAG;
-    set_header->free_elems = _elem;
-    set_header->active_count--;
-}
-
-/** Removes element from the set by its index  */
-CVAPI(void)   cvSetRemove( CvSet* set_header, int index );
-
-/** Returns a set element by index. If the element doesn't belong to the set,
-   NULL is returned */
-CV_INLINE CvSetElem* cvGetSetElem( const CvSet* set_header, int idx )
-{
-    CvSetElem* elem = (CvSetElem*)(void *)cvGetSeqElem( (CvSeq*)set_header, idx );
-    return elem && CV_IS_SET_ELEM( elem ) ? elem : 0;
-}
-
-/** Removes all the elements from the set */
-CVAPI(void)  cvClearSet( CvSet* set_header );
-
-/** Creates new graph */
-CVAPI(CvGraph*)  cvCreateGraph( int graph_flags, int header_size,
-                                int vtx_size, int edge_size,
-                                CvMemStorage* storage );
-
-/** Adds new vertex to the graph */
-CVAPI(int)  cvGraphAddVtx( CvGraph* graph, const CvGraphVtx* vtx CV_DEFAULT(NULL),
-                           CvGraphVtx** inserted_vtx CV_DEFAULT(NULL) );
-
-
-/** Removes vertex from the graph together with all incident edges */
-CVAPI(int)  cvGraphRemoveVtx( CvGraph* graph, int index );
-CVAPI(int)  cvGraphRemoveVtxByPtr( CvGraph* graph, CvGraphVtx* vtx );
-
-
-/** Link two vertices specified by indices or pointers if they
-   are not connected or return pointer to already existing edge
-   connecting the vertices.
-   Functions return 1 if a new edge was created, 0 otherwise */
-CVAPI(int)  cvGraphAddEdge( CvGraph* graph,
-                            int start_idx, int end_idx,
-                            const CvGraphEdge* edge CV_DEFAULT(NULL),
-                            CvGraphEdge** inserted_edge CV_DEFAULT(NULL) );
-
-CVAPI(int)  cvGraphAddEdgeByPtr( CvGraph* graph,
-                               CvGraphVtx* start_vtx, CvGraphVtx* end_vtx,
-                               const CvGraphEdge* edge CV_DEFAULT(NULL),
-                               CvGraphEdge** inserted_edge CV_DEFAULT(NULL) );
-
-/** Remove edge connecting two vertices */
-CVAPI(void)  cvGraphRemoveEdge( CvGraph* graph, int start_idx, int end_idx );
-CVAPI(void)  cvGraphRemoveEdgeByPtr( CvGraph* graph, CvGraphVtx* start_vtx,
-                                     CvGraphVtx* end_vtx );
-
-/** Find edge connecting two vertices */
-CVAPI(CvGraphEdge*)  cvFindGraphEdge( const CvGraph* graph, int start_idx, int end_idx );
-CVAPI(CvGraphEdge*)  cvFindGraphEdgeByPtr( const CvGraph* graph,
-                                           const CvGraphVtx* start_vtx,
-                                           const CvGraphVtx* end_vtx );
-#define cvGraphFindEdge cvFindGraphEdge
-#define cvGraphFindEdgeByPtr cvFindGraphEdgeByPtr
-
-/** Remove all vertices and edges from the graph */
-CVAPI(void)  cvClearGraph( CvGraph* graph );
-
-
-/** Count number of edges incident to the vertex */
-CVAPI(int)  cvGraphVtxDegree( const CvGraph* graph, int vtx_idx );
-CVAPI(int)  cvGraphVtxDegreeByPtr( const CvGraph* graph, const CvGraphVtx* vtx );
-
-
-/** Retrieves graph vertex by given index */
-#define cvGetGraphVtx( graph, idx ) (CvGraphVtx*)cvGetSetElem((CvSet*)(graph), (idx))
-
-/** Retrieves index of a graph vertex given its pointer */
-#define cvGraphVtxIdx( graph, vtx ) ((vtx)->flags & CV_SET_ELEM_IDX_MASK)
-
-/** Retrieves index of a graph edge given its pointer */
-#define cvGraphEdgeIdx( graph, edge ) ((edge)->flags & CV_SET_ELEM_IDX_MASK)
-
-#define cvGraphGetVtxCount( graph ) ((graph)->active_count)
-#define cvGraphGetEdgeCount( graph ) ((graph)->edges->active_count)
-
-#define  CV_GRAPH_VERTEX        1
-#define  CV_GRAPH_TREE_EDGE     2
-#define  CV_GRAPH_BACK_EDGE     4
-#define  CV_GRAPH_FORWARD_EDGE  8
-#define  CV_GRAPH_CROSS_EDGE    16
-#define  CV_GRAPH_ANY_EDGE      30
-#define  CV_GRAPH_NEW_TREE      32
-#define  CV_GRAPH_BACKTRACKING  64
-#define  CV_GRAPH_OVER          -1
-
-#define  CV_GRAPH_ALL_ITEMS    -1
-
-/** flags for graph vertices and edges */
-#define  CV_GRAPH_ITEM_VISITED_FLAG  (1 << 30)
-#define  CV_IS_GRAPH_VERTEX_VISITED(vtx) \
-    (((CvGraphVtx*)(vtx))->flags & CV_GRAPH_ITEM_VISITED_FLAG)
-#define  CV_IS_GRAPH_EDGE_VISITED(edge) \
-    (((CvGraphEdge*)(edge))->flags & CV_GRAPH_ITEM_VISITED_FLAG)
-#define  CV_GRAPH_SEARCH_TREE_NODE_FLAG   (1 << 29)
-#define  CV_GRAPH_FORWARD_EDGE_FLAG       (1 << 28)
-
-typedef struct CvGraphScanner
-{
-    CvGraphVtx* vtx;       /* current graph vertex (or current edge origin) */
-    CvGraphVtx* dst;       /* current graph edge destination vertex */
-    CvGraphEdge* edge;     /* current edge */
-
-    CvGraph* graph;        /* the graph */
-    CvSeq*   stack;        /* the graph vertex stack */
-    int      index;        /* the lower bound of certainly visited vertices */
-    int      mask;         /* event mask */
-}
-CvGraphScanner;
-
-/** Creates new graph scanner. */
-CVAPI(CvGraphScanner*)  cvCreateGraphScanner( CvGraph* graph,
-                                             CvGraphVtx* vtx CV_DEFAULT(NULL),
-                                             int mask CV_DEFAULT(CV_GRAPH_ALL_ITEMS));
-
-/** Releases graph scanner. */
-CVAPI(void) cvReleaseGraphScanner( CvGraphScanner** scanner );
-
-/** Get next graph element */
-CVAPI(int)  cvNextGraphItem( CvGraphScanner* scanner );
-
-/** Creates a copy of graph */
-CVAPI(CvGraph*) cvCloneGraph( const CvGraph* graph, CvMemStorage* storage );
-
-
-/** Does look-up transformation. Elements of the source array
-   (that should be 8uC1 or 8sC1) are used as indexes in lutarr 256-element table */
-CVAPI(void) cvLUT( const CvArr* src, CvArr* dst, const CvArr* lut );
-
-
-/******************* Iteration through the sequence tree *****************/
-typedef struct CvTreeNodeIterator
-{
-    const void* node;
-    int level;
-    int max_level;
-}
-CvTreeNodeIterator;
-
-CVAPI(void) cvInitTreeNodeIterator( CvTreeNodeIterator* tree_iterator,
-                                   const void* first, int max_level );
-CVAPI(void*) cvNextTreeNode( CvTreeNodeIterator* tree_iterator );
-CVAPI(void*) cvPrevTreeNode( CvTreeNodeIterator* tree_iterator );
-
-/** Inserts sequence into tree with specified "parent" sequence.
-   If parent is equal to frame (e.g. the most external contour),
-   then added contour will have null pointer to parent. */
-CVAPI(void) cvInsertNodeIntoTree( void* node, void* parent, void* frame );
-
-/** Removes contour from tree (together with the contour children). */
-CVAPI(void) cvRemoveNodeFromTree( void* node, void* frame );
-
-/** Gathers pointers to all the sequences,
-   accessible from the `first`, to the single sequence */
-CVAPI(CvSeq*) cvTreeToNodeSeq( const void* first, int header_size,
-                              CvMemStorage* storage );
-
-/** The function implements the K-means algorithm for clustering an array of sample
-   vectors in a specified number of classes */
-#define CV_KMEANS_USE_INITIAL_LABELS    1
-CVAPI(int) cvKMeans2( const CvArr* samples, int cluster_count, CvArr* labels,
-                      CvTermCriteria termcrit, int attempts CV_DEFAULT(1),
-                      CvRNG* rng CV_DEFAULT(0), int flags CV_DEFAULT(0),
-                      CvArr* _centers CV_DEFAULT(0), double* compactness CV_DEFAULT(0) );
-
-/****************************************************************************************\
-*                                    System functions                                    *
-\****************************************************************************************/
-
-/** Loads optimized functions from IPP, MKL etc. or switches back to pure C code */
-CVAPI(int)  cvUseOptimized( int on_off );
-
-typedef IplImage* (CV_STDCALL* Cv_iplCreateImageHeader)
-                            (int,int,int,char*,char*,int,int,int,int,int,
-                            IplROI*,IplImage*,void*,IplTileInfo*);
-typedef void (CV_STDCALL* Cv_iplAllocateImageData)(IplImage*,int,int);
-typedef void (CV_STDCALL* Cv_iplDeallocate)(IplImage*,int);
-typedef IplROI* (CV_STDCALL* Cv_iplCreateROI)(int,int,int,int,int);
-typedef IplImage* (CV_STDCALL* Cv_iplCloneImage)(const IplImage*);
-
-/** @brief Makes OpenCV use IPL functions for allocating IplImage and IplROI structures.
-
-Normally, the function is not called directly. Instead, a simple macro
-CV_TURN_ON_IPL_COMPATIBILITY() is used that calls cvSetIPLAllocators and passes there pointers
-to IPL allocation functions. :
-@code
-    ...
-    CV_TURN_ON_IPL_COMPATIBILITY()
-    ...
-@endcode
-@param create_header pointer to a function, creating IPL image header.
-@param allocate_data pointer to a function, allocating IPL image data.
-@param deallocate pointer to a function, deallocating IPL image.
-@param create_roi pointer to a function, creating IPL image ROI (i.e. Region of Interest).
-@param clone_image pointer to a function, cloning an IPL image.
- */
-CVAPI(void) cvSetIPLAllocators( Cv_iplCreateImageHeader create_header,
-                               Cv_iplAllocateImageData allocate_data,
-                               Cv_iplDeallocate deallocate,
-                               Cv_iplCreateROI create_roi,
-                               Cv_iplCloneImage clone_image );
-
-#define CV_TURN_ON_IPL_COMPATIBILITY()                                  \
-    cvSetIPLAllocators( iplCreateImageHeader, iplAllocateImage,         \
-                        iplDeallocate, iplCreateROI, iplCloneImage )
-
-/****************************************************************************************\
-*                                    Data Persistence                                    *
-\****************************************************************************************/
-
-/********************************** High-level functions ********************************/
-
-/** @brief Opens file storage for reading or writing data.
-
-The function opens file storage for reading or writing data. In the latter case, a new file is
-created or an existing file is rewritten. The type of the read or written file is determined by the
-filename extension: .xml for XML, .yml or .yaml for YAML and .json for JSON.
-
-At the same time, it also supports adding parameters like "example.xml?base64".
-
-The function returns a pointer to the CvFileStorage structure.
-If the file cannot be opened then the function returns NULL.
-@param filename Name of the file associated with the storage
-@param memstorage Memory storage used for temporary data and for
-:   storing dynamic structures, such as CvSeq or CvGraph . If it is NULL, a temporary memory
-    storage is created and used.
-@param flags Can be one of the following:
-> -   **CV_STORAGE_READ** the storage is open for reading
-> -   **CV_STORAGE_WRITE** the storage is open for writing
-      (use **CV_STORAGE_WRITE | CV_STORAGE_WRITE_BASE64** to write rawdata in Base64)
-@param encoding
- */
-CVAPI(CvFileStorage*)  cvOpenFileStorage( const char* filename, CvMemStorage* memstorage,
-                                          int flags, const char* encoding CV_DEFAULT(NULL) );
-
-/** @brief Releases file storage.
-
-The function closes the file associated with the storage and releases all the temporary structures.
-It must be called after all I/O operations with the storage are finished.
-@param fs Double pointer to the released file storage
- */
-CVAPI(void) cvReleaseFileStorage( CvFileStorage** fs );
-
-/** returns attribute value or 0 (NULL) if there is no such attribute */
-CVAPI(const char*) cvAttrValue( const CvAttrList* attr, const char* attr_name );
-
-/** @brief Starts writing a new structure.
-
-The function starts writing a compound structure (collection) that can be a sequence or a map. After
-all the structure fields, which can be scalars or structures, are written, cvEndWriteStruct should
-be called. The function can be used to group some objects or to implement the write function for a
-some user object (see CvTypeInfo).
-@param fs File storage
-@param name Name of the written structure. The structure can be accessed by this name when the
-storage is read.
-@param struct_flags A combination one of the following values:
--   **CV_NODE_SEQ** the written structure is a sequence (see discussion of CvFileStorage ),
-    that is, its elements do not have a name.
--   **CV_NODE_MAP** the written structure is a map (see discussion of CvFileStorage ), that
-    is, all its elements have names.
-One and only one of the two above flags must be specified
--   **CV_NODE_FLOW** the optional flag that makes sense only for YAML streams. It means that
-     the structure is written as a flow (not as a block), which is more compact. It is
-     recommended to use this flag for structures or arrays whose elements are all scalars.
-@param type_name Optional parameter - the object type name. In
-    case of XML it is written as a type_id attribute of the structure opening tag. In the case of
-    YAML it is written after a colon following the structure name (see the example in
-    CvFileStorage description). In case of JSON it is written as a name/value pair.
-    Mainly it is used with user objects. When the storage is read, the
-    encoded type name is used to determine the object type (see CvTypeInfo and cvFindType ).
-@param attributes This parameter is not used in the current implementation
- */
-CVAPI(void) cvStartWriteStruct( CvFileStorage* fs, const char* name,
-                                int struct_flags, const char* type_name CV_DEFAULT(NULL),
-                                CvAttrList attributes CV_DEFAULT(cvAttrList()));
-
-/** @brief Finishes writing to a file node collection.
-@param fs File storage
-@sa cvStartWriteStruct.
- */
-CVAPI(void) cvEndWriteStruct( CvFileStorage* fs );
-
-/** @brief Writes an integer value.
-
-The function writes a single integer value (with or without a name) to the file storage.
-@param fs File storage
-@param name Name of the written value. Should be NULL if and only if the parent structure is a
-sequence.
-@param value The written value
- */
-CVAPI(void) cvWriteInt( CvFileStorage* fs, const char* name, int value );
-
-/** @brief Writes a floating-point value.
-
-The function writes a single floating-point value (with or without a name) to file storage. Special
-values are encoded as follows: NaN (Not A Number) as .NaN, infinity as +.Inf or -.Inf.
-
-The following example shows how to use the low-level writing functions to store custom structures,
-such as termination criteria, without registering a new type. :
-@code
-    void write_termcriteria( CvFileStorage* fs, const char* struct_name,
-                             CvTermCriteria* termcrit )
-    {
-        cvStartWriteStruct( fs, struct_name, CV_NODE_MAP, NULL, cvAttrList(0,0));
-        cvWriteComment( fs, "termination criteria", 1 ); // just a description
-        if( termcrit->type & CV_TERMCRIT_ITER )
-            cvWriteInteger( fs, "max_iterations", termcrit->max_iter );
-        if( termcrit->type & CV_TERMCRIT_EPS )
-            cvWriteReal( fs, "accuracy", termcrit->epsilon );
-        cvEndWriteStruct( fs );
-    }
-@endcode
-@param fs File storage
-@param name Name of the written value. Should be NULL if and only if the parent structure is a
-sequence.
-@param value The written value
-*/
-CVAPI(void) cvWriteReal( CvFileStorage* fs, const char* name, double value );
-
-/** @brief Writes a text string.
-
-The function writes a text string to file storage.
-@param fs File storage
-@param name Name of the written string . Should be NULL if and only if the parent structure is a
-sequence.
-@param str The written text string
-@param quote If non-zero, the written string is put in quotes, regardless of whether they are
-required. Otherwise, if the flag is zero, quotes are used only when they are required (e.g. when
-the string starts with a digit or contains spaces).
- */
-CVAPI(void) cvWriteString( CvFileStorage* fs, const char* name,
-                           const char* str, int quote CV_DEFAULT(0) );
-
-/** @brief Writes a comment.
-
-The function writes a comment into file storage. The comments are skipped when the storage is read.
-@param fs File storage
-@param comment The written comment, single-line or multi-line
-@param eol_comment If non-zero, the function tries to put the comment at the end of current line.
-If the flag is zero, if the comment is multi-line, or if it does not fit at the end of the current
-line, the comment starts a new line.
- */
-CVAPI(void) cvWriteComment( CvFileStorage* fs, const char* comment,
-                            int eol_comment );
-
-/** @brief Writes an object to file storage.
-
-The function writes an object to file storage. First, the appropriate type info is found using
-cvTypeOf. Then, the write method associated with the type info is called.
-
-Attributes are used to customize the writing procedure. The standard types support the following
-attributes (all the dt attributes have the same format as in cvWriteRawData):
-
--# CvSeq
-    -   **header_dt** description of user fields of the sequence header that follow CvSeq, or
-        CvChain (if the sequence is a Freeman chain) or CvContour (if the sequence is a contour or
-        point sequence)
-    -   **dt** description of the sequence elements.
-    -   **recursive** if the attribute is present and is not equal to "0" or "false", the whole
-        tree of sequences (contours) is stored.
--# CvGraph
-    -   **header_dt** description of user fields of the graph header that follows CvGraph;
-    -   **vertex_dt** description of user fields of graph vertices
-    -   **edge_dt** description of user fields of graph edges (note that the edge weight is
-        always written, so there is no need to specify it explicitly)
-
-Below is the code that creates the YAML file shown in the CvFileStorage description:
-@code
-    #include "cxcore.h"
-
-    int main( int argc, char** argv )
-    {
-        CvMat* mat = cvCreateMat( 3, 3, CV_32F );
-        CvFileStorage* fs = cvOpenFileStorage( "example.yml", 0, CV_STORAGE_WRITE );
-
-        cvSetIdentity( mat );
-        cvWrite( fs, "A", mat, cvAttrList(0,0) );
-
-        cvReleaseFileStorage( &fs );
-        cvReleaseMat( &mat );
-        return 0;
-    }
-@endcode
-@param fs File storage
-@param name Name of the written object. Should be NULL if and only if the parent structure is a
-sequence.
-@param ptr Pointer to the object
-@param attributes The attributes of the object. They are specific for each particular type (see
-the discussion below).
- */
-CVAPI(void) cvWrite( CvFileStorage* fs, const char* name, const void* ptr,
-                         CvAttrList attributes CV_DEFAULT(cvAttrList()));
-
-/** @brief Starts the next stream.
-
-The function finishes the currently written stream and starts the next stream. In the case of XML
-the file with multiple streams looks like this:
-@code{.xml}
-    <opencv_storage>
-    <!-- stream #1 data -->
-    </opencv_storage>
-    <opencv_storage>
-    <!-- stream #2 data -->
-    </opencv_storage>
-    ...
-@endcode
-The YAML file will look like this:
-@code{.yaml}
-    %YAML 1.0
-    # stream #1 data
-    ...
-    ---
-    # stream #2 data
-@endcode
-This is useful for concatenating files or for resuming the writing process.
-@param fs File storage
- */
-CVAPI(void) cvStartNextStream( CvFileStorage* fs );
-
-/** @brief Writes multiple numbers.
-
-The function writes an array, whose elements consist of single or multiple numbers. The function
-call can be replaced with a loop containing a few cvWriteInt and cvWriteReal calls, but a single
-call is more efficient. Note that because none of the elements have a name, they should be written
-to a sequence rather than a map.
-@param fs File storage
-@param src Pointer to the written array
-@param len Number of the array elements to write
-@param dt Specification of each array element, see @ref format_spec "format specification"
- */
-CVAPI(void) cvWriteRawData( CvFileStorage* fs, const void* src,
-                                int len, const char* dt );
-
-/** @brief Writes multiple numbers in Base64.
-
-If either CV_STORAGE_WRITE_BASE64 or cv::FileStorage::WRITE_BASE64 is used,
-this function will be the same as cvWriteRawData. If neither, the main
-difference is that it outputs a sequence in Base64 encoding rather than
-in plain text.
-
-This function can only be used to write a sequence with a type "binary".
-
-@param fs File storage
-@param src Pointer to the written array
-@param len Number of the array elements to write
-@param dt Specification of each array element, see @ref format_spec "format specification"
-*/
-CVAPI(void) cvWriteRawDataBase64( CvFileStorage* fs, const void* src,
-                                 int len, const char* dt );
-
-/** @brief Returns a unique pointer for a given name.
-
-The function returns a unique pointer for each particular file node name. This pointer can be then
-passed to the cvGetFileNode function that is faster than cvGetFileNodeByName because it compares
-text strings by comparing pointers rather than the strings' content.
-
-Consider the following example where an array of points is encoded as a sequence of 2-entry maps:
-@code
-    points:
-      - { x: 10, y: 10 }
-      - { x: 20, y: 20 }
-      - { x: 30, y: 30 }
-      # ...
-@endcode
-Then, it is possible to get hashed "x" and "y" pointers to speed up decoding of the points. :
-@code
-    #include "cxcore.h"
-
-    int main( int argc, char** argv )
-    {
-        CvFileStorage* fs = cvOpenFileStorage( "points.yml", 0, CV_STORAGE_READ );
-        CvStringHashNode* x_key = cvGetHashedNode( fs, "x", -1, 1 );
-        CvStringHashNode* y_key = cvGetHashedNode( fs, "y", -1, 1 );
-        CvFileNode* points = cvGetFileNodeByName( fs, 0, "points" );
-
-        if( CV_NODE_IS_SEQ(points->tag) )
-        {
-            CvSeq* seq = points->data.seq;
-            int i, total = seq->total;
-            CvSeqReader reader;
-            cvStartReadSeq( seq, &reader, 0 );
-            for( i = 0; i < total; i++ )
-            {
-                CvFileNode* pt = (CvFileNode*)reader.ptr;
-    #if 1 // faster variant
-                CvFileNode* xnode = cvGetFileNode( fs, pt, x_key, 0 );
-                CvFileNode* ynode = cvGetFileNode( fs, pt, y_key, 0 );
-                assert( xnode && CV_NODE_IS_INT(xnode->tag) &&
-                        ynode && CV_NODE_IS_INT(ynode->tag));
-                int x = xnode->data.i; // or x = cvReadInt( xnode, 0 );
-                int y = ynode->data.i; // or y = cvReadInt( ynode, 0 );
-    #elif 1 // slower variant; does not use x_key & y_key
-                CvFileNode* xnode = cvGetFileNodeByName( fs, pt, "x" );
-                CvFileNode* ynode = cvGetFileNodeByName( fs, pt, "y" );
-                assert( xnode && CV_NODE_IS_INT(xnode->tag) &&
-                        ynode && CV_NODE_IS_INT(ynode->tag));
-                int x = xnode->data.i; // or x = cvReadInt( xnode, 0 );
-                int y = ynode->data.i; // or y = cvReadInt( ynode, 0 );
-    #else // the slowest yet the easiest to use variant
-                int x = cvReadIntByName( fs, pt, "x", 0 );
-                int y = cvReadIntByName( fs, pt, "y", 0 );
-    #endif
-                CV_NEXT_SEQ_ELEM( seq->elem_size, reader );
-                printf("
-            }
-        }
-        cvReleaseFileStorage( &fs );
-        return 0;
-    }
-@endcode
-Please note that whatever method of accessing a map you are using, it is still much slower than
-using plain sequences; for example, in the above example, it is more efficient to encode the points
-as pairs of integers in a single numeric sequence.
-@param fs File storage
-@param name Literal node name
-@param len Length of the name (if it is known apriori), or -1 if it needs to be calculated
-@param create_missing Flag that specifies, whether an absent key should be added into the hash table
-*/
-CVAPI(CvStringHashNode*) cvGetHashedKey( CvFileStorage* fs, const char* name,
-                                        int len CV_DEFAULT(-1),
-                                        int create_missing CV_DEFAULT(0));
-
-/** @brief Retrieves one of the top-level nodes of the file storage.
-
-The function returns one of the top-level file nodes. The top-level nodes do not have a name, they
-correspond to the streams that are stored one after another in the file storage. If the index is out
-of range, the function returns a NULL pointer, so all the top-level nodes can be iterated by
-subsequent calls to the function with stream_index=0,1,..., until the NULL pointer is returned.
-This function can be used as a base for recursive traversal of the file storage.
-@param fs File storage
-@param stream_index Zero-based index of the stream. See cvStartNextStream . In most cases,
-there is only one stream in the file; however, there can be several.
- */
-CVAPI(CvFileNode*) cvGetRootFileNode( const CvFileStorage* fs,
-                                     int stream_index CV_DEFAULT(0) );
-
-/** @brief Finds a node in a map or file storage.
-
-The function finds a file node. It is a faster version of cvGetFileNodeByName (see
-cvGetHashedKey discussion). Also, the function can insert a new node, if it is not in the map yet.
-@param fs File storage
-@param map The parent map. If it is NULL, the function searches a top-level node. If both map and
-key are NULLs, the function returns the root file node - a map that contains top-level nodes.
-@param key Unique pointer to the node name, retrieved with cvGetHashedKey
-@param create_missing Flag that specifies whether an absent node should be added to the map
- */
-CVAPI(CvFileNode*) cvGetFileNode( CvFileStorage* fs, CvFileNode* map,
-                                 const CvStringHashNode* key,
-                                 int create_missing CV_DEFAULT(0) );
-
-/** @brief Finds a node in a map or file storage.
-
-The function finds a file node by name. The node is searched either in map or, if the pointer is
-NULL, among the top-level file storage nodes. Using this function for maps and cvGetSeqElem (or
-sequence reader) for sequences, it is possible to navigate through the file storage. To speed up
-multiple queries for a certain key (e.g., in the case of an array of structures) one may use a
-combination of cvGetHashedKey and cvGetFileNode.
-@param fs File storage
-@param map The parent map. If it is NULL, the function searches in all the top-level nodes
-(streams), starting with the first one.
-@param name The file node name
- */
-CVAPI(CvFileNode*) cvGetFileNodeByName( const CvFileStorage* fs,
-                                       const CvFileNode* map,
-                                       const char* name );
-
-/** @brief Retrieves an integer value from a file node.
-
-The function returns an integer that is represented by the file node. If the file node is NULL, the
-default_value is returned (thus, it is convenient to call the function right after cvGetFileNode
-without checking for a NULL pointer). If the file node has type CV_NODE_INT, then node-\>data.i is
-returned. If the file node has type CV_NODE_REAL, then node-\>data.f is converted to an integer
-and returned. Otherwise the error is reported.
-@param node File node
-@param default_value The value that is returned if node is NULL
- */
-CV_INLINE int cvReadInt( const CvFileNode* node, int default_value CV_DEFAULT(0) )
-{
-    return !node ? default_value :
-        CV_NODE_IS_INT(node->tag) ? node->data.i :
-        CV_NODE_IS_REAL(node->tag) ? cvRound(node->data.f) : 0x7fffffff;
-}
-
-/** @brief Finds a file node and returns its value.
-
-The function is a simple superposition of cvGetFileNodeByName and cvReadInt.
-@param fs File storage
-@param map The parent map. If it is NULL, the function searches a top-level node.
-@param name The node name
-@param default_value The value that is returned if the file node is not found
- */
-CV_INLINE int cvReadIntByName( const CvFileStorage* fs, const CvFileNode* map,
-                         const char* name, int default_value CV_DEFAULT(0) )
-{
-    return cvReadInt( cvGetFileNodeByName( fs, map, name ), default_value );
-}
-
-/** @brief Retrieves a floating-point value from a file node.
-
-The function returns a floating-point value that is represented by the file node. If the file node
-is NULL, the default_value is returned (thus, it is convenient to call the function right after
-cvGetFileNode without checking for a NULL pointer). If the file node has type CV_NODE_REAL ,
-then node-\>data.f is returned. If the file node has type CV_NODE_INT , then node-:math:\>data.f
-is converted to floating-point and returned. Otherwise the result is not determined.
-@param node File node
-@param default_value The value that is returned if node is NULL
- */
-CV_INLINE double cvReadReal( const CvFileNode* node, double default_value CV_DEFAULT(0.) )
-{
-    return !node ? default_value :
-        CV_NODE_IS_INT(node->tag) ? (double)node->data.i :
-        CV_NODE_IS_REAL(node->tag) ? node->data.f : 1e300;
-}
-
-/** @brief Finds a file node and returns its value.
-
-The function is a simple superposition of cvGetFileNodeByName and cvReadReal .
-@param fs File storage
-@param map The parent map. If it is NULL, the function searches a top-level node.
-@param name The node name
-@param default_value The value that is returned if the file node is not found
- */
-CV_INLINE double cvReadRealByName( const CvFileStorage* fs, const CvFileNode* map,
-                        const char* name, double default_value CV_DEFAULT(0.) )
-{
-    return cvReadReal( cvGetFileNodeByName( fs, map, name ), default_value );
-}
-
-/** @brief Retrieves a text string from a file node.
-
-The function returns a text string that is represented by the file node. If the file node is NULL,
-the default_value is returned (thus, it is convenient to call the function right after
-cvGetFileNode without checking for a NULL pointer). If the file node has type CV_NODE_STR , then
-node-:math:\>data.str.ptr is returned. Otherwise the result is not determined.
-@param node File node
-@param default_value The value that is returned if node is NULL
- */
-CV_INLINE const char* cvReadString( const CvFileNode* node,
-                        const char* default_value CV_DEFAULT(NULL) )
-{
-    return !node ? default_value : CV_NODE_IS_STRING(node->tag) ? node->data.str.ptr : 0;
-}
-
-/** @brief Finds a file node by its name and returns its value.
-
-The function is a simple superposition of cvGetFileNodeByName and cvReadString .
-@param fs File storage
-@param map The parent map. If it is NULL, the function searches a top-level node.
-@param name The node name
-@param default_value The value that is returned if the file node is not found
- */
-CV_INLINE const char* cvReadStringByName( const CvFileStorage* fs, const CvFileNode* map,
-                        const char* name, const char* default_value CV_DEFAULT(NULL) )
-{
-    return cvReadString( cvGetFileNodeByName( fs, map, name ), default_value );
-}
-
-
-/** @brief Decodes an object and returns a pointer to it.
-
-The function decodes a user object (creates an object in a native representation from the file
-storage subtree) and returns it. The object to be decoded must be an instance of a registered type
-that supports the read method (see CvTypeInfo). The type of the object is determined by the type
-name that is encoded in the file. If the object is a dynamic structure, it is created either in
-memory storage and passed to cvOpenFileStorage or, if a NULL pointer was passed, in temporary
-memory storage, which is released when cvReleaseFileStorage is called. Otherwise, if the object is
-not a dynamic structure, it is created in a heap and should be released with a specialized function
-or by using the generic cvRelease.
-@param fs File storage
-@param node The root object node
-@param attributes Unused parameter
- */
-CVAPI(void*) cvRead( CvFileStorage* fs, CvFileNode* node,
-                        CvAttrList* attributes CV_DEFAULT(NULL));
-
-/** @brief Finds an object by name and decodes it.
-
-The function is a simple superposition of cvGetFileNodeByName and cvRead.
-@param fs File storage
-@param map The parent map. If it is NULL, the function searches a top-level node.
-@param name The node name
-@param attributes Unused parameter
- */
-CV_INLINE void* cvReadByName( CvFileStorage* fs, const CvFileNode* map,
-                              const char* name, CvAttrList* attributes CV_DEFAULT(NULL) )
-{
-    return cvRead( fs, cvGetFileNodeByName( fs, map, name ), attributes );
-}
-
-
-/** @brief Initializes the file node sequence reader.
-
-The function initializes the sequence reader to read data from a file node. The initialized reader
-can be then passed to cvReadRawDataSlice.
-@param fs File storage
-@param src The file node (a sequence) to read numbers from
-@param reader Pointer to the sequence reader
- */
-CVAPI(void) cvStartReadRawData( const CvFileStorage* fs, const CvFileNode* src,
-                               CvSeqReader* reader );
-
-/** @brief Initializes file node sequence reader.
-
-The function reads one or more elements from the file node, representing a sequence, to a
-user-specified array. The total number of read sequence elements is a product of total and the
-number of components in each array element. For example, if dt=2if, the function will read total\*3
-sequence elements. As with any sequence, some parts of the file node sequence can be skipped or read
-repeatedly by repositioning the reader using cvSetSeqReaderPos.
-@param fs File storage
-@param reader The sequence reader. Initialize it with cvStartReadRawData .
-@param count The number of elements to read
-@param dst Pointer to the destination array
-@param dt Specification of each array element. It has the same format as in cvWriteRawData .
- */
-CVAPI(void) cvReadRawDataSlice( const CvFileStorage* fs, CvSeqReader* reader,
-                               int count, void* dst, const char* dt );
-
-/** @brief Reads multiple numbers.
-
-The function reads elements from a file node that represents a sequence of scalars.
-@param fs File storage
-@param src The file node (a sequence) to read numbers from
-@param dst Pointer to the destination array
-@param dt Specification of each array element. It has the same format as in cvWriteRawData .
- */
-CVAPI(void) cvReadRawData( const CvFileStorage* fs, const CvFileNode* src,
-                          void* dst, const char* dt );
-
-/** @brief Writes a file node to another file storage.
-
-The function writes a copy of a file node to file storage. Possible applications of the function are
-merging several file storages into one and conversion between XML, YAML and JSON formats.
-@param fs Destination file storage
-@param new_node_name New name of the file node in the destination file storage. To keep the
-existing name, use cvcvGetFileNodeName
-@param node The written node
-@param embed If the written node is a collection and this parameter is not zero, no extra level of
-hierarchy is created. Instead, all the elements of node are written into the currently written
-structure. Of course, map elements can only be embedded into another map, and sequence elements
-can only be embedded into another sequence.
- */
-CVAPI(void) cvWriteFileNode( CvFileStorage* fs, const char* new_node_name,
-                            const CvFileNode* node, int embed );
-
-/** @brief Returns the name of a file node.
-
-The function returns the name of a file node or NULL, if the file node does not have a name or if
-node is NULL.
-@param node File node
- */
-CVAPI(const char*) cvGetFileNodeName( const CvFileNode* node );
-
-/*********************************** Adding own types ***********************************/
-
-/** @brief Registers a new type.
-
-The function registers a new type, which is described by info . The function creates a copy of the
-structure, so the user should delete it after calling the function.
-@param info Type info structure
- */
-CVAPI(void) cvRegisterType( const CvTypeInfo* info );
-
-/** @brief Unregisters the type.
-
-The function unregisters a type with a specified name. If the name is unknown, it is possible to
-locate the type info by an instance of the type using cvTypeOf or by iterating the type list,
-starting from cvFirstType, and then calling cvUnregisterType(info-\>typeName).
-@param type_name Name of an unregistered type
- */
-CVAPI(void) cvUnregisterType( const char* type_name );
-
-/** @brief Returns the beginning of a type list.
-
-The function returns the first type in the list of registered types. Navigation through the list can
-be done via the prev and next fields of the CvTypeInfo structure.
- */
-CVAPI(CvTypeInfo*) cvFirstType(void);
-
-/** @brief Finds a type by its name.
-
-The function finds a registered type by its name. It returns NULL if there is no type with the
-specified name.
-@param type_name Type name
- */
-CVAPI(CvTypeInfo*) cvFindType( const char* type_name );
-
-/** @brief Returns the type of an object.
-
-The function finds the type of a given object. It iterates through the list of registered types and
-calls the is_instance function/method for every type info structure with that object until one of
-them returns non-zero or until the whole list has been traversed. In the latter case, the function
-returns NULL.
-@param struct_ptr The object pointer
- */
-CVAPI(CvTypeInfo*) cvTypeOf( const void* struct_ptr );
-
-/** @brief Releases an object.
-
-The function finds the type of a given object and calls release with the double pointer.
-@param struct_ptr Double pointer to the object
- */
-CVAPI(void) cvRelease( void** struct_ptr );
-
-/** @brief Makes a clone of an object.
-
-The function finds the type of a given object and calls clone with the passed object. Of course, if
-you know the object type, for example, struct_ptr is CvMat\*, it is faster to call the specific
-function, like cvCloneMat.
-@param struct_ptr The object to clone
- */
-CVAPI(void*) cvClone( const void* struct_ptr );
-
-/** @brief Saves an object to a file.
-
-The function saves an object to a file. It provides a simple interface to cvWrite .
-@param filename File name
-@param struct_ptr Object to save
-@param name Optional object name. If it is NULL, the name will be formed from filename .
-@param comment Optional comment to put in the beginning of the file
-@param attributes Optional attributes passed to cvWrite
- */
-CVAPI(void) cvSave( const char* filename, const void* struct_ptr,
-                    const char* name CV_DEFAULT(NULL),
-                    const char* comment CV_DEFAULT(NULL),
-                    CvAttrList attributes CV_DEFAULT(cvAttrList()));
-
-/** @brief Loads an object from a file.
-
-The function loads an object from a file. It basically reads the specified file, find the first
-top-level node and calls cvRead for that node. If the file node does not have type information or
-the type information can not be found by the type name, the function returns NULL. After the object
-is loaded, the file storage is closed and all the temporary buffers are deleted. Thus, to load a
-dynamic structure, such as a sequence, contour, or graph, one should pass a valid memory storage
-destination to the function.
-@param filename File name
-@param memstorage Memory storage for dynamic structures, such as CvSeq or CvGraph . It is not used
-for matrices or images.
-@param name Optional object name. If it is NULL, the first top-level object in the storage will be
-loaded.
-@param real_name Optional output parameter that will contain the name of the loaded object
-(useful if name=NULL )
- */
-CVAPI(void*) cvLoad( const char* filename,
-                     CvMemStorage* memstorage CV_DEFAULT(NULL),
-                     const char* name CV_DEFAULT(NULL),
-                     const char** real_name CV_DEFAULT(NULL) );
-
-/*********************************** Measuring Execution Time ***************************/
-
-/** helper functions for RNG initialization and accurate time measurement:
-   uses internal clock counter on x86 */
-CVAPI(int64)  cvGetTickCount( void );
-CVAPI(double) cvGetTickFrequency( void );
-
-/*********************************** CPU capabilities ***********************************/
-
-CVAPI(int) cvCheckHardwareSupport(int feature);
-
-/*********************************** Multi-Threading ************************************/
-
-/** retrieve/set the number of threads used in OpenMP implementations */
-CVAPI(int)  cvGetNumThreads( void );
-CVAPI(void) cvSetNumThreads( int threads CV_DEFAULT(0) );
-/** get index of the thread being executed */
-CVAPI(int)  cvGetThreadNum( void );
-
-
-/********************************** Error Handling **************************************/
-
-/** Get current OpenCV error status */
-CVAPI(int) cvGetErrStatus( void );
-
-/** Sets error status silently */
-CVAPI(void) cvSetErrStatus( int status );
-
-#define CV_ErrModeLeaf     0   /* Print error and exit program */
-#define CV_ErrModeParent   1   /* Print error and continue */
-#define CV_ErrModeSilent   2   /* Don't print and continue */
-
-/** Retrieves current error processing mode */
-CVAPI(int)  cvGetErrMode( void );
-
-/** Sets error processing mode, returns previously used mode */
-CVAPI(int) cvSetErrMode( int mode );
-
-/** Sets error status and performs some additional actions (displaying message box,
- writing message to stderr, terminating application etc.)
- depending on the current error mode */
-CVAPI(void) cvError( int status, const char* func_name,
-                    const char* err_msg, const char* file_name, int line );
-
-/** Retrieves textual description of the error given its code */
-CVAPI(const char*) cvErrorStr( int status );
-
-/** Retrieves detailed information about the last error occurred */
-CVAPI(int) cvGetErrInfo( const char** errcode_desc, const char** description,
-                        const char** filename, int* line );
-
-/** Maps IPP error codes to the counterparts from OpenCV */
-CVAPI(int) cvErrorFromIppStatus( int ipp_status );
-
-typedef int (CV_CDECL *CvErrorCallback)( int status, const char* func_name,
-                                        const char* err_msg, const char* file_name, int line, void* userdata );
-
-/** Assigns a new error-handling function */
-CVAPI(CvErrorCallback) cvRedirectError( CvErrorCallback error_handler,
-                                       void* userdata CV_DEFAULT(NULL),
-                                       void** prev_userdata CV_DEFAULT(NULL) );
-
-/** Output nothing */
-CVAPI(int) cvNulDevReport( int status, const char* func_name, const char* err_msg,
-                          const char* file_name, int line, void* userdata );
-
-/** Output to console(fprintf(stderr,...)) */
-CVAPI(int) cvStdErrReport( int status, const char* func_name, const char* err_msg,
-                          const char* file_name, int line, void* userdata );
-
-/** Output to MessageBox(WIN32) */
-CVAPI(int) cvGuiBoxReport( int status, const char* func_name, const char* err_msg,
-                          const char* file_name, int line, void* userdata );
-
-#define OPENCV_ERROR(status,func,context)                           \
-cvError((status),(func),(context),__FILE__,__LINE__)
-
-#define OPENCV_ASSERT(expr,func,context)                            \
-{if (! (expr))                                      \
-{OPENCV_ERROR(CV_StsInternal,(func),(context));}}
-
-#define OPENCV_CALL( Func )                                         \
-{                                                                   \
-Func;                                                           \
-}
-
-
-/** CV_FUNCNAME macro defines icvFuncName constant which is used by CV_ERROR macro */
-#ifdef CV_NO_FUNC_NAMES
-#define CV_FUNCNAME( Name )
-#define cvFuncName ""
-#else
-#define CV_FUNCNAME( Name )  \
-static char cvFuncName[] = Name
-#endif
-
-
-/**
- CV_ERROR macro unconditionally raises error with passed code and message.
- After raising error, control will be transferred to the exit label.
- */
-#define CV_ERROR( Code, Msg )                                       \
-{                                                                   \
-    cvError( (Code), cvFuncName, Msg, __FILE__, __LINE__ );        \
-    __CV_EXIT__;                                                   \
-}
-
-/**
- CV_CHECK macro checks error status after CV (or IPL)
- function call. If error detected, control will be transferred to the exit
- label.
- */
-#define CV_CHECK()                                                  \
-{                                                                   \
-    if( cvGetErrStatus() < 0 )                                      \
-        CV_ERROR( CV_StsBackTrace, "Inner function failed." );      \
-}
-
-
-/**
- CV_CALL macro calls CV (or IPL) function, checks error status and
- signals a error if the function failed. Useful in "parent node"
- error processing mode
- */
-#define CV_CALL( Func )                                             \
-{                                                                   \
-    Func;                                                           \
-    CV_CHECK();                                                     \
-}
-
-
-/** Runtime assertion macro */
-#define CV_ASSERT( Condition )                                          \
-{                                                                       \
-    if( !(Condition) )                                                  \
-        CV_ERROR( CV_StsInternal, "Assertion: " #Condition " failed" ); \
-}
-
-#define __CV_BEGIN__       {
-#define __CV_END__         goto exit; exit: ; }
-#define __CV_EXIT__        goto exit
-
-/** @} core_c */
-
-#ifdef __cplusplus
-} // extern "C"
-#endif
-
-#ifdef __cplusplus
-
-//! @addtogroup core_c_glue
-//! @{
-
-//! class for automatic module/RTTI data registration/unregistration
-struct CV_EXPORTS CvType
-{
-    CvType( const char* type_name,
-            CvIsInstanceFunc is_instance, CvReleaseFunc release=0,
-            CvReadFunc read=0, CvWriteFunc write=0, CvCloneFunc clone=0 );
-    ~CvType();
-    CvTypeInfo* info;
-
-    static CvTypeInfo* first;
-    static CvTypeInfo* last;
-};
-
-//! @}
-
-#include "opencv2/core/utility.hpp"
-
-namespace cv
-{
-
-//! @addtogroup core_c_glue
-//! @{
-
-/////////////////////////////////////////// glue ///////////////////////////////////////////
-
-//! converts array (CvMat or IplImage) to cv::Mat
-CV_EXPORTS Mat cvarrToMat(const CvArr* arr, bool copyData=false,
-                          bool allowND=true, int coiMode=0,
-                          AutoBuffer<double>* buf=0);
-
-static inline Mat cvarrToMatND(const CvArr* arr, bool copyData=false, int coiMode=0)
-{
-    return cvarrToMat(arr, copyData, true, coiMode);
-}
-
-
-//! extracts Channel of Interest from CvMat or IplImage and makes cv::Mat out of it.
-CV_EXPORTS void extractImageCOI(const CvArr* arr, OutputArray coiimg, int coi=-1);
-//! inserts single-channel cv::Mat into a multi-channel CvMat or IplImage
-CV_EXPORTS void insertImageCOI(InputArray coiimg, CvArr* arr, int coi=-1);
-
-
-
-////// specialized implementations of DefaultDeleter::operator() for classic OpenCV types //////
-
-template<> CV_EXPORTS void DefaultDeleter<CvMat>::operator ()(CvMat* obj) const;
-template<> CV_EXPORTS void DefaultDeleter<IplImage>::operator ()(IplImage* obj) const;
-template<> CV_EXPORTS void DefaultDeleter<CvMatND>::operator ()(CvMatND* obj) const;
-template<> CV_EXPORTS void DefaultDeleter<CvSparseMat>::operator ()(CvSparseMat* obj) const;
-template<> CV_EXPORTS void DefaultDeleter<CvMemStorage>::operator ()(CvMemStorage* obj) const;
-
-////////////// convenient wrappers for operating old-style dynamic structures //////////////
-
-template<typename _Tp> class SeqIterator;
-
-typedef Ptr<CvMemStorage> MemStorage;
-
-/*!
- Template Sequence Class derived from CvSeq
-
- The class provides more convenient access to sequence elements,
- STL-style operations and iterators.
-
- \note The class is targeted for simple data types,
-    i.e. no constructors or destructors
-    are called for the sequence elements.
-*/
-template<typename _Tp> class Seq
-{
-public:
-    typedef SeqIterator<_Tp> iterator;
-    typedef SeqIterator<_Tp> const_iterator;
-
-    //! the default constructor
-    Seq();
-    //! the constructor for wrapping CvSeq structure. The real element type in CvSeq should match _Tp.
-    Seq(const CvSeq* seq);
-    //! creates the empty sequence that resides in the specified storage
-    Seq(MemStorage& storage, int headerSize = sizeof(CvSeq));
-    //! returns read-write reference to the specified element
-    _Tp& operator [](int idx);
-    //! returns read-only reference to the specified element
-    const _Tp& operator[](int idx) const;
-    //! returns iterator pointing to the beginning of the sequence
-    SeqIterator<_Tp> begin() const;
-    //! returns iterator pointing to the element following the last sequence element
-    SeqIterator<_Tp> end() const;
-    //! returns the number of elements in the sequence
-    size_t size() const;
-    //! returns the type of sequence elements (CV_8UC1 ... CV_64FC(CV_CN_MAX) ...)
-    int type() const;
-    //! returns the depth of sequence elements (CV_8U ... CV_64F)
-    int depth() const;
-    //! returns the number of channels in each sequence element
-    int channels() const;
-    //! returns the size of each sequence element
-    size_t elemSize() const;
-    //! returns index of the specified sequence element
-    size_t index(const _Tp& elem) const;
-    //! appends the specified element to the end of the sequence
-    void push_back(const _Tp& elem);
-    //! appends the specified element to the front of the sequence
-    void push_front(const _Tp& elem);
-    //! appends zero or more elements to the end of the sequence
-    void push_back(const _Tp* elems, size_t count);
-    //! appends zero or more elements to the front of the sequence
-    void push_front(const _Tp* elems, size_t count);
-    //! inserts the specified element to the specified position
-    void insert(int idx, const _Tp& elem);
-    //! inserts zero or more elements to the specified position
-    void insert(int idx, const _Tp* elems, size_t count);
-    //! removes element at the specified position
-    void remove(int idx);
-    //! removes the specified subsequence
-    void remove(const Range& r);
-
-    //! returns reference to the first sequence element
-    _Tp& front();
-    //! returns read-only reference to the first sequence element
-    const _Tp& front() const;
-    //! returns reference to the last sequence element
-    _Tp& back();
-    //! returns read-only reference to the last sequence element
-    const _Tp& back() const;
-    //! returns true iff the sequence contains no elements
-    bool empty() const;
-
-    //! removes all the elements from the sequence
-    void clear();
-    //! removes the first element from the sequence
-    void pop_front();
-    //! removes the last element from the sequence
-    void pop_back();
-    //! removes zero or more elements from the beginning of the sequence
-    void pop_front(_Tp* elems, size_t count);
-    //! removes zero or more elements from the end of the sequence
-    void pop_back(_Tp* elems, size_t count);
-
-    //! copies the whole sequence or the sequence slice to the specified vector
-    void copyTo(std::vector<_Tp>& vec, const Range& range=Range::all()) const;
-    //! returns the vector containing all the sequence elements
-    operator std::vector<_Tp>() const;
-
-    CvSeq* seq;
-};
-
-
-/*!
- STL-style Sequence Iterator inherited from the CvSeqReader structure
-*/
-template<typename _Tp> class SeqIterator : public CvSeqReader
-{
-public:
-    //! the default constructor
-    SeqIterator();
-    //! the constructor setting the iterator to the beginning or to the end of the sequence
-    SeqIterator(const Seq<_Tp>& seq, bool seekEnd=false);
-    //! positions the iterator within the sequence
-    void seek(size_t pos);
-    //! reports the current iterator position
-    size_t tell() const;
-    //! returns reference to the current sequence element
-    _Tp& operator *();
-    //! returns read-only reference to the current sequence element
-    const _Tp& operator *() const;
-    //! moves iterator to the next sequence element
-    SeqIterator& operator ++();
-    //! moves iterator to the next sequence element
-    SeqIterator operator ++(int) const;
-    //! moves iterator to the previous sequence element
-    SeqIterator& operator --();
-    //! moves iterator to the previous sequence element
-    SeqIterator operator --(int) const;
-
-    //! moves iterator forward by the specified offset (possibly negative)
-    SeqIterator& operator +=(int);
-    //! moves iterator backward by the specified offset (possibly negative)
-    SeqIterator& operator -=(int);
-
-    // this is index of the current element module seq->total*2
-    // (to distinguish between 0 and seq->total)
-    int index;
-};
-
-
-
-// bridge C++ => C Seq API
-CV_EXPORTS schar*  seqPush( CvSeq* seq, const void* element=0);
-CV_EXPORTS schar*  seqPushFront( CvSeq* seq, const void* element=0);
-CV_EXPORTS void  seqPop( CvSeq* seq, void* element=0);
-CV_EXPORTS void  seqPopFront( CvSeq* seq, void* element=0);
-CV_EXPORTS void  seqPopMulti( CvSeq* seq, void* elements,
-                              int count, int in_front=0 );
-CV_EXPORTS void  seqRemove( CvSeq* seq, int index );
-CV_EXPORTS void  clearSeq( CvSeq* seq );
-CV_EXPORTS schar*  getSeqElem( const CvSeq* seq, int index );
-CV_EXPORTS void  seqRemoveSlice( CvSeq* seq, CvSlice slice );
-CV_EXPORTS void  seqInsertSlice( CvSeq* seq, int before_index, const CvArr* from_arr );
-
-template<typename _Tp> inline Seq<_Tp>::Seq() : seq(0) {}
-template<typename _Tp> inline Seq<_Tp>::Seq( const CvSeq* _seq ) : seq((CvSeq*)_seq)
-{
-    CV_Assert(!_seq || _seq->elem_size == sizeof(_Tp));
-}
-
-template<typename _Tp> inline Seq<_Tp>::Seq( MemStorage& storage,
-                                             int headerSize )
-{
-    CV_Assert(headerSize >= (int)sizeof(CvSeq));
-    seq = cvCreateSeq(DataType<_Tp>::type, headerSize, sizeof(_Tp), storage);
-}
-
-template<typename _Tp> inline _Tp& Seq<_Tp>::operator [](int idx)
-{ return *(_Tp*)getSeqElem(seq, idx); }
-
-template<typename _Tp> inline const _Tp& Seq<_Tp>::operator [](int idx) const
-{ return *(_Tp*)getSeqElem(seq, idx); }
-
-template<typename _Tp> inline SeqIterator<_Tp> Seq<_Tp>::begin() const
-{ return SeqIterator<_Tp>(*this); }
-
-template<typename _Tp> inline SeqIterator<_Tp> Seq<_Tp>::end() const
-{ return SeqIterator<_Tp>(*this, true); }
-
-template<typename _Tp> inline size_t Seq<_Tp>::size() const
-{ return seq ? seq->total : 0; }
-
-template<typename _Tp> inline int Seq<_Tp>::type() const
-{ return seq ? CV_MAT_TYPE(seq->flags) : 0; }
-
-template<typename _Tp> inline int Seq<_Tp>::depth() const
-{ return seq ? CV_MAT_DEPTH(seq->flags) : 0; }
-
-template<typename _Tp> inline int Seq<_Tp>::channels() const
-{ return seq ? CV_MAT_CN(seq->flags) : 0; }
-
-template<typename _Tp> inline size_t Seq<_Tp>::elemSize() const
-{ return seq ? seq->elem_size : 0; }
-
-template<typename _Tp> inline size_t Seq<_Tp>::index(const _Tp& elem) const
-{ return cvSeqElemIdx(seq, &elem); }
-
-template<typename _Tp> inline void Seq<_Tp>::push_back(const _Tp& elem)
-{ cvSeqPush(seq, &elem); }
-
-template<typename _Tp> inline void Seq<_Tp>::push_front(const _Tp& elem)
-{ cvSeqPushFront(seq, &elem); }
-
-template<typename _Tp> inline void Seq<_Tp>::push_back(const _Tp* elem, size_t count)
-{ cvSeqPushMulti(seq, elem, (int)count, 0); }
-
-template<typename _Tp> inline void Seq<_Tp>::push_front(const _Tp* elem, size_t count)
-{ cvSeqPushMulti(seq, elem, (int)count, 1); }
-
-template<typename _Tp> inline _Tp& Seq<_Tp>::back()
-{ return *(_Tp*)getSeqElem(seq, -1); }
-
-template<typename _Tp> inline const _Tp& Seq<_Tp>::back() const
-{ return *(const _Tp*)getSeqElem(seq, -1); }
-
-template<typename _Tp> inline _Tp& Seq<_Tp>::front()
-{ return *(_Tp*)getSeqElem(seq, 0); }
-
-template<typename _Tp> inline const _Tp& Seq<_Tp>::front() const
-{ return *(const _Tp*)getSeqElem(seq, 0); }
-
-template<typename _Tp> inline bool Seq<_Tp>::empty() const
-{ return !seq || seq->total == 0; }
-
-template<typename _Tp> inline void Seq<_Tp>::clear()
-{ if(seq) clearSeq(seq); }
-
-template<typename _Tp> inline void Seq<_Tp>::pop_back()
-{ seqPop(seq); }
-
-template<typename _Tp> inline void Seq<_Tp>::pop_front()
-{ seqPopFront(seq); }
-
-template<typename _Tp> inline void Seq<_Tp>::pop_back(_Tp* elem, size_t count)
-{ seqPopMulti(seq, elem, (int)count, 0); }
-
-template<typename _Tp> inline void Seq<_Tp>::pop_front(_Tp* elem, size_t count)
-{ seqPopMulti(seq, elem, (int)count, 1); }
-
-template<typename _Tp> inline void Seq<_Tp>::insert(int idx, const _Tp& elem)
-{ seqInsert(seq, idx, &elem); }
-
-template<typename _Tp> inline void Seq<_Tp>::insert(int idx, const _Tp* elems, size_t count)
-{
-    CvMat m = cvMat(1, count, DataType<_Tp>::type, elems);
-    seqInsertSlice(seq, idx, &m);
-}
-
-template<typename _Tp> inline void Seq<_Tp>::remove(int idx)
-{ seqRemove(seq, idx); }
-
-template<typename _Tp> inline void Seq<_Tp>::remove(const Range& r)
-{ seqRemoveSlice(seq, cvSlice(r.start, r.end)); }
-
-template<typename _Tp> inline void Seq<_Tp>::copyTo(std::vector<_Tp>& vec, const Range& range) const
-{
-    size_t len = !seq ? 0 : range == Range::all() ? seq->total : range.end - range.start;
-    vec.resize(len);
-    if( seq && len )
-        cvCvtSeqToArray(seq, &vec[0], range);
-}
-
-template<typename _Tp> inline Seq<_Tp>::operator std::vector<_Tp>() const
-{
-    std::vector<_Tp> vec;
-    copyTo(vec);
-    return vec;
-}
-
-template<typename _Tp> inline SeqIterator<_Tp>::SeqIterator()
-{ memset(this, 0, sizeof(*this)); }
-
-template<typename _Tp> inline SeqIterator<_Tp>::SeqIterator(const Seq<_Tp>& _seq, bool seekEnd)
-{
-    cvStartReadSeq(_seq.seq, this);
-    index = seekEnd ? _seq.seq->total : 0;
-}
-
-template<typename _Tp> inline void SeqIterator<_Tp>::seek(size_t pos)
-{
-    cvSetSeqReaderPos(this, (int)pos, false);
-    index = pos;
-}
-
-template<typename _Tp> inline size_t SeqIterator<_Tp>::tell() const
-{ return index; }
-
-template<typename _Tp> inline _Tp& SeqIterator<_Tp>::operator *()
-{ return *(_Tp*)ptr; }
-
-template<typename _Tp> inline const _Tp& SeqIterator<_Tp>::operator *() const
-{ return *(const _Tp*)ptr; }
-
-template<typename _Tp> inline SeqIterator<_Tp>& SeqIterator<_Tp>::operator ++()
-{
-    CV_NEXT_SEQ_ELEM(sizeof(_Tp), *this);
-    if( ++index >= seq->total*2 )
-        index = 0;
-    return *this;
-}
-
-template<typename _Tp> inline SeqIterator<_Tp> SeqIterator<_Tp>::operator ++(int) const
-{
-    SeqIterator<_Tp> it = *this;
-    ++*this;
-    return it;
-}
-
-template<typename _Tp> inline SeqIterator<_Tp>& SeqIterator<_Tp>::operator --()
-{
-    CV_PREV_SEQ_ELEM(sizeof(_Tp), *this);
-    if( --index < 0 )
-        index = seq->total*2-1;
-    return *this;
-}
-
-template<typename _Tp> inline SeqIterator<_Tp> SeqIterator<_Tp>::operator --(int) const
-{
-    SeqIterator<_Tp> it = *this;
-    --*this;
-    return it;
-}
-
-template<typename _Tp> inline SeqIterator<_Tp>& SeqIterator<_Tp>::operator +=(int delta)
-{
-    cvSetSeqReaderPos(this, delta, 1);
-    index += delta;
-    int n = seq->total*2;
-    if( index < 0 )
-        index += n;
-    if( index >= n )
-        index -= n;
-    return *this;
-}
-
-template<typename _Tp> inline SeqIterator<_Tp>& SeqIterator<_Tp>::operator -=(int delta)
-{
-    return (*this += -delta);
-}
-
-template<typename _Tp> inline ptrdiff_t operator - (const SeqIterator<_Tp>& a,
-                                                    const SeqIterator<_Tp>& b)
-{
-    ptrdiff_t delta = a.index - b.index, n = a.seq->total;
-    if( delta > n || delta < -n )
-        delta += delta < 0 ? n : -n;
-    return delta;
-}
-
-template<typename _Tp> inline bool operator == (const SeqIterator<_Tp>& a,
-                                                const SeqIterator<_Tp>& b)
-{
-    return a.seq == b.seq && a.index == b.index;
-}
-
-template<typename _Tp> inline bool operator != (const SeqIterator<_Tp>& a,
-                                                const SeqIterator<_Tp>& b)
-{
-    return !(a == b);
-}
-
-//! @}
-
-} // cv
-
-#endif
-
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda.hpp
deleted file mode 100755
index 820aba7..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda.hpp
+++ /dev/null
@@ -1,1049 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_CORE_CUDA_HPP
-#define OPENCV_CORE_CUDA_HPP
-
-#ifndef __cplusplus
-#  error cuda.hpp header must be compiled as C++
-#endif
-
-#include "opencv2/core.hpp"
-#include "opencv2/core/cuda_types.hpp"
-
-/**
-  @defgroup cuda CUDA-accelerated Computer Vision
-  @{
-    @defgroup cudacore Core part
-    @{
-      @defgroup cudacore_init Initialization and Information
-      @defgroup cudacore_struct Data Structures
-    @}
-  @}
- */
-
-namespace cv { namespace cuda {
-
-//! @addtogroup cudacore_struct
-//! @{
-
-//===================================================================================
-// GpuMat
-//===================================================================================
-
-/** @brief Base storage class for GPU memory with reference counting.
-
-Its interface matches the Mat interface with the following limitations:
-
--   no arbitrary dimensions support (only 2D)
--   no functions that return references to their data (because references on GPU are not valid for
-    CPU)
--   no expression templates technique support
-
-Beware that the latter limitation may lead to overloaded matrix operators that cause memory
-allocations. The GpuMat class is convertible to cuda::PtrStepSz and cuda::PtrStep so it can be
-passed directly to the kernel.
-
-@note In contrast with Mat, in most cases GpuMat::isContinuous() == false . This means that rows are
-aligned to a size depending on the hardware. Single-row GpuMat is always a continuous matrix.
-
-@note You are not recommended to leave static or global GpuMat variables allocated, that is, to rely
-on its destructor. The destruction order of such variables and CUDA context is undefined. GPU memory
-release function returns error if the CUDA context has been destroyed before.
-
-Some member functions are described as a "Blocking Call" while some are described as a
-"Non-Blocking Call". Blocking functions are synchronous to host. It is guaranteed that the GPU
-operation is finished when the function returns. However, non-blocking functions are asynchronous to
-host. Those functions may return even if the GPU operation is not finished.
-
-Compared to their blocking counterpart, non-blocking functions accept Stream as an additional
-argument. If a non-default stream is passed, the GPU operation may overlap with operations in other
-streams.
-
-@sa Mat
- */
-class CV_EXPORTS GpuMat
-{
-public:
-    class CV_EXPORTS Allocator
-    {
-    public:
-        virtual ~Allocator() {}
-
-        // allocator must fill data, step and refcount fields
-        virtual bool allocate(GpuMat* mat, int rows, int cols, size_t elemSize) = 0;
-        virtual void free(GpuMat* mat) = 0;
-    };
-
-    //! default allocator
-    static Allocator* defaultAllocator();
-    static void setDefaultAllocator(Allocator* allocator);
-
-    //! default constructor
-    explicit GpuMat(Allocator* allocator = defaultAllocator());
-
-    //! constructs GpuMat of the specified size and type
-    GpuMat(int rows, int cols, int type, Allocator* allocator = defaultAllocator());
-    GpuMat(Size size, int type, Allocator* allocator = defaultAllocator());
-
-    //! constucts GpuMat and fills it with the specified value _s
-    GpuMat(int rows, int cols, int type, Scalar s, Allocator* allocator = defaultAllocator());
-    GpuMat(Size size, int type, Scalar s, Allocator* allocator = defaultAllocator());
-
-    //! copy constructor
-    GpuMat(const GpuMat& m);
-
-    //! constructor for GpuMat headers pointing to user-allocated data
-    GpuMat(int rows, int cols, int type, void* data, size_t step = Mat::AUTO_STEP);
-    GpuMat(Size size, int type, void* data, size_t step = Mat::AUTO_STEP);
-
-    //! creates a GpuMat header for a part of the bigger matrix
-    GpuMat(const GpuMat& m, Range rowRange, Range colRange);
-    GpuMat(const GpuMat& m, Rect roi);
-
-    //! builds GpuMat from host memory (Blocking call)
-    explicit GpuMat(InputArray arr, Allocator* allocator = defaultAllocator());
-
-    //! destructor - calls release()
-    ~GpuMat();
-
-    //! assignment operators
-    GpuMat& operator =(const GpuMat& m);
-
-    //! allocates new GpuMat data unless the GpuMat already has specified size and type
-    void create(int rows, int cols, int type);
-    void create(Size size, int type);
-
-    //! decreases reference counter, deallocate the data when reference counter reaches 0
-    void release();
-
-    //! swaps with other smart pointer
-    void swap(GpuMat& mat);
-
-    /** @brief Performs data upload to GpuMat (Blocking call)
-
-    This function copies data from host memory to device memory. As being a blocking call, it is
-    guaranteed that the copy operation is finished when this function returns.
-    */
-    void upload(InputArray arr);
-
-    /** @brief Performs data upload to GpuMat (Non-Blocking call)
-
-    This function copies data from host memory to device memory. As being a non-blocking call, this
-    function may return even if the copy operation is not finished.
-
-    The copy operation may be overlapped with operations in other non-default streams if \p stream is
-    not the default stream and \p dst is HostMem allocated with HostMem::PAGE_LOCKED option.
-    */
-    void upload(InputArray arr, Stream& stream);
-
-    /** @brief Performs data download from GpuMat (Blocking call)
-
-    This function copies data from device memory to host memory. As being a blocking call, it is
-    guaranteed that the copy operation is finished when this function returns.
-    */
-    void download(OutputArray dst) const;
-
-    /** @brief Performs data download from GpuMat (Non-Blocking call)
-
-    This function copies data from device memory to host memory. As being a non-blocking call, this
-    function may return even if the copy operation is not finished.
-
-    The copy operation may be overlapped with operations in other non-default streams if \p stream is
-    not the default stream and \p dst is HostMem allocated with HostMem::PAGE_LOCKED option.
-    */
-    void download(OutputArray dst, Stream& stream) const;
-
-    //! returns deep copy of the GpuMat, i.e. the data is copied
-    GpuMat clone() const;
-
-    //! copies the GpuMat content to device memory (Blocking call)
-    void copyTo(OutputArray dst) const;
-
-    //! copies the GpuMat content to device memory (Non-Blocking call)
-    void copyTo(OutputArray dst, Stream& stream) const;
-
-    //! copies those GpuMat elements to "m" that are marked with non-zero mask elements (Blocking call)
-    void copyTo(OutputArray dst, InputArray mask) const;
-
-    //! copies those GpuMat elements to "m" that are marked with non-zero mask elements (Non-Blocking call)
-    void copyTo(OutputArray dst, InputArray mask, Stream& stream) const;
-
-    //! sets some of the GpuMat elements to s (Blocking call)
-    GpuMat& setTo(Scalar s);
-
-    //! sets some of the GpuMat elements to s (Non-Blocking call)
-    GpuMat& setTo(Scalar s, Stream& stream);
-
-    //! sets some of the GpuMat elements to s, according to the mask (Blocking call)
-    GpuMat& setTo(Scalar s, InputArray mask);
-
-    //! sets some of the GpuMat elements to s, according to the mask (Non-Blocking call)
-    GpuMat& setTo(Scalar s, InputArray mask, Stream& stream);
-
-    //! converts GpuMat to another datatype (Blocking call)
-    void convertTo(OutputArray dst, int rtype) const;
-
-    //! converts GpuMat to another datatype (Non-Blocking call)
-    void convertTo(OutputArray dst, int rtype, Stream& stream) const;
-
-    //! converts GpuMat to another datatype with scaling (Blocking call)
-    void convertTo(OutputArray dst, int rtype, double alpha, double beta = 0.0) const;
-
-    //! converts GpuMat to another datatype with scaling (Non-Blocking call)
-    void convertTo(OutputArray dst, int rtype, double alpha, Stream& stream) const;
-
-    //! converts GpuMat to another datatype with scaling (Non-Blocking call)
-    void convertTo(OutputArray dst, int rtype, double alpha, double beta, Stream& stream) const;
-
-    void assignTo(GpuMat& m, int type=-1) const;
-
-    //! returns pointer to y-th row
-    uchar* ptr(int y = 0);
-    const uchar* ptr(int y = 0) const;
-
-    //! template version of the above method
-    template<typename _Tp> _Tp* ptr(int y = 0);
-    template<typename _Tp> const _Tp* ptr(int y = 0) const;
-
-    template <typename _Tp> operator PtrStepSz<_Tp>() const;
-    template <typename _Tp> operator PtrStep<_Tp>() const;
-
-    //! returns a new GpuMat header for the specified row
-    GpuMat row(int y) const;
-
-    //! returns a new GpuMat header for the specified column
-    GpuMat col(int x) const;
-
-    //! ... for the specified row span
-    GpuMat rowRange(int startrow, int endrow) const;
-    GpuMat rowRange(Range r) const;
-
-    //! ... for the specified column span
-    GpuMat colRange(int startcol, int endcol) const;
-    GpuMat colRange(Range r) const;
-
-    //! extracts a rectangular sub-GpuMat (this is a generalized form of row, rowRange etc.)
-    GpuMat operator ()(Range rowRange, Range colRange) const;
-    GpuMat operator ()(Rect roi) const;
-
-    //! creates alternative GpuMat header for the same data, with different
-    //! number of channels and/or different number of rows
-    GpuMat reshape(int cn, int rows = 0) const;
-
-    //! locates GpuMat header within a parent GpuMat
-    void locateROI(Size& wholeSize, Point& ofs) const;
-
-    //! moves/resizes the current GpuMat ROI inside the parent GpuMat
-    GpuMat& adjustROI(int dtop, int dbottom, int dleft, int dright);
-
-    //! returns true iff the GpuMat data is continuous
-    //! (i.e. when there are no gaps between successive rows)
-    bool isContinuous() const;
-
-    //! returns element size in bytes
-    size_t elemSize() const;
-
-    //! returns the size of element channel in bytes
-    size_t elemSize1() const;
-
-    //! returns element type
-    int type() const;
-
-    //! returns element type
-    int depth() const;
-
-    //! returns number of channels
-    int channels() const;
-
-    //! returns step/elemSize1()
-    size_t step1() const;
-
-    //! returns GpuMat size : width == number of columns, height == number of rows
-    Size size() const;
-
-    //! returns true if GpuMat data is NULL
-    bool empty() const;
-
-    //! internal use method: updates the continuity flag
-    void updateContinuityFlag();
-
-    /*! includes several bit-fields:
-    - the magic signature
-    - continuity flag
-    - depth
-    - number of channels
-    */
-    int flags;
-
-    //! the number of rows and columns
-    int rows, cols;
-
-    //! a distance between successive rows in bytes; includes the gap if any
-    size_t step;
-
-    //! pointer to the data
-    uchar* data;
-
-    //! pointer to the reference counter;
-    //! when GpuMat points to user-allocated data, the pointer is NULL
-    int* refcount;
-
-    //! helper fields used in locateROI and adjustROI
-    uchar* datastart;
-    const uchar* dataend;
-
-    //! allocator
-    Allocator* allocator;
-};
-
-/** @brief Creates a continuous matrix.
-
-@param rows Row count.
-@param cols Column count.
-@param type Type of the matrix.
-@param arr Destination matrix. This parameter changes only if it has a proper type and area (
-\f$\texttt{rows} \times \texttt{cols}\f$ ).
-
-Matrix is called continuous if its elements are stored continuously, that is, without gaps at the
-end of each row.
- */
-CV_EXPORTS void createContinuous(int rows, int cols, int type, OutputArray arr);
-
-/** @brief Ensures that the size of a matrix is big enough and the matrix has a proper type.
-
-@param rows Minimum desired number of rows.
-@param cols Minimum desired number of columns.
-@param type Desired matrix type.
-@param arr Destination matrix.
-
-The function does not reallocate memory if the matrix has proper attributes already.
- */
-CV_EXPORTS void ensureSizeIsEnough(int rows, int cols, int type, OutputArray arr);
-
-/** @brief BufferPool for use with CUDA streams
-
-BufferPool utilizes Stream's allocator to create new buffers for GpuMat's. It is
-only useful when enabled with #setBufferPoolUsage.
-
-@code
-    setBufferPoolUsage(true);
-@endcode
-
-@note #setBufferPoolUsage must be called \em before any Stream declaration.
-
-Users may specify custom allocator for Stream and may implement their own stream based
-functions utilizing the same underlying GPU memory management.
-
-If custom allocator is not specified, BufferPool utilizes StackAllocator by
-default. StackAllocator allocates a chunk of GPU device memory beforehand,
-and when GpuMat is declared later on, it is given the pre-allocated memory.
-This kind of strategy reduces the number of calls for memory allocating APIs
-such as cudaMalloc or cudaMallocPitch.
-
-Below is an example that utilizes BufferPool with StackAllocator:
-
-@code
-    #include <opencv2/opencv.hpp>
-
-    using namespace cv;
-    using namespace cv::cuda
-
-    int main()
-    {
-        setBufferPoolUsage(true);                               // Tell OpenCV that we are going to utilize BufferPool
-        setBufferPoolConfig(getDevice(), 1024 * 1024 * 64, 2);  // Allocate 64 MB, 2 stacks (default is 10 MB, 5 stacks)
-
-        Stream stream1, stream2;                                // Each stream uses 1 stack
-        BufferPool pool1(stream1), pool2(stream2);
-
-        GpuMat d_src1 = pool1.getBuffer(4096, 4096, CV_8UC1);   // 16MB
-        GpuMat d_dst1 = pool1.getBuffer(4096, 4096, CV_8UC3);   // 48MB, pool1 is now full
-
-        GpuMat d_src2 = pool2.getBuffer(1024, 1024, CV_8UC1);   // 1MB
-        GpuMat d_dst2 = pool2.getBuffer(1024, 1024, CV_8UC3);   // 3MB
-
-        cvtColor(d_src1, d_dst1, CV_GRAY2BGR, 0, stream1);
-        cvtColor(d_src2, d_dst2, CV_GRAY2BGR, 0, stream2);
-    }
-@endcode
-
-If we allocate another GpuMat on pool1 in the above example, it will be carried out by
-the DefaultAllocator since the stack for pool1 is full.
-
-@code
-    GpuMat d_add1 = pool1.getBuffer(1024, 1024, CV_8UC1);   // Stack for pool1 is full, memory is allocated with DefaultAllocator
-@endcode
-
-If a third stream is declared in the above example, allocating with #getBuffer
-within that stream will also be carried out by the DefaultAllocator because we've run out of
-stacks.
-
-@code
-    Stream stream3;                                         // Only 2 stacks were allocated, we've run out of stacks
-    BufferPool pool3(stream3);
-    GpuMat d_src3 = pool3.getBuffer(1024, 1024, CV_8UC1);   // Memory is allocated with DefaultAllocator
-@endcode
-
-@warning When utilizing StackAllocator, deallocation order is important.
-
-Just like a stack, deallocation must be done in LIFO order. Below is an example of
-erroneous usage that violates LIFO rule. If OpenCV is compiled in Debug mode, this
-sample code will emit CV_Assert error.
-
-@code
-    int main()
-    {
-        setBufferPoolUsage(true);                               // Tell OpenCV that we are going to utilize BufferPool
-        Stream stream;                                          // A default size (10 MB) stack is allocated to this stream
-        BufferPool pool(stream);
-
-        GpuMat mat1 = pool.getBuffer(1024, 1024, CV_8UC1);      // Allocate mat1 (1MB)
-        GpuMat mat2 = pool.getBuffer(1024, 1024, CV_8UC1);      // Allocate mat2 (1MB)
-
-        mat1.release();                                         // erroneous usage : mat2 must be deallocated before mat1
-    }
-@endcode
-
-Since C++ local variables are destroyed in the reverse order of construction,
-the code sample below satisfies the LIFO rule. Local GpuMat's are deallocated
-and the corresponding memory is automatically returned to the pool for later usage.
-
-@code
-    int main()
-    {
-        setBufferPoolUsage(true);                               // Tell OpenCV that we are going to utilize BufferPool
-        setBufferPoolConfig(getDevice(), 1024 * 1024 * 64, 2);  // Allocate 64 MB, 2 stacks (default is 10 MB, 5 stacks)
-
-        Stream stream1, stream2;                                // Each stream uses 1 stack
-        BufferPool pool1(stream1), pool2(stream2);
-
-        for (int i = 0; i < 10; i++)
-        {
-            GpuMat d_src1 = pool1.getBuffer(4096, 4096, CV_8UC1);   // 16MB
-            GpuMat d_dst1 = pool1.getBuffer(4096, 4096, CV_8UC3);   // 48MB, pool1 is now full
-
-            GpuMat d_src2 = pool2.getBuffer(1024, 1024, CV_8UC1);   // 1MB
-            GpuMat d_dst2 = pool2.getBuffer(1024, 1024, CV_8UC3);   // 3MB
-
-            d_src1.setTo(Scalar(i), stream1);
-            d_src2.setTo(Scalar(i), stream2);
-
-            cvtColor(d_src1, d_dst1, CV_GRAY2BGR, 0, stream1);
-            cvtColor(d_src2, d_dst2, CV_GRAY2BGR, 0, stream2);
-                                                                    // The order of destruction of the local variables is:
-                                                                    //   d_dst2 => d_src2 => d_dst1 => d_src1
-                                                                    // LIFO rule is satisfied, this code runs without error
-        }
-    }
-@endcode
- */
-class CV_EXPORTS BufferPool
-{
-public:
-
-    //! Gets the BufferPool for the given stream.
-    explicit BufferPool(Stream& stream);
-
-    //! Allocates a new GpuMat of given size and type.
-    GpuMat getBuffer(int rows, int cols, int type);
-
-    //! Allocates a new GpuMat of given size and type.
-    GpuMat getBuffer(Size size, int type) { return getBuffer(size.height, size.width, type); }
-
-    //! Returns the allocator associated with the stream.
-    Ptr<GpuMat::Allocator> getAllocator() const { return allocator_; }
-
-private:
-    Ptr<GpuMat::Allocator> allocator_;
-};
-
-//! BufferPool management (must be called before Stream creation)
-CV_EXPORTS void setBufferPoolUsage(bool on);
-CV_EXPORTS void setBufferPoolConfig(int deviceId, size_t stackSize, int stackCount);
-
-//===================================================================================
-// HostMem
-//===================================================================================
-
-/** @brief Class with reference counting wrapping special memory type allocation functions from CUDA.
-
-Its interface is also Mat-like but with additional memory type parameters.
-
--   **PAGE_LOCKED** sets a page locked memory type used commonly for fast and asynchronous
-    uploading/downloading data from/to GPU.
--   **SHARED** specifies a zero copy memory allocation that enables mapping the host memory to GPU
-    address space, if supported.
--   **WRITE_COMBINED** sets the write combined buffer that is not cached by CPU. Such buffers are
-    used to supply GPU with data when GPU only reads it. The advantage is a better CPU cache
-    utilization.
-
-@note Allocation size of such memory types is usually limited. For more details, see *CUDA 2.2
-Pinned Memory APIs* document or *CUDA C Programming Guide*.
- */
-class CV_EXPORTS HostMem
-{
-public:
-    enum AllocType { PAGE_LOCKED = 1, SHARED = 2, WRITE_COMBINED = 4 };
-
-    static MatAllocator* getAllocator(AllocType alloc_type = PAGE_LOCKED);
-
-    explicit HostMem(AllocType alloc_type = PAGE_LOCKED);
-
-    HostMem(const HostMem& m);
-
-    HostMem(int rows, int cols, int type, AllocType alloc_type = PAGE_LOCKED);
-    HostMem(Size size, int type, AllocType alloc_type = PAGE_LOCKED);
-
-    //! creates from host memory with coping data
-    explicit HostMem(InputArray arr, AllocType alloc_type = PAGE_LOCKED);
-
-    ~HostMem();
-
-    HostMem& operator =(const HostMem& m);
-
-    //! swaps with other smart pointer
-    void swap(HostMem& b);
-
-    //! returns deep copy of the matrix, i.e. the data is copied
-    HostMem clone() const;
-
-    //! allocates new matrix data unless the matrix already has specified size and type.
-    void create(int rows, int cols, int type);
-    void create(Size size, int type);
-
-    //! creates alternative HostMem header for the same data, with different
-    //! number of channels and/or different number of rows
-    HostMem reshape(int cn, int rows = 0) const;
-
-    //! decrements reference counter and released memory if needed.
-    void release();
-
-    //! returns matrix header with disabled reference counting for HostMem data.
-    Mat createMatHeader() const;
-
-    /** @brief Maps CPU memory to GPU address space and creates the cuda::GpuMat header without reference counting
-    for it.
-
-    This can be done only if memory was allocated with the SHARED flag and if it is supported by the
-    hardware. Laptops often share video and CPU memory, so address spaces can be mapped, which
-    eliminates an extra copy.
-     */
-    GpuMat createGpuMatHeader() const;
-
-    // Please see cv::Mat for descriptions
-    bool isContinuous() const;
-    size_t elemSize() const;
-    size_t elemSize1() const;
-    int type() const;
-    int depth() const;
-    int channels() const;
-    size_t step1() const;
-    Size size() const;
-    bool empty() const;
-
-    // Please see cv::Mat for descriptions
-    int flags;
-    int rows, cols;
-    size_t step;
-
-    uchar* data;
-    int* refcount;
-
-    uchar* datastart;
-    const uchar* dataend;
-
-    AllocType alloc_type;
-};
-
-/** @brief Page-locks the memory of matrix and maps it for the device(s).
-
-@param m Input matrix.
- */
-CV_EXPORTS void registerPageLocked(Mat& m);
-
-/** @brief Unmaps the memory of matrix and makes it pageable again.
-
-@param m Input matrix.
- */
-CV_EXPORTS void unregisterPageLocked(Mat& m);
-
-//===================================================================================
-// Stream
-//===================================================================================
-
-/** @brief This class encapsulates a queue of asynchronous calls.
-
-@note Currently, you may face problems if an operation is enqueued twice with different data. Some
-functions use the constant GPU memory, and next call may update the memory before the previous one
-has been finished. But calling different operations asynchronously is safe because each operation
-has its own constant buffer. Memory copy/upload/download/set operations to the buffers you hold are
-also safe.
-
-@note The Stream class is not thread-safe. Please use different Stream objects for different CPU threads.
-
-@code
-void thread1()
-{
-    cv::cuda::Stream stream1;
-    cv::cuda::func1(..., stream1);
-}
-
-void thread2()
-{
-    cv::cuda::Stream stream2;
-    cv::cuda::func2(..., stream2);
-}
-@endcode
-
-@note By default all CUDA routines are launched in Stream::Null() object, if the stream is not specified by user.
-In multi-threading environment the stream objects must be passed explicitly (see previous note).
- */
-class CV_EXPORTS Stream
-{
-    typedef void (Stream::*bool_type)() const;
-    void this_type_does_not_support_comparisons() const {}
-
-public:
-    typedef void (*StreamCallback)(int status, void* userData);
-
-    //! creates a new asynchronous stream
-    Stream();
-
-    //! creates a new asynchronous stream with custom allocator
-    Stream(const Ptr<GpuMat::Allocator>& allocator);
-
-    /** @brief Returns true if the current stream queue is finished. Otherwise, it returns false.
-    */
-    bool queryIfComplete() const;
-
-    /** @brief Blocks the current CPU thread until all operations in the stream are complete.
-    */
-    void waitForCompletion();
-
-    /** @brief Makes a compute stream wait on an event.
-    */
-    void waitEvent(const Event& event);
-
-    /** @brief Adds a callback to be called on the host after all currently enqueued items in the stream have
-    completed.
-
-    @note Callbacks must not make any CUDA API calls. Callbacks must not perform any synchronization
-    that may depend on outstanding device work or other callbacks that are not mandated to run earlier.
-    Callbacks without a mandated order (in independent streams) execute in undefined order and may be
-    serialized.
-     */
-    void enqueueHostCallback(StreamCallback callback, void* userData);
-
-    //! return Stream object for default CUDA stream
-    static Stream& Null();
-
-    //! returns true if stream object is not default (!= 0)
-    operator bool_type() const;
-
-    class Impl;
-
-private:
-    Ptr<Impl> impl_;
-    Stream(const Ptr<Impl>& impl);
-
-    friend struct StreamAccessor;
-    friend class BufferPool;
-    friend class DefaultDeviceInitializer;
-};
-
-class CV_EXPORTS Event
-{
-public:
-    enum CreateFlags
-    {
-        DEFAULT        = 0x00,  /**< Default event flag */
-        BLOCKING_SYNC  = 0x01,  /**< Event uses blocking synchronization */
-        DISABLE_TIMING = 0x02,  /**< Event will not record timing data */
-        INTERPROCESS   = 0x04   /**< Event is suitable for interprocess use. DisableTiming must be set */
-    };
-
-    explicit Event(CreateFlags flags = DEFAULT);
-
-    //! records an event
-    void record(Stream& stream = Stream::Null());
-
-    //! queries an event's status
-    bool queryIfComplete() const;
-
-    //! waits for an event to complete
-    void waitForCompletion();
-
-    //! computes the elapsed time between events
-    static float elapsedTime(const Event& start, const Event& end);
-
-    class Impl;
-
-private:
-    Ptr<Impl> impl_;
-    Event(const Ptr<Impl>& impl);
-
-    friend struct EventAccessor;
-};
-
-//! @} cudacore_struct
-
-//===================================================================================
-// Initialization & Info
-//===================================================================================
-
-//! @addtogroup cudacore_init
-//! @{
-
-/** @brief Returns the number of installed CUDA-enabled devices.
-
-Use this function before any other CUDA functions calls. If OpenCV is compiled without CUDA support,
-this function returns 0. If the CUDA driver is not installed, or is incompatible, this function
-returns -1.
- */
-CV_EXPORTS int getCudaEnabledDeviceCount();
-
-/** @brief Sets a device and initializes it for the current thread.
-
-@param device System index of a CUDA device starting with 0.
-
-If the call of this function is omitted, a default device is initialized at the fist CUDA usage.
- */
-CV_EXPORTS void setDevice(int device);
-
-/** @brief Returns the current device index set by cuda::setDevice or initialized by default.
- */
-CV_EXPORTS int getDevice();
-
-/** @brief Explicitly destroys and cleans up all resources associated with the current device in the current
-process.
-
-Any subsequent API call to this device will reinitialize the device.
- */
-CV_EXPORTS void resetDevice();
-
-/** @brief Enumeration providing CUDA computing features.
- */
-enum FeatureSet
-{
-    FEATURE_SET_COMPUTE_10 = 10,
-    FEATURE_SET_COMPUTE_11 = 11,
-    FEATURE_SET_COMPUTE_12 = 12,
-    FEATURE_SET_COMPUTE_13 = 13,
-    FEATURE_SET_COMPUTE_20 = 20,
-    FEATURE_SET_COMPUTE_21 = 21,
-    FEATURE_SET_COMPUTE_30 = 30,
-    FEATURE_SET_COMPUTE_32 = 32,
-    FEATURE_SET_COMPUTE_35 = 35,
-    FEATURE_SET_COMPUTE_50 = 50,
-
-    GLOBAL_ATOMICS = FEATURE_SET_COMPUTE_11,
-    SHARED_ATOMICS = FEATURE_SET_COMPUTE_12,
-    NATIVE_DOUBLE = FEATURE_SET_COMPUTE_13,
-    WARP_SHUFFLE_FUNCTIONS = FEATURE_SET_COMPUTE_30,
-    DYNAMIC_PARALLELISM = FEATURE_SET_COMPUTE_35
-};
-
-//! checks whether current device supports the given feature
-CV_EXPORTS bool deviceSupports(FeatureSet feature_set);
-
-/** @brief Class providing a set of static methods to check what NVIDIA\* card architecture the CUDA module was
-built for.
-
-According to the CUDA C Programming Guide Version 3.2: "PTX code produced for some specific compute
-capability can always be compiled to binary code of greater or equal compute capability".
- */
-class CV_EXPORTS TargetArchs
-{
-public:
-    /** @brief The following method checks whether the module was built with the support of the given feature:
-
-    @param feature_set Features to be checked. See :ocvcuda::FeatureSet.
-     */
-    static bool builtWith(FeatureSet feature_set);
-
-    /** @brief There is a set of methods to check whether the module contains intermediate (PTX) or binary CUDA
-    code for the given architecture(s):
-
-    @param major Major compute capability version.
-    @param minor Minor compute capability version.
-     */
-    static bool has(int major, int minor);
-    static bool hasPtx(int major, int minor);
-    static bool hasBin(int major, int minor);
-
-    static bool hasEqualOrLessPtx(int major, int minor);
-    static bool hasEqualOrGreater(int major, int minor);
-    static bool hasEqualOrGreaterPtx(int major, int minor);
-    static bool hasEqualOrGreaterBin(int major, int minor);
-};
-
-/** @brief Class providing functionality for querying the specified GPU properties.
- */
-class CV_EXPORTS DeviceInfo
-{
-public:
-    //! creates DeviceInfo object for the current GPU
-    DeviceInfo();
-
-    /** @brief The constructors.
-
-    @param device_id System index of the CUDA device starting with 0.
-
-    Constructs the DeviceInfo object for the specified device. If device_id parameter is missed, it
-    constructs an object for the current device.
-     */
-    DeviceInfo(int device_id);
-
-    /** @brief Returns system index of the CUDA device starting with 0.
-    */
-    int deviceID() const;
-
-    //! ASCII string identifying device
-    const char* name() const;
-
-    //! global memory available on device in bytes
-    size_t totalGlobalMem() const;
-
-    //! shared memory available per block in bytes
-    size_t sharedMemPerBlock() const;
-
-    //! 32-bit registers available per block
-    int regsPerBlock() const;
-
-    //! warp size in threads
-    int warpSize() const;
-
-    //! maximum pitch in bytes allowed by memory copies
-    size_t memPitch() const;
-
-    //! maximum number of threads per block
-    int maxThreadsPerBlock() const;
-
-    //! maximum size of each dimension of a block
-    Vec3i maxThreadsDim() const;
-
-    //! maximum size of each dimension of a grid
-    Vec3i maxGridSize() const;
-
-    //! clock frequency in kilohertz
-    int clockRate() const;
-
-    //! constant memory available on device in bytes
-    size_t totalConstMem() const;
-
-    //! major compute capability
-    int majorVersion() const;
-
-    //! minor compute capability
-    int minorVersion() const;
-
-    //! alignment requirement for textures
-    size_t textureAlignment() const;
-
-    //! pitch alignment requirement for texture references bound to pitched memory
-    size_t texturePitchAlignment() const;
-
-    //! number of multiprocessors on device
-    int multiProcessorCount() const;
-
-    //! specified whether there is a run time limit on kernels
-    bool kernelExecTimeoutEnabled() const;
-
-    //! device is integrated as opposed to discrete
-    bool integrated() const;
-
-    //! device can map host memory with cudaHostAlloc/cudaHostGetDevicePointer
-    bool canMapHostMemory() const;
-
-    enum ComputeMode
-    {
-        ComputeModeDefault,         /**< default compute mode (Multiple threads can use cudaSetDevice with this device) */
-        ComputeModeExclusive,       /**< compute-exclusive-thread mode (Only one thread in one process will be able to use cudaSetDevice with this device) */
-        ComputeModeProhibited,      /**< compute-prohibited mode (No threads can use cudaSetDevice with this device) */
-        ComputeModeExclusiveProcess /**< compute-exclusive-process mode (Many threads in one process will be able to use cudaSetDevice with this device) */
-    };
-
-    //! compute mode
-    ComputeMode computeMode() const;
-
-    //! maximum 1D texture size
-    int maxTexture1D() const;
-
-    //! maximum 1D mipmapped texture size
-    int maxTexture1DMipmap() const;
-
-    //! maximum size for 1D textures bound to linear memory
-    int maxTexture1DLinear() const;
-
-    //! maximum 2D texture dimensions
-    Vec2i maxTexture2D() const;
-
-    //! maximum 2D mipmapped texture dimensions
-    Vec2i maxTexture2DMipmap() const;
-
-    //! maximum dimensions (width, height, pitch) for 2D textures bound to pitched memory
-    Vec3i maxTexture2DLinear() const;
-
-    //! maximum 2D texture dimensions if texture gather operations have to be performed
-    Vec2i maxTexture2DGather() const;
-
-    //! maximum 3D texture dimensions
-    Vec3i maxTexture3D() const;
-
-    //! maximum Cubemap texture dimensions
-    int maxTextureCubemap() const;
-
-    //! maximum 1D layered texture dimensions
-    Vec2i maxTexture1DLayered() const;
-
-    //! maximum 2D layered texture dimensions
-    Vec3i maxTexture2DLayered() const;
-
-    //! maximum Cubemap layered texture dimensions
-    Vec2i maxTextureCubemapLayered() const;
-
-    //! maximum 1D surface size
-    int maxSurface1D() const;
-
-    //! maximum 2D surface dimensions
-    Vec2i maxSurface2D() const;
-
-    //! maximum 3D surface dimensions
-    Vec3i maxSurface3D() const;
-
-    //! maximum 1D layered surface dimensions
-    Vec2i maxSurface1DLayered() const;
-
-    //! maximum 2D layered surface dimensions
-    Vec3i maxSurface2DLayered() const;
-
-    //! maximum Cubemap surface dimensions
-    int maxSurfaceCubemap() const;
-
-    //! maximum Cubemap layered surface dimensions
-    Vec2i maxSurfaceCubemapLayered() const;
-
-    //! alignment requirements for surfaces
-    size_t surfaceAlignment() const;
-
-    //! device can possibly execute multiple kernels concurrently
-    bool concurrentKernels() const;
-
-    //! device has ECC support enabled
-    bool ECCEnabled() const;
-
-    //! PCI bus ID of the device
-    int pciBusID() const;
-
-    //! PCI device ID of the device
-    int pciDeviceID() const;
-
-    //! PCI domain ID of the device
-    int pciDomainID() const;
-
-    //! true if device is a Tesla device using TCC driver, false otherwise
-    bool tccDriver() const;
-
-    //! number of asynchronous engines
-    int asyncEngineCount() const;
-
-    //! device shares a unified address space with the host
-    bool unifiedAddressing() const;
-
-    //! peak memory clock frequency in kilohertz
-    int memoryClockRate() const;
-
-    //! global memory bus width in bits
-    int memoryBusWidth() const;
-
-    //! size of L2 cache in bytes
-    int l2CacheSize() const;
-
-    //! maximum resident threads per multiprocessor
-    int maxThreadsPerMultiProcessor() const;
-
-    //! gets free and total device memory
-    void queryMemory(size_t& totalMemory, size_t& freeMemory) const;
-    size_t freeMemory() const;
-    size_t totalMemory() const;
-
-    /** @brief Provides information on CUDA feature support.
-
-    @param feature_set Features to be checked. See cuda::FeatureSet.
-
-    This function returns true if the device has the specified CUDA feature. Otherwise, it returns false
-     */
-    bool supports(FeatureSet feature_set) const;
-
-    /** @brief Checks the CUDA module and device compatibility.
-
-    This function returns true if the CUDA module can be run on the specified device. Otherwise, it
-    returns false .
-     */
-    bool isCompatible() const;
-
-private:
-    int device_id_;
-};
-
-CV_EXPORTS void printCudaDeviceInfo(int device);
-CV_EXPORTS void printShortCudaDeviceInfo(int device);
-
-/** @brief Converts an array to half precision floating number.
-
-@param _src input array.
-@param _dst output array.
-@param stream Stream for the asynchronous version.
-@sa convertFp16
-*/
-CV_EXPORTS void convertFp16(InputArray _src, OutputArray _dst, Stream& stream = Stream::Null());
-
-//! @} cudacore_init
-
-}} // namespace cv { namespace cuda {
-
-
-#include "opencv2/core/cuda.inl.hpp"
-
-#endif /* OPENCV_CORE_CUDA_HPP */
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda.inl.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda.inl.hpp
deleted file mode 100755
index 35ae2e4..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda.inl.hpp
+++ /dev/null
@@ -1,631 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_CORE_CUDAINL_HPP
-#define OPENCV_CORE_CUDAINL_HPP
-
-#include "opencv2/core/cuda.hpp"
-
-//! @cond IGNORED
-
-namespace cv { namespace cuda {
-
-//===================================================================================
-// GpuMat
-//===================================================================================
-
-inline
-GpuMat::GpuMat(Allocator* allocator_)
-    : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), allocator(allocator_)
-{}
-
-inline
-GpuMat::GpuMat(int rows_, int cols_, int type_, Allocator* allocator_)
-    : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), allocator(allocator_)
-{
-    if (rows_ > 0 && cols_ > 0)
-        create(rows_, cols_, type_);
-}
-
-inline
-GpuMat::GpuMat(Size size_, int type_, Allocator* allocator_)
-    : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), allocator(allocator_)
-{
-    if (size_.height > 0 && size_.width > 0)
-        create(size_.height, size_.width, type_);
-}
-
-inline
-GpuMat::GpuMat(int rows_, int cols_, int type_, Scalar s_, Allocator* allocator_)
-    : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), allocator(allocator_)
-{
-    if (rows_ > 0 && cols_ > 0)
-    {
-        create(rows_, cols_, type_);
-        setTo(s_);
-    }
-}
-
-inline
-GpuMat::GpuMat(Size size_, int type_, Scalar s_, Allocator* allocator_)
-    : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), allocator(allocator_)
-{
-    if (size_.height > 0 && size_.width > 0)
-    {
-        create(size_.height, size_.width, type_);
-        setTo(s_);
-    }
-}
-
-inline
-GpuMat::GpuMat(const GpuMat& m)
-    : flags(m.flags), rows(m.rows), cols(m.cols), step(m.step), data(m.data), refcount(m.refcount), datastart(m.datastart), dataend(m.dataend), allocator(m.allocator)
-{
-    if (refcount)
-        CV_XADD(refcount, 1);
-}
-
-inline
-GpuMat::GpuMat(InputArray arr, Allocator* allocator_) :
-    flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), allocator(allocator_)
-{
-    upload(arr);
-}
-
-inline
-GpuMat::~GpuMat()
-{
-    release();
-}
-
-inline
-GpuMat& GpuMat::operator =(const GpuMat& m)
-{
-    if (this != &m)
-    {
-        GpuMat temp(m);
-        swap(temp);
-    }
-
-    return *this;
-}
-
-inline
-void GpuMat::create(Size size_, int type_)
-{
-    create(size_.height, size_.width, type_);
-}
-
-inline
-void GpuMat::swap(GpuMat& b)
-{
-    std::swap(flags, b.flags);
-    std::swap(rows, b.rows);
-    std::swap(cols, b.cols);
-    std::swap(step, b.step);
-    std::swap(data, b.data);
-    std::swap(datastart, b.datastart);
-    std::swap(dataend, b.dataend);
-    std::swap(refcount, b.refcount);
-    std::swap(allocator, b.allocator);
-}
-
-inline
-GpuMat GpuMat::clone() const
-{
-    GpuMat m;
-    copyTo(m);
-    return m;
-}
-
-inline
-void GpuMat::copyTo(OutputArray dst, InputArray mask) const
-{
-    copyTo(dst, mask, Stream::Null());
-}
-
-inline
-GpuMat& GpuMat::setTo(Scalar s)
-{
-    return setTo(s, Stream::Null());
-}
-
-inline
-GpuMat& GpuMat::setTo(Scalar s, InputArray mask)
-{
-    return setTo(s, mask, Stream::Null());
-}
-
-inline
-void GpuMat::convertTo(OutputArray dst, int rtype) const
-{
-    convertTo(dst, rtype, Stream::Null());
-}
-
-inline
-void GpuMat::convertTo(OutputArray dst, int rtype, double alpha, double beta) const
-{
-    convertTo(dst, rtype, alpha, beta, Stream::Null());
-}
-
-inline
-void GpuMat::convertTo(OutputArray dst, int rtype, double alpha, Stream& stream) const
-{
-    convertTo(dst, rtype, alpha, 0.0, stream);
-}
-
-inline
-void GpuMat::assignTo(GpuMat& m, int _type) const
-{
-    if (_type < 0)
-        m = *this;
-    else
-        convertTo(m, _type);
-}
-
-inline
-uchar* GpuMat::ptr(int y)
-{
-    CV_DbgAssert( (unsigned)y < (unsigned)rows );
-    return data + step * y;
-}
-
-inline
-const uchar* GpuMat::ptr(int y) const
-{
-    CV_DbgAssert( (unsigned)y < (unsigned)rows );
-    return data + step * y;
-}
-
-template<typename _Tp> inline
-_Tp* GpuMat::ptr(int y)
-{
-    return (_Tp*)ptr(y);
-}
-
-template<typename _Tp> inline
-const _Tp* GpuMat::ptr(int y) const
-{
-    return (const _Tp*)ptr(y);
-}
-
-template <class T> inline
-GpuMat::operator PtrStepSz<T>() const
-{
-    return PtrStepSz<T>(rows, cols, (T*)data, step);
-}
-
-template <class T> inline
-GpuMat::operator PtrStep<T>() const
-{
-    return PtrStep<T>((T*)data, step);
-}
-
-inline
-GpuMat GpuMat::row(int y) const
-{
-    return GpuMat(*this, Range(y, y+1), Range::all());
-}
-
-inline
-GpuMat GpuMat::col(int x) const
-{
-    return GpuMat(*this, Range::all(), Range(x, x+1));
-}
-
-inline
-GpuMat GpuMat::rowRange(int startrow, int endrow) const
-{
-    return GpuMat(*this, Range(startrow, endrow), Range::all());
-}
-
-inline
-GpuMat GpuMat::rowRange(Range r) const
-{
-    return GpuMat(*this, r, Range::all());
-}
-
-inline
-GpuMat GpuMat::colRange(int startcol, int endcol) const
-{
-    return GpuMat(*this, Range::all(), Range(startcol, endcol));
-}
-
-inline
-GpuMat GpuMat::colRange(Range r) const
-{
-    return GpuMat(*this, Range::all(), r);
-}
-
-inline
-GpuMat GpuMat::operator ()(Range rowRange_, Range colRange_) const
-{
-    return GpuMat(*this, rowRange_, colRange_);
-}
-
-inline
-GpuMat GpuMat::operator ()(Rect roi) const
-{
-    return GpuMat(*this, roi);
-}
-
-inline
-bool GpuMat::isContinuous() const
-{
-    return (flags & Mat::CONTINUOUS_FLAG) != 0;
-}
-
-inline
-size_t GpuMat::elemSize() const
-{
-    return CV_ELEM_SIZE(flags);
-}
-
-inline
-size_t GpuMat::elemSize1() const
-{
-    return CV_ELEM_SIZE1(flags);
-}
-
-inline
-int GpuMat::type() const
-{
-    return CV_MAT_TYPE(flags);
-}
-
-inline
-int GpuMat::depth() const
-{
-    return CV_MAT_DEPTH(flags);
-}
-
-inline
-int GpuMat::channels() const
-{
-    return CV_MAT_CN(flags);
-}
-
-inline
-size_t GpuMat::step1() const
-{
-    return step / elemSize1();
-}
-
-inline
-Size GpuMat::size() const
-{
-    return Size(cols, rows);
-}
-
-inline
-bool GpuMat::empty() const
-{
-    return data == 0;
-}
-
-static inline
-GpuMat createContinuous(int rows, int cols, int type)
-{
-    GpuMat m;
-    createContinuous(rows, cols, type, m);
-    return m;
-}
-
-static inline
-void createContinuous(Size size, int type, OutputArray arr)
-{
-    createContinuous(size.height, size.width, type, arr);
-}
-
-static inline
-GpuMat createContinuous(Size size, int type)
-{
-    GpuMat m;
-    createContinuous(size, type, m);
-    return m;
-}
-
-static inline
-void ensureSizeIsEnough(Size size, int type, OutputArray arr)
-{
-    ensureSizeIsEnough(size.height, size.width, type, arr);
-}
-
-static inline
-void swap(GpuMat& a, GpuMat& b)
-{
-    a.swap(b);
-}
-
-//===================================================================================
-// HostMem
-//===================================================================================
-
-inline
-HostMem::HostMem(AllocType alloc_type_)
-    : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), alloc_type(alloc_type_)
-{
-}
-
-inline
-HostMem::HostMem(const HostMem& m)
-    : flags(m.flags), rows(m.rows), cols(m.cols), step(m.step), data(m.data), refcount(m.refcount), datastart(m.datastart), dataend(m.dataend), alloc_type(m.alloc_type)
-{
-    if( refcount )
-        CV_XADD(refcount, 1);
-}
-
-inline
-HostMem::HostMem(int rows_, int cols_, int type_, AllocType alloc_type_)
-    : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), alloc_type(alloc_type_)
-{
-    if (rows_ > 0 && cols_ > 0)
-        create(rows_, cols_, type_);
-}
-
-inline
-HostMem::HostMem(Size size_, int type_, AllocType alloc_type_)
-    : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), alloc_type(alloc_type_)
-{
-    if (size_.height > 0 && size_.width > 0)
-        create(size_.height, size_.width, type_);
-}
-
-inline
-HostMem::HostMem(InputArray arr, AllocType alloc_type_)
-    : flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), alloc_type(alloc_type_)
-{
-    arr.getMat().copyTo(*this);
-}
-
-inline
-HostMem::~HostMem()
-{
-    release();
-}
-
-inline
-HostMem& HostMem::operator =(const HostMem& m)
-{
-    if (this != &m)
-    {
-        HostMem temp(m);
-        swap(temp);
-    }
-
-    return *this;
-}
-
-inline
-void HostMem::swap(HostMem& b)
-{
-    std::swap(flags, b.flags);
-    std::swap(rows, b.rows);
-    std::swap(cols, b.cols);
-    std::swap(step, b.step);
-    std::swap(data, b.data);
-    std::swap(datastart, b.datastart);
-    std::swap(dataend, b.dataend);
-    std::swap(refcount, b.refcount);
-    std::swap(alloc_type, b.alloc_type);
-}
-
-inline
-HostMem HostMem::clone() const
-{
-    HostMem m(size(), type(), alloc_type);
-    createMatHeader().copyTo(m);
-    return m;
-}
-
-inline
-void HostMem::create(Size size_, int type_)
-{
-    create(size_.height, size_.width, type_);
-}
-
-inline
-Mat HostMem::createMatHeader() const
-{
-    return Mat(size(), type(), data, step);
-}
-
-inline
-bool HostMem::isContinuous() const
-{
-    return (flags & Mat::CONTINUOUS_FLAG) != 0;
-}
-
-inline
-size_t HostMem::elemSize() const
-{
-    return CV_ELEM_SIZE(flags);
-}
-
-inline
-size_t HostMem::elemSize1() const
-{
-    return CV_ELEM_SIZE1(flags);
-}
-
-inline
-int HostMem::type() const
-{
-    return CV_MAT_TYPE(flags);
-}
-
-inline
-int HostMem::depth() const
-{
-    return CV_MAT_DEPTH(flags);
-}
-
-inline
-int HostMem::channels() const
-{
-    return CV_MAT_CN(flags);
-}
-
-inline
-size_t HostMem::step1() const
-{
-    return step / elemSize1();
-}
-
-inline
-Size HostMem::size() const
-{
-    return Size(cols, rows);
-}
-
-inline
-bool HostMem::empty() const
-{
-    return data == 0;
-}
-
-static inline
-void swap(HostMem& a, HostMem& b)
-{
-    a.swap(b);
-}
-
-//===================================================================================
-// Stream
-//===================================================================================
-
-inline
-Stream::Stream(const Ptr<Impl>& impl)
-    : impl_(impl)
-{
-}
-
-//===================================================================================
-// Event
-//===================================================================================
-
-inline
-Event::Event(const Ptr<Impl>& impl)
-    : impl_(impl)
-{
-}
-
-//===================================================================================
-// Initialization & Info
-//===================================================================================
-
-inline
-bool TargetArchs::has(int major, int minor)
-{
-    return hasPtx(major, minor) || hasBin(major, minor);
-}
-
-inline
-bool TargetArchs::hasEqualOrGreater(int major, int minor)
-{
-    return hasEqualOrGreaterPtx(major, minor) || hasEqualOrGreaterBin(major, minor);
-}
-
-inline
-DeviceInfo::DeviceInfo()
-{
-    device_id_ = getDevice();
-}
-
-inline
-DeviceInfo::DeviceInfo(int device_id)
-{
-    CV_Assert( device_id >= 0 && device_id < getCudaEnabledDeviceCount() );
-    device_id_ = device_id;
-}
-
-inline
-int DeviceInfo::deviceID() const
-{
-    return device_id_;
-}
-
-inline
-size_t DeviceInfo::freeMemory() const
-{
-    size_t _totalMemory = 0, _freeMemory = 0;
-    queryMemory(_totalMemory, _freeMemory);
-    return _freeMemory;
-}
-
-inline
-size_t DeviceInfo::totalMemory() const
-{
-    size_t _totalMemory = 0, _freeMemory = 0;
-    queryMemory(_totalMemory, _freeMemory);
-    return _totalMemory;
-}
-
-inline
-bool DeviceInfo::supports(FeatureSet feature_set) const
-{
-    int version = majorVersion() * 10 + minorVersion();
-    return version >= feature_set;
-}
-
-
-}} // namespace cv { namespace cuda {
-
-//===================================================================================
-// Mat
-//===================================================================================
-
-namespace cv {
-
-inline
-Mat::Mat(const cuda::GpuMat& m)
-    : flags(0), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0), datalimit(0), allocator(0), u(0), size(&rows)
-{
-    m.download(*this);
-}
-
-}
-
-//! @endcond
-
-#endif // OPENCV_CORE_CUDAINL_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/block.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/block.hpp
deleted file mode 100755
index c277f0e..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/block.hpp
+++ /dev/null
@@ -1,211 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_CUDA_DEVICE_BLOCK_HPP
-#define OPENCV_CUDA_DEVICE_BLOCK_HPP
-
-/** @file
- * @deprecated Use @ref cudev instead.
- */
-
-//! @cond IGNORED
-
-namespace cv { namespace cuda { namespace device
-{
-    struct Block
-    {
-        static __device__ __forceinline__ unsigned int id()
-        {
-            return blockIdx.x;
-        }
-
-        static __device__ __forceinline__ unsigned int stride()
-        {
-            return blockDim.x * blockDim.y * blockDim.z;
-        }
-
-        static __device__ __forceinline__ void sync()
-        {
-            __syncthreads();
-        }
-
-        static __device__ __forceinline__ int flattenedThreadId()
-        {
-            return threadIdx.z * blockDim.x * blockDim.y + threadIdx.y * blockDim.x + threadIdx.x;
-        }
-
-        template<typename It, typename T>
-        static __device__ __forceinline__ void fill(It beg, It end, const T& value)
-        {
-            int STRIDE = stride();
-            It t = beg + flattenedThreadId();
-
-            for(; t < end; t += STRIDE)
-                *t = value;
-        }
-
-        template<typename OutIt, typename T>
-        static __device__ __forceinline__ void yota(OutIt beg, OutIt end, T value)
-        {
-            int STRIDE = stride();
-            int tid = flattenedThreadId();
-            value += tid;
-
-            for(OutIt t = beg + tid; t < end; t += STRIDE, value += STRIDE)
-                *t = value;
-        }
-
-        template<typename InIt, typename OutIt>
-        static __device__ __forceinline__ void copy(InIt beg, InIt end, OutIt out)
-        {
-            int STRIDE = stride();
-            InIt  t = beg + flattenedThreadId();
-            OutIt o = out + (t - beg);
-
-            for(; t < end; t += STRIDE, o += STRIDE)
-                *o = *t;
-        }
-
-        template<typename InIt, typename OutIt, class UnOp>
-        static __device__ __forceinline__ void transform(InIt beg, InIt end, OutIt out, UnOp op)
-        {
-            int STRIDE = stride();
-            InIt  t = beg + flattenedThreadId();
-            OutIt o = out + (t - beg);
-
-            for(; t < end; t += STRIDE, o += STRIDE)
-                *o = op(*t);
-        }
-
-        template<typename InIt1, typename InIt2, typename OutIt, class BinOp>
-        static __device__ __forceinline__ void transform(InIt1 beg1, InIt1 end1, InIt2 beg2, OutIt out, BinOp op)
-        {
-            int STRIDE = stride();
-            InIt1 t1 = beg1 + flattenedThreadId();
-            InIt2 t2 = beg2 + flattenedThreadId();
-            OutIt o  = out + (t1 - beg1);
-
-            for(; t1 < end1; t1 += STRIDE, t2 += STRIDE, o += STRIDE)
-                *o = op(*t1, *t2);
-        }
-
-        template<int CTA_SIZE, typename T, class BinOp>
-        static __device__ __forceinline__ void reduce(volatile T* buffer, BinOp op)
-        {
-            int tid = flattenedThreadId();
-            T val =  buffer[tid];
-
-            if (CTA_SIZE >= 1024) { if (tid < 512) buffer[tid] = val = op(val, buffer[tid + 512]); __syncthreads(); }
-            if (CTA_SIZE >=  512) { if (tid < 256) buffer[tid] = val = op(val, buffer[tid + 256]); __syncthreads(); }
-            if (CTA_SIZE >=  256) { if (tid < 128) buffer[tid] = val = op(val, buffer[tid + 128]); __syncthreads(); }
-            if (CTA_SIZE >=  128) { if (tid <  64) buffer[tid] = val = op(val, buffer[tid +  64]); __syncthreads(); }
-
-            if (tid < 32)
-            {
-                if (CTA_SIZE >=   64) { buffer[tid] = val = op(val, buffer[tid +  32]); }
-                if (CTA_SIZE >=   32) { buffer[tid] = val = op(val, buffer[tid +  16]); }
-                if (CTA_SIZE >=   16) { buffer[tid] = val = op(val, buffer[tid +   8]); }
-                if (CTA_SIZE >=    8) { buffer[tid] = val = op(val, buffer[tid +   4]); }
-                if (CTA_SIZE >=    4) { buffer[tid] = val = op(val, buffer[tid +   2]); }
-                if (CTA_SIZE >=    2) { buffer[tid] = val = op(val, buffer[tid +   1]); }
-            }
-        }
-
-        template<int CTA_SIZE, typename T, class BinOp>
-        static __device__ __forceinline__ T reduce(volatile T* buffer, T init, BinOp op)
-        {
-            int tid = flattenedThreadId();
-            T val =  buffer[tid] = init;
-            __syncthreads();
-
-            if (CTA_SIZE >= 1024) { if (tid < 512) buffer[tid] = val = op(val, buffer[tid + 512]); __syncthreads(); }
-            if (CTA_SIZE >=  512) { if (tid < 256) buffer[tid] = val = op(val, buffer[tid + 256]); __syncthreads(); }
-            if (CTA_SIZE >=  256) { if (tid < 128) buffer[tid] = val = op(val, buffer[tid + 128]); __syncthreads(); }
-            if (CTA_SIZE >=  128) { if (tid <  64) buffer[tid] = val = op(val, buffer[tid +  64]); __syncthreads(); }
-
-            if (tid < 32)
-            {
-                if (CTA_SIZE >=   64) { buffer[tid] = val = op(val, buffer[tid +  32]); }
-                if (CTA_SIZE >=   32) { buffer[tid] = val = op(val, buffer[tid +  16]); }
-                if (CTA_SIZE >=   16) { buffer[tid] = val = op(val, buffer[tid +   8]); }
-                if (CTA_SIZE >=    8) { buffer[tid] = val = op(val, buffer[tid +   4]); }
-                if (CTA_SIZE >=    4) { buffer[tid] = val = op(val, buffer[tid +   2]); }
-                if (CTA_SIZE >=    2) { buffer[tid] = val = op(val, buffer[tid +   1]); }
-            }
-            __syncthreads();
-            return buffer[0];
-        }
-
-        template <typename T, class BinOp>
-        static __device__ __forceinline__ void reduce_n(T* data, unsigned int n, BinOp op)
-        {
-            int ftid = flattenedThreadId();
-            int sft = stride();
-
-            if (sft < n)
-            {
-                for (unsigned int i = sft + ftid; i < n; i += sft)
-                    data[ftid] = op(data[ftid], data[i]);
-
-                __syncthreads();
-
-                n = sft;
-            }
-
-            while (n > 1)
-            {
-                unsigned int half = n/2;
-
-                if (ftid < half)
-                    data[ftid] = op(data[ftid], data[n - ftid - 1]);
-
-                __syncthreads();
-
-                n = n - half;
-            }
-        }
-    };
-}}}
-
-//! @endcond
-
-#endif /* OPENCV_CUDA_DEVICE_BLOCK_HPP */
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/border_interpolate.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/border_interpolate.hpp
deleted file mode 100755
index 874f705..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/border_interpolate.hpp
+++ /dev/null
@@ -1,722 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_CUDA_BORDER_INTERPOLATE_HPP
-#define OPENCV_CUDA_BORDER_INTERPOLATE_HPP
-
-#include "saturate_cast.hpp"
-#include "vec_traits.hpp"
-#include "vec_math.hpp"
-
-/** @file
- * @deprecated Use @ref cudev instead.
- */
-
-//! @cond IGNORED
-
-namespace cv { namespace cuda { namespace device
-{
-    //////////////////////////////////////////////////////////////
-    // BrdConstant
-
-    template <typename D> struct BrdRowConstant
-    {
-        typedef D result_type;
-
-        explicit __host__ __device__ __forceinline__ BrdRowConstant(int width_, const D& val_ = VecTraits<D>::all(0)) : width(width_), val(val_) {}
-
-        template <typename T> __device__ __forceinline__ D at_low(int x, const T* data) const
-        {
-            return x >= 0 ? saturate_cast<D>(data[x]) : val;
-        }
-
-        template <typename T> __device__ __forceinline__ D at_high(int x, const T* data) const
-        {
-            return x < width ? saturate_cast<D>(data[x]) : val;
-        }
-
-        template <typename T> __device__ __forceinline__ D at(int x, const T* data) const
-        {
-            return (x >= 0 && x < width) ? saturate_cast<D>(data[x]) : val;
-        }
-
-        int width;
-        D val;
-    };
-
-    template <typename D> struct BrdColConstant
-    {
-        typedef D result_type;
-
-        explicit __host__ __device__ __forceinline__ BrdColConstant(int height_, const D& val_ = VecTraits<D>::all(0)) : height(height_), val(val_) {}
-
-        template <typename T> __device__ __forceinline__ D at_low(int y, const T* data, size_t step) const
-        {
-            return y >= 0 ? saturate_cast<D>(*(const T*)((const char*)data + y * step)) : val;
-        }
-
-        template <typename T> __device__ __forceinline__ D at_high(int y, const T* data, size_t step) const
-        {
-            return y < height ? saturate_cast<D>(*(const T*)((const char*)data + y * step)) : val;
-        }
-
-        template <typename T> __device__ __forceinline__ D at(int y, const T* data, size_t step) const
-        {
-            return (y >= 0 && y < height) ? saturate_cast<D>(*(const T*)((const char*)data + y * step)) : val;
-        }
-
-        int height;
-        D val;
-    };
-
-    template <typename D> struct BrdConstant
-    {
-        typedef D result_type;
-
-        __host__ __device__ __forceinline__ BrdConstant(int height_, int width_, const D& val_ = VecTraits<D>::all(0)) : height(height_), width(width_), val(val_)
-        {
-        }
-
-        template <typename T> __device__ __forceinline__ D at(int y, int x, const T* data, size_t step) const
-        {
-            return (x >= 0 && x < width && y >= 0 && y < height) ? saturate_cast<D>(((const T*)((const uchar*)data + y * step))[x]) : val;
-        }
-
-        template <typename Ptr2D> __device__ __forceinline__ D at(typename Ptr2D::index_type y, typename Ptr2D::index_type x, const Ptr2D& src) const
-        {
-            return (x >= 0 && x < width && y >= 0 && y < height) ? saturate_cast<D>(src(y, x)) : val;
-        }
-
-        int height;
-        int width;
-        D val;
-    };
-
-    //////////////////////////////////////////////////////////////
-    // BrdReplicate
-
-    template <typename D> struct BrdRowReplicate
-    {
-        typedef D result_type;
-
-        explicit __host__ __device__ __forceinline__ BrdRowReplicate(int width) : last_col(width - 1) {}
-        template <typename U> __host__ __device__ __forceinline__ BrdRowReplicate(int width, U) : last_col(width - 1) {}
-
-        __device__ __forceinline__ int idx_col_low(int x) const
-        {
-            return ::max(x, 0);
-        }
-
-        __device__ __forceinline__ int idx_col_high(int x) const
-        {
-            return ::min(x, last_col);
-        }
-
-        __device__ __forceinline__ int idx_col(int x) const
-        {
-            return idx_col_low(idx_col_high(x));
-        }
-
-        template <typename T> __device__ __forceinline__ D at_low(int x, const T* data) const
-        {
-            return saturate_cast<D>(data[idx_col_low(x)]);
-        }
-
-        template <typename T> __device__ __forceinline__ D at_high(int x, const T* data) const
-        {
-            return saturate_cast<D>(data[idx_col_high(x)]);
-        }
-
-        template <typename T> __device__ __forceinline__ D at(int x, const T* data) const
-        {
-            return saturate_cast<D>(data[idx_col(x)]);
-        }
-
-        int last_col;
-    };
-
-    template <typename D> struct BrdColReplicate
-    {
-        typedef D result_type;
-
-        explicit __host__ __device__ __forceinline__ BrdColReplicate(int height) : last_row(height - 1) {}
-        template <typename U> __host__ __device__ __forceinline__ BrdColReplicate(int height, U) : last_row(height - 1) {}
-
-        __device__ __forceinline__ int idx_row_low(int y) const
-        {
-            return ::max(y, 0);
-        }
-
-        __device__ __forceinline__ int idx_row_high(int y) const
-        {
-            return ::min(y, last_row);
-        }
-
-        __device__ __forceinline__ int idx_row(int y) const
-        {
-            return idx_row_low(idx_row_high(y));
-        }
-
-        template <typename T> __device__ __forceinline__ D at_low(int y, const T* data, size_t step) const
-        {
-            return saturate_cast<D>(*(const T*)((const char*)data + idx_row_low(y) * step));
-        }
-
-        template <typename T> __device__ __forceinline__ D at_high(int y, const T* data, size_t step) const
-        {
-            return saturate_cast<D>(*(const T*)((const char*)data + idx_row_high(y) * step));
-        }
-
-        template <typename T> __device__ __forceinline__ D at(int y, const T* data, size_t step) const
-        {
-            return saturate_cast<D>(*(const T*)((const char*)data + idx_row(y) * step));
-        }
-
-        int last_row;
-    };
-
-    template <typename D> struct BrdReplicate
-    {
-        typedef D result_type;
-
-        __host__ __device__ __forceinline__ BrdReplicate(int height, int width) : last_row(height - 1), last_col(width - 1) {}
-        template <typename U> __host__ __device__ __forceinline__ BrdReplicate(int height, int width, U) : last_row(height - 1), last_col(width - 1) {}
-
-        __device__ __forceinline__ int idx_row_low(int y) const
-        {
-            return ::max(y, 0);
-        }
-
-        __device__ __forceinline__ int idx_row_high(int y) const
-        {
-            return ::min(y, last_row);
-        }
-
-        __device__ __forceinline__ int idx_row(int y) const
-        {
-            return idx_row_low(idx_row_high(y));
-        }
-
-        __device__ __forceinline__ int idx_col_low(int x) const
-        {
-            return ::max(x, 0);
-        }
-
-        __device__ __forceinline__ int idx_col_high(int x) const
-        {
-            return ::min(x, last_col);
-        }
-
-        __device__ __forceinline__ int idx_col(int x) const
-        {
-            return idx_col_low(idx_col_high(x));
-        }
-
-        template <typename T> __device__ __forceinline__ D at(int y, int x, const T* data, size_t step) const
-        {
-            return saturate_cast<D>(((const T*)((const char*)data + idx_row(y) * step))[idx_col(x)]);
-        }
-
-        template <typename Ptr2D> __device__ __forceinline__ D at(typename Ptr2D::index_type y, typename Ptr2D::index_type x, const Ptr2D& src) const
-        {
-            return saturate_cast<D>(src(idx_row(y), idx_col(x)));
-        }
-
-        int last_row;
-        int last_col;
-    };
-
-    //////////////////////////////////////////////////////////////
-    // BrdReflect101
-
-    template <typename D> struct BrdRowReflect101
-    {
-        typedef D result_type;
-
-        explicit __host__ __device__ __forceinline__ BrdRowReflect101(int width) : last_col(width - 1) {}
-        template <typename U> __host__ __device__ __forceinline__ BrdRowReflect101(int width, U) : last_col(width - 1) {}
-
-        __device__ __forceinline__ int idx_col_low(int x) const
-        {
-            return ::abs(x) % (last_col + 1);
-        }
-
-        __device__ __forceinline__ int idx_col_high(int x) const
-        {
-            return ::abs(last_col - ::abs(last_col - x)) % (last_col + 1);
-        }
-
-        __device__ __forceinline__ int idx_col(int x) const
-        {
-            return idx_col_low(idx_col_high(x));
-        }
-
-        template <typename T> __device__ __forceinline__ D at_low(int x, const T* data) const
-        {
-            return saturate_cast<D>(data[idx_col_low(x)]);
-        }
-
-        template <typename T> __device__ __forceinline__ D at_high(int x, const T* data) const
-        {
-            return saturate_cast<D>(data[idx_col_high(x)]);
-        }
-
-        template <typename T> __device__ __forceinline__ D at(int x, const T* data) const
-        {
-            return saturate_cast<D>(data[idx_col(x)]);
-        }
-
-        int last_col;
-    };
-
-    template <typename D> struct BrdColReflect101
-    {
-        typedef D result_type;
-
-        explicit __host__ __device__ __forceinline__ BrdColReflect101(int height) : last_row(height - 1) {}
-        template <typename U> __host__ __device__ __forceinline__ BrdColReflect101(int height, U) : last_row(height - 1) {}
-
-        __device__ __forceinline__ int idx_row_low(int y) const
-        {
-            return ::abs(y) % (last_row + 1);
-        }
-
-        __device__ __forceinline__ int idx_row_high(int y) const
-        {
-            return ::abs(last_row - ::abs(last_row - y)) % (last_row + 1);
-        }
-
-        __device__ __forceinline__ int idx_row(int y) const
-        {
-            return idx_row_low(idx_row_high(y));
-        }
-
-        template <typename T> __device__ __forceinline__ D at_low(int y, const T* data, size_t step) const
-        {
-            return saturate_cast<D>(*(const D*)((const char*)data + idx_row_low(y) * step));
-        }
-
-        template <typename T> __device__ __forceinline__ D at_high(int y, const T* data, size_t step) const
-        {
-            return saturate_cast<D>(*(const D*)((const char*)data + idx_row_high(y) * step));
-        }
-
-        template <typename T> __device__ __forceinline__ D at(int y, const T* data, size_t step) const
-        {
-            return saturate_cast<D>(*(const D*)((const char*)data + idx_row(y) * step));
-        }
-
-        int last_row;
-    };
-
-    template <typename D> struct BrdReflect101
-    {
-        typedef D result_type;
-
-        __host__ __device__ __forceinline__ BrdReflect101(int height, int width) : last_row(height - 1), last_col(width - 1) {}
-        template <typename U> __host__ __device__ __forceinline__ BrdReflect101(int height, int width, U) : last_row(height - 1), last_col(width - 1) {}
-
-        __device__ __forceinline__ int idx_row_low(int y) const
-        {
-            return ::abs(y) % (last_row + 1);
-        }
-
-        __device__ __forceinline__ int idx_row_high(int y) const
-        {
-            return ::abs(last_row - ::abs(last_row - y)) % (last_row + 1);
-        }
-
-        __device__ __forceinline__ int idx_row(int y) const
-        {
-            return idx_row_low(idx_row_high(y));
-        }
-
-        __device__ __forceinline__ int idx_col_low(int x) const
-        {
-            return ::abs(x) % (last_col + 1);
-        }
-
-        __device__ __forceinline__ int idx_col_high(int x) const
-        {
-            return ::abs(last_col - ::abs(last_col - x)) % (last_col + 1);
-        }
-
-        __device__ __forceinline__ int idx_col(int x) const
-        {
-            return idx_col_low(idx_col_high(x));
-        }
-
-        template <typename T> __device__ __forceinline__ D at(int y, int x, const T* data, size_t step) const
-        {
-            return saturate_cast<D>(((const T*)((const char*)data + idx_row(y) * step))[idx_col(x)]);
-        }
-
-        template <typename Ptr2D> __device__ __forceinline__ D at(typename Ptr2D::index_type y, typename Ptr2D::index_type x, const Ptr2D& src) const
-        {
-            return saturate_cast<D>(src(idx_row(y), idx_col(x)));
-        }
-
-        int last_row;
-        int last_col;
-    };
-
-    //////////////////////////////////////////////////////////////
-    // BrdReflect
-
-    template <typename D> struct BrdRowReflect
-    {
-        typedef D result_type;
-
-        explicit __host__ __device__ __forceinline__ BrdRowReflect(int width) : last_col(width - 1) {}
-        template <typename U> __host__ __device__ __forceinline__ BrdRowReflect(int width, U) : last_col(width - 1) {}
-
-        __device__ __forceinline__ int idx_col_low(int x) const
-        {
-            return (::abs(x) - (x < 0)) % (last_col + 1);
-        }
-
-        __device__ __forceinline__ int idx_col_high(int x) const
-        {
-            return ::abs(last_col - ::abs(last_col - x) + (x > last_col)) % (last_col + 1);
-        }
-
-        __device__ __forceinline__ int idx_col(int x) const
-        {
-            return idx_col_high(::abs(x) - (x < 0));
-        }
-
-        template <typename T> __device__ __forceinline__ D at_low(int x, const T* data) const
-        {
-            return saturate_cast<D>(data[idx_col_low(x)]);
-        }
-
-        template <typename T> __device__ __forceinline__ D at_high(int x, const T* data) const
-        {
-            return saturate_cast<D>(data[idx_col_high(x)]);
-        }
-
-        template <typename T> __device__ __forceinline__ D at(int x, const T* data) const
-        {
-            return saturate_cast<D>(data[idx_col(x)]);
-        }
-
-        int last_col;
-    };
-
-    template <typename D> struct BrdColReflect
-    {
-        typedef D result_type;
-
-        explicit __host__ __device__ __forceinline__ BrdColReflect(int height) : last_row(height - 1) {}
-        template <typename U> __host__ __device__ __forceinline__ BrdColReflect(int height, U) : last_row(height - 1) {}
-
-        __device__ __forceinline__ int idx_row_low(int y) const
-        {
-            return (::abs(y) - (y < 0)) % (last_row + 1);
-        }
-
-        __device__ __forceinline__ int idx_row_high(int y) const
-        {
-            return ::abs(last_row - ::abs(last_row - y) + (y > last_row)) % (last_row + 1);
-        }
-
-        __device__ __forceinline__ int idx_row(int y) const
-        {
-            return idx_row_high(::abs(y) - (y < 0));
-        }
-
-        template <typename T> __device__ __forceinline__ D at_low(int y, const T* data, size_t step) const
-        {
-            return saturate_cast<D>(*(const D*)((const char*)data + idx_row_low(y) * step));
-        }
-
-        template <typename T> __device__ __forceinline__ D at_high(int y, const T* data, size_t step) const
-        {
-            return saturate_cast<D>(*(const D*)((const char*)data + idx_row_high(y) * step));
-        }
-
-        template <typename T> __device__ __forceinline__ D at(int y, const T* data, size_t step) const
-        {
-            return saturate_cast<D>(*(const D*)((const char*)data + idx_row(y) * step));
-        }
-
-        int last_row;
-    };
-
-    template <typename D> struct BrdReflect
-    {
-        typedef D result_type;
-
-        __host__ __device__ __forceinline__ BrdReflect(int height, int width) : last_row(height - 1), last_col(width - 1) {}
-        template <typename U> __host__ __device__ __forceinline__ BrdReflect(int height, int width, U) : last_row(height - 1), last_col(width - 1) {}
-
-        __device__ __forceinline__ int idx_row_low(int y) const
-        {
-            return (::abs(y) - (y < 0)) % (last_row + 1);
-        }
-
-        __device__ __forceinline__ int idx_row_high(int y) const
-        {
-            return /*::abs*/(last_row - ::abs(last_row - y) + (y > last_row)) /*% (last_row + 1)*/;
-        }
-
-        __device__ __forceinline__ int idx_row(int y) const
-        {
-            return idx_row_low(idx_row_high(y));
-        }
-
-        __device__ __forceinline__ int idx_col_low(int x) const
-        {
-            return (::abs(x) - (x < 0)) % (last_col + 1);
-        }
-
-        __device__ __forceinline__ int idx_col_high(int x) const
-        {
-            return (last_col - ::abs(last_col - x) + (x > last_col));
-        }
-
-        __device__ __forceinline__ int idx_col(int x) const
-        {
-            return idx_col_low(idx_col_high(x));
-        }
-
-        template <typename T> __device__ __forceinline__ D at(int y, int x, const T* data, size_t step) const
-        {
-            return saturate_cast<D>(((const T*)((const char*)data + idx_row(y) * step))[idx_col(x)]);
-        }
-
-        template <typename Ptr2D> __device__ __forceinline__ D at(typename Ptr2D::index_type y, typename Ptr2D::index_type x, const Ptr2D& src) const
-        {
-            return saturate_cast<D>(src(idx_row(y), idx_col(x)));
-        }
-
-        int last_row;
-        int last_col;
-    };
-
-    //////////////////////////////////////////////////////////////
-    // BrdWrap
-
-    template <typename D> struct BrdRowWrap
-    {
-        typedef D result_type;
-
-        explicit __host__ __device__ __forceinline__ BrdRowWrap(int width_) : width(width_) {}
-        template <typename U> __host__ __device__ __forceinline__ BrdRowWrap(int width_, U) : width(width_) {}
-
-        __device__ __forceinline__ int idx_col_low(int x) const
-        {
-            return (x >= 0) * x + (x < 0) * (x - ((x - width + 1) / width) * width);
-        }
-
-        __device__ __forceinline__ int idx_col_high(int x) const
-        {
-            return (x < width) * x + (x >= width) * (x % width);
-        }
-
-        __device__ __forceinline__ int idx_col(int x) const
-        {
-            return idx_col_high(idx_col_low(x));
-        }
-
-        template <typename T> __device__ __forceinline__ D at_low(int x, const T* data) const
-        {
-            return saturate_cast<D>(data[idx_col_low(x)]);
-        }
-
-        template <typename T> __device__ __forceinline__ D at_high(int x, const T* data) const
-        {
-            return saturate_cast<D>(data[idx_col_high(x)]);
-        }
-
-        template <typename T> __device__ __forceinline__ D at(int x, const T* data) const
-        {
-            return saturate_cast<D>(data[idx_col(x)]);
-        }
-
-        int width;
-    };
-
-    template <typename D> struct BrdColWrap
-    {
-        typedef D result_type;
-
-        explicit __host__ __device__ __forceinline__ BrdColWrap(int height_) : height(height_) {}
-        template <typename U> __host__ __device__ __forceinline__ BrdColWrap(int height_, U) : height(height_) {}
-
-        __device__ __forceinline__ int idx_row_low(int y) const
-        {
-            return (y >= 0) * y + (y < 0) * (y - ((y - height + 1) / height) * height);
-        }
-
-        __device__ __forceinline__ int idx_row_high(int y) const
-        {
-            return (y < height) * y + (y >= height) * (y % height);
-        }
-
-        __device__ __forceinline__ int idx_row(int y) const
-        {
-            return idx_row_high(idx_row_low(y));
-        }
-
-        template <typename T> __device__ __forceinline__ D at_low(int y, const T* data, size_t step) const
-        {
-            return saturate_cast<D>(*(const D*)((const char*)data + idx_row_low(y) * step));
-        }
-
-        template <typename T> __device__ __forceinline__ D at_high(int y, const T* data, size_t step) const
-        {
-            return saturate_cast<D>(*(const D*)((const char*)data + idx_row_high(y) * step));
-        }
-
-        template <typename T> __device__ __forceinline__ D at(int y, const T* data, size_t step) const
-        {
-            return saturate_cast<D>(*(const D*)((const char*)data + idx_row(y) * step));
-        }
-
-        int height;
-    };
-
-    template <typename D> struct BrdWrap
-    {
-        typedef D result_type;
-
-        __host__ __device__ __forceinline__ BrdWrap(int height_, int width_) :
-            height(height_), width(width_)
-        {
-        }
-        template <typename U>
-        __host__ __device__ __forceinline__ BrdWrap(int height_, int width_, U) :
-            height(height_), width(width_)
-        {
-        }
-
-        __device__ __forceinline__ int idx_row_low(int y) const
-        {
-            return (y >= 0) ? y : (y - ((y - height + 1) / height) * height);
-        }
-
-        __device__ __forceinline__ int idx_row_high(int y) const
-        {
-            return (y < height) ? y : (y % height);
-        }
-
-        __device__ __forceinline__ int idx_row(int y) const
-        {
-            return idx_row_high(idx_row_low(y));
-        }
-
-        __device__ __forceinline__ int idx_col_low(int x) const
-        {
-            return (x >= 0) ? x : (x - ((x - width + 1) / width) * width);
-        }
-
-        __device__ __forceinline__ int idx_col_high(int x) const
-        {
-            return (x < width) ? x : (x % width);
-        }
-
-        __device__ __forceinline__ int idx_col(int x) const
-        {
-            return idx_col_high(idx_col_low(x));
-        }
-
-        template <typename T> __device__ __forceinline__ D at(int y, int x, const T* data, size_t step) const
-        {
-            return saturate_cast<D>(((const T*)((const char*)data + idx_row(y) * step))[idx_col(x)]);
-        }
-
-        template <typename Ptr2D> __device__ __forceinline__ D at(typename Ptr2D::index_type y, typename Ptr2D::index_type x, const Ptr2D& src) const
-        {
-            return saturate_cast<D>(src(idx_row(y), idx_col(x)));
-        }
-
-        int height;
-        int width;
-    };
-
-    //////////////////////////////////////////////////////////////
-    // BorderReader
-
-    template <typename Ptr2D, typename B> struct BorderReader
-    {
-        typedef typename B::result_type elem_type;
-        typedef typename Ptr2D::index_type index_type;
-
-        __host__ __device__ __forceinline__ BorderReader(const Ptr2D& ptr_, const B& b_) : ptr(ptr_), b(b_) {}
-
-        __device__ __forceinline__ elem_type operator ()(index_type y, index_type x) const
-        {
-            return b.at(y, x, ptr);
-        }
-
-        Ptr2D ptr;
-        B b;
-    };
-
-    // under win32 there is some bug with templated types that passed as kernel parameters
-    // with this specialization all works fine
-    template <typename Ptr2D, typename D> struct BorderReader< Ptr2D, BrdConstant<D> >
-    {
-        typedef typename BrdConstant<D>::result_type elem_type;
-        typedef typename Ptr2D::index_type index_type;
-
-        __host__ __device__ __forceinline__ BorderReader(const Ptr2D& src_, const BrdConstant<D>& b) :
-            src(src_), height(b.height), width(b.width), val(b.val)
-        {
-        }
-
-        __device__ __forceinline__ D operator ()(index_type y, index_type x) const
-        {
-            return (x >= 0 && x < width && y >= 0 && y < height) ? saturate_cast<D>(src(y, x)) : val;
-        }
-
-        Ptr2D src;
-        int height;
-        int width;
-        D val;
-    };
-}}} // namespace cv { namespace cuda { namespace cudev
-
-//! @endcond
-
-#endif // OPENCV_CUDA_BORDER_INTERPOLATE_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/color.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/color.hpp
deleted file mode 100755
index dcce280..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/color.hpp
+++ /dev/null
@@ -1,309 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_CUDA_COLOR_HPP
-#define OPENCV_CUDA_COLOR_HPP
-
-#include "detail/color_detail.hpp"
-
-/** @file
- * @deprecated Use @ref cudev instead.
- */
-
-//! @cond IGNORED
-
-namespace cv { namespace cuda { namespace device
-{
-    // All OPENCV_CUDA_IMPLEMENT_*_TRAITS(ColorSpace1_to_ColorSpace2, ...) macros implements
-    // template <typename T> class ColorSpace1_to_ColorSpace2_traits
-    // {
-    //     typedef ... functor_type;
-    //     static __host__ __device__ functor_type create_functor();
-    // };
-
-    OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS(bgr_to_rgb, 3, 3, 2)
-    OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS(bgr_to_bgra, 3, 4, 0)
-    OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS(bgr_to_rgba, 3, 4, 2)
-    OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS(bgra_to_bgr, 4, 3, 0)
-    OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS(bgra_to_rgb, 4, 3, 2)
-    OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS(bgra_to_rgba, 4, 4, 2)
-
-    #undef OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS
-
-    OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(bgr_to_bgr555, 3, 0, 5)
-    OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(bgr_to_bgr565, 3, 0, 6)
-    OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(rgb_to_bgr555, 3, 2, 5)
-    OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(rgb_to_bgr565, 3, 2, 6)
-    OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(bgra_to_bgr555, 4, 0, 5)
-    OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(bgra_to_bgr565, 4, 0, 6)
-    OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(rgba_to_bgr555, 4, 2, 5)
-    OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(rgba_to_bgr565, 4, 2, 6)
-
-    #undef OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS
-
-    OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr555_to_rgb, 3, 2, 5)
-    OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr565_to_rgb, 3, 2, 6)
-    OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr555_to_bgr, 3, 0, 5)
-    OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr565_to_bgr, 3, 0, 6)
-    OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr555_to_rgba, 4, 2, 5)
-    OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr565_to_rgba, 4, 2, 6)
-    OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr555_to_bgra, 4, 0, 5)
-    OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr565_to_bgra, 4, 0, 6)
-
-    #undef OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS
-
-    OPENCV_CUDA_IMPLEMENT_GRAY2RGB_TRAITS(gray_to_bgr, 3)
-    OPENCV_CUDA_IMPLEMENT_GRAY2RGB_TRAITS(gray_to_bgra, 4)
-
-    #undef OPENCV_CUDA_IMPLEMENT_GRAY2RGB_TRAITS
-
-    OPENCV_CUDA_IMPLEMENT_GRAY2RGB5x5_TRAITS(gray_to_bgr555, 5)
-    OPENCV_CUDA_IMPLEMENT_GRAY2RGB5x5_TRAITS(gray_to_bgr565, 6)
-
-    #undef OPENCV_CUDA_IMPLEMENT_GRAY2RGB5x5_TRAITS
-
-    OPENCV_CUDA_IMPLEMENT_RGB5x52GRAY_TRAITS(bgr555_to_gray, 5)
-    OPENCV_CUDA_IMPLEMENT_RGB5x52GRAY_TRAITS(bgr565_to_gray, 6)
-
-    #undef OPENCV_CUDA_IMPLEMENT_RGB5x52GRAY_TRAITS
-
-    OPENCV_CUDA_IMPLEMENT_RGB2GRAY_TRAITS(rgb_to_gray, 3, 2)
-    OPENCV_CUDA_IMPLEMENT_RGB2GRAY_TRAITS(bgr_to_gray, 3, 0)
-    OPENCV_CUDA_IMPLEMENT_RGB2GRAY_TRAITS(rgba_to_gray, 4, 2)
-    OPENCV_CUDA_IMPLEMENT_RGB2GRAY_TRAITS(bgra_to_gray, 4, 0)
-
-    #undef OPENCV_CUDA_IMPLEMENT_RGB2GRAY_TRAITS
-
-    OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(rgb_to_yuv, 3, 3, 2)
-    OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(rgba_to_yuv, 4, 3, 2)
-    OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(rgb_to_yuv4, 3, 4, 2)
-    OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(rgba_to_yuv4, 4, 4, 2)
-    OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(bgr_to_yuv, 3, 3, 0)
-    OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(bgra_to_yuv, 4, 3, 0)
-    OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(bgr_to_yuv4, 3, 4, 0)
-    OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(bgra_to_yuv4, 4, 4, 0)
-
-    #undef OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS
-
-    OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv_to_rgb, 3, 3, 2)
-    OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv_to_rgba, 3, 4, 2)
-    OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv4_to_rgb, 4, 3, 2)
-    OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv4_to_rgba, 4, 4, 2)
-    OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv_to_bgr, 3, 3, 0)
-    OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv_to_bgra, 3, 4, 0)
-    OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv4_to_bgr, 4, 3, 0)
-    OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv4_to_bgra, 4, 4, 0)
-
-    #undef OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS
-
-    OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(rgb_to_YCrCb, 3, 3, 2)
-    OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(rgba_to_YCrCb, 4, 3, 2)
-    OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(rgb_to_YCrCb4, 3, 4, 2)
-    OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(rgba_to_YCrCb4, 4, 4, 2)
-    OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(bgr_to_YCrCb, 3, 3, 0)
-    OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(bgra_to_YCrCb, 4, 3, 0)
-    OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(bgr_to_YCrCb4, 3, 4, 0)
-    OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(bgra_to_YCrCb4, 4, 4, 0)
-
-    #undef OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS
-
-    OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb_to_rgb, 3, 3, 2)
-    OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb_to_rgba, 3, 4, 2)
-    OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb4_to_rgb, 4, 3, 2)
-    OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb4_to_rgba, 4, 4, 2)
-    OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb_to_bgr, 3, 3, 0)
-    OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb_to_bgra, 3, 4, 0)
-    OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb4_to_bgr, 4, 3, 0)
-    OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb4_to_bgra, 4, 4, 0)
-
-    #undef OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS
-
-    OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(rgb_to_xyz, 3, 3, 2)
-    OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(rgba_to_xyz, 4, 3, 2)
-    OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(rgb_to_xyz4, 3, 4, 2)
-    OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(rgba_to_xyz4, 4, 4, 2)
-    OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(bgr_to_xyz, 3, 3, 0)
-    OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(bgra_to_xyz, 4, 3, 0)
-    OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(bgr_to_xyz4, 3, 4, 0)
-    OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(bgra_to_xyz4, 4, 4, 0)
-
-    #undef OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS
-
-    OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz_to_rgb, 3, 3, 2)
-    OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz4_to_rgb, 4, 3, 2)
-    OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz_to_rgba, 3, 4, 2)
-    OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz4_to_rgba, 4, 4, 2)
-    OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz_to_bgr, 3, 3, 0)
-    OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz4_to_bgr, 4, 3, 0)
-    OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz_to_bgra, 3, 4, 0)
-    OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz4_to_bgra, 4, 4, 0)
-
-    #undef OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS
-
-    OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(rgb_to_hsv, 3, 3, 2)
-    OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(rgba_to_hsv, 4, 3, 2)
-    OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(rgb_to_hsv4, 3, 4, 2)
-    OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(rgba_to_hsv4, 4, 4, 2)
-    OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(bgr_to_hsv, 3, 3, 0)
-    OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(bgra_to_hsv, 4, 3, 0)
-    OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(bgr_to_hsv4, 3, 4, 0)
-    OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(bgra_to_hsv4, 4, 4, 0)
-
-    #undef OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS
-
-    OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv_to_rgb, 3, 3, 2)
-    OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv_to_rgba, 3, 4, 2)
-    OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv4_to_rgb, 4, 3, 2)
-    OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv4_to_rgba, 4, 4, 2)
-    OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv_to_bgr, 3, 3, 0)
-    OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv_to_bgra, 3, 4, 0)
-    OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv4_to_bgr, 4, 3, 0)
-    OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv4_to_bgra, 4, 4, 0)
-
-    #undef OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS
-
-    OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(rgb_to_hls, 3, 3, 2)
-    OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(rgba_to_hls, 4, 3, 2)
-    OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(rgb_to_hls4, 3, 4, 2)
-    OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(rgba_to_hls4, 4, 4, 2)
-    OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(bgr_to_hls, 3, 3, 0)
-    OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(bgra_to_hls, 4, 3, 0)
-    OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(bgr_to_hls4, 3, 4, 0)
-    OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(bgra_to_hls4, 4, 4, 0)
-
-    #undef OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS
-
-    OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls_to_rgb, 3, 3, 2)
-    OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls_to_rgba, 3, 4, 2)
-    OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls4_to_rgb, 4, 3, 2)
-    OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls4_to_rgba, 4, 4, 2)
-    OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls_to_bgr, 3, 3, 0)
-    OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls_to_bgra, 3, 4, 0)
-    OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls4_to_bgr, 4, 3, 0)
-    OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls4_to_bgra, 4, 4, 0)
-
-    #undef OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS
-
-    OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(rgb_to_lab, 3, 3, true, 2)
-    OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(rgba_to_lab, 4, 3, true, 2)
-    OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(rgb_to_lab4, 3, 4, true, 2)
-    OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(rgba_to_lab4, 4, 4, true, 2)
-    OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(bgr_to_lab, 3, 3, true, 0)
-    OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(bgra_to_lab, 4, 3, true, 0)
-    OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(bgr_to_lab4, 3, 4, true, 0)
-    OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(bgra_to_lab4, 4, 4, true, 0)
-
-    OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lrgb_to_lab, 3, 3, false, 2)
-    OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lrgba_to_lab, 4, 3, false, 2)
-    OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lrgb_to_lab4, 3, 4, false, 2)
-    OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lrgba_to_lab4, 4, 4, false, 2)
-    OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lbgr_to_lab, 3, 3, false, 0)
-    OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lbgra_to_lab, 4, 3, false, 0)
-    OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lbgr_to_lab4, 3, 4, false, 0)
-    OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lbgra_to_lab4, 4, 4, false, 0)
-
-    #undef OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS
-
-    OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_rgb, 3, 3, true, 2)
-    OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_rgb, 4, 3, true, 2)
-    OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_rgba, 3, 4, true, 2)
-    OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_rgba, 4, 4, true, 2)
-    OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_bgr, 3, 3, true, 0)
-    OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_bgr, 4, 3, true, 0)
-    OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_bgra, 3, 4, true, 0)
-    OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_bgra, 4, 4, true, 0)
-
-    OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_lrgb, 3, 3, false, 2)
-    OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_lrgb, 4, 3, false, 2)
-    OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_lrgba, 3, 4, false, 2)
-    OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_lrgba, 4, 4, false, 2)
-    OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_lbgr, 3, 3, false, 0)
-    OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_lbgr, 4, 3, false, 0)
-    OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_lbgra, 3, 4, false, 0)
-    OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_lbgra, 4, 4, false, 0)
-
-    #undef OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS
-
-    OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(rgb_to_luv, 3, 3, true, 2)
-    OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(rgba_to_luv, 4, 3, true, 2)
-    OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(rgb_to_luv4, 3, 4, true, 2)
-    OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(rgba_to_luv4, 4, 4, true, 2)
-    OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(bgr_to_luv, 3, 3, true, 0)
-    OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(bgra_to_luv, 4, 3, true, 0)
-    OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(bgr_to_luv4, 3, 4, true, 0)
-    OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(bgra_to_luv4, 4, 4, true, 0)
-
-    OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lrgb_to_luv, 3, 3, false, 2)
-    OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lrgba_to_luv, 4, 3, false, 2)
-    OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lrgb_to_luv4, 3, 4, false, 2)
-    OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lrgba_to_luv4, 4, 4, false, 2)
-    OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lbgr_to_luv, 3, 3, false, 0)
-    OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lbgra_to_luv, 4, 3, false, 0)
-    OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lbgr_to_luv4, 3, 4, false, 0)
-    OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lbgra_to_luv4, 4, 4, false, 0)
-
-    #undef OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS
-
-    OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_rgb, 3, 3, true, 2)
-    OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_rgb, 4, 3, true, 2)
-    OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_rgba, 3, 4, true, 2)
-    OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_rgba, 4, 4, true, 2)
-    OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_bgr, 3, 3, true, 0)
-    OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_bgr, 4, 3, true, 0)
-    OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_bgra, 3, 4, true, 0)
-    OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_bgra, 4, 4, true, 0)
-
-    OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_lrgb, 3, 3, false, 2)
-    OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_lrgb, 4, 3, false, 2)
-    OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_lrgba, 3, 4, false, 2)
-    OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_lrgba, 4, 4, false, 2)
-    OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_lbgr, 3, 3, false, 0)
-    OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_lbgr, 4, 3, false, 0)
-    OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_lbgra, 3, 4, false, 0)
-    OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_lbgra, 4, 4, false, 0)
-
-    #undef OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS
-}}} // namespace cv { namespace cuda { namespace cudev
-
-//! @endcond
-
-#endif // OPENCV_CUDA_COLOR_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/common.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/common.hpp
deleted file mode 100755
index 14b1f3f..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/common.hpp
+++ /dev/null
@@ -1,109 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_CUDA_COMMON_HPP
-#define OPENCV_CUDA_COMMON_HPP
-
-#include <cuda_runtime.h>
-#include "opencv2/core/cuda_types.hpp"
-#include "opencv2/core/cvdef.h"
-#include "opencv2/core/base.hpp"
-
-/** @file
- * @deprecated Use @ref cudev instead.
- */
-
-//! @cond IGNORED
-
-#ifndef CV_PI_F
-    #ifndef CV_PI
-        #define CV_PI_F 3.14159265f
-    #else
-        #define CV_PI_F ((float)CV_PI)
-    #endif
-#endif
-
-namespace cv { namespace cuda {
-    static inline void checkCudaError(cudaError_t err, const char* file, const int line, const char* func)
-    {
-        if (cudaSuccess != err)
-            cv::error(cv::Error::GpuApiCallError, cudaGetErrorString(err), func, file, line);
-    }
-}}
-
-#ifndef cudaSafeCall
-    #define cudaSafeCall(expr)  cv::cuda::checkCudaError(expr, __FILE__, __LINE__, CV_Func)
-#endif
-
-namespace cv { namespace cuda
-{
-    template <typename T> static inline bool isAligned(const T* ptr, size_t size)
-    {
-        return reinterpret_cast<size_t>(ptr) % size == 0;
-    }
-
-    static inline bool isAligned(size_t step, size_t size)
-    {
-        return step % size == 0;
-    }
-}}
-
-namespace cv { namespace cuda
-{
-    namespace device
-    {
-        __host__ __device__ __forceinline__ int divUp(int total, int grain)
-        {
-            return (total + grain - 1) / grain;
-        }
-
-        template<class T> inline void bindTexture(const textureReference* tex, const PtrStepSz<T>& img)
-        {
-            cudaChannelFormatDesc desc = cudaCreateChannelDesc<T>();
-            cudaSafeCall( cudaBindTexture2D(0, tex, img.ptr(), &desc, img.cols, img.rows, img.step) );
-        }
-    }
-}}
-
-//! @endcond
-
-#endif // OPENCV_CUDA_COMMON_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/datamov_utils.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/datamov_utils.hpp
deleted file mode 100755
index 6820d0f..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/datamov_utils.hpp
+++ /dev/null
@@ -1,113 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_CUDA_DATAMOV_UTILS_HPP
-#define OPENCV_CUDA_DATAMOV_UTILS_HPP
-
-#include "common.hpp"
-
-/** @file
- * @deprecated Use @ref cudev instead.
- */
-
-//! @cond IGNORED
-
-namespace cv { namespace cuda { namespace device
-{
-    #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 200
-
-        // for Fermi memory space is detected automatically
-        template <typename T> struct ForceGlob
-        {
-            __device__ __forceinline__ static void Load(const T* ptr, int offset, T& val)  { val = ptr[offset];  }
-        };
-
-    #else // __CUDA_ARCH__ >= 200
-
-        #if defined(_WIN64) || defined(__LP64__)
-            // 64-bit register modifier for inlined asm
-            #define OPENCV_CUDA_ASM_PTR "l"
-        #else
-            // 32-bit register modifier for inlined asm
-            #define OPENCV_CUDA_ASM_PTR "r"
-        #endif
-
-        template<class T> struct ForceGlob;
-
-        #define OPENCV_CUDA_DEFINE_FORCE_GLOB(base_type, ptx_type, reg_mod) \
-            template <> struct ForceGlob<base_type> \
-            { \
-                __device__ __forceinline__ static void Load(const base_type* ptr, int offset, base_type& val) \
-                { \
-                    asm("ld.global."#ptx_type" %0, [%1];" : "="#reg_mod(val) : OPENCV_CUDA_ASM_PTR(ptr + offset)); \
-                } \
-            };
-
-        #define OPENCV_CUDA_DEFINE_FORCE_GLOB_B(base_type, ptx_type) \
-            template <> struct ForceGlob<base_type> \
-            { \
-                __device__ __forceinline__ static void Load(const base_type* ptr, int offset, base_type& val) \
-                { \
-                    asm("ld.global."#ptx_type" %0, [%1];" : "=r"(*reinterpret_cast<uint*>(&val)) : OPENCV_CUDA_ASM_PTR(ptr + offset)); \
-                } \
-            };
-
-            OPENCV_CUDA_DEFINE_FORCE_GLOB_B(uchar,  u8)
-            OPENCV_CUDA_DEFINE_FORCE_GLOB_B(schar,  s8)
-            OPENCV_CUDA_DEFINE_FORCE_GLOB_B(char,   b8)
-            OPENCV_CUDA_DEFINE_FORCE_GLOB  (ushort, u16, h)
-            OPENCV_CUDA_DEFINE_FORCE_GLOB  (short,  s16, h)
-            OPENCV_CUDA_DEFINE_FORCE_GLOB  (uint,   u32, r)
-            OPENCV_CUDA_DEFINE_FORCE_GLOB  (int,    s32, r)
-            OPENCV_CUDA_DEFINE_FORCE_GLOB  (float,  f32, f)
-            OPENCV_CUDA_DEFINE_FORCE_GLOB  (double, f64, d)
-
-        #undef OPENCV_CUDA_DEFINE_FORCE_GLOB
-        #undef OPENCV_CUDA_DEFINE_FORCE_GLOB_B
-        #undef OPENCV_CUDA_ASM_PTR
-
-    #endif // __CUDA_ARCH__ >= 200
-}}} // namespace cv { namespace cuda { namespace cudev
-
-//! @endcond
-
-#endif // OPENCV_CUDA_DATAMOV_UTILS_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/detail/color_detail.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/detail/color_detail.hpp
deleted file mode 100755
index bfb4055..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/detail/color_detail.hpp
+++ /dev/null
@@ -1,1980 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_CUDA_COLOR_DETAIL_HPP
-#define OPENCV_CUDA_COLOR_DETAIL_HPP
-
-#include "../common.hpp"
-#include "../vec_traits.hpp"
-#include "../saturate_cast.hpp"
-#include "../limits.hpp"
-#include "../functional.hpp"
-
-//! @cond IGNORED
-
-namespace cv { namespace cuda { namespace device
-{
-    #ifndef CV_DESCALE
-        #define CV_DESCALE(x, n) (((x) + (1 << ((n)-1))) >> (n))
-    #endif
-
-    namespace color_detail
-    {
-        template<typename T> struct ColorChannel
-        {
-            typedef float worktype_f;
-            static __device__ __forceinline__ T max() { return numeric_limits<T>::max(); }
-            static __device__ __forceinline__ T half() { return (T)(max()/2 + 1); }
-        };
-
-        template<> struct ColorChannel<float>
-        {
-            typedef float worktype_f;
-            static __device__ __forceinline__ float max() { return 1.f; }
-            static __device__ __forceinline__ float half() { return 0.5f; }
-        };
-
-        template <typename T> static __device__ __forceinline__ void setAlpha(typename TypeVec<T, 3>::vec_type& vec, T val)
-        {
-        }
-
-        template <typename T> static __device__ __forceinline__ void setAlpha(typename TypeVec<T, 4>::vec_type& vec, T val)
-        {
-            vec.w = val;
-        }
-
-        template <typename T> static __device__ __forceinline__ T getAlpha(const typename TypeVec<T, 3>::vec_type& vec)
-        {
-            return ColorChannel<T>::max();
-        }
-
-        template <typename T> static __device__ __forceinline__ T getAlpha(const typename TypeVec<T, 4>::vec_type& vec)
-        {
-            return vec.w;
-        }
-
-        enum
-        {
-            yuv_shift  = 14,
-            xyz_shift  = 12,
-            R2Y        = 4899,
-            G2Y        = 9617,
-            B2Y        = 1868,
-            BLOCK_SIZE = 256
-        };
-    }
-
-////////////////// Various 3/4-channel to 3/4-channel RGB transformations /////////////////
-
-    namespace color_detail
-    {
-        template <typename T, int scn, int dcn, int bidx> struct RGB2RGB
-            : unary_function<typename TypeVec<T, scn>::vec_type, typename TypeVec<T, dcn>::vec_type>
-        {
-            __device__ typename TypeVec<T, dcn>::vec_type operator()(const typename TypeVec<T, scn>::vec_type& src) const
-            {
-                typename TypeVec<T, dcn>::vec_type dst;
-
-                dst.x = (&src.x)[bidx];
-                dst.y = src.y;
-                dst.z = (&src.x)[bidx^2];
-                setAlpha(dst, getAlpha<T>(src));
-
-                return dst;
-            }
-
-            __host__ __device__ __forceinline__ RGB2RGB() {}
-            __host__ __device__ __forceinline__ RGB2RGB(const RGB2RGB&) {}
-        };
-
-        template <> struct RGB2RGB<uchar, 4, 4, 2> : unary_function<uint, uint>
-        {
-            __device__ uint operator()(uint src) const
-            {
-                uint dst = 0;
-
-                dst |= (0xffu & (src >> 16));
-                dst |= (0xffu & (src >> 8)) << 8;
-                dst |= (0xffu & (src)) << 16;
-                dst |= (0xffu & (src >> 24)) << 24;
-
-                return dst;
-            }
-
-            __host__ __device__ __forceinline__ RGB2RGB() {}
-            __host__ __device__ __forceinline__ RGB2RGB(const RGB2RGB&) {}
-        };
-    }
-
-#define OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS(name, scn, dcn, bidx) \
-    template <typename T> struct name ## _traits \
-    { \
-        typedef ::cv::cuda::device::color_detail::RGB2RGB<T, scn, dcn, bidx> functor_type; \
-        static __host__ __device__ __forceinline__ functor_type create_functor() \
-        { \
-            return functor_type(); \
-        } \
-    };
-
-/////////// Transforming 16-bit (565 or 555) RGB to/from 24/32-bit (888[8]) RGB //////////
-
-    namespace color_detail
-    {
-        template <int green_bits, int bidx> struct RGB2RGB5x5Converter;
-        template<int bidx> struct RGB2RGB5x5Converter<6, bidx>
-        {
-            static __device__ __forceinline__ ushort cvt(const uchar3& src)
-            {
-                return (ushort)(((&src.x)[bidx] >> 3) | ((src.y & ~3) << 3) | (((&src.x)[bidx^2] & ~7) << 8));
-            }
-
-            static __device__ __forceinline__ ushort cvt(uint src)
-            {
-                uint b = 0xffu & (src >> (bidx * 8));
-                uint g = 0xffu & (src >> 8);
-                uint r = 0xffu & (src >> ((bidx ^ 2) * 8));
-                return (ushort)((b >> 3) | ((g & ~3) << 3) | ((r & ~7) << 8));
-            }
-        };
-
-        template<int bidx> struct RGB2RGB5x5Converter<5, bidx>
-        {
-            static __device__ __forceinline__ ushort cvt(const uchar3& src)
-            {
-                return (ushort)(((&src.x)[bidx] >> 3) | ((src.y & ~7) << 2) | (((&src.x)[bidx^2] & ~7) << 7));
-            }
-
-            static __device__ __forceinline__ ushort cvt(uint src)
-            {
-                uint b = 0xffu & (src >> (bidx * 8));
-                uint g = 0xffu & (src >> 8);
-                uint r = 0xffu & (src >> ((bidx ^ 2) * 8));
-                uint a = 0xffu & (src >> 24);
-                return (ushort)((b >> 3) | ((g & ~7) << 2) | ((r & ~7) << 7) | (a * 0x8000));
-            }
-        };
-
-        template<int scn, int bidx, int green_bits> struct RGB2RGB5x5;
-
-        template<int bidx, int green_bits> struct RGB2RGB5x5<3, bidx,green_bits> : unary_function<uchar3, ushort>
-        {
-            __device__ __forceinline__ ushort operator()(const uchar3& src) const
-            {
-                return RGB2RGB5x5Converter<green_bits, bidx>::cvt(src);
-            }
-
-            __host__ __device__ __forceinline__ RGB2RGB5x5() {}
-            __host__ __device__ __forceinline__ RGB2RGB5x5(const RGB2RGB5x5&) {}
-        };
-
-        template<int bidx, int green_bits> struct RGB2RGB5x5<4, bidx,green_bits> : unary_function<uint, ushort>
-        {
-            __device__ __forceinline__ ushort operator()(uint src) const
-            {
-                return RGB2RGB5x5Converter<green_bits, bidx>::cvt(src);
-            }
-
-            __host__ __device__ __forceinline__ RGB2RGB5x5() {}
-            __host__ __device__ __forceinline__ RGB2RGB5x5(const RGB2RGB5x5&) {}
-        };
-    }
-
-#define OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(name, scn, bidx, green_bits) \
-    struct name ## _traits \
-    { \
-        typedef ::cv::cuda::device::color_detail::RGB2RGB5x5<scn, bidx, green_bits> functor_type; \
-        static __host__ __device__ __forceinline__ functor_type create_functor() \
-        { \
-            return functor_type(); \
-        } \
-    };
-
-    namespace color_detail
-    {
-        template <int green_bits, int bidx> struct RGB5x52RGBConverter;
-
-        template <int bidx> struct RGB5x52RGBConverter<5, bidx>
-        {
-            static __device__ __forceinline__ void cvt(uint src, uchar3& dst)
-            {
-                (&dst.x)[bidx] = src << 3;
-                dst.y = (src >> 2) & ~7;
-                (&dst.x)[bidx ^ 2] = (src >> 7) & ~7;
-            }
-
-            static __device__ __forceinline__ void cvt(uint src, uint& dst)
-            {
-                dst = 0;
-
-                dst |= (0xffu & (src << 3)) << (bidx * 8);
-                dst |= (0xffu & ((src >> 2) & ~7)) << 8;
-                dst |= (0xffu & ((src >> 7) & ~7)) << ((bidx ^ 2) * 8);
-                dst |= ((src & 0x8000) * 0xffu) << 24;
-            }
-        };
-
-        template <int bidx> struct RGB5x52RGBConverter<6, bidx>
-        {
-            static __device__ __forceinline__ void cvt(uint src, uchar3& dst)
-            {
-                (&dst.x)[bidx] = src << 3;
-                dst.y = (src >> 3) & ~3;
-                (&dst.x)[bidx ^ 2] = (src >> 8) & ~7;
-            }
-
-            static __device__ __forceinline__ void cvt(uint src, uint& dst)
-            {
-                dst = 0xffu << 24;
-
-                dst |= (0xffu & (src << 3)) << (bidx * 8);
-                dst |= (0xffu &((src >> 3) & ~3)) << 8;
-                dst |= (0xffu & ((src >> 8) & ~7)) << ((bidx ^ 2) * 8);
-            }
-        };
-
-        template <int dcn, int bidx, int green_bits> struct RGB5x52RGB;
-
-        template <int bidx, int green_bits> struct RGB5x52RGB<3, bidx, green_bits> : unary_function<ushort, uchar3>
-        {
-            __device__ __forceinline__ uchar3 operator()(ushort src) const
-            {
-                uchar3 dst;
-                RGB5x52RGBConverter<green_bits, bidx>::cvt(src, dst);
-                return dst;
-            }
-            __host__ __device__ __forceinline__ RGB5x52RGB() {}
-            __host__ __device__ __forceinline__ RGB5x52RGB(const RGB5x52RGB&) {}
-
-        };
-
-        template <int bidx, int green_bits> struct RGB5x52RGB<4, bidx, green_bits> : unary_function<ushort, uint>
-        {
-            __device__ __forceinline__ uint operator()(ushort src) const
-            {
-                uint dst;
-                RGB5x52RGBConverter<green_bits, bidx>::cvt(src, dst);
-                return dst;
-            }
-            __host__ __device__ __forceinline__ RGB5x52RGB() {}
-            __host__ __device__ __forceinline__ RGB5x52RGB(const RGB5x52RGB&) {}
-        };
-    }
-
-#define OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(name, dcn, bidx, green_bits) \
-    struct name ## _traits \
-    { \
-        typedef ::cv::cuda::device::color_detail::RGB5x52RGB<dcn, bidx, green_bits> functor_type; \
-        static __host__ __device__ __forceinline__ functor_type create_functor() \
-        { \
-            return functor_type(); \
-        } \
-    };
-
-///////////////////////////////// Grayscale to Color ////////////////////////////////
-
-    namespace color_detail
-    {
-        template <typename T, int dcn> struct Gray2RGB : unary_function<T, typename TypeVec<T, dcn>::vec_type>
-        {
-            __device__ __forceinline__ typename TypeVec<T, dcn>::vec_type operator()(T src) const
-            {
-                typename TypeVec<T, dcn>::vec_type dst;
-
-                dst.z = dst.y = dst.x = src;
-                setAlpha(dst, ColorChannel<T>::max());
-
-                return dst;
-            }
-            __host__ __device__ __forceinline__ Gray2RGB() {}
-            __host__ __device__ __forceinline__ Gray2RGB(const Gray2RGB&) {}
-        };
-
-        template <> struct Gray2RGB<uchar, 4> : unary_function<uchar, uint>
-        {
-            __device__ __forceinline__ uint operator()(uint src) const
-            {
-                uint dst = 0xffu << 24;
-
-                dst |= src;
-                dst |= src << 8;
-                dst |= src << 16;
-
-                return dst;
-            }
-            __host__ __device__ __forceinline__ Gray2RGB() {}
-            __host__ __device__ __forceinline__ Gray2RGB(const Gray2RGB&) {}
-        };
-    }
-
-#define OPENCV_CUDA_IMPLEMENT_GRAY2RGB_TRAITS(name, dcn) \
-    template <typename T> struct name ## _traits \
-    { \
-        typedef ::cv::cuda::device::color_detail::Gray2RGB<T, dcn> functor_type; \
-        static __host__ __device__ __forceinline__ functor_type create_functor() \
-        { \
-            return functor_type(); \
-        } \
-    };
-
-    namespace color_detail
-    {
-        template <int green_bits> struct Gray2RGB5x5Converter;
-        template<> struct Gray2RGB5x5Converter<6>
-        {
-            static __device__ __forceinline__ ushort cvt(uint t)
-            {
-                return (ushort)((t >> 3) | ((t & ~3) << 3) | ((t & ~7) << 8));
-            }
-        };
-
-        template<> struct Gray2RGB5x5Converter<5>
-        {
-            static __device__ __forceinline__ ushort cvt(uint t)
-            {
-                t >>= 3;
-                return (ushort)(t | (t << 5) | (t << 10));
-            }
-        };
-
-        template<int green_bits> struct Gray2RGB5x5 : unary_function<uchar, ushort>
-        {
-            __device__ __forceinline__ ushort operator()(uint src) const
-            {
-                return Gray2RGB5x5Converter<green_bits>::cvt(src);
-            }
-
-            __host__ __device__ __forceinline__ Gray2RGB5x5() {}
-            __host__ __device__ __forceinline__ Gray2RGB5x5(const Gray2RGB5x5&) {}
-        };
-    }
-
-#define OPENCV_CUDA_IMPLEMENT_GRAY2RGB5x5_TRAITS(name, green_bits) \
-    struct name ## _traits \
-    { \
-        typedef ::cv::cuda::device::color_detail::Gray2RGB5x5<green_bits> functor_type; \
-        static __host__ __device__ __forceinline__ functor_type create_functor() \
-        { \
-            return functor_type(); \
-        } \
-    };
-
-///////////////////////////////// Color to Grayscale ////////////////////////////////
-
-    namespace color_detail
-    {
-        template <int green_bits> struct RGB5x52GrayConverter;
-        template <> struct RGB5x52GrayConverter<6>
-        {
-            static __device__ __forceinline__ uchar cvt(uint t)
-            {
-                return (uchar)CV_DESCALE(((t << 3) & 0xf8) * B2Y + ((t >> 3) & 0xfc) * G2Y + ((t >> 8) & 0xf8) * R2Y, yuv_shift);
-            }
-        };
-
-        template <> struct RGB5x52GrayConverter<5>
-        {
-            static __device__ __forceinline__ uchar cvt(uint t)
-            {
-                return (uchar)CV_DESCALE(((t << 3) & 0xf8) * B2Y + ((t >> 2) & 0xf8) * G2Y + ((t >> 7) & 0xf8) * R2Y, yuv_shift);
-            }
-        };
-
-        template<int green_bits> struct RGB5x52Gray : unary_function<ushort, uchar>
-        {
-            __device__ __forceinline__ uchar operator()(uint src) const
-            {
-                return RGB5x52GrayConverter<green_bits>::cvt(src);
-            }
-            __host__ __device__ __forceinline__ RGB5x52Gray() {}
-            __host__ __device__ __forceinline__ RGB5x52Gray(const RGB5x52Gray&) {}
-        };
-    }
-
-#define OPENCV_CUDA_IMPLEMENT_RGB5x52GRAY_TRAITS(name, green_bits) \
-    struct name ## _traits \
-    { \
-        typedef ::cv::cuda::device::color_detail::RGB5x52Gray<green_bits> functor_type; \
-        static __host__ __device__ __forceinline__ functor_type create_functor() \
-        { \
-            return functor_type(); \
-        } \
-    };
-
-    namespace color_detail
-    {
-        template <int bidx, typename T> static __device__ __forceinline__ T RGB2GrayConvert(const T* src)
-        {
-            return (T)CV_DESCALE((unsigned)(src[bidx] * B2Y + src[1] * G2Y + src[bidx^2] * R2Y), yuv_shift);
-        }
-
-        template <int bidx> static __device__ __forceinline__ uchar RGB2GrayConvert(uint src)
-        {
-            uint b = 0xffu & (src >> (bidx * 8));
-            uint g = 0xffu & (src >> 8);
-            uint r = 0xffu & (src >> ((bidx ^ 2) * 8));
-            return CV_DESCALE((uint)(b * B2Y + g * G2Y + r * R2Y), yuv_shift);
-        }
-
-        template <int bidx> static __device__ __forceinline__ float RGB2GrayConvert(const float* src)
-        {
-            return src[bidx] * 0.114f + src[1] * 0.587f + src[bidx^2] * 0.299f;
-        }
-
-        template <typename T, int scn, int bidx> struct RGB2Gray : unary_function<typename TypeVec<T, scn>::vec_type, T>
-        {
-            __device__ __forceinline__ T operator()(const typename TypeVec<T, scn>::vec_type& src) const
-            {
-                return RGB2GrayConvert<bidx>(&src.x);
-            }
-            __host__ __device__ __forceinline__ RGB2Gray() {}
-            __host__ __device__ __forceinline__ RGB2Gray(const RGB2Gray&) {}
-        };
-
-        template <int bidx> struct RGB2Gray<uchar, 4, bidx> : unary_function<uint, uchar>
-        {
-            __device__ __forceinline__ uchar operator()(uint src) const
-            {
-                return RGB2GrayConvert<bidx>(src);
-            }
-            __host__ __device__ __forceinline__ RGB2Gray() {}
-            __host__ __device__ __forceinline__ RGB2Gray(const RGB2Gray&) {}
-        };
-    }
-
-#define OPENCV_CUDA_IMPLEMENT_RGB2GRAY_TRAITS(name, scn, bidx) \
-    template <typename T> struct name ## _traits \
-    { \
-        typedef ::cv::cuda::device::color_detail::RGB2Gray<T, scn, bidx> functor_type; \
-        static __host__ __device__ __forceinline__ functor_type create_functor() \
-        { \
-            return functor_type(); \
-        } \
-    };
-
-///////////////////////////////////// RGB <-> YUV //////////////////////////////////////
-
-    namespace color_detail
-    {
-        __constant__ float c_RGB2YUVCoeffs_f[5] = { 0.114f, 0.587f, 0.299f, 0.492f, 0.877f };
-        __constant__ int   c_RGB2YUVCoeffs_i[5] = { B2Y, G2Y, R2Y, 8061, 14369 };
-
-        template <int bidx, typename T, typename D> static __device__ void RGB2YUVConvert(const T* src, D& dst)
-        {
-            const int delta = ColorChannel<T>::half() * (1 << yuv_shift);
-
-            const int Y = CV_DESCALE(src[0] * c_RGB2YUVCoeffs_i[bidx^2] + src[1] * c_RGB2YUVCoeffs_i[1] + src[2] * c_RGB2YUVCoeffs_i[bidx], yuv_shift);
-            const int Cr = CV_DESCALE((src[bidx^2] - Y) * c_RGB2YUVCoeffs_i[3] + delta, yuv_shift);
-            const int Cb = CV_DESCALE((src[bidx] - Y) * c_RGB2YUVCoeffs_i[4] + delta, yuv_shift);
-
-            dst.x = saturate_cast<T>(Y);
-            dst.y = saturate_cast<T>(Cr);
-            dst.z = saturate_cast<T>(Cb);
-        }
-
-        template <int bidx, typename D> static __device__ __forceinline__ void RGB2YUVConvert(const float* src, D& dst)
-        {
-            dst.x = src[0] * c_RGB2YUVCoeffs_f[bidx^2] + src[1] * c_RGB2YUVCoeffs_f[1] + src[2] * c_RGB2YUVCoeffs_f[bidx];
-            dst.y = (src[bidx^2] - dst.x) * c_RGB2YUVCoeffs_f[3] + ColorChannel<float>::half();
-            dst.z = (src[bidx] - dst.x) * c_RGB2YUVCoeffs_f[4] + ColorChannel<float>::half();
-        }
-
-        template <typename T, int scn, int dcn, int bidx> struct RGB2YUV
-            : unary_function<typename TypeVec<T, scn>::vec_type, typename TypeVec<T, dcn>::vec_type>
-        {
-            __device__ __forceinline__ typename TypeVec<T, dcn>::vec_type operator ()(const typename TypeVec<T, scn>::vec_type& src) const
-            {
-                typename TypeVec<T, dcn>::vec_type dst;
-                RGB2YUVConvert<bidx>(&src.x, dst);
-                return dst;
-            }
-            __host__ __device__ __forceinline__ RGB2YUV() {}
-            __host__ __device__ __forceinline__ RGB2YUV(const RGB2YUV&) {}
-        };
-    }
-
-#define OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(name, scn, dcn, bidx) \
-    template <typename T> struct name ## _traits \
-    { \
-        typedef ::cv::cuda::device::color_detail::RGB2YUV<T, scn, dcn, bidx> functor_type; \
-        static __host__ __device__ __forceinline__ functor_type create_functor() \
-        { \
-            return functor_type(); \
-        } \
-    };
-
-    namespace color_detail
-    {
-        __constant__ float c_YUV2RGBCoeffs_f[5] = { 2.032f, -0.395f, -0.581f, 1.140f };
-        __constant__ int   c_YUV2RGBCoeffs_i[5] = { 33292, -6472, -9519, 18678 };
-
-        template <int bidx, typename T, typename D> static __device__ void YUV2RGBConvert(const T& src, D* dst)
-        {
-            const int b = src.x + CV_DESCALE((src.z - ColorChannel<D>::half()) * c_YUV2RGBCoeffs_i[3], yuv_shift);
-
-            const int g = src.x + CV_DESCALE((src.z - ColorChannel<D>::half()) * c_YUV2RGBCoeffs_i[2]
-                                             + (src.y - ColorChannel<D>::half()) * c_YUV2RGBCoeffs_i[1], yuv_shift);
-
-            const int r = src.x + CV_DESCALE((src.y - ColorChannel<D>::half()) * c_YUV2RGBCoeffs_i[0], yuv_shift);
-
-            dst[bidx] = saturate_cast<D>(b);
-            dst[1] = saturate_cast<D>(g);
-            dst[bidx^2] = saturate_cast<D>(r);
-        }
-
-        template <int bidx> static __device__ uint YUV2RGBConvert(uint src)
-        {
-            const int x = 0xff & (src);
-            const int y = 0xff & (src >> 8);
-            const int z = 0xff & (src >> 16);
-
-            const int b = x + CV_DESCALE((z - ColorChannel<uchar>::half()) * c_YUV2RGBCoeffs_i[3], yuv_shift);
-
-            const int g = x + CV_DESCALE((z - ColorChannel<uchar>::half()) * c_YUV2RGBCoeffs_i[2]
-                                         + (y - ColorChannel<uchar>::half()) * c_YUV2RGBCoeffs_i[1], yuv_shift);
-
-            const int r = x + CV_DESCALE((y - ColorChannel<uchar>::half()) * c_YUV2RGBCoeffs_i[0], yuv_shift);
-
-            uint dst = 0xffu << 24;
-
-            dst |= saturate_cast<uchar>(b) << (bidx * 8);
-            dst |= saturate_cast<uchar>(g) << 8;
-            dst |= saturate_cast<uchar>(r) << ((bidx ^ 2) * 8);
-
-            return dst;
-        }
-
-        template <int bidx, typename T> static __device__ __forceinline__ void YUV2RGBConvert(const T& src, float* dst)
-        {
-            dst[bidx] = src.x + (src.z - ColorChannel<float>::half()) * c_YUV2RGBCoeffs_f[3];
-
-            dst[1] = src.x + (src.z - ColorChannel<float>::half()) * c_YUV2RGBCoeffs_f[2]
-                     + (src.y - ColorChannel<float>::half()) * c_YUV2RGBCoeffs_f[1];
-
-            dst[bidx^2] = src.x + (src.y - ColorChannel<float>::half()) * c_YUV2RGBCoeffs_f[0];
-        }
-
-        template <typename T, int scn, int dcn, int bidx> struct YUV2RGB
-            : unary_function<typename TypeVec<T, scn>::vec_type, typename TypeVec<T, dcn>::vec_type>
-        {
-            __device__ __forceinline__ typename TypeVec<T, dcn>::vec_type operator ()(const typename TypeVec<T, scn>::vec_type& src) const
-            {
-                typename TypeVec<T, dcn>::vec_type dst;
-
-                YUV2RGBConvert<bidx>(src, &dst.x);
-                setAlpha(dst, ColorChannel<T>::max());
-
-                return dst;
-            }
-            __host__ __device__ __forceinline__ YUV2RGB() {}
-            __host__ __device__ __forceinline__ YUV2RGB(const YUV2RGB&) {}
-        };
-
-        template <int bidx> struct YUV2RGB<uchar, 4, 4, bidx> : unary_function<uint, uint>
-        {
-            __device__ __forceinline__ uint operator ()(uint src) const
-            {
-                return YUV2RGBConvert<bidx>(src);
-            }
-            __host__ __device__ __forceinline__ YUV2RGB() {}
-            __host__ __device__ __forceinline__ YUV2RGB(const YUV2RGB&) {}
-        };
-    }
-
-#define OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(name, scn, dcn, bidx) \
-    template <typename T> struct name ## _traits \
-    { \
-        typedef ::cv::cuda::device::color_detail::YUV2RGB<T, scn, dcn, bidx> functor_type; \
-        static __host__ __device__ __forceinline__ functor_type create_functor() \
-        { \
-            return functor_type(); \
-        } \
-    };
-
-///////////////////////////////////// RGB <-> YCrCb //////////////////////////////////////
-
-    namespace color_detail
-    {
-        __constant__ float c_RGB2YCrCbCoeffs_f[5] = {0.299f, 0.587f, 0.114f, 0.713f, 0.564f};
-        __constant__ int   c_RGB2YCrCbCoeffs_i[5] = {R2Y, G2Y, B2Y, 11682, 9241};
-
-        template <int bidx, typename T, typename D> static __device__ void RGB2YCrCbConvert(const T* src, D& dst)
-        {
-            const int delta = ColorChannel<T>::half() * (1 << yuv_shift);
-
-            const int Y = CV_DESCALE(src[0] * c_RGB2YCrCbCoeffs_i[bidx^2] + src[1] * c_RGB2YCrCbCoeffs_i[1] + src[2] * c_RGB2YCrCbCoeffs_i[bidx], yuv_shift);
-            const int Cr = CV_DESCALE((src[bidx^2] - Y) * c_RGB2YCrCbCoeffs_i[3] + delta, yuv_shift);
-            const int Cb = CV_DESCALE((src[bidx] - Y) * c_RGB2YCrCbCoeffs_i[4] + delta, yuv_shift);
-
-            dst.x = saturate_cast<T>(Y);
-            dst.y = saturate_cast<T>(Cr);
-            dst.z = saturate_cast<T>(Cb);
-        }
-
-        template <int bidx> static __device__ uint RGB2YCrCbConvert(uint src)
-        {
-            const int delta = ColorChannel<uchar>::half() * (1 << yuv_shift);
-
-            const int Y = CV_DESCALE((0xffu & src) * c_RGB2YCrCbCoeffs_i[bidx^2] + (0xffu & (src >> 8)) * c_RGB2YCrCbCoeffs_i[1] + (0xffu & (src >> 16)) * c_RGB2YCrCbCoeffs_i[bidx], yuv_shift);
-            const int Cr = CV_DESCALE(((0xffu & (src >> ((bidx ^ 2) * 8))) - Y) * c_RGB2YCrCbCoeffs_i[3] + delta, yuv_shift);
-            const int Cb = CV_DESCALE(((0xffu & (src >> (bidx * 8))) - Y) * c_RGB2YCrCbCoeffs_i[4] + delta, yuv_shift);
-
-            uint dst = 0;
-
-            dst |= saturate_cast<uchar>(Y);
-            dst |= saturate_cast<uchar>(Cr) << 8;
-            dst |= saturate_cast<uchar>(Cb) << 16;
-
-            return dst;
-        }
-
-        template <int bidx, typename D> static __device__ __forceinline__ void RGB2YCrCbConvert(const float* src, D& dst)
-        {
-            dst.x = src[0] * c_RGB2YCrCbCoeffs_f[bidx^2] + src[1] * c_RGB2YCrCbCoeffs_f[1] + src[2] * c_RGB2YCrCbCoeffs_f[bidx];
-            dst.y = (src[bidx^2] - dst.x) * c_RGB2YCrCbCoeffs_f[3] + ColorChannel<float>::half();
-            dst.z = (src[bidx] - dst.x) * c_RGB2YCrCbCoeffs_f[4] + ColorChannel<float>::half();
-        }
-
-        template <typename T, int scn, int dcn, int bidx> struct RGB2YCrCb
-            : unary_function<typename TypeVec<T, scn>::vec_type, typename TypeVec<T, dcn>::vec_type>
-        {
-            __device__ __forceinline__ typename TypeVec<T, dcn>::vec_type operator ()(const typename TypeVec<T, scn>::vec_type& src) const
-            {
-                typename TypeVec<T, dcn>::vec_type dst;
-                RGB2YCrCbConvert<bidx>(&src.x, dst);
-                return dst;
-            }
-            __host__ __device__ __forceinline__ RGB2YCrCb() {}
-            __host__ __device__ __forceinline__ RGB2YCrCb(const RGB2YCrCb&) {}
-        };
-
-        template <int bidx> struct RGB2YCrCb<uchar, 4, 4, bidx> : unary_function<uint, uint>
-        {
-            __device__ __forceinline__ uint operator ()(uint src) const
-            {
-                return RGB2YCrCbConvert<bidx>(src);
-            }
-
-            __host__ __device__ __forceinline__ RGB2YCrCb() {}
-            __host__ __device__ __forceinline__ RGB2YCrCb(const RGB2YCrCb&) {}
-        };
-    }
-
-#define OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(name, scn, dcn, bidx) \
-    template <typename T> struct name ## _traits \
-    { \
-        typedef ::cv::cuda::device::color_detail::RGB2YCrCb<T, scn, dcn, bidx> functor_type; \
-        static __host__ __device__ __forceinline__ functor_type create_functor() \
-        { \
-            return functor_type(); \
-        } \
-    };
-
-    namespace color_detail
-    {
-        __constant__ float c_YCrCb2RGBCoeffs_f[5] = {1.403f, -0.714f, -0.344f, 1.773f};
-        __constant__ int   c_YCrCb2RGBCoeffs_i[5] = {22987, -11698, -5636, 29049};
-
-        template <int bidx, typename T, typename D> static __device__ void YCrCb2RGBConvert(const T& src, D* dst)
-        {
-            const int b = src.x + CV_DESCALE((src.z - ColorChannel<D>::half()) * c_YCrCb2RGBCoeffs_i[3], yuv_shift);
-            const int g = src.x + CV_DESCALE((src.z - ColorChannel<D>::half()) * c_YCrCb2RGBCoeffs_i[2] + (src.y - ColorChannel<D>::half()) * c_YCrCb2RGBCoeffs_i[1], yuv_shift);
-            const int r = src.x + CV_DESCALE((src.y - ColorChannel<D>::half()) * c_YCrCb2RGBCoeffs_i[0], yuv_shift);
-
-            dst[bidx] = saturate_cast<D>(b);
-            dst[1] = saturate_cast<D>(g);
-            dst[bidx^2] = saturate_cast<D>(r);
-        }
-
-        template <int bidx> static __device__ uint YCrCb2RGBConvert(uint src)
-        {
-            const int x = 0xff & (src);
-            const int y = 0xff & (src >> 8);
-            const int z = 0xff & (src >> 16);
-
-            const int b = x + CV_DESCALE((z - ColorChannel<uchar>::half()) * c_YCrCb2RGBCoeffs_i[3], yuv_shift);
-            const int g = x + CV_DESCALE((z - ColorChannel<uchar>::half()) * c_YCrCb2RGBCoeffs_i[2] + (y - ColorChannel<uchar>::half()) * c_YCrCb2RGBCoeffs_i[1], yuv_shift);
-            const int r = x + CV_DESCALE((y - ColorChannel<uchar>::half()) * c_YCrCb2RGBCoeffs_i[0], yuv_shift);
-
-            uint dst = 0xffu << 24;
-
-            dst |= saturate_cast<uchar>(b) << (bidx * 8);
-            dst |= saturate_cast<uchar>(g) << 8;
-            dst |= saturate_cast<uchar>(r) << ((bidx ^ 2) * 8);
-
-            return dst;
-        }
-
-        template <int bidx, typename T> __device__ __forceinline__ void YCrCb2RGBConvert(const T& src, float* dst)
-        {
-            dst[bidx] = src.x + (src.z - ColorChannel<float>::half()) * c_YCrCb2RGBCoeffs_f[3];
-            dst[1] = src.x + (src.z - ColorChannel<float>::half()) * c_YCrCb2RGBCoeffs_f[2] + (src.y - ColorChannel<float>::half()) * c_YCrCb2RGBCoeffs_f[1];
-            dst[bidx^2] = src.x + (src.y - ColorChannel<float>::half()) * c_YCrCb2RGBCoeffs_f[0];
-        }
-
-        template <typename T, int scn, int dcn, int bidx> struct YCrCb2RGB
-            : unary_function<typename TypeVec<T, scn>::vec_type, typename TypeVec<T, dcn>::vec_type>
-        {
-            __device__ __forceinline__ typename TypeVec<T, dcn>::vec_type operator ()(const typename TypeVec<T, scn>::vec_type& src) const
-            {
-                typename TypeVec<T, dcn>::vec_type dst;
-
-                YCrCb2RGBConvert<bidx>(src, &dst.x);
-                setAlpha(dst, ColorChannel<T>::max());
-
-                return dst;
-            }
-            __host__ __device__ __forceinline__ YCrCb2RGB() {}
-            __host__ __device__ __forceinline__ YCrCb2RGB(const YCrCb2RGB&) {}
-        };
-
-        template <int bidx> struct YCrCb2RGB<uchar, 4, 4, bidx> : unary_function<uint, uint>
-        {
-            __device__ __forceinline__ uint operator ()(uint src) const
-            {
-                return YCrCb2RGBConvert<bidx>(src);
-            }
-            __host__ __device__ __forceinline__ YCrCb2RGB() {}
-            __host__ __device__ __forceinline__ YCrCb2RGB(const YCrCb2RGB&) {}
-        };
-    }
-
-#define OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(name, scn, dcn, bidx) \
-    template <typename T> struct name ## _traits \
-    { \
-        typedef ::cv::cuda::device::color_detail::YCrCb2RGB<T, scn, dcn, bidx> functor_type; \
-        static __host__ __device__ __forceinline__ functor_type create_functor() \
-        { \
-            return functor_type(); \
-        } \
-    };
-
-////////////////////////////////////// RGB <-> XYZ ///////////////////////////////////////
-
-    namespace color_detail
-    {
-        __constant__ float c_RGB2XYZ_D65f[9] = { 0.412453f, 0.357580f, 0.180423f, 0.212671f, 0.715160f, 0.072169f, 0.019334f, 0.119193f, 0.950227f };
-        __constant__ int   c_RGB2XYZ_D65i[9] = { 1689, 1465, 739, 871, 2929, 296, 79, 488, 3892 };
-
-        template <int bidx, typename T, typename D> static __device__ __forceinline__ void RGB2XYZConvert(const T* src, D& dst)
-        {
-            dst.z = saturate_cast<T>(CV_DESCALE(src[bidx^2] * c_RGB2XYZ_D65i[6] + src[1] * c_RGB2XYZ_D65i[7] + src[bidx] * c_RGB2XYZ_D65i[8], xyz_shift));
-            dst.x = saturate_cast<T>(CV_DESCALE(src[bidx^2] * c_RGB2XYZ_D65i[0] + src[1] * c_RGB2XYZ_D65i[1] + src[bidx] * c_RGB2XYZ_D65i[2], xyz_shift));
-            dst.y = saturate_cast<T>(CV_DESCALE(src[bidx^2] * c_RGB2XYZ_D65i[3] + src[1] * c_RGB2XYZ_D65i[4] + src[bidx] * c_RGB2XYZ_D65i[5], xyz_shift));
-        }
-
-        template <int bidx> static __device__ __forceinline__ uint RGB2XYZConvert(uint src)
-        {
-            const uint b = 0xffu & (src >> (bidx * 8));
-            const uint g = 0xffu & (src >> 8);
-            const uint r = 0xffu & (src >> ((bidx ^ 2) * 8));
-
-            const uint x = saturate_cast<uchar>(CV_DESCALE(r * c_RGB2XYZ_D65i[0] + g * c_RGB2XYZ_D65i[1] + b * c_RGB2XYZ_D65i[2], xyz_shift));
-            const uint y = saturate_cast<uchar>(CV_DESCALE(r * c_RGB2XYZ_D65i[3] + g * c_RGB2XYZ_D65i[4] + b * c_RGB2XYZ_D65i[5], xyz_shift));
-            const uint z = saturate_cast<uchar>(CV_DESCALE(r * c_RGB2XYZ_D65i[6] + g * c_RGB2XYZ_D65i[7] + b * c_RGB2XYZ_D65i[8], xyz_shift));
-
-            uint dst = 0;
-
-            dst |= x;
-            dst |= y << 8;
-            dst |= z << 16;
-
-            return dst;
-        }
-
-        template <int bidx, typename D> static __device__ __forceinline__ void RGB2XYZConvert(const float* src, D& dst)
-        {
-            dst.x = src[bidx^2] * c_RGB2XYZ_D65f[0] + src[1] * c_RGB2XYZ_D65f[1] + src[bidx] * c_RGB2XYZ_D65f[2];
-            dst.y = src[bidx^2] * c_RGB2XYZ_D65f[3] + src[1] * c_RGB2XYZ_D65f[4] + src[bidx] * c_RGB2XYZ_D65f[5];
-            dst.z = src[bidx^2] * c_RGB2XYZ_D65f[6] + src[1] * c_RGB2XYZ_D65f[7] + src[bidx] * c_RGB2XYZ_D65f[8];
-        }
-
-        template <typename T, int scn, int dcn, int bidx> struct RGB2XYZ
-            : unary_function<typename TypeVec<T, scn>::vec_type, typename TypeVec<T, dcn>::vec_type>
-        {
-            __device__ __forceinline__ typename TypeVec<T, dcn>::vec_type operator()(const typename TypeVec<T, scn>::vec_type& src) const
-            {
-                typename TypeVec<T, dcn>::vec_type dst;
-
-                RGB2XYZConvert<bidx>(&src.x, dst);
-
-                return dst;
-            }
-            __host__ __device__ __forceinline__ RGB2XYZ() {}
-            __host__ __device__ __forceinline__ RGB2XYZ(const RGB2XYZ&) {}
-        };
-
-        template <int bidx> struct RGB2XYZ<uchar, 4, 4, bidx> : unary_function<uint, uint>
-        {
-            __device__ __forceinline__ uint operator()(uint src) const
-            {
-                return RGB2XYZConvert<bidx>(src);
-            }
-            __host__ __device__ __forceinline__ RGB2XYZ() {}
-            __host__ __device__ __forceinline__ RGB2XYZ(const RGB2XYZ&) {}
-        };
-    }
-
-#define OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(name, scn, dcn, bidx) \
-    template <typename T> struct name ## _traits \
-    { \
-        typedef ::cv::cuda::device::color_detail::RGB2XYZ<T, scn, dcn, bidx> functor_type; \
-        static __host__ __device__ __forceinline__ functor_type create_functor() \
-        { \
-            return functor_type(); \
-        } \
-    };
-
-    namespace color_detail
-    {
-        __constant__ float c_XYZ2sRGB_D65f[9] = { 3.240479f, -1.53715f, -0.498535f, -0.969256f, 1.875991f, 0.041556f, 0.055648f, -0.204043f, 1.057311f };
-        __constant__ int   c_XYZ2sRGB_D65i[9] = { 13273, -6296, -2042, -3970, 7684, 170, 228, -836, 4331 };
-
-        template <int bidx, typename T, typename D> static __device__ __forceinline__ void XYZ2RGBConvert(const T& src, D* dst)
-        {
-            dst[bidx^2] = saturate_cast<D>(CV_DESCALE(src.x * c_XYZ2sRGB_D65i[0] + src.y * c_XYZ2sRGB_D65i[1] + src.z * c_XYZ2sRGB_D65i[2], xyz_shift));
-            dst[1]      = saturate_cast<D>(CV_DESCALE(src.x * c_XYZ2sRGB_D65i[3] + src.y * c_XYZ2sRGB_D65i[4] + src.z * c_XYZ2sRGB_D65i[5], xyz_shift));
-            dst[bidx]   = saturate_cast<D>(CV_DESCALE(src.x * c_XYZ2sRGB_D65i[6] + src.y * c_XYZ2sRGB_D65i[7] + src.z * c_XYZ2sRGB_D65i[8], xyz_shift));
-        }
-
-        template <int bidx> static __device__ __forceinline__ uint XYZ2RGBConvert(uint src)
-        {
-            const int x = 0xff & src;
-            const int y = 0xff & (src >> 8);
-            const int z = 0xff & (src >> 16);
-
-            const uint r = saturate_cast<uchar>(CV_DESCALE(x * c_XYZ2sRGB_D65i[0] + y * c_XYZ2sRGB_D65i[1] + z * c_XYZ2sRGB_D65i[2], xyz_shift));
-            const uint g = saturate_cast<uchar>(CV_DESCALE(x * c_XYZ2sRGB_D65i[3] + y * c_XYZ2sRGB_D65i[4] + z * c_XYZ2sRGB_D65i[5], xyz_shift));
-            const uint b = saturate_cast<uchar>(CV_DESCALE(x * c_XYZ2sRGB_D65i[6] + y * c_XYZ2sRGB_D65i[7] + z * c_XYZ2sRGB_D65i[8], xyz_shift));
-
-            uint dst = 0xffu << 24;
-
-            dst |= b << (bidx * 8);
-            dst |= g << 8;
-            dst |= r << ((bidx ^ 2) * 8);
-
-            return dst;
-        }
-
-        template <int bidx, typename T> static __device__ __forceinline__ void XYZ2RGBConvert(const T& src, float* dst)
-        {
-            dst[bidx^2] = src.x * c_XYZ2sRGB_D65f[0] + src.y * c_XYZ2sRGB_D65f[1] + src.z * c_XYZ2sRGB_D65f[2];
-            dst[1]      = src.x * c_XYZ2sRGB_D65f[3] + src.y * c_XYZ2sRGB_D65f[4] + src.z * c_XYZ2sRGB_D65f[5];
-            dst[bidx]   = src.x * c_XYZ2sRGB_D65f[6] + src.y * c_XYZ2sRGB_D65f[7] + src.z * c_XYZ2sRGB_D65f[8];
-        }
-
-        template <typename T, int scn, int dcn, int bidx> struct XYZ2RGB
-            : unary_function<typename TypeVec<T, scn>::vec_type, typename TypeVec<T, dcn>::vec_type>
-        {
-            __device__ __forceinline__ typename TypeVec<T, dcn>::vec_type operator()(const typename TypeVec<T, scn>::vec_type& src) const
-            {
-                typename TypeVec<T, dcn>::vec_type dst;
-
-                XYZ2RGBConvert<bidx>(src, &dst.x);
-                setAlpha(dst, ColorChannel<T>::max());
-
-                return dst;
-            }
-            __host__ __device__ __forceinline__ XYZ2RGB() {}
-            __host__ __device__ __forceinline__ XYZ2RGB(const XYZ2RGB&) {}
-        };
-
-        template <int bidx> struct XYZ2RGB<uchar, 4, 4, bidx> : unary_function<uint, uint>
-        {
-            __device__ __forceinline__ uint operator()(uint src) const
-            {
-                return XYZ2RGBConvert<bidx>(src);
-            }
-            __host__ __device__ __forceinline__ XYZ2RGB() {}
-            __host__ __device__ __forceinline__ XYZ2RGB(const XYZ2RGB&) {}
-        };
-    }
-
-#define OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(name, scn, dcn, bidx) \
-    template <typename T> struct name ## _traits \
-    { \
-        typedef ::cv::cuda::device::color_detail::XYZ2RGB<T, scn, dcn, bidx> functor_type; \
-        static __host__ __device__ __forceinline__ functor_type create_functor() \
-        { \
-            return functor_type(); \
-        } \
-    };
-
-////////////////////////////////////// RGB <-> HSV ///////////////////////////////////////
-
-    namespace color_detail
-    {
-        __constant__ int c_HsvDivTable   [256] = {0, 1044480, 522240, 348160, 261120, 208896, 174080, 149211, 130560, 116053, 104448, 94953, 87040, 80345, 74606, 69632, 65280, 61440, 58027, 54973, 52224, 49737, 47476, 45412, 43520, 41779, 40172, 38684, 37303, 36017, 34816, 33693, 32640, 31651, 30720, 29842, 29013, 28229, 27486, 26782, 26112, 25475, 24869, 24290, 23738, 23211, 22706, 22223, 21760, 21316, 20890, 20480, 20086, 19707, 19342, 18991, 18651, 18324, 18008, 17703, 17408, 17123, 16846, 16579, 16320, 16069, 15825, 15589, 15360, 15137, 14921, 14711, 14507, 14308, 14115, 13926, 13743, 13565, 13391, 13221, 13056, 12895, 12738, 12584, 12434, 12288, 12145, 12006, 11869, 11736, 11605, 11478, 11353, 11231, 11111, 10995, 10880, 10768, 10658, 10550, 10445, 10341, 10240, 10141, 10043, 9947, 9854, 9761, 9671, 9582, 9495, 9410, 9326, 9243, 9162, 9082, 9004, 8927, 8852, 8777, 8704, 8632, 8561, 8492, 8423, 8356, 8290, 8224, 8160, 8097, 8034, 7973, 7913, 7853, 7795, 7737, 7680, 7624, 7569, 7514, 7461, 7408, 7355, 7304, 7253, 7203, 7154, 7105, 7057, 7010, 6963, 6917, 6872, 6827, 6782, 6739, 6695, 6653, 6611, 6569, 6528, 6487, 6447, 6408, 6369, 6330, 6292, 6254, 6217, 6180, 6144, 6108, 6073, 6037, 6003, 5968, 5935, 5901, 5868, 5835, 5803, 5771, 5739, 5708, 5677, 5646, 5615, 5585, 5556, 5526, 5497, 5468, 5440, 5412, 5384, 5356, 5329, 5302, 5275, 5249, 5222, 5196, 5171, 5145, 5120, 5095, 5070, 5046, 5022, 4998, 4974, 4950, 4927, 4904, 4881, 4858, 4836, 4813, 4791, 4769, 4748, 4726, 4705, 4684, 4663, 4642, 4622, 4601, 4581, 4561, 4541, 4522, 4502, 4483, 4464, 4445, 4426, 4407, 4389, 4370, 4352, 4334, 4316, 4298, 4281, 4263, 4246, 4229, 4212, 4195, 4178, 4161, 4145, 4128, 4112, 4096};
-        __constant__ int c_HsvDivTable180[256] = {0, 122880, 61440, 40960, 30720, 24576, 20480, 17554, 15360, 13653, 12288, 11171, 10240, 9452, 8777, 8192, 7680, 7228, 6827, 6467, 6144, 5851, 5585, 5343, 5120, 4915, 4726, 4551, 4389, 4237, 4096, 3964, 3840, 3724, 3614, 3511, 3413, 3321, 3234, 3151, 3072, 2997, 2926, 2858, 2793, 2731, 2671, 2614, 2560, 2508, 2458, 2409, 2363, 2318, 2276, 2234, 2194, 2156, 2119, 2083, 2048, 2014, 1982, 1950, 1920, 1890, 1862, 1834, 1807, 1781, 1755, 1731, 1707, 1683, 1661, 1638, 1617, 1596, 1575, 1555, 1536, 1517, 1499, 1480, 1463, 1446, 1429, 1412, 1396, 1381, 1365, 1350, 1336, 1321, 1307, 1293, 1280, 1267, 1254, 1241, 1229, 1217, 1205, 1193, 1182, 1170, 1159, 1148, 1138, 1127, 1117, 1107, 1097, 1087, 1078, 1069, 1059, 1050, 1041, 1033, 1024, 1016, 1007, 999, 991, 983, 975, 968, 960, 953, 945, 938, 931, 924, 917, 910, 904, 897, 890, 884, 878, 871, 865, 859, 853, 847, 842, 836, 830, 825, 819, 814, 808, 803, 798, 793, 788, 783, 778, 773, 768, 763, 759, 754, 749, 745, 740, 736, 731, 727, 723, 719, 714, 710, 706, 702, 698, 694, 690, 686, 683, 679, 675, 671, 668, 664, 661, 657, 654, 650, 647, 643, 640, 637, 633, 630, 627, 624, 621, 617, 614, 611, 608, 605, 602, 599, 597, 594, 591, 588, 585, 582, 580, 577, 574, 572, 569, 566, 564, 561, 559, 556, 554, 551, 549, 546, 544, 541, 539, 537, 534, 532, 530, 527, 525, 523, 521, 518, 516, 514, 512, 510, 508, 506, 504, 502, 500, 497, 495, 493, 492, 490, 488, 486, 484, 482};
-        __constant__ int c_HsvDivTable256[256] = {0, 174763, 87381, 58254, 43691, 34953, 29127, 24966, 21845, 19418, 17476, 15888, 14564, 13443, 12483, 11651, 10923, 10280, 9709, 9198, 8738, 8322, 7944, 7598, 7282, 6991, 6722, 6473, 6242, 6026, 5825, 5638, 5461, 5296, 5140, 4993, 4855, 4723, 4599, 4481, 4369, 4263, 4161, 4064, 3972, 3884, 3799, 3718, 3641, 3567, 3495, 3427, 3361, 3297, 3236, 3178, 3121, 3066, 3013, 2962, 2913, 2865, 2819, 2774, 2731, 2689, 2648, 2608, 2570, 2533, 2497, 2461, 2427, 2394, 2362, 2330, 2300, 2270, 2241, 2212, 2185, 2158, 2131, 2106, 2081, 2056, 2032, 2009, 1986, 1964, 1942, 1920, 1900, 1879, 1859, 1840, 1820, 1802, 1783, 1765, 1748, 1730, 1713, 1697, 1680, 1664, 1649, 1633, 1618, 1603, 1589, 1574, 1560, 1547, 1533, 1520, 1507, 1494, 1481, 1469, 1456, 1444, 1432, 1421, 1409, 1398, 1387, 1376, 1365, 1355, 1344, 1334, 1324, 1314, 1304, 1295, 1285, 1276, 1266, 1257, 1248, 1239, 1231, 1222, 1214, 1205, 1197, 1189, 1181, 1173, 1165, 1157, 1150, 1142, 1135, 1128, 1120, 1113, 1106, 1099, 1092, 1085, 1079, 1072, 1066, 1059, 1053, 1046, 1040, 1034, 1028, 1022, 1016, 1010, 1004, 999, 993, 987, 982, 976, 971, 966, 960, 955, 950, 945, 940, 935, 930, 925, 920, 915, 910, 906, 901, 896, 892, 887, 883, 878, 874, 869, 865, 861, 857, 853, 848, 844, 840, 836, 832, 828, 824, 820, 817, 813, 809, 805, 802, 798, 794, 791, 787, 784, 780, 777, 773, 770, 767, 763, 760, 757, 753, 750, 747, 744, 741, 737, 734, 731, 728, 725, 722, 719, 716, 713, 710, 708, 705, 702, 699, 696, 694, 691, 688, 685};
-
-        template <int bidx, int hr, typename D> static __device__ void RGB2HSVConvert(const uchar* src, D& dst)
-        {
-            const int hsv_shift = 12;
-            const int* hdiv_table = hr == 180 ? c_HsvDivTable180 : c_HsvDivTable256;
-
-            int b = src[bidx], g = src[1], r = src[bidx^2];
-            int h, s, v = b;
-            int vmin = b, diff;
-            int vr, vg;
-
-            v = ::max(v, g);
-            v = ::max(v, r);
-            vmin = ::min(vmin, g);
-            vmin = ::min(vmin, r);
-
-            diff = v - vmin;
-            vr = (v == r) * -1;
-            vg = (v == g) * -1;
-
-            s = (diff * c_HsvDivTable[v] + (1 << (hsv_shift-1))) >> hsv_shift;
-            h = (vr & (g - b)) + (~vr & ((vg & (b - r + 2 * diff)) + ((~vg) & (r - g + 4 * diff))));
-            h = (h * hdiv_table[diff] + (1 << (hsv_shift-1))) >> hsv_shift;
-            h += (h < 0) * hr;
-
-            dst.x = saturate_cast<uchar>(h);
-            dst.y = (uchar)s;
-            dst.z = (uchar)v;
-        }
-
-        template <int bidx, int hr> static __device__ uint RGB2HSVConvert(uint src)
-        {
-            const int hsv_shift = 12;
-            const int* hdiv_table = hr == 180 ? c_HsvDivTable180 : c_HsvDivTable256;
-
-            const int b = 0xff & (src >> (bidx * 8));
-            const int g = 0xff & (src >> 8);
-            const int r = 0xff & (src >> ((bidx ^ 2) * 8));
-
-            int h, s, v = b;
-            int vmin = b, diff;
-            int vr, vg;
-
-            v = ::max(v, g);
-            v = ::max(v, r);
-            vmin = ::min(vmin, g);
-            vmin = ::min(vmin, r);
-
-            diff = v - vmin;
-            vr = (v == r) * -1;
-            vg = (v == g) * -1;
-
-            s = (diff * c_HsvDivTable[v] + (1 << (hsv_shift-1))) >> hsv_shift;
-            h = (vr & (g - b)) + (~vr & ((vg & (b - r + 2 * diff)) + ((~vg) & (r - g + 4 * diff))));
-            h = (h * hdiv_table[diff] + (1 << (hsv_shift-1))) >> hsv_shift;
-            h += (h < 0) * hr;
-
-            uint dst = 0;
-
-            dst |= saturate_cast<uchar>(h);
-            dst |= (0xffu & s) << 8;
-            dst |= (0xffu & v) << 16;
-
-            return dst;
-        }
-
-        template <int bidx, int hr, typename D> static __device__ void RGB2HSVConvert(const float* src, D& dst)
-        {
-            const float hscale = hr * (1.f / 360.f);
-
-            float b = src[bidx], g = src[1], r = src[bidx^2];
-            float h, s, v;
-
-            float vmin, diff;
-
-            v = vmin = r;
-            v = fmax(v, g);
-            v = fmax(v, b);
-            vmin = fmin(vmin, g);
-            vmin = fmin(vmin, b);
-
-            diff = v - vmin;
-            s = diff / (float)(::fabs(v) + numeric_limits<float>::epsilon());
-            diff = (float)(60. / (diff + numeric_limits<float>::epsilon()));
-
-            h  = (v == r) * (g - b) * diff;
-            h += (v != r && v == g) * ((b - r) * diff + 120.f);
-            h += (v != r && v != g) * ((r - g) * diff + 240.f);
-            h += (h < 0) * 360.f;
-
-            dst.x = h * hscale;
-            dst.y = s;
-            dst.z = v;
-        }
-
-        template <typename T, int scn, int dcn, int bidx, int hr> struct RGB2HSV
-            : unary_function<typename TypeVec<T, scn>::vec_type, typename TypeVec<T, dcn>::vec_type>
-        {
-            __device__ __forceinline__ typename TypeVec<T, dcn>::vec_type operator()(const typename TypeVec<T, scn>::vec_type& src) const
-            {
-                typename TypeVec<T, dcn>::vec_type dst;
-
-                RGB2HSVConvert<bidx, hr>(&src.x, dst);
-
-                return dst;
-            }
-            __host__ __device__ __forceinline__ RGB2HSV() {}
-            __host__ __device__ __forceinline__ RGB2HSV(const RGB2HSV&) {}
-        };
-
-        template <int bidx, int hr> struct RGB2HSV<uchar, 4, 4, bidx, hr> : unary_function<uint, uint>
-        {
-            __device__ __forceinline__ uint operator()(uint src) const
-            {
-                return RGB2HSVConvert<bidx, hr>(src);
-            }
-            __host__ __device__ __forceinline__ RGB2HSV() {}
-            __host__ __device__ __forceinline__ RGB2HSV(const RGB2HSV&) {}
-        };
-    }
-
-#define OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(name, scn, dcn, bidx) \
-    template <typename T> struct name ## _traits \
-    { \
-        typedef ::cv::cuda::device::color_detail::RGB2HSV<T, scn, dcn, bidx, 180> functor_type; \
-        static __host__ __device__ __forceinline__ functor_type create_functor() \
-        { \
-            return functor_type(); \
-        } \
-    }; \
-    template <typename T> struct name ## _full_traits \
-    { \
-        typedef ::cv::cuda::device::color_detail::RGB2HSV<T, scn, dcn, bidx, 256> functor_type; \
-        static __host__ __device__ __forceinline__ functor_type create_functor() \
-        { \
-            return functor_type(); \
-        } \
-    }; \
-    template <> struct name ## _traits<float> \
-    { \
-        typedef ::cv::cuda::device::color_detail::RGB2HSV<float, scn, dcn, bidx, 360> functor_type; \
-        static __host__ __device__ __forceinline__ functor_type create_functor() \
-        { \
-            return functor_type(); \
-        } \
-    }; \
-    template <> struct name ## _full_traits<float> \
-    { \
-        typedef ::cv::cuda::device::color_detail::RGB2HSV<float, scn, dcn, bidx, 360> functor_type; \
-        static __host__ __device__ __forceinline__ functor_type create_functor() \
-        { \
-            return functor_type(); \
-        } \
-    };
-
-    namespace color_detail
-    {
-        __constant__ int c_HsvSectorData[6][3] = { {1,3,0}, {1,0,2}, {3,0,1}, {0,2,1}, {0,1,3}, {2,1,0} };
-
-        template <int bidx, int hr, typename T> static __device__ void HSV2RGBConvert(const T& src, float* dst)
-        {
-            const float hscale = 6.f / hr;
-
-            float h = src.x, s = src.y, v = src.z;
-            float b = v, g = v, r = v;
-
-            if (s != 0)
-            {
-                h *= hscale;
-
-                if( h < 0 )
-                    do h += 6; while( h < 0 );
-                else if( h >= 6 )
-                    do h -= 6; while( h >= 6 );
-
-                int sector = __float2int_rd(h);
-                h -= sector;
-
-                if ( (unsigned)sector >= 6u )
-                {
-                    sector = 0;
-                    h = 0.f;
-                }
-
-                float tab[4];
-                tab[0] = v;
-                tab[1] = v * (1.f - s);
-                tab[2] = v * (1.f - s * h);
-                tab[3] = v * (1.f - s * (1.f - h));
-
-                b = tab[c_HsvSectorData[sector][0]];
-                g = tab[c_HsvSectorData[sector][1]];
-                r = tab[c_HsvSectorData[sector][2]];
-            }
-
-            dst[bidx] = b;
-            dst[1] = g;
-            dst[bidx^2] = r;
-        }
-
-        template <int bidx, int HR, typename T> static __device__ void HSV2RGBConvert(const T& src, uchar* dst)
-        {
-            float3 buf;
-
-            buf.x = src.x;
-            buf.y = src.y * (1.f / 255.f);
-            buf.z = src.z * (1.f / 255.f);
-
-            HSV2RGBConvert<bidx, HR>(buf, &buf.x);
-
-            dst[0] = saturate_cast<uchar>(buf.x * 255.f);
-            dst[1] = saturate_cast<uchar>(buf.y * 255.f);
-            dst[2] = saturate_cast<uchar>(buf.z * 255.f);
-        }
-
-        template <int bidx, int hr> static __device__ uint HSV2RGBConvert(uint src)
-        {
-            float3 buf;
-
-            buf.x = src & 0xff;
-            buf.y = ((src >> 8) & 0xff) * (1.f/255.f);
-            buf.z = ((src >> 16) & 0xff) * (1.f/255.f);
-
-            HSV2RGBConvert<bidx, hr>(buf, &buf.x);
-
-            uint dst = 0xffu << 24;
-
-            dst |= saturate_cast<uchar>(buf.x * 255.f);
-            dst |= saturate_cast<uchar>(buf.y * 255.f) << 8;
-            dst |= saturate_cast<uchar>(buf.z * 255.f) << 16;
-
-            return dst;
-        }
-
-        template <typename T, int scn, int dcn, int bidx, int hr> struct HSV2RGB
-            : unary_function<typename TypeVec<T, scn>::vec_type, typename TypeVec<T, dcn>::vec_type>
-        {
-            __device__ __forceinline__ typename TypeVec<T, dcn>::vec_type operator()(const typename TypeVec<T, scn>::vec_type& src) const
-            {
-                typename TypeVec<T, dcn>::vec_type dst;
-
-                HSV2RGBConvert<bidx, hr>(src, &dst.x);
-                setAlpha(dst, ColorChannel<T>::max());
-
-                return dst;
-            }
-            __host__ __device__ __forceinline__ HSV2RGB() {}
-            __host__ __device__ __forceinline__ HSV2RGB(const HSV2RGB&) {}
-        };
-
-        template <int bidx, int hr> struct HSV2RGB<uchar, 4, 4, bidx, hr> : unary_function<uint, uint>
-        {
-            __device__ __forceinline__ uint operator()(uint src) const
-            {
-                return HSV2RGBConvert<bidx, hr>(src);
-            }
-            __host__ __device__ __forceinline__ HSV2RGB() {}
-            __host__ __device__ __forceinline__ HSV2RGB(const HSV2RGB&) {}
-        };
-    }
-
-#define OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(name, scn, dcn, bidx) \
-    template <typename T> struct name ## _traits \
-    { \
-        typedef ::cv::cuda::device::color_detail::HSV2RGB<T, scn, dcn, bidx, 180> functor_type; \
-        static __host__ __device__ __forceinline__ functor_type create_functor() \
-        { \
-            return functor_type(); \
-        } \
-    }; \
-    template <typename T> struct name ## _full_traits \
-    { \
-        typedef ::cv::cuda::device::color_detail::HSV2RGB<T, scn, dcn, bidx, 255> functor_type; \
-        static __host__ __device__ __forceinline__ functor_type create_functor() \
-        { \
-            return functor_type(); \
-        } \
-    }; \
-    template <> struct name ## _traits<float> \
-    { \
-        typedef ::cv::cuda::device::color_detail::HSV2RGB<float, scn, dcn, bidx, 360> functor_type; \
-        static __host__ __device__ __forceinline__ functor_type create_functor() \
-        { \
-            return functor_type(); \
-        } \
-    }; \
-    template <> struct name ## _full_traits<float> \
-    { \
-        typedef ::cv::cuda::device::color_detail::HSV2RGB<float, scn, dcn, bidx, 360> functor_type; \
-        static __host__ __device__ __forceinline__ functor_type create_functor() \
-        { \
-            return functor_type(); \
-        } \
-    };
-
-/////////////////////////////////////// RGB <-> HLS ////////////////////////////////////////
-
-    namespace color_detail
-    {
-        template <int bidx, int hr, typename D> static __device__ void RGB2HLSConvert(const float* src, D& dst)
-        {
-            const float hscale = hr * (1.f / 360.f);
-
-            float b = src[bidx], g = src[1], r = src[bidx^2];
-            float h = 0.f, s = 0.f, l;
-            float vmin, vmax, diff;
-
-            vmax = vmin = r;
-            vmax = fmax(vmax, g);
-            vmax = fmax(vmax, b);
-            vmin = fmin(vmin, g);
-            vmin = fmin(vmin, b);
-
-            diff = vmax - vmin;
-            l = (vmax + vmin) * 0.5f;
-
-            if (diff > numeric_limits<float>::epsilon())
-            {
-                s = (l < 0.5f) * diff / (vmax + vmin);
-                s += (l >= 0.5f) * diff / (2.0f - vmax - vmin);
-
-                diff = 60.f / diff;
-
-                h  = (vmax == r) * (g - b) * diff;
-                h += (vmax != r && vmax == g) * ((b - r) * diff + 120.f);
-                h += (vmax != r && vmax != g) * ((r - g) * diff + 240.f);
-                h += (h < 0.f) * 360.f;
-            }
-
-            dst.x = h * hscale;
-            dst.y = l;
-            dst.z = s;
-        }
-
-        template <int bidx, int hr, typename D> static __device__ void RGB2HLSConvert(const uchar* src, D& dst)
-        {
-            float3 buf;
-
-            buf.x = src[0] * (1.f / 255.f);
-            buf.y = src[1] * (1.f / 255.f);
-            buf.z = src[2] * (1.f / 255.f);
-
-            RGB2HLSConvert<bidx, hr>(&buf.x, buf);
-
-            dst.x = saturate_cast<uchar>(buf.x);
-            dst.y = saturate_cast<uchar>(buf.y*255.f);
-            dst.z = saturate_cast<uchar>(buf.z*255.f);
-        }
-
-        template <int bidx, int hr> static __device__ uint RGB2HLSConvert(uint src)
-        {
-            float3 buf;
-
-            buf.x = (0xff & src) * (1.f / 255.f);
-            buf.y = (0xff & (src >> 8)) * (1.f / 255.f);
-            buf.z = (0xff & (src >> 16)) * (1.f / 255.f);
-
-            RGB2HLSConvert<bidx, hr>(&buf.x, buf);
-
-            uint dst = 0xffu << 24;
-
-            dst |= saturate_cast<uchar>(buf.x);
-            dst |= saturate_cast<uchar>(buf.y * 255.f) << 8;
-            dst |= saturate_cast<uchar>(buf.z * 255.f) << 16;
-
-            return dst;
-        }
-
-        template <typename T, int scn, int dcn, int bidx, int hr> struct RGB2HLS
-            : unary_function<typename TypeVec<T, scn>::vec_type, typename TypeVec<T, dcn>::vec_type>
-        {
-            __device__ __forceinline__ typename TypeVec<T, dcn>::vec_type operator()(const typename TypeVec<T, scn>::vec_type& src) const
-            {
-                typename TypeVec<T, dcn>::vec_type dst;
-
-                RGB2HLSConvert<bidx, hr>(&src.x, dst);
-
-                return dst;
-            }
-            __host__ __device__ __forceinline__ RGB2HLS() {}
-            __host__ __device__ __forceinline__ RGB2HLS(const RGB2HLS&) {}
-        };
-
-        template <int bidx, int hr> struct RGB2HLS<uchar, 4, 4, bidx, hr> : unary_function<uint, uint>
-        {
-            __device__ __forceinline__ uint operator()(uint src) const
-            {
-                return RGB2HLSConvert<bidx, hr>(src);
-            }
-            __host__ __device__ __forceinline__ RGB2HLS() {}
-            __host__ __device__ __forceinline__ RGB2HLS(const RGB2HLS&) {}
-        };
-    }
-
-#define OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(name, scn, dcn, bidx) \
-    template <typename T> struct name ## _traits \
-    { \
-        typedef ::cv::cuda::device::color_detail::RGB2HLS<T, scn, dcn, bidx, 180> functor_type; \
-        static __host__ __device__ __forceinline__ functor_type create_functor() \
-        { \
-            return functor_type(); \
-        } \
-    }; \
-    template <typename T> struct name ## _full_traits \
-    { \
-        typedef ::cv::cuda::device::color_detail::RGB2HLS<T, scn, dcn, bidx, 256> functor_type; \
-        static __host__ __device__ __forceinline__ functor_type create_functor() \
-        { \
-            return functor_type(); \
-        } \
-    }; \
-    template <> struct name ## _traits<float> \
-    { \
-        typedef ::cv::cuda::device::color_detail::RGB2HLS<float, scn, dcn, bidx, 360> functor_type; \
-        static __host__ __device__ __forceinline__ functor_type create_functor() \
-        { \
-            return functor_type(); \
-        } \
-    }; \
-    template <> struct name ## _full_traits<float> \
-    { \
-        typedef ::cv::cuda::device::color_detail::RGB2HLS<float, scn, dcn, bidx, 360> functor_type; \
-        static __host__ __device__ __forceinline__ functor_type create_functor() \
-        { \
-            return functor_type(); \
-        } \
-    };
-
-    namespace color_detail
-    {
-        __constant__ int c_HlsSectorData[6][3] = { {1,3,0}, {1,0,2}, {3,0,1}, {0,2,1}, {0,1,3}, {2,1,0} };
-
-        template <int bidx, int hr, typename T> static __device__ void HLS2RGBConvert(const T& src, float* dst)
-        {
-            const float hscale = 6.0f / hr;
-
-            float h = src.x, l = src.y, s = src.z;
-            float b = l, g = l, r = l;
-
-            if (s != 0)
-            {
-                float p2  = (l <= 0.5f) * l * (1 + s);
-                      p2 += (l > 0.5f) * (l + s - l * s);
-                float p1 = 2 * l - p2;
-
-                h *= hscale;
-
-                if( h < 0 )
-                    do h += 6; while( h < 0 );
-                else if( h >= 6 )
-                    do h -= 6; while( h >= 6 );
-
-                int sector;
-                sector = __float2int_rd(h);
-
-                h -= sector;
-
-                float tab[4];
-                tab[0] = p2;
-                tab[1] = p1;
-                tab[2] = p1 + (p2 - p1) * (1 - h);
-                tab[3] = p1 + (p2 - p1) * h;
-
-                b = tab[c_HlsSectorData[sector][0]];
-                g = tab[c_HlsSectorData[sector][1]];
-                r = tab[c_HlsSectorData[sector][2]];
-            }
-
-            dst[bidx] = b;
-            dst[1] = g;
-            dst[bidx^2] = r;
-        }
-
-        template <int bidx, int hr, typename T> static __device__ void HLS2RGBConvert(const T& src, uchar* dst)
-        {
-            float3 buf;
-
-            buf.x = src.x;
-            buf.y = src.y * (1.f / 255.f);
-            buf.z = src.z * (1.f / 255.f);
-
-            HLS2RGBConvert<bidx, hr>(buf, &buf.x);
-
-            dst[0] = saturate_cast<uchar>(buf.x * 255.f);
-            dst[1] = saturate_cast<uchar>(buf.y * 255.f);
-            dst[2] = saturate_cast<uchar>(buf.z * 255.f);
-        }
-
-        template <int bidx, int hr> static __device__ uint HLS2RGBConvert(uint src)
-        {
-            float3 buf;
-
-            buf.x = 0xff & src;
-            buf.y = (0xff & (src >> 8)) * (1.f / 255.f);
-            buf.z = (0xff & (src >> 16)) * (1.f / 255.f);
-
-            HLS2RGBConvert<bidx, hr>(buf, &buf.x);
-
-            uint dst = 0xffu << 24;
-
-            dst |= saturate_cast<uchar>(buf.x * 255.f);
-            dst |= saturate_cast<uchar>(buf.y * 255.f) << 8;
-            dst |= saturate_cast<uchar>(buf.z * 255.f) << 16;
-
-            return dst;
-        }
-
-        template <typename T, int scn, int dcn, int bidx, int hr> struct HLS2RGB
-            : unary_function<typename TypeVec<T, scn>::vec_type, typename TypeVec<T, dcn>::vec_type>
-        {
-            __device__ __forceinline__ typename TypeVec<T, dcn>::vec_type operator()(const typename TypeVec<T, scn>::vec_type& src) const
-            {
-                typename TypeVec<T, dcn>::vec_type dst;
-
-                HLS2RGBConvert<bidx, hr>(src, &dst.x);
-                setAlpha(dst, ColorChannel<T>::max());
-
-                return dst;
-            }
-            __host__ __device__ __forceinline__ HLS2RGB() {}
-            __host__ __device__ __forceinline__ HLS2RGB(const HLS2RGB&) {}
-        };
-
-        template <int bidx, int hr> struct HLS2RGB<uchar, 4, 4, bidx, hr> : unary_function<uint, uint>
-        {
-            __device__ __forceinline__ uint operator()(uint src) const
-            {
-                return HLS2RGBConvert<bidx, hr>(src);
-            }
-            __host__ __device__ __forceinline__ HLS2RGB() {}
-            __host__ __device__ __forceinline__ HLS2RGB(const HLS2RGB&) {}
-        };
-    }
-
-#define OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(name, scn, dcn, bidx) \
-    template <typename T> struct name ## _traits \
-    { \
-        typedef ::cv::cuda::device::color_detail::HLS2RGB<T, scn, dcn, bidx, 180> functor_type; \
-        static __host__ __device__ __forceinline__ functor_type create_functor() \
-        { \
-            return functor_type(); \
-        } \
-    }; \
-    template <typename T> struct name ## _full_traits \
-    { \
-        typedef ::cv::cuda::device::color_detail::HLS2RGB<T, scn, dcn, bidx, 255> functor_type; \
-        static __host__ __device__ __forceinline__ functor_type create_functor() \
-        { \
-            return functor_type(); \
-        } \
-    }; \
-    template <> struct name ## _traits<float> \
-    { \
-        typedef ::cv::cuda::device::color_detail::HLS2RGB<float, scn, dcn, bidx, 360> functor_type; \
-        static __host__ __device__ __forceinline__ functor_type create_functor() \
-        { \
-            return functor_type(); \
-        } \
-    }; \
-    template <> struct name ## _full_traits<float> \
-    { \
-        typedef ::cv::cuda::device::color_detail::HLS2RGB<float, scn, dcn, bidx, 360> functor_type; \
-        static __host__ __device__ __forceinline__ functor_type create_functor() \
-        { \
-            return functor_type(); \
-        } \
-    };
-
-///////////////////////////////////// RGB <-> Lab /////////////////////////////////////
-
-    namespace color_detail
-    {
-        enum
-        {
-            LAB_CBRT_TAB_SIZE = 1024,
-            GAMMA_TAB_SIZE = 1024,
-            lab_shift = xyz_shift,
-            gamma_shift = 3,
-            lab_shift2 = (lab_shift + gamma_shift),
-            LAB_CBRT_TAB_SIZE_B = (256 * 3 / 2 * (1 << gamma_shift))
-        };
-
-        __constant__ ushort c_sRGBGammaTab_b[] = {0,1,1,2,2,3,4,4,5,6,6,7,8,8,9,10,11,11,12,13,14,15,16,17,19,20,21,22,24,25,26,28,29,31,33,34,36,38,40,41,43,45,47,49,51,54,56,58,60,63,65,68,70,73,75,78,81,83,86,89,92,95,98,101,105,108,111,115,118,121,125,129,132,136,140,144,147,151,155,160,164,168,172,176,181,185,190,194,199,204,209,213,218,223,228,233,239,244,249,255,260,265,271,277,282,288,294,300,306,312,318,324,331,337,343,350,356,363,370,376,383,390,397,404,411,418,426,433,440,448,455,463,471,478,486,494,502,510,518,527,535,543,552,560,569,578,586,595,604,613,622,631,641,650,659,669,678,688,698,707,717,727,737,747,757,768,778,788,799,809,820,831,842,852,863,875,886,897,908,920,931,943,954,966,978,990,1002,1014,1026,1038,1050,1063,1075,1088,1101,1113,1126,1139,1152,1165,1178,1192,1205,1218,1232,1245,1259,1273,1287,1301,1315,1329,1343,1357,1372,1386,1401,1415,1430,1445,1460,1475,1490,1505,1521,1536,1551,1567,1583,1598,1614,1630,1646,1662,1678,1695,1711,1728,1744,1761,1778,1794,1811,1828,1846,1863,1880,1897,1915,1933,1950,1968,1986,2004,2022,2040};
-
-        __device__ __forceinline__ int LabCbrt_b(int i)
-        {
-            float x = i * (1.f / (255.f * (1 << gamma_shift)));
-            return (1 << lab_shift2) * (x < 0.008856f ? x * 7.787f + 0.13793103448275862f : ::cbrtf(x));
-        }
-
-        template <bool srgb, int blueIdx, typename T, typename D>
-        __device__ __forceinline__ void RGB2LabConvert_b(const T& src, D& dst)
-        {
-            const int Lscale = (116 * 255 + 50) / 100;
-            const int Lshift = -((16 * 255 * (1 << lab_shift2) + 50) / 100);
-
-            int B = blueIdx == 0 ? src.x : src.z;
-            int G = src.y;
-            int R = blueIdx == 0 ? src.z : src.x;
-
-            if (srgb)
-            {
-                B = c_sRGBGammaTab_b[B];
-                G = c_sRGBGammaTab_b[G];
-                R = c_sRGBGammaTab_b[R];
-            }
-            else
-            {
-                B <<= 3;
-                G <<= 3;
-                R <<= 3;
-            }
-
-            int fX = LabCbrt_b(CV_DESCALE(B * 778 + G * 1541 + R * 1777, lab_shift));
-            int fY = LabCbrt_b(CV_DESCALE(B * 296 + G * 2929 + R * 871, lab_shift));
-            int fZ = LabCbrt_b(CV_DESCALE(B * 3575 + G * 448 + R * 73, lab_shift));
-
-            int L = CV_DESCALE(Lscale * fY + Lshift, lab_shift2);
-            int a = CV_DESCALE(500 * (fX - fY) + 128 * (1 << lab_shift2), lab_shift2);
-            int b = CV_DESCALE(200 * (fY - fZ) + 128 * (1 << lab_shift2), lab_shift2);
-
-            dst.x = saturate_cast<uchar>(L);
-            dst.y = saturate_cast<uchar>(a);
-            dst.z = saturate_cast<uchar>(b);
-        }
-
-        __device__ __forceinline__ float splineInterpolate(float x, const float* tab, int n)
-        {
-            int ix = ::min(::max(int(x), 0), n-1);
-            x -= ix;
-            tab += ix * 4;
-            return ((tab[3] * x + tab[2]) * x + tab[1]) * x + tab[0];
-        }
-
-        __constant__ float c_sRGBGammaTab[] = 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-
-        template <bool srgb, int blueIdx, typename T, typename D>
-        __device__ __forceinline__ void RGB2LabConvert_f(const T& src, D& dst)
-        {
-            const float _1_3 = 1.0f / 3.0f;
-            const float _a = 16.0f / 116.0f;
-
-            float B = blueIdx == 0 ? src.x : src.z;
-            float G = src.y;
-            float R = blueIdx == 0 ? src.z : src.x;
-
-            if (srgb)
-            {
-                B = splineInterpolate(B * GAMMA_TAB_SIZE, c_sRGBGammaTab, GAMMA_TAB_SIZE);
-                G = splineInterpolate(G * GAMMA_TAB_SIZE, c_sRGBGammaTab, GAMMA_TAB_SIZE);
-                R = splineInterpolate(R * GAMMA_TAB_SIZE, c_sRGBGammaTab, GAMMA_TAB_SIZE);
-            }
-
-            float X = B * 0.189828f + G * 0.376219f + R * 0.433953f;
-            float Y = B * 0.072169f + G * 0.715160f + R * 0.212671f;
-            float Z = B * 0.872766f + G * 0.109477f + R * 0.017758f;
-
-            float FX = X > 0.008856f ? ::powf(X, _1_3) : (7.787f * X + _a);
-            float FY = Y > 0.008856f ? ::powf(Y, _1_3) : (7.787f * Y + _a);
-            float FZ = Z > 0.008856f ? ::powf(Z, _1_3) : (7.787f * Z + _a);
-
-            float L = Y > 0.008856f ? (116.f * FY - 16.f) : (903.3f * Y);
-            float a = 500.f * (FX - FY);
-            float b = 200.f * (FY - FZ);
-
-            dst.x = L;
-            dst.y = a;
-            dst.z = b;
-        }
-
-        template <typename T, int scn, int dcn, bool srgb, int blueIdx> struct RGB2Lab;
-        template <int scn, int dcn, bool srgb, int blueIdx>
-        struct RGB2Lab<uchar, scn, dcn, srgb, blueIdx>
-            : unary_function<typename TypeVec<uchar, scn>::vec_type, typename TypeVec<uchar, dcn>::vec_type>
-        {
-            __device__ __forceinline__ typename TypeVec<uchar, dcn>::vec_type operator ()(const typename TypeVec<uchar, scn>::vec_type& src) const
-            {
-                typename TypeVec<uchar, dcn>::vec_type dst;
-
-                RGB2LabConvert_b<srgb, blueIdx>(src, dst);
-
-                return dst;
-            }
-            __host__ __device__ __forceinline__ RGB2Lab() {}
-            __host__ __device__ __forceinline__ RGB2Lab(const RGB2Lab&) {}
-        };
-        template <int scn, int dcn, bool srgb, int blueIdx>
-        struct RGB2Lab<float, scn, dcn, srgb, blueIdx>
-            : unary_function<typename TypeVec<float, scn>::vec_type, typename TypeVec<float, dcn>::vec_type>
-        {
-            __device__ __forceinline__ typename TypeVec<float, dcn>::vec_type operator ()(const typename TypeVec<float, scn>::vec_type& src) const
-            {
-                typename TypeVec<float, dcn>::vec_type dst;
-
-                RGB2LabConvert_f<srgb, blueIdx>(src, dst);
-
-                return dst;
-            }
-            __host__ __device__ __forceinline__ RGB2Lab() {}
-            __host__ __device__ __forceinline__ RGB2Lab(const RGB2Lab&) {}
-        };
-    }
-
-#define OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(name, scn, dcn, srgb, blueIdx) \
-    template <typename T> struct name ## _traits \
-    { \
-        typedef ::cv::cuda::device::color_detail::RGB2Lab<T, scn, dcn, srgb, blueIdx> functor_type; \
-        static __host__ __device__ __forceinline__ functor_type create_functor() \
-        { \
-            return functor_type(); \
-        } \
-    };
-
-    namespace color_detail
-    {
-        __constant__ float c_sRGBInvGammaTab[] = 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-
-        template <bool srgb, int blueIdx, typename T, typename D>
-        __device__ __forceinline__ void Lab2RGBConvert_f(const T& src, D& dst)
-        {
-            const float lThresh = 0.008856f * 903.3f;
-            const float fThresh = 7.787f * 0.008856f + 16.0f / 116.0f;
-
-            float Y, fy;
-
-            if (src.x <= lThresh)
-            {
-                Y = src.x / 903.3f;
-                fy = 7.787f * Y + 16.0f / 116.0f;
-            }
-            else
-            {
-                fy = (src.x + 16.0f) / 116.0f;
-                Y = fy * fy * fy;
-            }
-
-            float X = src.y / 500.0f + fy;
-            float Z = fy - src.z / 200.0f;
-
-            if (X <= fThresh)
-                X = (X - 16.0f / 116.0f) / 7.787f;
-            else
-                X = X * X * X;
-
-            if (Z <= fThresh)
-                Z = (Z - 16.0f / 116.0f) / 7.787f;
-            else
-                Z = Z * Z * Z;
-
-            float B = 0.052891f * X - 0.204043f * Y + 1.151152f * Z;
-            float G = -0.921235f * X + 1.875991f * Y + 0.045244f * Z;
-            float R = 3.079933f * X - 1.537150f * Y - 0.542782f * Z;
-
-            if (srgb)
-            {
-                B = splineInterpolate(B * GAMMA_TAB_SIZE, c_sRGBInvGammaTab, GAMMA_TAB_SIZE);
-                G = splineInterpolate(G * GAMMA_TAB_SIZE, c_sRGBInvGammaTab, GAMMA_TAB_SIZE);
-                R = splineInterpolate(R * GAMMA_TAB_SIZE, c_sRGBInvGammaTab, GAMMA_TAB_SIZE);
-            }
-
-            dst.x = blueIdx == 0 ? B : R;
-            dst.y = G;
-            dst.z = blueIdx == 0 ? R : B;
-            setAlpha(dst, ColorChannel<float>::max());
-        }
-
-        template <bool srgb, int blueIdx, typename T, typename D>
-        __device__ __forceinline__ void Lab2RGBConvert_b(const T& src, D& dst)
-        {
-            float3 srcf, dstf;
-
-            srcf.x = src.x * (100.f / 255.f);
-            srcf.y = src.y - 128;
-            srcf.z = src.z - 128;
-
-            Lab2RGBConvert_f<srgb, blueIdx>(srcf, dstf);
-
-            dst.x = saturate_cast<uchar>(dstf.x * 255.f);
-            dst.y = saturate_cast<uchar>(dstf.y * 255.f);
-            dst.z = saturate_cast<uchar>(dstf.z * 255.f);
-            setAlpha(dst, ColorChannel<uchar>::max());
-        }
-
-        template <typename T, int scn, int dcn, bool srgb, int blueIdx> struct Lab2RGB;
-        template <int scn, int dcn, bool srgb, int blueIdx>
-        struct Lab2RGB<uchar, scn, dcn, srgb, blueIdx>
-            : unary_function<typename TypeVec<uchar, scn>::vec_type, typename TypeVec<uchar, dcn>::vec_type>
-        {
-            __device__ __forceinline__ typename TypeVec<uchar, dcn>::vec_type operator ()(const typename TypeVec<uchar, scn>::vec_type& src) const
-            {
-                typename TypeVec<uchar, dcn>::vec_type dst;
-
-                Lab2RGBConvert_b<srgb, blueIdx>(src, dst);
-
-                return dst;
-            }
-            __host__ __device__ __forceinline__ Lab2RGB() {}
-            __host__ __device__ __forceinline__ Lab2RGB(const Lab2RGB&) {}
-        };
-        template <int scn, int dcn, bool srgb, int blueIdx>
-        struct Lab2RGB<float, scn, dcn, srgb, blueIdx>
-            : unary_function<typename TypeVec<float, scn>::vec_type, typename TypeVec<float, dcn>::vec_type>
-        {
-            __device__ __forceinline__ typename TypeVec<float, dcn>::vec_type operator ()(const typename TypeVec<float, scn>::vec_type& src) const
-            {
-                typename TypeVec<float, dcn>::vec_type dst;
-
-                Lab2RGBConvert_f<srgb, blueIdx>(src, dst);
-
-                return dst;
-            }
-            __host__ __device__ __forceinline__ Lab2RGB() {}
-            __host__ __device__ __forceinline__ Lab2RGB(const Lab2RGB&) {}
-        };
-    }
-
-#define OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(name, scn, dcn, srgb, blueIdx) \
-    template <typename T> struct name ## _traits \
-    { \
-        typedef ::cv::cuda::device::color_detail::Lab2RGB<T, scn, dcn, srgb, blueIdx> functor_type; \
-        static __host__ __device__ __forceinline__ functor_type create_functor() \
-        { \
-            return functor_type(); \
-        } \
-    };
-
-///////////////////////////////////// RGB <-> Luv /////////////////////////////////////
-
-    namespace color_detail
-    {
-        __constant__ float c_LabCbrtTab[] = 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-
-        template <bool srgb, int blueIdx, typename T, typename D>
-        __device__ __forceinline__ void RGB2LuvConvert_f(const T& src, D& dst)
-        {
-            const float _d = 1.f / (0.950456f + 15 + 1.088754f * 3);
-            const float _un = 13 * (4 * 0.950456f * _d);
-            const float _vn = 13 * (9 * _d);
-
-            float B = blueIdx == 0 ? src.x : src.z;
-            float G = src.y;
-            float R = blueIdx == 0 ? src.z : src.x;
-
-            if (srgb)
-            {
-                B = splineInterpolate(B * GAMMA_TAB_SIZE, c_sRGBGammaTab, GAMMA_TAB_SIZE);
-                G = splineInterpolate(G * GAMMA_TAB_SIZE, c_sRGBGammaTab, GAMMA_TAB_SIZE);
-                R = splineInterpolate(R * GAMMA_TAB_SIZE, c_sRGBGammaTab, GAMMA_TAB_SIZE);
-            }
-
-            float X = R * 0.412453f + G * 0.357580f + B * 0.180423f;
-            float Y = R * 0.212671f + G * 0.715160f + B * 0.072169f;
-            float Z = R * 0.019334f + G * 0.119193f + B * 0.950227f;
-
-            float L = splineInterpolate(Y * (LAB_CBRT_TAB_SIZE / 1.5f), c_LabCbrtTab, LAB_CBRT_TAB_SIZE);
-            L = 116.f * L - 16.f;
-
-            const float d = (4 * 13) / ::fmaxf(X + 15 * Y + 3 * Z, numeric_limits<float>::epsilon());
-            float u = L * (X * d - _un);
-            float v = L * ((9 * 0.25f) * Y * d - _vn);
-
-            dst.x = L;
-            dst.y = u;
-            dst.z = v;
-        }
-
-        template <bool srgb, int blueIdx, typename T, typename D>
-        __device__ __forceinline__ void RGB2LuvConvert_b(const T& src, D& dst)
-        {
-            float3 srcf, dstf;
-
-            srcf.x = src.x * (1.f / 255.f);
-            srcf.y = src.y * (1.f / 255.f);
-            srcf.z = src.z * (1.f / 255.f);
-
-            RGB2LuvConvert_f<srgb, blueIdx>(srcf, dstf);
-
-            dst.x = saturate_cast<uchar>(dstf.x * 2.55f);
-            dst.y = saturate_cast<uchar>(dstf.y * 0.72033898305084743f + 96.525423728813564f);
-            dst.z = saturate_cast<uchar>(dstf.z * 0.9732824427480916f + 136.259541984732824f);
-        }
-
-        template <typename T, int scn, int dcn, bool srgb, int blueIdx> struct RGB2Luv;
-        template <int scn, int dcn, bool srgb, int blueIdx>
-        struct RGB2Luv<uchar, scn, dcn, srgb, blueIdx>
-            : unary_function<typename TypeVec<uchar, scn>::vec_type, typename TypeVec<uchar, dcn>::vec_type>
-        {
-            __device__ __forceinline__ typename TypeVec<uchar, dcn>::vec_type operator ()(const typename TypeVec<uchar, scn>::vec_type& src) const
-            {
-                typename TypeVec<uchar, dcn>::vec_type dst;
-
-                RGB2LuvConvert_b<srgb, blueIdx>(src, dst);
-
-                return dst;
-            }
-            __host__ __device__ __forceinline__ RGB2Luv() {}
-            __host__ __device__ __forceinline__ RGB2Luv(const RGB2Luv&) {}
-        };
-        template <int scn, int dcn, bool srgb, int blueIdx>
-        struct RGB2Luv<float, scn, dcn, srgb, blueIdx>
-            : unary_function<typename TypeVec<float, scn>::vec_type, typename TypeVec<float, dcn>::vec_type>
-        {
-            __device__ __forceinline__ typename TypeVec<float, dcn>::vec_type operator ()(const typename TypeVec<float, scn>::vec_type& src) const
-            {
-                typename TypeVec<float, dcn>::vec_type dst;
-
-                RGB2LuvConvert_f<srgb, blueIdx>(src, dst);
-
-                return dst;
-            }
-            __host__ __device__ __forceinline__ RGB2Luv() {}
-            __host__ __device__ __forceinline__ RGB2Luv(const RGB2Luv&) {}
-        };
-    }
-
-#define OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(name, scn, dcn, srgb, blueIdx) \
-    template <typename T> struct name ## _traits \
-    { \
-        typedef ::cv::cuda::device::color_detail::RGB2Luv<T, scn, dcn, srgb, blueIdx> functor_type; \
-        static __host__ __device__ __forceinline__ functor_type create_functor() \
-        { \
-            return functor_type(); \
-        } \
-    };
-
-    namespace color_detail
-    {
-        template <bool srgb, int blueIdx, typename T, typename D>
-        __device__ __forceinline__ void Luv2RGBConvert_f(const T& src, D& dst)
-        {
-            const float _d = 1.f / (0.950456f + 15 + 1.088754f * 3);
-            const float _un = 4 * 0.950456f * _d;
-            const float _vn = 9 * _d;
-
-            float L = src.x;
-            float u = src.y;
-            float v = src.z;
-
-            float Y = (L + 16.f) * (1.f / 116.f);
-            Y = Y * Y * Y;
-
-            float d = (1.f / 13.f) / L;
-            u = u * d + _un;
-            v = v * d + _vn;
-
-            float iv = 1.f / v;
-            float X = 2.25f * u * Y * iv;
-            float Z = (12 - 3 * u - 20 * v) * Y * 0.25f * iv;
-
-            float B = 0.055648f * X - 0.204043f * Y + 1.057311f * Z;
-            float G = -0.969256f * X + 1.875991f * Y + 0.041556f * Z;
-            float R = 3.240479f * X - 1.537150f * Y - 0.498535f * Z;
-
-            if (srgb)
-            {
-                B = splineInterpolate(B * GAMMA_TAB_SIZE, c_sRGBInvGammaTab, GAMMA_TAB_SIZE);
-                G = splineInterpolate(G * GAMMA_TAB_SIZE, c_sRGBInvGammaTab, GAMMA_TAB_SIZE);
-                R = splineInterpolate(R * GAMMA_TAB_SIZE, c_sRGBInvGammaTab, GAMMA_TAB_SIZE);
-            }
-
-            dst.x = blueIdx == 0 ? B : R;
-            dst.y = G;
-            dst.z = blueIdx == 0 ? R : B;
-            setAlpha(dst, ColorChannel<float>::max());
-        }
-
-        template <bool srgb, int blueIdx, typename T, typename D>
-        __device__ __forceinline__ void Luv2RGBConvert_b(const T& src, D& dst)
-        {
-            float3 srcf, dstf;
-
-            srcf.x = src.x * (100.f / 255.f);
-            srcf.y = src.y * 1.388235294117647f - 134.f;
-            srcf.z = src.z * 1.027450980392157f - 140.f;
-
-            Luv2RGBConvert_f<srgb, blueIdx>(srcf, dstf);
-
-            dst.x = saturate_cast<uchar>(dstf.x * 255.f);
-            dst.y = saturate_cast<uchar>(dstf.y * 255.f);
-            dst.z = saturate_cast<uchar>(dstf.z * 255.f);
-            setAlpha(dst, ColorChannel<uchar>::max());
-        }
-
-        template <typename T, int scn, int dcn, bool srgb, int blueIdx> struct Luv2RGB;
-        template <int scn, int dcn, bool srgb, int blueIdx>
-        struct Luv2RGB<uchar, scn, dcn, srgb, blueIdx>
-            : unary_function<typename TypeVec<uchar, scn>::vec_type, typename TypeVec<uchar, dcn>::vec_type>
-        {
-            __device__ __forceinline__ typename TypeVec<uchar, dcn>::vec_type operator ()(const typename TypeVec<uchar, scn>::vec_type& src) const
-            {
-                typename TypeVec<uchar, dcn>::vec_type dst;
-
-                Luv2RGBConvert_b<srgb, blueIdx>(src, dst);
-
-                return dst;
-            }
-            __host__ __device__ __forceinline__ Luv2RGB() {}
-            __host__ __device__ __forceinline__ Luv2RGB(const Luv2RGB&) {}
-        };
-        template <int scn, int dcn, bool srgb, int blueIdx>
-        struct Luv2RGB<float, scn, dcn, srgb, blueIdx>
-            : unary_function<typename TypeVec<float, scn>::vec_type, typename TypeVec<float, dcn>::vec_type>
-        {
-            __device__ __forceinline__ typename TypeVec<float, dcn>::vec_type operator ()(const typename TypeVec<float, scn>::vec_type& src) const
-            {
-                typename TypeVec<float, dcn>::vec_type dst;
-
-                Luv2RGBConvert_f<srgb, blueIdx>(src, dst);
-
-                return dst;
-            }
-            __host__ __device__ __forceinline__ Luv2RGB() {}
-            __host__ __device__ __forceinline__ Luv2RGB(const Luv2RGB&) {}
-        };
-    }
-
-#define OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(name, scn, dcn, srgb, blueIdx) \
-    template <typename T> struct name ## _traits \
-    { \
-        typedef ::cv::cuda::device::color_detail::Luv2RGB<T, scn, dcn, srgb, blueIdx> functor_type; \
-        static __host__ __device__ __forceinline__ functor_type create_functor() \
-        { \
-            return functor_type(); \
-        } \
-    };
-
-    #undef CV_DESCALE
-
-}}} // namespace cv { namespace cuda { namespace cudev
-
-//! @endcond
-
-#endif // OPENCV_CUDA_COLOR_DETAIL_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/detail/reduce.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/detail/reduce.hpp
deleted file mode 100755
index ff82c3c..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/detail/reduce.hpp
+++ /dev/null
@@ -1,365 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_CUDA_REDUCE_DETAIL_HPP
-#define OPENCV_CUDA_REDUCE_DETAIL_HPP
-
-#include <thrust/tuple.h>
-#include "../warp.hpp"
-#include "../warp_shuffle.hpp"
-
-//! @cond IGNORED
-
-namespace cv { namespace cuda { namespace device
-{
-    namespace reduce_detail
-    {
-        template <typename T> struct GetType;
-        template <typename T> struct GetType<T*>
-        {
-            typedef T type;
-        };
-        template <typename T> struct GetType<volatile T*>
-        {
-            typedef T type;
-        };
-        template <typename T> struct GetType<T&>
-        {
-            typedef T type;
-        };
-
-        template <unsigned int I, unsigned int N>
-        struct For
-        {
-            template <class PointerTuple, class ValTuple>
-            static __device__ void loadToSmem(const PointerTuple& smem, const ValTuple& val, unsigned int tid)
-            {
-                thrust::get<I>(smem)[tid] = thrust::get<I>(val);
-
-                For<I + 1, N>::loadToSmem(smem, val, tid);
-            }
-            template <class PointerTuple, class ValTuple>
-            static __device__ void loadFromSmem(const PointerTuple& smem, const ValTuple& val, unsigned int tid)
-            {
-                thrust::get<I>(val) = thrust::get<I>(smem)[tid];
-
-                For<I + 1, N>::loadFromSmem(smem, val, tid);
-            }
-
-            template <class PointerTuple, class ValTuple, class OpTuple>
-            static __device__ void merge(const PointerTuple& smem, const ValTuple& val, unsigned int tid, unsigned int delta, const OpTuple& op)
-            {
-                typename GetType<typename thrust::tuple_element<I, PointerTuple>::type>::type reg = thrust::get<I>(smem)[tid + delta];
-                thrust::get<I>(smem)[tid] = thrust::get<I>(val) = thrust::get<I>(op)(thrust::get<I>(val), reg);
-
-                For<I + 1, N>::merge(smem, val, tid, delta, op);
-            }
-            template <class ValTuple, class OpTuple>
-            static __device__ void mergeShfl(const ValTuple& val, unsigned int delta, unsigned int width, const OpTuple& op)
-            {
-                typename GetType<typename thrust::tuple_element<I, ValTuple>::type>::type reg = shfl_down(thrust::get<I>(val), delta, width);
-                thrust::get<I>(val) = thrust::get<I>(op)(thrust::get<I>(val), reg);
-
-                For<I + 1, N>::mergeShfl(val, delta, width, op);
-            }
-        };
-        template <unsigned int N>
-        struct For<N, N>
-        {
-            template <class PointerTuple, class ValTuple>
-            static __device__ void loadToSmem(const PointerTuple&, const ValTuple&, unsigned int)
-            {
-            }
-            template <class PointerTuple, class ValTuple>
-            static __device__ void loadFromSmem(const PointerTuple&, const ValTuple&, unsigned int)
-            {
-            }
-
-            template <class PointerTuple, class ValTuple, class OpTuple>
-            static __device__ void merge(const PointerTuple&, const ValTuple&, unsigned int, unsigned int, const OpTuple&)
-            {
-            }
-            template <class ValTuple, class OpTuple>
-            static __device__ void mergeShfl(const ValTuple&, unsigned int, unsigned int, const OpTuple&)
-            {
-            }
-        };
-
-        template <typename T>
-        __device__ __forceinline__ void loadToSmem(volatile T* smem, T& val, unsigned int tid)
-        {
-            smem[tid] = val;
-        }
-        template <typename T>
-        __device__ __forceinline__ void loadFromSmem(volatile T* smem, T& val, unsigned int tid)
-        {
-            val = smem[tid];
-        }
-        template <typename P0, typename P1, typename P2, typename P3, typename P4, typename P5, typename P6, typename P7, typename P8, typename P9,
-                  typename R0, typename R1, typename R2, typename R3, typename R4, typename R5, typename R6, typename R7, typename R8, typename R9>
-        __device__ __forceinline__ void loadToSmem(const thrust::tuple<P0, P1, P2, P3, P4, P5, P6, P7, P8, P9>& smem,
-                                                       const thrust::tuple<R0, R1, R2, R3, R4, R5, R6, R7, R8, R9>& val,
-                                                       unsigned int tid)
-        {
-            For<0, thrust::tuple_size<thrust::tuple<P0, P1, P2, P3, P4, P5, P6, P7, P8, P9> >::value>::loadToSmem(smem, val, tid);
-        }
-        template <typename P0, typename P1, typename P2, typename P3, typename P4, typename P5, typename P6, typename P7, typename P8, typename P9,
-                  typename R0, typename R1, typename R2, typename R3, typename R4, typename R5, typename R6, typename R7, typename R8, typename R9>
-        __device__ __forceinline__ void loadFromSmem(const thrust::tuple<P0, P1, P2, P3, P4, P5, P6, P7, P8, P9>& smem,
-                                                         const thrust::tuple<R0, R1, R2, R3, R4, R5, R6, R7, R8, R9>& val,
-                                                         unsigned int tid)
-        {
-            For<0, thrust::tuple_size<thrust::tuple<P0, P1, P2, P3, P4, P5, P6, P7, P8, P9> >::value>::loadFromSmem(smem, val, tid);
-        }
-
-        template <typename T, class Op>
-        __device__ __forceinline__ void merge(volatile T* smem, T& val, unsigned int tid, unsigned int delta, const Op& op)
-        {
-            T reg = smem[tid + delta];
-            smem[tid] = val = op(val, reg);
-        }
-        template <typename T, class Op>
-        __device__ __forceinline__ void mergeShfl(T& val, unsigned int delta, unsigned int width, const Op& op)
-        {
-            T reg = shfl_down(val, delta, width);
-            val = op(val, reg);
-        }
-        template <typename P0, typename P1, typename P2, typename P3, typename P4, typename P5, typename P6, typename P7, typename P8, typename P9,
-                  typename R0, typename R1, typename R2, typename R3, typename R4, typename R5, typename R6, typename R7, typename R8, typename R9,
-                  class Op0, class Op1, class Op2, class Op3, class Op4, class Op5, class Op6, class Op7, class Op8, class Op9>
-        __device__ __forceinline__ void merge(const thrust::tuple<P0, P1, P2, P3, P4, P5, P6, P7, P8, P9>& smem,
-                                              const thrust::tuple<R0, R1, R2, R3, R4, R5, R6, R7, R8, R9>& val,
-                                              unsigned int tid,
-                                              unsigned int delta,
-                                              const thrust::tuple<Op0, Op1, Op2, Op3, Op4, Op5, Op6, Op7, Op8, Op9>& op)
-        {
-            For<0, thrust::tuple_size<thrust::tuple<P0, P1, P2, P3, P4, P5, P6, P7, P8, P9> >::value>::merge(smem, val, tid, delta, op);
-        }
-        template <typename R0, typename R1, typename R2, typename R3, typename R4, typename R5, typename R6, typename R7, typename R8, typename R9,
-                  class Op0, class Op1, class Op2, class Op3, class Op4, class Op5, class Op6, class Op7, class Op8, class Op9>
-        __device__ __forceinline__ void mergeShfl(const thrust::tuple<R0, R1, R2, R3, R4, R5, R6, R7, R8, R9>& val,
-                                                  unsigned int delta,
-                                                  unsigned int width,
-                                                  const thrust::tuple<Op0, Op1, Op2, Op3, Op4, Op5, Op6, Op7, Op8, Op9>& op)
-        {
-            For<0, thrust::tuple_size<thrust::tuple<R0, R1, R2, R3, R4, R5, R6, R7, R8, R9> >::value>::mergeShfl(val, delta, width, op);
-        }
-
-        template <unsigned int N> struct Generic
-        {
-            template <typename Pointer, typename Reference, class Op>
-            static __device__ void reduce(Pointer smem, Reference val, unsigned int tid, Op op)
-            {
-                loadToSmem(smem, val, tid);
-                if (N >= 32)
-                    __syncthreads();
-
-                if (N >= 2048)
-                {
-                    if (tid < 1024)
-                        merge(smem, val, tid, 1024, op);
-
-                    __syncthreads();
-                }
-                if (N >= 1024)
-                {
-                    if (tid < 512)
-                        merge(smem, val, tid, 512, op);
-
-                    __syncthreads();
-                }
-                if (N >= 512)
-                {
-                    if (tid < 256)
-                        merge(smem, val, tid, 256, op);
-
-                    __syncthreads();
-                }
-                if (N >= 256)
-                {
-                    if (tid < 128)
-                        merge(smem, val, tid, 128, op);
-
-                    __syncthreads();
-                }
-                if (N >= 128)
-                {
-                    if (tid < 64)
-                        merge(smem, val, tid, 64, op);
-
-                    __syncthreads();
-                }
-                if (N >= 64)
-                {
-                    if (tid < 32)
-                        merge(smem, val, tid, 32, op);
-                }
-
-                if (tid < 16)
-                {
-                    merge(smem, val, tid, 16, op);
-                    merge(smem, val, tid, 8, op);
-                    merge(smem, val, tid, 4, op);
-                    merge(smem, val, tid, 2, op);
-                    merge(smem, val, tid, 1, op);
-                }
-            }
-        };
-
-        template <unsigned int I, typename Pointer, typename Reference, class Op>
-        struct Unroll
-        {
-            static __device__ void loopShfl(Reference val, Op op, unsigned int N)
-            {
-                mergeShfl(val, I, N, op);
-                Unroll<I / 2, Pointer, Reference, Op>::loopShfl(val, op, N);
-            }
-            static __device__ void loop(Pointer smem, Reference val, unsigned int tid, Op op)
-            {
-                merge(smem, val, tid, I, op);
-                Unroll<I / 2, Pointer, Reference, Op>::loop(smem, val, tid, op);
-            }
-        };
-        template <typename Pointer, typename Reference, class Op>
-        struct Unroll<0, Pointer, Reference, Op>
-        {
-            static __device__ void loopShfl(Reference, Op, unsigned int)
-            {
-            }
-            static __device__ void loop(Pointer, Reference, unsigned int, Op)
-            {
-            }
-        };
-
-        template <unsigned int N> struct WarpOptimized
-        {
-            template <typename Pointer, typename Reference, class Op>
-            static __device__ void reduce(Pointer smem, Reference val, unsigned int tid, Op op)
-            {
-            #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300
-                (void) smem;
-                (void) tid;
-
-                Unroll<N / 2, Pointer, Reference, Op>::loopShfl(val, op, N);
-            #else
-                loadToSmem(smem, val, tid);
-
-                if (tid < N / 2)
-                    Unroll<N / 2, Pointer, Reference, Op>::loop(smem, val, tid, op);
-            #endif
-            }
-        };
-
-        template <unsigned int N> struct GenericOptimized32
-        {
-            enum { M = N / 32 };
-
-            template <typename Pointer, typename Reference, class Op>
-            static __device__ void reduce(Pointer smem, Reference val, unsigned int tid, Op op)
-            {
-                const unsigned int laneId = Warp::laneId();
-
-            #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300
-                Unroll<16, Pointer, Reference, Op>::loopShfl(val, op, warpSize);
-
-                if (laneId == 0)
-                    loadToSmem(smem, val, tid / 32);
-            #else
-                loadToSmem(smem, val, tid);
-
-                if (laneId < 16)
-                    Unroll<16, Pointer, Reference, Op>::loop(smem, val, tid, op);
-
-                __syncthreads();
-
-                if (laneId == 0)
-                    loadToSmem(smem, val, tid / 32);
-            #endif
-
-                __syncthreads();
-
-                loadFromSmem(smem, val, tid);
-
-                if (tid < 32)
-                {
-                #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300
-                    Unroll<M / 2, Pointer, Reference, Op>::loopShfl(val, op, M);
-                #else
-                    Unroll<M / 2, Pointer, Reference, Op>::loop(smem, val, tid, op);
-                #endif
-                }
-            }
-        };
-
-        template <bool val, class T1, class T2> struct StaticIf;
-        template <class T1, class T2> struct StaticIf<true, T1, T2>
-        {
-            typedef T1 type;
-        };
-        template <class T1, class T2> struct StaticIf<false, T1, T2>
-        {
-            typedef T2 type;
-        };
-
-        template <unsigned int N> struct IsPowerOf2
-        {
-            enum { value = ((N != 0) && !(N & (N - 1))) };
-        };
-
-        template <unsigned int N> struct Dispatcher
-        {
-            typedef typename StaticIf<
-                (N <= 32) && IsPowerOf2<N>::value,
-                WarpOptimized<N>,
-                typename StaticIf<
-                    (N <= 1024) && IsPowerOf2<N>::value,
-                    GenericOptimized32<N>,
-                    Generic<N>
-                >::type
-            >::type reductor;
-        };
-    }
-}}}
-
-//! @endcond
-
-#endif // OPENCV_CUDA_REDUCE_DETAIL_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/detail/reduce_key_val.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/detail/reduce_key_val.hpp
deleted file mode 100755
index 6a537c9..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/detail/reduce_key_val.hpp
+++ /dev/null
@@ -1,502 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_CUDA_PRED_VAL_REDUCE_DETAIL_HPP
-#define OPENCV_CUDA_PRED_VAL_REDUCE_DETAIL_HPP
-
-#include <thrust/tuple.h>
-#include "../warp.hpp"
-#include "../warp_shuffle.hpp"
-
-//! @cond IGNORED
-
-namespace cv { namespace cuda { namespace device
-{
-    namespace reduce_key_val_detail
-    {
-        template <typename T> struct GetType;
-        template <typename T> struct GetType<T*>
-        {
-            typedef T type;
-        };
-        template <typename T> struct GetType<volatile T*>
-        {
-            typedef T type;
-        };
-        template <typename T> struct GetType<T&>
-        {
-            typedef T type;
-        };
-
-        template <unsigned int I, unsigned int N>
-        struct For
-        {
-            template <class PointerTuple, class ReferenceTuple>
-            static __device__ void loadToSmem(const PointerTuple& smem, const ReferenceTuple& data, unsigned int tid)
-            {
-                thrust::get<I>(smem)[tid] = thrust::get<I>(data);
-
-                For<I + 1, N>::loadToSmem(smem, data, tid);
-            }
-            template <class PointerTuple, class ReferenceTuple>
-            static __device__ void loadFromSmem(const PointerTuple& smem, const ReferenceTuple& data, unsigned int tid)
-            {
-                thrust::get<I>(data) = thrust::get<I>(smem)[tid];
-
-                For<I + 1, N>::loadFromSmem(smem, data, tid);
-            }
-
-            template <class ReferenceTuple>
-            static __device__ void copyShfl(const ReferenceTuple& val, unsigned int delta, int width)
-            {
-                thrust::get<I>(val) = shfl_down(thrust::get<I>(val), delta, width);
-
-                For<I + 1, N>::copyShfl(val, delta, width);
-            }
-            template <class PointerTuple, class ReferenceTuple>
-            static __device__ void copy(const PointerTuple& svals, const ReferenceTuple& val, unsigned int tid, unsigned int delta)
-            {
-                thrust::get<I>(svals)[tid] = thrust::get<I>(val) = thrust::get<I>(svals)[tid + delta];
-
-                For<I + 1, N>::copy(svals, val, tid, delta);
-            }
-
-            template <class KeyReferenceTuple, class ValReferenceTuple, class CmpTuple>
-            static __device__ void mergeShfl(const KeyReferenceTuple& key, const ValReferenceTuple& val, const CmpTuple& cmp, unsigned int delta, int width)
-            {
-                typename GetType<typename thrust::tuple_element<I, KeyReferenceTuple>::type>::type reg = shfl_down(thrust::get<I>(key), delta, width);
-
-                if (thrust::get<I>(cmp)(reg, thrust::get<I>(key)))
-                {
-                    thrust::get<I>(key) = reg;
-                    thrust::get<I>(val) = shfl_down(thrust::get<I>(val), delta, width);
-                }
-
-                For<I + 1, N>::mergeShfl(key, val, cmp, delta, width);
-            }
-            template <class KeyPointerTuple, class KeyReferenceTuple, class ValPointerTuple, class ValReferenceTuple, class CmpTuple>
-            static __device__ void merge(const KeyPointerTuple& skeys, const KeyReferenceTuple& key,
-                                         const ValPointerTuple& svals, const ValReferenceTuple& val,
-                                         const CmpTuple& cmp,
-                                         unsigned int tid, unsigned int delta)
-            {
-                typename GetType<typename thrust::tuple_element<I, KeyPointerTuple>::type>::type reg = thrust::get<I>(skeys)[tid + delta];
-
-                if (thrust::get<I>(cmp)(reg, thrust::get<I>(key)))
-                {
-                    thrust::get<I>(skeys)[tid] = thrust::get<I>(key) = reg;
-                    thrust::get<I>(svals)[tid] = thrust::get<I>(val) = thrust::get<I>(svals)[tid + delta];
-                }
-
-                For<I + 1, N>::merge(skeys, key, svals, val, cmp, tid, delta);
-            }
-        };
-        template <unsigned int N>
-        struct For<N, N>
-        {
-            template <class PointerTuple, class ReferenceTuple>
-            static __device__ void loadToSmem(const PointerTuple&, const ReferenceTuple&, unsigned int)
-            {
-            }
-            template <class PointerTuple, class ReferenceTuple>
-            static __device__ void loadFromSmem(const PointerTuple&, const ReferenceTuple&, unsigned int)
-            {
-            }
-
-            template <class ReferenceTuple>
-            static __device__ void copyShfl(const ReferenceTuple&, unsigned int, int)
-            {
-            }
-            template <class PointerTuple, class ReferenceTuple>
-            static __device__ void copy(const PointerTuple&, const ReferenceTuple&, unsigned int, unsigned int)
-            {
-            }
-
-            template <class KeyReferenceTuple, class ValReferenceTuple, class CmpTuple>
-            static __device__ void mergeShfl(const KeyReferenceTuple&, const ValReferenceTuple&, const CmpTuple&, unsigned int, int)
-            {
-            }
-            template <class KeyPointerTuple, class KeyReferenceTuple, class ValPointerTuple, class ValReferenceTuple, class CmpTuple>
-            static __device__ void merge(const KeyPointerTuple&, const KeyReferenceTuple&,
-                                         const ValPointerTuple&, const ValReferenceTuple&,
-                                         const CmpTuple&,
-                                         unsigned int, unsigned int)
-            {
-            }
-        };
-
-        //////////////////////////////////////////////////////
-        // loadToSmem
-
-        template <typename T>
-        __device__ __forceinline__ void loadToSmem(volatile T* smem, T& data, unsigned int tid)
-        {
-            smem[tid] = data;
-        }
-        template <typename T>
-        __device__ __forceinline__ void loadFromSmem(volatile T* smem, T& data, unsigned int tid)
-        {
-            data = smem[tid];
-        }
-        template <typename VP0, typename VP1, typename VP2, typename VP3, typename VP4, typename VP5, typename VP6, typename VP7, typename VP8, typename VP9,
-                  typename VR0, typename VR1, typename VR2, typename VR3, typename VR4, typename VR5, typename VR6, typename VR7, typename VR8, typename VR9>
-        __device__ __forceinline__ void loadToSmem(const thrust::tuple<VP0, VP1, VP2, VP3, VP4, VP5, VP6, VP7, VP8, VP9>& smem,
-                                                   const thrust::tuple<VR0, VR1, VR2, VR3, VR4, VR5, VR6, VR7, VR8, VR9>& data,
-                                                   unsigned int tid)
-        {
-            For<0, thrust::tuple_size<thrust::tuple<VP0, VP1, VP2, VP3, VP4, VP5, VP6, VP7, VP8, VP9> >::value>::loadToSmem(smem, data, tid);
-        }
-        template <typename VP0, typename VP1, typename VP2, typename VP3, typename VP4, typename VP5, typename VP6, typename VP7, typename VP8, typename VP9,
-                  typename VR0, typename VR1, typename VR2, typename VR3, typename VR4, typename VR5, typename VR6, typename VR7, typename VR8, typename VR9>
-        __device__ __forceinline__ void loadFromSmem(const thrust::tuple<VP0, VP1, VP2, VP3, VP4, VP5, VP6, VP7, VP8, VP9>& smem,
-                                                     const thrust::tuple<VR0, VR1, VR2, VR3, VR4, VR5, VR6, VR7, VR8, VR9>& data,
-                                                     unsigned int tid)
-        {
-            For<0, thrust::tuple_size<thrust::tuple<VP0, VP1, VP2, VP3, VP4, VP5, VP6, VP7, VP8, VP9> >::value>::loadFromSmem(smem, data, tid);
-        }
-
-        //////////////////////////////////////////////////////
-        // copyVals
-
-        template <typename V>
-        __device__ __forceinline__ void copyValsShfl(V& val, unsigned int delta, int width)
-        {
-            val = shfl_down(val, delta, width);
-        }
-        template <typename V>
-        __device__ __forceinline__ void copyVals(volatile V* svals, V& val, unsigned int tid, unsigned int delta)
-        {
-            svals[tid] = val = svals[tid + delta];
-        }
-        template <typename VR0, typename VR1, typename VR2, typename VR3, typename VR4, typename VR5, typename VR6, typename VR7, typename VR8, typename VR9>
-        __device__ __forceinline__ void copyValsShfl(const thrust::tuple<VR0, VR1, VR2, VR3, VR4, VR5, VR6, VR7, VR8, VR9>& val,
-                                                     unsigned int delta,
-                                                     int width)
-        {
-            For<0, thrust::tuple_size<thrust::tuple<VR0, VR1, VR2, VR3, VR4, VR5, VR6, VR7, VR8, VR9> >::value>::copyShfl(val, delta, width);
-        }
-        template <typename VP0, typename VP1, typename VP2, typename VP3, typename VP4, typename VP5, typename VP6, typename VP7, typename VP8, typename VP9,
-                  typename VR0, typename VR1, typename VR2, typename VR3, typename VR4, typename VR5, typename VR6, typename VR7, typename VR8, typename VR9>
-        __device__ __forceinline__ void copyVals(const thrust::tuple<VP0, VP1, VP2, VP3, VP4, VP5, VP6, VP7, VP8, VP9>& svals,
-                                                 const thrust::tuple<VR0, VR1, VR2, VR3, VR4, VR5, VR6, VR7, VR8, VR9>& val,
-                                                 unsigned int tid, unsigned int delta)
-        {
-            For<0, thrust::tuple_size<thrust::tuple<VP0, VP1, VP2, VP3, VP4, VP5, VP6, VP7, VP8, VP9> >::value>::copy(svals, val, tid, delta);
-        }
-
-        //////////////////////////////////////////////////////
-        // merge
-
-        template <typename K, typename V, class Cmp>
-        __device__ __forceinline__ void mergeShfl(K& key, V& val, const Cmp& cmp, unsigned int delta, int width)
-        {
-            K reg = shfl_down(key, delta, width);
-
-            if (cmp(reg, key))
-            {
-                key = reg;
-                copyValsShfl(val, delta, width);
-            }
-        }
-        template <typename K, typename V, class Cmp>
-        __device__ __forceinline__ void merge(volatile K* skeys, K& key, volatile V* svals, V& val, const Cmp& cmp, unsigned int tid, unsigned int delta)
-        {
-            K reg = skeys[tid + delta];
-
-            if (cmp(reg, key))
-            {
-                skeys[tid] = key = reg;
-                copyVals(svals, val, tid, delta);
-            }
-        }
-        template <typename K,
-                  typename VR0, typename VR1, typename VR2, typename VR3, typename VR4, typename VR5, typename VR6, typename VR7, typename VR8, typename VR9,
-                  class Cmp>
-        __device__ __forceinline__ void mergeShfl(K& key,
-                                                  const thrust::tuple<VR0, VR1, VR2, VR3, VR4, VR5, VR6, VR7, VR8, VR9>& val,
-                                                  const Cmp& cmp,
-                                                  unsigned int delta, int width)
-        {
-            K reg = shfl_down(key, delta, width);
-
-            if (cmp(reg, key))
-            {
-                key = reg;
-                copyValsShfl(val, delta, width);
-            }
-        }
-        template <typename K,
-                  typename VP0, typename VP1, typename VP2, typename VP3, typename VP4, typename VP5, typename VP6, typename VP7, typename VP8, typename VP9,
-                  typename VR0, typename VR1, typename VR2, typename VR3, typename VR4, typename VR5, typename VR6, typename VR7, typename VR8, typename VR9,
-                  class Cmp>
-        __device__ __forceinline__ void merge(volatile K* skeys, K& key,
-                                              const thrust::tuple<VP0, VP1, VP2, VP3, VP4, VP5, VP6, VP7, VP8, VP9>& svals,
-                                              const thrust::tuple<VR0, VR1, VR2, VR3, VR4, VR5, VR6, VR7, VR8, VR9>& val,
-                                              const Cmp& cmp, unsigned int tid, unsigned int delta)
-        {
-            K reg = skeys[tid + delta];
-
-            if (cmp(reg, key))
-            {
-                skeys[tid] = key = reg;
-                copyVals(svals, val, tid, delta);
-            }
-        }
-        template <typename KR0, typename KR1, typename KR2, typename KR3, typename KR4, typename KR5, typename KR6, typename KR7, typename KR8, typename KR9,
-                  typename VR0, typename VR1, typename VR2, typename VR3, typename VR4, typename VR5, typename VR6, typename VR7, typename VR8, typename VR9,
-                  class Cmp0, class Cmp1, class Cmp2, class Cmp3, class Cmp4, class Cmp5, class Cmp6, class Cmp7, class Cmp8, class Cmp9>
-        __device__ __forceinline__ void mergeShfl(const thrust::tuple<KR0, KR1, KR2, KR3, KR4, KR5, KR6, KR7, KR8, KR9>& key,
-                                                  const thrust::tuple<VR0, VR1, VR2, VR3, VR4, VR5, VR6, VR7, VR8, VR9>& val,
-                                                  const thrust::tuple<Cmp0, Cmp1, Cmp2, Cmp3, Cmp4, Cmp5, Cmp6, Cmp7, Cmp8, Cmp9>& cmp,
-                                                  unsigned int delta, int width)
-        {
-            For<0, thrust::tuple_size<thrust::tuple<KR0, KR1, KR2, KR3, KR4, KR5, KR6, KR7, KR8, KR9> >::value>::mergeShfl(key, val, cmp, delta, width);
-        }
-        template <typename KP0, typename KP1, typename KP2, typename KP3, typename KP4, typename KP5, typename KP6, typename KP7, typename KP8, typename KP9,
-                  typename KR0, typename KR1, typename KR2, typename KR3, typename KR4, typename KR5, typename KR6, typename KR7, typename KR8, typename KR9,
-                  typename VP0, typename VP1, typename VP2, typename VP3, typename VP4, typename VP5, typename VP6, typename VP7, typename VP8, typename VP9,
-                  typename VR0, typename VR1, typename VR2, typename VR3, typename VR4, typename VR5, typename VR6, typename VR7, typename VR8, typename VR9,
-                  class Cmp0, class Cmp1, class Cmp2, class Cmp3, class Cmp4, class Cmp5, class Cmp6, class Cmp7, class Cmp8, class Cmp9>
-        __device__ __forceinline__ void merge(const thrust::tuple<KP0, KP1, KP2, KP3, KP4, KP5, KP6, KP7, KP8, KP9>& skeys,
-                                              const thrust::tuple<KR0, KR1, KR2, KR3, KR4, KR5, KR6, KR7, KR8, KR9>& key,
-                                              const thrust::tuple<VP0, VP1, VP2, VP3, VP4, VP5, VP6, VP7, VP8, VP9>& svals,
-                                              const thrust::tuple<VR0, VR1, VR2, VR3, VR4, VR5, VR6, VR7, VR8, VR9>& val,
-                                              const thrust::tuple<Cmp0, Cmp1, Cmp2, Cmp3, Cmp4, Cmp5, Cmp6, Cmp7, Cmp8, Cmp9>& cmp,
-                                              unsigned int tid, unsigned int delta)
-        {
-            For<0, thrust::tuple_size<thrust::tuple<VP0, VP1, VP2, VP3, VP4, VP5, VP6, VP7, VP8, VP9> >::value>::merge(skeys, key, svals, val, cmp, tid, delta);
-        }
-
-        //////////////////////////////////////////////////////
-        // Generic
-
-        template <unsigned int N> struct Generic
-        {
-            template <class KP, class KR, class VP, class VR, class Cmp>
-            static __device__ void reduce(KP skeys, KR key, VP svals, VR val, unsigned int tid, Cmp cmp)
-            {
-                loadToSmem(skeys, key, tid);
-                loadValsToSmem(svals, val, tid);
-                if (N >= 32)
-                    __syncthreads();
-
-                if (N >= 2048)
-                {
-                    if (tid < 1024)
-                        merge(skeys, key, svals, val, cmp, tid, 1024);
-
-                    __syncthreads();
-                }
-                if (N >= 1024)
-                {
-                    if (tid < 512)
-                        merge(skeys, key, svals, val, cmp, tid, 512);
-
-                    __syncthreads();
-                }
-                if (N >= 512)
-                {
-                    if (tid < 256)
-                        merge(skeys, key, svals, val, cmp, tid, 256);
-
-                    __syncthreads();
-                }
-                if (N >= 256)
-                {
-                    if (tid < 128)
-                        merge(skeys, key, svals, val, cmp, tid, 128);
-
-                    __syncthreads();
-                }
-                if (N >= 128)
-                {
-                    if (tid < 64)
-                        merge(skeys, key, svals, val, cmp, tid, 64);
-
-                    __syncthreads();
-                }
-                if (N >= 64)
-                {
-                    if (tid < 32)
-                        merge(skeys, key, svals, val, cmp, tid, 32);
-                }
-
-                if (tid < 16)
-                {
-                    merge(skeys, key, svals, val, cmp, tid, 16);
-                    merge(skeys, key, svals, val, cmp, tid, 8);
-                    merge(skeys, key, svals, val, cmp, tid, 4);
-                    merge(skeys, key, svals, val, cmp, tid, 2);
-                    merge(skeys, key, svals, val, cmp, tid, 1);
-                }
-            }
-        };
-
-        template <unsigned int I, class KP, class KR, class VP, class VR, class Cmp>
-        struct Unroll
-        {
-            static __device__ void loopShfl(KR key, VR val, Cmp cmp, unsigned int N)
-            {
-                mergeShfl(key, val, cmp, I, N);
-                Unroll<I / 2, KP, KR, VP, VR, Cmp>::loopShfl(key, val, cmp, N);
-            }
-            static __device__ void loop(KP skeys, KR key, VP svals, VR val, unsigned int tid, Cmp cmp)
-            {
-                merge(skeys, key, svals, val, cmp, tid, I);
-                Unroll<I / 2, KP, KR, VP, VR, Cmp>::loop(skeys, key, svals, val, tid, cmp);
-            }
-        };
-        template <class KP, class KR, class VP, class VR, class Cmp>
-        struct Unroll<0, KP, KR, VP, VR, Cmp>
-        {
-            static __device__ void loopShfl(KR, VR, Cmp, unsigned int)
-            {
-            }
-            static __device__ void loop(KP, KR, VP, VR, unsigned int, Cmp)
-            {
-            }
-        };
-
-        template <unsigned int N> struct WarpOptimized
-        {
-            template <class KP, class KR, class VP, class VR, class Cmp>
-            static __device__ void reduce(KP skeys, KR key, VP svals, VR val, unsigned int tid, Cmp cmp)
-            {
-            #if 0 // __CUDA_ARCH__ >= 300
-                (void) skeys;
-                (void) svals;
-                (void) tid;
-
-                Unroll<N / 2, KP, KR, VP, VR, Cmp>::loopShfl(key, val, cmp, N);
-            #else
-                loadToSmem(skeys, key, tid);
-                loadToSmem(svals, val, tid);
-
-                if (tid < N / 2)
-                    Unroll<N / 2, KP, KR, VP, VR, Cmp>::loop(skeys, key, svals, val, tid, cmp);
-            #endif
-            }
-        };
-
-        template <unsigned int N> struct GenericOptimized32
-        {
-            enum { M = N / 32 };
-
-            template <class KP, class KR, class VP, class VR, class Cmp>
-            static __device__ void reduce(KP skeys, KR key, VP svals, VR val, unsigned int tid, Cmp cmp)
-            {
-                const unsigned int laneId = Warp::laneId();
-
-            #if 0 // __CUDA_ARCH__ >= 300
-                Unroll<16, KP, KR, VP, VR, Cmp>::loopShfl(key, val, cmp, warpSize);
-
-                if (laneId == 0)
-                {
-                    loadToSmem(skeys, key, tid / 32);
-                    loadToSmem(svals, val, tid / 32);
-                }
-            #else
-                loadToSmem(skeys, key, tid);
-                loadToSmem(svals, val, tid);
-
-                if (laneId < 16)
-                    Unroll<16, KP, KR, VP, VR, Cmp>::loop(skeys, key, svals, val, tid, cmp);
-
-                __syncthreads();
-
-                if (laneId == 0)
-                {
-                    loadToSmem(skeys, key, tid / 32);
-                    loadToSmem(svals, val, tid / 32);
-                }
-            #endif
-
-                __syncthreads();
-
-                loadFromSmem(skeys, key, tid);
-
-                if (tid < 32)
-                {
-                #if 0 // __CUDA_ARCH__ >= 300
-                    loadFromSmem(svals, val, tid);
-
-                    Unroll<M / 2, KP, KR, VP, VR, Cmp>::loopShfl(key, val, cmp, M);
-                #else
-                    Unroll<M / 2, KP, KR, VP, VR, Cmp>::loop(skeys, key, svals, val, tid, cmp);
-                #endif
-                }
-            }
-        };
-
-        template <bool val, class T1, class T2> struct StaticIf;
-        template <class T1, class T2> struct StaticIf<true, T1, T2>
-        {
-            typedef T1 type;
-        };
-        template <class T1, class T2> struct StaticIf<false, T1, T2>
-        {
-            typedef T2 type;
-        };
-
-        template <unsigned int N> struct IsPowerOf2
-        {
-            enum { value = ((N != 0) && !(N & (N - 1))) };
-        };
-
-        template <unsigned int N> struct Dispatcher
-        {
-            typedef typename StaticIf<
-                (N <= 32) && IsPowerOf2<N>::value,
-                WarpOptimized<N>,
-                typename StaticIf<
-                    (N <= 1024) && IsPowerOf2<N>::value,
-                    GenericOptimized32<N>,
-                    Generic<N>
-                >::type
-            >::type reductor;
-        };
-    }
-}}}
-
-//! @endcond
-
-#endif // OPENCV_CUDA_PRED_VAL_REDUCE_DETAIL_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/detail/transform_detail.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/detail/transform_detail.hpp
deleted file mode 100755
index 3b72b03..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/detail/transform_detail.hpp
+++ /dev/null
@@ -1,399 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_CUDA_TRANSFORM_DETAIL_HPP
-#define OPENCV_CUDA_TRANSFORM_DETAIL_HPP
-
-#include "../common.hpp"
-#include "../vec_traits.hpp"
-#include "../functional.hpp"
-
-//! @cond IGNORED
-
-namespace cv { namespace cuda { namespace device
-{
-    namespace transform_detail
-    {
-        //! Read Write Traits
-
-        template <typename T, typename D, int shift> struct UnaryReadWriteTraits
-        {
-            typedef typename TypeVec<T, shift>::vec_type read_type;
-            typedef typename TypeVec<D, shift>::vec_type write_type;
-        };
-
-        template <typename T1, typename T2, typename D, int shift> struct BinaryReadWriteTraits
-        {
-            typedef typename TypeVec<T1, shift>::vec_type read_type1;
-            typedef typename TypeVec<T2, shift>::vec_type read_type2;
-            typedef typename TypeVec<D, shift>::vec_type write_type;
-        };
-
-        //! Transform kernels
-
-        template <int shift> struct OpUnroller;
-        template <> struct OpUnroller<1>
-        {
-            template <typename T, typename D, typename UnOp, typename Mask>
-            static __device__ __forceinline__ void unroll(const T& src, D& dst, const Mask& mask, UnOp& op, int x_shifted, int y)
-            {
-                if (mask(y, x_shifted))
-                    dst.x = op(src.x);
-            }
-
-            template <typename T1, typename T2, typename D, typename BinOp, typename Mask>
-            static __device__ __forceinline__ void unroll(const T1& src1, const T2& src2, D& dst, const Mask& mask, BinOp& op, int x_shifted, int y)
-            {
-                if (mask(y, x_shifted))
-                    dst.x = op(src1.x, src2.x);
-            }
-        };
-        template <> struct OpUnroller<2>
-        {
-            template <typename T, typename D, typename UnOp, typename Mask>
-            static __device__ __forceinline__ void unroll(const T& src, D& dst, const Mask& mask, UnOp& op, int x_shifted, int y)
-            {
-                if (mask(y, x_shifted))
-                    dst.x = op(src.x);
-                if (mask(y, x_shifted + 1))
-                    dst.y = op(src.y);
-            }
-
-            template <typename T1, typename T2, typename D, typename BinOp, typename Mask>
-            static __device__ __forceinline__ void unroll(const T1& src1, const T2& src2, D& dst, const Mask& mask, BinOp& op, int x_shifted, int y)
-            {
-                if (mask(y, x_shifted))
-                    dst.x = op(src1.x, src2.x);
-                if (mask(y, x_shifted + 1))
-                    dst.y = op(src1.y, src2.y);
-            }
-        };
-        template <> struct OpUnroller<3>
-        {
-            template <typename T, typename D, typename UnOp, typename Mask>
-            static __device__ __forceinline__ void unroll(const T& src, D& dst, const Mask& mask, const UnOp& op, int x_shifted, int y)
-            {
-                if (mask(y, x_shifted))
-                    dst.x = op(src.x);
-                if (mask(y, x_shifted + 1))
-                    dst.y = op(src.y);
-                if (mask(y, x_shifted + 2))
-                    dst.z = op(src.z);
-            }
-
-            template <typename T1, typename T2, typename D, typename BinOp, typename Mask>
-            static __device__ __forceinline__ void unroll(const T1& src1, const T2& src2, D& dst, const Mask& mask, const BinOp& op, int x_shifted, int y)
-            {
-                if (mask(y, x_shifted))
-                    dst.x = op(src1.x, src2.x);
-                if (mask(y, x_shifted + 1))
-                    dst.y = op(src1.y, src2.y);
-                if (mask(y, x_shifted + 2))
-                    dst.z = op(src1.z, src2.z);
-            }
-        };
-        template <> struct OpUnroller<4>
-        {
-            template <typename T, typename D, typename UnOp, typename Mask>
-            static __device__ __forceinline__ void unroll(const T& src, D& dst, const Mask& mask, const UnOp& op, int x_shifted, int y)
-            {
-                if (mask(y, x_shifted))
-                    dst.x = op(src.x);
-                if (mask(y, x_shifted + 1))
-                    dst.y = op(src.y);
-                if (mask(y, x_shifted + 2))
-                    dst.z = op(src.z);
-                if (mask(y, x_shifted + 3))
-                    dst.w = op(src.w);
-            }
-
-            template <typename T1, typename T2, typename D, typename BinOp, typename Mask>
-            static __device__ __forceinline__ void unroll(const T1& src1, const T2& src2, D& dst, const Mask& mask, const BinOp& op, int x_shifted, int y)
-            {
-                if (mask(y, x_shifted))
-                    dst.x = op(src1.x, src2.x);
-                if (mask(y, x_shifted + 1))
-                    dst.y = op(src1.y, src2.y);
-                if (mask(y, x_shifted + 2))
-                    dst.z = op(src1.z, src2.z);
-                if (mask(y, x_shifted + 3))
-                    dst.w = op(src1.w, src2.w);
-            }
-        };
-        template <> struct OpUnroller<8>
-        {
-            template <typename T, typename D, typename UnOp, typename Mask>
-            static __device__ __forceinline__ void unroll(const T& src, D& dst, const Mask& mask, const UnOp& op, int x_shifted, int y)
-            {
-                if (mask(y, x_shifted))
-                    dst.a0 = op(src.a0);
-                if (mask(y, x_shifted + 1))
-                    dst.a1 = op(src.a1);
-                if (mask(y, x_shifted + 2))
-                    dst.a2 = op(src.a2);
-                if (mask(y, x_shifted + 3))
-                    dst.a3 = op(src.a3);
-                if (mask(y, x_shifted + 4))
-                    dst.a4 = op(src.a4);
-                if (mask(y, x_shifted + 5))
-                    dst.a5 = op(src.a5);
-                if (mask(y, x_shifted + 6))
-                    dst.a6 = op(src.a6);
-                if (mask(y, x_shifted + 7))
-                    dst.a7 = op(src.a7);
-            }
-
-            template <typename T1, typename T2, typename D, typename BinOp, typename Mask>
-            static __device__ __forceinline__ void unroll(const T1& src1, const T2& src2, D& dst, const Mask& mask, const BinOp& op, int x_shifted, int y)
-            {
-                if (mask(y, x_shifted))
-                    dst.a0 = op(src1.a0, src2.a0);
-                if (mask(y, x_shifted + 1))
-                    dst.a1 = op(src1.a1, src2.a1);
-                if (mask(y, x_shifted + 2))
-                    dst.a2 = op(src1.a2, src2.a2);
-                if (mask(y, x_shifted + 3))
-                    dst.a3 = op(src1.a3, src2.a3);
-                if (mask(y, x_shifted + 4))
-                    dst.a4 = op(src1.a4, src2.a4);
-                if (mask(y, x_shifted + 5))
-                    dst.a5 = op(src1.a5, src2.a5);
-                if (mask(y, x_shifted + 6))
-                    dst.a6 = op(src1.a6, src2.a6);
-                if (mask(y, x_shifted + 7))
-                    dst.a7 = op(src1.a7, src2.a7);
-            }
-        };
-
-        template <typename T, typename D, typename UnOp, typename Mask>
-        static __global__ void transformSmart(const PtrStepSz<T> src_, PtrStep<D> dst_, const Mask mask, const UnOp op)
-        {
-            typedef TransformFunctorTraits<UnOp> ft;
-            typedef typename UnaryReadWriteTraits<T, D, ft::smart_shift>::read_type read_type;
-            typedef typename UnaryReadWriteTraits<T, D, ft::smart_shift>::write_type write_type;
-
-            const int x = threadIdx.x + blockIdx.x * blockDim.x;
-            const int y = threadIdx.y + blockIdx.y * blockDim.y;
-            const int x_shifted = x * ft::smart_shift;
-
-            if (y < src_.rows)
-            {
-                const T* src = src_.ptr(y);
-                D* dst = dst_.ptr(y);
-
-                if (x_shifted + ft::smart_shift - 1 < src_.cols)
-                {
-                    const read_type src_n_el = ((const read_type*)src)[x];
-                    write_type dst_n_el = ((const write_type*)dst)[x];
-
-                    OpUnroller<ft::smart_shift>::unroll(src_n_el, dst_n_el, mask, op, x_shifted, y);
-
-                    ((write_type*)dst)[x] = dst_n_el;
-                }
-                else
-                {
-                    for (int real_x = x_shifted; real_x < src_.cols; ++real_x)
-                    {
-                        if (mask(y, real_x))
-                            dst[real_x] = op(src[real_x]);
-                    }
-                }
-            }
-        }
-
-        template <typename T, typename D, typename UnOp, typename Mask>
-        __global__ static void transformSimple(const PtrStepSz<T> src, PtrStep<D> dst, const Mask mask, const UnOp op)
-        {
-            const int x = blockDim.x * blockIdx.x + threadIdx.x;
-            const int y = blockDim.y * blockIdx.y + threadIdx.y;
-
-            if (x < src.cols && y < src.rows && mask(y, x))
-            {
-                dst.ptr(y)[x] = op(src.ptr(y)[x]);
-            }
-        }
-
-        template <typename T1, typename T2, typename D, typename BinOp, typename Mask>
-        static __global__ void transformSmart(const PtrStepSz<T1> src1_, const PtrStep<T2> src2_, PtrStep<D> dst_,
-            const Mask mask, const BinOp op)
-        {
-            typedef TransformFunctorTraits<BinOp> ft;
-            typedef typename BinaryReadWriteTraits<T1, T2, D, ft::smart_shift>::read_type1 read_type1;
-            typedef typename BinaryReadWriteTraits<T1, T2, D, ft::smart_shift>::read_type2 read_type2;
-            typedef typename BinaryReadWriteTraits<T1, T2, D, ft::smart_shift>::write_type write_type;
-
-            const int x = threadIdx.x + blockIdx.x * blockDim.x;
-            const int y = threadIdx.y + blockIdx.y * blockDim.y;
-            const int x_shifted = x * ft::smart_shift;
-
-            if (y < src1_.rows)
-            {
-                const T1* src1 = src1_.ptr(y);
-                const T2* src2 = src2_.ptr(y);
-                D* dst = dst_.ptr(y);
-
-                if (x_shifted + ft::smart_shift - 1 < src1_.cols)
-                {
-                    const read_type1 src1_n_el = ((const read_type1*)src1)[x];
-                    const read_type2 src2_n_el = ((const read_type2*)src2)[x];
-                    write_type dst_n_el = ((const write_type*)dst)[x];
-
-                    OpUnroller<ft::smart_shift>::unroll(src1_n_el, src2_n_el, dst_n_el, mask, op, x_shifted, y);
-
-                    ((write_type*)dst)[x] = dst_n_el;
-                }
-                else
-                {
-                    for (int real_x = x_shifted; real_x < src1_.cols; ++real_x)
-                    {
-                        if (mask(y, real_x))
-                            dst[real_x] = op(src1[real_x], src2[real_x]);
-                    }
-                }
-            }
-        }
-
-        template <typename T1, typename T2, typename D, typename BinOp, typename Mask>
-        static __global__ void transformSimple(const PtrStepSz<T1> src1, const PtrStep<T2> src2, PtrStep<D> dst,
-            const Mask mask, const BinOp op)
-        {
-            const int x = blockDim.x * blockIdx.x + threadIdx.x;
-            const int y = blockDim.y * blockIdx.y + threadIdx.y;
-
-            if (x < src1.cols && y < src1.rows && mask(y, x))
-            {
-                const T1 src1_data = src1.ptr(y)[x];
-                const T2 src2_data = src2.ptr(y)[x];
-                dst.ptr(y)[x] = op(src1_data, src2_data);
-            }
-        }
-
-        template <bool UseSmart> struct TransformDispatcher;
-        template<> struct TransformDispatcher<false>
-        {
-            template <typename T, typename D, typename UnOp, typename Mask>
-            static void call(PtrStepSz<T> src, PtrStepSz<D> dst, UnOp op, Mask mask, cudaStream_t stream)
-            {
-                typedef TransformFunctorTraits<UnOp> ft;
-
-                const dim3 threads(ft::simple_block_dim_x, ft::simple_block_dim_y, 1);
-                const dim3 grid(divUp(src.cols, threads.x), divUp(src.rows, threads.y), 1);
-
-                transformSimple<T, D><<<grid, threads, 0, stream>>>(src, dst, mask, op);
-                cudaSafeCall( cudaGetLastError() );
-
-                if (stream == 0)
-                    cudaSafeCall( cudaDeviceSynchronize() );
-            }
-
-            template <typename T1, typename T2, typename D, typename BinOp, typename Mask>
-            static void call(PtrStepSz<T1> src1, PtrStepSz<T2> src2, PtrStepSz<D> dst, BinOp op, Mask mask, cudaStream_t stream)
-            {
-                typedef TransformFunctorTraits<BinOp> ft;
-
-                const dim3 threads(ft::simple_block_dim_x, ft::simple_block_dim_y, 1);
-                const dim3 grid(divUp(src1.cols, threads.x), divUp(src1.rows, threads.y), 1);
-
-                transformSimple<T1, T2, D><<<grid, threads, 0, stream>>>(src1, src2, dst, mask, op);
-                cudaSafeCall( cudaGetLastError() );
-
-                if (stream == 0)
-                    cudaSafeCall( cudaDeviceSynchronize() );
-            }
-        };
-        template<> struct TransformDispatcher<true>
-        {
-            template <typename T, typename D, typename UnOp, typename Mask>
-            static void call(PtrStepSz<T> src, PtrStepSz<D> dst, UnOp op, Mask mask, cudaStream_t stream)
-            {
-                typedef TransformFunctorTraits<UnOp> ft;
-
-                CV_StaticAssert(ft::smart_shift != 1, "");
-
-                if (!isAligned(src.data, ft::smart_shift * sizeof(T)) || !isAligned(src.step, ft::smart_shift * sizeof(T)) ||
-                    !isAligned(dst.data, ft::smart_shift * sizeof(D)) || !isAligned(dst.step, ft::smart_shift * sizeof(D)))
-                {
-                    TransformDispatcher<false>::call(src, dst, op, mask, stream);
-                    return;
-                }
-
-                const dim3 threads(ft::smart_block_dim_x, ft::smart_block_dim_y, 1);
-                const dim3 grid(divUp(src.cols, threads.x * ft::smart_shift), divUp(src.rows, threads.y), 1);
-
-                transformSmart<T, D><<<grid, threads, 0, stream>>>(src, dst, mask, op);
-                cudaSafeCall( cudaGetLastError() );
-
-                if (stream == 0)
-                    cudaSafeCall( cudaDeviceSynchronize() );
-            }
-
-            template <typename T1, typename T2, typename D, typename BinOp, typename Mask>
-            static void call(PtrStepSz<T1> src1, PtrStepSz<T2> src2, PtrStepSz<D> dst, BinOp op, Mask mask, cudaStream_t stream)
-            {
-                typedef TransformFunctorTraits<BinOp> ft;
-
-                CV_StaticAssert(ft::smart_shift != 1, "");
-
-                if (!isAligned(src1.data, ft::smart_shift * sizeof(T1)) || !isAligned(src1.step, ft::smart_shift * sizeof(T1)) ||
-                    !isAligned(src2.data, ft::smart_shift * sizeof(T2)) || !isAligned(src2.step, ft::smart_shift * sizeof(T2)) ||
-                    !isAligned(dst.data, ft::smart_shift * sizeof(D)) || !isAligned(dst.step, ft::smart_shift * sizeof(D)))
-                {
-                    TransformDispatcher<false>::call(src1, src2, dst, op, mask, stream);
-                    return;
-                }
-
-                const dim3 threads(ft::smart_block_dim_x, ft::smart_block_dim_y, 1);
-                const dim3 grid(divUp(src1.cols, threads.x * ft::smart_shift), divUp(src1.rows, threads.y), 1);
-
-                transformSmart<T1, T2, D><<<grid, threads, 0, stream>>>(src1, src2, dst, mask, op);
-                cudaSafeCall( cudaGetLastError() );
-
-                if (stream == 0)
-                    cudaSafeCall( cudaDeviceSynchronize() );
-            }
-        };
-    } // namespace transform_detail
-}}} // namespace cv { namespace cuda { namespace cudev
-
-//! @endcond
-
-#endif // OPENCV_CUDA_TRANSFORM_DETAIL_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/detail/type_traits_detail.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/detail/type_traits_detail.hpp
deleted file mode 100755
index a78bd2c..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/detail/type_traits_detail.hpp
+++ /dev/null
@@ -1,191 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_CUDA_TYPE_TRAITS_DETAIL_HPP
-#define OPENCV_CUDA_TYPE_TRAITS_DETAIL_HPP
-
-#include "../common.hpp"
-#include "../vec_traits.hpp"
-
-//! @cond IGNORED
-
-namespace cv { namespace cuda { namespace device
-{
-    namespace type_traits_detail
-    {
-        template <bool, typename T1, typename T2> struct Select { typedef T1 type; };
-        template <typename T1, typename T2> struct Select<false, T1, T2> { typedef T2 type; };
-
-        template <typename T> struct IsSignedIntergral { enum {value = 0}; };
-        template <> struct IsSignedIntergral<schar> { enum {value = 1}; };
-        template <> struct IsSignedIntergral<char1> { enum {value = 1}; };
-        template <> struct IsSignedIntergral<short> { enum {value = 1}; };
-        template <> struct IsSignedIntergral<short1> { enum {value = 1}; };
-        template <> struct IsSignedIntergral<int> { enum {value = 1}; };
-        template <> struct IsSignedIntergral<int1> { enum {value = 1}; };
-
-        template <typename T> struct IsUnsignedIntegral { enum {value = 0}; };
-        template <> struct IsUnsignedIntegral<uchar> { enum {value = 1}; };
-        template <> struct IsUnsignedIntegral<uchar1> { enum {value = 1}; };
-        template <> struct IsUnsignedIntegral<ushort> { enum {value = 1}; };
-        template <> struct IsUnsignedIntegral<ushort1> { enum {value = 1}; };
-        template <> struct IsUnsignedIntegral<uint> { enum {value = 1}; };
-        template <> struct IsUnsignedIntegral<uint1> { enum {value = 1}; };
-
-        template <typename T> struct IsIntegral { enum {value = IsSignedIntergral<T>::value || IsUnsignedIntegral<T>::value}; };
-        template <> struct IsIntegral<char> { enum {value = 1}; };
-        template <> struct IsIntegral<bool> { enum {value = 1}; };
-
-        template <typename T> struct IsFloat { enum {value = 0}; };
-        template <> struct IsFloat<float> { enum {value = 1}; };
-        template <> struct IsFloat<double> { enum {value = 1}; };
-
-        template <typename T> struct IsVec { enum {value = 0}; };
-        template <> struct IsVec<uchar1> { enum {value = 1}; };
-        template <> struct IsVec<uchar2> { enum {value = 1}; };
-        template <> struct IsVec<uchar3> { enum {value = 1}; };
-        template <> struct IsVec<uchar4> { enum {value = 1}; };
-        template <> struct IsVec<uchar8> { enum {value = 1}; };
-        template <> struct IsVec<char1> { enum {value = 1}; };
-        template <> struct IsVec<char2> { enum {value = 1}; };
-        template <> struct IsVec<char3> { enum {value = 1}; };
-        template <> struct IsVec<char4> { enum {value = 1}; };
-        template <> struct IsVec<char8> { enum {value = 1}; };
-        template <> struct IsVec<ushort1> { enum {value = 1}; };
-        template <> struct IsVec<ushort2> { enum {value = 1}; };
-        template <> struct IsVec<ushort3> { enum {value = 1}; };
-        template <> struct IsVec<ushort4> { enum {value = 1}; };
-        template <> struct IsVec<ushort8> { enum {value = 1}; };
-        template <> struct IsVec<short1> { enum {value = 1}; };
-        template <> struct IsVec<short2> { enum {value = 1}; };
-        template <> struct IsVec<short3> { enum {value = 1}; };
-        template <> struct IsVec<short4> { enum {value = 1}; };
-        template <> struct IsVec<short8> { enum {value = 1}; };
-        template <> struct IsVec<uint1> { enum {value = 1}; };
-        template <> struct IsVec<uint2> { enum {value = 1}; };
-        template <> struct IsVec<uint3> { enum {value = 1}; };
-        template <> struct IsVec<uint4> { enum {value = 1}; };
-        template <> struct IsVec<uint8> { enum {value = 1}; };
-        template <> struct IsVec<int1> { enum {value = 1}; };
-        template <> struct IsVec<int2> { enum {value = 1}; };
-        template <> struct IsVec<int3> { enum {value = 1}; };
-        template <> struct IsVec<int4> { enum {value = 1}; };
-        template <> struct IsVec<int8> { enum {value = 1}; };
-        template <> struct IsVec<float1> { enum {value = 1}; };
-        template <> struct IsVec<float2> { enum {value = 1}; };
-        template <> struct IsVec<float3> { enum {value = 1}; };
-        template <> struct IsVec<float4> { enum {value = 1}; };
-        template <> struct IsVec<float8> { enum {value = 1}; };
-        template <> struct IsVec<double1> { enum {value = 1}; };
-        template <> struct IsVec<double2> { enum {value = 1}; };
-        template <> struct IsVec<double3> { enum {value = 1}; };
-        template <> struct IsVec<double4> { enum {value = 1}; };
-        template <> struct IsVec<double8> { enum {value = 1}; };
-
-        template <class U> struct AddParameterType { typedef const U& type; };
-        template <class U> struct AddParameterType<U&> { typedef U& type; };
-        template <> struct AddParameterType<void> { typedef void type; };
-
-        template <class U> struct ReferenceTraits
-        {
-            enum { value = false };
-            typedef U type;
-        };
-        template <class U> struct ReferenceTraits<U&>
-        {
-            enum { value = true };
-            typedef U type;
-        };
-
-        template <class U> struct PointerTraits
-        {
-            enum { value = false };
-            typedef void type;
-        };
-        template <class U> struct PointerTraits<U*>
-        {
-            enum { value = true };
-            typedef U type;
-        };
-        template <class U> struct PointerTraits<U*&>
-        {
-            enum { value = true };
-            typedef U type;
-        };
-
-        template <class U> struct UnConst
-        {
-            typedef U type;
-            enum { value = 0 };
-        };
-        template <class U> struct UnConst<const U>
-        {
-            typedef U type;
-            enum { value = 1 };
-        };
-        template <class U> struct UnConst<const U&>
-        {
-            typedef U& type;
-            enum { value = 1 };
-        };
-
-        template <class U> struct UnVolatile
-        {
-            typedef U type;
-            enum { value = 0 };
-        };
-        template <class U> struct UnVolatile<volatile U>
-        {
-            typedef U type;
-            enum { value = 1 };
-        };
-        template <class U> struct UnVolatile<volatile U&>
-        {
-            typedef U& type;
-            enum { value = 1 };
-        };
-    } // namespace type_traits_detail
-}}} // namespace cv { namespace cuda { namespace cudev
-
-//! @endcond
-
-#endif // OPENCV_CUDA_TYPE_TRAITS_DETAIL_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/detail/vec_distance_detail.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/detail/vec_distance_detail.hpp
deleted file mode 100755
index 8283a99..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/detail/vec_distance_detail.hpp
+++ /dev/null
@@ -1,121 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_CUDA_VEC_DISTANCE_DETAIL_HPP
-#define OPENCV_CUDA_VEC_DISTANCE_DETAIL_HPP
-
-#include "../datamov_utils.hpp"
-
-//! @cond IGNORED
-
-namespace cv { namespace cuda { namespace device
-{
-    namespace vec_distance_detail
-    {
-        template <int THREAD_DIM, int N> struct UnrollVecDiffCached
-        {
-            template <typename Dist, typename T1, typename T2>
-            static __device__ void calcCheck(const T1* vecCached, const T2* vecGlob, int len, Dist& dist, int ind)
-            {
-                if (ind < len)
-                {
-                    T1 val1 = *vecCached++;
-
-                    T2 val2;
-                    ForceGlob<T2>::Load(vecGlob, ind, val2);
-
-                    dist.reduceIter(val1, val2);
-
-                    UnrollVecDiffCached<THREAD_DIM, N - 1>::calcCheck(vecCached, vecGlob, len, dist, ind + THREAD_DIM);
-                }
-            }
-
-            template <typename Dist, typename T1, typename T2>
-            static __device__ void calcWithoutCheck(const T1* vecCached, const T2* vecGlob, Dist& dist)
-            {
-                T1 val1 = *vecCached++;
-
-                T2 val2;
-                ForceGlob<T2>::Load(vecGlob, 0, val2);
-                vecGlob += THREAD_DIM;
-
-                dist.reduceIter(val1, val2);
-
-                UnrollVecDiffCached<THREAD_DIM, N - 1>::calcWithoutCheck(vecCached, vecGlob, dist);
-            }
-        };
-        template <int THREAD_DIM> struct UnrollVecDiffCached<THREAD_DIM, 0>
-        {
-            template <typename Dist, typename T1, typename T2>
-            static __device__ __forceinline__ void calcCheck(const T1*, const T2*, int, Dist&, int)
-            {
-            }
-
-            template <typename Dist, typename T1, typename T2>
-            static __device__ __forceinline__ void calcWithoutCheck(const T1*, const T2*, Dist&)
-            {
-            }
-        };
-
-        template <int THREAD_DIM, int MAX_LEN, bool LEN_EQ_MAX_LEN> struct VecDiffCachedCalculator;
-        template <int THREAD_DIM, int MAX_LEN> struct VecDiffCachedCalculator<THREAD_DIM, MAX_LEN, false>
-        {
-            template <typename Dist, typename T1, typename T2>
-            static __device__ __forceinline__ void calc(const T1* vecCached, const T2* vecGlob, int len, Dist& dist, int tid)
-            {
-                UnrollVecDiffCached<THREAD_DIM, MAX_LEN / THREAD_DIM>::calcCheck(vecCached, vecGlob, len, dist, tid);
-            }
-        };
-        template <int THREAD_DIM, int MAX_LEN> struct VecDiffCachedCalculator<THREAD_DIM, MAX_LEN, true>
-        {
-            template <typename Dist, typename T1, typename T2>
-            static __device__ __forceinline__ void calc(const T1* vecCached, const T2* vecGlob, int len, Dist& dist, int tid)
-            {
-                UnrollVecDiffCached<THREAD_DIM, MAX_LEN / THREAD_DIM>::calcWithoutCheck(vecCached, vecGlob + tid, dist);
-            }
-        };
-    } // namespace vec_distance_detail
-}}} // namespace cv { namespace cuda { namespace cudev
-
-//! @endcond
-
-#endif // OPENCV_CUDA_VEC_DISTANCE_DETAIL_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/dynamic_smem.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/dynamic_smem.hpp
deleted file mode 100755
index 42570c6..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/dynamic_smem.hpp
+++ /dev/null
@@ -1,88 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_CUDA_DYNAMIC_SMEM_HPP
-#define OPENCV_CUDA_DYNAMIC_SMEM_HPP
-
-/** @file
- * @deprecated Use @ref cudev instead.
- */
-
-//! @cond IGNORED
-
-namespace cv { namespace cuda { namespace device
-{
-    template<class T> struct DynamicSharedMem
-    {
-        __device__ __forceinline__ operator T*()
-        {
-            extern __shared__ int __smem[];
-            return (T*)__smem;
-        }
-
-        __device__ __forceinline__ operator const T*() const
-        {
-            extern __shared__ int __smem[];
-            return (T*)__smem;
-        }
-    };
-
-    // specialize for double to avoid unaligned memory access compile errors
-    template<> struct DynamicSharedMem<double>
-    {
-        __device__ __forceinline__ operator double*()
-        {
-            extern __shared__ double __smem_d[];
-            return (double*)__smem_d;
-        }
-
-        __device__ __forceinline__ operator const double*() const
-        {
-            extern __shared__ double __smem_d[];
-            return (double*)__smem_d;
-        }
-    };
-}}}
-
-//! @endcond
-
-#endif // OPENCV_CUDA_DYNAMIC_SMEM_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/emulation.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/emulation.hpp
deleted file mode 100755
index d346865..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/emulation.hpp
+++ /dev/null
@@ -1,269 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_CUDA_EMULATION_HPP_
-#define OPENCV_CUDA_EMULATION_HPP_
-
-#include "common.hpp"
-#include "warp_reduce.hpp"
-
-/** @file
- * @deprecated Use @ref cudev instead.
- */
-
-//! @cond IGNORED
-
-namespace cv { namespace cuda { namespace device
-{
-    struct Emulation
-    {
-
-        static __device__ __forceinline__ int syncthreadsOr(int pred)
-        {
-#if defined (__CUDA_ARCH__) && (__CUDA_ARCH__ < 200)
-                // just campilation stab
-                return 0;
-#else
-                return __syncthreads_or(pred);
-#endif
-        }
-
-        template<int CTA_SIZE>
-        static __forceinline__ __device__ int Ballot(int predicate)
-        {
-#if defined (__CUDA_ARCH__) && (__CUDA_ARCH__ >= 200)
-            return __ballot(predicate);
-#else
-            __shared__ volatile int cta_buffer[CTA_SIZE];
-
-            int tid = threadIdx.x;
-            cta_buffer[tid] = predicate ? (1 << (tid & 31)) : 0;
-            return warp_reduce(cta_buffer);
-#endif
-        }
-
-        struct smem
-        {
-            enum { TAG_MASK = (1U << ( (sizeof(unsigned int) << 3) - 5U)) - 1U };
-
-            template<typename T>
-            static __device__ __forceinline__ T atomicInc(T* address, T val)
-            {
-#if defined (__CUDA_ARCH__) && (__CUDA_ARCH__ < 120)
-                T count;
-                unsigned int tag = threadIdx.x << ( (sizeof(unsigned int) << 3) - 5U);
-                do
-                {
-                    count = *address & TAG_MASK;
-                    count = tag | (count + 1);
-                    *address = count;
-                } while (*address != count);
-
-                return (count & TAG_MASK) - 1;
-#else
-                return ::atomicInc(address, val);
-#endif
-            }
-
-            template<typename T>
-            static __device__ __forceinline__ T atomicAdd(T* address, T val)
-            {
-#if defined (__CUDA_ARCH__) && (__CUDA_ARCH__ < 120)
-                T count;
-                unsigned int tag = threadIdx.x << ( (sizeof(unsigned int) << 3) - 5U);
-                do
-                {
-                    count = *address & TAG_MASK;
-                    count = tag | (count + val);
-                    *address = count;
-                } while (*address != count);
-
-                return (count & TAG_MASK) - val;
-#else
-                return ::atomicAdd(address, val);
-#endif
-            }
-
-            template<typename T>
-            static __device__ __forceinline__ T atomicMin(T* address, T val)
-            {
-#if defined (__CUDA_ARCH__) && (__CUDA_ARCH__ < 120)
-                T count = ::min(*address, val);
-                do
-                {
-                    *address = count;
-                } while (*address > count);
-
-                return count;
-#else
-                return ::atomicMin(address, val);
-#endif
-            }
-        }; // struct cmem
-
-        struct glob
-        {
-            static __device__ __forceinline__ int atomicAdd(int* address, int val)
-            {
-                return ::atomicAdd(address, val);
-            }
-            static __device__ __forceinline__ unsigned int atomicAdd(unsigned int* address, unsigned int val)
-            {
-                return ::atomicAdd(address, val);
-            }
-            static __device__ __forceinline__ float atomicAdd(float* address, float val)
-            {
-            #if __CUDA_ARCH__ >= 200
-                return ::atomicAdd(address, val);
-            #else
-                int* address_as_i = (int*) address;
-                int old = *address_as_i, assumed;
-                do {
-                    assumed = old;
-                    old = ::atomicCAS(address_as_i, assumed,
-                        __float_as_int(val + __int_as_float(assumed)));
-                } while (assumed != old);
-                return __int_as_float(old);
-            #endif
-            }
-            static __device__ __forceinline__ double atomicAdd(double* address, double val)
-            {
-            #if __CUDA_ARCH__ >= 130
-                unsigned long long int* address_as_ull = (unsigned long long int*) address;
-                unsigned long long int old = *address_as_ull, assumed;
-                do {
-                    assumed = old;
-                    old = ::atomicCAS(address_as_ull, assumed,
-                        __double_as_longlong(val + __longlong_as_double(assumed)));
-                } while (assumed != old);
-                return __longlong_as_double(old);
-            #else
-                (void) address;
-                (void) val;
-                return 0.0;
-            #endif
-            }
-
-            static __device__ __forceinline__ int atomicMin(int* address, int val)
-            {
-                return ::atomicMin(address, val);
-            }
-            static __device__ __forceinline__ float atomicMin(float* address, float val)
-            {
-            #if __CUDA_ARCH__ >= 120
-                int* address_as_i = (int*) address;
-                int old = *address_as_i, assumed;
-                do {
-                    assumed = old;
-                    old = ::atomicCAS(address_as_i, assumed,
-                        __float_as_int(::fminf(val, __int_as_float(assumed))));
-                } while (assumed != old);
-                return __int_as_float(old);
-            #else
-                (void) address;
-                (void) val;
-                return 0.0f;
-            #endif
-            }
-            static __device__ __forceinline__ double atomicMin(double* address, double val)
-            {
-            #if __CUDA_ARCH__ >= 130
-                unsigned long long int* address_as_ull = (unsigned long long int*) address;
-                unsigned long long int old = *address_as_ull, assumed;
-                do {
-                    assumed = old;
-                    old = ::atomicCAS(address_as_ull, assumed,
-                        __double_as_longlong(::fmin(val, __longlong_as_double(assumed))));
-                } while (assumed != old);
-                return __longlong_as_double(old);
-            #else
-                (void) address;
-                (void) val;
-                return 0.0;
-            #endif
-            }
-
-            static __device__ __forceinline__ int atomicMax(int* address, int val)
-            {
-                return ::atomicMax(address, val);
-            }
-            static __device__ __forceinline__ float atomicMax(float* address, float val)
-            {
-            #if __CUDA_ARCH__ >= 120
-                int* address_as_i = (int*) address;
-                int old = *address_as_i, assumed;
-                do {
-                    assumed = old;
-                    old = ::atomicCAS(address_as_i, assumed,
-                        __float_as_int(::fmaxf(val, __int_as_float(assumed))));
-                } while (assumed != old);
-                return __int_as_float(old);
-            #else
-                (void) address;
-                (void) val;
-                return 0.0f;
-            #endif
-            }
-            static __device__ __forceinline__ double atomicMax(double* address, double val)
-            {
-            #if __CUDA_ARCH__ >= 130
-                unsigned long long int* address_as_ull = (unsigned long long int*) address;
-                unsigned long long int old = *address_as_ull, assumed;
-                do {
-                    assumed = old;
-                    old = ::atomicCAS(address_as_ull, assumed,
-                        __double_as_longlong(::fmax(val, __longlong_as_double(assumed))));
-                } while (assumed != old);
-                return __longlong_as_double(old);
-            #else
-                (void) address;
-                (void) val;
-                return 0.0;
-            #endif
-            }
-        };
-    }; //struct Emulation
-}}} // namespace cv { namespace cuda { namespace cudev
-
-//! @endcond
-
-#endif /* OPENCV_CUDA_EMULATION_HPP_ */
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/filters.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/filters.hpp
deleted file mode 100755
index c2e24dd..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/filters.hpp
+++ /dev/null
@@ -1,286 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_CUDA_FILTERS_HPP
-#define OPENCV_CUDA_FILTERS_HPP
-
-#include "saturate_cast.hpp"
-#include "vec_traits.hpp"
-#include "vec_math.hpp"
-#include "type_traits.hpp"
-
-/** @file
- * @deprecated Use @ref cudev instead.
- */
-
-//! @cond IGNORED
-
-namespace cv { namespace cuda { namespace device
-{
-    template <typename Ptr2D> struct PointFilter
-    {
-        typedef typename Ptr2D::elem_type elem_type;
-        typedef float index_type;
-
-        explicit __host__ __device__ __forceinline__ PointFilter(const Ptr2D& src_, float fx = 0.f, float fy = 0.f)
-        : src(src_)
-        {
-            (void)fx;
-            (void)fy;
-        }
-
-        __device__ __forceinline__ elem_type operator ()(float y, float x) const
-        {
-            return src(__float2int_rz(y), __float2int_rz(x));
-        }
-
-        Ptr2D src;
-    };
-
-    template <typename Ptr2D> struct LinearFilter
-    {
-        typedef typename Ptr2D::elem_type elem_type;
-        typedef float index_type;
-
-        explicit __host__ __device__ __forceinline__ LinearFilter(const Ptr2D& src_, float fx = 0.f, float fy = 0.f)
-        : src(src_)
-        {
-            (void)fx;
-            (void)fy;
-        }
-        __device__ __forceinline__ elem_type operator ()(float y, float x) const
-        {
-            typedef typename TypeVec<float, VecTraits<elem_type>::cn>::vec_type work_type;
-
-            work_type out = VecTraits<work_type>::all(0);
-
-            const int x1 = __float2int_rd(x);
-            const int y1 = __float2int_rd(y);
-            const int x2 = x1 + 1;
-            const int y2 = y1 + 1;
-
-            elem_type src_reg = src(y1, x1);
-            out = out + src_reg * ((x2 - x) * (y2 - y));
-
-            src_reg = src(y1, x2);
-            out = out + src_reg * ((x - x1) * (y2 - y));
-
-            src_reg = src(y2, x1);
-            out = out + src_reg * ((x2 - x) * (y - y1));
-
-            src_reg = src(y2, x2);
-            out = out + src_reg * ((x - x1) * (y - y1));
-
-            return saturate_cast<elem_type>(out);
-        }
-
-        Ptr2D src;
-    };
-
-    template <typename Ptr2D> struct CubicFilter
-    {
-        typedef typename Ptr2D::elem_type elem_type;
-        typedef float index_type;
-        typedef typename TypeVec<float, VecTraits<elem_type>::cn>::vec_type work_type;
-
-        explicit __host__ __device__ __forceinline__ CubicFilter(const Ptr2D& src_, float fx = 0.f, float fy = 0.f)
-        : src(src_)
-        {
-            (void)fx;
-            (void)fy;
-        }
-
-        static __device__ __forceinline__ float bicubicCoeff(float x_)
-        {
-            float x = fabsf(x_);
-            if (x <= 1.0f)
-            {
-                return x * x * (1.5f * x - 2.5f) + 1.0f;
-            }
-            else if (x < 2.0f)
-            {
-                return x * (x * (-0.5f * x + 2.5f) - 4.0f) + 2.0f;
-            }
-            else
-            {
-                return 0.0f;
-            }
-        }
-
-        __device__ elem_type operator ()(float y, float x) const
-        {
-            const float xmin = ::ceilf(x - 2.0f);
-            const float xmax = ::floorf(x + 2.0f);
-
-            const float ymin = ::ceilf(y - 2.0f);
-            const float ymax = ::floorf(y + 2.0f);
-
-            work_type sum = VecTraits<work_type>::all(0);
-            float wsum = 0.0f;
-
-            for (float cy = ymin; cy <= ymax; cy += 1.0f)
-            {
-                for (float cx = xmin; cx <= xmax; cx += 1.0f)
-                {
-                    const float w = bicubicCoeff(x - cx) * bicubicCoeff(y - cy);
-                    sum = sum + w * src(__float2int_rd(cy), __float2int_rd(cx));
-                    wsum += w;
-                }
-            }
-
-            work_type res = (!wsum)? VecTraits<work_type>::all(0) : sum / wsum;
-
-            return saturate_cast<elem_type>(res);
-        }
-
-        Ptr2D src;
-    };
-    // for integer scaling
-    template <typename Ptr2D> struct IntegerAreaFilter
-    {
-        typedef typename Ptr2D::elem_type elem_type;
-        typedef float index_type;
-
-        explicit __host__ __device__ __forceinline__ IntegerAreaFilter(const Ptr2D& src_, float scale_x_, float scale_y_)
-            : src(src_), scale_x(scale_x_), scale_y(scale_y_), scale(1.f / (scale_x * scale_y)) {}
-
-        __device__ __forceinline__ elem_type operator ()(float y, float x) const
-        {
-            float fsx1 = x * scale_x;
-            float fsx2 = fsx1 + scale_x;
-
-            int sx1 = __float2int_ru(fsx1);
-            int sx2 = __float2int_rd(fsx2);
-
-            float fsy1 = y * scale_y;
-            float fsy2 = fsy1 + scale_y;
-
-            int sy1 = __float2int_ru(fsy1);
-            int sy2 = __float2int_rd(fsy2);
-
-            typedef typename TypeVec<float, VecTraits<elem_type>::cn>::vec_type work_type;
-            work_type out = VecTraits<work_type>::all(0.f);
-
-            for(int dy = sy1; dy < sy2; ++dy)
-                for(int dx = sx1; dx < sx2; ++dx)
-                {
-                    out = out + src(dy, dx) * scale;
-                }
-
-            return saturate_cast<elem_type>(out);
-        }
-
-        Ptr2D src;
-        float scale_x, scale_y ,scale;
-    };
-
-    template <typename Ptr2D> struct AreaFilter
-    {
-        typedef typename Ptr2D::elem_type elem_type;
-        typedef float index_type;
-
-        explicit __host__ __device__ __forceinline__ AreaFilter(const Ptr2D& src_, float scale_x_, float scale_y_)
-            : src(src_), scale_x(scale_x_), scale_y(scale_y_){}
-
-        __device__ __forceinline__ elem_type operator ()(float y, float x) const
-        {
-            float fsx1 = x * scale_x;
-            float fsx2 = fsx1 + scale_x;
-
-            int sx1 = __float2int_ru(fsx1);
-            int sx2 = __float2int_rd(fsx2);
-
-            float fsy1 = y * scale_y;
-            float fsy2 = fsy1 + scale_y;
-
-            int sy1 = __float2int_ru(fsy1);
-            int sy2 = __float2int_rd(fsy2);
-
-            float scale = 1.f / (fminf(scale_x, src.width - fsx1) * fminf(scale_y, src.height - fsy1));
-
-            typedef typename TypeVec<float, VecTraits<elem_type>::cn>::vec_type work_type;
-            work_type out = VecTraits<work_type>::all(0.f);
-
-            for (int dy = sy1; dy < sy2; ++dy)
-            {
-                for (int dx = sx1; dx < sx2; ++dx)
-                    out = out + src(dy, dx) * scale;
-
-                if (sx1 > fsx1)
-                    out = out + src(dy, (sx1 -1) ) * ((sx1 - fsx1) * scale);
-
-                if (sx2 < fsx2)
-                    out = out + src(dy, sx2) * ((fsx2 -sx2) * scale);
-            }
-
-            if (sy1 > fsy1)
-                for (int dx = sx1; dx < sx2; ++dx)
-                    out = out + src( (sy1 - 1) , dx) * ((sy1 -fsy1) * scale);
-
-            if (sy2 < fsy2)
-                for (int dx = sx1; dx < sx2; ++dx)
-                    out = out + src(sy2, dx) * ((fsy2 -sy2) * scale);
-
-            if ((sy1 > fsy1) &&  (sx1 > fsx1))
-                out = out + src( (sy1 - 1) , (sx1 - 1)) * ((sy1 -fsy1) * (sx1 -fsx1) * scale);
-
-            if ((sy1 > fsy1) &&  (sx2 < fsx2))
-                out = out + src( (sy1 - 1) , sx2) * ((sy1 -fsy1) * (fsx2 -sx2) * scale);
-
-            if ((sy2 < fsy2) &&  (sx2 < fsx2))
-                out = out + src(sy2, sx2) * ((fsy2 -sy2) * (fsx2 -sx2) * scale);
-
-            if ((sy2 < fsy2) &&  (sx1 > fsx1))
-                out = out + src(sy2, (sx1 - 1)) * ((fsy2 -sy2) * (sx1 -fsx1) * scale);
-
-            return saturate_cast<elem_type>(out);
-        }
-
-        Ptr2D src;
-        float scale_x, scale_y;
-        int width, haight;
-    };
-}}} // namespace cv { namespace cuda { namespace cudev
-
-//! @endcond
-
-#endif // OPENCV_CUDA_FILTERS_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/funcattrib.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/funcattrib.hpp
deleted file mode 100755
index f582080..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/funcattrib.hpp
+++ /dev/null
@@ -1,79 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_CUDA_DEVICE_FUNCATTRIB_HPP
-#define OPENCV_CUDA_DEVICE_FUNCATTRIB_HPP
-
-#include <cstdio>
-
-/** @file
- * @deprecated Use @ref cudev instead.
- */
-
-//! @cond IGNORED
-
-namespace cv { namespace cuda { namespace device
-{
-    template<class Func>
-    void printFuncAttrib(Func& func)
-    {
-
-        cudaFuncAttributes attrs;
-        cudaFuncGetAttributes(&attrs, func);
-
-        printf("=== Function stats ===\n");
-        printf("Name: \n");
-        printf("sharedSizeBytes    = %d\n", attrs.sharedSizeBytes);
-        printf("constSizeBytes     = %d\n", attrs.constSizeBytes);
-        printf("localSizeBytes     = %d\n", attrs.localSizeBytes);
-        printf("maxThreadsPerBlock = %d\n", attrs.maxThreadsPerBlock);
-        printf("numRegs            = %d\n", attrs.numRegs);
-        printf("ptxVersion         = %d\n", attrs.ptxVersion);
-        printf("binaryVersion      = %d\n", attrs.binaryVersion);
-        printf("\n");
-        fflush(stdout);
-    }
-}}} // namespace cv { namespace cuda { namespace cudev
-
-//! @endcond
-
-#endif  /* OPENCV_CUDA_DEVICE_FUNCATTRIB_HPP */
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/functional.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/functional.hpp
deleted file mode 100755
index b28f7a5..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/functional.hpp
+++ /dev/null
@@ -1,811 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_CUDA_FUNCTIONAL_HPP
-#define OPENCV_CUDA_FUNCTIONAL_HPP
-
-#include <functional>
-#include "saturate_cast.hpp"
-#include "vec_traits.hpp"
-#include "type_traits.hpp"
-#include "device_functions.h"
-
-/** @file
- * @deprecated Use @ref cudev instead.
- */
-
-//! @cond IGNORED
-
-namespace cv { namespace cuda { namespace device
-{
-    // Function Objects
-#ifdef CV_CXX11
-    template<typename Argument, typename Result> struct unary_function
-    {
-        typedef Argument argument_type;
-        typedef Result result_type;
-    };
-    template<typename Argument1, typename Argument2, typename Result> struct binary_function
-    {
-        typedef Argument1 first_argument_type;
-        typedef Argument2 second_argument_type;
-        typedef Result result_type;
-    };
-#else
-    template<typename Argument, typename Result> struct unary_function : public std::unary_function<Argument, Result> {};
-    template<typename Argument1, typename Argument2, typename Result> struct binary_function : public std::binary_function<Argument1, Argument2, Result> {};
-#endif
-
-    // Arithmetic Operations
-    template <typename T> struct plus : binary_function<T, T, T>
-    {
-        __device__ __forceinline__ T operator ()(typename TypeTraits<T>::ParameterType a,
-                                                 typename TypeTraits<T>::ParameterType b) const
-        {
-            return a + b;
-        }
-        __host__ __device__ __forceinline__ plus() {}
-        __host__ __device__ __forceinline__ plus(const plus&) {}
-    };
-
-    template <typename T> struct minus : binary_function<T, T, T>
-    {
-        __device__ __forceinline__ T operator ()(typename TypeTraits<T>::ParameterType a,
-                                                 typename TypeTraits<T>::ParameterType b) const
-        {
-            return a - b;
-        }
-        __host__ __device__ __forceinline__ minus() {}
-        __host__ __device__ __forceinline__ minus(const minus&) {}
-    };
-
-    template <typename T> struct multiplies : binary_function<T, T, T>
-    {
-        __device__ __forceinline__ T operator ()(typename TypeTraits<T>::ParameterType a,
-                                                 typename TypeTraits<T>::ParameterType b) const
-        {
-            return a * b;
-        }
-        __host__ __device__ __forceinline__ multiplies() {}
-        __host__ __device__ __forceinline__ multiplies(const multiplies&) {}
-    };
-
-    template <typename T> struct divides : binary_function<T, T, T>
-    {
-        __device__ __forceinline__ T operator ()(typename TypeTraits<T>::ParameterType a,
-                                                 typename TypeTraits<T>::ParameterType b) const
-        {
-            return a / b;
-        }
-        __host__ __device__ __forceinline__ divides() {}
-        __host__ __device__ __forceinline__ divides(const divides&) {}
-    };
-
-    template <typename T> struct modulus : binary_function<T, T, T>
-    {
-        __device__ __forceinline__ T operator ()(typename TypeTraits<T>::ParameterType a,
-                                                 typename TypeTraits<T>::ParameterType b) const
-        {
-            return a % b;
-        }
-        __host__ __device__ __forceinline__ modulus() {}
-        __host__ __device__ __forceinline__ modulus(const modulus&) {}
-    };
-
-    template <typename T> struct negate : unary_function<T, T>
-    {
-        __device__ __forceinline__ T operator ()(typename TypeTraits<T>::ParameterType a) const
-        {
-            return -a;
-        }
-        __host__ __device__ __forceinline__ negate() {}
-        __host__ __device__ __forceinline__ negate(const negate&) {}
-    };
-
-    // Comparison Operations
-    template <typename T> struct equal_to : binary_function<T, T, bool>
-    {
-        __device__ __forceinline__ bool operator ()(typename TypeTraits<T>::ParameterType a,
-                                                    typename TypeTraits<T>::ParameterType b) const
-        {
-            return a == b;
-        }
-        __host__ __device__ __forceinline__ equal_to() {}
-        __host__ __device__ __forceinline__ equal_to(const equal_to&) {}
-    };
-
-    template <typename T> struct not_equal_to : binary_function<T, T, bool>
-    {
-        __device__ __forceinline__ bool operator ()(typename TypeTraits<T>::ParameterType a,
-                                                    typename TypeTraits<T>::ParameterType b) const
-        {
-            return a != b;
-        }
-        __host__ __device__ __forceinline__ not_equal_to() {}
-        __host__ __device__ __forceinline__ not_equal_to(const not_equal_to&) {}
-    };
-
-    template <typename T> struct greater : binary_function<T, T, bool>
-    {
-        __device__ __forceinline__ bool operator ()(typename TypeTraits<T>::ParameterType a,
-                                                    typename TypeTraits<T>::ParameterType b) const
-        {
-            return a > b;
-        }
-        __host__ __device__ __forceinline__ greater() {}
-        __host__ __device__ __forceinline__ greater(const greater&) {}
-    };
-
-    template <typename T> struct less : binary_function<T, T, bool>
-    {
-        __device__ __forceinline__ bool operator ()(typename TypeTraits<T>::ParameterType a,
-                                                    typename TypeTraits<T>::ParameterType b) const
-        {
-            return a < b;
-        }
-        __host__ __device__ __forceinline__ less() {}
-        __host__ __device__ __forceinline__ less(const less&) {}
-    };
-
-    template <typename T> struct greater_equal : binary_function<T, T, bool>
-    {
-        __device__ __forceinline__ bool operator ()(typename TypeTraits<T>::ParameterType a,
-                                                    typename TypeTraits<T>::ParameterType b) const
-        {
-            return a >= b;
-        }
-        __host__ __device__ __forceinline__ greater_equal() {}
-        __host__ __device__ __forceinline__ greater_equal(const greater_equal&) {}
-    };
-
-    template <typename T> struct less_equal : binary_function<T, T, bool>
-    {
-        __device__ __forceinline__ bool operator ()(typename TypeTraits<T>::ParameterType a,
-                                                    typename TypeTraits<T>::ParameterType b) const
-        {
-            return a <= b;
-        }
-        __host__ __device__ __forceinline__ less_equal() {}
-        __host__ __device__ __forceinline__ less_equal(const less_equal&) {}
-    };
-
-    // Logical Operations
-    template <typename T> struct logical_and : binary_function<T, T, bool>
-    {
-        __device__ __forceinline__ bool operator ()(typename TypeTraits<T>::ParameterType a,
-                                                    typename TypeTraits<T>::ParameterType b) const
-        {
-            return a && b;
-        }
-        __host__ __device__ __forceinline__ logical_and() {}
-        __host__ __device__ __forceinline__ logical_and(const logical_and&) {}
-    };
-
-    template <typename T> struct logical_or : binary_function<T, T, bool>
-    {
-        __device__ __forceinline__ bool operator ()(typename TypeTraits<T>::ParameterType a,
-                                                    typename TypeTraits<T>::ParameterType b) const
-        {
-            return a || b;
-        }
-        __host__ __device__ __forceinline__ logical_or() {}
-        __host__ __device__ __forceinline__ logical_or(const logical_or&) {}
-    };
-
-    template <typename T> struct logical_not : unary_function<T, bool>
-    {
-        __device__ __forceinline__ bool operator ()(typename TypeTraits<T>::ParameterType a) const
-        {
-            return !a;
-        }
-        __host__ __device__ __forceinline__ logical_not() {}
-        __host__ __device__ __forceinline__ logical_not(const logical_not&) {}
-    };
-
-    // Bitwise Operations
-    template <typename T> struct bit_and : binary_function<T, T, T>
-    {
-        __device__ __forceinline__ T operator ()(typename TypeTraits<T>::ParameterType a,
-                                                 typename TypeTraits<T>::ParameterType b) const
-        {
-            return a & b;
-        }
-        __host__ __device__ __forceinline__ bit_and() {}
-        __host__ __device__ __forceinline__ bit_and(const bit_and&) {}
-    };
-
-    template <typename T> struct bit_or : binary_function<T, T, T>
-    {
-        __device__ __forceinline__ T operator ()(typename TypeTraits<T>::ParameterType a,
-                                                 typename TypeTraits<T>::ParameterType b) const
-        {
-            return a | b;
-        }
-        __host__ __device__ __forceinline__ bit_or() {}
-        __host__ __device__ __forceinline__ bit_or(const bit_or&) {}
-    };
-
-    template <typename T> struct bit_xor : binary_function<T, T, T>
-    {
-        __device__ __forceinline__ T operator ()(typename TypeTraits<T>::ParameterType a,
-                                                 typename TypeTraits<T>::ParameterType b) const
-        {
-            return a ^ b;
-        }
-        __host__ __device__ __forceinline__ bit_xor() {}
-        __host__ __device__ __forceinline__ bit_xor(const bit_xor&) {}
-    };
-
-    template <typename T> struct bit_not : unary_function<T, T>
-    {
-        __device__ __forceinline__ T operator ()(typename TypeTraits<T>::ParameterType v) const
-        {
-            return ~v;
-        }
-        __host__ __device__ __forceinline__ bit_not() {}
-        __host__ __device__ __forceinline__ bit_not(const bit_not&) {}
-    };
-
-    // Generalized Identity Operations
-    template <typename T> struct identity : unary_function<T, T>
-    {
-        __device__ __forceinline__ typename TypeTraits<T>::ParameterType operator()(typename TypeTraits<T>::ParameterType x) const
-        {
-            return x;
-        }
-        __host__ __device__ __forceinline__ identity() {}
-        __host__ __device__ __forceinline__ identity(const identity&) {}
-    };
-
-    template <typename T1, typename T2> struct project1st : binary_function<T1, T2, T1>
-    {
-        __device__ __forceinline__ typename TypeTraits<T1>::ParameterType operator()(typename TypeTraits<T1>::ParameterType lhs, typename TypeTraits<T2>::ParameterType rhs) const
-        {
-            return lhs;
-        }
-        __host__ __device__ __forceinline__ project1st() {}
-        __host__ __device__ __forceinline__ project1st(const project1st&) {}
-    };
-
-    template <typename T1, typename T2> struct project2nd : binary_function<T1, T2, T2>
-    {
-        __device__ __forceinline__ typename TypeTraits<T2>::ParameterType operator()(typename TypeTraits<T1>::ParameterType lhs, typename TypeTraits<T2>::ParameterType rhs) const
-        {
-            return rhs;
-        }
-        __host__ __device__ __forceinline__ project2nd() {}
-        __host__ __device__ __forceinline__ project2nd(const project2nd&) {}
-    };
-
-    // Min/Max Operations
-
-#define OPENCV_CUDA_IMPLEMENT_MINMAX(name, type, op) \
-    template <> struct name<type> : binary_function<type, type, type> \
-    { \
-        __device__ __forceinline__ type operator()(type lhs, type rhs) const {return op(lhs, rhs);} \
-        __host__ __device__ __forceinline__ name() {}\
-        __host__ __device__ __forceinline__ name(const name&) {}\
-    };
-
-    template <typename T> struct maximum : binary_function<T, T, T>
-    {
-        __device__ __forceinline__ T operator()(typename TypeTraits<T>::ParameterType lhs, typename TypeTraits<T>::ParameterType rhs) const
-        {
-            return max(lhs, rhs);
-        }
-        __host__ __device__ __forceinline__ maximum() {}
-        __host__ __device__ __forceinline__ maximum(const maximum&) {}
-    };
-
-    OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, uchar, ::max)
-    OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, schar, ::max)
-    OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, char, ::max)
-    OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, ushort, ::max)
-    OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, short, ::max)
-    OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, int, ::max)
-    OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, uint, ::max)
-    OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, float, ::fmax)
-    OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, double, ::fmax)
-
-    template <typename T> struct minimum : binary_function<T, T, T>
-    {
-        __device__ __forceinline__ T operator()(typename TypeTraits<T>::ParameterType lhs, typename TypeTraits<T>::ParameterType rhs) const
-        {
-            return min(lhs, rhs);
-        }
-        __host__ __device__ __forceinline__ minimum() {}
-        __host__ __device__ __forceinline__ minimum(const minimum&) {}
-    };
-
-    OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, uchar, ::min)
-    OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, schar, ::min)
-    OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, char, ::min)
-    OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, ushort, ::min)
-    OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, short, ::min)
-    OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, int, ::min)
-    OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, uint, ::min)
-    OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, float, ::fmin)
-    OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, double, ::fmin)
-
-#undef OPENCV_CUDA_IMPLEMENT_MINMAX
-
-    // Math functions
-
-    template <typename T> struct abs_func : unary_function<T, T>
-    {
-        __device__ __forceinline__ T operator ()(typename TypeTraits<T>::ParameterType x) const
-        {
-            return abs(x);
-        }
-
-        __host__ __device__ __forceinline__ abs_func() {}
-        __host__ __device__ __forceinline__ abs_func(const abs_func&) {}
-    };
-    template <> struct abs_func<unsigned char> : unary_function<unsigned char, unsigned char>
-    {
-        __device__ __forceinline__ unsigned char operator ()(unsigned char x) const
-        {
-            return x;
-        }
-
-        __host__ __device__ __forceinline__ abs_func() {}
-        __host__ __device__ __forceinline__ abs_func(const abs_func&) {}
-    };
-    template <> struct abs_func<signed char> : unary_function<signed char, signed char>
-    {
-        __device__ __forceinline__ signed char operator ()(signed char x) const
-        {
-            return ::abs((int)x);
-        }
-
-        __host__ __device__ __forceinline__ abs_func() {}
-        __host__ __device__ __forceinline__ abs_func(const abs_func&) {}
-    };
-    template <> struct abs_func<char> : unary_function<char, char>
-    {
-        __device__ __forceinline__ char operator ()(char x) const
-        {
-            return ::abs((int)x);
-        }
-
-        __host__ __device__ __forceinline__ abs_func() {}
-        __host__ __device__ __forceinline__ abs_func(const abs_func&) {}
-    };
-    template <> struct abs_func<unsigned short> : unary_function<unsigned short, unsigned short>
-    {
-        __device__ __forceinline__ unsigned short operator ()(unsigned short x) const
-        {
-            return x;
-        }
-
-        __host__ __device__ __forceinline__ abs_func() {}
-        __host__ __device__ __forceinline__ abs_func(const abs_func&) {}
-    };
-    template <> struct abs_func<short> : unary_function<short, short>
-    {
-        __device__ __forceinline__ short operator ()(short x) const
-        {
-            return ::abs((int)x);
-        }
-
-        __host__ __device__ __forceinline__ abs_func() {}
-        __host__ __device__ __forceinline__ abs_func(const abs_func&) {}
-    };
-    template <> struct abs_func<unsigned int> : unary_function<unsigned int, unsigned int>
-    {
-        __device__ __forceinline__ unsigned int operator ()(unsigned int x) const
-        {
-            return x;
-        }
-
-        __host__ __device__ __forceinline__ abs_func() {}
-        __host__ __device__ __forceinline__ abs_func(const abs_func&) {}
-    };
-    template <> struct abs_func<int> : unary_function<int, int>
-    {
-        __device__ __forceinline__ int operator ()(int x) const
-        {
-            return ::abs(x);
-        }
-
-        __host__ __device__ __forceinline__ abs_func() {}
-        __host__ __device__ __forceinline__ abs_func(const abs_func&) {}
-    };
-    template <> struct abs_func<float> : unary_function<float, float>
-    {
-        __device__ __forceinline__ float operator ()(float x) const
-        {
-            return ::fabsf(x);
-        }
-
-        __host__ __device__ __forceinline__ abs_func() {}
-        __host__ __device__ __forceinline__ abs_func(const abs_func&) {}
-    };
-    template <> struct abs_func<double> : unary_function<double, double>
-    {
-        __device__ __forceinline__ double operator ()(double x) const
-        {
-            return ::fabs(x);
-        }
-
-        __host__ __device__ __forceinline__ abs_func() {}
-        __host__ __device__ __forceinline__ abs_func(const abs_func&) {}
-    };
-
-#define OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(name, func) \
-    template <typename T> struct name ## _func : unary_function<T, float> \
-    { \
-        __device__ __forceinline__ float operator ()(typename TypeTraits<T>::ParameterType v) const \
-        { \
-            return func ## f(v); \
-        } \
-        __host__ __device__ __forceinline__ name ## _func() {} \
-        __host__ __device__ __forceinline__ name ## _func(const name ## _func&) {} \
-    }; \
-    template <> struct name ## _func<double> : unary_function<double, double> \
-    { \
-        __device__ __forceinline__ double operator ()(double v) const \
-        { \
-            return func(v); \
-        } \
-        __host__ __device__ __forceinline__ name ## _func() {} \
-        __host__ __device__ __forceinline__ name ## _func(const name ## _func&) {} \
-    };
-
-#define OPENCV_CUDA_IMPLEMENT_BIN_FUNCTOR(name, func) \
-    template <typename T> struct name ## _func : binary_function<T, T, float> \
-    { \
-        __device__ __forceinline__ float operator ()(typename TypeTraits<T>::ParameterType v1, typename TypeTraits<T>::ParameterType v2) const \
-        { \
-            return func ## f(v1, v2); \
-        } \
-        __host__ __device__ __forceinline__ name ## _func() {} \
-        __host__ __device__ __forceinline__ name ## _func(const name ## _func&) {} \
-    }; \
-    template <> struct name ## _func<double> : binary_function<double, double, double> \
-    { \
-        __device__ __forceinline__ double operator ()(double v1, double v2) const \
-        { \
-            return func(v1, v2); \
-        } \
-        __host__ __device__ __forceinline__ name ## _func() {} \
-        __host__ __device__ __forceinline__ name ## _func(const name ## _func&) {} \
-    };
-
-    OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(sqrt, ::sqrt)
-    OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(exp, ::exp)
-    OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(exp2, ::exp2)
-    OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(exp10, ::exp10)
-    OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(log, ::log)
-    OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(log2, ::log2)
-    OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(log10, ::log10)
-    OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(sin, ::sin)
-    OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(cos, ::cos)
-    OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(tan, ::tan)
-    OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(asin, ::asin)
-    OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(acos, ::acos)
-    OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(atan, ::atan)
-    OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(sinh, ::sinh)
-    OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(cosh, ::cosh)
-    OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(tanh, ::tanh)
-    OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(asinh, ::asinh)
-    OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(acosh, ::acosh)
-    OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(atanh, ::atanh)
-
-    OPENCV_CUDA_IMPLEMENT_BIN_FUNCTOR(hypot, ::hypot)
-    OPENCV_CUDA_IMPLEMENT_BIN_FUNCTOR(atan2, ::atan2)
-    OPENCV_CUDA_IMPLEMENT_BIN_FUNCTOR(pow, ::pow)
-
-    #undef OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR
-    #undef OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR_NO_DOUBLE
-    #undef OPENCV_CUDA_IMPLEMENT_BIN_FUNCTOR
-
-    template<typename T> struct hypot_sqr_func : binary_function<T, T, float>
-    {
-        __device__ __forceinline__ T operator ()(typename TypeTraits<T>::ParameterType src1, typename TypeTraits<T>::ParameterType src2) const
-        {
-            return src1 * src1 + src2 * src2;
-        }
-        __host__ __device__ __forceinline__ hypot_sqr_func() {}
-        __host__ __device__ __forceinline__ hypot_sqr_func(const hypot_sqr_func&) {}
-    };
-
-    // Saturate Cast Functor
-    template <typename T, typename D> struct saturate_cast_func : unary_function<T, D>
-    {
-        __device__ __forceinline__ D operator ()(typename TypeTraits<T>::ParameterType v) const
-        {
-            return saturate_cast<D>(v);
-        }
-        __host__ __device__ __forceinline__ saturate_cast_func() {}
-        __host__ __device__ __forceinline__ saturate_cast_func(const saturate_cast_func&) {}
-    };
-
-    // Threshold Functors
-    template <typename T> struct thresh_binary_func : unary_function<T, T>
-    {
-        __host__ __device__ __forceinline__ thresh_binary_func(T thresh_, T maxVal_) : thresh(thresh_), maxVal(maxVal_) {}
-
-        __device__ __forceinline__ T operator()(typename TypeTraits<T>::ParameterType src) const
-        {
-            return (src > thresh) * maxVal;
-        }
-
-        __host__ __device__ __forceinline__ thresh_binary_func() {}
-        __host__ __device__ __forceinline__ thresh_binary_func(const thresh_binary_func& other)
-            : thresh(other.thresh), maxVal(other.maxVal) {}
-
-        T thresh;
-        T maxVal;
-    };
-
-    template <typename T> struct thresh_binary_inv_func : unary_function<T, T>
-    {
-        __host__ __device__ __forceinline__ thresh_binary_inv_func(T thresh_, T maxVal_) : thresh(thresh_), maxVal(maxVal_) {}
-
-        __device__ __forceinline__ T operator()(typename TypeTraits<T>::ParameterType src) const
-        {
-            return (src <= thresh) * maxVal;
-        }
-
-        __host__ __device__ __forceinline__ thresh_binary_inv_func() {}
-        __host__ __device__ __forceinline__ thresh_binary_inv_func(const thresh_binary_inv_func& other)
-            : thresh(other.thresh), maxVal(other.maxVal) {}
-
-        T thresh;
-        T maxVal;
-    };
-
-    template <typename T> struct thresh_trunc_func : unary_function<T, T>
-    {
-        explicit __host__ __device__ __forceinline__ thresh_trunc_func(T thresh_, T maxVal_ = 0) : thresh(thresh_) {(void)maxVal_;}
-
-        __device__ __forceinline__ T operator()(typename TypeTraits<T>::ParameterType src) const
-        {
-            return minimum<T>()(src, thresh);
-        }
-
-        __host__ __device__ __forceinline__ thresh_trunc_func() {}
-        __host__ __device__ __forceinline__ thresh_trunc_func(const thresh_trunc_func& other)
-            : thresh(other.thresh) {}
-
-        T thresh;
-    };
-
-    template <typename T> struct thresh_to_zero_func : unary_function<T, T>
-    {
-        explicit __host__ __device__ __forceinline__ thresh_to_zero_func(T thresh_, T maxVal_ = 0) : thresh(thresh_) {(void)maxVal_;}
-
-        __device__ __forceinline__ T operator()(typename TypeTraits<T>::ParameterType src) const
-        {
-            return (src > thresh) * src;
-        }
-
-        __host__ __device__ __forceinline__ thresh_to_zero_func() {}
-       __host__  __device__ __forceinline__ thresh_to_zero_func(const thresh_to_zero_func& other)
-            : thresh(other.thresh) {}
-
-        T thresh;
-    };
-
-    template <typename T> struct thresh_to_zero_inv_func : unary_function<T, T>
-    {
-        explicit __host__ __device__ __forceinline__ thresh_to_zero_inv_func(T thresh_, T maxVal_ = 0) : thresh(thresh_) {(void)maxVal_;}
-
-        __device__ __forceinline__ T operator()(typename TypeTraits<T>::ParameterType src) const
-        {
-            return (src <= thresh) * src;
-        }
-
-        __host__ __device__ __forceinline__ thresh_to_zero_inv_func() {}
-        __host__ __device__ __forceinline__ thresh_to_zero_inv_func(const thresh_to_zero_inv_func& other)
-            : thresh(other.thresh) {}
-
-        T thresh;
-    };
-
-    // Function Object Adaptors
-    template <typename Predicate> struct unary_negate : unary_function<typename Predicate::argument_type, bool>
-    {
-      explicit __host__ __device__ __forceinline__ unary_negate(const Predicate& p) : pred(p) {}
-
-      __device__ __forceinline__ bool operator()(typename TypeTraits<typename Predicate::argument_type>::ParameterType x) const
-      {
-          return !pred(x);
-      }
-
-      __host__ __device__ __forceinline__ unary_negate() {}
-      __host__ __device__ __forceinline__ unary_negate(const unary_negate& other) : pred(other.pred) {}
-
-      Predicate pred;
-    };
-
-    template <typename Predicate> __host__ __device__ __forceinline__ unary_negate<Predicate> not1(const Predicate& pred)
-    {
-        return unary_negate<Predicate>(pred);
-    }
-
-    template <typename Predicate> struct binary_negate : binary_function<typename Predicate::first_argument_type, typename Predicate::second_argument_type, bool>
-    {
-        explicit __host__ __device__ __forceinline__ binary_negate(const Predicate& p) : pred(p) {}
-
-        __device__ __forceinline__ bool operator()(typename TypeTraits<typename Predicate::first_argument_type>::ParameterType x,
-                                                   typename TypeTraits<typename Predicate::second_argument_type>::ParameterType y) const
-        {
-            return !pred(x,y);
-        }
-
-        __host__ __device__ __forceinline__ binary_negate() {}
-        __host__ __device__ __forceinline__ binary_negate(const binary_negate& other) : pred(other.pred) {}
-
-        Predicate pred;
-    };
-
-    template <typename BinaryPredicate> __host__ __device__ __forceinline__ binary_negate<BinaryPredicate> not2(const BinaryPredicate& pred)
-    {
-        return binary_negate<BinaryPredicate>(pred);
-    }
-
-    template <typename Op> struct binder1st : unary_function<typename Op::second_argument_type, typename Op::result_type>
-    {
-        __host__ __device__ __forceinline__ binder1st(const Op& op_, const typename Op::first_argument_type& arg1_) : op(op_), arg1(arg1_) {}
-
-        __device__ __forceinline__ typename Op::result_type operator ()(typename TypeTraits<typename Op::second_argument_type>::ParameterType a) const
-        {
-            return op(arg1, a);
-        }
-
-        __host__ __device__ __forceinline__ binder1st() {}
-        __host__ __device__ __forceinline__ binder1st(const binder1st& other) : op(other.op), arg1(other.arg1) {}
-
-        Op op;
-        typename Op::first_argument_type arg1;
-    };
-
-    template <typename Op, typename T> __host__ __device__ __forceinline__ binder1st<Op> bind1st(const Op& op, const T& x)
-    {
-        return binder1st<Op>(op, typename Op::first_argument_type(x));
-    }
-
-    template <typename Op> struct binder2nd : unary_function<typename Op::first_argument_type, typename Op::result_type>
-    {
-        __host__ __device__ __forceinline__ binder2nd(const Op& op_, const typename Op::second_argument_type& arg2_) : op(op_), arg2(arg2_) {}
-
-        __forceinline__ __device__ typename Op::result_type operator ()(typename TypeTraits<typename Op::first_argument_type>::ParameterType a) const
-        {
-            return op(a, arg2);
-        }
-
-        __host__ __device__ __forceinline__ binder2nd() {}
-        __host__ __device__ __forceinline__ binder2nd(const binder2nd& other) : op(other.op), arg2(other.arg2) {}
-
-        Op op;
-        typename Op::second_argument_type arg2;
-    };
-
-    template <typename Op, typename T> __host__ __device__ __forceinline__ binder2nd<Op> bind2nd(const Op& op, const T& x)
-    {
-        return binder2nd<Op>(op, typename Op::second_argument_type(x));
-    }
-
-    // Functor Traits
-    template <typename F> struct IsUnaryFunction
-    {
-        typedef char Yes;
-        struct No {Yes a[2];};
-
-        template <typename T, typename D> static Yes check(unary_function<T, D>);
-        static No check(...);
-
-        static F makeF();
-
-        enum { value = (sizeof(check(makeF())) == sizeof(Yes)) };
-    };
-
-    template <typename F> struct IsBinaryFunction
-    {
-        typedef char Yes;
-        struct No {Yes a[2];};
-
-        template <typename T1, typename T2, typename D> static Yes check(binary_function<T1, T2, D>);
-        static No check(...);
-
-        static F makeF();
-
-        enum { value = (sizeof(check(makeF())) == sizeof(Yes)) };
-    };
-
-    namespace functional_detail
-    {
-        template <size_t src_elem_size, size_t dst_elem_size> struct UnOpShift { enum { shift = 1 }; };
-        template <size_t src_elem_size> struct UnOpShift<src_elem_size, 1> { enum { shift = 4 }; };
-        template <size_t src_elem_size> struct UnOpShift<src_elem_size, 2> { enum { shift = 2 }; };
-
-        template <typename T, typename D> struct DefaultUnaryShift
-        {
-            enum { shift = UnOpShift<sizeof(T), sizeof(D)>::shift };
-        };
-
-        template <size_t src_elem_size1, size_t src_elem_size2, size_t dst_elem_size> struct BinOpShift { enum { shift = 1 }; };
-        template <size_t src_elem_size1, size_t src_elem_size2> struct BinOpShift<src_elem_size1, src_elem_size2, 1> { enum { shift = 4 }; };
-        template <size_t src_elem_size1, size_t src_elem_size2> struct BinOpShift<src_elem_size1, src_elem_size2, 2> { enum { shift = 2 }; };
-
-        template <typename T1, typename T2, typename D> struct DefaultBinaryShift
-        {
-            enum { shift = BinOpShift<sizeof(T1), sizeof(T2), sizeof(D)>::shift };
-        };
-
-        template <typename Func, bool unary = IsUnaryFunction<Func>::value> struct ShiftDispatcher;
-        template <typename Func> struct ShiftDispatcher<Func, true>
-        {
-            enum { shift = DefaultUnaryShift<typename Func::argument_type, typename Func::result_type>::shift };
-        };
-        template <typename Func> struct ShiftDispatcher<Func, false>
-        {
-            enum { shift = DefaultBinaryShift<typename Func::first_argument_type, typename Func::second_argument_type, typename Func::result_type>::shift };
-        };
-    }
-
-    template <typename Func> struct DefaultTransformShift
-    {
-        enum { shift = functional_detail::ShiftDispatcher<Func>::shift };
-    };
-
-    template <typename Func> struct DefaultTransformFunctorTraits
-    {
-        enum { simple_block_dim_x = 16 };
-        enum { simple_block_dim_y = 16 };
-
-        enum { smart_block_dim_x = 16 };
-        enum { smart_block_dim_y = 16 };
-        enum { smart_shift = DefaultTransformShift<Func>::shift };
-    };
-
-    template <typename Func> struct TransformFunctorTraits : DefaultTransformFunctorTraits<Func> {};
-
-#define OPENCV_CUDA_TRANSFORM_FUNCTOR_TRAITS(type) \
-    template <> struct TransformFunctorTraits< type > : DefaultTransformFunctorTraits< type >
-}}} // namespace cv { namespace cuda { namespace cudev
-
-//! @endcond
-
-#endif // OPENCV_CUDA_FUNCTIONAL_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/limits.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/limits.hpp
deleted file mode 100755
index 7e15ed6..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/limits.hpp
+++ /dev/null
@@ -1,128 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_CUDA_LIMITS_HPP
-#define OPENCV_CUDA_LIMITS_HPP
-
-#include <limits.h>
-#include <float.h>
-#include "common.hpp"
-
-/** @file
- * @deprecated Use @ref cudev instead.
- */
-
-//! @cond IGNORED
-
-namespace cv { namespace cuda { namespace device
-{
-template <class T> struct numeric_limits;
-
-template <> struct numeric_limits<bool>
-{
-    __device__ __forceinline__ static bool min() { return false; }
-    __device__ __forceinline__ static bool max() { return true;  }
-    static const bool is_signed = false;
-};
-
-template <> struct numeric_limits<signed char>
-{
-    __device__ __forceinline__ static signed char min() { return SCHAR_MIN; }
-    __device__ __forceinline__ static signed char max() { return SCHAR_MAX; }
-    static const bool is_signed = true;
-};
-
-template <> struct numeric_limits<unsigned char>
-{
-    __device__ __forceinline__ static unsigned char min() { return 0; }
-    __device__ __forceinline__ static unsigned char max() { return UCHAR_MAX; }
-    static const bool is_signed = false;
-};
-
-template <> struct numeric_limits<short>
-{
-    __device__ __forceinline__ static short min() { return SHRT_MIN; }
-    __device__ __forceinline__ static short max() { return SHRT_MAX; }
-    static const bool is_signed = true;
-};
-
-template <> struct numeric_limits<unsigned short>
-{
-    __device__ __forceinline__ static unsigned short min() { return 0; }
-    __device__ __forceinline__ static unsigned short max() { return USHRT_MAX; }
-    static const bool is_signed = false;
-};
-
-template <> struct numeric_limits<int>
-{
-    __device__ __forceinline__ static int min() { return INT_MIN; }
-    __device__ __forceinline__ static int max() { return INT_MAX; }
-    static const bool is_signed = true;
-};
-
-template <> struct numeric_limits<unsigned int>
-{
-    __device__ __forceinline__ static unsigned int min() { return 0; }
-    __device__ __forceinline__ static unsigned int max() { return UINT_MAX; }
-    static const bool is_signed = false;
-};
-
-template <> struct numeric_limits<float>
-{
-    __device__ __forceinline__ static float min() { return FLT_MIN; }
-    __device__ __forceinline__ static float max() { return FLT_MAX; }
-    __device__ __forceinline__ static float epsilon() { return FLT_EPSILON; }
-    static const bool is_signed = true;
-};
-
-template <> struct numeric_limits<double>
-{
-    __device__ __forceinline__ static double min() { return DBL_MIN; }
-    __device__ __forceinline__ static double max() { return DBL_MAX; }
-    __device__ __forceinline__ static double epsilon() { return DBL_EPSILON; }
-    static const bool is_signed = true;
-};
-}}} // namespace cv { namespace cuda { namespace cudev {
-
-//! @endcond
-
-#endif // OPENCV_CUDA_LIMITS_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/reduce.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/reduce.hpp
deleted file mode 100755
index 5de3650..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/reduce.hpp
+++ /dev/null
@@ -1,209 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_CUDA_REDUCE_HPP
-#define OPENCV_CUDA_REDUCE_HPP
-
-#ifndef THRUST_DEBUG // eliminate -Wundef warning
-#define THRUST_DEBUG 0
-#endif
-
-#include <thrust/tuple.h>
-#include "detail/reduce.hpp"
-#include "detail/reduce_key_val.hpp"
-
-/** @file
- * @deprecated Use @ref cudev instead.
- */
-
-//! @cond IGNORED
-
-namespace cv { namespace cuda { namespace device
-{
-    template <int N, typename T, class Op>
-    __device__ __forceinline__ void reduce(volatile T* smem, T& val, unsigned int tid, const Op& op)
-    {
-        reduce_detail::Dispatcher<N>::reductor::template reduce<volatile T*, T&, const Op&>(smem, val, tid, op);
-    }
-    template <int N,
-              typename P0, typename P1, typename P2, typename P3, typename P4, typename P5, typename P6, typename P7, typename P8, typename P9,
-              typename R0, typename R1, typename R2, typename R3, typename R4, typename R5, typename R6, typename R7, typename R8, typename R9,
-              class Op0, class Op1, class Op2, class Op3, class Op4, class Op5, class Op6, class Op7, class Op8, class Op9>
-    __device__ __forceinline__ void reduce(const thrust::tuple<P0, P1, P2, P3, P4, P5, P6, P7, P8, P9>& smem,
-                                           const thrust::tuple<R0, R1, R2, R3, R4, R5, R6, R7, R8, R9>& val,
-                                           unsigned int tid,
-                                           const thrust::tuple<Op0, Op1, Op2, Op3, Op4, Op5, Op6, Op7, Op8, Op9>& op)
-    {
-        reduce_detail::Dispatcher<N>::reductor::template reduce<
-                const thrust::tuple<P0, P1, P2, P3, P4, P5, P6, P7, P8, P9>&,
-                const thrust::tuple<R0, R1, R2, R3, R4, R5, R6, R7, R8, R9>&,
-                const thrust::tuple<Op0, Op1, Op2, Op3, Op4, Op5, Op6, Op7, Op8, Op9>&>(smem, val, tid, op);
-    }
-
-    template <unsigned int N, typename K, typename V, class Cmp>
-    __device__ __forceinline__ void reduceKeyVal(volatile K* skeys, K& key, volatile V* svals, V& val, unsigned int tid, const Cmp& cmp)
-    {
-        reduce_key_val_detail::Dispatcher<N>::reductor::template reduce<volatile K*, K&, volatile V*, V&, const Cmp&>(skeys, key, svals, val, tid, cmp);
-    }
-    template <unsigned int N,
-              typename K,
-              typename VP0, typename VP1, typename VP2, typename VP3, typename VP4, typename VP5, typename VP6, typename VP7, typename VP8, typename VP9,
-              typename VR0, typename VR1, typename VR2, typename VR3, typename VR4, typename VR5, typename VR6, typename VR7, typename VR8, typename VR9,
-              class Cmp>
-    __device__ __forceinline__ void reduceKeyVal(volatile K* skeys, K& key,
-                                                 const thrust::tuple<VP0, VP1, VP2, VP3, VP4, VP5, VP6, VP7, VP8, VP9>& svals,
-                                                 const thrust::tuple<VR0, VR1, VR2, VR3, VR4, VR5, VR6, VR7, VR8, VR9>& val,
-                                                 unsigned int tid, const Cmp& cmp)
-    {
-        reduce_key_val_detail::Dispatcher<N>::reductor::template reduce<volatile K*, K&,
-                const thrust::tuple<VP0, VP1, VP2, VP3, VP4, VP5, VP6, VP7, VP8, VP9>&,
-                const thrust::tuple<VR0, VR1, VR2, VR3, VR4, VR5, VR6, VR7, VR8, VR9>&,
-                const Cmp&>(skeys, key, svals, val, tid, cmp);
-    }
-    template <unsigned int N,
-              typename KP0, typename KP1, typename KP2, typename KP3, typename KP4, typename KP5, typename KP6, typename KP7, typename KP8, typename KP9,
-              typename KR0, typename KR1, typename KR2, typename KR3, typename KR4, typename KR5, typename KR6, typename KR7, typename KR8, typename KR9,
-              typename VP0, typename VP1, typename VP2, typename VP3, typename VP4, typename VP5, typename VP6, typename VP7, typename VP8, typename VP9,
-              typename VR0, typename VR1, typename VR2, typename VR3, typename VR4, typename VR5, typename VR6, typename VR7, typename VR8, typename VR9,
-              class Cmp0, class Cmp1, class Cmp2, class Cmp3, class Cmp4, class Cmp5, class Cmp6, class Cmp7, class Cmp8, class Cmp9>
-    __device__ __forceinline__ void reduceKeyVal(const thrust::tuple<KP0, KP1, KP2, KP3, KP4, KP5, KP6, KP7, KP8, KP9>& skeys,
-                                                 const thrust::tuple<KR0, KR1, KR2, KR3, KR4, KR5, KR6, KR7, KR8, KR9>& key,
-                                                 const thrust::tuple<VP0, VP1, VP2, VP3, VP4, VP5, VP6, VP7, VP8, VP9>& svals,
-                                                 const thrust::tuple<VR0, VR1, VR2, VR3, VR4, VR5, VR6, VR7, VR8, VR9>& val,
-                                                 unsigned int tid,
-                                                 const thrust::tuple<Cmp0, Cmp1, Cmp2, Cmp3, Cmp4, Cmp5, Cmp6, Cmp7, Cmp8, Cmp9>& cmp)
-    {
-        reduce_key_val_detail::Dispatcher<N>::reductor::template reduce<
-                const thrust::tuple<KP0, KP1, KP2, KP3, KP4, KP5, KP6, KP7, KP8, KP9>&,
-                const thrust::tuple<KR0, KR1, KR2, KR3, KR4, KR5, KR6, KR7, KR8, KR9>&,
-                const thrust::tuple<VP0, VP1, VP2, VP3, VP4, VP5, VP6, VP7, VP8, VP9>&,
-                const thrust::tuple<VR0, VR1, VR2, VR3, VR4, VR5, VR6, VR7, VR8, VR9>&,
-                const thrust::tuple<Cmp0, Cmp1, Cmp2, Cmp3, Cmp4, Cmp5, Cmp6, Cmp7, Cmp8, Cmp9>&
-                >(skeys, key, svals, val, tid, cmp);
-    }
-
-    // smem_tuple
-
-    template <typename T0>
-    __device__ __forceinline__
-    thrust::tuple<volatile T0*>
-    smem_tuple(T0* t0)
-    {
-        return thrust::make_tuple((volatile T0*) t0);
-    }
-
-    template <typename T0, typename T1>
-    __device__ __forceinline__
-    thrust::tuple<volatile T0*, volatile T1*>
-    smem_tuple(T0* t0, T1* t1)
-    {
-        return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1);
-    }
-
-    template <typename T0, typename T1, typename T2>
-    __device__ __forceinline__
-    thrust::tuple<volatile T0*, volatile T1*, volatile T2*>
-    smem_tuple(T0* t0, T1* t1, T2* t2)
-    {
-        return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2);
-    }
-
-    template <typename T0, typename T1, typename T2, typename T3>
-    __device__ __forceinline__
-    thrust::tuple<volatile T0*, volatile T1*, volatile T2*, volatile T3*>
-    smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3)
-    {
-        return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3);
-    }
-
-    template <typename T0, typename T1, typename T2, typename T3, typename T4>
-    __device__ __forceinline__
-    thrust::tuple<volatile T0*, volatile T1*, volatile T2*, volatile T3*, volatile T4*>
-    smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3, T4* t4)
-    {
-        return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3, (volatile T4*) t4);
-    }
-
-    template <typename T0, typename T1, typename T2, typename T3, typename T4, typename T5>
-    __device__ __forceinline__
-    thrust::tuple<volatile T0*, volatile T1*, volatile T2*, volatile T3*, volatile T4*, volatile T5*>
-    smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3, T4* t4, T5* t5)
-    {
-        return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3, (volatile T4*) t4, (volatile T5*) t5);
-    }
-
-    template <typename T0, typename T1, typename T2, typename T3, typename T4, typename T5, typename T6>
-    __device__ __forceinline__
-    thrust::tuple<volatile T0*, volatile T1*, volatile T2*, volatile T3*, volatile T4*, volatile T5*, volatile T6*>
-    smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3, T4* t4, T5* t5, T6* t6)
-    {
-        return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3, (volatile T4*) t4, (volatile T5*) t5, (volatile T6*) t6);
-    }
-
-    template <typename T0, typename T1, typename T2, typename T3, typename T4, typename T5, typename T6, typename T7>
-    __device__ __forceinline__
-    thrust::tuple<volatile T0*, volatile T1*, volatile T2*, volatile T3*, volatile T4*, volatile T5*, volatile T6*, volatile T7*>
-    smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3, T4* t4, T5* t5, T6* t6, T7* t7)
-    {
-        return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3, (volatile T4*) t4, (volatile T5*) t5, (volatile T6*) t6, (volatile T7*) t7);
-    }
-
-    template <typename T0, typename T1, typename T2, typename T3, typename T4, typename T5, typename T6, typename T7, typename T8>
-    __device__ __forceinline__
-    thrust::tuple<volatile T0*, volatile T1*, volatile T2*, volatile T3*, volatile T4*, volatile T5*, volatile T6*, volatile T7*, volatile T8*>
-    smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3, T4* t4, T5* t5, T6* t6, T7* t7, T8* t8)
-    {
-        return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3, (volatile T4*) t4, (volatile T5*) t5, (volatile T6*) t6, (volatile T7*) t7, (volatile T8*) t8);
-    }
-
-    template <typename T0, typename T1, typename T2, typename T3, typename T4, typename T5, typename T6, typename T7, typename T8, typename T9>
-    __device__ __forceinline__
-    thrust::tuple<volatile T0*, volatile T1*, volatile T2*, volatile T3*, volatile T4*, volatile T5*, volatile T6*, volatile T7*, volatile T8*, volatile T9*>
-    smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3, T4* t4, T5* t5, T6* t6, T7* t7, T8* t8, T9* t9)
-    {
-        return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3, (volatile T4*) t4, (volatile T5*) t5, (volatile T6*) t6, (volatile T7*) t7, (volatile T8*) t8, (volatile T9*) t9);
-    }
-}}}
-
-//! @endcond
-
-#endif // OPENCV_CUDA_REDUCE_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/saturate_cast.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/saturate_cast.hpp
deleted file mode 100755
index c3a3d1c..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/saturate_cast.hpp
+++ /dev/null
@@ -1,292 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_CUDA_SATURATE_CAST_HPP
-#define OPENCV_CUDA_SATURATE_CAST_HPP
-
-#include "common.hpp"
-
-/** @file
- * @deprecated Use @ref cudev instead.
- */
-
-//! @cond IGNORED
-
-namespace cv { namespace cuda { namespace device
-{
-    template<typename _Tp> __device__ __forceinline__ _Tp saturate_cast(uchar v) { return _Tp(v); }
-    template<typename _Tp> __device__ __forceinline__ _Tp saturate_cast(schar v) { return _Tp(v); }
-    template<typename _Tp> __device__ __forceinline__ _Tp saturate_cast(ushort v) { return _Tp(v); }
-    template<typename _Tp> __device__ __forceinline__ _Tp saturate_cast(short v) { return _Tp(v); }
-    template<typename _Tp> __device__ __forceinline__ _Tp saturate_cast(uint v) { return _Tp(v); }
-    template<typename _Tp> __device__ __forceinline__ _Tp saturate_cast(int v) { return _Tp(v); }
-    template<typename _Tp> __device__ __forceinline__ _Tp saturate_cast(float v) { return _Tp(v); }
-    template<typename _Tp> __device__ __forceinline__ _Tp saturate_cast(double v) { return _Tp(v); }
-
-    template<> __device__ __forceinline__ uchar saturate_cast<uchar>(schar v)
-    {
-        uint res = 0;
-        int vi = v;
-        asm("cvt.sat.u8.s8 %0, %1;" : "=r"(res) : "r"(vi));
-        return res;
-    }
-    template<> __device__ __forceinline__ uchar saturate_cast<uchar>(short v)
-    {
-        uint res = 0;
-        asm("cvt.sat.u8.s16 %0, %1;" : "=r"(res) : "h"(v));
-        return res;
-    }
-    template<> __device__ __forceinline__ uchar saturate_cast<uchar>(ushort v)
-    {
-        uint res = 0;
-        asm("cvt.sat.u8.u16 %0, %1;" : "=r"(res) : "h"(v));
-        return res;
-    }
-    template<> __device__ __forceinline__ uchar saturate_cast<uchar>(int v)
-    {
-        uint res = 0;
-        asm("cvt.sat.u8.s32 %0, %1;" : "=r"(res) : "r"(v));
-        return res;
-    }
-    template<> __device__ __forceinline__ uchar saturate_cast<uchar>(uint v)
-    {
-        uint res = 0;
-        asm("cvt.sat.u8.u32 %0, %1;" : "=r"(res) : "r"(v));
-        return res;
-    }
-    template<> __device__ __forceinline__ uchar saturate_cast<uchar>(float v)
-    {
-        uint res = 0;
-        asm("cvt.rni.sat.u8.f32 %0, %1;" : "=r"(res) : "f"(v));
-        return res;
-    }
-    template<> __device__ __forceinline__ uchar saturate_cast<uchar>(double v)
-    {
-    #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 130
-        uint res = 0;
-        asm("cvt.rni.sat.u8.f64 %0, %1;" : "=r"(res) : "d"(v));
-        return res;
-    #else
-        return saturate_cast<uchar>((float)v);
-    #endif
-    }
-
-    template<> __device__ __forceinline__ schar saturate_cast<schar>(uchar v)
-    {
-        uint res = 0;
-        uint vi = v;
-        asm("cvt.sat.s8.u8 %0, %1;" : "=r"(res) : "r"(vi));
-        return res;
-    }
-    template<> __device__ __forceinline__ schar saturate_cast<schar>(short v)
-    {
-        uint res = 0;
-        asm("cvt.sat.s8.s16 %0, %1;" : "=r"(res) : "h"(v));
-        return res;
-    }
-    template<> __device__ __forceinline__ schar saturate_cast<schar>(ushort v)
-    {
-        uint res = 0;
-        asm("cvt.sat.s8.u16 %0, %1;" : "=r"(res) : "h"(v));
-        return res;
-    }
-    template<> __device__ __forceinline__ schar saturate_cast<schar>(int v)
-    {
-        uint res = 0;
-        asm("cvt.sat.s8.s32 %0, %1;" : "=r"(res) : "r"(v));
-        return res;
-    }
-    template<> __device__ __forceinline__ schar saturate_cast<schar>(uint v)
-    {
-        uint res = 0;
-        asm("cvt.sat.s8.u32 %0, %1;" : "=r"(res) : "r"(v));
-        return res;
-    }
-    template<> __device__ __forceinline__ schar saturate_cast<schar>(float v)
-    {
-        uint res = 0;
-        asm("cvt.rni.sat.s8.f32 %0, %1;" : "=r"(res) : "f"(v));
-        return res;
-    }
-    template<> __device__ __forceinline__ schar saturate_cast<schar>(double v)
-    {
-    #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 130
-        uint res = 0;
-        asm("cvt.rni.sat.s8.f64 %0, %1;" : "=r"(res) : "d"(v));
-        return res;
-    #else
-        return saturate_cast<schar>((float)v);
-    #endif
-    }
-
-    template<> __device__ __forceinline__ ushort saturate_cast<ushort>(schar v)
-    {
-        ushort res = 0;
-        int vi = v;
-        asm("cvt.sat.u16.s8 %0, %1;" : "=h"(res) : "r"(vi));
-        return res;
-    }
-    template<> __device__ __forceinline__ ushort saturate_cast<ushort>(short v)
-    {
-        ushort res = 0;
-        asm("cvt.sat.u16.s16 %0, %1;" : "=h"(res) : "h"(v));
-        return res;
-    }
-    template<> __device__ __forceinline__ ushort saturate_cast<ushort>(int v)
-    {
-        ushort res = 0;
-        asm("cvt.sat.u16.s32 %0, %1;" : "=h"(res) : "r"(v));
-        return res;
-    }
-    template<> __device__ __forceinline__ ushort saturate_cast<ushort>(uint v)
-    {
-        ushort res = 0;
-        asm("cvt.sat.u16.u32 %0, %1;" : "=h"(res) : "r"(v));
-        return res;
-    }
-    template<> __device__ __forceinline__ ushort saturate_cast<ushort>(float v)
-    {
-        ushort res = 0;
-        asm("cvt.rni.sat.u16.f32 %0, %1;" : "=h"(res) : "f"(v));
-        return res;
-    }
-    template<> __device__ __forceinline__ ushort saturate_cast<ushort>(double v)
-    {
-    #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 130
-        ushort res = 0;
-        asm("cvt.rni.sat.u16.f64 %0, %1;" : "=h"(res) : "d"(v));
-        return res;
-    #else
-        return saturate_cast<ushort>((float)v);
-    #endif
-    }
-
-    template<> __device__ __forceinline__ short saturate_cast<short>(ushort v)
-    {
-        short res = 0;
-        asm("cvt.sat.s16.u16 %0, %1;" : "=h"(res) : "h"(v));
-        return res;
-    }
-    template<> __device__ __forceinline__ short saturate_cast<short>(int v)
-    {
-        short res = 0;
-        asm("cvt.sat.s16.s32 %0, %1;" : "=h"(res) : "r"(v));
-        return res;
-    }
-    template<> __device__ __forceinline__ short saturate_cast<short>(uint v)
-    {
-        short res = 0;
-        asm("cvt.sat.s16.u32 %0, %1;" : "=h"(res) : "r"(v));
-        return res;
-    }
-    template<> __device__ __forceinline__ short saturate_cast<short>(float v)
-    {
-        short res = 0;
-        asm("cvt.rni.sat.s16.f32 %0, %1;" : "=h"(res) : "f"(v));
-        return res;
-    }
-    template<> __device__ __forceinline__ short saturate_cast<short>(double v)
-    {
-    #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 130
-        short res = 0;
-        asm("cvt.rni.sat.s16.f64 %0, %1;" : "=h"(res) : "d"(v));
-        return res;
-    #else
-        return saturate_cast<short>((float)v);
-    #endif
-    }
-
-    template<> __device__ __forceinline__ int saturate_cast<int>(uint v)
-    {
-        int res = 0;
-        asm("cvt.sat.s32.u32 %0, %1;" : "=r"(res) : "r"(v));
-        return res;
-    }
-    template<> __device__ __forceinline__ int saturate_cast<int>(float v)
-    {
-        return __float2int_rn(v);
-    }
-    template<> __device__ __forceinline__ int saturate_cast<int>(double v)
-    {
-    #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 130
-        return __double2int_rn(v);
-    #else
-        return saturate_cast<int>((float)v);
-    #endif
-    }
-
-    template<> __device__ __forceinline__ uint saturate_cast<uint>(schar v)
-    {
-        uint res = 0;
-        int vi = v;
-        asm("cvt.sat.u32.s8 %0, %1;" : "=r"(res) : "r"(vi));
-        return res;
-    }
-    template<> __device__ __forceinline__ uint saturate_cast<uint>(short v)
-    {
-        uint res = 0;
-        asm("cvt.sat.u32.s16 %0, %1;" : "=r"(res) : "h"(v));
-        return res;
-    }
-    template<> __device__ __forceinline__ uint saturate_cast<uint>(int v)
-    {
-        uint res = 0;
-        asm("cvt.sat.u32.s32 %0, %1;" : "=r"(res) : "r"(v));
-        return res;
-    }
-    template<> __device__ __forceinline__ uint saturate_cast<uint>(float v)
-    {
-        return __float2uint_rn(v);
-    }
-    template<> __device__ __forceinline__ uint saturate_cast<uint>(double v)
-    {
-    #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 130
-        return __double2uint_rn(v);
-    #else
-        return saturate_cast<uint>((float)v);
-    #endif
-    }
-}}}
-
-//! @endcond
-
-#endif /* OPENCV_CUDA_SATURATE_CAST_HPP */
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/scan.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/scan.hpp
deleted file mode 100755
index e07ee65..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/scan.hpp
+++ /dev/null
@@ -1,258 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_CUDA_SCAN_HPP
-#define OPENCV_CUDA_SCAN_HPP
-
-#include "opencv2/core/cuda/common.hpp"
-#include "opencv2/core/cuda/utility.hpp"
-#include "opencv2/core/cuda/warp.hpp"
-#include "opencv2/core/cuda/warp_shuffle.hpp"
-
-/** @file
- * @deprecated Use @ref cudev instead.
- */
-
-//! @cond IGNORED
-
-namespace cv { namespace cuda { namespace device
-{
-    enum ScanKind { EXCLUSIVE = 0,  INCLUSIVE = 1 };
-
-    template <ScanKind Kind, typename T, typename F> struct WarpScan
-    {
-        __device__ __forceinline__ WarpScan() {}
-        __device__ __forceinline__ WarpScan(const WarpScan& other) { (void)other; }
-
-        __device__ __forceinline__ T operator()( volatile T *ptr , const unsigned int idx)
-        {
-            const unsigned int lane = idx & 31;
-            F op;
-
-            if ( lane >=  1) ptr [idx ] = op(ptr [idx -  1], ptr [idx]);
-            if ( lane >=  2) ptr [idx ] = op(ptr [idx -  2], ptr [idx]);
-            if ( lane >=  4) ptr [idx ] = op(ptr [idx -  4], ptr [idx]);
-            if ( lane >=  8) ptr [idx ] = op(ptr [idx -  8], ptr [idx]);
-            if ( lane >= 16) ptr [idx ] = op(ptr [idx - 16], ptr [idx]);
-
-            if( Kind == INCLUSIVE )
-                return ptr [idx];
-            else
-                return (lane > 0) ? ptr [idx - 1] : 0;
-        }
-
-        __device__ __forceinline__ unsigned int index(const unsigned int tid)
-        {
-            return tid;
-        }
-
-        __device__ __forceinline__ void init(volatile T *ptr){}
-
-        static const int warp_offset      = 0;
-
-        typedef WarpScan<INCLUSIVE, T, F>  merge;
-    };
-
-    template <ScanKind Kind , typename T, typename F> struct WarpScanNoComp
-    {
-        __device__ __forceinline__ WarpScanNoComp() {}
-        __device__ __forceinline__ WarpScanNoComp(const WarpScanNoComp& other) { (void)other; }
-
-        __device__ __forceinline__ T operator()( volatile T *ptr , const unsigned int idx)
-        {
-            const unsigned int lane = threadIdx.x & 31;
-            F op;
-
-            ptr [idx ] = op(ptr [idx -  1], ptr [idx]);
-            ptr [idx ] = op(ptr [idx -  2], ptr [idx]);
-            ptr [idx ] = op(ptr [idx -  4], ptr [idx]);
-            ptr [idx ] = op(ptr [idx -  8], ptr [idx]);
-            ptr [idx ] = op(ptr [idx - 16], ptr [idx]);
-
-            if( Kind == INCLUSIVE )
-                return ptr [idx];
-            else
-                return (lane > 0) ? ptr [idx - 1] : 0;
-        }
-
-        __device__ __forceinline__ unsigned int index(const unsigned int tid)
-        {
-            return (tid >> warp_log) * warp_smem_stride + 16 + (tid & warp_mask);
-        }
-
-        __device__ __forceinline__ void init(volatile T *ptr)
-        {
-            ptr[threadIdx.x] = 0;
-        }
-
-        static const int warp_smem_stride = 32 + 16 + 1;
-        static const int warp_offset      = 16;
-        static const int warp_log         = 5;
-        static const int warp_mask        = 31;
-
-        typedef WarpScanNoComp<INCLUSIVE, T, F> merge;
-    };
-
-    template <ScanKind Kind , typename T, typename Sc, typename F> struct BlockScan
-    {
-        __device__ __forceinline__ BlockScan() {}
-        __device__ __forceinline__ BlockScan(const BlockScan& other) { (void)other; }
-
-        __device__ __forceinline__ T operator()(volatile T *ptr)
-        {
-            const unsigned int tid  = threadIdx.x;
-            const unsigned int lane = tid & warp_mask;
-            const unsigned int warp = tid >> warp_log;
-
-            Sc scan;
-            typename Sc::merge merge_scan;
-            const unsigned int idx = scan.index(tid);
-
-            T val = scan(ptr, idx);
-            __syncthreads ();
-
-            if( warp == 0)
-                scan.init(ptr);
-            __syncthreads ();
-
-            if( lane == 31 )
-                ptr [scan.warp_offset + warp ] = (Kind == INCLUSIVE) ? val : ptr [idx];
-            __syncthreads ();
-
-            if( warp == 0 )
-                merge_scan(ptr, idx);
-            __syncthreads();
-
-            if ( warp > 0)
-                val = ptr [scan.warp_offset + warp - 1] + val;
-            __syncthreads ();
-
-            ptr[idx] = val;
-            __syncthreads ();
-
-            return val ;
-        }
-
-        static const int warp_log  = 5;
-        static const int warp_mask = 31;
-    };
-
-    template <typename T>
-    __device__ T warpScanInclusive(T idata, volatile T* s_Data, unsigned int tid)
-    {
-    #if __CUDA_ARCH__ >= 300
-        const unsigned int laneId = cv::cuda::device::Warp::laneId();
-
-        // scan on shuffl functions
-        #pragma unroll
-        for (int i = 1; i <= (OPENCV_CUDA_WARP_SIZE / 2); i *= 2)
-        {
-            const T n = cv::cuda::device::shfl_up(idata, i);
-            if (laneId >= i)
-                  idata += n;
-        }
-
-        return idata;
-    #else
-        unsigned int pos = 2 * tid - (tid & (OPENCV_CUDA_WARP_SIZE - 1));
-        s_Data[pos] = 0;
-        pos += OPENCV_CUDA_WARP_SIZE;
-        s_Data[pos] = idata;
-
-        s_Data[pos] += s_Data[pos - 1];
-        s_Data[pos] += s_Data[pos - 2];
-        s_Data[pos] += s_Data[pos - 4];
-        s_Data[pos] += s_Data[pos - 8];
-        s_Data[pos] += s_Data[pos - 16];
-
-        return s_Data[pos];
-    #endif
-    }
-
-    template <typename T>
-    __device__ __forceinline__ T warpScanExclusive(T idata, volatile T* s_Data, unsigned int tid)
-    {
-        return warpScanInclusive(idata, s_Data, tid) - idata;
-    }
-
-    template <int tiNumScanThreads, typename T>
-    __device__ T blockScanInclusive(T idata, volatile T* s_Data, unsigned int tid)
-    {
-        if (tiNumScanThreads > OPENCV_CUDA_WARP_SIZE)
-        {
-            //Bottom-level inclusive warp scan
-            T warpResult = warpScanInclusive(idata, s_Data, tid);
-
-            //Save top elements of each warp for exclusive warp scan
-            //sync to wait for warp scans to complete (because s_Data is being overwritten)
-            __syncthreads();
-            if ((tid & (OPENCV_CUDA_WARP_SIZE - 1)) == (OPENCV_CUDA_WARP_SIZE - 1))
-            {
-                s_Data[tid >> OPENCV_CUDA_LOG_WARP_SIZE] = warpResult;
-            }
-
-            //wait for warp scans to complete
-            __syncthreads();
-
-            if (tid < (tiNumScanThreads / OPENCV_CUDA_WARP_SIZE) )
-            {
-                //grab top warp elements
-                T val = s_Data[tid];
-                //calculate exclusive scan and write back to shared memory
-                s_Data[tid] = warpScanExclusive(val, s_Data, tid);
-            }
-
-            //return updated warp scans with exclusive scan results
-            __syncthreads();
-
-            return warpResult + s_Data[tid >> OPENCV_CUDA_LOG_WARP_SIZE];
-        }
-        else
-        {
-            return warpScanInclusive(idata, s_Data, tid);
-        }
-    }
-}}}
-
-//! @endcond
-
-#endif // OPENCV_CUDA_SCAN_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/simd_functions.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/simd_functions.hpp
deleted file mode 100755
index 3d8c2e0..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/simd_functions.hpp
+++ /dev/null
@@ -1,869 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-/*
- * Copyright (c) 2013 NVIDIA Corporation. All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *
- *   Redistributions of source code must retain the above copyright notice,
- *   this list of conditions and the following disclaimer.
- *
- *   Redistributions in binary form must reproduce the above copyright notice,
- *   this list of conditions and the following disclaimer in the documentation
- *   and/or other materials provided with the distribution.
- *
- *   Neither the name of NVIDIA Corporation nor the names of its contributors
- *   may be used to endorse or promote products derived from this software
- *   without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
- * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
- * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
- * ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
- * LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
- * CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
- * SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
- * INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
- * CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
- * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
- * POSSIBILITY OF SUCH DAMAGE.
- */
-
-#ifndef OPENCV_CUDA_SIMD_FUNCTIONS_HPP
-#define OPENCV_CUDA_SIMD_FUNCTIONS_HPP
-
-#include "common.hpp"
-
-/** @file
- * @deprecated Use @ref cudev instead.
- */
-
-//! @cond IGNORED
-
-namespace cv { namespace cuda { namespace device
-{
-    // 2
-
-    static __device__ __forceinline__ unsigned int vadd2(unsigned int a, unsigned int b)
-    {
-        unsigned int r = 0;
-
-    #if __CUDA_ARCH__ >= 300
-        asm("vadd2.u32.u32.u32.sat %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #elif __CUDA_ARCH__ >= 200
-        asm("vadd.u32.u32.u32.sat %0.h0, %1.h0, %2.h0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-        asm("vadd.u32.u32.u32.sat %0.h1, %1.h1, %2.h1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #else
-        unsigned int s;
-        s = a ^ b;          // sum bits
-        r = a + b;          // actual sum
-        s = s ^ r;          // determine carry-ins for each bit position
-        s = s & 0x00010000; // carry-in to high word (= carry-out from low word)
-        r = r - s;          // subtract out carry-out from low word
-    #endif
-
-        return r;
-    }
-
-    static __device__ __forceinline__ unsigned int vsub2(unsigned int a, unsigned int b)
-    {
-        unsigned int r = 0;
-
-    #if __CUDA_ARCH__ >= 300
-        asm("vsub2.u32.u32.u32.sat %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #elif __CUDA_ARCH__ >= 200
-        asm("vsub.u32.u32.u32.sat %0.h0, %1.h0, %2.h0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-        asm("vsub.u32.u32.u32.sat %0.h1, %1.h1, %2.h1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #else
-        unsigned int s;
-        s = a ^ b;          // sum bits
-        r = a - b;          // actual sum
-        s = s ^ r;          // determine carry-ins for each bit position
-        s = s & 0x00010000; // borrow to high word
-        r = r + s;          // compensate for borrow from low word
-    #endif
-
-        return r;
-    }
-
-    static __device__ __forceinline__ unsigned int vabsdiff2(unsigned int a, unsigned int b)
-    {
-        unsigned int r = 0;
-
-    #if __CUDA_ARCH__ >= 300
-        asm("vabsdiff2.u32.u32.u32.sat %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #elif __CUDA_ARCH__ >= 200
-        asm("vabsdiff.u32.u32.u32.sat %0.h0, %1.h0, %2.h0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-        asm("vabsdiff.u32.u32.u32.sat %0.h1, %1.h1, %2.h1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #else
-        unsigned int s, t, u, v;
-        s = a & 0x0000ffff; // extract low halfword
-        r = b & 0x0000ffff; // extract low halfword
-        u = ::max(r, s);    // maximum of low halfwords
-        v = ::min(r, s);    // minimum of low halfwords
-        s = a & 0xffff0000; // extract high halfword
-        r = b & 0xffff0000; // extract high halfword
-        t = ::max(r, s);    // maximum of high halfwords
-        s = ::min(r, s);    // minimum of high halfwords
-        r = u | t;          // maximum of both halfwords
-        s = v | s;          // minimum of both halfwords
-        r = r - s;          // |a - b| = max(a,b) - min(a,b);
-    #endif
-
-        return r;
-    }
-
-    static __device__ __forceinline__ unsigned int vavg2(unsigned int a, unsigned int b)
-    {
-        unsigned int r, s;
-
-        // HAKMEM #23: a + b = 2 * (a & b) + (a ^ b) ==>
-        // (a + b) / 2 = (a & b) + ((a ^ b) >> 1)
-        s = a ^ b;
-        r = a & b;
-        s = s & 0xfffefffe; // ensure shift doesn't cross halfword boundaries
-        s = s >> 1;
-        s = r + s;
-
-        return s;
-    }
-
-    static __device__ __forceinline__ unsigned int vavrg2(unsigned int a, unsigned int b)
-    {
-        unsigned int r = 0;
-
-    #if __CUDA_ARCH__ >= 300
-        asm("vavrg2.u32.u32.u32 %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #else
-        // HAKMEM #23: a + b = 2 * (a | b) - (a ^ b) ==>
-        // (a + b + 1) / 2 = (a | b) - ((a ^ b) >> 1)
-        unsigned int s;
-        s = a ^ b;
-        r = a | b;
-        s = s & 0xfffefffe; // ensure shift doesn't cross half-word boundaries
-        s = s >> 1;
-        r = r - s;
-    #endif
-
-        return r;
-    }
-
-    static __device__ __forceinline__ unsigned int vseteq2(unsigned int a, unsigned int b)
-    {
-        unsigned int r = 0;
-
-    #if __CUDA_ARCH__ >= 300
-        asm("vset2.u32.u32.eq %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #else
-        // inspired by Alan Mycroft's null-byte detection algorithm:
-        // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080))
-        unsigned int c;
-        r = a ^ b;          // 0x0000 if a == b
-        c = r | 0x80008000; // set msbs, to catch carry out
-        r = r ^ c;          // extract msbs, msb = 1 if r < 0x8000
-        c = c - 0x00010001; // msb = 0, if r was 0x0000 or 0x8000
-        c = r & ~c;         // msb = 1, if r was 0x0000
-        r = c >> 15;        // convert to bool
-    #endif
-
-        return r;
-    }
-
-    static __device__ __forceinline__ unsigned int vcmpeq2(unsigned int a, unsigned int b)
-    {
-        unsigned int r, c;
-
-    #if __CUDA_ARCH__ >= 300
-        r = vseteq2(a, b);
-        c = r << 16;        // convert bool
-        r = c - r;          //  into mask
-    #else
-        // inspired by Alan Mycroft's null-byte detection algorithm:
-        // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080))
-        r = a ^ b;          // 0x0000 if a == b
-        c = r | 0x80008000; // set msbs, to catch carry out
-        r = r ^ c;          // extract msbs, msb = 1 if r < 0x8000
-        c = c - 0x00010001; // msb = 0, if r was 0x0000 or 0x8000
-        c = r & ~c;         // msb = 1, if r was 0x0000
-        r = c >> 15;        // convert
-        r = c - r;          //  msbs to
-        r = c | r;          //   mask
-    #endif
-
-        return r;
-    }
-
-    static __device__ __forceinline__ unsigned int vsetge2(unsigned int a, unsigned int b)
-    {
-        unsigned int r = 0;
-
-    #if __CUDA_ARCH__ >= 300
-        asm("vset2.u32.u32.ge %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #else
-        unsigned int c;
-        asm("not.b32 %0, %0;" : "+r"(b));
-        c = vavrg2(a, b);   // (a + ~b + 1) / 2 = (a - b) / 2
-        c = c & 0x80008000; // msb = carry-outs
-        r = c >> 15;        // convert to bool
-    #endif
-
-        return r;
-    }
-
-    static __device__ __forceinline__ unsigned int vcmpge2(unsigned int a, unsigned int b)
-    {
-        unsigned int r, c;
-
-    #if __CUDA_ARCH__ >= 300
-        r = vsetge2(a, b);
-        c = r << 16;        // convert bool
-        r = c - r;          //  into mask
-    #else
-        asm("not.b32 %0, %0;" : "+r"(b));
-        c = vavrg2(a, b);   // (a + ~b + 1) / 2 = (a - b) / 2
-        c = c & 0x80008000; // msb = carry-outs
-        r = c >> 15;        // convert
-        r = c - r;          //  msbs to
-        r = c | r;          //   mask
-    #endif
-
-        return r;
-    }
-
-    static __device__ __forceinline__ unsigned int vsetgt2(unsigned int a, unsigned int b)
-    {
-        unsigned int r = 0;
-
-    #if __CUDA_ARCH__ >= 300
-        asm("vset2.u32.u32.gt %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #else
-        unsigned int c;
-        asm("not.b32 %0, %0;" : "+r"(b));
-        c = vavg2(a, b);    // (a + ~b) / 2 = (a - b) / 2 [rounded down]
-        c = c & 0x80008000; // msbs = carry-outs
-        r = c >> 15;        // convert to bool
-    #endif
-
-        return r;
-    }
-
-    static __device__ __forceinline__ unsigned int vcmpgt2(unsigned int a, unsigned int b)
-    {
-        unsigned int r, c;
-
-    #if __CUDA_ARCH__ >= 300
-        r = vsetgt2(a, b);
-        c = r << 16;        // convert bool
-        r = c - r;          //  into mask
-    #else
-        asm("not.b32 %0, %0;" : "+r"(b));
-        c = vavg2(a, b);    // (a + ~b) / 2 = (a - b) / 2 [rounded down]
-        c = c & 0x80008000; // msbs = carry-outs
-        r = c >> 15;        // convert
-        r = c - r;          //  msbs to
-        r = c | r;          //   mask
-    #endif
-
-        return r;
-    }
-
-    static __device__ __forceinline__ unsigned int vsetle2(unsigned int a, unsigned int b)
-    {
-        unsigned int r = 0;
-
-    #if __CUDA_ARCH__ >= 300
-        asm("vset2.u32.u32.le %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #else
-        unsigned int c;
-        asm("not.b32 %0, %0;" : "+r"(a));
-        c = vavrg2(a, b);   // (b + ~a + 1) / 2 = (b - a) / 2
-        c = c & 0x80008000; // msb = carry-outs
-        r = c >> 15;        // convert to bool
-    #endif
-
-        return r;
-    }
-
-    static __device__ __forceinline__ unsigned int vcmple2(unsigned int a, unsigned int b)
-    {
-        unsigned int r, c;
-
-    #if __CUDA_ARCH__ >= 300
-        r = vsetle2(a, b);
-        c = r << 16;        // convert bool
-        r = c - r;          //  into mask
-    #else
-        asm("not.b32 %0, %0;" : "+r"(a));
-        c = vavrg2(a, b);   // (b + ~a + 1) / 2 = (b - a) / 2
-        c = c & 0x80008000; // msb = carry-outs
-        r = c >> 15;        // convert
-        r = c - r;          //  msbs to
-        r = c | r;          //   mask
-    #endif
-
-        return r;
-    }
-
-    static __device__ __forceinline__ unsigned int vsetlt2(unsigned int a, unsigned int b)
-    {
-        unsigned int r = 0;
-
-    #if __CUDA_ARCH__ >= 300
-        asm("vset2.u32.u32.lt %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #else
-        unsigned int c;
-        asm("not.b32 %0, %0;" : "+r"(a));
-        c = vavg2(a, b);    // (b + ~a) / 2 = (b - a) / 2 [rounded down]
-        c = c & 0x80008000; // msb = carry-outs
-        r = c >> 15;        // convert to bool
-    #endif
-
-        return r;
-    }
-
-    static __device__ __forceinline__ unsigned int vcmplt2(unsigned int a, unsigned int b)
-    {
-        unsigned int r, c;
-
-    #if __CUDA_ARCH__ >= 300
-        r = vsetlt2(a, b);
-        c = r << 16;        // convert bool
-        r = c - r;          //  into mask
-    #else
-        asm("not.b32 %0, %0;" : "+r"(a));
-        c = vavg2(a, b);    // (b + ~a) / 2 = (b - a) / 2 [rounded down]
-        c = c & 0x80008000; // msb = carry-outs
-        r = c >> 15;        // convert
-        r = c - r;          //  msbs to
-        r = c | r;          //   mask
-    #endif
-
-        return r;
-    }
-
-    static __device__ __forceinline__ unsigned int vsetne2(unsigned int a, unsigned int b)
-    {
-        unsigned int r = 0;
-
-    #if __CUDA_ARCH__ >= 300
-        asm ("vset2.u32.u32.ne %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #else
-        // inspired by Alan Mycroft's null-byte detection algorithm:
-        // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080))
-        unsigned int c;
-        r = a ^ b;          // 0x0000 if a == b
-        c = r | 0x80008000; // set msbs, to catch carry out
-        c = c - 0x00010001; // msb = 0, if r was 0x0000 or 0x8000
-        c = r | c;          // msb = 1, if r was not 0x0000
-        c = c & 0x80008000; // extract msbs
-        r = c >> 15;        // convert to bool
-    #endif
-
-        return r;
-    }
-
-    static __device__ __forceinline__ unsigned int vcmpne2(unsigned int a, unsigned int b)
-    {
-        unsigned int r, c;
-
-    #if __CUDA_ARCH__ >= 300
-        r = vsetne2(a, b);
-        c = r << 16;        // convert bool
-        r = c - r;          //  into mask
-    #else
-        // inspired by Alan Mycroft's null-byte detection algorithm:
-        // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080))
-        r = a ^ b;          // 0x0000 if a == b
-        c = r | 0x80008000; // set msbs, to catch carry out
-        c = c - 0x00010001; // msb = 0, if r was 0x0000 or 0x8000
-        c = r | c;          // msb = 1, if r was not 0x0000
-        c = c & 0x80008000; // extract msbs
-        r = c >> 15;        // convert
-        r = c - r;          //  msbs to
-        r = c | r;          //   mask
-    #endif
-
-        return r;
-    }
-
-    static __device__ __forceinline__ unsigned int vmax2(unsigned int a, unsigned int b)
-    {
-        unsigned int r = 0;
-
-    #if __CUDA_ARCH__ >= 300
-        asm("vmax2.u32.u32.u32 %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #elif __CUDA_ARCH__ >= 200
-        asm("vmax.u32.u32.u32 %0.h0, %1.h0, %2.h0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-        asm("vmax.u32.u32.u32 %0.h1, %1.h1, %2.h1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #else
-        unsigned int s, t, u;
-        r = a & 0x0000ffff; // extract low halfword
-        s = b & 0x0000ffff; // extract low halfword
-        t = ::max(r, s);    // maximum of low halfwords
-        r = a & 0xffff0000; // extract high halfword
-        s = b & 0xffff0000; // extract high halfword
-        u = ::max(r, s);    // maximum of high halfwords
-        r = t | u;          // combine halfword maximums
-    #endif
-
-        return r;
-    }
-
-    static __device__ __forceinline__ unsigned int vmin2(unsigned int a, unsigned int b)
-    {
-        unsigned int r = 0;
-
-    #if __CUDA_ARCH__ >= 300
-        asm("vmin2.u32.u32.u32 %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #elif __CUDA_ARCH__ >= 200
-        asm("vmin.u32.u32.u32 %0.h0, %1.h0, %2.h0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-        asm("vmin.u32.u32.u32 %0.h1, %1.h1, %2.h1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #else
-        unsigned int s, t, u;
-        r = a & 0x0000ffff; // extract low halfword
-        s = b & 0x0000ffff; // extract low halfword
-        t = ::min(r, s);    // minimum of low halfwords
-        r = a & 0xffff0000; // extract high halfword
-        s = b & 0xffff0000; // extract high halfword
-        u = ::min(r, s);    // minimum of high halfwords
-        r = t | u;          // combine halfword minimums
-    #endif
-
-        return r;
-    }
-
-    // 4
-
-    static __device__ __forceinline__ unsigned int vadd4(unsigned int a, unsigned int b)
-    {
-        unsigned int r = 0;
-
-    #if __CUDA_ARCH__ >= 300
-        asm("vadd4.u32.u32.u32.sat %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #elif __CUDA_ARCH__ >= 200
-        asm("vadd.u32.u32.u32.sat %0.b0, %1.b0, %2.b0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-        asm("vadd.u32.u32.u32.sat %0.b1, %1.b1, %2.b1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-        asm("vadd.u32.u32.u32.sat %0.b2, %1.b2, %2.b2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-        asm("vadd.u32.u32.u32.sat %0.b3, %1.b3, %2.b3, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #else
-        unsigned int s, t;
-        s = a ^ b;          // sum bits
-        r = a & 0x7f7f7f7f; // clear msbs
-        t = b & 0x7f7f7f7f; // clear msbs
-        s = s & 0x80808080; // msb sum bits
-        r = r + t;          // add without msbs, record carry-out in msbs
-        r = r ^ s;          // sum of msb sum and carry-in bits, w/o carry-out
-    #endif /* __CUDA_ARCH__ >= 300 */
-
-        return r;
-    }
-
-    static __device__ __forceinline__ unsigned int vsub4(unsigned int a, unsigned int b)
-    {
-        unsigned int r = 0;
-
-    #if __CUDA_ARCH__ >= 300
-        asm("vsub4.u32.u32.u32.sat %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #elif __CUDA_ARCH__ >= 200
-        asm("vsub.u32.u32.u32.sat %0.b0, %1.b0, %2.b0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-        asm("vsub.u32.u32.u32.sat %0.b1, %1.b1, %2.b1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-        asm("vsub.u32.u32.u32.sat %0.b2, %1.b2, %2.b2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-        asm("vsub.u32.u32.u32.sat %0.b3, %1.b3, %2.b3, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #else
-        unsigned int s, t;
-        s = a ^ ~b;         // inverted sum bits
-        r = a | 0x80808080; // set msbs
-        t = b & 0x7f7f7f7f; // clear msbs
-        s = s & 0x80808080; // inverted msb sum bits
-        r = r - t;          // subtract w/o msbs, record inverted borrows in msb
-        r = r ^ s;          // combine inverted msb sum bits and borrows
-    #endif
-
-        return r;
-    }
-
-    static __device__ __forceinline__ unsigned int vavg4(unsigned int a, unsigned int b)
-    {
-        unsigned int r, s;
-
-        // HAKMEM #23: a + b = 2 * (a & b) + (a ^ b) ==>
-        // (a + b) / 2 = (a & b) + ((a ^ b) >> 1)
-        s = a ^ b;
-        r = a & b;
-        s = s & 0xfefefefe; // ensure following shift doesn't cross byte boundaries
-        s = s >> 1;
-        s = r + s;
-
-        return s;
-    }
-
-    static __device__ __forceinline__ unsigned int vavrg4(unsigned int a, unsigned int b)
-    {
-        unsigned int r = 0;
-
-    #if __CUDA_ARCH__ >= 300
-        asm("vavrg4.u32.u32.u32 %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #else
-        // HAKMEM #23: a + b = 2 * (a | b) - (a ^ b) ==>
-        // (a + b + 1) / 2 = (a | b) - ((a ^ b) >> 1)
-        unsigned int c;
-        c = a ^ b;
-        r = a | b;
-        c = c & 0xfefefefe; // ensure following shift doesn't cross byte boundaries
-        c = c >> 1;
-        r = r - c;
-    #endif
-
-        return r;
-    }
-
-    static __device__ __forceinline__ unsigned int vseteq4(unsigned int a, unsigned int b)
-    {
-        unsigned int r = 0;
-
-    #if __CUDA_ARCH__ >= 300
-        asm("vset4.u32.u32.eq %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #else
-        // inspired by Alan Mycroft's null-byte detection algorithm:
-        // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080))
-        unsigned int c;
-        r = a ^ b;          // 0x00 if a == b
-        c = r | 0x80808080; // set msbs, to catch carry out
-        r = r ^ c;          // extract msbs, msb = 1 if r < 0x80
-        c = c - 0x01010101; // msb = 0, if r was 0x00 or 0x80
-        c = r & ~c;         // msb = 1, if r was 0x00
-        r = c >> 7;         // convert to bool
-    #endif
-
-        return r;
-    }
-
-    static __device__ __forceinline__ unsigned int vcmpeq4(unsigned int a, unsigned int b)
-    {
-        unsigned int r, t;
-
-    #if __CUDA_ARCH__ >= 300
-        r = vseteq4(a, b);
-        t = r << 8;         // convert bool
-        r = t - r;          //  to mask
-    #else
-        // inspired by Alan Mycroft's null-byte detection algorithm:
-        // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080))
-        t = a ^ b;          // 0x00 if a == b
-        r = t | 0x80808080; // set msbs, to catch carry out
-        t = t ^ r;          // extract msbs, msb = 1 if t < 0x80
-        r = r - 0x01010101; // msb = 0, if t was 0x00 or 0x80
-        r = t & ~r;         // msb = 1, if t was 0x00
-        t = r >> 7;         // build mask
-        t = r - t;          //  from
-        r = t | r;          //   msbs
-    #endif
-
-        return r;
-    }
-
-    static __device__ __forceinline__ unsigned int vsetle4(unsigned int a, unsigned int b)
-    {
-        unsigned int r = 0;
-
-    #if __CUDA_ARCH__ >= 300
-        asm("vset4.u32.u32.le %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #else
-        unsigned int c;
-        asm("not.b32 %0, %0;" : "+r"(a));
-        c = vavrg4(a, b);   // (b + ~a + 1) / 2 = (b - a) / 2
-        c = c & 0x80808080; // msb = carry-outs
-        r = c >> 7;         // convert to bool
-    #endif
-
-        return r;
-    }
-
-    static __device__ __forceinline__ unsigned int vcmple4(unsigned int a, unsigned int b)
-    {
-        unsigned int r, c;
-
-    #if __CUDA_ARCH__ >= 300
-        r = vsetle4(a, b);
-        c = r << 8;         // convert bool
-        r = c - r;          //  to mask
-    #else
-        asm("not.b32 %0, %0;" : "+r"(a));
-        c = vavrg4(a, b);   // (b + ~a + 1) / 2 = (b - a) / 2
-        c = c & 0x80808080; // msbs = carry-outs
-        r = c >> 7;         // convert
-        r = c - r;          //  msbs to
-        r = c | r;          //   mask
-    #endif
-
-        return r;
-    }
-
-    static __device__ __forceinline__ unsigned int vsetlt4(unsigned int a, unsigned int b)
-    {
-        unsigned int r = 0;
-
-    #if __CUDA_ARCH__ >= 300
-        asm("vset4.u32.u32.lt %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #else
-        unsigned int c;
-        asm("not.b32 %0, %0;" : "+r"(a));
-        c = vavg4(a, b);    // (b + ~a) / 2 = (b - a) / 2 [rounded down]
-        c = c & 0x80808080; // msb = carry-outs
-        r = c >> 7;         // convert to bool
-    #endif
-
-        return r;
-    }
-
-    static __device__ __forceinline__ unsigned int vcmplt4(unsigned int a, unsigned int b)
-    {
-        unsigned int r, c;
-
-    #if __CUDA_ARCH__ >= 300
-        r = vsetlt4(a, b);
-        c = r << 8;         // convert bool
-        r = c - r;          //  to mask
-    #else
-        asm("not.b32 %0, %0;" : "+r"(a));
-        c = vavg4(a, b);    // (b + ~a) / 2 = (b - a) / 2 [rounded down]
-        c = c & 0x80808080; // msbs = carry-outs
-        r = c >> 7;         // convert
-        r = c - r;          //  msbs to
-        r = c | r;          //   mask
-    #endif
-
-        return r;
-    }
-
-    static __device__ __forceinline__ unsigned int vsetge4(unsigned int a, unsigned int b)
-    {
-        unsigned int r = 0;
-
-    #if __CUDA_ARCH__ >= 300
-        asm("vset4.u32.u32.ge %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #else
-        unsigned int c;
-        asm("not.b32 %0, %0;" : "+r"(b));
-        c = vavrg4(a, b);   // (a + ~b + 1) / 2 = (a - b) / 2
-        c = c & 0x80808080; // msb = carry-outs
-        r = c >> 7;         // convert to bool
-    #endif
-
-        return r;
-    }
-
-    static __device__ __forceinline__ unsigned int vcmpge4(unsigned int a, unsigned int b)
-    {
-        unsigned int r, s;
-
-    #if __CUDA_ARCH__ >= 300
-        r = vsetge4(a, b);
-        s = r << 8;         // convert bool
-        r = s - r;          //  to mask
-    #else
-        asm ("not.b32 %0,%0;" : "+r"(b));
-        r = vavrg4 (a, b);  // (a + ~b + 1) / 2 = (a - b) / 2
-        r = r & 0x80808080; // msb = carry-outs
-        s = r >> 7;         // build mask
-        s = r - s;          //  from
-        r = s | r;          //   msbs
-    #endif
-
-        return r;
-    }
-
-    static __device__ __forceinline__ unsigned int vsetgt4(unsigned int a, unsigned int b)
-    {
-        unsigned int r = 0;
-
-    #if __CUDA_ARCH__ >= 300
-        asm("vset4.u32.u32.gt %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #else
-        unsigned int c;
-        asm("not.b32 %0, %0;" : "+r"(b));
-        c = vavg4(a, b);    // (a + ~b) / 2 = (a - b) / 2 [rounded down]
-        c = c & 0x80808080; // msb = carry-outs
-        r = c >> 7;         // convert to bool
-    #endif
-
-        return r;
-    }
-
-    static __device__ __forceinline__ unsigned int vcmpgt4(unsigned int a, unsigned int b)
-    {
-        unsigned int r, c;
-
-    #if __CUDA_ARCH__ >= 300
-        r = vsetgt4(a, b);
-        c = r << 8;         // convert bool
-        r = c - r;          //  to mask
-    #else
-        asm("not.b32 %0, %0;" : "+r"(b));
-        c = vavg4(a, b);    // (a + ~b) / 2 = (a - b) / 2 [rounded down]
-        c = c & 0x80808080; // msb = carry-outs
-        r = c >> 7;         // convert
-        r = c - r;          //  msbs to
-        r = c | r;          //   mask
-    #endif
-
-        return r;
-    }
-
-    static __device__ __forceinline__ unsigned int vsetne4(unsigned int a, unsigned int b)
-    {
-        unsigned int r = 0;
-
-    #if __CUDA_ARCH__ >= 300
-        asm("vset4.u32.u32.ne %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #else
-        // inspired by Alan Mycroft's null-byte detection algorithm:
-        // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080))
-        unsigned int c;
-        r = a ^ b;          // 0x00 if a == b
-        c = r | 0x80808080; // set msbs, to catch carry out
-        c = c - 0x01010101; // msb = 0, if r was 0x00 or 0x80
-        c = r | c;          // msb = 1, if r was not 0x00
-        c = c & 0x80808080; // extract msbs
-        r = c >> 7;         // convert to bool
-    #endif
-
-        return r;
-    }
-
-    static __device__ __forceinline__ unsigned int vcmpne4(unsigned int a, unsigned int b)
-    {
-        unsigned int r, c;
-
-    #if __CUDA_ARCH__ >= 300
-        r = vsetne4(a, b);
-        c = r << 8;         // convert bool
-        r = c - r;          //  to mask
-    #else
-        // inspired by Alan Mycroft's null-byte detection algorithm:
-        // null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080))
-        r = a ^ b;          // 0x00 if a == b
-        c = r | 0x80808080; // set msbs, to catch carry out
-        c = c - 0x01010101; // msb = 0, if r was 0x00 or 0x80
-        c = r | c;          // msb = 1, if r was not 0x00
-        c = c & 0x80808080; // extract msbs
-        r = c >> 7;         // convert
-        r = c - r;          //  msbs to
-        r = c | r;          //   mask
-    #endif
-
-        return r;
-    }
-
-    static __device__ __forceinline__ unsigned int vabsdiff4(unsigned int a, unsigned int b)
-    {
-        unsigned int r = 0;
-
-    #if __CUDA_ARCH__ >= 300
-        asm("vabsdiff4.u32.u32.u32.sat %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #elif __CUDA_ARCH__ >= 200
-        asm("vabsdiff.u32.u32.u32.sat %0.b0, %1.b0, %2.b0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-        asm("vabsdiff.u32.u32.u32.sat %0.b1, %1.b1, %2.b1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-        asm("vabsdiff.u32.u32.u32.sat %0.b2, %1.b2, %2.b2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-        asm("vabsdiff.u32.u32.u32.sat %0.b3, %1.b3, %2.b3, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #else
-        unsigned int s;
-        s = vcmpge4(a, b);  // mask = 0xff if a >= b
-        r = a ^ b;          //
-        s = (r &  s) ^ b;   // select a when a >= b, else select b => max(a,b)
-        r = s ^ r;          // select a when b >= a, else select b => min(a,b)
-        r = s - r;          // |a - b| = max(a,b) - min(a,b);
-    #endif
-
-        return r;
-    }
-
-    static __device__ __forceinline__ unsigned int vmax4(unsigned int a, unsigned int b)
-    {
-        unsigned int r = 0;
-
-    #if __CUDA_ARCH__ >= 300
-        asm("vmax4.u32.u32.u32 %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #elif __CUDA_ARCH__ >= 200
-        asm("vmax.u32.u32.u32 %0.b0, %1.b0, %2.b0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-        asm("vmax.u32.u32.u32 %0.b1, %1.b1, %2.b1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-        asm("vmax.u32.u32.u32 %0.b2, %1.b2, %2.b2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-        asm("vmax.u32.u32.u32 %0.b3, %1.b3, %2.b3, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #else
-        unsigned int s;
-        s = vcmpge4(a, b);  // mask = 0xff if a >= b
-        r = a & s;          // select a when b >= a
-        s = b & ~s;         // select b when b < a
-        r = r | s;          // combine byte selections
-    #endif
-
-        return r;           // byte-wise unsigned maximum
-    }
-
-    static __device__ __forceinline__ unsigned int vmin4(unsigned int a, unsigned int b)
-    {
-        unsigned int r = 0;
-
-    #if __CUDA_ARCH__ >= 300
-        asm("vmin4.u32.u32.u32 %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #elif __CUDA_ARCH__ >= 200
-        asm("vmin.u32.u32.u32 %0.b0, %1.b0, %2.b0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-        asm("vmin.u32.u32.u32 %0.b1, %1.b1, %2.b1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-        asm("vmin.u32.u32.u32 %0.b2, %1.b2, %2.b2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-        asm("vmin.u32.u32.u32 %0.b3, %1.b3, %2.b3, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
-    #else
-        unsigned int s;
-        s = vcmpge4(b, a);  // mask = 0xff if a >= b
-        r = a & s;          // select a when b >= a
-        s = b & ~s;         // select b when b < a
-        r = r | s;          // combine byte selections
-    #endif
-
-        return r;
-    }
-}}}
-
-//! @endcond
-
-#endif // OPENCV_CUDA_SIMD_FUNCTIONS_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/transform.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/transform.hpp
deleted file mode 100755
index 42aa6ea..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/transform.hpp
+++ /dev/null
@@ -1,75 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_CUDA_TRANSFORM_HPP
-#define OPENCV_CUDA_TRANSFORM_HPP
-
-#include "common.hpp"
-#include "utility.hpp"
-#include "detail/transform_detail.hpp"
-
-/** @file
- * @deprecated Use @ref cudev instead.
- */
-
-//! @cond IGNORED
-
-namespace cv { namespace cuda { namespace device
-{
-    template <typename T, typename D, typename UnOp, typename Mask>
-    static inline void transform(PtrStepSz<T> src, PtrStepSz<D> dst, UnOp op, const Mask& mask, cudaStream_t stream)
-    {
-        typedef TransformFunctorTraits<UnOp> ft;
-        transform_detail::TransformDispatcher<VecTraits<T>::cn == 1 && VecTraits<D>::cn == 1 && ft::smart_shift != 1>::call(src, dst, op, mask, stream);
-    }
-
-    template <typename T1, typename T2, typename D, typename BinOp, typename Mask>
-    static inline void transform(PtrStepSz<T1> src1, PtrStepSz<T2> src2, PtrStepSz<D> dst, BinOp op, const Mask& mask, cudaStream_t stream)
-    {
-        typedef TransformFunctorTraits<BinOp> ft;
-        transform_detail::TransformDispatcher<VecTraits<T1>::cn == 1 && VecTraits<T2>::cn == 1 && VecTraits<D>::cn == 1 && ft::smart_shift != 1>::call(src1, src2, dst, op, mask, stream);
-    }
-}}}
-
-//! @endcond
-
-#endif // OPENCV_CUDA_TRANSFORM_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/type_traits.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/type_traits.hpp
deleted file mode 100755
index 8b7a3fd..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/type_traits.hpp
+++ /dev/null
@@ -1,90 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_CUDA_TYPE_TRAITS_HPP
-#define OPENCV_CUDA_TYPE_TRAITS_HPP
-
-#include "detail/type_traits_detail.hpp"
-
-/** @file
- * @deprecated Use @ref cudev instead.
- */
-
-//! @cond IGNORED
-
-namespace cv { namespace cuda { namespace device
-{
-    template <typename T> struct IsSimpleParameter
-    {
-        enum {value = type_traits_detail::IsIntegral<T>::value || type_traits_detail::IsFloat<T>::value ||
-            type_traits_detail::PointerTraits<typename type_traits_detail::ReferenceTraits<T>::type>::value};
-    };
-
-    template <typename T> struct TypeTraits
-    {
-        typedef typename type_traits_detail::UnConst<T>::type                                                NonConstType;
-        typedef typename type_traits_detail::UnVolatile<T>::type                                             NonVolatileType;
-        typedef typename type_traits_detail::UnVolatile<typename type_traits_detail::UnConst<T>::type>::type UnqualifiedType;
-        typedef typename type_traits_detail::PointerTraits<UnqualifiedType>::type                            PointeeType;
-        typedef typename type_traits_detail::ReferenceTraits<T>::type                                        ReferredType;
-
-        enum { isConst          = type_traits_detail::UnConst<T>::value };
-        enum { isVolatile       = type_traits_detail::UnVolatile<T>::value };
-
-        enum { isReference      = type_traits_detail::ReferenceTraits<UnqualifiedType>::value };
-        enum { isPointer        = type_traits_detail::PointerTraits<typename type_traits_detail::ReferenceTraits<UnqualifiedType>::type>::value };
-
-        enum { isUnsignedInt    = type_traits_detail::IsUnsignedIntegral<UnqualifiedType>::value };
-        enum { isSignedInt      = type_traits_detail::IsSignedIntergral<UnqualifiedType>::value };
-        enum { isIntegral       = type_traits_detail::IsIntegral<UnqualifiedType>::value };
-        enum { isFloat          = type_traits_detail::IsFloat<UnqualifiedType>::value };
-        enum { isArith          = isIntegral || isFloat };
-        enum { isVec            = type_traits_detail::IsVec<UnqualifiedType>::value };
-
-        typedef typename type_traits_detail::Select<IsSimpleParameter<UnqualifiedType>::value,
-            T, typename type_traits_detail::AddParameterType<T>::type>::type ParameterType;
-    };
-}}}
-
-//! @endcond
-
-#endif // OPENCV_CUDA_TYPE_TRAITS_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/utility.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/utility.hpp
deleted file mode 100755
index 7f5db48..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/utility.hpp
+++ /dev/null
@@ -1,230 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_CUDA_UTILITY_HPP
-#define OPENCV_CUDA_UTILITY_HPP
-
-#include "saturate_cast.hpp"
-#include "datamov_utils.hpp"
-
-/** @file
- * @deprecated Use @ref cudev instead.
- */
-
-//! @cond IGNORED
-
-namespace cv { namespace cuda { namespace device
-{
-    struct CV_EXPORTS ThrustAllocator
-    {
-        typedef uchar value_type;
-        virtual ~ThrustAllocator();
-        virtual __device__ __host__ uchar* allocate(size_t numBytes) = 0;
-        virtual __device__ __host__ void deallocate(uchar* ptr, size_t numBytes) = 0;
-        static ThrustAllocator& getAllocator();
-        static void setAllocator(ThrustAllocator* allocator);
-    };
-    #define OPENCV_CUDA_LOG_WARP_SIZE        (5)
-    #define OPENCV_CUDA_WARP_SIZE            (1 << OPENCV_CUDA_LOG_WARP_SIZE)
-    #define OPENCV_CUDA_LOG_MEM_BANKS        ((__CUDA_ARCH__ >= 200) ? 5 : 4) // 32 banks on fermi, 16 on tesla
-    #define OPENCV_CUDA_MEM_BANKS            (1 << OPENCV_CUDA_LOG_MEM_BANKS)
-
-    ///////////////////////////////////////////////////////////////////////////////
-    // swap
-
-    template <typename T> void __device__ __host__ __forceinline__ swap(T& a, T& b)
-    {
-        const T temp = a;
-        a = b;
-        b = temp;
-    }
-
-    ///////////////////////////////////////////////////////////////////////////////
-    // Mask Reader
-
-    struct SingleMask
-    {
-        explicit __host__ __device__ __forceinline__ SingleMask(PtrStepb mask_) : mask(mask_) {}
-        __host__ __device__ __forceinline__ SingleMask(const SingleMask& mask_): mask(mask_.mask){}
-
-        __device__ __forceinline__ bool operator()(int y, int x) const
-        {
-            return mask.ptr(y)[x] != 0;
-        }
-
-        PtrStepb mask;
-    };
-
-    struct SingleMaskChannels
-    {
-        __host__ __device__ __forceinline__ SingleMaskChannels(PtrStepb mask_, int channels_)
-        : mask(mask_), channels(channels_) {}
-        __host__ __device__ __forceinline__ SingleMaskChannels(const SingleMaskChannels& mask_)
-            :mask(mask_.mask), channels(mask_.channels){}
-
-        __device__ __forceinline__ bool operator()(int y, int x) const
-        {
-            return mask.ptr(y)[x / channels] != 0;
-        }
-
-        PtrStepb mask;
-        int channels;
-    };
-
-    struct MaskCollection
-    {
-        explicit __host__ __device__ __forceinline__ MaskCollection(PtrStepb* maskCollection_)
-            : maskCollection(maskCollection_) {}
-
-        __device__ __forceinline__ MaskCollection(const MaskCollection& masks_)
-            : maskCollection(masks_.maskCollection), curMask(masks_.curMask){}
-
-        __device__ __forceinline__ void next()
-        {
-            curMask = *maskCollection++;
-        }
-        __device__ __forceinline__ void setMask(int z)
-        {
-            curMask = maskCollection[z];
-        }
-
-        __device__ __forceinline__ bool operator()(int y, int x) const
-        {
-            uchar val;
-            return curMask.data == 0 || (ForceGlob<uchar>::Load(curMask.ptr(y), x, val), (val != 0));
-        }
-
-        const PtrStepb* maskCollection;
-        PtrStepb curMask;
-    };
-
-    struct WithOutMask
-    {
-        __host__ __device__ __forceinline__ WithOutMask(){}
-        __host__ __device__ __forceinline__ WithOutMask(const WithOutMask&){}
-
-        __device__ __forceinline__ void next() const
-        {
-        }
-        __device__ __forceinline__ void setMask(int) const
-        {
-        }
-
-        __device__ __forceinline__ bool operator()(int, int) const
-        {
-            return true;
-        }
-
-        __device__ __forceinline__ bool operator()(int, int, int) const
-        {
-            return true;
-        }
-
-        static __device__ __forceinline__ bool check(int, int)
-        {
-            return true;
-        }
-
-        static __device__ __forceinline__ bool check(int, int, int)
-        {
-            return true;
-        }
-    };
-
-    ///////////////////////////////////////////////////////////////////////////////
-    // Solve linear system
-
-    // solve 2x2 linear system Ax=b
-    template <typename T> __device__ __forceinline__ bool solve2x2(const T A[2][2], const T b[2], T x[2])
-    {
-        T det = A[0][0] * A[1][1] - A[1][0] * A[0][1];
-
-        if (det != 0)
-        {
-            double invdet = 1.0 / det;
-
-            x[0] = saturate_cast<T>(invdet * (b[0] * A[1][1] - b[1] * A[0][1]));
-
-            x[1] = saturate_cast<T>(invdet * (A[0][0] * b[1] - A[1][0] * b[0]));
-
-            return true;
-        }
-
-        return false;
-    }
-
-    // solve 3x3 linear system Ax=b
-    template <typename T> __device__ __forceinline__ bool solve3x3(const T A[3][3], const T b[3], T x[3])
-    {
-        T det = A[0][0] * (A[1][1] * A[2][2] - A[1][2] * A[2][1])
-              - A[0][1] * (A[1][0] * A[2][2] - A[1][2] * A[2][0])
-              + A[0][2] * (A[1][0] * A[2][1] - A[1][1] * A[2][0]);
-
-        if (det != 0)
-        {
-            double invdet = 1.0 / det;
-
-            x[0] = saturate_cast<T>(invdet *
-                (b[0]    * (A[1][1] * A[2][2] - A[1][2] * A[2][1]) -
-                 A[0][1] * (b[1]    * A[2][2] - A[1][2] * b[2]   ) +
-                 A[0][2] * (b[1]    * A[2][1] - A[1][1] * b[2]   )));
-
-            x[1] = saturate_cast<T>(invdet *
-                (A[0][0] * (b[1]    * A[2][2] - A[1][2] * b[2]   ) -
-                 b[0]    * (A[1][0] * A[2][2] - A[1][2] * A[2][0]) +
-                 A[0][2] * (A[1][0] * b[2]    - b[1]    * A[2][0])));
-
-            x[2] = saturate_cast<T>(invdet *
-                (A[0][0] * (A[1][1] * b[2]    - b[1]    * A[2][1]) -
-                 A[0][1] * (A[1][0] * b[2]    - b[1]    * A[2][0]) +
-                 b[0]    * (A[1][0] * A[2][1] - A[1][1] * A[2][0])));
-
-            return true;
-        }
-
-        return false;
-    }
-}}} // namespace cv { namespace cuda { namespace cudev
-
-//! @endcond
-
-#endif // OPENCV_CUDA_UTILITY_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/vec_distance.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/vec_distance.hpp
deleted file mode 100755
index ef6e510..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/vec_distance.hpp
+++ /dev/null
@@ -1,232 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_CUDA_VEC_DISTANCE_HPP
-#define OPENCV_CUDA_VEC_DISTANCE_HPP
-
-#include "reduce.hpp"
-#include "functional.hpp"
-#include "detail/vec_distance_detail.hpp"
-
-/** @file
- * @deprecated Use @ref cudev instead.
- */
-
-//! @cond IGNORED
-
-namespace cv { namespace cuda { namespace device
-{
-    template <typename T> struct L1Dist
-    {
-        typedef int value_type;
-        typedef int result_type;
-
-        __device__ __forceinline__ L1Dist() : mySum(0) {}
-
-        __device__ __forceinline__ void reduceIter(int val1, int val2)
-        {
-            mySum = __sad(val1, val2, mySum);
-        }
-
-        template <int THREAD_DIM> __device__ __forceinline__ void reduceAll(int* smem, int tid)
-        {
-            reduce<THREAD_DIM>(smem, mySum, tid, plus<int>());
-        }
-
-        __device__ __forceinline__ operator int() const
-        {
-            return mySum;
-        }
-
-        int mySum;
-    };
-    template <> struct L1Dist<float>
-    {
-        typedef float value_type;
-        typedef float result_type;
-
-        __device__ __forceinline__ L1Dist() : mySum(0.0f) {}
-
-        __device__ __forceinline__ void reduceIter(float val1, float val2)
-        {
-            mySum += ::fabs(val1 - val2);
-        }
-
-        template <int THREAD_DIM> __device__ __forceinline__ void reduceAll(float* smem, int tid)
-        {
-            reduce<THREAD_DIM>(smem, mySum, tid, plus<float>());
-        }
-
-        __device__ __forceinline__ operator float() const
-        {
-            return mySum;
-        }
-
-        float mySum;
-    };
-
-    struct L2Dist
-    {
-        typedef float value_type;
-        typedef float result_type;
-
-        __device__ __forceinline__ L2Dist() : mySum(0.0f) {}
-
-        __device__ __forceinline__ void reduceIter(float val1, float val2)
-        {
-            float reg = val1 - val2;
-            mySum += reg * reg;
-        }
-
-        template <int THREAD_DIM> __device__ __forceinline__ void reduceAll(float* smem, int tid)
-        {
-            reduce<THREAD_DIM>(smem, mySum, tid, plus<float>());
-        }
-
-        __device__ __forceinline__ operator float() const
-        {
-            return sqrtf(mySum);
-        }
-
-        float mySum;
-    };
-
-    struct HammingDist
-    {
-        typedef int value_type;
-        typedef int result_type;
-
-        __device__ __forceinline__ HammingDist() : mySum(0) {}
-
-        __device__ __forceinline__ void reduceIter(int val1, int val2)
-        {
-            mySum += __popc(val1 ^ val2);
-        }
-
-        template <int THREAD_DIM> __device__ __forceinline__ void reduceAll(int* smem, int tid)
-        {
-            reduce<THREAD_DIM>(smem, mySum, tid, plus<int>());
-        }
-
-        __device__ __forceinline__ operator int() const
-        {
-            return mySum;
-        }
-
-        int mySum;
-    };
-
-    // calc distance between two vectors in global memory
-    template <int THREAD_DIM, typename Dist, typename T1, typename T2>
-    __device__ void calcVecDiffGlobal(const T1* vec1, const T2* vec2, int len, Dist& dist, typename Dist::result_type* smem, int tid)
-    {
-        for (int i = tid; i < len; i += THREAD_DIM)
-        {
-            T1 val1;
-            ForceGlob<T1>::Load(vec1, i, val1);
-
-            T2 val2;
-            ForceGlob<T2>::Load(vec2, i, val2);
-
-            dist.reduceIter(val1, val2);
-        }
-
-        dist.reduceAll<THREAD_DIM>(smem, tid);
-    }
-
-    // calc distance between two vectors, first vector is cached in register or shared memory, second vector is in global memory
-    template <int THREAD_DIM, int MAX_LEN, bool LEN_EQ_MAX_LEN, typename Dist, typename T1, typename T2>
-    __device__ __forceinline__ void calcVecDiffCached(const T1* vecCached, const T2* vecGlob, int len, Dist& dist, typename Dist::result_type* smem, int tid)
-    {
-        vec_distance_detail::VecDiffCachedCalculator<THREAD_DIM, MAX_LEN, LEN_EQ_MAX_LEN>::calc(vecCached, vecGlob, len, dist, tid);
-
-        dist.reduceAll<THREAD_DIM>(smem, tid);
-    }
-
-    // calc distance between two vectors in global memory
-    template <int THREAD_DIM, typename T1> struct VecDiffGlobal
-    {
-        explicit __device__ __forceinline__ VecDiffGlobal(const T1* vec1_, int = 0, void* = 0, int = 0, int = 0)
-        {
-            vec1 = vec1_;
-        }
-
-        template <typename T2, typename Dist>
-        __device__ __forceinline__ void calc(const T2* vec2, int len, Dist& dist, typename Dist::result_type* smem, int tid) const
-        {
-            calcVecDiffGlobal<THREAD_DIM>(vec1, vec2, len, dist, smem, tid);
-        }
-
-        const T1* vec1;
-    };
-
-    // calc distance between two vectors, first vector is cached in register memory, second vector is in global memory
-    template <int THREAD_DIM, int MAX_LEN, bool LEN_EQ_MAX_LEN, typename U> struct VecDiffCachedRegister
-    {
-        template <typename T1> __device__ __forceinline__ VecDiffCachedRegister(const T1* vec1, int len, U* smem, int glob_tid, int tid)
-        {
-            if (glob_tid < len)
-                smem[glob_tid] = vec1[glob_tid];
-            __syncthreads();
-
-            U* vec1ValsPtr = vec1Vals;
-
-            #pragma unroll
-            for (int i = tid; i < MAX_LEN; i += THREAD_DIM)
-                *vec1ValsPtr++ = smem[i];
-
-            __syncthreads();
-        }
-
-        template <typename T2, typename Dist>
-        __device__ __forceinline__ void calc(const T2* vec2, int len, Dist& dist, typename Dist::result_type* smem, int tid) const
-        {
-            calcVecDiffCached<THREAD_DIM, MAX_LEN, LEN_EQ_MAX_LEN>(vec1Vals, vec2, len, dist, smem, tid);
-        }
-
-        U vec1Vals[MAX_LEN / THREAD_DIM];
-    };
-}}} // namespace cv { namespace cuda { namespace cudev
-
-//! @endcond
-
-#endif // OPENCV_CUDA_VEC_DISTANCE_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/vec_math.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/vec_math.hpp
deleted file mode 100755
index 9085b92..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/vec_math.hpp
+++ /dev/null
@@ -1,930 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_CUDA_VECMATH_HPP
-#define OPENCV_CUDA_VECMATH_HPP
-
-#include "vec_traits.hpp"
-#include "saturate_cast.hpp"
-
-/** @file
- * @deprecated Use @ref cudev instead.
- */
-
-//! @cond IGNORED
-
-namespace cv { namespace cuda { namespace device
-{
-
-// saturate_cast
-
-namespace vec_math_detail
-{
-    template <int cn, typename VecD> struct SatCastHelper;
-    template <typename VecD> struct SatCastHelper<1, VecD>
-    {
-        template <typename VecS> static __device__ __forceinline__ VecD cast(const VecS& v)
-        {
-            typedef typename VecTraits<VecD>::elem_type D;
-            return VecTraits<VecD>::make(saturate_cast<D>(v.x));
-        }
-    };
-    template <typename VecD> struct SatCastHelper<2, VecD>
-    {
-        template <typename VecS> static __device__ __forceinline__ VecD cast(const VecS& v)
-        {
-            typedef typename VecTraits<VecD>::elem_type D;
-            return VecTraits<VecD>::make(saturate_cast<D>(v.x), saturate_cast<D>(v.y));
-        }
-    };
-    template <typename VecD> struct SatCastHelper<3, VecD>
-    {
-        template <typename VecS> static __device__ __forceinline__ VecD cast(const VecS& v)
-        {
-            typedef typename VecTraits<VecD>::elem_type D;
-            return VecTraits<VecD>::make(saturate_cast<D>(v.x), saturate_cast<D>(v.y), saturate_cast<D>(v.z));
-        }
-    };
-    template <typename VecD> struct SatCastHelper<4, VecD>
-    {
-        template <typename VecS> static __device__ __forceinline__ VecD cast(const VecS& v)
-        {
-            typedef typename VecTraits<VecD>::elem_type D;
-            return VecTraits<VecD>::make(saturate_cast<D>(v.x), saturate_cast<D>(v.y), saturate_cast<D>(v.z), saturate_cast<D>(v.w));
-        }
-    };
-
-    template <typename VecD, typename VecS> static __device__ __forceinline__ VecD saturate_cast_helper(const VecS& v)
-    {
-        return SatCastHelper<VecTraits<VecD>::cn, VecD>::cast(v);
-    }
-}
-
-template<typename T> static __device__ __forceinline__ T saturate_cast(const uchar1& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
-template<typename T> static __device__ __forceinline__ T saturate_cast(const char1& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
-template<typename T> static __device__ __forceinline__ T saturate_cast(const ushort1& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
-template<typename T> static __device__ __forceinline__ T saturate_cast(const short1& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
-template<typename T> static __device__ __forceinline__ T saturate_cast(const uint1& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
-template<typename T> static __device__ __forceinline__ T saturate_cast(const int1& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
-template<typename T> static __device__ __forceinline__ T saturate_cast(const float1& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
-template<typename T> static __device__ __forceinline__ T saturate_cast(const double1& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
-
-template<typename T> static __device__ __forceinline__ T saturate_cast(const uchar2& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
-template<typename T> static __device__ __forceinline__ T saturate_cast(const char2& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
-template<typename T> static __device__ __forceinline__ T saturate_cast(const ushort2& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
-template<typename T> static __device__ __forceinline__ T saturate_cast(const short2& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
-template<typename T> static __device__ __forceinline__ T saturate_cast(const uint2& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
-template<typename T> static __device__ __forceinline__ T saturate_cast(const int2& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
-template<typename T> static __device__ __forceinline__ T saturate_cast(const float2& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
-template<typename T> static __device__ __forceinline__ T saturate_cast(const double2& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
-
-template<typename T> static __device__ __forceinline__ T saturate_cast(const uchar3& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
-template<typename T> static __device__ __forceinline__ T saturate_cast(const char3& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
-template<typename T> static __device__ __forceinline__ T saturate_cast(const ushort3& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
-template<typename T> static __device__ __forceinline__ T saturate_cast(const short3& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
-template<typename T> static __device__ __forceinline__ T saturate_cast(const uint3& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
-template<typename T> static __device__ __forceinline__ T saturate_cast(const int3& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
-template<typename T> static __device__ __forceinline__ T saturate_cast(const float3& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
-template<typename T> static __device__ __forceinline__ T saturate_cast(const double3& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
-
-template<typename T> static __device__ __forceinline__ T saturate_cast(const uchar4& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
-template<typename T> static __device__ __forceinline__ T saturate_cast(const char4& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
-template<typename T> static __device__ __forceinline__ T saturate_cast(const ushort4& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
-template<typename T> static __device__ __forceinline__ T saturate_cast(const short4& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
-template<typename T> static __device__ __forceinline__ T saturate_cast(const uint4& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
-template<typename T> static __device__ __forceinline__ T saturate_cast(const int4& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
-template<typename T> static __device__ __forceinline__ T saturate_cast(const float4& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
-template<typename T> static __device__ __forceinline__ T saturate_cast(const double4& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
-
-// unary operators
-
-#define CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(op, input_type, output_type) \
-    __device__ __forceinline__ output_type ## 1 operator op(const input_type ## 1 & a) \
-    { \
-        return VecTraits<output_type ## 1>::make(op (a.x)); \
-    } \
-    __device__ __forceinline__ output_type ## 2 operator op(const input_type ## 2 & a) \
-    { \
-        return VecTraits<output_type ## 2>::make(op (a.x), op (a.y)); \
-    } \
-    __device__ __forceinline__ output_type ## 3 operator op(const input_type ## 3 & a) \
-    { \
-        return VecTraits<output_type ## 3>::make(op (a.x), op (a.y), op (a.z)); \
-    } \
-    __device__ __forceinline__ output_type ## 4 operator op(const input_type ## 4 & a) \
-    { \
-        return VecTraits<output_type ## 4>::make(op (a.x), op (a.y), op (a.z), op (a.w)); \
-    }
-
-CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(-, char, char)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(-, short, short)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(-, int, int)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(-, float, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(-, double, double)
-
-CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, uchar, uchar)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, char, uchar)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, ushort, uchar)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, short, uchar)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, int, uchar)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, uint, uchar)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, float, uchar)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, double, uchar)
-
-CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(~, uchar, uchar)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(~, char, char)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(~, ushort, ushort)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(~, short, short)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(~, int, int)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(~, uint, uint)
-
-#undef CV_CUDEV_IMPLEMENT_VEC_UNARY_OP
-
-// unary functions
-
-#define CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(func_name, func, input_type, output_type) \
-    __device__ __forceinline__ output_type ## 1 func_name(const input_type ## 1 & a) \
-    { \
-        return VecTraits<output_type ## 1>::make(func (a.x)); \
-    } \
-    __device__ __forceinline__ output_type ## 2 func_name(const input_type ## 2 & a) \
-    { \
-        return VecTraits<output_type ## 2>::make(func (a.x), func (a.y)); \
-    } \
-    __device__ __forceinline__ output_type ## 3 func_name(const input_type ## 3 & a) \
-    { \
-        return VecTraits<output_type ## 3>::make(func (a.x), func (a.y), func (a.z)); \
-    } \
-    __device__ __forceinline__ output_type ## 4 func_name(const input_type ## 4 & a) \
-    { \
-        return VecTraits<output_type ## 4>::make(func (a.x), func (a.y), func (a.z), func (a.w)); \
-    }
-
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, /*::abs*/, uchar, uchar)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, ::abs, char, char)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, /*::abs*/, ushort, ushort)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, ::abs, short, short)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, ::abs, int, int)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, /*::abs*/, uint, uint)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, ::fabsf, float, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, ::fabs, double, double)
-
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, uchar, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, char, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, ushort, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, short, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, int, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, uint, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, float, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrt, double, double)
-
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, uchar, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, char, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, ushort, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, short, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, int, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, uint, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, float, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::exp, double, double)
-
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, uchar, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, char, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, ushort, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, short, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, int, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, uint, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, float, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2, double, double)
-
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, uchar, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, char, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, ushort, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, short, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, int, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, uint, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, float, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10, double, double)
-
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, uchar, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, char, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, ushort, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, short, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, int, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, uint, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, float, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::log, double, double)
-
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, uchar, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, char, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, ushort, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, short, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, int, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, uint, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, float, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2, double, double)
-
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, uchar, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, char, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, ushort, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, short, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, int, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, uint, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, float, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10, double, double)
-
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, uchar, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, char, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, ushort, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, short, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, int, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, uint, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, float, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sin, double, double)
-
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, uchar, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, char, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, ushort, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, short, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, int, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, uint, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, float, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cos, double, double)
-
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, uchar, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, char, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, ushort, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, short, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, int, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, uint, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, float, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tan, double, double)
-
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, uchar, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, char, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, ushort, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, short, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, int, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, uint, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, float, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asin, double, double)
-
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, uchar, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, char, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, ushort, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, short, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, int, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, uint, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, float, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acos, double, double)
-
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, uchar, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, char, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, ushort, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, short, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, int, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, uint, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, float, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atan, double, double)
-
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, uchar, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, char, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, ushort, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, short, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, int, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, uint, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, float, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinh, double, double)
-
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, uchar, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, char, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, ushort, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, short, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, int, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, uint, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, float, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::cosh, double, double)
-
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, uchar, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, char, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, ushort, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, short, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, int, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, uint, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, float, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanh, double, double)
-
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, uchar, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, char, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, ushort, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, short, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, int, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, uint, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, float, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinh, double, double)
-
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, uchar, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, char, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, ushort, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, short, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, int, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, uint, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, float, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acosh, double, double)
-
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, uchar, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, char, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, ushort, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, short, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, int, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, uint, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, float, float)
-CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanh, double, double)
-
-#undef CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC
-
-// binary operators (vec & vec)
-
-#define CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(op, input_type, output_type) \
-    __device__ __forceinline__ output_type ## 1 operator op(const input_type ## 1 & a, const input_type ## 1 & b) \
-    { \
-        return VecTraits<output_type ## 1>::make(a.x op b.x); \
-    } \
-    __device__ __forceinline__ output_type ## 2 operator op(const input_type ## 2 & a, const input_type ## 2 & b) \
-    { \
-        return VecTraits<output_type ## 2>::make(a.x op b.x, a.y op b.y); \
-    } \
-    __device__ __forceinline__ output_type ## 3 operator op(const input_type ## 3 & a, const input_type ## 3 & b) \
-    { \
-        return VecTraits<output_type ## 3>::make(a.x op b.x, a.y op b.y, a.z op b.z); \
-    } \
-    __device__ __forceinline__ output_type ## 4 operator op(const input_type ## 4 & a, const input_type ## 4 & b) \
-    { \
-        return VecTraits<output_type ## 4>::make(a.x op b.x, a.y op b.y, a.z op b.z, a.w op b.w); \
-    }
-
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, uchar, int)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, char, int)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, ushort, int)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, short, int)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, int, int)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, uint, uint)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, float, float)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, double, double)
-
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, uchar, int)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, char, int)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, ushort, int)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, short, int)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, int, int)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, uint, uint)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, float, float)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, double, double)
-
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, uchar, int)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, char, int)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, ushort, int)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, short, int)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, int, int)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, uint, uint)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, float, float)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, double, double)
-
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, uchar, int)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, char, int)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, ushort, int)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, short, int)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, int, int)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, uint, uint)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, float, float)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, double, double)
-
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, uchar, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, char, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, ushort, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, short, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, int, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, uint, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, float, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, double, uchar)
-
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, uchar, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, char, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, ushort, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, short, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, int, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, uint, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, float, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, double, uchar)
-
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, uchar, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, char, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, ushort, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, short, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, int, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, uint, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, float, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, double, uchar)
-
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, uchar, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, char, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, ushort, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, short, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, int, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, uint, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, float, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, double, uchar)
-
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, uchar, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, char, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, ushort, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, short, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, int, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, uint, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, float, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, double, uchar)
-
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, uchar, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, char, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, ushort, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, short, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, int, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, uint, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, float, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, double, uchar)
-
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, uchar, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, char, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, ushort, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, short, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, int, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, uint, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, float, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, double, uchar)
-
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, uchar, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, char, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, ushort, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, short, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, int, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, uint, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, float, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, double, uchar)
-
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&, uchar, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&, char, char)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&, ushort, ushort)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&, short, short)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&, int, int)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&, uint, uint)
-
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(|, uchar, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(|, char, char)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(|, ushort, ushort)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(|, short, short)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(|, int, int)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(|, uint, uint)
-
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(^, uchar, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(^, char, char)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(^, ushort, ushort)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(^, short, short)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(^, int, int)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(^, uint, uint)
-
-#undef CV_CUDEV_IMPLEMENT_VEC_BINARY_OP
-
-// binary operators (vec & scalar)
-
-#define CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(op, input_type, scalar_type, output_type) \
-    __device__ __forceinline__ output_type ## 1 operator op(const input_type ## 1 & a, scalar_type s) \
-    { \
-        return VecTraits<output_type ## 1>::make(a.x op s); \
-    } \
-    __device__ __forceinline__ output_type ## 1 operator op(scalar_type s, const input_type ## 1 & b) \
-    { \
-        return VecTraits<output_type ## 1>::make(s op b.x); \
-    } \
-    __device__ __forceinline__ output_type ## 2 operator op(const input_type ## 2 & a, scalar_type s) \
-    { \
-        return VecTraits<output_type ## 2>::make(a.x op s, a.y op s); \
-    } \
-    __device__ __forceinline__ output_type ## 2 operator op(scalar_type s, const input_type ## 2 & b) \
-    { \
-        return VecTraits<output_type ## 2>::make(s op b.x, s op b.y); \
-    } \
-    __device__ __forceinline__ output_type ## 3 operator op(const input_type ## 3 & a, scalar_type s) \
-    { \
-        return VecTraits<output_type ## 3>::make(a.x op s, a.y op s, a.z op s); \
-    } \
-    __device__ __forceinline__ output_type ## 3 operator op(scalar_type s, const input_type ## 3 & b) \
-    { \
-        return VecTraits<output_type ## 3>::make(s op b.x, s op b.y, s op b.z); \
-    } \
-    __device__ __forceinline__ output_type ## 4 operator op(const input_type ## 4 & a, scalar_type s) \
-    { \
-        return VecTraits<output_type ## 4>::make(a.x op s, a.y op s, a.z op s, a.w op s); \
-    } \
-    __device__ __forceinline__ output_type ## 4 operator op(scalar_type s, const input_type ## 4 & b) \
-    { \
-        return VecTraits<output_type ## 4>::make(s op b.x, s op b.y, s op b.z, s op b.w); \
-    }
-
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, uchar, int, int)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, uchar, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, uchar, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, char, int, int)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, char, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, char, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, ushort, int, int)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, ushort, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, ushort, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, short, int, int)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, short, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, short, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, int, int, int)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, int, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, int, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, uint, uint, uint)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, uint, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, uint, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, float, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, float, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, double, double, double)
-
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, uchar, int, int)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, uchar, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, uchar, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, char, int, int)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, char, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, char, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, ushort, int, int)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, ushort, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, ushort, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, short, int, int)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, short, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, short, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, int, int, int)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, int, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, int, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, uint, uint, uint)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, uint, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, uint, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, float, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, float, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, double, double, double)
-
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, uchar, int, int)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, uchar, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, uchar, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, char, int, int)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, char, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, char, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, ushort, int, int)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, ushort, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, ushort, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, short, int, int)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, short, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, short, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, int, int, int)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, int, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, int, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, uint, uint, uint)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, uint, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, uint, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, float, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, float, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, double, double, double)
-
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, uchar, int, int)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, uchar, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, uchar, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, char, int, int)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, char, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, char, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, ushort, int, int)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, ushort, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, ushort, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, short, int, int)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, short, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, short, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, int, int, int)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, int, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, int, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, uint, uint, uint)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, uint, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, uint, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, float, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, float, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, double, double, double)
-
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, uchar, uchar, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, char, char, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, ushort, ushort, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, short, short, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, int, int, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, uint, uint, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, float, float, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, double, double, uchar)
-
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, uchar, uchar, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, char, char, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, ushort, ushort, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, short, short, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, int, int, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, uint, uint, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, float, float, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, double, double, uchar)
-
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, uchar, uchar, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, char, char, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, ushort, ushort, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, short, short, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, int, int, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, uint, uint, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, float, float, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, double, double, uchar)
-
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, uchar, uchar, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, char, char, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, ushort, ushort, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, short, short, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, int, int, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, uint, uint, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, float, float, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, double, double, uchar)
-
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, uchar, uchar, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, char, char, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, ushort, ushort, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, short, short, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, int, int, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, uint, uint, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, float, float, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, double, double, uchar)
-
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, uchar, uchar, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, char, char, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, ushort, ushort, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, short, short, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, int, int, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, uint, uint, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, float, float, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, double, double, uchar)
-
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, uchar, uchar, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, char, char, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, ushort, ushort, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, short, short, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, int, int, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, uint, uint, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, float, float, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, double, double, uchar)
-
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, uchar, uchar, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, char, char, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, ushort, ushort, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, short, short, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, int, int, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, uint, uint, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, float, float, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, double, double, uchar)
-
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&, uchar, uchar, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&, char, char, char)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&, ushort, ushort, ushort)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&, short, short, short)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&, int, int, int)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&, uint, uint, uint)
-
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(|, uchar, uchar, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(|, char, char, char)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(|, ushort, ushort, ushort)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(|, short, short, short)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(|, int, int, int)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(|, uint, uint, uint)
-
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(^, uchar, uchar, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(^, char, char, char)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(^, ushort, ushort, ushort)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(^, short, short, short)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(^, int, int, int)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(^, uint, uint, uint)
-
-#undef CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP
-
-// binary function (vec & vec)
-
-#define CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(func_name, func, input_type, output_type) \
-    __device__ __forceinline__ output_type ## 1 func_name(const input_type ## 1 & a, const input_type ## 1 & b) \
-    { \
-        return VecTraits<output_type ## 1>::make(func (a.x, b.x)); \
-    } \
-    __device__ __forceinline__ output_type ## 2 func_name(const input_type ## 2 & a, const input_type ## 2 & b) \
-    { \
-        return VecTraits<output_type ## 2>::make(func (a.x, b.x), func (a.y, b.y)); \
-    } \
-    __device__ __forceinline__ output_type ## 3 func_name(const input_type ## 3 & a, const input_type ## 3 & b) \
-    { \
-        return VecTraits<output_type ## 3>::make(func (a.x, b.x), func (a.y, b.y), func (a.z, b.z)); \
-    } \
-    __device__ __forceinline__ output_type ## 4 func_name(const input_type ## 4 & a, const input_type ## 4 & b) \
-    { \
-        return VecTraits<output_type ## 4>::make(func (a.x, b.x), func (a.y, b.y), func (a.z, b.z), func (a.w, b.w)); \
-    }
-
-CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::max, uchar, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::max, char, char)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::max, ushort, ushort)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::max, short, short)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::max, uint, uint)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::max, int, int)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::fmaxf, float, float)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::fmax, double, double)
-
-CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::min, uchar, uchar)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::min, char, char)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::min, ushort, ushort)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::min, short, short)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::min, uint, uint)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::min, int, int)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::fminf, float, float)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::fmin, double, double)
-
-CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, uchar, float)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, char, float)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, ushort, float)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, short, float)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, uint, float)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, int, float)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, float, float)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypot, double, double)
-
-CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, uchar, float)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, char, float)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, ushort, float)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, short, float)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, uint, float)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, int, float)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, float, float)
-CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2, double, double)
-
-#undef CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC
-
-// binary function (vec & scalar)
-
-#define CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(func_name, func, input_type, scalar_type, output_type) \
-    __device__ __forceinline__ output_type ## 1 func_name(const input_type ## 1 & a, scalar_type s) \
-    { \
-        return VecTraits<output_type ## 1>::make(func ((output_type) a.x, (output_type) s)); \
-    } \
-    __device__ __forceinline__ output_type ## 1 func_name(scalar_type s, const input_type ## 1 & b) \
-    { \
-        return VecTraits<output_type ## 1>::make(func ((output_type) s, (output_type) b.x)); \
-    } \
-    __device__ __forceinline__ output_type ## 2 func_name(const input_type ## 2 & a, scalar_type s) \
-    { \
-        return VecTraits<output_type ## 2>::make(func ((output_type) a.x, (output_type) s), func ((output_type) a.y, (output_type) s)); \
-    } \
-    __device__ __forceinline__ output_type ## 2 func_name(scalar_type s, const input_type ## 2 & b) \
-    { \
-        return VecTraits<output_type ## 2>::make(func ((output_type) s, (output_type) b.x), func ((output_type) s, (output_type) b.y)); \
-    } \
-    __device__ __forceinline__ output_type ## 3 func_name(const input_type ## 3 & a, scalar_type s) \
-    { \
-        return VecTraits<output_type ## 3>::make(func ((output_type) a.x, (output_type) s), func ((output_type) a.y, (output_type) s), func ((output_type) a.z, (output_type) s)); \
-    } \
-    __device__ __forceinline__ output_type ## 3 func_name(scalar_type s, const input_type ## 3 & b) \
-    { \
-        return VecTraits<output_type ## 3>::make(func ((output_type) s, (output_type) b.x), func ((output_type) s, (output_type) b.y), func ((output_type) s, (output_type) b.z)); \
-    } \
-    __device__ __forceinline__ output_type ## 4 func_name(const input_type ## 4 & a, scalar_type s) \
-    { \
-        return VecTraits<output_type ## 4>::make(func ((output_type) a.x, (output_type) s), func ((output_type) a.y, (output_type) s), func ((output_type) a.z, (output_type) s), func ((output_type) a.w, (output_type) s)); \
-    } \
-    __device__ __forceinline__ output_type ## 4 func_name(scalar_type s, const input_type ## 4 & b) \
-    { \
-        return VecTraits<output_type ## 4>::make(func ((output_type) s, (output_type) b.x), func ((output_type) s, (output_type) b.y), func ((output_type) s, (output_type) b.z), func ((output_type) s, (output_type) b.w)); \
-    }
-
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::max, uchar, uchar, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, uchar, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, uchar, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::max, char, char, char)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, char, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, char, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::max, ushort, ushort, ushort)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, ushort, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, ushort, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::max, short, short, short)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, short, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, short, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::max, uint, uint, uint)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, uint, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, uint, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::max, int, int, int)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, int, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, int, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, float, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, float, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, double, double, double)
-
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::min, uchar, uchar, uchar)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, uchar, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, uchar, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::min, char, char, char)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, char, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, char, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::min, ushort, ushort, ushort)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, ushort, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, ushort, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::min, short, short, short)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, short, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, short, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::min, uint, uint, uint)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, uint, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, uint, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::min, int, int, int)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, int, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, int, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, float, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, float, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, double, double, double)
-
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, uchar, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, uchar, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, char, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, char, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, ushort, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, ushort, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, short, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, short, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, uint, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, uint, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, int, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, int, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, float, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, float, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, double, double, double)
-
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, uchar, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, uchar, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, char, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, char, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, ushort, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, ushort, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, short, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, short, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, uint, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, uint, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, int, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, int, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, float, float, float)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, float, double, double)
-CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, double, double, double)
-
-#undef CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC
-
-}}} // namespace cv { namespace cuda { namespace device
-
-//! @endcond
-
-#endif // OPENCV_CUDA_VECMATH_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/vec_traits.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/vec_traits.hpp
deleted file mode 100755
index b5ff281..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/vec_traits.hpp
+++ /dev/null
@@ -1,288 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_CUDA_VEC_TRAITS_HPP
-#define OPENCV_CUDA_VEC_TRAITS_HPP
-
-#include "common.hpp"
-
-/** @file
- * @deprecated Use @ref cudev instead.
- */
-
-//! @cond IGNORED
-
-namespace cv { namespace cuda { namespace device
-{
-    template<typename T, int N> struct TypeVec;
-
-    struct __align__(8) uchar8
-    {
-        uchar a0, a1, a2, a3, a4, a5, a6, a7;
-    };
-    static __host__ __device__ __forceinline__ uchar8 make_uchar8(uchar a0, uchar a1, uchar a2, uchar a3, uchar a4, uchar a5, uchar a6, uchar a7)
-    {
-        uchar8 val = {a0, a1, a2, a3, a4, a5, a6, a7};
-        return val;
-    }
-    struct __align__(8) char8
-    {
-        schar a0, a1, a2, a3, a4, a5, a6, a7;
-    };
-    static __host__ __device__ __forceinline__ char8 make_char8(schar a0, schar a1, schar a2, schar a3, schar a4, schar a5, schar a6, schar a7)
-    {
-        char8 val = {a0, a1, a2, a3, a4, a5, a6, a7};
-        return val;
-    }
-    struct __align__(16) ushort8
-    {
-        ushort a0, a1, a2, a3, a4, a5, a6, a7;
-    };
-    static __host__ __device__ __forceinline__ ushort8 make_ushort8(ushort a0, ushort a1, ushort a2, ushort a3, ushort a4, ushort a5, ushort a6, ushort a7)
-    {
-        ushort8 val = {a0, a1, a2, a3, a4, a5, a6, a7};
-        return val;
-    }
-    struct __align__(16) short8
-    {
-        short a0, a1, a2, a3, a4, a5, a6, a7;
-    };
-    static __host__ __device__ __forceinline__ short8 make_short8(short a0, short a1, short a2, short a3, short a4, short a5, short a6, short a7)
-    {
-        short8 val = {a0, a1, a2, a3, a4, a5, a6, a7};
-        return val;
-    }
-    struct __align__(32) uint8
-    {
-        uint a0, a1, a2, a3, a4, a5, a6, a7;
-    };
-    static __host__ __device__ __forceinline__ uint8 make_uint8(uint a0, uint a1, uint a2, uint a3, uint a4, uint a5, uint a6, uint a7)
-    {
-        uint8 val = {a0, a1, a2, a3, a4, a5, a6, a7};
-        return val;
-    }
-    struct __align__(32) int8
-    {
-        int a0, a1, a2, a3, a4, a5, a6, a7;
-    };
-    static __host__ __device__ __forceinline__ int8 make_int8(int a0, int a1, int a2, int a3, int a4, int a5, int a6, int a7)
-    {
-        int8 val = {a0, a1, a2, a3, a4, a5, a6, a7};
-        return val;
-    }
-    struct __align__(32) float8
-    {
-        float a0, a1, a2, a3, a4, a5, a6, a7;
-    };
-    static __host__ __device__ __forceinline__ float8 make_float8(float a0, float a1, float a2, float a3, float a4, float a5, float a6, float a7)
-    {
-        float8 val = {a0, a1, a2, a3, a4, a5, a6, a7};
-        return val;
-    }
-    struct double8
-    {
-        double a0, a1, a2, a3, a4, a5, a6, a7;
-    };
-    static __host__ __device__ __forceinline__ double8 make_double8(double a0, double a1, double a2, double a3, double a4, double a5, double a6, double a7)
-    {
-        double8 val = {a0, a1, a2, a3, a4, a5, a6, a7};
-        return val;
-    }
-
-#define OPENCV_CUDA_IMPLEMENT_TYPE_VEC(type) \
-    template<> struct TypeVec<type, 1> { typedef type vec_type; }; \
-    template<> struct TypeVec<type ## 1, 1> { typedef type ## 1 vec_type; }; \
-    template<> struct TypeVec<type, 2> { typedef type ## 2 vec_type; }; \
-    template<> struct TypeVec<type ## 2, 2> { typedef type ## 2 vec_type; }; \
-    template<> struct TypeVec<type, 3> { typedef type ## 3 vec_type; }; \
-    template<> struct TypeVec<type ## 3, 3> { typedef type ## 3 vec_type; }; \
-    template<> struct TypeVec<type, 4> { typedef type ## 4 vec_type; }; \
-    template<> struct TypeVec<type ## 4, 4> { typedef type ## 4 vec_type; }; \
-    template<> struct TypeVec<type, 8> { typedef type ## 8 vec_type; }; \
-    template<> struct TypeVec<type ## 8, 8> { typedef type ## 8 vec_type; };
-
-    OPENCV_CUDA_IMPLEMENT_TYPE_VEC(uchar)
-    OPENCV_CUDA_IMPLEMENT_TYPE_VEC(char)
-    OPENCV_CUDA_IMPLEMENT_TYPE_VEC(ushort)
-    OPENCV_CUDA_IMPLEMENT_TYPE_VEC(short)
-    OPENCV_CUDA_IMPLEMENT_TYPE_VEC(int)
-    OPENCV_CUDA_IMPLEMENT_TYPE_VEC(uint)
-    OPENCV_CUDA_IMPLEMENT_TYPE_VEC(float)
-    OPENCV_CUDA_IMPLEMENT_TYPE_VEC(double)
-
-    #undef OPENCV_CUDA_IMPLEMENT_TYPE_VEC
-
-    template<> struct TypeVec<schar, 1> { typedef schar vec_type; };
-    template<> struct TypeVec<schar, 2> { typedef char2 vec_type; };
-    template<> struct TypeVec<schar, 3> { typedef char3 vec_type; };
-    template<> struct TypeVec<schar, 4> { typedef char4 vec_type; };
-    template<> struct TypeVec<schar, 8> { typedef char8 vec_type; };
-
-    template<> struct TypeVec<bool, 1> { typedef uchar vec_type; };
-    template<> struct TypeVec<bool, 2> { typedef uchar2 vec_type; };
-    template<> struct TypeVec<bool, 3> { typedef uchar3 vec_type; };
-    template<> struct TypeVec<bool, 4> { typedef uchar4 vec_type; };
-    template<> struct TypeVec<bool, 8> { typedef uchar8 vec_type; };
-
-    template<typename T> struct VecTraits;
-
-#define OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(type) \
-    template<> struct VecTraits<type> \
-    { \
-        typedef type elem_type; \
-        enum {cn=1}; \
-        static __device__ __host__ __forceinline__ type all(type v) {return v;} \
-        static __device__ __host__ __forceinline__ type make(type x) {return x;} \
-        static __device__ __host__ __forceinline__ type make(const type* v) {return *v;} \
-    }; \
-    template<> struct VecTraits<type ## 1> \
-    { \
-        typedef type elem_type; \
-        enum {cn=1}; \
-        static __device__ __host__ __forceinline__ type ## 1 all(type v) {return make_ ## type ## 1(v);} \
-        static __device__ __host__ __forceinline__ type ## 1 make(type x) {return make_ ## type ## 1(x);} \
-        static __device__ __host__ __forceinline__ type ## 1 make(const type* v) {return make_ ## type ## 1(*v);} \
-    }; \
-    template<> struct VecTraits<type ## 2> \
-    { \
-        typedef type elem_type; \
-        enum {cn=2}; \
-        static __device__ __host__ __forceinline__ type ## 2 all(type v) {return make_ ## type ## 2(v, v);} \
-        static __device__ __host__ __forceinline__ type ## 2 make(type x, type y) {return make_ ## type ## 2(x, y);} \
-        static __device__ __host__ __forceinline__ type ## 2 make(const type* v) {return make_ ## type ## 2(v[0], v[1]);} \
-    }; \
-    template<> struct VecTraits<type ## 3> \
-    { \
-        typedef type elem_type; \
-        enum {cn=3}; \
-        static __device__ __host__ __forceinline__ type ## 3 all(type v) {return make_ ## type ## 3(v, v, v);} \
-        static __device__ __host__ __forceinline__ type ## 3 make(type x, type y, type z) {return make_ ## type ## 3(x, y, z);} \
-        static __device__ __host__ __forceinline__ type ## 3 make(const type* v) {return make_ ## type ## 3(v[0], v[1], v[2]);} \
-    }; \
-    template<> struct VecTraits<type ## 4> \
-    { \
-        typedef type elem_type; \
-        enum {cn=4}; \
-        static __device__ __host__ __forceinline__ type ## 4 all(type v) {return make_ ## type ## 4(v, v, v, v);} \
-        static __device__ __host__ __forceinline__ type ## 4 make(type x, type y, type z, type w) {return make_ ## type ## 4(x, y, z, w);} \
-        static __device__ __host__ __forceinline__ type ## 4 make(const type* v) {return make_ ## type ## 4(v[0], v[1], v[2], v[3]);} \
-    }; \
-    template<> struct VecTraits<type ## 8> \
-    { \
-        typedef type elem_type; \
-        enum {cn=8}; \
-        static __device__ __host__ __forceinline__ type ## 8 all(type v) {return make_ ## type ## 8(v, v, v, v, v, v, v, v);} \
-        static __device__ __host__ __forceinline__ type ## 8 make(type a0, type a1, type a2, type a3, type a4, type a5, type a6, type a7) {return make_ ## type ## 8(a0, a1, a2, a3, a4, a5, a6, a7);} \
-        static __device__ __host__ __forceinline__ type ## 8 make(const type* v) {return make_ ## type ## 8(v[0], v[1], v[2], v[3], v[4], v[5], v[6], v[7]);} \
-    };
-
-    OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(uchar)
-    OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(ushort)
-    OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(short)
-    OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(int)
-    OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(uint)
-    OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(float)
-    OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(double)
-
-    #undef OPENCV_CUDA_IMPLEMENT_VEC_TRAITS
-
-    template<> struct VecTraits<char>
-    {
-        typedef char elem_type;
-        enum {cn=1};
-        static __device__ __host__ __forceinline__ char all(char v) {return v;}
-        static __device__ __host__ __forceinline__ char make(char x) {return x;}
-        static __device__ __host__ __forceinline__ char make(const char* x) {return *x;}
-    };
-    template<> struct VecTraits<schar>
-    {
-        typedef schar elem_type;
-        enum {cn=1};
-        static __device__ __host__ __forceinline__ schar all(schar v) {return v;}
-        static __device__ __host__ __forceinline__ schar make(schar x) {return x;}
-        static __device__ __host__ __forceinline__ schar make(const schar* x) {return *x;}
-    };
-    template<> struct VecTraits<char1>
-    {
-        typedef schar elem_type;
-        enum {cn=1};
-        static __device__ __host__ __forceinline__ char1 all(schar v) {return make_char1(v);}
-        static __device__ __host__ __forceinline__ char1 make(schar x) {return make_char1(x);}
-        static __device__ __host__ __forceinline__ char1 make(const schar* v) {return make_char1(v[0]);}
-    };
-    template<> struct VecTraits<char2>
-    {
-        typedef schar elem_type;
-        enum {cn=2};
-        static __device__ __host__ __forceinline__ char2 all(schar v) {return make_char2(v, v);}
-        static __device__ __host__ __forceinline__ char2 make(schar x, schar y) {return make_char2(x, y);}
-        static __device__ __host__ __forceinline__ char2 make(const schar* v) {return make_char2(v[0], v[1]);}
-    };
-    template<> struct VecTraits<char3>
-    {
-        typedef schar elem_type;
-        enum {cn=3};
-        static __device__ __host__ __forceinline__ char3 all(schar v) {return make_char3(v, v, v);}
-        static __device__ __host__ __forceinline__ char3 make(schar x, schar y, schar z) {return make_char3(x, y, z);}
-        static __device__ __host__ __forceinline__ char3 make(const schar* v) {return make_char3(v[0], v[1], v[2]);}
-    };
-    template<> struct VecTraits<char4>
-    {
-        typedef schar elem_type;
-        enum {cn=4};
-        static __device__ __host__ __forceinline__ char4 all(schar v) {return make_char4(v, v, v, v);}
-        static __device__ __host__ __forceinline__ char4 make(schar x, schar y, schar z, schar w) {return make_char4(x, y, z, w);}
-        static __device__ __host__ __forceinline__ char4 make(const schar* v) {return make_char4(v[0], v[1], v[2], v[3]);}
-    };
-    template<> struct VecTraits<char8>
-    {
-        typedef schar elem_type;
-        enum {cn=8};
-        static __device__ __host__ __forceinline__ char8 all(schar v) {return make_char8(v, v, v, v, v, v, v, v);}
-        static __device__ __host__ __forceinline__ char8 make(schar a0, schar a1, schar a2, schar a3, schar a4, schar a5, schar a6, schar a7) {return make_char8(a0, a1, a2, a3, a4, a5, a6, a7);}
-        static __device__ __host__ __forceinline__ char8 make(const schar* v) {return make_char8(v[0], v[1], v[2], v[3], v[4], v[5], v[6], v[7]);}
-    };
-}}} // namespace cv { namespace cuda { namespace cudev
-
-//! @endcond
-
-#endif // OPENCV_CUDA_VEC_TRAITS_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/warp.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/warp.hpp
deleted file mode 100755
index 8af7e6a..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/warp.hpp
+++ /dev/null
@@ -1,139 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_CUDA_DEVICE_WARP_HPP
-#define OPENCV_CUDA_DEVICE_WARP_HPP
-
-/** @file
- * @deprecated Use @ref cudev instead.
- */
-
-//! @cond IGNORED
-
-namespace cv { namespace cuda { namespace device
-{
-    struct Warp
-    {
-        enum
-        {
-            LOG_WARP_SIZE = 5,
-            WARP_SIZE     = 1 << LOG_WARP_SIZE,
-            STRIDE        = WARP_SIZE
-        };
-
-        /** \brief Returns the warp lane ID of the calling thread. */
-        static __device__ __forceinline__ unsigned int laneId()
-        {
-            unsigned int ret;
-            asm("mov.u32 %0, %%laneid;" : "=r"(ret) );
-            return ret;
-        }
-
-        template<typename It, typename T>
-        static __device__ __forceinline__ void fill(It beg, It end, const T& value)
-        {
-            for(It t = beg + laneId(); t < end; t += STRIDE)
-                *t = value;
-        }
-
-        template<typename InIt, typename OutIt>
-        static __device__ __forceinline__ OutIt copy(InIt beg, InIt end, OutIt out)
-        {
-            for(InIt t = beg + laneId(); t < end; t += STRIDE, out += STRIDE)
-                *out = *t;
-            return out;
-        }
-
-        template<typename InIt, typename OutIt, class UnOp>
-        static __device__ __forceinline__ OutIt transform(InIt beg, InIt end, OutIt out, UnOp op)
-        {
-            for(InIt t = beg + laneId(); t < end; t += STRIDE, out += STRIDE)
-                *out = op(*t);
-            return out;
-        }
-
-        template<typename InIt1, typename InIt2, typename OutIt, class BinOp>
-        static __device__ __forceinline__ OutIt transform(InIt1 beg1, InIt1 end1, InIt2 beg2, OutIt out, BinOp op)
-        {
-            unsigned int lane = laneId();
-
-            InIt1 t1 = beg1 + lane;
-            InIt2 t2 = beg2 + lane;
-            for(; t1 < end1; t1 += STRIDE, t2 += STRIDE, out += STRIDE)
-                *out = op(*t1, *t2);
-            return out;
-        }
-
-        template <class T, class BinOp>
-        static __device__ __forceinline__ T reduce(volatile T *ptr, BinOp op)
-        {
-            const unsigned int lane = laneId();
-
-            if (lane < 16)
-            {
-                T partial = ptr[lane];
-
-                ptr[lane] = partial = op(partial, ptr[lane + 16]);
-                ptr[lane] = partial = op(partial, ptr[lane + 8]);
-                ptr[lane] = partial = op(partial, ptr[lane + 4]);
-                ptr[lane] = partial = op(partial, ptr[lane + 2]);
-                ptr[lane] = partial = op(partial, ptr[lane + 1]);
-            }
-
-            return *ptr;
-        }
-
-        template<typename OutIt, typename T>
-        static __device__ __forceinline__ void yota(OutIt beg, OutIt end, T value)
-        {
-            unsigned int lane = laneId();
-            value += lane;
-
-            for(OutIt t = beg + lane; t < end; t += STRIDE, value += STRIDE)
-                *t = value;
-        }
-    };
-}}} // namespace cv { namespace cuda { namespace cudev
-
-//! @endcond
-
-#endif /* OPENCV_CUDA_DEVICE_WARP_HPP */
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/warp_reduce.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/warp_reduce.hpp
deleted file mode 100755
index 530303d..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/warp_reduce.hpp
+++ /dev/null
@@ -1,76 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_CUDA_WARP_REDUCE_HPP__
-#define OPENCV_CUDA_WARP_REDUCE_HPP__
-
-/** @file
- * @deprecated Use @ref cudev instead.
- */
-
-//! @cond IGNORED
-
-namespace cv { namespace cuda { namespace device
-{
-    template <class T>
-    __device__ __forceinline__ T warp_reduce(volatile T *ptr , const unsigned int tid = threadIdx.x)
-    {
-        const unsigned int lane = tid & 31; // index of thread in warp (0..31)
-
-        if (lane < 16)
-        {
-            T partial = ptr[tid];
-
-            ptr[tid] = partial = partial + ptr[tid + 16];
-            ptr[tid] = partial = partial + ptr[tid + 8];
-            ptr[tid] = partial = partial + ptr[tid + 4];
-            ptr[tid] = partial = partial + ptr[tid + 2];
-            ptr[tid] = partial = partial + ptr[tid + 1];
-        }
-
-        return ptr[tid - lane];
-    }
-}}} // namespace cv { namespace cuda { namespace cudev {
-
-//! @endcond
-
-#endif /* OPENCV_CUDA_WARP_REDUCE_HPP__ */
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/warp_shuffle.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/warp_shuffle.hpp
deleted file mode 100755
index 0da54ae..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda/warp_shuffle.hpp
+++ /dev/null
@@ -1,162 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_CUDA_WARP_SHUFFLE_HPP
-#define OPENCV_CUDA_WARP_SHUFFLE_HPP
-
-/** @file
- * @deprecated Use @ref cudev instead.
- */
-
-//! @cond IGNORED
-
-namespace cv { namespace cuda { namespace device
-{
-#if __CUDACC_VER_MAJOR__ >= 9
-#  define __shfl(x, y, z) __shfl_sync(0xFFFFFFFFU, x, y, z)
-#  define __shfl_up(x, y, z) __shfl_up_sync(0xFFFFFFFFU, x, y, z)
-#  define __shfl_down(x, y, z) __shfl_down_sync(0xFFFFFFFFU, x, y, z)
-#endif
-    template <typename T>
-    __device__ __forceinline__ T shfl(T val, int srcLane, int width = warpSize)
-    {
-    #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300
-        return __shfl(val, srcLane, width);
-    #else
-        return T();
-    #endif
-    }
-    __device__ __forceinline__ unsigned int shfl(unsigned int val, int srcLane, int width = warpSize)
-    {
-    #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300
-        return (unsigned int) __shfl((int) val, srcLane, width);
-    #else
-        return 0;
-    #endif
-    }
-    __device__ __forceinline__ double shfl(double val, int srcLane, int width = warpSize)
-    {
-    #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300
-        int lo = __double2loint(val);
-        int hi = __double2hiint(val);
-
-        lo = __shfl(lo, srcLane, width);
-        hi = __shfl(hi, srcLane, width);
-
-        return __hiloint2double(hi, lo);
-    #else
-        return 0.0;
-    #endif
-    }
-
-    template <typename T>
-    __device__ __forceinline__ T shfl_down(T val, unsigned int delta, int width = warpSize)
-    {
-    #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300
-        return __shfl_down(val, delta, width);
-    #else
-        return T();
-    #endif
-    }
-    __device__ __forceinline__ unsigned int shfl_down(unsigned int val, unsigned int delta, int width = warpSize)
-    {
-    #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300
-        return (unsigned int) __shfl_down((int) val, delta, width);
-    #else
-        return 0;
-    #endif
-    }
-    __device__ __forceinline__ double shfl_down(double val, unsigned int delta, int width = warpSize)
-    {
-    #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300
-        int lo = __double2loint(val);
-        int hi = __double2hiint(val);
-
-        lo = __shfl_down(lo, delta, width);
-        hi = __shfl_down(hi, delta, width);
-
-        return __hiloint2double(hi, lo);
-    #else
-        return 0.0;
-    #endif
-    }
-
-    template <typename T>
-    __device__ __forceinline__ T shfl_up(T val, unsigned int delta, int width = warpSize)
-    {
-    #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300
-        return __shfl_up(val, delta, width);
-    #else
-        return T();
-    #endif
-    }
-    __device__ __forceinline__ unsigned int shfl_up(unsigned int val, unsigned int delta, int width = warpSize)
-    {
-    #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300
-        return (unsigned int) __shfl_up((int) val, delta, width);
-    #else
-        return 0;
-    #endif
-    }
-    __device__ __forceinline__ double shfl_up(double val, unsigned int delta, int width = warpSize)
-    {
-    #if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300
-        int lo = __double2loint(val);
-        int hi = __double2hiint(val);
-
-        lo = __shfl_up(lo, delta, width);
-        hi = __shfl_up(hi, delta, width);
-
-        return __hiloint2double(hi, lo);
-    #else
-        return 0.0;
-    #endif
-    }
-}}}
-
-#  undef __shfl
-#  undef __shfl_up
-#  undef __shfl_down
-
-//! @endcond
-
-#endif // OPENCV_CUDA_WARP_SHUFFLE_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda_stream_accessor.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda_stream_accessor.hpp
deleted file mode 100755
index deaf356..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda_stream_accessor.hpp
+++ /dev/null
@@ -1,86 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_CORE_CUDA_STREAM_ACCESSOR_HPP
-#define OPENCV_CORE_CUDA_STREAM_ACCESSOR_HPP
-
-#ifndef __cplusplus
-#  error cuda_stream_accessor.hpp header must be compiled as C++
-#endif
-
-/** @file cuda_stream_accessor.hpp
- * This is only header file that depends on CUDA Runtime API. All other headers are independent.
- */
-
-#include <cuda_runtime.h>
-#include "opencv2/core/cuda.hpp"
-
-namespace cv
-{
-    namespace cuda
-    {
-
-//! @addtogroup cudacore_struct
-//! @{
-
-        /** @brief Class that enables getting cudaStream_t from cuda::Stream
-         */
-        struct StreamAccessor
-        {
-            CV_EXPORTS static cudaStream_t getStream(const Stream& stream);
-            CV_EXPORTS static Stream wrapStream(cudaStream_t stream);
-        };
-
-        /** @brief Class that enables getting cudaEvent_t from cuda::Event
-         */
-        struct EventAccessor
-        {
-            CV_EXPORTS static cudaEvent_t getEvent(const Event& event);
-            CV_EXPORTS static Event wrapEvent(cudaEvent_t event);
-        };
-
-//! @}
-
-    }
-}
-
-#endif /* OPENCV_CORE_CUDA_STREAM_ACCESSOR_HPP */
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda_types.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda_types.hpp
deleted file mode 100755
index e2647c0..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cuda_types.hpp
+++ /dev/null
@@ -1,142 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_CORE_CUDA_TYPES_HPP
-#define OPENCV_CORE_CUDA_TYPES_HPP
-
-#ifndef __cplusplus
-#  error cuda_types.hpp header must be compiled as C++
-#endif
-
-#if defined(__OPENCV_BUILD) && defined(__clang__)
-#pragma clang diagnostic ignored "-Winconsistent-missing-override"
-#endif
-#if defined(__OPENCV_BUILD) && defined(__GNUC__) && __GNUC__ >= 5
-#pragma GCC diagnostic ignored "-Wsuggest-override"
-#endif
-
-/** @file
- * @deprecated Use @ref cudev instead.
- */
-
-//! @cond IGNORED
-
-#ifdef __CUDACC__
-    #define __CV_CUDA_HOST_DEVICE__ __host__ __device__ __forceinline__
-#else
-    #define __CV_CUDA_HOST_DEVICE__
-#endif
-
-namespace cv
-{
-    namespace cuda
-    {
-
-        // Simple lightweight structures that encapsulates information about an image on device.
-        // It is intended to pass to nvcc-compiled code. GpuMat depends on headers that nvcc can't compile
-
-        template <typename T> struct DevPtr
-        {
-            typedef T elem_type;
-            typedef int index_type;
-
-            enum { elem_size = sizeof(elem_type) };
-
-            T* data;
-
-            __CV_CUDA_HOST_DEVICE__ DevPtr() : data(0) {}
-            __CV_CUDA_HOST_DEVICE__ DevPtr(T* data_) : data(data_) {}
-
-            __CV_CUDA_HOST_DEVICE__ size_t elemSize() const { return elem_size; }
-            __CV_CUDA_HOST_DEVICE__ operator       T*()       { return data; }
-            __CV_CUDA_HOST_DEVICE__ operator const T*() const { return data; }
-        };
-
-        template <typename T> struct PtrSz : public DevPtr<T>
-        {
-            __CV_CUDA_HOST_DEVICE__ PtrSz() : size(0) {}
-            __CV_CUDA_HOST_DEVICE__ PtrSz(T* data_, size_t size_) : DevPtr<T>(data_), size(size_) {}
-
-            size_t size;
-        };
-
-        template <typename T> struct PtrStep : public DevPtr<T>
-        {
-            __CV_CUDA_HOST_DEVICE__ PtrStep() : step(0) {}
-            __CV_CUDA_HOST_DEVICE__ PtrStep(T* data_, size_t step_) : DevPtr<T>(data_), step(step_) {}
-
-            size_t step;
-
-            __CV_CUDA_HOST_DEVICE__       T* ptr(int y = 0)       { return (      T*)( (      char*)DevPtr<T>::data + y * step); }
-            __CV_CUDA_HOST_DEVICE__ const T* ptr(int y = 0) const { return (const T*)( (const char*)DevPtr<T>::data + y * step); }
-
-            __CV_CUDA_HOST_DEVICE__       T& operator ()(int y, int x)       { return ptr(y)[x]; }
-            __CV_CUDA_HOST_DEVICE__ const T& operator ()(int y, int x) const { return ptr(y)[x]; }
-        };
-
-        template <typename T> struct PtrStepSz : public PtrStep<T>
-        {
-            __CV_CUDA_HOST_DEVICE__ PtrStepSz() : cols(0), rows(0) {}
-            __CV_CUDA_HOST_DEVICE__ PtrStepSz(int rows_, int cols_, T* data_, size_t step_)
-                : PtrStep<T>(data_, step_), cols(cols_), rows(rows_) {}
-
-            template <typename U>
-            explicit PtrStepSz(const PtrStepSz<U>& d) : PtrStep<T>((T*)d.data, d.step), cols(d.cols), rows(d.rows){}
-
-            int cols;
-            int rows;
-        };
-
-        typedef PtrStepSz<unsigned char> PtrStepSzb;
-        typedef PtrStepSz<float> PtrStepSzf;
-        typedef PtrStepSz<int> PtrStepSzi;
-
-        typedef PtrStep<unsigned char> PtrStepb;
-        typedef PtrStep<float> PtrStepf;
-        typedef PtrStep<int> PtrStepi;
-
-    }
-}
-
-//! @endcond
-
-#endif /* OPENCV_CORE_CUDA_TYPES_HPP */
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cv_cpu_dispatch.h b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cv_cpu_dispatch.h
deleted file mode 100755
index bb0fed4..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cv_cpu_dispatch.h
+++ /dev/null
@@ -1,239 +0,0 @@
-// This file is part of OpenCV project.
-// It is subject to the license terms in the LICENSE file found in the top-level directory
-// of this distribution and at http://opencv.org/license.html.
-
-#if defined __OPENCV_BUILD \
-
-#include "cv_cpu_config.h"
-#include "cv_cpu_helper.h"
-
-#ifdef CV_CPU_DISPATCH_MODE
-#define CV_CPU_OPTIMIZATION_NAMESPACE __CV_CAT(opt_, CV_CPU_DISPATCH_MODE)
-#define CV_CPU_OPTIMIZATION_NAMESPACE_BEGIN namespace __CV_CAT(opt_, CV_CPU_DISPATCH_MODE) {
-#define CV_CPU_OPTIMIZATION_NAMESPACE_END }
-#else
-#define CV_CPU_OPTIMIZATION_NAMESPACE cpu_baseline
-#define CV_CPU_OPTIMIZATION_NAMESPACE_BEGIN namespace cpu_baseline {
-#define CV_CPU_OPTIMIZATION_NAMESPACE_END }
-#endif
-
-
-#define __CV_CPU_DISPATCH_CHAIN_END(fn, args, mode, ...)  /* done */
-#define __CV_CPU_DISPATCH(fn, args, mode, ...) __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
-#define __CV_CPU_DISPATCH_EXPAND(fn, args, ...) __CV_EXPAND(__CV_CPU_DISPATCH(fn, args, __VA_ARGS__))
-#define CV_CPU_DISPATCH(fn, args, ...) __CV_CPU_DISPATCH_EXPAND(fn, args, __VA_ARGS__, END) // expand macros
-
-
-#if defined CV_ENABLE_INTRINSICS \
-    && !defined CV_DISABLE_OPTIMIZATION \
-    && !defined __CUDACC__ /* do not include SSE/AVX/NEON headers for NVCC compiler */ \
-
-#ifdef CV_CPU_COMPILE_SSE2
-#  include <emmintrin.h>
-#  define CV_MMX 1
-#  define CV_SSE 1
-#  define CV_SSE2 1
-#endif
-#ifdef CV_CPU_COMPILE_SSE3
-#  include <pmmintrin.h>
-#  define CV_SSE3 1
-#endif
-#ifdef CV_CPU_COMPILE_SSSE3
-#  include <tmmintrin.h>
-#  define CV_SSSE3 1
-#endif
-#ifdef CV_CPU_COMPILE_SSE4_1
-#  include <smmintrin.h>
-#  define CV_SSE4_1 1
-#endif
-#ifdef CV_CPU_COMPILE_SSE4_2
-#  include <nmmintrin.h>
-#  define CV_SSE4_2 1
-#endif
-#ifdef CV_CPU_COMPILE_POPCNT
-#  ifdef _MSC_VER
-#    include <nmmintrin.h>
-#    if defined(_M_X64)
-#      define CV_POPCNT_U64 _mm_popcnt_u64
-#    endif
-#    define CV_POPCNT_U32 _mm_popcnt_u32
-#  else
-#    include <popcntintrin.h>
-#    if defined(__x86_64__)
-#      define CV_POPCNT_U64 __builtin_popcountll
-#    endif
-#    define CV_POPCNT_U32 __builtin_popcount
-#  endif
-#  define CV_POPCNT 1
-#endif
-#ifdef CV_CPU_COMPILE_AVX
-#  include <immintrin.h>
-#  define CV_AVX 1
-#endif
-#ifdef CV_CPU_COMPILE_FP16
-#  if defined(__arm__) || defined(__aarch64__) || defined(_M_ARM)
-#    include <arm_neon.h>
-#  else
-#    include <immintrin.h>
-#  endif
-#  define CV_FP16 1
-#endif
-#ifdef CV_CPU_COMPILE_AVX2
-#  include <immintrin.h>
-#  define CV_AVX2 1
-#endif
-#ifdef CV_CPU_COMPILE_AVX_512F
-#  include <immintrin.h>
-#  define CV_AVX_512F 1
-#endif
-#ifdef CV_CPU_COMPILE_AVX512_SKX
-#  include <immintrin.h>
-#  define CV_AVX512_SKX 1
-#endif
-#ifdef CV_CPU_COMPILE_FMA3
-#  define CV_FMA3 1
-#endif
-
-#if defined _WIN32 && defined(_M_ARM)
-# include <Intrin.h>
-# include <arm_neon.h>
-# define CV_NEON 1
-#elif defined(__ARM_NEON__) || (defined (__ARM_NEON) && defined(__aarch64__))
-#  include <arm_neon.h>
-#  define CV_NEON 1
-#endif
-
-#if defined(__ARM_NEON__) || defined(__aarch64__)
-#  include <arm_neon.h>
-#endif
-
-#if defined(__VSX__) && defined(__PPC64__) && defined(__LITTLE_ENDIAN__)
-#  include <altivec.h>
-#  undef vector
-#  undef pixel
-#  undef bool
-#  define CV_VSX 1
-#endif
-
-#endif // CV_ENABLE_INTRINSICS && !CV_DISABLE_OPTIMIZATION && !__CUDACC__
-
-#if defined CV_CPU_COMPILE_AVX && !defined CV_CPU_BASELINE_COMPILE_AVX
-struct VZeroUpperGuard {
-#ifdef __GNUC__
-    __attribute__((always_inline))
-#endif
-    inline ~VZeroUpperGuard() { _mm256_zeroupper(); }
-};
-#define __CV_AVX_GUARD VZeroUpperGuard __vzeroupper_guard; (void)__vzeroupper_guard;
-#endif
-
-#ifdef __CV_AVX_GUARD
-#define CV_AVX_GUARD __CV_AVX_GUARD
-#else
-#define CV_AVX_GUARD
-#endif
-
-#endif // __OPENCV_BUILD
-
-
-
-#if !defined __OPENCV_BUILD /* Compatibility code */ \
-    && !defined __CUDACC__ /* do not include SSE/AVX/NEON headers for NVCC compiler */
-#if defined __SSE2__ || defined _M_X64 || (defined _M_IX86_FP && _M_IX86_FP >= 2)
-#  include <emmintrin.h>
-#  define CV_MMX 1
-#  define CV_SSE 1
-#  define CV_SSE2 1
-#elif defined _WIN32 && defined(_M_ARM)
-# include <Intrin.h>
-# include <arm_neon.h>
-# define CV_NEON 1
-#elif defined(__ARM_NEON__) || (defined (__ARM_NEON) && defined(__aarch64__))
-#  include <arm_neon.h>
-#  define CV_NEON 1
-#elif defined(__VSX__) && defined(__PPC64__) && defined(__LITTLE_ENDIAN__)
-#  include <altivec.h>
-#  undef vector
-#  undef pixel
-#  undef bool
-#  define CV_VSX 1
-#endif
-
-#endif // !__OPENCV_BUILD && !__CUDACC (Compatibility code)
-
-
-
-#ifndef CV_MMX
-#  define CV_MMX 0
-#endif
-#ifndef CV_SSE
-#  define CV_SSE 0
-#endif
-#ifndef CV_SSE2
-#  define CV_SSE2 0
-#endif
-#ifndef CV_SSE3
-#  define CV_SSE3 0
-#endif
-#ifndef CV_SSSE3
-#  define CV_SSSE3 0
-#endif
-#ifndef CV_SSE4_1
-#  define CV_SSE4_1 0
-#endif
-#ifndef CV_SSE4_2
-#  define CV_SSE4_2 0
-#endif
-#ifndef CV_POPCNT
-#  define CV_POPCNT 0
-#endif
-#ifndef CV_AVX
-#  define CV_AVX 0
-#endif
-#ifndef CV_FP16
-#  define CV_FP16 0
-#endif
-#ifndef CV_AVX2
-#  define CV_AVX2 0
-#endif
-#ifndef CV_FMA3
-#  define CV_FMA3 0
-#endif
-#ifndef CV_AVX_512F
-#  define CV_AVX_512F 0
-#endif
-#ifndef CV_AVX_512BW
-#  define CV_AVX_512BW 0
-#endif
-#ifndef CV_AVX_512CD
-#  define CV_AVX_512CD 0
-#endif
-#ifndef CV_AVX_512DQ
-#  define CV_AVX_512DQ 0
-#endif
-#ifndef CV_AVX_512ER
-#  define CV_AVX_512ER 0
-#endif
-#ifndef CV_AVX_512IFMA512
-#  define CV_AVX_512IFMA512 0
-#endif
-#ifndef CV_AVX_512PF
-#  define CV_AVX_512PF 0
-#endif
-#ifndef CV_AVX_512VBMI
-#  define CV_AVX_512VBMI 0
-#endif
-#ifndef CV_AVX_512VL
-#  define CV_AVX_512VL 0
-#endif
-#ifndef CV_AVX512_SKX
-#  define CV_AVX512_SKX 0
-#endif
-
-#ifndef CV_NEON
-#  define CV_NEON 0
-#endif
-
-#ifndef CV_VSX
-#  define CV_VSX 0
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cv_cpu_helper.h b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cv_cpu_helper.h
deleted file mode 100755
index 84f489b..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cv_cpu_helper.h
+++ /dev/null
@@ -1,319 +0,0 @@
-// AUTOGENERATED, DO NOT EDIT
-
-#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_SSE
-#  define CV_TRY_SSE 1
-#  define CV_CPU_FORCE_SSE 1
-#  define CV_CPU_HAS_SUPPORT_SSE 1
-#  define CV_CPU_CALL_SSE(fn, args) return (cpu_baseline::fn args)
-#  define CV_CPU_CALL_SSE_(fn, args) return (opt_SSE::fn args)
-#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_SSE
-#  define CV_TRY_SSE 1
-#  define CV_CPU_FORCE_SSE 0
-#  define CV_CPU_HAS_SUPPORT_SSE (cv::checkHardwareSupport(CV_CPU_SSE))
-#  define CV_CPU_CALL_SSE(fn, args) if (CV_CPU_HAS_SUPPORT_SSE) return (opt_SSE::fn args)
-#  define CV_CPU_CALL_SSE_(fn, args) if (CV_CPU_HAS_SUPPORT_SSE) return (opt_SSE::fn args)
-#else
-#  define CV_TRY_SSE 0
-#  define CV_CPU_FORCE_SSE 0
-#  define CV_CPU_HAS_SUPPORT_SSE 0
-#  define CV_CPU_CALL_SSE(fn, args)
-#  define CV_CPU_CALL_SSE_(fn, args)
-#endif
-#define __CV_CPU_DISPATCH_CHAIN_SSE(fn, args, mode, ...)  CV_CPU_CALL_SSE(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
-
-#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_SSE2
-#  define CV_TRY_SSE2 1
-#  define CV_CPU_FORCE_SSE2 1
-#  define CV_CPU_HAS_SUPPORT_SSE2 1
-#  define CV_CPU_CALL_SSE2(fn, args) return (cpu_baseline::fn args)
-#  define CV_CPU_CALL_SSE2_(fn, args) return (opt_SSE2::fn args)
-#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_SSE2
-#  define CV_TRY_SSE2 1
-#  define CV_CPU_FORCE_SSE2 0
-#  define CV_CPU_HAS_SUPPORT_SSE2 (cv::checkHardwareSupport(CV_CPU_SSE2))
-#  define CV_CPU_CALL_SSE2(fn, args) if (CV_CPU_HAS_SUPPORT_SSE2) return (opt_SSE2::fn args)
-#  define CV_CPU_CALL_SSE2_(fn, args) if (CV_CPU_HAS_SUPPORT_SSE2) return (opt_SSE2::fn args)
-#else
-#  define CV_TRY_SSE2 0
-#  define CV_CPU_FORCE_SSE2 0
-#  define CV_CPU_HAS_SUPPORT_SSE2 0
-#  define CV_CPU_CALL_SSE2(fn, args)
-#  define CV_CPU_CALL_SSE2_(fn, args)
-#endif
-#define __CV_CPU_DISPATCH_CHAIN_SSE2(fn, args, mode, ...)  CV_CPU_CALL_SSE2(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
-
-#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_SSE3
-#  define CV_TRY_SSE3 1
-#  define CV_CPU_FORCE_SSE3 1
-#  define CV_CPU_HAS_SUPPORT_SSE3 1
-#  define CV_CPU_CALL_SSE3(fn, args) return (cpu_baseline::fn args)
-#  define CV_CPU_CALL_SSE3_(fn, args) return (opt_SSE3::fn args)
-#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_SSE3
-#  define CV_TRY_SSE3 1
-#  define CV_CPU_FORCE_SSE3 0
-#  define CV_CPU_HAS_SUPPORT_SSE3 (cv::checkHardwareSupport(CV_CPU_SSE3))
-#  define CV_CPU_CALL_SSE3(fn, args) if (CV_CPU_HAS_SUPPORT_SSE3) return (opt_SSE3::fn args)
-#  define CV_CPU_CALL_SSE3_(fn, args) if (CV_CPU_HAS_SUPPORT_SSE3) return (opt_SSE3::fn args)
-#else
-#  define CV_TRY_SSE3 0
-#  define CV_CPU_FORCE_SSE3 0
-#  define CV_CPU_HAS_SUPPORT_SSE3 0
-#  define CV_CPU_CALL_SSE3(fn, args)
-#  define CV_CPU_CALL_SSE3_(fn, args)
-#endif
-#define __CV_CPU_DISPATCH_CHAIN_SSE3(fn, args, mode, ...)  CV_CPU_CALL_SSE3(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
-
-#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_SSSE3
-#  define CV_TRY_SSSE3 1
-#  define CV_CPU_FORCE_SSSE3 1
-#  define CV_CPU_HAS_SUPPORT_SSSE3 1
-#  define CV_CPU_CALL_SSSE3(fn, args) return (cpu_baseline::fn args)
-#  define CV_CPU_CALL_SSSE3_(fn, args) return (opt_SSSE3::fn args)
-#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_SSSE3
-#  define CV_TRY_SSSE3 1
-#  define CV_CPU_FORCE_SSSE3 0
-#  define CV_CPU_HAS_SUPPORT_SSSE3 (cv::checkHardwareSupport(CV_CPU_SSSE3))
-#  define CV_CPU_CALL_SSSE3(fn, args) if (CV_CPU_HAS_SUPPORT_SSSE3) return (opt_SSSE3::fn args)
-#  define CV_CPU_CALL_SSSE3_(fn, args) if (CV_CPU_HAS_SUPPORT_SSSE3) return (opt_SSSE3::fn args)
-#else
-#  define CV_TRY_SSSE3 0
-#  define CV_CPU_FORCE_SSSE3 0
-#  define CV_CPU_HAS_SUPPORT_SSSE3 0
-#  define CV_CPU_CALL_SSSE3(fn, args)
-#  define CV_CPU_CALL_SSSE3_(fn, args)
-#endif
-#define __CV_CPU_DISPATCH_CHAIN_SSSE3(fn, args, mode, ...)  CV_CPU_CALL_SSSE3(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
-
-#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_SSE4_1
-#  define CV_TRY_SSE4_1 1
-#  define CV_CPU_FORCE_SSE4_1 1
-#  define CV_CPU_HAS_SUPPORT_SSE4_1 1
-#  define CV_CPU_CALL_SSE4_1(fn, args) return (cpu_baseline::fn args)
-#  define CV_CPU_CALL_SSE4_1_(fn, args) return (opt_SSE4_1::fn args)
-#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_SSE4_1
-#  define CV_TRY_SSE4_1 1
-#  define CV_CPU_FORCE_SSE4_1 0
-#  define CV_CPU_HAS_SUPPORT_SSE4_1 (cv::checkHardwareSupport(CV_CPU_SSE4_1))
-#  define CV_CPU_CALL_SSE4_1(fn, args) if (CV_CPU_HAS_SUPPORT_SSE4_1) return (opt_SSE4_1::fn args)
-#  define CV_CPU_CALL_SSE4_1_(fn, args) if (CV_CPU_HAS_SUPPORT_SSE4_1) return (opt_SSE4_1::fn args)
-#else
-#  define CV_TRY_SSE4_1 0
-#  define CV_CPU_FORCE_SSE4_1 0
-#  define CV_CPU_HAS_SUPPORT_SSE4_1 0
-#  define CV_CPU_CALL_SSE4_1(fn, args)
-#  define CV_CPU_CALL_SSE4_1_(fn, args)
-#endif
-#define __CV_CPU_DISPATCH_CHAIN_SSE4_1(fn, args, mode, ...)  CV_CPU_CALL_SSE4_1(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
-
-#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_SSE4_2
-#  define CV_TRY_SSE4_2 1
-#  define CV_CPU_FORCE_SSE4_2 1
-#  define CV_CPU_HAS_SUPPORT_SSE4_2 1
-#  define CV_CPU_CALL_SSE4_2(fn, args) return (cpu_baseline::fn args)
-#  define CV_CPU_CALL_SSE4_2_(fn, args) return (opt_SSE4_2::fn args)
-#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_SSE4_2
-#  define CV_TRY_SSE4_2 1
-#  define CV_CPU_FORCE_SSE4_2 0
-#  define CV_CPU_HAS_SUPPORT_SSE4_2 (cv::checkHardwareSupport(CV_CPU_SSE4_2))
-#  define CV_CPU_CALL_SSE4_2(fn, args) if (CV_CPU_HAS_SUPPORT_SSE4_2) return (opt_SSE4_2::fn args)
-#  define CV_CPU_CALL_SSE4_2_(fn, args) if (CV_CPU_HAS_SUPPORT_SSE4_2) return (opt_SSE4_2::fn args)
-#else
-#  define CV_TRY_SSE4_2 0
-#  define CV_CPU_FORCE_SSE4_2 0
-#  define CV_CPU_HAS_SUPPORT_SSE4_2 0
-#  define CV_CPU_CALL_SSE4_2(fn, args)
-#  define CV_CPU_CALL_SSE4_2_(fn, args)
-#endif
-#define __CV_CPU_DISPATCH_CHAIN_SSE4_2(fn, args, mode, ...)  CV_CPU_CALL_SSE4_2(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
-
-#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_POPCNT
-#  define CV_TRY_POPCNT 1
-#  define CV_CPU_FORCE_POPCNT 1
-#  define CV_CPU_HAS_SUPPORT_POPCNT 1
-#  define CV_CPU_CALL_POPCNT(fn, args) return (cpu_baseline::fn args)
-#  define CV_CPU_CALL_POPCNT_(fn, args) return (opt_POPCNT::fn args)
-#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_POPCNT
-#  define CV_TRY_POPCNT 1
-#  define CV_CPU_FORCE_POPCNT 0
-#  define CV_CPU_HAS_SUPPORT_POPCNT (cv::checkHardwareSupport(CV_CPU_POPCNT))
-#  define CV_CPU_CALL_POPCNT(fn, args) if (CV_CPU_HAS_SUPPORT_POPCNT) return (opt_POPCNT::fn args)
-#  define CV_CPU_CALL_POPCNT_(fn, args) if (CV_CPU_HAS_SUPPORT_POPCNT) return (opt_POPCNT::fn args)
-#else
-#  define CV_TRY_POPCNT 0
-#  define CV_CPU_FORCE_POPCNT 0
-#  define CV_CPU_HAS_SUPPORT_POPCNT 0
-#  define CV_CPU_CALL_POPCNT(fn, args)
-#  define CV_CPU_CALL_POPCNT_(fn, args)
-#endif
-#define __CV_CPU_DISPATCH_CHAIN_POPCNT(fn, args, mode, ...)  CV_CPU_CALL_POPCNT(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
-
-#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_AVX
-#  define CV_TRY_AVX 1
-#  define CV_CPU_FORCE_AVX 1
-#  define CV_CPU_HAS_SUPPORT_AVX 1
-#  define CV_CPU_CALL_AVX(fn, args) return (cpu_baseline::fn args)
-#  define CV_CPU_CALL_AVX_(fn, args) return (opt_AVX::fn args)
-#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_AVX
-#  define CV_TRY_AVX 1
-#  define CV_CPU_FORCE_AVX 0
-#  define CV_CPU_HAS_SUPPORT_AVX (cv::checkHardwareSupport(CV_CPU_AVX))
-#  define CV_CPU_CALL_AVX(fn, args) if (CV_CPU_HAS_SUPPORT_AVX) return (opt_AVX::fn args)
-#  define CV_CPU_CALL_AVX_(fn, args) if (CV_CPU_HAS_SUPPORT_AVX) return (opt_AVX::fn args)
-#else
-#  define CV_TRY_AVX 0
-#  define CV_CPU_FORCE_AVX 0
-#  define CV_CPU_HAS_SUPPORT_AVX 0
-#  define CV_CPU_CALL_AVX(fn, args)
-#  define CV_CPU_CALL_AVX_(fn, args)
-#endif
-#define __CV_CPU_DISPATCH_CHAIN_AVX(fn, args, mode, ...)  CV_CPU_CALL_AVX(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
-
-#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_FP16
-#  define CV_TRY_FP16 1
-#  define CV_CPU_FORCE_FP16 1
-#  define CV_CPU_HAS_SUPPORT_FP16 1
-#  define CV_CPU_CALL_FP16(fn, args) return (cpu_baseline::fn args)
-#  define CV_CPU_CALL_FP16_(fn, args) return (opt_FP16::fn args)
-#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_FP16
-#  define CV_TRY_FP16 1
-#  define CV_CPU_FORCE_FP16 0
-#  define CV_CPU_HAS_SUPPORT_FP16 (cv::checkHardwareSupport(CV_CPU_FP16))
-#  define CV_CPU_CALL_FP16(fn, args) if (CV_CPU_HAS_SUPPORT_FP16) return (opt_FP16::fn args)
-#  define CV_CPU_CALL_FP16_(fn, args) if (CV_CPU_HAS_SUPPORT_FP16) return (opt_FP16::fn args)
-#else
-#  define CV_TRY_FP16 0
-#  define CV_CPU_FORCE_FP16 0
-#  define CV_CPU_HAS_SUPPORT_FP16 0
-#  define CV_CPU_CALL_FP16(fn, args)
-#  define CV_CPU_CALL_FP16_(fn, args)
-#endif
-#define __CV_CPU_DISPATCH_CHAIN_FP16(fn, args, mode, ...)  CV_CPU_CALL_FP16(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
-
-#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_AVX2
-#  define CV_TRY_AVX2 1
-#  define CV_CPU_FORCE_AVX2 1
-#  define CV_CPU_HAS_SUPPORT_AVX2 1
-#  define CV_CPU_CALL_AVX2(fn, args) return (cpu_baseline::fn args)
-#  define CV_CPU_CALL_AVX2_(fn, args) return (opt_AVX2::fn args)
-#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_AVX2
-#  define CV_TRY_AVX2 1
-#  define CV_CPU_FORCE_AVX2 0
-#  define CV_CPU_HAS_SUPPORT_AVX2 (cv::checkHardwareSupport(CV_CPU_AVX2))
-#  define CV_CPU_CALL_AVX2(fn, args) if (CV_CPU_HAS_SUPPORT_AVX2) return (opt_AVX2::fn args)
-#  define CV_CPU_CALL_AVX2_(fn, args) if (CV_CPU_HAS_SUPPORT_AVX2) return (opt_AVX2::fn args)
-#else
-#  define CV_TRY_AVX2 0
-#  define CV_CPU_FORCE_AVX2 0
-#  define CV_CPU_HAS_SUPPORT_AVX2 0
-#  define CV_CPU_CALL_AVX2(fn, args)
-#  define CV_CPU_CALL_AVX2_(fn, args)
-#endif
-#define __CV_CPU_DISPATCH_CHAIN_AVX2(fn, args, mode, ...)  CV_CPU_CALL_AVX2(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
-
-#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_FMA3
-#  define CV_TRY_FMA3 1
-#  define CV_CPU_FORCE_FMA3 1
-#  define CV_CPU_HAS_SUPPORT_FMA3 1
-#  define CV_CPU_CALL_FMA3(fn, args) return (cpu_baseline::fn args)
-#  define CV_CPU_CALL_FMA3_(fn, args) return (opt_FMA3::fn args)
-#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_FMA3
-#  define CV_TRY_FMA3 1
-#  define CV_CPU_FORCE_FMA3 0
-#  define CV_CPU_HAS_SUPPORT_FMA3 (cv::checkHardwareSupport(CV_CPU_FMA3))
-#  define CV_CPU_CALL_FMA3(fn, args) if (CV_CPU_HAS_SUPPORT_FMA3) return (opt_FMA3::fn args)
-#  define CV_CPU_CALL_FMA3_(fn, args) if (CV_CPU_HAS_SUPPORT_FMA3) return (opt_FMA3::fn args)
-#else
-#  define CV_TRY_FMA3 0
-#  define CV_CPU_FORCE_FMA3 0
-#  define CV_CPU_HAS_SUPPORT_FMA3 0
-#  define CV_CPU_CALL_FMA3(fn, args)
-#  define CV_CPU_CALL_FMA3_(fn, args)
-#endif
-#define __CV_CPU_DISPATCH_CHAIN_FMA3(fn, args, mode, ...)  CV_CPU_CALL_FMA3(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
-
-#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_AVX_512F
-#  define CV_TRY_AVX_512F 1
-#  define CV_CPU_FORCE_AVX_512F 1
-#  define CV_CPU_HAS_SUPPORT_AVX_512F 1
-#  define CV_CPU_CALL_AVX_512F(fn, args) return (cpu_baseline::fn args)
-#  define CV_CPU_CALL_AVX_512F_(fn, args) return (opt_AVX_512F::fn args)
-#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_AVX_512F
-#  define CV_TRY_AVX_512F 1
-#  define CV_CPU_FORCE_AVX_512F 0
-#  define CV_CPU_HAS_SUPPORT_AVX_512F (cv::checkHardwareSupport(CV_CPU_AVX_512F))
-#  define CV_CPU_CALL_AVX_512F(fn, args) if (CV_CPU_HAS_SUPPORT_AVX_512F) return (opt_AVX_512F::fn args)
-#  define CV_CPU_CALL_AVX_512F_(fn, args) if (CV_CPU_HAS_SUPPORT_AVX_512F) return (opt_AVX_512F::fn args)
-#else
-#  define CV_TRY_AVX_512F 0
-#  define CV_CPU_FORCE_AVX_512F 0
-#  define CV_CPU_HAS_SUPPORT_AVX_512F 0
-#  define CV_CPU_CALL_AVX_512F(fn, args)
-#  define CV_CPU_CALL_AVX_512F_(fn, args)
-#endif
-#define __CV_CPU_DISPATCH_CHAIN_AVX_512F(fn, args, mode, ...)  CV_CPU_CALL_AVX_512F(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
-
-#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_AVX512_SKX
-#  define CV_TRY_AVX512_SKX 1
-#  define CV_CPU_FORCE_AVX512_SKX 1
-#  define CV_CPU_HAS_SUPPORT_AVX512_SKX 1
-#  define CV_CPU_CALL_AVX512_SKX(fn, args) return (cpu_baseline::fn args)
-#  define CV_CPU_CALL_AVX512_SKX_(fn, args) return (opt_AVX512_SKX::fn args)
-#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_AVX512_SKX
-#  define CV_TRY_AVX512_SKX 1
-#  define CV_CPU_FORCE_AVX512_SKX 0
-#  define CV_CPU_HAS_SUPPORT_AVX512_SKX (cv::checkHardwareSupport(CV_CPU_AVX512_SKX))
-#  define CV_CPU_CALL_AVX512_SKX(fn, args) if (CV_CPU_HAS_SUPPORT_AVX512_SKX) return (opt_AVX512_SKX::fn args)
-#  define CV_CPU_CALL_AVX512_SKX_(fn, args) if (CV_CPU_HAS_SUPPORT_AVX512_SKX) return (opt_AVX512_SKX::fn args)
-#else
-#  define CV_TRY_AVX512_SKX 0
-#  define CV_CPU_FORCE_AVX512_SKX 0
-#  define CV_CPU_HAS_SUPPORT_AVX512_SKX 0
-#  define CV_CPU_CALL_AVX512_SKX(fn, args)
-#  define CV_CPU_CALL_AVX512_SKX_(fn, args)
-#endif
-#define __CV_CPU_DISPATCH_CHAIN_AVX512_SKX(fn, args, mode, ...)  CV_CPU_CALL_AVX512_SKX(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
-
-#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_NEON
-#  define CV_TRY_NEON 1
-#  define CV_CPU_FORCE_NEON 1
-#  define CV_CPU_HAS_SUPPORT_NEON 1
-#  define CV_CPU_CALL_NEON(fn, args) return (cpu_baseline::fn args)
-#  define CV_CPU_CALL_NEON_(fn, args) return (opt_NEON::fn args)
-#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_NEON
-#  define CV_TRY_NEON 1
-#  define CV_CPU_FORCE_NEON 0
-#  define CV_CPU_HAS_SUPPORT_NEON (cv::checkHardwareSupport(CV_CPU_NEON))
-#  define CV_CPU_CALL_NEON(fn, args) if (CV_CPU_HAS_SUPPORT_NEON) return (opt_NEON::fn args)
-#  define CV_CPU_CALL_NEON_(fn, args) if (CV_CPU_HAS_SUPPORT_NEON) return (opt_NEON::fn args)
-#else
-#  define CV_TRY_NEON 0
-#  define CV_CPU_FORCE_NEON 0
-#  define CV_CPU_HAS_SUPPORT_NEON 0
-#  define CV_CPU_CALL_NEON(fn, args)
-#  define CV_CPU_CALL_NEON_(fn, args)
-#endif
-#define __CV_CPU_DISPATCH_CHAIN_NEON(fn, args, mode, ...)  CV_CPU_CALL_NEON(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
-
-#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_VSX
-#  define CV_TRY_VSX 1
-#  define CV_CPU_FORCE_VSX 1
-#  define CV_CPU_HAS_SUPPORT_VSX 1
-#  define CV_CPU_CALL_VSX(fn, args) return (cpu_baseline::fn args)
-#  define CV_CPU_CALL_VSX_(fn, args) return (opt_VSX::fn args)
-#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_VSX
-#  define CV_TRY_VSX 1
-#  define CV_CPU_FORCE_VSX 0
-#  define CV_CPU_HAS_SUPPORT_VSX (cv::checkHardwareSupport(CV_CPU_VSX))
-#  define CV_CPU_CALL_VSX(fn, args) if (CV_CPU_HAS_SUPPORT_VSX) return (opt_VSX::fn args)
-#  define CV_CPU_CALL_VSX_(fn, args) if (CV_CPU_HAS_SUPPORT_VSX) return (opt_VSX::fn args)
-#else
-#  define CV_TRY_VSX 0
-#  define CV_CPU_FORCE_VSX 0
-#  define CV_CPU_HAS_SUPPORT_VSX 0
-#  define CV_CPU_CALL_VSX(fn, args)
-#  define CV_CPU_CALL_VSX_(fn, args)
-#endif
-#define __CV_CPU_DISPATCH_CHAIN_VSX(fn, args, mode, ...)  CV_CPU_CALL_VSX(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
-
-#define CV_CPU_CALL_BASELINE(fn, args) return (cpu_baseline::fn args)
-#define __CV_CPU_DISPATCH_CHAIN_BASELINE(fn, args, mode, ...)  CV_CPU_CALL_BASELINE(fn, args) /* last in sequence */
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cvdef.h b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cvdef.h
deleted file mode 100755
index 4ab72b3..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cvdef.h
+++ /dev/null
@@ -1,531 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
-// Copyright (C) 2015, Itseez Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_CORE_CVDEF_H
-#define OPENCV_CORE_CVDEF_H
-
-//! @addtogroup core_utils
-//! @{
-
-#if !defined CV_DOXYGEN && !defined CV_IGNORE_DEBUG_BUILD_GUARD
-#if (defined(_MSC_VER) && (defined(DEBUG) || defined(_DEBUG))) || \
-    (defined(_GLIBCXX_DEBUG) || defined(_GLIBCXX_DEBUG_PEDANTIC))
-// Guard to prevent using of binary incompatible binaries / runtimes
-// https://github.com/opencv/opencv/pull/9161
-#define CV__DEBUG_NS_BEGIN namespace debug_build_guard {
-#define CV__DEBUG_NS_END }
-namespace cv { namespace debug_build_guard { } using namespace debug_build_guard; }
-#endif
-#endif
-
-#ifndef CV__DEBUG_NS_BEGIN
-#define CV__DEBUG_NS_BEGIN
-#define CV__DEBUG_NS_END
-#endif
-
-
-#ifdef __OPENCV_BUILD
-#include "cvconfig.h"
-#endif
-
-#ifndef __CV_EXPAND
-#define __CV_EXPAND(x) x
-#endif
-
-#ifndef __CV_CAT
-#define __CV_CAT__(x, y) x ## y
-#define __CV_CAT_(x, y) __CV_CAT__(x, y)
-#define __CV_CAT(x, y) __CV_CAT_(x, y)
-#endif
-
-
-// undef problematic defines sometimes defined by system headers (windows.h in particular)
-#undef small
-#undef min
-#undef max
-#undef abs
-#undef Complex
-
-#include <limits.h>
-#include "opencv2/core/hal/interface.h"
-
-#if defined __ICL
-#  define CV_ICC   __ICL
-#elif defined __ICC
-#  define CV_ICC   __ICC
-#elif defined __ECL
-#  define CV_ICC   __ECL
-#elif defined __ECC
-#  define CV_ICC   __ECC
-#elif defined __INTEL_COMPILER
-#  define CV_ICC   __INTEL_COMPILER
-#endif
-
-#ifndef CV_INLINE
-#  if defined __cplusplus
-#    define CV_INLINE static inline
-#  elif defined _MSC_VER
-#    define CV_INLINE __inline
-#  else
-#    define CV_INLINE static
-#  endif
-#endif
-
-#if defined CV_DISABLE_OPTIMIZATION || (defined CV_ICC && !defined CV_ENABLE_UNROLLED)
-#  define CV_ENABLE_UNROLLED 0
-#else
-#  define CV_ENABLE_UNROLLED 1
-#endif
-
-#ifdef __GNUC__
-#  define CV_DECL_ALIGNED(x) __attribute__ ((aligned (x)))
-#elif defined _MSC_VER
-#  define CV_DECL_ALIGNED(x) __declspec(align(x))
-#else
-#  define CV_DECL_ALIGNED(x)
-#endif
-
-/* CPU features and intrinsics support */
-#define CV_CPU_NONE             0
-#define CV_CPU_MMX              1
-#define CV_CPU_SSE              2
-#define CV_CPU_SSE2             3
-#define CV_CPU_SSE3             4
-#define CV_CPU_SSSE3            5
-#define CV_CPU_SSE4_1           6
-#define CV_CPU_SSE4_2           7
-#define CV_CPU_POPCNT           8
-#define CV_CPU_FP16             9
-#define CV_CPU_AVX              10
-#define CV_CPU_AVX2             11
-#define CV_CPU_FMA3             12
-
-#define CV_CPU_AVX_512F         13
-#define CV_CPU_AVX_512BW        14
-#define CV_CPU_AVX_512CD        15
-#define CV_CPU_AVX_512DQ        16
-#define CV_CPU_AVX_512ER        17
-#define CV_CPU_AVX_512IFMA512   18 // deprecated
-#define CV_CPU_AVX_512IFMA      18
-#define CV_CPU_AVX_512PF        19
-#define CV_CPU_AVX_512VBMI      20
-#define CV_CPU_AVX_512VL        21
-
-#define CV_CPU_NEON   100
-
-#define CV_CPU_VSX 200
-
-// CPU features groups
-#define CV_CPU_AVX512_SKX       256
-
-// when adding to this list remember to update the following enum
-#define CV_HARDWARE_MAX_FEATURE 512
-
-/** @brief Available CPU features.
-*/
-enum CpuFeatures {
-    CPU_MMX             = 1,
-    CPU_SSE             = 2,
-    CPU_SSE2            = 3,
-    CPU_SSE3            = 4,
-    CPU_SSSE3           = 5,
-    CPU_SSE4_1          = 6,
-    CPU_SSE4_2          = 7,
-    CPU_POPCNT          = 8,
-    CPU_FP16            = 9,
-    CPU_AVX             = 10,
-    CPU_AVX2            = 11,
-    CPU_FMA3            = 12,
-
-    CPU_AVX_512F        = 13,
-    CPU_AVX_512BW       = 14,
-    CPU_AVX_512CD       = 15,
-    CPU_AVX_512DQ       = 16,
-    CPU_AVX_512ER       = 17,
-    CPU_AVX_512IFMA512  = 18, // deprecated
-    CPU_AVX_512IFMA     = 18,
-    CPU_AVX_512PF       = 19,
-    CPU_AVX_512VBMI     = 20,
-    CPU_AVX_512VL       = 21,
-
-    CPU_NEON            = 100,
-
-    CPU_VSX             = 200,
-
-    CPU_AVX512_SKX      = 256, //!< Skylake-X with AVX-512F/CD/BW/DQ/VL
-
-    CPU_MAX_FEATURE     = 512  // see CV_HARDWARE_MAX_FEATURE
-};
-
-
-#include "cv_cpu_dispatch.h"
-
-
-/* fundamental constants */
-#define CV_PI   3.1415926535897932384626433832795
-#define CV_2PI  6.283185307179586476925286766559
-#define CV_LOG2 0.69314718055994530941723212145818
-
-#if defined __ARM_FP16_FORMAT_IEEE \
-    && !defined __CUDACC__
-#  define CV_FP16_TYPE 1
-#else
-#  define CV_FP16_TYPE 0
-#endif
-
-typedef union Cv16suf
-{
-    short i;
-#if CV_FP16_TYPE
-    __fp16 h;
-#endif
-    struct _fp16Format
-    {
-        unsigned int significand : 10;
-        unsigned int exponent    : 5;
-        unsigned int sign        : 1;
-    } fmt;
-}
-Cv16suf;
-
-typedef union Cv32suf
-{
-    int i;
-    unsigned u;
-    float f;
-    struct _fp32Format
-    {
-        unsigned int significand : 23;
-        unsigned int exponent    : 8;
-        unsigned int sign        : 1;
-    } fmt;
-}
-Cv32suf;
-
-typedef union Cv64suf
-{
-    int64 i;
-    uint64 u;
-    double f;
-}
-Cv64suf;
-
-#define OPENCV_ABI_COMPATIBILITY 300
-
-#ifdef __OPENCV_BUILD
-#  define DISABLE_OPENCV_24_COMPATIBILITY
-#endif
-
-#ifdef CVAPI_EXPORTS
-# if (defined _WIN32 || defined WINCE || defined __CYGWIN__)
-#   define CV_EXPORTS __declspec(dllexport)
-# elif defined __GNUC__ && __GNUC__ >= 4
-#   define CV_EXPORTS __attribute__ ((visibility ("default")))
-# endif
-#endif
-
-#ifndef CV_EXPORTS
-# define CV_EXPORTS
-#endif
-
-#ifdef _MSC_VER
-#   define CV_EXPORTS_TEMPLATE
-#else
-#   define CV_EXPORTS_TEMPLATE CV_EXPORTS
-#endif
-
-#ifndef CV_DEPRECATED
-#  if defined(__GNUC__)
-#    define CV_DEPRECATED __attribute__ ((deprecated))
-#  elif defined(_MSC_VER)
-#    define CV_DEPRECATED __declspec(deprecated)
-#  else
-#    define CV_DEPRECATED
-#  endif
-#endif
-
-#ifndef CV_EXTERN_C
-#  ifdef __cplusplus
-#    define CV_EXTERN_C extern "C"
-#  else
-#    define CV_EXTERN_C
-#  endif
-#endif
-
-/* special informative macros for wrapper generators */
-#define CV_EXPORTS_W CV_EXPORTS
-#define CV_EXPORTS_W_SIMPLE CV_EXPORTS
-#define CV_EXPORTS_AS(synonym) CV_EXPORTS
-#define CV_EXPORTS_W_MAP CV_EXPORTS
-#define CV_IN_OUT
-#define CV_OUT
-#define CV_PROP
-#define CV_PROP_RW
-#define CV_WRAP
-#define CV_WRAP_AS(synonym)
-
-/****************************************************************************************\
-*                                  Matrix type (Mat)                                     *
-\****************************************************************************************/
-
-#define CV_MAT_CN_MASK          ((CV_CN_MAX - 1) << CV_CN_SHIFT)
-#define CV_MAT_CN(flags)        ((((flags) & CV_MAT_CN_MASK) >> CV_CN_SHIFT) + 1)
-#define CV_MAT_TYPE_MASK        (CV_DEPTH_MAX*CV_CN_MAX - 1)
-#define CV_MAT_TYPE(flags)      ((flags) & CV_MAT_TYPE_MASK)
-#define CV_MAT_CONT_FLAG_SHIFT  14
-#define CV_MAT_CONT_FLAG        (1 << CV_MAT_CONT_FLAG_SHIFT)
-#define CV_IS_MAT_CONT(flags)   ((flags) & CV_MAT_CONT_FLAG)
-#define CV_IS_CONT_MAT          CV_IS_MAT_CONT
-#define CV_SUBMAT_FLAG_SHIFT    15
-#define CV_SUBMAT_FLAG          (1 << CV_SUBMAT_FLAG_SHIFT)
-#define CV_IS_SUBMAT(flags)     ((flags) & CV_MAT_SUBMAT_FLAG)
-
-/** Size of each channel item,
-   0x8442211 = 1000 0100 0100 0010 0010 0001 0001 ~ array of sizeof(arr_type_elem) */
-#define CV_ELEM_SIZE1(type) \
-    ((((sizeof(size_t)<<28)|0x8442211) >> CV_MAT_DEPTH(type)*4) & 15)
-
-/** 0x3a50 = 11 10 10 01 01 00 00 ~ array of log2(sizeof(arr_type_elem)) */
-#define CV_ELEM_SIZE(type) \
-    (CV_MAT_CN(type) << ((((sizeof(size_t)/4+1)*16384|0x3a50) >> CV_MAT_DEPTH(type)*2) & 3))
-
-#ifndef MIN
-#  define MIN(a,b)  ((a) > (b) ? (b) : (a))
-#endif
-
-#ifndef MAX
-#  define MAX(a,b)  ((a) < (b) ? (b) : (a))
-#endif
-
-/****************************************************************************************\
-*                                    static analysys                                     *
-\****************************************************************************************/
-
-// In practice, some macro are not processed correctly (noreturn is not detected).
-// We need to use simplified definition for them.
-#ifndef CV_STATIC_ANALYSIS
-# if defined(__KLOCWORK__) || defined(__clang_analyzer__) || defined(__COVERITY__)
-#   define CV_STATIC_ANALYSIS
-# endif
-#endif
-
-/****************************************************************************************\
-*                                    Thread sanitizer                                    *
-\****************************************************************************************/
-#ifndef CV_THREAD_SANITIZER
-# if defined(__has_feature)
-#   if __has_feature(thread_sanitizer)
-#     define CV_THREAD_SANITIZER
-#   endif
-# endif
-#endif
-
-/****************************************************************************************\
-*          exchange-add operation for atomic operations on reference counters            *
-\****************************************************************************************/
-
-#ifdef CV_XADD
-  // allow to use user-defined macro
-#elif defined __GNUC__ || defined __clang__
-#  if defined __clang__ && __clang_major__ >= 3 && !defined __ANDROID__ && !defined __EMSCRIPTEN__ && !defined(__CUDACC__)
-#    ifdef __ATOMIC_ACQ_REL
-#      define CV_XADD(addr, delta) __c11_atomic_fetch_add((_Atomic(int)*)(addr), delta, __ATOMIC_ACQ_REL)
-#    else
-#      define CV_XADD(addr, delta) __atomic_fetch_add((_Atomic(int)*)(addr), delta, 4)
-#    endif
-#  else
-#    if defined __ATOMIC_ACQ_REL && !defined __clang__
-       // version for gcc >= 4.7
-#      define CV_XADD(addr, delta) (int)__atomic_fetch_add((unsigned*)(addr), (unsigned)(delta), __ATOMIC_ACQ_REL)
-#    else
-#      define CV_XADD(addr, delta) (int)__sync_fetch_and_add((unsigned*)(addr), (unsigned)(delta))
-#    endif
-#  endif
-#elif defined _MSC_VER && !defined RC_INVOKED
-#  include <intrin.h>
-#  define CV_XADD(addr, delta) (int)_InterlockedExchangeAdd((long volatile*)addr, delta)
-#else
-   CV_INLINE CV_XADD(int* addr, int delta) { int tmp = *addr; *addr += delta; return tmp; }
-#endif
-
-
-/****************************************************************************************\
-*                                  CV_NORETURN attribute                                 *
-\****************************************************************************************/
-
-#ifndef CV_NORETURN
-#  if defined(__GNUC__)
-#    define CV_NORETURN __attribute__((__noreturn__))
-#  elif defined(_MSC_VER) && (_MSC_VER >= 1300)
-#    define CV_NORETURN __declspec(noreturn)
-#  else
-#    define CV_NORETURN /* nothing by default */
-#  endif
-#endif
-
-
-/****************************************************************************************\
-*                                    C++ 11                                              *
-\****************************************************************************************/
-#ifndef CV_CXX11
-#  if __cplusplus >= 201103L || (defined(_MSC_VER) && _MSC_VER >= 1800)
-#    define CV_CXX11 1
-#  endif
-#else
-#  if CV_CXX11 == 0
-#    undef CV_CXX11
-#  endif
-#endif
-
-
-/****************************************************************************************\
-*                                    C++ Move semantics                                  *
-\****************************************************************************************/
-
-#ifndef CV_CXX_MOVE_SEMANTICS
-#  if __cplusplus >= 201103L || defined(__GXX_EXPERIMENTAL_CXX0X__) || (defined(_MSC_VER) && _MSC_VER >= 1600)
-#    define CV_CXX_MOVE_SEMANTICS 1
-#  elif defined(__clang)
-#    if __has_feature(cxx_rvalue_references)
-#      define CV_CXX_MOVE_SEMANTICS 1
-#    endif
-#  endif
-#else
-#  if CV_CXX_MOVE_SEMANTICS == 0
-#    undef CV_CXX_MOVE_SEMANTICS
-#  endif
-#endif
-
-/****************************************************************************************\
-*                                    C++11 std::array                                    *
-\****************************************************************************************/
-
-#ifndef CV_CXX_STD_ARRAY
-#  if __cplusplus >= 201103L || (defined(_MSC_VER) && _MSC_VER >= 1900/*MSVS 2015*/)
-#    define CV_CXX_STD_ARRAY 1
-#    include <array>
-#  endif
-#else
-#  if CV_CXX_STD_ARRAY == 0
-#    undef CV_CXX_STD_ARRAY
-#  endif
-#endif
-
-
-/****************************************************************************************\
-*                                 C++11 override / final                                 *
-\****************************************************************************************/
-
-#ifndef CV_OVERRIDE
-#  ifdef CV_CXX11
-#    define CV_OVERRIDE override
-#  endif
-#endif
-#ifndef CV_OVERRIDE
-#  define CV_OVERRIDE
-#endif
-
-#ifndef CV_FINAL
-#  ifdef CV_CXX11
-#    define CV_FINAL final
-#  endif
-#endif
-#ifndef CV_FINAL
-#  define CV_FINAL
-#endif
-
-
-
-// Integer types portatibility
-#ifdef OPENCV_STDINT_HEADER
-#include OPENCV_STDINT_HEADER
-#elif defined(__cplusplus)
-#if defined(_MSC_VER) && _MSC_VER < 1600 /* MSVS 2010 */
-namespace cv {
-typedef signed char int8_t;
-typedef unsigned char uint8_t;
-typedef signed short int16_t;
-typedef unsigned short uint16_t;
-typedef signed int int32_t;
-typedef unsigned int uint32_t;
-typedef signed __int64 int64_t;
-typedef unsigned __int64 uint64_t;
-}
-#elif defined(_MSC_VER) || __cplusplus >= 201103L
-#include <cstdint>
-namespace cv {
-using std::int8_t;
-using std::uint8_t;
-using std::int16_t;
-using std::uint16_t;
-using std::int32_t;
-using std::uint32_t;
-using std::int64_t;
-using std::uint64_t;
-}
-#else
-#include <stdint.h>
-namespace cv {
-typedef ::int8_t int8_t;
-typedef ::uint8_t uint8_t;
-typedef ::int16_t int16_t;
-typedef ::uint16_t uint16_t;
-typedef ::int32_t int32_t;
-typedef ::uint32_t uint32_t;
-typedef ::int64_t int64_t;
-typedef ::uint64_t uint64_t;
-}
-#endif
-#else // pure C
-#include <stdint.h>
-#endif
-
-
-//! @}
-
-#ifndef __cplusplus
-#include "opencv2/core/fast_math.hpp" // define cvRound(double)
-#endif
-
-#endif // OPENCV_CORE_CVDEF_H
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cvstd.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cvstd.hpp
deleted file mode 100755
index 0a3f553..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cvstd.hpp
+++ /dev/null
@@ -1,1040 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_CORE_CVSTD_HPP
-#define OPENCV_CORE_CVSTD_HPP
-
-#ifndef __cplusplus
-#  error cvstd.hpp header must be compiled as C++
-#endif
-
-#include "opencv2/core/cvdef.h"
-#include <cstddef>
-#include <cstring>
-#include <cctype>
-
-#include <string>
-
-// import useful primitives from stl
-#  include <algorithm>
-#  include <utility>
-#  include <cstdlib> //for abs(int)
-#  include <cmath>
-
-namespace cv
-{
-    static inline uchar abs(uchar a) { return a; }
-    static inline ushort abs(ushort a) { return a; }
-    static inline unsigned abs(unsigned a) { return a; }
-    static inline uint64 abs(uint64 a) { return a; }
-
-    using std::min;
-    using std::max;
-    using std::abs;
-    using std::swap;
-    using std::sqrt;
-    using std::exp;
-    using std::pow;
-    using std::log;
-}
-
-namespace cv {
-
-//! @addtogroup core_utils
-//! @{
-
-//////////////////////////// memory management functions ////////////////////////////
-
-/** @brief Allocates an aligned memory buffer.
-
-The function allocates the buffer of the specified size and returns it. When the buffer size is 16
-bytes or more, the returned buffer is aligned to 16 bytes.
-@param bufSize Allocated buffer size.
- */
-CV_EXPORTS void* fastMalloc(size_t bufSize);
-
-/** @brief Deallocates a memory buffer.
-
-The function deallocates the buffer allocated with fastMalloc . If NULL pointer is passed, the
-function does nothing. C version of the function clears the pointer *pptr* to avoid problems with
-double memory deallocation.
-@param ptr Pointer to the allocated buffer.
- */
-CV_EXPORTS void fastFree(void* ptr);
-
-/*!
-  The STL-compilant memory Allocator based on cv::fastMalloc() and cv::fastFree()
-*/
-template<typename _Tp> class Allocator
-{
-public:
-    typedef _Tp value_type;
-    typedef value_type* pointer;
-    typedef const value_type* const_pointer;
-    typedef value_type& reference;
-    typedef const value_type& const_reference;
-    typedef size_t size_type;
-    typedef ptrdiff_t difference_type;
-    template<typename U> class rebind { typedef Allocator<U> other; };
-
-    explicit Allocator() {}
-    ~Allocator() {}
-    explicit Allocator(Allocator const&) {}
-    template<typename U>
-    explicit Allocator(Allocator<U> const&) {}
-
-    // address
-    pointer address(reference r) { return &r; }
-    const_pointer address(const_reference r) { return &r; }
-
-    pointer allocate(size_type count, const void* =0) { return reinterpret_cast<pointer>(fastMalloc(count * sizeof (_Tp))); }
-    void deallocate(pointer p, size_type) { fastFree(p); }
-
-    void construct(pointer p, const _Tp& v) { new(static_cast<void*>(p)) _Tp(v); }
-    void destroy(pointer p) { p->~_Tp(); }
-
-    size_type max_size() const { return cv::max(static_cast<_Tp>(-1)/sizeof(_Tp), 1); }
-};
-
-//! @} core_utils
-
-//! @cond IGNORED
-
-namespace detail
-{
-
-// Metafunction to avoid taking a reference to void.
-template<typename T>
-struct RefOrVoid { typedef T& type; };
-
-template<>
-struct RefOrVoid<void>{ typedef void type; };
-
-template<>
-struct RefOrVoid<const void>{ typedef const void type; };
-
-template<>
-struct RefOrVoid<volatile void>{ typedef volatile void type; };
-
-template<>
-struct RefOrVoid<const volatile void>{ typedef const volatile void type; };
-
-// This class would be private to Ptr, if it didn't have to be a non-template.
-struct PtrOwner;
-
-}
-
-template<typename Y>
-struct DefaultDeleter
-{
-    void operator () (Y* p) const;
-};
-
-//! @endcond
-
-//! @addtogroup core_basic
-//! @{
-
-/** @brief Template class for smart pointers with shared ownership
-
-A Ptr\<T\> pretends to be a pointer to an object of type T. Unlike an ordinary pointer, however, the
-object will be automatically cleaned up once all Ptr instances pointing to it are destroyed.
-
-Ptr is similar to boost::shared_ptr that is part of the Boost library
-(<http://www.boost.org/doc/libs/release/libs/smart_ptr/shared_ptr.htm>) and std::shared_ptr from
-the [C++11](http://en.wikipedia.org/wiki/C++11) standard.
-
-This class provides the following advantages:
--   Default constructor, copy constructor, and assignment operator for an arbitrary C++ class or C
-    structure. For some objects, like files, windows, mutexes, sockets, and others, a copy
-    constructor or an assignment operator are difficult to define. For some other objects, like
-    complex classifiers in OpenCV, copy constructors are absent and not easy to implement. Finally,
-    some of complex OpenCV and your own data structures may be written in C. However, copy
-    constructors and default constructors can simplify programming a lot. Besides, they are often
-    required (for example, by STL containers). By using a Ptr to such an object instead of the
-    object itself, you automatically get all of the necessary constructors and the assignment
-    operator.
--   *O(1)* complexity of the above-mentioned operations. While some structures, like std::vector,
-    provide a copy constructor and an assignment operator, the operations may take a considerable
-    amount of time if the data structures are large. But if the structures are put into a Ptr, the
-    overhead is small and independent of the data size.
--   Automatic and customizable cleanup, even for C structures. See the example below with FILE\*.
--   Heterogeneous collections of objects. The standard STL and most other C++ and OpenCV containers
-    can store only objects of the same type and the same size. The classical solution to store
-    objects of different types in the same container is to store pointers to the base class (Base\*)
-    instead but then you lose the automatic memory management. Again, by using Ptr\<Base\> instead
-    of raw pointers, you can solve the problem.
-
-A Ptr is said to *own* a pointer - that is, for each Ptr there is a pointer that will be deleted
-once all Ptr instances that own it are destroyed. The owned pointer may be null, in which case
-nothing is deleted. Each Ptr also *stores* a pointer. The stored pointer is the pointer the Ptr
-pretends to be; that is, the one you get when you use Ptr::get or the conversion to T\*. It's
-usually the same as the owned pointer, but if you use casts or the general shared-ownership
-constructor, the two may diverge: the Ptr will still own the original pointer, but will itself point
-to something else.
-
-The owned pointer is treated as a black box. The only thing Ptr needs to know about it is how to
-delete it. This knowledge is encapsulated in the *deleter* - an auxiliary object that is associated
-with the owned pointer and shared between all Ptr instances that own it. The default deleter is an
-instance of DefaultDeleter, which uses the standard C++ delete operator; as such it will work with
-any pointer allocated with the standard new operator.
-
-However, if the pointer must be deleted in a different way, you must specify a custom deleter upon
-Ptr construction. A deleter is simply a callable object that accepts the pointer as its sole
-argument. For example, if you want to wrap FILE, you may do so as follows:
-@code
-    Ptr<FILE> f(fopen("myfile.txt", "w"), fclose);
-    if(!f) throw ...;
-    fprintf(f, ....);
-    ...
-    // the file will be closed automatically by f's destructor.
-@endcode
-Alternatively, if you want all pointers of a particular type to be deleted the same way, you can
-specialize DefaultDeleter<T>::operator() for that type, like this:
-@code
-    namespace cv {
-    template<> void DefaultDeleter<FILE>::operator ()(FILE * obj) const
-    {
-        fclose(obj);
-    }
-    }
-@endcode
-For convenience, the following types from the OpenCV C API already have such a specialization that
-calls the appropriate release function:
--   CvCapture
--   CvFileStorage
--   CvHaarClassifierCascade
--   CvMat
--   CvMatND
--   CvMemStorage
--   CvSparseMat
--   CvVideoWriter
--   IplImage
-@note The shared ownership mechanism is implemented with reference counting. As such, cyclic
-ownership (e.g. when object a contains a Ptr to object b, which contains a Ptr to object a) will
-lead to all involved objects never being cleaned up. Avoid such situations.
-@note It is safe to concurrently read (but not write) a Ptr instance from multiple threads and
-therefore it is normally safe to use it in multi-threaded applications. The same is true for Mat and
-other C++ OpenCV classes that use internal reference counts.
-*/
-template<typename T>
-struct Ptr
-{
-    /** Generic programming support. */
-    typedef T element_type;
-
-    /** The default constructor creates a null Ptr - one that owns and stores a null pointer.
-    */
-    Ptr();
-
-    /**
-    If p is null, these are equivalent to the default constructor.
-    Otherwise, these constructors assume ownership of p - that is, the created Ptr owns and stores p
-    and assumes it is the sole owner of it. Don't use them if p is already owned by another Ptr, or
-    else p will get deleted twice.
-    With the first constructor, DefaultDeleter\<Y\>() becomes the associated deleter (so p will
-    eventually be deleted with the standard delete operator). Y must be a complete type at the point
-    of invocation.
-    With the second constructor, d becomes the associated deleter.
-    Y\* must be convertible to T\*.
-    @param p Pointer to own.
-    @note It is often easier to use makePtr instead.
-     */
-    template<typename Y>
-#ifdef DISABLE_OPENCV_24_COMPATIBILITY
-    explicit
-#endif
-    Ptr(Y* p);
-
-    /** @overload
-    @param d Deleter to use for the owned pointer.
-    @param p Pointer to own.
-    */
-    template<typename Y, typename D>
-    Ptr(Y* p, D d);
-
-    /**
-    These constructors create a Ptr that shares ownership with another Ptr - that is, own the same
-    pointer as o.
-    With the first two, the same pointer is stored, as well; for the second, Y\* must be convertible
-    to T\*.
-    With the third, p is stored, and Y may be any type. This constructor allows to have completely
-    unrelated owned and stored pointers, and should be used with care to avoid confusion. A relatively
-    benign use is to create a non-owning Ptr, like this:
-    @code
-        ptr = Ptr<T>(Ptr<T>(), dont_delete_me); // owns nothing; will not delete the pointer.
-    @endcode
-    @param o Ptr to share ownership with.
-    */
-    Ptr(const Ptr& o);
-
-    /** @overload
-    @param o Ptr to share ownership with.
-    */
-    template<typename Y>
-    Ptr(const Ptr<Y>& o);
-
-    /** @overload
-    @param o Ptr to share ownership with.
-    @param p Pointer to store.
-    */
-    template<typename Y>
-    Ptr(const Ptr<Y>& o, T* p);
-
-    /** The destructor is equivalent to calling Ptr::release. */
-    ~Ptr();
-
-    /**
-    Assignment replaces the current Ptr instance with one that owns and stores same pointers as o and
-    then destroys the old instance.
-    @param o Ptr to share ownership with.
-     */
-    Ptr& operator = (const Ptr& o);
-
-    /** @overload */
-    template<typename Y>
-    Ptr& operator = (const Ptr<Y>& o);
-
-    /** If no other Ptr instance owns the owned pointer, deletes it with the associated deleter. Then sets
-    both the owned and the stored pointers to NULL.
-    */
-    void release();
-
-    /**
-    `ptr.reset(...)` is equivalent to `ptr = Ptr<T>(...)`.
-    @param p Pointer to own.
-    */
-    template<typename Y>
-    void reset(Y* p);
-
-    /** @overload
-    @param d Deleter to use for the owned pointer.
-    @param p Pointer to own.
-    */
-    template<typename Y, typename D>
-    void reset(Y* p, D d);
-
-    /**
-    Swaps the owned and stored pointers (and deleters, if any) of this and o.
-    @param o Ptr to swap with.
-    */
-    void swap(Ptr& o);
-
-    /** Returns the stored pointer. */
-    T* get() const;
-
-    /** Ordinary pointer emulation. */
-    typename detail::RefOrVoid<T>::type operator * () const;
-
-    /** Ordinary pointer emulation. */
-    T* operator -> () const;
-
-    /** Equivalent to get(). */
-    operator T* () const;
-
-    /** ptr.empty() is equivalent to `!ptr.get()`. */
-    bool empty() const;
-
-    /** Returns a Ptr that owns the same pointer as this, and stores the same
-       pointer as this, except converted via static_cast to Y*.
-    */
-    template<typename Y>
-    Ptr<Y> staticCast() const;
-
-    /** Ditto for const_cast. */
-    template<typename Y>
-    Ptr<Y> constCast() const;
-
-    /** Ditto for dynamic_cast. */
-    template<typename Y>
-    Ptr<Y> dynamicCast() const;
-
-#ifdef CV_CXX_MOVE_SEMANTICS
-    Ptr(Ptr&& o);
-    Ptr& operator = (Ptr&& o);
-#endif
-
-private:
-    detail::PtrOwner* owner;
-    T* stored;
-
-    template<typename Y>
-    friend struct Ptr; // have to do this for the cross-type copy constructor
-};
-
-/** Equivalent to ptr1.swap(ptr2). Provided to help write generic algorithms. */
-template<typename T>
-void swap(Ptr<T>& ptr1, Ptr<T>& ptr2);
-
-/** Return whether ptr1.get() and ptr2.get() are equal and not equal, respectively. */
-template<typename T>
-bool operator == (const Ptr<T>& ptr1, const Ptr<T>& ptr2);
-template<typename T>
-bool operator != (const Ptr<T>& ptr1, const Ptr<T>& ptr2);
-
-/** `makePtr<T>(...)` is equivalent to `Ptr<T>(new T(...))`. It is shorter than the latter, and it's
-marginally safer than using a constructor or Ptr::reset, since it ensures that the owned pointer
-is new and thus not owned by any other Ptr instance.
-Unfortunately, perfect forwarding is impossible to implement in C++03, and so makePtr is limited
-to constructors of T that have up to 10 arguments, none of which are non-const references.
- */
-template<typename T>
-Ptr<T> makePtr();
-/** @overload */
-template<typename T, typename A1>
-Ptr<T> makePtr(const A1& a1);
-/** @overload */
-template<typename T, typename A1, typename A2>
-Ptr<T> makePtr(const A1& a1, const A2& a2);
-/** @overload */
-template<typename T, typename A1, typename A2, typename A3>
-Ptr<T> makePtr(const A1& a1, const A2& a2, const A3& a3);
-/** @overload */
-template<typename T, typename A1, typename A2, typename A3, typename A4>
-Ptr<T> makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4);
-/** @overload */
-template<typename T, typename A1, typename A2, typename A3, typename A4, typename A5>
-Ptr<T> makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5);
-/** @overload */
-template<typename T, typename A1, typename A2, typename A3, typename A4, typename A5, typename A6>
-Ptr<T> makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6);
-/** @overload */
-template<typename T, typename A1, typename A2, typename A3, typename A4, typename A5, typename A6, typename A7>
-Ptr<T> makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6, const A7& a7);
-/** @overload */
-template<typename T, typename A1, typename A2, typename A3, typename A4, typename A5, typename A6, typename A7, typename A8>
-Ptr<T> makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6, const A7& a7, const A8& a8);
-/** @overload */
-template<typename T, typename A1, typename A2, typename A3, typename A4, typename A5, typename A6, typename A7, typename A8, typename A9>
-Ptr<T> makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6, const A7& a7, const A8& a8, const A9& a9);
-/** @overload */
-template<typename T, typename A1, typename A2, typename A3, typename A4, typename A5, typename A6, typename A7, typename A8, typename A9, typename A10>
-Ptr<T> makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6, const A7& a7, const A8& a8, const A9& a9, const A10& a10);
-
-//////////////////////////////// string class ////////////////////////////////
-
-class CV_EXPORTS FileNode; //for string constructor from FileNode
-
-class CV_EXPORTS String
-{
-public:
-    typedef char value_type;
-    typedef char& reference;
-    typedef const char& const_reference;
-    typedef char* pointer;
-    typedef const char* const_pointer;
-    typedef ptrdiff_t difference_type;
-    typedef size_t size_type;
-    typedef char* iterator;
-    typedef const char* const_iterator;
-
-    static const size_t npos = size_t(-1);
-
-    String();
-    String(const String& str);
-    String(const String& str, size_t pos, size_t len = npos);
-    String(const char* s);
-    String(const char* s, size_t n);
-    String(size_t n, char c);
-    String(const char* first, const char* last);
-    template<typename Iterator> String(Iterator first, Iterator last);
-    explicit String(const FileNode& fn);
-    ~String();
-
-    String& operator=(const String& str);
-    String& operator=(const char* s);
-    String& operator=(char c);
-
-    String& operator+=(const String& str);
-    String& operator+=(const char* s);
-    String& operator+=(char c);
-
-    size_t size() const;
-    size_t length() const;
-
-    char operator[](size_t idx) const;
-    char operator[](int idx) const;
-
-    const char* begin() const;
-    const char* end() const;
-
-    const char* c_str() const;
-
-    bool empty() const;
-    void clear();
-
-    int compare(const char* s) const;
-    int compare(const String& str) const;
-
-    void swap(String& str);
-    String substr(size_t pos = 0, size_t len = npos) const;
-
-    size_t find(const char* s, size_t pos, size_t n) const;
-    size_t find(char c, size_t pos = 0) const;
-    size_t find(const String& str, size_t pos = 0) const;
-    size_t find(const char* s, size_t pos = 0) const;
-
-    size_t rfind(const char* s, size_t pos, size_t n) const;
-    size_t rfind(char c, size_t pos = npos) const;
-    size_t rfind(const String& str, size_t pos = npos) const;
-    size_t rfind(const char* s, size_t pos = npos) const;
-
-    size_t find_first_of(const char* s, size_t pos, size_t n) const;
-    size_t find_first_of(char c, size_t pos = 0) const;
-    size_t find_first_of(const String& str, size_t pos = 0) const;
-    size_t find_first_of(const char* s, size_t pos = 0) const;
-
-    size_t find_last_of(const char* s, size_t pos, size_t n) const;
-    size_t find_last_of(char c, size_t pos = npos) const;
-    size_t find_last_of(const String& str, size_t pos = npos) const;
-    size_t find_last_of(const char* s, size_t pos = npos) const;
-
-    friend String operator+ (const String& lhs, const String& rhs);
-    friend String operator+ (const String& lhs, const char*   rhs);
-    friend String operator+ (const char*   lhs, const String& rhs);
-    friend String operator+ (const String& lhs, char          rhs);
-    friend String operator+ (char          lhs, const String& rhs);
-
-    String toLowerCase() const;
-
-    String(const std::string& str);
-    String(const std::string& str, size_t pos, size_t len = npos);
-    String& operator=(const std::string& str);
-    String& operator+=(const std::string& str);
-    operator std::string() const;
-
-    friend String operator+ (const String& lhs, const std::string& rhs);
-    friend String operator+ (const std::string& lhs, const String& rhs);
-
-private:
-    char*  cstr_;
-    size_t len_;
-
-    char* allocate(size_t len); // len without trailing 0
-    void deallocate();
-
-    String(int); // disabled and invalid. Catch invalid usages like, commandLineParser.has(0) problem
-};
-
-//! @} core_basic
-
-////////////////////////// cv::String implementation /////////////////////////
-
-//! @cond IGNORED
-
-inline
-String::String()
-    : cstr_(0), len_(0)
-{}
-
-inline
-String::String(const String& str)
-    : cstr_(str.cstr_), len_(str.len_)
-{
-    if (cstr_)
-        CV_XADD(((int*)cstr_)-1, 1);
-}
-
-inline
-String::String(const String& str, size_t pos, size_t len)
-    : cstr_(0), len_(0)
-{
-    pos = min(pos, str.len_);
-    len = min(str.len_ - pos, len);
-    if (!len) return;
-    if (len == str.len_)
-    {
-        CV_XADD(((int*)str.cstr_)-1, 1);
-        cstr_ = str.cstr_;
-        len_ = str.len_;
-        return;
-    }
-    memcpy(allocate(len), str.cstr_ + pos, len);
-}
-
-inline
-String::String(const char* s)
-    : cstr_(0), len_(0)
-{
-    if (!s) return;
-    size_t len = strlen(s);
-    if (!len) return;
-    memcpy(allocate(len), s, len);
-}
-
-inline
-String::String(const char* s, size_t n)
-    : cstr_(0), len_(0)
-{
-    if (!n) return;
-    if (!s) return;
-    memcpy(allocate(n), s, n);
-}
-
-inline
-String::String(size_t n, char c)
-    : cstr_(0), len_(0)
-{
-    if (!n) return;
-    memset(allocate(n), c, n);
-}
-
-inline
-String::String(const char* first, const char* last)
-    : cstr_(0), len_(0)
-{
-    size_t len = (size_t)(last - first);
-    if (!len) return;
-    memcpy(allocate(len), first, len);
-}
-
-template<typename Iterator> inline
-String::String(Iterator first, Iterator last)
-    : cstr_(0), len_(0)
-{
-    size_t len = (size_t)(last - first);
-    if (!len) return;
-    char* str = allocate(len);
-    while (first != last)
-    {
-        *str++ = *first;
-        ++first;
-    }
-}
-
-inline
-String::~String()
-{
-    deallocate();
-}
-
-inline
-String& String::operator=(const String& str)
-{
-    if (&str == this) return *this;
-
-    deallocate();
-    if (str.cstr_) CV_XADD(((int*)str.cstr_)-1, 1);
-    cstr_ = str.cstr_;
-    len_ = str.len_;
-    return *this;
-}
-
-inline
-String& String::operator=(const char* s)
-{
-    deallocate();
-    if (!s) return *this;
-    size_t len = strlen(s);
-    if (len) memcpy(allocate(len), s, len);
-    return *this;
-}
-
-inline
-String& String::operator=(char c)
-{
-    deallocate();
-    allocate(1)[0] = c;
-    return *this;
-}
-
-inline
-String& String::operator+=(const String& str)
-{
-    *this = *this + str;
-    return *this;
-}
-
-inline
-String& String::operator+=(const char* s)
-{
-    *this = *this + s;
-    return *this;
-}
-
-inline
-String& String::operator+=(char c)
-{
-    *this = *this + c;
-    return *this;
-}
-
-inline
-size_t String::size() const
-{
-    return len_;
-}
-
-inline
-size_t String::length() const
-{
-    return len_;
-}
-
-inline
-char String::operator[](size_t idx) const
-{
-    return cstr_[idx];
-}
-
-inline
-char String::operator[](int idx) const
-{
-    return cstr_[idx];
-}
-
-inline
-const char* String::begin() const
-{
-    return cstr_;
-}
-
-inline
-const char* String::end() const
-{
-    return len_ ? cstr_ + len_ : NULL;
-}
-
-inline
-bool String::empty() const
-{
-    return len_ == 0;
-}
-
-inline
-const char* String::c_str() const
-{
-    return cstr_ ? cstr_ : "";
-}
-
-inline
-void String::swap(String& str)
-{
-    cv::swap(cstr_, str.cstr_);
-    cv::swap(len_, str.len_);
-}
-
-inline
-void String::clear()
-{
-    deallocate();
-}
-
-inline
-int String::compare(const char* s) const
-{
-    if (cstr_ == s) return 0;
-    return strcmp(c_str(), s);
-}
-
-inline
-int String::compare(const String& str) const
-{
-    if (cstr_ == str.cstr_) return 0;
-    return strcmp(c_str(), str.c_str());
-}
-
-inline
-String String::substr(size_t pos, size_t len) const
-{
-    return String(*this, pos, len);
-}
-
-inline
-size_t String::find(const char* s, size_t pos, size_t n) const
-{
-    if (n == 0 || pos + n > len_) return npos;
-    const char* lmax = cstr_ + len_ - n;
-    for (const char* i = cstr_ + pos; i <= lmax; ++i)
-    {
-        size_t j = 0;
-        while (j < n && s[j] == i[j]) ++j;
-        if (j == n) return (size_t)(i - cstr_);
-    }
-    return npos;
-}
-
-inline
-size_t String::find(char c, size_t pos) const
-{
-    return find(&c, pos, 1);
-}
-
-inline
-size_t String::find(const String& str, size_t pos) const
-{
-    return find(str.c_str(), pos, str.len_);
-}
-
-inline
-size_t String::find(const char* s, size_t pos) const
-{
-    if (pos >= len_ || !s[0]) return npos;
-    const char* lmax = cstr_ + len_;
-    for (const char* i = cstr_ + pos; i < lmax; ++i)
-    {
-        size_t j = 0;
-        while (s[j] && s[j] == i[j])
-        {   if(i + j >= lmax) return npos;
-            ++j;
-        }
-        if (!s[j]) return (size_t)(i - cstr_);
-    }
-    return npos;
-}
-
-inline
-size_t String::rfind(const char* s, size_t pos, size_t n) const
-{
-    if (n > len_) return npos;
-    if (pos > len_ - n) pos = len_ - n;
-    for (const char* i = cstr_ + pos; i >= cstr_; --i)
-    {
-        size_t j = 0;
-        while (j < n && s[j] == i[j]) ++j;
-        if (j == n) return (size_t)(i - cstr_);
-    }
-    return npos;
-}
-
-inline
-size_t String::rfind(char c, size_t pos) const
-{
-    return rfind(&c, pos, 1);
-}
-
-inline
-size_t String::rfind(const String& str, size_t pos) const
-{
-    return rfind(str.c_str(), pos, str.len_);
-}
-
-inline
-size_t String::rfind(const char* s, size_t pos) const
-{
-    return rfind(s, pos, strlen(s));
-}
-
-inline
-size_t String::find_first_of(const char* s, size_t pos, size_t n) const
-{
-    if (n == 0 || pos + n > len_) return npos;
-    const char* lmax = cstr_ + len_;
-    for (const char* i = cstr_ + pos; i < lmax; ++i)
-    {
-        for (size_t j = 0; j < n; ++j)
-            if (s[j] == *i)
-                return (size_t)(i - cstr_);
-    }
-    return npos;
-}
-
-inline
-size_t String::find_first_of(char c, size_t pos) const
-{
-    return find_first_of(&c, pos, 1);
-}
-
-inline
-size_t String::find_first_of(const String& str, size_t pos) const
-{
-    return find_first_of(str.c_str(), pos, str.len_);
-}
-
-inline
-size_t String::find_first_of(const char* s, size_t pos) const
-{
-    if (len_ == 0) return npos;
-    if (pos >= len_ || !s[0]) return npos;
-    const char* lmax = cstr_ + len_;
-    for (const char* i = cstr_ + pos; i < lmax; ++i)
-    {
-        for (size_t j = 0; s[j]; ++j)
-            if (s[j] == *i)
-                return (size_t)(i - cstr_);
-    }
-    return npos;
-}
-
-inline
-size_t String::find_last_of(const char* s, size_t pos, size_t n) const
-{
-    if (len_ == 0) return npos;
-    if (pos >= len_) pos = len_ - 1;
-    for (const char* i = cstr_ + pos; i >= cstr_; --i)
-    {
-        for (size_t j = 0; j < n; ++j)
-            if (s[j] == *i)
-                return (size_t)(i - cstr_);
-    }
-    return npos;
-}
-
-inline
-size_t String::find_last_of(char c, size_t pos) const
-{
-    return find_last_of(&c, pos, 1);
-}
-
-inline
-size_t String::find_last_of(const String& str, size_t pos) const
-{
-    return find_last_of(str.c_str(), pos, str.len_);
-}
-
-inline
-size_t String::find_last_of(const char* s, size_t pos) const
-{
-    if (len_ == 0) return npos;
-    if (pos >= len_) pos = len_ - 1;
-    for (const char* i = cstr_ + pos; i >= cstr_; --i)
-    {
-        for (size_t j = 0; s[j]; ++j)
-            if (s[j] == *i)
-                return (size_t)(i - cstr_);
-    }
-    return npos;
-}
-
-inline
-String String::toLowerCase() const
-{
-    if (!cstr_)
-        return String();
-    String res(cstr_, len_);
-    for (size_t i = 0; i < len_; ++i)
-        res.cstr_[i] = (char) ::tolower(cstr_[i]);
-
-    return res;
-}
-
-//! @endcond
-
-// ************************* cv::String non-member functions *************************
-
-//! @relates cv::String
-//! @{
-
-inline
-String operator + (const String& lhs, const String& rhs)
-{
-    String s;
-    s.allocate(lhs.len_ + rhs.len_);
-    if (lhs.len_) memcpy(s.cstr_, lhs.cstr_, lhs.len_);
-    if (rhs.len_) memcpy(s.cstr_ + lhs.len_, rhs.cstr_, rhs.len_);
-    return s;
-}
-
-inline
-String operator + (const String& lhs, const char* rhs)
-{
-    String s;
-    size_t rhslen = strlen(rhs);
-    s.allocate(lhs.len_ + rhslen);
-    if (lhs.len_) memcpy(s.cstr_, lhs.cstr_, lhs.len_);
-    if (rhslen) memcpy(s.cstr_ + lhs.len_, rhs, rhslen);
-    return s;
-}
-
-inline
-String operator + (const char* lhs, const String& rhs)
-{
-    String s;
-    size_t lhslen = strlen(lhs);
-    s.allocate(lhslen + rhs.len_);
-    if (lhslen) memcpy(s.cstr_, lhs, lhslen);
-    if (rhs.len_) memcpy(s.cstr_ + lhslen, rhs.cstr_, rhs.len_);
-    return s;
-}
-
-inline
-String operator + (const String& lhs, char rhs)
-{
-    String s;
-    s.allocate(lhs.len_ + 1);
-    if (lhs.len_) memcpy(s.cstr_, lhs.cstr_, lhs.len_);
-    s.cstr_[lhs.len_] = rhs;
-    return s;
-}
-
-inline
-String operator + (char lhs, const String& rhs)
-{
-    String s;
-    s.allocate(rhs.len_ + 1);
-    s.cstr_[0] = lhs;
-    if (rhs.len_) memcpy(s.cstr_ + 1, rhs.cstr_, rhs.len_);
-    return s;
-}
-
-static inline bool operator== (const String& lhs, const String& rhs) { return 0 == lhs.compare(rhs); }
-static inline bool operator== (const char*   lhs, const String& rhs) { return 0 == rhs.compare(lhs); }
-static inline bool operator== (const String& lhs, const char*   rhs) { return 0 == lhs.compare(rhs); }
-static inline bool operator!= (const String& lhs, const String& rhs) { return 0 != lhs.compare(rhs); }
-static inline bool operator!= (const char*   lhs, const String& rhs) { return 0 != rhs.compare(lhs); }
-static inline bool operator!= (const String& lhs, const char*   rhs) { return 0 != lhs.compare(rhs); }
-static inline bool operator<  (const String& lhs, const String& rhs) { return lhs.compare(rhs) <  0; }
-static inline bool operator<  (const char*   lhs, const String& rhs) { return rhs.compare(lhs) >  0; }
-static inline bool operator<  (const String& lhs, const char*   rhs) { return lhs.compare(rhs) <  0; }
-static inline bool operator<= (const String& lhs, const String& rhs) { return lhs.compare(rhs) <= 0; }
-static inline bool operator<= (const char*   lhs, const String& rhs) { return rhs.compare(lhs) >= 0; }
-static inline bool operator<= (const String& lhs, const char*   rhs) { return lhs.compare(rhs) <= 0; }
-static inline bool operator>  (const String& lhs, const String& rhs) { return lhs.compare(rhs) >  0; }
-static inline bool operator>  (const char*   lhs, const String& rhs) { return rhs.compare(lhs) <  0; }
-static inline bool operator>  (const String& lhs, const char*   rhs) { return lhs.compare(rhs) >  0; }
-static inline bool operator>= (const String& lhs, const String& rhs) { return lhs.compare(rhs) >= 0; }
-static inline bool operator>= (const char*   lhs, const String& rhs) { return rhs.compare(lhs) <= 0; }
-static inline bool operator>= (const String& lhs, const char*   rhs) { return lhs.compare(rhs) >= 0; }
-
-//! @} relates cv::String
-
-} // cv
-
-namespace std
-{
-    static inline void swap(cv::String& a, cv::String& b) { a.swap(b); }
-}
-
-#include "opencv2/core/ptr.inl.hpp"
-
-#endif //OPENCV_CORE_CVSTD_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cvstd.inl.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cvstd.inl.hpp
deleted file mode 100755
index 631fe94..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/cvstd.inl.hpp
+++ /dev/null
@@ -1,280 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_CORE_CVSTDINL_HPP
-#define OPENCV_CORE_CVSTDINL_HPP
-
-#include <complex>
-#include <ostream>
-
-//! @cond IGNORED
-
-#ifdef _MSC_VER
-#pragma warning( push )
-#pragma warning( disable: 4127 )
-#endif
-
-namespace cv
-{
-
-template<typename _Tp> class DataType< std::complex<_Tp> >
-{
-public:
-    typedef std::complex<_Tp>  value_type;
-    typedef value_type         work_type;
-    typedef _Tp                channel_type;
-
-    enum { generic_type = 0,
-           depth        = DataType<channel_type>::depth,
-           channels     = 2,
-           fmt          = DataType<channel_type>::fmt + ((channels - 1) << 8),
-           type         = CV_MAKETYPE(depth, channels) };
-
-    typedef Vec<channel_type, channels> vec_type;
-};
-
-inline
-String::String(const std::string& str)
-    : cstr_(0), len_(0)
-{
-    size_t len = str.size();
-    if (len) memcpy(allocate(len), str.c_str(), len);
-}
-
-inline
-String::String(const std::string& str, size_t pos, size_t len)
-    : cstr_(0), len_(0)
-{
-    size_t strlen = str.size();
-    pos = min(pos, strlen);
-    len = min(strlen - pos, len);
-    if (!len) return;
-    memcpy(allocate(len), str.c_str() + pos, len);
-}
-
-inline
-String& String::operator = (const std::string& str)
-{
-    deallocate();
-    size_t len = str.size();
-    if (len) memcpy(allocate(len), str.c_str(), len);
-    return *this;
-}
-
-inline
-String& String::operator += (const std::string& str)
-{
-    *this = *this + str;
-    return *this;
-}
-
-inline
-String::operator std::string() const
-{
-    return std::string(cstr_, len_);
-}
-
-inline
-String operator + (const String& lhs, const std::string& rhs)
-{
-    String s;
-    size_t rhslen = rhs.size();
-    s.allocate(lhs.len_ + rhslen);
-    if (lhs.len_) memcpy(s.cstr_, lhs.cstr_, lhs.len_);
-    if (rhslen) memcpy(s.cstr_ + lhs.len_, rhs.c_str(), rhslen);
-    return s;
-}
-
-inline
-String operator + (const std::string& lhs, const String& rhs)
-{
-    String s;
-    size_t lhslen = lhs.size();
-    s.allocate(lhslen + rhs.len_);
-    if (lhslen) memcpy(s.cstr_, lhs.c_str(), lhslen);
-    if (rhs.len_) memcpy(s.cstr_ + lhslen, rhs.cstr_, rhs.len_);
-    return s;
-}
-
-inline
-FileNode::operator std::string() const
-{
-    String value;
-    read(*this, value, value);
-    return value;
-}
-
-template<> inline
-void operator >> (const FileNode& n, std::string& value)
-{
-    read(n, value, std::string());
-}
-
-template<> inline
-FileStorage& operator << (FileStorage& fs, const std::string& value)
-{
-    return fs << cv::String(value);
-}
-
-static inline
-std::ostream& operator << (std::ostream& os, const String& str)
-{
-    return os << str.c_str();
-}
-
-static inline
-std::ostream& operator << (std::ostream& out, Ptr<Formatted> fmtd)
-{
-    fmtd->reset();
-    for(const char* str = fmtd->next(); str; str = fmtd->next())
-        out << str;
-    return out;
-}
-
-static inline
-std::ostream& operator << (std::ostream& out, const Mat& mtx)
-{
-    return out << Formatter::get()->format(mtx);
-}
-
-static inline
-std::ostream& operator << (std::ostream& out, const UMat& m)
-{
-    return out << m.getMat(ACCESS_READ);
-}
-
-template<typename _Tp> static inline
-std::ostream& operator << (std::ostream& out, const Complex<_Tp>& c)
-{
-    return out << "(" << c.re << "," << c.im << ")";
-}
-
-template<typename _Tp> static inline
-std::ostream& operator << (std::ostream& out, const std::vector<Point_<_Tp> >& vec)
-{
-    return out << Formatter::get()->format(Mat(vec));
-}
-
-
-template<typename _Tp> static inline
-std::ostream& operator << (std::ostream& out, const std::vector<Point3_<_Tp> >& vec)
-{
-    return out << Formatter::get()->format(Mat(vec));
-}
-
-
-template<typename _Tp, int m, int n> static inline
-std::ostream& operator << (std::ostream& out, const Matx<_Tp, m, n>& matx)
-{
-    return out << Formatter::get()->format(Mat(matx));
-}
-
-template<typename _Tp> static inline
-std::ostream& operator << (std::ostream& out, const Point_<_Tp>& p)
-{
-    out << "[" << p.x << ", " << p.y << "]";
-    return out;
-}
-
-template<typename _Tp> static inline
-std::ostream& operator << (std::ostream& out, const Point3_<_Tp>& p)
-{
-    out << "[" << p.x << ", " << p.y << ", " << p.z << "]";
-    return out;
-}
-
-template<typename _Tp, int n> static inline
-std::ostream& operator << (std::ostream& out, const Vec<_Tp, n>& vec)
-{
-    out << "[";
-    if (cv::traits::Depth<_Tp>::value <= CV_32S)
-    {
-        for (int i = 0; i < n - 1; ++i) {
-            out << (int)vec[i] << ", ";
-        }
-        out << (int)vec[n-1] << "]";
-    }
-    else
-    {
-        for (int i = 0; i < n - 1; ++i) {
-            out << vec[i] << ", ";
-        }
-        out << vec[n-1] << "]";
-    }
-
-    return out;
-}
-
-template<typename _Tp> static inline
-std::ostream& operator << (std::ostream& out, const Size_<_Tp>& size)
-{
-    return out << "[" << size.width << " x " << size.height << "]";
-}
-
-template<typename _Tp> static inline
-std::ostream& operator << (std::ostream& out, const Rect_<_Tp>& rect)
-{
-    return out << "[" << rect.width << " x " << rect.height << " from (" << rect.x << ", " << rect.y << ")]";
-}
-
-static inline std::ostream& operator << (std::ostream& out, const MatSize& msize)
-{
-    int i, dims = msize.dims();
-    for( i = 0; i < dims; i++ )
-    {
-        out << msize[i];
-        if( i < dims-1 )
-            out << " x ";
-    }
-    return out;
-}
-
-} // cv
-
-#ifdef _MSC_VER
-#pragma warning( pop )
-#endif
-
-//! @endcond
-
-#endif // OPENCV_CORE_CVSTDINL_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/directx.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/directx.hpp
deleted file mode 100755
index 056a85a..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/directx.hpp
+++ /dev/null
@@ -1,184 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2010-2013, Advanced Micro Devices, Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors as is and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the copyright holders or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_CORE_DIRECTX_HPP
-#define OPENCV_CORE_DIRECTX_HPP
-
-#include "mat.hpp"
-#include "ocl.hpp"
-
-#if !defined(__d3d11_h__)
-struct ID3D11Device;
-struct ID3D11Texture2D;
-#endif
-
-#if !defined(__d3d10_h__)
-struct ID3D10Device;
-struct ID3D10Texture2D;
-#endif
-
-#if !defined(_D3D9_H_)
-struct IDirect3DDevice9;
-struct IDirect3DDevice9Ex;
-struct IDirect3DSurface9;
-#endif
-
-
-namespace cv { namespace directx {
-
-namespace ocl {
-using namespace cv::ocl;
-
-//! @addtogroup core_directx
-// This section describes OpenCL and DirectX interoperability.
-//
-// To enable DirectX support, configure OpenCV using CMake with WITH_DIRECTX=ON . Note, DirectX is
-// supported only on Windows.
-//
-// To use OpenCL functionality you should first initialize OpenCL context from DirectX resource.
-//
-//! @{
-
-// TODO static functions in the Context class
-//! @brief Creates OpenCL context from D3D11 device
-//
-//! @param pD3D11Device - pointer to D3D11 device
-//! @return Returns reference to OpenCL Context
-CV_EXPORTS Context& initializeContextFromD3D11Device(ID3D11Device* pD3D11Device);
-
-//! @brief Creates OpenCL context from D3D10 device
-//
-//! @param pD3D10Device - pointer to D3D10 device
-//! @return Returns reference to OpenCL Context
-CV_EXPORTS Context& initializeContextFromD3D10Device(ID3D10Device* pD3D10Device);
-
-//! @brief Creates OpenCL context from Direct3DDevice9Ex device
-//
-//! @param pDirect3DDevice9Ex - pointer to Direct3DDevice9Ex device
-//! @return Returns reference to OpenCL Context
-CV_EXPORTS Context& initializeContextFromDirect3DDevice9Ex(IDirect3DDevice9Ex* pDirect3DDevice9Ex);
-
-//! @brief Creates OpenCL context from Direct3DDevice9 device
-//
-//! @param pDirect3DDevice9 - pointer to Direct3Device9 device
-//! @return Returns reference to OpenCL Context
-CV_EXPORTS Context& initializeContextFromDirect3DDevice9(IDirect3DDevice9* pDirect3DDevice9);
-
-//! @}
-
-} // namespace cv::directx::ocl
-
-//! @addtogroup core_directx
-//! @{
-
-//! @brief Converts InputArray to ID3D11Texture2D. If destination texture format is DXGI_FORMAT_NV12 then
-//!        input UMat expected to be in BGR format and data will be downsampled and color-converted to NV12.
-//
-//! @note Note: Destination texture must be allocated by application. Function does memory copy from src to
-//!             pD3D11Texture2D
-//
-//! @param src - source InputArray
-//! @param pD3D11Texture2D - destination D3D11 texture
-CV_EXPORTS void convertToD3D11Texture2D(InputArray src, ID3D11Texture2D* pD3D11Texture2D);
-
-//! @brief Converts ID3D11Texture2D to OutputArray. If input texture format is DXGI_FORMAT_NV12 then
-//!        data will be upsampled and color-converted to BGR format.
-//
-//! @note Note: Destination matrix will be re-allocated if it has not enough memory to match texture size.
-//!             function does memory copy from pD3D11Texture2D to dst
-//
-//! @param pD3D11Texture2D - source D3D11 texture
-//! @param dst             - destination OutputArray
-CV_EXPORTS void convertFromD3D11Texture2D(ID3D11Texture2D* pD3D11Texture2D, OutputArray dst);
-
-//! @brief Converts InputArray to ID3D10Texture2D
-//
-//! @note Note: function does memory copy from src to
-//!             pD3D10Texture2D
-//
-//! @param src             - source InputArray
-//! @param pD3D10Texture2D - destination D3D10 texture
-CV_EXPORTS void convertToD3D10Texture2D(InputArray src, ID3D10Texture2D* pD3D10Texture2D);
-
-//! @brief Converts ID3D10Texture2D to OutputArray
-//
-//! @note Note: function does memory copy from pD3D10Texture2D
-//!             to dst
-//
-//! @param pD3D10Texture2D - source D3D10 texture
-//! @param dst             - destination OutputArray
-CV_EXPORTS void convertFromD3D10Texture2D(ID3D10Texture2D* pD3D10Texture2D, OutputArray dst);
-
-//! @brief Converts InputArray to IDirect3DSurface9
-//
-//! @note Note: function does memory copy from src to
-//!             pDirect3DSurface9
-//
-//! @param src                 - source InputArray
-//! @param pDirect3DSurface9   - destination D3D10 texture
-//! @param surfaceSharedHandle - shared handle
-CV_EXPORTS void convertToDirect3DSurface9(InputArray src, IDirect3DSurface9* pDirect3DSurface9, void* surfaceSharedHandle = NULL);
-
-//! @brief Converts IDirect3DSurface9 to OutputArray
-//
-//! @note Note: function does memory copy from pDirect3DSurface9
-//!             to dst
-//
-//! @param pDirect3DSurface9   - source D3D10 texture
-//! @param dst                 - destination OutputArray
-//! @param surfaceSharedHandle - shared handle
-CV_EXPORTS void convertFromDirect3DSurface9(IDirect3DSurface9* pDirect3DSurface9, OutputArray dst, void* surfaceSharedHandle = NULL);
-
-//! @brief Get OpenCV type from DirectX type
-//! @param iDXGI_FORMAT - enum DXGI_FORMAT for D3D10/D3D11
-//! @return OpenCV type or -1 if there is no equivalent
-CV_EXPORTS int getTypeFromDXGI_FORMAT(const int iDXGI_FORMAT); // enum DXGI_FORMAT for D3D10/D3D11
-
-//! @brief Get OpenCV type from DirectX type
-//! @param iD3DFORMAT - enum D3DTYPE for D3D9
-//! @return OpenCV type or -1 if there is no equivalent
-CV_EXPORTS int getTypeFromD3DFORMAT(const int iD3DFORMAT); // enum D3DTYPE for D3D9
-
-//! @}
-
-} } // namespace cv::directx
-
-#endif // OPENCV_CORE_DIRECTX_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/eigen.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/eigen.hpp
deleted file mode 100755
index c8603ac..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/eigen.hpp
+++ /dev/null
@@ -1,280 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-
-#ifndef OPENCV_CORE_EIGEN_HPP
-#define OPENCV_CORE_EIGEN_HPP
-
-#include "opencv2/core.hpp"
-
-#if defined _MSC_VER && _MSC_VER >= 1200
-#pragma warning( disable: 4714 ) //__forceinline is not inlined
-#pragma warning( disable: 4127 ) //conditional expression is constant
-#pragma warning( disable: 4244 ) //conversion from '__int64' to 'int', possible loss of data
-#endif
-
-namespace cv
-{
-
-//! @addtogroup core_eigen
-//! @{
-
-template<typename _Tp, int _rows, int _cols, int _options, int _maxRows, int _maxCols> static inline
-void eigen2cv( const Eigen::Matrix<_Tp, _rows, _cols, _options, _maxRows, _maxCols>& src, Mat& dst )
-{
-    if( !(src.Flags & Eigen::RowMajorBit) )
-    {
-        Mat _src(src.cols(), src.rows(), traits::Type<_Tp>::value,
-              (void*)src.data(), src.outerStride()*sizeof(_Tp));
-        transpose(_src, dst);
-    }
-    else
-    {
-        Mat _src(src.rows(), src.cols(), traits::Type<_Tp>::value,
-                 (void*)src.data(), src.outerStride()*sizeof(_Tp));
-        _src.copyTo(dst);
-    }
-}
-
-// Matx case
-template<typename _Tp, int _rows, int _cols, int _options, int _maxRows, int _maxCols> static inline
-void eigen2cv( const Eigen::Matrix<_Tp, _rows, _cols, _options, _maxRows, _maxCols>& src,
-               Matx<_Tp, _rows, _cols>& dst )
-{
-    if( !(src.Flags & Eigen::RowMajorBit) )
-    {
-        dst = Matx<_Tp, _cols, _rows>(static_cast<const _Tp*>(src.data())).t();
-    }
-    else
-    {
-        dst = Matx<_Tp, _rows, _cols>(static_cast<const _Tp*>(src.data()));
-    }
-}
-
-template<typename _Tp, int _rows, int _cols, int _options, int _maxRows, int _maxCols> static inline
-void cv2eigen( const Mat& src,
-               Eigen::Matrix<_Tp, _rows, _cols, _options, _maxRows, _maxCols>& dst )
-{
-    CV_DbgAssert(src.rows == _rows && src.cols == _cols);
-    if( !(dst.Flags & Eigen::RowMajorBit) )
-    {
-        const Mat _dst(src.cols, src.rows, traits::Type<_Tp>::value,
-                 dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
-        if( src.type() == _dst.type() )
-            transpose(src, _dst);
-        else if( src.cols == src.rows )
-        {
-            src.convertTo(_dst, _dst.type());
-            transpose(_dst, _dst);
-        }
-        else
-            Mat(src.t()).convertTo(_dst, _dst.type());
-    }
-    else
-    {
-        const Mat _dst(src.rows, src.cols, traits::Type<_Tp>::value,
-                 dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
-        src.convertTo(_dst, _dst.type());
-    }
-}
-
-// Matx case
-template<typename _Tp, int _rows, int _cols, int _options, int _maxRows, int _maxCols> static inline
-void cv2eigen( const Matx<_Tp, _rows, _cols>& src,
-               Eigen::Matrix<_Tp, _rows, _cols, _options, _maxRows, _maxCols>& dst )
-{
-    if( !(dst.Flags & Eigen::RowMajorBit) )
-    {
-        const Mat _dst(_cols, _rows, traits::Type<_Tp>::value,
-                 dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
-        transpose(src, _dst);
-    }
-    else
-    {
-        const Mat _dst(_rows, _cols, traits::Type<_Tp>::value,
-                 dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
-        Mat(src).copyTo(_dst);
-    }
-}
-
-template<typename _Tp>  static inline
-void cv2eigen( const Mat& src,
-               Eigen::Matrix<_Tp, Eigen::Dynamic, Eigen::Dynamic>& dst )
-{
-    dst.resize(src.rows, src.cols);
-    if( !(dst.Flags & Eigen::RowMajorBit) )
-    {
-        const Mat _dst(src.cols, src.rows, traits::Type<_Tp>::value,
-             dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
-        if( src.type() == _dst.type() )
-            transpose(src, _dst);
-        else if( src.cols == src.rows )
-        {
-            src.convertTo(_dst, _dst.type());
-            transpose(_dst, _dst);
-        }
-        else
-            Mat(src.t()).convertTo(_dst, _dst.type());
-    }
-    else
-    {
-        const Mat _dst(src.rows, src.cols, traits::Type<_Tp>::value,
-                 dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
-        src.convertTo(_dst, _dst.type());
-    }
-}
-
-// Matx case
-template<typename _Tp, int _rows, int _cols> static inline
-void cv2eigen( const Matx<_Tp, _rows, _cols>& src,
-               Eigen::Matrix<_Tp, Eigen::Dynamic, Eigen::Dynamic>& dst )
-{
-    dst.resize(_rows, _cols);
-    if( !(dst.Flags & Eigen::RowMajorBit) )
-    {
-        const Mat _dst(_cols, _rows, traits::Type<_Tp>::value,
-             dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
-        transpose(src, _dst);
-    }
-    else
-    {
-        const Mat _dst(_rows, _cols, traits::Type<_Tp>::value,
-                 dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
-        Mat(src).copyTo(_dst);
-    }
-}
-
-template<typename _Tp> static inline
-void cv2eigen( const Mat& src,
-               Eigen::Matrix<_Tp, Eigen::Dynamic, 1>& dst )
-{
-    CV_Assert(src.cols == 1);
-    dst.resize(src.rows);
-
-    if( !(dst.Flags & Eigen::RowMajorBit) )
-    {
-        const Mat _dst(src.cols, src.rows, traits::Type<_Tp>::value,
-                 dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
-        if( src.type() == _dst.type() )
-            transpose(src, _dst);
-        else
-            Mat(src.t()).convertTo(_dst, _dst.type());
-    }
-    else
-    {
-        const Mat _dst(src.rows, src.cols, traits::Type<_Tp>::value,
-                 dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
-        src.convertTo(_dst, _dst.type());
-    }
-}
-
-// Matx case
-template<typename _Tp, int _rows> static inline
-void cv2eigen( const Matx<_Tp, _rows, 1>& src,
-               Eigen::Matrix<_Tp, Eigen::Dynamic, 1>& dst )
-{
-    dst.resize(_rows);
-
-    if( !(dst.Flags & Eigen::RowMajorBit) )
-    {
-        const Mat _dst(1, _rows, traits::Type<_Tp>::value,
-                 dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
-        transpose(src, _dst);
-    }
-    else
-    {
-        const Mat _dst(_rows, 1, traits::Type<_Tp>::value,
-                 dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
-        src.copyTo(_dst);
-    }
-}
-
-
-template<typename _Tp> static inline
-void cv2eigen( const Mat& src,
-               Eigen::Matrix<_Tp, 1, Eigen::Dynamic>& dst )
-{
-    CV_Assert(src.rows == 1);
-    dst.resize(src.cols);
-    if( !(dst.Flags & Eigen::RowMajorBit) )
-    {
-        const Mat _dst(src.cols, src.rows, traits::Type<_Tp>::value,
-                 dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
-        if( src.type() == _dst.type() )
-            transpose(src, _dst);
-        else
-            Mat(src.t()).convertTo(_dst, _dst.type());
-    }
-    else
-    {
-        const Mat _dst(src.rows, src.cols, traits::Type<_Tp>::value,
-                 dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
-        src.convertTo(_dst, _dst.type());
-    }
-}
-
-//Matx
-template<typename _Tp, int _cols> static inline
-void cv2eigen( const Matx<_Tp, 1, _cols>& src,
-               Eigen::Matrix<_Tp, 1, Eigen::Dynamic>& dst )
-{
-    dst.resize(_cols);
-    if( !(dst.Flags & Eigen::RowMajorBit) )
-    {
-        const Mat _dst(_cols, 1, traits::Type<_Tp>::value,
-                 dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
-        transpose(src, _dst);
-    }
-    else
-    {
-        const Mat _dst(1, _cols, traits::Type<_Tp>::value,
-                 dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
-        Mat(src).copyTo(_dst);
-    }
-}
-
-//! @}
-
-} // cv
-
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/fast_math.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/fast_math.hpp
deleted file mode 100755
index 7858d40..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/fast_math.hpp
+++ /dev/null
@@ -1,271 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
-// Copyright (C) 2015, Itseez Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_CORE_FAST_MATH_HPP
-#define OPENCV_CORE_FAST_MATH_HPP
-
-#include "opencv2/core/cvdef.h"
-
-#if ((defined _MSC_VER && defined _M_X64) || (defined __GNUC__ && defined __x86_64__ \
-    && defined __SSE2__ && !defined __APPLE__)) && !defined(__CUDACC__)
-#include <emmintrin.h>
-#endif
-
-
-//! @addtogroup core_utils
-//! @{
-
-/****************************************************************************************\
-*                                      fast math                                         *
-\****************************************************************************************/
-
-#ifdef __cplusplus
-#  include <cmath>
-#else
-#  ifdef __BORLANDC__
-#    include <fastmath.h>
-#  else
-#    include <math.h>
-#  endif
-#endif
-
-#ifdef HAVE_TEGRA_OPTIMIZATION
-#  include "tegra_round.hpp"
-#endif
-
-#if defined __GNUC__ && defined __arm__ && (defined __ARM_PCS_VFP || defined __ARM_VFPV3__ || defined __ARM_NEON__) && !defined __SOFTFP__ && !defined(__CUDACC__)
-    // 1. general scheme
-    #define ARM_ROUND(_value, _asm_string) \
-        int res; \
-        float temp; \
-        (void)temp; \
-        __asm__(_asm_string : [res] "=r" (res), [temp] "=w" (temp) : [value] "w" (_value)); \
-        return res
-    // 2. version for double
-    #ifdef __clang__
-        #define ARM_ROUND_DBL(value) ARM_ROUND(value, "vcvtr.s32.f64 %[temp], %[value] \n vmov %[res], %[temp]")
-    #else
-        #define ARM_ROUND_DBL(value) ARM_ROUND(value, "vcvtr.s32.f64 %[temp], %P[value] \n vmov %[res], %[temp]")
-    #endif
-    // 3. version for float
-    #define ARM_ROUND_FLT(value) ARM_ROUND(value, "vcvtr.s32.f32 %[temp], %[value]\n vmov %[res], %[temp]")
-#endif
-
-/** @brief Rounds floating-point number to the nearest integer
-
- @param value floating-point number. If the value is outside of INT_MIN ... INT_MAX range, the
- result is not defined.
- */
-CV_INLINE int
-cvRound( double value )
-{
-#if ((defined _MSC_VER && defined _M_X64) || (defined __GNUC__ && defined __x86_64__ \
-    && defined __SSE2__ && !defined __APPLE__) || CV_SSE2) && !defined(__CUDACC__)
-    __m128d t = _mm_set_sd( value );
-    return _mm_cvtsd_si32(t);
-#elif defined _MSC_VER && defined _M_IX86
-    int t;
-    __asm
-    {
-        fld value;
-        fistp t;
-    }
-    return t;
-#elif ((defined _MSC_VER && defined _M_ARM) || defined CV_ICC || \
-        defined __GNUC__) && defined HAVE_TEGRA_OPTIMIZATION
-    TEGRA_ROUND_DBL(value);
-#elif defined CV_ICC || defined __GNUC__
-# if defined ARM_ROUND_DBL
-    ARM_ROUND_DBL(value);
-# else
-    return (int)lrint(value);
-# endif
-#else
-    /* it's ok if round does not comply with IEEE754 standard;
-       the tests should allow +/-1 difference when the tested functions use round */
-    return (int)(value + (value >= 0 ? 0.5 : -0.5));
-#endif
-}
-
-
-/** @brief Rounds floating-point number to the nearest integer not larger than the original.
-
- The function computes an integer i such that:
- \f[i \le \texttt{value} < i+1\f]
- @param value floating-point number. If the value is outside of INT_MIN ... INT_MAX range, the
- result is not defined.
- */
-CV_INLINE int cvFloor( double value )
-{
-    int i = (int)value;
-    return i - (i > value);
-}
-
-/** @brief Rounds floating-point number to the nearest integer not smaller than the original.
-
- The function computes an integer i such that:
- \f[i \le \texttt{value} < i+1\f]
- @param value floating-point number. If the value is outside of INT_MIN ... INT_MAX range, the
- result is not defined.
- */
-CV_INLINE int cvCeil( double value )
-{
-    int i = (int)value;
-    return i + (i < value);
-}
-
-/** @brief Determines if the argument is Not A Number.
-
- @param value The input floating-point value
-
- The function returns 1 if the argument is Not A Number (as defined by IEEE754 standard), 0
- otherwise. */
-CV_INLINE int cvIsNaN( double value )
-{
-    Cv64suf ieee754;
-    ieee754.f = value;
-    return ((unsigned)(ieee754.u >> 32) & 0x7fffffff) +
-           ((unsigned)ieee754.u != 0) > 0x7ff00000;
-}
-
-/** @brief Determines if the argument is Infinity.
-
- @param value The input floating-point value
-
- The function returns 1 if the argument is a plus or minus infinity (as defined by IEEE754 standard)
- and 0 otherwise. */
-CV_INLINE int cvIsInf( double value )
-{
-    Cv64suf ieee754;
-    ieee754.f = value;
-    return ((unsigned)(ieee754.u >> 32) & 0x7fffffff) == 0x7ff00000 &&
-            (unsigned)ieee754.u == 0;
-}
-
-#ifdef __cplusplus
-
-/** @overload */
-CV_INLINE int cvRound(float value)
-{
-#if ((defined _MSC_VER && defined _M_X64) || (defined __GNUC__ && defined __x86_64__ \
-    && defined __SSE2__ && !defined __APPLE__) || CV_SSE2) && !defined(__CUDACC__)
-    __m128 t = _mm_set_ss( value );
-    return _mm_cvtss_si32(t);
-#elif defined _MSC_VER && defined _M_IX86
-    int t;
-    __asm
-    {
-        fld value;
-        fistp t;
-    }
-    return t;
-#elif ((defined _MSC_VER && defined _M_ARM) || defined CV_ICC || \
-        defined __GNUC__) && defined HAVE_TEGRA_OPTIMIZATION
-    TEGRA_ROUND_FLT(value);
-#elif defined CV_ICC || defined __GNUC__
-# if defined ARM_ROUND_FLT
-    ARM_ROUND_FLT(value);
-# else
-    return (int)lrintf(value);
-# endif
-#else
-    /* it's ok if round does not comply with IEEE754 standard;
-     the tests should allow +/-1 difference when the tested functions use round */
-    return (int)(value + (value >= 0 ? 0.5f : -0.5f));
-#endif
-}
-
-/** @overload */
-CV_INLINE int cvRound( int value )
-{
-    return value;
-}
-
-/** @overload */
-CV_INLINE int cvFloor( float value )
-{
-    int i = (int)value;
-    return i - (i > value);
-}
-
-/** @overload */
-CV_INLINE int cvFloor( int value )
-{
-    return value;
-}
-
-/** @overload */
-CV_INLINE int cvCeil( float value )
-{
-    int i = (int)value;
-    return i + (i < value);
-}
-
-/** @overload */
-CV_INLINE int cvCeil( int value )
-{
-    return value;
-}
-
-/** @overload */
-CV_INLINE int cvIsNaN( float value )
-{
-    Cv32suf ieee754;
-    ieee754.f = value;
-    return (ieee754.u & 0x7fffffff) > 0x7f800000;
-}
-
-/** @overload */
-CV_INLINE int cvIsInf( float value )
-{
-    Cv32suf ieee754;
-    ieee754.f = value;
-    return (ieee754.u & 0x7fffffff) == 0x7f800000;
-}
-
-#endif // __cplusplus
-
-//! @} core_utils
-
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/hal/hal.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/hal/hal.hpp
deleted file mode 100755
index 68900ec..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/hal/hal.hpp
+++ /dev/null
@@ -1,250 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
-// Copyright (C) 2015, Itseez Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_HAL_HPP
-#define OPENCV_HAL_HPP
-
-#include "opencv2/core/cvdef.h"
-#include "opencv2/core/cvstd.hpp"
-#include "opencv2/core/hal/interface.h"
-
-namespace cv { namespace hal {
-
-//! @addtogroup core_hal_functions
-//! @{
-
-CV_EXPORTS int normHamming(const uchar* a, int n);
-CV_EXPORTS int normHamming(const uchar* a, const uchar* b, int n);
-
-CV_EXPORTS int normHamming(const uchar* a, int n, int cellSize);
-CV_EXPORTS int normHamming(const uchar* a, const uchar* b, int n, int cellSize);
-
-CV_EXPORTS int LU32f(float* A, size_t astep, int m, float* b, size_t bstep, int n);
-CV_EXPORTS int LU64f(double* A, size_t astep, int m, double* b, size_t bstep, int n);
-CV_EXPORTS bool Cholesky32f(float* A, size_t astep, int m, float* b, size_t bstep, int n);
-CV_EXPORTS bool Cholesky64f(double* A, size_t astep, int m, double* b, size_t bstep, int n);
-CV_EXPORTS void SVD32f(float* At, size_t astep, float* W, float* U, size_t ustep, float* Vt, size_t vstep, int m, int n, int flags);
-CV_EXPORTS void SVD64f(double* At, size_t astep, double* W, double* U, size_t ustep, double* Vt, size_t vstep, int m, int n, int flags);
-CV_EXPORTS int QR32f(float* A, size_t astep, int m, int n, int k, float* b, size_t bstep, float* hFactors);
-CV_EXPORTS int QR64f(double* A, size_t astep, int m, int n, int k, double* b, size_t bstep, double* hFactors);
-
-CV_EXPORTS void gemm32f(const float* src1, size_t src1_step, const float* src2, size_t src2_step,
-                        float alpha, const float* src3, size_t src3_step, float beta, float* dst, size_t dst_step,
-                        int m_a, int n_a, int n_d, int flags);
-CV_EXPORTS void gemm64f(const double* src1, size_t src1_step, const double* src2, size_t src2_step,
-                        double alpha, const double* src3, size_t src3_step, double beta, double* dst, size_t dst_step,
-                        int m_a, int n_a, int n_d, int flags);
-CV_EXPORTS void gemm32fc(const float* src1, size_t src1_step, const float* src2, size_t src2_step,
-                        float alpha, const float* src3, size_t src3_step, float beta, float* dst, size_t dst_step,
-                        int m_a, int n_a, int n_d, int flags);
-CV_EXPORTS void gemm64fc(const double* src1, size_t src1_step, const double* src2, size_t src2_step,
-                        double alpha, const double* src3, size_t src3_step, double beta, double* dst, size_t dst_step,
-                        int m_a, int n_a, int n_d, int flags);
-
-CV_EXPORTS int normL1_(const uchar* a, const uchar* b, int n);
-CV_EXPORTS float normL1_(const float* a, const float* b, int n);
-CV_EXPORTS float normL2Sqr_(const float* a, const float* b, int n);
-
-CV_EXPORTS void exp32f(const float* src, float* dst, int n);
-CV_EXPORTS void exp64f(const double* src, double* dst, int n);
-CV_EXPORTS void log32f(const float* src, float* dst, int n);
-CV_EXPORTS void log64f(const double* src, double* dst, int n);
-
-CV_EXPORTS void fastAtan32f(const float* y, const float* x, float* dst, int n, bool angleInDegrees);
-CV_EXPORTS void fastAtan64f(const double* y, const double* x, double* dst, int n, bool angleInDegrees);
-CV_EXPORTS void magnitude32f(const float* x, const float* y, float* dst, int n);
-CV_EXPORTS void magnitude64f(const double* x, const double* y, double* dst, int n);
-CV_EXPORTS void sqrt32f(const float* src, float* dst, int len);
-CV_EXPORTS void sqrt64f(const double* src, double* dst, int len);
-CV_EXPORTS void invSqrt32f(const float* src, float* dst, int len);
-CV_EXPORTS void invSqrt64f(const double* src, double* dst, int len);
-
-CV_EXPORTS void split8u(const uchar* src, uchar** dst, int len, int cn );
-CV_EXPORTS void split16u(const ushort* src, ushort** dst, int len, int cn );
-CV_EXPORTS void split32s(const int* src, int** dst, int len, int cn );
-CV_EXPORTS void split64s(const int64* src, int64** dst, int len, int cn );
-
-CV_EXPORTS void merge8u(const uchar** src, uchar* dst, int len, int cn );
-CV_EXPORTS void merge16u(const ushort** src, ushort* dst, int len, int cn );
-CV_EXPORTS void merge32s(const int** src, int* dst, int len, int cn );
-CV_EXPORTS void merge64s(const int64** src, int64* dst, int len, int cn );
-
-CV_EXPORTS void add8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* );
-CV_EXPORTS void add8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* );
-CV_EXPORTS void add16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* );
-CV_EXPORTS void add16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* );
-CV_EXPORTS void add32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* );
-CV_EXPORTS void add32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* );
-CV_EXPORTS void add64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* );
-
-CV_EXPORTS void sub8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* );
-CV_EXPORTS void sub8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* );
-CV_EXPORTS void sub16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* );
-CV_EXPORTS void sub16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* );
-CV_EXPORTS void sub32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* );
-CV_EXPORTS void sub32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* );
-CV_EXPORTS void sub64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* );
-
-CV_EXPORTS void max8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* );
-CV_EXPORTS void max8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* );
-CV_EXPORTS void max16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* );
-CV_EXPORTS void max16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* );
-CV_EXPORTS void max32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* );
-CV_EXPORTS void max32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* );
-CV_EXPORTS void max64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* );
-
-CV_EXPORTS void min8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* );
-CV_EXPORTS void min8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* );
-CV_EXPORTS void min16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* );
-CV_EXPORTS void min16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* );
-CV_EXPORTS void min32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* );
-CV_EXPORTS void min32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* );
-CV_EXPORTS void min64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* );
-
-CV_EXPORTS void absdiff8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* );
-CV_EXPORTS void absdiff8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* );
-CV_EXPORTS void absdiff16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* );
-CV_EXPORTS void absdiff16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* );
-CV_EXPORTS void absdiff32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* );
-CV_EXPORTS void absdiff32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* );
-CV_EXPORTS void absdiff64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* );
-
-CV_EXPORTS void and8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* );
-CV_EXPORTS void or8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* );
-CV_EXPORTS void xor8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* );
-CV_EXPORTS void not8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* );
-
-CV_EXPORTS void cmp8u(const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop);
-CV_EXPORTS void cmp8s(const schar* src1, size_t step1, const schar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop);
-CV_EXPORTS void cmp16u(const ushort* src1, size_t step1, const ushort* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop);
-CV_EXPORTS void cmp16s(const short* src1, size_t step1, const short* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop);
-CV_EXPORTS void cmp32s(const int* src1, size_t step1, const int* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop);
-CV_EXPORTS void cmp32f(const float* src1, size_t step1, const float* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop);
-CV_EXPORTS void cmp64f(const double* src1, size_t step1, const double* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop);
-
-CV_EXPORTS void mul8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* scale);
-CV_EXPORTS void mul8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* scale);
-CV_EXPORTS void mul16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* scale);
-CV_EXPORTS void mul16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* scale);
-CV_EXPORTS void mul32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* scale);
-CV_EXPORTS void mul32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* scale);
-CV_EXPORTS void mul64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* scale);
-
-CV_EXPORTS void div8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* scale);
-CV_EXPORTS void div8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* scale);
-CV_EXPORTS void div16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* scale);
-CV_EXPORTS void div16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* scale);
-CV_EXPORTS void div32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* scale);
-CV_EXPORTS void div32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* scale);
-CV_EXPORTS void div64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* scale);
-
-CV_EXPORTS void recip8u( const uchar *, size_t, const uchar * src2, size_t step2, uchar* dst, size_t step, int width, int height, void* scale);
-CV_EXPORTS void recip8s( const schar *, size_t, const schar * src2, size_t step2, schar* dst, size_t step, int width, int height, void* scale);
-CV_EXPORTS void recip16u( const ushort *, size_t, const ushort * src2, size_t step2, ushort* dst, size_t step, int width, int height, void* scale);
-CV_EXPORTS void recip16s( const short *, size_t, const short * src2, size_t step2, short* dst, size_t step, int width, int height, void* scale);
-CV_EXPORTS void recip32s( const int *, size_t, const int * src2, size_t step2, int* dst, size_t step, int width, int height, void* scale);
-CV_EXPORTS void recip32f( const float *, size_t, const float * src2, size_t step2, float* dst, size_t step, int width, int height, void* scale);
-CV_EXPORTS void recip64f( const double *, size_t, const double * src2, size_t step2, double* dst, size_t step, int width, int height, void* scale);
-
-CV_EXPORTS void addWeighted8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _scalars );
-CV_EXPORTS void addWeighted8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* scalars );
-CV_EXPORTS void addWeighted16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* scalars );
-CV_EXPORTS void addWeighted16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* scalars );
-CV_EXPORTS void addWeighted32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* scalars );
-CV_EXPORTS void addWeighted32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* scalars );
-CV_EXPORTS void addWeighted64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* scalars );
-
-struct CV_EXPORTS DFT1D
-{
-    static Ptr<DFT1D> create(int len, int count, int depth, int flags, bool * useBuffer = 0);
-    virtual void apply(const uchar *src, uchar *dst) = 0;
-    virtual ~DFT1D() {}
-};
-
-struct CV_EXPORTS DFT2D
-{
-    static Ptr<DFT2D> create(int width, int height, int depth,
-                             int src_channels, int dst_channels,
-                             int flags, int nonzero_rows = 0);
-    virtual void apply(const uchar *src_data, size_t src_step, uchar *dst_data, size_t dst_step) = 0;
-    virtual ~DFT2D() {}
-};
-
-struct CV_EXPORTS DCT2D
-{
-    static Ptr<DCT2D> create(int width, int height, int depth, int flags);
-    virtual void apply(const uchar *src_data, size_t src_step, uchar *dst_data, size_t dst_step) = 0;
-    virtual ~DCT2D() {}
-};
-
-//! @} core_hal
-
-//=============================================================================
-// for binary compatibility with 3.0
-
-//! @cond IGNORED
-
-CV_EXPORTS int LU(float* A, size_t astep, int m, float* b, size_t bstep, int n);
-CV_EXPORTS int LU(double* A, size_t astep, int m, double* b, size_t bstep, int n);
-CV_EXPORTS bool Cholesky(float* A, size_t astep, int m, float* b, size_t bstep, int n);
-CV_EXPORTS bool Cholesky(double* A, size_t astep, int m, double* b, size_t bstep, int n);
-
-CV_EXPORTS void exp(const float* src, float* dst, int n);
-CV_EXPORTS void exp(const double* src, double* dst, int n);
-CV_EXPORTS void log(const float* src, float* dst, int n);
-CV_EXPORTS void log(const double* src, double* dst, int n);
-
-CV_EXPORTS void fastAtan2(const float* y, const float* x, float* dst, int n, bool angleInDegrees);
-CV_EXPORTS void magnitude(const float* x, const float* y, float* dst, int n);
-CV_EXPORTS void magnitude(const double* x, const double* y, double* dst, int n);
-CV_EXPORTS void sqrt(const float* src, float* dst, int len);
-CV_EXPORTS void sqrt(const double* src, double* dst, int len);
-CV_EXPORTS void invSqrt(const float* src, float* dst, int len);
-CV_EXPORTS void invSqrt(const double* src, double* dst, int len);
-
-//! @endcond
-
-}} //cv::hal
-
-#endif //OPENCV_HAL_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/hal/interface.h b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/hal/interface.h
deleted file mode 100755
index 8f64025..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/hal/interface.h
+++ /dev/null
@@ -1,182 +0,0 @@
-#ifndef OPENCV_CORE_HAL_INTERFACE_H
-#define OPENCV_CORE_HAL_INTERFACE_H
-
-//! @addtogroup core_hal_interface
-//! @{
-
-//! @name Return codes
-//! @{
-#define CV_HAL_ERROR_OK 0
-#define CV_HAL_ERROR_NOT_IMPLEMENTED 1
-#define CV_HAL_ERROR_UNKNOWN -1
-//! @}
-
-#ifdef __cplusplus
-#include <cstddef>
-#else
-#include <stddef.h>
-#include <stdbool.h>
-#endif
-
-//! @name Data types
-//! primitive types
-//! - schar  - signed 1 byte integer
-//! - uchar  - unsigned 1 byte integer
-//! - short  - signed 2 byte integer
-//! - ushort - unsigned 2 byte integer
-//! - int    - signed 4 byte integer
-//! - uint   - unsigned 4 byte integer
-//! - int64  - signed 8 byte integer
-//! - uint64 - unsigned 8 byte integer
-//! @{
-#if !defined _MSC_VER && !defined __BORLANDC__
-#  if defined __cplusplus && __cplusplus >= 201103L && !defined __APPLE__
-#    include <cstdint>
-#    ifdef __NEWLIB__
-        typedef unsigned int uint;
-#    else
-        typedef std::uint32_t uint;
-#    endif
-#  else
-#    include <stdint.h>
-     typedef uint32_t uint;
-#  endif
-#else
-   typedef unsigned uint;
-#endif
-
-typedef signed char schar;
-
-#ifndef __IPL_H__
-   typedef unsigned char uchar;
-   typedef unsigned short ushort;
-#endif
-
-#if defined _MSC_VER || defined __BORLANDC__
-   typedef __int64 int64;
-   typedef unsigned __int64 uint64;
-#  define CV_BIG_INT(n)   n##I64
-#  define CV_BIG_UINT(n)  n##UI64
-#else
-   typedef int64_t int64;
-   typedef uint64_t uint64;
-#  define CV_BIG_INT(n)   n##LL
-#  define CV_BIG_UINT(n)  n##ULL
-#endif
-
-#define CV_CN_MAX     512
-#define CV_CN_SHIFT   3
-#define CV_DEPTH_MAX  (1 << CV_CN_SHIFT)
-
-#define CV_8U   0
-#define CV_8S   1
-#define CV_16U  2
-#define CV_16S  3
-#define CV_32S  4
-#define CV_32F  5
-#define CV_64F  6
-#define CV_USRTYPE1 7
-
-#define CV_MAT_DEPTH_MASK       (CV_DEPTH_MAX - 1)
-#define CV_MAT_DEPTH(flags)     ((flags) & CV_MAT_DEPTH_MASK)
-
-#define CV_MAKETYPE(depth,cn) (CV_MAT_DEPTH(depth) + (((cn)-1) << CV_CN_SHIFT))
-#define CV_MAKE_TYPE CV_MAKETYPE
-
-#define CV_8UC1 CV_MAKETYPE(CV_8U,1)
-#define CV_8UC2 CV_MAKETYPE(CV_8U,2)
-#define CV_8UC3 CV_MAKETYPE(CV_8U,3)
-#define CV_8UC4 CV_MAKETYPE(CV_8U,4)
-#define CV_8UC(n) CV_MAKETYPE(CV_8U,(n))
-
-#define CV_8SC1 CV_MAKETYPE(CV_8S,1)
-#define CV_8SC2 CV_MAKETYPE(CV_8S,2)
-#define CV_8SC3 CV_MAKETYPE(CV_8S,3)
-#define CV_8SC4 CV_MAKETYPE(CV_8S,4)
-#define CV_8SC(n) CV_MAKETYPE(CV_8S,(n))
-
-#define CV_16UC1 CV_MAKETYPE(CV_16U,1)
-#define CV_16UC2 CV_MAKETYPE(CV_16U,2)
-#define CV_16UC3 CV_MAKETYPE(CV_16U,3)
-#define CV_16UC4 CV_MAKETYPE(CV_16U,4)
-#define CV_16UC(n) CV_MAKETYPE(CV_16U,(n))
-
-#define CV_16SC1 CV_MAKETYPE(CV_16S,1)
-#define CV_16SC2 CV_MAKETYPE(CV_16S,2)
-#define CV_16SC3 CV_MAKETYPE(CV_16S,3)
-#define CV_16SC4 CV_MAKETYPE(CV_16S,4)
-#define CV_16SC(n) CV_MAKETYPE(CV_16S,(n))
-
-#define CV_32SC1 CV_MAKETYPE(CV_32S,1)
-#define CV_32SC2 CV_MAKETYPE(CV_32S,2)
-#define CV_32SC3 CV_MAKETYPE(CV_32S,3)
-#define CV_32SC4 CV_MAKETYPE(CV_32S,4)
-#define CV_32SC(n) CV_MAKETYPE(CV_32S,(n))
-
-#define CV_32FC1 CV_MAKETYPE(CV_32F,1)
-#define CV_32FC2 CV_MAKETYPE(CV_32F,2)
-#define CV_32FC3 CV_MAKETYPE(CV_32F,3)
-#define CV_32FC4 CV_MAKETYPE(CV_32F,4)
-#define CV_32FC(n) CV_MAKETYPE(CV_32F,(n))
-
-#define CV_64FC1 CV_MAKETYPE(CV_64F,1)
-#define CV_64FC2 CV_MAKETYPE(CV_64F,2)
-#define CV_64FC3 CV_MAKETYPE(CV_64F,3)
-#define CV_64FC4 CV_MAKETYPE(CV_64F,4)
-#define CV_64FC(n) CV_MAKETYPE(CV_64F,(n))
-//! @}
-
-//! @name Comparison operation
-//! @sa cv::CmpTypes
-//! @{
-#define CV_HAL_CMP_EQ 0
-#define CV_HAL_CMP_GT 1
-#define CV_HAL_CMP_GE 2
-#define CV_HAL_CMP_LT 3
-#define CV_HAL_CMP_LE 4
-#define CV_HAL_CMP_NE 5
-//! @}
-
-//! @name Border processing modes
-//! @sa cv::BorderTypes
-//! @{
-#define CV_HAL_BORDER_CONSTANT 0
-#define CV_HAL_BORDER_REPLICATE 1
-#define CV_HAL_BORDER_REFLECT 2
-#define CV_HAL_BORDER_WRAP 3
-#define CV_HAL_BORDER_REFLECT_101 4
-#define CV_HAL_BORDER_TRANSPARENT 5
-#define CV_HAL_BORDER_ISOLATED 16
-//! @}
-
-//! @name DFT flags
-//! @{
-#define CV_HAL_DFT_INVERSE        1
-#define CV_HAL_DFT_SCALE          2
-#define CV_HAL_DFT_ROWS           4
-#define CV_HAL_DFT_COMPLEX_OUTPUT 16
-#define CV_HAL_DFT_REAL_OUTPUT    32
-#define CV_HAL_DFT_TWO_STAGE      64
-#define CV_HAL_DFT_STAGE_COLS    128
-#define CV_HAL_DFT_IS_CONTINUOUS 512
-#define CV_HAL_DFT_IS_INPLACE 1024
-//! @}
-
-//! @name SVD flags
-//! @{
-#define CV_HAL_SVD_NO_UV    1
-#define CV_HAL_SVD_SHORT_UV 2
-#define CV_HAL_SVD_MODIFY_A 4
-#define CV_HAL_SVD_FULL_UV  8
-//! @}
-
-//! @name Gemm flags
-//! @{
-#define CV_HAL_GEMM_1_T 1
-#define CV_HAL_GEMM_2_T 2
-#define CV_HAL_GEMM_3_T 4
-//! @}
-
-//! @}
-
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/hal/intrin.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/hal/intrin.hpp
deleted file mode 100755
index 9dcfc56..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/hal/intrin.hpp
+++ /dev/null
@@ -1,472 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
-// Copyright (C) 2015, Itseez Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_HAL_INTRIN_HPP
-#define OPENCV_HAL_INTRIN_HPP
-
-#include <cmath>
-#include <float.h>
-#include <stdlib.h>
-#include "opencv2/core/cvdef.h"
-
-#define OPENCV_HAL_ADD(a, b) ((a) + (b))
-#define OPENCV_HAL_AND(a, b) ((a) & (b))
-#define OPENCV_HAL_NOP(a) (a)
-#define OPENCV_HAL_1ST(a, b) (a)
-
-// unlike HAL API, which is in cv::hal,
-// we put intrinsics into cv namespace to make its
-// access from within opencv code more accessible
-namespace cv {
-
-#ifndef CV_DOXYGEN
-
-#ifdef CV_CPU_DISPATCH_MODE
-#define CV_CPU_OPTIMIZATION_HAL_NAMESPACE __CV_CAT(hal_, CV_CPU_DISPATCH_MODE)
-#define CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN namespace __CV_CAT(hal_, CV_CPU_DISPATCH_MODE) {
-#define CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END }
-#else
-#define CV_CPU_OPTIMIZATION_HAL_NAMESPACE hal_baseline
-#define CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN namespace hal_baseline {
-#define CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END }
-#endif
-
-
-CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN
-CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END
-using namespace CV_CPU_OPTIMIZATION_HAL_NAMESPACE;
-CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN
-#endif
-
-//! @addtogroup core_hal_intrin
-//! @{
-
-//! @cond IGNORED
-template<typename _Tp> struct V_TypeTraits
-{
-    typedef _Tp int_type;
-    typedef _Tp uint_type;
-    typedef _Tp abs_type;
-    typedef _Tp sum_type;
-
-    enum { delta = 0, shift = 0 };
-
-    static int_type reinterpret_int(_Tp x) { return x; }
-    static uint_type reinterpet_uint(_Tp x) { return x; }
-    static _Tp reinterpret_from_int(int_type x) { return (_Tp)x; }
-};
-
-template<> struct V_TypeTraits<uchar>
-{
-    typedef uchar value_type;
-    typedef schar int_type;
-    typedef uchar uint_type;
-    typedef uchar abs_type;
-    typedef int sum_type;
-
-    typedef ushort w_type;
-    typedef unsigned q_type;
-
-    enum { delta = 128, shift = 8 };
-
-    static int_type reinterpret_int(value_type x) { return (int_type)x; }
-    static uint_type reinterpret_uint(value_type x) { return (uint_type)x; }
-    static value_type reinterpret_from_int(int_type x) { return (value_type)x; }
-};
-
-template<> struct V_TypeTraits<schar>
-{
-    typedef schar value_type;
-    typedef schar int_type;
-    typedef uchar uint_type;
-    typedef uchar abs_type;
-    typedef int sum_type;
-
-    typedef short w_type;
-    typedef int q_type;
-
-    enum { delta = 128, shift = 8 };
-
-    static int_type reinterpret_int(value_type x) { return (int_type)x; }
-    static uint_type reinterpret_uint(value_type x) { return (uint_type)x; }
-    static value_type reinterpret_from_int(int_type x) { return (value_type)x; }
-};
-
-template<> struct V_TypeTraits<ushort>
-{
-    typedef ushort value_type;
-    typedef short int_type;
-    typedef ushort uint_type;
-    typedef ushort abs_type;
-    typedef int sum_type;
-
-    typedef unsigned w_type;
-    typedef uchar nu_type;
-
-    enum { delta = 32768, shift = 16 };
-
-    static int_type reinterpret_int(value_type x) { return (int_type)x; }
-    static uint_type reinterpret_uint(value_type x) { return (uint_type)x; }
-    static value_type reinterpret_from_int(int_type x) { return (value_type)x; }
-};
-
-template<> struct V_TypeTraits<short>
-{
-    typedef short value_type;
-    typedef short int_type;
-    typedef ushort uint_type;
-    typedef ushort abs_type;
-    typedef int sum_type;
-
-    typedef int w_type;
-    typedef uchar nu_type;
-    typedef schar n_type;
-
-    enum { delta = 128, shift = 8 };
-
-    static int_type reinterpret_int(value_type x) { return (int_type)x; }
-    static uint_type reinterpret_uint(value_type x) { return (uint_type)x; }
-    static value_type reinterpret_from_int(int_type x) { return (value_type)x; }
-};
-
-template<> struct V_TypeTraits<unsigned>
-{
-    typedef unsigned value_type;
-    typedef int int_type;
-    typedef unsigned uint_type;
-    typedef unsigned abs_type;
-    typedef unsigned sum_type;
-
-    typedef uint64 w_type;
-    typedef ushort nu_type;
-
-    static int_type reinterpret_int(value_type x) { return (int_type)x; }
-    static uint_type reinterpret_uint(value_type x) { return (uint_type)x; }
-    static value_type reinterpret_from_int(int_type x) { return (value_type)x; }
-};
-
-template<> struct V_TypeTraits<int>
-{
-    typedef int value_type;
-    typedef int int_type;
-    typedef unsigned uint_type;
-    typedef unsigned abs_type;
-    typedef int sum_type;
-
-    typedef int64 w_type;
-    typedef short n_type;
-    typedef ushort nu_type;
-
-    static int_type reinterpret_int(value_type x) { return (int_type)x; }
-    static uint_type reinterpret_uint(value_type x) { return (uint_type)x; }
-    static value_type reinterpret_from_int(int_type x) { return (value_type)x; }
-};
-
-template<> struct V_TypeTraits<uint64>
-{
-    typedef uint64 value_type;
-    typedef int64 int_type;
-    typedef uint64 uint_type;
-    typedef uint64 abs_type;
-    typedef uint64 sum_type;
-
-    typedef unsigned nu_type;
-
-    static int_type reinterpret_int(value_type x) { return (int_type)x; }
-    static uint_type reinterpret_uint(value_type x) { return (uint_type)x; }
-    static value_type reinterpret_from_int(int_type x) { return (value_type)x; }
-};
-
-template<> struct V_TypeTraits<int64>
-{
-    typedef int64 value_type;
-    typedef int64 int_type;
-    typedef uint64 uint_type;
-    typedef uint64 abs_type;
-    typedef int64 sum_type;
-
-    typedef int nu_type;
-
-    static int_type reinterpret_int(value_type x) { return (int_type)x; }
-    static uint_type reinterpret_uint(value_type x) { return (uint_type)x; }
-    static value_type reinterpret_from_int(int_type x) { return (value_type)x; }
-};
-
-
-template<> struct V_TypeTraits<float>
-{
-    typedef float value_type;
-    typedef int int_type;
-    typedef unsigned uint_type;
-    typedef float abs_type;
-    typedef float sum_type;
-
-    typedef double w_type;
-
-    static int_type reinterpret_int(value_type x)
-    {
-        Cv32suf u;
-        u.f = x;
-        return u.i;
-    }
-    static uint_type reinterpet_uint(value_type x)
-    {
-        Cv32suf u;
-        u.f = x;
-        return u.u;
-    }
-    static value_type reinterpret_from_int(int_type x)
-    {
-        Cv32suf u;
-        u.i = x;
-        return u.f;
-    }
-};
-
-template<> struct V_TypeTraits<double>
-{
-    typedef double value_type;
-    typedef int64 int_type;
-    typedef uint64 uint_type;
-    typedef double abs_type;
-    typedef double sum_type;
-    static int_type reinterpret_int(value_type x)
-    {
-        Cv64suf u;
-        u.f = x;
-        return u.i;
-    }
-    static uint_type reinterpet_uint(value_type x)
-    {
-        Cv64suf u;
-        u.f = x;
-        return u.u;
-    }
-    static value_type reinterpret_from_int(int_type x)
-    {
-        Cv64suf u;
-        u.i = x;
-        return u.f;
-    }
-};
-
-template <typename T> struct V_SIMD128Traits
-{
-    enum { nlanes = 16 / sizeof(T) };
-};
-
-//! @endcond
-
-//! @}
-
-#ifndef CV_DOXYGEN
-CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END
-#endif
-}
-
-#ifdef CV_DOXYGEN
-#   undef CV_SSE2
-#   undef CV_NEON
-#   undef CV_VSX
-#endif
-
-#if CV_SSE2
-
-#include "opencv2/core/hal/intrin_sse.hpp"
-
-#elif CV_NEON
-
-#include "opencv2/core/hal/intrin_neon.hpp"
-
-#elif CV_VSX
-
-#include "opencv2/core/hal/intrin_vsx.hpp"
-
-#else
-
-#include "opencv2/core/hal/intrin_cpp.hpp"
-
-#endif
-
-//! @addtogroup core_hal_intrin
-//! @{
-
-#ifndef CV_SIMD128
-//! Set to 1 if current compiler supports vector extensions (NEON or SSE is enabled)
-#define CV_SIMD128 0
-#endif
-
-#ifndef CV_SIMD128_64F
-//! Set to 1 if current intrinsics implementation supports 64-bit float vectors
-#define CV_SIMD128_64F 0
-#endif
-
-//! @}
-
-//==================================================================================================
-
-//! @cond IGNORED
-
-namespace cv {
-
-#ifndef CV_DOXYGEN
-CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN
-#endif
-
-template <typename R> struct V_RegTrait128;
-
-template <> struct V_RegTrait128<uchar> {
-    typedef v_uint8x16 reg;
-    typedef v_uint16x8 w_reg;
-    typedef v_uint32x4 q_reg;
-    typedef v_uint8x16 u_reg;
-    static v_uint8x16 zero() { return v_setzero_u8(); }
-    static v_uint8x16 all(uchar val) { return v_setall_u8(val); }
-};
-
-template <> struct V_RegTrait128<schar> {
-    typedef v_int8x16 reg;
-    typedef v_int16x8 w_reg;
-    typedef v_int32x4 q_reg;
-    typedef v_uint8x16 u_reg;
-    static v_int8x16 zero() { return v_setzero_s8(); }
-    static v_int8x16 all(schar val) { return v_setall_s8(val); }
-};
-
-template <> struct V_RegTrait128<ushort> {
-    typedef v_uint16x8 reg;
-    typedef v_uint32x4 w_reg;
-    typedef v_int16x8 int_reg;
-    typedef v_uint16x8 u_reg;
-    static v_uint16x8 zero() { return v_setzero_u16(); }
-    static v_uint16x8 all(ushort val) { return v_setall_u16(val); }
-};
-
-template <> struct V_RegTrait128<short> {
-    typedef v_int16x8 reg;
-    typedef v_int32x4 w_reg;
-    typedef v_uint16x8 u_reg;
-    static v_int16x8 zero() { return v_setzero_s16(); }
-    static v_int16x8 all(short val) { return v_setall_s16(val); }
-};
-
-template <> struct V_RegTrait128<unsigned> {
-    typedef v_uint32x4 reg;
-    typedef v_uint64x2 w_reg;
-    typedef v_int32x4 int_reg;
-    typedef v_uint32x4 u_reg;
-    static v_uint32x4 zero() { return v_setzero_u32(); }
-    static v_uint32x4 all(unsigned val) { return v_setall_u32(val); }
-};
-
-template <> struct V_RegTrait128<int> {
-    typedef v_int32x4 reg;
-    typedef v_int64x2 w_reg;
-    typedef v_uint32x4 u_reg;
-    static v_int32x4 zero() { return v_setzero_s32(); }
-    static v_int32x4 all(int val) { return v_setall_s32(val); }
-};
-
-template <> struct V_RegTrait128<uint64> {
-    typedef v_uint64x2 reg;
-    static v_uint64x2 zero() { return v_setzero_u64(); }
-    static v_uint64x2 all(uint64 val) { return v_setall_u64(val); }
-};
-
-template <> struct V_RegTrait128<int64> {
-    typedef v_int64x2 reg;
-    static v_int64x2 zero() { return v_setzero_s64(); }
-    static v_int64x2 all(int64 val) { return v_setall_s64(val); }
-};
-
-template <> struct V_RegTrait128<float> {
-    typedef v_float32x4 reg;
-    typedef v_int32x4 int_reg;
-    typedef v_float32x4 u_reg;
-    static v_float32x4 zero() { return v_setzero_f32(); }
-    static v_float32x4 all(float val) { return v_setall_f32(val); }
-};
-
-#if CV_SIMD128_64F
-template <> struct V_RegTrait128<double> {
-    typedef v_float64x2 reg;
-    typedef v_int32x4 int_reg;
-    typedef v_float64x2 u_reg;
-    static v_float64x2 zero() { return v_setzero_f64(); }
-    static v_float64x2 all(double val) { return v_setall_f64(val); }
-};
-#endif
-
-inline unsigned int trailingZeros32(unsigned int value) {
-#if defined(_MSC_VER)
-#if (_MSC_VER < 1700) || defined(_M_ARM)
-    unsigned long index = 0;
-    _BitScanForward(&index, value);
-    return (unsigned int)index;
-#else
-    return _tzcnt_u32(value);
-#endif
-#elif defined(__GNUC__) || defined(__GNUG__)
-    return __builtin_ctz(value);
-#elif defined(__ICC) || defined(__INTEL_COMPILER)
-    return _bit_scan_forward(value);
-#elif defined(__clang__)
-    return llvm.cttz.i32(value, true);
-#else
-    static const int MultiplyDeBruijnBitPosition[32] = {
-        0, 1, 28, 2, 29, 14, 24, 3, 30, 22, 20, 15, 25, 17, 4, 8,
-        31, 27, 13, 23, 21, 19, 16, 7, 26, 12, 18, 6, 11, 5, 10, 9 };
-    return MultiplyDeBruijnBitPosition[((uint32_t)((value & -value) * 0x077CB531U)) >> 27];
-#endif
-}
-
-#ifndef CV_DOXYGEN
-CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END
-#endif
-
-} // cv::
-
-//! @endcond
-
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/hal/intrin_cpp.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/hal/intrin_cpp.hpp
deleted file mode 100755
index c7cbb57..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/hal/intrin_cpp.hpp
+++ /dev/null
@@ -1,1988 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
-// Copyright (C) 2015, Itseez Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_HAL_INTRIN_CPP_HPP
-#define OPENCV_HAL_INTRIN_CPP_HPP
-
-#include <limits>
-#include <cstring>
-#include <algorithm>
-#include "opencv2/core/saturate.hpp"
-
-namespace cv
-{
-
-#ifndef CV_DOXYGEN
-CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN
-#endif
-
-/** @addtogroup core_hal_intrin
-
-"Universal intrinsics" is a types and functions set intended to simplify vectorization of code on
-different platforms. Currently there are two supported SIMD extensions: __SSE/SSE2__ on x86
-architectures and __NEON__ on ARM architectures, both allow working with 128 bit registers
-containing packed values of different types. In case when there is no SIMD extension available
-during compilation, fallback C++ implementation of intrinsics will be chosen and code will work as
-expected although it could be slower.
-
-### Types
-
-There are several types representing 128-bit register as a vector of packed values, each type is
-implemented as a structure based on a one SIMD register.
-
-- cv::v_uint8x16 and cv::v_int8x16: sixteen 8-bit integer values (unsigned/signed) - char
-- cv::v_uint16x8 and cv::v_int16x8: eight 16-bit integer values (unsigned/signed) - short
-- cv::v_uint32x4 and cv::v_int32x4: four 32-bit integer values (unsgined/signed) - int
-- cv::v_uint64x2 and cv::v_int64x2: two 64-bit integer values (unsigned/signed) - int64
-- cv::v_float32x4: four 32-bit floating point values (signed) - float
-- cv::v_float64x2: two 64-bit floating point valies (signed) - double
-
-@note
-cv::v_float64x2 is not implemented in NEON variant, if you want to use this type, don't forget to
-check the CV_SIMD128_64F preprocessor definition:
-@code
-#if CV_SIMD128_64F
-//...
-#endif
-@endcode
-
-### Load and store operations
-
-These operations allow to set contents of the register explicitly or by loading it from some memory
-block and to save contents of the register to memory block.
-
-- Constructors:
-@ref v_reg::v_reg(const _Tp *ptr) "from memory",
-@ref v_reg::v_reg(_Tp s0, _Tp s1) "from two values", ...
-- Other create methods:
-@ref v_setall_s8, @ref v_setall_u8, ...,
-@ref v_setzero_u8, @ref v_setzero_s8, ...
-- Memory operations:
-@ref v_load, @ref v_load_aligned, @ref v_load_low, @ref v_load_halves,
-@ref v_store, @ref v_store_aligned,
-@ref v_store_high, @ref v_store_low
-
-### Value reordering
-
-These operations allow to reorder or recombine elements in one or multiple vectors.
-
-- Interleave, deinterleave (2, 3 and 4 channels): @ref v_load_deinterleave, @ref v_store_interleave
-- Expand: @ref v_load_expand, @ref v_load_expand_q, @ref v_expand
-- Pack: @ref v_pack, @ref v_pack_u, @ref v_rshr_pack, @ref v_rshr_pack_u,
-@ref v_pack_store, @ref v_pack_u_store, @ref v_rshr_pack_store, @ref v_rshr_pack_u_store
-- Recombine: @ref v_zip, @ref v_recombine, @ref v_combine_low, @ref v_combine_high
-- Extract: @ref v_extract
-
-
-### Arithmetic, bitwise and comparison operations
-
-Element-wise binary and unary operations.
-
-- Arithmetics:
-@ref operator +(const v_reg &a, const v_reg &b) "+",
-@ref operator -(const v_reg &a, const v_reg &b) "-",
-@ref operator *(const v_reg &a, const v_reg &b) "*",
-@ref operator /(const v_reg &a, const v_reg &b) "/",
-@ref v_mul_expand
-
-- Non-saturating arithmetics: @ref v_add_wrap, @ref v_sub_wrap
-
-- Bitwise shifts:
-@ref operator <<(const v_reg &a, int s) "<<",
-@ref operator >>(const v_reg &a, int s) ">>",
-@ref v_shl, @ref v_shr
-
-- Bitwise logic:
-@ref operator&(const v_reg &a, const v_reg &b) "&",
-@ref operator |(const v_reg &a, const v_reg &b) "|",
-@ref operator ^(const v_reg &a, const v_reg &b) "^",
-@ref operator ~(const v_reg &a) "~"
-
-- Comparison:
-@ref operator >(const v_reg &a, const v_reg &b) ">",
-@ref operator >=(const v_reg &a, const v_reg &b) ">=",
-@ref operator <(const v_reg &a, const v_reg &b) "<",
-@ref operator <=(const v_reg &a, const v_reg &b) "<=",
-@ref operator==(const v_reg &a, const v_reg &b) "==",
-@ref operator !=(const v_reg &a, const v_reg &b) "!="
-
-- min/max: @ref v_min, @ref v_max
-
-### Reduce and mask
-
-Most of these operations return only one value.
-
-- Reduce: @ref v_reduce_min, @ref v_reduce_max, @ref v_reduce_sum, @ref v_popcount
-- Mask: @ref v_signmask, @ref v_check_all, @ref v_check_any, @ref v_select
-
-### Other math
-
-- Some frequent operations: @ref v_sqrt, @ref v_invsqrt, @ref v_magnitude, @ref v_sqr_magnitude
-- Absolute values: @ref v_abs, @ref v_absdiff
-
-### Conversions
-
-Different type conversions and casts:
-
-- Rounding: @ref v_round, @ref v_floor, @ref v_ceil, @ref v_trunc,
-- To float: @ref v_cvt_f32, @ref v_cvt_f64
-- Reinterpret: @ref v_reinterpret_as_u8, @ref v_reinterpret_as_s8, ...
-
-### Matrix operations
-
-In these operations vectors represent matrix rows/columns: @ref v_dotprod, @ref v_matmul, @ref v_transpose4x4
-
-### Usability
-
-Most operations are implemented only for some subset of the available types, following matrices
-shows the applicability of different operations to the types.
-
-Regular integers:
-
-| Operations\\Types | uint 8x16 | int 8x16 | uint 16x8 | int 16x8 | uint 32x4 | int 32x4 |
-|-------------------|:-:|:-:|:-:|:-:|:-:|:-:|
-|load, store        | x | x | x | x | x | x |
-|interleave         | x | x | x | x | x | x |
-|expand             | x | x | x | x | x | x |
-|expand_q           | x | x |   |   |   |   |
-|add, sub           | x | x | x | x | x | x |
-|add_wrap, sub_wrap | x | x | x | x |   |   |
-|mul                |   |   | x | x | x | x |
-|mul_expand         |   |   | x | x | x |   |
-|compare            | x | x | x | x | x | x |
-|shift              |   |   | x | x | x | x |
-|dotprod            |   |   |   | x |   |   |
-|logical            | x | x | x | x | x | x |
-|min, max           | x | x | x | x | x | x |
-|absdiff            | x | x | x | x | x | x |
-|reduce             |   |   |   |   | x | x |
-|mask               | x | x | x | x | x | x |
-|pack               | x | x | x | x | x | x |
-|pack_u             | x |   | x |   |   |   |
-|unpack             | x | x | x | x | x | x |
-|extract            | x | x | x | x | x | x |
-|rotate (lanes)     | x | x | x | x | x | x |
-|cvt_flt32          |   |   |   |   |   | x |
-|cvt_flt64          |   |   |   |   |   | x |
-|transpose4x4       |   |   |   |   | x | x |
-
-Big integers:
-
-| Operations\\Types | uint 64x2 | int 64x2 |
-|-------------------|:-:|:-:|
-|load, store        | x | x |
-|add, sub           | x | x |
-|shift              | x | x |
-|logical            | x | x |
-|extract            | x | x |
-|rotate (lanes)     | x | x |
-
-Floating point:
-
-| Operations\\Types | float 32x4 | float 64x2 |
-|-------------------|:-:|:-:|
-|load, store        | x | x |
-|interleave         | x |   |
-|add, sub           | x | x |
-|mul                | x | x |
-|div                | x | x |
-|compare            | x | x |
-|min, max           | x | x |
-|absdiff            | x | x |
-|reduce             | x |   |
-|mask               | x | x |
-|unpack             | x | x |
-|cvt_flt32          |   | x |
-|cvt_flt64          | x |   |
-|sqrt, abs          | x | x |
-|float math         | x | x |
-|transpose4x4       | x |   |
-|extract            | x | x |
-|rotate (lanes)     | x | x |
-
- @{ */
-
-template<typename _Tp, int n> struct v_reg
-{
-//! @cond IGNORED
-    typedef _Tp lane_type;
-    typedef v_reg<typename V_TypeTraits<_Tp>::int_type, n> int_vec;
-    typedef v_reg<typename V_TypeTraits<_Tp>::abs_type, n> abs_vec;
-    enum { nlanes = n };
-// !@endcond
-
-    /** @brief Constructor
-
-    Initializes register with data from memory
-    @param ptr pointer to memory block with data for register */
-    explicit v_reg(const _Tp* ptr) { for( int i = 0; i < n; i++ ) s[i] = ptr[i]; }
-
-    /** @brief Constructor
-
-    Initializes register with two 64-bit values */
-    v_reg(_Tp s0, _Tp s1) { s[0] = s0; s[1] = s1; }
-
-    /** @brief Constructor
-
-    Initializes register with four 32-bit values */
-    v_reg(_Tp s0, _Tp s1, _Tp s2, _Tp s3) { s[0] = s0; s[1] = s1; s[2] = s2; s[3] = s3; }
-
-    /** @brief Constructor
-
-    Initializes register with eight 16-bit values */
-    v_reg(_Tp s0, _Tp s1, _Tp s2, _Tp s3,
-           _Tp s4, _Tp s5, _Tp s6, _Tp s7)
-    {
-        s[0] = s0; s[1] = s1; s[2] = s2; s[3] = s3;
-        s[4] = s4; s[5] = s5; s[6] = s6; s[7] = s7;
-    }
-
-    /** @brief Constructor
-
-    Initializes register with sixteen 8-bit values */
-    v_reg(_Tp s0, _Tp s1, _Tp s2, _Tp s3,
-           _Tp s4, _Tp s5, _Tp s6, _Tp s7,
-           _Tp s8, _Tp s9, _Tp s10, _Tp s11,
-           _Tp s12, _Tp s13, _Tp s14, _Tp s15)
-    {
-        s[0] = s0; s[1] = s1; s[2] = s2; s[3] = s3;
-        s[4] = s4; s[5] = s5; s[6] = s6; s[7] = s7;
-        s[8] = s8; s[9] = s9; s[10] = s10; s[11] = s11;
-        s[12] = s12; s[13] = s13; s[14] = s14; s[15] = s15;
-    }
-
-    /** @brief Default constructor
-
-    Does not initialize anything*/
-    v_reg() {}
-
-    /** @brief Copy constructor */
-    v_reg(const v_reg<_Tp, n> & r)
-    {
-        for( int i = 0; i < n; i++ )
-            s[i] = r.s[i];
-    }
-    /** @brief Access first value
-
-    Returns value of the first lane according to register type, for example:
-    @code{.cpp}
-    v_int32x4 r(1, 2, 3, 4);
-    int v = r.get0(); // returns 1
-    v_uint64x2 r(1, 2);
-    uint64_t v = r.get0(); // returns 1
-    @endcode
-    */
-    _Tp get0() const { return s[0]; }
-
-//! @cond IGNORED
-    _Tp get(const int i) const { return s[i]; }
-    v_reg<_Tp, n> high() const
-    {
-        v_reg<_Tp, n> c;
-        int i;
-        for( i = 0; i < n/2; i++ )
-        {
-            c.s[i] = s[i+(n/2)];
-            c.s[i+(n/2)] = 0;
-        }
-        return c;
-    }
-
-    static v_reg<_Tp, n> zero()
-    {
-        v_reg<_Tp, n> c;
-        for( int i = 0; i < n; i++ )
-            c.s[i] = (_Tp)0;
-        return c;
-    }
-
-    static v_reg<_Tp, n> all(_Tp s)
-    {
-        v_reg<_Tp, n> c;
-        for( int i = 0; i < n; i++ )
-            c.s[i] = s;
-        return c;
-    }
-
-    template<typename _Tp2, int n2> v_reg<_Tp2, n2> reinterpret_as() const
-    {
-        size_t bytes = std::min(sizeof(_Tp2)*n2, sizeof(_Tp)*n);
-        v_reg<_Tp2, n2> c;
-        std::memcpy(&c.s[0], &s[0], bytes);
-        return c;
-    }
-
-    _Tp s[n];
-//! @endcond
-};
-
-/** @brief Sixteen 8-bit unsigned integer values */
-typedef v_reg<uchar, 16> v_uint8x16;
-/** @brief Sixteen 8-bit signed integer values */
-typedef v_reg<schar, 16> v_int8x16;
-/** @brief Eight 16-bit unsigned integer values */
-typedef v_reg<ushort, 8> v_uint16x8;
-/** @brief Eight 16-bit signed integer values */
-typedef v_reg<short, 8> v_int16x8;
-/** @brief Four 32-bit unsigned integer values */
-typedef v_reg<unsigned, 4> v_uint32x4;
-/** @brief Four 32-bit signed integer values */
-typedef v_reg<int, 4> v_int32x4;
-/** @brief Four 32-bit floating point values (single precision) */
-typedef v_reg<float, 4> v_float32x4;
-/** @brief Two 64-bit floating point values (double precision) */
-typedef v_reg<double, 2> v_float64x2;
-/** @brief Two 64-bit unsigned integer values */
-typedef v_reg<uint64, 2> v_uint64x2;
-/** @brief Two 64-bit signed integer values */
-typedef v_reg<int64, 2> v_int64x2;
-
-//! @brief Helper macro
-//! @ingroup core_hal_intrin_impl
-#define OPENCV_HAL_IMPL_BIN_OP(bin_op) \
-template<typename _Tp, int n> inline v_reg<_Tp, n> \
-    operator bin_op (const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \
-{ \
-    v_reg<_Tp, n> c; \
-    for( int i = 0; i < n; i++ ) \
-        c.s[i] = saturate_cast<_Tp>(a.s[i] bin_op b.s[i]); \
-    return c; \
-} \
-template<typename _Tp, int n> inline v_reg<_Tp, n>& \
-    operator bin_op##= (v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \
-{ \
-    for( int i = 0; i < n; i++ ) \
-        a.s[i] = saturate_cast<_Tp>(a.s[i] bin_op b.s[i]); \
-    return a; \
-}
-
-/** @brief Add values
-
-For all types. */
-OPENCV_HAL_IMPL_BIN_OP(+)
-
-/** @brief Subtract values
-
-For all types. */
-OPENCV_HAL_IMPL_BIN_OP(-)
-
-/** @brief Multiply values
-
-For 16- and 32-bit integer types and floating types. */
-OPENCV_HAL_IMPL_BIN_OP(*)
-
-/** @brief Divide values
-
-For floating types only. */
-OPENCV_HAL_IMPL_BIN_OP(/)
-
-//! @brief Helper macro
-//! @ingroup core_hal_intrin_impl
-#define OPENCV_HAL_IMPL_BIT_OP(bit_op) \
-template<typename _Tp, int n> inline v_reg<_Tp, n> operator bit_op \
-    (const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \
-{ \
-    v_reg<_Tp, n> c; \
-    typedef typename V_TypeTraits<_Tp>::int_type itype; \
-    for( int i = 0; i < n; i++ ) \
-        c.s[i] = V_TypeTraits<_Tp>::reinterpret_from_int((itype)(V_TypeTraits<_Tp>::reinterpret_int(a.s[i]) bit_op \
-                                                        V_TypeTraits<_Tp>::reinterpret_int(b.s[i]))); \
-    return c; \
-} \
-template<typename _Tp, int n> inline v_reg<_Tp, n>& operator \
-    bit_op##= (v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \
-{ \
-    typedef typename V_TypeTraits<_Tp>::int_type itype; \
-    for( int i = 0; i < n; i++ ) \
-        a.s[i] = V_TypeTraits<_Tp>::reinterpret_from_int((itype)(V_TypeTraits<_Tp>::reinterpret_int(a.s[i]) bit_op \
-                                                        V_TypeTraits<_Tp>::reinterpret_int(b.s[i]))); \
-    return a; \
-}
-
-/** @brief Bitwise AND
-
-Only for integer types. */
-OPENCV_HAL_IMPL_BIT_OP(&)
-
-/** @brief Bitwise OR
-
-Only for integer types. */
-OPENCV_HAL_IMPL_BIT_OP(|)
-
-/** @brief Bitwise XOR
-
-Only for integer types.*/
-OPENCV_HAL_IMPL_BIT_OP(^)
-
-/** @brief Bitwise NOT
-
-Only for integer types.*/
-template<typename _Tp, int n> inline v_reg<_Tp, n> operator ~ (const v_reg<_Tp, n>& a)
-{
-    v_reg<_Tp, n> c;
-    for( int i = 0; i < n; i++ )
-    {
-        c.s[i] = V_TypeTraits<_Tp>::reinterpret_from_int(~V_TypeTraits<_Tp>::reinterpret_int(a.s[i]));
-    }
-    return c;
-}
-
-//! @brief Helper macro
-//! @ingroup core_hal_intrin_impl
-#define OPENCV_HAL_IMPL_MATH_FUNC(func, cfunc, _Tp2) \
-template<typename _Tp, int n> inline v_reg<_Tp2, n> func(const v_reg<_Tp, n>& a) \
-{ \
-    v_reg<_Tp2, n> c; \
-    for( int i = 0; i < n; i++ ) \
-        c.s[i] = cfunc(a.s[i]); \
-    return c; \
-}
-
-/** @brief Square root of elements
-
-Only for floating point types.*/
-OPENCV_HAL_IMPL_MATH_FUNC(v_sqrt, std::sqrt, _Tp)
-
-//! @cond IGNORED
-OPENCV_HAL_IMPL_MATH_FUNC(v_sin, std::sin, _Tp)
-OPENCV_HAL_IMPL_MATH_FUNC(v_cos, std::cos, _Tp)
-OPENCV_HAL_IMPL_MATH_FUNC(v_exp, std::exp, _Tp)
-OPENCV_HAL_IMPL_MATH_FUNC(v_log, std::log, _Tp)
-//! @endcond
-
-/** @brief Absolute value of elements
-
-Only for floating point types.*/
-OPENCV_HAL_IMPL_MATH_FUNC(v_abs, (typename V_TypeTraits<_Tp>::abs_type)std::abs,
-                          typename V_TypeTraits<_Tp>::abs_type)
-
-/** @brief Round elements
-
-Only for floating point types.*/
-OPENCV_HAL_IMPL_MATH_FUNC(v_round, cvRound, int)
-
-/** @brief Floor elements
-
-Only for floating point types.*/
-OPENCV_HAL_IMPL_MATH_FUNC(v_floor, cvFloor, int)
-
-/** @brief Ceil elements
-
-Only for floating point types.*/
-OPENCV_HAL_IMPL_MATH_FUNC(v_ceil, cvCeil, int)
-
-/** @brief Truncate elements
-
-Only for floating point types.*/
-OPENCV_HAL_IMPL_MATH_FUNC(v_trunc, int, int)
-
-//! @brief Helper macro
-//! @ingroup core_hal_intrin_impl
-#define OPENCV_HAL_IMPL_MINMAX_FUNC(func, cfunc) \
-template<typename _Tp, int n> inline v_reg<_Tp, n> func(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \
-{ \
-    v_reg<_Tp, n> c; \
-    for( int i = 0; i < n; i++ ) \
-        c.s[i] = cfunc(a.s[i], b.s[i]); \
-    return c; \
-}
-
-//! @brief Helper macro
-//! @ingroup core_hal_intrin_impl
-#define OPENCV_HAL_IMPL_REDUCE_MINMAX_FUNC(func, cfunc) \
-template<typename _Tp, int n> inline _Tp func(const v_reg<_Tp, n>& a) \
-{ \
-    _Tp c = a.s[0]; \
-    for( int i = 1; i < n; i++ ) \
-        c = cfunc(c, a.s[i]); \
-    return c; \
-}
-
-/** @brief Choose min values for each pair
-
-Scheme:
-@code
-{A1 A2 ...}
-{B1 B2 ...}
---------------
-{min(A1,B1) min(A2,B2) ...}
-@endcode
-For all types except 64-bit integer. */
-OPENCV_HAL_IMPL_MINMAX_FUNC(v_min, std::min)
-
-/** @brief Choose max values for each pair
-
-Scheme:
-@code
-{A1 A2 ...}
-{B1 B2 ...}
---------------
-{max(A1,B1) max(A2,B2) ...}
-@endcode
-For all types except 64-bit integer. */
-OPENCV_HAL_IMPL_MINMAX_FUNC(v_max, std::max)
-
-/** @brief Find one min value
-
-Scheme:
-@code
-{A1 A2 A3 ...} => min(A1,A2,A3,...)
-@endcode
-For 32-bit integer and 32-bit floating point types. */
-OPENCV_HAL_IMPL_REDUCE_MINMAX_FUNC(v_reduce_min, std::min)
-
-/** @brief Find one max value
-
-Scheme:
-@code
-{A1 A2 A3 ...} => max(A1,A2,A3,...)
-@endcode
-For 32-bit integer and 32-bit floating point types. */
-OPENCV_HAL_IMPL_REDUCE_MINMAX_FUNC(v_reduce_max, std::max)
-
-static const unsigned char popCountTable[] =
-{
-    0, 1, 1, 2, 1, 2, 2, 3, 1, 2, 2, 3, 2, 3, 3, 4,
-    1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5,
-    1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5,
-    2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6,
-    1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5,
-    2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6,
-    2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6,
-    3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7,
-    1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5,
-    2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6,
-    2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6,
-    3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7,
-    2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6,
-    3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7,
-    3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7,
-    4, 5, 5, 6, 5, 6, 6, 7, 5, 6, 6, 7, 6, 7, 7, 8,
-};
-/** @brief Count the 1 bits in the vector and return 4 values
-
-Scheme:
-@code
-{A1 A2 A3 ...} => popcount(A1)
-@endcode
-Any types but result will be in v_uint32x4*/
-template<typename _Tp, int n> inline v_uint32x4 v_popcount(const v_reg<_Tp, n>& a)
-{
-    v_uint8x16 b;
-    b = v_reinterpret_as_u8(a);
-    for( int i = 0; i < v_uint8x16::nlanes; i++ )
-    {
-        b.s[i] = popCountTable[b.s[i]];
-    }
-    v_uint32x4 c;
-    for( int i = 0; i < v_uint32x4::nlanes; i++ )
-    {
-        c.s[i] = b.s[i*4] + b.s[i*4+1] + b.s[i*4+2] + b.s[i*4+3];
-    }
-    return c;
-}
-
-
-//! @cond IGNORED
-template<typename _Tp, int n>
-inline void v_minmax( const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b,
-                      v_reg<_Tp, n>& minval, v_reg<_Tp, n>& maxval )
-{
-    for( int i = 0; i < n; i++ )
-    {
-        minval.s[i] = std::min(a.s[i], b.s[i]);
-        maxval.s[i] = std::max(a.s[i], b.s[i]);
-    }
-}
-//! @endcond
-
-//! @brief Helper macro
-//! @ingroup core_hal_intrin_impl
-#define OPENCV_HAL_IMPL_CMP_OP(cmp_op) \
-template<typename _Tp, int n> \
-inline v_reg<_Tp, n> operator cmp_op(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \
-{ \
-    typedef typename V_TypeTraits<_Tp>::int_type itype; \
-    v_reg<_Tp, n> c; \
-    for( int i = 0; i < n; i++ ) \
-        c.s[i] = V_TypeTraits<_Tp>::reinterpret_from_int((itype)-(int)(a.s[i] cmp_op b.s[i])); \
-    return c; \
-}
-
-/** @brief Less-than comparison
-
-For all types except 64-bit integer values. */
-OPENCV_HAL_IMPL_CMP_OP(<)
-
-/** @brief Greater-than comparison
-
-For all types except 64-bit integer values. */
-OPENCV_HAL_IMPL_CMP_OP(>)
-
-/** @brief Less-than or equal comparison
-
-For all types except 64-bit integer values. */
-OPENCV_HAL_IMPL_CMP_OP(<=)
-
-/** @brief Greater-than or equal comparison
-
-For all types except 64-bit integer values. */
-OPENCV_HAL_IMPL_CMP_OP(>=)
-
-/** @brief Equal comparison
-
-For all types except 64-bit integer values. */
-OPENCV_HAL_IMPL_CMP_OP(==)
-
-/** @brief Not equal comparison
-
-For all types except 64-bit integer values. */
-OPENCV_HAL_IMPL_CMP_OP(!=)
-
-//! @brief Helper macro
-//! @ingroup core_hal_intrin_impl
-#define OPENCV_HAL_IMPL_ADD_SUB_OP(func, bin_op, cast_op, _Tp2) \
-template<typename _Tp, int n> \
-inline v_reg<_Tp2, n> func(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \
-{ \
-    typedef _Tp2 rtype; \
-    v_reg<rtype, n> c; \
-    for( int i = 0; i < n; i++ ) \
-        c.s[i] = cast_op(a.s[i] bin_op b.s[i]); \
-    return c; \
-}
-
-/** @brief Add values without saturation
-
-For 8- and 16-bit integer values. */
-OPENCV_HAL_IMPL_ADD_SUB_OP(v_add_wrap, +, (_Tp), _Tp)
-
-/** @brief Subtract values without saturation
-
-For 8- and 16-bit integer values. */
-OPENCV_HAL_IMPL_ADD_SUB_OP(v_sub_wrap, -, (_Tp), _Tp)
-
-//! @cond IGNORED
-template<typename T> inline T _absdiff(T a, T b)
-{
-    return a > b ? a - b : b - a;
-}
-//! @endcond
-
-/** @brief Absolute difference
-
-Returns \f$ |a - b| \f$ converted to corresponding unsigned type.
-Example:
-@code{.cpp}
-v_int32x4 a, b; // {1, 2, 3, 4} and {4, 3, 2, 1}
-v_uint32x4 c = v_absdiff(a, b); // result is {3, 1, 1, 3}
-@endcode
-For 8-, 16-, 32-bit integer source types. */
-template<typename _Tp, int n>
-inline v_reg<typename V_TypeTraits<_Tp>::abs_type, n> v_absdiff(const v_reg<_Tp, n>& a, const v_reg<_Tp, n> & b)
-{
-    typedef typename V_TypeTraits<_Tp>::abs_type rtype;
-    v_reg<rtype, n> c;
-    const rtype mask = (rtype)(std::numeric_limits<_Tp>::is_signed ? (1 << (sizeof(rtype)*8 - 1)) : 0);
-    for( int i = 0; i < n; i++ )
-    {
-        rtype ua = a.s[i] ^ mask;
-        rtype ub = b.s[i] ^ mask;
-        c.s[i] = _absdiff(ua, ub);
-    }
-    return c;
-}
-
-/** @overload
-
-For 32-bit floating point values */
-inline v_float32x4 v_absdiff(const v_float32x4& a, const v_float32x4& b)
-{
-    v_float32x4 c;
-    for( int i = 0; i < c.nlanes; i++ )
-        c.s[i] = _absdiff(a.s[i], b.s[i]);
-    return c;
-}
-
-/** @overload
-
-For 64-bit floating point values */
-inline v_float64x2 v_absdiff(const v_float64x2& a, const v_float64x2& b)
-{
-    v_float64x2 c;
-    for( int i = 0; i < c.nlanes; i++ )
-        c.s[i] = _absdiff(a.s[i], b.s[i]);
-    return c;
-}
-
-/** @brief Inversed square root
-
-Returns \f$ 1/sqrt(a) \f$
-For floating point types only. */
-template<typename _Tp, int n>
-inline v_reg<_Tp, n> v_invsqrt(const v_reg<_Tp, n>& a)
-{
-    v_reg<_Tp, n> c;
-    for( int i = 0; i < n; i++ )
-        c.s[i] = 1.f/std::sqrt(a.s[i]);
-    return c;
-}
-
-/** @brief Magnitude
-
-Returns \f$ sqrt(a^2 + b^2) \f$
-For floating point types only. */
-template<typename _Tp, int n>
-inline v_reg<_Tp, n> v_magnitude(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b)
-{
-    v_reg<_Tp, n> c;
-    for( int i = 0; i < n; i++ )
-        c.s[i] = std::sqrt(a.s[i]*a.s[i] + b.s[i]*b.s[i]);
-    return c;
-}
-
-/** @brief Square of the magnitude
-
-Returns \f$ a^2 + b^2 \f$
-For floating point types only. */
-template<typename _Tp, int n>
-inline v_reg<_Tp, n> v_sqr_magnitude(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b)
-{
-    v_reg<_Tp, n> c;
-    for( int i = 0; i < n; i++ )
-        c.s[i] = a.s[i]*a.s[i] + b.s[i]*b.s[i];
-    return c;
-}
-
-/** @brief Multiply and add
-
-Returns \f$ a*b + c \f$
-For floating point types and signed 32bit int only. */
-template<typename _Tp, int n>
-inline v_reg<_Tp, n> v_muladd(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b,
-                              const v_reg<_Tp, n>& c)
-{
-    v_reg<_Tp, n> d;
-    for( int i = 0; i < n; i++ )
-        d.s[i] = a.s[i]*b.s[i] + c.s[i];
-    return d;
-}
-
-/** @brief Dot product of elements
-
-Multiply values in two registers and sum adjacent result pairs.
-Scheme:
-@code
-  {A1 A2 ...} // 16-bit
-x {B1 B2 ...} // 16-bit
--------------
-{A1B1+A2B2 ...} // 32-bit
-@endcode
-Implemented only for 16-bit signed source type (v_int16x8).
-*/
-template<typename _Tp, int n> inline v_reg<typename V_TypeTraits<_Tp>::w_type, n/2>
-    v_dotprod(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b)
-{
-    typedef typename V_TypeTraits<_Tp>::w_type w_type;
-    v_reg<w_type, n/2> c;
-    for( int i = 0; i < (n/2); i++ )
-        c.s[i] = (w_type)a.s[i*2]*b.s[i*2] + (w_type)a.s[i*2+1]*b.s[i*2+1];
-    return c;
-}
-
-/** @brief Dot product of elements
-
-Same as cv::v_dotprod, but add a third element to the sum of adjacent pairs.
-Scheme:
-@code
-  {A1 A2 ...} // 16-bit
-x {B1 B2 ...} // 16-bit
--------------
-  {A1B1+A2B2+C1 ...} // 32-bit
-
-@endcode
-Implemented only for 16-bit signed source type (v_int16x8).
-*/
-template<typename _Tp, int n> inline v_reg<typename V_TypeTraits<_Tp>::w_type, n/2>
-    v_dotprod(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b, const v_reg<typename V_TypeTraits<_Tp>::w_type, n / 2>& c)
-{
-    typedef typename V_TypeTraits<_Tp>::w_type w_type;
-    v_reg<w_type, n/2> s;
-    for( int i = 0; i < (n/2); i++ )
-        s.s[i] = (w_type)a.s[i*2]*b.s[i*2] + (w_type)a.s[i*2+1]*b.s[i*2+1] + c.s[i];
-    return s;
-}
-
-/** @brief Multiply and expand
-
-Multiply values two registers and store results in two registers with wider pack type.
-Scheme:
-@code
-  {A B C D} // 32-bit
-x {E F G H} // 32-bit
----------------
-{AE BF}         // 64-bit
-        {CG DH} // 64-bit
-@endcode
-Example:
-@code{.cpp}
-v_uint32x4 a, b; // {1,2,3,4} and {2,2,2,2}
-v_uint64x2 c, d; // results
-v_mul_expand(a, b, c, d); // c, d = {2,4}, {6, 8}
-@endcode
-Implemented only for 16- and unsigned 32-bit source types (v_int16x8, v_uint16x8, v_uint32x4).
-*/
-template<typename _Tp, int n> inline void v_mul_expand(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b,
-                                                       v_reg<typename V_TypeTraits<_Tp>::w_type, n/2>& c,
-                                                       v_reg<typename V_TypeTraits<_Tp>::w_type, n/2>& d)
-{
-    typedef typename V_TypeTraits<_Tp>::w_type w_type;
-    for( int i = 0; i < (n/2); i++ )
-    {
-        c.s[i] = (w_type)a.s[i]*b.s[i];
-        d.s[i] = (w_type)a.s[i+(n/2)]*b.s[i+(n/2)];
-    }
-}
-
-//! @cond IGNORED
-template<typename _Tp, int n> inline void v_hsum(const v_reg<_Tp, n>& a,
-                                                 v_reg<typename V_TypeTraits<_Tp>::w_type, n/2>& c)
-{
-    typedef typename V_TypeTraits<_Tp>::w_type w_type;
-    for( int i = 0; i < (n/2); i++ )
-    {
-        c.s[i] = (w_type)a.s[i*2] + a.s[i*2+1];
-    }
-}
-//! @endcond
-
-//! @brief Helper macro
-//! @ingroup core_hal_intrin_impl
-#define OPENCV_HAL_IMPL_SHIFT_OP(shift_op) \
-template<typename _Tp, int n> inline v_reg<_Tp, n> operator shift_op(const v_reg<_Tp, n>& a, int imm) \
-{ \
-    v_reg<_Tp, n> c; \
-    for( int i = 0; i < n; i++ ) \
-        c.s[i] = (_Tp)(a.s[i] shift_op imm); \
-    return c; \
-}
-
-/** @brief Bitwise shift left
-
-For 16-, 32- and 64-bit integer values. */
-OPENCV_HAL_IMPL_SHIFT_OP(<< )
-
-/** @brief Bitwise shift right
-
-For 16-, 32- and 64-bit integer values. */
-OPENCV_HAL_IMPL_SHIFT_OP(>> )
-
-/** @brief Element shift left among vector
-
-For all type */
-#define OPENCV_HAL_IMPL_ROTATE_SHIFT_OP(suffix,opA,opB) \
-template<int imm, typename _Tp, int n> inline v_reg<_Tp, n> v_rotate_##suffix(const v_reg<_Tp, n>& a) \
-{ \
-    v_reg<_Tp, n> b; \
-    for (int i = 0; i < n; i++) \
-    { \
-        int sIndex = i opA imm; \
-        if (0 <= sIndex && sIndex < n) \
-        { \
-            b.s[i] = a.s[sIndex]; \
-        } \
-        else \
-        { \
-            b.s[i] = 0; \
-        } \
-    } \
-    return b; \
-} \
-template<int imm, typename _Tp, int n> inline v_reg<_Tp, n> v_rotate_##suffix(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b) \
-{ \
-    v_reg<_Tp, n> c; \
-    for (int i = 0; i < n; i++) \
-    { \
-        int aIndex = i opA imm; \
-        int bIndex = i opA imm opB n; \
-        if (0 <= bIndex && bIndex < n) \
-        { \
-            c.s[i] = b.s[bIndex]; \
-        } \
-        else if (0 <= aIndex && aIndex < n) \
-        { \
-            c.s[i] = a.s[aIndex]; \
-        } \
-        else \
-        { \
-            c.s[i] = 0; \
-        } \
-    } \
-    return c; \
-}
-
-OPENCV_HAL_IMPL_ROTATE_SHIFT_OP(left,  -, +)
-OPENCV_HAL_IMPL_ROTATE_SHIFT_OP(right, +, -)
-
-/** @brief Sum packed values
-
-Scheme:
-@code
-{A1 A2 A3 ...} => sum{A1,A2,A3,...}
-@endcode
-For 32-bit integer and 32-bit floating point types.*/
-template<typename _Tp, int n> inline typename V_TypeTraits<_Tp>::sum_type v_reduce_sum(const v_reg<_Tp, n>& a)
-{
-    typename V_TypeTraits<_Tp>::sum_type c = a.s[0];
-    for( int i = 1; i < n; i++ )
-        c += a.s[i];
-    return c;
-}
-
-/** @brief Sums all elements of each input vector, returns the vector of sums
-
- Scheme:
- @code
- result[0] = a[0] + a[1] + a[2] + a[3]
- result[1] = b[0] + b[1] + b[2] + b[3]
- result[2] = c[0] + c[1] + c[2] + c[3]
- result[3] = d[0] + d[1] + d[2] + d[3]
- @endcode
-*/
-inline v_float32x4 v_reduce_sum4(const v_float32x4& a, const v_float32x4& b,
-                                 const v_float32x4& c, const v_float32x4& d)
-{
-    v_float32x4 r;
-    r.s[0] = a.s[0] + a.s[1] + a.s[2] + a.s[3];
-    r.s[1] = b.s[0] + b.s[1] + b.s[2] + b.s[3];
-    r.s[2] = c.s[0] + c.s[1] + c.s[2] + c.s[3];
-    r.s[3] = d.s[0] + d.s[1] + d.s[2] + d.s[3];
-    return r;
-}
-
-/** @brief Get negative values mask
-
-Returned value is a bit mask with bits set to 1 on places corresponding to negative packed values indexes.
-Example:
-@code{.cpp}
-v_int32x4 r; // set to {-1, -1, 1, 1}
-int mask = v_signmask(r); // mask = 3 <== 00000000 00000000 00000000 00000011
-@endcode
-For all types except 64-bit. */
-template<typename _Tp, int n> inline int v_signmask(const v_reg<_Tp, n>& a)
-{
-    int mask = 0;
-    for( int i = 0; i < n; i++ )
-        mask |= (V_TypeTraits<_Tp>::reinterpret_int(a.s[i]) < 0) << i;
-    return mask;
-}
-
-/** @brief Check if all packed values are less than zero
-
-Unsigned values will be casted to signed: `uchar 254 => char -2`.
-For all types except 64-bit. */
-template<typename _Tp, int n> inline bool v_check_all(const v_reg<_Tp, n>& a)
-{
-    for( int i = 0; i < n; i++ )
-        if( V_TypeTraits<_Tp>::reinterpret_int(a.s[i]) >= 0 )
-            return false;
-    return true;
-}
-
-/** @brief Check if any of packed values is less than zero
-
-Unsigned values will be casted to signed: `uchar 254 => char -2`.
-For all types except 64-bit. */
-template<typename _Tp, int n> inline bool v_check_any(const v_reg<_Tp, n>& a)
-{
-    for( int i = 0; i < n; i++ )
-        if( V_TypeTraits<_Tp>::reinterpret_int(a.s[i]) < 0 )
-            return true;
-    return false;
-}
-
-/** @brief Per-element select (blend operation)
-
-Return value will be built by combining values _a_ and _b_ using the following scheme:
-    result[i] = mask[i] ? a[i] : b[i];
-
-@note: _mask_ element values are restricted to these values:
-- 0: select element from _b_
-- 0xff/0xffff/etc: select element from _a_
-(fully compatible with bitwise-based operator)
-*/
-template<typename _Tp, int n> inline v_reg<_Tp, n> v_select(const v_reg<_Tp, n>& mask,
-                                                           const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b)
-{
-    typedef V_TypeTraits<_Tp> Traits;
-    typedef typename Traits::int_type int_type;
-    v_reg<_Tp, n> c;
-    for( int i = 0; i < n; i++ )
-    {
-        int_type m = Traits::reinterpret_int(mask.s[i]);
-        CV_DbgAssert(m == 0 || m == (~(int_type)0));  // restrict mask values: 0 or 0xff/0xffff/etc
-        c.s[i] = m ? a.s[i] : b.s[i];
-    }
-    return c;
-}
-
-/** @brief Expand values to the wider pack type
-
-Copy contents of register to two registers with 2x wider pack type.
-Scheme:
-@code
- int32x4     int64x2 int64x2
-{A B C D} ==> {A B} , {C D}
-@endcode */
-template<typename _Tp, int n> inline void v_expand(const v_reg<_Tp, n>& a,
-                            v_reg<typename V_TypeTraits<_Tp>::w_type, n/2>& b0,
-                            v_reg<typename V_TypeTraits<_Tp>::w_type, n/2>& b1)
-{
-    for( int i = 0; i < (n/2); i++ )
-    {
-        b0.s[i] = a.s[i];
-        b1.s[i] = a.s[i+(n/2)];
-    }
-}
-
-//! @cond IGNORED
-template<typename _Tp, int n> inline v_reg<typename V_TypeTraits<_Tp>::int_type, n>
-    v_reinterpret_as_int(const v_reg<_Tp, n>& a)
-{
-    v_reg<typename V_TypeTraits<_Tp>::int_type, n> c;
-    for( int i = 0; i < n; i++ )
-        c.s[i] = V_TypeTraits<_Tp>::reinterpret_int(a.s[i]);
-    return c;
-}
-
-template<typename _Tp, int n> inline v_reg<typename V_TypeTraits<_Tp>::uint_type, n>
-    v_reinterpret_as_uint(const v_reg<_Tp, n>& a)
-{
-    v_reg<typename V_TypeTraits<_Tp>::uint_type, n> c;
-    for( int i = 0; i < n; i++ )
-        c.s[i] = V_TypeTraits<_Tp>::reinterpret_uint(a.s[i]);
-    return c;
-}
-//! @endcond
-
-/** @brief Interleave two vectors
-
-Scheme:
-@code
-  {A1 A2 A3 A4}
-  {B1 B2 B3 B4}
----------------
-  {A1 B1 A2 B2} and {A3 B3 A4 B4}
-@endcode
-For all types except 64-bit.
-*/
-template<typename _Tp, int n> inline void v_zip( const v_reg<_Tp, n>& a0, const v_reg<_Tp, n>& a1,
-                                               v_reg<_Tp, n>& b0, v_reg<_Tp, n>& b1 )
-{
-    int i;
-    for( i = 0; i < n/2; i++ )
-    {
-        b0.s[i*2] = a0.s[i];
-        b0.s[i*2+1] = a1.s[i];
-    }
-    for( ; i < n; i++ )
-    {
-        b1.s[i*2-n] = a0.s[i];
-        b1.s[i*2-n+1] = a1.s[i];
-    }
-}
-
-/** @brief Load register contents from memory
-
-@param ptr pointer to memory block with data
-@return register object
-
-@note Returned type will be detected from passed pointer type, for example uchar ==> cv::v_uint8x16, int ==> cv::v_int32x4, etc.
- */
-template<typename _Tp>
-inline v_reg<_Tp, V_SIMD128Traits<_Tp>::nlanes> v_load(const _Tp* ptr)
-{
-    return v_reg<_Tp, V_SIMD128Traits<_Tp>::nlanes>(ptr);
-}
-
-/** @brief Load register contents from memory (aligned)
-
-similar to cv::v_load, but source memory block should be aligned (to 16-byte boundary)
- */
-template<typename _Tp>
-inline v_reg<_Tp, V_SIMD128Traits<_Tp>::nlanes> v_load_aligned(const _Tp* ptr)
-{
-    return v_reg<_Tp, V_SIMD128Traits<_Tp>::nlanes>(ptr);
-}
-
-/** @brief Load 64-bits of data to lower part (high part is undefined).
-
-@param ptr memory block containing data for first half (0..n/2)
-
-@code{.cpp}
-int lo[2] = { 1, 2 };
-v_int32x4 r = v_load_low(lo);
-@endcode
- */
-template<typename _Tp>
-inline v_reg<_Tp, V_SIMD128Traits<_Tp>::nlanes> v_load_low(const _Tp* ptr)
-{
-    v_reg<_Tp, V_SIMD128Traits<_Tp>::nlanes> c;
-    for( int i = 0; i < c.nlanes/2; i++ )
-    {
-        c.s[i] = ptr[i];
-    }
-    return c;
-}
-
-/** @brief Load register contents from two memory blocks
-
-@param loptr memory block containing data for first half (0..n/2)
-@param hiptr memory block containing data for second half (n/2..n)
-
-@code{.cpp}
-int lo[2] = { 1, 2 }, hi[2] = { 3, 4 };
-v_int32x4 r = v_load_halves(lo, hi);
-@endcode
- */
-template<typename _Tp>
-inline v_reg<_Tp, V_SIMD128Traits<_Tp>::nlanes> v_load_halves(const _Tp* loptr, const _Tp* hiptr)
-{
-    v_reg<_Tp, V_SIMD128Traits<_Tp>::nlanes> c;
-    for( int i = 0; i < c.nlanes/2; i++ )
-    {
-        c.s[i] = loptr[i];
-        c.s[i+c.nlanes/2] = hiptr[i];
-    }
-    return c;
-}
-
-/** @brief Load register contents from memory with double expand
-
-Same as cv::v_load, but result pack type will be 2x wider than memory type.
-
-@code{.cpp}
-short buf[4] = {1, 2, 3, 4}; // type is int16
-v_int32x4 r = v_load_expand(buf); // r = {1, 2, 3, 4} - type is int32
-@endcode
-For 8-, 16-, 32-bit integer source types. */
-template<typename _Tp>
-inline v_reg<typename V_TypeTraits<_Tp>::w_type, V_SIMD128Traits<_Tp>::nlanes / 2>
-v_load_expand(const _Tp* ptr)
-{
-    typedef typename V_TypeTraits<_Tp>::w_type w_type;
-    v_reg<w_type, V_SIMD128Traits<w_type>::nlanes> c;
-    for( int i = 0; i < c.nlanes; i++ )
-    {
-        c.s[i] = ptr[i];
-    }
-    return c;
-}
-
-/** @brief Load register contents from memory with quad expand
-
-Same as cv::v_load_expand, but result type is 4 times wider than source.
-@code{.cpp}
-char buf[4] = {1, 2, 3, 4}; // type is int8
-v_int32x4 r = v_load_q(buf); // r = {1, 2, 3, 4} - type is int32
-@endcode
-For 8-bit integer source types. */
-template<typename _Tp>
-inline v_reg<typename V_TypeTraits<_Tp>::q_type, V_SIMD128Traits<_Tp>::nlanes / 4>
-v_load_expand_q(const _Tp* ptr)
-{
-    typedef typename V_TypeTraits<_Tp>::q_type q_type;
-    v_reg<q_type, V_SIMD128Traits<q_type>::nlanes> c;
-    for( int i = 0; i < c.nlanes; i++ )
-    {
-        c.s[i] = ptr[i];
-    }
-    return c;
-}
-
-/** @brief Load and deinterleave (2 channels)
-
-Load data from memory deinterleave and store to 2 registers.
-Scheme:
-@code
-{A1 B1 A2 B2 ...} ==> {A1 A2 ...}, {B1 B2 ...}
-@endcode
-For all types except 64-bit. */
-template<typename _Tp, int n> inline void v_load_deinterleave(const _Tp* ptr, v_reg<_Tp, n>& a,
-                                                            v_reg<_Tp, n>& b)
-{
-    int i, i2;
-    for( i = i2 = 0; i < n; i++, i2 += 2 )
-    {
-        a.s[i] = ptr[i2];
-        b.s[i] = ptr[i2+1];
-    }
-}
-
-/** @brief Load and deinterleave (3 channels)
-
-Load data from memory deinterleave and store to 3 registers.
-Scheme:
-@code
-{A1 B1 C1 A2 B2 C2 ...} ==> {A1 A2 ...}, {B1 B2 ...}, {C1 C2 ...}
-@endcode
-For all types except 64-bit. */
-template<typename _Tp, int n> inline void v_load_deinterleave(const _Tp* ptr, v_reg<_Tp, n>& a,
-                                                            v_reg<_Tp, n>& b, v_reg<_Tp, n>& c)
-{
-    int i, i3;
-    for( i = i3 = 0; i < n; i++, i3 += 3 )
-    {
-        a.s[i] = ptr[i3];
-        b.s[i] = ptr[i3+1];
-        c.s[i] = ptr[i3+2];
-    }
-}
-
-/** @brief Load and deinterleave (4 channels)
-
-Load data from memory deinterleave and store to 4 registers.
-Scheme:
-@code
-{A1 B1 C1 D1 A2 B2 C2 D2 ...} ==> {A1 A2 ...}, {B1 B2 ...}, {C1 C2 ...}, {D1 D2 ...}
-@endcode
-For all types except 64-bit. */
-template<typename _Tp, int n>
-inline void v_load_deinterleave(const _Tp* ptr, v_reg<_Tp, n>& a,
-                                v_reg<_Tp, n>& b, v_reg<_Tp, n>& c,
-                                v_reg<_Tp, n>& d)
-{
-    int i, i4;
-    for( i = i4 = 0; i < n; i++, i4 += 4 )
-    {
-        a.s[i] = ptr[i4];
-        b.s[i] = ptr[i4+1];
-        c.s[i] = ptr[i4+2];
-        d.s[i] = ptr[i4+3];
-    }
-}
-
-/** @brief Interleave and store (2 channels)
-
-Interleave and store data from 2 registers to memory.
-Scheme:
-@code
-{A1 A2 ...}, {B1 B2 ...} ==> {A1 B1 A2 B2 ...}
-@endcode
-For all types except 64-bit. */
-template<typename _Tp, int n>
-inline void v_store_interleave( _Tp* ptr, const v_reg<_Tp, n>& a,
-                               const v_reg<_Tp, n>& b)
-{
-    int i, i2;
-    for( i = i2 = 0; i < n; i++, i2 += 2 )
-    {
-        ptr[i2] = a.s[i];
-        ptr[i2+1] = b.s[i];
-    }
-}
-
-/** @brief Interleave and store (3 channels)
-
-Interleave and store data from 3 registers to memory.
-Scheme:
-@code
-{A1 A2 ...}, {B1 B2 ...}, {C1 C2 ...} ==> {A1 B1 C1 A2 B2 C2 ...}
-@endcode
-For all types except 64-bit. */
-template<typename _Tp, int n>
-inline void v_store_interleave( _Tp* ptr, const v_reg<_Tp, n>& a,
-                                const v_reg<_Tp, n>& b, const v_reg<_Tp, n>& c)
-{
-    int i, i3;
-    for( i = i3 = 0; i < n; i++, i3 += 3 )
-    {
-        ptr[i3] = a.s[i];
-        ptr[i3+1] = b.s[i];
-        ptr[i3+2] = c.s[i];
-    }
-}
-
-/** @brief Interleave and store (4 channels)
-
-Interleave and store data from 4 registers to memory.
-Scheme:
-@code
-{A1 A2 ...}, {B1 B2 ...}, {C1 C2 ...}, {D1 D2 ...} ==> {A1 B1 C1 D1 A2 B2 C2 D2 ...}
-@endcode
-For all types except 64-bit. */
-template<typename _Tp, int n> inline void v_store_interleave( _Tp* ptr, const v_reg<_Tp, n>& a,
-                                                            const v_reg<_Tp, n>& b, const v_reg<_Tp, n>& c,
-                                                            const v_reg<_Tp, n>& d)
-{
-    int i, i4;
-    for( i = i4 = 0; i < n; i++, i4 += 4 )
-    {
-        ptr[i4] = a.s[i];
-        ptr[i4+1] = b.s[i];
-        ptr[i4+2] = c.s[i];
-        ptr[i4+3] = d.s[i];
-    }
-}
-
-/** @brief Store data to memory
-
-Store register contents to memory.
-Scheme:
-@code
-  REG {A B C D} ==> MEM {A B C D}
-@endcode
-Pointer can be unaligned. */
-template<typename _Tp, int n>
-inline void v_store(_Tp* ptr, const v_reg<_Tp, n>& a)
-{
-    for( int i = 0; i < n; i++ )
-        ptr[i] = a.s[i];
-}
-
-/** @brief Store data to memory (lower half)
-
-Store lower half of register contents to memory.
-Scheme:
-@code
-  REG {A B C D} ==> MEM {A B}
-@endcode */
-template<typename _Tp, int n>
-inline void v_store_low(_Tp* ptr, const v_reg<_Tp, n>& a)
-{
-    for( int i = 0; i < (n/2); i++ )
-        ptr[i] = a.s[i];
-}
-
-/** @brief Store data to memory (higher half)
-
-Store higher half of register contents to memory.
-Scheme:
-@code
-  REG {A B C D} ==> MEM {C D}
-@endcode */
-template<typename _Tp, int n>
-inline void v_store_high(_Tp* ptr, const v_reg<_Tp, n>& a)
-{
-    for( int i = 0; i < (n/2); i++ )
-        ptr[i] = a.s[i+(n/2)];
-}
-
-/** @brief Store data to memory (aligned)
-
-Store register contents to memory.
-Scheme:
-@code
-  REG {A B C D} ==> MEM {A B C D}
-@endcode
-Pointer __should__ be aligned by 16-byte boundary. */
-template<typename _Tp, int n>
-inline void v_store_aligned(_Tp* ptr, const v_reg<_Tp, n>& a)
-{
-    for( int i = 0; i < n; i++ )
-        ptr[i] = a.s[i];
-}
-
-/** @brief Combine vector from first elements of two vectors
-
-Scheme:
-@code
-  {A1 A2 A3 A4}
-  {B1 B2 B3 B4}
----------------
-  {A1 A2 B1 B2}
-@endcode
-For all types except 64-bit. */
-template<typename _Tp, int n>
-inline v_reg<_Tp, n> v_combine_low(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b)
-{
-    v_reg<_Tp, n> c;
-    for( int i = 0; i < (n/2); i++ )
-    {
-        c.s[i] = a.s[i];
-        c.s[i+(n/2)] = b.s[i];
-    }
-    return c;
-}
-
-/** @brief Combine vector from last elements of two vectors
-
-Scheme:
-@code
-  {A1 A2 A3 A4}
-  {B1 B2 B3 B4}
----------------
-  {A3 A4 B3 B4}
-@endcode
-For all types except 64-bit. */
-template<typename _Tp, int n>
-inline v_reg<_Tp, n> v_combine_high(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b)
-{
-    v_reg<_Tp, n> c;
-    for( int i = 0; i < (n/2); i++ )
-    {
-        c.s[i] = a.s[i+(n/2)];
-        c.s[i+(n/2)] = b.s[i+(n/2)];
-    }
-    return c;
-}
-
-/** @brief Combine two vectors from lower and higher parts of two other vectors
-
-@code{.cpp}
-low = cv::v_combine_low(a, b);
-high = cv::v_combine_high(a, b);
-@endcode */
-template<typename _Tp, int n>
-inline void v_recombine(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b,
-                        v_reg<_Tp, n>& low, v_reg<_Tp, n>& high)
-{
-    for( int i = 0; i < (n/2); i++ )
-    {
-        low.s[i] = a.s[i];
-        low.s[i+(n/2)] = b.s[i];
-        high.s[i] = a.s[i+(n/2)];
-        high.s[i+(n/2)] = b.s[i+(n/2)];
-    }
-}
-
-/** @brief Vector extract
-
-Scheme:
-@code
-  {A1 A2 A3 A4}
-  {B1 B2 B3 B4}
-========================
-shift = 1  {A2 A3 A4 B1}
-shift = 2  {A3 A4 B1 B2}
-shift = 3  {A4 B1 B2 B3}
-@endcode
-Restriction: 0 <= shift < nlanes
-
-Usage:
-@code
-v_int32x4 a, b, c;
-c = v_extract<2>(a, b);
-@endcode
-For all types. */
-template<int s, typename _Tp, int n>
-inline v_reg<_Tp, n> v_extract(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b)
-{
-    v_reg<_Tp, n> r;
-    const int shift = n - s;
-    int i = 0;
-    for (; i < shift; ++i)
-        r.s[i] = a.s[i+s];
-    for (; i < n; ++i)
-        r.s[i] = b.s[i-shift];
-    return r;
-}
-
-/** @brief Round
-
-Rounds each value. Input type is float vector ==> output type is int vector.*/
-template<int n> inline v_reg<int, n> v_round(const v_reg<float, n>& a)
-{
-    v_reg<int, n> c;
-    for( int i = 0; i < n; i++ )
-        c.s[i] = cvRound(a.s[i]);
-    return c;
-}
-
-/** @brief Floor
-
-Floor each value. Input type is float vector ==> output type is int vector.*/
-template<int n> inline v_reg<int, n> v_floor(const v_reg<float, n>& a)
-{
-    v_reg<int, n> c;
-    for( int i = 0; i < n; i++ )
-        c.s[i] = cvFloor(a.s[i]);
-    return c;
-}
-
-/** @brief Ceil
-
-Ceil each value. Input type is float vector ==> output type is int vector.*/
-template<int n> inline v_reg<int, n> v_ceil(const v_reg<float, n>& a)
-{
-    v_reg<int, n> c;
-    for( int i = 0; i < n; i++ )
-        c.s[i] = cvCeil(a.s[i]);
-    return c;
-}
-
-/** @brief Trunc
-
-Truncate each value. Input type is float vector ==> output type is int vector.*/
-template<int n> inline v_reg<int, n> v_trunc(const v_reg<float, n>& a)
-{
-    v_reg<int, n> c;
-    for( int i = 0; i < n; i++ )
-        c.s[i] = (int)(a.s[i]);
-    return c;
-}
-
-/** @overload */
-template<int n> inline v_reg<int, n*2> v_round(const v_reg<double, n>& a)
-{
-    v_reg<int, n*2> c;
-    for( int i = 0; i < n; i++ )
-    {
-        c.s[i] = cvRound(a.s[i]);
-        c.s[i+n] = 0;
-    }
-    return c;
-}
-
-/** @overload */
-template<int n> inline v_reg<int, n*2> v_floor(const v_reg<double, n>& a)
-{
-    v_reg<int, n> c;
-    for( int i = 0; i < n; i++ )
-    {
-        c.s[i] = cvFloor(a.s[i]);
-        c.s[i+n] = 0;
-    }
-    return c;
-}
-
-/** @overload */
-template<int n> inline v_reg<int, n*2> v_ceil(const v_reg<double, n>& a)
-{
-    v_reg<int, n> c;
-    for( int i = 0; i < n; i++ )
-    {
-        c.s[i] = cvCeil(a.s[i]);
-        c.s[i+n] = 0;
-    }
-    return c;
-}
-
-/** @overload */
-template<int n> inline v_reg<int, n*2> v_trunc(const v_reg<double, n>& a)
-{
-    v_reg<int, n> c;
-    for( int i = 0; i < n; i++ )
-    {
-        c.s[i] = cvCeil(a.s[i]);
-        c.s[i+n] = 0;
-    }
-    return c;
-}
-
-/** @brief Convert to float
-
-Supported input type is cv::v_int32x4. */
-template<int n> inline v_reg<float, n> v_cvt_f32(const v_reg<int, n>& a)
-{
-    v_reg<float, n> c;
-    for( int i = 0; i < n; i++ )
-        c.s[i] = (float)a.s[i];
-    return c;
-}
-
-/** @brief Convert to double
-
-Supported input type is cv::v_int32x4. */
-template<int n> inline v_reg<double, n> v_cvt_f64(const v_reg<int, n*2>& a)
-{
-    v_reg<double, n> c;
-    for( int i = 0; i < n; i++ )
-        c.s[i] = (double)a.s[i];
-    return c;
-}
-
-/** @brief Convert to double
-
-Supported input type is cv::v_float32x4. */
-template<int n> inline v_reg<double, n> v_cvt_f64(const v_reg<float, n*2>& a)
-{
-    v_reg<double, n> c;
-    for( int i = 0; i < n; i++ )
-        c.s[i] = (double)a.s[i];
-    return c;
-}
-
-/** @brief Transpose 4x4 matrix
-
-Scheme:
-@code
-a0  {A1 A2 A3 A4}
-a1  {B1 B2 B3 B4}
-a2  {C1 C2 C3 C4}
-a3  {D1 D2 D3 D4}
-===============
-b0  {A1 B1 C1 D1}
-b1  {A2 B2 C2 D2}
-b2  {A3 B3 C3 D3}
-b3  {A4 B4 C4 D4}
-@endcode
-*/
-template<typename _Tp>
-inline void v_transpose4x4( v_reg<_Tp, 4>& a0, const v_reg<_Tp, 4>& a1,
-                            const v_reg<_Tp, 4>& a2, const v_reg<_Tp, 4>& a3,
-                            v_reg<_Tp, 4>& b0, v_reg<_Tp, 4>& b1,
-                            v_reg<_Tp, 4>& b2, v_reg<_Tp, 4>& b3 )
-{
-    b0 = v_reg<_Tp, 4>(a0.s[0], a1.s[0], a2.s[0], a3.s[0]);
-    b1 = v_reg<_Tp, 4>(a0.s[1], a1.s[1], a2.s[1], a3.s[1]);
-    b2 = v_reg<_Tp, 4>(a0.s[2], a1.s[2], a2.s[2], a3.s[2]);
-    b3 = v_reg<_Tp, 4>(a0.s[3], a1.s[3], a2.s[3], a3.s[3]);
-}
-
-//! @brief Helper macro
-//! @ingroup core_hal_intrin_impl
-#define OPENCV_HAL_IMPL_C_INIT_ZERO(_Tpvec, _Tp, suffix) \
-inline _Tpvec v_setzero_##suffix() { return _Tpvec::zero(); }
-
-//! @name Init with zero
-//! @{
-//! @brief Create new vector with zero elements
-OPENCV_HAL_IMPL_C_INIT_ZERO(v_uint8x16, uchar, u8)
-OPENCV_HAL_IMPL_C_INIT_ZERO(v_int8x16, schar, s8)
-OPENCV_HAL_IMPL_C_INIT_ZERO(v_uint16x8, ushort, u16)
-OPENCV_HAL_IMPL_C_INIT_ZERO(v_int16x8, short, s16)
-OPENCV_HAL_IMPL_C_INIT_ZERO(v_uint32x4, unsigned, u32)
-OPENCV_HAL_IMPL_C_INIT_ZERO(v_int32x4, int, s32)
-OPENCV_HAL_IMPL_C_INIT_ZERO(v_float32x4, float, f32)
-OPENCV_HAL_IMPL_C_INIT_ZERO(v_float64x2, double, f64)
-OPENCV_HAL_IMPL_C_INIT_ZERO(v_uint64x2, uint64, u64)
-OPENCV_HAL_IMPL_C_INIT_ZERO(v_int64x2, int64, s64)
-//! @}
-
-//! @brief Helper macro
-//! @ingroup core_hal_intrin_impl
-#define OPENCV_HAL_IMPL_C_INIT_VAL(_Tpvec, _Tp, suffix) \
-inline _Tpvec v_setall_##suffix(_Tp val) { return _Tpvec::all(val); }
-
-//! @name Init with value
-//! @{
-//! @brief Create new vector with elements set to a specific value
-OPENCV_HAL_IMPL_C_INIT_VAL(v_uint8x16, uchar, u8)
-OPENCV_HAL_IMPL_C_INIT_VAL(v_int8x16, schar, s8)
-OPENCV_HAL_IMPL_C_INIT_VAL(v_uint16x8, ushort, u16)
-OPENCV_HAL_IMPL_C_INIT_VAL(v_int16x8, short, s16)
-OPENCV_HAL_IMPL_C_INIT_VAL(v_uint32x4, unsigned, u32)
-OPENCV_HAL_IMPL_C_INIT_VAL(v_int32x4, int, s32)
-OPENCV_HAL_IMPL_C_INIT_VAL(v_float32x4, float, f32)
-OPENCV_HAL_IMPL_C_INIT_VAL(v_float64x2, double, f64)
-OPENCV_HAL_IMPL_C_INIT_VAL(v_uint64x2, uint64, u64)
-OPENCV_HAL_IMPL_C_INIT_VAL(v_int64x2, int64, s64)
-//! @}
-
-//! @brief Helper macro
-//! @ingroup core_hal_intrin_impl
-#define OPENCV_HAL_IMPL_C_REINTERPRET(_Tpvec, _Tp, suffix) \
-template<typename _Tp0, int n0> inline _Tpvec \
-    v_reinterpret_as_##suffix(const v_reg<_Tp0, n0>& a) \
-{ return a.template reinterpret_as<_Tp, _Tpvec::nlanes>(); }
-
-//! @name Reinterpret
-//! @{
-//! @brief Convert vector to different type without modifying underlying data.
-OPENCV_HAL_IMPL_C_REINTERPRET(v_uint8x16, uchar, u8)
-OPENCV_HAL_IMPL_C_REINTERPRET(v_int8x16, schar, s8)
-OPENCV_HAL_IMPL_C_REINTERPRET(v_uint16x8, ushort, u16)
-OPENCV_HAL_IMPL_C_REINTERPRET(v_int16x8, short, s16)
-OPENCV_HAL_IMPL_C_REINTERPRET(v_uint32x4, unsigned, u32)
-OPENCV_HAL_IMPL_C_REINTERPRET(v_int32x4, int, s32)
-OPENCV_HAL_IMPL_C_REINTERPRET(v_float32x4, float, f32)
-OPENCV_HAL_IMPL_C_REINTERPRET(v_float64x2, double, f64)
-OPENCV_HAL_IMPL_C_REINTERPRET(v_uint64x2, uint64, u64)
-OPENCV_HAL_IMPL_C_REINTERPRET(v_int64x2, int64, s64)
-//! @}
-
-//! @brief Helper macro
-//! @ingroup core_hal_intrin_impl
-#define OPENCV_HAL_IMPL_C_SHIFTL(_Tpvec, _Tp) \
-template<int n> inline _Tpvec v_shl(const _Tpvec& a) \
-{ return a << n; }
-
-//! @name Left shift
-//! @{
-//! @brief Shift left
-OPENCV_HAL_IMPL_C_SHIFTL(v_uint16x8, ushort)
-OPENCV_HAL_IMPL_C_SHIFTL(v_int16x8, short)
-OPENCV_HAL_IMPL_C_SHIFTL(v_uint32x4, unsigned)
-OPENCV_HAL_IMPL_C_SHIFTL(v_int32x4, int)
-OPENCV_HAL_IMPL_C_SHIFTL(v_uint64x2, uint64)
-OPENCV_HAL_IMPL_C_SHIFTL(v_int64x2, int64)
-//! @}
-
-//! @brief Helper macro
-//! @ingroup core_hal_intrin_impl
-#define OPENCV_HAL_IMPL_C_SHIFTR(_Tpvec, _Tp) \
-template<int n> inline _Tpvec v_shr(const _Tpvec& a) \
-{ return a >> n; }
-
-//! @name Right shift
-//! @{
-//! @brief Shift right
-OPENCV_HAL_IMPL_C_SHIFTR(v_uint16x8, ushort)
-OPENCV_HAL_IMPL_C_SHIFTR(v_int16x8, short)
-OPENCV_HAL_IMPL_C_SHIFTR(v_uint32x4, unsigned)
-OPENCV_HAL_IMPL_C_SHIFTR(v_int32x4, int)
-OPENCV_HAL_IMPL_C_SHIFTR(v_uint64x2, uint64)
-OPENCV_HAL_IMPL_C_SHIFTR(v_int64x2, int64)
-//! @}
-
-//! @brief Helper macro
-//! @ingroup core_hal_intrin_impl
-#define OPENCV_HAL_IMPL_C_RSHIFTR(_Tpvec, _Tp) \
-template<int n> inline _Tpvec v_rshr(const _Tpvec& a) \
-{ \
-    _Tpvec c; \
-    for( int i = 0; i < _Tpvec::nlanes; i++ ) \
-        c.s[i] = (_Tp)((a.s[i] + ((_Tp)1 << (n - 1))) >> n); \
-    return c; \
-}
-
-//! @name Rounding shift
-//! @{
-//! @brief Rounding shift right
-OPENCV_HAL_IMPL_C_RSHIFTR(v_uint16x8, ushort)
-OPENCV_HAL_IMPL_C_RSHIFTR(v_int16x8, short)
-OPENCV_HAL_IMPL_C_RSHIFTR(v_uint32x4, unsigned)
-OPENCV_HAL_IMPL_C_RSHIFTR(v_int32x4, int)
-OPENCV_HAL_IMPL_C_RSHIFTR(v_uint64x2, uint64)
-OPENCV_HAL_IMPL_C_RSHIFTR(v_int64x2, int64)
-//! @}
-
-//! @brief Helper macro
-//! @ingroup core_hal_intrin_impl
-#define OPENCV_HAL_IMPL_C_PACK(_Tpvec, _Tpnvec, _Tpn, pack_suffix, cast) \
-inline _Tpnvec v_##pack_suffix(const _Tpvec& a, const _Tpvec& b) \
-{ \
-    _Tpnvec c; \
-    for( int i = 0; i < _Tpvec::nlanes; i++ ) \
-    { \
-        c.s[i] = cast<_Tpn>(a.s[i]); \
-        c.s[i+_Tpvec::nlanes] = cast<_Tpn>(b.s[i]); \
-    } \
-    return c; \
-}
-
-//! @name Pack
-//! @{
-//! @brief Pack values from two vectors to one
-//!
-//! Return vector type have twice more elements than input vector types. Variant with _u_ suffix also
-//! converts to corresponding unsigned type.
-//!
-//! - pack: for 16-, 32- and 64-bit integer input types
-//! - pack_u: for 16- and 32-bit signed integer input types
-//!
-//! @note All variants except 64-bit use saturation.
-OPENCV_HAL_IMPL_C_PACK(v_uint16x8, v_uint8x16, uchar, pack, saturate_cast)
-OPENCV_HAL_IMPL_C_PACK(v_int16x8, v_int8x16, schar, pack, saturate_cast)
-OPENCV_HAL_IMPL_C_PACK(v_uint32x4, v_uint16x8, ushort, pack, saturate_cast)
-OPENCV_HAL_IMPL_C_PACK(v_int32x4, v_int16x8, short, pack, saturate_cast)
-OPENCV_HAL_IMPL_C_PACK(v_uint64x2, v_uint32x4, unsigned, pack, static_cast)
-OPENCV_HAL_IMPL_C_PACK(v_int64x2, v_int32x4, int, pack, static_cast)
-OPENCV_HAL_IMPL_C_PACK(v_int16x8, v_uint8x16, uchar, pack_u, saturate_cast)
-OPENCV_HAL_IMPL_C_PACK(v_int32x4, v_uint16x8, ushort, pack_u, saturate_cast)
-//! @}
-
-//! @brief Helper macro
-//! @ingroup core_hal_intrin_impl
-#define OPENCV_HAL_IMPL_C_RSHR_PACK(_Tpvec, _Tp, _Tpnvec, _Tpn, pack_suffix, cast) \
-template<int n> inline _Tpnvec v_rshr_##pack_suffix(const _Tpvec& a, const _Tpvec& b) \
-{ \
-    _Tpnvec c; \
-    for( int i = 0; i < _Tpvec::nlanes; i++ ) \
-    { \
-        c.s[i] = cast<_Tpn>((a.s[i] + ((_Tp)1 << (n - 1))) >> n); \
-        c.s[i+_Tpvec::nlanes] = cast<_Tpn>((b.s[i] + ((_Tp)1 << (n - 1))) >> n); \
-    } \
-    return c; \
-}
-
-//! @name Pack with rounding shift
-//! @{
-//! @brief Pack values from two vectors to one with rounding shift
-//!
-//! Values from the input vectors will be shifted right by _n_ bits with rounding, converted to narrower
-//! type and returned in the result vector. Variant with _u_ suffix converts to unsigned type.
-//!
-//! - pack: for 16-, 32- and 64-bit integer input types
-//! - pack_u: for 16- and 32-bit signed integer input types
-//!
-//! @note All variants except 64-bit use saturation.
-OPENCV_HAL_IMPL_C_RSHR_PACK(v_uint16x8, ushort, v_uint8x16, uchar, pack, saturate_cast)
-OPENCV_HAL_IMPL_C_RSHR_PACK(v_int16x8, short, v_int8x16, schar, pack, saturate_cast)
-OPENCV_HAL_IMPL_C_RSHR_PACK(v_uint32x4, unsigned, v_uint16x8, ushort, pack, saturate_cast)
-OPENCV_HAL_IMPL_C_RSHR_PACK(v_int32x4, int, v_int16x8, short, pack, saturate_cast)
-OPENCV_HAL_IMPL_C_RSHR_PACK(v_uint64x2, uint64, v_uint32x4, unsigned, pack, static_cast)
-OPENCV_HAL_IMPL_C_RSHR_PACK(v_int64x2, int64, v_int32x4, int, pack, static_cast)
-OPENCV_HAL_IMPL_C_RSHR_PACK(v_int16x8, short, v_uint8x16, uchar, pack_u, saturate_cast)
-OPENCV_HAL_IMPL_C_RSHR_PACK(v_int32x4, int, v_uint16x8, ushort, pack_u, saturate_cast)
-//! @}
-
-//! @brief Helper macro
-//! @ingroup core_hal_intrin_impl
-#define OPENCV_HAL_IMPL_C_PACK_STORE(_Tpvec, _Tp, _Tpnvec, _Tpn, pack_suffix, cast) \
-inline void v_##pack_suffix##_store(_Tpn* ptr, const _Tpvec& a) \
-{ \
-    for( int i = 0; i < _Tpvec::nlanes; i++ ) \
-        ptr[i] = cast<_Tpn>(a.s[i]); \
-}
-
-//! @name Pack and store
-//! @{
-//! @brief Store values from the input vector into memory with pack
-//!
-//! Values will be stored into memory with conversion to narrower type.
-//! Variant with _u_ suffix converts to corresponding unsigned type.
-//!
-//! - pack: for 16-, 32- and 64-bit integer input types
-//! - pack_u: for 16- and 32-bit signed integer input types
-//!
-//! @note All variants except 64-bit use saturation.
-OPENCV_HAL_IMPL_C_PACK_STORE(v_uint16x8, ushort, v_uint8x16, uchar, pack, saturate_cast)
-OPENCV_HAL_IMPL_C_PACK_STORE(v_int16x8, short, v_int8x16, schar, pack, saturate_cast)
-OPENCV_HAL_IMPL_C_PACK_STORE(v_uint32x4, unsigned, v_uint16x8, ushort, pack, saturate_cast)
-OPENCV_HAL_IMPL_C_PACK_STORE(v_int32x4, int, v_int16x8, short, pack, saturate_cast)
-OPENCV_HAL_IMPL_C_PACK_STORE(v_uint64x2, uint64, v_uint32x4, unsigned, pack, static_cast)
-OPENCV_HAL_IMPL_C_PACK_STORE(v_int64x2, int64, v_int32x4, int, pack, static_cast)
-OPENCV_HAL_IMPL_C_PACK_STORE(v_int16x8, short, v_uint8x16, uchar, pack_u, saturate_cast)
-OPENCV_HAL_IMPL_C_PACK_STORE(v_int32x4, int, v_uint16x8, ushort, pack_u, saturate_cast)
-//! @}
-
-//! @brief Helper macro
-//! @ingroup core_hal_intrin_impl
-#define OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(_Tpvec, _Tp, _Tpnvec, _Tpn, pack_suffix, cast) \
-template<int n> inline void v_rshr_##pack_suffix##_store(_Tpn* ptr, const _Tpvec& a) \
-{ \
-    for( int i = 0; i < _Tpvec::nlanes; i++ ) \
-        ptr[i] = cast<_Tpn>((a.s[i] + ((_Tp)1 << (n - 1))) >> n); \
-}
-
-//! @name Pack and store with rounding shift
-//! @{
-//! @brief Store values from the input vector into memory with pack
-//!
-//! Values will be shifted _n_ bits right with rounding, converted to narrower type and stored into
-//! memory. Variant with _u_ suffix converts to unsigned type.
-//!
-//! - pack: for 16-, 32- and 64-bit integer input types
-//! - pack_u: for 16- and 32-bit signed integer input types
-//!
-//! @note All variants except 64-bit use saturation.
-OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(v_uint16x8, ushort, v_uint8x16, uchar, pack, saturate_cast)
-OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(v_int16x8, short, v_int8x16, schar, pack, saturate_cast)
-OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(v_uint32x4, unsigned, v_uint16x8, ushort, pack, saturate_cast)
-OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(v_int32x4, int, v_int16x8, short, pack, saturate_cast)
-OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(v_uint64x2, uint64, v_uint32x4, unsigned, pack, static_cast)
-OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(v_int64x2, int64, v_int32x4, int, pack, static_cast)
-OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(v_int16x8, short, v_uint8x16, uchar, pack_u, saturate_cast)
-OPENCV_HAL_IMPL_C_RSHR_PACK_STORE(v_int32x4, int, v_uint16x8, ushort, pack_u, saturate_cast)
-//! @}
-
-/** @brief Matrix multiplication
-
-Scheme:
-@code
-{A0 A1 A2 A3}   |V0|
-{B0 B1 B2 B3}   |V1|
-{C0 C1 C2 C3}   |V2|
-{D0 D1 D2 D3} x |V3|
-====================
-{R0 R1 R2 R3}, where:
-R0 = A0V0 + A1V1 + A2V2 + A3V3,
-R1 = B0V0 + B1V1 + B2V2 + B3V3
-...
-@endcode
-*/
-inline v_float32x4 v_matmul(const v_float32x4& v, const v_float32x4& m0,
-                            const v_float32x4& m1, const v_float32x4& m2,
-                            const v_float32x4& m3)
-{
-    return v_float32x4(v.s[0]*m0.s[0] + v.s[1]*m1.s[0] + v.s[2]*m2.s[0] + v.s[3]*m3.s[0],
-                       v.s[0]*m0.s[1] + v.s[1]*m1.s[1] + v.s[2]*m2.s[1] + v.s[3]*m3.s[1],
-                       v.s[0]*m0.s[2] + v.s[1]*m1.s[2] + v.s[2]*m2.s[2] + v.s[3]*m3.s[2],
-                       v.s[0]*m0.s[3] + v.s[1]*m1.s[3] + v.s[2]*m2.s[3] + v.s[3]*m3.s[3]);
-}
-
-/** @brief Matrix multiplication and add
-
-Scheme:
-@code
-{A0 A1 A2   }   |V0|   |D0|
-{B0 B1 B2   }   |V1|   |D1|
-{C0 C1 C2   } x |V2| + |D2|
-====================
-{R0 R1 R2 R3}, where:
-R0 = A0V0 + A1V1 + A2V2 + D0,
-R1 = B0V0 + B1V1 + B2V2 + D1
-...
-@endcode
-*/
-inline v_float32x4 v_matmuladd(const v_float32x4& v, const v_float32x4& m0,
-                               const v_float32x4& m1, const v_float32x4& m2,
-                               const v_float32x4& m3)
-{
-    return v_float32x4(v.s[0]*m0.s[0] + v.s[1]*m1.s[0] + v.s[2]*m2.s[0] + m3.s[0],
-                       v.s[0]*m0.s[1] + v.s[1]*m1.s[1] + v.s[2]*m2.s[1] + m3.s[1],
-                       v.s[0]*m0.s[2] + v.s[1]*m1.s[2] + v.s[2]*m2.s[2] + m3.s[2],
-                       v.s[0]*m0.s[3] + v.s[1]*m1.s[3] + v.s[2]*m2.s[3] + m3.s[3]);
-}
-
-//! @}
-
-//! @name Check SIMD support
-//! @{
-//! @brief Check CPU capability of SIMD operation
-static inline bool hasSIMD128()
-{
-    return false;
-}
-
-//! @}
-
-#ifndef CV_DOXYGEN
-CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END
-#endif
-}
-
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/hal/intrin_neon.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/hal/intrin_neon.hpp
deleted file mode 100755
index 9dadab5..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/hal/intrin_neon.hpp
+++ /dev/null
@@ -1,1345 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
-// Copyright (C) 2015, Itseez Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_HAL_INTRIN_NEON_HPP
-#define OPENCV_HAL_INTRIN_NEON_HPP
-
-#include <algorithm>
-#include "opencv2/core/utility.hpp"
-
-namespace cv
-{
-
-//! @cond IGNORED
-
-CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN
-
-#define CV_SIMD128 1
-#if defined(__aarch64__)
-#define CV_SIMD128_64F 1
-#else
-#define CV_SIMD128_64F 0
-#endif
-
-#if CV_SIMD128_64F
-#define OPENCV_HAL_IMPL_NEON_REINTERPRET(_Tpv, suffix) \
-template <typename T> static inline \
-_Tpv vreinterpretq_##suffix##_f64(T a) { return (_Tpv) a; } \
-template <typename T> static inline \
-float64x2_t vreinterpretq_f64_##suffix(T a) { return (float64x2_t) a; }
-OPENCV_HAL_IMPL_NEON_REINTERPRET(uint8x16_t, u8)
-OPENCV_HAL_IMPL_NEON_REINTERPRET(int8x16_t, s8)
-OPENCV_HAL_IMPL_NEON_REINTERPRET(uint16x8_t, u16)
-OPENCV_HAL_IMPL_NEON_REINTERPRET(int16x8_t, s16)
-OPENCV_HAL_IMPL_NEON_REINTERPRET(uint32x4_t, u32)
-OPENCV_HAL_IMPL_NEON_REINTERPRET(int32x4_t, s32)
-OPENCV_HAL_IMPL_NEON_REINTERPRET(uint64x2_t, u64)
-OPENCV_HAL_IMPL_NEON_REINTERPRET(int64x2_t, s64)
-OPENCV_HAL_IMPL_NEON_REINTERPRET(float32x4_t, f32)
-#endif
-
-struct v_uint8x16
-{
-    typedef uchar lane_type;
-    enum { nlanes = 16 };
-
-    v_uint8x16() {}
-    explicit v_uint8x16(uint8x16_t v) : val(v) {}
-    v_uint8x16(uchar v0, uchar v1, uchar v2, uchar v3, uchar v4, uchar v5, uchar v6, uchar v7,
-               uchar v8, uchar v9, uchar v10, uchar v11, uchar v12, uchar v13, uchar v14, uchar v15)
-    {
-        uchar v[] = {v0, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15};
-        val = vld1q_u8(v);
-    }
-    uchar get0() const
-    {
-        return vgetq_lane_u8(val, 0);
-    }
-
-    uint8x16_t val;
-};
-
-struct v_int8x16
-{
-    typedef schar lane_type;
-    enum { nlanes = 16 };
-
-    v_int8x16() {}
-    explicit v_int8x16(int8x16_t v) : val(v) {}
-    v_int8x16(schar v0, schar v1, schar v2, schar v3, schar v4, schar v5, schar v6, schar v7,
-               schar v8, schar v9, schar v10, schar v11, schar v12, schar v13, schar v14, schar v15)
-    {
-        schar v[] = {v0, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15};
-        val = vld1q_s8(v);
-    }
-    schar get0() const
-    {
-        return vgetq_lane_s8(val, 0);
-    }
-
-    int8x16_t val;
-};
-
-struct v_uint16x8
-{
-    typedef ushort lane_type;
-    enum { nlanes = 8 };
-
-    v_uint16x8() {}
-    explicit v_uint16x8(uint16x8_t v) : val(v) {}
-    v_uint16x8(ushort v0, ushort v1, ushort v2, ushort v3, ushort v4, ushort v5, ushort v6, ushort v7)
-    {
-        ushort v[] = {v0, v1, v2, v3, v4, v5, v6, v7};
-        val = vld1q_u16(v);
-    }
-    ushort get0() const
-    {
-        return vgetq_lane_u16(val, 0);
-    }
-
-    uint16x8_t val;
-};
-
-struct v_int16x8
-{
-    typedef short lane_type;
-    enum { nlanes = 8 };
-
-    v_int16x8() {}
-    explicit v_int16x8(int16x8_t v) : val(v) {}
-    v_int16x8(short v0, short v1, short v2, short v3, short v4, short v5, short v6, short v7)
-    {
-        short v[] = {v0, v1, v2, v3, v4, v5, v6, v7};
-        val = vld1q_s16(v);
-    }
-    short get0() const
-    {
-        return vgetq_lane_s16(val, 0);
-    }
-
-    int16x8_t val;
-};
-
-struct v_uint32x4
-{
-    typedef unsigned lane_type;
-    enum { nlanes = 4 };
-
-    v_uint32x4() {}
-    explicit v_uint32x4(uint32x4_t v) : val(v) {}
-    v_uint32x4(unsigned v0, unsigned v1, unsigned v2, unsigned v3)
-    {
-        unsigned v[] = {v0, v1, v2, v3};
-        val = vld1q_u32(v);
-    }
-    unsigned get0() const
-    {
-        return vgetq_lane_u32(val, 0);
-    }
-
-    uint32x4_t val;
-};
-
-struct v_int32x4
-{
-    typedef int lane_type;
-    enum { nlanes = 4 };
-
-    v_int32x4() {}
-    explicit v_int32x4(int32x4_t v) : val(v) {}
-    v_int32x4(int v0, int v1, int v2, int v3)
-    {
-        int v[] = {v0, v1, v2, v3};
-        val = vld1q_s32(v);
-    }
-    int get0() const
-    {
-        return vgetq_lane_s32(val, 0);
-    }
-    int32x4_t val;
-};
-
-struct v_float32x4
-{
-    typedef float lane_type;
-    enum { nlanes = 4 };
-
-    v_float32x4() {}
-    explicit v_float32x4(float32x4_t v) : val(v) {}
-    v_float32x4(float v0, float v1, float v2, float v3)
-    {
-        float v[] = {v0, v1, v2, v3};
-        val = vld1q_f32(v);
-    }
-    float get0() const
-    {
-        return vgetq_lane_f32(val, 0);
-    }
-    float32x4_t val;
-};
-
-struct v_uint64x2
-{
-    typedef uint64 lane_type;
-    enum { nlanes = 2 };
-
-    v_uint64x2() {}
-    explicit v_uint64x2(uint64x2_t v) : val(v) {}
-    v_uint64x2(uint64 v0, uint64 v1)
-    {
-        uint64 v[] = {v0, v1};
-        val = vld1q_u64(v);
-    }
-    uint64 get0() const
-    {
-        return vgetq_lane_u64(val, 0);
-    }
-    uint64x2_t val;
-};
-
-struct v_int64x2
-{
-    typedef int64 lane_type;
-    enum { nlanes = 2 };
-
-    v_int64x2() {}
-    explicit v_int64x2(int64x2_t v) : val(v) {}
-    v_int64x2(int64 v0, int64 v1)
-    {
-        int64 v[] = {v0, v1};
-        val = vld1q_s64(v);
-    }
-    int64 get0() const
-    {
-        return vgetq_lane_s64(val, 0);
-    }
-    int64x2_t val;
-};
-
-#if CV_SIMD128_64F
-struct v_float64x2
-{
-    typedef double lane_type;
-    enum { nlanes = 2 };
-
-    v_float64x2() {}
-    explicit v_float64x2(float64x2_t v) : val(v) {}
-    v_float64x2(double v0, double v1)
-    {
-        double v[] = {v0, v1};
-        val = vld1q_f64(v);
-    }
-    double get0() const
-    {
-        return vgetq_lane_f64(val, 0);
-    }
-    float64x2_t val;
-};
-#endif
-
-#if CV_FP16
-// Workaround for old compilers
-template <typename T> static inline int16x4_t vreinterpret_s16_f16(T a)
-{ return (int16x4_t)a; }
-template <typename T> static inline float16x4_t vreinterpret_f16_s16(T a)
-{ return (float16x4_t)a; }
-template <typename T> static inline float16x4_t cv_vld1_f16(const T* ptr)
-{
-#ifndef vld1_f16 // APPLE compiler defines vld1_f16 as macro
-    return vreinterpret_f16_s16(vld1_s16((const short*)ptr));
-#else
-    return vld1_f16((const __fp16*)ptr);
-#endif
-}
-template <typename T> static inline void cv_vst1_f16(T* ptr, float16x4_t a)
-{
-#ifndef vst1_f16 // APPLE compiler defines vst1_f16 as macro
-    vst1_s16((short*)ptr, vreinterpret_s16_f16(a));
-#else
-    vst1_f16((__fp16*)ptr, a);
-#endif
-}
-
-struct v_float16x4
-{
-    typedef short lane_type;
-    enum { nlanes = 4 };
-
-    v_float16x4() {}
-    explicit v_float16x4(float16x4_t v) : val(v) {}
-    v_float16x4(short v0, short v1, short v2, short v3)
-    {
-        short v[] = {v0, v1, v2, v3};
-        val = cv_vld1_f16(v);
-    }
-    short get0() const
-    {
-        return vget_lane_s16(vreinterpret_s16_f16(val), 0);
-    }
-    float16x4_t val;
-};
-#endif
-
-#define OPENCV_HAL_IMPL_NEON_INIT(_Tpv, _Tp, suffix) \
-inline v_##_Tpv v_setzero_##suffix() { return v_##_Tpv(vdupq_n_##suffix((_Tp)0)); } \
-inline v_##_Tpv v_setall_##suffix(_Tp v) { return v_##_Tpv(vdupq_n_##suffix(v)); } \
-inline _Tpv##_t vreinterpretq_##suffix##_##suffix(_Tpv##_t v) { return v; } \
-inline v_uint8x16 v_reinterpret_as_u8(const v_##_Tpv& v) { return v_uint8x16(vreinterpretq_u8_##suffix(v.val)); } \
-inline v_int8x16 v_reinterpret_as_s8(const v_##_Tpv& v) { return v_int8x16(vreinterpretq_s8_##suffix(v.val)); } \
-inline v_uint16x8 v_reinterpret_as_u16(const v_##_Tpv& v) { return v_uint16x8(vreinterpretq_u16_##suffix(v.val)); } \
-inline v_int16x8 v_reinterpret_as_s16(const v_##_Tpv& v) { return v_int16x8(vreinterpretq_s16_##suffix(v.val)); } \
-inline v_uint32x4 v_reinterpret_as_u32(const v_##_Tpv& v) { return v_uint32x4(vreinterpretq_u32_##suffix(v.val)); } \
-inline v_int32x4 v_reinterpret_as_s32(const v_##_Tpv& v) { return v_int32x4(vreinterpretq_s32_##suffix(v.val)); } \
-inline v_uint64x2 v_reinterpret_as_u64(const v_##_Tpv& v) { return v_uint64x2(vreinterpretq_u64_##suffix(v.val)); } \
-inline v_int64x2 v_reinterpret_as_s64(const v_##_Tpv& v) { return v_int64x2(vreinterpretq_s64_##suffix(v.val)); } \
-inline v_float32x4 v_reinterpret_as_f32(const v_##_Tpv& v) { return v_float32x4(vreinterpretq_f32_##suffix(v.val)); }
-
-OPENCV_HAL_IMPL_NEON_INIT(uint8x16, uchar, u8)
-OPENCV_HAL_IMPL_NEON_INIT(int8x16, schar, s8)
-OPENCV_HAL_IMPL_NEON_INIT(uint16x8, ushort, u16)
-OPENCV_HAL_IMPL_NEON_INIT(int16x8, short, s16)
-OPENCV_HAL_IMPL_NEON_INIT(uint32x4, unsigned, u32)
-OPENCV_HAL_IMPL_NEON_INIT(int32x4, int, s32)
-OPENCV_HAL_IMPL_NEON_INIT(uint64x2, uint64, u64)
-OPENCV_HAL_IMPL_NEON_INIT(int64x2, int64, s64)
-OPENCV_HAL_IMPL_NEON_INIT(float32x4, float, f32)
-#if CV_SIMD128_64F
-#define OPENCV_HAL_IMPL_NEON_INIT_64(_Tpv, suffix) \
-inline v_float64x2 v_reinterpret_as_f64(const v_##_Tpv& v) { return v_float64x2(vreinterpretq_f64_##suffix(v.val)); }
-OPENCV_HAL_IMPL_NEON_INIT(float64x2, double, f64)
-OPENCV_HAL_IMPL_NEON_INIT_64(uint8x16, u8)
-OPENCV_HAL_IMPL_NEON_INIT_64(int8x16, s8)
-OPENCV_HAL_IMPL_NEON_INIT_64(uint16x8, u16)
-OPENCV_HAL_IMPL_NEON_INIT_64(int16x8, s16)
-OPENCV_HAL_IMPL_NEON_INIT_64(uint32x4, u32)
-OPENCV_HAL_IMPL_NEON_INIT_64(int32x4, s32)
-OPENCV_HAL_IMPL_NEON_INIT_64(uint64x2, u64)
-OPENCV_HAL_IMPL_NEON_INIT_64(int64x2, s64)
-OPENCV_HAL_IMPL_NEON_INIT_64(float32x4, f32)
-OPENCV_HAL_IMPL_NEON_INIT_64(float64x2, f64)
-#endif
-
-#define OPENCV_HAL_IMPL_NEON_PACK(_Tpvec, _Tp, hreg, suffix, _Tpwvec, pack, mov, rshr) \
-inline _Tpvec v_##pack(const _Tpwvec& a, const _Tpwvec& b) \
-{ \
-    hreg a1 = mov(a.val), b1 = mov(b.val); \
-    return _Tpvec(vcombine_##suffix(a1, b1)); \
-} \
-inline void v_##pack##_store(_Tp* ptr, const _Tpwvec& a) \
-{ \
-    hreg a1 = mov(a.val); \
-    vst1_##suffix(ptr, a1); \
-} \
-template<int n> inline \
-_Tpvec v_rshr_##pack(const _Tpwvec& a, const _Tpwvec& b) \
-{ \
-    hreg a1 = rshr(a.val, n); \
-    hreg b1 = rshr(b.val, n); \
-    return _Tpvec(vcombine_##suffix(a1, b1)); \
-} \
-template<int n> inline \
-void v_rshr_##pack##_store(_Tp* ptr, const _Tpwvec& a) \
-{ \
-    hreg a1 = rshr(a.val, n); \
-    vst1_##suffix(ptr, a1); \
-}
-
-OPENCV_HAL_IMPL_NEON_PACK(v_uint8x16, uchar, uint8x8_t, u8, v_uint16x8, pack, vqmovn_u16, vqrshrn_n_u16)
-OPENCV_HAL_IMPL_NEON_PACK(v_int8x16, schar, int8x8_t, s8, v_int16x8, pack, vqmovn_s16, vqrshrn_n_s16)
-OPENCV_HAL_IMPL_NEON_PACK(v_uint16x8, ushort, uint16x4_t, u16, v_uint32x4, pack, vqmovn_u32, vqrshrn_n_u32)
-OPENCV_HAL_IMPL_NEON_PACK(v_int16x8, short, int16x4_t, s16, v_int32x4, pack, vqmovn_s32, vqrshrn_n_s32)
-OPENCV_HAL_IMPL_NEON_PACK(v_uint32x4, unsigned, uint32x2_t, u32, v_uint64x2, pack, vmovn_u64, vrshrn_n_u64)
-OPENCV_HAL_IMPL_NEON_PACK(v_int32x4, int, int32x2_t, s32, v_int64x2, pack, vmovn_s64, vrshrn_n_s64)
-
-OPENCV_HAL_IMPL_NEON_PACK(v_uint8x16, uchar, uint8x8_t, u8, v_int16x8, pack_u, vqmovun_s16, vqrshrun_n_s16)
-OPENCV_HAL_IMPL_NEON_PACK(v_uint16x8, ushort, uint16x4_t, u16, v_int32x4, pack_u, vqmovun_s32, vqrshrun_n_s32)
-
-inline v_float32x4 v_matmul(const v_float32x4& v, const v_float32x4& m0,
-                            const v_float32x4& m1, const v_float32x4& m2,
-                            const v_float32x4& m3)
-{
-    float32x2_t vl = vget_low_f32(v.val), vh = vget_high_f32(v.val);
-    float32x4_t res = vmulq_lane_f32(m0.val, vl, 0);
-    res = vmlaq_lane_f32(res, m1.val, vl, 1);
-    res = vmlaq_lane_f32(res, m2.val, vh, 0);
-    res = vmlaq_lane_f32(res, m3.val, vh, 1);
-    return v_float32x4(res);
-}
-
-inline v_float32x4 v_matmuladd(const v_float32x4& v, const v_float32x4& m0,
-                               const v_float32x4& m1, const v_float32x4& m2,
-                               const v_float32x4& a)
-{
-    float32x2_t vl = vget_low_f32(v.val), vh = vget_high_f32(v.val);
-    float32x4_t res = vmulq_lane_f32(m0.val, vl, 0);
-    res = vmlaq_lane_f32(res, m1.val, vl, 1);
-    res = vmlaq_lane_f32(res, m2.val, vh, 0);
-    res = vaddq_f32(res, a.val);
-    return v_float32x4(res);
-}
-
-#define OPENCV_HAL_IMPL_NEON_BIN_OP(bin_op, _Tpvec, intrin) \
-inline _Tpvec operator bin_op (const _Tpvec& a, const _Tpvec& b) \
-{ \
-    return _Tpvec(intrin(a.val, b.val)); \
-} \
-inline _Tpvec& operator bin_op##= (_Tpvec& a, const _Tpvec& b) \
-{ \
-    a.val = intrin(a.val, b.val); \
-    return a; \
-}
-
-OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_uint8x16, vqaddq_u8)
-OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_uint8x16, vqsubq_u8)
-OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_int8x16, vqaddq_s8)
-OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_int8x16, vqsubq_s8)
-OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_uint16x8, vqaddq_u16)
-OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_uint16x8, vqsubq_u16)
-OPENCV_HAL_IMPL_NEON_BIN_OP(*, v_uint16x8, vmulq_u16)
-OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_int16x8, vqaddq_s16)
-OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_int16x8, vqsubq_s16)
-OPENCV_HAL_IMPL_NEON_BIN_OP(*, v_int16x8, vmulq_s16)
-OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_int32x4, vaddq_s32)
-OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_int32x4, vsubq_s32)
-OPENCV_HAL_IMPL_NEON_BIN_OP(*, v_int32x4, vmulq_s32)
-OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_uint32x4, vaddq_u32)
-OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_uint32x4, vsubq_u32)
-OPENCV_HAL_IMPL_NEON_BIN_OP(*, v_uint32x4, vmulq_u32)
-OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_float32x4, vaddq_f32)
-OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_float32x4, vsubq_f32)
-OPENCV_HAL_IMPL_NEON_BIN_OP(*, v_float32x4, vmulq_f32)
-OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_int64x2, vaddq_s64)
-OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_int64x2, vsubq_s64)
-OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_uint64x2, vaddq_u64)
-OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_uint64x2, vsubq_u64)
-#if CV_SIMD128_64F
-OPENCV_HAL_IMPL_NEON_BIN_OP(/, v_float32x4, vdivq_f32)
-OPENCV_HAL_IMPL_NEON_BIN_OP(+, v_float64x2, vaddq_f64)
-OPENCV_HAL_IMPL_NEON_BIN_OP(-, v_float64x2, vsubq_f64)
-OPENCV_HAL_IMPL_NEON_BIN_OP(*, v_float64x2, vmulq_f64)
-OPENCV_HAL_IMPL_NEON_BIN_OP(/, v_float64x2, vdivq_f64)
-#else
-inline v_float32x4 operator / (const v_float32x4& a, const v_float32x4& b)
-{
-    float32x4_t reciprocal = vrecpeq_f32(b.val);
-    reciprocal = vmulq_f32(vrecpsq_f32(b.val, reciprocal), reciprocal);
-    reciprocal = vmulq_f32(vrecpsq_f32(b.val, reciprocal), reciprocal);
-    return v_float32x4(vmulq_f32(a.val, reciprocal));
-}
-inline v_float32x4& operator /= (v_float32x4& a, const v_float32x4& b)
-{
-    float32x4_t reciprocal = vrecpeq_f32(b.val);
-    reciprocal = vmulq_f32(vrecpsq_f32(b.val, reciprocal), reciprocal);
-    reciprocal = vmulq_f32(vrecpsq_f32(b.val, reciprocal), reciprocal);
-    a.val = vmulq_f32(a.val, reciprocal);
-    return a;
-}
-#endif
-
-inline void v_mul_expand(const v_int16x8& a, const v_int16x8& b,
-                         v_int32x4& c, v_int32x4& d)
-{
-    c.val = vmull_s16(vget_low_s16(a.val), vget_low_s16(b.val));
-    d.val = vmull_s16(vget_high_s16(a.val), vget_high_s16(b.val));
-}
-
-inline void v_mul_expand(const v_uint16x8& a, const v_uint16x8& b,
-                         v_uint32x4& c, v_uint32x4& d)
-{
-    c.val = vmull_u16(vget_low_u16(a.val), vget_low_u16(b.val));
-    d.val = vmull_u16(vget_high_u16(a.val), vget_high_u16(b.val));
-}
-
-inline void v_mul_expand(const v_uint32x4& a, const v_uint32x4& b,
-                         v_uint64x2& c, v_uint64x2& d)
-{
-    c.val = vmull_u32(vget_low_u32(a.val), vget_low_u32(b.val));
-    d.val = vmull_u32(vget_high_u32(a.val), vget_high_u32(b.val));
-}
-
-inline v_int32x4 v_dotprod(const v_int16x8& a, const v_int16x8& b)
-{
-    int32x4_t c = vmull_s16(vget_low_s16(a.val), vget_low_s16(b.val));
-    int32x4_t d = vmull_s16(vget_high_s16(a.val), vget_high_s16(b.val));
-    int32x4x2_t cd = vuzpq_s32(c, d);
-    return v_int32x4(vaddq_s32(cd.val[0], cd.val[1]));
-}
-
-inline v_int32x4 v_dotprod(const v_int16x8& a, const v_int16x8& b, const v_int32x4& c)
-{
-    v_int32x4 s = v_dotprod(a, b);
-    return v_int32x4(vaddq_s32(s.val , c.val));
-}
-
-#define OPENCV_HAL_IMPL_NEON_LOGIC_OP(_Tpvec, suffix) \
-    OPENCV_HAL_IMPL_NEON_BIN_OP(&, _Tpvec, vandq_##suffix) \
-    OPENCV_HAL_IMPL_NEON_BIN_OP(|, _Tpvec, vorrq_##suffix) \
-    OPENCV_HAL_IMPL_NEON_BIN_OP(^, _Tpvec, veorq_##suffix) \
-    inline _Tpvec operator ~ (const _Tpvec& a) \
-    { \
-        return _Tpvec(vreinterpretq_##suffix##_u8(vmvnq_u8(vreinterpretq_u8_##suffix(a.val)))); \
-    }
-
-OPENCV_HAL_IMPL_NEON_LOGIC_OP(v_uint8x16, u8)
-OPENCV_HAL_IMPL_NEON_LOGIC_OP(v_int8x16, s8)
-OPENCV_HAL_IMPL_NEON_LOGIC_OP(v_uint16x8, u16)
-OPENCV_HAL_IMPL_NEON_LOGIC_OP(v_int16x8, s16)
-OPENCV_HAL_IMPL_NEON_LOGIC_OP(v_uint32x4, u32)
-OPENCV_HAL_IMPL_NEON_LOGIC_OP(v_int32x4, s32)
-OPENCV_HAL_IMPL_NEON_LOGIC_OP(v_uint64x2, u64)
-OPENCV_HAL_IMPL_NEON_LOGIC_OP(v_int64x2, s64)
-
-#define OPENCV_HAL_IMPL_NEON_FLT_BIT_OP(bin_op, intrin) \
-inline v_float32x4 operator bin_op (const v_float32x4& a, const v_float32x4& b) \
-{ \
-    return v_float32x4(vreinterpretq_f32_s32(intrin(vreinterpretq_s32_f32(a.val), vreinterpretq_s32_f32(b.val)))); \
-} \
-inline v_float32x4& operator bin_op##= (v_float32x4& a, const v_float32x4& b) \
-{ \
-    a.val = vreinterpretq_f32_s32(intrin(vreinterpretq_s32_f32(a.val), vreinterpretq_s32_f32(b.val))); \
-    return a; \
-}
-
-OPENCV_HAL_IMPL_NEON_FLT_BIT_OP(&, vandq_s32)
-OPENCV_HAL_IMPL_NEON_FLT_BIT_OP(|, vorrq_s32)
-OPENCV_HAL_IMPL_NEON_FLT_BIT_OP(^, veorq_s32)
-
-inline v_float32x4 operator ~ (const v_float32x4& a)
-{
-    return v_float32x4(vreinterpretq_f32_s32(vmvnq_s32(vreinterpretq_s32_f32(a.val))));
-}
-
-#if CV_SIMD128_64F
-inline v_float32x4 v_sqrt(const v_float32x4& x)
-{
-    return v_float32x4(vsqrtq_f32(x.val));
-}
-
-inline v_float32x4 v_invsqrt(const v_float32x4& x)
-{
-    v_float32x4 one = v_setall_f32(1.0f);
-    return one / v_sqrt(x);
-}
-#else
-inline v_float32x4 v_sqrt(const v_float32x4& x)
-{
-    float32x4_t x1 = vmaxq_f32(x.val, vdupq_n_f32(FLT_MIN));
-    float32x4_t e = vrsqrteq_f32(x1);
-    e = vmulq_f32(vrsqrtsq_f32(vmulq_f32(x1, e), e), e);
-    e = vmulq_f32(vrsqrtsq_f32(vmulq_f32(x1, e), e), e);
-    return v_float32x4(vmulq_f32(x.val, e));
-}
-
-inline v_float32x4 v_invsqrt(const v_float32x4& x)
-{
-    float32x4_t e = vrsqrteq_f32(x.val);
-    e = vmulq_f32(vrsqrtsq_f32(vmulq_f32(x.val, e), e), e);
-    e = vmulq_f32(vrsqrtsq_f32(vmulq_f32(x.val, e), e), e);
-    return v_float32x4(e);
-}
-#endif
-
-#define OPENCV_HAL_IMPL_NEON_ABS(_Tpuvec, _Tpsvec, usuffix, ssuffix) \
-inline _Tpuvec v_abs(const _Tpsvec& a) { return v_reinterpret_as_##usuffix(_Tpsvec(vabsq_##ssuffix(a.val))); }
-
-OPENCV_HAL_IMPL_NEON_ABS(v_uint8x16, v_int8x16, u8, s8)
-OPENCV_HAL_IMPL_NEON_ABS(v_uint16x8, v_int16x8, u16, s16)
-OPENCV_HAL_IMPL_NEON_ABS(v_uint32x4, v_int32x4, u32, s32)
-
-inline v_float32x4 v_abs(v_float32x4 x)
-{ return v_float32x4(vabsq_f32(x.val)); }
-
-#if CV_SIMD128_64F
-#define OPENCV_HAL_IMPL_NEON_DBL_BIT_OP(bin_op, intrin) \
-inline v_float64x2 operator bin_op (const v_float64x2& a, const v_float64x2& b) \
-{ \
-    return v_float64x2(vreinterpretq_f64_s64(intrin(vreinterpretq_s64_f64(a.val), vreinterpretq_s64_f64(b.val)))); \
-} \
-inline v_float64x2& operator bin_op##= (v_float64x2& a, const v_float64x2& b) \
-{ \
-    a.val = vreinterpretq_f64_s64(intrin(vreinterpretq_s64_f64(a.val), vreinterpretq_s64_f64(b.val))); \
-    return a; \
-}
-
-OPENCV_HAL_IMPL_NEON_DBL_BIT_OP(&, vandq_s64)
-OPENCV_HAL_IMPL_NEON_DBL_BIT_OP(|, vorrq_s64)
-OPENCV_HAL_IMPL_NEON_DBL_BIT_OP(^, veorq_s64)
-
-inline v_float64x2 operator ~ (const v_float64x2& a)
-{
-    return v_float64x2(vreinterpretq_f64_s32(vmvnq_s32(vreinterpretq_s32_f64(a.val))));
-}
-
-inline v_float64x2 v_sqrt(const v_float64x2& x)
-{
-    return v_float64x2(vsqrtq_f64(x.val));
-}
-
-inline v_float64x2 v_invsqrt(const v_float64x2& x)
-{
-    v_float64x2 one = v_setall_f64(1.0f);
-    return one / v_sqrt(x);
-}
-
-inline v_float64x2 v_abs(v_float64x2 x)
-{ return v_float64x2(vabsq_f64(x.val)); }
-#endif
-
-// TODO: exp, log, sin, cos
-
-#define OPENCV_HAL_IMPL_NEON_BIN_FUNC(_Tpvec, func, intrin) \
-inline _Tpvec func(const _Tpvec& a, const _Tpvec& b) \
-{ \
-    return _Tpvec(intrin(a.val, b.val)); \
-}
-
-OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint8x16, v_min, vminq_u8)
-OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint8x16, v_max, vmaxq_u8)
-OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int8x16, v_min, vminq_s8)
-OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int8x16, v_max, vmaxq_s8)
-OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint16x8, v_min, vminq_u16)
-OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint16x8, v_max, vmaxq_u16)
-OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int16x8, v_min, vminq_s16)
-OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int16x8, v_max, vmaxq_s16)
-OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint32x4, v_min, vminq_u32)
-OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint32x4, v_max, vmaxq_u32)
-OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int32x4, v_min, vminq_s32)
-OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int32x4, v_max, vmaxq_s32)
-OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_float32x4, v_min, vminq_f32)
-OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_float32x4, v_max, vmaxq_f32)
-#if CV_SIMD128_64F
-OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_float64x2, v_min, vminq_f64)
-OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_float64x2, v_max, vmaxq_f64)
-#endif
-
-#if CV_SIMD128_64F
-inline int64x2_t vmvnq_s64(int64x2_t a)
-{
-    int64x2_t vx = vreinterpretq_s64_u32(vdupq_n_u32(0xFFFFFFFF));
-    return veorq_s64(a, vx);
-}
-inline uint64x2_t vmvnq_u64(uint64x2_t a)
-{
-    uint64x2_t vx = vreinterpretq_u64_u32(vdupq_n_u32(0xFFFFFFFF));
-    return veorq_u64(a, vx);
-}
-#endif
-#define OPENCV_HAL_IMPL_NEON_INT_CMP_OP(_Tpvec, cast, suffix, not_suffix) \
-inline _Tpvec operator == (const _Tpvec& a, const _Tpvec& b) \
-{ return _Tpvec(cast(vceqq_##suffix(a.val, b.val))); } \
-inline _Tpvec operator != (const _Tpvec& a, const _Tpvec& b) \
-{ return _Tpvec(cast(vmvnq_##not_suffix(vceqq_##suffix(a.val, b.val)))); } \
-inline _Tpvec operator < (const _Tpvec& a, const _Tpvec& b) \
-{ return _Tpvec(cast(vcltq_##suffix(a.val, b.val))); } \
-inline _Tpvec operator > (const _Tpvec& a, const _Tpvec& b) \
-{ return _Tpvec(cast(vcgtq_##suffix(a.val, b.val))); } \
-inline _Tpvec operator <= (const _Tpvec& a, const _Tpvec& b) \
-{ return _Tpvec(cast(vcleq_##suffix(a.val, b.val))); } \
-inline _Tpvec operator >= (const _Tpvec& a, const _Tpvec& b) \
-{ return _Tpvec(cast(vcgeq_##suffix(a.val, b.val))); }
-
-OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_uint8x16, OPENCV_HAL_NOP, u8, u8)
-OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_int8x16, vreinterpretq_s8_u8, s8, u8)
-OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_uint16x8, OPENCV_HAL_NOP, u16, u16)
-OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_int16x8, vreinterpretq_s16_u16, s16, u16)
-OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_uint32x4, OPENCV_HAL_NOP, u32, u32)
-OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_int32x4, vreinterpretq_s32_u32, s32, u32)
-OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_float32x4, vreinterpretq_f32_u32, f32, u32)
-#if CV_SIMD128_64F
-OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_uint64x2, OPENCV_HAL_NOP, u64, u64)
-OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_int64x2, vreinterpretq_s64_u64, s64, u64)
-OPENCV_HAL_IMPL_NEON_INT_CMP_OP(v_float64x2, vreinterpretq_f64_u64, f64, u64)
-#endif
-
-OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint8x16, v_add_wrap, vaddq_u8)
-OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int8x16, v_add_wrap, vaddq_s8)
-OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint16x8, v_add_wrap, vaddq_u16)
-OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int16x8, v_add_wrap, vaddq_s16)
-OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint8x16, v_sub_wrap, vsubq_u8)
-OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int8x16, v_sub_wrap, vsubq_s8)
-OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint16x8, v_sub_wrap, vsubq_u16)
-OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_int16x8, v_sub_wrap, vsubq_s16)
-
-// TODO: absdiff for signed integers
-OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint8x16, v_absdiff, vabdq_u8)
-OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint16x8, v_absdiff, vabdq_u16)
-OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_uint32x4, v_absdiff, vabdq_u32)
-OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_float32x4, v_absdiff, vabdq_f32)
-#if CV_SIMD128_64F
-OPENCV_HAL_IMPL_NEON_BIN_FUNC(v_float64x2, v_absdiff, vabdq_f64)
-#endif
-
-#define OPENCV_HAL_IMPL_NEON_BIN_FUNC2(_Tpvec, _Tpvec2, cast, func, intrin) \
-inline _Tpvec2 func(const _Tpvec& a, const _Tpvec& b) \
-{ \
-    return _Tpvec2(cast(intrin(a.val, b.val))); \
-}
-
-OPENCV_HAL_IMPL_NEON_BIN_FUNC2(v_int8x16, v_uint8x16, vreinterpretq_u8_s8, v_absdiff, vabdq_s8)
-OPENCV_HAL_IMPL_NEON_BIN_FUNC2(v_int16x8, v_uint16x8, vreinterpretq_u16_s16, v_absdiff, vabdq_s16)
-OPENCV_HAL_IMPL_NEON_BIN_FUNC2(v_int32x4, v_uint32x4, vreinterpretq_u32_s32, v_absdiff, vabdq_s32)
-
-inline v_float32x4 v_magnitude(const v_float32x4& a, const v_float32x4& b)
-{
-    v_float32x4 x(vmlaq_f32(vmulq_f32(a.val, a.val), b.val, b.val));
-    return v_sqrt(x);
-}
-
-inline v_float32x4 v_sqr_magnitude(const v_float32x4& a, const v_float32x4& b)
-{
-    return v_float32x4(vmlaq_f32(vmulq_f32(a.val, a.val), b.val, b.val));
-}
-
-inline v_float32x4 v_muladd(const v_float32x4& a, const v_float32x4& b, const v_float32x4& c)
-{
-    return v_float32x4(vmlaq_f32(c.val, a.val, b.val));
-}
-
-inline v_int32x4 v_muladd(const v_int32x4& a, const v_int32x4& b, const v_int32x4& c)
-{
-    return v_int32x4(vmlaq_s32(c.val, a.val, b.val));
-}
-
-#if CV_SIMD128_64F
-inline v_float64x2 v_magnitude(const v_float64x2& a, const v_float64x2& b)
-{
-    v_float64x2 x(vaddq_f64(vmulq_f64(a.val, a.val), vmulq_f64(b.val, b.val)));
-    return v_sqrt(x);
-}
-
-inline v_float64x2 v_sqr_magnitude(const v_float64x2& a, const v_float64x2& b)
-{
-    return v_float64x2(vaddq_f64(vmulq_f64(a.val, a.val), vmulq_f64(b.val, b.val)));
-}
-
-inline v_float64x2 v_muladd(const v_float64x2& a, const v_float64x2& b, const v_float64x2& c)
-{
-    return v_float64x2(vaddq_f64(c.val, vmulq_f64(a.val, b.val)));
-}
-#endif
-
-// trade efficiency for convenience
-#define OPENCV_HAL_IMPL_NEON_SHIFT_OP(_Tpvec, suffix, _Tps, ssuffix) \
-inline _Tpvec operator << (const _Tpvec& a, int n) \
-{ return _Tpvec(vshlq_##suffix(a.val, vdupq_n_##ssuffix((_Tps)n))); } \
-inline _Tpvec operator >> (const _Tpvec& a, int n) \
-{ return _Tpvec(vshlq_##suffix(a.val, vdupq_n_##ssuffix((_Tps)-n))); } \
-template<int n> inline _Tpvec v_shl(const _Tpvec& a) \
-{ return _Tpvec(vshlq_n_##suffix(a.val, n)); } \
-template<int n> inline _Tpvec v_shr(const _Tpvec& a) \
-{ return _Tpvec(vshrq_n_##suffix(a.val, n)); } \
-template<int n> inline _Tpvec v_rshr(const _Tpvec& a) \
-{ return _Tpvec(vrshrq_n_##suffix(a.val, n)); }
-
-OPENCV_HAL_IMPL_NEON_SHIFT_OP(v_uint8x16, u8, schar, s8)
-OPENCV_HAL_IMPL_NEON_SHIFT_OP(v_int8x16, s8, schar, s8)
-OPENCV_HAL_IMPL_NEON_SHIFT_OP(v_uint16x8, u16, short, s16)
-OPENCV_HAL_IMPL_NEON_SHIFT_OP(v_int16x8, s16, short, s16)
-OPENCV_HAL_IMPL_NEON_SHIFT_OP(v_uint32x4, u32, int, s32)
-OPENCV_HAL_IMPL_NEON_SHIFT_OP(v_int32x4, s32, int, s32)
-OPENCV_HAL_IMPL_NEON_SHIFT_OP(v_uint64x2, u64, int64, s64)
-OPENCV_HAL_IMPL_NEON_SHIFT_OP(v_int64x2, s64, int64, s64)
-
-#define OPENCV_HAL_IMPL_NEON_ROTATE_OP(_Tpvec, suffix) \
-template<int n> inline _Tpvec v_rotate_right(const _Tpvec& a) \
-{ return _Tpvec(vextq_##suffix(a.val, vdupq_n_##suffix(0), n)); } \
-template<int n> inline _Tpvec v_rotate_left(const _Tpvec& a) \
-{ return _Tpvec(vextq_##suffix(vdupq_n_##suffix(0), a.val, _Tpvec::nlanes - n)); } \
-template<> inline _Tpvec v_rotate_left<0>(const _Tpvec& a) \
-{ return a; } \
-template<int n> inline _Tpvec v_rotate_right(const _Tpvec& a, const _Tpvec& b) \
-{ return _Tpvec(vextq_##suffix(a.val, b.val, n)); } \
-template<int n> inline _Tpvec v_rotate_left(const _Tpvec& a, const _Tpvec& b) \
-{ return _Tpvec(vextq_##suffix(b.val, a.val, _Tpvec::nlanes - n)); } \
-template<> inline _Tpvec v_rotate_left<0>(const _Tpvec& a, const _Tpvec& b) \
-{ CV_UNUSED(b); return a; }
-
-OPENCV_HAL_IMPL_NEON_ROTATE_OP(v_uint8x16, u8)
-OPENCV_HAL_IMPL_NEON_ROTATE_OP(v_int8x16, s8)
-OPENCV_HAL_IMPL_NEON_ROTATE_OP(v_uint16x8, u16)
-OPENCV_HAL_IMPL_NEON_ROTATE_OP(v_int16x8, s16)
-OPENCV_HAL_IMPL_NEON_ROTATE_OP(v_uint32x4, u32)
-OPENCV_HAL_IMPL_NEON_ROTATE_OP(v_int32x4, s32)
-OPENCV_HAL_IMPL_NEON_ROTATE_OP(v_float32x4, f32)
-OPENCV_HAL_IMPL_NEON_ROTATE_OP(v_uint64x2, u64)
-OPENCV_HAL_IMPL_NEON_ROTATE_OP(v_int64x2, s64)
-#if CV_SIMD128_64F
-OPENCV_HAL_IMPL_NEON_ROTATE_OP(v_float64x2, f64)
-#endif
-
-#define OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(_Tpvec, _Tp, suffix) \
-inline _Tpvec v_load(const _Tp* ptr) \
-{ return _Tpvec(vld1q_##suffix(ptr)); } \
-inline _Tpvec v_load_aligned(const _Tp* ptr) \
-{ return _Tpvec(vld1q_##suffix(ptr)); } \
-inline _Tpvec v_load_low(const _Tp* ptr) \
-{ return _Tpvec(vcombine_##suffix(vld1_##suffix(ptr), vdup_n_##suffix((_Tp)0))); } \
-inline _Tpvec v_load_halves(const _Tp* ptr0, const _Tp* ptr1) \
-{ return _Tpvec(vcombine_##suffix(vld1_##suffix(ptr0), vld1_##suffix(ptr1))); } \
-inline void v_store(_Tp* ptr, const _Tpvec& a) \
-{ vst1q_##suffix(ptr, a.val); } \
-inline void v_store_aligned(_Tp* ptr, const _Tpvec& a) \
-{ vst1q_##suffix(ptr, a.val); } \
-inline void v_store_low(_Tp* ptr, const _Tpvec& a) \
-{ vst1_##suffix(ptr, vget_low_##suffix(a.val)); } \
-inline void v_store_high(_Tp* ptr, const _Tpvec& a) \
-{ vst1_##suffix(ptr, vget_high_##suffix(a.val)); }
-
-OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_uint8x16, uchar, u8)
-OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_int8x16, schar, s8)
-OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_uint16x8, ushort, u16)
-OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_int16x8, short, s16)
-OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_uint32x4, unsigned, u32)
-OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_int32x4, int, s32)
-OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_uint64x2, uint64, u64)
-OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_int64x2, int64, s64)
-OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_float32x4, float, f32)
-#if CV_SIMD128_64F
-OPENCV_HAL_IMPL_NEON_LOADSTORE_OP(v_float64x2, double, f64)
-#endif
-
-#if CV_FP16
-// Workaround for old comiplers
-inline v_float16x4 v_load_f16(const short* ptr)
-{ return v_float16x4(cv_vld1_f16(ptr)); }
-inline void v_store_f16(short* ptr, v_float16x4& a)
-{ cv_vst1_f16(ptr, a.val); }
-#endif
-
-#define OPENCV_HAL_IMPL_NEON_REDUCE_OP_8(_Tpvec, _Tpnvec, scalartype, func, vectorfunc, suffix) \
-inline scalartype v_reduce_##func(const _Tpvec& a) \
-{ \
-    _Tpnvec##_t a0 = vp##vectorfunc##_##suffix(vget_low_##suffix(a.val), vget_high_##suffix(a.val)); \
-    a0 = vp##vectorfunc##_##suffix(a0, a0); \
-    return (scalartype)vget_lane_##suffix(vp##vectorfunc##_##suffix(a0, a0),0); \
-}
-
-OPENCV_HAL_IMPL_NEON_REDUCE_OP_8(v_uint16x8, uint16x4, unsigned short, sum, add, u16)
-OPENCV_HAL_IMPL_NEON_REDUCE_OP_8(v_uint16x8, uint16x4, unsigned short, max, max, u16)
-OPENCV_HAL_IMPL_NEON_REDUCE_OP_8(v_uint16x8, uint16x4, unsigned short, min, min, u16)
-OPENCV_HAL_IMPL_NEON_REDUCE_OP_8(v_int16x8, int16x4, short, sum, add, s16)
-OPENCV_HAL_IMPL_NEON_REDUCE_OP_8(v_int16x8, int16x4, short, max, max, s16)
-OPENCV_HAL_IMPL_NEON_REDUCE_OP_8(v_int16x8, int16x4, short, min, min, s16)
-
-#define OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(_Tpvec, _Tpnvec, scalartype, func, vectorfunc, suffix) \
-inline scalartype v_reduce_##func(const _Tpvec& a) \
-{ \
-    _Tpnvec##_t a0 = vp##vectorfunc##_##suffix(vget_low_##suffix(a.val), vget_high_##suffix(a.val)); \
-    return (scalartype)vget_lane_##suffix(vp##vectorfunc##_##suffix(a0, vget_high_##suffix(a.val)),0); \
-}
-
-OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_uint32x4, uint32x2, unsigned, sum, add, u32)
-OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_uint32x4, uint32x2, unsigned, max, max, u32)
-OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_uint32x4, uint32x2, unsigned, min, min, u32)
-OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_int32x4, int32x2, int, sum, add, s32)
-OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_int32x4, int32x2, int, max, max, s32)
-OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_int32x4, int32x2, int, min, min, s32)
-OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_float32x4, float32x2, float, sum, add, f32)
-OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_float32x4, float32x2, float, max, max, f32)
-OPENCV_HAL_IMPL_NEON_REDUCE_OP_4(v_float32x4, float32x2, float, min, min, f32)
-
-inline v_float32x4 v_reduce_sum4(const v_float32x4& a, const v_float32x4& b,
-                                 const v_float32x4& c, const v_float32x4& d)
-{
-    float32x4x2_t ab = vtrnq_f32(a.val, b.val);
-    float32x4x2_t cd = vtrnq_f32(c.val, d.val);
-
-    float32x4_t u0 = vaddq_f32(ab.val[0], ab.val[1]); // a0+a1 b0+b1 a2+a3 b2+b3
-    float32x4_t u1 = vaddq_f32(cd.val[0], cd.val[1]); // c0+c1 d0+d1 c2+c3 d2+d3
-
-    float32x4_t v0 = vcombine_f32(vget_low_f32(u0), vget_low_f32(u1));
-    float32x4_t v1 = vcombine_f32(vget_high_f32(u0), vget_high_f32(u1));
-
-    return v_float32x4(vaddq_f32(v0, v1));
-}
-
-#define OPENCV_HAL_IMPL_NEON_POPCOUNT(_Tpvec, cast) \
-inline v_uint32x4 v_popcount(const _Tpvec& a) \
-{ \
-    uint8x16_t t = vcntq_u8(cast(a.val)); \
-    uint16x8_t t0 = vpaddlq_u8(t);  /* 16 -> 8 */ \
-    uint32x4_t t1 = vpaddlq_u16(t0); /* 8 -> 4 */ \
-    return v_uint32x4(t1); \
-}
-
-OPENCV_HAL_IMPL_NEON_POPCOUNT(v_uint8x16, OPENCV_HAL_NOP)
-OPENCV_HAL_IMPL_NEON_POPCOUNT(v_uint16x8, vreinterpretq_u8_u16)
-OPENCV_HAL_IMPL_NEON_POPCOUNT(v_uint32x4, vreinterpretq_u8_u32)
-OPENCV_HAL_IMPL_NEON_POPCOUNT(v_int8x16, vreinterpretq_u8_s8)
-OPENCV_HAL_IMPL_NEON_POPCOUNT(v_int16x8, vreinterpretq_u8_s16)
-OPENCV_HAL_IMPL_NEON_POPCOUNT(v_int32x4, vreinterpretq_u8_s32)
-
-inline int v_signmask(const v_uint8x16& a)
-{
-    int8x8_t m0 = vcreate_s8(CV_BIG_UINT(0x0706050403020100));
-    uint8x16_t v0 = vshlq_u8(vshrq_n_u8(a.val, 7), vcombine_s8(m0, m0));
-    uint64x2_t v1 = vpaddlq_u32(vpaddlq_u16(vpaddlq_u8(v0)));
-    return (int)vgetq_lane_u64(v1, 0) + ((int)vgetq_lane_u64(v1, 1) << 8);
-}
-inline int v_signmask(const v_int8x16& a)
-{ return v_signmask(v_reinterpret_as_u8(a)); }
-
-inline int v_signmask(const v_uint16x8& a)
-{
-    int16x4_t m0 = vcreate_s16(CV_BIG_UINT(0x0003000200010000));
-    uint16x8_t v0 = vshlq_u16(vshrq_n_u16(a.val, 15), vcombine_s16(m0, m0));
-    uint64x2_t v1 = vpaddlq_u32(vpaddlq_u16(v0));
-    return (int)vgetq_lane_u64(v1, 0) + ((int)vgetq_lane_u64(v1, 1) << 4);
-}
-inline int v_signmask(const v_int16x8& a)
-{ return v_signmask(v_reinterpret_as_u16(a)); }
-
-inline int v_signmask(const v_uint32x4& a)
-{
-    int32x2_t m0 = vcreate_s32(CV_BIG_UINT(0x0000000100000000));
-    uint32x4_t v0 = vshlq_u32(vshrq_n_u32(a.val, 31), vcombine_s32(m0, m0));
-    uint64x2_t v1 = vpaddlq_u32(v0);
-    return (int)vgetq_lane_u64(v1, 0) + ((int)vgetq_lane_u64(v1, 1) << 2);
-}
-inline int v_signmask(const v_int32x4& a)
-{ return v_signmask(v_reinterpret_as_u32(a)); }
-inline int v_signmask(const v_float32x4& a)
-{ return v_signmask(v_reinterpret_as_u32(a)); }
-#if CV_SIMD128_64F
-inline int v_signmask(const v_uint64x2& a)
-{
-    int64x1_t m0 = vdup_n_s64(0);
-    uint64x2_t v0 = vshlq_u64(vshrq_n_u64(a.val, 63), vcombine_s64(m0, m0));
-    return (int)vgetq_lane_u64(v0, 0) + ((int)vgetq_lane_u64(v0, 1) << 1);
-}
-inline int v_signmask(const v_float64x2& a)
-{ return v_signmask(v_reinterpret_as_u64(a)); }
-#endif
-
-#define OPENCV_HAL_IMPL_NEON_CHECK_ALLANY(_Tpvec, suffix, shift) \
-inline bool v_check_all(const v_##_Tpvec& a) \
-{ \
-    _Tpvec##_t v0 = vshrq_n_##suffix(vmvnq_##suffix(a.val), shift); \
-    uint64x2_t v1 = vreinterpretq_u64_##suffix(v0); \
-    return (vgetq_lane_u64(v1, 0) | vgetq_lane_u64(v1, 1)) == 0; \
-} \
-inline bool v_check_any(const v_##_Tpvec& a) \
-{ \
-    _Tpvec##_t v0 = vshrq_n_##suffix(a.val, shift); \
-    uint64x2_t v1 = vreinterpretq_u64_##suffix(v0); \
-    return (vgetq_lane_u64(v1, 0) | vgetq_lane_u64(v1, 1)) != 0; \
-}
-
-OPENCV_HAL_IMPL_NEON_CHECK_ALLANY(uint8x16, u8, 7)
-OPENCV_HAL_IMPL_NEON_CHECK_ALLANY(uint16x8, u16, 15)
-OPENCV_HAL_IMPL_NEON_CHECK_ALLANY(uint32x4, u32, 31)
-#if CV_SIMD128_64F
-OPENCV_HAL_IMPL_NEON_CHECK_ALLANY(uint64x2, u64, 63)
-#endif
-
-inline bool v_check_all(const v_int8x16& a)
-{ return v_check_all(v_reinterpret_as_u8(a)); }
-inline bool v_check_all(const v_int16x8& a)
-{ return v_check_all(v_reinterpret_as_u16(a)); }
-inline bool v_check_all(const v_int32x4& a)
-{ return v_check_all(v_reinterpret_as_u32(a)); }
-inline bool v_check_all(const v_float32x4& a)
-{ return v_check_all(v_reinterpret_as_u32(a)); }
-
-inline bool v_check_any(const v_int8x16& a)
-{ return v_check_any(v_reinterpret_as_u8(a)); }
-inline bool v_check_any(const v_int16x8& a)
-{ return v_check_any(v_reinterpret_as_u16(a)); }
-inline bool v_check_any(const v_int32x4& a)
-{ return v_check_any(v_reinterpret_as_u32(a)); }
-inline bool v_check_any(const v_float32x4& a)
-{ return v_check_any(v_reinterpret_as_u32(a)); }
-
-#if CV_SIMD128_64F
-inline bool v_check_all(const v_int64x2& a)
-{ return v_check_all(v_reinterpret_as_u64(a)); }
-inline bool v_check_all(const v_float64x2& a)
-{ return v_check_all(v_reinterpret_as_u64(a)); }
-inline bool v_check_any(const v_int64x2& a)
-{ return v_check_any(v_reinterpret_as_u64(a)); }
-inline bool v_check_any(const v_float64x2& a)
-{ return v_check_any(v_reinterpret_as_u64(a)); }
-#endif
-
-#define OPENCV_HAL_IMPL_NEON_SELECT(_Tpvec, suffix, usuffix) \
-inline _Tpvec v_select(const _Tpvec& mask, const _Tpvec& a, const _Tpvec& b) \
-{ \
-    return _Tpvec(vbslq_##suffix(vreinterpretq_##usuffix##_##suffix(mask.val), a.val, b.val)); \
-}
-
-OPENCV_HAL_IMPL_NEON_SELECT(v_uint8x16, u8, u8)
-OPENCV_HAL_IMPL_NEON_SELECT(v_int8x16, s8, u8)
-OPENCV_HAL_IMPL_NEON_SELECT(v_uint16x8, u16, u16)
-OPENCV_HAL_IMPL_NEON_SELECT(v_int16x8, s16, u16)
-OPENCV_HAL_IMPL_NEON_SELECT(v_uint32x4, u32, u32)
-OPENCV_HAL_IMPL_NEON_SELECT(v_int32x4, s32, u32)
-OPENCV_HAL_IMPL_NEON_SELECT(v_float32x4, f32, u32)
-#if CV_SIMD128_64F
-OPENCV_HAL_IMPL_NEON_SELECT(v_float64x2, f64, u64)
-#endif
-
-#define OPENCV_HAL_IMPL_NEON_EXPAND(_Tpvec, _Tpwvec, _Tp, suffix) \
-inline void v_expand(const _Tpvec& a, _Tpwvec& b0, _Tpwvec& b1) \
-{ \
-    b0.val = vmovl_##suffix(vget_low_##suffix(a.val)); \
-    b1.val = vmovl_##suffix(vget_high_##suffix(a.val)); \
-} \
-inline _Tpwvec v_load_expand(const _Tp* ptr) \
-{ \
-    return _Tpwvec(vmovl_##suffix(vld1_##suffix(ptr))); \
-}
-
-OPENCV_HAL_IMPL_NEON_EXPAND(v_uint8x16, v_uint16x8, uchar, u8)
-OPENCV_HAL_IMPL_NEON_EXPAND(v_int8x16, v_int16x8, schar, s8)
-OPENCV_HAL_IMPL_NEON_EXPAND(v_uint16x8, v_uint32x4, ushort, u16)
-OPENCV_HAL_IMPL_NEON_EXPAND(v_int16x8, v_int32x4, short, s16)
-OPENCV_HAL_IMPL_NEON_EXPAND(v_uint32x4, v_uint64x2, uint, u32)
-OPENCV_HAL_IMPL_NEON_EXPAND(v_int32x4, v_int64x2, int, s32)
-
-inline v_uint32x4 v_load_expand_q(const uchar* ptr)
-{
-    uint8x8_t v0 = vcreate_u8(*(unsigned*)ptr);
-    uint16x4_t v1 = vget_low_u16(vmovl_u8(v0));
-    return v_uint32x4(vmovl_u16(v1));
-}
-
-inline v_int32x4 v_load_expand_q(const schar* ptr)
-{
-    int8x8_t v0 = vcreate_s8(*(unsigned*)ptr);
-    int16x4_t v1 = vget_low_s16(vmovl_s8(v0));
-    return v_int32x4(vmovl_s16(v1));
-}
-
-#if defined(__aarch64__)
-#define OPENCV_HAL_IMPL_NEON_UNPACKS(_Tpvec, suffix) \
-inline void v_zip(const v_##_Tpvec& a0, const v_##_Tpvec& a1, v_##_Tpvec& b0, v_##_Tpvec& b1) \
-{ \
-    b0.val = vzip1q_##suffix(a0.val, a1.val); \
-    b1.val = vzip2q_##suffix(a0.val, a1.val); \
-} \
-inline v_##_Tpvec v_combine_low(const v_##_Tpvec& a, const v_##_Tpvec& b) \
-{ \
-    return v_##_Tpvec(vcombine_##suffix(vget_low_##suffix(a.val), vget_low_##suffix(b.val))); \
-} \
-inline v_##_Tpvec v_combine_high(const v_##_Tpvec& a, const v_##_Tpvec& b) \
-{ \
-    return v_##_Tpvec(vcombine_##suffix(vget_high_##suffix(a.val), vget_high_##suffix(b.val))); \
-} \
-inline void v_recombine(const v_##_Tpvec& a, const v_##_Tpvec& b, v_##_Tpvec& c, v_##_Tpvec& d) \
-{ \
-    c.val = vcombine_##suffix(vget_low_##suffix(a.val), vget_low_##suffix(b.val)); \
-    d.val = vcombine_##suffix(vget_high_##suffix(a.val), vget_high_##suffix(b.val)); \
-}
-#else
-#define OPENCV_HAL_IMPL_NEON_UNPACKS(_Tpvec, suffix) \
-inline void v_zip(const v_##_Tpvec& a0, const v_##_Tpvec& a1, v_##_Tpvec& b0, v_##_Tpvec& b1) \
-{ \
-    _Tpvec##x2_t p = vzipq_##suffix(a0.val, a1.val); \
-    b0.val = p.val[0]; \
-    b1.val = p.val[1]; \
-} \
-inline v_##_Tpvec v_combine_low(const v_##_Tpvec& a, const v_##_Tpvec& b) \
-{ \
-    return v_##_Tpvec(vcombine_##suffix(vget_low_##suffix(a.val), vget_low_##suffix(b.val))); \
-} \
-inline v_##_Tpvec v_combine_high(const v_##_Tpvec& a, const v_##_Tpvec& b) \
-{ \
-    return v_##_Tpvec(vcombine_##suffix(vget_high_##suffix(a.val), vget_high_##suffix(b.val))); \
-} \
-inline void v_recombine(const v_##_Tpvec& a, const v_##_Tpvec& b, v_##_Tpvec& c, v_##_Tpvec& d) \
-{ \
-    c.val = vcombine_##suffix(vget_low_##suffix(a.val), vget_low_##suffix(b.val)); \
-    d.val = vcombine_##suffix(vget_high_##suffix(a.val), vget_high_##suffix(b.val)); \
-}
-#endif
-
-OPENCV_HAL_IMPL_NEON_UNPACKS(uint8x16, u8)
-OPENCV_HAL_IMPL_NEON_UNPACKS(int8x16, s8)
-OPENCV_HAL_IMPL_NEON_UNPACKS(uint16x8, u16)
-OPENCV_HAL_IMPL_NEON_UNPACKS(int16x8, s16)
-OPENCV_HAL_IMPL_NEON_UNPACKS(uint32x4, u32)
-OPENCV_HAL_IMPL_NEON_UNPACKS(int32x4, s32)
-OPENCV_HAL_IMPL_NEON_UNPACKS(float32x4, f32)
-#if CV_SIMD128_64F
-OPENCV_HAL_IMPL_NEON_UNPACKS(float64x2, f64)
-#endif
-
-#define OPENCV_HAL_IMPL_NEON_EXTRACT(_Tpvec, suffix) \
-template <int s> \
-inline v_##_Tpvec v_extract(const v_##_Tpvec& a, const v_##_Tpvec& b) \
-{ \
-    return v_##_Tpvec(vextq_##suffix(a.val, b.val, s)); \
-}
-
-OPENCV_HAL_IMPL_NEON_EXTRACT(uint8x16, u8)
-OPENCV_HAL_IMPL_NEON_EXTRACT(int8x16, s8)
-OPENCV_HAL_IMPL_NEON_EXTRACT(uint16x8, u16)
-OPENCV_HAL_IMPL_NEON_EXTRACT(int16x8, s16)
-OPENCV_HAL_IMPL_NEON_EXTRACT(uint32x4, u32)
-OPENCV_HAL_IMPL_NEON_EXTRACT(int32x4, s32)
-OPENCV_HAL_IMPL_NEON_EXTRACT(uint64x2, u64)
-OPENCV_HAL_IMPL_NEON_EXTRACT(int64x2, s64)
-OPENCV_HAL_IMPL_NEON_EXTRACT(float32x4, f32)
-#if CV_SIMD128_64F
-OPENCV_HAL_IMPL_NEON_EXTRACT(float64x2, f64)
-#endif
-
-#if CV_SIMD128_64F
-inline v_int32x4 v_round(const v_float32x4& a)
-{
-    float32x4_t a_ = a.val;
-    int32x4_t result;
-    __asm__ ("fcvtns %0.4s, %1.4s"
-             : "=w"(result)
-             : "w"(a_)
-             : /* No clobbers */);
-    return v_int32x4(result);
-}
-#else
-inline v_int32x4 v_round(const v_float32x4& a)
-{
-    static const int32x4_t v_sign = vdupq_n_s32(1 << 31),
-        v_05 = vreinterpretq_s32_f32(vdupq_n_f32(0.5f));
-
-    int32x4_t v_addition = vorrq_s32(v_05, vandq_s32(v_sign, vreinterpretq_s32_f32(a.val)));
-    return v_int32x4(vcvtq_s32_f32(vaddq_f32(a.val, vreinterpretq_f32_s32(v_addition))));
-}
-#endif
-inline v_int32x4 v_floor(const v_float32x4& a)
-{
-    int32x4_t a1 = vcvtq_s32_f32(a.val);
-    uint32x4_t mask = vcgtq_f32(vcvtq_f32_s32(a1), a.val);
-    return v_int32x4(vaddq_s32(a1, vreinterpretq_s32_u32(mask)));
-}
-
-inline v_int32x4 v_ceil(const v_float32x4& a)
-{
-    int32x4_t a1 = vcvtq_s32_f32(a.val);
-    uint32x4_t mask = vcgtq_f32(a.val, vcvtq_f32_s32(a1));
-    return v_int32x4(vsubq_s32(a1, vreinterpretq_s32_u32(mask)));
-}
-
-inline v_int32x4 v_trunc(const v_float32x4& a)
-{ return v_int32x4(vcvtq_s32_f32(a.val)); }
-
-#if CV_SIMD128_64F
-inline v_int32x4 v_round(const v_float64x2& a)
-{
-    static const int32x2_t zero = vdup_n_s32(0);
-    return v_int32x4(vcombine_s32(vmovn_s64(vcvtaq_s64_f64(a.val)), zero));
-}
-
-inline v_int32x4 v_floor(const v_float64x2& a)
-{
-    static const int32x2_t zero = vdup_n_s32(0);
-    int64x2_t a1 = vcvtq_s64_f64(a.val);
-    uint64x2_t mask = vcgtq_f64(vcvtq_f64_s64(a1), a.val);
-    a1 = vaddq_s64(a1, vreinterpretq_s64_u64(mask));
-    return v_int32x4(vcombine_s32(vmovn_s64(a1), zero));
-}
-
-inline v_int32x4 v_ceil(const v_float64x2& a)
-{
-    static const int32x2_t zero = vdup_n_s32(0);
-    int64x2_t a1 = vcvtq_s64_f64(a.val);
-    uint64x2_t mask = vcgtq_f64(a.val, vcvtq_f64_s64(a1));
-    a1 = vsubq_s64(a1, vreinterpretq_s64_u64(mask));
-    return v_int32x4(vcombine_s32(vmovn_s64(a1), zero));
-}
-
-inline v_int32x4 v_trunc(const v_float64x2& a)
-{
-    static const int32x2_t zero = vdup_n_s32(0);
-    return v_int32x4(vcombine_s32(vmovn_s64(vcvtaq_s64_f64(a.val)), zero));
-}
-#endif
-
-#define OPENCV_HAL_IMPL_NEON_TRANSPOSE4x4(_Tpvec, suffix) \
-inline void v_transpose4x4(const v_##_Tpvec& a0, const v_##_Tpvec& a1, \
-                         const v_##_Tpvec& a2, const v_##_Tpvec& a3, \
-                         v_##_Tpvec& b0, v_##_Tpvec& b1, \
-                         v_##_Tpvec& b2, v_##_Tpvec& b3) \
-{ \
-    /* m00 m01 m02 m03 */ \
-    /* m10 m11 m12 m13 */ \
-    /* m20 m21 m22 m23 */ \
-    /* m30 m31 m32 m33 */ \
-    _Tpvec##x2_t t0 = vtrnq_##suffix(a0.val, a1.val); \
-    _Tpvec##x2_t t1 = vtrnq_##suffix(a2.val, a3.val); \
-    /* m00 m10 m02 m12 */ \
-    /* m01 m11 m03 m13 */ \
-    /* m20 m30 m22 m32 */ \
-    /* m21 m31 m23 m33 */ \
-    b0.val = vcombine_##suffix(vget_low_##suffix(t0.val[0]), vget_low_##suffix(t1.val[0])); \
-    b1.val = vcombine_##suffix(vget_low_##suffix(t0.val[1]), vget_low_##suffix(t1.val[1])); \
-    b2.val = vcombine_##suffix(vget_high_##suffix(t0.val[0]), vget_high_##suffix(t1.val[0])); \
-    b3.val = vcombine_##suffix(vget_high_##suffix(t0.val[1]), vget_high_##suffix(t1.val[1])); \
-}
-
-OPENCV_HAL_IMPL_NEON_TRANSPOSE4x4(uint32x4, u32)
-OPENCV_HAL_IMPL_NEON_TRANSPOSE4x4(int32x4, s32)
-OPENCV_HAL_IMPL_NEON_TRANSPOSE4x4(float32x4, f32)
-
-#define OPENCV_HAL_IMPL_NEON_INTERLEAVED(_Tpvec, _Tp, suffix) \
-inline void v_load_deinterleave(const _Tp* ptr, v_##_Tpvec& a, v_##_Tpvec& b) \
-{ \
-    _Tpvec##x2_t v = vld2q_##suffix(ptr); \
-    a.val = v.val[0]; \
-    b.val = v.val[1]; \
-} \
-inline void v_load_deinterleave(const _Tp* ptr, v_##_Tpvec& a, v_##_Tpvec& b, v_##_Tpvec& c) \
-{ \
-    _Tpvec##x3_t v = vld3q_##suffix(ptr); \
-    a.val = v.val[0]; \
-    b.val = v.val[1]; \
-    c.val = v.val[2]; \
-} \
-inline void v_load_deinterleave(const _Tp* ptr, v_##_Tpvec& a, v_##_Tpvec& b, \
-                                v_##_Tpvec& c, v_##_Tpvec& d) \
-{ \
-    _Tpvec##x4_t v = vld4q_##suffix(ptr); \
-    a.val = v.val[0]; \
-    b.val = v.val[1]; \
-    c.val = v.val[2]; \
-    d.val = v.val[3]; \
-} \
-inline void v_store_interleave( _Tp* ptr, const v_##_Tpvec& a, const v_##_Tpvec& b) \
-{ \
-    _Tpvec##x2_t v; \
-    v.val[0] = a.val; \
-    v.val[1] = b.val; \
-    vst2q_##suffix(ptr, v); \
-} \
-inline void v_store_interleave( _Tp* ptr, const v_##_Tpvec& a, const v_##_Tpvec& b, const v_##_Tpvec& c) \
-{ \
-    _Tpvec##x3_t v; \
-    v.val[0] = a.val; \
-    v.val[1] = b.val; \
-    v.val[2] = c.val; \
-    vst3q_##suffix(ptr, v); \
-} \
-inline void v_store_interleave( _Tp* ptr, const v_##_Tpvec& a, const v_##_Tpvec& b, \
-                               const v_##_Tpvec& c, const v_##_Tpvec& d) \
-{ \
-    _Tpvec##x4_t v; \
-    v.val[0] = a.val; \
-    v.val[1] = b.val; \
-    v.val[2] = c.val; \
-    v.val[3] = d.val; \
-    vst4q_##suffix(ptr, v); \
-}
-
-OPENCV_HAL_IMPL_NEON_INTERLEAVED(uint8x16, uchar, u8)
-OPENCV_HAL_IMPL_NEON_INTERLEAVED(int8x16, schar, s8)
-OPENCV_HAL_IMPL_NEON_INTERLEAVED(uint16x8, ushort, u16)
-OPENCV_HAL_IMPL_NEON_INTERLEAVED(int16x8, short, s16)
-OPENCV_HAL_IMPL_NEON_INTERLEAVED(uint32x4, unsigned, u32)
-OPENCV_HAL_IMPL_NEON_INTERLEAVED(int32x4, int, s32)
-OPENCV_HAL_IMPL_NEON_INTERLEAVED(float32x4, float, f32)
-#if CV_SIMD128_64F
-OPENCV_HAL_IMPL_NEON_INTERLEAVED(float64x2, double, f64)
-#endif
-
-inline v_float32x4 v_cvt_f32(const v_int32x4& a)
-{
-    return v_float32x4(vcvtq_f32_s32(a.val));
-}
-
-#if CV_SIMD128_64F
-inline v_float32x4 v_cvt_f32(const v_float64x2& a)
-{
-    float32x2_t zero = vdup_n_f32(0.0f);
-    return v_float32x4(vcombine_f32(vcvt_f32_f64(a.val), zero));
-}
-
-inline v_float64x2 v_cvt_f64(const v_int32x4& a)
-{
-    return v_float64x2(vcvt_f64_f32(vcvt_f32_s32(vget_low_s32(a.val))));
-}
-
-inline v_float64x2 v_cvt_f64_high(const v_int32x4& a)
-{
-    return v_float64x2(vcvt_f64_f32(vcvt_f32_s32(vget_high_s32(a.val))));
-}
-
-inline v_float64x2 v_cvt_f64(const v_float32x4& a)
-{
-    return v_float64x2(vcvt_f64_f32(vget_low_f32(a.val)));
-}
-
-inline v_float64x2 v_cvt_f64_high(const v_float32x4& a)
-{
-    return v_float64x2(vcvt_f64_f32(vget_high_f32(a.val)));
-}
-#endif
-
-#if CV_FP16
-inline v_float32x4 v_cvt_f32(const v_float16x4& a)
-{
-    return v_float32x4(vcvt_f32_f16(a.val));
-}
-
-inline v_float16x4 v_cvt_f16(const v_float32x4& a)
-{
-    return v_float16x4(vcvt_f16_f32(a.val));
-}
-#endif
-
-//! @name Check SIMD support
-//! @{
-//! @brief Check CPU capability of SIMD operation
-static inline bool hasSIMD128()
-{
-    return (CV_CPU_HAS_SUPPORT_NEON) ? true : false;
-}
-
-//! @}
-
-CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END
-
-//! @endcond
-
-}
-
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/hal/intrin_sse.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/hal/intrin_sse.hpp
deleted file mode 100755
index 22c0668..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/hal/intrin_sse.hpp
+++ /dev/null
@@ -1,2234 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
-// Copyright (C) 2015, Itseez Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_HAL_SSE_HPP
-#define OPENCV_HAL_SSE_HPP
-
-#include <algorithm>
-#include "opencv2/core/utility.hpp"
-
-#define CV_SIMD128 1
-#define CV_SIMD128_64F 1
-
-namespace cv
-{
-
-//! @cond IGNORED
-
-CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN
-
-struct v_uint8x16
-{
-    typedef uchar lane_type;
-    typedef __m128i vector_type;
-    enum { nlanes = 16 };
-
-    v_uint8x16() : val(_mm_setzero_si128()) {}
-    explicit v_uint8x16(__m128i v) : val(v) {}
-    v_uint8x16(uchar v0, uchar v1, uchar v2, uchar v3, uchar v4, uchar v5, uchar v6, uchar v7,
-               uchar v8, uchar v9, uchar v10, uchar v11, uchar v12, uchar v13, uchar v14, uchar v15)
-    {
-        val = _mm_setr_epi8((char)v0, (char)v1, (char)v2, (char)v3,
-                            (char)v4, (char)v5, (char)v6, (char)v7,
-                            (char)v8, (char)v9, (char)v10, (char)v11,
-                            (char)v12, (char)v13, (char)v14, (char)v15);
-    }
-    uchar get0() const
-    {
-        return (uchar)_mm_cvtsi128_si32(val);
-    }
-
-    __m128i val;
-};
-
-struct v_int8x16
-{
-    typedef schar lane_type;
-    typedef __m128i vector_type;
-    enum { nlanes = 16 };
-
-    v_int8x16() : val(_mm_setzero_si128()) {}
-    explicit v_int8x16(__m128i v) : val(v) {}
-    v_int8x16(schar v0, schar v1, schar v2, schar v3, schar v4, schar v5, schar v6, schar v7,
-              schar v8, schar v9, schar v10, schar v11, schar v12, schar v13, schar v14, schar v15)
-    {
-        val = _mm_setr_epi8((char)v0, (char)v1, (char)v2, (char)v3,
-                            (char)v4, (char)v5, (char)v6, (char)v7,
-                            (char)v8, (char)v9, (char)v10, (char)v11,
-                            (char)v12, (char)v13, (char)v14, (char)v15);
-    }
-    schar get0() const
-    {
-        return (schar)_mm_cvtsi128_si32(val);
-    }
-
-    __m128i val;
-};
-
-struct v_uint16x8
-{
-    typedef ushort lane_type;
-    typedef __m128i vector_type;
-    enum { nlanes = 8 };
-
-    v_uint16x8() : val(_mm_setzero_si128()) {}
-    explicit v_uint16x8(__m128i v) : val(v) {}
-    v_uint16x8(ushort v0, ushort v1, ushort v2, ushort v3, ushort v4, ushort v5, ushort v6, ushort v7)
-    {
-        val = _mm_setr_epi16((short)v0, (short)v1, (short)v2, (short)v3,
-                             (short)v4, (short)v5, (short)v6, (short)v7);
-    }
-    ushort get0() const
-    {
-        return (ushort)_mm_cvtsi128_si32(val);
-    }
-
-    __m128i val;
-};
-
-struct v_int16x8
-{
-    typedef short lane_type;
-    typedef __m128i vector_type;
-    enum { nlanes = 8 };
-
-    v_int16x8() : val(_mm_setzero_si128()) {}
-    explicit v_int16x8(__m128i v) : val(v) {}
-    v_int16x8(short v0, short v1, short v2, short v3, short v4, short v5, short v6, short v7)
-    {
-        val = _mm_setr_epi16((short)v0, (short)v1, (short)v2, (short)v3,
-                             (short)v4, (short)v5, (short)v6, (short)v7);
-    }
-    short get0() const
-    {
-        return (short)_mm_cvtsi128_si32(val);
-    }
-    __m128i val;
-};
-
-struct v_uint32x4
-{
-    typedef unsigned lane_type;
-    typedef __m128i vector_type;
-    enum { nlanes = 4 };
-
-    v_uint32x4() : val(_mm_setzero_si128()) {}
-    explicit v_uint32x4(__m128i v) : val(v) {}
-    v_uint32x4(unsigned v0, unsigned v1, unsigned v2, unsigned v3)
-    {
-        val = _mm_setr_epi32((int)v0, (int)v1, (int)v2, (int)v3);
-    }
-    unsigned get0() const
-    {
-        return (unsigned)_mm_cvtsi128_si32(val);
-    }
-    __m128i val;
-};
-
-struct v_int32x4
-{
-    typedef int lane_type;
-    typedef __m128i vector_type;
-    enum { nlanes = 4 };
-
-    v_int32x4() : val(_mm_setzero_si128()) {}
-    explicit v_int32x4(__m128i v) : val(v) {}
-    v_int32x4(int v0, int v1, int v2, int v3)
-    {
-        val = _mm_setr_epi32(v0, v1, v2, v3);
-    }
-    int get0() const
-    {
-        return _mm_cvtsi128_si32(val);
-    }
-    __m128i val;
-};
-
-struct v_float32x4
-{
-    typedef float lane_type;
-    typedef __m128 vector_type;
-    enum { nlanes = 4 };
-
-    v_float32x4() : val(_mm_setzero_ps()) {}
-    explicit v_float32x4(__m128 v) : val(v) {}
-    v_float32x4(float v0, float v1, float v2, float v3)
-    {
-        val = _mm_setr_ps(v0, v1, v2, v3);
-    }
-    float get0() const
-    {
-        return _mm_cvtss_f32(val);
-    }
-    __m128 val;
-};
-
-struct v_uint64x2
-{
-    typedef uint64 lane_type;
-    typedef __m128i vector_type;
-    enum { nlanes = 2 };
-
-    v_uint64x2() : val(_mm_setzero_si128()) {}
-    explicit v_uint64x2(__m128i v) : val(v) {}
-    v_uint64x2(uint64 v0, uint64 v1)
-    {
-        val = _mm_setr_epi32((int)v0, (int)(v0 >> 32), (int)v1, (int)(v1 >> 32));
-    }
-    uint64 get0() const
-    {
-        int a = _mm_cvtsi128_si32(val);
-        int b = _mm_cvtsi128_si32(_mm_srli_epi64(val, 32));
-        return (unsigned)a | ((uint64)(unsigned)b << 32);
-    }
-    __m128i val;
-};
-
-struct v_int64x2
-{
-    typedef int64 lane_type;
-    typedef __m128i vector_type;
-    enum { nlanes = 2 };
-
-    v_int64x2() : val(_mm_setzero_si128()) {}
-    explicit v_int64x2(__m128i v) : val(v) {}
-    v_int64x2(int64 v0, int64 v1)
-    {
-        val = _mm_setr_epi32((int)v0, (int)(v0 >> 32), (int)v1, (int)(v1 >> 32));
-    }
-    int64 get0() const
-    {
-        int a = _mm_cvtsi128_si32(val);
-        int b = _mm_cvtsi128_si32(_mm_srli_epi64(val, 32));
-        return (int64)((unsigned)a | ((uint64)(unsigned)b << 32));
-    }
-    __m128i val;
-};
-
-struct v_float64x2
-{
-    typedef double lane_type;
-    typedef __m128d vector_type;
-    enum { nlanes = 2 };
-
-    v_float64x2() : val(_mm_setzero_pd()) {}
-    explicit v_float64x2(__m128d v) : val(v) {}
-    v_float64x2(double v0, double v1)
-    {
-        val = _mm_setr_pd(v0, v1);
-    }
-    double get0() const
-    {
-        return _mm_cvtsd_f64(val);
-    }
-    __m128d val;
-};
-
-#if CV_FP16
-struct v_float16x4
-{
-    typedef short lane_type;
-    typedef __m128i vector_type;
-    enum { nlanes = 4 };
-
-    v_float16x4() : val(_mm_setzero_si128()) {}
-    explicit v_float16x4(__m128i v) : val(v) {}
-    v_float16x4(short v0, short v1, short v2, short v3)
-    {
-        val = _mm_setr_epi16(v0, v1, v2, v3, 0, 0, 0, 0);
-    }
-    short get0() const
-    {
-        return (short)_mm_cvtsi128_si32(val);
-    }
-    __m128i val;
-};
-#endif
-
-namespace hal_sse_internal
-{
-    template <typename to_sse_type, typename from_sse_type>
-    to_sse_type v_sse_reinterpret_as(const from_sse_type& val);
-
-#define OPENCV_HAL_IMPL_SSE_REINTERPRET_RAW(to_sse_type, from_sse_type, sse_cast_intrin) \
-    template<> inline \
-    to_sse_type v_sse_reinterpret_as(const from_sse_type& a) \
-    { return sse_cast_intrin(a); }
-
-    OPENCV_HAL_IMPL_SSE_REINTERPRET_RAW(__m128i, __m128i, OPENCV_HAL_NOP);
-    OPENCV_HAL_IMPL_SSE_REINTERPRET_RAW(__m128i, __m128, _mm_castps_si128);
-    OPENCV_HAL_IMPL_SSE_REINTERPRET_RAW(__m128i, __m128d, _mm_castpd_si128);
-    OPENCV_HAL_IMPL_SSE_REINTERPRET_RAW(__m128, __m128i, _mm_castsi128_ps);
-    OPENCV_HAL_IMPL_SSE_REINTERPRET_RAW(__m128, __m128, OPENCV_HAL_NOP);
-    OPENCV_HAL_IMPL_SSE_REINTERPRET_RAW(__m128, __m128d, _mm_castpd_ps);
-    OPENCV_HAL_IMPL_SSE_REINTERPRET_RAW(__m128d, __m128i, _mm_castsi128_pd);
-    OPENCV_HAL_IMPL_SSE_REINTERPRET_RAW(__m128d, __m128, _mm_castps_pd);
-    OPENCV_HAL_IMPL_SSE_REINTERPRET_RAW(__m128d, __m128d, OPENCV_HAL_NOP);
-}
-
-#define OPENCV_HAL_IMPL_SSE_INITVEC(_Tpvec, _Tp, suffix, zsuffix, ssuffix, _Tps, cast) \
-inline _Tpvec v_setzero_##suffix() { return _Tpvec(_mm_setzero_##zsuffix()); } \
-inline _Tpvec v_setall_##suffix(_Tp v) { return _Tpvec(_mm_set1_##ssuffix((_Tps)v)); } \
-template<typename _Tpvec0> inline _Tpvec v_reinterpret_as_##suffix(const _Tpvec0& a) \
-{ return _Tpvec(cast(a.val)); }
-
-OPENCV_HAL_IMPL_SSE_INITVEC(v_uint8x16, uchar, u8, si128, epi8, char, OPENCV_HAL_NOP)
-OPENCV_HAL_IMPL_SSE_INITVEC(v_int8x16, schar, s8, si128, epi8, char, OPENCV_HAL_NOP)
-OPENCV_HAL_IMPL_SSE_INITVEC(v_uint16x8, ushort, u16, si128, epi16, short, OPENCV_HAL_NOP)
-OPENCV_HAL_IMPL_SSE_INITVEC(v_int16x8, short, s16, si128, epi16, short, OPENCV_HAL_NOP)
-OPENCV_HAL_IMPL_SSE_INITVEC(v_uint32x4, unsigned, u32, si128, epi32, int, OPENCV_HAL_NOP)
-OPENCV_HAL_IMPL_SSE_INITVEC(v_int32x4, int, s32, si128, epi32, int, OPENCV_HAL_NOP)
-OPENCV_HAL_IMPL_SSE_INITVEC(v_float32x4, float, f32, ps, ps, float, _mm_castsi128_ps)
-OPENCV_HAL_IMPL_SSE_INITVEC(v_float64x2, double, f64, pd, pd, double, _mm_castsi128_pd)
-
-inline v_uint64x2 v_setzero_u64() { return v_uint64x2(_mm_setzero_si128()); }
-inline v_int64x2 v_setzero_s64() { return v_int64x2(_mm_setzero_si128()); }
-inline v_uint64x2 v_setall_u64(uint64 val) { return v_uint64x2(val, val); }
-inline v_int64x2 v_setall_s64(int64 val) { return v_int64x2(val, val); }
-
-template<typename _Tpvec> inline
-v_uint64x2 v_reinterpret_as_u64(const _Tpvec& a) { return v_uint64x2(a.val); }
-template<typename _Tpvec> inline
-v_int64x2 v_reinterpret_as_s64(const _Tpvec& a) { return v_int64x2(a.val); }
-inline v_float32x4 v_reinterpret_as_f32(const v_uint64x2& a)
-{ return v_float32x4(_mm_castsi128_ps(a.val)); }
-inline v_float32x4 v_reinterpret_as_f32(const v_int64x2& a)
-{ return v_float32x4(_mm_castsi128_ps(a.val)); }
-inline v_float64x2 v_reinterpret_as_f64(const v_uint64x2& a)
-{ return v_float64x2(_mm_castsi128_pd(a.val)); }
-inline v_float64x2 v_reinterpret_as_f64(const v_int64x2& a)
-{ return v_float64x2(_mm_castsi128_pd(a.val)); }
-
-#define OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(_Tpvec, suffix) \
-inline _Tpvec v_reinterpret_as_##suffix(const v_float32x4& a) \
-{ return _Tpvec(_mm_castps_si128(a.val)); } \
-inline _Tpvec v_reinterpret_as_##suffix(const v_float64x2& a) \
-{ return _Tpvec(_mm_castpd_si128(a.val)); }
-
-OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(v_uint8x16, u8)
-OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(v_int8x16, s8)
-OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(v_uint16x8, u16)
-OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(v_int16x8, s16)
-OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(v_uint32x4, u32)
-OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(v_int32x4, s32)
-OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(v_uint64x2, u64)
-OPENCV_HAL_IMPL_SSE_INIT_FROM_FLT(v_int64x2, s64)
-
-inline v_float32x4 v_reinterpret_as_f32(const v_float32x4& a) {return a; }
-inline v_float64x2 v_reinterpret_as_f64(const v_float64x2& a) {return a; }
-inline v_float32x4 v_reinterpret_as_f32(const v_float64x2& a) {return v_float32x4(_mm_castpd_ps(a.val)); }
-inline v_float64x2 v_reinterpret_as_f64(const v_float32x4& a) {return v_float64x2(_mm_castps_pd(a.val)); }
-
-//////////////// PACK ///////////////
-inline v_uint8x16 v_pack(const v_uint16x8& a, const v_uint16x8& b)
-{
-    __m128i delta = _mm_set1_epi16(255);
-    return v_uint8x16(_mm_packus_epi16(_mm_subs_epu16(a.val, _mm_subs_epu16(a.val, delta)),
-                                       _mm_subs_epu16(b.val, _mm_subs_epu16(b.val, delta))));
-}
-
-inline void v_pack_store(uchar* ptr, const v_uint16x8& a)
-{
-    __m128i delta = _mm_set1_epi16(255);
-    __m128i a1 = _mm_subs_epu16(a.val, _mm_subs_epu16(a.val, delta));
-    _mm_storel_epi64((__m128i*)ptr, _mm_packus_epi16(a1, a1));
-}
-
-inline v_uint8x16 v_pack_u(const v_int16x8& a, const v_int16x8& b)
-{ return v_uint8x16(_mm_packus_epi16(a.val, b.val)); }
-
-inline void v_pack_u_store(uchar* ptr, const v_int16x8& a)
-{ _mm_storel_epi64((__m128i*)ptr, _mm_packus_epi16(a.val, a.val)); }
-
-template<int n> inline
-v_uint8x16 v_rshr_pack(const v_uint16x8& a, const v_uint16x8& b)
-{
-    // we assume that n > 0, and so the shifted 16-bit values can be treated as signed numbers.
-    __m128i delta = _mm_set1_epi16((short)(1 << (n-1)));
-    return v_uint8x16(_mm_packus_epi16(_mm_srli_epi16(_mm_adds_epu16(a.val, delta), n),
-                                       _mm_srli_epi16(_mm_adds_epu16(b.val, delta), n)));
-}
-
-template<int n> inline
-void v_rshr_pack_store(uchar* ptr, const v_uint16x8& a)
-{
-    __m128i delta = _mm_set1_epi16((short)(1 << (n-1)));
-    __m128i a1 = _mm_srli_epi16(_mm_adds_epu16(a.val, delta), n);
-    _mm_storel_epi64((__m128i*)ptr, _mm_packus_epi16(a1, a1));
-}
-
-template<int n> inline
-v_uint8x16 v_rshr_pack_u(const v_int16x8& a, const v_int16x8& b)
-{
-    __m128i delta = _mm_set1_epi16((short)(1 << (n-1)));
-    return v_uint8x16(_mm_packus_epi16(_mm_srai_epi16(_mm_adds_epi16(a.val, delta), n),
-                                       _mm_srai_epi16(_mm_adds_epi16(b.val, delta), n)));
-}
-
-template<int n> inline
-void v_rshr_pack_u_store(uchar* ptr, const v_int16x8& a)
-{
-    __m128i delta = _mm_set1_epi16((short)(1 << (n-1)));
-    __m128i a1 = _mm_srai_epi16(_mm_adds_epi16(a.val, delta), n);
-    _mm_storel_epi64((__m128i*)ptr, _mm_packus_epi16(a1, a1));
-}
-
-inline v_int8x16 v_pack(const v_int16x8& a, const v_int16x8& b)
-{ return v_int8x16(_mm_packs_epi16(a.val, b.val)); }
-
-inline void v_pack_store(schar* ptr, v_int16x8& a)
-{ _mm_storel_epi64((__m128i*)ptr, _mm_packs_epi16(a.val, a.val)); }
-
-template<int n> inline
-v_int8x16 v_rshr_pack(const v_int16x8& a, const v_int16x8& b)
-{
-    // we assume that n > 0, and so the shifted 16-bit values can be treated as signed numbers.
-    __m128i delta = _mm_set1_epi16((short)(1 << (n-1)));
-    return v_int8x16(_mm_packs_epi16(_mm_srai_epi16(_mm_adds_epi16(a.val, delta), n),
-                                     _mm_srai_epi16(_mm_adds_epi16(b.val, delta), n)));
-}
-template<int n> inline
-void v_rshr_pack_store(schar* ptr, const v_int16x8& a)
-{
-    // we assume that n > 0, and so the shifted 16-bit values can be treated as signed numbers.
-    __m128i delta = _mm_set1_epi16((short)(1 << (n-1)));
-    __m128i a1 = _mm_srai_epi16(_mm_adds_epi16(a.val, delta), n);
-    _mm_storel_epi64((__m128i*)ptr, _mm_packs_epi16(a1, a1));
-}
-
-
-// byte-wise "mask ? a : b"
-inline __m128i v_select_si128(__m128i mask, __m128i a, __m128i b)
-{
-#if CV_SSE4_1
-    return _mm_blendv_epi8(b, a, mask);
-#else
-    return _mm_xor_si128(b, _mm_and_si128(_mm_xor_si128(a, b), mask));
-#endif
-}
-
-inline v_uint16x8 v_pack(const v_uint32x4& a, const v_uint32x4& b)
-{
-    __m128i z = _mm_setzero_si128(), maxval32 = _mm_set1_epi32(65535), delta32 = _mm_set1_epi32(32768);
-    __m128i a1 = _mm_sub_epi32(v_select_si128(_mm_cmpgt_epi32(z, a.val), maxval32, a.val), delta32);
-    __m128i b1 = _mm_sub_epi32(v_select_si128(_mm_cmpgt_epi32(z, b.val), maxval32, b.val), delta32);
-    __m128i r = _mm_packs_epi32(a1, b1);
-    return v_uint16x8(_mm_sub_epi16(r, _mm_set1_epi16(-32768)));
-}
-
-inline void v_pack_store(ushort* ptr, const v_uint32x4& a)
-{
-    __m128i z = _mm_setzero_si128(), maxval32 = _mm_set1_epi32(65535), delta32 = _mm_set1_epi32(32768);
-    __m128i a1 = _mm_sub_epi32(v_select_si128(_mm_cmpgt_epi32(z, a.val), maxval32, a.val), delta32);
-    __m128i r = _mm_packs_epi32(a1, a1);
-    _mm_storel_epi64((__m128i*)ptr, _mm_sub_epi16(r, _mm_set1_epi16(-32768)));
-}
-
-template<int n> inline
-v_uint16x8 v_rshr_pack(const v_uint32x4& a, const v_uint32x4& b)
-{
-    __m128i delta = _mm_set1_epi32(1 << (n-1)), delta32 = _mm_set1_epi32(32768);
-    __m128i a1 = _mm_sub_epi32(_mm_srli_epi32(_mm_add_epi32(a.val, delta), n), delta32);
-    __m128i b1 = _mm_sub_epi32(_mm_srli_epi32(_mm_add_epi32(b.val, delta), n), delta32);
-    return v_uint16x8(_mm_sub_epi16(_mm_packs_epi32(a1, b1), _mm_set1_epi16(-32768)));
-}
-
-template<int n> inline
-void v_rshr_pack_store(ushort* ptr, const v_uint32x4& a)
-{
-    __m128i delta = _mm_set1_epi32(1 << (n-1)), delta32 = _mm_set1_epi32(32768);
-    __m128i a1 = _mm_sub_epi32(_mm_srli_epi32(_mm_add_epi32(a.val, delta), n), delta32);
-    __m128i a2 = _mm_sub_epi16(_mm_packs_epi32(a1, a1), _mm_set1_epi16(-32768));
-    _mm_storel_epi64((__m128i*)ptr, a2);
-}
-
-inline v_uint16x8 v_pack_u(const v_int32x4& a, const v_int32x4& b)
-{
-    __m128i delta32 = _mm_set1_epi32(32768);
-    __m128i r = _mm_packs_epi32(_mm_sub_epi32(a.val, delta32), _mm_sub_epi32(b.val, delta32));
-    return v_uint16x8(_mm_sub_epi16(r, _mm_set1_epi16(-32768)));
-}
-
-inline void v_pack_u_store(ushort* ptr, const v_int32x4& a)
-{
-    __m128i delta32 = _mm_set1_epi32(32768);
-    __m128i a1 = _mm_sub_epi32(a.val, delta32);
-    __m128i r = _mm_sub_epi16(_mm_packs_epi32(a1, a1), _mm_set1_epi16(-32768));
-    _mm_storel_epi64((__m128i*)ptr, r);
-}
-
-template<int n> inline
-v_uint16x8 v_rshr_pack_u(const v_int32x4& a, const v_int32x4& b)
-{
-    __m128i delta = _mm_set1_epi32(1 << (n-1)), delta32 = _mm_set1_epi32(32768);
-    __m128i a1 = _mm_sub_epi32(_mm_srai_epi32(_mm_add_epi32(a.val, delta), n), delta32);
-    __m128i a2 = _mm_sub_epi16(_mm_packs_epi32(a1, a1), _mm_set1_epi16(-32768));
-    __m128i b1 = _mm_sub_epi32(_mm_srai_epi32(_mm_add_epi32(b.val, delta), n), delta32);
-    __m128i b2 = _mm_sub_epi16(_mm_packs_epi32(b1, b1), _mm_set1_epi16(-32768));
-    return v_uint16x8(_mm_unpacklo_epi64(a2, b2));
-}
-
-template<int n> inline
-void v_rshr_pack_u_store(ushort* ptr, const v_int32x4& a)
-{
-    __m128i delta = _mm_set1_epi32(1 << (n-1)), delta32 = _mm_set1_epi32(32768);
-    __m128i a1 = _mm_sub_epi32(_mm_srai_epi32(_mm_add_epi32(a.val, delta), n), delta32);
-    __m128i a2 = _mm_sub_epi16(_mm_packs_epi32(a1, a1), _mm_set1_epi16(-32768));
-    _mm_storel_epi64((__m128i*)ptr, a2);
-}
-
-inline v_int16x8 v_pack(const v_int32x4& a, const v_int32x4& b)
-{ return v_int16x8(_mm_packs_epi32(a.val, b.val)); }
-
-inline void v_pack_store(short* ptr, const v_int32x4& a)
-{
-    _mm_storel_epi64((__m128i*)ptr, _mm_packs_epi32(a.val, a.val));
-}
-
-template<int n> inline
-v_int16x8 v_rshr_pack(const v_int32x4& a, const v_int32x4& b)
-{
-    __m128i delta = _mm_set1_epi32(1 << (n-1));
-    return v_int16x8(_mm_packs_epi32(_mm_srai_epi32(_mm_add_epi32(a.val, delta), n),
-                                     _mm_srai_epi32(_mm_add_epi32(b.val, delta), n)));
-}
-
-template<int n> inline
-void v_rshr_pack_store(short* ptr, const v_int32x4& a)
-{
-    __m128i delta = _mm_set1_epi32(1 << (n-1));
-    __m128i a1 = _mm_srai_epi32(_mm_add_epi32(a.val, delta), n);
-    _mm_storel_epi64((__m128i*)ptr, _mm_packs_epi32(a1, a1));
-}
-
-
-// [a0 0 | b0 0]  [a1 0 | b1 0]
-inline v_uint32x4 v_pack(const v_uint64x2& a, const v_uint64x2& b)
-{
-    __m128i v0 = _mm_unpacklo_epi32(a.val, b.val); // a0 a1 0 0
-    __m128i v1 = _mm_unpackhi_epi32(a.val, b.val); // b0 b1 0 0
-    return v_uint32x4(_mm_unpacklo_epi32(v0, v1));
-}
-
-inline void v_pack_store(unsigned* ptr, const v_uint64x2& a)
-{
-    __m128i a1 = _mm_shuffle_epi32(a.val, _MM_SHUFFLE(0, 2, 2, 0));
-    _mm_storel_epi64((__m128i*)ptr, a1);
-}
-
-// [a0 0 | b0 0]  [a1 0 | b1 0]
-inline v_int32x4 v_pack(const v_int64x2& a, const v_int64x2& b)
-{
-    __m128i v0 = _mm_unpacklo_epi32(a.val, b.val); // a0 a1 0 0
-    __m128i v1 = _mm_unpackhi_epi32(a.val, b.val); // b0 b1 0 0
-    return v_int32x4(_mm_unpacklo_epi32(v0, v1));
-}
-
-inline void v_pack_store(int* ptr, const v_int64x2& a)
-{
-    __m128i a1 = _mm_shuffle_epi32(a.val, _MM_SHUFFLE(0, 2, 2, 0));
-    _mm_storel_epi64((__m128i*)ptr, a1);
-}
-
-template<int n> inline
-v_uint32x4 v_rshr_pack(const v_uint64x2& a, const v_uint64x2& b)
-{
-    uint64 delta = (uint64)1 << (n-1);
-    v_uint64x2 delta2(delta, delta);
-    __m128i a1 = _mm_srli_epi64(_mm_add_epi64(a.val, delta2.val), n);
-    __m128i b1 = _mm_srli_epi64(_mm_add_epi64(b.val, delta2.val), n);
-    __m128i v0 = _mm_unpacklo_epi32(a1, b1); // a0 a1 0 0
-    __m128i v1 = _mm_unpackhi_epi32(a1, b1); // b0 b1 0 0
-    return v_uint32x4(_mm_unpacklo_epi32(v0, v1));
-}
-
-template<int n> inline
-void v_rshr_pack_store(unsigned* ptr, const v_uint64x2& a)
-{
-    uint64 delta = (uint64)1 << (n-1);
-    v_uint64x2 delta2(delta, delta);
-    __m128i a1 = _mm_srli_epi64(_mm_add_epi64(a.val, delta2.val), n);
-    __m128i a2 = _mm_shuffle_epi32(a1, _MM_SHUFFLE(0, 2, 2, 0));
-    _mm_storel_epi64((__m128i*)ptr, a2);
-}
-
-inline __m128i v_sign_epi64(__m128i a)
-{
-    return _mm_shuffle_epi32(_mm_srai_epi32(a, 31), _MM_SHUFFLE(3, 3, 1, 1)); // x m0 | x m1
-}
-
-inline __m128i v_srai_epi64(__m128i a, int imm)
-{
-    __m128i smask = v_sign_epi64(a);
-    return _mm_xor_si128(_mm_srli_epi64(_mm_xor_si128(a, smask), imm), smask);
-}
-
-template<int n> inline
-v_int32x4 v_rshr_pack(const v_int64x2& a, const v_int64x2& b)
-{
-    int64 delta = (int64)1 << (n-1);
-    v_int64x2 delta2(delta, delta);
-    __m128i a1 = v_srai_epi64(_mm_add_epi64(a.val, delta2.val), n);
-    __m128i b1 = v_srai_epi64(_mm_add_epi64(b.val, delta2.val), n);
-    __m128i v0 = _mm_unpacklo_epi32(a1, b1); // a0 a1 0 0
-    __m128i v1 = _mm_unpackhi_epi32(a1, b1); // b0 b1 0 0
-    return v_int32x4(_mm_unpacklo_epi32(v0, v1));
-}
-
-template<int n> inline
-void v_rshr_pack_store(int* ptr, const v_int64x2& a)
-{
-    int64 delta = (int64)1 << (n-1);
-    v_int64x2 delta2(delta, delta);
-    __m128i a1 = v_srai_epi64(_mm_add_epi64(a.val, delta2.val), n);
-    __m128i a2 = _mm_shuffle_epi32(a1, _MM_SHUFFLE(0, 2, 2, 0));
-    _mm_storel_epi64((__m128i*)ptr, a2);
-}
-
-inline v_float32x4 v_matmul(const v_float32x4& v, const v_float32x4& m0,
-                            const v_float32x4& m1, const v_float32x4& m2,
-                            const v_float32x4& m3)
-{
-    __m128 v0 = _mm_mul_ps(_mm_shuffle_ps(v.val, v.val, _MM_SHUFFLE(0, 0, 0, 0)), m0.val);
-    __m128 v1 = _mm_mul_ps(_mm_shuffle_ps(v.val, v.val, _MM_SHUFFLE(1, 1, 1, 1)), m1.val);
-    __m128 v2 = _mm_mul_ps(_mm_shuffle_ps(v.val, v.val, _MM_SHUFFLE(2, 2, 2, 2)), m2.val);
-    __m128 v3 = _mm_mul_ps(_mm_shuffle_ps(v.val, v.val, _MM_SHUFFLE(3, 3, 3, 3)), m3.val);
-
-    return v_float32x4(_mm_add_ps(_mm_add_ps(v0, v1), _mm_add_ps(v2, v3)));
-}
-
-inline v_float32x4 v_matmuladd(const v_float32x4& v, const v_float32x4& m0,
-                               const v_float32x4& m1, const v_float32x4& m2,
-                               const v_float32x4& a)
-{
-    __m128 v0 = _mm_mul_ps(_mm_shuffle_ps(v.val, v.val, _MM_SHUFFLE(0, 0, 0, 0)), m0.val);
-    __m128 v1 = _mm_mul_ps(_mm_shuffle_ps(v.val, v.val, _MM_SHUFFLE(1, 1, 1, 1)), m1.val);
-    __m128 v2 = _mm_mul_ps(_mm_shuffle_ps(v.val, v.val, _MM_SHUFFLE(2, 2, 2, 2)), m2.val);
-
-    return v_float32x4(_mm_add_ps(_mm_add_ps(v0, v1), _mm_add_ps(v2, a.val)));
-}
-
-#define OPENCV_HAL_IMPL_SSE_BIN_OP(bin_op, _Tpvec, intrin) \
-    inline _Tpvec operator bin_op (const _Tpvec& a, const _Tpvec& b) \
-    { \
-        return _Tpvec(intrin(a.val, b.val)); \
-    } \
-    inline _Tpvec& operator bin_op##= (_Tpvec& a, const _Tpvec& b) \
-    { \
-        a.val = intrin(a.val, b.val); \
-        return a; \
-    }
-
-OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_uint8x16, _mm_adds_epu8)
-OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_uint8x16, _mm_subs_epu8)
-OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_int8x16, _mm_adds_epi8)
-OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_int8x16, _mm_subs_epi8)
-OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_uint16x8, _mm_adds_epu16)
-OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_uint16x8, _mm_subs_epu16)
-OPENCV_HAL_IMPL_SSE_BIN_OP(*, v_uint16x8, _mm_mullo_epi16)
-OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_int16x8, _mm_adds_epi16)
-OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_int16x8, _mm_subs_epi16)
-OPENCV_HAL_IMPL_SSE_BIN_OP(*, v_int16x8, _mm_mullo_epi16)
-OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_uint32x4, _mm_add_epi32)
-OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_uint32x4, _mm_sub_epi32)
-OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_int32x4, _mm_add_epi32)
-OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_int32x4, _mm_sub_epi32)
-OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_float32x4, _mm_add_ps)
-OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_float32x4, _mm_sub_ps)
-OPENCV_HAL_IMPL_SSE_BIN_OP(*, v_float32x4, _mm_mul_ps)
-OPENCV_HAL_IMPL_SSE_BIN_OP(/, v_float32x4, _mm_div_ps)
-OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_float64x2, _mm_add_pd)
-OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_float64x2, _mm_sub_pd)
-OPENCV_HAL_IMPL_SSE_BIN_OP(*, v_float64x2, _mm_mul_pd)
-OPENCV_HAL_IMPL_SSE_BIN_OP(/, v_float64x2, _mm_div_pd)
-OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_uint64x2, _mm_add_epi64)
-OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_uint64x2, _mm_sub_epi64)
-OPENCV_HAL_IMPL_SSE_BIN_OP(+, v_int64x2, _mm_add_epi64)
-OPENCV_HAL_IMPL_SSE_BIN_OP(-, v_int64x2, _mm_sub_epi64)
-
-inline v_uint32x4 operator * (const v_uint32x4& a, const v_uint32x4& b)
-{
-    __m128i c0 = _mm_mul_epu32(a.val, b.val);
-    __m128i c1 = _mm_mul_epu32(_mm_srli_epi64(a.val, 32), _mm_srli_epi64(b.val, 32));
-    __m128i d0 = _mm_unpacklo_epi32(c0, c1);
-    __m128i d1 = _mm_unpackhi_epi32(c0, c1);
-    return v_uint32x4(_mm_unpacklo_epi64(d0, d1));
-}
-inline v_int32x4 operator * (const v_int32x4& a, const v_int32x4& b)
-{
-    __m128i c0 = _mm_mul_epu32(a.val, b.val);
-    __m128i c1 = _mm_mul_epu32(_mm_srli_epi64(a.val, 32), _mm_srli_epi64(b.val, 32));
-    __m128i d0 = _mm_unpacklo_epi32(c0, c1);
-    __m128i d1 = _mm_unpackhi_epi32(c0, c1);
-    return v_int32x4(_mm_unpacklo_epi64(d0, d1));
-}
-inline v_uint32x4& operator *= (v_uint32x4& a, const v_uint32x4& b)
-{
-    a = a * b;
-    return a;
-}
-inline v_int32x4& operator *= (v_int32x4& a, const v_int32x4& b)
-{
-    a = a * b;
-    return a;
-}
-
-inline void v_mul_expand(const v_int16x8& a, const v_int16x8& b,
-                         v_int32x4& c, v_int32x4& d)
-{
-    __m128i v0 = _mm_mullo_epi16(a.val, b.val);
-    __m128i v1 = _mm_mulhi_epi16(a.val, b.val);
-    c.val = _mm_unpacklo_epi16(v0, v1);
-    d.val = _mm_unpackhi_epi16(v0, v1);
-}
-
-inline void v_mul_expand(const v_uint16x8& a, const v_uint16x8& b,
-                         v_uint32x4& c, v_uint32x4& d)
-{
-    __m128i v0 = _mm_mullo_epi16(a.val, b.val);
-    __m128i v1 = _mm_mulhi_epu16(a.val, b.val);
-    c.val = _mm_unpacklo_epi16(v0, v1);
-    d.val = _mm_unpackhi_epi16(v0, v1);
-}
-
-inline void v_mul_expand(const v_uint32x4& a, const v_uint32x4& b,
-                         v_uint64x2& c, v_uint64x2& d)
-{
-    __m128i c0 = _mm_mul_epu32(a.val, b.val);
-    __m128i c1 = _mm_mul_epu32(_mm_srli_epi64(a.val, 32), _mm_srli_epi64(b.val, 32));
-    c.val = _mm_unpacklo_epi64(c0, c1);
-    d.val = _mm_unpackhi_epi64(c0, c1);
-}
-
-inline v_int32x4 v_dotprod(const v_int16x8& a, const v_int16x8& b)
-{
-    return v_int32x4(_mm_madd_epi16(a.val, b.val));
-}
-
-inline v_int32x4 v_dotprod(const v_int16x8& a, const v_int16x8& b, const v_int32x4& c)
-{
-    return v_int32x4(_mm_add_epi32(_mm_madd_epi16(a.val, b.val), c.val));
-}
-
-#define OPENCV_HAL_IMPL_SSE_LOGIC_OP(_Tpvec, suffix, not_const) \
-    OPENCV_HAL_IMPL_SSE_BIN_OP(&, _Tpvec, _mm_and_##suffix) \
-    OPENCV_HAL_IMPL_SSE_BIN_OP(|, _Tpvec, _mm_or_##suffix) \
-    OPENCV_HAL_IMPL_SSE_BIN_OP(^, _Tpvec, _mm_xor_##suffix) \
-    inline _Tpvec operator ~ (const _Tpvec& a) \
-    { \
-        return _Tpvec(_mm_xor_##suffix(a.val, not_const)); \
-    }
-
-OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_uint8x16, si128, _mm_set1_epi32(-1))
-OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_int8x16, si128, _mm_set1_epi32(-1))
-OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_uint16x8, si128, _mm_set1_epi32(-1))
-OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_int16x8, si128, _mm_set1_epi32(-1))
-OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_uint32x4, si128, _mm_set1_epi32(-1))
-OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_int32x4, si128, _mm_set1_epi32(-1))
-OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_uint64x2, si128, _mm_set1_epi32(-1))
-OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_int64x2, si128, _mm_set1_epi32(-1))
-OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_float32x4, ps, _mm_castsi128_ps(_mm_set1_epi32(-1)))
-OPENCV_HAL_IMPL_SSE_LOGIC_OP(v_float64x2, pd, _mm_castsi128_pd(_mm_set1_epi32(-1)))
-
-inline v_float32x4 v_sqrt(const v_float32x4& x)
-{ return v_float32x4(_mm_sqrt_ps(x.val)); }
-
-inline v_float32x4 v_invsqrt(const v_float32x4& x)
-{
-    static const __m128 _0_5 = _mm_set1_ps(0.5f), _1_5 = _mm_set1_ps(1.5f);
-    __m128 t = x.val;
-    __m128 h = _mm_mul_ps(t, _0_5);
-    t = _mm_rsqrt_ps(t);
-    t = _mm_mul_ps(t, _mm_sub_ps(_1_5, _mm_mul_ps(_mm_mul_ps(t, t), h)));
-    return v_float32x4(t);
-}
-
-inline v_float64x2 v_sqrt(const v_float64x2& x)
-{ return v_float64x2(_mm_sqrt_pd(x.val)); }
-
-inline v_float64x2 v_invsqrt(const v_float64x2& x)
-{
-    static const __m128d v_1 = _mm_set1_pd(1.);
-    return v_float64x2(_mm_div_pd(v_1, _mm_sqrt_pd(x.val)));
-}
-
-#define OPENCV_HAL_IMPL_SSE_ABS_INT_FUNC(_Tpuvec, _Tpsvec, func, suffix, subWidth) \
-inline _Tpuvec v_abs(const _Tpsvec& x) \
-{ return _Tpuvec(_mm_##func##_ep##suffix(x.val, _mm_sub_ep##subWidth(_mm_setzero_si128(), x.val))); }
-
-OPENCV_HAL_IMPL_SSE_ABS_INT_FUNC(v_uint8x16, v_int8x16, min, u8, i8)
-OPENCV_HAL_IMPL_SSE_ABS_INT_FUNC(v_uint16x8, v_int16x8, max, i16, i16)
-inline v_uint32x4 v_abs(const v_int32x4& x)
-{
-    __m128i s = _mm_srli_epi32(x.val, 31);
-    __m128i f = _mm_srai_epi32(x.val, 31);
-    return v_uint32x4(_mm_add_epi32(_mm_xor_si128(x.val, f), s));
-}
-inline v_float32x4 v_abs(const v_float32x4& x)
-{ return v_float32x4(_mm_and_ps(x.val, _mm_castsi128_ps(_mm_set1_epi32(0x7fffffff)))); }
-inline v_float64x2 v_abs(const v_float64x2& x)
-{
-    return v_float64x2(_mm_and_pd(x.val,
-        _mm_castsi128_pd(_mm_srli_epi64(_mm_set1_epi32(-1), 1))));
-}
-
-// TODO: exp, log, sin, cos
-
-#define OPENCV_HAL_IMPL_SSE_BIN_FUNC(_Tpvec, func, intrin) \
-inline _Tpvec func(const _Tpvec& a, const _Tpvec& b) \
-{ \
-    return _Tpvec(intrin(a.val, b.val)); \
-}
-
-OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_uint8x16, v_min, _mm_min_epu8)
-OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_uint8x16, v_max, _mm_max_epu8)
-OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_int16x8, v_min, _mm_min_epi16)
-OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_int16x8, v_max, _mm_max_epi16)
-OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_float32x4, v_min, _mm_min_ps)
-OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_float32x4, v_max, _mm_max_ps)
-OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_float64x2, v_min, _mm_min_pd)
-OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_float64x2, v_max, _mm_max_pd)
-
-inline v_int8x16 v_min(const v_int8x16& a, const v_int8x16& b)
-{
-#if CV_SSE4_1
-    return v_int8x16(_mm_min_epi8(a.val, b.val));
-#else
-    __m128i delta = _mm_set1_epi8((char)-128);
-    return v_int8x16(_mm_xor_si128(delta, _mm_min_epu8(_mm_xor_si128(a.val, delta),
-                                                       _mm_xor_si128(b.val, delta))));
-#endif
-}
-inline v_int8x16 v_max(const v_int8x16& a, const v_int8x16& b)
-{
-#if CV_SSE4_1
-    return v_int8x16(_mm_max_epi8(a.val, b.val));
-#else
-    __m128i delta = _mm_set1_epi8((char)-128);
-    return v_int8x16(_mm_xor_si128(delta, _mm_max_epu8(_mm_xor_si128(a.val, delta),
-                                                       _mm_xor_si128(b.val, delta))));
-#endif
-}
-inline v_uint16x8 v_min(const v_uint16x8& a, const v_uint16x8& b)
-{
-#if CV_SSE4_1
-    return v_uint16x8(_mm_min_epu16(a.val, b.val));
-#else
-    return v_uint16x8(_mm_subs_epu16(a.val, _mm_subs_epu16(a.val, b.val)));
-#endif
-}
-inline v_uint16x8 v_max(const v_uint16x8& a, const v_uint16x8& b)
-{
-#if CV_SSE4_1
-    return v_uint16x8(_mm_max_epu16(a.val, b.val));
-#else
-    return v_uint16x8(_mm_adds_epu16(_mm_subs_epu16(a.val, b.val), b.val));
-#endif
-}
-inline v_uint32x4 v_min(const v_uint32x4& a, const v_uint32x4& b)
-{
-#if CV_SSE4_1
-    return v_uint32x4(_mm_min_epu32(a.val, b.val));
-#else
-    __m128i delta = _mm_set1_epi32((int)0x80000000);
-    __m128i mask = _mm_cmpgt_epi32(_mm_xor_si128(a.val, delta), _mm_xor_si128(b.val, delta));
-    return v_uint32x4(v_select_si128(mask, b.val, a.val));
-#endif
-}
-inline v_uint32x4 v_max(const v_uint32x4& a, const v_uint32x4& b)
-{
-#if CV_SSE4_1
-    return v_uint32x4(_mm_max_epu32(a.val, b.val));
-#else
-    __m128i delta = _mm_set1_epi32((int)0x80000000);
-    __m128i mask = _mm_cmpgt_epi32(_mm_xor_si128(a.val, delta), _mm_xor_si128(b.val, delta));
-    return v_uint32x4(v_select_si128(mask, a.val, b.val));
-#endif
-}
-inline v_int32x4 v_min(const v_int32x4& a, const v_int32x4& b)
-{
-#if CV_SSE4_1
-    return v_int32x4(_mm_min_epi32(a.val, b.val));
-#else
-    return v_int32x4(v_select_si128(_mm_cmpgt_epi32(a.val, b.val), b.val, a.val));
-#endif
-}
-inline v_int32x4 v_max(const v_int32x4& a, const v_int32x4& b)
-{
-#if CV_SSE4_1
-    return v_int32x4(_mm_max_epi32(a.val, b.val));
-#else
-    return v_int32x4(v_select_si128(_mm_cmpgt_epi32(a.val, b.val), a.val, b.val));
-#endif
-}
-
-#define OPENCV_HAL_IMPL_SSE_INT_CMP_OP(_Tpuvec, _Tpsvec, suffix, sbit) \
-inline _Tpuvec operator == (const _Tpuvec& a, const _Tpuvec& b) \
-{ return _Tpuvec(_mm_cmpeq_##suffix(a.val, b.val)); } \
-inline _Tpuvec operator != (const _Tpuvec& a, const _Tpuvec& b) \
-{ \
-    __m128i not_mask = _mm_set1_epi32(-1); \
-    return _Tpuvec(_mm_xor_si128(_mm_cmpeq_##suffix(a.val, b.val), not_mask)); \
-} \
-inline _Tpsvec operator == (const _Tpsvec& a, const _Tpsvec& b) \
-{ return _Tpsvec(_mm_cmpeq_##suffix(a.val, b.val)); } \
-inline _Tpsvec operator != (const _Tpsvec& a, const _Tpsvec& b) \
-{ \
-    __m128i not_mask = _mm_set1_epi32(-1); \
-    return _Tpsvec(_mm_xor_si128(_mm_cmpeq_##suffix(a.val, b.val), not_mask)); \
-} \
-inline _Tpuvec operator < (const _Tpuvec& a, const _Tpuvec& b) \
-{ \
-    __m128i smask = _mm_set1_##suffix(sbit); \
-    return _Tpuvec(_mm_cmpgt_##suffix(_mm_xor_si128(b.val, smask), _mm_xor_si128(a.val, smask))); \
-} \
-inline _Tpuvec operator > (const _Tpuvec& a, const _Tpuvec& b) \
-{ \
-    __m128i smask = _mm_set1_##suffix(sbit); \
-    return _Tpuvec(_mm_cmpgt_##suffix(_mm_xor_si128(a.val, smask), _mm_xor_si128(b.val, smask))); \
-} \
-inline _Tpuvec operator <= (const _Tpuvec& a, const _Tpuvec& b) \
-{ \
-    __m128i smask = _mm_set1_##suffix(sbit); \
-    __m128i not_mask = _mm_set1_epi32(-1); \
-    __m128i res = _mm_cmpgt_##suffix(_mm_xor_si128(a.val, smask), _mm_xor_si128(b.val, smask)); \
-    return _Tpuvec(_mm_xor_si128(res, not_mask)); \
-} \
-inline _Tpuvec operator >= (const _Tpuvec& a, const _Tpuvec& b) \
-{ \
-    __m128i smask = _mm_set1_##suffix(sbit); \
-    __m128i not_mask = _mm_set1_epi32(-1); \
-    __m128i res = _mm_cmpgt_##suffix(_mm_xor_si128(b.val, smask), _mm_xor_si128(a.val, smask)); \
-    return _Tpuvec(_mm_xor_si128(res, not_mask)); \
-} \
-inline _Tpsvec operator < (const _Tpsvec& a, const _Tpsvec& b) \
-{ \
-    return _Tpsvec(_mm_cmpgt_##suffix(b.val, a.val)); \
-} \
-inline _Tpsvec operator > (const _Tpsvec& a, const _Tpsvec& b) \
-{ \
-    return _Tpsvec(_mm_cmpgt_##suffix(a.val, b.val)); \
-} \
-inline _Tpsvec operator <= (const _Tpsvec& a, const _Tpsvec& b) \
-{ \
-    __m128i not_mask = _mm_set1_epi32(-1); \
-    return _Tpsvec(_mm_xor_si128(_mm_cmpgt_##suffix(a.val, b.val), not_mask)); \
-} \
-inline _Tpsvec operator >= (const _Tpsvec& a, const _Tpsvec& b) \
-{ \
-    __m128i not_mask = _mm_set1_epi32(-1); \
-    return _Tpsvec(_mm_xor_si128(_mm_cmpgt_##suffix(b.val, a.val), not_mask)); \
-}
-
-OPENCV_HAL_IMPL_SSE_INT_CMP_OP(v_uint8x16, v_int8x16, epi8, (char)-128)
-OPENCV_HAL_IMPL_SSE_INT_CMP_OP(v_uint16x8, v_int16x8, epi16, (short)-32768)
-OPENCV_HAL_IMPL_SSE_INT_CMP_OP(v_uint32x4, v_int32x4, epi32, (int)0x80000000)
-
-#define OPENCV_HAL_IMPL_SSE_FLT_CMP_OP(_Tpvec, suffix) \
-inline _Tpvec operator == (const _Tpvec& a, const _Tpvec& b) \
-{ return _Tpvec(_mm_cmpeq_##suffix(a.val, b.val)); } \
-inline _Tpvec operator != (const _Tpvec& a, const _Tpvec& b) \
-{ return _Tpvec(_mm_cmpneq_##suffix(a.val, b.val)); } \
-inline _Tpvec operator < (const _Tpvec& a, const _Tpvec& b) \
-{ return _Tpvec(_mm_cmplt_##suffix(a.val, b.val)); } \
-inline _Tpvec operator > (const _Tpvec& a, const _Tpvec& b) \
-{ return _Tpvec(_mm_cmpgt_##suffix(a.val, b.val)); } \
-inline _Tpvec operator <= (const _Tpvec& a, const _Tpvec& b) \
-{ return _Tpvec(_mm_cmple_##suffix(a.val, b.val)); } \
-inline _Tpvec operator >= (const _Tpvec& a, const _Tpvec& b) \
-{ return _Tpvec(_mm_cmpge_##suffix(a.val, b.val)); }
-
-OPENCV_HAL_IMPL_SSE_FLT_CMP_OP(v_float32x4, ps)
-OPENCV_HAL_IMPL_SSE_FLT_CMP_OP(v_float64x2, pd)
-
-#define OPENCV_HAL_IMPL_SSE_64BIT_CMP_OP(_Tpvec, cast) \
-inline _Tpvec operator == (const _Tpvec& a, const _Tpvec& b) \
-{ return cast(v_reinterpret_as_f64(a) == v_reinterpret_as_f64(b)); } \
-inline _Tpvec operator != (const _Tpvec& a, const _Tpvec& b) \
-{ return cast(v_reinterpret_as_f64(a) != v_reinterpret_as_f64(b)); }
-
-OPENCV_HAL_IMPL_SSE_64BIT_CMP_OP(v_uint64x2, v_reinterpret_as_u64);
-OPENCV_HAL_IMPL_SSE_64BIT_CMP_OP(v_int64x2, v_reinterpret_as_s64);
-
-OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_uint8x16, v_add_wrap, _mm_add_epi8)
-OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_int8x16, v_add_wrap, _mm_add_epi8)
-OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_uint16x8, v_add_wrap, _mm_add_epi16)
-OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_int16x8, v_add_wrap, _mm_add_epi16)
-OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_uint8x16, v_sub_wrap, _mm_sub_epi8)
-OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_int8x16, v_sub_wrap, _mm_sub_epi8)
-OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_uint16x8, v_sub_wrap, _mm_sub_epi16)
-OPENCV_HAL_IMPL_SSE_BIN_FUNC(v_int16x8, v_sub_wrap, _mm_sub_epi16)
-
-#define OPENCV_HAL_IMPL_SSE_ABSDIFF_8_16(_Tpuvec, _Tpsvec, bits, smask32) \
-inline _Tpuvec v_absdiff(const _Tpuvec& a, const _Tpuvec& b) \
-{ \
-    return _Tpuvec(_mm_add_epi##bits(_mm_subs_epu##bits(a.val, b.val), _mm_subs_epu##bits(b.val, a.val))); \
-} \
-inline _Tpuvec v_absdiff(const _Tpsvec& a, const _Tpsvec& b) \
-{ \
-    __m128i smask = _mm_set1_epi32(smask32); \
-    __m128i a1 = _mm_xor_si128(a.val, smask); \
-    __m128i b1 = _mm_xor_si128(b.val, smask); \
-    return _Tpuvec(_mm_add_epi##bits(_mm_subs_epu##bits(a1, b1), _mm_subs_epu##bits(b1, a1))); \
-}
-
-OPENCV_HAL_IMPL_SSE_ABSDIFF_8_16(v_uint8x16, v_int8x16, 8, (int)0x80808080)
-OPENCV_HAL_IMPL_SSE_ABSDIFF_8_16(v_uint16x8, v_int16x8, 16, (int)0x80008000)
-
-inline v_uint32x4 v_absdiff(const v_uint32x4& a, const v_uint32x4& b)
-{
-    return v_max(a, b) - v_min(a, b);
-}
-
-inline v_uint32x4 v_absdiff(const v_int32x4& a, const v_int32x4& b)
-{
-    __m128i d = _mm_sub_epi32(a.val, b.val);
-    __m128i m = _mm_cmpgt_epi32(b.val, a.val);
-    return v_uint32x4(_mm_sub_epi32(_mm_xor_si128(d, m), m));
-}
-inline v_int32x4 v_muladd(const v_int32x4& a, const v_int32x4& b, const v_int32x4& c)
-{
-    return a * b + c;
-}
-
-#define OPENCV_HAL_IMPL_SSE_MISC_FLT_OP(_Tpvec, _Tp, _Tpreg, suffix, absmask_vec) \
-inline _Tpvec v_absdiff(const _Tpvec& a, const _Tpvec& b) \
-{ \
-    _Tpreg absmask = _mm_castsi128_##suffix(absmask_vec); \
-    return _Tpvec(_mm_and_##suffix(_mm_sub_##suffix(a.val, b.val), absmask)); \
-} \
-inline _Tpvec v_magnitude(const _Tpvec& a, const _Tpvec& b) \
-{ \
-    _Tpreg res = _mm_add_##suffix(_mm_mul_##suffix(a.val, a.val), _mm_mul_##suffix(b.val, b.val)); \
-    return _Tpvec(_mm_sqrt_##suffix(res)); \
-} \
-inline _Tpvec v_sqr_magnitude(const _Tpvec& a, const _Tpvec& b) \
-{ \
-    _Tpreg res = _mm_add_##suffix(_mm_mul_##suffix(a.val, a.val), _mm_mul_##suffix(b.val, b.val)); \
-    return _Tpvec(res); \
-} \
-inline _Tpvec v_muladd(const _Tpvec& a, const _Tpvec& b, const _Tpvec& c) \
-{ \
-    return _Tpvec(_mm_add_##suffix(_mm_mul_##suffix(a.val, b.val), c.val)); \
-}
-
-OPENCV_HAL_IMPL_SSE_MISC_FLT_OP(v_float32x4, float, __m128, ps, _mm_set1_epi32((int)0x7fffffff))
-OPENCV_HAL_IMPL_SSE_MISC_FLT_OP(v_float64x2, double, __m128d, pd, _mm_srli_epi64(_mm_set1_epi32(-1), 1))
-
-#define OPENCV_HAL_IMPL_SSE_SHIFT_OP(_Tpuvec, _Tpsvec, suffix, srai) \
-inline _Tpuvec operator << (const _Tpuvec& a, int imm) \
-{ \
-    return _Tpuvec(_mm_slli_##suffix(a.val, imm)); \
-} \
-inline _Tpsvec operator << (const _Tpsvec& a, int imm) \
-{ \
-    return _Tpsvec(_mm_slli_##suffix(a.val, imm)); \
-} \
-inline _Tpuvec operator >> (const _Tpuvec& a, int imm) \
-{ \
-    return _Tpuvec(_mm_srli_##suffix(a.val, imm)); \
-} \
-inline _Tpsvec operator >> (const _Tpsvec& a, int imm) \
-{ \
-    return _Tpsvec(srai(a.val, imm)); \
-} \
-template<int imm> \
-inline _Tpuvec v_shl(const _Tpuvec& a) \
-{ \
-    return _Tpuvec(_mm_slli_##suffix(a.val, imm)); \
-} \
-template<int imm> \
-inline _Tpsvec v_shl(const _Tpsvec& a) \
-{ \
-    return _Tpsvec(_mm_slli_##suffix(a.val, imm)); \
-} \
-template<int imm> \
-inline _Tpuvec v_shr(const _Tpuvec& a) \
-{ \
-    return _Tpuvec(_mm_srli_##suffix(a.val, imm)); \
-} \
-template<int imm> \
-inline _Tpsvec v_shr(const _Tpsvec& a) \
-{ \
-    return _Tpsvec(srai(a.val, imm)); \
-}
-
-OPENCV_HAL_IMPL_SSE_SHIFT_OP(v_uint16x8, v_int16x8, epi16, _mm_srai_epi16)
-OPENCV_HAL_IMPL_SSE_SHIFT_OP(v_uint32x4, v_int32x4, epi32, _mm_srai_epi32)
-OPENCV_HAL_IMPL_SSE_SHIFT_OP(v_uint64x2, v_int64x2, epi64, v_srai_epi64)
-
-namespace hal_sse_internal
-{
-    template <int imm,
-        bool is_invalid = ((imm < 0) || (imm > 16)),
-        bool is_first = (imm == 0),
-        bool is_half = (imm == 8),
-        bool is_second = (imm == 16),
-        bool is_other = (((imm > 0) && (imm < 8)) || ((imm > 8) && (imm < 16)))>
-    class v_sse_palignr_u8_class;
-
-    template <int imm>
-    class v_sse_palignr_u8_class<imm, true, false, false, false, false>;
-
-    template <int imm>
-    class v_sse_palignr_u8_class<imm, false, true, false, false, false>
-    {
-    public:
-        inline __m128i operator()(const __m128i& a, const __m128i&) const
-        {
-            return a;
-        }
-    };
-
-    template <int imm>
-    class v_sse_palignr_u8_class<imm, false, false, true, false, false>
-    {
-    public:
-        inline __m128i operator()(const __m128i& a, const __m128i& b) const
-        {
-            return _mm_unpacklo_epi64(_mm_unpackhi_epi64(a, a), b);
-        }
-    };
-
-    template <int imm>
-    class v_sse_palignr_u8_class<imm, false, false, false, true, false>
-    {
-    public:
-        inline __m128i operator()(const __m128i&, const __m128i& b) const
-        {
-            return b;
-        }
-    };
-
-    template <int imm>
-    class v_sse_palignr_u8_class<imm, false, false, false, false, true>
-    {
-#if CV_SSSE3
-    public:
-        inline __m128i operator()(const __m128i& a, const __m128i& b) const
-        {
-            return _mm_alignr_epi8(b, a, imm);
-        }
-#else
-    public:
-        inline __m128i operator()(const __m128i& a, const __m128i& b) const
-        {
-            enum { imm2 = (sizeof(__m128i) - imm) };
-            return _mm_or_si128(_mm_srli_si128(a, imm), _mm_slli_si128(b, imm2));
-        }
-#endif
-    };
-
-    template <int imm>
-    inline __m128i v_sse_palignr_u8(const __m128i& a, const __m128i& b)
-    {
-        CV_StaticAssert((imm >= 0) && (imm <= 16), "Invalid imm for v_sse_palignr_u8.");
-        return v_sse_palignr_u8_class<imm>()(a, b);
-    }
-}
-
-template<int imm, typename _Tpvec>
-inline _Tpvec v_rotate_right(const _Tpvec &a)
-{
-    using namespace hal_sse_internal;
-    enum { imm2 = (imm * sizeof(typename _Tpvec::lane_type)) };
-    return _Tpvec(v_sse_reinterpret_as<typename _Tpvec::vector_type>(
-        _mm_srli_si128(
-            v_sse_reinterpret_as<__m128i>(a.val), imm2)));
-}
-
-template<int imm, typename _Tpvec>
-inline _Tpvec v_rotate_left(const _Tpvec &a)
-{
-    using namespace hal_sse_internal;
-    enum { imm2 = (imm * sizeof(typename _Tpvec::lane_type)) };
-    return _Tpvec(v_sse_reinterpret_as<typename _Tpvec::vector_type>(
-        _mm_slli_si128(
-            v_sse_reinterpret_as<__m128i>(a.val), imm2)));
-}
-
-template<int imm, typename _Tpvec>
-inline _Tpvec v_rotate_right(const _Tpvec &a, const _Tpvec &b)
-{
-    using namespace hal_sse_internal;
-    enum { imm2 = (imm * sizeof(typename _Tpvec::lane_type)) };
-    return _Tpvec(v_sse_reinterpret_as<typename _Tpvec::vector_type>(
-        v_sse_palignr_u8<imm2>(
-            v_sse_reinterpret_as<__m128i>(a.val),
-            v_sse_reinterpret_as<__m128i>(b.val))));
-}
-
-template<int imm, typename _Tpvec>
-inline _Tpvec v_rotate_left(const _Tpvec &a, const _Tpvec &b)
-{
-    using namespace hal_sse_internal;
-    enum { imm2 = ((_Tpvec::nlanes - imm) * sizeof(typename _Tpvec::lane_type)) };
-    return _Tpvec(v_sse_reinterpret_as<typename _Tpvec::vector_type>(
-        v_sse_palignr_u8<imm2>(
-            v_sse_reinterpret_as<__m128i>(b.val),
-            v_sse_reinterpret_as<__m128i>(a.val))));
-}
-
-#define OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(_Tpvec, _Tp) \
-inline _Tpvec v_load(const _Tp* ptr) \
-{ return _Tpvec(_mm_loadu_si128((const __m128i*)ptr)); } \
-inline _Tpvec v_load_aligned(const _Tp* ptr) \
-{ return _Tpvec(_mm_load_si128((const __m128i*)ptr)); } \
-inline _Tpvec v_load_low(const _Tp* ptr) \
-{ return _Tpvec(_mm_loadl_epi64((const __m128i*)ptr)); } \
-inline _Tpvec v_load_halves(const _Tp* ptr0, const _Tp* ptr1) \
-{ \
-    return _Tpvec(_mm_unpacklo_epi64(_mm_loadl_epi64((const __m128i*)ptr0), \
-                                     _mm_loadl_epi64((const __m128i*)ptr1))); \
-} \
-inline void v_store(_Tp* ptr, const _Tpvec& a) \
-{ _mm_storeu_si128((__m128i*)ptr, a.val); } \
-inline void v_store_aligned(_Tp* ptr, const _Tpvec& a) \
-{ _mm_store_si128((__m128i*)ptr, a.val); } \
-inline void v_store_low(_Tp* ptr, const _Tpvec& a) \
-{ _mm_storel_epi64((__m128i*)ptr, a.val); } \
-inline void v_store_high(_Tp* ptr, const _Tpvec& a) \
-{ _mm_storel_epi64((__m128i*)ptr, _mm_unpackhi_epi64(a.val, a.val)); }
-
-OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(v_uint8x16, uchar)
-OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(v_int8x16, schar)
-OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(v_uint16x8, ushort)
-OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(v_int16x8, short)
-OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(v_uint32x4, unsigned)
-OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(v_int32x4, int)
-OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(v_uint64x2, uint64)
-OPENCV_HAL_IMPL_SSE_LOADSTORE_INT_OP(v_int64x2, int64)
-
-#define OPENCV_HAL_IMPL_SSE_LOADSTORE_FLT_OP(_Tpvec, _Tp, suffix) \
-inline _Tpvec v_load(const _Tp* ptr) \
-{ return _Tpvec(_mm_loadu_##suffix(ptr)); } \
-inline _Tpvec v_load_aligned(const _Tp* ptr) \
-{ return _Tpvec(_mm_load_##suffix(ptr)); } \
-inline _Tpvec v_load_low(const _Tp* ptr) \
-{ return _Tpvec(_mm_castsi128_##suffix(_mm_loadl_epi64((const __m128i*)ptr))); } \
-inline _Tpvec v_load_halves(const _Tp* ptr0, const _Tp* ptr1) \
-{ \
-    return _Tpvec(_mm_castsi128_##suffix( \
-        _mm_unpacklo_epi64(_mm_loadl_epi64((const __m128i*)ptr0), \
-                           _mm_loadl_epi64((const __m128i*)ptr1)))); \
-} \
-inline void v_store(_Tp* ptr, const _Tpvec& a) \
-{ _mm_storeu_##suffix(ptr, a.val); } \
-inline void v_store_aligned(_Tp* ptr, const _Tpvec& a) \
-{ _mm_store_##suffix(ptr, a.val); } \
-inline void v_store_low(_Tp* ptr, const _Tpvec& a) \
-{ _mm_storel_epi64((__m128i*)ptr, _mm_cast##suffix##_si128(a.val)); } \
-inline void v_store_high(_Tp* ptr, const _Tpvec& a) \
-{ \
-    __m128i a1 = _mm_cast##suffix##_si128(a.val); \
-    _mm_storel_epi64((__m128i*)ptr, _mm_unpackhi_epi64(a1, a1)); \
-}
-
-OPENCV_HAL_IMPL_SSE_LOADSTORE_FLT_OP(v_float32x4, float, ps)
-OPENCV_HAL_IMPL_SSE_LOADSTORE_FLT_OP(v_float64x2, double, pd)
-
-#if CV_FP16
-inline v_float16x4 v_load_f16(const short* ptr)
-{ return v_float16x4(_mm_loadl_epi64((const __m128i*)ptr)); }
-inline void v_store_f16(short* ptr, v_float16x4& a)
-{ _mm_storel_epi64((__m128i*)ptr, a.val); }
-#endif
-
-#define OPENCV_HAL_IMPL_SSE_REDUCE_OP_8(_Tpvec, scalartype, func, suffix, sbit) \
-inline scalartype v_reduce_##func(const v_##_Tpvec& a) \
-{ \
-    __m128i val = a.val; \
-    val = _mm_##func##_##suffix(val, _mm_srli_si128(val,8)); \
-    val = _mm_##func##_##suffix(val, _mm_srli_si128(val,4)); \
-    val = _mm_##func##_##suffix(val, _mm_srli_si128(val,2)); \
-    return (scalartype)_mm_cvtsi128_si32(val); \
-} \
-inline unsigned scalartype v_reduce_##func(const v_u##_Tpvec& a) \
-{ \
-    __m128i val = a.val; \
-    __m128i smask = _mm_set1_epi16(sbit); \
-    val = _mm_xor_si128(val, smask); \
-    val = _mm_##func##_##suffix(val, _mm_srli_si128(val,8)); \
-    val = _mm_##func##_##suffix(val, _mm_srli_si128(val,4)); \
-    val = _mm_##func##_##suffix(val, _mm_srli_si128(val,2)); \
-    return (unsigned scalartype)(_mm_cvtsi128_si32(val) ^  sbit); \
-}
-#define OPENCV_HAL_IMPL_SSE_REDUCE_OP_8_SUM(_Tpvec, scalartype, suffix) \
-inline scalartype v_reduce_sum(const v_##_Tpvec& a) \
-{ \
-    __m128i val = a.val; \
-    val = _mm_adds_epi##suffix(val, _mm_srli_si128(val, 8)); \
-    val = _mm_adds_epi##suffix(val, _mm_srli_si128(val, 4)); \
-    val = _mm_adds_epi##suffix(val, _mm_srli_si128(val, 2)); \
-    return (scalartype)_mm_cvtsi128_si32(val); \
-} \
-inline unsigned scalartype v_reduce_sum(const v_u##_Tpvec& a) \
-{ \
-    __m128i val = a.val; \
-    val = _mm_adds_epu##suffix(val, _mm_srli_si128(val, 8)); \
-    val = _mm_adds_epu##suffix(val, _mm_srli_si128(val, 4)); \
-    val = _mm_adds_epu##suffix(val, _mm_srli_si128(val, 2)); \
-    return (unsigned scalartype)_mm_cvtsi128_si32(val); \
-}
-OPENCV_HAL_IMPL_SSE_REDUCE_OP_8(int16x8, short, max, epi16, (short)-32768)
-OPENCV_HAL_IMPL_SSE_REDUCE_OP_8(int16x8, short, min, epi16, (short)-32768)
-OPENCV_HAL_IMPL_SSE_REDUCE_OP_8_SUM(int16x8, short, 16)
-
-#define OPENCV_HAL_IMPL_SSE_REDUCE_OP_4_SUM(_Tpvec, scalartype, regtype, suffix, cast_from, cast_to, extract) \
-inline scalartype v_reduce_sum(const _Tpvec& a) \
-{ \
-    regtype val = a.val; \
-    val = _mm_add_##suffix(val, cast_to(_mm_srli_si128(cast_from(val), 8))); \
-    val = _mm_add_##suffix(val, cast_to(_mm_srli_si128(cast_from(val), 4))); \
-    return (scalartype)_mm_cvt##extract(val); \
-}
-
-#define OPENCV_HAL_IMPL_SSE_REDUCE_OP_4(_Tpvec, scalartype, func, scalar_func) \
-inline scalartype v_reduce_##func(const _Tpvec& a) \
-{ \
-    scalartype CV_DECL_ALIGNED(16) buf[4]; \
-    v_store_aligned(buf, a); \
-    scalartype s0 = scalar_func(buf[0], buf[1]); \
-    scalartype s1 = scalar_func(buf[2], buf[3]); \
-    return scalar_func(s0, s1); \
-}
-
-OPENCV_HAL_IMPL_SSE_REDUCE_OP_4_SUM(v_uint32x4, unsigned, __m128i, epi32, OPENCV_HAL_NOP, OPENCV_HAL_NOP, si128_si32)
-OPENCV_HAL_IMPL_SSE_REDUCE_OP_4_SUM(v_int32x4, int, __m128i, epi32, OPENCV_HAL_NOP, OPENCV_HAL_NOP, si128_si32)
-OPENCV_HAL_IMPL_SSE_REDUCE_OP_4_SUM(v_float32x4, float, __m128, ps, _mm_castps_si128, _mm_castsi128_ps, ss_f32)
-
-inline v_float32x4 v_reduce_sum4(const v_float32x4& a, const v_float32x4& b,
-                                 const v_float32x4& c, const v_float32x4& d)
-{
-#if CV_SSE3
-    __m128 ab = _mm_hadd_ps(a.val, b.val);
-    __m128 cd = _mm_hadd_ps(c.val, d.val);
-    return v_float32x4(_mm_hadd_ps(ab, cd));
-#else
-    __m128 ac = _mm_add_ps(_mm_unpacklo_ps(a.val, c.val), _mm_unpackhi_ps(a.val, c.val));
-    __m128 bd = _mm_add_ps(_mm_unpacklo_ps(b.val, d.val), _mm_unpackhi_ps(b.val, d.val));
-    return v_float32x4(_mm_add_ps(_mm_unpacklo_ps(ac, bd), _mm_unpackhi_ps(ac, bd)));
-#endif
-}
-
-OPENCV_HAL_IMPL_SSE_REDUCE_OP_4(v_uint32x4, unsigned, max, std::max)
-OPENCV_HAL_IMPL_SSE_REDUCE_OP_4(v_uint32x4, unsigned, min, std::min)
-OPENCV_HAL_IMPL_SSE_REDUCE_OP_4(v_int32x4, int, max, std::max)
-OPENCV_HAL_IMPL_SSE_REDUCE_OP_4(v_int32x4, int, min, std::min)
-OPENCV_HAL_IMPL_SSE_REDUCE_OP_4(v_float32x4, float, max, std::max)
-OPENCV_HAL_IMPL_SSE_REDUCE_OP_4(v_float32x4, float, min, std::min)
-
-#define OPENCV_HAL_IMPL_SSE_POPCOUNT(_Tpvec) \
-inline v_uint32x4 v_popcount(const _Tpvec& a) \
-{ \
-    __m128i m1 = _mm_set1_epi32(0x55555555); \
-    __m128i m2 = _mm_set1_epi32(0x33333333); \
-    __m128i m4 = _mm_set1_epi32(0x0f0f0f0f); \
-    __m128i p = a.val; \
-    p = _mm_add_epi32(_mm_and_si128(_mm_srli_epi32(p, 1), m1), _mm_and_si128(p, m1)); \
-    p = _mm_add_epi32(_mm_and_si128(_mm_srli_epi32(p, 2), m2), _mm_and_si128(p, m2)); \
-    p = _mm_add_epi32(_mm_and_si128(_mm_srli_epi32(p, 4), m4), _mm_and_si128(p, m4)); \
-    p = _mm_adds_epi8(p, _mm_srli_si128(p, 1)); \
-    p = _mm_adds_epi8(p, _mm_srli_si128(p, 2)); \
-    return v_uint32x4(_mm_and_si128(p, _mm_set1_epi32(0x000000ff))); \
-}
-
-OPENCV_HAL_IMPL_SSE_POPCOUNT(v_uint8x16)
-OPENCV_HAL_IMPL_SSE_POPCOUNT(v_uint16x8)
-OPENCV_HAL_IMPL_SSE_POPCOUNT(v_uint32x4)
-OPENCV_HAL_IMPL_SSE_POPCOUNT(v_int8x16)
-OPENCV_HAL_IMPL_SSE_POPCOUNT(v_int16x8)
-OPENCV_HAL_IMPL_SSE_POPCOUNT(v_int32x4)
-
-#define OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(_Tpvec, suffix, pack_op, and_op, signmask, allmask) \
-inline int v_signmask(const _Tpvec& a) \
-{ \
-    return and_op(_mm_movemask_##suffix(pack_op(a.val)), signmask); \
-} \
-inline bool v_check_all(const _Tpvec& a) \
-{ return and_op(_mm_movemask_##suffix(a.val), allmask) == allmask; } \
-inline bool v_check_any(const _Tpvec& a) \
-{ return and_op(_mm_movemask_##suffix(a.val), allmask) != 0; }
-
-#define OPENCV_HAL_PACKS(a) _mm_packs_epi16(a, a)
-inline __m128i v_packq_epi32(__m128i a)
-{
-    __m128i b = _mm_packs_epi32(a, a);
-    return _mm_packs_epi16(b, b);
-}
-
-OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(v_uint8x16, epi8, OPENCV_HAL_NOP, OPENCV_HAL_1ST, 65535, 65535)
-OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(v_int8x16, epi8, OPENCV_HAL_NOP, OPENCV_HAL_1ST, 65535, 65535)
-OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(v_uint16x8, epi8, OPENCV_HAL_PACKS, OPENCV_HAL_AND, 255, (int)0xaaaa)
-OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(v_int16x8, epi8, OPENCV_HAL_PACKS, OPENCV_HAL_AND, 255, (int)0xaaaa)
-OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(v_uint32x4, epi8, v_packq_epi32, OPENCV_HAL_AND, 15, (int)0x8888)
-OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(v_int32x4, epi8, v_packq_epi32, OPENCV_HAL_AND, 15, (int)0x8888)
-OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(v_float32x4, ps, OPENCV_HAL_NOP, OPENCV_HAL_1ST, 15, 15)
-OPENCV_HAL_IMPL_SSE_CHECK_SIGNS(v_float64x2, pd, OPENCV_HAL_NOP, OPENCV_HAL_1ST, 3, 3)
-
-#if CV_SSE4_1
-#define OPENCV_HAL_IMPL_SSE_SELECT(_Tpvec, cast_ret, cast, suffix) \
-inline _Tpvec v_select(const _Tpvec& mask, const _Tpvec& a, const _Tpvec& b) \
-{ \
-    return _Tpvec(cast_ret(_mm_blendv_##suffix(cast(b.val), cast(a.val), cast(mask.val)))); \
-}
-
-OPENCV_HAL_IMPL_SSE_SELECT(v_uint8x16, OPENCV_HAL_NOP, OPENCV_HAL_NOP, epi8)
-OPENCV_HAL_IMPL_SSE_SELECT(v_int8x16, OPENCV_HAL_NOP, OPENCV_HAL_NOP, epi8)
-OPENCV_HAL_IMPL_SSE_SELECT(v_uint16x8, OPENCV_HAL_NOP, OPENCV_HAL_NOP, epi8)
-OPENCV_HAL_IMPL_SSE_SELECT(v_int16x8, OPENCV_HAL_NOP, OPENCV_HAL_NOP, epi8)
-OPENCV_HAL_IMPL_SSE_SELECT(v_uint32x4, _mm_castps_si128, _mm_castsi128_ps, ps)
-OPENCV_HAL_IMPL_SSE_SELECT(v_int32x4, _mm_castps_si128, _mm_castsi128_ps, ps)
-// OPENCV_HAL_IMPL_SSE_SELECT(v_uint64x2, TBD, TBD, pd)
-// OPENCV_HAL_IMPL_SSE_SELECT(v_int64x2, TBD, TBD, ps)
-OPENCV_HAL_IMPL_SSE_SELECT(v_float32x4, OPENCV_HAL_NOP, OPENCV_HAL_NOP, ps)
-OPENCV_HAL_IMPL_SSE_SELECT(v_float64x2, OPENCV_HAL_NOP, OPENCV_HAL_NOP, pd)
-
-#else // CV_SSE4_1
-
-#define OPENCV_HAL_IMPL_SSE_SELECT(_Tpvec, suffix) \
-inline _Tpvec v_select(const _Tpvec& mask, const _Tpvec& a, const _Tpvec& b) \
-{ \
-    return _Tpvec(_mm_xor_##suffix(b.val, _mm_and_##suffix(_mm_xor_##suffix(b.val, a.val), mask.val))); \
-}
-
-OPENCV_HAL_IMPL_SSE_SELECT(v_uint8x16, si128)
-OPENCV_HAL_IMPL_SSE_SELECT(v_int8x16, si128)
-OPENCV_HAL_IMPL_SSE_SELECT(v_uint16x8, si128)
-OPENCV_HAL_IMPL_SSE_SELECT(v_int16x8, si128)
-OPENCV_HAL_IMPL_SSE_SELECT(v_uint32x4, si128)
-OPENCV_HAL_IMPL_SSE_SELECT(v_int32x4, si128)
-// OPENCV_HAL_IMPL_SSE_SELECT(v_uint64x2, si128)
-// OPENCV_HAL_IMPL_SSE_SELECT(v_int64x2, si128)
-OPENCV_HAL_IMPL_SSE_SELECT(v_float32x4, ps)
-OPENCV_HAL_IMPL_SSE_SELECT(v_float64x2, pd)
-#endif
-
-#define OPENCV_HAL_IMPL_SSE_EXPAND(_Tpuvec, _Tpwuvec, _Tpu, _Tpsvec, _Tpwsvec, _Tps, suffix, wsuffix, shift) \
-inline void v_expand(const _Tpuvec& a, _Tpwuvec& b0, _Tpwuvec& b1) \
-{ \
-    __m128i z = _mm_setzero_si128(); \
-    b0.val = _mm_unpacklo_##suffix(a.val, z); \
-    b1.val = _mm_unpackhi_##suffix(a.val, z); \
-} \
-inline _Tpwuvec v_load_expand(const _Tpu* ptr) \
-{ \
-    __m128i z = _mm_setzero_si128(); \
-    return _Tpwuvec(_mm_unpacklo_##suffix(_mm_loadl_epi64((const __m128i*)ptr), z)); \
-} \
-inline void v_expand(const _Tpsvec& a, _Tpwsvec& b0, _Tpwsvec& b1) \
-{ \
-    b0.val = _mm_srai_##wsuffix(_mm_unpacklo_##suffix(a.val, a.val), shift); \
-    b1.val = _mm_srai_##wsuffix(_mm_unpackhi_##suffix(a.val, a.val), shift); \
-} \
-inline _Tpwsvec v_load_expand(const _Tps* ptr) \
-{ \
-    __m128i a = _mm_loadl_epi64((const __m128i*)ptr); \
-    return _Tpwsvec(_mm_srai_##wsuffix(_mm_unpacklo_##suffix(a, a), shift)); \
-}
-
-OPENCV_HAL_IMPL_SSE_EXPAND(v_uint8x16, v_uint16x8, uchar, v_int8x16, v_int16x8, schar, epi8, epi16, 8)
-OPENCV_HAL_IMPL_SSE_EXPAND(v_uint16x8, v_uint32x4, ushort, v_int16x8, v_int32x4, short, epi16, epi32, 16)
-
-inline void v_expand(const v_uint32x4& a, v_uint64x2& b0, v_uint64x2& b1)
-{
-    __m128i z = _mm_setzero_si128();
-    b0.val = _mm_unpacklo_epi32(a.val, z);
-    b1.val = _mm_unpackhi_epi32(a.val, z);
-}
-inline v_uint64x2 v_load_expand(const unsigned* ptr)
-{
-    __m128i z = _mm_setzero_si128();
-    return v_uint64x2(_mm_unpacklo_epi32(_mm_loadl_epi64((const __m128i*)ptr), z));
-}
-inline void v_expand(const v_int32x4& a, v_int64x2& b0, v_int64x2& b1)
-{
-    __m128i s = _mm_srai_epi32(a.val, 31);
-    b0.val = _mm_unpacklo_epi32(a.val, s);
-    b1.val = _mm_unpackhi_epi32(a.val, s);
-}
-inline v_int64x2 v_load_expand(const int* ptr)
-{
-    __m128i a = _mm_loadl_epi64((const __m128i*)ptr);
-    __m128i s = _mm_srai_epi32(a, 31);
-    return v_int64x2(_mm_unpacklo_epi32(a, s));
-}
-
-inline v_uint32x4 v_load_expand_q(const uchar* ptr)
-{
-    __m128i z = _mm_setzero_si128();
-    __m128i a = _mm_cvtsi32_si128(*(const int*)ptr);
-    return v_uint32x4(_mm_unpacklo_epi16(_mm_unpacklo_epi8(a, z), z));
-}
-
-inline v_int32x4 v_load_expand_q(const schar* ptr)
-{
-    __m128i a = _mm_cvtsi32_si128(*(const int*)ptr);
-    a = _mm_unpacklo_epi8(a, a);
-    a = _mm_unpacklo_epi8(a, a);
-    return v_int32x4(_mm_srai_epi32(a, 24));
-}
-
-#define OPENCV_HAL_IMPL_SSE_UNPACKS(_Tpvec, suffix, cast_from, cast_to) \
-inline void v_zip(const _Tpvec& a0, const _Tpvec& a1, _Tpvec& b0, _Tpvec& b1) \
-{ \
-    b0.val = _mm_unpacklo_##suffix(a0.val, a1.val); \
-    b1.val = _mm_unpackhi_##suffix(a0.val, a1.val); \
-} \
-inline _Tpvec v_combine_low(const _Tpvec& a, const _Tpvec& b) \
-{ \
-    __m128i a1 = cast_from(a.val), b1 = cast_from(b.val); \
-    return _Tpvec(cast_to(_mm_unpacklo_epi64(a1, b1))); \
-} \
-inline _Tpvec v_combine_high(const _Tpvec& a, const _Tpvec& b) \
-{ \
-    __m128i a1 = cast_from(a.val), b1 = cast_from(b.val); \
-    return _Tpvec(cast_to(_mm_unpackhi_epi64(a1, b1))); \
-} \
-inline void v_recombine(const _Tpvec& a, const _Tpvec& b, _Tpvec& c, _Tpvec& d) \
-{ \
-    __m128i a1 = cast_from(a.val), b1 = cast_from(b.val); \
-    c.val = cast_to(_mm_unpacklo_epi64(a1, b1)); \
-    d.val = cast_to(_mm_unpackhi_epi64(a1, b1)); \
-}
-
-OPENCV_HAL_IMPL_SSE_UNPACKS(v_uint8x16, epi8, OPENCV_HAL_NOP, OPENCV_HAL_NOP)
-OPENCV_HAL_IMPL_SSE_UNPACKS(v_int8x16, epi8, OPENCV_HAL_NOP, OPENCV_HAL_NOP)
-OPENCV_HAL_IMPL_SSE_UNPACKS(v_uint16x8, epi16, OPENCV_HAL_NOP, OPENCV_HAL_NOP)
-OPENCV_HAL_IMPL_SSE_UNPACKS(v_int16x8, epi16, OPENCV_HAL_NOP, OPENCV_HAL_NOP)
-OPENCV_HAL_IMPL_SSE_UNPACKS(v_uint32x4, epi32, OPENCV_HAL_NOP, OPENCV_HAL_NOP)
-OPENCV_HAL_IMPL_SSE_UNPACKS(v_int32x4, epi32, OPENCV_HAL_NOP, OPENCV_HAL_NOP)
-OPENCV_HAL_IMPL_SSE_UNPACKS(v_float32x4, ps, _mm_castps_si128, _mm_castsi128_ps)
-OPENCV_HAL_IMPL_SSE_UNPACKS(v_float64x2, pd, _mm_castpd_si128, _mm_castsi128_pd)
-
-template<int s, typename _Tpvec>
-inline _Tpvec v_extract(const _Tpvec& a, const _Tpvec& b)
-{
-    return v_rotate_right<s>(a, b);
-}
-
-inline v_int32x4 v_round(const v_float32x4& a)
-{ return v_int32x4(_mm_cvtps_epi32(a.val)); }
-
-inline v_int32x4 v_floor(const v_float32x4& a)
-{
-    __m128i a1 = _mm_cvtps_epi32(a.val);
-    __m128i mask = _mm_castps_si128(_mm_cmpgt_ps(_mm_cvtepi32_ps(a1), a.val));
-    return v_int32x4(_mm_add_epi32(a1, mask));
-}
-
-inline v_int32x4 v_ceil(const v_float32x4& a)
-{
-    __m128i a1 = _mm_cvtps_epi32(a.val);
-    __m128i mask = _mm_castps_si128(_mm_cmpgt_ps(a.val, _mm_cvtepi32_ps(a1)));
-    return v_int32x4(_mm_sub_epi32(a1, mask));
-}
-
-inline v_int32x4 v_trunc(const v_float32x4& a)
-{ return v_int32x4(_mm_cvttps_epi32(a.val)); }
-
-inline v_int32x4 v_round(const v_float64x2& a)
-{ return v_int32x4(_mm_cvtpd_epi32(a.val)); }
-
-inline v_int32x4 v_floor(const v_float64x2& a)
-{
-    __m128i a1 = _mm_cvtpd_epi32(a.val);
-    __m128i mask = _mm_castpd_si128(_mm_cmpgt_pd(_mm_cvtepi32_pd(a1), a.val));
-    mask = _mm_srli_si128(_mm_slli_si128(mask, 4), 8); // m0 m0 m1 m1 => m0 m1 0 0
-    return v_int32x4(_mm_add_epi32(a1, mask));
-}
-
-inline v_int32x4 v_ceil(const v_float64x2& a)
-{
-    __m128i a1 = _mm_cvtpd_epi32(a.val);
-    __m128i mask = _mm_castpd_si128(_mm_cmpgt_pd(a.val, _mm_cvtepi32_pd(a1)));
-    mask = _mm_srli_si128(_mm_slli_si128(mask, 4), 8); // m0 m0 m1 m1 => m0 m1 0 0
-    return v_int32x4(_mm_sub_epi32(a1, mask));
-}
-
-inline v_int32x4 v_trunc(const v_float64x2& a)
-{ return v_int32x4(_mm_cvttpd_epi32(a.val)); }
-
-#define OPENCV_HAL_IMPL_SSE_TRANSPOSE4x4(_Tpvec, suffix, cast_from, cast_to) \
-inline void v_transpose4x4(const _Tpvec& a0, const _Tpvec& a1, \
-                           const _Tpvec& a2, const _Tpvec& a3, \
-                           _Tpvec& b0, _Tpvec& b1, \
-                           _Tpvec& b2, _Tpvec& b3) \
-{ \
-    __m128i t0 = cast_from(_mm_unpacklo_##suffix(a0.val, a1.val)); \
-    __m128i t1 = cast_from(_mm_unpacklo_##suffix(a2.val, a3.val)); \
-    __m128i t2 = cast_from(_mm_unpackhi_##suffix(a0.val, a1.val)); \
-    __m128i t3 = cast_from(_mm_unpackhi_##suffix(a2.val, a3.val)); \
-\
-    b0.val = cast_to(_mm_unpacklo_epi64(t0, t1)); \
-    b1.val = cast_to(_mm_unpackhi_epi64(t0, t1)); \
-    b2.val = cast_to(_mm_unpacklo_epi64(t2, t3)); \
-    b3.val = cast_to(_mm_unpackhi_epi64(t2, t3)); \
-}
-
-OPENCV_HAL_IMPL_SSE_TRANSPOSE4x4(v_uint32x4, epi32, OPENCV_HAL_NOP, OPENCV_HAL_NOP)
-OPENCV_HAL_IMPL_SSE_TRANSPOSE4x4(v_int32x4, epi32, OPENCV_HAL_NOP, OPENCV_HAL_NOP)
-OPENCV_HAL_IMPL_SSE_TRANSPOSE4x4(v_float32x4, ps, _mm_castps_si128, _mm_castsi128_ps)
-
-// adopted from sse_utils.hpp
-inline void v_load_deinterleave(const uchar* ptr, v_uint8x16& a, v_uint8x16& b)
-{
-    __m128i t00 = _mm_loadu_si128((const __m128i*)ptr);
-    __m128i t01 = _mm_loadu_si128((const __m128i*)(ptr + 16));
-
-    __m128i t10 = _mm_unpacklo_epi8(t00, t01);
-    __m128i t11 = _mm_unpackhi_epi8(t00, t01);
-
-    __m128i t20 = _mm_unpacklo_epi8(t10, t11);
-    __m128i t21 = _mm_unpackhi_epi8(t10, t11);
-
-    __m128i t30 = _mm_unpacklo_epi8(t20, t21);
-    __m128i t31 = _mm_unpackhi_epi8(t20, t21);
-
-    a.val = _mm_unpacklo_epi8(t30, t31);
-    b.val = _mm_unpackhi_epi8(t30, t31);
-}
-
-inline void v_load_deinterleave(const uchar* ptr, v_uint8x16& a, v_uint8x16& b, v_uint8x16& c)
-{
-#if CV_SSSE3
-    static const __m128i m0 = _mm_setr_epi8(0, 3, 6, 9, 12, 15, 1, 4, 7, 10, 13, 2, 5, 8, 11, 14);
-    static const __m128i m1 = _mm_alignr_epi8(m0, m0, 11);
-    static const __m128i m2 = _mm_alignr_epi8(m0, m0, 6);
-
-    __m128i t0 = _mm_loadu_si128((const __m128i*)ptr);
-    __m128i t1 = _mm_loadu_si128((const __m128i*)(ptr + 16));
-    __m128i t2 = _mm_loadu_si128((const __m128i*)(ptr + 32));
-
-    __m128i s0 = _mm_shuffle_epi8(t0, m0);
-    __m128i s1 = _mm_shuffle_epi8(t1, m1);
-    __m128i s2 = _mm_shuffle_epi8(t2, m2);
-
-    t0 = _mm_alignr_epi8(s1, _mm_slli_si128(s0, 10), 5);
-    a.val = _mm_alignr_epi8(s2, t0, 5);
-
-    t1 = _mm_alignr_epi8(_mm_srli_si128(s1, 5), _mm_slli_si128(s0, 5), 6);
-    b.val = _mm_alignr_epi8(_mm_srli_si128(s2, 5), t1, 5);
-
-    t2 = _mm_alignr_epi8(_mm_srli_si128(s2, 10), s1, 11);
-    c.val = _mm_alignr_epi8(t2, s0, 11);
-#else
-    __m128i t00 = _mm_loadu_si128((const __m128i*)ptr);
-    __m128i t01 = _mm_loadu_si128((const __m128i*)(ptr + 16));
-    __m128i t02 = _mm_loadu_si128((const __m128i*)(ptr + 32));
-
-    __m128i t10 = _mm_unpacklo_epi8(t00, _mm_unpackhi_epi64(t01, t01));
-    __m128i t11 = _mm_unpacklo_epi8(_mm_unpackhi_epi64(t00, t00), t02);
-    __m128i t12 = _mm_unpacklo_epi8(t01, _mm_unpackhi_epi64(t02, t02));
-
-    __m128i t20 = _mm_unpacklo_epi8(t10, _mm_unpackhi_epi64(t11, t11));
-    __m128i t21 = _mm_unpacklo_epi8(_mm_unpackhi_epi64(t10, t10), t12);
-    __m128i t22 = _mm_unpacklo_epi8(t11, _mm_unpackhi_epi64(t12, t12));
-
-    __m128i t30 = _mm_unpacklo_epi8(t20, _mm_unpackhi_epi64(t21, t21));
-    __m128i t31 = _mm_unpacklo_epi8(_mm_unpackhi_epi64(t20, t20), t22);
-    __m128i t32 = _mm_unpacklo_epi8(t21, _mm_unpackhi_epi64(t22, t22));
-
-    a.val = _mm_unpacklo_epi8(t30, _mm_unpackhi_epi64(t31, t31));
-    b.val = _mm_unpacklo_epi8(_mm_unpackhi_epi64(t30, t30), t32);
-    c.val = _mm_unpacklo_epi8(t31, _mm_unpackhi_epi64(t32, t32));
-#endif
-}
-
-inline void v_load_deinterleave(const uchar* ptr, v_uint8x16& a, v_uint8x16& b, v_uint8x16& c, v_uint8x16& d)
-{
-    __m128i u0 = _mm_loadu_si128((const __m128i*)ptr); // a0 b0 c0 d0 a1 b1 c1 d1 ...
-    __m128i u1 = _mm_loadu_si128((const __m128i*)(ptr + 16)); // a4 b4 c4 d4 ...
-    __m128i u2 = _mm_loadu_si128((const __m128i*)(ptr + 32)); // a8 b8 c8 d8 ...
-    __m128i u3 = _mm_loadu_si128((const __m128i*)(ptr + 48)); // a12 b12 c12 d12 ...
-
-    __m128i v0 = _mm_unpacklo_epi8(u0, u2); // a0 a8 b0 b8 ...
-    __m128i v1 = _mm_unpackhi_epi8(u0, u2); // a2 a10 b2 b10 ...
-    __m128i v2 = _mm_unpacklo_epi8(u1, u3); // a4 a12 b4 b12 ...
-    __m128i v3 = _mm_unpackhi_epi8(u1, u3); // a6 a14 b6 b14 ...
-
-    u0 = _mm_unpacklo_epi8(v0, v2); // a0 a4 a8 a12 ...
-    u1 = _mm_unpacklo_epi8(v1, v3); // a2 a6 a10 a14 ...
-    u2 = _mm_unpackhi_epi8(v0, v2); // a1 a5 a9 a13 ...
-    u3 = _mm_unpackhi_epi8(v1, v3); // a3 a7 a11 a15 ...
-
-    v0 = _mm_unpacklo_epi8(u0, u1); // a0 a2 a4 a6 ...
-    v1 = _mm_unpacklo_epi8(u2, u3); // a1 a3 a5 a7 ...
-    v2 = _mm_unpackhi_epi8(u0, u1); // c0 c2 c4 c6 ...
-    v3 = _mm_unpackhi_epi8(u2, u3); // c1 c3 c5 c7 ...
-
-    a.val = _mm_unpacklo_epi8(v0, v1);
-    b.val = _mm_unpackhi_epi8(v0, v1);
-    c.val = _mm_unpacklo_epi8(v2, v3);
-    d.val = _mm_unpackhi_epi8(v2, v3);
-}
-
-inline void v_load_deinterleave(const ushort* ptr, v_uint16x8& a, v_uint16x8& b, v_uint16x8& c)
-{
-    __m128i t00 = _mm_loadu_si128((const __m128i*)ptr);
-    __m128i t01 = _mm_loadu_si128((const __m128i*)(ptr + 8));
-    __m128i t02 = _mm_loadu_si128((const __m128i*)(ptr + 16));
-
-    __m128i t10 = _mm_unpacklo_epi16(t00, _mm_unpackhi_epi64(t01, t01));
-    __m128i t11 = _mm_unpacklo_epi16(_mm_unpackhi_epi64(t00, t00), t02);
-    __m128i t12 = _mm_unpacklo_epi16(t01, _mm_unpackhi_epi64(t02, t02));
-
-    __m128i t20 = _mm_unpacklo_epi16(t10, _mm_unpackhi_epi64(t11, t11));
-    __m128i t21 = _mm_unpacklo_epi16(_mm_unpackhi_epi64(t10, t10), t12);
-    __m128i t22 = _mm_unpacklo_epi16(t11, _mm_unpackhi_epi64(t12, t12));
-
-    a.val = _mm_unpacklo_epi16(t20, _mm_unpackhi_epi64(t21, t21));
-    b.val = _mm_unpacklo_epi16(_mm_unpackhi_epi64(t20, t20), t22);
-    c.val = _mm_unpacklo_epi16(t21, _mm_unpackhi_epi64(t22, t22));
-}
-
-inline void v_load_deinterleave(const ushort* ptr, v_uint16x8& a, v_uint16x8& b, v_uint16x8& c, v_uint16x8& d)
-{
-    __m128i u0 = _mm_loadu_si128((const __m128i*)ptr); // a0 b0 c0 d0 a1 b1 c1 d1
-    __m128i u1 = _mm_loadu_si128((const __m128i*)(ptr + 8)); // a2 b2 c2 d2 ...
-    __m128i u2 = _mm_loadu_si128((const __m128i*)(ptr + 16)); // a4 b4 c4 d4 ...
-    __m128i u3 = _mm_loadu_si128((const __m128i*)(ptr + 24)); // a6 b6 c6 d6 ...
-
-    __m128i v0 = _mm_unpacklo_epi16(u0, u2); // a0 a4 b0 b4 ...
-    __m128i v1 = _mm_unpackhi_epi16(u0, u2); // a1 a5 b1 b5 ...
-    __m128i v2 = _mm_unpacklo_epi16(u1, u3); // a2 a6 b2 b6 ...
-    __m128i v3 = _mm_unpackhi_epi16(u1, u3); // a3 a7 b3 b7 ...
-
-    u0 = _mm_unpacklo_epi16(v0, v2); // a0 a2 a4 a6 ...
-    u1 = _mm_unpacklo_epi16(v1, v3); // a1 a3 a5 a7 ...
-    u2 = _mm_unpackhi_epi16(v0, v2); // c0 c2 c4 c6 ...
-    u3 = _mm_unpackhi_epi16(v1, v3); // c1 c3 c5 c7 ...
-
-    a.val = _mm_unpacklo_epi16(u0, u1);
-    b.val = _mm_unpackhi_epi16(u0, u1);
-    c.val = _mm_unpacklo_epi16(u2, u3);
-    d.val = _mm_unpackhi_epi16(u2, u3);
-}
-
-inline void v_load_deinterleave(const unsigned* ptr, v_uint32x4& a, v_uint32x4& b, v_uint32x4& c)
-{
-    __m128i t00 = _mm_loadu_si128((const __m128i*)ptr);
-    __m128i t01 = _mm_loadu_si128((const __m128i*)(ptr + 4));
-    __m128i t02 = _mm_loadu_si128((const __m128i*)(ptr + 8));
-
-    __m128i t10 = _mm_unpacklo_epi32(t00, _mm_unpackhi_epi64(t01, t01));
-    __m128i t11 = _mm_unpacklo_epi32(_mm_unpackhi_epi64(t00, t00), t02);
-    __m128i t12 = _mm_unpacklo_epi32(t01, _mm_unpackhi_epi64(t02, t02));
-
-    a.val = _mm_unpacklo_epi32(t10, _mm_unpackhi_epi64(t11, t11));
-    b.val = _mm_unpacklo_epi32(_mm_unpackhi_epi64(t10, t10), t12);
-    c.val = _mm_unpacklo_epi32(t11, _mm_unpackhi_epi64(t12, t12));
-}
-
-inline void v_load_deinterleave(const unsigned* ptr, v_uint32x4& a, v_uint32x4& b, v_uint32x4& c, v_uint32x4& d)
-{
-    v_uint32x4 u0(_mm_loadu_si128((const __m128i*)ptr));        // a0 b0 c0 d0
-    v_uint32x4 u1(_mm_loadu_si128((const __m128i*)(ptr + 4))); // a1 b1 c1 d1
-    v_uint32x4 u2(_mm_loadu_si128((const __m128i*)(ptr + 8))); // a2 b2 c2 d2
-    v_uint32x4 u3(_mm_loadu_si128((const __m128i*)(ptr + 12))); // a3 b3 c3 d3
-
-    v_transpose4x4(u0, u1, u2, u3, a, b, c, d);
-}
-
-inline void v_load_deinterleave(const float* ptr, v_float32x4& a, v_float32x4& b, v_float32x4& c)
-{
-    __m128 t0 = _mm_loadu_ps(ptr + 0);
-    __m128 t1 = _mm_loadu_ps(ptr + 4);
-    __m128 t2 = _mm_loadu_ps(ptr + 8);
-
-    __m128 at12 = _mm_shuffle_ps(t1, t2, _MM_SHUFFLE(0, 1, 0, 2));
-    a.val = _mm_shuffle_ps(t0, at12, _MM_SHUFFLE(2, 0, 3, 0));
-
-    __m128 bt01 = _mm_shuffle_ps(t0, t1, _MM_SHUFFLE(0, 0, 0, 1));
-    __m128 bt12 = _mm_shuffle_ps(t1, t2, _MM_SHUFFLE(0, 2, 0, 3));
-    b.val = _mm_shuffle_ps(bt01, bt12, _MM_SHUFFLE(2, 0, 2, 0));
-
-    __m128 ct01 = _mm_shuffle_ps(t0, t1, _MM_SHUFFLE(0, 1, 0, 2));
-    c.val = _mm_shuffle_ps(ct01, t2, _MM_SHUFFLE(3, 0, 2, 0));
-}
-
-inline void v_load_deinterleave(const float* ptr, v_float32x4& a, v_float32x4& b, v_float32x4& c, v_float32x4& d)
-{
-    __m128 t0 = _mm_loadu_ps(ptr +  0);
-    __m128 t1 = _mm_loadu_ps(ptr +  4);
-    __m128 t2 = _mm_loadu_ps(ptr +  8);
-    __m128 t3 = _mm_loadu_ps(ptr + 12);
-    __m128 t02lo = _mm_unpacklo_ps(t0, t2);
-    __m128 t13lo = _mm_unpacklo_ps(t1, t3);
-    __m128 t02hi = _mm_unpackhi_ps(t0, t2);
-    __m128 t13hi = _mm_unpackhi_ps(t1, t3);
-    a.val = _mm_unpacklo_ps(t02lo, t13lo);
-    b.val = _mm_unpackhi_ps(t02lo, t13lo);
-    c.val = _mm_unpacklo_ps(t02hi, t13hi);
-    d.val = _mm_unpackhi_ps(t02hi, t13hi);
-}
-
-inline void v_load_deinterleave(const uint64 *ptr, v_uint64x2& a, v_uint64x2& b, v_uint64x2& c)
-{
-    __m128i t0 = _mm_loadu_si128((const __m128i*)ptr);
-    __m128i t1 = _mm_loadu_si128((const __m128i*)(ptr + 2));
-    __m128i t2 = _mm_loadu_si128((const __m128i*)(ptr + 4));
-
-    a = v_uint64x2(_mm_unpacklo_epi64(t0, _mm_unpackhi_epi64(t1, t1)));
-    b = v_uint64x2(_mm_unpacklo_epi64(_mm_unpackhi_epi64(t0, t0), t2));
-    c = v_uint64x2(_mm_unpacklo_epi64(t1, _mm_unpackhi_epi64(t2, t2)));
-}
-
-inline void v_load_deinterleave(const int64 *ptr, v_int64x2& a, v_int64x2& b, v_int64x2& c)
-{
-    v_uint64x2 t0, t1, t2;
-    v_load_deinterleave((const uint64*)ptr, t0, t1, t2);
-    a = v_reinterpret_as_s64(t0);
-    b = v_reinterpret_as_s64(t1);
-    c = v_reinterpret_as_s64(t2);
-}
-
-inline void v_load_deinterleave(const double *ptr, v_float64x2& a, v_float64x2& b, v_float64x2& c)
-{
-    v_uint64x2 t0, t1, t2;
-    v_load_deinterleave((const uint64*)ptr, t0, t1, t2);
-    a = v_reinterpret_as_f64(t0);
-    b = v_reinterpret_as_f64(t1);
-    c = v_reinterpret_as_f64(t2);
-}
-
-// 2-channel
-inline void v_load_deinterleave(const float* ptr, v_float32x4& a, v_float32x4& b)
-{
-    const int mask_lo = _MM_SHUFFLE(2, 0, 2, 0), mask_hi = _MM_SHUFFLE(3, 1, 3, 1);
-
-    __m128 u0 = _mm_loadu_ps(ptr);       // a0 b0 a1 b1
-    __m128 u1 = _mm_loadu_ps((ptr + 4)); // a2 b2 a3 b3
-
-    a.val = _mm_shuffle_ps(u0, u1, mask_lo); // a0 a1 a2 a3
-    b.val = _mm_shuffle_ps(u0, u1, mask_hi); // b0 b1 ab b3
-}
-
-inline void v_load_deinterleave(const short* ptr, v_int16x8& a, v_int16x8& b)
-{
-    __m128i v0 = _mm_loadu_si128((__m128i*)(ptr));     // a0 b0 a1 b1 a2 b2 a3 b3
-    __m128i v1 = _mm_loadu_si128((__m128i*)(ptr + 8)); // a4 b4 a5 b5 a6 b6 a7 b7
-
-    __m128i v2 = _mm_unpacklo_epi16(v0, v1); // a0 a4 b0 b4 a1 a5 b1 b5
-    __m128i v3 = _mm_unpackhi_epi16(v0, v1); // a2 a6 b2 b6 a3 a7 b3 b7
-    __m128i v4 = _mm_unpacklo_epi16(v2, v3); // a0 a2 a4 a6 b0 b2 b4 b6
-    __m128i v5 = _mm_unpackhi_epi16(v2, v3); // a1 a3 a5 a7 b1 b3 b5 b7
-
-    a.val = _mm_unpacklo_epi16(v4, v5); // a0 a1 a2 a3 a4 a5 a6 a7
-    b.val = _mm_unpackhi_epi16(v4, v5); // b0 b1 ab b3 b4 b5 b6 b7
-}
-
-inline void v_load_deinterleave(const ushort*ptr, v_uint16x8& a, v_uint16x8& b)
-{
-    v_int16x8 sa, sb;
-    v_load_deinterleave((const short*)ptr, sa, sb);
-    a = v_reinterpret_as_u16(sa);
-    b = v_reinterpret_as_u16(sb);
-}
-
-inline void v_store_interleave(short* ptr, const v_int16x8& a, const v_int16x8& b)
-{
-    __m128i t0, t1;
-    t0 = _mm_unpacklo_epi16(a.val, b.val);
-    t1 = _mm_unpackhi_epi16(a.val, b.val);
-    _mm_storeu_si128((__m128i*)(ptr), t0);
-    _mm_storeu_si128((__m128i*)(ptr + 8), t1);
-}
-
-inline void v_store_interleave( uchar* ptr, const v_uint8x16& a, const v_uint8x16& b)
-{
-    __m128i v0 = _mm_unpacklo_epi8(a.val, b.val);
-    __m128i v1 = _mm_unpackhi_epi8(a.val, b.val);
-
-    _mm_storeu_si128((__m128i*)(ptr), v0);
-    _mm_storeu_si128((__m128i*)(ptr + 16), v1);
-}
-
-inline void v_store_interleave( uchar* ptr, const v_uint8x16& a, const v_uint8x16& b,
-                                const v_uint8x16& c )
-{
-#if CV_SSSE3
-    static const __m128i m0 = _mm_setr_epi8(0, 6, 11, 1, 7, 12, 2, 8, 13, 3, 9, 14, 4, 10, 15, 5);
-    static const __m128i m1 = _mm_setr_epi8(5, 11, 0, 6, 12, 1, 7, 13, 2, 8, 14, 3, 9, 15, 4, 10);
-    static const __m128i m2 = _mm_setr_epi8(10, 0, 5, 11, 1, 6, 12, 2, 7, 13, 3, 8, 14, 4, 9, 15);
-
-    __m128i t0 = _mm_alignr_epi8(b.val, _mm_slli_si128(a.val, 10), 5);
-    t0 = _mm_alignr_epi8(c.val, t0, 5);
-    __m128i s0 = _mm_shuffle_epi8(t0, m0);
-
-    __m128i t1 = _mm_alignr_epi8(_mm_srli_si128(b.val, 5), _mm_slli_si128(a.val, 5), 6);
-    t1 = _mm_alignr_epi8(_mm_srli_si128(c.val, 5), t1, 5);
-    __m128i s1 = _mm_shuffle_epi8(t1, m1);
-
-    __m128i t2 = _mm_alignr_epi8(_mm_srli_si128(c.val, 10), b.val, 11);
-    t2 = _mm_alignr_epi8(t2, a.val, 11);
-    __m128i s2 = _mm_shuffle_epi8(t2, m2);
-
-    _mm_storeu_si128((__m128i*)ptr, s0);
-    _mm_storeu_si128((__m128i*)(ptr + 16), s1);
-    _mm_storeu_si128((__m128i*)(ptr + 32), s2);
-#else
-    __m128i z = _mm_setzero_si128();
-    __m128i ab0 = _mm_unpacklo_epi8(a.val, b.val);
-    __m128i ab1 = _mm_unpackhi_epi8(a.val, b.val);
-    __m128i c0 = _mm_unpacklo_epi8(c.val, z);
-    __m128i c1 = _mm_unpackhi_epi8(c.val, z);
-
-    __m128i p00 = _mm_unpacklo_epi16(ab0, c0);
-    __m128i p01 = _mm_unpackhi_epi16(ab0, c0);
-    __m128i p02 = _mm_unpacklo_epi16(ab1, c1);
-    __m128i p03 = _mm_unpackhi_epi16(ab1, c1);
-
-    __m128i p10 = _mm_unpacklo_epi32(p00, p01);
-    __m128i p11 = _mm_unpackhi_epi32(p00, p01);
-    __m128i p12 = _mm_unpacklo_epi32(p02, p03);
-    __m128i p13 = _mm_unpackhi_epi32(p02, p03);
-
-    __m128i p20 = _mm_unpacklo_epi64(p10, p11);
-    __m128i p21 = _mm_unpackhi_epi64(p10, p11);
-    __m128i p22 = _mm_unpacklo_epi64(p12, p13);
-    __m128i p23 = _mm_unpackhi_epi64(p12, p13);
-
-    p20 = _mm_slli_si128(p20, 1);
-    p22 = _mm_slli_si128(p22, 1);
-
-    __m128i p30 = _mm_slli_epi64(_mm_unpacklo_epi32(p20, p21), 8);
-    __m128i p31 = _mm_srli_epi64(_mm_unpackhi_epi32(p20, p21), 8);
-    __m128i p32 = _mm_slli_epi64(_mm_unpacklo_epi32(p22, p23), 8);
-    __m128i p33 = _mm_srli_epi64(_mm_unpackhi_epi32(p22, p23), 8);
-
-    __m128i p40 = _mm_unpacklo_epi64(p30, p31);
-    __m128i p41 = _mm_unpackhi_epi64(p30, p31);
-    __m128i p42 = _mm_unpacklo_epi64(p32, p33);
-    __m128i p43 = _mm_unpackhi_epi64(p32, p33);
-
-    __m128i v0 = _mm_or_si128(_mm_srli_si128(p40, 2), _mm_slli_si128(p41, 10));
-    __m128i v1 = _mm_or_si128(_mm_srli_si128(p41, 6), _mm_slli_si128(p42, 6));
-    __m128i v2 = _mm_or_si128(_mm_srli_si128(p42, 10), _mm_slli_si128(p43, 2));
-
-    _mm_storeu_si128((__m128i*)(ptr), v0);
-    _mm_storeu_si128((__m128i*)(ptr + 16), v1);
-    _mm_storeu_si128((__m128i*)(ptr + 32), v2);
-#endif
-}
-
-inline void v_store_interleave( uchar* ptr, const v_uint8x16& a, const v_uint8x16& b,
-                                const v_uint8x16& c, const v_uint8x16& d)
-{
-    // a0 a1 a2 a3 ....
-    // b0 b1 b2 b3 ....
-    // c0 c1 c2 c3 ....
-    // d0 d1 d2 d3 ....
-    __m128i u0 = _mm_unpacklo_epi8(a.val, c.val); // a0 c0 a1 c1 ...
-    __m128i u1 = _mm_unpackhi_epi8(a.val, c.val); // a8 c8 a9 c9 ...
-    __m128i u2 = _mm_unpacklo_epi8(b.val, d.val); // b0 d0 b1 d1 ...
-    __m128i u3 = _mm_unpackhi_epi8(b.val, d.val); // b8 d8 b9 d9 ...
-
-    __m128i v0 = _mm_unpacklo_epi8(u0, u2); // a0 b0 c0 d0 ...
-    __m128i v1 = _mm_unpacklo_epi8(u1, u3); // a8 b8 c8 d8 ...
-    __m128i v2 = _mm_unpackhi_epi8(u0, u2); // a4 b4 c4 d4 ...
-    __m128i v3 = _mm_unpackhi_epi8(u1, u3); // a12 b12 c12 d12 ...
-
-    _mm_storeu_si128((__m128i*)ptr, v0);
-    _mm_storeu_si128((__m128i*)(ptr + 16), v2);
-    _mm_storeu_si128((__m128i*)(ptr + 32), v1);
-    _mm_storeu_si128((__m128i*)(ptr + 48), v3);
-}
-
-inline void v_store_interleave( ushort* ptr, const v_uint16x8& a,
-                                const v_uint16x8& b,
-                                const v_uint16x8& c )
-{
-    __m128i z = _mm_setzero_si128();
-    __m128i ab0 = _mm_unpacklo_epi16(a.val, b.val);
-    __m128i ab1 = _mm_unpackhi_epi16(a.val, b.val);
-    __m128i c0 = _mm_unpacklo_epi16(c.val, z);
-    __m128i c1 = _mm_unpackhi_epi16(c.val, z);
-
-    __m128i p10 = _mm_unpacklo_epi32(ab0, c0);
-    __m128i p11 = _mm_unpackhi_epi32(ab0, c0);
-    __m128i p12 = _mm_unpacklo_epi32(ab1, c1);
-    __m128i p13 = _mm_unpackhi_epi32(ab1, c1);
-
-    __m128i p20 = _mm_unpacklo_epi64(p10, p11);
-    __m128i p21 = _mm_unpackhi_epi64(p10, p11);
-    __m128i p22 = _mm_unpacklo_epi64(p12, p13);
-    __m128i p23 = _mm_unpackhi_epi64(p12, p13);
-
-    p20 = _mm_slli_si128(p20, 2);
-    p22 = _mm_slli_si128(p22, 2);
-
-    __m128i p30 = _mm_unpacklo_epi64(p20, p21);
-    __m128i p31 = _mm_unpackhi_epi64(p20, p21);
-    __m128i p32 = _mm_unpacklo_epi64(p22, p23);
-    __m128i p33 = _mm_unpackhi_epi64(p22, p23);
-
-    __m128i v0 = _mm_or_si128(_mm_srli_si128(p30, 2), _mm_slli_si128(p31, 10));
-    __m128i v1 = _mm_or_si128(_mm_srli_si128(p31, 6), _mm_slli_si128(p32, 6));
-    __m128i v2 = _mm_or_si128(_mm_srli_si128(p32, 10), _mm_slli_si128(p33, 2));
-
-    _mm_storeu_si128((__m128i*)(ptr), v0);
-    _mm_storeu_si128((__m128i*)(ptr + 8), v1);
-    _mm_storeu_si128((__m128i*)(ptr + 16), v2);
-}
-
-inline void v_store_interleave( ushort* ptr, const v_uint16x8& a, const v_uint16x8& b,
-                                const v_uint16x8& c, const v_uint16x8& d)
-{
-    // a0 a1 a2 a3 ....
-    // b0 b1 b2 b3 ....
-    // c0 c1 c2 c3 ....
-    // d0 d1 d2 d3 ....
-    __m128i u0 = _mm_unpacklo_epi16(a.val, c.val); // a0 c0 a1 c1 ...
-    __m128i u1 = _mm_unpackhi_epi16(a.val, c.val); // a4 c4 a5 c5 ...
-    __m128i u2 = _mm_unpacklo_epi16(b.val, d.val); // b0 d0 b1 d1 ...
-    __m128i u3 = _mm_unpackhi_epi16(b.val, d.val); // b4 d4 b5 d5 ...
-
-    __m128i v0 = _mm_unpacklo_epi16(u0, u2); // a0 b0 c0 d0 ...
-    __m128i v1 = _mm_unpacklo_epi16(u1, u3); // a4 b4 c4 d4 ...
-    __m128i v2 = _mm_unpackhi_epi16(u0, u2); // a2 b2 c2 d2 ...
-    __m128i v3 = _mm_unpackhi_epi16(u1, u3); // a6 b6 c6 d6 ...
-
-    _mm_storeu_si128((__m128i*)ptr, v0);
-    _mm_storeu_si128((__m128i*)(ptr + 8), v2);
-    _mm_storeu_si128((__m128i*)(ptr + 16), v1);
-    _mm_storeu_si128((__m128i*)(ptr + 24), v3);
-}
-
-inline void v_store_interleave( unsigned* ptr, const v_uint32x4& a, const v_uint32x4& b,
-                                const v_uint32x4& c )
-{
-    v_uint32x4 z = v_setzero_u32(), u0, u1, u2, u3;
-    v_transpose4x4(a, b, c, z, u0, u1, u2, u3);
-
-    __m128i v0 = _mm_or_si128(u0.val, _mm_slli_si128(u1.val, 12));
-    __m128i v1 = _mm_or_si128(_mm_srli_si128(u1.val, 4), _mm_slli_si128(u2.val, 8));
-    __m128i v2 = _mm_or_si128(_mm_srli_si128(u2.val, 8), _mm_slli_si128(u3.val, 4));
-
-    _mm_storeu_si128((__m128i*)ptr, v0);
-    _mm_storeu_si128((__m128i*)(ptr + 4), v1);
-    _mm_storeu_si128((__m128i*)(ptr + 8), v2);
-}
-
-inline void v_store_interleave(unsigned* ptr, const v_uint32x4& a, const v_uint32x4& b,
-                               const v_uint32x4& c, const v_uint32x4& d)
-{
-    v_uint32x4 t0, t1, t2, t3;
-    v_transpose4x4(a, b, c, d, t0, t1, t2, t3);
-    v_store(ptr, t0);
-    v_store(ptr + 4, t1);
-    v_store(ptr + 8, t2);
-    v_store(ptr + 12, t3);
-}
-
-// 2-channel, float only
-inline void v_store_interleave(float* ptr, const v_float32x4& a, const v_float32x4& b)
-{
-    // a0 a1 a2 a3 ...
-    // b0 b1 b2 b3 ...
-    __m128 u0 = _mm_unpacklo_ps(a.val, b.val); // a0 b0 a1 b1
-    __m128 u1 = _mm_unpackhi_ps(a.val, b.val); // a2 b2 a3 b3
-
-    _mm_storeu_ps(ptr, u0);
-    _mm_storeu_ps((ptr + 4), u1);
-}
-
-inline void v_store_interleave(float* ptr, const v_float32x4& a, const v_float32x4& b, const v_float32x4& c)
-{
-    __m128 u0 = _mm_shuffle_ps(a.val, b.val, _MM_SHUFFLE(0, 0, 0, 0));
-    __m128 u1 = _mm_shuffle_ps(c.val, a.val, _MM_SHUFFLE(1, 1, 0, 0));
-    __m128 v0 = _mm_shuffle_ps(u0, u1, _MM_SHUFFLE(2, 0, 2, 0));
-    __m128 u2 = _mm_shuffle_ps(b.val, c.val, _MM_SHUFFLE(1, 1, 1, 1));
-    __m128 u3 = _mm_shuffle_ps(a.val, b.val, _MM_SHUFFLE(2, 2, 2, 2));
-    __m128 v1 = _mm_shuffle_ps(u2, u3, _MM_SHUFFLE(2, 0, 2, 0));
-    __m128 u4 = _mm_shuffle_ps(c.val, a.val, _MM_SHUFFLE(3, 3, 2, 2));
-    __m128 u5 = _mm_shuffle_ps(b.val, c.val, _MM_SHUFFLE(3, 3, 3, 3));
-    __m128 v2 = _mm_shuffle_ps(u4, u5, _MM_SHUFFLE(2, 0, 2, 0));
-
-    _mm_storeu_ps(ptr + 0, v0);
-    _mm_storeu_ps(ptr + 4, v1);
-    _mm_storeu_ps(ptr + 8, v2);
-}
-
-inline void v_store_interleave(float* ptr, const v_float32x4& a, const v_float32x4& b,
-                               const v_float32x4& c, const v_float32x4& d)
-{
-    __m128 u0 = _mm_unpacklo_ps(a.val, c.val);
-    __m128 u1 = _mm_unpacklo_ps(b.val, d.val);
-    __m128 u2 = _mm_unpackhi_ps(a.val, c.val);
-    __m128 u3 = _mm_unpackhi_ps(b.val, d.val);
-    __m128 v0 = _mm_unpacklo_ps(u0, u1);
-    __m128 v2 = _mm_unpacklo_ps(u2, u3);
-    __m128 v1 = _mm_unpackhi_ps(u0, u1);
-    __m128 v3 = _mm_unpackhi_ps(u2, u3);
-
-    _mm_storeu_ps(ptr +  0, v0);
-    _mm_storeu_ps(ptr +  4, v1);
-    _mm_storeu_ps(ptr +  8, v2);
-    _mm_storeu_ps(ptr + 12, v3);
-}
-
-inline void v_store_interleave(uint64 *ptr, const v_uint64x2& a, const v_uint64x2& b, const v_uint64x2& c)
-{
-    __m128i t0 = _mm_unpacklo_epi64(a.val, b.val);
-    __m128i t1 = _mm_unpacklo_epi64(c.val, _mm_unpackhi_epi64(a.val, a.val));
-    __m128i t2 = _mm_unpackhi_epi64(b.val, c.val);
-
-    _mm_storeu_si128((__m128i*)ptr, t0);
-    _mm_storeu_si128((__m128i*)(ptr + 2), t1);
-    _mm_storeu_si128((__m128i*)(ptr + 4), t2);
-}
-
-inline void v_store_interleave(int64 *ptr, const v_int64x2& a, const v_int64x2& b, const v_int64x2& c)
-{
-    v_store_interleave((uint64*)ptr, v_reinterpret_as_u64(a), v_reinterpret_as_u64(b), v_reinterpret_as_u64(c));
-}
-
-inline void v_store_interleave(double *ptr, const v_float64x2& a, const v_float64x2& b, const v_float64x2& c)
-{
-    v_store_interleave((uint64*)ptr, v_reinterpret_as_u64(a), v_reinterpret_as_u64(b), v_reinterpret_as_u64(c));
-}
-
-#define OPENCV_HAL_IMPL_SSE_LOADSTORE_INTERLEAVE(_Tpvec, _Tp, suffix, _Tpuvec, _Tpu, usuffix) \
-inline void v_load_deinterleave( const _Tp* ptr, _Tpvec& a0, \
-                                 _Tpvec& b0, _Tpvec& c0 ) \
-{ \
-    _Tpuvec a1, b1, c1; \
-    v_load_deinterleave((const _Tpu*)ptr, a1, b1, c1); \
-    a0 = v_reinterpret_as_##suffix(a1); \
-    b0 = v_reinterpret_as_##suffix(b1); \
-    c0 = v_reinterpret_as_##suffix(c1); \
-} \
-inline void v_load_deinterleave( const _Tp* ptr, _Tpvec& a0, \
-                                 _Tpvec& b0, _Tpvec& c0, _Tpvec& d0 ) \
-{ \
-    _Tpuvec a1, b1, c1, d1; \
-    v_load_deinterleave((const _Tpu*)ptr, a1, b1, c1, d1); \
-    a0 = v_reinterpret_as_##suffix(a1); \
-    b0 = v_reinterpret_as_##suffix(b1); \
-    c0 = v_reinterpret_as_##suffix(c1); \
-    d0 = v_reinterpret_as_##suffix(d1); \
-} \
-inline void v_store_interleave( _Tp* ptr, const _Tpvec& a0, \
-                               const _Tpvec& b0, const _Tpvec& c0 ) \
-{ \
-    _Tpuvec a1 = v_reinterpret_as_##usuffix(a0); \
-    _Tpuvec b1 = v_reinterpret_as_##usuffix(b0); \
-    _Tpuvec c1 = v_reinterpret_as_##usuffix(c0); \
-    v_store_interleave((_Tpu*)ptr, a1, b1, c1); \
-} \
-inline void v_store_interleave( _Tp* ptr, const _Tpvec& a0, const _Tpvec& b0, \
-                               const _Tpvec& c0, const _Tpvec& d0 ) \
-{ \
-    _Tpuvec a1 = v_reinterpret_as_##usuffix(a0); \
-    _Tpuvec b1 = v_reinterpret_as_##usuffix(b0); \
-    _Tpuvec c1 = v_reinterpret_as_##usuffix(c0); \
-    _Tpuvec d1 = v_reinterpret_as_##usuffix(d0); \
-    v_store_interleave((_Tpu*)ptr, a1, b1, c1, d1); \
-}
-
-OPENCV_HAL_IMPL_SSE_LOADSTORE_INTERLEAVE(v_int8x16, schar, s8, v_uint8x16, uchar, u8)
-OPENCV_HAL_IMPL_SSE_LOADSTORE_INTERLEAVE(v_int16x8, short, s16, v_uint16x8, ushort, u16)
-OPENCV_HAL_IMPL_SSE_LOADSTORE_INTERLEAVE(v_int32x4, int, s32, v_uint32x4, unsigned, u32)
-//OPENCV_HAL_IMPL_SSE_LOADSTORE_INTERLEAVE(v_float32x4, float, f32, v_uint32x4, unsigned, u32)
-
-inline v_float32x4 v_cvt_f32(const v_int32x4& a)
-{
-    return v_float32x4(_mm_cvtepi32_ps(a.val));
-}
-
-inline v_float32x4 v_cvt_f32(const v_float64x2& a)
-{
-    return v_float32x4(_mm_cvtpd_ps(a.val));
-}
-
-inline v_float64x2 v_cvt_f64(const v_int32x4& a)
-{
-    return v_float64x2(_mm_cvtepi32_pd(a.val));
-}
-
-inline v_float64x2 v_cvt_f64_high(const v_int32x4& a)
-{
-    return v_float64x2(_mm_cvtepi32_pd(_mm_srli_si128(a.val,8)));
-}
-
-inline v_float64x2 v_cvt_f64(const v_float32x4& a)
-{
-    return v_float64x2(_mm_cvtps_pd(a.val));
-}
-
-inline v_float64x2 v_cvt_f64_high(const v_float32x4& a)
-{
-    return v_float64x2(_mm_cvtps_pd(_mm_castsi128_ps(_mm_srli_si128(_mm_castps_si128(a.val),8))));
-}
-
-#if CV_FP16
-inline v_float32x4 v_cvt_f32(const v_float16x4& a)
-{
-    return v_float32x4(_mm_cvtph_ps(a.val));
-}
-
-inline v_float16x4 v_cvt_f16(const v_float32x4& a)
-{
-    return v_float16x4(_mm_cvtps_ph(a.val, 0));
-}
-#endif
-
-//! @name Check SIMD support
-//! @{
-//! @brief Check CPU capability of SIMD operation
-static inline bool hasSIMD128()
-{
-    return (CV_CPU_HAS_SUPPORT_SSE2) ? true : false;
-}
-
-//! @}
-
-CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END
-
-//! @endcond
-
-}
-
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/hal/intrin_vsx.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/hal/intrin_vsx.hpp
deleted file mode 100755
index 8b76dd8..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/hal/intrin_vsx.hpp
+++ /dev/null
@@ -1,917 +0,0 @@
-// This file is part of OpenCV project.
-// It is subject to the license terms in the LICENSE file found in the top-level directory
-// of this distribution and at http://opencv.org/license.html
-
-#ifndef OPENCV_HAL_VSX_HPP
-#define OPENCV_HAL_VSX_HPP
-
-#include <algorithm>
-#include "opencv2/core/utility.hpp"
-
-#define CV_SIMD128 1
-#define CV_SIMD128_64F 1
-
-/**
- * todo: supporting half precision for power9
- * convert instractions xvcvhpsp, xvcvsphp
-**/
-
-namespace cv
-{
-
-//! @cond IGNORED
-
-CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN
-
-///////// Types ////////////
-
-struct v_uint8x16
-{
-    typedef uchar lane_type;
-    enum { nlanes = 16 };
-    vec_uchar16 val;
-
-    explicit v_uint8x16(const vec_uchar16& v) : val(v)
-    {}
-    v_uint8x16() : val(vec_uchar16_z)
-    {}
-    v_uint8x16(vec_bchar16 v) : val(vec_uchar16_c(v))
-    {}
-    v_uint8x16(uchar v0, uchar v1, uchar v2, uchar v3, uchar v4, uchar v5, uchar v6, uchar v7,
-               uchar v8, uchar v9, uchar v10, uchar v11, uchar v12, uchar v13, uchar v14, uchar v15)
-        : val(vec_uchar16_set(v0, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15))
-    {}
-    uchar get0() const
-    { return vec_extract(val, 0); }
-};
-
-struct v_int8x16
-{
-    typedef schar lane_type;
-    enum { nlanes = 16 };
-    vec_char16 val;
-
-    explicit v_int8x16(const vec_char16& v) : val(v)
-    {}
-    v_int8x16() : val(vec_char16_z)
-    {}
-    v_int8x16(vec_bchar16 v) : val(vec_char16_c(v))
-    {}
-    v_int8x16(schar v0, schar v1, schar v2, schar v3, schar v4, schar v5, schar v6, schar v7,
-              schar v8, schar v9, schar v10, schar v11, schar v12, schar v13, schar v14, schar v15)
-        : val(vec_char16_set(v0, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14, v15))
-    {}
-    schar get0() const
-    { return vec_extract(val, 0); }
-};
-
-struct v_uint16x8
-{
-    typedef ushort lane_type;
-    enum { nlanes = 8 };
-    vec_ushort8 val;
-
-    explicit v_uint16x8(const vec_ushort8& v) : val(v)
-    {}
-    v_uint16x8() : val(vec_ushort8_z)
-    {}
-    v_uint16x8(vec_bshort8 v) : val(vec_ushort8_c(v))
-    {}
-    v_uint16x8(ushort v0, ushort v1, ushort v2, ushort v3, ushort v4, ushort v5, ushort v6, ushort v7)
-        : val(vec_ushort8_set(v0, v1, v2, v3, v4, v5, v6, v7))
-    {}
-    ushort get0() const
-    { return vec_extract(val, 0); }
-};
-
-struct v_int16x8
-{
-    typedef short lane_type;
-    enum { nlanes = 8 };
-    vec_short8 val;
-
-    explicit v_int16x8(const vec_short8& v) : val(v)
-    {}
-    v_int16x8() : val(vec_short8_z)
-    {}
-    v_int16x8(vec_bshort8 v) : val(vec_short8_c(v))
-    {}
-    v_int16x8(short v0, short v1, short v2, short v3, short v4, short v5, short v6, short v7)
-        : val(vec_short8_set(v0, v1, v2, v3, v4, v5, v6, v7))
-    {}
-    short get0() const
-    { return vec_extract(val, 0); }
-};
-
-struct v_uint32x4
-{
-    typedef unsigned lane_type;
-    enum { nlanes = 4 };
-    vec_uint4 val;
-
-    explicit v_uint32x4(const vec_uint4& v) : val(v)
-    {}
-    v_uint32x4() : val(vec_uint4_z)
-    {}
-    v_uint32x4(vec_bint4 v) : val(vec_uint4_c(v))
-    {}
-    v_uint32x4(unsigned v0, unsigned v1, unsigned v2, unsigned v3) : val(vec_uint4_set(v0, v1, v2, v3))
-    {}
-    uint get0() const
-    { return vec_extract(val, 0); }
-};
-
-struct v_int32x4
-{
-    typedef int lane_type;
-    enum { nlanes = 4 };
-    vec_int4 val;
-
-    explicit v_int32x4(const vec_int4& v) : val(v)
-    {}
-    v_int32x4() : val(vec_int4_z)
-    {}
-    v_int32x4(vec_bint4 v) : val(vec_int4_c(v))
-    {}
-    v_int32x4(int v0, int v1, int v2, int v3) : val(vec_int4_set(v0, v1, v2, v3))
-    {}
-    int get0() const
-    { return vec_extract(val, 0); }
-};
-
-struct v_float32x4
-{
-    typedef float lane_type;
-    enum { nlanes = 4 };
-    vec_float4 val;
-
-    explicit v_float32x4(const vec_float4& v) : val(v)
-    {}
-    v_float32x4() : val(vec_float4_z)
-    {}
-    v_float32x4(vec_bint4 v) : val(vec_float4_c(v))
-    {}
-    v_float32x4(float v0, float v1, float v2, float v3) : val(vec_float4_set(v0, v1, v2, v3))
-    {}
-    float get0() const
-    { return vec_extract(val, 0); }
-};
-
-struct v_uint64x2
-{
-    typedef uint64 lane_type;
-    enum { nlanes = 2 };
-    vec_udword2 val;
-
-    explicit v_uint64x2(const vec_udword2& v) : val(v)
-    {}
-    v_uint64x2() : val(vec_udword2_z)
-    {}
-    v_uint64x2(vec_bdword2 v) : val(vec_udword2_c(v))
-    {}
-    v_uint64x2(uint64 v0, uint64 v1) : val(vec_udword2_set(v0, v1))
-    {}
-    uint64 get0() const
-    { return vec_extract(val, 0); }
-};
-
-struct v_int64x2
-{
-    typedef int64 lane_type;
-    enum { nlanes = 2 };
-    vec_dword2 val;
-
-    explicit v_int64x2(const vec_dword2& v) : val(v)
-    {}
-    v_int64x2() : val(vec_dword2_z)
-    {}
-    v_int64x2(vec_bdword2 v) : val(vec_dword2_c(v))
-    {}
-    v_int64x2(int64 v0, int64 v1) : val(vec_dword2_set(v0, v1))
-    {}
-    int64 get0() const
-    { return vec_extract(val, 0); }
-};
-
-struct v_float64x2
-{
-    typedef double lane_type;
-    enum { nlanes = 2 };
-    vec_double2 val;
-
-    explicit v_float64x2(const vec_double2& v) : val(v)
-    {}
-    v_float64x2() : val(vec_double2_z)
-    {}
-    v_float64x2(vec_bdword2 v) : val(vec_double2_c(v))
-    {}
-    v_float64x2(double v0, double v1) : val(vec_double2_set(v0, v1))
-    {}
-    double get0() const
-    { return vec_extract(val, 0); }
-};
-
-//////////////// Load and store operations ///////////////
-
-/*
- * clang-5 aborted during parse "vec_xxx_c" only if it's
- * inside a function template which is defined by preprocessor macro.
- *
- * if vec_xxx_c defined as C++ cast, clang-5 will pass it
-*/
-#define OPENCV_HAL_IMPL_VSX_INITVEC(_Tpvec, _Tp, suffix, cast)                        \
-inline _Tpvec v_setzero_##suffix() { return _Tpvec(); }                               \
-inline _Tpvec v_setall_##suffix(_Tp v) { return _Tpvec(vec_splats((_Tp)v));}          \
-template<typename _Tpvec0> inline _Tpvec v_reinterpret_as_##suffix(const _Tpvec0 &a)  \
-{ return _Tpvec((cast)a.val); }
-
-OPENCV_HAL_IMPL_VSX_INITVEC(v_uint8x16, uchar, u8, vec_uchar16)
-OPENCV_HAL_IMPL_VSX_INITVEC(v_int8x16, schar, s8, vec_char16)
-OPENCV_HAL_IMPL_VSX_INITVEC(v_uint16x8, ushort, u16, vec_ushort8)
-OPENCV_HAL_IMPL_VSX_INITVEC(v_int16x8, short, s16, vec_short8)
-OPENCV_HAL_IMPL_VSX_INITVEC(v_uint32x4, uint, u32, vec_uint4)
-OPENCV_HAL_IMPL_VSX_INITVEC(v_int32x4, int, s32, vec_int4)
-OPENCV_HAL_IMPL_VSX_INITVEC(v_uint64x2, uint64, u64, vec_udword2)
-OPENCV_HAL_IMPL_VSX_INITVEC(v_int64x2, int64, s64, vec_dword2)
-OPENCV_HAL_IMPL_VSX_INITVEC(v_float32x4, float, f32, vec_float4)
-OPENCV_HAL_IMPL_VSX_INITVEC(v_float64x2, double, f64, vec_double2)
-
-#define OPENCV_HAL_IMPL_VSX_LOADSTORE_C(_Tpvec, _Tp, ld, ld_a, st, st_a)    \
-inline _Tpvec v_load(const _Tp* ptr)                                        \
-{ return _Tpvec(ld(0, ptr)); }                                              \
-inline _Tpvec v_load_aligned(VSX_UNUSED(const _Tp* ptr))                    \
-{ return _Tpvec(ld_a(0, ptr)); }                                            \
-inline _Tpvec v_load_low(const _Tp* ptr)                                    \
-{ return _Tpvec(vec_ld_l8(ptr)); }                                          \
-inline _Tpvec v_load_halves(const _Tp* ptr0, const _Tp* ptr1)               \
-{ return _Tpvec(vec_mergesqh(vec_ld_l8(ptr0), vec_ld_l8(ptr1))); }          \
-inline void v_store(_Tp* ptr, const _Tpvec& a)                              \
-{ st(a.val, 0, ptr); }                                                      \
-inline void v_store_aligned(VSX_UNUSED(_Tp* ptr), const _Tpvec& a)          \
-{ st_a(a.val, 0, ptr); }                                                    \
-inline void v_store_low(_Tp* ptr, const _Tpvec& a)                          \
-{ vec_st_l8(a.val, ptr); }                                                  \
-inline void v_store_high(_Tp* ptr, const _Tpvec& a)                         \
-{ vec_st_h8(a.val, ptr); }
-
-#define OPENCV_HAL_IMPL_VSX_LOADSTORE(_Tpvec, _Tp) \
-OPENCV_HAL_IMPL_VSX_LOADSTORE_C(_Tpvec, _Tp, vsx_ld, vec_ld, vsx_st, vec_st)
-
-OPENCV_HAL_IMPL_VSX_LOADSTORE(v_uint8x16,  uchar)
-OPENCV_HAL_IMPL_VSX_LOADSTORE(v_int8x16,   schar)
-OPENCV_HAL_IMPL_VSX_LOADSTORE(v_uint16x8,  ushort)
-OPENCV_HAL_IMPL_VSX_LOADSTORE(v_int16x8,   short)
-OPENCV_HAL_IMPL_VSX_LOADSTORE(v_uint32x4,  uint)
-OPENCV_HAL_IMPL_VSX_LOADSTORE(v_int32x4,   int)
-OPENCV_HAL_IMPL_VSX_LOADSTORE(v_float32x4, float)
-
-OPENCV_HAL_IMPL_VSX_LOADSTORE_C(v_float64x2, double, vsx_ld,  vsx_ld,  vsx_st,  vsx_st)
-OPENCV_HAL_IMPL_VSX_LOADSTORE_C(v_uint64x2,  uint64, vsx_ld2, vsx_ld2, vsx_st2, vsx_st2)
-OPENCV_HAL_IMPL_VSX_LOADSTORE_C(v_int64x2,    int64, vsx_ld2, vsx_ld2, vsx_st2, vsx_st2)
-
-//////////////// Value reordering ///////////////
-
-/* de&interleave */
-#define OPENCV_HAL_IMPL_VSX_INTERLEAVE(_Tp, _Tpvec)                          \
-inline void v_load_deinterleave(const _Tp* ptr, _Tpvec& a, _Tpvec& b)        \
-{ vec_ld_deinterleave(ptr, a.val, b.val);}                                   \
-inline void v_load_deinterleave(const _Tp* ptr, _Tpvec& a,                   \
-                                _Tpvec& b, _Tpvec& c)                        \
-{ vec_ld_deinterleave(ptr, a.val, b.val, c.val); }                           \
-inline void v_load_deinterleave(const _Tp* ptr, _Tpvec& a, _Tpvec& b,        \
-                                                _Tpvec& c, _Tpvec& d)        \
-{ vec_ld_deinterleave(ptr, a.val, b.val, c.val, d.val); }                    \
-inline void v_store_interleave(_Tp* ptr, const _Tpvec& a, const _Tpvec& b)   \
-{ vec_st_interleave(a.val, b.val, ptr); }                                    \
-inline void v_store_interleave(_Tp* ptr, const _Tpvec& a,                    \
-                               const _Tpvec& b, const _Tpvec& c)             \
-{ vec_st_interleave(a.val, b.val, c.val, ptr); }                             \
-inline void v_store_interleave(_Tp* ptr, const _Tpvec& a, const _Tpvec& b,   \
-                                         const _Tpvec& c, const _Tpvec& d)   \
-{ vec_st_interleave(a.val, b.val, c.val, d.val, ptr); }
-
-OPENCV_HAL_IMPL_VSX_INTERLEAVE(uchar, v_uint8x16)
-OPENCV_HAL_IMPL_VSX_INTERLEAVE(schar, v_int8x16)
-OPENCV_HAL_IMPL_VSX_INTERLEAVE(ushort, v_uint16x8)
-OPENCV_HAL_IMPL_VSX_INTERLEAVE(short, v_int16x8)
-OPENCV_HAL_IMPL_VSX_INTERLEAVE(uint, v_uint32x4)
-OPENCV_HAL_IMPL_VSX_INTERLEAVE(int, v_int32x4)
-OPENCV_HAL_IMPL_VSX_INTERLEAVE(float, v_float32x4)
-OPENCV_HAL_IMPL_VSX_INTERLEAVE(double, v_float64x2)
-
-/* Expand */
-#define OPENCV_HAL_IMPL_VSX_EXPAND(_Tpvec, _Tpwvec, _Tp, fl, fh)  \
-inline void v_expand(const _Tpvec& a, _Tpwvec& b0, _Tpwvec& b1)   \
-{                                                                 \
-    b0.val = fh(a.val);                                           \
-    b1.val = fl(a.val);                                           \
-}                                                                 \
-inline _Tpwvec v_load_expand(const _Tp* ptr)                      \
-{ return _Tpwvec(fh(vec_ld_l8(ptr))); }
-
-OPENCV_HAL_IMPL_VSX_EXPAND(v_uint8x16, v_uint16x8, uchar, vec_unpacklu, vec_unpackhu)
-OPENCV_HAL_IMPL_VSX_EXPAND(v_int8x16, v_int16x8, schar, vec_unpackl, vec_unpackh)
-OPENCV_HAL_IMPL_VSX_EXPAND(v_uint16x8, v_uint32x4, ushort, vec_unpacklu, vec_unpackhu)
-OPENCV_HAL_IMPL_VSX_EXPAND(v_int16x8, v_int32x4, short, vec_unpackl, vec_unpackh)
-OPENCV_HAL_IMPL_VSX_EXPAND(v_uint32x4, v_uint64x2, uint, vec_unpacklu, vec_unpackhu)
-OPENCV_HAL_IMPL_VSX_EXPAND(v_int32x4, v_int64x2, int, vec_unpackl, vec_unpackh)
-
-inline v_uint32x4 v_load_expand_q(const uchar* ptr)
-{ return v_uint32x4(vec_uint4_set(ptr[0], ptr[1], ptr[2], ptr[3])); }
-
-inline v_int32x4 v_load_expand_q(const schar* ptr)
-{ return v_int32x4(vec_int4_set(ptr[0], ptr[1], ptr[2], ptr[3])); }
-
-/* pack */
-#define OPENCV_HAL_IMPL_VSX_PACK(_Tpvec, _Tp, _Tpwvec, _Tpvn, _Tpdel, sfnc, pkfnc, addfnc, pack)    \
-inline _Tpvec v_##pack(const _Tpwvec& a, const _Tpwvec& b)                                          \
-{                                                                                                   \
-    return _Tpvec(pkfnc(a.val, b.val));                                                             \
-}                                                                                                   \
-inline void v_##pack##_store(_Tp* ptr, const _Tpwvec& a)                                            \
-{                                                                                                   \
-    vec_st_l8(pkfnc(a.val, a.val), ptr);                                                            \
-}                                                                                                   \
-template<int n>                                                                                     \
-inline _Tpvec v_rshr_##pack(const _Tpwvec& a, const _Tpwvec& b)                                     \
-{                                                                                                   \
-    const __vector _Tpvn vn = vec_splats((_Tpvn)n);                                                 \
-    const __vector _Tpdel delta = vec_splats((_Tpdel)((_Tpdel)1 << (n-1)));                         \
-    return _Tpvec(pkfnc(sfnc(addfnc(a.val, delta), vn), sfnc(addfnc(b.val, delta), vn)));           \
-}                                                                                                   \
-template<int n>                                                                                     \
-inline void v_rshr_##pack##_store(_Tp* ptr, const _Tpwvec& a)                                       \
-{                                                                                                   \
-    const __vector _Tpvn vn = vec_splats((_Tpvn)n);                                                 \
-    const __vector _Tpdel delta = vec_splats((_Tpdel)((_Tpdel)1 << (n-1)));                         \
-    vec_st_l8(pkfnc(sfnc(addfnc(a.val, delta), vn), delta), ptr);                                   \
-}
-
-OPENCV_HAL_IMPL_VSX_PACK(v_uint8x16, uchar, v_uint16x8, unsigned short, unsigned short,
-                         vec_sr, vec_packs, vec_adds, pack)
-OPENCV_HAL_IMPL_VSX_PACK(v_int8x16, schar, v_int16x8, unsigned short, short,
-                         vec_sra, vec_packs, vec_adds, pack)
-
-OPENCV_HAL_IMPL_VSX_PACK(v_uint16x8, ushort, v_uint32x4, unsigned int, unsigned int,
-                         vec_sr, vec_packs, vec_add, pack)
-OPENCV_HAL_IMPL_VSX_PACK(v_int16x8, short, v_int32x4, unsigned int, int,
-                         vec_sra, vec_packs, vec_add, pack)
-
-OPENCV_HAL_IMPL_VSX_PACK(v_uint32x4, uint, v_uint64x2, unsigned long long, unsigned long long,
-                         vec_sr, vec_pack, vec_add, pack)
-OPENCV_HAL_IMPL_VSX_PACK(v_int32x4, int, v_int64x2, unsigned long long, long long,
-                         vec_sra, vec_pack, vec_add, pack)
-
-OPENCV_HAL_IMPL_VSX_PACK(v_uint8x16, uchar, v_int16x8, unsigned short, short,
-                         vec_sra, vec_packsu, vec_adds, pack_u)
-OPENCV_HAL_IMPL_VSX_PACK(v_uint16x8, ushort, v_int32x4, unsigned int, int,
-                         vec_sra, vec_packsu, vec_add, pack_u)
-// Following variant is not implemented on other platforms:
-//OPENCV_HAL_IMPL_VSX_PACK(v_uint32x4, uint, v_int64x2, unsigned long long, long long,
-//                         vec_sra, vec_packsu, vec_add, pack_u)
-
-/* Recombine */
-template <typename _Tpvec>
-inline void v_zip(const _Tpvec& a0, const _Tpvec& a1, _Tpvec& b0, _Tpvec& b1)
-{
-    b0.val = vec_mergeh(a0.val, a1.val);
-    b1.val = vec_mergel(a0.val, a1.val);
-}
-
-template <typename _Tpvec>
-inline _Tpvec v_combine_high(const _Tpvec& a, const _Tpvec& b)
-{ return _Tpvec(vec_mergesql(a.val, b.val)); }
-
-template <typename _Tpvec>
-inline _Tpvec v_combine_low(const _Tpvec& a, const _Tpvec& b)
-{ return _Tpvec(vec_mergesqh(a.val, b.val)); }
-
-template <typename _Tpvec>
-inline void v_recombine(const _Tpvec& a, const _Tpvec& b, _Tpvec& c, _Tpvec& d)
-{
-    c.val = vec_mergesqh(a.val, b.val);
-    d.val = vec_mergesql(a.val, b.val);
-}
-
-////////// Arithmetic, bitwise and comparison operations /////////
-
-/* Element-wise binary and unary operations */
-/** Arithmetics **/
-#define OPENCV_HAL_IMPL_VSX_BIN_OP(bin_op, _Tpvec, intrin)       \
-inline _Tpvec operator bin_op (const _Tpvec& a, const _Tpvec& b) \
-{ return _Tpvec(intrin(a.val, b.val)); }                         \
-inline _Tpvec& operator bin_op##= (_Tpvec& a, const _Tpvec& b)   \
-{ a.val = intrin(a.val, b.val); return a; }
-
-OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_uint8x16, vec_adds)
-OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_uint8x16, vec_subs)
-OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_int8x16,  vec_adds)
-OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_int8x16, vec_subs)
-OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_uint16x8, vec_adds)
-OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_uint16x8, vec_subs)
-OPENCV_HAL_IMPL_VSX_BIN_OP(*, v_uint16x8, vec_mul)
-OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_int16x8, vec_adds)
-OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_int16x8, vec_subs)
-OPENCV_HAL_IMPL_VSX_BIN_OP(*, v_int16x8, vec_mul)
-OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_uint32x4, vec_add)
-OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_uint32x4, vec_sub)
-OPENCV_HAL_IMPL_VSX_BIN_OP(*, v_uint32x4, vec_mul)
-OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_int32x4, vec_add)
-OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_int32x4, vec_sub)
-OPENCV_HAL_IMPL_VSX_BIN_OP(*, v_int32x4, vec_mul)
-OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_float32x4, vec_add)
-OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_float32x4, vec_sub)
-OPENCV_HAL_IMPL_VSX_BIN_OP(*, v_float32x4, vec_mul)
-OPENCV_HAL_IMPL_VSX_BIN_OP(/, v_float32x4, vec_div)
-OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_float64x2, vec_add)
-OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_float64x2, vec_sub)
-OPENCV_HAL_IMPL_VSX_BIN_OP(*, v_float64x2, vec_mul)
-OPENCV_HAL_IMPL_VSX_BIN_OP(/, v_float64x2, vec_div)
-OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_uint64x2, vec_add)
-OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_uint64x2, vec_sub)
-OPENCV_HAL_IMPL_VSX_BIN_OP(+, v_int64x2, vec_add)
-OPENCV_HAL_IMPL_VSX_BIN_OP(-, v_int64x2, vec_sub)
-
-inline void v_mul_expand(const v_int16x8& a, const v_int16x8& b, v_int32x4& c, v_int32x4& d)
-{
-    c.val = vec_mul(vec_unpackh(a.val), vec_unpackh(b.val));
-    d.val = vec_mul(vec_unpackl(a.val), vec_unpackl(b.val));
-}
-inline void v_mul_expand(const v_uint16x8& a, const v_uint16x8& b, v_uint32x4& c, v_uint32x4& d)
-{
-    c.val = vec_mul(vec_unpackhu(a.val), vec_unpackhu(b.val));
-    d.val = vec_mul(vec_unpacklu(a.val), vec_unpacklu(b.val));
-}
-inline void v_mul_expand(const v_uint32x4& a, const v_uint32x4& b, v_uint64x2& c, v_uint64x2& d)
-{
-    c.val = vec_mul(vec_unpackhu(a.val), vec_unpackhu(b.val));
-    d.val = vec_mul(vec_unpacklu(a.val), vec_unpacklu(b.val));
-}
-
-/** Non-saturating arithmetics **/
-#define OPENCV_HAL_IMPL_VSX_BIN_FUNC(func, intrin)    \
-template<typename _Tpvec>                             \
-inline _Tpvec func(const _Tpvec& a, const _Tpvec& b)  \
-{ return _Tpvec(intrin(a.val, b.val)); }
-
-OPENCV_HAL_IMPL_VSX_BIN_FUNC(v_add_wrap, vec_add)
-OPENCV_HAL_IMPL_VSX_BIN_FUNC(v_sub_wrap, vec_sub)
-
-/** Bitwise shifts **/
-#define OPENCV_HAL_IMPL_VSX_SHIFT_OP(_Tpvec, shr, splfunc)   \
-inline _Tpvec operator << (const _Tpvec& a, int imm)         \
-{ return _Tpvec(vec_sl(a.val, splfunc(imm))); }              \
-inline _Tpvec operator >> (const _Tpvec& a, int imm)         \
-{ return _Tpvec(shr(a.val, splfunc(imm))); }                 \
-template<int imm> inline _Tpvec v_shl(const _Tpvec& a)       \
-{ return _Tpvec(vec_sl(a.val, splfunc(imm))); }              \
-template<int imm> inline _Tpvec v_shr(const _Tpvec& a)       \
-{ return _Tpvec(shr(a.val, splfunc(imm))); }
-
-OPENCV_HAL_IMPL_VSX_SHIFT_OP(v_uint8x16, vec_sr, vec_uchar16_sp)
-OPENCV_HAL_IMPL_VSX_SHIFT_OP(v_uint16x8, vec_sr, vec_ushort8_sp)
-OPENCV_HAL_IMPL_VSX_SHIFT_OP(v_uint32x4, vec_sr, vec_uint4_sp)
-OPENCV_HAL_IMPL_VSX_SHIFT_OP(v_uint64x2, vec_sr, vec_udword2_sp)
-// algebraic right shift
-OPENCV_HAL_IMPL_VSX_SHIFT_OP(v_int8x16, vec_sra, vec_uchar16_sp)
-OPENCV_HAL_IMPL_VSX_SHIFT_OP(v_int16x8, vec_sra, vec_ushort8_sp)
-OPENCV_HAL_IMPL_VSX_SHIFT_OP(v_int32x4, vec_sra, vec_uint4_sp)
-OPENCV_HAL_IMPL_VSX_SHIFT_OP(v_int64x2, vec_sra, vec_udword2_sp)
-
-/** Bitwise logic **/
-#define OPENCV_HAL_IMPL_VSX_LOGIC_OP(_Tpvec)    \
-OPENCV_HAL_IMPL_VSX_BIN_OP(&, _Tpvec, vec_and)  \
-OPENCV_HAL_IMPL_VSX_BIN_OP(|, _Tpvec, vec_or)   \
-OPENCV_HAL_IMPL_VSX_BIN_OP(^, _Tpvec, vec_xor)  \
-inline _Tpvec operator ~ (const _Tpvec& a)      \
-{ return _Tpvec(vec_not(a.val)); }
-
-OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_uint8x16)
-OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_int8x16)
-OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_uint16x8)
-OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_int16x8)
-OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_uint32x4)
-OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_int32x4)
-OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_uint64x2)
-OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_int64x2)
-OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_float32x4)
-OPENCV_HAL_IMPL_VSX_LOGIC_OP(v_float64x2)
-
-/** Bitwise select **/
-#define OPENCV_HAL_IMPL_VSX_SELECT(_Tpvec, cast)                             \
-inline _Tpvec v_select(const _Tpvec& mask, const _Tpvec& a, const _Tpvec& b) \
-{ return _Tpvec(vec_sel(b.val, a.val, cast(mask.val))); }
-
-OPENCV_HAL_IMPL_VSX_SELECT(v_uint8x16, vec_bchar16_c)
-OPENCV_HAL_IMPL_VSX_SELECT(v_int8x16, vec_bchar16_c)
-OPENCV_HAL_IMPL_VSX_SELECT(v_uint16x8, vec_bshort8_c)
-OPENCV_HAL_IMPL_VSX_SELECT(v_int16x8, vec_bshort8_c)
-OPENCV_HAL_IMPL_VSX_SELECT(v_uint32x4, vec_bint4_c)
-OPENCV_HAL_IMPL_VSX_SELECT(v_int32x4, vec_bint4_c)
-OPENCV_HAL_IMPL_VSX_SELECT(v_float32x4, vec_bint4_c)
-OPENCV_HAL_IMPL_VSX_SELECT(v_float64x2, vec_bdword2_c)
-
-/** Comparison **/
-#define OPENCV_HAL_IMPL_VSX_INT_CMP_OP(_Tpvec)                 \
-inline _Tpvec operator == (const _Tpvec& a, const _Tpvec& b)   \
-{ return _Tpvec(vec_cmpeq(a.val, b.val)); }                    \
-inline _Tpvec operator != (const _Tpvec& a, const _Tpvec& b)   \
-{ return _Tpvec(vec_cmpne(a.val, b.val)); }                    \
-inline _Tpvec operator < (const _Tpvec& a, const _Tpvec& b)    \
-{ return _Tpvec(vec_cmplt(a.val, b.val)); }                    \
-inline _Tpvec operator > (const _Tpvec& a, const _Tpvec& b)    \
-{ return _Tpvec(vec_cmpgt(a.val, b.val)); }                    \
-inline _Tpvec operator <= (const _Tpvec& a, const _Tpvec& b)   \
-{ return _Tpvec(vec_cmple(a.val, b.val)); }                    \
-inline _Tpvec operator >= (const _Tpvec& a, const _Tpvec& b)   \
-{ return _Tpvec(vec_cmpge(a.val, b.val)); }
-
-OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_uint8x16)
-OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_int8x16)
-OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_uint16x8)
-OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_int16x8)
-OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_uint32x4)
-OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_int32x4)
-OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_float32x4)
-OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_float64x2)
-OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_uint64x2)
-OPENCV_HAL_IMPL_VSX_INT_CMP_OP(v_int64x2)
-
-/** min/max **/
-OPENCV_HAL_IMPL_VSX_BIN_FUNC(v_min, vec_min)
-OPENCV_HAL_IMPL_VSX_BIN_FUNC(v_max, vec_max)
-
-/** Rotate **/
-#define OPENCV_IMPL_VSX_ROTATE(_Tpvec, suffix, shf, cast)                       \
-template<int imm>                                                               \
-inline _Tpvec v_rotate_##suffix(const _Tpvec& a)                                \
-{                                                                               \
-    const int wd = imm * sizeof(typename _Tpvec::lane_type);                    \
-    if (wd > 15)                                                                \
-        return _Tpvec();                                                        \
-    return _Tpvec((cast)shf(vec_uchar16_c(a.val), vec_uchar16_sp(wd << 3)));    \
-}
-
-#define OPENCV_IMPL_VSX_ROTATE_LR(_Tpvec, cast)     \
-OPENCV_IMPL_VSX_ROTATE(_Tpvec, left, vec_slo, cast) \
-OPENCV_IMPL_VSX_ROTATE(_Tpvec, right, vec_sro, cast)
-
-OPENCV_IMPL_VSX_ROTATE_LR(v_uint8x16, vec_uchar16)
-OPENCV_IMPL_VSX_ROTATE_LR(v_int8x16,  vec_char16)
-OPENCV_IMPL_VSX_ROTATE_LR(v_uint16x8, vec_ushort8)
-OPENCV_IMPL_VSX_ROTATE_LR(v_int16x8,  vec_short8)
-OPENCV_IMPL_VSX_ROTATE_LR(v_uint32x4, vec_uint4)
-OPENCV_IMPL_VSX_ROTATE_LR(v_int32x4,  vec_int4)
-OPENCV_IMPL_VSX_ROTATE_LR(v_float32x4, vec_float4)
-OPENCV_IMPL_VSX_ROTATE_LR(v_uint64x2, vec_udword2)
-OPENCV_IMPL_VSX_ROTATE_LR(v_int64x2,  vec_dword2)
-OPENCV_IMPL_VSX_ROTATE_LR(v_float64x2, vec_double2)
-
-template<int imm, typename _Tpvec>
-inline _Tpvec v_rotate_right(const _Tpvec& a, const _Tpvec& b)
-{
-    enum { CV_SHIFT = 16 - imm * (sizeof(typename _Tpvec::lane_type)) };
-    if (CV_SHIFT == 16)
-        return a;
-#ifdef __IBMCPP__
-    return _Tpvec(vec_sld(b.val, a.val, CV_SHIFT & 15));
-#else
-    return _Tpvec(vec_sld(b.val, a.val, CV_SHIFT));
-#endif
-}
-
-template<int imm, typename _Tpvec>
-inline _Tpvec v_rotate_left(const _Tpvec& a, const _Tpvec& b)
-{
-    enum { CV_SHIFT = imm * (sizeof(typename _Tpvec::lane_type)) };
-    if (CV_SHIFT == 16)
-        return b;
-    return _Tpvec(vec_sld(a.val, b.val, CV_SHIFT));
-}
-
-#define OPENCV_IMPL_VSX_ROTATE_64_2RG(_Tpvec, suffix, rg1, rg2)   \
-template<int imm>                                                 \
-inline _Tpvec v_rotate_##suffix(const _Tpvec& a, const _Tpvec& b) \
-{                                                                 \
-    if (imm == 1)                                                 \
-        return _Tpvec(vec_permi(rg1.val, rg2.val, 2));            \
-    return imm ? b : a;                                           \
-}
-
-#define OPENCV_IMPL_VSX_ROTATE_64_2RG_LR(_Tpvec)    \
-OPENCV_IMPL_VSX_ROTATE_64_2RG(_Tpvec, left,  b, a)  \
-OPENCV_IMPL_VSX_ROTATE_64_2RG(_Tpvec, right, a, b)
-
-OPENCV_IMPL_VSX_ROTATE_64_2RG_LR(v_float64x2)
-OPENCV_IMPL_VSX_ROTATE_64_2RG_LR(v_uint64x2)
-OPENCV_IMPL_VSX_ROTATE_64_2RG_LR(v_int64x2)
-
-/* Extract */
-template<int s, typename _Tpvec>
-inline _Tpvec v_extract(const _Tpvec& a, const _Tpvec& b)
-{ return v_rotate_right<s>(a, b); }
-
-////////// Reduce and mask /////////
-
-/** Reduce **/
-inline short v_reduce_sum(const v_int16x8& a)
-{
-    const vec_int4 zero = vec_int4_z;
-    return saturate_cast<short>(vec_extract(vec_sums(vec_sum4s(a.val, zero), zero), 3));
-}
-inline ushort v_reduce_sum(const v_uint16x8& a)
-{
-    const vec_int4 v4 = vec_int4_c(vec_unpackhu(vec_adds(a.val, vec_sld(a.val, a.val, 8))));
-    return saturate_cast<ushort>(vec_extract(vec_sums(v4, vec_int4_z), 3));
-}
-
-#define OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(_Tpvec, _Tpvec2, scalartype, suffix, func) \
-inline scalartype v_reduce_##suffix(const _Tpvec& a)                               \
-{                                                                                  \
-    const _Tpvec2 rs = func(a.val, vec_sld(a.val, a.val, 8));                      \
-    return vec_extract(func(rs, vec_sld(rs, rs, 4)), 0);                           \
-}
-OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(v_uint32x4, vec_uint4, uint, sum, vec_add)
-OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(v_uint32x4, vec_uint4, uint, max, vec_max)
-OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(v_uint32x4, vec_uint4, uint, min, vec_min)
-OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(v_int32x4, vec_int4, int, sum, vec_add)
-OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(v_int32x4, vec_int4, int, max, vec_max)
-OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(v_int32x4, vec_int4, int, min, vec_min)
-OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(v_float32x4, vec_float4, float, sum, vec_add)
-OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(v_float32x4, vec_float4, float, max, vec_max)
-OPENCV_HAL_IMPL_VSX_REDUCE_OP_4(v_float32x4, vec_float4, float, min, vec_min)
-
-#define OPENCV_HAL_IMPL_VSX_REDUCE_OP_8(_Tpvec, _Tpvec2, scalartype, suffix, func) \
-inline scalartype v_reduce_##suffix(const _Tpvec& a)                               \
-{                                                                                  \
-    _Tpvec2 rs = func(a.val, vec_sld(a.val, a.val, 8));                            \
-    rs = func(rs, vec_sld(rs, rs, 4));                                             \
-    return vec_extract(func(rs, vec_sld(rs, rs, 2)), 0);                           \
-}
-OPENCV_HAL_IMPL_VSX_REDUCE_OP_8(v_uint16x8, vec_ushort8, ushort, max, vec_max)
-OPENCV_HAL_IMPL_VSX_REDUCE_OP_8(v_uint16x8, vec_ushort8, ushort, min, vec_min)
-OPENCV_HAL_IMPL_VSX_REDUCE_OP_8(v_int16x8, vec_short8, short, max, vec_max)
-OPENCV_HAL_IMPL_VSX_REDUCE_OP_8(v_int16x8, vec_short8, short, min, vec_min)
-
-inline v_float32x4 v_reduce_sum4(const v_float32x4& a, const v_float32x4& b,
-                                 const v_float32x4& c, const v_float32x4& d)
-{
-    vec_float4 ac = vec_add(vec_mergel(a.val, c.val), vec_mergeh(a.val, c.val));
-    ac = vec_add(ac, vec_sld(ac, ac, 8));
-
-    vec_float4 bd = vec_add(vec_mergel(b.val, d.val), vec_mergeh(b.val, d.val));
-    bd = vec_add(bd, vec_sld(bd, bd, 8));
-    return v_float32x4(vec_mergeh(ac, bd));
-}
-
-/** Popcount **/
-template<typename _Tpvec>
-inline v_uint32x4 v_popcount(const _Tpvec& a)
-{ return v_uint32x4(vec_popcntu(vec_uint4_c(a.val))); }
-
-/** Mask **/
-inline int v_signmask(const v_uint8x16& a)
-{
-    vec_uchar16 sv  = vec_sr(a.val, vec_uchar16_sp(7));
-    static const vec_uchar16 slm = {0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7};
-    sv = vec_sl(sv, slm);
-    vec_uint4 sv4 = vec_sum4s(sv, vec_uint4_z);
-    static const vec_uint4 slm4 = {0, 0, 8, 8};
-    sv4 = vec_sl(sv4, slm4);
-    return vec_extract(vec_sums((vec_int4) sv4, vec_int4_z), 3);
-}
-inline int v_signmask(const v_int8x16& a)
-{ return v_signmask(v_reinterpret_as_u8(a)); }
-
-inline int v_signmask(const v_int16x8& a)
-{
-    static const vec_ushort8 slm = {0, 1, 2, 3, 4, 5, 6, 7};
-    vec_short8 sv = vec_sr(a.val, vec_ushort8_sp(15));
-    sv = vec_sl(sv, slm);
-    vec_int4 svi = vec_int4_z;
-    svi = vec_sums(vec_sum4s(sv, svi), svi);
-    return vec_extract(svi, 3);
-}
-inline int v_signmask(const v_uint16x8& a)
-{ return v_signmask(v_reinterpret_as_s16(a)); }
-
-inline int v_signmask(const v_int32x4& a)
-{
-    static const vec_uint4 slm = {0, 1, 2, 3};
-    vec_int4 sv = vec_sr(a.val, vec_uint4_sp(31));
-    sv = vec_sl(sv, slm);
-    sv = vec_sums(sv, vec_int4_z);
-    return vec_extract(sv, 3);
-}
-inline int v_signmask(const v_uint32x4& a)
-{ return v_signmask(v_reinterpret_as_s32(a)); }
-inline int v_signmask(const v_float32x4& a)
-{ return v_signmask(v_reinterpret_as_s32(a)); }
-
-inline int v_signmask(const v_int64x2& a)
-{
-    VSX_UNUSED(const vec_dword2) sv = vec_sr(a.val, vec_udword2_sp(63));
-    return (int)vec_extract(sv, 0) | (int)vec_extract(sv, 1) << 1;
-}
-inline int v_signmask(const v_uint64x2& a)
-{ return v_signmask(v_reinterpret_as_s64(a)); }
-inline int v_signmask(const v_float64x2& a)
-{ return v_signmask(v_reinterpret_as_s64(a)); }
-
-template<typename _Tpvec>
-inline bool v_check_all(const _Tpvec& a)
-{ return vec_all_lt(a.val, _Tpvec().val); }
-inline bool v_check_all(const v_uint8x16& a)
-{ return v_check_all(v_reinterpret_as_s8(a)); }
-inline bool v_check_all(const v_uint16x8& a)
-{ return v_check_all(v_reinterpret_as_s16(a)); }
-inline bool v_check_all(const v_uint32x4& a)
-{ return v_check_all(v_reinterpret_as_s32(a)); }
-inline bool v_check_all(const v_float32x4& a)
-{ return v_check_all(v_reinterpret_as_s32(a)); }
-inline bool v_check_all(const v_float64x2& a)
-{ return v_check_all(v_reinterpret_as_s64(a)); }
-
-template<typename _Tpvec>
-inline bool v_check_any(const _Tpvec& a)
-{ return vec_any_lt(a.val, _Tpvec().val); }
-inline bool v_check_any(const v_uint8x16& a)
-{ return v_check_any(v_reinterpret_as_s8(a)); }
-inline bool v_check_any(const v_uint16x8& a)
-{ return v_check_any(v_reinterpret_as_s16(a)); }
-inline bool v_check_any(const v_uint32x4& a)
-{ return v_check_any(v_reinterpret_as_s32(a)); }
-inline bool v_check_any(const v_float32x4& a)
-{ return v_check_any(v_reinterpret_as_s32(a)); }
-inline bool v_check_any(const v_float64x2& a)
-{ return v_check_any(v_reinterpret_as_s64(a)); }
-
-////////// Other math /////////
-
-/** Some frequent operations **/
-inline v_float32x4 v_sqrt(const v_float32x4& x)
-{ return v_float32x4(vec_sqrt(x.val)); }
-inline v_float64x2 v_sqrt(const v_float64x2& x)
-{ return v_float64x2(vec_sqrt(x.val)); }
-
-inline v_float32x4 v_invsqrt(const v_float32x4& x)
-{ return v_float32x4(vec_rsqrt(x.val)); }
-inline v_float64x2 v_invsqrt(const v_float64x2& x)
-{ return v_float64x2(vec_rsqrt(x.val)); }
-
-#define OPENCV_HAL_IMPL_VSX_MULADD(_Tpvec)                                  \
-inline _Tpvec v_magnitude(const _Tpvec& a, const _Tpvec& b)                 \
-{ return _Tpvec(vec_sqrt(vec_madd(a.val, a.val, vec_mul(b.val, b.val)))); } \
-inline _Tpvec v_sqr_magnitude(const _Tpvec& a, const _Tpvec& b)             \
-{ return _Tpvec(vec_madd(a.val, a.val, vec_mul(b.val, b.val))); }           \
-inline _Tpvec v_muladd(const _Tpvec& a, const _Tpvec& b, const _Tpvec& c)   \
-{ return _Tpvec(vec_madd(a.val, b.val, c.val)); }
-
-OPENCV_HAL_IMPL_VSX_MULADD(v_float32x4)
-OPENCV_HAL_IMPL_VSX_MULADD(v_float64x2)
-
-inline v_int32x4 v_muladd(const v_int32x4& a, const v_int32x4& b, const v_int32x4& c)
-{ return a * b + c; }
-
-// TODO: exp, log, sin, cos
-
-/** Absolute values **/
-inline v_uint8x16 v_abs(const v_int8x16& x)
-{ return v_uint8x16(vec_uchar16_c(vec_abs(x.val))); }
-
-inline v_uint16x8 v_abs(const v_int16x8& x)
-{ return v_uint16x8(vec_ushort8_c(vec_abs(x.val))); }
-
-inline v_uint32x4 v_abs(const v_int32x4& x)
-{ return v_uint32x4(vec_uint4_c(vec_abs(x.val))); }
-
-inline v_float32x4 v_abs(const v_float32x4& x)
-{ return v_float32x4(vec_abs(x.val)); }
-
-inline v_float64x2 v_abs(const v_float64x2& x)
-{ return v_float64x2(vec_abs(x.val)); }
-
-OPENCV_HAL_IMPL_VSX_BIN_FUNC(v_absdiff, vec_absd)
-
-#define OPENCV_HAL_IMPL_VSX_BIN_FUNC2(_Tpvec, _Tpvec2, cast, func, intrin)  \
-inline _Tpvec2 func(const _Tpvec& a, const _Tpvec& b)                       \
-{ return _Tpvec2(cast(intrin(a.val, b.val))); }
-
-OPENCV_HAL_IMPL_VSX_BIN_FUNC2(v_int8x16, v_uint8x16, vec_uchar16_c, v_absdiff, vec_absd)
-OPENCV_HAL_IMPL_VSX_BIN_FUNC2(v_int16x8, v_uint16x8, vec_ushort8_c, v_absdiff, vec_absd)
-OPENCV_HAL_IMPL_VSX_BIN_FUNC2(v_int32x4, v_uint32x4, vec_uint4_c, v_absdiff, vec_absd)
-OPENCV_HAL_IMPL_VSX_BIN_FUNC2(v_int64x2, v_uint64x2, vec_udword2_c, v_absdiff, vec_absd)
-
-////////// Conversions /////////
-
-/** Rounding **/
-inline v_int32x4 v_round(const v_float32x4& a)
-{ return v_int32x4(vec_cts(vec_round(a.val))); }
-
-inline v_int32x4 v_round(const v_float64x2& a)
-{ return v_int32x4(vec_mergesqo(vec_ctso(vec_round(a.val)), vec_int4_z)); }
-
-inline v_int32x4 v_floor(const v_float32x4& a)
-{ return v_int32x4(vec_cts(vec_floor(a.val))); }
-
-inline v_int32x4 v_floor(const v_float64x2& a)
-{ return v_int32x4(vec_mergesqo(vec_ctso(vec_floor(a.val)), vec_int4_z)); }
-
-inline v_int32x4 v_ceil(const v_float32x4& a)
-{ return v_int32x4(vec_cts(vec_ceil(a.val))); }
-
-inline v_int32x4 v_ceil(const v_float64x2& a)
-{ return v_int32x4(vec_mergesqo(vec_ctso(vec_ceil(a.val)), vec_int4_z)); }
-
-inline v_int32x4 v_trunc(const v_float32x4& a)
-{ return v_int32x4(vec_cts(a.val)); }
-
-inline v_int32x4 v_trunc(const v_float64x2& a)
-{ return v_int32x4(vec_mergesqo(vec_ctso(a.val), vec_int4_z)); }
-
-/** To float **/
-inline v_float32x4 v_cvt_f32(const v_int32x4& a)
-{ return v_float32x4(vec_ctf(a.val)); }
-
-inline v_float32x4 v_cvt_f32(const v_float64x2& a)
-{ return v_float32x4(vec_mergesqo(vec_cvfo(a.val), vec_float4_z)); }
-
-inline v_float64x2 v_cvt_f64(const v_int32x4& a)
-{ return v_float64x2(vec_ctdo(vec_mergeh(a.val, a.val))); }
-
-inline v_float64x2 v_cvt_f64_high(const v_int32x4& a)
-{ return v_float64x2(vec_ctdo(vec_mergel(a.val, a.val))); }
-
-inline v_float64x2 v_cvt_f64(const v_float32x4& a)
-{ return v_float64x2(vec_cvfo(vec_mergeh(a.val, a.val))); }
-
-inline v_float64x2 v_cvt_f64_high(const v_float32x4& a)
-{ return v_float64x2(vec_cvfo(vec_mergel(a.val, a.val))); }
-
-/** Reinterpret **/
-/** its up there with load and store operations **/
-
-////////// Matrix operations /////////
-
-inline v_int32x4 v_dotprod(const v_int16x8& a, const v_int16x8& b)
-{ return v_int32x4(vec_msum(a.val, b.val, vec_int4_z)); }
-
-inline v_int32x4 v_dotprod(const v_int16x8& a, const v_int16x8& b, const v_int32x4& c)
-{ return v_int32x4(vec_msum(a.val, b.val, c.val)); }
-
-inline v_float32x4 v_matmul(const v_float32x4& v, const v_float32x4& m0,
-                            const v_float32x4& m1, const v_float32x4& m2,
-                            const v_float32x4& m3)
-{
-    const vec_float4 v0 = vec_splat(v.val, 0);
-    const vec_float4 v1 = vec_splat(v.val, 1);
-    const vec_float4 v2 = vec_splat(v.val, 2);
-    VSX_UNUSED(const vec_float4) v3 = vec_splat(v.val, 3);
-    return v_float32x4(vec_madd(v0, m0.val, vec_madd(v1, m1.val, vec_madd(v2, m2.val, vec_mul(v3, m3.val)))));
-}
-
-inline v_float32x4 v_matmuladd(const v_float32x4& v, const v_float32x4& m0,
-                               const v_float32x4& m1, const v_float32x4& m2,
-                               const v_float32x4& a)
-{
-    const vec_float4 v0 = vec_splat(v.val, 0);
-    const vec_float4 v1 = vec_splat(v.val, 1);
-    const vec_float4 v2 = vec_splat(v.val, 2);
-    return v_float32x4(vec_madd(v0, m0.val, vec_madd(v1, m1.val, vec_madd(v2, m2.val, a.val))));
-}
-
-#define OPENCV_HAL_IMPL_VSX_TRANSPOSE4x4(_Tpvec, _Tpvec2)                        \
-inline void v_transpose4x4(const _Tpvec& a0, const _Tpvec& a1,                   \
-                           const _Tpvec& a2, const _Tpvec& a3,                   \
-                           _Tpvec& b0, _Tpvec& b1, _Tpvec& b2, _Tpvec& b3)       \
-{                                                                                \
-    _Tpvec2 a02 = vec_mergeh(a0.val, a2.val);                                    \
-    _Tpvec2 a13 = vec_mergeh(a1.val, a3.val);                                    \
-    b0.val = vec_mergeh(a02, a13);                                               \
-    b1.val = vec_mergel(a02, a13);                                               \
-    a02 = vec_mergel(a0.val, a2.val);                                            \
-    a13 = vec_mergel(a1.val, a3.val);                                            \
-    b2.val  = vec_mergeh(a02, a13);                                              \
-    b3.val  = vec_mergel(a02, a13);                                              \
-}
-OPENCV_HAL_IMPL_VSX_TRANSPOSE4x4(v_uint32x4, vec_uint4)
-OPENCV_HAL_IMPL_VSX_TRANSPOSE4x4(v_int32x4, vec_int4)
-OPENCV_HAL_IMPL_VSX_TRANSPOSE4x4(v_float32x4, vec_float4)
-
-//! @name Check SIMD support
-//! @{
-//! @brief Check CPU capability of SIMD operation
-static inline bool hasSIMD128()
-{
-    return (CV_CPU_HAS_SUPPORT_VSX) ? true : false;
-}
-
-//! @}
-
-CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END
-
-//! @endcond
-
-}
-
-#endif // OPENCV_HAL_VSX_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/ippasync.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/ippasync.hpp
deleted file mode 100755
index 0ed8264..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/ippasync.hpp
+++ /dev/null
@@ -1,195 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2015, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
-// Copyright (C) 2015, Itseez Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_CORE_IPPASYNC_HPP
-#define OPENCV_CORE_IPPASYNC_HPP
-
-#ifdef HAVE_IPP_A
-
-#include "opencv2/core.hpp"
-#include <ipp_async_op.h>
-#include <ipp_async_accel.h>
-
-namespace cv
-{
-
-namespace hpp
-{
-
-/** @addtogroup core_ipp
-This section describes conversion between OpenCV and [Intel&reg; IPP Asynchronous
-C/C++](http://software.intel.com/en-us/intel-ipp-preview) library. [Getting Started
-Guide](http://registrationcenter.intel.com/irc_nas/3727/ipp_async_get_started.htm) help you to
-install the library, configure header and library build paths.
- */
-//! @{
-
-    //! convert OpenCV data type to hppDataType
-    inline int toHppType(const int cvType)
-    {
-        int depth = CV_MAT_DEPTH(cvType);
-        int hppType = depth == CV_8U ? HPP_DATA_TYPE_8U :
-                     depth == CV_16U ? HPP_DATA_TYPE_16U :
-                     depth == CV_16S ? HPP_DATA_TYPE_16S :
-                     depth == CV_32S ? HPP_DATA_TYPE_32S :
-                     depth == CV_32F ? HPP_DATA_TYPE_32F :
-                     depth == CV_64F ? HPP_DATA_TYPE_64F : -1;
-        CV_Assert( hppType >= 0 );
-        return hppType;
-    }
-
-    //! convert hppDataType to OpenCV data type
-    inline int toCvType(const int hppType)
-    {
-        int cvType = hppType == HPP_DATA_TYPE_8U ? CV_8U :
-                    hppType == HPP_DATA_TYPE_16U ? CV_16U :
-                    hppType == HPP_DATA_TYPE_16S ? CV_16S :
-                    hppType == HPP_DATA_TYPE_32S ? CV_32S :
-                    hppType == HPP_DATA_TYPE_32F ? CV_32F :
-                    hppType == HPP_DATA_TYPE_64F ? CV_64F : -1;
-        CV_Assert( cvType >= 0 );
-        return cvType;
-    }
-
-    /** @brief Convert hppiMatrix to Mat.
-
-    This function allocates and initializes new matrix (if needed) that has the same size and type as
-    input matrix. Supports CV_8U, CV_16U, CV_16S, CV_32S, CV_32F, CV_64F.
-    @param src input hppiMatrix.
-    @param dst output matrix.
-    @param accel accelerator instance (see hpp::getHpp for the list of acceleration framework types).
-    @param cn number of channels.
-     */
-    inline void copyHppToMat(hppiMatrix* src, Mat& dst, hppAccel accel, int cn)
-    {
-        hppDataType type;
-        hpp32u width, height;
-        hppStatus sts;
-
-        if (src == NULL)
-            return dst.release();
-
-        sts = hppiInquireMatrix(src, &type, &width, &height);
-
-        CV_Assert( sts == HPP_STATUS_NO_ERROR);
-
-        int matType = CV_MAKETYPE(toCvType(type), cn);
-
-        CV_Assert(width%cn == 0);
-
-        width /= cn;
-
-        dst.create((int)height, (int)width, (int)matType);
-
-        size_t newSize = (size_t)(height*(hpp32u)(dst.step));
-
-        sts = hppiGetMatrixData(accel,src,(hpp32u)(dst.step),dst.data,&newSize);
-
-        CV_Assert( sts == HPP_STATUS_NO_ERROR);
-    }
-
-    /** @brief Create Mat from hppiMatrix.
-
-    This function allocates and initializes the Mat that has the same size and type as input matrix.
-    Supports CV_8U, CV_16U, CV_16S, CV_32S, CV_32F, CV_64F.
-    @param src input hppiMatrix.
-    @param accel accelerator instance (see hpp::getHpp for the list of acceleration framework types).
-    @param cn number of channels.
-    @sa howToUseIPPAconversion, hpp::copyHppToMat, hpp::getHpp.
-     */
-    inline Mat getMat(hppiMatrix* src, hppAccel accel, int cn)
-    {
-        Mat dst;
-        copyHppToMat(src, dst, accel, cn);
-        return dst;
-    }
-
-    /** @brief Create hppiMatrix from Mat.
-
-    This function allocates and initializes the hppiMatrix that has the same size and type as input
-    matrix, returns the hppiMatrix*.
-
-    If you want to use zero-copy for GPU you should to have 4KB aligned matrix data. See details
-    [hppiCreateSharedMatrix](http://software.intel.com/ru-ru/node/501697).
-
-    Supports CV_8U, CV_16U, CV_16S, CV_32S, CV_32F, CV_64F.
-
-    @note The hppiMatrix pointer to the image buffer in system memory refers to the src.data. Control
-    the lifetime of the matrix and don't change its data, if there is no special need.
-    @param src input matrix.
-    @param accel accelerator instance. Supports type:
-    -   **HPP_ACCEL_TYPE_CPU** - accelerated by optimized CPU instructions.
-    -   **HPP_ACCEL_TYPE_GPU** - accelerated by GPU programmable units or fixed-function
-        accelerators.
-    -   **HPP_ACCEL_TYPE_ANY** - any acceleration or no acceleration available.
-    @sa howToUseIPPAconversion, hpp::getMat
-     */
-    inline hppiMatrix* getHpp(const Mat& src, hppAccel accel)
-    {
-        int htype = toHppType(src.type());
-        int cn = src.channels();
-
-        CV_Assert(src.data);
-        hppAccelType accelType = hppQueryAccelType(accel);
-
-        if (accelType!=HPP_ACCEL_TYPE_CPU)
-        {
-            hpp32u pitch, size;
-            hppQueryMatrixAllocParams(accel, src.cols*cn, src.rows, htype, &pitch, &size);
-            if (pitch!=0 && size!=0)
-                if ((int)(src.data)%4096==0 && pitch==(hpp32u)(src.step))
-                {
-                    return hppiCreateSharedMatrix(htype, src.cols*cn, src.rows, src.data, pitch, size);
-                }
-        }
-
-        return hppiCreateMatrix(htype, src.cols*cn, src.rows, src.data, (hpp32s)(src.step));;
-    }
-
-//! @}
-}}
-
-#endif
-
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/mat.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/mat.hpp
deleted file mode 100755
index 7be97c8..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/mat.hpp
+++ /dev/null
@@ -1,3669 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_CORE_MAT_HPP
-#define OPENCV_CORE_MAT_HPP
-
-#ifndef __cplusplus
-#  error mat.hpp header must be compiled as C++
-#endif
-
-#include "opencv2/core/matx.hpp"
-#include "opencv2/core/types.hpp"
-
-#include "opencv2/core/bufferpool.hpp"
-
-#ifdef CV_CXX11
-#include <type_traits>
-#endif
-
-namespace cv
-{
-
-//! @addtogroup core_basic
-//! @{
-
-enum { ACCESS_READ=1<<24, ACCESS_WRITE=1<<25,
-    ACCESS_RW=3<<24, ACCESS_MASK=ACCESS_RW, ACCESS_FAST=1<<26 };
-
-CV__DEBUG_NS_BEGIN
-
-class CV_EXPORTS _OutputArray;
-
-//////////////////////// Input/Output Array Arguments /////////////////////////////////
-
-/** @brief This is the proxy class for passing read-only input arrays into OpenCV functions.
-
-It is defined as:
-@code
-    typedef const _InputArray& InputArray;
-@endcode
-where _InputArray is a class that can be constructed from `Mat`, `Mat_<T>`, `Matx<T, m, n>`,
-`std::vector<T>`, `std::vector<std::vector<T> >`, `std::vector<Mat>`, `std::vector<Mat_<T> >`,
-`UMat`, `std::vector<UMat>` or `double`. It can also be constructed from a matrix expression.
-
-Since this is mostly implementation-level class, and its interface may change in future versions, we
-do not describe it in details. There are a few key things, though, that should be kept in mind:
-
--   When you see in the reference manual or in OpenCV source code a function that takes
-    InputArray, it means that you can actually pass `Mat`, `Matx`, `vector<T>` etc. (see above the
-    complete list).
--   Optional input arguments: If some of the input arrays may be empty, pass cv::noArray() (or
-    simply cv::Mat() as you probably did before).
--   The class is designed solely for passing parameters. That is, normally you *should not*
-    declare class members, local and global variables of this type.
--   If you want to design your own function or a class method that can operate of arrays of
-    multiple types, you can use InputArray (or OutputArray) for the respective parameters. Inside
-    a function you should use _InputArray::getMat() method to construct a matrix header for the
-    array (without copying data). _InputArray::kind() can be used to distinguish Mat from
-    `vector<>` etc., but normally it is not needed.
-
-Here is how you can use a function that takes InputArray :
-@code
-    std::vector<Point2f> vec;
-    // points or a circle
-    for( int i = 0; i < 30; i++ )
-        vec.push_back(Point2f((float)(100 + 30*cos(i*CV_PI*2/5)),
-                              (float)(100 - 30*sin(i*CV_PI*2/5))));
-    cv::transform(vec, vec, cv::Matx23f(0.707, -0.707, 10, 0.707, 0.707, 20));
-@endcode
-That is, we form an STL vector containing points, and apply in-place affine transformation to the
-vector using the 2x3 matrix created inline as `Matx<float, 2, 3>` instance.
-
-Here is how such a function can be implemented (for simplicity, we implement a very specific case of
-it, according to the assertion statement inside) :
-@code
-    void myAffineTransform(InputArray _src, OutputArray _dst, InputArray _m)
-    {
-        // get Mat headers for input arrays. This is O(1) operation,
-        // unless _src and/or _m are matrix expressions.
-        Mat src = _src.getMat(), m = _m.getMat();
-        CV_Assert( src.type() == CV_32FC2 && m.type() == CV_32F && m.size() == Size(3, 2) );
-
-        // [re]create the output array so that it has the proper size and type.
-        // In case of Mat it calls Mat::create, in case of STL vector it calls vector::resize.
-        _dst.create(src.size(), src.type());
-        Mat dst = _dst.getMat();
-
-        for( int i = 0; i < src.rows; i++ )
-            for( int j = 0; j < src.cols; j++ )
-            {
-                Point2f pt = src.at<Point2f>(i, j);
-                dst.at<Point2f>(i, j) = Point2f(m.at<float>(0, 0)*pt.x +
-                                                m.at<float>(0, 1)*pt.y +
-                                                m.at<float>(0, 2),
-                                                m.at<float>(1, 0)*pt.x +
-                                                m.at<float>(1, 1)*pt.y +
-                                                m.at<float>(1, 2));
-            }
-    }
-@endcode
-There is another related type, InputArrayOfArrays, which is currently defined as a synonym for
-InputArray:
-@code
-    typedef InputArray InputArrayOfArrays;
-@endcode
-It denotes function arguments that are either vectors of vectors or vectors of matrices. A separate
-synonym is needed to generate Python/Java etc. wrappers properly. At the function implementation
-level their use is similar, but _InputArray::getMat(idx) should be used to get header for the
-idx-th component of the outer vector and _InputArray::size().area() should be used to find the
-number of components (vectors/matrices) of the outer vector.
- */
-class CV_EXPORTS _InputArray
-{
-public:
-    enum {
-        KIND_SHIFT = 16,
-        FIXED_TYPE = 0x8000 << KIND_SHIFT,
-        FIXED_SIZE = 0x4000 << KIND_SHIFT,
-        KIND_MASK = 31 << KIND_SHIFT,
-
-        NONE              = 0 << KIND_SHIFT,
-        MAT               = 1 << KIND_SHIFT,
-        MATX              = 2 << KIND_SHIFT,
-        STD_VECTOR        = 3 << KIND_SHIFT,
-        STD_VECTOR_VECTOR = 4 << KIND_SHIFT,
-        STD_VECTOR_MAT    = 5 << KIND_SHIFT,
-        EXPR              = 6 << KIND_SHIFT,
-        OPENGL_BUFFER     = 7 << KIND_SHIFT,
-        CUDA_HOST_MEM     = 8 << KIND_SHIFT,
-        CUDA_GPU_MAT      = 9 << KIND_SHIFT,
-        UMAT              =10 << KIND_SHIFT,
-        STD_VECTOR_UMAT   =11 << KIND_SHIFT,
-        STD_BOOL_VECTOR   =12 << KIND_SHIFT,
-        STD_VECTOR_CUDA_GPU_MAT = 13 << KIND_SHIFT,
-        STD_ARRAY         =14 << KIND_SHIFT,
-        STD_ARRAY_MAT     =15 << KIND_SHIFT
-    };
-
-    _InputArray();
-    _InputArray(int _flags, void* _obj);
-    _InputArray(const Mat& m);
-    _InputArray(const MatExpr& expr);
-    _InputArray(const std::vector<Mat>& vec);
-    template<typename _Tp> _InputArray(const Mat_<_Tp>& m);
-    template<typename _Tp> _InputArray(const std::vector<_Tp>& vec);
-    _InputArray(const std::vector<bool>& vec);
-    template<typename _Tp> _InputArray(const std::vector<std::vector<_Tp> >& vec);
-    _InputArray(const std::vector<std::vector<bool> >&);
-    template<typename _Tp> _InputArray(const std::vector<Mat_<_Tp> >& vec);
-    template<typename _Tp> _InputArray(const _Tp* vec, int n);
-    template<typename _Tp, int m, int n> _InputArray(const Matx<_Tp, m, n>& matx);
-    _InputArray(const double& val);
-    _InputArray(const cuda::GpuMat& d_mat);
-    _InputArray(const std::vector<cuda::GpuMat>& d_mat_array);
-    _InputArray(const ogl::Buffer& buf);
-    _InputArray(const cuda::HostMem& cuda_mem);
-    template<typename _Tp> _InputArray(const cudev::GpuMat_<_Tp>& m);
-    _InputArray(const UMat& um);
-    _InputArray(const std::vector<UMat>& umv);
-
-#ifdef CV_CXX_STD_ARRAY
-    template<typename _Tp, std::size_t _Nm> _InputArray(const std::array<_Tp, _Nm>& arr);
-    template<std::size_t _Nm> _InputArray(const std::array<Mat, _Nm>& arr);
-#endif
-
-    Mat getMat(int idx=-1) const;
-    Mat getMat_(int idx=-1) const;
-    UMat getUMat(int idx=-1) const;
-    void getMatVector(std::vector<Mat>& mv) const;
-    void getUMatVector(std::vector<UMat>& umv) const;
-    void getGpuMatVector(std::vector<cuda::GpuMat>& gpumv) const;
-    cuda::GpuMat getGpuMat() const;
-    ogl::Buffer getOGlBuffer() const;
-
-    int getFlags() const;
-    void* getObj() const;
-    Size getSz() const;
-
-    int kind() const;
-    int dims(int i=-1) const;
-    int cols(int i=-1) const;
-    int rows(int i=-1) const;
-    Size size(int i=-1) const;
-    int sizend(int* sz, int i=-1) const;
-    bool sameSize(const _InputArray& arr) const;
-    size_t total(int i=-1) const;
-    int type(int i=-1) const;
-    int depth(int i=-1) const;
-    int channels(int i=-1) const;
-    bool isContinuous(int i=-1) const;
-    bool isSubmatrix(int i=-1) const;
-    bool empty() const;
-    void copyTo(const _OutputArray& arr) const;
-    void copyTo(const _OutputArray& arr, const _InputArray & mask) const;
-    size_t offset(int i=-1) const;
-    size_t step(int i=-1) const;
-    bool isMat() const;
-    bool isUMat() const;
-    bool isMatVector() const;
-    bool isUMatVector() const;
-    bool isMatx() const;
-    bool isVector() const;
-    bool isGpuMat() const;
-    bool isGpuMatVector() const;
-    ~_InputArray();
-
-protected:
-    int flags;
-    void* obj;
-    Size sz;
-
-    void init(int _flags, const void* _obj);
-    void init(int _flags, const void* _obj, Size _sz);
-};
-
-
-/** @brief This type is very similar to InputArray except that it is used for input/output and output function
-parameters.
-
-Just like with InputArray, OpenCV users should not care about OutputArray, they just pass `Mat`,
-`vector<T>` etc. to the functions. The same limitation as for `InputArray`: *Do not explicitly
-create OutputArray instances* applies here too.
-
-If you want to make your function polymorphic (i.e. accept different arrays as output parameters),
-it is also not very difficult. Take the sample above as the reference. Note that
-_OutputArray::create() needs to be called before _OutputArray::getMat(). This way you guarantee
-that the output array is properly allocated.
-
-Optional output parameters. If you do not need certain output array to be computed and returned to
-you, pass cv::noArray(), just like you would in the case of optional input array. At the
-implementation level, use _OutputArray::needed() to check if certain output array needs to be
-computed or not.
-
-There are several synonyms for OutputArray that are used to assist automatic Python/Java/... wrapper
-generators:
-@code
-    typedef OutputArray OutputArrayOfArrays;
-    typedef OutputArray InputOutputArray;
-    typedef OutputArray InputOutputArrayOfArrays;
-@endcode
- */
-class CV_EXPORTS _OutputArray : public _InputArray
-{
-public:
-    enum
-    {
-        DEPTH_MASK_8U = 1 << CV_8U,
-        DEPTH_MASK_8S = 1 << CV_8S,
-        DEPTH_MASK_16U = 1 << CV_16U,
-        DEPTH_MASK_16S = 1 << CV_16S,
-        DEPTH_MASK_32S = 1 << CV_32S,
-        DEPTH_MASK_32F = 1 << CV_32F,
-        DEPTH_MASK_64F = 1 << CV_64F,
-        DEPTH_MASK_ALL = (DEPTH_MASK_64F<<1)-1,
-        DEPTH_MASK_ALL_BUT_8S = DEPTH_MASK_ALL & ~DEPTH_MASK_8S,
-        DEPTH_MASK_FLT = DEPTH_MASK_32F + DEPTH_MASK_64F
-    };
-
-    _OutputArray();
-    _OutputArray(int _flags, void* _obj);
-    _OutputArray(Mat& m);
-    _OutputArray(std::vector<Mat>& vec);
-    _OutputArray(cuda::GpuMat& d_mat);
-    _OutputArray(std::vector<cuda::GpuMat>& d_mat);
-    _OutputArray(ogl::Buffer& buf);
-    _OutputArray(cuda::HostMem& cuda_mem);
-    template<typename _Tp> _OutputArray(cudev::GpuMat_<_Tp>& m);
-    template<typename _Tp> _OutputArray(std::vector<_Tp>& vec);
-    _OutputArray(std::vector<bool>& vec);
-    template<typename _Tp> _OutputArray(std::vector<std::vector<_Tp> >& vec);
-    _OutputArray(std::vector<std::vector<bool> >&);
-    template<typename _Tp> _OutputArray(std::vector<Mat_<_Tp> >& vec);
-    template<typename _Tp> _OutputArray(Mat_<_Tp>& m);
-    template<typename _Tp> _OutputArray(_Tp* vec, int n);
-    template<typename _Tp, int m, int n> _OutputArray(Matx<_Tp, m, n>& matx);
-    _OutputArray(UMat& m);
-    _OutputArray(std::vector<UMat>& vec);
-
-    _OutputArray(const Mat& m);
-    _OutputArray(const std::vector<Mat>& vec);
-    _OutputArray(const cuda::GpuMat& d_mat);
-    _OutputArray(const std::vector<cuda::GpuMat>& d_mat);
-    _OutputArray(const ogl::Buffer& buf);
-    _OutputArray(const cuda::HostMem& cuda_mem);
-    template<typename _Tp> _OutputArray(const cudev::GpuMat_<_Tp>& m);
-    template<typename _Tp> _OutputArray(const std::vector<_Tp>& vec);
-    template<typename _Tp> _OutputArray(const std::vector<std::vector<_Tp> >& vec);
-    template<typename _Tp> _OutputArray(const std::vector<Mat_<_Tp> >& vec);
-    template<typename _Tp> _OutputArray(const Mat_<_Tp>& m);
-    template<typename _Tp> _OutputArray(const _Tp* vec, int n);
-    template<typename _Tp, int m, int n> _OutputArray(const Matx<_Tp, m, n>& matx);
-    _OutputArray(const UMat& m);
-    _OutputArray(const std::vector<UMat>& vec);
-
-#ifdef CV_CXX_STD_ARRAY
-    template<typename _Tp, std::size_t _Nm> _OutputArray(std::array<_Tp, _Nm>& arr);
-    template<typename _Tp, std::size_t _Nm> _OutputArray(const std::array<_Tp, _Nm>& arr);
-    template<std::size_t _Nm> _OutputArray(std::array<Mat, _Nm>& arr);
-    template<std::size_t _Nm> _OutputArray(const std::array<Mat, _Nm>& arr);
-#endif
-
-    bool fixedSize() const;
-    bool fixedType() const;
-    bool needed() const;
-    Mat& getMatRef(int i=-1) const;
-    UMat& getUMatRef(int i=-1) const;
-    cuda::GpuMat& getGpuMatRef() const;
-    std::vector<cuda::GpuMat>& getGpuMatVecRef() const;
-    ogl::Buffer& getOGlBufferRef() const;
-    cuda::HostMem& getHostMemRef() const;
-    void create(Size sz, int type, int i=-1, bool allowTransposed=false, int fixedDepthMask=0) const;
-    void create(int rows, int cols, int type, int i=-1, bool allowTransposed=false, int fixedDepthMask=0) const;
-    void create(int dims, const int* size, int type, int i=-1, bool allowTransposed=false, int fixedDepthMask=0) const;
-    void createSameSize(const _InputArray& arr, int mtype) const;
-    void release() const;
-    void clear() const;
-    void setTo(const _InputArray& value, const _InputArray & mask = _InputArray()) const;
-
-    void assign(const UMat& u) const;
-    void assign(const Mat& m) const;
-
-    void assign(const std::vector<UMat>& v) const;
-    void assign(const std::vector<Mat>& v) const;
-};
-
-
-class CV_EXPORTS _InputOutputArray : public _OutputArray
-{
-public:
-    _InputOutputArray();
-    _InputOutputArray(int _flags, void* _obj);
-    _InputOutputArray(Mat& m);
-    _InputOutputArray(std::vector<Mat>& vec);
-    _InputOutputArray(cuda::GpuMat& d_mat);
-    _InputOutputArray(ogl::Buffer& buf);
-    _InputOutputArray(cuda::HostMem& cuda_mem);
-    template<typename _Tp> _InputOutputArray(cudev::GpuMat_<_Tp>& m);
-    template<typename _Tp> _InputOutputArray(std::vector<_Tp>& vec);
-    _InputOutputArray(std::vector<bool>& vec);
-    template<typename _Tp> _InputOutputArray(std::vector<std::vector<_Tp> >& vec);
-    template<typename _Tp> _InputOutputArray(std::vector<Mat_<_Tp> >& vec);
-    template<typename _Tp> _InputOutputArray(Mat_<_Tp>& m);
-    template<typename _Tp> _InputOutputArray(_Tp* vec, int n);
-    template<typename _Tp, int m, int n> _InputOutputArray(Matx<_Tp, m, n>& matx);
-    _InputOutputArray(UMat& m);
-    _InputOutputArray(std::vector<UMat>& vec);
-
-    _InputOutputArray(const Mat& m);
-    _InputOutputArray(const std::vector<Mat>& vec);
-    _InputOutputArray(const cuda::GpuMat& d_mat);
-    _InputOutputArray(const std::vector<cuda::GpuMat>& d_mat);
-    _InputOutputArray(const ogl::Buffer& buf);
-    _InputOutputArray(const cuda::HostMem& cuda_mem);
-    template<typename _Tp> _InputOutputArray(const cudev::GpuMat_<_Tp>& m);
-    template<typename _Tp> _InputOutputArray(const std::vector<_Tp>& vec);
-    template<typename _Tp> _InputOutputArray(const std::vector<std::vector<_Tp> >& vec);
-    template<typename _Tp> _InputOutputArray(const std::vector<Mat_<_Tp> >& vec);
-    template<typename _Tp> _InputOutputArray(const Mat_<_Tp>& m);
-    template<typename _Tp> _InputOutputArray(const _Tp* vec, int n);
-    template<typename _Tp, int m, int n> _InputOutputArray(const Matx<_Tp, m, n>& matx);
-    _InputOutputArray(const UMat& m);
-    _InputOutputArray(const std::vector<UMat>& vec);
-
-#ifdef CV_CXX_STD_ARRAY
-    template<typename _Tp, std::size_t _Nm> _InputOutputArray(std::array<_Tp, _Nm>& arr);
-    template<typename _Tp, std::size_t _Nm> _InputOutputArray(const std::array<_Tp, _Nm>& arr);
-    template<std::size_t _Nm> _InputOutputArray(std::array<Mat, _Nm>& arr);
-    template<std::size_t _Nm> _InputOutputArray(const std::array<Mat, _Nm>& arr);
-#endif
-
-};
-
-CV__DEBUG_NS_END
-
-typedef const _InputArray& InputArray;
-typedef InputArray InputArrayOfArrays;
-typedef const _OutputArray& OutputArray;
-typedef OutputArray OutputArrayOfArrays;
-typedef const _InputOutputArray& InputOutputArray;
-typedef InputOutputArray InputOutputArrayOfArrays;
-
-CV_EXPORTS InputOutputArray noArray();
-
-/////////////////////////////////// MatAllocator //////////////////////////////////////
-
-//! Usage flags for allocator
-enum UMatUsageFlags
-{
-    USAGE_DEFAULT = 0,
-
-    // buffer allocation policy is platform and usage specific
-    USAGE_ALLOCATE_HOST_MEMORY = 1 << 0,
-    USAGE_ALLOCATE_DEVICE_MEMORY = 1 << 1,
-    USAGE_ALLOCATE_SHARED_MEMORY = 1 << 2, // It is not equal to: USAGE_ALLOCATE_HOST_MEMORY | USAGE_ALLOCATE_DEVICE_MEMORY
-
-    __UMAT_USAGE_FLAGS_32BIT = 0x7fffffff // Binary compatibility hint
-};
-
-struct CV_EXPORTS UMatData;
-
-/** @brief  Custom array allocator
-*/
-class CV_EXPORTS MatAllocator
-{
-public:
-    MatAllocator() {}
-    virtual ~MatAllocator() {}
-
-    // let's comment it off for now to detect and fix all the uses of allocator
-    //virtual void allocate(int dims, const int* sizes, int type, int*& refcount,
-    //                      uchar*& datastart, uchar*& data, size_t* step) = 0;
-    //virtual void deallocate(int* refcount, uchar* datastart, uchar* data) = 0;
-    virtual UMatData* allocate(int dims, const int* sizes, int type,
-                               void* data, size_t* step, int flags, UMatUsageFlags usageFlags) const = 0;
-    virtual bool allocate(UMatData* data, int accessflags, UMatUsageFlags usageFlags) const = 0;
-    virtual void deallocate(UMatData* data) const = 0;
-    virtual void map(UMatData* data, int accessflags) const;
-    virtual void unmap(UMatData* data) const;
-    virtual void download(UMatData* data, void* dst, int dims, const size_t sz[],
-                          const size_t srcofs[], const size_t srcstep[],
-                          const size_t dststep[]) const;
-    virtual void upload(UMatData* data, const void* src, int dims, const size_t sz[],
-                        const size_t dstofs[], const size_t dststep[],
-                        const size_t srcstep[]) const;
-    virtual void copy(UMatData* srcdata, UMatData* dstdata, int dims, const size_t sz[],
-                      const size_t srcofs[], const size_t srcstep[],
-                      const size_t dstofs[], const size_t dststep[], bool sync) const;
-
-    // default implementation returns DummyBufferPoolController
-    virtual BufferPoolController* getBufferPoolController(const char* id = NULL) const;
-};
-
-
-//////////////////////////////// MatCommaInitializer //////////////////////////////////
-
-/** @brief  Comma-separated Matrix Initializer
-
- The class instances are usually not created explicitly.
- Instead, they are created on "matrix << firstValue" operator.
-
- The sample below initializes 2x2 rotation matrix:
-
- \code
- double angle = 30, a = cos(angle*CV_PI/180), b = sin(angle*CV_PI/180);
- Mat R = (Mat_<double>(2,2) << a, -b, b, a);
- \endcode
-*/
-template<typename _Tp> class MatCommaInitializer_
-{
-public:
-    //! the constructor, created by "matrix << firstValue" operator, where matrix is cv::Mat
-    MatCommaInitializer_(Mat_<_Tp>* _m);
-    //! the operator that takes the next value and put it to the matrix
-    template<typename T2> MatCommaInitializer_<_Tp>& operator , (T2 v);
-    //! another form of conversion operator
-    operator Mat_<_Tp>() const;
-protected:
-    MatIterator_<_Tp> it;
-};
-
-
-/////////////////////////////////////// Mat ///////////////////////////////////////////
-
-// note that umatdata might be allocated together
-// with the matrix data, not as a separate object.
-// therefore, it does not have constructor or destructor;
-// it should be explicitly initialized using init().
-struct CV_EXPORTS UMatData
-{
-    enum { COPY_ON_MAP=1, HOST_COPY_OBSOLETE=2,
-        DEVICE_COPY_OBSOLETE=4, TEMP_UMAT=8, TEMP_COPIED_UMAT=24,
-        USER_ALLOCATED=32, DEVICE_MEM_MAPPED=64,
-        ASYNC_CLEANUP=128
-    };
-    UMatData(const MatAllocator* allocator);
-    ~UMatData();
-
-    // provide atomic access to the structure
-    void lock();
-    void unlock();
-
-    bool hostCopyObsolete() const;
-    bool deviceCopyObsolete() const;
-    bool deviceMemMapped() const;
-    bool copyOnMap() const;
-    bool tempUMat() const;
-    bool tempCopiedUMat() const;
-    void markHostCopyObsolete(bool flag);
-    void markDeviceCopyObsolete(bool flag);
-    void markDeviceMemMapped(bool flag);
-
-    const MatAllocator* prevAllocator;
-    const MatAllocator* currAllocator;
-    int urefcount;
-    int refcount;
-    uchar* data;
-    uchar* origdata;
-    size_t size;
-
-    int flags;
-    void* handle;
-    void* userdata;
-    int allocatorFlags_;
-    int mapcount;
-    UMatData* originalUMatData;
-};
-
-
-struct CV_EXPORTS MatSize
-{
-    explicit MatSize(int* _p);
-    int dims() const;
-    Size operator()() const;
-    const int& operator[](int i) const;
-    int& operator[](int i);
-    operator const int*() const;  // TODO OpenCV 4.0: drop this
-    bool operator == (const MatSize& sz) const;
-    bool operator != (const MatSize& sz) const;
-
-    int* p;
-};
-
-struct CV_EXPORTS MatStep
-{
-    MatStep();
-    explicit MatStep(size_t s);
-    const size_t& operator[](int i) const;
-    size_t& operator[](int i);
-    operator size_t() const;
-    MatStep& operator = (size_t s);
-
-    size_t* p;
-    size_t buf[2];
-protected:
-    MatStep& operator = (const MatStep&);
-};
-
-/** @example cout_mat.cpp
-An example demonstrating the serial out capabilities of cv::Mat
-*/
-
- /** @brief n-dimensional dense array class \anchor CVMat_Details
-
-The class Mat represents an n-dimensional dense numerical single-channel or multi-channel array. It
-can be used to store real or complex-valued vectors and matrices, grayscale or color images, voxel
-volumes, vector fields, point clouds, tensors, histograms (though, very high-dimensional histograms
-may be better stored in a SparseMat ). The data layout of the array `M` is defined by the array
-`M.step[]`, so that the address of element \f$(i_0,...,i_{M.dims-1})\f$, where \f$0\leq i_k<M.size[k]\f$, is
-computed as:
-\f[addr(M_{i_0,...,i_{M.dims-1}}) = M.data + M.step[0]*i_0 + M.step[1]*i_1 + ... + M.step[M.dims-1]*i_{M.dims-1}\f]
-In case of a 2-dimensional array, the above formula is reduced to:
-\f[addr(M_{i,j}) = M.data + M.step[0]*i + M.step[1]*j\f]
-Note that `M.step[i] >= M.step[i+1]` (in fact, `M.step[i] >= M.step[i+1]*M.size[i+1]` ). This means
-that 2-dimensional matrices are stored row-by-row, 3-dimensional matrices are stored plane-by-plane,
-and so on. M.step[M.dims-1] is minimal and always equal to the element size M.elemSize() .
-
-So, the data layout in Mat is fully compatible with CvMat, IplImage, and CvMatND types from OpenCV
-1.x. It is also compatible with the majority of dense array types from the standard toolkits and
-SDKs, such as Numpy (ndarray), Win32 (independent device bitmaps), and others, that is, with any
-array that uses *steps* (or *strides*) to compute the position of a pixel. Due to this
-compatibility, it is possible to make a Mat header for user-allocated data and process it in-place
-using OpenCV functions.
-
-There are many different ways to create a Mat object. The most popular options are listed below:
-
-- Use the create(nrows, ncols, type) method or the similar Mat(nrows, ncols, type[, fillValue])
-constructor. A new array of the specified size and type is allocated. type has the same meaning as
-in the cvCreateMat method. For example, CV_8UC1 means a 8-bit single-channel array, CV_32FC2
-means a 2-channel (complex) floating-point array, and so on.
-@code
-    // make a 7x7 complex matrix filled with 1+3j.
-    Mat M(7,7,CV_32FC2,Scalar(1,3));
-    // and now turn M to a 100x60 15-channel 8-bit matrix.
-    // The old content will be deallocated
-    M.create(100,60,CV_8UC(15));
-@endcode
-As noted in the introduction to this chapter, create() allocates only a new array when the shape
-or type of the current array are different from the specified ones.
-
-- Create a multi-dimensional array:
-@code
-    // create a 100x100x100 8-bit array
-    int sz[] = {100, 100, 100};
-    Mat bigCube(3, sz, CV_8U, Scalar::all(0));
-@endcode
-It passes the number of dimensions =1 to the Mat constructor but the created array will be
-2-dimensional with the number of columns set to 1. So, Mat::dims is always \>= 2 (can also be 0
-when the array is empty).
-
-- Use a copy constructor or assignment operator where there can be an array or expression on the
-right side (see below). As noted in the introduction, the array assignment is an O(1) operation
-because it only copies the header and increases the reference counter. The Mat::clone() method can
-be used to get a full (deep) copy of the array when you need it.
-
-- Construct a header for a part of another array. It can be a single row, single column, several
-rows, several columns, rectangular region in the array (called a *minor* in algebra) or a
-diagonal. Such operations are also O(1) because the new header references the same data. You can
-actually modify a part of the array using this feature, for example:
-@code
-    // add the 5-th row, multiplied by 3 to the 3rd row
-    M.row(3) = M.row(3) + M.row(5)*3;
-    // now copy the 7-th column to the 1-st column
-    // M.col(1) = M.col(7); // this will not work
-    Mat M1 = M.col(1);
-    M.col(7).copyTo(M1);
-    // create a new 320x240 image
-    Mat img(Size(320,240),CV_8UC3);
-    // select a ROI
-    Mat roi(img, Rect(10,10,100,100));
-    // fill the ROI with (0,255,0) (which is green in RGB space);
-    // the original 320x240 image will be modified
-    roi = Scalar(0,255,0);
-@endcode
-Due to the additional datastart and dataend members, it is possible to compute a relative
-sub-array position in the main *container* array using locateROI():
-@code
-    Mat A = Mat::eye(10, 10, CV_32S);
-    // extracts A columns, 1 (inclusive) to 3 (exclusive).
-    Mat B = A(Range::all(), Range(1, 3));
-    // extracts B rows, 5 (inclusive) to 9 (exclusive).
-    // that is, C \~ A(Range(5, 9), Range(1, 3))
-    Mat C = B(Range(5, 9), Range::all());
-    Size size; Point ofs;
-    C.locateROI(size, ofs);
-    // size will be (width=10,height=10) and the ofs will be (x=1, y=5)
-@endcode
-As in case of whole matrices, if you need a deep copy, use the `clone()` method of the extracted
-sub-matrices.
-
-- Make a header for user-allocated data. It can be useful to do the following:
-    -# Process "foreign" data using OpenCV (for example, when you implement a DirectShow\* filter or
-    a processing module for gstreamer, and so on). For example:
-    @code
-        void process_video_frame(const unsigned char* pixels,
-                                 int width, int height, int step)
-        {
-            Mat img(height, width, CV_8UC3, pixels, step);
-            GaussianBlur(img, img, Size(7,7), 1.5, 1.5);
-        }
-    @endcode
-    -# Quickly initialize small matrices and/or get a super-fast element access.
-    @code
-        double m[3][3] = {{a, b, c}, {d, e, f}, {g, h, i}};
-        Mat M = Mat(3, 3, CV_64F, m).inv();
-    @endcode
-    .
-    Partial yet very common cases of this *user-allocated data* case are conversions from CvMat and
-    IplImage to Mat. For this purpose, there is function cv::cvarrToMat taking pointers to CvMat or
-    IplImage and the optional flag indicating whether to copy the data or not.
-    @snippet samples/cpp/image.cpp iplimage
-
-- Use MATLAB-style array initializers, zeros(), ones(), eye(), for example:
-@code
-    // create a double-precision identity matrix and add it to M.
-    M += Mat::eye(M.rows, M.cols, CV_64F);
-@endcode
-
-- Use a comma-separated initializer:
-@code
-    // create a 3x3 double-precision identity matrix
-    Mat M = (Mat_<double>(3,3) << 1, 0, 0, 0, 1, 0, 0, 0, 1);
-@endcode
-With this approach, you first call a constructor of the Mat class with the proper parameters, and
-then you just put `<< operator` followed by comma-separated values that can be constants,
-variables, expressions, and so on. Also, note the extra parentheses required to avoid compilation
-errors.
-
-Once the array is created, it is automatically managed via a reference-counting mechanism. If the
-array header is built on top of user-allocated data, you should handle the data by yourself. The
-array data is deallocated when no one points to it. If you want to release the data pointed by a
-array header before the array destructor is called, use Mat::release().
-
-The next important thing to learn about the array class is element access. This manual already
-described how to compute an address of each array element. Normally, you are not required to use the
-formula directly in the code. If you know the array element type (which can be retrieved using the
-method Mat::type() ), you can access the element \f$M_{ij}\f$ of a 2-dimensional array as:
-@code
-    M.at<double>(i,j) += 1.f;
-@endcode
-assuming that `M` is a double-precision floating-point array. There are several variants of the method
-at for a different number of dimensions.
-
-If you need to process a whole row of a 2D array, the most efficient way is to get the pointer to
-the row first, and then just use the plain C operator [] :
-@code
-    // compute sum of positive matrix elements
-    // (assuming that M is a double-precision matrix)
-    double sum=0;
-    for(int i = 0; i < M.rows; i++)
-    {
-        const double* Mi = M.ptr<double>(i);
-        for(int j = 0; j < M.cols; j++)
-            sum += std::max(Mi[j], 0.);
-    }
-@endcode
-Some operations, like the one above, do not actually depend on the array shape. They just process
-elements of an array one by one (or elements from multiple arrays that have the same coordinates,
-for example, array addition). Such operations are called *element-wise*. It makes sense to check
-whether all the input/output arrays are continuous, namely, have no gaps at the end of each row. If
-yes, process them as a long single row:
-@code
-    // compute the sum of positive matrix elements, optimized variant
-    double sum=0;
-    int cols = M.cols, rows = M.rows;
-    if(M.isContinuous())
-    {
-        cols *= rows;
-        rows = 1;
-    }
-    for(int i = 0; i < rows; i++)
-    {
-        const double* Mi = M.ptr<double>(i);
-        for(int j = 0; j < cols; j++)
-            sum += std::max(Mi[j], 0.);
-    }
-@endcode
-In case of the continuous matrix, the outer loop body is executed just once. So, the overhead is
-smaller, which is especially noticeable in case of small matrices.
-
-Finally, there are STL-style iterators that are smart enough to skip gaps between successive rows:
-@code
-    // compute sum of positive matrix elements, iterator-based variant
-    double sum=0;
-    MatConstIterator_<double> it = M.begin<double>(), it_end = M.end<double>();
-    for(; it != it_end; ++it)
-        sum += std::max(*it, 0.);
-@endcode
-The matrix iterators are random-access iterators, so they can be passed to any STL algorithm,
-including std::sort().
-
-@note Matrix Expressions and arithmetic see MatExpr
-*/
-class CV_EXPORTS Mat
-{
-public:
-    /**
-    These are various constructors that form a matrix. As noted in the AutomaticAllocation, often
-    the default constructor is enough, and the proper matrix will be allocated by an OpenCV function.
-    The constructed matrix can further be assigned to another matrix or matrix expression or can be
-    allocated with Mat::create . In the former case, the old content is de-referenced.
-     */
-    Mat();
-
-    /** @overload
-    @param rows Number of rows in a 2D array.
-    @param cols Number of columns in a 2D array.
-    @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or
-    CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices.
-    */
-    Mat(int rows, int cols, int type);
-
-    /** @overload
-    @param size 2D array size: Size(cols, rows) . In the Size() constructor, the number of rows and the
-    number of columns go in the reverse order.
-    @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or
-    CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices.
-      */
-    Mat(Size size, int type);
-
-    /** @overload
-    @param rows Number of rows in a 2D array.
-    @param cols Number of columns in a 2D array.
-    @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or
-    CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices.
-    @param s An optional value to initialize each matrix element with. To set all the matrix elements to
-    the particular value after the construction, use the assignment operator
-    Mat::operator=(const Scalar& value) .
-    */
-    Mat(int rows, int cols, int type, const Scalar& s);
-
-    /** @overload
-    @param size 2D array size: Size(cols, rows) . In the Size() constructor, the number of rows and the
-    number of columns go in the reverse order.
-    @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or
-    CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices.
-    @param s An optional value to initialize each matrix element with. To set all the matrix elements to
-    the particular value after the construction, use the assignment operator
-    Mat::operator=(const Scalar& value) .
-      */
-    Mat(Size size, int type, const Scalar& s);
-
-    /** @overload
-    @param ndims Array dimensionality.
-    @param sizes Array of integers specifying an n-dimensional array shape.
-    @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or
-    CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices.
-    */
-    Mat(int ndims, const int* sizes, int type);
-
-    /** @overload
-    @param sizes Array of integers specifying an n-dimensional array shape.
-    @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or
-    CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices.
-    */
-    Mat(const std::vector<int>& sizes, int type);
-
-    /** @overload
-    @param ndims Array dimensionality.
-    @param sizes Array of integers specifying an n-dimensional array shape.
-    @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or
-    CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices.
-    @param s An optional value to initialize each matrix element with. To set all the matrix elements to
-    the particular value after the construction, use the assignment operator
-    Mat::operator=(const Scalar& value) .
-    */
-    Mat(int ndims, const int* sizes, int type, const Scalar& s);
-
-    /** @overload
-    @param sizes Array of integers specifying an n-dimensional array shape.
-    @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or
-    CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices.
-    @param s An optional value to initialize each matrix element with. To set all the matrix elements to
-    the particular value after the construction, use the assignment operator
-    Mat::operator=(const Scalar& value) .
-    */
-    Mat(const std::vector<int>& sizes, int type, const Scalar& s);
-
-
-    /** @overload
-    @param m Array that (as a whole or partly) is assigned to the constructed matrix. No data is copied
-    by these constructors. Instead, the header pointing to m data or its sub-array is constructed and
-    associated with it. The reference counter, if any, is incremented. So, when you modify the matrix
-    formed using such a constructor, you also modify the corresponding elements of m . If you want to
-    have an independent copy of the sub-array, use Mat::clone() .
-    */
-    Mat(const Mat& m);
-
-    /** @overload
-    @param rows Number of rows in a 2D array.
-    @param cols Number of columns in a 2D array.
-    @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or
-    CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices.
-    @param data Pointer to the user data. Matrix constructors that take data and step parameters do not
-    allocate matrix data. Instead, they just initialize the matrix header that points to the specified
-    data, which means that no data is copied. This operation is very efficient and can be used to
-    process external data using OpenCV functions. The external data is not automatically deallocated, so
-    you should take care of it.
-    @param step Number of bytes each matrix row occupies. The value should include the padding bytes at
-    the end of each row, if any. If the parameter is missing (set to AUTO_STEP ), no padding is assumed
-    and the actual step is calculated as cols*elemSize(). See Mat::elemSize.
-    */
-    Mat(int rows, int cols, int type, void* data, size_t step=AUTO_STEP);
-
-    /** @overload
-    @param size 2D array size: Size(cols, rows) . In the Size() constructor, the number of rows and the
-    number of columns go in the reverse order.
-    @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or
-    CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices.
-    @param data Pointer to the user data. Matrix constructors that take data and step parameters do not
-    allocate matrix data. Instead, they just initialize the matrix header that points to the specified
-    data, which means that no data is copied. This operation is very efficient and can be used to
-    process external data using OpenCV functions. The external data is not automatically deallocated, so
-    you should take care of it.
-    @param step Number of bytes each matrix row occupies. The value should include the padding bytes at
-    the end of each row, if any. If the parameter is missing (set to AUTO_STEP ), no padding is assumed
-    and the actual step is calculated as cols*elemSize(). See Mat::elemSize.
-    */
-    Mat(Size size, int type, void* data, size_t step=AUTO_STEP);
-
-    /** @overload
-    @param ndims Array dimensionality.
-    @param sizes Array of integers specifying an n-dimensional array shape.
-    @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or
-    CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices.
-    @param data Pointer to the user data. Matrix constructors that take data and step parameters do not
-    allocate matrix data. Instead, they just initialize the matrix header that points to the specified
-    data, which means that no data is copied. This operation is very efficient and can be used to
-    process external data using OpenCV functions. The external data is not automatically deallocated, so
-    you should take care of it.
-    @param steps Array of ndims-1 steps in case of a multi-dimensional array (the last step is always
-    set to the element size). If not specified, the matrix is assumed to be continuous.
-    */
-    Mat(int ndims, const int* sizes, int type, void* data, const size_t* steps=0);
-
-    /** @overload
-    @param sizes Array of integers specifying an n-dimensional array shape.
-    @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or
-    CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices.
-    @param data Pointer to the user data. Matrix constructors that take data and step parameters do not
-    allocate matrix data. Instead, they just initialize the matrix header that points to the specified
-    data, which means that no data is copied. This operation is very efficient and can be used to
-    process external data using OpenCV functions. The external data is not automatically deallocated, so
-    you should take care of it.
-    @param steps Array of ndims-1 steps in case of a multi-dimensional array (the last step is always
-    set to the element size). If not specified, the matrix is assumed to be continuous.
-    */
-    Mat(const std::vector<int>& sizes, int type, void* data, const size_t* steps=0);
-
-    /** @overload
-    @param m Array that (as a whole or partly) is assigned to the constructed matrix. No data is copied
-    by these constructors. Instead, the header pointing to m data or its sub-array is constructed and
-    associated with it. The reference counter, if any, is incremented. So, when you modify the matrix
-    formed using such a constructor, you also modify the corresponding elements of m . If you want to
-    have an independent copy of the sub-array, use Mat::clone() .
-    @param rowRange Range of the m rows to take. As usual, the range start is inclusive and the range
-    end is exclusive. Use Range::all() to take all the rows.
-    @param colRange Range of the m columns to take. Use Range::all() to take all the columns.
-    */
-    Mat(const Mat& m, const Range& rowRange, const Range& colRange=Range::all());
-
-    /** @overload
-    @param m Array that (as a whole or partly) is assigned to the constructed matrix. No data is copied
-    by these constructors. Instead, the header pointing to m data or its sub-array is constructed and
-    associated with it. The reference counter, if any, is incremented. So, when you modify the matrix
-    formed using such a constructor, you also modify the corresponding elements of m . If you want to
-    have an independent copy of the sub-array, use Mat::clone() .
-    @param roi Region of interest.
-    */
-    Mat(const Mat& m, const Rect& roi);
-
-    /** @overload
-    @param m Array that (as a whole or partly) is assigned to the constructed matrix. No data is copied
-    by these constructors. Instead, the header pointing to m data or its sub-array is constructed and
-    associated with it. The reference counter, if any, is incremented. So, when you modify the matrix
-    formed using such a constructor, you also modify the corresponding elements of m . If you want to
-    have an independent copy of the sub-array, use Mat::clone() .
-    @param ranges Array of selected ranges of m along each dimensionality.
-    */
-    Mat(const Mat& m, const Range* ranges);
-
-    /** @overload
-    @param m Array that (as a whole or partly) is assigned to the constructed matrix. No data is copied
-    by these constructors. Instead, the header pointing to m data or its sub-array is constructed and
-    associated with it. The reference counter, if any, is incremented. So, when you modify the matrix
-    formed using such a constructor, you also modify the corresponding elements of m . If you want to
-    have an independent copy of the sub-array, use Mat::clone() .
-    @param ranges Array of selected ranges of m along each dimensionality.
-    */
-    Mat(const Mat& m, const std::vector<Range>& ranges);
-
-    /** @overload
-    @param vec STL vector whose elements form the matrix. The matrix has a single column and the number
-    of rows equal to the number of vector elements. Type of the matrix matches the type of vector
-    elements. The constructor can handle arbitrary types, for which there is a properly declared
-    DataType . This means that the vector elements must be primitive numbers or uni-type numerical
-    tuples of numbers. Mixed-type structures are not supported. The corresponding constructor is
-    explicit. Since STL vectors are not automatically converted to Mat instances, you should write
-    Mat(vec) explicitly. Unless you copy the data into the matrix ( copyData=true ), no new elements
-    will be added to the vector because it can potentially yield vector data reallocation, and, thus,
-    the matrix data pointer will be invalid.
-    @param copyData Flag to specify whether the underlying data of the STL vector should be copied
-    to (true) or shared with (false) the newly constructed matrix. When the data is copied, the
-    allocated buffer is managed using Mat reference counting mechanism. While the data is shared,
-    the reference counter is NULL, and you should not deallocate the data until the matrix is not
-    destructed.
-    */
-    template<typename _Tp> explicit Mat(const std::vector<_Tp>& vec, bool copyData=false);
-
-#ifdef CV_CXX11
-    /** @overload
-    */
-    template<typename _Tp, typename = typename std::enable_if<std::is_arithmetic<_Tp>::value>::type>
-    explicit Mat(const std::initializer_list<_Tp> list);
-
-    /** @overload
-    */
-    template<typename _Tp> explicit Mat(const std::initializer_list<int> sizes, const std::initializer_list<_Tp> list);
-#endif
-
-#ifdef CV_CXX_STD_ARRAY
-    /** @overload
-    */
-    template<typename _Tp, size_t _Nm> explicit Mat(const std::array<_Tp, _Nm>& arr, bool copyData=false);
-#endif
-
-    /** @overload
-    */
-    template<typename _Tp, int n> explicit Mat(const Vec<_Tp, n>& vec, bool copyData=true);
-
-    /** @overload
-    */
-    template<typename _Tp, int m, int n> explicit Mat(const Matx<_Tp, m, n>& mtx, bool copyData=true);
-
-    /** @overload
-    */
-    template<typename _Tp> explicit Mat(const Point_<_Tp>& pt, bool copyData=true);
-
-    /** @overload
-    */
-    template<typename _Tp> explicit Mat(const Point3_<_Tp>& pt, bool copyData=true);
-
-    /** @overload
-    */
-    template<typename _Tp> explicit Mat(const MatCommaInitializer_<_Tp>& commaInitializer);
-
-    //! download data from GpuMat
-    explicit Mat(const cuda::GpuMat& m);
-
-    //! destructor - calls release()
-    ~Mat();
-
-    /** @brief assignment operators
-
-    These are available assignment operators. Since they all are very different, make sure to read the
-    operator parameters description.
-    @param m Assigned, right-hand-side matrix. Matrix assignment is an O(1) operation. This means that
-    no data is copied but the data is shared and the reference counter, if any, is incremented. Before
-    assigning new data, the old data is de-referenced via Mat::release .
-     */
-    Mat& operator = (const Mat& m);
-
-    /** @overload
-    @param expr Assigned matrix expression object. As opposite to the first form of the assignment
-    operation, the second form can reuse already allocated matrix if it has the right size and type to
-    fit the matrix expression result. It is automatically handled by the real function that the matrix
-    expressions is expanded to. For example, C=A+B is expanded to add(A, B, C), and add takes care of
-    automatic C reallocation.
-    */
-    Mat& operator = (const MatExpr& expr);
-
-    //! retrieve UMat from Mat
-    UMat getUMat(int accessFlags, UMatUsageFlags usageFlags = USAGE_DEFAULT) const;
-
-    /** @brief Creates a matrix header for the specified matrix row.
-
-    The method makes a new header for the specified matrix row and returns it. This is an O(1)
-    operation, regardless of the matrix size. The underlying data of the new matrix is shared with the
-    original matrix. Here is the example of one of the classical basic matrix processing operations,
-    axpy, used by LU and many other algorithms:
-    @code
-        inline void matrix_axpy(Mat& A, int i, int j, double alpha)
-        {
-            A.row(i) += A.row(j)*alpha;
-        }
-    @endcode
-    @note In the current implementation, the following code does not work as expected:
-    @code
-        Mat A;
-        ...
-        A.row(i) = A.row(j); // will not work
-    @endcode
-    This happens because A.row(i) forms a temporary header that is further assigned to another header.
-    Remember that each of these operations is O(1), that is, no data is copied. Thus, the above
-    assignment is not true if you may have expected the j-th row to be copied to the i-th row. To
-    achieve that, you should either turn this simple assignment into an expression or use the
-    Mat::copyTo method:
-    @code
-        Mat A;
-        ...
-        // works, but looks a bit obscure.
-        A.row(i) = A.row(j) + 0;
-        // this is a bit longer, but the recommended method.
-        A.row(j).copyTo(A.row(i));
-    @endcode
-    @param y A 0-based row index.
-     */
-    Mat row(int y) const;
-
-    /** @brief Creates a matrix header for the specified matrix column.
-
-    The method makes a new header for the specified matrix column and returns it. This is an O(1)
-    operation, regardless of the matrix size. The underlying data of the new matrix is shared with the
-    original matrix. See also the Mat::row description.
-    @param x A 0-based column index.
-     */
-    Mat col(int x) const;
-
-    /** @brief Creates a matrix header for the specified row span.
-
-    The method makes a new header for the specified row span of the matrix. Similarly to Mat::row and
-    Mat::col , this is an O(1) operation.
-    @param startrow An inclusive 0-based start index of the row span.
-    @param endrow An exclusive 0-based ending index of the row span.
-     */
-    Mat rowRange(int startrow, int endrow) const;
-
-    /** @overload
-    @param r Range structure containing both the start and the end indices.
-    */
-    Mat rowRange(const Range& r) const;
-
-    /** @brief Creates a matrix header for the specified column span.
-
-    The method makes a new header for the specified column span of the matrix. Similarly to Mat::row and
-    Mat::col , this is an O(1) operation.
-    @param startcol An inclusive 0-based start index of the column span.
-    @param endcol An exclusive 0-based ending index of the column span.
-     */
-    Mat colRange(int startcol, int endcol) const;
-
-    /** @overload
-    @param r Range structure containing both the start and the end indices.
-    */
-    Mat colRange(const Range& r) const;
-
-    /** @brief Extracts a diagonal from a matrix
-
-    The method makes a new header for the specified matrix diagonal. The new matrix is represented as a
-    single-column matrix. Similarly to Mat::row and Mat::col, this is an O(1) operation.
-    @param d index of the diagonal, with the following values:
-    - `d=0` is the main diagonal.
-    - `d<0` is a diagonal from the lower half. For example, d=-1 means the diagonal is set
-      immediately below the main one.
-    - `d>0` is a diagonal from the upper half. For example, d=1 means the diagonal is set
-      immediately above the main one.
-    For example:
-    @code
-        Mat m = (Mat_<int>(3,3) <<
-                    1,2,3,
-                    4,5,6,
-                    7,8,9);
-        Mat d0 = m.diag(0);
-        Mat d1 = m.diag(1);
-        Mat d_1 = m.diag(-1);
-    @endcode
-    The resulting matrices are
-    @code
-     d0 =
-       [1;
-        5;
-        9]
-     d1 =
-       [2;
-        6]
-     d_1 =
-       [4;
-        8]
-    @endcode
-     */
-    Mat diag(int d=0) const;
-
-    /** @brief creates a diagonal matrix
-
-    The method creates a square diagonal matrix from specified main diagonal.
-    @param d One-dimensional matrix that represents the main diagonal.
-     */
-    static Mat diag(const Mat& d);
-
-    /** @brief Creates a full copy of the array and the underlying data.
-
-    The method creates a full copy of the array. The original step[] is not taken into account. So, the
-    array copy is a continuous array occupying total()*elemSize() bytes.
-     */
-    Mat clone() const;
-
-    /** @brief Copies the matrix to another one.
-
-    The method copies the matrix data to another matrix. Before copying the data, the method invokes :
-    @code
-        m.create(this->size(), this->type());
-    @endcode
-    so that the destination matrix is reallocated if needed. While m.copyTo(m); works flawlessly, the
-    function does not handle the case of a partial overlap between the source and the destination
-    matrices.
-
-    When the operation mask is specified, if the Mat::create call shown above reallocates the matrix,
-    the newly allocated matrix is initialized with all zeros before copying the data.
-    @param m Destination matrix. If it does not have a proper size or type before the operation, it is
-    reallocated.
-     */
-    void copyTo( OutputArray m ) const;
-
-    /** @overload
-    @param m Destination matrix. If it does not have a proper size or type before the operation, it is
-    reallocated.
-    @param mask Operation mask of the same size as \*this. Its non-zero elements indicate which matrix
-    elements need to be copied. The mask has to be of type CV_8U and can have 1 or multiple channels.
-    */
-    void copyTo( OutputArray m, InputArray mask ) const;
-
-    /** @brief Converts an array to another data type with optional scaling.
-
-    The method converts source pixel values to the target data type. saturate_cast\<\> is applied at
-    the end to avoid possible overflows:
-
-    \f[m(x,y) = saturate \_ cast<rType>( \alpha (*this)(x,y) +  \beta )\f]
-    @param m output matrix; if it does not have a proper size or type before the operation, it is
-    reallocated.
-    @param rtype desired output matrix type or, rather, the depth since the number of channels are the
-    same as the input has; if rtype is negative, the output matrix will have the same type as the input.
-    @param alpha optional scale factor.
-    @param beta optional delta added to the scaled values.
-     */
-    void convertTo( OutputArray m, int rtype, double alpha=1, double beta=0 ) const;
-
-    /** @brief Provides a functional form of convertTo.
-
-    This is an internally used method called by the @ref MatrixExpressions engine.
-    @param m Destination array.
-    @param type Desired destination array depth (or -1 if it should be the same as the source type).
-     */
-    void assignTo( Mat& m, int type=-1 ) const;
-
-    /** @brief Sets all or some of the array elements to the specified value.
-    @param s Assigned scalar converted to the actual array type.
-    */
-    Mat& operator = (const Scalar& s);
-
-    /** @brief Sets all or some of the array elements to the specified value.
-
-    This is an advanced variant of the Mat::operator=(const Scalar& s) operator.
-    @param value Assigned scalar converted to the actual array type.
-    @param mask Operation mask of the same size as \*this. Its non-zero elements indicate which matrix
-    elements need to be copied. The mask has to be of type CV_8U and can have 1 or multiple channels
-     */
-    Mat& setTo(InputArray value, InputArray mask=noArray());
-
-    /** @brief Changes the shape and/or the number of channels of a 2D matrix without copying the data.
-
-    The method makes a new matrix header for \*this elements. The new matrix may have a different size
-    and/or different number of channels. Any combination is possible if:
-    -   No extra elements are included into the new matrix and no elements are excluded. Consequently,
-        the product rows\*cols\*channels() must stay the same after the transformation.
-    -   No data is copied. That is, this is an O(1) operation. Consequently, if you change the number of
-        rows, or the operation changes the indices of elements row in some other way, the matrix must be
-        continuous. See Mat::isContinuous .
-
-    For example, if there is a set of 3D points stored as an STL vector, and you want to represent the
-    points as a 3xN matrix, do the following:
-    @code
-        std::vector<Point3f> vec;
-        ...
-        Mat pointMat = Mat(vec). // convert vector to Mat, O(1) operation
-                          reshape(1). // make Nx3 1-channel matrix out of Nx1 3-channel.
-                                      // Also, an O(1) operation
-                             t(); // finally, transpose the Nx3 matrix.
-                                  // This involves copying all the elements
-    @endcode
-    @param cn New number of channels. If the parameter is 0, the number of channels remains the same.
-    @param rows New number of rows. If the parameter is 0, the number of rows remains the same.
-     */
-    Mat reshape(int cn, int rows=0) const;
-
-    /** @overload */
-    Mat reshape(int cn, int newndims, const int* newsz) const;
-
-    /** @overload */
-    Mat reshape(int cn, const std::vector<int>& newshape) const;
-
-    /** @brief Transposes a matrix.
-
-    The method performs matrix transposition by means of matrix expressions. It does not perform the
-    actual transposition but returns a temporary matrix transposition object that can be further used as
-    a part of more complex matrix expressions or can be assigned to a matrix:
-    @code
-        Mat A1 = A + Mat::eye(A.size(), A.type())*lambda;
-        Mat C = A1.t()*A1; // compute (A + lambda*I)^t * (A + lamda*I)
-    @endcode
-     */
-    MatExpr t() const;
-
-    /** @brief Inverses a matrix.
-
-    The method performs a matrix inversion by means of matrix expressions. This means that a temporary
-    matrix inversion object is returned by the method and can be used further as a part of more complex
-    matrix expressions or can be assigned to a matrix.
-    @param method Matrix inversion method. One of cv::DecompTypes
-     */
-    MatExpr inv(int method=DECOMP_LU) const;
-
-    /** @brief Performs an element-wise multiplication or division of the two matrices.
-
-    The method returns a temporary object encoding per-element array multiplication, with optional
-    scale. Note that this is not a matrix multiplication that corresponds to a simpler "\*" operator.
-
-    Example:
-    @code
-        Mat C = A.mul(5/B); // equivalent to divide(A, B, C, 5)
-    @endcode
-    @param m Another array of the same type and the same size as \*this, or a matrix expression.
-    @param scale Optional scale factor.
-     */
-    MatExpr mul(InputArray m, double scale=1) const;
-
-    /** @brief Computes a cross-product of two 3-element vectors.
-
-    The method computes a cross-product of two 3-element vectors. The vectors must be 3-element
-    floating-point vectors of the same shape and size. The result is another 3-element vector of the
-    same shape and type as operands.
-    @param m Another cross-product operand.
-     */
-    Mat cross(InputArray m) const;
-
-    /** @brief Computes a dot-product of two vectors.
-
-    The method computes a dot-product of two matrices. If the matrices are not single-column or
-    single-row vectors, the top-to-bottom left-to-right scan ordering is used to treat them as 1D
-    vectors. The vectors must have the same size and type. If the matrices have more than one channel,
-    the dot products from all the channels are summed together.
-    @param m another dot-product operand.
-     */
-    double dot(InputArray m) const;
-
-    /** @brief Returns a zero array of the specified size and type.
-
-    The method returns a Matlab-style zero array initializer. It can be used to quickly form a constant
-    array as a function parameter, part of a matrix expression, or as a matrix initializer. :
-    @code
-        Mat A;
-        A = Mat::zeros(3, 3, CV_32F);
-    @endcode
-    In the example above, a new matrix is allocated only if A is not a 3x3 floating-point matrix.
-    Otherwise, the existing matrix A is filled with zeros.
-    @param rows Number of rows.
-    @param cols Number of columns.
-    @param type Created matrix type.
-     */
-    static MatExpr zeros(int rows, int cols, int type);
-
-    /** @overload
-    @param size Alternative to the matrix size specification Size(cols, rows) .
-    @param type Created matrix type.
-    */
-    static MatExpr zeros(Size size, int type);
-
-    /** @overload
-    @param ndims Array dimensionality.
-    @param sz Array of integers specifying the array shape.
-    @param type Created matrix type.
-    */
-    static MatExpr zeros(int ndims, const int* sz, int type);
-
-    /** @brief Returns an array of all 1's of the specified size and type.
-
-    The method returns a Matlab-style 1's array initializer, similarly to Mat::zeros. Note that using
-    this method you can initialize an array with an arbitrary value, using the following Matlab idiom:
-    @code
-        Mat A = Mat::ones(100, 100, CV_8U)*3; // make 100x100 matrix filled with 3.
-    @endcode
-    The above operation does not form a 100x100 matrix of 1's and then multiply it by 3. Instead, it
-    just remembers the scale factor (3 in this case) and use it when actually invoking the matrix
-    initializer.
-    @param rows Number of rows.
-    @param cols Number of columns.
-    @param type Created matrix type.
-     */
-    static MatExpr ones(int rows, int cols, int type);
-
-    /** @overload
-    @param size Alternative to the matrix size specification Size(cols, rows) .
-    @param type Created matrix type.
-    */
-    static MatExpr ones(Size size, int type);
-
-    /** @overload
-    @param ndims Array dimensionality.
-    @param sz Array of integers specifying the array shape.
-    @param type Created matrix type.
-    */
-    static MatExpr ones(int ndims, const int* sz, int type);
-
-    /** @brief Returns an identity matrix of the specified size and type.
-
-    The method returns a Matlab-style identity matrix initializer, similarly to Mat::zeros. Similarly to
-    Mat::ones, you can use a scale operation to create a scaled identity matrix efficiently:
-    @code
-        // make a 4x4 diagonal matrix with 0.1's on the diagonal.
-        Mat A = Mat::eye(4, 4, CV_32F)*0.1;
-    @endcode
-    @param rows Number of rows.
-    @param cols Number of columns.
-    @param type Created matrix type.
-     */
-    static MatExpr eye(int rows, int cols, int type);
-
-    /** @overload
-    @param size Alternative matrix size specification as Size(cols, rows) .
-    @param type Created matrix type.
-    */
-    static MatExpr eye(Size size, int type);
-
-    /** @brief Allocates new array data if needed.
-
-    This is one of the key Mat methods. Most new-style OpenCV functions and methods that produce arrays
-    call this method for each output array. The method uses the following algorithm:
-
-    -# If the current array shape and the type match the new ones, return immediately. Otherwise,
-       de-reference the previous data by calling Mat::release.
-    -# Initialize the new header.
-    -# Allocate the new data of total()\*elemSize() bytes.
-    -# Allocate the new, associated with the data, reference counter and set it to 1.
-
-    Such a scheme makes the memory management robust and efficient at the same time and helps avoid
-    extra typing for you. This means that usually there is no need to explicitly allocate output arrays.
-    That is, instead of writing:
-    @code
-        Mat color;
-        ...
-        Mat gray(color.rows, color.cols, color.depth());
-        cvtColor(color, gray, COLOR_BGR2GRAY);
-    @endcode
-    you can simply write:
-    @code
-        Mat color;
-        ...
-        Mat gray;
-        cvtColor(color, gray, COLOR_BGR2GRAY);
-    @endcode
-    because cvtColor, as well as the most of OpenCV functions, calls Mat::create() for the output array
-    internally.
-    @param rows New number of rows.
-    @param cols New number of columns.
-    @param type New matrix type.
-     */
-    void create(int rows, int cols, int type);
-
-    /** @overload
-    @param size Alternative new matrix size specification: Size(cols, rows)
-    @param type New matrix type.
-    */
-    void create(Size size, int type);
-
-    /** @overload
-    @param ndims New array dimensionality.
-    @param sizes Array of integers specifying a new array shape.
-    @param type New matrix type.
-    */
-    void create(int ndims, const int* sizes, int type);
-
-    /** @overload
-    @param sizes Array of integers specifying a new array shape.
-    @param type New matrix type.
-    */
-    void create(const std::vector<int>& sizes, int type);
-
-    /** @brief Increments the reference counter.
-
-    The method increments the reference counter associated with the matrix data. If the matrix header
-    points to an external data set (see Mat::Mat ), the reference counter is NULL, and the method has no
-    effect in this case. Normally, to avoid memory leaks, the method should not be called explicitly. It
-    is called implicitly by the matrix assignment operator. The reference counter increment is an atomic
-    operation on the platforms that support it. Thus, it is safe to operate on the same matrices
-    asynchronously in different threads.
-     */
-    void addref();
-
-    /** @brief Decrements the reference counter and deallocates the matrix if needed.
-
-    The method decrements the reference counter associated with the matrix data. When the reference
-    counter reaches 0, the matrix data is deallocated and the data and the reference counter pointers
-    are set to NULL's. If the matrix header points to an external data set (see Mat::Mat ), the
-    reference counter is NULL, and the method has no effect in this case.
-
-    This method can be called manually to force the matrix data deallocation. But since this method is
-    automatically called in the destructor, or by any other method that changes the data pointer, it is
-    usually not needed. The reference counter decrement and check for 0 is an atomic operation on the
-    platforms that support it. Thus, it is safe to operate on the same matrices asynchronously in
-    different threads.
-     */
-    void release();
-
-    //! internal use function, consider to use 'release' method instead; deallocates the matrix data
-    void deallocate();
-    //! internal use function; properly re-allocates _size, _step arrays
-    void copySize(const Mat& m);
-
-    /** @brief Reserves space for the certain number of rows.
-
-    The method reserves space for sz rows. If the matrix already has enough space to store sz rows,
-    nothing happens. If the matrix is reallocated, the first Mat::rows rows are preserved. The method
-    emulates the corresponding method of the STL vector class.
-    @param sz Number of rows.
-     */
-    void reserve(size_t sz);
-
-    /** @brief Reserves space for the certain number of bytes.
-
-    The method reserves space for sz bytes. If the matrix already has enough space to store sz bytes,
-    nothing happens. If matrix has to be reallocated its previous content could be lost.
-    @param sz Number of bytes.
-    */
-    void reserveBuffer(size_t sz);
-
-    /** @brief Changes the number of matrix rows.
-
-    The methods change the number of matrix rows. If the matrix is reallocated, the first
-    min(Mat::rows, sz) rows are preserved. The methods emulate the corresponding methods of the STL
-    vector class.
-    @param sz New number of rows.
-     */
-    void resize(size_t sz);
-
-    /** @overload
-    @param sz New number of rows.
-    @param s Value assigned to the newly added elements.
-     */
-    void resize(size_t sz, const Scalar& s);
-
-    //! internal function
-    void push_back_(const void* elem);
-
-    /** @brief Adds elements to the bottom of the matrix.
-
-    The methods add one or more elements to the bottom of the matrix. They emulate the corresponding
-    method of the STL vector class. When elem is Mat , its type and the number of columns must be the
-    same as in the container matrix.
-    @param elem Added element(s).
-     */
-    template<typename _Tp> void push_back(const _Tp& elem);
-
-    /** @overload
-    @param elem Added element(s).
-    */
-    template<typename _Tp> void push_back(const Mat_<_Tp>& elem);
-
-    /** @overload
-    @param elem Added element(s).
-    */
-    template<typename _Tp> void push_back(const std::vector<_Tp>& elem);
-
-    /** @overload
-    @param m Added line(s).
-    */
-    void push_back(const Mat& m);
-
-    /** @brief Removes elements from the bottom of the matrix.
-
-    The method removes one or more rows from the bottom of the matrix.
-    @param nelems Number of removed rows. If it is greater than the total number of rows, an exception
-    is thrown.
-     */
-    void pop_back(size_t nelems=1);
-
-    /** @brief Locates the matrix header within a parent matrix.
-
-    After you extracted a submatrix from a matrix using Mat::row, Mat::col, Mat::rowRange,
-    Mat::colRange, and others, the resultant submatrix points just to the part of the original big
-    matrix. However, each submatrix contains information (represented by datastart and dataend
-    fields) that helps reconstruct the original matrix size and the position of the extracted
-    submatrix within the original matrix. The method locateROI does exactly that.
-    @param wholeSize Output parameter that contains the size of the whole matrix containing *this*
-    as a part.
-    @param ofs Output parameter that contains an offset of *this* inside the whole matrix.
-     */
-    void locateROI( Size& wholeSize, Point& ofs ) const;
-
-    /** @brief Adjusts a submatrix size and position within the parent matrix.
-
-    The method is complimentary to Mat::locateROI . The typical use of these functions is to determine
-    the submatrix position within the parent matrix and then shift the position somehow. Typically, it
-    can be required for filtering operations when pixels outside of the ROI should be taken into
-    account. When all the method parameters are positive, the ROI needs to grow in all directions by the
-    specified amount, for example:
-    @code
-        A.adjustROI(2, 2, 2, 2);
-    @endcode
-    In this example, the matrix size is increased by 4 elements in each direction. The matrix is shifted
-    by 2 elements to the left and 2 elements up, which brings in all the necessary pixels for the
-    filtering with the 5x5 kernel.
-
-    adjustROI forces the adjusted ROI to be inside of the parent matrix that is boundaries of the
-    adjusted ROI are constrained by boundaries of the parent matrix. For example, if the submatrix A is
-    located in the first row of a parent matrix and you called A.adjustROI(2, 2, 2, 2) then A will not
-    be increased in the upward direction.
-
-    The function is used internally by the OpenCV filtering functions, like filter2D , morphological
-    operations, and so on.
-    @param dtop Shift of the top submatrix boundary upwards.
-    @param dbottom Shift of the bottom submatrix boundary downwards.
-    @param dleft Shift of the left submatrix boundary to the left.
-    @param dright Shift of the right submatrix boundary to the right.
-    @sa copyMakeBorder
-     */
-    Mat& adjustROI( int dtop, int dbottom, int dleft, int dright );
-
-    /** @brief Extracts a rectangular submatrix.
-
-    The operators make a new header for the specified sub-array of \*this . They are the most
-    generalized forms of Mat::row, Mat::col, Mat::rowRange, and Mat::colRange . For example,
-    `A(Range(0, 10), Range::all())` is equivalent to `A.rowRange(0, 10)`. Similarly to all of the above,
-    the operators are O(1) operations, that is, no matrix data is copied.
-    @param rowRange Start and end row of the extracted submatrix. The upper boundary is not included. To
-    select all the rows, use Range::all().
-    @param colRange Start and end column of the extracted submatrix. The upper boundary is not included.
-    To select all the columns, use Range::all().
-     */
-    Mat operator()( Range rowRange, Range colRange ) const;
-
-    /** @overload
-    @param roi Extracted submatrix specified as a rectangle.
-    */
-    Mat operator()( const Rect& roi ) const;
-
-    /** @overload
-    @param ranges Array of selected ranges along each array dimension.
-    */
-    Mat operator()( const Range* ranges ) const;
-
-    /** @overload
-    @param ranges Array of selected ranges along each array dimension.
-    */
-    Mat operator()(const std::vector<Range>& ranges) const;
-
-    // //! converts header to CvMat; no data is copied
-    // operator CvMat() const;
-    // //! converts header to CvMatND; no data is copied
-    // operator CvMatND() const;
-    // //! converts header to IplImage; no data is copied
-    // operator IplImage() const;
-
-    template<typename _Tp> operator std::vector<_Tp>() const;
-    template<typename _Tp, int n> operator Vec<_Tp, n>() const;
-    template<typename _Tp, int m, int n> operator Matx<_Tp, m, n>() const;
-
-#ifdef CV_CXX_STD_ARRAY
-    template<typename _Tp, std::size_t _Nm> operator std::array<_Tp, _Nm>() const;
-#endif
-
-    /** @brief Reports whether the matrix is continuous or not.
-
-    The method returns true if the matrix elements are stored continuously without gaps at the end of
-    each row. Otherwise, it returns false. Obviously, 1x1 or 1xN matrices are always continuous.
-    Matrices created with Mat::create are always continuous. But if you extract a part of the matrix
-    using Mat::col, Mat::diag, and so on, or constructed a matrix header for externally allocated data,
-    such matrices may no longer have this property.
-
-    The continuity flag is stored as a bit in the Mat::flags field and is computed automatically when
-    you construct a matrix header. Thus, the continuity check is a very fast operation, though
-    theoretically it could be done as follows:
-    @code
-        // alternative implementation of Mat::isContinuous()
-        bool myCheckMatContinuity(const Mat& m)
-        {
-            //return (m.flags & Mat::CONTINUOUS_FLAG) != 0;
-            return m.rows == 1 || m.step == m.cols*m.elemSize();
-        }
-    @endcode
-    The method is used in quite a few of OpenCV functions. The point is that element-wise operations
-    (such as arithmetic and logical operations, math functions, alpha blending, color space
-    transformations, and others) do not depend on the image geometry. Thus, if all the input and output
-    arrays are continuous, the functions can process them as very long single-row vectors. The example
-    below illustrates how an alpha-blending function can be implemented:
-    @code
-        template<typename T>
-        void alphaBlendRGBA(const Mat& src1, const Mat& src2, Mat& dst)
-        {
-            const float alpha_scale = (float)std::numeric_limits<T>::max(),
-                        inv_scale = 1.f/alpha_scale;
-
-            CV_Assert( src1.type() == src2.type() &&
-                       src1.type() == CV_MAKETYPE(traits::Depth<T>::value, 4) &&
-                       src1.size() == src2.size());
-            Size size = src1.size();
-            dst.create(size, src1.type());
-
-            // here is the idiom: check the arrays for continuity and,
-            // if this is the case,
-            // treat the arrays as 1D vectors
-            if( src1.isContinuous() && src2.isContinuous() && dst.isContinuous() )
-            {
-                size.width *= size.height;
-                size.height = 1;
-            }
-            size.width *= 4;
-
-            for( int i = 0; i < size.height; i++ )
-            {
-                // when the arrays are continuous,
-                // the outer loop is executed only once
-                const T* ptr1 = src1.ptr<T>(i);
-                const T* ptr2 = src2.ptr<T>(i);
-                T* dptr = dst.ptr<T>(i);
-
-                for( int j = 0; j < size.width; j += 4 )
-                {
-                    float alpha = ptr1[j+3]*inv_scale, beta = ptr2[j+3]*inv_scale;
-                    dptr[j] = saturate_cast<T>(ptr1[j]*alpha + ptr2[j]*beta);
-                    dptr[j+1] = saturate_cast<T>(ptr1[j+1]*alpha + ptr2[j+1]*beta);
-                    dptr[j+2] = saturate_cast<T>(ptr1[j+2]*alpha + ptr2[j+2]*beta);
-                    dptr[j+3] = saturate_cast<T>((1 - (1-alpha)*(1-beta))*alpha_scale);
-                }
-            }
-        }
-    @endcode
-    This approach, while being very simple, can boost the performance of a simple element-operation by
-    10-20 percents, especially if the image is rather small and the operation is quite simple.
-
-    Another OpenCV idiom in this function, a call of Mat::create for the destination array, that
-    allocates the destination array unless it already has the proper size and type. And while the newly
-    allocated arrays are always continuous, you still need to check the destination array because
-    Mat::create does not always allocate a new matrix.
-     */
-    bool isContinuous() const;
-
-    //! returns true if the matrix is a submatrix of another matrix
-    bool isSubmatrix() const;
-
-    /** @brief Returns the matrix element size in bytes.
-
-    The method returns the matrix element size in bytes. For example, if the matrix type is CV_16SC3 ,
-    the method returns 3\*sizeof(short) or 6.
-     */
-    size_t elemSize() const;
-
-    /** @brief Returns the size of each matrix element channel in bytes.
-
-    The method returns the matrix element channel size in bytes, that is, it ignores the number of
-    channels. For example, if the matrix type is CV_16SC3 , the method returns sizeof(short) or 2.
-     */
-    size_t elemSize1() const;
-
-    /** @brief Returns the type of a matrix element.
-
-    The method returns a matrix element type. This is an identifier compatible with the CvMat type
-    system, like CV_16SC3 or 16-bit signed 3-channel array, and so on.
-     */
-    int type() const;
-
-    /** @brief Returns the depth of a matrix element.
-
-    The method returns the identifier of the matrix element depth (the type of each individual channel).
-    For example, for a 16-bit signed element array, the method returns CV_16S . A complete list of
-    matrix types contains the following values:
-    -   CV_8U - 8-bit unsigned integers ( 0..255 )
-    -   CV_8S - 8-bit signed integers ( -128..127 )
-    -   CV_16U - 16-bit unsigned integers ( 0..65535 )
-    -   CV_16S - 16-bit signed integers ( -32768..32767 )
-    -   CV_32S - 32-bit signed integers ( -2147483648..2147483647 )
-    -   CV_32F - 32-bit floating-point numbers ( -FLT_MAX..FLT_MAX, INF, NAN )
-    -   CV_64F - 64-bit floating-point numbers ( -DBL_MAX..DBL_MAX, INF, NAN )
-     */
-    int depth() const;
-
-    /** @brief Returns the number of matrix channels.
-
-    The method returns the number of matrix channels.
-     */
-    int channels() const;
-
-    /** @brief Returns a normalized step.
-
-    The method returns a matrix step divided by Mat::elemSize1() . It can be useful to quickly access an
-    arbitrary matrix element.
-     */
-    size_t step1(int i=0) const;
-
-    /** @brief Returns true if the array has no elements.
-
-    The method returns true if Mat::total() is 0 or if Mat::data is NULL. Because of pop_back() and
-    resize() methods `M.total() == 0` does not imply that `M.data == NULL`.
-     */
-    bool empty() const;
-
-    /** @brief Returns the total number of array elements.
-
-    The method returns the number of array elements (a number of pixels if the array represents an
-    image).
-     */
-    size_t total() const;
-
-    /** @brief Returns the total number of array elements.
-
-     The method returns the number of elements within a certain sub-array slice with startDim <= dim < endDim
-     */
-    size_t total(int startDim, int endDim=INT_MAX) const;
-
-    /**
-     * @param elemChannels Number of channels or number of columns the matrix should have.
-     *                     For a 2-D matrix, when the matrix has only 1 column, then it should have
-     *                     elemChannels channels; When the matrix has only 1 channel,
-     *                     then it should have elemChannels columns.
-     *                     For a 3-D matrix, it should have only one channel. Furthermore,
-     *                     if the number of planes is not one, then the number of rows
-     *                     within every plane has to be 1; if the number of rows within
-     *                     every plane is not 1, then the number of planes has to be 1.
-     * @param depth The depth the matrix should have. Set it to -1 when any depth is fine.
-     * @param requireContinuous Set it to true to require the matrix to be continuous
-     * @return -1 if the requirement is not satisfied.
-     *         Otherwise, it returns the number of elements in the matrix. Note
-     *         that an element may have multiple channels.
-     *
-     * The following code demonstrates its usage for a 2-d matrix:
-     * @snippet snippets/core_mat_checkVector.cpp example-2d
-     *
-     * The following code demonstrates its usage for a 3-d matrix:
-     * @snippet snippets/core_mat_checkVector.cpp example-3d
-     */
-    int checkVector(int elemChannels, int depth=-1, bool requireContinuous=true) const;
-
-    /** @brief Returns a pointer to the specified matrix row.
-
-    The methods return `uchar*` or typed pointer to the specified matrix row. See the sample in
-    Mat::isContinuous to know how to use these methods.
-    @param i0 A 0-based row index.
-     */
-    uchar* ptr(int i0=0);
-    /** @overload */
-    const uchar* ptr(int i0=0) const;
-
-    /** @overload
-    @param row Index along the dimension 0
-    @param col Index along the dimension 1
-    */
-    uchar* ptr(int row, int col);
-    /** @overload
-    @param row Index along the dimension 0
-    @param col Index along the dimension 1
-    */
-    const uchar* ptr(int row, int col) const;
-
-    /** @overload */
-    uchar* ptr(int i0, int i1, int i2);
-    /** @overload */
-    const uchar* ptr(int i0, int i1, int i2) const;
-
-    /** @overload */
-    uchar* ptr(const int* idx);
-    /** @overload */
-    const uchar* ptr(const int* idx) const;
-    /** @overload */
-    template<int n> uchar* ptr(const Vec<int, n>& idx);
-    /** @overload */
-    template<int n> const uchar* ptr(const Vec<int, n>& idx) const;
-
-    /** @overload */
-    template<typename _Tp> _Tp* ptr(int i0=0);
-    /** @overload */
-    template<typename _Tp> const _Tp* ptr(int i0=0) const;
-    /** @overload
-    @param row Index along the dimension 0
-    @param col Index along the dimension 1
-    */
-    template<typename _Tp> _Tp* ptr(int row, int col);
-    /** @overload
-    @param row Index along the dimension 0
-    @param col Index along the dimension 1
-    */
-    template<typename _Tp> const _Tp* ptr(int row, int col) const;
-    /** @overload */
-    template<typename _Tp> _Tp* ptr(int i0, int i1, int i2);
-    /** @overload */
-    template<typename _Tp> const _Tp* ptr(int i0, int i1, int i2) const;
-    /** @overload */
-    template<typename _Tp> _Tp* ptr(const int* idx);
-    /** @overload */
-    template<typename _Tp> const _Tp* ptr(const int* idx) const;
-    /** @overload */
-    template<typename _Tp, int n> _Tp* ptr(const Vec<int, n>& idx);
-    /** @overload */
-    template<typename _Tp, int n> const _Tp* ptr(const Vec<int, n>& idx) const;
-
-    /** @brief Returns a reference to the specified array element.
-
-    The template methods return a reference to the specified array element. For the sake of higher
-    performance, the index range checks are only performed in the Debug configuration.
-
-    Note that the variants with a single index (i) can be used to access elements of single-row or
-    single-column 2-dimensional arrays. That is, if, for example, A is a 1 x N floating-point matrix and
-    B is an M x 1 integer matrix, you can simply write `A.at<float>(k+4)` and `B.at<int>(2*i+1)`
-    instead of `A.at<float>(0,k+4)` and `B.at<int>(2*i+1,0)`, respectively.
-
-    The example below initializes a Hilbert matrix:
-    @code
-        Mat H(100, 100, CV_64F);
-        for(int i = 0; i < H.rows; i++)
-            for(int j = 0; j < H.cols; j++)
-                H.at<double>(i,j)=1./(i+j+1);
-    @endcode
-
-    Keep in mind that the size identifier used in the at operator cannot be chosen at random. It depends
-    on the image from which you are trying to retrieve the data. The table below gives a better insight in this:
-     - If matrix is of type `CV_8U` then use `Mat.at<uchar>(y,x)`.
-     - If matrix is of type `CV_8S` then use `Mat.at<schar>(y,x)`.
-     - If matrix is of type `CV_16U` then use `Mat.at<ushort>(y,x)`.
-     - If matrix is of type `CV_16S` then use `Mat.at<short>(y,x)`.
-     - If matrix is of type `CV_32S`  then use `Mat.at<int>(y,x)`.
-     - If matrix is of type `CV_32F`  then use `Mat.at<float>(y,x)`.
-     - If matrix is of type `CV_64F` then use `Mat.at<double>(y,x)`.
-
-    @param i0 Index along the dimension 0
-     */
-    template<typename _Tp> _Tp& at(int i0=0);
-    /** @overload
-    @param i0 Index along the dimension 0
-    */
-    template<typename _Tp> const _Tp& at(int i0=0) const;
-    /** @overload
-    @param row Index along the dimension 0
-    @param col Index along the dimension 1
-    */
-    template<typename _Tp> _Tp& at(int row, int col);
-    /** @overload
-    @param row Index along the dimension 0
-    @param col Index along the dimension 1
-    */
-    template<typename _Tp> const _Tp& at(int row, int col) const;
-
-    /** @overload
-    @param i0 Index along the dimension 0
-    @param i1 Index along the dimension 1
-    @param i2 Index along the dimension 2
-    */
-    template<typename _Tp> _Tp& at(int i0, int i1, int i2);
-    /** @overload
-    @param i0 Index along the dimension 0
-    @param i1 Index along the dimension 1
-    @param i2 Index along the dimension 2
-    */
-    template<typename _Tp> const _Tp& at(int i0, int i1, int i2) const;
-
-    /** @overload
-    @param idx Array of Mat::dims indices.
-    */
-    template<typename _Tp> _Tp& at(const int* idx);
-    /** @overload
-    @param idx Array of Mat::dims indices.
-    */
-    template<typename _Tp> const _Tp& at(const int* idx) const;
-
-    /** @overload */
-    template<typename _Tp, int n> _Tp& at(const Vec<int, n>& idx);
-    /** @overload */
-    template<typename _Tp, int n> const _Tp& at(const Vec<int, n>& idx) const;
-
-    /** @overload
-    special versions for 2D arrays (especially convenient for referencing image pixels)
-    @param pt Element position specified as Point(j,i) .
-    */
-    template<typename _Tp> _Tp& at(Point pt);
-    /** @overload
-    special versions for 2D arrays (especially convenient for referencing image pixels)
-    @param pt Element position specified as Point(j,i) .
-    */
-    template<typename _Tp> const _Tp& at(Point pt) const;
-
-    /** @brief Returns the matrix iterator and sets it to the first matrix element.
-
-    The methods return the matrix read-only or read-write iterators. The use of matrix iterators is very
-    similar to the use of bi-directional STL iterators. In the example below, the alpha blending
-    function is rewritten using the matrix iterators:
-    @code
-        template<typename T>
-        void alphaBlendRGBA(const Mat& src1, const Mat& src2, Mat& dst)
-        {
-            typedef Vec<T, 4> VT;
-
-            const float alpha_scale = (float)std::numeric_limits<T>::max(),
-                        inv_scale = 1.f/alpha_scale;
-
-            CV_Assert( src1.type() == src2.type() &&
-                       src1.type() == traits::Type<VT>::value &&
-                       src1.size() == src2.size());
-            Size size = src1.size();
-            dst.create(size, src1.type());
-
-            MatConstIterator_<VT> it1 = src1.begin<VT>(), it1_end = src1.end<VT>();
-            MatConstIterator_<VT> it2 = src2.begin<VT>();
-            MatIterator_<VT> dst_it = dst.begin<VT>();
-
-            for( ; it1 != it1_end; ++it1, ++it2, ++dst_it )
-            {
-                VT pix1 = *it1, pix2 = *it2;
-                float alpha = pix1[3]*inv_scale, beta = pix2[3]*inv_scale;
-                *dst_it = VT(saturate_cast<T>(pix1[0]*alpha + pix2[0]*beta),
-                             saturate_cast<T>(pix1[1]*alpha + pix2[1]*beta),
-                             saturate_cast<T>(pix1[2]*alpha + pix2[2]*beta),
-                             saturate_cast<T>((1 - (1-alpha)*(1-beta))*alpha_scale));
-            }
-        }
-    @endcode
-     */
-    template<typename _Tp> MatIterator_<_Tp> begin();
-    template<typename _Tp> MatConstIterator_<_Tp> begin() const;
-
-    /** @brief Returns the matrix iterator and sets it to the after-last matrix element.
-
-    The methods return the matrix read-only or read-write iterators, set to the point following the last
-    matrix element.
-     */
-    template<typename _Tp> MatIterator_<_Tp> end();
-    template<typename _Tp> MatConstIterator_<_Tp> end() const;
-
-    /** @brief Runs the given functor over all matrix elements in parallel.
-
-    The operation passed as argument has to be a function pointer, a function object or a lambda(C++11).
-
-    Example 1. All of the operations below put 0xFF the first channel of all matrix elements:
-    @code
-        Mat image(1920, 1080, CV_8UC3);
-        typedef cv::Point3_<uint8_t> Pixel;
-
-        // first. raw pointer access.
-        for (int r = 0; r < image.rows; ++r) {
-            Pixel* ptr = image.ptr<Pixel>(r, 0);
-            const Pixel* ptr_end = ptr + image.cols;
-            for (; ptr != ptr_end; ++ptr) {
-                ptr->x = 255;
-            }
-        }
-
-        // Using MatIterator. (Simple but there are a Iterator's overhead)
-        for (Pixel &p : cv::Mat_<Pixel>(image)) {
-            p.x = 255;
-        }
-
-        // Parallel execution with function object.
-        struct Operator {
-            void operator ()(Pixel &pixel, const int * position) {
-                pixel.x = 255;
-            }
-        };
-        image.forEach<Pixel>(Operator());
-
-        // Parallel execution using C++11 lambda.
-        image.forEach<Pixel>([](Pixel &p, const int * position) -> void {
-            p.x = 255;
-        });
-    @endcode
-    Example 2. Using the pixel's position:
-    @code
-        // Creating 3D matrix (255 x 255 x 255) typed uint8_t
-        // and initialize all elements by the value which equals elements position.
-        // i.e. pixels (x,y,z) = (1,2,3) is (b,g,r) = (1,2,3).
-
-        int sizes[] = { 255, 255, 255 };
-        typedef cv::Point3_<uint8_t> Pixel;
-
-        Mat_<Pixel> image = Mat::zeros(3, sizes, CV_8UC3);
-
-        image.forEach<Pixel>([&](Pixel& pixel, const int position[]) -> void {
-            pixel.x = position[0];
-            pixel.y = position[1];
-            pixel.z = position[2];
-        });
-    @endcode
-     */
-    template<typename _Tp, typename Functor> void forEach(const Functor& operation);
-    /** @overload */
-    template<typename _Tp, typename Functor> void forEach(const Functor& operation) const;
-
-#ifdef CV_CXX_MOVE_SEMANTICS
-    Mat(Mat&& m);
-    Mat& operator = (Mat&& m);
-#endif
-
-    enum { MAGIC_VAL  = 0x42FF0000, AUTO_STEP = 0, CONTINUOUS_FLAG = CV_MAT_CONT_FLAG, SUBMATRIX_FLAG = CV_SUBMAT_FLAG };
-    enum { MAGIC_MASK = 0xFFFF0000, TYPE_MASK = 0x00000FFF, DEPTH_MASK = 7 };
-
-    /*! includes several bit-fields:
-         - the magic signature
-         - continuity flag
-         - depth
-         - number of channels
-     */
-    int flags;
-    //! the matrix dimensionality, >= 2
-    int dims;
-    //! the number of rows and columns or (-1, -1) when the matrix has more than 2 dimensions
-    int rows, cols;
-    //! pointer to the data
-    uchar* data;
-
-    //! helper fields used in locateROI and adjustROI
-    const uchar* datastart;
-    const uchar* dataend;
-    const uchar* datalimit;
-
-    //! custom allocator
-    MatAllocator* allocator;
-    //! and the standard allocator
-    static MatAllocator* getStdAllocator();
-    static MatAllocator* getDefaultAllocator();
-    static void setDefaultAllocator(MatAllocator* allocator);
-
-    //! internal use method: updates the continuity flag
-    void updateContinuityFlag();
-
-    //! interaction with UMat
-    UMatData* u;
-
-    MatSize size;
-    MatStep step;
-
-protected:
-    template<typename _Tp, typename Functor> void forEach_impl(const Functor& operation);
-};
-
-
-///////////////////////////////// Mat_<_Tp> ////////////////////////////////////
-
-/** @brief Template matrix class derived from Mat
-
-@code{.cpp}
-    template<typename _Tp> class Mat_ : public Mat
-    {
-    public:
-        // ... some specific methods
-        //         and
-        // no new extra fields
-    };
-@endcode
-The class `Mat_<_Tp>` is a *thin* template wrapper on top of the Mat class. It does not have any
-extra data fields. Nor this class nor Mat has any virtual methods. Thus, references or pointers to
-these two classes can be freely but carefully converted one to another. For example:
-@code{.cpp}
-    // create a 100x100 8-bit matrix
-    Mat M(100,100,CV_8U);
-    // this will be compiled fine. no any data conversion will be done.
-    Mat_<float>& M1 = (Mat_<float>&)M;
-    // the program is likely to crash at the statement below
-    M1(99,99) = 1.f;
-@endcode
-While Mat is sufficient in most cases, Mat_ can be more convenient if you use a lot of element
-access operations and if you know matrix type at the compilation time. Note that
-`Mat::at(int y,int x)` and `Mat_::operator()(int y,int x)` do absolutely the same
-and run at the same speed, but the latter is certainly shorter:
-@code{.cpp}
-    Mat_<double> M(20,20);
-    for(int i = 0; i < M.rows; i++)
-        for(int j = 0; j < M.cols; j++)
-            M(i,j) = 1./(i+j+1);
-    Mat E, V;
-    eigen(M,E,V);
-    cout << E.at<double>(0,0)/E.at<double>(M.rows-1,0);
-@endcode
-To use Mat_ for multi-channel images/matrices, pass Vec as a Mat_ parameter:
-@code{.cpp}
-    // allocate a 320x240 color image and fill it with green (in RGB space)
-    Mat_<Vec3b> img(240, 320, Vec3b(0,255,0));
-    // now draw a diagonal white line
-    for(int i = 0; i < 100; i++)
-        img(i,i)=Vec3b(255,255,255);
-    // and now scramble the 2nd (red) channel of each pixel
-    for(int i = 0; i < img.rows; i++)
-        for(int j = 0; j < img.cols; j++)
-            img(i,j)[2] ^= (uchar)(i ^ j);
-@endcode
-Mat_ is fully compatible with C++11 range-based for loop. For example such loop
-can be used to safely apply look-up table:
-@code{.cpp}
-void applyTable(Mat_<uchar>& I, const uchar* const table)
-{
-    for(auto& pixel : I)
-    {
-        pixel = table[pixel];
-    }
-}
-@endcode
- */
-template<typename _Tp> class Mat_ : public Mat
-{
-public:
-    typedef _Tp value_type;
-    typedef typename DataType<_Tp>::channel_type channel_type;
-    typedef MatIterator_<_Tp> iterator;
-    typedef MatConstIterator_<_Tp> const_iterator;
-
-    //! default constructor
-    Mat_();
-    //! equivalent to Mat(_rows, _cols, DataType<_Tp>::type)
-    Mat_(int _rows, int _cols);
-    //! constructor that sets each matrix element to specified value
-    Mat_(int _rows, int _cols, const _Tp& value);
-    //! equivalent to Mat(_size, DataType<_Tp>::type)
-    explicit Mat_(Size _size);
-    //! constructor that sets each matrix element to specified value
-    Mat_(Size _size, const _Tp& value);
-    //! n-dim array constructor
-    Mat_(int _ndims, const int* _sizes);
-    //! n-dim array constructor that sets each matrix element to specified value
-    Mat_(int _ndims, const int* _sizes, const _Tp& value);
-    //! copy/conversion constructor. If m is of different type, it's converted
-    Mat_(const Mat& m);
-    //! copy constructor
-    Mat_(const Mat_& m);
-    //! constructs a matrix on top of user-allocated data. step is in bytes(!!!), regardless of the type
-    Mat_(int _rows, int _cols, _Tp* _data, size_t _step=AUTO_STEP);
-    //! constructs n-dim matrix on top of user-allocated data. steps are in bytes(!!!), regardless of the type
-    Mat_(int _ndims, const int* _sizes, _Tp* _data, const size_t* _steps=0);
-    //! selects a submatrix
-    Mat_(const Mat_& m, const Range& rowRange, const Range& colRange=Range::all());
-    //! selects a submatrix
-    Mat_(const Mat_& m, const Rect& roi);
-    //! selects a submatrix, n-dim version
-    Mat_(const Mat_& m, const Range* ranges);
-    //! selects a submatrix, n-dim version
-    Mat_(const Mat_& m, const std::vector<Range>& ranges);
-    //! from a matrix expression
-    explicit Mat_(const MatExpr& e);
-    //! makes a matrix out of Vec, std::vector, Point_ or Point3_. The matrix will have a single column
-    explicit Mat_(const std::vector<_Tp>& vec, bool copyData=false);
-    template<int n> explicit Mat_(const Vec<typename DataType<_Tp>::channel_type, n>& vec, bool copyData=true);
-    template<int m, int n> explicit Mat_(const Matx<typename DataType<_Tp>::channel_type, m, n>& mtx, bool copyData=true);
-    explicit Mat_(const Point_<typename DataType<_Tp>::channel_type>& pt, bool copyData=true);
-    explicit Mat_(const Point3_<typename DataType<_Tp>::channel_type>& pt, bool copyData=true);
-    explicit Mat_(const MatCommaInitializer_<_Tp>& commaInitializer);
-
-#ifdef CV_CXX11
-    Mat_(std::initializer_list<_Tp> values);
-    explicit Mat_(const std::initializer_list<int> sizes, const std::initializer_list<_Tp> values);
-#endif
-
-#ifdef CV_CXX_STD_ARRAY
-    template <std::size_t _Nm> explicit Mat_(const std::array<_Tp, _Nm>& arr, bool copyData=false);
-#endif
-
-    Mat_& operator = (const Mat& m);
-    Mat_& operator = (const Mat_& m);
-    //! set all the elements to s.
-    Mat_& operator = (const _Tp& s);
-    //! assign a matrix expression
-    Mat_& operator = (const MatExpr& e);
-
-    //! iterators; they are smart enough to skip gaps in the end of rows
-    iterator begin();
-    iterator end();
-    const_iterator begin() const;
-    const_iterator end() const;
-
-    //! template methods for for operation over all matrix elements.
-    // the operations take care of skipping gaps in the end of rows (if any)
-    template<typename Functor> void forEach(const Functor& operation);
-    template<typename Functor> void forEach(const Functor& operation) const;
-
-    //! equivalent to Mat::create(_rows, _cols, DataType<_Tp>::type)
-    void create(int _rows, int _cols);
-    //! equivalent to Mat::create(_size, DataType<_Tp>::type)
-    void create(Size _size);
-    //! equivalent to Mat::create(_ndims, _sizes, DatType<_Tp>::type)
-    void create(int _ndims, const int* _sizes);
-    //! equivalent to Mat::release()
-    void release();
-    //! cross-product
-    Mat_ cross(const Mat_& m) const;
-    //! data type conversion
-    template<typename T2> operator Mat_<T2>() const;
-    //! overridden forms of Mat::row() etc.
-    Mat_ row(int y) const;
-    Mat_ col(int x) const;
-    Mat_ diag(int d=0) const;
-    Mat_ clone() const;
-
-    //! overridden forms of Mat::elemSize() etc.
-    size_t elemSize() const;
-    size_t elemSize1() const;
-    int type() const;
-    int depth() const;
-    int channels() const;
-    size_t step1(int i=0) const;
-    //! returns step()/sizeof(_Tp)
-    size_t stepT(int i=0) const;
-
-    //! overridden forms of Mat::zeros() etc. Data type is omitted, of course
-    static MatExpr zeros(int rows, int cols);
-    static MatExpr zeros(Size size);
-    static MatExpr zeros(int _ndims, const int* _sizes);
-    static MatExpr ones(int rows, int cols);
-    static MatExpr ones(Size size);
-    static MatExpr ones(int _ndims, const int* _sizes);
-    static MatExpr eye(int rows, int cols);
-    static MatExpr eye(Size size);
-
-    //! some more overridden methods
-    Mat_& adjustROI( int dtop, int dbottom, int dleft, int dright );
-    Mat_ operator()( const Range& rowRange, const Range& colRange ) const;
-    Mat_ operator()( const Rect& roi ) const;
-    Mat_ operator()( const Range* ranges ) const;
-    Mat_ operator()(const std::vector<Range>& ranges) const;
-
-    //! more convenient forms of row and element access operators
-    _Tp* operator [](int y);
-    const _Tp* operator [](int y) const;
-
-    //! returns reference to the specified element
-    _Tp& operator ()(const int* idx);
-    //! returns read-only reference to the specified element
-    const _Tp& operator ()(const int* idx) const;
-
-    //! returns reference to the specified element
-    template<int n> _Tp& operator ()(const Vec<int, n>& idx);
-    //! returns read-only reference to the specified element
-    template<int n> const _Tp& operator ()(const Vec<int, n>& idx) const;
-
-    //! returns reference to the specified element (1D case)
-    _Tp& operator ()(int idx0);
-    //! returns read-only reference to the specified element (1D case)
-    const _Tp& operator ()(int idx0) const;
-    //! returns reference to the specified element (2D case)
-    _Tp& operator ()(int row, int col);
-    //! returns read-only reference to the specified element (2D case)
-    const _Tp& operator ()(int row, int col) const;
-    //! returns reference to the specified element (3D case)
-    _Tp& operator ()(int idx0, int idx1, int idx2);
-    //! returns read-only reference to the specified element (3D case)
-    const _Tp& operator ()(int idx0, int idx1, int idx2) const;
-
-    _Tp& operator ()(Point pt);
-    const _Tp& operator ()(Point pt) const;
-
-    //! conversion to vector.
-    operator std::vector<_Tp>() const;
-
-#ifdef CV_CXX_STD_ARRAY
-    //! conversion to array.
-    template<std::size_t _Nm> operator std::array<_Tp, _Nm>() const;
-#endif
-
-    //! conversion to Vec
-    template<int n> operator Vec<typename DataType<_Tp>::channel_type, n>() const;
-    //! conversion to Matx
-    template<int m, int n> operator Matx<typename DataType<_Tp>::channel_type, m, n>() const;
-
-#ifdef CV_CXX_MOVE_SEMANTICS
-    Mat_(Mat_&& m);
-    Mat_& operator = (Mat_&& m);
-
-    Mat_(Mat&& m);
-    Mat_& operator = (Mat&& m);
-
-    Mat_(MatExpr&& e);
-#endif
-};
-
-typedef Mat_<uchar> Mat1b;
-typedef Mat_<Vec2b> Mat2b;
-typedef Mat_<Vec3b> Mat3b;
-typedef Mat_<Vec4b> Mat4b;
-
-typedef Mat_<short> Mat1s;
-typedef Mat_<Vec2s> Mat2s;
-typedef Mat_<Vec3s> Mat3s;
-typedef Mat_<Vec4s> Mat4s;
-
-typedef Mat_<ushort> Mat1w;
-typedef Mat_<Vec2w> Mat2w;
-typedef Mat_<Vec3w> Mat3w;
-typedef Mat_<Vec4w> Mat4w;
-
-typedef Mat_<int>   Mat1i;
-typedef Mat_<Vec2i> Mat2i;
-typedef Mat_<Vec3i> Mat3i;
-typedef Mat_<Vec4i> Mat4i;
-
-typedef Mat_<float> Mat1f;
-typedef Mat_<Vec2f> Mat2f;
-typedef Mat_<Vec3f> Mat3f;
-typedef Mat_<Vec4f> Mat4f;
-
-typedef Mat_<double> Mat1d;
-typedef Mat_<Vec2d> Mat2d;
-typedef Mat_<Vec3d> Mat3d;
-typedef Mat_<Vec4d> Mat4d;
-
-/** @todo document */
-class CV_EXPORTS UMat
-{
-public:
-    //! default constructor
-    UMat(UMatUsageFlags usageFlags = USAGE_DEFAULT);
-    //! constructs 2D matrix of the specified size and type
-    // (_type is CV_8UC1, CV_64FC3, CV_32SC(12) etc.)
-    UMat(int rows, int cols, int type, UMatUsageFlags usageFlags = USAGE_DEFAULT);
-    UMat(Size size, int type, UMatUsageFlags usageFlags = USAGE_DEFAULT);
-    //! constucts 2D matrix and fills it with the specified value _s.
-    UMat(int rows, int cols, int type, const Scalar& s, UMatUsageFlags usageFlags = USAGE_DEFAULT);
-    UMat(Size size, int type, const Scalar& s, UMatUsageFlags usageFlags = USAGE_DEFAULT);
-
-    //! constructs n-dimensional matrix
-    UMat(int ndims, const int* sizes, int type, UMatUsageFlags usageFlags = USAGE_DEFAULT);
-    UMat(int ndims, const int* sizes, int type, const Scalar& s, UMatUsageFlags usageFlags = USAGE_DEFAULT);
-
-    //! copy constructor
-    UMat(const UMat& m);
-
-    //! creates a matrix header for a part of the bigger matrix
-    UMat(const UMat& m, const Range& rowRange, const Range& colRange=Range::all());
-    UMat(const UMat& m, const Rect& roi);
-    UMat(const UMat& m, const Range* ranges);
-    UMat(const UMat& m, const std::vector<Range>& ranges);
-    //! builds matrix from std::vector with or without copying the data
-    template<typename _Tp> explicit UMat(const std::vector<_Tp>& vec, bool copyData=false);
-
-    //! builds matrix from cv::Vec; the data is copied by default
-    template<typename _Tp, int n> explicit UMat(const Vec<_Tp, n>& vec, bool copyData=true);
-    //! builds matrix from cv::Matx; the data is copied by default
-    template<typename _Tp, int m, int n> explicit UMat(const Matx<_Tp, m, n>& mtx, bool copyData=true);
-    //! builds matrix from a 2D point
-    template<typename _Tp> explicit UMat(const Point_<_Tp>& pt, bool copyData=true);
-    //! builds matrix from a 3D point
-    template<typename _Tp> explicit UMat(const Point3_<_Tp>& pt, bool copyData=true);
-    //! builds matrix from comma initializer
-    template<typename _Tp> explicit UMat(const MatCommaInitializer_<_Tp>& commaInitializer);
-
-    //! destructor - calls release()
-    ~UMat();
-    //! assignment operators
-    UMat& operator = (const UMat& m);
-
-    Mat getMat(int flags) const;
-
-    //! returns a new matrix header for the specified row
-    UMat row(int y) const;
-    //! returns a new matrix header for the specified column
-    UMat col(int x) const;
-    //! ... for the specified row span
-    UMat rowRange(int startrow, int endrow) const;
-    UMat rowRange(const Range& r) const;
-    //! ... for the specified column span
-    UMat colRange(int startcol, int endcol) const;
-    UMat colRange(const Range& r) const;
-    //! ... for the specified diagonal
-    //! (d=0 - the main diagonal,
-    //!  >0 - a diagonal from the upper half,
-    //!  <0 - a diagonal from the lower half)
-    UMat diag(int d=0) const;
-    //! constructs a square diagonal matrix which main diagonal is vector "d"
-    static UMat diag(const UMat& d);
-
-    //! returns deep copy of the matrix, i.e. the data is copied
-    UMat clone() const;
-    //! copies the matrix content to "m".
-    // It calls m.create(this->size(), this->type()).
-    void copyTo( OutputArray m ) const;
-    //! copies those matrix elements to "m" that are marked with non-zero mask elements.
-    void copyTo( OutputArray m, InputArray mask ) const;
-    //! converts matrix to another datatype with optional scaling. See cvConvertScale.
-    void convertTo( OutputArray m, int rtype, double alpha=1, double beta=0 ) const;
-
-    void assignTo( UMat& m, int type=-1 ) const;
-
-    //! sets every matrix element to s
-    UMat& operator = (const Scalar& s);
-    //! sets some of the matrix elements to s, according to the mask
-    UMat& setTo(InputArray value, InputArray mask=noArray());
-    //! creates alternative matrix header for the same data, with different
-    // number of channels and/or different number of rows. see cvReshape.
-    UMat reshape(int cn, int rows=0) const;
-    UMat reshape(int cn, int newndims, const int* newsz) const;
-
-    //! matrix transposition by means of matrix expressions
-    UMat t() const;
-    //! matrix inversion by means of matrix expressions
-    UMat inv(int method=DECOMP_LU) const;
-    //! per-element matrix multiplication by means of matrix expressions
-    UMat mul(InputArray m, double scale=1) const;
-
-    //! computes dot-product
-    double dot(InputArray m) const;
-
-    //! Matlab-style matrix initialization
-    static UMat zeros(int rows, int cols, int type);
-    static UMat zeros(Size size, int type);
-    static UMat zeros(int ndims, const int* sz, int type);
-    static UMat ones(int rows, int cols, int type);
-    static UMat ones(Size size, int type);
-    static UMat ones(int ndims, const int* sz, int type);
-    static UMat eye(int rows, int cols, int type);
-    static UMat eye(Size size, int type);
-
-    //! allocates new matrix data unless the matrix already has specified size and type.
-    // previous data is unreferenced if needed.
-    void create(int rows, int cols, int type, UMatUsageFlags usageFlags = USAGE_DEFAULT);
-    void create(Size size, int type, UMatUsageFlags usageFlags = USAGE_DEFAULT);
-    void create(int ndims, const int* sizes, int type, UMatUsageFlags usageFlags = USAGE_DEFAULT);
-    void create(const std::vector<int>& sizes, int type, UMatUsageFlags usageFlags = USAGE_DEFAULT);
-
-    //! increases the reference counter; use with care to avoid memleaks
-    void addref();
-    //! decreases reference counter;
-    // deallocates the data when reference counter reaches 0.
-    void release();
-
-    //! deallocates the matrix data
-    void deallocate();
-    //! internal use function; properly re-allocates _size, _step arrays
-    void copySize(const UMat& m);
-
-    //! locates matrix header within a parent matrix. See below
-    void locateROI( Size& wholeSize, Point& ofs ) const;
-    //! moves/resizes the current matrix ROI inside the parent matrix.
-    UMat& adjustROI( int dtop, int dbottom, int dleft, int dright );
-    //! extracts a rectangular sub-matrix
-    // (this is a generalized form of row, rowRange etc.)
-    UMat operator()( Range rowRange, Range colRange ) const;
-    UMat operator()( const Rect& roi ) const;
-    UMat operator()( const Range* ranges ) const;
-    UMat operator()(const std::vector<Range>& ranges) const;
-
-    //! returns true iff the matrix data is continuous
-    // (i.e. when there are no gaps between successive rows).
-    // similar to CV_IS_MAT_CONT(cvmat->type)
-    bool isContinuous() const;
-
-    //! returns true if the matrix is a submatrix of another matrix
-    bool isSubmatrix() const;
-
-    //! returns element size in bytes,
-    // similar to CV_ELEM_SIZE(cvmat->type)
-    size_t elemSize() const;
-    //! returns the size of element channel in bytes.
-    size_t elemSize1() const;
-    //! returns element type, similar to CV_MAT_TYPE(cvmat->type)
-    int type() const;
-    //! returns element type, similar to CV_MAT_DEPTH(cvmat->type)
-    int depth() const;
-    //! returns element type, similar to CV_MAT_CN(cvmat->type)
-    int channels() const;
-    //! returns step/elemSize1()
-    size_t step1(int i=0) const;
-    //! returns true if matrix data is NULL
-    bool empty() const;
-    //! returns the total number of matrix elements
-    size_t total() const;
-
-    //! returns N if the matrix is 1-channel (N x ptdim) or ptdim-channel (1 x N) or (N x 1); negative number otherwise
-    int checkVector(int elemChannels, int depth=-1, bool requireContinuous=true) const;
-
-#ifdef CV_CXX_MOVE_SEMANTICS
-    UMat(UMat&& m);
-    UMat& operator = (UMat&& m);
-#endif
-
-    /*! Returns the OpenCL buffer handle on which UMat operates on.
-        The UMat instance should be kept alive during the use of the handle to prevent the buffer to be
-        returned to the OpenCV buffer pool.
-     */
-    void* handle(int accessFlags) const;
-    void ndoffset(size_t* ofs) const;
-
-    enum { MAGIC_VAL  = 0x42FF0000, AUTO_STEP = 0, CONTINUOUS_FLAG = CV_MAT_CONT_FLAG, SUBMATRIX_FLAG = CV_SUBMAT_FLAG };
-    enum { MAGIC_MASK = 0xFFFF0000, TYPE_MASK = 0x00000FFF, DEPTH_MASK = 7 };
-
-    /*! includes several bit-fields:
-         - the magic signature
-         - continuity flag
-         - depth
-         - number of channels
-     */
-    int flags;
-    //! the matrix dimensionality, >= 2
-    int dims;
-    //! the number of rows and columns or (-1, -1) when the matrix has more than 2 dimensions
-    int rows, cols;
-
-    //! custom allocator
-    MatAllocator* allocator;
-    UMatUsageFlags usageFlags; // usage flags for allocator
-    //! and the standard allocator
-    static MatAllocator* getStdAllocator();
-
-    //! internal use method: updates the continuity flag
-    void updateContinuityFlag();
-
-    // black-box container of UMat data
-    UMatData* u;
-
-    // offset of the submatrix (or 0)
-    size_t offset;
-
-    MatSize size;
-    MatStep step;
-
-protected:
-};
-
-
-/////////////////////////// multi-dimensional sparse matrix //////////////////////////
-
-/** @brief The class SparseMat represents multi-dimensional sparse numerical arrays.
-
-Such a sparse array can store elements of any type that Mat can store. *Sparse* means that only
-non-zero elements are stored (though, as a result of operations on a sparse matrix, some of its
-stored elements can actually become 0. It is up to you to detect such elements and delete them
-using SparseMat::erase ). The non-zero elements are stored in a hash table that grows when it is
-filled so that the search time is O(1) in average (regardless of whether element is there or not).
-Elements can be accessed using the following methods:
--   Query operations (SparseMat::ptr and the higher-level SparseMat::ref, SparseMat::value and
-    SparseMat::find), for example:
-    @code
-        const int dims = 5;
-        int size[5] = {10, 10, 10, 10, 10};
-        SparseMat sparse_mat(dims, size, CV_32F);
-        for(int i = 0; i < 1000; i++)
-        {
-            int idx[dims];
-            for(int k = 0; k < dims; k++)
-                idx[k] = rand() % size[k];
-            sparse_mat.ref<float>(idx) += 1.f;
-        }
-        cout << "nnz = " << sparse_mat.nzcount() << endl;
-    @endcode
--   Sparse matrix iterators. They are similar to MatIterator but different from NAryMatIterator.
-    That is, the iteration loop is familiar to STL users:
-    @code
-        // prints elements of a sparse floating-point matrix
-        // and the sum of elements.
-        SparseMatConstIterator_<float>
-            it = sparse_mat.begin<float>(),
-            it_end = sparse_mat.end<float>();
-        double s = 0;
-        int dims = sparse_mat.dims();
-        for(; it != it_end; ++it)
-        {
-            // print element indices and the element value
-            const SparseMat::Node* n = it.node();
-            printf("(");
-            for(int i = 0; i < dims; i++)
-                printf("%d%s", n->idx[i], i < dims-1 ? ", " : ")");
-            printf(": %g\n", it.value<float>());
-            s += *it;
-        }
-        printf("Element sum is %g\n", s);
-    @endcode
-    If you run this loop, you will notice that elements are not enumerated in a logical order
-    (lexicographical, and so on). They come in the same order as they are stored in the hash table
-    (semi-randomly). You may collect pointers to the nodes and sort them to get the proper ordering.
-    Note, however, that pointers to the nodes may become invalid when you add more elements to the
-    matrix. This may happen due to possible buffer reallocation.
--   Combination of the above 2 methods when you need to process 2 or more sparse matrices
-    simultaneously. For example, this is how you can compute unnormalized cross-correlation of the 2
-    floating-point sparse matrices:
-    @code
-        double cross_corr(const SparseMat& a, const SparseMat& b)
-        {
-            const SparseMat *_a = &a, *_b = &b;
-            // if b contains less elements than a,
-            // it is faster to iterate through b
-            if(_a->nzcount() > _b->nzcount())
-                std::swap(_a, _b);
-            SparseMatConstIterator_<float> it = _a->begin<float>(),
-                                           it_end = _a->end<float>();
-            double ccorr = 0;
-            for(; it != it_end; ++it)
-            {
-                // take the next element from the first matrix
-                float avalue = *it;
-                const Node* anode = it.node();
-                // and try to find an element with the same index in the second matrix.
-                // since the hash value depends only on the element index,
-                // reuse the hash value stored in the node
-                float bvalue = _b->value<float>(anode->idx,&anode->hashval);
-                ccorr += avalue*bvalue;
-            }
-            return ccorr;
-        }
-    @endcode
- */
-class CV_EXPORTS SparseMat
-{
-public:
-    typedef SparseMatIterator iterator;
-    typedef SparseMatConstIterator const_iterator;
-
-    enum { MAGIC_VAL=0x42FD0000, MAX_DIM=32, HASH_SCALE=0x5bd1e995, HASH_BIT=0x80000000 };
-
-    //! the sparse matrix header
-    struct CV_EXPORTS Hdr
-    {
-        Hdr(int _dims, const int* _sizes, int _type);
-        void clear();
-        int refcount;
-        int dims;
-        int valueOffset;
-        size_t nodeSize;
-        size_t nodeCount;
-        size_t freeList;
-        std::vector<uchar> pool;
-        std::vector<size_t> hashtab;
-        int size[MAX_DIM];
-    };
-
-    //! sparse matrix node - element of a hash table
-    struct CV_EXPORTS Node
-    {
-        //! hash value
-        size_t hashval;
-        //! index of the next node in the same hash table entry
-        size_t next;
-        //! index of the matrix element
-        int idx[MAX_DIM];
-    };
-
-    /** @brief Various SparseMat constructors.
-     */
-    SparseMat();
-
-    /** @overload
-    @param dims Array dimensionality.
-    @param _sizes Sparce matrix size on all dementions.
-    @param _type Sparse matrix data type.
-    */
-    SparseMat(int dims, const int* _sizes, int _type);
-
-    /** @overload
-    @param m Source matrix for copy constructor. If m is dense matrix (ocvMat) then it will be converted
-    to sparse representation.
-    */
-    SparseMat(const SparseMat& m);
-
-    /** @overload
-    @param m Source matrix for copy constructor. If m is dense matrix (ocvMat) then it will be converted
-    to sparse representation.
-    */
-    explicit SparseMat(const Mat& m);
-
-    //! the destructor
-    ~SparseMat();
-
-    //! assignment operator. This is O(1) operation, i.e. no data is copied
-    SparseMat& operator = (const SparseMat& m);
-    //! equivalent to the corresponding constructor
-    SparseMat& operator = (const Mat& m);
-
-    //! creates full copy of the matrix
-    SparseMat clone() const;
-
-    //! copies all the data to the destination matrix. All the previous content of m is erased
-    void copyTo( SparseMat& m ) const;
-    //! converts sparse matrix to dense matrix.
-    void copyTo( Mat& m ) const;
-    //! multiplies all the matrix elements by the specified scale factor alpha and converts the results to the specified data type
-    void convertTo( SparseMat& m, int rtype, double alpha=1 ) const;
-    //! converts sparse matrix to dense n-dim matrix with optional type conversion and scaling.
-    /*!
-        @param [out] m - output matrix; if it does not have a proper size or type before the operation,
-            it is reallocated
-        @param [in] rtype - desired output matrix type or, rather, the depth since the number of channels
-            are the same as the input has; if rtype is negative, the output matrix will have the
-            same type as the input.
-        @param [in] alpha - optional scale factor
-        @param [in] beta - optional delta added to the scaled values
-    */
-    void convertTo( Mat& m, int rtype, double alpha=1, double beta=0 ) const;
-
-    // not used now
-    void assignTo( SparseMat& m, int type=-1 ) const;
-
-    //! reallocates sparse matrix.
-    /*!
-        If the matrix already had the proper size and type,
-        it is simply cleared with clear(), otherwise,
-        the old matrix is released (using release()) and the new one is allocated.
-    */
-    void create(int dims, const int* _sizes, int _type);
-    //! sets all the sparse matrix elements to 0, which means clearing the hash table.
-    void clear();
-    //! manually increments the reference counter to the header.
-    void addref();
-    // decrements the header reference counter. When the counter reaches 0, the header and all the underlying data are deallocated.
-    void release();
-
-    //! converts sparse matrix to the old-style representation; all the elements are copied.
-    //operator CvSparseMat*() const;
-    //! returns the size of each element in bytes (not including the overhead - the space occupied by SparseMat::Node elements)
-    size_t elemSize() const;
-    //! returns elemSize()/channels()
-    size_t elemSize1() const;
-
-    //! returns type of sparse matrix elements
-    int type() const;
-    //! returns the depth of sparse matrix elements
-    int depth() const;
-    //! returns the number of channels
-    int channels() const;
-
-    //! returns the array of sizes, or NULL if the matrix is not allocated
-    const int* size() const;
-    //! returns the size of i-th matrix dimension (or 0)
-    int size(int i) const;
-    //! returns the matrix dimensionality
-    int dims() const;
-    //! returns the number of non-zero elements (=the number of hash table nodes)
-    size_t nzcount() const;
-
-    //! computes the element hash value (1D case)
-    size_t hash(int i0) const;
-    //! computes the element hash value (2D case)
-    size_t hash(int i0, int i1) const;
-    //! computes the element hash value (3D case)
-    size_t hash(int i0, int i1, int i2) const;
-    //! computes the element hash value (nD case)
-    size_t hash(const int* idx) const;
-
-    //!@{
-    /*!
-     specialized variants for 1D, 2D, 3D cases and the generic_type one for n-D case.
-     return pointer to the matrix element.
-      - if the element is there (it's non-zero), the pointer to it is returned
-      - if it's not there and createMissing=false, NULL pointer is returned
-      - if it's not there and createMissing=true, then the new element
-        is created and initialized with 0. Pointer to it is returned
-      - if the optional hashval pointer is not NULL, the element hash value is
-        not computed, but *hashval is taken instead.
-    */
-    //! returns pointer to the specified element (1D case)
-    uchar* ptr(int i0, bool createMissing, size_t* hashval=0);
-    //! returns pointer to the specified element (2D case)
-    uchar* ptr(int i0, int i1, bool createMissing, size_t* hashval=0);
-    //! returns pointer to the specified element (3D case)
-    uchar* ptr(int i0, int i1, int i2, bool createMissing, size_t* hashval=0);
-    //! returns pointer to the specified element (nD case)
-    uchar* ptr(const int* idx, bool createMissing, size_t* hashval=0);
-    //!@}
-
-    //!@{
-    /*!
-     return read-write reference to the specified sparse matrix element.
-
-     `ref<_Tp>(i0,...[,hashval])` is equivalent to `*(_Tp*)ptr(i0,...,true[,hashval])`.
-     The methods always return a valid reference.
-     If the element did not exist, it is created and initialiazed with 0.
-    */
-    //! returns reference to the specified element (1D case)
-    template<typename _Tp> _Tp& ref(int i0, size_t* hashval=0);
-    //! returns reference to the specified element (2D case)
-    template<typename _Tp> _Tp& ref(int i0, int i1, size_t* hashval=0);
-    //! returns reference to the specified element (3D case)
-    template<typename _Tp> _Tp& ref(int i0, int i1, int i2, size_t* hashval=0);
-    //! returns reference to the specified element (nD case)
-    template<typename _Tp> _Tp& ref(const int* idx, size_t* hashval=0);
-    //!@}
-
-    //!@{
-    /*!
-     return value of the specified sparse matrix element.
-
-     `value<_Tp>(i0,...[,hashval])` is equivalent to
-     @code
-     { const _Tp* p = find<_Tp>(i0,...[,hashval]); return p ? *p : _Tp(); }
-     @endcode
-
-     That is, if the element did not exist, the methods return 0.
-     */
-    //! returns value of the specified element (1D case)
-    template<typename _Tp> _Tp value(int i0, size_t* hashval=0) const;
-    //! returns value of the specified element (2D case)
-    template<typename _Tp> _Tp value(int i0, int i1, size_t* hashval=0) const;
-    //! returns value of the specified element (3D case)
-    template<typename _Tp> _Tp value(int i0, int i1, int i2, size_t* hashval=0) const;
-    //! returns value of the specified element (nD case)
-    template<typename _Tp> _Tp value(const int* idx, size_t* hashval=0) const;
-    //!@}
-
-    //!@{
-    /*!
-     Return pointer to the specified sparse matrix element if it exists
-
-     `find<_Tp>(i0,...[,hashval])` is equivalent to `(_const Tp*)ptr(i0,...false[,hashval])`.
-
-     If the specified element does not exist, the methods return NULL.
-    */
-    //! returns pointer to the specified element (1D case)
-    template<typename _Tp> const _Tp* find(int i0, size_t* hashval=0) const;
-    //! returns pointer to the specified element (2D case)
-    template<typename _Tp> const _Tp* find(int i0, int i1, size_t* hashval=0) const;
-    //! returns pointer to the specified element (3D case)
-    template<typename _Tp> const _Tp* find(int i0, int i1, int i2, size_t* hashval=0) const;
-    //! returns pointer to the specified element (nD case)
-    template<typename _Tp> const _Tp* find(const int* idx, size_t* hashval=0) const;
-    //!@}
-
-    //! erases the specified element (2D case)
-    void erase(int i0, int i1, size_t* hashval=0);
-    //! erases the specified element (3D case)
-    void erase(int i0, int i1, int i2, size_t* hashval=0);
-    //! erases the specified element (nD case)
-    void erase(const int* idx, size_t* hashval=0);
-
-    //!@{
-    /*!
-       return the sparse matrix iterator pointing to the first sparse matrix element
-    */
-    //! returns the sparse matrix iterator at the matrix beginning
-    SparseMatIterator begin();
-    //! returns the sparse matrix iterator at the matrix beginning
-    template<typename _Tp> SparseMatIterator_<_Tp> begin();
-    //! returns the read-only sparse matrix iterator at the matrix beginning
-    SparseMatConstIterator begin() const;
-    //! returns the read-only sparse matrix iterator at the matrix beginning
-    template<typename _Tp> SparseMatConstIterator_<_Tp> begin() const;
-    //!@}
-    /*!
-       return the sparse matrix iterator pointing to the element following the last sparse matrix element
-    */
-    //! returns the sparse matrix iterator at the matrix end
-    SparseMatIterator end();
-    //! returns the read-only sparse matrix iterator at the matrix end
-    SparseMatConstIterator end() const;
-    //! returns the typed sparse matrix iterator at the matrix end
-    template<typename _Tp> SparseMatIterator_<_Tp> end();
-    //! returns the typed read-only sparse matrix iterator at the matrix end
-    template<typename _Tp> SparseMatConstIterator_<_Tp> end() const;
-
-    //! returns the value stored in the sparse martix node
-    template<typename _Tp> _Tp& value(Node* n);
-    //! returns the value stored in the sparse martix node
-    template<typename _Tp> const _Tp& value(const Node* n) const;
-
-    ////////////// some internal-use methods ///////////////
-    Node* node(size_t nidx);
-    const Node* node(size_t nidx) const;
-
-    uchar* newNode(const int* idx, size_t hashval);
-    void removeNode(size_t hidx, size_t nidx, size_t previdx);
-    void resizeHashTab(size_t newsize);
-
-    int flags;
-    Hdr* hdr;
-};
-
-
-
-///////////////////////////////// SparseMat_<_Tp> ////////////////////////////////////
-
-/** @brief Template sparse n-dimensional array class derived from SparseMat
-
-SparseMat_ is a thin wrapper on top of SparseMat created in the same way as Mat_ . It simplifies
-notation of some operations:
-@code
-    int sz[] = {10, 20, 30};
-    SparseMat_<double> M(3, sz);
-    ...
-    M.ref(1, 2, 3) = M(4, 5, 6) + M(7, 8, 9);
-@endcode
- */
-template<typename _Tp> class SparseMat_ : public SparseMat
-{
-public:
-    typedef SparseMatIterator_<_Tp> iterator;
-    typedef SparseMatConstIterator_<_Tp> const_iterator;
-
-    //! the default constructor
-    SparseMat_();
-    //! the full constructor equivalent to SparseMat(dims, _sizes, DataType<_Tp>::type)
-    SparseMat_(int dims, const int* _sizes);
-    //! the copy constructor. If DataType<_Tp>.type != m.type(), the m elements are converted
-    SparseMat_(const SparseMat& m);
-    //! the copy constructor. This is O(1) operation - no data is copied
-    SparseMat_(const SparseMat_& m);
-    //! converts dense matrix to the sparse form
-    SparseMat_(const Mat& m);
-    //! converts the old-style sparse matrix to the C++ class. All the elements are copied
-    //SparseMat_(const CvSparseMat* m);
-    //! the assignment operator. If DataType<_Tp>.type != m.type(), the m elements are converted
-    SparseMat_& operator = (const SparseMat& m);
-    //! the assignment operator. This is O(1) operation - no data is copied
-    SparseMat_& operator = (const SparseMat_& m);
-    //! converts dense matrix to the sparse form
-    SparseMat_& operator = (const Mat& m);
-
-    //! makes full copy of the matrix. All the elements are duplicated
-    SparseMat_ clone() const;
-    //! equivalent to cv::SparseMat::create(dims, _sizes, DataType<_Tp>::type)
-    void create(int dims, const int* _sizes);
-    //! converts sparse matrix to the old-style CvSparseMat. All the elements are copied
-    //operator CvSparseMat*() const;
-
-    //! returns type of the matrix elements
-    int type() const;
-    //! returns depth of the matrix elements
-    int depth() const;
-    //! returns the number of channels in each matrix element
-    int channels() const;
-
-    //! equivalent to SparseMat::ref<_Tp>(i0, hashval)
-    _Tp& ref(int i0, size_t* hashval=0);
-    //! equivalent to SparseMat::ref<_Tp>(i0, i1, hashval)
-    _Tp& ref(int i0, int i1, size_t* hashval=0);
-    //! equivalent to SparseMat::ref<_Tp>(i0, i1, i2, hashval)
-    _Tp& ref(int i0, int i1, int i2, size_t* hashval=0);
-    //! equivalent to SparseMat::ref<_Tp>(idx, hashval)
-    _Tp& ref(const int* idx, size_t* hashval=0);
-
-    //! equivalent to SparseMat::value<_Tp>(i0, hashval)
-    _Tp operator()(int i0, size_t* hashval=0) const;
-    //! equivalent to SparseMat::value<_Tp>(i0, i1, hashval)
-    _Tp operator()(int i0, int i1, size_t* hashval=0) const;
-    //! equivalent to SparseMat::value<_Tp>(i0, i1, i2, hashval)
-    _Tp operator()(int i0, int i1, int i2, size_t* hashval=0) const;
-    //! equivalent to SparseMat::value<_Tp>(idx, hashval)
-    _Tp operator()(const int* idx, size_t* hashval=0) const;
-
-    //! returns sparse matrix iterator pointing to the first sparse matrix element
-    SparseMatIterator_<_Tp> begin();
-    //! returns read-only sparse matrix iterator pointing to the first sparse matrix element
-    SparseMatConstIterator_<_Tp> begin() const;
-    //! returns sparse matrix iterator pointing to the element following the last sparse matrix element
-    SparseMatIterator_<_Tp> end();
-    //! returns read-only sparse matrix iterator pointing to the element following the last sparse matrix element
-    SparseMatConstIterator_<_Tp> end() const;
-};
-
-
-
-////////////////////////////////// MatConstIterator //////////////////////////////////
-
-class CV_EXPORTS MatConstIterator
-{
-public:
-    typedef uchar* value_type;
-    typedef ptrdiff_t difference_type;
-    typedef const uchar** pointer;
-    typedef uchar* reference;
-
-    typedef std::random_access_iterator_tag iterator_category;
-
-    //! default constructor
-    MatConstIterator();
-    //! constructor that sets the iterator to the beginning of the matrix
-    MatConstIterator(const Mat* _m);
-    //! constructor that sets the iterator to the specified element of the matrix
-    MatConstIterator(const Mat* _m, int _row, int _col=0);
-    //! constructor that sets the iterator to the specified element of the matrix
-    MatConstIterator(const Mat* _m, Point _pt);
-    //! constructor that sets the iterator to the specified element of the matrix
-    MatConstIterator(const Mat* _m, const int* _idx);
-    //! copy constructor
-    MatConstIterator(const MatConstIterator& it);
-
-    //! copy operator
-    MatConstIterator& operator = (const MatConstIterator& it);
-    //! returns the current matrix element
-    const uchar* operator *() const;
-    //! returns the i-th matrix element, relative to the current
-    const uchar* operator [](ptrdiff_t i) const;
-
-    //! shifts the iterator forward by the specified number of elements
-    MatConstIterator& operator += (ptrdiff_t ofs);
-    //! shifts the iterator backward by the specified number of elements
-    MatConstIterator& operator -= (ptrdiff_t ofs);
-    //! decrements the iterator
-    MatConstIterator& operator --();
-    //! decrements the iterator
-    MatConstIterator operator --(int);
-    //! increments the iterator
-    MatConstIterator& operator ++();
-    //! increments the iterator
-    MatConstIterator operator ++(int);
-    //! returns the current iterator position
-    Point pos() const;
-    //! returns the current iterator position
-    void pos(int* _idx) const;
-
-    ptrdiff_t lpos() const;
-    void seek(ptrdiff_t ofs, bool relative = false);
-    void seek(const int* _idx, bool relative = false);
-
-    const Mat* m;
-    size_t elemSize;
-    const uchar* ptr;
-    const uchar* sliceStart;
-    const uchar* sliceEnd;
-};
-
-
-
-////////////////////////////////// MatConstIterator_ /////////////////////////////////
-
-/** @brief Matrix read-only iterator
- */
-template<typename _Tp>
-class MatConstIterator_ : public MatConstIterator
-{
-public:
-    typedef _Tp value_type;
-    typedef ptrdiff_t difference_type;
-    typedef const _Tp* pointer;
-    typedef const _Tp& reference;
-
-    typedef std::random_access_iterator_tag iterator_category;
-
-    //! default constructor
-    MatConstIterator_();
-    //! constructor that sets the iterator to the beginning of the matrix
-    MatConstIterator_(const Mat_<_Tp>* _m);
-    //! constructor that sets the iterator to the specified element of the matrix
-    MatConstIterator_(const Mat_<_Tp>* _m, int _row, int _col=0);
-    //! constructor that sets the iterator to the specified element of the matrix
-    MatConstIterator_(const Mat_<_Tp>* _m, Point _pt);
-    //! constructor that sets the iterator to the specified element of the matrix
-    MatConstIterator_(const Mat_<_Tp>* _m, const int* _idx);
-    //! copy constructor
-    MatConstIterator_(const MatConstIterator_& it);
-
-    //! copy operator
-    MatConstIterator_& operator = (const MatConstIterator_& it);
-    //! returns the current matrix element
-    const _Tp& operator *() const;
-    //! returns the i-th matrix element, relative to the current
-    const _Tp& operator [](ptrdiff_t i) const;
-
-    //! shifts the iterator forward by the specified number of elements
-    MatConstIterator_& operator += (ptrdiff_t ofs);
-    //! shifts the iterator backward by the specified number of elements
-    MatConstIterator_& operator -= (ptrdiff_t ofs);
-    //! decrements the iterator
-    MatConstIterator_& operator --();
-    //! decrements the iterator
-    MatConstIterator_ operator --(int);
-    //! increments the iterator
-    MatConstIterator_& operator ++();
-    //! increments the iterator
-    MatConstIterator_ operator ++(int);
-    //! returns the current iterator position
-    Point pos() const;
-};
-
-
-
-//////////////////////////////////// MatIterator_ ////////////////////////////////////
-
-/** @brief Matrix read-write iterator
-*/
-template<typename _Tp>
-class MatIterator_ : public MatConstIterator_<_Tp>
-{
-public:
-    typedef _Tp* pointer;
-    typedef _Tp& reference;
-
-    typedef std::random_access_iterator_tag iterator_category;
-
-    //! the default constructor
-    MatIterator_();
-    //! constructor that sets the iterator to the beginning of the matrix
-    MatIterator_(Mat_<_Tp>* _m);
-    //! constructor that sets the iterator to the specified element of the matrix
-    MatIterator_(Mat_<_Tp>* _m, int _row, int _col=0);
-    //! constructor that sets the iterator to the specified element of the matrix
-    MatIterator_(Mat_<_Tp>* _m, Point _pt);
-    //! constructor that sets the iterator to the specified element of the matrix
-    MatIterator_(Mat_<_Tp>* _m, const int* _idx);
-    //! copy constructor
-    MatIterator_(const MatIterator_& it);
-    //! copy operator
-    MatIterator_& operator = (const MatIterator_<_Tp>& it );
-
-    //! returns the current matrix element
-    _Tp& operator *() const;
-    //! returns the i-th matrix element, relative to the current
-    _Tp& operator [](ptrdiff_t i) const;
-
-    //! shifts the iterator forward by the specified number of elements
-    MatIterator_& operator += (ptrdiff_t ofs);
-    //! shifts the iterator backward by the specified number of elements
-    MatIterator_& operator -= (ptrdiff_t ofs);
-    //! decrements the iterator
-    MatIterator_& operator --();
-    //! decrements the iterator
-    MatIterator_ operator --(int);
-    //! increments the iterator
-    MatIterator_& operator ++();
-    //! increments the iterator
-    MatIterator_ operator ++(int);
-};
-
-
-
-/////////////////////////////// SparseMatConstIterator ///////////////////////////////
-
-/**  @brief Read-Only Sparse Matrix Iterator.
-
- Here is how to use the iterator to compute the sum of floating-point sparse matrix elements:
-
- \code
- SparseMatConstIterator it = m.begin(), it_end = m.end();
- double s = 0;
- CV_Assert( m.type() == CV_32F );
- for( ; it != it_end; ++it )
-    s += it.value<float>();
- \endcode
-*/
-class CV_EXPORTS SparseMatConstIterator
-{
-public:
-    //! the default constructor
-    SparseMatConstIterator();
-    //! the full constructor setting the iterator to the first sparse matrix element
-    SparseMatConstIterator(const SparseMat* _m);
-    //! the copy constructor
-    SparseMatConstIterator(const SparseMatConstIterator& it);
-
-    //! the assignment operator
-    SparseMatConstIterator& operator = (const SparseMatConstIterator& it);
-
-    //! template method returning the current matrix element
-    template<typename _Tp> const _Tp& value() const;
-    //! returns the current node of the sparse matrix. it.node->idx is the current element index
-    const SparseMat::Node* node() const;
-
-    //! moves iterator to the previous element
-    SparseMatConstIterator& operator --();
-    //! moves iterator to the previous element
-    SparseMatConstIterator operator --(int);
-    //! moves iterator to the next element
-    SparseMatConstIterator& operator ++();
-    //! moves iterator to the next element
-    SparseMatConstIterator operator ++(int);
-
-    //! moves iterator to the element after the last element
-    void seekEnd();
-
-    const SparseMat* m;
-    size_t hashidx;
-    uchar* ptr;
-};
-
-
-
-////////////////////////////////// SparseMatIterator /////////////////////////////////
-
-/** @brief  Read-write Sparse Matrix Iterator
-
- The class is similar to cv::SparseMatConstIterator,
- but can be used for in-place modification of the matrix elements.
-*/
-class CV_EXPORTS SparseMatIterator : public SparseMatConstIterator
-{
-public:
-    //! the default constructor
-    SparseMatIterator();
-    //! the full constructor setting the iterator to the first sparse matrix element
-    SparseMatIterator(SparseMat* _m);
-    //! the full constructor setting the iterator to the specified sparse matrix element
-    SparseMatIterator(SparseMat* _m, const int* idx);
-    //! the copy constructor
-    SparseMatIterator(const SparseMatIterator& it);
-
-    //! the assignment operator
-    SparseMatIterator& operator = (const SparseMatIterator& it);
-    //! returns read-write reference to the current sparse matrix element
-    template<typename _Tp> _Tp& value() const;
-    //! returns pointer to the current sparse matrix node. it.node->idx is the index of the current element (do not modify it!)
-    SparseMat::Node* node() const;
-
-    //! moves iterator to the next element
-    SparseMatIterator& operator ++();
-    //! moves iterator to the next element
-    SparseMatIterator operator ++(int);
-};
-
-
-
-/////////////////////////////// SparseMatConstIterator_ //////////////////////////////
-
-/** @brief  Template Read-Only Sparse Matrix Iterator Class.
-
- This is the derived from SparseMatConstIterator class that
- introduces more convenient operator *() for accessing the current element.
-*/
-template<typename _Tp> class SparseMatConstIterator_ : public SparseMatConstIterator
-{
-public:
-
-    typedef std::forward_iterator_tag iterator_category;
-
-    //! the default constructor
-    SparseMatConstIterator_();
-    //! the full constructor setting the iterator to the first sparse matrix element
-    SparseMatConstIterator_(const SparseMat_<_Tp>* _m);
-    SparseMatConstIterator_(const SparseMat* _m);
-    //! the copy constructor
-    SparseMatConstIterator_(const SparseMatConstIterator_& it);
-
-    //! the assignment operator
-    SparseMatConstIterator_& operator = (const SparseMatConstIterator_& it);
-    //! the element access operator
-    const _Tp& operator *() const;
-
-    //! moves iterator to the next element
-    SparseMatConstIterator_& operator ++();
-    //! moves iterator to the next element
-    SparseMatConstIterator_ operator ++(int);
-};
-
-
-
-///////////////////////////////// SparseMatIterator_ /////////////////////////////////
-
-/** @brief  Template Read-Write Sparse Matrix Iterator Class.
-
- This is the derived from cv::SparseMatConstIterator_ class that
- introduces more convenient operator *() for accessing the current element.
-*/
-template<typename _Tp> class SparseMatIterator_ : public SparseMatConstIterator_<_Tp>
-{
-public:
-
-    typedef std::forward_iterator_tag iterator_category;
-
-    //! the default constructor
-    SparseMatIterator_();
-    //! the full constructor setting the iterator to the first sparse matrix element
-    SparseMatIterator_(SparseMat_<_Tp>* _m);
-    SparseMatIterator_(SparseMat* _m);
-    //! the copy constructor
-    SparseMatIterator_(const SparseMatIterator_& it);
-
-    //! the assignment operator
-    SparseMatIterator_& operator = (const SparseMatIterator_& it);
-    //! returns the reference to the current element
-    _Tp& operator *() const;
-
-    //! moves the iterator to the next element
-    SparseMatIterator_& operator ++();
-    //! moves the iterator to the next element
-    SparseMatIterator_ operator ++(int);
-};
-
-
-
-/////////////////////////////////// NAryMatIterator //////////////////////////////////
-
-/** @brief n-ary multi-dimensional array iterator.
-
-Use the class to implement unary, binary, and, generally, n-ary element-wise operations on
-multi-dimensional arrays. Some of the arguments of an n-ary function may be continuous arrays, some
-may be not. It is possible to use conventional MatIterator 's for each array but incrementing all of
-the iterators after each small operations may be a big overhead. In this case consider using
-NAryMatIterator to iterate through several matrices simultaneously as long as they have the same
-geometry (dimensionality and all the dimension sizes are the same). On each iteration `it.planes[0]`,
-`it.planes[1]`,... will be the slices of the corresponding matrices.
-
-The example below illustrates how you can compute a normalized and threshold 3D color histogram:
-@code
-    void computeNormalizedColorHist(const Mat& image, Mat& hist, int N, double minProb)
-    {
-        const int histSize[] = {N, N, N};
-
-        // make sure that the histogram has a proper size and type
-        hist.create(3, histSize, CV_32F);
-
-        // and clear it
-        hist = Scalar(0);
-
-        // the loop below assumes that the image
-        // is a 8-bit 3-channel. check it.
-        CV_Assert(image.type() == CV_8UC3);
-        MatConstIterator_<Vec3b> it = image.begin<Vec3b>(),
-                                 it_end = image.end<Vec3b>();
-        for( ; it != it_end; ++it )
-        {
-            const Vec3b& pix = *it;
-            hist.at<float>(pix[0]*N/256, pix[1]*N/256, pix[2]*N/256) += 1.f;
-        }
-
-        minProb *= image.rows*image.cols;
-
-        // initialize iterator (the style is different from STL).
-        // after initialization the iterator will contain
-        // the number of slices or planes the iterator will go through.
-        // it simultaneously increments iterators for several matrices
-        // supplied as a null terminated list of pointers
-        const Mat* arrays[] = {&hist, 0};
-        Mat planes[1];
-        NAryMatIterator itNAry(arrays, planes, 1);
-        double s = 0;
-        // iterate through the matrix. on each iteration
-        // itNAry.planes[i] (of type Mat) will be set to the current plane
-        // of the i-th n-dim matrix passed to the iterator constructor.
-        for(int p = 0; p < itNAry.nplanes; p++, ++itNAry)
-        {
-            threshold(itNAry.planes[0], itNAry.planes[0], minProb, 0, THRESH_TOZERO);
-            s += sum(itNAry.planes[0])[0];
-        }
-
-        s = 1./s;
-        itNAry = NAryMatIterator(arrays, planes, 1);
-        for(int p = 0; p < itNAry.nplanes; p++, ++itNAry)
-            itNAry.planes[0] *= s;
-    }
-@endcode
- */
-class CV_EXPORTS NAryMatIterator
-{
-public:
-    //! the default constructor
-    NAryMatIterator();
-    //! the full constructor taking arbitrary number of n-dim matrices
-    NAryMatIterator(const Mat** arrays, uchar** ptrs, int narrays=-1);
-    //! the full constructor taking arbitrary number of n-dim matrices
-    NAryMatIterator(const Mat** arrays, Mat* planes, int narrays=-1);
-    //! the separate iterator initialization method
-    void init(const Mat** arrays, Mat* planes, uchar** ptrs, int narrays=-1);
-
-    //! proceeds to the next plane of every iterated matrix
-    NAryMatIterator& operator ++();
-    //! proceeds to the next plane of every iterated matrix (postfix increment operator)
-    NAryMatIterator operator ++(int);
-
-    //! the iterated arrays
-    const Mat** arrays;
-    //! the current planes
-    Mat* planes;
-    //! data pointers
-    uchar** ptrs;
-    //! the number of arrays
-    int narrays;
-    //! the number of hyper-planes that the iterator steps through
-    size_t nplanes;
-    //! the size of each segment (in elements)
-    size_t size;
-protected:
-    int iterdepth;
-    size_t idx;
-};
-
-
-
-///////////////////////////////// Matrix Expressions /////////////////////////////////
-
-class CV_EXPORTS MatOp
-{
-public:
-    MatOp();
-    virtual ~MatOp();
-
-    virtual bool elementWise(const MatExpr& expr) const;
-    virtual void assign(const MatExpr& expr, Mat& m, int type=-1) const = 0;
-    virtual void roi(const MatExpr& expr, const Range& rowRange,
-                     const Range& colRange, MatExpr& res) const;
-    virtual void diag(const MatExpr& expr, int d, MatExpr& res) const;
-    virtual void augAssignAdd(const MatExpr& expr, Mat& m) const;
-    virtual void augAssignSubtract(const MatExpr& expr, Mat& m) const;
-    virtual void augAssignMultiply(const MatExpr& expr, Mat& m) const;
-    virtual void augAssignDivide(const MatExpr& expr, Mat& m) const;
-    virtual void augAssignAnd(const MatExpr& expr, Mat& m) const;
-    virtual void augAssignOr(const MatExpr& expr, Mat& m) const;
-    virtual void augAssignXor(const MatExpr& expr, Mat& m) const;
-
-    virtual void add(const MatExpr& expr1, const MatExpr& expr2, MatExpr& res) const;
-    virtual void add(const MatExpr& expr1, const Scalar& s, MatExpr& res) const;
-
-    virtual void subtract(const MatExpr& expr1, const MatExpr& expr2, MatExpr& res) const;
-    virtual void subtract(const Scalar& s, const MatExpr& expr, MatExpr& res) const;
-
-    virtual void multiply(const MatExpr& expr1, const MatExpr& expr2, MatExpr& res, double scale=1) const;
-    virtual void multiply(const MatExpr& expr1, double s, MatExpr& res) const;
-
-    virtual void divide(const MatExpr& expr1, const MatExpr& expr2, MatExpr& res, double scale=1) const;
-    virtual void divide(double s, const MatExpr& expr, MatExpr& res) const;
-
-    virtual void abs(const MatExpr& expr, MatExpr& res) const;
-
-    virtual void transpose(const MatExpr& expr, MatExpr& res) const;
-    virtual void matmul(const MatExpr& expr1, const MatExpr& expr2, MatExpr& res) const;
-    virtual void invert(const MatExpr& expr, int method, MatExpr& res) const;
-
-    virtual Size size(const MatExpr& expr) const;
-    virtual int type(const MatExpr& expr) const;
-};
-
-/** @brief Matrix expression representation
-@anchor MatrixExpressions
-This is a list of implemented matrix operations that can be combined in arbitrary complex
-expressions (here A, B stand for matrices ( Mat ), s for a scalar ( Scalar ), alpha for a
-real-valued scalar ( double )):
--   Addition, subtraction, negation: `A+B`, `A-B`, `A+s`, `A-s`, `s+A`, `s-A`, `-A`
--   Scaling: `A*alpha`
--   Per-element multiplication and division: `A.mul(B)`, `A/B`, `alpha/A`
--   Matrix multiplication: `A*B`
--   Transposition: `A.t()` (means A<sup>T</sup>)
--   Matrix inversion and pseudo-inversion, solving linear systems and least-squares problems:
-    `A.inv([method]) (~ A<sup>-1</sup>)`,   `A.inv([method])*B (~ X: AX=B)`
--   Comparison: `A cmpop B`, `A cmpop alpha`, `alpha cmpop A`, where *cmpop* is one of
-  `>`, `>=`, `==`, `!=`, `<=`, `<`. The result of comparison is an 8-bit single channel mask whose
-    elements are set to 255 (if the particular element or pair of elements satisfy the condition) or
-    0.
--   Bitwise logical operations: `A logicop B`, `A logicop s`, `s logicop A`, `~A`, where *logicop* is one of
-  `&`, `|`, `^`.
--   Element-wise minimum and maximum: `min(A, B)`, `min(A, alpha)`, `max(A, B)`, `max(A, alpha)`
--   Element-wise absolute value: `abs(A)`
--   Cross-product, dot-product: `A.cross(B)`, `A.dot(B)`
--   Any function of matrix or matrices and scalars that returns a matrix or a scalar, such as norm,
-    mean, sum, countNonZero, trace, determinant, repeat, and others.
--   Matrix initializers ( Mat::eye(), Mat::zeros(), Mat::ones() ), matrix comma-separated
-    initializers, matrix constructors and operators that extract sub-matrices (see Mat description).
--   Mat_<destination_type>() constructors to cast the result to the proper type.
-@note Comma-separated initializers and probably some other operations may require additional
-explicit Mat() or Mat_<T>() constructor calls to resolve a possible ambiguity.
-
-Here are examples of matrix expressions:
-@code
-    // compute pseudo-inverse of A, equivalent to A.inv(DECOMP_SVD)
-    SVD svd(A);
-    Mat pinvA = svd.vt.t()*Mat::diag(1./svd.w)*svd.u.t();
-
-    // compute the new vector of parameters in the Levenberg-Marquardt algorithm
-    x -= (A.t()*A + lambda*Mat::eye(A.cols,A.cols,A.type())).inv(DECOMP_CHOLESKY)*(A.t()*err);
-
-    // sharpen image using "unsharp mask" algorithm
-    Mat blurred; double sigma = 1, threshold = 5, amount = 1;
-    GaussianBlur(img, blurred, Size(), sigma, sigma);
-    Mat lowContrastMask = abs(img - blurred) < threshold;
-    Mat sharpened = img*(1+amount) + blurred*(-amount);
-    img.copyTo(sharpened, lowContrastMask);
-@endcode
-*/
-class CV_EXPORTS MatExpr
-{
-public:
-    MatExpr();
-    explicit MatExpr(const Mat& m);
-
-    MatExpr(const MatOp* _op, int _flags, const Mat& _a = Mat(), const Mat& _b = Mat(),
-            const Mat& _c = Mat(), double _alpha = 1, double _beta = 1, const Scalar& _s = Scalar());
-
-    operator Mat() const;
-    template<typename _Tp> operator Mat_<_Tp>() const;
-
-    Size size() const;
-    int type() const;
-
-    MatExpr row(int y) const;
-    MatExpr col(int x) const;
-    MatExpr diag(int d = 0) const;
-    MatExpr operator()( const Range& rowRange, const Range& colRange ) const;
-    MatExpr operator()( const Rect& roi ) const;
-
-    MatExpr t() const;
-    MatExpr inv(int method = DECOMP_LU) const;
-    MatExpr mul(const MatExpr& e, double scale=1) const;
-    MatExpr mul(const Mat& m, double scale=1) const;
-
-    Mat cross(const Mat& m) const;
-    double dot(const Mat& m) const;
-
-    const MatOp* op;
-    int flags;
-
-    Mat a, b, c;
-    double alpha, beta;
-    Scalar s;
-};
-
-//! @} core_basic
-
-//! @relates cv::MatExpr
-//! @{
-CV_EXPORTS MatExpr operator + (const Mat& a, const Mat& b);
-CV_EXPORTS MatExpr operator + (const Mat& a, const Scalar& s);
-CV_EXPORTS MatExpr operator + (const Scalar& s, const Mat& a);
-CV_EXPORTS MatExpr operator + (const MatExpr& e, const Mat& m);
-CV_EXPORTS MatExpr operator + (const Mat& m, const MatExpr& e);
-CV_EXPORTS MatExpr operator + (const MatExpr& e, const Scalar& s);
-CV_EXPORTS MatExpr operator + (const Scalar& s, const MatExpr& e);
-CV_EXPORTS MatExpr operator + (const MatExpr& e1, const MatExpr& e2);
-
-CV_EXPORTS MatExpr operator - (const Mat& a, const Mat& b);
-CV_EXPORTS MatExpr operator - (const Mat& a, const Scalar& s);
-CV_EXPORTS MatExpr operator - (const Scalar& s, const Mat& a);
-CV_EXPORTS MatExpr operator - (const MatExpr& e, const Mat& m);
-CV_EXPORTS MatExpr operator - (const Mat& m, const MatExpr& e);
-CV_EXPORTS MatExpr operator - (const MatExpr& e, const Scalar& s);
-CV_EXPORTS MatExpr operator - (const Scalar& s, const MatExpr& e);
-CV_EXPORTS MatExpr operator - (const MatExpr& e1, const MatExpr& e2);
-
-CV_EXPORTS MatExpr operator - (const Mat& m);
-CV_EXPORTS MatExpr operator - (const MatExpr& e);
-
-CV_EXPORTS MatExpr operator * (const Mat& a, const Mat& b);
-CV_EXPORTS MatExpr operator * (const Mat& a, double s);
-CV_EXPORTS MatExpr operator * (double s, const Mat& a);
-CV_EXPORTS MatExpr operator * (const MatExpr& e, const Mat& m);
-CV_EXPORTS MatExpr operator * (const Mat& m, const MatExpr& e);
-CV_EXPORTS MatExpr operator * (const MatExpr& e, double s);
-CV_EXPORTS MatExpr operator * (double s, const MatExpr& e);
-CV_EXPORTS MatExpr operator * (const MatExpr& e1, const MatExpr& e2);
-
-CV_EXPORTS MatExpr operator / (const Mat& a, const Mat& b);
-CV_EXPORTS MatExpr operator / (const Mat& a, double s);
-CV_EXPORTS MatExpr operator / (double s, const Mat& a);
-CV_EXPORTS MatExpr operator / (const MatExpr& e, const Mat& m);
-CV_EXPORTS MatExpr operator / (const Mat& m, const MatExpr& e);
-CV_EXPORTS MatExpr operator / (const MatExpr& e, double s);
-CV_EXPORTS MatExpr operator / (double s, const MatExpr& e);
-CV_EXPORTS MatExpr operator / (const MatExpr& e1, const MatExpr& e2);
-
-CV_EXPORTS MatExpr operator < (const Mat& a, const Mat& b);
-CV_EXPORTS MatExpr operator < (const Mat& a, double s);
-CV_EXPORTS MatExpr operator < (double s, const Mat& a);
-
-CV_EXPORTS MatExpr operator <= (const Mat& a, const Mat& b);
-CV_EXPORTS MatExpr operator <= (const Mat& a, double s);
-CV_EXPORTS MatExpr operator <= (double s, const Mat& a);
-
-CV_EXPORTS MatExpr operator == (const Mat& a, const Mat& b);
-CV_EXPORTS MatExpr operator == (const Mat& a, double s);
-CV_EXPORTS MatExpr operator == (double s, const Mat& a);
-
-CV_EXPORTS MatExpr operator != (const Mat& a, const Mat& b);
-CV_EXPORTS MatExpr operator != (const Mat& a, double s);
-CV_EXPORTS MatExpr operator != (double s, const Mat& a);
-
-CV_EXPORTS MatExpr operator >= (const Mat& a, const Mat& b);
-CV_EXPORTS MatExpr operator >= (const Mat& a, double s);
-CV_EXPORTS MatExpr operator >= (double s, const Mat& a);
-
-CV_EXPORTS MatExpr operator > (const Mat& a, const Mat& b);
-CV_EXPORTS MatExpr operator > (const Mat& a, double s);
-CV_EXPORTS MatExpr operator > (double s, const Mat& a);
-
-CV_EXPORTS MatExpr operator & (const Mat& a, const Mat& b);
-CV_EXPORTS MatExpr operator & (const Mat& a, const Scalar& s);
-CV_EXPORTS MatExpr operator & (const Scalar& s, const Mat& a);
-
-CV_EXPORTS MatExpr operator | (const Mat& a, const Mat& b);
-CV_EXPORTS MatExpr operator | (const Mat& a, const Scalar& s);
-CV_EXPORTS MatExpr operator | (const Scalar& s, const Mat& a);
-
-CV_EXPORTS MatExpr operator ^ (const Mat& a, const Mat& b);
-CV_EXPORTS MatExpr operator ^ (const Mat& a, const Scalar& s);
-CV_EXPORTS MatExpr operator ^ (const Scalar& s, const Mat& a);
-
-CV_EXPORTS MatExpr operator ~(const Mat& m);
-
-CV_EXPORTS MatExpr min(const Mat& a, const Mat& b);
-CV_EXPORTS MatExpr min(const Mat& a, double s);
-CV_EXPORTS MatExpr min(double s, const Mat& a);
-
-CV_EXPORTS MatExpr max(const Mat& a, const Mat& b);
-CV_EXPORTS MatExpr max(const Mat& a, double s);
-CV_EXPORTS MatExpr max(double s, const Mat& a);
-
-/** @brief Calculates an absolute value of each matrix element.
-
-abs is a meta-function that is expanded to one of absdiff or convertScaleAbs forms:
-- C = abs(A-B) is equivalent to `absdiff(A, B, C)`
-- C = abs(A) is equivalent to `absdiff(A, Scalar::all(0), C)`
-- C = `Mat_<Vec<uchar,n> >(abs(A*alpha + beta))` is equivalent to `convertScaleAbs(A, C, alpha,
-beta)`
-
-The output matrix has the same size and the same type as the input one except for the last case,
-where C is depth=CV_8U .
-@param m matrix.
-@sa @ref MatrixExpressions, absdiff, convertScaleAbs
- */
-CV_EXPORTS MatExpr abs(const Mat& m);
-/** @overload
-@param e matrix expression.
-*/
-CV_EXPORTS MatExpr abs(const MatExpr& e);
-//! @} relates cv::MatExpr
-
-} // cv
-
-#include "opencv2/core/mat.inl.hpp"
-
-#endif // OPENCV_CORE_MAT_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/mat.inl.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/mat.inl.hpp
deleted file mode 100755
index 98346f1..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/mat.inl.hpp
+++ /dev/null
@@ -1,3945 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
-// Copyright (C) 2015, Itseez Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_CORE_MATRIX_OPERATIONS_HPP
-#define OPENCV_CORE_MATRIX_OPERATIONS_HPP
-
-#ifndef __cplusplus
-#  error mat.inl.hpp header must be compiled as C++
-#endif
-
-#ifdef _MSC_VER
-#pragma warning( push )
-#pragma warning( disable: 4127 )
-#endif
-
-namespace cv
-{
-CV__DEBUG_NS_BEGIN
-
-
-//! @cond IGNORED
-
-//////////////////////// Input/Output Arrays ////////////////////////
-
-inline void _InputArray::init(int _flags, const void* _obj)
-{ flags = _flags; obj = (void*)_obj; }
-
-inline void _InputArray::init(int _flags, const void* _obj, Size _sz)
-{ flags = _flags; obj = (void*)_obj; sz = _sz; }
-
-inline void* _InputArray::getObj() const { return obj; }
-inline int _InputArray::getFlags() const { return flags; }
-inline Size _InputArray::getSz() const { return sz; }
-
-inline _InputArray::_InputArray() { init(NONE, 0); }
-inline _InputArray::_InputArray(int _flags, void* _obj) { init(_flags, _obj); }
-inline _InputArray::_InputArray(const Mat& m) { init(MAT+ACCESS_READ, &m); }
-inline _InputArray::_InputArray(const std::vector<Mat>& vec) { init(STD_VECTOR_MAT+ACCESS_READ, &vec); }
-inline _InputArray::_InputArray(const UMat& m) { init(UMAT+ACCESS_READ, &m); }
-inline _InputArray::_InputArray(const std::vector<UMat>& vec) { init(STD_VECTOR_UMAT+ACCESS_READ, &vec); }
-
-template<typename _Tp> inline
-_InputArray::_InputArray(const std::vector<_Tp>& vec)
-{ init(FIXED_TYPE + STD_VECTOR + traits::Type<_Tp>::value + ACCESS_READ, &vec); }
-
-#ifdef CV_CXX_STD_ARRAY
-template<typename _Tp, std::size_t _Nm> inline
-_InputArray::_InputArray(const std::array<_Tp, _Nm>& arr)
-{ init(FIXED_TYPE + FIXED_SIZE + STD_ARRAY + traits::Type<_Tp>::value + ACCESS_READ, arr.data(), Size(1, _Nm)); }
-
-template<std::size_t _Nm> inline
-_InputArray::_InputArray(const std::array<Mat, _Nm>& arr)
-{ init(STD_ARRAY_MAT + ACCESS_READ, arr.data(), Size(1, _Nm)); }
-#endif
-
-inline
-_InputArray::_InputArray(const std::vector<bool>& vec)
-{ init(FIXED_TYPE + STD_BOOL_VECTOR + traits::Type<bool>::value + ACCESS_READ, &vec); }
-
-template<typename _Tp> inline
-_InputArray::_InputArray(const std::vector<std::vector<_Tp> >& vec)
-{ init(FIXED_TYPE + STD_VECTOR_VECTOR + traits::Type<_Tp>::value + ACCESS_READ, &vec); }
-
-inline
-_InputArray::_InputArray(const std::vector<std::vector<bool> >&)
-{ CV_Error(Error::StsUnsupportedFormat, "std::vector<std::vector<bool> > is not supported!\n"); }
-
-template<typename _Tp> inline
-_InputArray::_InputArray(const std::vector<Mat_<_Tp> >& vec)
-{ init(FIXED_TYPE + STD_VECTOR_MAT + traits::Type<_Tp>::value + ACCESS_READ, &vec); }
-
-template<typename _Tp, int m, int n> inline
-_InputArray::_InputArray(const Matx<_Tp, m, n>& mtx)
-{ init(FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_READ, &mtx, Size(n, m)); }
-
-template<typename _Tp> inline
-_InputArray::_InputArray(const _Tp* vec, int n)
-{ init(FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_READ, vec, Size(n, 1)); }
-
-template<typename _Tp> inline
-_InputArray::_InputArray(const Mat_<_Tp>& m)
-{ init(FIXED_TYPE + MAT + traits::Type<_Tp>::value + ACCESS_READ, &m); }
-
-inline _InputArray::_InputArray(const double& val)
-{ init(FIXED_TYPE + FIXED_SIZE + MATX + CV_64F + ACCESS_READ, &val, Size(1,1)); }
-
-inline _InputArray::_InputArray(const MatExpr& expr)
-{ init(FIXED_TYPE + FIXED_SIZE + EXPR + ACCESS_READ, &expr); }
-
-inline _InputArray::_InputArray(const cuda::GpuMat& d_mat)
-{ init(CUDA_GPU_MAT + ACCESS_READ, &d_mat); }
-
-inline _InputArray::_InputArray(const std::vector<cuda::GpuMat>& d_mat)
-{	init(STD_VECTOR_CUDA_GPU_MAT + ACCESS_READ, &d_mat);}
-
-inline _InputArray::_InputArray(const ogl::Buffer& buf)
-{ init(OPENGL_BUFFER + ACCESS_READ, &buf); }
-
-inline _InputArray::_InputArray(const cuda::HostMem& cuda_mem)
-{ init(CUDA_HOST_MEM + ACCESS_READ, &cuda_mem); }
-
-inline _InputArray::~_InputArray() {}
-
-inline Mat _InputArray::getMat(int i) const
-{
-    if( kind() == MAT && i < 0 )
-        return *(const Mat*)obj;
-    return getMat_(i);
-}
-
-inline bool _InputArray::isMat() const { return kind() == _InputArray::MAT; }
-inline bool _InputArray::isUMat() const  { return kind() == _InputArray::UMAT; }
-inline bool _InputArray::isMatVector() const { return kind() == _InputArray::STD_VECTOR_MAT; }
-inline bool _InputArray::isUMatVector() const  { return kind() == _InputArray::STD_VECTOR_UMAT; }
-inline bool _InputArray::isMatx() const { return kind() == _InputArray::MATX; }
-inline bool _InputArray::isVector() const { return kind() == _InputArray::STD_VECTOR ||
-                                                   kind() == _InputArray::STD_BOOL_VECTOR ||
-                                                   kind() == _InputArray::STD_ARRAY; }
-inline bool _InputArray::isGpuMat() const { return kind() == _InputArray::CUDA_GPU_MAT; }
-inline bool _InputArray::isGpuMatVector() const { return kind() == _InputArray::STD_VECTOR_CUDA_GPU_MAT; }
-
-////////////////////////////////////////////////////////////////////////////////////////
-
-inline _OutputArray::_OutputArray() { init(ACCESS_WRITE, 0); }
-inline _OutputArray::_OutputArray(int _flags, void* _obj) { init(_flags|ACCESS_WRITE, _obj); }
-inline _OutputArray::_OutputArray(Mat& m) { init(MAT+ACCESS_WRITE, &m); }
-inline _OutputArray::_OutputArray(std::vector<Mat>& vec) { init(STD_VECTOR_MAT+ACCESS_WRITE, &vec); }
-inline _OutputArray::_OutputArray(UMat& m) { init(UMAT+ACCESS_WRITE, &m); }
-inline _OutputArray::_OutputArray(std::vector<UMat>& vec) { init(STD_VECTOR_UMAT+ACCESS_WRITE, &vec); }
-
-template<typename _Tp> inline
-_OutputArray::_OutputArray(std::vector<_Tp>& vec)
-{ init(FIXED_TYPE + STD_VECTOR + traits::Type<_Tp>::value + ACCESS_WRITE, &vec); }
-
-#ifdef CV_CXX_STD_ARRAY
-template<typename _Tp, std::size_t _Nm> inline
-_OutputArray::_OutputArray(std::array<_Tp, _Nm>& arr)
-{ init(FIXED_TYPE + FIXED_SIZE + STD_ARRAY + traits::Type<_Tp>::value + ACCESS_WRITE, arr.data(), Size(1, _Nm)); }
-
-template<std::size_t _Nm> inline
-_OutputArray::_OutputArray(std::array<Mat, _Nm>& arr)
-{ init(STD_ARRAY_MAT + ACCESS_WRITE, arr.data(), Size(1, _Nm)); }
-#endif
-
-inline
-_OutputArray::_OutputArray(std::vector<bool>&)
-{ CV_Error(Error::StsUnsupportedFormat, "std::vector<bool> cannot be an output array\n"); }
-
-template<typename _Tp> inline
-_OutputArray::_OutputArray(std::vector<std::vector<_Tp> >& vec)
-{ init(FIXED_TYPE + STD_VECTOR_VECTOR + traits::Type<_Tp>::value + ACCESS_WRITE, &vec); }
-
-inline
-_OutputArray::_OutputArray(std::vector<std::vector<bool> >&)
-{ CV_Error(Error::StsUnsupportedFormat, "std::vector<std::vector<bool> > cannot be an output array\n"); }
-
-template<typename _Tp> inline
-_OutputArray::_OutputArray(std::vector<Mat_<_Tp> >& vec)
-{ init(FIXED_TYPE + STD_VECTOR_MAT + traits::Type<_Tp>::value + ACCESS_WRITE, &vec); }
-
-template<typename _Tp> inline
-_OutputArray::_OutputArray(Mat_<_Tp>& m)
-{ init(FIXED_TYPE + MAT + traits::Type<_Tp>::value + ACCESS_WRITE, &m); }
-
-template<typename _Tp, int m, int n> inline
-_OutputArray::_OutputArray(Matx<_Tp, m, n>& mtx)
-{ init(FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_WRITE, &mtx, Size(n, m)); }
-
-template<typename _Tp> inline
-_OutputArray::_OutputArray(_Tp* vec, int n)
-{ init(FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_WRITE, vec, Size(n, 1)); }
-
-template<typename _Tp> inline
-_OutputArray::_OutputArray(const std::vector<_Tp>& vec)
-{ init(FIXED_TYPE + FIXED_SIZE + STD_VECTOR + traits::Type<_Tp>::value + ACCESS_WRITE, &vec); }
-
-#ifdef CV_CXX_STD_ARRAY
-template<typename _Tp, std::size_t _Nm> inline
-_OutputArray::_OutputArray(const std::array<_Tp, _Nm>& arr)
-{ init(FIXED_TYPE + FIXED_SIZE + STD_ARRAY + traits::Type<_Tp>::value + ACCESS_WRITE, arr.data(), Size(1, _Nm)); }
-
-template<std::size_t _Nm> inline
-_OutputArray::_OutputArray(const std::array<Mat, _Nm>& arr)
-{ init(FIXED_SIZE + STD_ARRAY_MAT + ACCESS_WRITE, arr.data(), Size(1, _Nm)); }
-#endif
-
-template<typename _Tp> inline
-_OutputArray::_OutputArray(const std::vector<std::vector<_Tp> >& vec)
-{ init(FIXED_TYPE + FIXED_SIZE + STD_VECTOR_VECTOR + traits::Type<_Tp>::value + ACCESS_WRITE, &vec); }
-
-template<typename _Tp> inline
-_OutputArray::_OutputArray(const std::vector<Mat_<_Tp> >& vec)
-{ init(FIXED_TYPE + FIXED_SIZE + STD_VECTOR_MAT + traits::Type<_Tp>::value + ACCESS_WRITE, &vec); }
-
-template<typename _Tp> inline
-_OutputArray::_OutputArray(const Mat_<_Tp>& m)
-{ init(FIXED_TYPE + FIXED_SIZE + MAT + traits::Type<_Tp>::value + ACCESS_WRITE, &m); }
-
-template<typename _Tp, int m, int n> inline
-_OutputArray::_OutputArray(const Matx<_Tp, m, n>& mtx)
-{ init(FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_WRITE, &mtx, Size(n, m)); }
-
-template<typename _Tp> inline
-_OutputArray::_OutputArray(const _Tp* vec, int n)
-{ init(FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_WRITE, vec, Size(n, 1)); }
-
-inline _OutputArray::_OutputArray(cuda::GpuMat& d_mat)
-{ init(CUDA_GPU_MAT + ACCESS_WRITE, &d_mat); }
-
-inline _OutputArray::_OutputArray(std::vector<cuda::GpuMat>& d_mat)
-{	init(STD_VECTOR_CUDA_GPU_MAT + ACCESS_WRITE, &d_mat);}
-
-inline _OutputArray::_OutputArray(ogl::Buffer& buf)
-{ init(OPENGL_BUFFER + ACCESS_WRITE, &buf); }
-
-inline _OutputArray::_OutputArray(cuda::HostMem& cuda_mem)
-{ init(CUDA_HOST_MEM + ACCESS_WRITE, &cuda_mem); }
-
-inline _OutputArray::_OutputArray(const Mat& m)
-{ init(FIXED_TYPE + FIXED_SIZE + MAT + ACCESS_WRITE, &m); }
-
-inline _OutputArray::_OutputArray(const std::vector<Mat>& vec)
-{ init(FIXED_SIZE + STD_VECTOR_MAT + ACCESS_WRITE, &vec); }
-
-inline _OutputArray::_OutputArray(const UMat& m)
-{ init(FIXED_TYPE + FIXED_SIZE + UMAT + ACCESS_WRITE, &m); }
-
-inline _OutputArray::_OutputArray(const std::vector<UMat>& vec)
-{ init(FIXED_SIZE + STD_VECTOR_UMAT + ACCESS_WRITE, &vec); }
-
-inline _OutputArray::_OutputArray(const cuda::GpuMat& d_mat)
-{ init(FIXED_TYPE + FIXED_SIZE + CUDA_GPU_MAT + ACCESS_WRITE, &d_mat); }
-
-
-inline _OutputArray::_OutputArray(const ogl::Buffer& buf)
-{ init(FIXED_TYPE + FIXED_SIZE + OPENGL_BUFFER + ACCESS_WRITE, &buf); }
-
-inline _OutputArray::_OutputArray(const cuda::HostMem& cuda_mem)
-{ init(FIXED_TYPE + FIXED_SIZE + CUDA_HOST_MEM + ACCESS_WRITE, &cuda_mem); }
-
-///////////////////////////////////////////////////////////////////////////////////////////
-
-inline _InputOutputArray::_InputOutputArray() { init(ACCESS_RW, 0); }
-inline _InputOutputArray::_InputOutputArray(int _flags, void* _obj) { init(_flags|ACCESS_RW, _obj); }
-inline _InputOutputArray::_InputOutputArray(Mat& m) { init(MAT+ACCESS_RW, &m); }
-inline _InputOutputArray::_InputOutputArray(std::vector<Mat>& vec) { init(STD_VECTOR_MAT+ACCESS_RW, &vec); }
-inline _InputOutputArray::_InputOutputArray(UMat& m) { init(UMAT+ACCESS_RW, &m); }
-inline _InputOutputArray::_InputOutputArray(std::vector<UMat>& vec) { init(STD_VECTOR_UMAT+ACCESS_RW, &vec); }
-
-template<typename _Tp> inline
-_InputOutputArray::_InputOutputArray(std::vector<_Tp>& vec)
-{ init(FIXED_TYPE + STD_VECTOR + traits::Type<_Tp>::value + ACCESS_RW, &vec); }
-
-#ifdef CV_CXX_STD_ARRAY
-template<typename _Tp, std::size_t _Nm> inline
-_InputOutputArray::_InputOutputArray(std::array<_Tp, _Nm>& arr)
-{ init(FIXED_TYPE + FIXED_SIZE + STD_ARRAY + traits::Type<_Tp>::value + ACCESS_RW, arr.data(), Size(1, _Nm)); }
-
-template<std::size_t _Nm> inline
-_InputOutputArray::_InputOutputArray(std::array<Mat, _Nm>& arr)
-{ init(STD_ARRAY_MAT + ACCESS_RW, arr.data(), Size(1, _Nm)); }
-#endif
-
-inline _InputOutputArray::_InputOutputArray(std::vector<bool>&)
-{ CV_Error(Error::StsUnsupportedFormat, "std::vector<bool> cannot be an input/output array\n"); }
-
-template<typename _Tp> inline
-_InputOutputArray::_InputOutputArray(std::vector<std::vector<_Tp> >& vec)
-{ init(FIXED_TYPE + STD_VECTOR_VECTOR + traits::Type<_Tp>::value + ACCESS_RW, &vec); }
-
-template<typename _Tp> inline
-_InputOutputArray::_InputOutputArray(std::vector<Mat_<_Tp> >& vec)
-{ init(FIXED_TYPE + STD_VECTOR_MAT + traits::Type<_Tp>::value + ACCESS_RW, &vec); }
-
-template<typename _Tp> inline
-_InputOutputArray::_InputOutputArray(Mat_<_Tp>& m)
-{ init(FIXED_TYPE + MAT + traits::Type<_Tp>::value + ACCESS_RW, &m); }
-
-template<typename _Tp, int m, int n> inline
-_InputOutputArray::_InputOutputArray(Matx<_Tp, m, n>& mtx)
-{ init(FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_RW, &mtx, Size(n, m)); }
-
-template<typename _Tp> inline
-_InputOutputArray::_InputOutputArray(_Tp* vec, int n)
-{ init(FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_RW, vec, Size(n, 1)); }
-
-template<typename _Tp> inline
-_InputOutputArray::_InputOutputArray(const std::vector<_Tp>& vec)
-{ init(FIXED_TYPE + FIXED_SIZE + STD_VECTOR + traits::Type<_Tp>::value + ACCESS_RW, &vec); }
-
-#ifdef CV_CXX_STD_ARRAY
-template<typename _Tp, std::size_t _Nm> inline
-_InputOutputArray::_InputOutputArray(const std::array<_Tp, _Nm>& arr)
-{ init(FIXED_TYPE + FIXED_SIZE + STD_ARRAY + traits::Type<_Tp>::value + ACCESS_RW, arr.data(), Size(1, _Nm)); }
-
-template<std::size_t _Nm> inline
-_InputOutputArray::_InputOutputArray(const std::array<Mat, _Nm>& arr)
-{ init(FIXED_SIZE + STD_ARRAY_MAT + ACCESS_RW, arr.data(), Size(1, _Nm)); }
-#endif
-
-template<typename _Tp> inline
-_InputOutputArray::_InputOutputArray(const std::vector<std::vector<_Tp> >& vec)
-{ init(FIXED_TYPE + FIXED_SIZE + STD_VECTOR_VECTOR + traits::Type<_Tp>::value + ACCESS_RW, &vec); }
-
-template<typename _Tp> inline
-_InputOutputArray::_InputOutputArray(const std::vector<Mat_<_Tp> >& vec)
-{ init(FIXED_TYPE + FIXED_SIZE + STD_VECTOR_MAT + traits::Type<_Tp>::value + ACCESS_RW, &vec); }
-
-template<typename _Tp> inline
-_InputOutputArray::_InputOutputArray(const Mat_<_Tp>& m)
-{ init(FIXED_TYPE + FIXED_SIZE + MAT + traits::Type<_Tp>::value + ACCESS_RW, &m); }
-
-template<typename _Tp, int m, int n> inline
-_InputOutputArray::_InputOutputArray(const Matx<_Tp, m, n>& mtx)
-{ init(FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_RW, &mtx, Size(n, m)); }
-
-template<typename _Tp> inline
-_InputOutputArray::_InputOutputArray(const _Tp* vec, int n)
-{ init(FIXED_TYPE + FIXED_SIZE + MATX + traits::Type<_Tp>::value + ACCESS_RW, vec, Size(n, 1)); }
-
-inline _InputOutputArray::_InputOutputArray(cuda::GpuMat& d_mat)
-{ init(CUDA_GPU_MAT + ACCESS_RW, &d_mat); }
-
-inline _InputOutputArray::_InputOutputArray(ogl::Buffer& buf)
-{ init(OPENGL_BUFFER + ACCESS_RW, &buf); }
-
-inline _InputOutputArray::_InputOutputArray(cuda::HostMem& cuda_mem)
-{ init(CUDA_HOST_MEM + ACCESS_RW, &cuda_mem); }
-
-inline _InputOutputArray::_InputOutputArray(const Mat& m)
-{ init(FIXED_TYPE + FIXED_SIZE + MAT + ACCESS_RW, &m); }
-
-inline _InputOutputArray::_InputOutputArray(const std::vector<Mat>& vec)
-{ init(FIXED_SIZE + STD_VECTOR_MAT + ACCESS_RW, &vec); }
-
-inline _InputOutputArray::_InputOutputArray(const UMat& m)
-{ init(FIXED_TYPE + FIXED_SIZE + UMAT + ACCESS_RW, &m); }
-
-inline _InputOutputArray::_InputOutputArray(const std::vector<UMat>& vec)
-{ init(FIXED_SIZE + STD_VECTOR_UMAT + ACCESS_RW, &vec); }
-
-inline _InputOutputArray::_InputOutputArray(const cuda::GpuMat& d_mat)
-{ init(FIXED_TYPE + FIXED_SIZE + CUDA_GPU_MAT + ACCESS_RW, &d_mat); }
-
-inline _InputOutputArray::_InputOutputArray(const std::vector<cuda::GpuMat>& d_mat)
-{ init(FIXED_TYPE + FIXED_SIZE + STD_VECTOR_CUDA_GPU_MAT + ACCESS_RW, &d_mat);}
-
-template<> inline _InputOutputArray::_InputOutputArray(std::vector<cuda::GpuMat>& d_mat)
-{ init(FIXED_TYPE + FIXED_SIZE + STD_VECTOR_CUDA_GPU_MAT + ACCESS_RW, &d_mat);}
-
-inline _InputOutputArray::_InputOutputArray(const ogl::Buffer& buf)
-{ init(FIXED_TYPE + FIXED_SIZE + OPENGL_BUFFER + ACCESS_RW, &buf); }
-
-inline _InputOutputArray::_InputOutputArray(const cuda::HostMem& cuda_mem)
-{ init(FIXED_TYPE + FIXED_SIZE + CUDA_HOST_MEM + ACCESS_RW, &cuda_mem); }
-
-CV__DEBUG_NS_END
-
-//////////////////////////////////////////// Mat //////////////////////////////////////////
-
-inline
-Mat::Mat()
-    : flags(MAGIC_VAL), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0),
-      datalimit(0), allocator(0), u(0), size(&rows), step(0)
-{}
-
-inline
-Mat::Mat(int _rows, int _cols, int _type)
-    : flags(MAGIC_VAL), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0),
-      datalimit(0), allocator(0), u(0), size(&rows), step(0)
-{
-    create(_rows, _cols, _type);
-}
-
-inline
-Mat::Mat(int _rows, int _cols, int _type, const Scalar& _s)
-    : flags(MAGIC_VAL), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0),
-      datalimit(0), allocator(0), u(0), size(&rows), step(0)
-{
-    create(_rows, _cols, _type);
-    *this = _s;
-}
-
-inline
-Mat::Mat(Size _sz, int _type)
-    : flags(MAGIC_VAL), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0),
-      datalimit(0), allocator(0), u(0), size(&rows), step(0)
-{
-    create( _sz.height, _sz.width, _type );
-}
-
-inline
-Mat::Mat(Size _sz, int _type, const Scalar& _s)
-    : flags(MAGIC_VAL), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0),
-      datalimit(0), allocator(0), u(0), size(&rows), step(0)
-{
-    create(_sz.height, _sz.width, _type);
-    *this = _s;
-}
-
-inline
-Mat::Mat(int _dims, const int* _sz, int _type)
-    : flags(MAGIC_VAL), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0),
-      datalimit(0), allocator(0), u(0), size(&rows), step(0)
-{
-    create(_dims, _sz, _type);
-}
-
-inline
-Mat::Mat(int _dims, const int* _sz, int _type, const Scalar& _s)
-    : flags(MAGIC_VAL), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0),
-      datalimit(0), allocator(0), u(0), size(&rows), step(0)
-{
-    create(_dims, _sz, _type);
-    *this = _s;
-}
-
-inline
-Mat::Mat(const std::vector<int>& _sz, int _type)
-    : flags(MAGIC_VAL), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0),
-      datalimit(0), allocator(0), u(0), size(&rows), step(0)
-{
-    create(_sz, _type);
-}
-
-inline
-Mat::Mat(const std::vector<int>& _sz, int _type, const Scalar& _s)
-    : flags(MAGIC_VAL), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0),
-      datalimit(0), allocator(0), u(0), size(&rows), step(0)
-{
-    create(_sz, _type);
-    *this = _s;
-}
-
-inline
-Mat::Mat(const Mat& m)
-    : flags(m.flags), dims(m.dims), rows(m.rows), cols(m.cols), data(m.data),
-      datastart(m.datastart), dataend(m.dataend), datalimit(m.datalimit), allocator(m.allocator),
-      u(m.u), size(&rows), step(0)
-{
-    if( u )
-        CV_XADD(&u->refcount, 1);
-    if( m.dims <= 2 )
-    {
-        step[0] = m.step[0]; step[1] = m.step[1];
-    }
-    else
-    {
-        dims = 0;
-        copySize(m);
-    }
-}
-
-inline
-Mat::Mat(int _rows, int _cols, int _type, void* _data, size_t _step)
-    : flags(MAGIC_VAL + (_type & TYPE_MASK)), dims(2), rows(_rows), cols(_cols),
-      data((uchar*)_data), datastart((uchar*)_data), dataend(0), datalimit(0),
-      allocator(0), u(0), size(&rows)
-{
-    CV_Assert(total() == 0 || data != NULL);
-
-    size_t esz = CV_ELEM_SIZE(_type), esz1 = CV_ELEM_SIZE1(_type);
-    size_t minstep = cols * esz;
-    if( _step == AUTO_STEP )
-    {
-        _step = minstep;
-    }
-    else
-    {
-        CV_DbgAssert( _step >= minstep );
-        if (_step % esz1 != 0)
-        {
-            CV_Error(Error::BadStep, "Step must be a multiple of esz1");
-        }
-    }
-    step[0] = _step;
-    step[1] = esz;
-    datalimit = datastart + _step * rows;
-    dataend = datalimit - _step + minstep;
-    updateContinuityFlag();
-}
-
-inline
-Mat::Mat(Size _sz, int _type, void* _data, size_t _step)
-    : flags(MAGIC_VAL + (_type & TYPE_MASK)), dims(2), rows(_sz.height), cols(_sz.width),
-      data((uchar*)_data), datastart((uchar*)_data), dataend(0), datalimit(0),
-      allocator(0), u(0), size(&rows)
-{
-    CV_Assert(total() == 0 || data != NULL);
-
-    size_t esz = CV_ELEM_SIZE(_type), esz1 = CV_ELEM_SIZE1(_type);
-    size_t minstep = cols*esz;
-    if( _step == AUTO_STEP )
-    {
-        _step = minstep;
-    }
-    else
-    {
-        CV_DbgAssert( _step >= minstep );
-
-        if (_step % esz1 != 0)
-        {
-            CV_Error(Error::BadStep, "Step must be a multiple of esz1");
-        }
-    }
-    step[0] = _step;
-    step[1] = esz;
-    datalimit = datastart + _step*rows;
-    dataend = datalimit - _step + minstep;
-    updateContinuityFlag();
-}
-
-template<typename _Tp> inline
-Mat::Mat(const std::vector<_Tp>& vec, bool copyData)
-    : flags(MAGIC_VAL | traits::Type<_Tp>::value | CV_MAT_CONT_FLAG), dims(2), rows((int)vec.size()),
-      cols(1), data(0), datastart(0), dataend(0), datalimit(0), allocator(0), u(0), size(&rows), step(0)
-{
-    if(vec.empty())
-        return;
-    if( !copyData )
-    {
-        step[0] = step[1] = sizeof(_Tp);
-        datastart = data = (uchar*)&vec[0];
-        datalimit = dataend = datastart + rows * step[0];
-    }
-    else
-        Mat((int)vec.size(), 1, traits::Type<_Tp>::value, (uchar*)&vec[0]).copyTo(*this);
-}
-
-#ifdef CV_CXX11
-template<typename _Tp, typename> inline
-Mat::Mat(const std::initializer_list<_Tp> list)
-    : Mat()
-{
-    CV_Assert(list.size() != 0);
-    Mat((int)list.size(), 1, traits::Type<_Tp>::value, (uchar*)list.begin()).copyTo(*this);
-}
-
-template<typename _Tp> inline
-Mat::Mat(const std::initializer_list<int> sizes, const std::initializer_list<_Tp> list)
-    : Mat()
-{
-    size_t size_total = 1;
-    for(auto s : sizes)
-        size_total *= s;
-    CV_Assert(list.size() != 0);
-    CV_Assert(size_total == list.size());
-    Mat((int)sizes.size(), (int*)sizes.begin(), traits::Type<_Tp>::value, (uchar*)list.begin()).copyTo(*this);
-}
-#endif
-
-#ifdef CV_CXX_STD_ARRAY
-template<typename _Tp, std::size_t _Nm> inline
-Mat::Mat(const std::array<_Tp, _Nm>& arr, bool copyData)
-    : flags(MAGIC_VAL | traits::Type<_Tp>::value | CV_MAT_CONT_FLAG), dims(2), rows((int)arr.size()),
-      cols(1), data(0), datastart(0), dataend(0), datalimit(0), allocator(0), u(0), size(&rows), step(0)
-{
-    if(arr.empty())
-        return;
-    if( !copyData )
-    {
-        step[0] = step[1] = sizeof(_Tp);
-        datastart = data = (uchar*)arr.data();
-        datalimit = dataend = datastart + rows * step[0];
-    }
-    else
-        Mat((int)arr.size(), 1, traits::Type<_Tp>::value, (uchar*)arr.data()).copyTo(*this);
-}
-#endif
-
-template<typename _Tp, int n> inline
-Mat::Mat(const Vec<_Tp, n>& vec, bool copyData)
-    : flags(MAGIC_VAL | traits::Type<_Tp>::value | CV_MAT_CONT_FLAG), dims(2), rows(n), cols(1), data(0),
-      datastart(0), dataend(0), datalimit(0), allocator(0), u(0), size(&rows), step(0)
-{
-    if( !copyData )
-    {
-        step[0] = step[1] = sizeof(_Tp);
-        datastart = data = (uchar*)vec.val;
-        datalimit = dataend = datastart + rows * step[0];
-    }
-    else
-        Mat(n, 1, traits::Type<_Tp>::value, (void*)vec.val).copyTo(*this);
-}
-
-
-template<typename _Tp, int m, int n> inline
-Mat::Mat(const Matx<_Tp,m,n>& M, bool copyData)
-    : flags(MAGIC_VAL | traits::Type<_Tp>::value | CV_MAT_CONT_FLAG), dims(2), rows(m), cols(n), data(0),
-      datastart(0), dataend(0), datalimit(0), allocator(0), u(0), size(&rows), step(0)
-{
-    if( !copyData )
-    {
-        step[0] = cols * sizeof(_Tp);
-        step[1] = sizeof(_Tp);
-        datastart = data = (uchar*)M.val;
-        datalimit = dataend = datastart + rows * step[0];
-    }
-    else
-        Mat(m, n, traits::Type<_Tp>::value, (uchar*)M.val).copyTo(*this);
-}
-
-template<typename _Tp> inline
-Mat::Mat(const Point_<_Tp>& pt, bool copyData)
-    : flags(MAGIC_VAL | traits::Type<_Tp>::value | CV_MAT_CONT_FLAG), dims(2), rows(2), cols(1), data(0),
-      datastart(0), dataend(0), datalimit(0), allocator(0), u(0), size(&rows), step(0)
-{
-    if( !copyData )
-    {
-        step[0] = step[1] = sizeof(_Tp);
-        datastart = data = (uchar*)&pt.x;
-        datalimit = dataend = datastart + rows * step[0];
-    }
-    else
-    {
-        create(2, 1, traits::Type<_Tp>::value);
-        ((_Tp*)data)[0] = pt.x;
-        ((_Tp*)data)[1] = pt.y;
-    }
-}
-
-template<typename _Tp> inline
-Mat::Mat(const Point3_<_Tp>& pt, bool copyData)
-    : flags(MAGIC_VAL | traits::Type<_Tp>::value | CV_MAT_CONT_FLAG), dims(2), rows(3), cols(1), data(0),
-      datastart(0), dataend(0), datalimit(0), allocator(0), u(0), size(&rows), step(0)
-{
-    if( !copyData )
-    {
-        step[0] = step[1] = sizeof(_Tp);
-        datastart = data = (uchar*)&pt.x;
-        datalimit = dataend = datastart + rows * step[0];
-    }
-    else
-    {
-        create(3, 1, traits::Type<_Tp>::value);
-        ((_Tp*)data)[0] = pt.x;
-        ((_Tp*)data)[1] = pt.y;
-        ((_Tp*)data)[2] = pt.z;
-    }
-}
-
-template<typename _Tp> inline
-Mat::Mat(const MatCommaInitializer_<_Tp>& commaInitializer)
-    : flags(MAGIC_VAL | traits::Type<_Tp>::value | CV_MAT_CONT_FLAG), dims(0), rows(0), cols(0), data(0),
-      datastart(0), dataend(0), allocator(0), u(0), size(&rows)
-{
-    *this = commaInitializer.operator Mat_<_Tp>();
-}
-
-inline
-Mat::~Mat()
-{
-    release();
-    if( step.p != step.buf )
-        fastFree(step.p);
-}
-
-inline
-Mat& Mat::operator = (const Mat& m)
-{
-    if( this != &m )
-    {
-        if( m.u )
-            CV_XADD(&m.u->refcount, 1);
-        release();
-        flags = m.flags;
-        if( dims <= 2 && m.dims <= 2 )
-        {
-            dims = m.dims;
-            rows = m.rows;
-            cols = m.cols;
-            step[0] = m.step[0];
-            step[1] = m.step[1];
-        }
-        else
-            copySize(m);
-        data = m.data;
-        datastart = m.datastart;
-        dataend = m.dataend;
-        datalimit = m.datalimit;
-        allocator = m.allocator;
-        u = m.u;
-    }
-    return *this;
-}
-
-inline
-Mat Mat::row(int y) const
-{
-    return Mat(*this, Range(y, y + 1), Range::all());
-}
-
-inline
-Mat Mat::col(int x) const
-{
-    return Mat(*this, Range::all(), Range(x, x + 1));
-}
-
-inline
-Mat Mat::rowRange(int startrow, int endrow) const
-{
-    return Mat(*this, Range(startrow, endrow), Range::all());
-}
-
-inline
-Mat Mat::rowRange(const Range& r) const
-{
-    return Mat(*this, r, Range::all());
-}
-
-inline
-Mat Mat::colRange(int startcol, int endcol) const
-{
-    return Mat(*this, Range::all(), Range(startcol, endcol));
-}
-
-inline
-Mat Mat::colRange(const Range& r) const
-{
-    return Mat(*this, Range::all(), r);
-}
-
-inline
-Mat Mat::clone() const
-{
-    Mat m;
-    copyTo(m);
-    return m;
-}
-
-inline
-void Mat::assignTo( Mat& m, int _type ) const
-{
-    if( _type < 0 )
-        m = *this;
-    else
-        convertTo(m, _type);
-}
-
-inline
-void Mat::create(int _rows, int _cols, int _type)
-{
-    _type &= TYPE_MASK;
-    if( dims <= 2 && rows == _rows && cols == _cols && type() == _type && data )
-        return;
-    int sz[] = {_rows, _cols};
-    create(2, sz, _type);
-}
-
-inline
-void Mat::create(Size _sz, int _type)
-{
-    create(_sz.height, _sz.width, _type);
-}
-
-inline
-void Mat::addref()
-{
-    if( u )
-        CV_XADD(&u->refcount, 1);
-}
-
-inline
-void Mat::release()
-{
-    if( u && CV_XADD(&u->refcount, -1) == 1 )
-        deallocate();
-    u = NULL;
-    datastart = dataend = datalimit = data = 0;
-    for(int i = 0; i < dims; i++)
-        size.p[i] = 0;
-#ifdef _DEBUG
-    flags = MAGIC_VAL;
-    dims = rows = cols = 0;
-    if(step.p != step.buf)
-    {
-        fastFree(step.p);
-        step.p = step.buf;
-        size.p = &rows;
-    }
-#endif
-}
-
-inline
-Mat Mat::operator()( Range _rowRange, Range _colRange ) const
-{
-    return Mat(*this, _rowRange, _colRange);
-}
-
-inline
-Mat Mat::operator()( const Rect& roi ) const
-{
-    return Mat(*this, roi);
-}
-
-inline
-Mat Mat::operator()(const Range* ranges) const
-{
-    return Mat(*this, ranges);
-}
-
-inline
-Mat Mat::operator()(const std::vector<Range>& ranges) const
-{
-    return Mat(*this, ranges);
-}
-
-inline
-bool Mat::isContinuous() const
-{
-    return (flags & CONTINUOUS_FLAG) != 0;
-}
-
-inline
-bool Mat::isSubmatrix() const
-{
-    return (flags & SUBMATRIX_FLAG) != 0;
-}
-
-inline
-size_t Mat::elemSize() const
-{
-    return dims > 0 ? step.p[dims - 1] : 0;
-}
-
-inline
-size_t Mat::elemSize1() const
-{
-    return CV_ELEM_SIZE1(flags);
-}
-
-inline
-int Mat::type() const
-{
-    return CV_MAT_TYPE(flags);
-}
-
-inline
-int Mat::depth() const
-{
-    return CV_MAT_DEPTH(flags);
-}
-
-inline
-int Mat::channels() const
-{
-    return CV_MAT_CN(flags);
-}
-
-inline
-size_t Mat::step1(int i) const
-{
-    return step.p[i] / elemSize1();
-}
-
-inline
-bool Mat::empty() const
-{
-    return data == 0 || total() == 0 || dims == 0;
-}
-
-inline
-size_t Mat::total() const
-{
-    if( dims <= 2 )
-        return (size_t)rows * cols;
-    size_t p = 1;
-    for( int i = 0; i < dims; i++ )
-        p *= size[i];
-    return p;
-}
-
-inline
-size_t Mat::total(int startDim, int endDim) const
-{
-    CV_Assert( 0 <= startDim && startDim <= endDim);
-    size_t p = 1;
-    int endDim_ = endDim <= dims ? endDim : dims;
-    for( int i = startDim; i < endDim_; i++ )
-        p *= size[i];
-    return p;
-}
-
-inline
-uchar* Mat::ptr(int y)
-{
-    CV_DbgAssert( y == 0 || (data && dims >= 1 && (unsigned)y < (unsigned)size.p[0]) );
-    return data + step.p[0] * y;
-}
-
-inline
-const uchar* Mat::ptr(int y) const
-{
-    CV_DbgAssert( y == 0 || (data && dims >= 1 && (unsigned)y < (unsigned)size.p[0]) );
-    return data + step.p[0] * y;
-}
-
-template<typename _Tp> inline
-_Tp* Mat::ptr(int y)
-{
-    CV_DbgAssert( y == 0 || (data && dims >= 1 && (unsigned)y < (unsigned)size.p[0]) );
-    return (_Tp*)(data + step.p[0] * y);
-}
-
-template<typename _Tp> inline
-const _Tp* Mat::ptr(int y) const
-{
-    CV_DbgAssert( y == 0 || (data && dims >= 1 && data && (unsigned)y < (unsigned)size.p[0]) );
-    return (const _Tp*)(data + step.p[0] * y);
-}
-
-inline
-uchar* Mat::ptr(int i0, int i1)
-{
-    CV_DbgAssert(dims >= 2);
-    CV_DbgAssert(data);
-    CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]);
-    CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]);
-    return data + i0 * step.p[0] + i1 * step.p[1];
-}
-
-inline
-const uchar* Mat::ptr(int i0, int i1) const
-{
-    CV_DbgAssert(dims >= 2);
-    CV_DbgAssert(data);
-    CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]);
-    CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]);
-    return data + i0 * step.p[0] + i1 * step.p[1];
-}
-
-template<typename _Tp> inline
-_Tp* Mat::ptr(int i0, int i1)
-{
-    CV_DbgAssert(dims >= 2);
-    CV_DbgAssert(data);
-    CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]);
-    CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]);
-    return (_Tp*)(data + i0 * step.p[0] + i1 * step.p[1]);
-}
-
-template<typename _Tp> inline
-const _Tp* Mat::ptr(int i0, int i1) const
-{
-    CV_DbgAssert(dims >= 2);
-    CV_DbgAssert(data);
-    CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]);
-    CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]);
-    return (const _Tp*)(data + i0 * step.p[0] + i1 * step.p[1]);
-}
-
-inline
-uchar* Mat::ptr(int i0, int i1, int i2)
-{
-    CV_DbgAssert(dims >= 3);
-    CV_DbgAssert(data);
-    CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]);
-    CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]);
-    CV_DbgAssert((unsigned)i2 < (unsigned)size.p[2]);
-    return data + i0 * step.p[0] + i1 * step.p[1] + i2 * step.p[2];
-}
-
-inline
-const uchar* Mat::ptr(int i0, int i1, int i2) const
-{
-    CV_DbgAssert(dims >= 3);
-    CV_DbgAssert(data);
-    CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]);
-    CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]);
-    CV_DbgAssert((unsigned)i2 < (unsigned)size.p[2]);
-    return data + i0 * step.p[0] + i1 * step.p[1] + i2 * step.p[2];
-}
-
-template<typename _Tp> inline
-_Tp* Mat::ptr(int i0, int i1, int i2)
-{
-    CV_DbgAssert(dims >= 3);
-    CV_DbgAssert(data);
-    CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]);
-    CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]);
-    CV_DbgAssert((unsigned)i2 < (unsigned)size.p[2]);
-    return (_Tp*)(data + i0 * step.p[0] + i1 * step.p[1] + i2 * step.p[2]);
-}
-
-template<typename _Tp> inline
-const _Tp* Mat::ptr(int i0, int i1, int i2) const
-{
-    CV_DbgAssert(dims >= 3);
-    CV_DbgAssert(data);
-    CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]);
-    CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]);
-    CV_DbgAssert((unsigned)i2 < (unsigned)size.p[2]);
-    return (const _Tp*)(data + i0 * step.p[0] + i1 * step.p[1] + i2 * step.p[2]);
-}
-
-inline
-uchar* Mat::ptr(const int* idx)
-{
-    int i, d = dims;
-    uchar* p = data;
-    CV_DbgAssert( d >= 1 && p );
-    for( i = 0; i < d; i++ )
-    {
-        CV_DbgAssert( (unsigned)idx[i] < (unsigned)size.p[i] );
-        p += idx[i] * step.p[i];
-    }
-    return p;
-}
-
-inline
-const uchar* Mat::ptr(const int* idx) const
-{
-    int i, d = dims;
-    uchar* p = data;
-    CV_DbgAssert( d >= 1 && p );
-    for( i = 0; i < d; i++ )
-    {
-        CV_DbgAssert( (unsigned)idx[i] < (unsigned)size.p[i] );
-        p += idx[i] * step.p[i];
-    }
-    return p;
-}
-
-template<typename _Tp> inline
-_Tp* Mat::ptr(const int* idx)
-{
-    int i, d = dims;
-    uchar* p = data;
-    CV_DbgAssert( d >= 1 && p );
-    for( i = 0; i < d; i++ )
-    {
-        CV_DbgAssert( (unsigned)idx[i] < (unsigned)size.p[i] );
-        p += idx[i] * step.p[i];
-    }
-    return (_Tp*)p;
-}
-
-template<typename _Tp> inline
-const _Tp* Mat::ptr(const int* idx) const
-{
-    int i, d = dims;
-    uchar* p = data;
-    CV_DbgAssert( d >= 1 && p );
-    for( i = 0; i < d; i++ )
-    {
-        CV_DbgAssert( (unsigned)idx[i] < (unsigned)size.p[i] );
-        p += idx[i] * step.p[i];
-    }
-    return (const _Tp*)p;
-}
-
-template<typename _Tp> inline
-_Tp& Mat::at(int i0, int i1)
-{
-    CV_DbgAssert(dims <= 2);
-    CV_DbgAssert(data);
-    CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]);
-    CV_DbgAssert((unsigned)(i1 * DataType<_Tp>::channels) < (unsigned)(size.p[1] * channels()));
-    CV_DbgAssert(CV_ELEM_SIZE1(traits::Depth<_Tp>::value) == elemSize1());
-    return ((_Tp*)(data + step.p[0] * i0))[i1];
-}
-
-template<typename _Tp> inline
-const _Tp& Mat::at(int i0, int i1) const
-{
-    CV_DbgAssert(dims <= 2);
-    CV_DbgAssert(data);
-    CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]);
-    CV_DbgAssert((unsigned)(i1 * DataType<_Tp>::channels) < (unsigned)(size.p[1] * channels()));
-    CV_DbgAssert(CV_ELEM_SIZE1(traits::Depth<_Tp>::value) == elemSize1());
-    return ((const _Tp*)(data + step.p[0] * i0))[i1];
-}
-
-template<typename _Tp> inline
-_Tp& Mat::at(Point pt)
-{
-    CV_DbgAssert(dims <= 2);
-    CV_DbgAssert(data);
-    CV_DbgAssert((unsigned)pt.y < (unsigned)size.p[0]);
-    CV_DbgAssert((unsigned)(pt.x * DataType<_Tp>::channels) < (unsigned)(size.p[1] * channels()));
-    CV_DbgAssert(CV_ELEM_SIZE1(traits::Depth<_Tp>::value) == elemSize1());
-    return ((_Tp*)(data + step.p[0] * pt.y))[pt.x];
-}
-
-template<typename _Tp> inline
-const _Tp& Mat::at(Point pt) const
-{
-    CV_DbgAssert(dims <= 2);
-    CV_DbgAssert(data);
-    CV_DbgAssert((unsigned)pt.y < (unsigned)size.p[0]);
-    CV_DbgAssert((unsigned)(pt.x * DataType<_Tp>::channels) < (unsigned)(size.p[1] * channels()));
-    CV_DbgAssert(CV_ELEM_SIZE1(traits::Depth<_Tp>::value) == elemSize1());
-    return ((const _Tp*)(data + step.p[0] * pt.y))[pt.x];
-}
-
-template<typename _Tp> inline
-_Tp& Mat::at(int i0)
-{
-    CV_DbgAssert(dims <= 2);
-    CV_DbgAssert(data);
-    CV_DbgAssert((unsigned)i0 < (unsigned)(size.p[0] * size.p[1]));
-    CV_DbgAssert(elemSize() == sizeof(_Tp));
-    if( isContinuous() || size.p[0] == 1 )
-        return ((_Tp*)data)[i0];
-    if( size.p[1] == 1 )
-        return *(_Tp*)(data + step.p[0] * i0);
-    int i = i0 / cols, j = i0 - i * cols;
-    return ((_Tp*)(data + step.p[0] * i))[j];
-}
-
-template<typename _Tp> inline
-const _Tp& Mat::at(int i0) const
-{
-    CV_DbgAssert(dims <= 2);
-    CV_DbgAssert(data);
-    CV_DbgAssert((unsigned)i0 < (unsigned)(size.p[0] * size.p[1]));
-    CV_DbgAssert(elemSize() == sizeof(_Tp));
-    if( isContinuous() || size.p[0] == 1 )
-        return ((const _Tp*)data)[i0];
-    if( size.p[1] == 1 )
-        return *(const _Tp*)(data + step.p[0] * i0);
-    int i = i0 / cols, j = i0 - i * cols;
-    return ((const _Tp*)(data + step.p[0] * i))[j];
-}
-
-template<typename _Tp> inline
-_Tp& Mat::at(int i0, int i1, int i2)
-{
-    CV_DbgAssert( elemSize() == sizeof(_Tp) );
-    return *(_Tp*)ptr(i0, i1, i2);
-}
-
-template<typename _Tp> inline
-const _Tp& Mat::at(int i0, int i1, int i2) const
-{
-    CV_DbgAssert( elemSize() == sizeof(_Tp) );
-    return *(const _Tp*)ptr(i0, i1, i2);
-}
-
-template<typename _Tp> inline
-_Tp& Mat::at(const int* idx)
-{
-    CV_DbgAssert( elemSize() == sizeof(_Tp) );
-    return *(_Tp*)ptr(idx);
-}
-
-template<typename _Tp> inline
-const _Tp& Mat::at(const int* idx) const
-{
-    CV_DbgAssert( elemSize() == sizeof(_Tp) );
-    return *(const _Tp*)ptr(idx);
-}
-
-template<typename _Tp, int n> inline
-_Tp& Mat::at(const Vec<int, n>& idx)
-{
-    CV_DbgAssert( elemSize() == sizeof(_Tp) );
-    return *(_Tp*)ptr(idx.val);
-}
-
-template<typename _Tp, int n> inline
-const _Tp& Mat::at(const Vec<int, n>& idx) const
-{
-    CV_DbgAssert( elemSize() == sizeof(_Tp) );
-    return *(const _Tp*)ptr(idx.val);
-}
-
-template<typename _Tp> inline
-MatConstIterator_<_Tp> Mat::begin() const
-{
-    CV_DbgAssert( elemSize() == sizeof(_Tp) );
-    return MatConstIterator_<_Tp>((const Mat_<_Tp>*)this);
-}
-
-template<typename _Tp> inline
-MatConstIterator_<_Tp> Mat::end() const
-{
-    CV_DbgAssert( elemSize() == sizeof(_Tp) );
-    MatConstIterator_<_Tp> it((const Mat_<_Tp>*)this);
-    it += total();
-    return it;
-}
-
-template<typename _Tp> inline
-MatIterator_<_Tp> Mat::begin()
-{
-    CV_DbgAssert( elemSize() == sizeof(_Tp) );
-    return MatIterator_<_Tp>((Mat_<_Tp>*)this);
-}
-
-template<typename _Tp> inline
-MatIterator_<_Tp> Mat::end()
-{
-    CV_DbgAssert( elemSize() == sizeof(_Tp) );
-    MatIterator_<_Tp> it((Mat_<_Tp>*)this);
-    it += total();
-    return it;
-}
-
-template<typename _Tp, typename Functor> inline
-void Mat::forEach(const Functor& operation) {
-    this->forEach_impl<_Tp>(operation);
-}
-
-template<typename _Tp, typename Functor> inline
-void Mat::forEach(const Functor& operation) const {
-    // call as not const
-    (const_cast<Mat*>(this))->forEach<_Tp>(operation);
-}
-
-template<typename _Tp> inline
-Mat::operator std::vector<_Tp>() const
-{
-    std::vector<_Tp> v;
-    copyTo(v);
-    return v;
-}
-
-#ifdef CV_CXX_STD_ARRAY
-template<typename _Tp, std::size_t _Nm> inline
-Mat::operator std::array<_Tp, _Nm>() const
-{
-    std::array<_Tp, _Nm> v;
-    copyTo(v);
-    return v;
-}
-#endif
-
-template<typename _Tp, int n> inline
-Mat::operator Vec<_Tp, n>() const
-{
-    CV_Assert( data && dims <= 2 && (rows == 1 || cols == 1) &&
-               rows + cols - 1 == n && channels() == 1 );
-
-    if( isContinuous() && type() == traits::Type<_Tp>::value )
-        return Vec<_Tp, n>((_Tp*)data);
-    Vec<_Tp, n> v;
-    Mat tmp(rows, cols, traits::Type<_Tp>::value, v.val);
-    convertTo(tmp, tmp.type());
-    return v;
-}
-
-template<typename _Tp, int m, int n> inline
-Mat::operator Matx<_Tp, m, n>() const
-{
-    CV_Assert( data && dims <= 2 && rows == m && cols == n && channels() == 1 );
-
-    if( isContinuous() && type() == traits::Type<_Tp>::value )
-        return Matx<_Tp, m, n>((_Tp*)data);
-    Matx<_Tp, m, n> mtx;
-    Mat tmp(rows, cols, traits::Type<_Tp>::value, mtx.val);
-    convertTo(tmp, tmp.type());
-    return mtx;
-}
-
-template<typename _Tp> inline
-void Mat::push_back(const _Tp& elem)
-{
-    if( !data )
-    {
-        *this = Mat(1, 1, traits::Type<_Tp>::value, (void*)&elem).clone();
-        return;
-    }
-    CV_Assert(traits::Type<_Tp>::value == type() && cols == 1
-              /* && dims == 2 (cols == 1 implies dims == 2) */);
-    const uchar* tmp = dataend + step[0];
-    if( !isSubmatrix() && isContinuous() && tmp <= datalimit )
-    {
-        *(_Tp*)(data + (size.p[0]++) * step.p[0]) = elem;
-        dataend = tmp;
-    }
-    else
-        push_back_(&elem);
-}
-
-template<typename _Tp> inline
-void Mat::push_back(const Mat_<_Tp>& m)
-{
-    push_back((const Mat&)m);
-}
-
-template<> inline
-void Mat::push_back(const MatExpr& expr)
-{
-    push_back(static_cast<Mat>(expr));
-}
-
-
-template<typename _Tp> inline
-void Mat::push_back(const std::vector<_Tp>& v)
-{
-    push_back(Mat(v));
-}
-
-#ifdef CV_CXX_MOVE_SEMANTICS
-
-inline
-Mat::Mat(Mat&& m)
-    : flags(m.flags), dims(m.dims), rows(m.rows), cols(m.cols), data(m.data),
-      datastart(m.datastart), dataend(m.dataend), datalimit(m.datalimit), allocator(m.allocator),
-      u(m.u), size(&rows)
-{
-    if (m.dims <= 2)  // move new step/size info
-    {
-        step[0] = m.step[0];
-        step[1] = m.step[1];
-    }
-    else
-    {
-        CV_DbgAssert(m.step.p != m.step.buf);
-        step.p = m.step.p;
-        size.p = m.size.p;
-        m.step.p = m.step.buf;
-        m.size.p = &m.rows;
-    }
-    m.flags = MAGIC_VAL; m.dims = m.rows = m.cols = 0;
-    m.data = NULL; m.datastart = NULL; m.dataend = NULL; m.datalimit = NULL;
-    m.allocator = NULL;
-    m.u = NULL;
-}
-
-inline
-Mat& Mat::operator = (Mat&& m)
-{
-    if (this == &m)
-      return *this;
-
-    release();
-    flags = m.flags; dims = m.dims; rows = m.rows; cols = m.cols; data = m.data;
-    datastart = m.datastart; dataend = m.dataend; datalimit = m.datalimit; allocator = m.allocator;
-    u = m.u;
-    if (step.p != step.buf) // release self step/size
-    {
-        fastFree(step.p);
-        step.p = step.buf;
-        size.p = &rows;
-    }
-    if (m.dims <= 2) // move new step/size info
-    {
-        step[0] = m.step[0];
-        step[1] = m.step[1];
-    }
-    else
-    {
-        CV_DbgAssert(m.step.p != m.step.buf);
-        step.p = m.step.p;
-        size.p = m.size.p;
-        m.step.p = m.step.buf;
-        m.size.p = &m.rows;
-    }
-    m.flags = MAGIC_VAL; m.dims = m.rows = m.cols = 0;
-    m.data = NULL; m.datastart = NULL; m.dataend = NULL; m.datalimit = NULL;
-    m.allocator = NULL;
-    m.u = NULL;
-    return *this;
-}
-
-#endif
-
-
-///////////////////////////// MatSize ////////////////////////////
-
-inline
-MatSize::MatSize(int* _p)
-    : p(_p) {}
-
-inline
-int MatSize::dims() const
-{
-    return (p - 1)[0];
-}
-
-inline
-Size MatSize::operator()() const
-{
-    CV_DbgAssert(dims() <= 2);
-    return Size(p[1], p[0]);
-}
-
-inline
-const int& MatSize::operator[](int i) const
-{
-    CV_DbgAssert(i < dims());
-#ifdef __OPENCV_BUILD
-    CV_DbgAssert(i >= 0);
-#endif
-    return p[i];
-}
-
-inline
-int& MatSize::operator[](int i)
-{
-    CV_DbgAssert(i < dims());
-#ifdef __OPENCV_BUILD
-    CV_DbgAssert(i >= 0);
-#endif
-    return p[i];
-}
-
-inline
-MatSize::operator const int*() const
-{
-    return p;
-}
-
-inline
-bool MatSize::operator == (const MatSize& sz) const
-{
-    int d = dims();
-    int dsz = sz.dims();
-    if( d != dsz )
-        return false;
-    if( d == 2 )
-        return p[0] == sz.p[0] && p[1] == sz.p[1];
-
-    for( int i = 0; i < d; i++ )
-        if( p[i] != sz.p[i] )
-            return false;
-    return true;
-}
-
-inline
-bool MatSize::operator != (const MatSize& sz) const
-{
-    return !(*this == sz);
-}
-
-
-
-///////////////////////////// MatStep ////////////////////////////
-
-inline
-MatStep::MatStep()
-{
-    p = buf; p[0] = p[1] = 0;
-}
-
-inline
-MatStep::MatStep(size_t s)
-{
-    p = buf; p[0] = s; p[1] = 0;
-}
-
-inline
-const size_t& MatStep::operator[](int i) const
-{
-    return p[i];
-}
-
-inline
-size_t& MatStep::operator[](int i)
-{
-    return p[i];
-}
-
-inline MatStep::operator size_t() const
-{
-    CV_DbgAssert( p == buf );
-    return buf[0];
-}
-
-inline MatStep& MatStep::operator = (size_t s)
-{
-    CV_DbgAssert( p == buf );
-    buf[0] = s;
-    return *this;
-}
-
-
-
-////////////////////////////// Mat_<_Tp> ////////////////////////////
-
-template<typename _Tp> inline
-Mat_<_Tp>::Mat_()
-    : Mat()
-{
-    flags = (flags & ~CV_MAT_TYPE_MASK) | traits::Type<_Tp>::value;
-}
-
-template<typename _Tp> inline
-Mat_<_Tp>::Mat_(int _rows, int _cols)
-    : Mat(_rows, _cols, traits::Type<_Tp>::value)
-{
-}
-
-template<typename _Tp> inline
-Mat_<_Tp>::Mat_(int _rows, int _cols, const _Tp& value)
-    : Mat(_rows, _cols, traits::Type<_Tp>::value)
-{
-    *this = value;
-}
-
-template<typename _Tp> inline
-Mat_<_Tp>::Mat_(Size _sz)
-    : Mat(_sz.height, _sz.width, traits::Type<_Tp>::value)
-{}
-
-template<typename _Tp> inline
-Mat_<_Tp>::Mat_(Size _sz, const _Tp& value)
-    : Mat(_sz.height, _sz.width, traits::Type<_Tp>::value)
-{
-    *this = value;
-}
-
-template<typename _Tp> inline
-Mat_<_Tp>::Mat_(int _dims, const int* _sz)
-    : Mat(_dims, _sz, traits::Type<_Tp>::value)
-{}
-
-template<typename _Tp> inline
-Mat_<_Tp>::Mat_(int _dims, const int* _sz, const _Tp& _s)
-    : Mat(_dims, _sz, traits::Type<_Tp>::value, Scalar(_s))
-{}
-
-template<typename _Tp> inline
-Mat_<_Tp>::Mat_(int _dims, const int* _sz, _Tp* _data, const size_t* _steps)
-    : Mat(_dims, _sz, traits::Type<_Tp>::value, _data, _steps)
-{}
-
-template<typename _Tp> inline
-Mat_<_Tp>::Mat_(const Mat_<_Tp>& m, const Range* ranges)
-    : Mat(m, ranges)
-{}
-
-template<typename _Tp> inline
-Mat_<_Tp>::Mat_(const Mat_<_Tp>& m, const std::vector<Range>& ranges)
-    : Mat(m, ranges)
-{}
-
-template<typename _Tp> inline
-Mat_<_Tp>::Mat_(const Mat& m)
-    : Mat()
-{
-    flags = (flags & ~CV_MAT_TYPE_MASK) | traits::Type<_Tp>::value;
-    *this = m;
-}
-
-template<typename _Tp> inline
-Mat_<_Tp>::Mat_(const Mat_& m)
-    : Mat(m)
-{}
-
-template<typename _Tp> inline
-Mat_<_Tp>::Mat_(int _rows, int _cols, _Tp* _data, size_t steps)
-    : Mat(_rows, _cols, traits::Type<_Tp>::value, _data, steps)
-{}
-
-template<typename _Tp> inline
-Mat_<_Tp>::Mat_(const Mat_& m, const Range& _rowRange, const Range& _colRange)
-    : Mat(m, _rowRange, _colRange)
-{}
-
-template<typename _Tp> inline
-Mat_<_Tp>::Mat_(const Mat_& m, const Rect& roi)
-    : Mat(m, roi)
-{}
-
-template<typename _Tp> template<int n> inline
-Mat_<_Tp>::Mat_(const Vec<typename DataType<_Tp>::channel_type, n>& vec, bool copyData)
-    : Mat(n / DataType<_Tp>::channels, 1, traits::Type<_Tp>::value, (void*)&vec)
-{
-    CV_Assert(n%DataType<_Tp>::channels == 0);
-    if( copyData )
-        *this = clone();
-}
-
-template<typename _Tp> template<int m, int n> inline
-Mat_<_Tp>::Mat_(const Matx<typename DataType<_Tp>::channel_type, m, n>& M, bool copyData)
-    : Mat(m, n / DataType<_Tp>::channels, traits::Type<_Tp>::value, (void*)&M)
-{
-    CV_Assert(n % DataType<_Tp>::channels == 0);
-    if( copyData )
-        *this = clone();
-}
-
-template<typename _Tp> inline
-Mat_<_Tp>::Mat_(const Point_<typename DataType<_Tp>::channel_type>& pt, bool copyData)
-    : Mat(2 / DataType<_Tp>::channels, 1, traits::Type<_Tp>::value, (void*)&pt)
-{
-    CV_Assert(2 % DataType<_Tp>::channels == 0);
-    if( copyData )
-        *this = clone();
-}
-
-template<typename _Tp> inline
-Mat_<_Tp>::Mat_(const Point3_<typename DataType<_Tp>::channel_type>& pt, bool copyData)
-    : Mat(3 / DataType<_Tp>::channels, 1, traits::Type<_Tp>::value, (void*)&pt)
-{
-    CV_Assert(3 % DataType<_Tp>::channels == 0);
-    if( copyData )
-        *this = clone();
-}
-
-template<typename _Tp> inline
-Mat_<_Tp>::Mat_(const MatCommaInitializer_<_Tp>& commaInitializer)
-    : Mat(commaInitializer)
-{}
-
-template<typename _Tp> inline
-Mat_<_Tp>::Mat_(const std::vector<_Tp>& vec, bool copyData)
-    : Mat(vec, copyData)
-{}
-
-#ifdef CV_CXX11
-template<typename _Tp> inline
-Mat_<_Tp>::Mat_(std::initializer_list<_Tp> list)
-    : Mat(list)
-{}
-
-template<typename _Tp> inline
-Mat_<_Tp>::Mat_(const std::initializer_list<int> sizes, std::initializer_list<_Tp> list)
-    : Mat(sizes, list)
-{}
-#endif
-
-#ifdef CV_CXX_STD_ARRAY
-template<typename _Tp> template<std::size_t _Nm> inline
-Mat_<_Tp>::Mat_(const std::array<_Tp, _Nm>& arr, bool copyData)
-    : Mat(arr, copyData)
-{}
-#endif
-
-template<typename _Tp> inline
-Mat_<_Tp>& Mat_<_Tp>::operator = (const Mat& m)
-{
-    if( traits::Type<_Tp>::value == m.type() )
-    {
-        Mat::operator = (m);
-        return *this;
-    }
-    if( traits::Depth<_Tp>::value == m.depth() )
-    {
-        return (*this = m.reshape(DataType<_Tp>::channels, m.dims, 0));
-    }
-    CV_Assert(DataType<_Tp>::channels == m.channels() || m.empty());
-    m.convertTo(*this, type());
-    return *this;
-}
-
-template<typename _Tp> inline
-Mat_<_Tp>& Mat_<_Tp>::operator = (const Mat_& m)
-{
-    Mat::operator=(m);
-    return *this;
-}
-
-template<typename _Tp> inline
-Mat_<_Tp>& Mat_<_Tp>::operator = (const _Tp& s)
-{
-    typedef typename DataType<_Tp>::vec_type VT;
-    Mat::operator=(Scalar((const VT&)s));
-    return *this;
-}
-
-template<typename _Tp> inline
-void Mat_<_Tp>::create(int _rows, int _cols)
-{
-    Mat::create(_rows, _cols, traits::Type<_Tp>::value);
-}
-
-template<typename _Tp> inline
-void Mat_<_Tp>::create(Size _sz)
-{
-    Mat::create(_sz, traits::Type<_Tp>::value);
-}
-
-template<typename _Tp> inline
-void Mat_<_Tp>::create(int _dims, const int* _sz)
-{
-    Mat::create(_dims, _sz, traits::Type<_Tp>::value);
-}
-
-template<typename _Tp> inline
-void Mat_<_Tp>::release()
-{
-    Mat::release();
-#ifdef _DEBUG
-    flags = (flags & ~CV_MAT_TYPE_MASK) | traits::Type<_Tp>::value;
-#endif
-}
-
-template<typename _Tp> inline
-Mat_<_Tp> Mat_<_Tp>::cross(const Mat_& m) const
-{
-    return Mat_<_Tp>(Mat::cross(m));
-}
-
-template<typename _Tp> template<typename T2> inline
-Mat_<_Tp>::operator Mat_<T2>() const
-{
-    return Mat_<T2>(*this);
-}
-
-template<typename _Tp> inline
-Mat_<_Tp> Mat_<_Tp>::row(int y) const
-{
-    return Mat_(*this, Range(y, y+1), Range::all());
-}
-
-template<typename _Tp> inline
-Mat_<_Tp> Mat_<_Tp>::col(int x) const
-{
-    return Mat_(*this, Range::all(), Range(x, x+1));
-}
-
-template<typename _Tp> inline
-Mat_<_Tp> Mat_<_Tp>::diag(int d) const
-{
-    return Mat_(Mat::diag(d));
-}
-
-template<typename _Tp> inline
-Mat_<_Tp> Mat_<_Tp>::clone() const
-{
-    return Mat_(Mat::clone());
-}
-
-template<typename _Tp> inline
-size_t Mat_<_Tp>::elemSize() const
-{
-    CV_DbgAssert( Mat::elemSize() == sizeof(_Tp) );
-    return sizeof(_Tp);
-}
-
-template<typename _Tp> inline
-size_t Mat_<_Tp>::elemSize1() const
-{
-    CV_DbgAssert( Mat::elemSize1() == sizeof(_Tp) / DataType<_Tp>::channels );
-    return sizeof(_Tp) / DataType<_Tp>::channels;
-}
-
-template<typename _Tp> inline
-int Mat_<_Tp>::type() const
-{
-    CV_DbgAssert( Mat::type() == traits::Type<_Tp>::value );
-    return traits::Type<_Tp>::value;
-}
-
-template<typename _Tp> inline
-int Mat_<_Tp>::depth() const
-{
-    CV_DbgAssert( Mat::depth() == traits::Depth<_Tp>::value );
-    return traits::Depth<_Tp>::value;
-}
-
-template<typename _Tp> inline
-int Mat_<_Tp>::channels() const
-{
-    CV_DbgAssert( Mat::channels() == DataType<_Tp>::channels );
-    return DataType<_Tp>::channels;
-}
-
-template<typename _Tp> inline
-size_t Mat_<_Tp>::stepT(int i) const
-{
-    return step.p[i] / elemSize();
-}
-
-template<typename _Tp> inline
-size_t Mat_<_Tp>::step1(int i) const
-{
-    return step.p[i] / elemSize1();
-}
-
-template<typename _Tp> inline
-Mat_<_Tp>& Mat_<_Tp>::adjustROI( int dtop, int dbottom, int dleft, int dright )
-{
-    return (Mat_<_Tp>&)(Mat::adjustROI(dtop, dbottom, dleft, dright));
-}
-
-template<typename _Tp> inline
-Mat_<_Tp> Mat_<_Tp>::operator()( const Range& _rowRange, const Range& _colRange ) const
-{
-    return Mat_<_Tp>(*this, _rowRange, _colRange);
-}
-
-template<typename _Tp> inline
-Mat_<_Tp> Mat_<_Tp>::operator()( const Rect& roi ) const
-{
-    return Mat_<_Tp>(*this, roi);
-}
-
-template<typename _Tp> inline
-Mat_<_Tp> Mat_<_Tp>::operator()( const Range* ranges ) const
-{
-    return Mat_<_Tp>(*this, ranges);
-}
-
-template<typename _Tp> inline
-Mat_<_Tp> Mat_<_Tp>::operator()(const std::vector<Range>& ranges) const
-{
-    return Mat_<_Tp>(*this, ranges);
-}
-
-template<typename _Tp> inline
-_Tp* Mat_<_Tp>::operator [](int y)
-{
-    CV_DbgAssert( 0 <= y && y < size.p[0] );
-    return (_Tp*)(data + y*step.p[0]);
-}
-
-template<typename _Tp> inline
-const _Tp* Mat_<_Tp>::operator [](int y) const
-{
-    CV_DbgAssert( 0 <= y && y < size.p[0] );
-    return (const _Tp*)(data + y*step.p[0]);
-}
-
-template<typename _Tp> inline
-_Tp& Mat_<_Tp>::operator ()(int i0, int i1)
-{
-    CV_DbgAssert(dims <= 2);
-    CV_DbgAssert(data);
-    CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]);
-    CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]);
-    CV_DbgAssert(type() == traits::Type<_Tp>::value);
-    return ((_Tp*)(data + step.p[0] * i0))[i1];
-}
-
-template<typename _Tp> inline
-const _Tp& Mat_<_Tp>::operator ()(int i0, int i1) const
-{
-    CV_DbgAssert(dims <= 2);
-    CV_DbgAssert(data);
-    CV_DbgAssert((unsigned)i0 < (unsigned)size.p[0]);
-    CV_DbgAssert((unsigned)i1 < (unsigned)size.p[1]);
-    CV_DbgAssert(type() == traits::Type<_Tp>::value);
-    return ((const _Tp*)(data + step.p[0] * i0))[i1];
-}
-
-template<typename _Tp> inline
-_Tp& Mat_<_Tp>::operator ()(Point pt)
-{
-    CV_DbgAssert(dims <= 2);
-    CV_DbgAssert(data);
-    CV_DbgAssert((unsigned)pt.y < (unsigned)size.p[0]);
-    CV_DbgAssert((unsigned)pt.x < (unsigned)size.p[1]);
-    CV_DbgAssert(type() == traits::Type<_Tp>::value);
-    return ((_Tp*)(data + step.p[0] * pt.y))[pt.x];
-}
-
-template<typename _Tp> inline
-const _Tp& Mat_<_Tp>::operator ()(Point pt) const
-{
-    CV_DbgAssert(dims <= 2);
-    CV_DbgAssert(data);
-    CV_DbgAssert((unsigned)pt.y < (unsigned)size.p[0]);
-    CV_DbgAssert((unsigned)pt.x < (unsigned)size.p[1]);
-    CV_DbgAssert(type() == traits::Type<_Tp>::value);
-    return ((const _Tp*)(data + step.p[0] * pt.y))[pt.x];
-}
-
-template<typename _Tp> inline
-_Tp& Mat_<_Tp>::operator ()(const int* idx)
-{
-    return Mat::at<_Tp>(idx);
-}
-
-template<typename _Tp> inline
-const _Tp& Mat_<_Tp>::operator ()(const int* idx) const
-{
-    return Mat::at<_Tp>(idx);
-}
-
-template<typename _Tp> template<int n> inline
-_Tp& Mat_<_Tp>::operator ()(const Vec<int, n>& idx)
-{
-    return Mat::at<_Tp>(idx);
-}
-
-template<typename _Tp> template<int n> inline
-const _Tp& Mat_<_Tp>::operator ()(const Vec<int, n>& idx) const
-{
-    return Mat::at<_Tp>(idx);
-}
-
-template<typename _Tp> inline
-_Tp& Mat_<_Tp>::operator ()(int i0)
-{
-    return this->at<_Tp>(i0);
-}
-
-template<typename _Tp> inline
-const _Tp& Mat_<_Tp>::operator ()(int i0) const
-{
-    return this->at<_Tp>(i0);
-}
-
-template<typename _Tp> inline
-_Tp& Mat_<_Tp>::operator ()(int i0, int i1, int i2)
-{
-    return this->at<_Tp>(i0, i1, i2);
-}
-
-template<typename _Tp> inline
-const _Tp& Mat_<_Tp>::operator ()(int i0, int i1, int i2) const
-{
-    return this->at<_Tp>(i0, i1, i2);
-}
-
-template<typename _Tp> inline
-Mat_<_Tp>::operator std::vector<_Tp>() const
-{
-    std::vector<_Tp> v;
-    copyTo(v);
-    return v;
-}
-
-#ifdef CV_CXX_STD_ARRAY
-template<typename _Tp> template<std::size_t _Nm> inline
-Mat_<_Tp>::operator std::array<_Tp, _Nm>() const
-{
-    std::array<_Tp, _Nm> a;
-    copyTo(a);
-    return a;
-}
-#endif
-
-template<typename _Tp> template<int n> inline
-Mat_<_Tp>::operator Vec<typename DataType<_Tp>::channel_type, n>() const
-{
-    CV_Assert(n % DataType<_Tp>::channels == 0);
-
-#if defined _MSC_VER
-    const Mat* pMat = (const Mat*)this; // workaround for MSVS <= 2012 compiler bugs (but GCC 4.6 dislikes this workaround)
-    return pMat->operator Vec<typename DataType<_Tp>::channel_type, n>();
-#else
-    return this->Mat::operator Vec<typename DataType<_Tp>::channel_type, n>();
-#endif
-}
-
-template<typename _Tp> template<int m, int n> inline
-Mat_<_Tp>::operator Matx<typename DataType<_Tp>::channel_type, m, n>() const
-{
-    CV_Assert(n % DataType<_Tp>::channels == 0);
-
-#if defined _MSC_VER
-    const Mat* pMat = (const Mat*)this; // workaround for MSVS <= 2012 compiler bugs (but GCC 4.6 dislikes this workaround)
-    Matx<typename DataType<_Tp>::channel_type, m, n> res = pMat->operator Matx<typename DataType<_Tp>::channel_type, m, n>();
-    return res;
-#else
-    Matx<typename DataType<_Tp>::channel_type, m, n> res = this->Mat::operator Matx<typename DataType<_Tp>::channel_type, m, n>();
-    return res;
-#endif
-}
-
-template<typename _Tp> inline
-MatConstIterator_<_Tp> Mat_<_Tp>::begin() const
-{
-    return Mat::begin<_Tp>();
-}
-
-template<typename _Tp> inline
-MatConstIterator_<_Tp> Mat_<_Tp>::end() const
-{
-    return Mat::end<_Tp>();
-}
-
-template<typename _Tp> inline
-MatIterator_<_Tp> Mat_<_Tp>::begin()
-{
-    return Mat::begin<_Tp>();
-}
-
-template<typename _Tp> inline
-MatIterator_<_Tp> Mat_<_Tp>::end()
-{
-    return Mat::end<_Tp>();
-}
-
-template<typename _Tp> template<typename Functor> inline
-void Mat_<_Tp>::forEach(const Functor& operation) {
-    Mat::forEach<_Tp, Functor>(operation);
-}
-
-template<typename _Tp> template<typename Functor> inline
-void Mat_<_Tp>::forEach(const Functor& operation) const {
-    Mat::forEach<_Tp, Functor>(operation);
-}
-
-#ifdef CV_CXX_MOVE_SEMANTICS
-
-template<typename _Tp> inline
-Mat_<_Tp>::Mat_(Mat_&& m)
-    : Mat(m)
-{
-}
-
-template<typename _Tp> inline
-Mat_<_Tp>& Mat_<_Tp>::operator = (Mat_&& m)
-{
-    Mat::operator = (std::move(m));
-    return *this;
-}
-
-template<typename _Tp> inline
-Mat_<_Tp>::Mat_(Mat&& m)
-    : Mat()
-{
-    flags = (flags & ~CV_MAT_TYPE_MASK) | traits::Type<_Tp>::value;
-    *this = m;
-}
-
-template<typename _Tp> inline
-Mat_<_Tp>& Mat_<_Tp>::operator = (Mat&& m)
-{
-    if( traits::Type<_Tp>::value == m.type() )
-    {
-        Mat::operator = ((Mat&&)m);
-        return *this;
-    }
-    if( traits::Depth<_Tp>::value == m.depth() )
-    {
-        Mat::operator = ((Mat&&)m.reshape(DataType<_Tp>::channels, m.dims, 0));
-        return *this;
-    }
-    CV_DbgAssert(DataType<_Tp>::channels == m.channels());
-    m.convertTo(*this, type());
-    return *this;
-}
-
-template<typename _Tp> inline
-Mat_<_Tp>::Mat_(MatExpr&& e)
-    : Mat()
-{
-    flags = (flags & ~CV_MAT_TYPE_MASK) | traits::Type<_Tp>::value;
-    *this = Mat(e);
-}
-
-#endif
-
-///////////////////////////// SparseMat /////////////////////////////
-
-inline
-SparseMat::SparseMat()
-    : flags(MAGIC_VAL), hdr(0)
-{}
-
-inline
-SparseMat::SparseMat(int _dims, const int* _sizes, int _type)
-    : flags(MAGIC_VAL), hdr(0)
-{
-    create(_dims, _sizes, _type);
-}
-
-inline
-SparseMat::SparseMat(const SparseMat& m)
-    : flags(m.flags), hdr(m.hdr)
-{
-    addref();
-}
-
-inline
-SparseMat::~SparseMat()
-{
-    release();
-}
-
-inline
-SparseMat& SparseMat::operator = (const SparseMat& m)
-{
-    if( this != &m )
-    {
-        if( m.hdr )
-            CV_XADD(&m.hdr->refcount, 1);
-        release();
-        flags = m.flags;
-        hdr = m.hdr;
-    }
-    return *this;
-}
-
-inline
-SparseMat& SparseMat::operator = (const Mat& m)
-{
-    return (*this = SparseMat(m));
-}
-
-inline
-SparseMat SparseMat::clone() const
-{
-    SparseMat temp;
-    this->copyTo(temp);
-    return temp;
-}
-
-inline
-void SparseMat::assignTo( SparseMat& m, int _type ) const
-{
-    if( _type < 0 )
-        m = *this;
-    else
-        convertTo(m, _type);
-}
-
-inline
-void SparseMat::addref()
-{
-    if( hdr )
-        CV_XADD(&hdr->refcount, 1);
-}
-
-inline
-void SparseMat::release()
-{
-    if( hdr && CV_XADD(&hdr->refcount, -1) == 1 )
-        delete hdr;
-    hdr = 0;
-}
-
-inline
-size_t SparseMat::elemSize() const
-{
-    return CV_ELEM_SIZE(flags);
-}
-
-inline
-size_t SparseMat::elemSize1() const
-{
-    return CV_ELEM_SIZE1(flags);
-}
-
-inline
-int SparseMat::type() const
-{
-    return CV_MAT_TYPE(flags);
-}
-
-inline
-int SparseMat::depth() const
-{
-    return CV_MAT_DEPTH(flags);
-}
-
-inline
-int SparseMat::channels() const
-{
-    return CV_MAT_CN(flags);
-}
-
-inline
-const int* SparseMat::size() const
-{
-    return hdr ? hdr->size : 0;
-}
-
-inline
-int SparseMat::size(int i) const
-{
-    if( hdr )
-    {
-        CV_DbgAssert((unsigned)i < (unsigned)hdr->dims);
-        return hdr->size[i];
-    }
-    return 0;
-}
-
-inline
-int SparseMat::dims() const
-{
-    return hdr ? hdr->dims : 0;
-}
-
-inline
-size_t SparseMat::nzcount() const
-{
-    return hdr ? hdr->nodeCount : 0;
-}
-
-inline
-size_t SparseMat::hash(int i0) const
-{
-    return (size_t)i0;
-}
-
-inline
-size_t SparseMat::hash(int i0, int i1) const
-{
-    return (size_t)(unsigned)i0 * HASH_SCALE + (unsigned)i1;
-}
-
-inline
-size_t SparseMat::hash(int i0, int i1, int i2) const
-{
-    return ((size_t)(unsigned)i0 * HASH_SCALE + (unsigned)i1) * HASH_SCALE + (unsigned)i2;
-}
-
-inline
-size_t SparseMat::hash(const int* idx) const
-{
-    size_t h = (unsigned)idx[0];
-    if( !hdr )
-        return 0;
-    int d = hdr->dims;
-    for(int i = 1; i < d; i++ )
-        h = h * HASH_SCALE + (unsigned)idx[i];
-    return h;
-}
-
-template<typename _Tp> inline
-_Tp& SparseMat::ref(int i0, size_t* hashval)
-{
-    return *(_Tp*)((SparseMat*)this)->ptr(i0, true, hashval);
-}
-
-template<typename _Tp> inline
-_Tp& SparseMat::ref(int i0, int i1, size_t* hashval)
-{
-    return *(_Tp*)((SparseMat*)this)->ptr(i0, i1, true, hashval);
-}
-
-template<typename _Tp> inline
-_Tp& SparseMat::ref(int i0, int i1, int i2, size_t* hashval)
-{
-    return *(_Tp*)((SparseMat*)this)->ptr(i0, i1, i2, true, hashval);
-}
-
-template<typename _Tp> inline
-_Tp& SparseMat::ref(const int* idx, size_t* hashval)
-{
-    return *(_Tp*)((SparseMat*)this)->ptr(idx, true, hashval);
-}
-
-template<typename _Tp> inline
-_Tp SparseMat::value(int i0, size_t* hashval) const
-{
-    const _Tp* p = (const _Tp*)((SparseMat*)this)->ptr(i0, false, hashval);
-    return p ? *p : _Tp();
-}
-
-template<typename _Tp> inline
-_Tp SparseMat::value(int i0, int i1, size_t* hashval) const
-{
-    const _Tp* p = (const _Tp*)((SparseMat*)this)->ptr(i0, i1, false, hashval);
-    return p ? *p : _Tp();
-}
-
-template<typename _Tp> inline
-_Tp SparseMat::value(int i0, int i1, int i2, size_t* hashval) const
-{
-    const _Tp* p = (const _Tp*)((SparseMat*)this)->ptr(i0, i1, i2, false, hashval);
-    return p ? *p : _Tp();
-}
-
-template<typename _Tp> inline
-_Tp SparseMat::value(const int* idx, size_t* hashval) const
-{
-    const _Tp* p = (const _Tp*)((SparseMat*)this)->ptr(idx, false, hashval);
-    return p ? *p : _Tp();
-}
-
-template<typename _Tp> inline
-const _Tp* SparseMat::find(int i0, size_t* hashval) const
-{
-    return (const _Tp*)((SparseMat*)this)->ptr(i0, false, hashval);
-}
-
-template<typename _Tp> inline
-const _Tp* SparseMat::find(int i0, int i1, size_t* hashval) const
-{
-    return (const _Tp*)((SparseMat*)this)->ptr(i0, i1, false, hashval);
-}
-
-template<typename _Tp> inline
-const _Tp* SparseMat::find(int i0, int i1, int i2, size_t* hashval) const
-{
-    return (const _Tp*)((SparseMat*)this)->ptr(i0, i1, i2, false, hashval);
-}
-
-template<typename _Tp> inline
-const _Tp* SparseMat::find(const int* idx, size_t* hashval) const
-{
-    return (const _Tp*)((SparseMat*)this)->ptr(idx, false, hashval);
-}
-
-template<typename _Tp> inline
-_Tp& SparseMat::value(Node* n)
-{
-    return *(_Tp*)((uchar*)n + hdr->valueOffset);
-}
-
-template<typename _Tp> inline
-const _Tp& SparseMat::value(const Node* n) const
-{
-    return *(const _Tp*)((const uchar*)n + hdr->valueOffset);
-}
-
-inline
-SparseMat::Node* SparseMat::node(size_t nidx)
-{
-    return (Node*)(void*)&hdr->pool[nidx];
-}
-
-inline
-const SparseMat::Node* SparseMat::node(size_t nidx) const
-{
-    return (const Node*)(const void*)&hdr->pool[nidx];
-}
-
-inline
-SparseMatIterator SparseMat::begin()
-{
-    return SparseMatIterator(this);
-}
-
-inline
-SparseMatConstIterator SparseMat::begin() const
-{
-    return SparseMatConstIterator(this);
-}
-
-inline
-SparseMatIterator SparseMat::end()
-{
-    SparseMatIterator it(this);
-    it.seekEnd();
-    return it;
-}
-
-inline
-SparseMatConstIterator SparseMat::end() const
-{
-    SparseMatConstIterator it(this);
-    it.seekEnd();
-    return it;
-}
-
-template<typename _Tp> inline
-SparseMatIterator_<_Tp> SparseMat::begin()
-{
-    return SparseMatIterator_<_Tp>(this);
-}
-
-template<typename _Tp> inline
-SparseMatConstIterator_<_Tp> SparseMat::begin() const
-{
-    return SparseMatConstIterator_<_Tp>(this);
-}
-
-template<typename _Tp> inline
-SparseMatIterator_<_Tp> SparseMat::end()
-{
-    SparseMatIterator_<_Tp> it(this);
-    it.seekEnd();
-    return it;
-}
-
-template<typename _Tp> inline
-SparseMatConstIterator_<_Tp> SparseMat::end() const
-{
-    SparseMatConstIterator_<_Tp> it(this);
-    it.seekEnd();
-    return it;
-}
-
-
-
-///////////////////////////// SparseMat_ ////////////////////////////
-
-template<typename _Tp> inline
-SparseMat_<_Tp>::SparseMat_()
-{
-    flags = MAGIC_VAL | traits::Type<_Tp>::value;
-}
-
-template<typename _Tp> inline
-SparseMat_<_Tp>::SparseMat_(int _dims, const int* _sizes)
-    : SparseMat(_dims, _sizes, traits::Type<_Tp>::value)
-{}
-
-template<typename _Tp> inline
-SparseMat_<_Tp>::SparseMat_(const SparseMat& m)
-{
-    if( m.type() == traits::Type<_Tp>::value )
-        *this = (const SparseMat_<_Tp>&)m;
-    else
-        m.convertTo(*this, traits::Type<_Tp>::value);
-}
-
-template<typename _Tp> inline
-SparseMat_<_Tp>::SparseMat_(const SparseMat_<_Tp>& m)
-{
-    this->flags = m.flags;
-    this->hdr = m.hdr;
-    if( this->hdr )
-        CV_XADD(&this->hdr->refcount, 1);
-}
-
-template<typename _Tp> inline
-SparseMat_<_Tp>::SparseMat_(const Mat& m)
-{
-    SparseMat sm(m);
-    *this = sm;
-}
-
-template<typename _Tp> inline
-SparseMat_<_Tp>& SparseMat_<_Tp>::operator = (const SparseMat_<_Tp>& m)
-{
-    if( this != &m )
-    {
-        if( m.hdr ) CV_XADD(&m.hdr->refcount, 1);
-        release();
-        flags = m.flags;
-        hdr = m.hdr;
-    }
-    return *this;
-}
-
-template<typename _Tp> inline
-SparseMat_<_Tp>& SparseMat_<_Tp>::operator = (const SparseMat& m)
-{
-    if( m.type() == traits::Type<_Tp>::value )
-        return (*this = (const SparseMat_<_Tp>&)m);
-    m.convertTo(*this, traits::Type<_Tp>::value);
-    return *this;
-}
-
-template<typename _Tp> inline
-SparseMat_<_Tp>& SparseMat_<_Tp>::operator = (const Mat& m)
-{
-    return (*this = SparseMat(m));
-}
-
-template<typename _Tp> inline
-SparseMat_<_Tp> SparseMat_<_Tp>::clone() const
-{
-    SparseMat_<_Tp> m;
-    this->copyTo(m);
-    return m;
-}
-
-template<typename _Tp> inline
-void SparseMat_<_Tp>::create(int _dims, const int* _sizes)
-{
-    SparseMat::create(_dims, _sizes, traits::Type<_Tp>::value);
-}
-
-template<typename _Tp> inline
-int SparseMat_<_Tp>::type() const
-{
-    return traits::Type<_Tp>::value;
-}
-
-template<typename _Tp> inline
-int SparseMat_<_Tp>::depth() const
-{
-    return traits::Depth<_Tp>::value;
-}
-
-template<typename _Tp> inline
-int SparseMat_<_Tp>::channels() const
-{
-    return DataType<_Tp>::channels;
-}
-
-template<typename _Tp> inline
-_Tp& SparseMat_<_Tp>::ref(int i0, size_t* hashval)
-{
-    return SparseMat::ref<_Tp>(i0, hashval);
-}
-
-template<typename _Tp> inline
-_Tp SparseMat_<_Tp>::operator()(int i0, size_t* hashval) const
-{
-    return SparseMat::value<_Tp>(i0, hashval);
-}
-
-template<typename _Tp> inline
-_Tp& SparseMat_<_Tp>::ref(int i0, int i1, size_t* hashval)
-{
-    return SparseMat::ref<_Tp>(i0, i1, hashval);
-}
-
-template<typename _Tp> inline
-_Tp SparseMat_<_Tp>::operator()(int i0, int i1, size_t* hashval) const
-{
-    return SparseMat::value<_Tp>(i0, i1, hashval);
-}
-
-template<typename _Tp> inline
-_Tp& SparseMat_<_Tp>::ref(int i0, int i1, int i2, size_t* hashval)
-{
-    return SparseMat::ref<_Tp>(i0, i1, i2, hashval);
-}
-
-template<typename _Tp> inline
-_Tp SparseMat_<_Tp>::operator()(int i0, int i1, int i2, size_t* hashval) const
-{
-    return SparseMat::value<_Tp>(i0, i1, i2, hashval);
-}
-
-template<typename _Tp> inline
-_Tp& SparseMat_<_Tp>::ref(const int* idx, size_t* hashval)
-{
-    return SparseMat::ref<_Tp>(idx, hashval);
-}
-
-template<typename _Tp> inline
-_Tp SparseMat_<_Tp>::operator()(const int* idx, size_t* hashval) const
-{
-    return SparseMat::value<_Tp>(idx, hashval);
-}
-
-template<typename _Tp> inline
-SparseMatIterator_<_Tp> SparseMat_<_Tp>::begin()
-{
-    return SparseMatIterator_<_Tp>(this);
-}
-
-template<typename _Tp> inline
-SparseMatConstIterator_<_Tp> SparseMat_<_Tp>::begin() const
-{
-    return SparseMatConstIterator_<_Tp>(this);
-}
-
-template<typename _Tp> inline
-SparseMatIterator_<_Tp> SparseMat_<_Tp>::end()
-{
-    SparseMatIterator_<_Tp> it(this);
-    it.seekEnd();
-    return it;
-}
-
-template<typename _Tp> inline
-SparseMatConstIterator_<_Tp> SparseMat_<_Tp>::end() const
-{
-    SparseMatConstIterator_<_Tp> it(this);
-    it.seekEnd();
-    return it;
-}
-
-
-
-////////////////////////// MatConstIterator /////////////////////////
-
-inline
-MatConstIterator::MatConstIterator()
-    : m(0), elemSize(0), ptr(0), sliceStart(0), sliceEnd(0)
-{}
-
-inline
-MatConstIterator::MatConstIterator(const Mat* _m)
-    : m(_m), elemSize(_m->elemSize()), ptr(0), sliceStart(0), sliceEnd(0)
-{
-    if( m && m->isContinuous() )
-    {
-        sliceStart = m->ptr();
-        sliceEnd = sliceStart + m->total()*elemSize;
-    }
-    seek((const int*)0);
-}
-
-inline
-MatConstIterator::MatConstIterator(const Mat* _m, int _row, int _col)
-    : m(_m), elemSize(_m->elemSize()), ptr(0), sliceStart(0), sliceEnd(0)
-{
-    CV_Assert(m && m->dims <= 2);
-    if( m->isContinuous() )
-    {
-        sliceStart = m->ptr();
-        sliceEnd = sliceStart + m->total()*elemSize;
-    }
-    int idx[] = {_row, _col};
-    seek(idx);
-}
-
-inline
-MatConstIterator::MatConstIterator(const Mat* _m, Point _pt)
-    : m(_m), elemSize(_m->elemSize()), ptr(0), sliceStart(0), sliceEnd(0)
-{
-    CV_Assert(m && m->dims <= 2);
-    if( m->isContinuous() )
-    {
-        sliceStart = m->ptr();
-        sliceEnd = sliceStart + m->total()*elemSize;
-    }
-    int idx[] = {_pt.y, _pt.x};
-    seek(idx);
-}
-
-inline
-MatConstIterator::MatConstIterator(const MatConstIterator& it)
-    : m(it.m), elemSize(it.elemSize), ptr(it.ptr), sliceStart(it.sliceStart), sliceEnd(it.sliceEnd)
-{}
-
-inline
-MatConstIterator& MatConstIterator::operator = (const MatConstIterator& it )
-{
-    m = it.m; elemSize = it.elemSize; ptr = it.ptr;
-    sliceStart = it.sliceStart; sliceEnd = it.sliceEnd;
-    return *this;
-}
-
-inline
-const uchar* MatConstIterator::operator *() const
-{
-    return ptr;
-}
-
-inline MatConstIterator& MatConstIterator::operator += (ptrdiff_t ofs)
-{
-    if( !m || ofs == 0 )
-        return *this;
-    ptrdiff_t ofsb = ofs*elemSize;
-    ptr += ofsb;
-    if( ptr < sliceStart || sliceEnd <= ptr )
-    {
-        ptr -= ofsb;
-        seek(ofs, true);
-    }
-    return *this;
-}
-
-inline
-MatConstIterator& MatConstIterator::operator -= (ptrdiff_t ofs)
-{
-    return (*this += -ofs);
-}
-
-inline
-MatConstIterator& MatConstIterator::operator --()
-{
-    if( m && (ptr -= elemSize) < sliceStart )
-    {
-        ptr += elemSize;
-        seek(-1, true);
-    }
-    return *this;
-}
-
-inline
-MatConstIterator MatConstIterator::operator --(int)
-{
-    MatConstIterator b = *this;
-    *this += -1;
-    return b;
-}
-
-inline
-MatConstIterator& MatConstIterator::operator ++()
-{
-    if( m && (ptr += elemSize) >= sliceEnd )
-    {
-        ptr -= elemSize;
-        seek(1, true);
-    }
-    return *this;
-}
-
-inline MatConstIterator MatConstIterator::operator ++(int)
-{
-    MatConstIterator b = *this;
-    *this += 1;
-    return b;
-}
-
-
-static inline
-bool operator == (const MatConstIterator& a, const MatConstIterator& b)
-{
-    return a.m == b.m && a.ptr == b.ptr;
-}
-
-static inline
-bool operator != (const MatConstIterator& a, const MatConstIterator& b)
-{
-    return !(a == b);
-}
-
-static inline
-bool operator < (const MatConstIterator& a, const MatConstIterator& b)
-{
-    return a.ptr < b.ptr;
-}
-
-static inline
-bool operator > (const MatConstIterator& a, const MatConstIterator& b)
-{
-    return a.ptr > b.ptr;
-}
-
-static inline
-bool operator <= (const MatConstIterator& a, const MatConstIterator& b)
-{
-    return a.ptr <= b.ptr;
-}
-
-static inline
-bool operator >= (const MatConstIterator& a, const MatConstIterator& b)
-{
-    return a.ptr >= b.ptr;
-}
-
-static inline
-ptrdiff_t operator - (const MatConstIterator& b, const MatConstIterator& a)
-{
-    if( a.m != b.m )
-        return ((size_t)(-1) >> 1);
-    if( a.sliceEnd == b.sliceEnd )
-        return (b.ptr - a.ptr)/static_cast<ptrdiff_t>(b.elemSize);
-
-    return b.lpos() - a.lpos();
-}
-
-static inline
-MatConstIterator operator + (const MatConstIterator& a, ptrdiff_t ofs)
-{
-    MatConstIterator b = a;
-    return b += ofs;
-}
-
-static inline
-MatConstIterator operator + (ptrdiff_t ofs, const MatConstIterator& a)
-{
-    MatConstIterator b = a;
-    return b += ofs;
-}
-
-static inline
-MatConstIterator operator - (const MatConstIterator& a, ptrdiff_t ofs)
-{
-    MatConstIterator b = a;
-    return b += -ofs;
-}
-
-
-inline
-const uchar* MatConstIterator::operator [](ptrdiff_t i) const
-{
-    return *(*this + i);
-}
-
-
-
-///////////////////////// MatConstIterator_ /////////////////////////
-
-template<typename _Tp> inline
-MatConstIterator_<_Tp>::MatConstIterator_()
-{}
-
-template<typename _Tp> inline
-MatConstIterator_<_Tp>::MatConstIterator_(const Mat_<_Tp>* _m)
-    : MatConstIterator(_m)
-{}
-
-template<typename _Tp> inline
-MatConstIterator_<_Tp>::MatConstIterator_(const Mat_<_Tp>* _m, int _row, int _col)
-    : MatConstIterator(_m, _row, _col)
-{}
-
-template<typename _Tp> inline
-MatConstIterator_<_Tp>::MatConstIterator_(const Mat_<_Tp>* _m, Point _pt)
-    : MatConstIterator(_m, _pt)
-{}
-
-template<typename _Tp> inline
-MatConstIterator_<_Tp>::MatConstIterator_(const MatConstIterator_& it)
-    : MatConstIterator(it)
-{}
-
-template<typename _Tp> inline
-MatConstIterator_<_Tp>& MatConstIterator_<_Tp>::operator = (const MatConstIterator_& it )
-{
-    MatConstIterator::operator = (it);
-    return *this;
-}
-
-template<typename _Tp> inline
-const _Tp& MatConstIterator_<_Tp>::operator *() const
-{
-    return *(_Tp*)(this->ptr);
-}
-
-template<typename _Tp> inline
-MatConstIterator_<_Tp>& MatConstIterator_<_Tp>::operator += (ptrdiff_t ofs)
-{
-    MatConstIterator::operator += (ofs);
-    return *this;
-}
-
-template<typename _Tp> inline
-MatConstIterator_<_Tp>& MatConstIterator_<_Tp>::operator -= (ptrdiff_t ofs)
-{
-    return (*this += -ofs);
-}
-
-template<typename _Tp> inline
-MatConstIterator_<_Tp>& MatConstIterator_<_Tp>::operator --()
-{
-    MatConstIterator::operator --();
-    return *this;
-}
-
-template<typename _Tp> inline
-MatConstIterator_<_Tp> MatConstIterator_<_Tp>::operator --(int)
-{
-    MatConstIterator_ b = *this;
-    MatConstIterator::operator --();
-    return b;
-}
-
-template<typename _Tp> inline
-MatConstIterator_<_Tp>& MatConstIterator_<_Tp>::operator ++()
-{
-    MatConstIterator::operator ++();
-    return *this;
-}
-
-template<typename _Tp> inline
-MatConstIterator_<_Tp> MatConstIterator_<_Tp>::operator ++(int)
-{
-    MatConstIterator_ b = *this;
-    MatConstIterator::operator ++();
-    return b;
-}
-
-
-template<typename _Tp> inline
-Point MatConstIterator_<_Tp>::pos() const
-{
-    if( !m )
-        return Point();
-    CV_DbgAssert( m->dims <= 2 );
-    if( m->isContinuous() )
-    {
-        ptrdiff_t ofs = (const _Tp*)ptr - (const _Tp*)m->data;
-        int y = (int)(ofs / m->cols);
-        int x = (int)(ofs - (ptrdiff_t)y * m->cols);
-        return Point(x, y);
-    }
-    else
-    {
-        ptrdiff_t ofs = (uchar*)ptr - m->data;
-        int y = (int)(ofs / m->step);
-        int x = (int)((ofs - y * m->step)/sizeof(_Tp));
-        return Point(x, y);
-    }
-}
-
-
-template<typename _Tp> static inline
-bool operator == (const MatConstIterator_<_Tp>& a, const MatConstIterator_<_Tp>& b)
-{
-    return a.m == b.m && a.ptr == b.ptr;
-}
-
-template<typename _Tp> static inline
-bool operator != (const MatConstIterator_<_Tp>& a, const MatConstIterator_<_Tp>& b)
-{
-    return a.m != b.m || a.ptr != b.ptr;
-}
-
-template<typename _Tp> static inline
-MatConstIterator_<_Tp> operator + (const MatConstIterator_<_Tp>& a, ptrdiff_t ofs)
-{
-    MatConstIterator t = (const MatConstIterator&)a + ofs;
-    return (MatConstIterator_<_Tp>&)t;
-}
-
-template<typename _Tp> static inline
-MatConstIterator_<_Tp> operator + (ptrdiff_t ofs, const MatConstIterator_<_Tp>& a)
-{
-    MatConstIterator t = (const MatConstIterator&)a + ofs;
-    return (MatConstIterator_<_Tp>&)t;
-}
-
-template<typename _Tp> static inline
-MatConstIterator_<_Tp> operator - (const MatConstIterator_<_Tp>& a, ptrdiff_t ofs)
-{
-    MatConstIterator t = (const MatConstIterator&)a - ofs;
-    return (MatConstIterator_<_Tp>&)t;
-}
-
-template<typename _Tp> inline
-const _Tp& MatConstIterator_<_Tp>::operator [](ptrdiff_t i) const
-{
-    return *(_Tp*)MatConstIterator::operator [](i);
-}
-
-
-
-//////////////////////////// MatIterator_ ///////////////////////////
-
-template<typename _Tp> inline
-MatIterator_<_Tp>::MatIterator_()
-    : MatConstIterator_<_Tp>()
-{}
-
-template<typename _Tp> inline
-MatIterator_<_Tp>::MatIterator_(Mat_<_Tp>* _m)
-    : MatConstIterator_<_Tp>(_m)
-{}
-
-template<typename _Tp> inline
-MatIterator_<_Tp>::MatIterator_(Mat_<_Tp>* _m, int _row, int _col)
-    : MatConstIterator_<_Tp>(_m, _row, _col)
-{}
-
-template<typename _Tp> inline
-MatIterator_<_Tp>::MatIterator_(Mat_<_Tp>* _m, Point _pt)
-    : MatConstIterator_<_Tp>(_m, _pt)
-{}
-
-template<typename _Tp> inline
-MatIterator_<_Tp>::MatIterator_(Mat_<_Tp>* _m, const int* _idx)
-    : MatConstIterator_<_Tp>(_m, _idx)
-{}
-
-template<typename _Tp> inline
-MatIterator_<_Tp>::MatIterator_(const MatIterator_& it)
-    : MatConstIterator_<_Tp>(it)
-{}
-
-template<typename _Tp> inline
-MatIterator_<_Tp>& MatIterator_<_Tp>::operator = (const MatIterator_<_Tp>& it )
-{
-    MatConstIterator::operator = (it);
-    return *this;
-}
-
-template<typename _Tp> inline
-_Tp& MatIterator_<_Tp>::operator *() const
-{
-    return *(_Tp*)(this->ptr);
-}
-
-template<typename _Tp> inline
-MatIterator_<_Tp>& MatIterator_<_Tp>::operator += (ptrdiff_t ofs)
-{
-    MatConstIterator::operator += (ofs);
-    return *this;
-}
-
-template<typename _Tp> inline
-MatIterator_<_Tp>& MatIterator_<_Tp>::operator -= (ptrdiff_t ofs)
-{
-    MatConstIterator::operator += (-ofs);
-    return *this;
-}
-
-template<typename _Tp> inline
-MatIterator_<_Tp>& MatIterator_<_Tp>::operator --()
-{
-    MatConstIterator::operator --();
-    return *this;
-}
-
-template<typename _Tp> inline
-MatIterator_<_Tp> MatIterator_<_Tp>::operator --(int)
-{
-    MatIterator_ b = *this;
-    MatConstIterator::operator --();
-    return b;
-}
-
-template<typename _Tp> inline
-MatIterator_<_Tp>& MatIterator_<_Tp>::operator ++()
-{
-    MatConstIterator::operator ++();
-    return *this;
-}
-
-template<typename _Tp> inline
-MatIterator_<_Tp> MatIterator_<_Tp>::operator ++(int)
-{
-    MatIterator_ b = *this;
-    MatConstIterator::operator ++();
-    return b;
-}
-
-template<typename _Tp> inline
-_Tp& MatIterator_<_Tp>::operator [](ptrdiff_t i) const
-{
-    return *(*this + i);
-}
-
-
-template<typename _Tp> static inline
-bool operator == (const MatIterator_<_Tp>& a, const MatIterator_<_Tp>& b)
-{
-    return a.m == b.m && a.ptr == b.ptr;
-}
-
-template<typename _Tp> static inline
-bool operator != (const MatIterator_<_Tp>& a, const MatIterator_<_Tp>& b)
-{
-    return a.m != b.m || a.ptr != b.ptr;
-}
-
-template<typename _Tp> static inline
-MatIterator_<_Tp> operator + (const MatIterator_<_Tp>& a, ptrdiff_t ofs)
-{
-    MatConstIterator t = (const MatConstIterator&)a + ofs;
-    return (MatIterator_<_Tp>&)t;
-}
-
-template<typename _Tp> static inline
-MatIterator_<_Tp> operator + (ptrdiff_t ofs, const MatIterator_<_Tp>& a)
-{
-    MatConstIterator t = (const MatConstIterator&)a + ofs;
-    return (MatIterator_<_Tp>&)t;
-}
-
-template<typename _Tp> static inline
-MatIterator_<_Tp> operator - (const MatIterator_<_Tp>& a, ptrdiff_t ofs)
-{
-    MatConstIterator t = (const MatConstIterator&)a - ofs;
-    return (MatIterator_<_Tp>&)t;
-}
-
-
-
-/////////////////////// SparseMatConstIterator //////////////////////
-
-inline
-SparseMatConstIterator::SparseMatConstIterator()
-    : m(0), hashidx(0), ptr(0)
-{}
-
-inline
-SparseMatConstIterator::SparseMatConstIterator(const SparseMatConstIterator& it)
-    : m(it.m), hashidx(it.hashidx), ptr(it.ptr)
-{}
-
-inline SparseMatConstIterator& SparseMatConstIterator::operator = (const SparseMatConstIterator& it)
-{
-    if( this != &it )
-    {
-        m = it.m;
-        hashidx = it.hashidx;
-        ptr = it.ptr;
-    }
-    return *this;
-}
-
-template<typename _Tp> inline
-const _Tp& SparseMatConstIterator::value() const
-{
-    return *(const _Tp*)ptr;
-}
-
-inline
-const SparseMat::Node* SparseMatConstIterator::node() const
-{
-    return (ptr && m && m->hdr) ? (const SparseMat::Node*)(const void*)(ptr - m->hdr->valueOffset) : 0;
-}
-
-inline
-SparseMatConstIterator SparseMatConstIterator::operator ++(int)
-{
-    SparseMatConstIterator it = *this;
-    ++*this;
-    return it;
-}
-
-inline
-void SparseMatConstIterator::seekEnd()
-{
-    if( m && m->hdr )
-    {
-        hashidx = m->hdr->hashtab.size();
-        ptr = 0;
-    }
-}
-
-
-static inline
-bool operator == (const SparseMatConstIterator& it1, const SparseMatConstIterator& it2)
-{
-    return it1.m == it2.m && it1.ptr == it2.ptr;
-}
-
-static inline
-bool operator != (const SparseMatConstIterator& it1, const SparseMatConstIterator& it2)
-{
-    return !(it1 == it2);
-}
-
-
-
-///////////////////////// SparseMatIterator /////////////////////////
-
-inline
-SparseMatIterator::SparseMatIterator()
-{}
-
-inline
-SparseMatIterator::SparseMatIterator(SparseMat* _m)
-    : SparseMatConstIterator(_m)
-{}
-
-inline
-SparseMatIterator::SparseMatIterator(const SparseMatIterator& it)
-    : SparseMatConstIterator(it)
-{}
-
-inline
-SparseMatIterator& SparseMatIterator::operator = (const SparseMatIterator& it)
-{
-    (SparseMatConstIterator&)*this = it;
-    return *this;
-}
-
-template<typename _Tp> inline
-_Tp& SparseMatIterator::value() const
-{
-    return *(_Tp*)ptr;
-}
-
-inline
-SparseMat::Node* SparseMatIterator::node() const
-{
-    return (SparseMat::Node*)SparseMatConstIterator::node();
-}
-
-inline
-SparseMatIterator& SparseMatIterator::operator ++()
-{
-    SparseMatConstIterator::operator ++();
-    return *this;
-}
-
-inline
-SparseMatIterator SparseMatIterator::operator ++(int)
-{
-    SparseMatIterator it = *this;
-    ++*this;
-    return it;
-}
-
-
-
-////////////////////// SparseMatConstIterator_ //////////////////////
-
-template<typename _Tp> inline
-SparseMatConstIterator_<_Tp>::SparseMatConstIterator_()
-{}
-
-template<typename _Tp> inline
-SparseMatConstIterator_<_Tp>::SparseMatConstIterator_(const SparseMat_<_Tp>* _m)
-    : SparseMatConstIterator(_m)
-{}
-
-template<typename _Tp> inline
-SparseMatConstIterator_<_Tp>::SparseMatConstIterator_(const SparseMat* _m)
-    : SparseMatConstIterator(_m)
-{
-    CV_Assert( _m->type() == traits::Type<_Tp>::value );
-}
-
-template<typename _Tp> inline
-SparseMatConstIterator_<_Tp>::SparseMatConstIterator_(const SparseMatConstIterator_<_Tp>& it)
-    : SparseMatConstIterator(it)
-{}
-
-template<typename _Tp> inline
-SparseMatConstIterator_<_Tp>& SparseMatConstIterator_<_Tp>::operator = (const SparseMatConstIterator_<_Tp>& it)
-{
-    return reinterpret_cast<SparseMatConstIterator_<_Tp>&>
-         (*reinterpret_cast<SparseMatConstIterator*>(this) =
-           reinterpret_cast<const SparseMatConstIterator&>(it));
-}
-
-template<typename _Tp> inline
-const _Tp& SparseMatConstIterator_<_Tp>::operator *() const
-{
-    return *(const _Tp*)this->ptr;
-}
-
-template<typename _Tp> inline
-SparseMatConstIterator_<_Tp>& SparseMatConstIterator_<_Tp>::operator ++()
-{
-    SparseMatConstIterator::operator ++();
-    return *this;
-}
-
-template<typename _Tp> inline
-SparseMatConstIterator_<_Tp> SparseMatConstIterator_<_Tp>::operator ++(int)
-{
-    SparseMatConstIterator_<_Tp> it = *this;
-    SparseMatConstIterator::operator ++();
-    return it;
-}
-
-
-
-///////////////////////// SparseMatIterator_ ////////////////////////
-
-template<typename _Tp> inline
-SparseMatIterator_<_Tp>::SparseMatIterator_()
-{}
-
-template<typename _Tp> inline
-SparseMatIterator_<_Tp>::SparseMatIterator_(SparseMat_<_Tp>* _m)
-    : SparseMatConstIterator_<_Tp>(_m)
-{}
-
-template<typename _Tp> inline
-SparseMatIterator_<_Tp>::SparseMatIterator_(SparseMat* _m)
-    : SparseMatConstIterator_<_Tp>(_m)
-{}
-
-template<typename _Tp> inline
-SparseMatIterator_<_Tp>::SparseMatIterator_(const SparseMatIterator_<_Tp>& it)
-    : SparseMatConstIterator_<_Tp>(it)
-{}
-
-template<typename _Tp> inline
-SparseMatIterator_<_Tp>& SparseMatIterator_<_Tp>::operator = (const SparseMatIterator_<_Tp>& it)
-{
-    return reinterpret_cast<SparseMatIterator_<_Tp>&>
-         (*reinterpret_cast<SparseMatConstIterator*>(this) =
-           reinterpret_cast<const SparseMatConstIterator&>(it));
-}
-
-template<typename _Tp> inline
-_Tp& SparseMatIterator_<_Tp>::operator *() const
-{
-    return *(_Tp*)this->ptr;
-}
-
-template<typename _Tp> inline
-SparseMatIterator_<_Tp>& SparseMatIterator_<_Tp>::operator ++()
-{
-    SparseMatConstIterator::operator ++();
-    return *this;
-}
-
-template<typename _Tp> inline
-SparseMatIterator_<_Tp> SparseMatIterator_<_Tp>::operator ++(int)
-{
-    SparseMatIterator_<_Tp> it = *this;
-    SparseMatConstIterator::operator ++();
-    return it;
-}
-
-
-
-//////////////////////// MatCommaInitializer_ ///////////////////////
-
-template<typename _Tp> inline
-MatCommaInitializer_<_Tp>::MatCommaInitializer_(Mat_<_Tp>* _m)
-    : it(_m)
-{}
-
-template<typename _Tp> template<typename T2> inline
-MatCommaInitializer_<_Tp>& MatCommaInitializer_<_Tp>::operator , (T2 v)
-{
-    CV_DbgAssert( this->it < ((const Mat_<_Tp>*)this->it.m)->end() );
-    *this->it = _Tp(v);
-    ++this->it;
-    return *this;
-}
-
-template<typename _Tp> inline
-MatCommaInitializer_<_Tp>::operator Mat_<_Tp>() const
-{
-    CV_DbgAssert( this->it == ((const Mat_<_Tp>*)this->it.m)->end() );
-    return Mat_<_Tp>(*this->it.m);
-}
-
-
-template<typename _Tp, typename T2> static inline
-MatCommaInitializer_<_Tp> operator << (const Mat_<_Tp>& m, T2 val)
-{
-    MatCommaInitializer_<_Tp> commaInitializer((Mat_<_Tp>*)&m);
-    return (commaInitializer, val);
-}
-
-
-
-///////////////////////// Matrix Expressions ////////////////////////
-
-inline
-Mat& Mat::operator = (const MatExpr& e)
-{
-    e.op->assign(e, *this);
-    return *this;
-}
-
-template<typename _Tp> inline
-Mat_<_Tp>::Mat_(const MatExpr& e)
-{
-    e.op->assign(e, *this, traits::Type<_Tp>::value);
-}
-
-template<typename _Tp> inline
-Mat_<_Tp>& Mat_<_Tp>::operator = (const MatExpr& e)
-{
-    e.op->assign(e, *this, traits::Type<_Tp>::value);
-    return *this;
-}
-
-template<typename _Tp> inline
-MatExpr Mat_<_Tp>::zeros(int rows, int cols)
-{
-    return Mat::zeros(rows, cols, traits::Type<_Tp>::value);
-}
-
-template<typename _Tp> inline
-MatExpr Mat_<_Tp>::zeros(Size sz)
-{
-    return Mat::zeros(sz, traits::Type<_Tp>::value);
-}
-
-template<typename _Tp> inline
-MatExpr Mat_<_Tp>::ones(int rows, int cols)
-{
-    return Mat::ones(rows, cols, traits::Type<_Tp>::value);
-}
-
-template<typename _Tp> inline
-MatExpr Mat_<_Tp>::ones(Size sz)
-{
-    return Mat::ones(sz, traits::Type<_Tp>::value);
-}
-
-template<typename _Tp> inline
-MatExpr Mat_<_Tp>::eye(int rows, int cols)
-{
-    return Mat::eye(rows, cols, traits::Type<_Tp>::value);
-}
-
-template<typename _Tp> inline
-MatExpr Mat_<_Tp>::eye(Size sz)
-{
-    return Mat::eye(sz, traits::Type<_Tp>::value);
-}
-
-inline
-MatExpr::MatExpr()
-    : op(0), flags(0), a(Mat()), b(Mat()), c(Mat()), alpha(0), beta(0), s()
-{}
-
-inline
-MatExpr::MatExpr(const MatOp* _op, int _flags, const Mat& _a, const Mat& _b,
-                 const Mat& _c, double _alpha, double _beta, const Scalar& _s)
-    : op(_op), flags(_flags), a(_a), b(_b), c(_c), alpha(_alpha), beta(_beta), s(_s)
-{}
-
-inline
-MatExpr::operator Mat() const
-{
-    Mat m;
-    op->assign(*this, m);
-    return m;
-}
-
-template<typename _Tp> inline
-MatExpr::operator Mat_<_Tp>() const
-{
-    Mat_<_Tp> m;
-    op->assign(*this, m, traits::Type<_Tp>::value);
-    return m;
-}
-
-
-template<typename _Tp> static inline
-MatExpr min(const Mat_<_Tp>& a, const Mat_<_Tp>& b)
-{
-    return cv::min((const Mat&)a, (const Mat&)b);
-}
-
-template<typename _Tp> static inline
-MatExpr min(const Mat_<_Tp>& a, double s)
-{
-    return cv::min((const Mat&)a, s);
-}
-
-template<typename _Tp> static inline
-MatExpr min(double s, const Mat_<_Tp>& a)
-{
-    return cv::min((const Mat&)a, s);
-}
-
-template<typename _Tp> static inline
-MatExpr max(const Mat_<_Tp>& a, const Mat_<_Tp>& b)
-{
-    return cv::max((const Mat&)a, (const Mat&)b);
-}
-
-template<typename _Tp> static inline
-MatExpr max(const Mat_<_Tp>& a, double s)
-{
-    return cv::max((const Mat&)a, s);
-}
-
-template<typename _Tp> static inline
-MatExpr max(double s, const Mat_<_Tp>& a)
-{
-    return cv::max((const Mat&)a, s);
-}
-
-template<typename _Tp> static inline
-MatExpr abs(const Mat_<_Tp>& m)
-{
-    return cv::abs((const Mat&)m);
-}
-
-
-static inline
-Mat& operator += (Mat& a, const MatExpr& b)
-{
-    b.op->augAssignAdd(b, a);
-    return a;
-}
-
-static inline
-const Mat& operator += (const Mat& a, const MatExpr& b)
-{
-    b.op->augAssignAdd(b, (Mat&)a);
-    return a;
-}
-
-template<typename _Tp> static inline
-Mat_<_Tp>& operator += (Mat_<_Tp>& a, const MatExpr& b)
-{
-    b.op->augAssignAdd(b, a);
-    return a;
-}
-
-template<typename _Tp> static inline
-const Mat_<_Tp>& operator += (const Mat_<_Tp>& a, const MatExpr& b)
-{
-    b.op->augAssignAdd(b, (Mat&)a);
-    return a;
-}
-
-static inline
-Mat& operator -= (Mat& a, const MatExpr& b)
-{
-    b.op->augAssignSubtract(b, a);
-    return a;
-}
-
-static inline
-const Mat& operator -= (const Mat& a, const MatExpr& b)
-{
-    b.op->augAssignSubtract(b, (Mat&)a);
-    return a;
-}
-
-template<typename _Tp> static inline
-Mat_<_Tp>& operator -= (Mat_<_Tp>& a, const MatExpr& b)
-{
-    b.op->augAssignSubtract(b, a);
-    return a;
-}
-
-template<typename _Tp> static inline
-const Mat_<_Tp>& operator -= (const Mat_<_Tp>& a, const MatExpr& b)
-{
-    b.op->augAssignSubtract(b, (Mat&)a);
-    return a;
-}
-
-static inline
-Mat& operator *= (Mat& a, const MatExpr& b)
-{
-    b.op->augAssignMultiply(b, a);
-    return a;
-}
-
-static inline
-const Mat& operator *= (const Mat& a, const MatExpr& b)
-{
-    b.op->augAssignMultiply(b, (Mat&)a);
-    return a;
-}
-
-template<typename _Tp> static inline
-Mat_<_Tp>& operator *= (Mat_<_Tp>& a, const MatExpr& b)
-{
-    b.op->augAssignMultiply(b, a);
-    return a;
-}
-
-template<typename _Tp> static inline
-const Mat_<_Tp>& operator *= (const Mat_<_Tp>& a, const MatExpr& b)
-{
-    b.op->augAssignMultiply(b, (Mat&)a);
-    return a;
-}
-
-static inline
-Mat& operator /= (Mat& a, const MatExpr& b)
-{
-    b.op->augAssignDivide(b, a);
-    return a;
-}
-
-static inline
-const Mat& operator /= (const Mat& a, const MatExpr& b)
-{
-    b.op->augAssignDivide(b, (Mat&)a);
-    return a;
-}
-
-template<typename _Tp> static inline
-Mat_<_Tp>& operator /= (Mat_<_Tp>& a, const MatExpr& b)
-{
-    b.op->augAssignDivide(b, a);
-    return a;
-}
-
-template<typename _Tp> static inline
-const Mat_<_Tp>& operator /= (const Mat_<_Tp>& a, const MatExpr& b)
-{
-    b.op->augAssignDivide(b, (Mat&)a);
-    return a;
-}
-
-
-//////////////////////////////// UMat ////////////////////////////////
-
-inline
-UMat::UMat(UMatUsageFlags _usageFlags)
-: flags(MAGIC_VAL), dims(0), rows(0), cols(0), allocator(0), usageFlags(_usageFlags), u(0), offset(0), size(&rows)
-{}
-
-inline
-UMat::UMat(int _rows, int _cols, int _type, UMatUsageFlags _usageFlags)
-: flags(MAGIC_VAL), dims(0), rows(0), cols(0), allocator(0), usageFlags(_usageFlags), u(0), offset(0), size(&rows)
-{
-    create(_rows, _cols, _type);
-}
-
-inline
-UMat::UMat(int _rows, int _cols, int _type, const Scalar& _s, UMatUsageFlags _usageFlags)
-: flags(MAGIC_VAL), dims(0), rows(0), cols(0), allocator(0), usageFlags(_usageFlags), u(0), offset(0), size(&rows)
-{
-    create(_rows, _cols, _type);
-    *this = _s;
-}
-
-inline
-UMat::UMat(Size _sz, int _type, UMatUsageFlags _usageFlags)
-: flags(MAGIC_VAL), dims(0), rows(0), cols(0), allocator(0), usageFlags(_usageFlags), u(0), offset(0), size(&rows)
-{
-    create( _sz.height, _sz.width, _type );
-}
-
-inline
-UMat::UMat(Size _sz, int _type, const Scalar& _s, UMatUsageFlags _usageFlags)
-: flags(MAGIC_VAL), dims(0), rows(0), cols(0), allocator(0), usageFlags(_usageFlags), u(0), offset(0), size(&rows)
-{
-    create(_sz.height, _sz.width, _type);
-    *this = _s;
-}
-
-inline
-UMat::UMat(int _dims, const int* _sz, int _type, UMatUsageFlags _usageFlags)
-: flags(MAGIC_VAL), dims(0), rows(0), cols(0), allocator(0), usageFlags(_usageFlags), u(0), offset(0), size(&rows)
-{
-    create(_dims, _sz, _type);
-}
-
-inline
-UMat::UMat(int _dims, const int* _sz, int _type, const Scalar& _s, UMatUsageFlags _usageFlags)
-: flags(MAGIC_VAL), dims(0), rows(0), cols(0), allocator(0), usageFlags(_usageFlags), u(0), offset(0), size(&rows)
-{
-    create(_dims, _sz, _type);
-    *this = _s;
-}
-
-inline
-UMat::UMat(const UMat& m)
-: flags(m.flags), dims(m.dims), rows(m.rows), cols(m.cols), allocator(m.allocator),
-  usageFlags(m.usageFlags), u(m.u), offset(m.offset), size(&rows)
-{
-    addref();
-    if( m.dims <= 2 )
-    {
-        step[0] = m.step[0]; step[1] = m.step[1];
-    }
-    else
-    {
-        dims = 0;
-        copySize(m);
-    }
-}
-
-
-template<typename _Tp> inline
-UMat::UMat(const std::vector<_Tp>& vec, bool copyData)
-: flags(MAGIC_VAL | traits::Type<_Tp>::value | CV_MAT_CONT_FLAG), dims(2), rows((int)vec.size()),
-cols(1), allocator(0), usageFlags(USAGE_DEFAULT), u(0), offset(0), size(&rows)
-{
-    if(vec.empty())
-        return;
-    if( !copyData )
-    {
-        // !!!TODO!!!
-        CV_Error(Error::StsNotImplemented, "");
-    }
-    else
-        Mat((int)vec.size(), 1, traits::Type<_Tp>::value, (uchar*)&vec[0]).copyTo(*this);
-}
-
-inline
-UMat& UMat::operator = (const UMat& m)
-{
-    if( this != &m )
-    {
-        const_cast<UMat&>(m).addref();
-        release();
-        flags = m.flags;
-        if( dims <= 2 && m.dims <= 2 )
-        {
-            dims = m.dims;
-            rows = m.rows;
-            cols = m.cols;
-            step[0] = m.step[0];
-            step[1] = m.step[1];
-        }
-        else
-            copySize(m);
-        allocator = m.allocator;
-        if (usageFlags == USAGE_DEFAULT)
-            usageFlags = m.usageFlags;
-        u = m.u;
-        offset = m.offset;
-    }
-    return *this;
-}
-
-inline
-UMat UMat::row(int y) const
-{
-    return UMat(*this, Range(y, y + 1), Range::all());
-}
-
-inline
-UMat UMat::col(int x) const
-{
-    return UMat(*this, Range::all(), Range(x, x + 1));
-}
-
-inline
-UMat UMat::rowRange(int startrow, int endrow) const
-{
-    return UMat(*this, Range(startrow, endrow), Range::all());
-}
-
-inline
-UMat UMat::rowRange(const Range& r) const
-{
-    return UMat(*this, r, Range::all());
-}
-
-inline
-UMat UMat::colRange(int startcol, int endcol) const
-{
-    return UMat(*this, Range::all(), Range(startcol, endcol));
-}
-
-inline
-UMat UMat::colRange(const Range& r) const
-{
-    return UMat(*this, Range::all(), r);
-}
-
-inline
-UMat UMat::clone() const
-{
-    UMat m;
-    copyTo(m);
-    return m;
-}
-
-inline
-void UMat::assignTo( UMat& m, int _type ) const
-{
-    if( _type < 0 )
-        m = *this;
-    else
-        convertTo(m, _type);
-}
-
-inline
-void UMat::create(int _rows, int _cols, int _type, UMatUsageFlags _usageFlags)
-{
-    _type &= TYPE_MASK;
-    if( dims <= 2 && rows == _rows && cols == _cols && type() == _type && u )
-        return;
-    int sz[] = {_rows, _cols};
-    create(2, sz, _type, _usageFlags);
-}
-
-inline
-void UMat::create(Size _sz, int _type, UMatUsageFlags _usageFlags)
-{
-    create(_sz.height, _sz.width, _type, _usageFlags);
-}
-
-inline
-void UMat::addref()
-{
-    if( u )
-        CV_XADD(&(u->urefcount), 1);
-}
-
-inline void UMat::release()
-{
-    if( u && CV_XADD(&(u->urefcount), -1) == 1 )
-        deallocate();
-    for(int i = 0; i < dims; i++)
-        size.p[i] = 0;
-    u = 0;
-}
-
-inline
-UMat UMat::operator()( Range _rowRange, Range _colRange ) const
-{
-    return UMat(*this, _rowRange, _colRange);
-}
-
-inline
-UMat UMat::operator()( const Rect& roi ) const
-{
-    return UMat(*this, roi);
-}
-
-inline
-UMat UMat::operator()(const Range* ranges) const
-{
-    return UMat(*this, ranges);
-}
-
-inline
-UMat UMat::operator()(const std::vector<Range>& ranges) const
-{
-    return UMat(*this, ranges);
-}
-
-inline
-bool UMat::isContinuous() const
-{
-    return (flags & CONTINUOUS_FLAG) != 0;
-}
-
-inline
-bool UMat::isSubmatrix() const
-{
-    return (flags & SUBMATRIX_FLAG) != 0;
-}
-
-inline
-size_t UMat::elemSize() const
-{
-    return dims > 0 ? step.p[dims - 1] : 0;
-}
-
-inline
-size_t UMat::elemSize1() const
-{
-    return CV_ELEM_SIZE1(flags);
-}
-
-inline
-int UMat::type() const
-{
-    return CV_MAT_TYPE(flags);
-}
-
-inline
-int UMat::depth() const
-{
-    return CV_MAT_DEPTH(flags);
-}
-
-inline
-int UMat::channels() const
-{
-    return CV_MAT_CN(flags);
-}
-
-inline
-size_t UMat::step1(int i) const
-{
-    return step.p[i] / elemSize1();
-}
-
-inline
-bool UMat::empty() const
-{
-    return u == 0 || total() == 0 || dims == 0;
-}
-
-inline
-size_t UMat::total() const
-{
-    if( dims <= 2 )
-        return (size_t)rows * cols;
-    size_t p = 1;
-    for( int i = 0; i < dims; i++ )
-        p *= size[i];
-    return p;
-}
-
-#ifdef CV_CXX_MOVE_SEMANTICS
-
-inline
-UMat::UMat(UMat&& m)
-: flags(m.flags), dims(m.dims), rows(m.rows), cols(m.cols), allocator(m.allocator),
-  usageFlags(m.usageFlags), u(m.u), offset(m.offset), size(&rows)
-{
-    if (m.dims <= 2)  // move new step/size info
-    {
-        step[0] = m.step[0];
-        step[1] = m.step[1];
-    }
-    else
-    {
-        CV_DbgAssert(m.step.p != m.step.buf);
-        step.p = m.step.p;
-        size.p = m.size.p;
-        m.step.p = m.step.buf;
-        m.size.p = &m.rows;
-    }
-    m.flags = MAGIC_VAL; m.dims = m.rows = m.cols = 0;
-    m.allocator = NULL;
-    m.u = NULL;
-    m.offset = 0;
-}
-
-inline
-UMat& UMat::operator = (UMat&& m)
-{
-    if (this == &m)
-      return *this;
-    release();
-    flags = m.flags; dims = m.dims; rows = m.rows; cols = m.cols;
-    allocator = m.allocator; usageFlags = m.usageFlags;
-    u = m.u;
-    offset = m.offset;
-    if (step.p != step.buf) // release self step/size
-    {
-        fastFree(step.p);
-        step.p = step.buf;
-        size.p = &rows;
-    }
-    if (m.dims <= 2) // move new step/size info
-    {
-        step[0] = m.step[0];
-        step[1] = m.step[1];
-    }
-    else
-    {
-        CV_DbgAssert(m.step.p != m.step.buf);
-        step.p = m.step.p;
-        size.p = m.size.p;
-        m.step.p = m.step.buf;
-        m.size.p = &m.rows;
-    }
-    m.flags = MAGIC_VAL; m.dims = m.rows = m.cols = 0;
-    m.allocator = NULL;
-    m.u = NULL;
-    m.offset = 0;
-    return *this;
-}
-
-#endif
-
-
-inline bool UMatData::hostCopyObsolete() const { return (flags & HOST_COPY_OBSOLETE) != 0; }
-inline bool UMatData::deviceCopyObsolete() const { return (flags & DEVICE_COPY_OBSOLETE) != 0; }
-inline bool UMatData::deviceMemMapped() const { return (flags & DEVICE_MEM_MAPPED) != 0; }
-inline bool UMatData::copyOnMap() const { return (flags & COPY_ON_MAP) != 0; }
-inline bool UMatData::tempUMat() const { return (flags & TEMP_UMAT) != 0; }
-inline bool UMatData::tempCopiedUMat() const { return (flags & TEMP_COPIED_UMAT) == TEMP_COPIED_UMAT; }
-
-inline void UMatData::markDeviceMemMapped(bool flag)
-{
-  if(flag)
-    flags |= DEVICE_MEM_MAPPED;
-  else
-    flags &= ~DEVICE_MEM_MAPPED;
-}
-
-inline void UMatData::markHostCopyObsolete(bool flag)
-{
-    if(flag)
-        flags |= HOST_COPY_OBSOLETE;
-    else
-        flags &= ~HOST_COPY_OBSOLETE;
-}
-inline void UMatData::markDeviceCopyObsolete(bool flag)
-{
-    if(flag)
-        flags |= DEVICE_COPY_OBSOLETE;
-    else
-        flags &= ~DEVICE_COPY_OBSOLETE;
-}
-
-//! @endcond
-
-} //cv
-
-#ifdef _MSC_VER
-#pragma warning( pop )
-#endif
-
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/matx.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/matx.hpp
deleted file mode 100755
index e664a3e..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/matx.hpp
+++ /dev/null
@@ -1,1476 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_CORE_MATX_HPP
-#define OPENCV_CORE_MATX_HPP
-
-#ifndef __cplusplus
-#  error matx.hpp header must be compiled as C++
-#endif
-
-#include "opencv2/core/cvdef.h"
-#include "opencv2/core/base.hpp"
-#include "opencv2/core/traits.hpp"
-#include "opencv2/core/saturate.hpp"
-
-#ifdef CV_CXX11
-#include <initializer_list>
-#endif
-
-namespace cv
-{
-
-//! @addtogroup core_basic
-//! @{
-
-////////////////////////////// Small Matrix ///////////////////////////
-
-//! @cond IGNORED
-struct CV_EXPORTS Matx_AddOp {};
-struct CV_EXPORTS Matx_SubOp {};
-struct CV_EXPORTS Matx_ScaleOp {};
-struct CV_EXPORTS Matx_MulOp {};
-struct CV_EXPORTS Matx_DivOp {};
-struct CV_EXPORTS Matx_MatMulOp {};
-struct CV_EXPORTS Matx_TOp {};
-//! @endcond
-
-/** @brief Template class for small matrices whose type and size are known at compilation time
-
-If you need a more flexible type, use Mat . The elements of the matrix M are accessible using the
-M(i,j) notation. Most of the common matrix operations (see also @ref MatrixExpressions ) are
-available. To do an operation on Matx that is not implemented, you can easily convert the matrix to
-Mat and backwards:
-@code{.cpp}
-    Matx33f m(1, 2, 3,
-              4, 5, 6,
-              7, 8, 9);
-    cout << sum(Mat(m*m.t())) << endl;
-@endcode
-Except of the plain constructor which takes a list of elements, Matx can be initialized from a C-array:
-@code{.cpp}
-    float values[] = { 1, 2, 3};
-    Matx31f m(values);
-@endcode
-In case if C++11 features are available, std::initializer_list can be also used to initialize Matx:
-@code{.cpp}
-    Matx31f m = { 1, 2, 3};
-@endcode
- */
-template<typename _Tp, int m, int n> class Matx
-{
-public:
-    enum {
-           rows     = m,
-           cols     = n,
-           channels = rows*cols,
-#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED
-           depth    = traits::Type<_Tp>::value,
-           type     = CV_MAKETYPE(depth, channels),
-#endif
-           shortdim = (m < n ? m : n)
-         };
-
-    typedef _Tp                           value_type;
-    typedef Matx<_Tp, m, n>               mat_type;
-    typedef Matx<_Tp, shortdim, 1> diag_type;
-
-    //! default constructor
-    Matx();
-
-    Matx(_Tp v0); //!< 1x1 matrix
-    Matx(_Tp v0, _Tp v1); //!< 1x2 or 2x1 matrix
-    Matx(_Tp v0, _Tp v1, _Tp v2); //!< 1x3 or 3x1 matrix
-    Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3); //!< 1x4, 2x2 or 4x1 matrix
-    Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4); //!< 1x5 or 5x1 matrix
-    Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5); //!< 1x6, 2x3, 3x2 or 6x1 matrix
-    Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6); //!< 1x7 or 7x1 matrix
-    Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7); //!< 1x8, 2x4, 4x2 or 8x1 matrix
-    Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8); //!< 1x9, 3x3 or 9x1 matrix
-    Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9); //!< 1x10, 2x5 or 5x2 or 10x1 matrix
-    Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3,
-         _Tp v4, _Tp v5, _Tp v6, _Tp v7,
-         _Tp v8, _Tp v9, _Tp v10, _Tp v11); //!< 1x12, 2x6, 3x4, 4x3, 6x2 or 12x1 matrix
-    Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3,
-         _Tp v4, _Tp v5, _Tp v6, _Tp v7,
-         _Tp v8, _Tp v9, _Tp v10, _Tp v11,
-         _Tp v12, _Tp v13); //!< 1x14, 2x7, 7x2 or 14x1 matrix
-    Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3,
-         _Tp v4, _Tp v5, _Tp v6, _Tp v7,
-         _Tp v8, _Tp v9, _Tp v10, _Tp v11,
-         _Tp v12, _Tp v13, _Tp v14, _Tp v15); //!< 1x16, 4x4 or 16x1 matrix
-    explicit Matx(const _Tp* vals); //!< initialize from a plain array
-
-#ifdef CV_CXX11
-    Matx(std::initializer_list<_Tp>); //!< initialize from an initializer list
-#endif
-
-    static Matx all(_Tp alpha);
-    static Matx zeros();
-    static Matx ones();
-    static Matx eye();
-    static Matx diag(const diag_type& d);
-    static Matx randu(_Tp a, _Tp b);
-    static Matx randn(_Tp a, _Tp b);
-
-    //! dot product computed with the default precision
-    _Tp dot(const Matx<_Tp, m, n>& v) const;
-
-    //! dot product computed in double-precision arithmetics
-    double ddot(const Matx<_Tp, m, n>& v) const;
-
-    //! conversion to another data type
-    template<typename T2> operator Matx<T2, m, n>() const;
-
-    //! change the matrix shape
-    template<int m1, int n1> Matx<_Tp, m1, n1> reshape() const;
-
-    //! extract part of the matrix
-    template<int m1, int n1> Matx<_Tp, m1, n1> get_minor(int i, int j) const;
-
-    //! extract the matrix row
-    Matx<_Tp, 1, n> row(int i) const;
-
-    //! extract the matrix column
-    Matx<_Tp, m, 1> col(int i) const;
-
-    //! extract the matrix diagonal
-    diag_type diag() const;
-
-    //! transpose the matrix
-    Matx<_Tp, n, m> t() const;
-
-    //! invert the matrix
-    Matx<_Tp, n, m> inv(int method=DECOMP_LU, bool *p_is_ok = NULL) const;
-
-    //! solve linear system
-    template<int l> Matx<_Tp, n, l> solve(const Matx<_Tp, m, l>& rhs, int flags=DECOMP_LU) const;
-    Vec<_Tp, n> solve(const Vec<_Tp, m>& rhs, int method) const;
-
-    //! multiply two matrices element-wise
-    Matx<_Tp, m, n> mul(const Matx<_Tp, m, n>& a) const;
-
-    //! divide two matrices element-wise
-    Matx<_Tp, m, n> div(const Matx<_Tp, m, n>& a) const;
-
-    //! element access
-    const _Tp& operator ()(int i, int j) const;
-    _Tp& operator ()(int i, int j);
-
-    //! 1D element access
-    const _Tp& operator ()(int i) const;
-    _Tp& operator ()(int i);
-
-    Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_AddOp);
-    Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_SubOp);
-    template<typename _T2> Matx(const Matx<_Tp, m, n>& a, _T2 alpha, Matx_ScaleOp);
-    Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_MulOp);
-    Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_DivOp);
-    template<int l> Matx(const Matx<_Tp, m, l>& a, const Matx<_Tp, l, n>& b, Matx_MatMulOp);
-    Matx(const Matx<_Tp, n, m>& a, Matx_TOp);
-
-    _Tp val[m*n]; //< matrix elements
-};
-
-typedef Matx<float, 1, 2> Matx12f;
-typedef Matx<double, 1, 2> Matx12d;
-typedef Matx<float, 1, 3> Matx13f;
-typedef Matx<double, 1, 3> Matx13d;
-typedef Matx<float, 1, 4> Matx14f;
-typedef Matx<double, 1, 4> Matx14d;
-typedef Matx<float, 1, 6> Matx16f;
-typedef Matx<double, 1, 6> Matx16d;
-
-typedef Matx<float, 2, 1> Matx21f;
-typedef Matx<double, 2, 1> Matx21d;
-typedef Matx<float, 3, 1> Matx31f;
-typedef Matx<double, 3, 1> Matx31d;
-typedef Matx<float, 4, 1> Matx41f;
-typedef Matx<double, 4, 1> Matx41d;
-typedef Matx<float, 6, 1> Matx61f;
-typedef Matx<double, 6, 1> Matx61d;
-
-typedef Matx<float, 2, 2> Matx22f;
-typedef Matx<double, 2, 2> Matx22d;
-typedef Matx<float, 2, 3> Matx23f;
-typedef Matx<double, 2, 3> Matx23d;
-typedef Matx<float, 3, 2> Matx32f;
-typedef Matx<double, 3, 2> Matx32d;
-
-typedef Matx<float, 3, 3> Matx33f;
-typedef Matx<double, 3, 3> Matx33d;
-
-typedef Matx<float, 3, 4> Matx34f;
-typedef Matx<double, 3, 4> Matx34d;
-typedef Matx<float, 4, 3> Matx43f;
-typedef Matx<double, 4, 3> Matx43d;
-
-typedef Matx<float, 4, 4> Matx44f;
-typedef Matx<double, 4, 4> Matx44d;
-typedef Matx<float, 6, 6> Matx66f;
-typedef Matx<double, 6, 6> Matx66d;
-
-/*!
-  traits
-*/
-template<typename _Tp, int m, int n> class DataType< Matx<_Tp, m, n> >
-{
-public:
-    typedef Matx<_Tp, m, n>                               value_type;
-    typedef Matx<typename DataType<_Tp>::work_type, m, n> work_type;
-    typedef _Tp                                           channel_type;
-    typedef value_type                                    vec_type;
-
-    enum { generic_type = 0,
-           channels     = m * n,
-           fmt          = traits::SafeFmt<channel_type>::fmt + ((channels - 1) << 8)
-#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED
-           ,depth        = DataType<channel_type>::depth
-           ,type         = CV_MAKETYPE(depth, channels)
-#endif
-         };
-};
-
-namespace traits {
-template<typename _Tp, int m, int n>
-struct Depth< Matx<_Tp, m, n> > { enum { value = Depth<_Tp>::value }; };
-template<typename _Tp, int m, int n>
-struct Type< Matx<_Tp, m, n> > { enum { value = CV_MAKETYPE(Depth<_Tp>::value, n*m) }; };
-} // namespace
-
-
-/** @brief  Comma-separated Matrix Initializer
-*/
-template<typename _Tp, int m, int n> class MatxCommaInitializer
-{
-public:
-    MatxCommaInitializer(Matx<_Tp, m, n>* _mtx);
-    template<typename T2> MatxCommaInitializer<_Tp, m, n>& operator , (T2 val);
-    Matx<_Tp, m, n> operator *() const;
-
-    Matx<_Tp, m, n>* dst;
-    int idx;
-};
-
-/*
- Utility methods
-*/
-template<typename _Tp, int m> static double determinant(const Matx<_Tp, m, m>& a);
-template<typename _Tp, int m, int n> static double trace(const Matx<_Tp, m, n>& a);
-template<typename _Tp, int m, int n> static double norm(const Matx<_Tp, m, n>& M);
-template<typename _Tp, int m, int n> static double norm(const Matx<_Tp, m, n>& M, int normType);
-
-
-
-/////////////////////// Vec (used as element of multi-channel images /////////////////////
-
-/** @brief Template class for short numerical vectors, a partial case of Matx
-
-This template class represents short numerical vectors (of 1, 2, 3, 4 ... elements) on which you
-can perform basic arithmetical operations, access individual elements using [] operator etc. The
-vectors are allocated on stack, as opposite to std::valarray, std::vector, cv::Mat etc., which
-elements are dynamically allocated in the heap.
-
-The template takes 2 parameters:
-@tparam _Tp element type
-@tparam cn the number of elements
-
-In addition to the universal notation like Vec<float, 3>, you can use shorter aliases
-for the most popular specialized variants of Vec, e.g. Vec3f ~ Vec<float, 3>.
-
-It is possible to convert Vec\<T,2\> to/from Point_, Vec\<T,3\> to/from Point3_ , and Vec\<T,4\>
-to CvScalar or Scalar_. Use operator[] to access the elements of Vec.
-
-All the expected vector operations are also implemented:
--   v1 = v2 + v3
--   v1 = v2 - v3
--   v1 = v2 \* scale
--   v1 = scale \* v2
--   v1 = -v2
--   v1 += v2 and other augmenting operations
--   v1 == v2, v1 != v2
--   norm(v1) (euclidean norm)
-The Vec class is commonly used to describe pixel types of multi-channel arrays. See Mat for details.
-*/
-template<typename _Tp, int cn> class Vec : public Matx<_Tp, cn, 1>
-{
-public:
-    typedef _Tp value_type;
-    enum {
-           channels = cn,
-#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED
-           depth    = Matx<_Tp, cn, 1>::depth,
-           type     = CV_MAKETYPE(depth, channels),
-#endif
-           _dummy_enum_finalizer = 0
-         };
-
-    //! default constructor
-    Vec();
-
-    Vec(_Tp v0); //!< 1-element vector constructor
-    Vec(_Tp v0, _Tp v1); //!< 2-element vector constructor
-    Vec(_Tp v0, _Tp v1, _Tp v2); //!< 3-element vector constructor
-    Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3); //!< 4-element vector constructor
-    Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4); //!< 5-element vector constructor
-    Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5); //!< 6-element vector constructor
-    Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6); //!< 7-element vector constructor
-    Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7); //!< 8-element vector constructor
-    Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8); //!< 9-element vector constructor
-    Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9); //!< 10-element vector constructor
-    Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9, _Tp v10, _Tp v11, _Tp v12, _Tp v13); //!< 14-element vector constructor
-    explicit Vec(const _Tp* values);
-
-#ifdef CV_CXX11
-    Vec(std::initializer_list<_Tp>);
-#endif
-
-    Vec(const Vec<_Tp, cn>& v);
-
-    static Vec all(_Tp alpha);
-
-    //! per-element multiplication
-    Vec mul(const Vec<_Tp, cn>& v) const;
-
-    //! conjugation (makes sense for complex numbers and quaternions)
-    Vec conj() const;
-
-    /*!
-      cross product of the two 3D vectors.
-
-      For other dimensionalities the exception is raised
-    */
-    Vec cross(const Vec& v) const;
-    //! conversion to another data type
-    template<typename T2> operator Vec<T2, cn>() const;
-
-    /*! element access */
-    const _Tp& operator [](int i) const;
-    _Tp& operator[](int i);
-    const _Tp& operator ()(int i) const;
-    _Tp& operator ()(int i);
-
-    Vec(const Matx<_Tp, cn, 1>& a, const Matx<_Tp, cn, 1>& b, Matx_AddOp);
-    Vec(const Matx<_Tp, cn, 1>& a, const Matx<_Tp, cn, 1>& b, Matx_SubOp);
-    template<typename _T2> Vec(const Matx<_Tp, cn, 1>& a, _T2 alpha, Matx_ScaleOp);
-};
-
-/** @name Shorter aliases for the most popular specializations of Vec<T,n>
-  @{
-*/
-typedef Vec<uchar, 2> Vec2b;
-typedef Vec<uchar, 3> Vec3b;
-typedef Vec<uchar, 4> Vec4b;
-
-typedef Vec<short, 2> Vec2s;
-typedef Vec<short, 3> Vec3s;
-typedef Vec<short, 4> Vec4s;
-
-typedef Vec<ushort, 2> Vec2w;
-typedef Vec<ushort, 3> Vec3w;
-typedef Vec<ushort, 4> Vec4w;
-
-typedef Vec<int, 2> Vec2i;
-typedef Vec<int, 3> Vec3i;
-typedef Vec<int, 4> Vec4i;
-typedef Vec<int, 6> Vec6i;
-typedef Vec<int, 8> Vec8i;
-
-typedef Vec<float, 2> Vec2f;
-typedef Vec<float, 3> Vec3f;
-typedef Vec<float, 4> Vec4f;
-typedef Vec<float, 6> Vec6f;
-
-typedef Vec<double, 2> Vec2d;
-typedef Vec<double, 3> Vec3d;
-typedef Vec<double, 4> Vec4d;
-typedef Vec<double, 6> Vec6d;
-/** @} */
-
-/*!
-  traits
-*/
-template<typename _Tp, int cn> class DataType< Vec<_Tp, cn> >
-{
-public:
-    typedef Vec<_Tp, cn>                               value_type;
-    typedef Vec<typename DataType<_Tp>::work_type, cn> work_type;
-    typedef _Tp                                        channel_type;
-    typedef value_type                                 vec_type;
-
-    enum { generic_type = 0,
-           channels     = cn,
-           fmt          = DataType<channel_type>::fmt + ((channels - 1) << 8),
-#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED
-           depth        = DataType<channel_type>::depth,
-           type         = CV_MAKETYPE(depth, channels),
-#endif
-           _dummy_enum_finalizer = 0
-         };
-};
-
-namespace traits {
-template<typename _Tp, int cn>
-struct Depth< Vec<_Tp, cn> > { enum { value = Depth<_Tp>::value }; };
-template<typename _Tp, int cn>
-struct Type< Vec<_Tp, cn> > { enum { value = CV_MAKETYPE(Depth<_Tp>::value, cn) }; };
-} // namespace
-
-
-/** @brief  Comma-separated Vec Initializer
-*/
-template<typename _Tp, int m> class VecCommaInitializer : public MatxCommaInitializer<_Tp, m, 1>
-{
-public:
-    VecCommaInitializer(Vec<_Tp, m>* _vec);
-    template<typename T2> VecCommaInitializer<_Tp, m>& operator , (T2 val);
-    Vec<_Tp, m> operator *() const;
-};
-
-template<typename _Tp, int cn> static Vec<_Tp, cn> normalize(const Vec<_Tp, cn>& v);
-
-//! @} core_basic
-
-//! @cond IGNORED
-
-///////////////////////////////////// helper classes /////////////////////////////////////
-namespace internal
-{
-
-template<typename _Tp, int m> struct Matx_DetOp
-{
-    double operator ()(const Matx<_Tp, m, m>& a) const
-    {
-        Matx<_Tp, m, m> temp = a;
-        double p = LU(temp.val, m*sizeof(_Tp), m, 0, 0, 0);
-        if( p == 0 )
-            return p;
-        for( int i = 0; i < m; i++ )
-            p *= temp(i, i);
-        return p;
-    }
-};
-
-template<typename _Tp> struct Matx_DetOp<_Tp, 1>
-{
-    double operator ()(const Matx<_Tp, 1, 1>& a) const
-    {
-        return a(0,0);
-    }
-};
-
-template<typename _Tp> struct Matx_DetOp<_Tp, 2>
-{
-    double operator ()(const Matx<_Tp, 2, 2>& a) const
-    {
-        return a(0,0)*a(1,1) - a(0,1)*a(1,0);
-    }
-};
-
-template<typename _Tp> struct Matx_DetOp<_Tp, 3>
-{
-    double operator ()(const Matx<_Tp, 3, 3>& a) const
-    {
-        return a(0,0)*(a(1,1)*a(2,2) - a(2,1)*a(1,2)) -
-            a(0,1)*(a(1,0)*a(2,2) - a(2,0)*a(1,2)) +
-            a(0,2)*(a(1,0)*a(2,1) - a(2,0)*a(1,1));
-    }
-};
-
-template<typename _Tp> Vec<_Tp, 2> inline conjugate(const Vec<_Tp, 2>& v)
-{
-    return Vec<_Tp, 2>(v[0], -v[1]);
-}
-
-template<typename _Tp> Vec<_Tp, 4> inline conjugate(const Vec<_Tp, 4>& v)
-{
-    return Vec<_Tp, 4>(v[0], -v[1], -v[2], -v[3]);
-}
-
-} // internal
-
-
-
-////////////////////////////////// Matx Implementation ///////////////////////////////////
-
-template<typename _Tp, int m, int n> inline
-Matx<_Tp, m, n>::Matx()
-{
-    for(int i = 0; i < channels; i++) val[i] = _Tp(0);
-}
-
-template<typename _Tp, int m, int n> inline
-Matx<_Tp, m, n>::Matx(_Tp v0)
-{
-    val[0] = v0;
-    for(int i = 1; i < channels; i++) val[i] = _Tp(0);
-}
-
-template<typename _Tp, int m, int n> inline
-Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1)
-{
-    CV_StaticAssert(channels >= 2, "Matx should have at least 2 elements.");
-    val[0] = v0; val[1] = v1;
-    for(int i = 2; i < channels; i++) val[i] = _Tp(0);
-}
-
-template<typename _Tp, int m, int n> inline
-Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2)
-{
-    CV_StaticAssert(channels >= 3, "Matx should have at least 3 elements.");
-    val[0] = v0; val[1] = v1; val[2] = v2;
-    for(int i = 3; i < channels; i++) val[i] = _Tp(0);
-}
-
-template<typename _Tp, int m, int n> inline
-Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3)
-{
-    CV_StaticAssert(channels >= 4, "Matx should have at least 4 elements.");
-    val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3;
-    for(int i = 4; i < channels; i++) val[i] = _Tp(0);
-}
-
-template<typename _Tp, int m, int n> inline
-Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4)
-{
-    CV_StaticAssert(channels >= 5, "Matx should have at least 5 elements.");
-    val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3; val[4] = v4;
-    for(int i = 5; i < channels; i++) val[i] = _Tp(0);
-}
-
-template<typename _Tp, int m, int n> inline
-Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5)
-{
-    CV_StaticAssert(channels >= 6, "Matx should have at least 6 elements.");
-    val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3;
-    val[4] = v4; val[5] = v5;
-    for(int i = 6; i < channels; i++) val[i] = _Tp(0);
-}
-
-template<typename _Tp, int m, int n> inline
-Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6)
-{
-    CV_StaticAssert(channels >= 7, "Matx should have at least 7 elements.");
-    val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3;
-    val[4] = v4; val[5] = v5; val[6] = v6;
-    for(int i = 7; i < channels; i++) val[i] = _Tp(0);
-}
-
-template<typename _Tp, int m, int n> inline
-Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7)
-{
-    CV_StaticAssert(channels >= 8, "Matx should have at least 8 elements.");
-    val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3;
-    val[4] = v4; val[5] = v5; val[6] = v6; val[7] = v7;
-    for(int i = 8; i < channels; i++) val[i] = _Tp(0);
-}
-
-template<typename _Tp, int m, int n> inline
-Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8)
-{
-    CV_StaticAssert(channels >= 9, "Matx should have at least 9 elements.");
-    val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3;
-    val[4] = v4; val[5] = v5; val[6] = v6; val[7] = v7;
-    val[8] = v8;
-    for(int i = 9; i < channels; i++) val[i] = _Tp(0);
-}
-
-template<typename _Tp, int m, int n> inline
-Matx<_Tp, m, n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9)
-{
-    CV_StaticAssert(channels >= 10, "Matx should have at least 10 elements.");
-    val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3;
-    val[4] = v4; val[5] = v5; val[6] = v6; val[7] = v7;
-    val[8] = v8; val[9] = v9;
-    for(int i = 10; i < channels; i++) val[i] = _Tp(0);
-}
-
-
-template<typename _Tp, int m, int n> inline
-Matx<_Tp,m,n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9, _Tp v10, _Tp v11)
-{
-    CV_StaticAssert(channels >= 12, "Matx should have at least 12 elements.");
-    val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3;
-    val[4] = v4; val[5] = v5; val[6] = v6; val[7] = v7;
-    val[8] = v8; val[9] = v9; val[10] = v10; val[11] = v11;
-    for(int i = 12; i < channels; i++) val[i] = _Tp(0);
-}
-
-template<typename _Tp, int m, int n> inline
-Matx<_Tp,m,n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9, _Tp v10, _Tp v11, _Tp v12, _Tp v13)
-{
-    CV_StaticAssert(channels >= 14, "Matx should have at least 14 elements.");
-    val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3;
-    val[4] = v4; val[5] = v5; val[6] = v6; val[7] = v7;
-    val[8] = v8; val[9] = v9; val[10] = v10; val[11] = v11;
-    val[12] = v12; val[13] = v13;
-    for (int i = 14; i < channels; i++) val[i] = _Tp(0);
-}
-
-
-template<typename _Tp, int m, int n> inline
-Matx<_Tp,m,n>::Matx(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9, _Tp v10, _Tp v11, _Tp v12, _Tp v13, _Tp v14, _Tp v15)
-{
-    CV_StaticAssert(channels >= 16, "Matx should have at least 16 elements.");
-    val[0] = v0; val[1] = v1; val[2] = v2; val[3] = v3;
-    val[4] = v4; val[5] = v5; val[6] = v6; val[7] = v7;
-    val[8] = v8; val[9] = v9; val[10] = v10; val[11] = v11;
-    val[12] = v12; val[13] = v13; val[14] = v14; val[15] = v15;
-    for(int i = 16; i < channels; i++) val[i] = _Tp(0);
-}
-
-template<typename _Tp, int m, int n> inline
-Matx<_Tp, m, n>::Matx(const _Tp* values)
-{
-    for( int i = 0; i < channels; i++ ) val[i] = values[i];
-}
-
-#ifdef CV_CXX11
-template<typename _Tp, int m, int n> inline
-Matx<_Tp, m, n>::Matx(std::initializer_list<_Tp> list)
-{
-    CV_DbgAssert(list.size() == channels);
-    int i = 0;
-    for(const auto& elem : list)
-    {
-        val[i++] = elem;
-    }
-}
-#endif
-
-template<typename _Tp, int m, int n> inline
-Matx<_Tp, m, n> Matx<_Tp, m, n>::all(_Tp alpha)
-{
-    Matx<_Tp, m, n> M;
-    for( int i = 0; i < m*n; i++ ) M.val[i] = alpha;
-    return M;
-}
-
-template<typename _Tp, int m, int n> inline
-Matx<_Tp,m,n> Matx<_Tp,m,n>::zeros()
-{
-    return all(0);
-}
-
-template<typename _Tp, int m, int n> inline
-Matx<_Tp,m,n> Matx<_Tp,m,n>::ones()
-{
-    return all(1);
-}
-
-template<typename _Tp, int m, int n> inline
-Matx<_Tp,m,n> Matx<_Tp,m,n>::eye()
-{
-    Matx<_Tp,m,n> M;
-    for(int i = 0; i < shortdim; i++)
-        M(i,i) = 1;
-    return M;
-}
-
-template<typename _Tp, int m, int n> inline
-_Tp Matx<_Tp, m, n>::dot(const Matx<_Tp, m, n>& M) const
-{
-    _Tp s = 0;
-    for( int i = 0; i < channels; i++ ) s += val[i]*M.val[i];
-    return s;
-}
-
-template<typename _Tp, int m, int n> inline
-double Matx<_Tp, m, n>::ddot(const Matx<_Tp, m, n>& M) const
-{
-    double s = 0;
-    for( int i = 0; i < channels; i++ ) s += (double)val[i]*M.val[i];
-    return s;
-}
-
-template<typename _Tp, int m, int n> inline
-Matx<_Tp,m,n> Matx<_Tp,m,n>::diag(const typename Matx<_Tp,m,n>::diag_type& d)
-{
-    Matx<_Tp,m,n> M;
-    for(int i = 0; i < shortdim; i++)
-        M(i,i) = d(i, 0);
-    return M;
-}
-
-template<typename _Tp, int m, int n> template<typename T2>
-inline Matx<_Tp, m, n>::operator Matx<T2, m, n>() const
-{
-    Matx<T2, m, n> M;
-    for( int i = 0; i < m*n; i++ ) M.val[i] = saturate_cast<T2>(val[i]);
-    return M;
-}
-
-template<typename _Tp, int m, int n> template<int m1, int n1> inline
-Matx<_Tp, m1, n1> Matx<_Tp, m, n>::reshape() const
-{
-    CV_StaticAssert(m1*n1 == m*n, "Input and destnarion matrices must have the same number of elements");
-    return (const Matx<_Tp, m1, n1>&)*this;
-}
-
-template<typename _Tp, int m, int n>
-template<int m1, int n1> inline
-Matx<_Tp, m1, n1> Matx<_Tp, m, n>::get_minor(int i, int j) const
-{
-    CV_DbgAssert(0 <= i && i+m1 <= m && 0 <= j && j+n1 <= n);
-    Matx<_Tp, m1, n1> s;
-    for( int di = 0; di < m1; di++ )
-        for( int dj = 0; dj < n1; dj++ )
-            s(di, dj) = (*this)(i+di, j+dj);
-    return s;
-}
-
-template<typename _Tp, int m, int n> inline
-Matx<_Tp, 1, n> Matx<_Tp, m, n>::row(int i) const
-{
-    CV_DbgAssert((unsigned)i < (unsigned)m);
-    return Matx<_Tp, 1, n>(&val[i*n]);
-}
-
-template<typename _Tp, int m, int n> inline
-Matx<_Tp, m, 1> Matx<_Tp, m, n>::col(int j) const
-{
-    CV_DbgAssert((unsigned)j < (unsigned)n);
-    Matx<_Tp, m, 1> v;
-    for( int i = 0; i < m; i++ )
-        v.val[i] = val[i*n + j];
-    return v;
-}
-
-template<typename _Tp, int m, int n> inline
-typename Matx<_Tp, m, n>::diag_type Matx<_Tp, m, n>::diag() const
-{
-    diag_type d;
-    for( int i = 0; i < shortdim; i++ )
-        d.val[i] = val[i*n + i];
-    return d;
-}
-
-template<typename _Tp, int m, int n> inline
-const _Tp& Matx<_Tp, m, n>::operator()(int i, int j) const
-{
-    CV_DbgAssert( (unsigned)i < (unsigned)m && (unsigned)j < (unsigned)n );
-    return this->val[i*n + j];
-}
-
-template<typename _Tp, int m, int n> inline
-_Tp& Matx<_Tp, m, n>::operator ()(int i, int j)
-{
-    CV_DbgAssert( (unsigned)i < (unsigned)m && (unsigned)j < (unsigned)n );
-    return val[i*n + j];
-}
-
-template<typename _Tp, int m, int n> inline
-const _Tp& Matx<_Tp, m, n>::operator ()(int i) const
-{
-    CV_StaticAssert(m == 1 || n == 1, "Single index indexation requires matrix to be a column or a row");
-    CV_DbgAssert( (unsigned)i < (unsigned)(m+n-1) );
-    return val[i];
-}
-
-template<typename _Tp, int m, int n> inline
-_Tp& Matx<_Tp, m, n>::operator ()(int i)
-{
-    CV_StaticAssert(m == 1 || n == 1, "Single index indexation requires matrix to be a column or a row");
-    CV_DbgAssert( (unsigned)i < (unsigned)(m+n-1) );
-    return val[i];
-}
-
-template<typename _Tp, int m, int n> inline
-Matx<_Tp,m,n>::Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_AddOp)
-{
-    for( int i = 0; i < channels; i++ )
-        val[i] = saturate_cast<_Tp>(a.val[i] + b.val[i]);
-}
-
-template<typename _Tp, int m, int n> inline
-Matx<_Tp,m,n>::Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_SubOp)
-{
-    for( int i = 0; i < channels; i++ )
-        val[i] = saturate_cast<_Tp>(a.val[i] - b.val[i]);
-}
-
-template<typename _Tp, int m, int n> template<typename _T2> inline
-Matx<_Tp,m,n>::Matx(const Matx<_Tp, m, n>& a, _T2 alpha, Matx_ScaleOp)
-{
-    for( int i = 0; i < channels; i++ )
-        val[i] = saturate_cast<_Tp>(a.val[i] * alpha);
-}
-
-template<typename _Tp, int m, int n> inline
-Matx<_Tp,m,n>::Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_MulOp)
-{
-    for( int i = 0; i < channels; i++ )
-        val[i] = saturate_cast<_Tp>(a.val[i] * b.val[i]);
-}
-
-template<typename _Tp, int m, int n> inline
-Matx<_Tp,m,n>::Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_DivOp)
-{
-    for( int i = 0; i < channels; i++ )
-        val[i] = saturate_cast<_Tp>(a.val[i] / b.val[i]);
-}
-
-template<typename _Tp, int m, int n> template<int l> inline
-Matx<_Tp,m,n>::Matx(const Matx<_Tp, m, l>& a, const Matx<_Tp, l, n>& b, Matx_MatMulOp)
-{
-    for( int i = 0; i < m; i++ )
-        for( int j = 0; j < n; j++ )
-        {
-            _Tp s = 0;
-            for( int k = 0; k < l; k++ )
-                s += a(i, k) * b(k, j);
-            val[i*n + j] = s;
-        }
-}
-
-template<typename _Tp, int m, int n> inline
-Matx<_Tp,m,n>::Matx(const Matx<_Tp, n, m>& a, Matx_TOp)
-{
-    for( int i = 0; i < m; i++ )
-        for( int j = 0; j < n; j++ )
-            val[i*n + j] = a(j, i);
-}
-
-template<typename _Tp, int m, int n> inline
-Matx<_Tp, m, n> Matx<_Tp, m, n>::mul(const Matx<_Tp, m, n>& a) const
-{
-    return Matx<_Tp, m, n>(*this, a, Matx_MulOp());
-}
-
-template<typename _Tp, int m, int n> inline
-Matx<_Tp, m, n> Matx<_Tp, m, n>::div(const Matx<_Tp, m, n>& a) const
-{
-    return Matx<_Tp, m, n>(*this, a, Matx_DivOp());
-}
-
-template<typename _Tp, int m, int n> inline
-Matx<_Tp, n, m> Matx<_Tp, m, n>::t() const
-{
-    return Matx<_Tp, n, m>(*this, Matx_TOp());
-}
-
-template<typename _Tp, int m, int n> inline
-Vec<_Tp, n> Matx<_Tp, m, n>::solve(const Vec<_Tp, m>& rhs, int method) const
-{
-    Matx<_Tp, n, 1> x = solve((const Matx<_Tp, m, 1>&)(rhs), method);
-    return (Vec<_Tp, n>&)(x);
-}
-
-template<typename _Tp, int m> static inline
-double determinant(const Matx<_Tp, m, m>& a)
-{
-    return cv::internal::Matx_DetOp<_Tp, m>()(a);
-}
-
-template<typename _Tp, int m, int n> static inline
-double trace(const Matx<_Tp, m, n>& a)
-{
-    _Tp s = 0;
-    for( int i = 0; i < std::min(m, n); i++ )
-        s += a(i,i);
-    return s;
-}
-
-template<typename _Tp, int m, int n> static inline
-double norm(const Matx<_Tp, m, n>& M)
-{
-    return std::sqrt(normL2Sqr<_Tp, double>(M.val, m*n));
-}
-
-template<typename _Tp, int m, int n> static inline
-double norm(const Matx<_Tp, m, n>& M, int normType)
-{
-    switch(normType) {
-    case NORM_INF:
-        return (double)normInf<_Tp, typename DataType<_Tp>::work_type>(M.val, m*n);
-    case NORM_L1:
-        return (double)normL1<_Tp, typename DataType<_Tp>::work_type>(M.val, m*n);
-    case NORM_L2SQR:
-        return (double)normL2Sqr<_Tp, typename DataType<_Tp>::work_type>(M.val, m*n);
-    default:
-    case NORM_L2:
-        return std::sqrt((double)normL2Sqr<_Tp, typename DataType<_Tp>::work_type>(M.val, m*n));
-    }
-}
-
-
-
-//////////////////////////////// matx comma initializer //////////////////////////////////
-
-template<typename _Tp, typename _T2, int m, int n> static inline
-MatxCommaInitializer<_Tp, m, n> operator << (const Matx<_Tp, m, n>& mtx, _T2 val)
-{
-    MatxCommaInitializer<_Tp, m, n> commaInitializer((Matx<_Tp, m, n>*)&mtx);
-    return (commaInitializer, val);
-}
-
-template<typename _Tp, int m, int n> inline
-MatxCommaInitializer<_Tp, m, n>::MatxCommaInitializer(Matx<_Tp, m, n>* _mtx)
-    : dst(_mtx), idx(0)
-{}
-
-template<typename _Tp, int m, int n> template<typename _T2> inline
-MatxCommaInitializer<_Tp, m, n>& MatxCommaInitializer<_Tp, m, n>::operator , (_T2 value)
-{
-    CV_DbgAssert( idx < m*n );
-    dst->val[idx++] = saturate_cast<_Tp>(value);
-    return *this;
-}
-
-template<typename _Tp, int m, int n> inline
-Matx<_Tp, m, n> MatxCommaInitializer<_Tp, m, n>::operator *() const
-{
-    CV_DbgAssert( idx == n*m );
-    return *dst;
-}
-
-
-
-/////////////////////////////////// Vec Implementation ///////////////////////////////////
-
-template<typename _Tp, int cn> inline
-Vec<_Tp, cn>::Vec() {}
-
-template<typename _Tp, int cn> inline
-Vec<_Tp, cn>::Vec(_Tp v0)
-    : Matx<_Tp, cn, 1>(v0) {}
-
-template<typename _Tp, int cn> inline
-Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1)
-    : Matx<_Tp, cn, 1>(v0, v1) {}
-
-template<typename _Tp, int cn> inline
-Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2)
-    : Matx<_Tp, cn, 1>(v0, v1, v2) {}
-
-template<typename _Tp, int cn> inline
-Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3)
-    : Matx<_Tp, cn, 1>(v0, v1, v2, v3) {}
-
-template<typename _Tp, int cn> inline
-Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4)
-    : Matx<_Tp, cn, 1>(v0, v1, v2, v3, v4) {}
-
-template<typename _Tp, int cn> inline
-Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5)
-    : Matx<_Tp, cn, 1>(v0, v1, v2, v3, v4, v5) {}
-
-template<typename _Tp, int cn> inline
-Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6)
-    : Matx<_Tp, cn, 1>(v0, v1, v2, v3, v4, v5, v6) {}
-
-template<typename _Tp, int cn> inline
-Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7)
-    : Matx<_Tp, cn, 1>(v0, v1, v2, v3, v4, v5, v6, v7) {}
-
-template<typename _Tp, int cn> inline
-Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8)
-    : Matx<_Tp, cn, 1>(v0, v1, v2, v3, v4, v5, v6, v7, v8) {}
-
-template<typename _Tp, int cn> inline
-Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9)
-    : Matx<_Tp, cn, 1>(v0, v1, v2, v3, v4, v5, v6, v7, v8, v9) {}
-
-template<typename _Tp, int cn> inline
-Vec<_Tp, cn>::Vec(_Tp v0, _Tp v1, _Tp v2, _Tp v3, _Tp v4, _Tp v5, _Tp v6, _Tp v7, _Tp v8, _Tp v9, _Tp v10, _Tp v11, _Tp v12, _Tp v13)
-    : Matx<_Tp, cn, 1>(v0, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13) {}
-
-template<typename _Tp, int cn> inline
-Vec<_Tp, cn>::Vec(const _Tp* values)
-    : Matx<_Tp, cn, 1>(values) {}
-
-#ifdef CV_CXX11
-template<typename _Tp, int cn> inline
-Vec<_Tp, cn>::Vec(std::initializer_list<_Tp> list)
-    : Matx<_Tp, cn, 1>(list) {}
-#endif
-
-template<typename _Tp, int cn> inline
-Vec<_Tp, cn>::Vec(const Vec<_Tp, cn>& m)
-    : Matx<_Tp, cn, 1>(m.val) {}
-
-template<typename _Tp, int cn> inline
-Vec<_Tp, cn>::Vec(const Matx<_Tp, cn, 1>& a, const Matx<_Tp, cn, 1>& b, Matx_AddOp op)
-    : Matx<_Tp, cn, 1>(a, b, op) {}
-
-template<typename _Tp, int cn> inline
-Vec<_Tp, cn>::Vec(const Matx<_Tp, cn, 1>& a, const Matx<_Tp, cn, 1>& b, Matx_SubOp op)
-    : Matx<_Tp, cn, 1>(a, b, op) {}
-
-template<typename _Tp, int cn> template<typename _T2> inline
-Vec<_Tp, cn>::Vec(const Matx<_Tp, cn, 1>& a, _T2 alpha, Matx_ScaleOp op)
-    : Matx<_Tp, cn, 1>(a, alpha, op) {}
-
-template<typename _Tp, int cn> inline
-Vec<_Tp, cn> Vec<_Tp, cn>::all(_Tp alpha)
-{
-    Vec v;
-    for( int i = 0; i < cn; i++ ) v.val[i] = alpha;
-    return v;
-}
-
-template<typename _Tp, int cn> inline
-Vec<_Tp, cn> Vec<_Tp, cn>::mul(const Vec<_Tp, cn>& v) const
-{
-    Vec<_Tp, cn> w;
-    for( int i = 0; i < cn; i++ ) w.val[i] = saturate_cast<_Tp>(this->val[i]*v.val[i]);
-    return w;
-}
-
-template<> inline
-Vec<float, 2> Vec<float, 2>::conj() const
-{
-    return cv::internal::conjugate(*this);
-}
-
-template<> inline
-Vec<double, 2> Vec<double, 2>::conj() const
-{
-    return cv::internal::conjugate(*this);
-}
-
-template<> inline
-Vec<float, 4> Vec<float, 4>::conj() const
-{
-    return cv::internal::conjugate(*this);
-}
-
-template<> inline
-Vec<double, 4> Vec<double, 4>::conj() const
-{
-    return cv::internal::conjugate(*this);
-}
-
-template<typename _Tp, int cn> inline
-Vec<_Tp, cn> Vec<_Tp, cn>::cross(const Vec<_Tp, cn>&) const
-{
-    CV_StaticAssert(cn == 3, "for arbitrary-size vector there is no cross-product defined");
-    return Vec<_Tp, cn>();
-}
-
-template<> inline
-Vec<float, 3> Vec<float, 3>::cross(const Vec<float, 3>& v) const
-{
-    return Vec<float,3>(this->val[1]*v.val[2] - this->val[2]*v.val[1],
-                     this->val[2]*v.val[0] - this->val[0]*v.val[2],
-                     this->val[0]*v.val[1] - this->val[1]*v.val[0]);
-}
-
-template<> inline
-Vec<double, 3> Vec<double, 3>::cross(const Vec<double, 3>& v) const
-{
-    return Vec<double,3>(this->val[1]*v.val[2] - this->val[2]*v.val[1],
-                     this->val[2]*v.val[0] - this->val[0]*v.val[2],
-                     this->val[0]*v.val[1] - this->val[1]*v.val[0]);
-}
-
-template<typename _Tp, int cn> template<typename T2> inline
-Vec<_Tp, cn>::operator Vec<T2, cn>() const
-{
-    Vec<T2, cn> v;
-    for( int i = 0; i < cn; i++ ) v.val[i] = saturate_cast<T2>(this->val[i]);
-    return v;
-}
-
-template<typename _Tp, int cn> inline
-const _Tp& Vec<_Tp, cn>::operator [](int i) const
-{
-    CV_DbgAssert( (unsigned)i < (unsigned)cn );
-    return this->val[i];
-}
-
-template<typename _Tp, int cn> inline
-_Tp& Vec<_Tp, cn>::operator [](int i)
-{
-    CV_DbgAssert( (unsigned)i < (unsigned)cn );
-    return this->val[i];
-}
-
-template<typename _Tp, int cn> inline
-const _Tp& Vec<_Tp, cn>::operator ()(int i) const
-{
-    CV_DbgAssert( (unsigned)i < (unsigned)cn );
-    return this->val[i];
-}
-
-template<typename _Tp, int cn> inline
-_Tp& Vec<_Tp, cn>::operator ()(int i)
-{
-    CV_DbgAssert( (unsigned)i < (unsigned)cn );
-    return this->val[i];
-}
-
-template<typename _Tp, int cn> inline
-Vec<_Tp, cn> normalize(const Vec<_Tp, cn>& v)
-{
-    double nv = norm(v);
-    return v * (nv ? 1./nv : 0.);
-}
-
-
-
-//////////////////////////////// vec comma initializer //////////////////////////////////
-
-
-template<typename _Tp, typename _T2, int cn> static inline
-VecCommaInitializer<_Tp, cn> operator << (const Vec<_Tp, cn>& vec, _T2 val)
-{
-    VecCommaInitializer<_Tp, cn> commaInitializer((Vec<_Tp, cn>*)&vec);
-    return (commaInitializer, val);
-}
-
-template<typename _Tp, int cn> inline
-VecCommaInitializer<_Tp, cn>::VecCommaInitializer(Vec<_Tp, cn>* _vec)
-    : MatxCommaInitializer<_Tp, cn, 1>(_vec)
-{}
-
-template<typename _Tp, int cn> template<typename _T2> inline
-VecCommaInitializer<_Tp, cn>& VecCommaInitializer<_Tp, cn>::operator , (_T2 value)
-{
-    CV_DbgAssert( this->idx < cn );
-    this->dst->val[this->idx++] = saturate_cast<_Tp>(value);
-    return *this;
-}
-
-template<typename _Tp, int cn> inline
-Vec<_Tp, cn> VecCommaInitializer<_Tp, cn>::operator *() const
-{
-    CV_DbgAssert( this->idx == cn );
-    return *this->dst;
-}
-
-//! @endcond
-
-///////////////////////////// Matx out-of-class operators ////////////////////////////////
-
-//! @relates cv::Matx
-//! @{
-
-template<typename _Tp1, typename _Tp2, int m, int n> static inline
-Matx<_Tp1, m, n>& operator += (Matx<_Tp1, m, n>& a, const Matx<_Tp2, m, n>& b)
-{
-    for( int i = 0; i < m*n; i++ )
-        a.val[i] = saturate_cast<_Tp1>(a.val[i] + b.val[i]);
-    return a;
-}
-
-template<typename _Tp1, typename _Tp2, int m, int n> static inline
-Matx<_Tp1, m, n>& operator -= (Matx<_Tp1, m, n>& a, const Matx<_Tp2, m, n>& b)
-{
-    for( int i = 0; i < m*n; i++ )
-        a.val[i] = saturate_cast<_Tp1>(a.val[i] - b.val[i]);
-    return a;
-}
-
-template<typename _Tp, int m, int n> static inline
-Matx<_Tp, m, n> operator + (const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b)
-{
-    return Matx<_Tp, m, n>(a, b, Matx_AddOp());
-}
-
-template<typename _Tp, int m, int n> static inline
-Matx<_Tp, m, n> operator - (const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b)
-{
-    return Matx<_Tp, m, n>(a, b, Matx_SubOp());
-}
-
-template<typename _Tp, int m, int n> static inline
-Matx<_Tp, m, n>& operator *= (Matx<_Tp, m, n>& a, int alpha)
-{
-    for( int i = 0; i < m*n; i++ )
-        a.val[i] = saturate_cast<_Tp>(a.val[i] * alpha);
-    return a;
-}
-
-template<typename _Tp, int m, int n> static inline
-Matx<_Tp, m, n>& operator *= (Matx<_Tp, m, n>& a, float alpha)
-{
-    for( int i = 0; i < m*n; i++ )
-        a.val[i] = saturate_cast<_Tp>(a.val[i] * alpha);
-    return a;
-}
-
-template<typename _Tp, int m, int n> static inline
-Matx<_Tp, m, n>& operator *= (Matx<_Tp, m, n>& a, double alpha)
-{
-    for( int i = 0; i < m*n; i++ )
-        a.val[i] = saturate_cast<_Tp>(a.val[i] * alpha);
-    return a;
-}
-
-template<typename _Tp, int m, int n> static inline
-Matx<_Tp, m, n> operator * (const Matx<_Tp, m, n>& a, int alpha)
-{
-    return Matx<_Tp, m, n>(a, alpha, Matx_ScaleOp());
-}
-
-template<typename _Tp, int m, int n> static inline
-Matx<_Tp, m, n> operator * (const Matx<_Tp, m, n>& a, float alpha)
-{
-    return Matx<_Tp, m, n>(a, alpha, Matx_ScaleOp());
-}
-
-template<typename _Tp, int m, int n> static inline
-Matx<_Tp, m, n> operator * (const Matx<_Tp, m, n>& a, double alpha)
-{
-    return Matx<_Tp, m, n>(a, alpha, Matx_ScaleOp());
-}
-
-template<typename _Tp, int m, int n> static inline
-Matx<_Tp, m, n> operator * (int alpha, const Matx<_Tp, m, n>& a)
-{
-    return Matx<_Tp, m, n>(a, alpha, Matx_ScaleOp());
-}
-
-template<typename _Tp, int m, int n> static inline
-Matx<_Tp, m, n> operator * (float alpha, const Matx<_Tp, m, n>& a)
-{
-    return Matx<_Tp, m, n>(a, alpha, Matx_ScaleOp());
-}
-
-template<typename _Tp, int m, int n> static inline
-Matx<_Tp, m, n> operator * (double alpha, const Matx<_Tp, m, n>& a)
-{
-    return Matx<_Tp, m, n>(a, alpha, Matx_ScaleOp());
-}
-
-template<typename _Tp, int m, int n> static inline
-Matx<_Tp, m, n> operator - (const Matx<_Tp, m, n>& a)
-{
-    return Matx<_Tp, m, n>(a, -1, Matx_ScaleOp());
-}
-
-template<typename _Tp, int m, int n, int l> static inline
-Matx<_Tp, m, n> operator * (const Matx<_Tp, m, l>& a, const Matx<_Tp, l, n>& b)
-{
-    return Matx<_Tp, m, n>(a, b, Matx_MatMulOp());
-}
-
-template<typename _Tp, int m, int n> static inline
-Vec<_Tp, m> operator * (const Matx<_Tp, m, n>& a, const Vec<_Tp, n>& b)
-{
-    Matx<_Tp, m, 1> c(a, b, Matx_MatMulOp());
-    return (const Vec<_Tp, m>&)(c);
-}
-
-template<typename _Tp, int m, int n> static inline
-bool operator == (const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b)
-{
-    for( int i = 0; i < m*n; i++ )
-        if( a.val[i] != b.val[i] ) return false;
-    return true;
-}
-
-template<typename _Tp, int m, int n> static inline
-bool operator != (const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b)
-{
-    return !(a == b);
-}
-
-//! @}
-
-////////////////////////////// Vec out-of-class operators ////////////////////////////////
-
-//! @relates cv::Vec
-//! @{
-
-template<typename _Tp1, typename _Tp2, int cn> static inline
-Vec<_Tp1, cn>& operator += (Vec<_Tp1, cn>& a, const Vec<_Tp2, cn>& b)
-{
-    for( int i = 0; i < cn; i++ )
-        a.val[i] = saturate_cast<_Tp1>(a.val[i] + b.val[i]);
-    return a;
-}
-
-template<typename _Tp1, typename _Tp2, int cn> static inline
-Vec<_Tp1, cn>& operator -= (Vec<_Tp1, cn>& a, const Vec<_Tp2, cn>& b)
-{
-    for( int i = 0; i < cn; i++ )
-        a.val[i] = saturate_cast<_Tp1>(a.val[i] - b.val[i]);
-    return a;
-}
-
-template<typename _Tp, int cn> static inline
-Vec<_Tp, cn> operator + (const Vec<_Tp, cn>& a, const Vec<_Tp, cn>& b)
-{
-    return Vec<_Tp, cn>(a, b, Matx_AddOp());
-}
-
-template<typename _Tp, int cn> static inline
-Vec<_Tp, cn> operator - (const Vec<_Tp, cn>& a, const Vec<_Tp, cn>& b)
-{
-    return Vec<_Tp, cn>(a, b, Matx_SubOp());
-}
-
-template<typename _Tp, int cn> static inline
-Vec<_Tp, cn>& operator *= (Vec<_Tp, cn>& a, int alpha)
-{
-    for( int i = 0; i < cn; i++ )
-        a[i] = saturate_cast<_Tp>(a[i]*alpha);
-    return a;
-}
-
-template<typename _Tp, int cn> static inline
-Vec<_Tp, cn>& operator *= (Vec<_Tp, cn>& a, float alpha)
-{
-    for( int i = 0; i < cn; i++ )
-        a[i] = saturate_cast<_Tp>(a[i]*alpha);
-    return a;
-}
-
-template<typename _Tp, int cn> static inline
-Vec<_Tp, cn>& operator *= (Vec<_Tp, cn>& a, double alpha)
-{
-    for( int i = 0; i < cn; i++ )
-        a[i] = saturate_cast<_Tp>(a[i]*alpha);
-    return a;
-}
-
-template<typename _Tp, int cn> static inline
-Vec<_Tp, cn>& operator /= (Vec<_Tp, cn>& a, int alpha)
-{
-    double ialpha = 1./alpha;
-    for( int i = 0; i < cn; i++ )
-        a[i] = saturate_cast<_Tp>(a[i]*ialpha);
-    return a;
-}
-
-template<typename _Tp, int cn> static inline
-Vec<_Tp, cn>& operator /= (Vec<_Tp, cn>& a, float alpha)
-{
-    float ialpha = 1.f/alpha;
-    for( int i = 0; i < cn; i++ )
-        a[i] = saturate_cast<_Tp>(a[i]*ialpha);
-    return a;
-}
-
-template<typename _Tp, int cn> static inline
-Vec<_Tp, cn>& operator /= (Vec<_Tp, cn>& a, double alpha)
-{
-    double ialpha = 1./alpha;
-    for( int i = 0; i < cn; i++ )
-        a[i] = saturate_cast<_Tp>(a[i]*ialpha);
-    return a;
-}
-
-template<typename _Tp, int cn> static inline
-Vec<_Tp, cn> operator * (const Vec<_Tp, cn>& a, int alpha)
-{
-    return Vec<_Tp, cn>(a, alpha, Matx_ScaleOp());
-}
-
-template<typename _Tp, int cn> static inline
-Vec<_Tp, cn> operator * (int alpha, const Vec<_Tp, cn>& a)
-{
-    return Vec<_Tp, cn>(a, alpha, Matx_ScaleOp());
-}
-
-template<typename _Tp, int cn> static inline
-Vec<_Tp, cn> operator * (const Vec<_Tp, cn>& a, float alpha)
-{
-    return Vec<_Tp, cn>(a, alpha, Matx_ScaleOp());
-}
-
-template<typename _Tp, int cn> static inline
-Vec<_Tp, cn> operator * (float alpha, const Vec<_Tp, cn>& a)
-{
-    return Vec<_Tp, cn>(a, alpha, Matx_ScaleOp());
-}
-
-template<typename _Tp, int cn> static inline
-Vec<_Tp, cn> operator * (const Vec<_Tp, cn>& a, double alpha)
-{
-    return Vec<_Tp, cn>(a, alpha, Matx_ScaleOp());
-}
-
-template<typename _Tp, int cn> static inline
-Vec<_Tp, cn> operator * (double alpha, const Vec<_Tp, cn>& a)
-{
-    return Vec<_Tp, cn>(a, alpha, Matx_ScaleOp());
-}
-
-template<typename _Tp, int cn> static inline
-Vec<_Tp, cn> operator / (const Vec<_Tp, cn>& a, int alpha)
-{
-    return Vec<_Tp, cn>(a, 1./alpha, Matx_ScaleOp());
-}
-
-template<typename _Tp, int cn> static inline
-Vec<_Tp, cn> operator / (const Vec<_Tp, cn>& a, float alpha)
-{
-    return Vec<_Tp, cn>(a, 1.f/alpha, Matx_ScaleOp());
-}
-
-template<typename _Tp, int cn> static inline
-Vec<_Tp, cn> operator / (const Vec<_Tp, cn>& a, double alpha)
-{
-    return Vec<_Tp, cn>(a, 1./alpha, Matx_ScaleOp());
-}
-
-template<typename _Tp, int cn> static inline
-Vec<_Tp, cn> operator - (const Vec<_Tp, cn>& a)
-{
-    Vec<_Tp,cn> t;
-    for( int i = 0; i < cn; i++ ) t.val[i] = saturate_cast<_Tp>(-a.val[i]);
-    return t;
-}
-
-template<typename _Tp> inline Vec<_Tp, 4> operator * (const Vec<_Tp, 4>& v1, const Vec<_Tp, 4>& v2)
-{
-    return Vec<_Tp, 4>(saturate_cast<_Tp>(v1[0]*v2[0] - v1[1]*v2[1] - v1[2]*v2[2] - v1[3]*v2[3]),
-                       saturate_cast<_Tp>(v1[0]*v2[1] + v1[1]*v2[0] + v1[2]*v2[3] - v1[3]*v2[2]),
-                       saturate_cast<_Tp>(v1[0]*v2[2] - v1[1]*v2[3] + v1[2]*v2[0] + v1[3]*v2[1]),
-                       saturate_cast<_Tp>(v1[0]*v2[3] + v1[1]*v2[2] - v1[2]*v2[1] + v1[3]*v2[0]));
-}
-
-template<typename _Tp> inline Vec<_Tp, 4>& operator *= (Vec<_Tp, 4>& v1, const Vec<_Tp, 4>& v2)
-{
-    v1 = v1 * v2;
-    return v1;
-}
-
-//! @}
-
-} // cv
-
-#endif // OPENCV_CORE_MATX_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/neon_utils.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/neon_utils.hpp
deleted file mode 100755
index 573ba99..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/neon_utils.hpp
+++ /dev/null
@@ -1,128 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2015, Itseez Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_HAL_NEON_UTILS_HPP
-#define OPENCV_HAL_NEON_UTILS_HPP
-
-#include "opencv2/core/cvdef.h"
-
-//! @addtogroup core_utils_neon
-//! @{
-
-#if CV_NEON
-
-inline int32x2_t cv_vrnd_s32_f32(float32x2_t v)
-{
-    static int32x2_t v_sign = vdup_n_s32(1 << 31),
-        v_05 = vreinterpret_s32_f32(vdup_n_f32(0.5f));
-
-    int32x2_t v_addition = vorr_s32(v_05, vand_s32(v_sign, vreinterpret_s32_f32(v)));
-    return vcvt_s32_f32(vadd_f32(v, vreinterpret_f32_s32(v_addition)));
-}
-
-inline int32x4_t cv_vrndq_s32_f32(float32x4_t v)
-{
-    static int32x4_t v_sign = vdupq_n_s32(1 << 31),
-        v_05 = vreinterpretq_s32_f32(vdupq_n_f32(0.5f));
-
-    int32x4_t v_addition = vorrq_s32(v_05, vandq_s32(v_sign, vreinterpretq_s32_f32(v)));
-    return vcvtq_s32_f32(vaddq_f32(v, vreinterpretq_f32_s32(v_addition)));
-}
-
-inline uint32x2_t cv_vrnd_u32_f32(float32x2_t v)
-{
-    static float32x2_t v_05 = vdup_n_f32(0.5f);
-    return vcvt_u32_f32(vadd_f32(v, v_05));
-}
-
-inline uint32x4_t cv_vrndq_u32_f32(float32x4_t v)
-{
-    static float32x4_t v_05 = vdupq_n_f32(0.5f);
-    return vcvtq_u32_f32(vaddq_f32(v, v_05));
-}
-
-inline float32x4_t cv_vrecpq_f32(float32x4_t val)
-{
-    float32x4_t reciprocal = vrecpeq_f32(val);
-    reciprocal = vmulq_f32(vrecpsq_f32(val, reciprocal), reciprocal);
-    reciprocal = vmulq_f32(vrecpsq_f32(val, reciprocal), reciprocal);
-    return reciprocal;
-}
-
-inline float32x2_t cv_vrecp_f32(float32x2_t val)
-{
-    float32x2_t reciprocal = vrecpe_f32(val);
-    reciprocal = vmul_f32(vrecps_f32(val, reciprocal), reciprocal);
-    reciprocal = vmul_f32(vrecps_f32(val, reciprocal), reciprocal);
-    return reciprocal;
-}
-
-inline float32x4_t cv_vrsqrtq_f32(float32x4_t val)
-{
-    float32x4_t e = vrsqrteq_f32(val);
-    e = vmulq_f32(vrsqrtsq_f32(vmulq_f32(e, e), val), e);
-    e = vmulq_f32(vrsqrtsq_f32(vmulq_f32(e, e), val), e);
-    return e;
-}
-
-inline float32x2_t cv_vrsqrt_f32(float32x2_t val)
-{
-    float32x2_t e = vrsqrte_f32(val);
-    e = vmul_f32(vrsqrts_f32(vmul_f32(e, e), val), e);
-    e = vmul_f32(vrsqrts_f32(vmul_f32(e, e), val), e);
-    return e;
-}
-
-inline float32x4_t cv_vsqrtq_f32(float32x4_t val)
-{
-    return cv_vrecpq_f32(cv_vrsqrtq_f32(val));
-}
-
-inline float32x2_t cv_vsqrt_f32(float32x2_t val)
-{
-    return cv_vrecp_f32(cv_vrsqrt_f32(val));
-}
-
-#endif
-
-//! @}
-
-#endif // OPENCV_HAL_NEON_UTILS_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/ocl.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/ocl.hpp
deleted file mode 100755
index 888477e..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/ocl.hpp
+++ /dev/null
@@ -1,842 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the OpenCV Foundation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_OPENCL_HPP
-#define OPENCV_OPENCL_HPP
-
-#include "opencv2/core.hpp"
-
-namespace cv { namespace ocl {
-
-//! @addtogroup core_opencl
-//! @{
-
-CV_EXPORTS_W bool haveOpenCL();
-CV_EXPORTS_W bool useOpenCL();
-CV_EXPORTS_W bool haveAmdBlas();
-CV_EXPORTS_W bool haveAmdFft();
-CV_EXPORTS_W void setUseOpenCL(bool flag);
-CV_EXPORTS_W void finish();
-
-CV_EXPORTS bool haveSVM();
-
-class CV_EXPORTS Context;
-class CV_EXPORTS Device;
-class CV_EXPORTS Kernel;
-class CV_EXPORTS Program;
-class CV_EXPORTS ProgramSource;
-class CV_EXPORTS Queue;
-class CV_EXPORTS PlatformInfo;
-class CV_EXPORTS Image2D;
-
-class CV_EXPORTS Device
-{
-public:
-    Device();
-    explicit Device(void* d);
-    Device(const Device& d);
-    Device& operator = (const Device& d);
-    ~Device();
-
-    void set(void* d);
-
-    enum
-    {
-        TYPE_DEFAULT     = (1 << 0),
-        TYPE_CPU         = (1 << 1),
-        TYPE_GPU         = (1 << 2),
-        TYPE_ACCELERATOR = (1 << 3),
-        TYPE_DGPU        = TYPE_GPU + (1 << 16),
-        TYPE_IGPU        = TYPE_GPU + (1 << 17),
-        TYPE_ALL         = 0xFFFFFFFF
-    };
-
-    String name() const;
-    String extensions() const;
-    bool isExtensionSupported(const String& extensionName) const;
-    String version() const;
-    String vendorName() const;
-    String OpenCL_C_Version() const;
-    String OpenCLVersion() const;
-    int deviceVersionMajor() const;
-    int deviceVersionMinor() const;
-    String driverVersion() const;
-    void* ptr() const;
-
-    int type() const;
-
-    int addressBits() const;
-    bool available() const;
-    bool compilerAvailable() const;
-    bool linkerAvailable() const;
-
-    enum
-    {
-        FP_DENORM=(1 << 0),
-        FP_INF_NAN=(1 << 1),
-        FP_ROUND_TO_NEAREST=(1 << 2),
-        FP_ROUND_TO_ZERO=(1 << 3),
-        FP_ROUND_TO_INF=(1 << 4),
-        FP_FMA=(1 << 5),
-        FP_SOFT_FLOAT=(1 << 6),
-        FP_CORRECTLY_ROUNDED_DIVIDE_SQRT=(1 << 7)
-    };
-    int doubleFPConfig() const;
-    int singleFPConfig() const;
-    int halfFPConfig() const;
-
-    bool endianLittle() const;
-    bool errorCorrectionSupport() const;
-
-    enum
-    {
-        EXEC_KERNEL=(1 << 0),
-        EXEC_NATIVE_KERNEL=(1 << 1)
-    };
-    int executionCapabilities() const;
-
-    size_t globalMemCacheSize() const;
-
-    enum
-    {
-        NO_CACHE=0,
-        READ_ONLY_CACHE=1,
-        READ_WRITE_CACHE=2
-    };
-    int globalMemCacheType() const;
-    int globalMemCacheLineSize() const;
-    size_t globalMemSize() const;
-
-    size_t localMemSize() const;
-    enum
-    {
-        NO_LOCAL_MEM=0,
-        LOCAL_IS_LOCAL=1,
-        LOCAL_IS_GLOBAL=2
-    };
-    int localMemType() const;
-    bool hostUnifiedMemory() const;
-
-    bool imageSupport() const;
-
-    bool imageFromBufferSupport() const;
-    uint imagePitchAlignment() const;
-    uint imageBaseAddressAlignment() const;
-
-    /// deprecated, use isExtensionSupported() method (probably with "cl_khr_subgroups" value)
-    bool intelSubgroupsSupport() const;
-
-    size_t image2DMaxWidth() const;
-    size_t image2DMaxHeight() const;
-
-    size_t image3DMaxWidth() const;
-    size_t image3DMaxHeight() const;
-    size_t image3DMaxDepth() const;
-
-    size_t imageMaxBufferSize() const;
-    size_t imageMaxArraySize() const;
-
-    enum
-    {
-        UNKNOWN_VENDOR=0,
-        VENDOR_AMD=1,
-        VENDOR_INTEL=2,
-        VENDOR_NVIDIA=3
-    };
-    int vendorID() const;
-    // FIXIT
-    // dev.isAMD() doesn't work for OpenCL CPU devices from AMD OpenCL platform.
-    // This method should use platform name instead of vendor name.
-    // After fix restore code in arithm.cpp: ocl_compare()
-    inline bool isAMD() const { return vendorID() == VENDOR_AMD; }
-    inline bool isIntel() const { return vendorID() == VENDOR_INTEL; }
-    inline bool isNVidia() const { return vendorID() == VENDOR_NVIDIA; }
-
-    int maxClockFrequency() const;
-    int maxComputeUnits() const;
-    int maxConstantArgs() const;
-    size_t maxConstantBufferSize() const;
-
-    size_t maxMemAllocSize() const;
-    size_t maxParameterSize() const;
-
-    int maxReadImageArgs() const;
-    int maxWriteImageArgs() const;
-    int maxSamplers() const;
-
-    size_t maxWorkGroupSize() const;
-    int maxWorkItemDims() const;
-    void maxWorkItemSizes(size_t*) const;
-
-    int memBaseAddrAlign() const;
-
-    int nativeVectorWidthChar() const;
-    int nativeVectorWidthShort() const;
-    int nativeVectorWidthInt() const;
-    int nativeVectorWidthLong() const;
-    int nativeVectorWidthFloat() const;
-    int nativeVectorWidthDouble() const;
-    int nativeVectorWidthHalf() const;
-
-    int preferredVectorWidthChar() const;
-    int preferredVectorWidthShort() const;
-    int preferredVectorWidthInt() const;
-    int preferredVectorWidthLong() const;
-    int preferredVectorWidthFloat() const;
-    int preferredVectorWidthDouble() const;
-    int preferredVectorWidthHalf() const;
-
-    size_t printfBufferSize() const;
-    size_t profilingTimerResolution() const;
-
-    static const Device& getDefault();
-
-protected:
-    struct Impl;
-    Impl* p;
-};
-
-
-class CV_EXPORTS Context
-{
-public:
-    Context();
-    explicit Context(int dtype);
-    ~Context();
-    Context(const Context& c);
-    Context& operator = (const Context& c);
-
-    bool create();
-    bool create(int dtype);
-    size_t ndevices() const;
-    const Device& device(size_t idx) const;
-    Program getProg(const ProgramSource& prog,
-                    const String& buildopt, String& errmsg);
-    void unloadProg(Program& prog);
-
-    static Context& getDefault(bool initialize = true);
-    void* ptr() const;
-
-    friend void initializeContextFromHandle(Context& ctx, void* platform, void* context, void* device);
-
-    bool useSVM() const;
-    void setUseSVM(bool enabled);
-
-    struct Impl;
-    inline Impl* getImpl() const { return (Impl*)p; }
-//protected:
-    Impl* p;
-};
-
-class CV_EXPORTS Platform
-{
-public:
-    Platform();
-    ~Platform();
-    Platform(const Platform& p);
-    Platform& operator = (const Platform& p);
-
-    void* ptr() const;
-    static Platform& getDefault();
-
-    friend void initializeContextFromHandle(Context& ctx, void* platform, void* context, void* device);
-protected:
-    struct Impl;
-    Impl* p;
-};
-
-/** @brief Attaches OpenCL context to OpenCV
-@note
-  OpenCV will check if available OpenCL platform has platformName name, then assign context to
-  OpenCV and call `clRetainContext` function. The deviceID device will be used as target device and
-  new command queue will be created.
-@param platformName name of OpenCL platform to attach, this string is used to check if platform is available to OpenCV at runtime
-@param platformID ID of platform attached context was created for
-@param context OpenCL context to be attached to OpenCV
-@param deviceID ID of device, must be created from attached context
-*/
-CV_EXPORTS void attachContext(const String& platformName, void* platformID, void* context, void* deviceID);
-
-/** @brief Convert OpenCL buffer to UMat
-@note
-  OpenCL buffer (cl_mem_buffer) should contain 2D image data, compatible with OpenCV. Memory
-  content is not copied from `clBuffer` to UMat. Instead, buffer handle assigned to UMat and
-  `clRetainMemObject` is called.
-@param cl_mem_buffer source clBuffer handle
-@param step num of bytes in single row
-@param rows number of rows
-@param cols number of cols
-@param type OpenCV type of image
-@param dst destination UMat
-*/
-CV_EXPORTS void convertFromBuffer(void* cl_mem_buffer, size_t step, int rows, int cols, int type, UMat& dst);
-
-/** @brief Convert OpenCL image2d_t to UMat
-@note
-  OpenCL `image2d_t` (cl_mem_image), should be compatible with OpenCV UMat formats. Memory content
-  is copied from image to UMat with `clEnqueueCopyImageToBuffer` function.
-@param cl_mem_image source image2d_t handle
-@param dst destination UMat
-*/
-CV_EXPORTS void convertFromImage(void* cl_mem_image, UMat& dst);
-
-// TODO Move to internal header
-void initializeContextFromHandle(Context& ctx, void* platform, void* context, void* device);
-
-class CV_EXPORTS Queue
-{
-public:
-    Queue();
-    explicit Queue(const Context& c, const Device& d=Device());
-    ~Queue();
-    Queue(const Queue& q);
-    Queue& operator = (const Queue& q);
-
-    bool create(const Context& c=Context(), const Device& d=Device());
-    void finish();
-    void* ptr() const;
-    static Queue& getDefault();
-
-    /// @brief Returns OpenCL command queue with enable profiling mode support
-    const Queue& getProfilingQueue() const;
-
-    struct Impl; friend struct Impl;
-    inline Impl* getImpl() const { return p; }
-protected:
-    Impl* p;
-};
-
-
-class CV_EXPORTS KernelArg
-{
-public:
-    enum { LOCAL=1, READ_ONLY=2, WRITE_ONLY=4, READ_WRITE=6, CONSTANT=8, PTR_ONLY = 16, NO_SIZE=256 };
-    KernelArg(int _flags, UMat* _m, int wscale=1, int iwscale=1, const void* _obj=0, size_t _sz=0);
-    KernelArg();
-
-    static KernelArg Local() { return KernelArg(LOCAL, 0); }
-    static KernelArg PtrWriteOnly(const UMat& m)
-    { return KernelArg(PTR_ONLY+WRITE_ONLY, (UMat*)&m); }
-    static KernelArg PtrReadOnly(const UMat& m)
-    { return KernelArg(PTR_ONLY+READ_ONLY, (UMat*)&m); }
-    static KernelArg PtrReadWrite(const UMat& m)
-    { return KernelArg(PTR_ONLY+READ_WRITE, (UMat*)&m); }
-    static KernelArg ReadWrite(const UMat& m, int wscale=1, int iwscale=1)
-    { return KernelArg(READ_WRITE, (UMat*)&m, wscale, iwscale); }
-    static KernelArg ReadWriteNoSize(const UMat& m, int wscale=1, int iwscale=1)
-    { return KernelArg(READ_WRITE+NO_SIZE, (UMat*)&m, wscale, iwscale); }
-    static KernelArg ReadOnly(const UMat& m, int wscale=1, int iwscale=1)
-    { return KernelArg(READ_ONLY, (UMat*)&m, wscale, iwscale); }
-    static KernelArg WriteOnly(const UMat& m, int wscale=1, int iwscale=1)
-    { return KernelArg(WRITE_ONLY, (UMat*)&m, wscale, iwscale); }
-    static KernelArg ReadOnlyNoSize(const UMat& m, int wscale=1, int iwscale=1)
-    { return KernelArg(READ_ONLY+NO_SIZE, (UMat*)&m, wscale, iwscale); }
-    static KernelArg WriteOnlyNoSize(const UMat& m, int wscale=1, int iwscale=1)
-    { return KernelArg(WRITE_ONLY+NO_SIZE, (UMat*)&m, wscale, iwscale); }
-    static KernelArg Constant(const Mat& m);
-    template<typename _Tp> static KernelArg Constant(const _Tp* arr, size_t n)
-    { return KernelArg(CONSTANT, 0, 1, 1, (void*)arr, n); }
-
-    int flags;
-    UMat* m;
-    const void* obj;
-    size_t sz;
-    int wscale, iwscale;
-};
-
-
-class CV_EXPORTS Kernel
-{
-public:
-    Kernel();
-    Kernel(const char* kname, const Program& prog);
-    Kernel(const char* kname, const ProgramSource& prog,
-           const String& buildopts = String(), String* errmsg=0);
-    ~Kernel();
-    Kernel(const Kernel& k);
-    Kernel& operator = (const Kernel& k);
-
-    bool empty() const;
-    bool create(const char* kname, const Program& prog);
-    bool create(const char* kname, const ProgramSource& prog,
-                const String& buildopts, String* errmsg=0);
-
-    int set(int i, const void* value, size_t sz);
-    int set(int i, const Image2D& image2D);
-    int set(int i, const UMat& m);
-    int set(int i, const KernelArg& arg);
-    template<typename _Tp> int set(int i, const _Tp& value)
-    { return set(i, &value, sizeof(value)); }
-
-    template<typename _Tp0>
-    Kernel& args(const _Tp0& a0)
-    {
-        set(0, a0); return *this;
-    }
-
-    template<typename _Tp0, typename _Tp1>
-    Kernel& args(const _Tp0& a0, const _Tp1& a1)
-    {
-        int i = set(0, a0); set(i, a1); return *this;
-    }
-
-    template<typename _Tp0, typename _Tp1, typename _Tp2>
-    Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2)
-    {
-        int i = set(0, a0); i = set(i, a1); set(i, a2); return *this;
-    }
-
-    template<typename _Tp0, typename _Tp1, typename _Tp2, typename _Tp3>
-    Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3)
-    {
-        int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); return *this;
-    }
-
-    template<typename _Tp0, typename _Tp1, typename _Tp2, typename _Tp3, typename _Tp4>
-    Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2,
-                 const _Tp3& a3, const _Tp4& a4)
-    {
-        int i = set(0, a0); i = set(i, a1); i = set(i, a2);
-        i = set(i, a3); set(i, a4); return *this;
-    }
-
-    template<typename _Tp0, typename _Tp1, typename _Tp2,
-             typename _Tp3, typename _Tp4, typename _Tp5>
-    Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2,
-                 const _Tp3& a3, const _Tp4& a4, const _Tp5& a5)
-    {
-        int i = set(0, a0); i = set(i, a1); i = set(i, a2);
-        i = set(i, a3); i = set(i, a4); set(i, a5); return *this;
-    }
-
-    template<typename _Tp0, typename _Tp1, typename _Tp2, typename _Tp3,
-             typename _Tp4, typename _Tp5, typename _Tp6>
-    Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3,
-                 const _Tp4& a4, const _Tp5& a5, const _Tp6& a6)
-    {
-        int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3);
-        i = set(i, a4); i = set(i, a5); set(i, a6); return *this;
-    }
-
-    template<typename _Tp0, typename _Tp1, typename _Tp2, typename _Tp3,
-             typename _Tp4, typename _Tp5, typename _Tp6, typename _Tp7>
-    Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3,
-                 const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7)
-    {
-        int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3);
-        i = set(i, a4); i = set(i, a5); i = set(i, a6); set(i, a7); return *this;
-    }
-
-    template<typename _Tp0, typename _Tp1, typename _Tp2, typename _Tp3, typename _Tp4,
-             typename _Tp5, typename _Tp6, typename _Tp7, typename _Tp8>
-    Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3,
-                 const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7,
-                 const _Tp8& a8)
-    {
-        int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); i = set(i, a4);
-        i = set(i, a5); i = set(i, a6); i = set(i, a7); set(i, a8); return *this;
-    }
-
-    template<typename _Tp0, typename _Tp1, typename _Tp2, typename _Tp3, typename _Tp4,
-             typename _Tp5, typename _Tp6, typename _Tp7, typename _Tp8, typename _Tp9>
-    Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3,
-                 const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7,
-                 const _Tp8& a8, const _Tp9& a9)
-    {
-        int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); i = set(i, a4); i = set(i, a5);
-        i = set(i, a6); i = set(i, a7); i = set(i, a8); set(i, a9); return *this;
-    }
-
-    template<typename _Tp0, typename _Tp1, typename _Tp2, typename _Tp3,
-             typename _Tp4, typename _Tp5, typename _Tp6, typename _Tp7,
-             typename _Tp8, typename _Tp9, typename _Tp10>
-    Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3,
-                 const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7,
-                 const _Tp8& a8, const _Tp9& a9, const _Tp10& a10)
-    {
-        int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); i = set(i, a4); i = set(i, a5);
-        i = set(i, a6); i = set(i, a7); i = set(i, a8); i = set(i, a9); set(i, a10); return *this;
-    }
-
-    template<typename _Tp0, typename _Tp1, typename _Tp2, typename _Tp3,
-             typename _Tp4, typename _Tp5, typename _Tp6, typename _Tp7,
-             typename _Tp8, typename _Tp9, typename _Tp10, typename _Tp11>
-    Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3,
-                 const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7,
-                 const _Tp8& a8, const _Tp9& a9, const _Tp10& a10, const _Tp11& a11)
-    {
-        int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); i = set(i, a4); i = set(i, a5);
-        i = set(i, a6); i = set(i, a7); i = set(i, a8); i = set(i, a9); i = set(i, a10); set(i, a11); return *this;
-    }
-
-    template<typename _Tp0, typename _Tp1, typename _Tp2, typename _Tp3,
-             typename _Tp4, typename _Tp5, typename _Tp6, typename _Tp7,
-             typename _Tp8, typename _Tp9, typename _Tp10, typename _Tp11, typename _Tp12>
-    Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3,
-                 const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7,
-                 const _Tp8& a8, const _Tp9& a9, const _Tp10& a10, const _Tp11& a11,
-                 const _Tp12& a12)
-    {
-        int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); i = set(i, a4); i = set(i, a5);
-        i = set(i, a6); i = set(i, a7); i = set(i, a8); i = set(i, a9); i = set(i, a10); i = set(i, a11);
-        set(i, a12); return *this;
-    }
-
-    template<typename _Tp0, typename _Tp1, typename _Tp2, typename _Tp3,
-             typename _Tp4, typename _Tp5, typename _Tp6, typename _Tp7,
-             typename _Tp8, typename _Tp9, typename _Tp10, typename _Tp11, typename _Tp12,
-             typename _Tp13>
-    Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3,
-                 const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7,
-                 const _Tp8& a8, const _Tp9& a9, const _Tp10& a10, const _Tp11& a11,
-                 const _Tp12& a12, const _Tp13& a13)
-    {
-        int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); i = set(i, a4); i = set(i, a5);
-        i = set(i, a6); i = set(i, a7); i = set(i, a8); i = set(i, a9); i = set(i, a10); i = set(i, a11);
-        i = set(i, a12); set(i, a13); return *this;
-    }
-
-    template<typename _Tp0, typename _Tp1, typename _Tp2, typename _Tp3,
-             typename _Tp4, typename _Tp5, typename _Tp6, typename _Tp7,
-             typename _Tp8, typename _Tp9, typename _Tp10, typename _Tp11, typename _Tp12,
-             typename _Tp13, typename _Tp14>
-    Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3,
-                 const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7,
-                 const _Tp8& a8, const _Tp9& a9, const _Tp10& a10, const _Tp11& a11,
-                 const _Tp12& a12, const _Tp13& a13, const _Tp14& a14)
-    {
-        int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); i = set(i, a4); i = set(i, a5);
-        i = set(i, a6); i = set(i, a7); i = set(i, a8); i = set(i, a9); i = set(i, a10); i = set(i, a11);
-        i = set(i, a12); i = set(i, a13); set(i, a14); return *this;
-    }
-
-    template<typename _Tp0, typename _Tp1, typename _Tp2, typename _Tp3,
-             typename _Tp4, typename _Tp5, typename _Tp6, typename _Tp7,
-             typename _Tp8, typename _Tp9, typename _Tp10, typename _Tp11, typename _Tp12,
-             typename _Tp13, typename _Tp14, typename _Tp15>
-    Kernel& args(const _Tp0& a0, const _Tp1& a1, const _Tp2& a2, const _Tp3& a3,
-                 const _Tp4& a4, const _Tp5& a5, const _Tp6& a6, const _Tp7& a7,
-                 const _Tp8& a8, const _Tp9& a9, const _Tp10& a10, const _Tp11& a11,
-                 const _Tp12& a12, const _Tp13& a13, const _Tp14& a14, const _Tp15& a15)
-    {
-        int i = set(0, a0); i = set(i, a1); i = set(i, a2); i = set(i, a3); i = set(i, a4); i = set(i, a5);
-        i = set(i, a6); i = set(i, a7); i = set(i, a8); i = set(i, a9); i = set(i, a10); i = set(i, a11);
-        i = set(i, a12); i = set(i, a13); i = set(i, a14); set(i, a15); return *this;
-    }
-    /** @brief Run the OpenCL kernel.
-    @param dims the work problem dimensions. It is the length of globalsize and localsize. It can be either 1, 2 or 3.
-    @param globalsize work items for each dimension. It is not the final globalsize passed to
-      OpenCL. Each dimension will be adjusted to the nearest integer divisible by the corresponding
-      value in localsize. If localsize is NULL, it will still be adjusted depending on dims. The
-      adjusted values are greater than or equal to the original values.
-    @param localsize work-group size for each dimension.
-    @param sync specify whether to wait for OpenCL computation to finish before return.
-    @param q command queue
-    */
-    bool run(int dims, size_t globalsize[],
-             size_t localsize[], bool sync, const Queue& q=Queue());
-    bool runTask(bool sync, const Queue& q=Queue());
-
-    /** @brief Similar to synchronized run() call with returning of kernel execution time
-     * Separate OpenCL command queue may be used (with CL_QUEUE_PROFILING_ENABLE)
-     * @return Execution time in nanoseconds or negative number on error
-     */
-    int64 runProfiling(int dims, size_t globalsize[], size_t localsize[], const Queue& q=Queue());
-
-    size_t workGroupSize() const;
-    size_t preferedWorkGroupSizeMultiple() const;
-    bool compileWorkGroupSize(size_t wsz[]) const;
-    size_t localMemSize() const;
-
-    void* ptr() const;
-    struct Impl;
-
-protected:
-    Impl* p;
-};
-
-class CV_EXPORTS Program
-{
-public:
-    Program();
-    Program(const ProgramSource& src,
-            const String& buildflags, String& errmsg);
-    Program(const Program& prog);
-
-    Program& operator = (const Program& prog);
-    ~Program();
-
-    bool create(const ProgramSource& src,
-                const String& buildflags, String& errmsg);
-
-    void* ptr() const;
-
-    /**
-     * @brief Query device-specific program binary.
-     *
-     * Returns RAW OpenCL executable binary without additional attachments.
-     *
-     * @sa ProgramSource::fromBinary
-     *
-     * @param[out] binary output buffer
-     */
-    void getBinary(std::vector<char>& binary) const;
-
-    struct Impl; friend struct Impl;
-    inline Impl* getImpl() const { return (Impl*)p; }
-protected:
-    Impl* p;
-public:
-#ifndef OPENCV_REMOVE_DEPRECATED_API
-    // TODO Remove this
-    CV_DEPRECATED bool read(const String& buf, const String& buildflags); // removed, use ProgramSource instead
-    CV_DEPRECATED bool write(String& buf) const; // removed, use getBinary() method instead (RAW OpenCL binary)
-    CV_DEPRECATED const ProgramSource& source() const; // implementation removed
-    CV_DEPRECATED String getPrefix() const; // deprecated, implementation replaced
-    CV_DEPRECATED static String getPrefix(const String& buildflags); // deprecated, implementation replaced
-#endif
-};
-
-
-class CV_EXPORTS ProgramSource
-{
-public:
-    typedef uint64 hash_t; // deprecated
-
-    ProgramSource();
-    explicit ProgramSource(const String& module, const String& name, const String& codeStr, const String& codeHash);
-    explicit ProgramSource(const String& prog); // deprecated
-    explicit ProgramSource(const char* prog); // deprecated
-    ~ProgramSource();
-    ProgramSource(const ProgramSource& prog);
-    ProgramSource& operator = (const ProgramSource& prog);
-
-    const String& source() const; // deprecated
-    hash_t hash() const; // deprecated
-
-
-    /** @brief Describe OpenCL program binary.
-     * Do not call clCreateProgramWithBinary() and/or clBuildProgram().
-     *
-     * Caller should guarantee binary buffer lifetime greater than ProgramSource object (and any of its copies).
-     *
-     * This kind of binary is not portable between platforms in general - it is specific to OpenCL vendor / device / driver version.
-     *
-     * @param module name of program owner module
-     * @param name unique name of program (module+name is used as key for OpenCL program caching)
-     * @param binary buffer address. See buffer lifetime requirement in description.
-     * @param size buffer size
-     * @param buildOptions additional program-related build options passed to clBuildProgram()
-     * @return created ProgramSource object
-     */
-    static ProgramSource fromBinary(const String& module, const String& name,
-            const unsigned char* binary, const size_t size,
-            const cv::String& buildOptions = cv::String());
-
-    /** @brief Describe OpenCL program in SPIR format.
-     * Do not call clCreateProgramWithBinary() and/or clBuildProgram().
-     *
-     * Supports SPIR 1.2 by default (pass '-spir-std=X.Y' in buildOptions to override this behavior)
-     *
-     * Caller should guarantee binary buffer lifetime greater than ProgramSource object (and any of its copies).
-     *
-     * Programs in this format are portable between OpenCL implementations with 'khr_spir' extension:
-     * https://www.khronos.org/registry/OpenCL/sdk/2.0/docs/man/xhtml/cl_khr_spir.html
-     * (but they are not portable between different platforms: 32-bit / 64-bit)
-     *
-     * Note: these programs can't support vendor specific extensions, like 'cl_intel_subgroups'.
-     *
-     * @param module name of program owner module
-     * @param name unique name of program (module+name is used as key for OpenCL program caching)
-     * @param binary buffer address. See buffer lifetime requirement in description.
-     * @param size buffer size
-     * @param buildOptions additional program-related build options passed to clBuildProgram()
-     *        (these options are added automatically: '-x spir' and '-spir-std=1.2')
-     * @return created ProgramSource object.
-     */
-    static ProgramSource fromSPIR(const String& module, const String& name,
-            const unsigned char* binary, const size_t size,
-            const cv::String& buildOptions = cv::String());
-
-    //OpenCL 2.1+ only
-    //static Program fromSPIRV(const String& module, const String& name,
-    //        const unsigned char* binary, const size_t size,
-    //        const cv::String& buildOptions = cv::String());
-
-    struct Impl; friend struct Impl;
-    inline Impl* getImpl() const { return (Impl*)p; }
-protected:
-    Impl* p;
-};
-
-class CV_EXPORTS PlatformInfo
-{
-public:
-    PlatformInfo();
-    explicit PlatformInfo(void* id);
-    ~PlatformInfo();
-
-    PlatformInfo(const PlatformInfo& i);
-    PlatformInfo& operator =(const PlatformInfo& i);
-
-    String name() const;
-    String vendor() const;
-    String version() const;
-    int deviceNumber() const;
-    void getDevice(Device& device, int d) const;
-
-protected:
-    struct Impl;
-    Impl* p;
-};
-
-CV_EXPORTS const char* convertTypeStr(int sdepth, int ddepth, int cn, char* buf);
-CV_EXPORTS const char* typeToStr(int t);
-CV_EXPORTS const char* memopTypeToStr(int t);
-CV_EXPORTS const char* vecopTypeToStr(int t);
-CV_EXPORTS const char* getOpenCLErrorString(int errorCode);
-CV_EXPORTS String kernelToStr(InputArray _kernel, int ddepth = -1, const char * name = NULL);
-CV_EXPORTS void getPlatfomsInfo(std::vector<PlatformInfo>& platform_info);
-
-
-enum OclVectorStrategy
-{
-    // all matrices have its own vector width
-    OCL_VECTOR_OWN = 0,
-    // all matrices have maximal vector width among all matrices
-    // (useful for cases when matrices have different data types)
-    OCL_VECTOR_MAX = 1,
-
-    // default strategy
-    OCL_VECTOR_DEFAULT = OCL_VECTOR_OWN
-};
-
-CV_EXPORTS int predictOptimalVectorWidth(InputArray src1, InputArray src2 = noArray(), InputArray src3 = noArray(),
-                                         InputArray src4 = noArray(), InputArray src5 = noArray(), InputArray src6 = noArray(),
-                                         InputArray src7 = noArray(), InputArray src8 = noArray(), InputArray src9 = noArray(),
-                                         OclVectorStrategy strat = OCL_VECTOR_DEFAULT);
-
-CV_EXPORTS int checkOptimalVectorWidth(const int *vectorWidths,
-                                       InputArray src1, InputArray src2 = noArray(), InputArray src3 = noArray(),
-                                       InputArray src4 = noArray(), InputArray src5 = noArray(), InputArray src6 = noArray(),
-                                       InputArray src7 = noArray(), InputArray src8 = noArray(), InputArray src9 = noArray(),
-                                       OclVectorStrategy strat = OCL_VECTOR_DEFAULT);
-
-// with OCL_VECTOR_MAX strategy
-CV_EXPORTS int predictOptimalVectorWidthMax(InputArray src1, InputArray src2 = noArray(), InputArray src3 = noArray(),
-                                            InputArray src4 = noArray(), InputArray src5 = noArray(), InputArray src6 = noArray(),
-                                            InputArray src7 = noArray(), InputArray src8 = noArray(), InputArray src9 = noArray());
-
-CV_EXPORTS void buildOptionsAddMatrixDescription(String& buildOptions, const String& name, InputArray _m);
-
-class CV_EXPORTS Image2D
-{
-public:
-    Image2D();
-
-    /**
-    @param src UMat object from which to get image properties and data
-    @param norm flag to enable the use of normalized channel data types
-    @param alias flag indicating that the image should alias the src UMat. If true, changes to the
-        image or src will be reflected in both objects.
-    */
-    explicit Image2D(const UMat &src, bool norm = false, bool alias = false);
-    Image2D(const Image2D & i);
-    ~Image2D();
-
-    Image2D & operator = (const Image2D & i);
-
-    /** Indicates if creating an aliased image should succeed.
-    Depends on the underlying platform and the dimensions of the UMat.
-    */
-    static bool canCreateAlias(const UMat &u);
-
-    /** Indicates if the image format is supported.
-    */
-    static bool isFormatSupported(int depth, int cn, bool norm);
-
-    void* ptr() const;
-protected:
-    struct Impl;
-    Impl* p;
-};
-
-class CV_EXPORTS Timer
-{
-public:
-    Timer(const Queue& q);
-    ~Timer();
-    void start();
-    void stop();
-
-    uint64 durationNS() const; //< duration in nanoseconds
-
-protected:
-    struct Impl;
-    Impl* const p;
-
-private:
-    Timer(const Timer&); // disabled
-    Timer& operator=(const Timer&); // disabled
-};
-
-CV_EXPORTS MatAllocator* getOpenCLAllocator();
-
-
-#ifdef __OPENCV_BUILD
-namespace internal {
-
-CV_EXPORTS bool isOpenCLForced();
-#define OCL_FORCE_CHECK(condition) (cv::ocl::internal::isOpenCLForced() || (condition))
-
-CV_EXPORTS bool isPerformanceCheckBypassed();
-#define OCL_PERFORMANCE_CHECK(condition) (cv::ocl::internal::isPerformanceCheckBypassed() || (condition))
-
-CV_EXPORTS bool isCLBuffer(UMat& u);
-
-} // namespace internal
-#endif
-
-//! @}
-
-}}
-
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/ocl_genbase.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/ocl_genbase.hpp
deleted file mode 100755
index 5334cf1..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/ocl_genbase.hpp
+++ /dev/null
@@ -1,69 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the OpenCV Foundation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_OPENCL_GENBASE_HPP
-#define OPENCV_OPENCL_GENBASE_HPP
-
-//! @cond IGNORED
-
-namespace cv {
-namespace ocl {
-
-class ProgramSource;
-
-namespace internal {
-
-struct CV_EXPORTS ProgramEntry
-{
-    const char* module;
-    const char* name;
-    const char* programCode;
-    const char* programHash;
-    ProgramSource* pProgramSource;
-
-    operator ProgramSource& () const;
-};
-
-} } } // namespace
-
-//! @endcond
-
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/opencl/ocl_defs.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/opencl/ocl_defs.hpp
deleted file mode 100755
index 605a65f..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/opencl/ocl_defs.hpp
+++ /dev/null
@@ -1,75 +0,0 @@
-// This file is part of OpenCV project.
-// It is subject to the license terms in the LICENSE file found in the top-level directory
-// of this distribution and at http://opencv.org/license.html.
-
-// Copyright (C) 2014, Advanced Micro Devices, Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-
-#ifndef OPENCV_CORE_OPENCL_DEFS_HPP
-#define OPENCV_CORE_OPENCL_DEFS_HPP
-
-#include "opencv2/core/utility.hpp"
-#include "cvconfig.h"
-
-namespace cv { namespace ocl {
-#ifdef HAVE_OPENCL
-/// Call is similar to useOpenCL() but doesn't try to load OpenCL runtime or create OpenCL context
-CV_EXPORTS bool isOpenCLActivated();
-#else
-static inline bool isOpenCLActivated() { return false; }
-#endif
-}} // namespace
-
-
-//#define CV_OPENCL_RUN_ASSERT
-
-#ifdef HAVE_OPENCL
-
-#ifdef CV_OPENCL_RUN_VERBOSE
-#define CV_OCL_RUN_(condition, func, ...)                                   \
-    {                                                                       \
-        if (cv::ocl::isOpenCLActivated() && (condition) && func)            \
-        {                                                                   \
-            printf("%s: OpenCL implementation is running\n", CV_Func);      \
-            fflush(stdout);                                                 \
-            CV_IMPL_ADD(CV_IMPL_OCL);                                       \
-            return __VA_ARGS__;                                             \
-        }                                                                   \
-        else                                                                \
-        {                                                                   \
-            printf("%s: Plain implementation is running\n", CV_Func);       \
-            fflush(stdout);                                                 \
-        }                                                                   \
-    }
-#elif defined CV_OPENCL_RUN_ASSERT
-#define CV_OCL_RUN_(condition, func, ...)                                   \
-    {                                                                       \
-        if (cv::ocl::isOpenCLActivated() && (condition))                    \
-        {                                                                   \
-            if(func)                                                        \
-            {                                                               \
-                CV_IMPL_ADD(CV_IMPL_OCL);                                   \
-            }                                                               \
-            else                                                            \
-            {                                                               \
-                CV_Error(cv::Error::StsAssert, #func);                      \
-            }                                                               \
-            return __VA_ARGS__;                                             \
-        }                                                                   \
-    }
-#else
-#define CV_OCL_RUN_(condition, func, ...)                                   \
-    if (cv::ocl::isOpenCLActivated() && (condition) && func)                \
-    {                                                                       \
-        CV_IMPL_ADD(CV_IMPL_OCL);                                           \
-        return __VA_ARGS__;                                                 \
-    }
-#endif
-
-#else
-#define CV_OCL_RUN_(condition, func, ...)
-#endif
-
-#define CV_OCL_RUN(condition, func) CV_OCL_RUN_(condition, func)
-
-#endif // OPENCV_CORE_OPENCL_DEFS_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/opencl/opencl_info.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/opencl/opencl_info.hpp
deleted file mode 100755
index b5d3739..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/opencl/opencl_info.hpp
+++ /dev/null
@@ -1,198 +0,0 @@
-// This file is part of OpenCV project.
-// It is subject to the license terms in the LICENSE file found in the top-level directory
-// of this distribution and at http://opencv.org/license.html.
-
-#include <iostream>
-
-#include <opencv2/core.hpp>
-#include <opencv2/core/ocl.hpp>
-
-#ifndef DUMP_CONFIG_PROPERTY
-#define DUMP_CONFIG_PROPERTY(...)
-#endif
-
-#ifndef DUMP_MESSAGE_STDOUT
-#define DUMP_MESSAGE_STDOUT(...) do { std::cout << __VA_ARGS__ << std::endl; } while (false)
-#endif
-
-namespace cv {
-
-namespace {
-static std::string bytesToStringRepr(size_t value)
-{
-    size_t b = value % 1024;
-    value /= 1024;
-
-    size_t kb = value % 1024;
-    value /= 1024;
-
-    size_t mb = value % 1024;
-    value /= 1024;
-
-    size_t gb = value;
-
-    std::ostringstream stream;
-
-    if (gb > 0)
-        stream << gb << " GB ";
-    if (mb > 0)
-        stream << mb << " MB ";
-    if (kb > 0)
-        stream << kb << " KB ";
-    if (b > 0)
-        stream << b << " B";
-
-    std::string s = stream.str();
-    if (s[s.size() - 1] == ' ')
-        s = s.substr(0, s.size() - 1);
-    return s;
-}
-} // namespace
-
-static void dumpOpenCLInformation()
-{
-    using namespace cv::ocl;
-
-    try
-    {
-        if (!haveOpenCL() || !useOpenCL())
-        {
-            DUMP_MESSAGE_STDOUT("OpenCL is disabled");
-            DUMP_CONFIG_PROPERTY("cv_ocl", "disabled");
-            return;
-        }
-
-        std::vector<PlatformInfo> platforms;
-        cv::ocl::getPlatfomsInfo(platforms);
-        if (platforms.size() > 0)
-        {
-            DUMP_MESSAGE_STDOUT("OpenCL Platforms: ");
-            for (size_t i = 0; i < platforms.size(); i++)
-            {
-                const PlatformInfo* platform = &platforms[i];
-                DUMP_MESSAGE_STDOUT("    " << platform->name().c_str());
-                Device current_device;
-                for (int j = 0; j < platform->deviceNumber(); j++)
-                {
-                    platform->getDevice(current_device, j);
-                    const char* deviceTypeStr = current_device.type() == Device::TYPE_CPU
-                        ? ("CPU") : (current_device.type() == Device::TYPE_GPU ? current_device.hostUnifiedMemory() ? "iGPU" : "dGPU" : "unknown");
-                    DUMP_MESSAGE_STDOUT( "        " << deviceTypeStr << ": " << current_device.name().c_str() << " (" << current_device.version().c_str() << ")");
-                    DUMP_CONFIG_PROPERTY( cv::format("cv_ocl_platform_%d_device_%d", (int)i, (int)j ),
-                        cv::format("(Platform=%s)(Type=%s)(Name=%s)(Version=%s)",
-                        platform->name().c_str(), deviceTypeStr, current_device.name().c_str(), current_device.version().c_str()) );
-                }
-            }
-        }
-        else
-        {
-            DUMP_MESSAGE_STDOUT("OpenCL is not available");
-            DUMP_CONFIG_PROPERTY("cv_ocl", "not available");
-            return;
-        }
-
-        const Device& device = Device::getDefault();
-        if (!device.available())
-            CV_Error(Error::OpenCLInitError, "OpenCL device is not available");
-
-        DUMP_MESSAGE_STDOUT("Current OpenCL device: ");
-
-#if 0
-        DUMP_MESSAGE_STDOUT("    Platform = " << device.getPlatform().name());
-        DUMP_CONFIG_PROPERTY("cv_ocl_current_platformName", device.getPlatform().name());
-#endif
-
-        const char* deviceTypeStr = device.type() == Device::TYPE_CPU
-            ? ("CPU") : (device.type() == Device::TYPE_GPU ? device.hostUnifiedMemory() ? "iGPU" : "dGPU" : "unknown");
-        DUMP_MESSAGE_STDOUT("    Type = " << deviceTypeStr);
-        DUMP_CONFIG_PROPERTY("cv_ocl_current_deviceType", deviceTypeStr);
-
-        DUMP_MESSAGE_STDOUT("    Name = " << device.name());
-        DUMP_CONFIG_PROPERTY("cv_ocl_current_deviceName", device.name());
-
-        DUMP_MESSAGE_STDOUT("    Version = " << device.version());
-        DUMP_CONFIG_PROPERTY("cv_ocl_current_deviceVersion", device.version());
-
-        DUMP_MESSAGE_STDOUT("    Driver version = " << device.driverVersion());
-        DUMP_CONFIG_PROPERTY("cv_ocl_current_driverVersion", device.driverVersion());
-
-        DUMP_MESSAGE_STDOUT("    Address bits = " << device.addressBits());
-        DUMP_CONFIG_PROPERTY("cv_ocl_current_addressBits", device.addressBits());
-
-        DUMP_MESSAGE_STDOUT("    Compute units = " << device.maxComputeUnits());
-        DUMP_CONFIG_PROPERTY("cv_ocl_current_maxComputeUnits", device.maxComputeUnits());
-
-        DUMP_MESSAGE_STDOUT("    Max work group size = " << device.maxWorkGroupSize());
-        DUMP_CONFIG_PROPERTY("cv_ocl_current_maxWorkGroupSize", device.maxWorkGroupSize());
-
-        std::string localMemorySizeStr = bytesToStringRepr(device.localMemSize());
-        DUMP_MESSAGE_STDOUT("    Local memory size = " << localMemorySizeStr);
-        DUMP_CONFIG_PROPERTY("cv_ocl_current_localMemSize", device.localMemSize());
-
-        std::string maxMemAllocSizeStr = bytesToStringRepr(device.maxMemAllocSize());
-        DUMP_MESSAGE_STDOUT("    Max memory allocation size = " << maxMemAllocSizeStr);
-        DUMP_CONFIG_PROPERTY("cv_ocl_current_maxMemAllocSize", device.maxMemAllocSize());
-
-        const char* doubleSupportStr = device.doubleFPConfig() > 0 ? "Yes" : "No";
-        DUMP_MESSAGE_STDOUT("    Double support = " << doubleSupportStr);
-        DUMP_CONFIG_PROPERTY("cv_ocl_current_haveDoubleSupport", device.doubleFPConfig() > 0);
-
-        const char* isUnifiedMemoryStr = device.hostUnifiedMemory() ? "Yes" : "No";
-        DUMP_MESSAGE_STDOUT("    Host unified memory = " << isUnifiedMemoryStr);
-        DUMP_CONFIG_PROPERTY("cv_ocl_current_hostUnifiedMemory", device.hostUnifiedMemory());
-
-        DUMP_MESSAGE_STDOUT("    Device extensions:");
-        String extensionsStr = device.extensions();
-        size_t pos = 0;
-        while (pos < extensionsStr.size())
-        {
-            size_t pos2 = extensionsStr.find(' ', pos);
-            if (pos2 == String::npos)
-                pos2 = extensionsStr.size();
-            if (pos2 > pos)
-            {
-                String extensionName = extensionsStr.substr(pos, pos2 - pos);
-                DUMP_MESSAGE_STDOUT("        " << extensionName);
-            }
-            pos = pos2 + 1;
-        }
-        DUMP_CONFIG_PROPERTY("cv_ocl_current_extensions", extensionsStr.c_str());
-
-        const char* haveAmdBlasStr = haveAmdBlas() ? "Yes" : "No";
-        DUMP_MESSAGE_STDOUT("    Has AMD Blas = " << haveAmdBlasStr);
-        DUMP_CONFIG_PROPERTY("cv_ocl_current_AmdBlas", haveAmdBlas());
-
-        const char* haveAmdFftStr = haveAmdFft() ? "Yes" : "No";
-        DUMP_MESSAGE_STDOUT("    Has AMD Fft = " << haveAmdFftStr);
-        DUMP_CONFIG_PROPERTY("cv_ocl_current_AmdFft", haveAmdFft());
-
-
-        DUMP_MESSAGE_STDOUT("    Preferred vector width char = " << device.preferredVectorWidthChar());
-        DUMP_CONFIG_PROPERTY("cv_ocl_current_preferredVectorWidthChar", device.preferredVectorWidthChar());
-
-        DUMP_MESSAGE_STDOUT("    Preferred vector width short = " << device.preferredVectorWidthShort());
-        DUMP_CONFIG_PROPERTY("cv_ocl_current_preferredVectorWidthShort", device.preferredVectorWidthShort());
-
-        DUMP_MESSAGE_STDOUT("    Preferred vector width int = " << device.preferredVectorWidthInt());
-        DUMP_CONFIG_PROPERTY("cv_ocl_current_preferredVectorWidthInt", device.preferredVectorWidthInt());
-
-        DUMP_MESSAGE_STDOUT("    Preferred vector width long = " << device.preferredVectorWidthLong());
-        DUMP_CONFIG_PROPERTY("cv_ocl_current_preferredVectorWidthLong", device.preferredVectorWidthLong());
-
-        DUMP_MESSAGE_STDOUT("    Preferred vector width float = " << device.preferredVectorWidthFloat());
-        DUMP_CONFIG_PROPERTY("cv_ocl_current_preferredVectorWidthFloat", device.preferredVectorWidthFloat());
-
-        DUMP_MESSAGE_STDOUT("    Preferred vector width double = " << device.preferredVectorWidthDouble());
-        DUMP_CONFIG_PROPERTY("cv_ocl_current_preferredVectorWidthDouble", device.preferredVectorWidthDouble());
-    }
-    catch (...)
-    {
-        DUMP_MESSAGE_STDOUT("Exception. Can't dump OpenCL info");
-        DUMP_MESSAGE_STDOUT("OpenCL device not available");
-        DUMP_CONFIG_PROPERTY("cv_ocl", "not available");
-    }
-}
-#undef DUMP_MESSAGE_STDOUT
-#undef DUMP_CONFIG_PROPERTY
-
-} // namespace
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/opencl/opencl_svm.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/opencl/opencl_svm.hpp
deleted file mode 100755
index 7453082..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/opencl/opencl_svm.hpp
+++ /dev/null
@@ -1,81 +0,0 @@
-/* See LICENSE file in the root OpenCV directory */
-
-#ifndef OPENCV_CORE_OPENCL_SVM_HPP
-#define OPENCV_CORE_OPENCL_SVM_HPP
-
-//
-// Internal usage only (binary compatibility is not guaranteed)
-//
-#ifndef __OPENCV_BUILD
-#error Internal header file
-#endif
-
-#if defined(HAVE_OPENCL) && defined(HAVE_OPENCL_SVM)
-#include "runtime/opencl_core.hpp"
-#include "runtime/opencl_svm_20.hpp"
-#include "runtime/opencl_svm_hsa_extension.hpp"
-
-namespace cv { namespace ocl { namespace svm {
-
-struct SVMCapabilities
-{
-    enum Value
-    {
-        SVM_COARSE_GRAIN_BUFFER = (1 << 0),
-        SVM_FINE_GRAIN_BUFFER = (1 << 1),
-        SVM_FINE_GRAIN_SYSTEM = (1 << 2),
-        SVM_ATOMICS = (1 << 3),
-    };
-    int value_;
-
-    SVMCapabilities(int capabilities = 0) : value_(capabilities) { }
-    operator int() const { return value_; }
-
-    inline bool isNoSVMSupport() const { return value_ == 0; }
-    inline bool isSupportCoarseGrainBuffer() const { return (value_ & SVM_COARSE_GRAIN_BUFFER) != 0; }
-    inline bool isSupportFineGrainBuffer() const { return (value_ & SVM_FINE_GRAIN_BUFFER) != 0; }
-    inline bool isSupportFineGrainSystem() const { return (value_ & SVM_FINE_GRAIN_SYSTEM) != 0; }
-    inline bool isSupportAtomics() const { return (value_ & SVM_ATOMICS) != 0; }
-};
-
-CV_EXPORTS const SVMCapabilities getSVMCapabilitites(const ocl::Context& context);
-
-struct SVMFunctions
-{
-    clSVMAllocAMD_fn fn_clSVMAlloc;
-    clSVMFreeAMD_fn fn_clSVMFree;
-    clSetKernelArgSVMPointerAMD_fn fn_clSetKernelArgSVMPointer;
-    //clSetKernelExecInfoAMD_fn fn_clSetKernelExecInfo;
-    //clEnqueueSVMFreeAMD_fn fn_clEnqueueSVMFree;
-    clEnqueueSVMMemcpyAMD_fn fn_clEnqueueSVMMemcpy;
-    clEnqueueSVMMemFillAMD_fn fn_clEnqueueSVMMemFill;
-    clEnqueueSVMMapAMD_fn fn_clEnqueueSVMMap;
-    clEnqueueSVMUnmapAMD_fn fn_clEnqueueSVMUnmap;
-
-    inline SVMFunctions()
-        : fn_clSVMAlloc(NULL), fn_clSVMFree(NULL),
-          fn_clSetKernelArgSVMPointer(NULL), /*fn_clSetKernelExecInfo(NULL),*/
-          /*fn_clEnqueueSVMFree(NULL),*/ fn_clEnqueueSVMMemcpy(NULL), fn_clEnqueueSVMMemFill(NULL),
-          fn_clEnqueueSVMMap(NULL), fn_clEnqueueSVMUnmap(NULL)
-    {
-        // nothing
-    }
-
-    inline bool isValid() const
-    {
-        return fn_clSVMAlloc != NULL && fn_clSVMFree && fn_clSetKernelArgSVMPointer &&
-                /*fn_clSetKernelExecInfo && fn_clEnqueueSVMFree &&*/ fn_clEnqueueSVMMemcpy &&
-                fn_clEnqueueSVMMemFill && fn_clEnqueueSVMMap && fn_clEnqueueSVMUnmap;
-    }
-};
-
-// We should guarantee that SVMFunctions lifetime is not less than context's lifetime
-CV_EXPORTS const SVMFunctions* getSVMFunctions(const ocl::Context& context);
-
-CV_EXPORTS bool useSVM(UMatUsageFlags usageFlags);
-
-}}} //namespace cv::ocl::svm
-#endif
-
-#endif // OPENCV_CORE_OPENCL_SVM_HPP
-/* End of file. */
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/opencl/runtime/autogenerated/opencl_clamdblas.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/opencl/runtime/autogenerated/opencl_clamdblas.hpp
deleted file mode 100755
index 65c8493..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/opencl/runtime/autogenerated/opencl_clamdblas.hpp
+++ /dev/null
@@ -1,714 +0,0 @@
-//
-// AUTOGENERATED, DO NOT EDIT
-//
-#ifndef OPENCV_CORE_OCL_RUNTIME_CLAMDBLAS_HPP
-#error "Invalid usage"
-#endif
-
-// generated by parser_clamdblas.py
-#define clAmdBlasAddScratchImage clAmdBlasAddScratchImage_
-#define clAmdBlasCaxpy clAmdBlasCaxpy_
-#define clAmdBlasCcopy clAmdBlasCcopy_
-#define clAmdBlasCdotc clAmdBlasCdotc_
-#define clAmdBlasCdotu clAmdBlasCdotu_
-#define clAmdBlasCgbmv clAmdBlasCgbmv_
-#define clAmdBlasCgemm clAmdBlasCgemm_
-#define clAmdBlasCgemmEx clAmdBlasCgemmEx_
-#define clAmdBlasCgemv clAmdBlasCgemv_
-#define clAmdBlasCgemvEx clAmdBlasCgemvEx_
-#define clAmdBlasCgerc clAmdBlasCgerc_
-#define clAmdBlasCgeru clAmdBlasCgeru_
-#define clAmdBlasChbmv clAmdBlasChbmv_
-#define clAmdBlasChemm clAmdBlasChemm_
-#define clAmdBlasChemv clAmdBlasChemv_
-#define clAmdBlasCher clAmdBlasCher_
-#define clAmdBlasCher2 clAmdBlasCher2_
-#define clAmdBlasCher2k clAmdBlasCher2k_
-#define clAmdBlasCherk clAmdBlasCherk_
-#define clAmdBlasChpmv clAmdBlasChpmv_
-#define clAmdBlasChpr clAmdBlasChpr_
-#define clAmdBlasChpr2 clAmdBlasChpr2_
-#define clAmdBlasCrotg clAmdBlasCrotg_
-#define clAmdBlasCscal clAmdBlasCscal_
-#define clAmdBlasCsrot clAmdBlasCsrot_
-#define clAmdBlasCsscal clAmdBlasCsscal_
-#define clAmdBlasCswap clAmdBlasCswap_
-#define clAmdBlasCsymm clAmdBlasCsymm_
-#define clAmdBlasCsyr2k clAmdBlasCsyr2k_
-#define clAmdBlasCsyr2kEx clAmdBlasCsyr2kEx_
-#define clAmdBlasCsyrk clAmdBlasCsyrk_
-#define clAmdBlasCsyrkEx clAmdBlasCsyrkEx_
-#define clAmdBlasCtbmv clAmdBlasCtbmv_
-#define clAmdBlasCtbsv clAmdBlasCtbsv_
-#define clAmdBlasCtpmv clAmdBlasCtpmv_
-#define clAmdBlasCtpsv clAmdBlasCtpsv_
-#define clAmdBlasCtrmm clAmdBlasCtrmm_
-#define clAmdBlasCtrmmEx clAmdBlasCtrmmEx_
-#define clAmdBlasCtrmv clAmdBlasCtrmv_
-#define clAmdBlasCtrsm clAmdBlasCtrsm_
-#define clAmdBlasCtrsmEx clAmdBlasCtrsmEx_
-#define clAmdBlasCtrsv clAmdBlasCtrsv_
-#define clAmdBlasDasum clAmdBlasDasum_
-#define clAmdBlasDaxpy clAmdBlasDaxpy_
-#define clAmdBlasDcopy clAmdBlasDcopy_
-#define clAmdBlasDdot clAmdBlasDdot_
-#define clAmdBlasDgbmv clAmdBlasDgbmv_
-#define clAmdBlasDgemm clAmdBlasDgemm_
-#define clAmdBlasDgemmEx clAmdBlasDgemmEx_
-#define clAmdBlasDgemv clAmdBlasDgemv_
-#define clAmdBlasDgemvEx clAmdBlasDgemvEx_
-#define clAmdBlasDger clAmdBlasDger_
-#define clAmdBlasDnrm2 clAmdBlasDnrm2_
-#define clAmdBlasDrot clAmdBlasDrot_
-#define clAmdBlasDrotg clAmdBlasDrotg_
-#define clAmdBlasDrotm clAmdBlasDrotm_
-#define clAmdBlasDrotmg clAmdBlasDrotmg_
-#define clAmdBlasDsbmv clAmdBlasDsbmv_
-#define clAmdBlasDscal clAmdBlasDscal_
-#define clAmdBlasDspmv clAmdBlasDspmv_
-#define clAmdBlasDspr clAmdBlasDspr_
-#define clAmdBlasDspr2 clAmdBlasDspr2_
-#define clAmdBlasDswap clAmdBlasDswap_
-#define clAmdBlasDsymm clAmdBlasDsymm_
-#define clAmdBlasDsymv clAmdBlasDsymv_
-#define clAmdBlasDsymvEx clAmdBlasDsymvEx_
-#define clAmdBlasDsyr clAmdBlasDsyr_
-#define clAmdBlasDsyr2 clAmdBlasDsyr2_
-#define clAmdBlasDsyr2k clAmdBlasDsyr2k_
-#define clAmdBlasDsyr2kEx clAmdBlasDsyr2kEx_
-#define clAmdBlasDsyrk clAmdBlasDsyrk_
-#define clAmdBlasDsyrkEx clAmdBlasDsyrkEx_
-#define clAmdBlasDtbmv clAmdBlasDtbmv_
-#define clAmdBlasDtbsv clAmdBlasDtbsv_
-#define clAmdBlasDtpmv clAmdBlasDtpmv_
-#define clAmdBlasDtpsv clAmdBlasDtpsv_
-#define clAmdBlasDtrmm clAmdBlasDtrmm_
-#define clAmdBlasDtrmmEx clAmdBlasDtrmmEx_
-#define clAmdBlasDtrmv clAmdBlasDtrmv_
-#define clAmdBlasDtrsm clAmdBlasDtrsm_
-#define clAmdBlasDtrsmEx clAmdBlasDtrsmEx_
-#define clAmdBlasDtrsv clAmdBlasDtrsv_
-#define clAmdBlasDzasum clAmdBlasDzasum_
-#define clAmdBlasDznrm2 clAmdBlasDznrm2_
-#define clAmdBlasGetVersion clAmdBlasGetVersion_
-#define clAmdBlasRemoveScratchImage clAmdBlasRemoveScratchImage_
-#define clAmdBlasSasum clAmdBlasSasum_
-#define clAmdBlasSaxpy clAmdBlasSaxpy_
-#define clAmdBlasScasum clAmdBlasScasum_
-#define clAmdBlasScnrm2 clAmdBlasScnrm2_
-#define clAmdBlasScopy clAmdBlasScopy_
-#define clAmdBlasSdot clAmdBlasSdot_
-#define clAmdBlasSetup clAmdBlasSetup_
-#define clAmdBlasSgbmv clAmdBlasSgbmv_
-#define clAmdBlasSgemm clAmdBlasSgemm_
-#define clAmdBlasSgemmEx clAmdBlasSgemmEx_
-#define clAmdBlasSgemv clAmdBlasSgemv_
-#define clAmdBlasSgemvEx clAmdBlasSgemvEx_
-#define clAmdBlasSger clAmdBlasSger_
-#define clAmdBlasSnrm2 clAmdBlasSnrm2_
-#define clAmdBlasSrot clAmdBlasSrot_
-#define clAmdBlasSrotg clAmdBlasSrotg_
-#define clAmdBlasSrotm clAmdBlasSrotm_
-#define clAmdBlasSrotmg clAmdBlasSrotmg_
-#define clAmdBlasSsbmv clAmdBlasSsbmv_
-#define clAmdBlasSscal clAmdBlasSscal_
-#define clAmdBlasSspmv clAmdBlasSspmv_
-#define clAmdBlasSspr clAmdBlasSspr_
-#define clAmdBlasSspr2 clAmdBlasSspr2_
-#define clAmdBlasSswap clAmdBlasSswap_
-#define clAmdBlasSsymm clAmdBlasSsymm_
-#define clAmdBlasSsymv clAmdBlasSsymv_
-#define clAmdBlasSsymvEx clAmdBlasSsymvEx_
-#define clAmdBlasSsyr clAmdBlasSsyr_
-#define clAmdBlasSsyr2 clAmdBlasSsyr2_
-#define clAmdBlasSsyr2k clAmdBlasSsyr2k_
-#define clAmdBlasSsyr2kEx clAmdBlasSsyr2kEx_
-#define clAmdBlasSsyrk clAmdBlasSsyrk_
-#define clAmdBlasSsyrkEx clAmdBlasSsyrkEx_
-#define clAmdBlasStbmv clAmdBlasStbmv_
-#define clAmdBlasStbsv clAmdBlasStbsv_
-#define clAmdBlasStpmv clAmdBlasStpmv_
-#define clAmdBlasStpsv clAmdBlasStpsv_
-#define clAmdBlasStrmm clAmdBlasStrmm_
-#define clAmdBlasStrmmEx clAmdBlasStrmmEx_
-#define clAmdBlasStrmv clAmdBlasStrmv_
-#define clAmdBlasStrsm clAmdBlasStrsm_
-#define clAmdBlasStrsmEx clAmdBlasStrsmEx_
-#define clAmdBlasStrsv clAmdBlasStrsv_
-#define clAmdBlasTeardown clAmdBlasTeardown_
-#define clAmdBlasZaxpy clAmdBlasZaxpy_
-#define clAmdBlasZcopy clAmdBlasZcopy_
-#define clAmdBlasZdotc clAmdBlasZdotc_
-#define clAmdBlasZdotu clAmdBlasZdotu_
-#define clAmdBlasZdrot clAmdBlasZdrot_
-#define clAmdBlasZdscal clAmdBlasZdscal_
-#define clAmdBlasZgbmv clAmdBlasZgbmv_
-#define clAmdBlasZgemm clAmdBlasZgemm_
-#define clAmdBlasZgemmEx clAmdBlasZgemmEx_
-#define clAmdBlasZgemv clAmdBlasZgemv_
-#define clAmdBlasZgemvEx clAmdBlasZgemvEx_
-#define clAmdBlasZgerc clAmdBlasZgerc_
-#define clAmdBlasZgeru clAmdBlasZgeru_
-#define clAmdBlasZhbmv clAmdBlasZhbmv_
-#define clAmdBlasZhemm clAmdBlasZhemm_
-#define clAmdBlasZhemv clAmdBlasZhemv_
-#define clAmdBlasZher clAmdBlasZher_
-#define clAmdBlasZher2 clAmdBlasZher2_
-#define clAmdBlasZher2k clAmdBlasZher2k_
-#define clAmdBlasZherk clAmdBlasZherk_
-#define clAmdBlasZhpmv clAmdBlasZhpmv_
-#define clAmdBlasZhpr clAmdBlasZhpr_
-#define clAmdBlasZhpr2 clAmdBlasZhpr2_
-#define clAmdBlasZrotg clAmdBlasZrotg_
-#define clAmdBlasZscal clAmdBlasZscal_
-#define clAmdBlasZswap clAmdBlasZswap_
-#define clAmdBlasZsymm clAmdBlasZsymm_
-#define clAmdBlasZsyr2k clAmdBlasZsyr2k_
-#define clAmdBlasZsyr2kEx clAmdBlasZsyr2kEx_
-#define clAmdBlasZsyrk clAmdBlasZsyrk_
-#define clAmdBlasZsyrkEx clAmdBlasZsyrkEx_
-#define clAmdBlasZtbmv clAmdBlasZtbmv_
-#define clAmdBlasZtbsv clAmdBlasZtbsv_
-#define clAmdBlasZtpmv clAmdBlasZtpmv_
-#define clAmdBlasZtpsv clAmdBlasZtpsv_
-#define clAmdBlasZtrmm clAmdBlasZtrmm_
-#define clAmdBlasZtrmmEx clAmdBlasZtrmmEx_
-#define clAmdBlasZtrmv clAmdBlasZtrmv_
-#define clAmdBlasZtrsm clAmdBlasZtrsm_
-#define clAmdBlasZtrsmEx clAmdBlasZtrsmEx_
-#define clAmdBlasZtrsv clAmdBlasZtrsv_
-#define clAmdBlasiCamax clAmdBlasiCamax_
-#define clAmdBlasiDamax clAmdBlasiDamax_
-#define clAmdBlasiSamax clAmdBlasiSamax_
-#define clAmdBlasiZamax clAmdBlasiZamax_
-
-#include <clAmdBlas.h>
-
-// generated by parser_clamdblas.py
-#undef clAmdBlasAddScratchImage
-//#define clAmdBlasAddScratchImage clAmdBlasAddScratchImage_pfn
-#undef clAmdBlasCaxpy
-//#define clAmdBlasCaxpy clAmdBlasCaxpy_pfn
-#undef clAmdBlasCcopy
-//#define clAmdBlasCcopy clAmdBlasCcopy_pfn
-#undef clAmdBlasCdotc
-//#define clAmdBlasCdotc clAmdBlasCdotc_pfn
-#undef clAmdBlasCdotu
-//#define clAmdBlasCdotu clAmdBlasCdotu_pfn
-#undef clAmdBlasCgbmv
-//#define clAmdBlasCgbmv clAmdBlasCgbmv_pfn
-#undef clAmdBlasCgemm
-//#define clAmdBlasCgemm clAmdBlasCgemm_pfn
-#undef clAmdBlasCgemmEx
-#define clAmdBlasCgemmEx clAmdBlasCgemmEx_pfn
-#undef clAmdBlasCgemv
-//#define clAmdBlasCgemv clAmdBlasCgemv_pfn
-#undef clAmdBlasCgemvEx
-//#define clAmdBlasCgemvEx clAmdBlasCgemvEx_pfn
-#undef clAmdBlasCgerc
-//#define clAmdBlasCgerc clAmdBlasCgerc_pfn
-#undef clAmdBlasCgeru
-//#define clAmdBlasCgeru clAmdBlasCgeru_pfn
-#undef clAmdBlasChbmv
-//#define clAmdBlasChbmv clAmdBlasChbmv_pfn
-#undef clAmdBlasChemm
-//#define clAmdBlasChemm clAmdBlasChemm_pfn
-#undef clAmdBlasChemv
-//#define clAmdBlasChemv clAmdBlasChemv_pfn
-#undef clAmdBlasCher
-//#define clAmdBlasCher clAmdBlasCher_pfn
-#undef clAmdBlasCher2
-//#define clAmdBlasCher2 clAmdBlasCher2_pfn
-#undef clAmdBlasCher2k
-//#define clAmdBlasCher2k clAmdBlasCher2k_pfn
-#undef clAmdBlasCherk
-//#define clAmdBlasCherk clAmdBlasCherk_pfn
-#undef clAmdBlasChpmv
-//#define clAmdBlasChpmv clAmdBlasChpmv_pfn
-#undef clAmdBlasChpr
-//#define clAmdBlasChpr clAmdBlasChpr_pfn
-#undef clAmdBlasChpr2
-//#define clAmdBlasChpr2 clAmdBlasChpr2_pfn
-#undef clAmdBlasCrotg
-//#define clAmdBlasCrotg clAmdBlasCrotg_pfn
-#undef clAmdBlasCscal
-//#define clAmdBlasCscal clAmdBlasCscal_pfn
-#undef clAmdBlasCsrot
-//#define clAmdBlasCsrot clAmdBlasCsrot_pfn
-#undef clAmdBlasCsscal
-//#define clAmdBlasCsscal clAmdBlasCsscal_pfn
-#undef clAmdBlasCswap
-//#define clAmdBlasCswap clAmdBlasCswap_pfn
-#undef clAmdBlasCsymm
-//#define clAmdBlasCsymm clAmdBlasCsymm_pfn
-#undef clAmdBlasCsyr2k
-//#define clAmdBlasCsyr2k clAmdBlasCsyr2k_pfn
-#undef clAmdBlasCsyr2kEx
-//#define clAmdBlasCsyr2kEx clAmdBlasCsyr2kEx_pfn
-#undef clAmdBlasCsyrk
-//#define clAmdBlasCsyrk clAmdBlasCsyrk_pfn
-#undef clAmdBlasCsyrkEx
-//#define clAmdBlasCsyrkEx clAmdBlasCsyrkEx_pfn
-#undef clAmdBlasCtbmv
-//#define clAmdBlasCtbmv clAmdBlasCtbmv_pfn
-#undef clAmdBlasCtbsv
-//#define clAmdBlasCtbsv clAmdBlasCtbsv_pfn
-#undef clAmdBlasCtpmv
-//#define clAmdBlasCtpmv clAmdBlasCtpmv_pfn
-#undef clAmdBlasCtpsv
-//#define clAmdBlasCtpsv clAmdBlasCtpsv_pfn
-#undef clAmdBlasCtrmm
-//#define clAmdBlasCtrmm clAmdBlasCtrmm_pfn
-#undef clAmdBlasCtrmmEx
-//#define clAmdBlasCtrmmEx clAmdBlasCtrmmEx_pfn
-#undef clAmdBlasCtrmv
-//#define clAmdBlasCtrmv clAmdBlasCtrmv_pfn
-#undef clAmdBlasCtrsm
-//#define clAmdBlasCtrsm clAmdBlasCtrsm_pfn
-#undef clAmdBlasCtrsmEx
-//#define clAmdBlasCtrsmEx clAmdBlasCtrsmEx_pfn
-#undef clAmdBlasCtrsv
-//#define clAmdBlasCtrsv clAmdBlasCtrsv_pfn
-#undef clAmdBlasDasum
-//#define clAmdBlasDasum clAmdBlasDasum_pfn
-#undef clAmdBlasDaxpy
-//#define clAmdBlasDaxpy clAmdBlasDaxpy_pfn
-#undef clAmdBlasDcopy
-//#define clAmdBlasDcopy clAmdBlasDcopy_pfn
-#undef clAmdBlasDdot
-//#define clAmdBlasDdot clAmdBlasDdot_pfn
-#undef clAmdBlasDgbmv
-//#define clAmdBlasDgbmv clAmdBlasDgbmv_pfn
-#undef clAmdBlasDgemm
-//#define clAmdBlasDgemm clAmdBlasDgemm_pfn
-#undef clAmdBlasDgemmEx
-#define clAmdBlasDgemmEx clAmdBlasDgemmEx_pfn
-#undef clAmdBlasDgemv
-//#define clAmdBlasDgemv clAmdBlasDgemv_pfn
-#undef clAmdBlasDgemvEx
-//#define clAmdBlasDgemvEx clAmdBlasDgemvEx_pfn
-#undef clAmdBlasDger
-//#define clAmdBlasDger clAmdBlasDger_pfn
-#undef clAmdBlasDnrm2
-//#define clAmdBlasDnrm2 clAmdBlasDnrm2_pfn
-#undef clAmdBlasDrot
-//#define clAmdBlasDrot clAmdBlasDrot_pfn
-#undef clAmdBlasDrotg
-//#define clAmdBlasDrotg clAmdBlasDrotg_pfn
-#undef clAmdBlasDrotm
-//#define clAmdBlasDrotm clAmdBlasDrotm_pfn
-#undef clAmdBlasDrotmg
-//#define clAmdBlasDrotmg clAmdBlasDrotmg_pfn
-#undef clAmdBlasDsbmv
-//#define clAmdBlasDsbmv clAmdBlasDsbmv_pfn
-#undef clAmdBlasDscal
-//#define clAmdBlasDscal clAmdBlasDscal_pfn
-#undef clAmdBlasDspmv
-//#define clAmdBlasDspmv clAmdBlasDspmv_pfn
-#undef clAmdBlasDspr
-//#define clAmdBlasDspr clAmdBlasDspr_pfn
-#undef clAmdBlasDspr2
-//#define clAmdBlasDspr2 clAmdBlasDspr2_pfn
-#undef clAmdBlasDswap
-//#define clAmdBlasDswap clAmdBlasDswap_pfn
-#undef clAmdBlasDsymm
-//#define clAmdBlasDsymm clAmdBlasDsymm_pfn
-#undef clAmdBlasDsymv
-//#define clAmdBlasDsymv clAmdBlasDsymv_pfn
-#undef clAmdBlasDsymvEx
-//#define clAmdBlasDsymvEx clAmdBlasDsymvEx_pfn
-#undef clAmdBlasDsyr
-//#define clAmdBlasDsyr clAmdBlasDsyr_pfn
-#undef clAmdBlasDsyr2
-//#define clAmdBlasDsyr2 clAmdBlasDsyr2_pfn
-#undef clAmdBlasDsyr2k
-//#define clAmdBlasDsyr2k clAmdBlasDsyr2k_pfn
-#undef clAmdBlasDsyr2kEx
-//#define clAmdBlasDsyr2kEx clAmdBlasDsyr2kEx_pfn
-#undef clAmdBlasDsyrk
-//#define clAmdBlasDsyrk clAmdBlasDsyrk_pfn
-#undef clAmdBlasDsyrkEx
-//#define clAmdBlasDsyrkEx clAmdBlasDsyrkEx_pfn
-#undef clAmdBlasDtbmv
-//#define clAmdBlasDtbmv clAmdBlasDtbmv_pfn
-#undef clAmdBlasDtbsv
-//#define clAmdBlasDtbsv clAmdBlasDtbsv_pfn
-#undef clAmdBlasDtpmv
-//#define clAmdBlasDtpmv clAmdBlasDtpmv_pfn
-#undef clAmdBlasDtpsv
-//#define clAmdBlasDtpsv clAmdBlasDtpsv_pfn
-#undef clAmdBlasDtrmm
-//#define clAmdBlasDtrmm clAmdBlasDtrmm_pfn
-#undef clAmdBlasDtrmmEx
-//#define clAmdBlasDtrmmEx clAmdBlasDtrmmEx_pfn
-#undef clAmdBlasDtrmv
-//#define clAmdBlasDtrmv clAmdBlasDtrmv_pfn
-#undef clAmdBlasDtrsm
-//#define clAmdBlasDtrsm clAmdBlasDtrsm_pfn
-#undef clAmdBlasDtrsmEx
-//#define clAmdBlasDtrsmEx clAmdBlasDtrsmEx_pfn
-#undef clAmdBlasDtrsv
-//#define clAmdBlasDtrsv clAmdBlasDtrsv_pfn
-#undef clAmdBlasDzasum
-//#define clAmdBlasDzasum clAmdBlasDzasum_pfn
-#undef clAmdBlasDznrm2
-//#define clAmdBlasDznrm2 clAmdBlasDznrm2_pfn
-#undef clAmdBlasGetVersion
-//#define clAmdBlasGetVersion clAmdBlasGetVersion_pfn
-#undef clAmdBlasRemoveScratchImage
-//#define clAmdBlasRemoveScratchImage clAmdBlasRemoveScratchImage_pfn
-#undef clAmdBlasSasum
-//#define clAmdBlasSasum clAmdBlasSasum_pfn
-#undef clAmdBlasSaxpy
-//#define clAmdBlasSaxpy clAmdBlasSaxpy_pfn
-#undef clAmdBlasScasum
-//#define clAmdBlasScasum clAmdBlasScasum_pfn
-#undef clAmdBlasScnrm2
-//#define clAmdBlasScnrm2 clAmdBlasScnrm2_pfn
-#undef clAmdBlasScopy
-//#define clAmdBlasScopy clAmdBlasScopy_pfn
-#undef clAmdBlasSdot
-//#define clAmdBlasSdot clAmdBlasSdot_pfn
-#undef clAmdBlasSetup
-#define clAmdBlasSetup clAmdBlasSetup_pfn
-#undef clAmdBlasSgbmv
-//#define clAmdBlasSgbmv clAmdBlasSgbmv_pfn
-#undef clAmdBlasSgemm
-//#define clAmdBlasSgemm clAmdBlasSgemm_pfn
-#undef clAmdBlasSgemmEx
-#define clAmdBlasSgemmEx clAmdBlasSgemmEx_pfn
-#undef clAmdBlasSgemv
-//#define clAmdBlasSgemv clAmdBlasSgemv_pfn
-#undef clAmdBlasSgemvEx
-//#define clAmdBlasSgemvEx clAmdBlasSgemvEx_pfn
-#undef clAmdBlasSger
-//#define clAmdBlasSger clAmdBlasSger_pfn
-#undef clAmdBlasSnrm2
-//#define clAmdBlasSnrm2 clAmdBlasSnrm2_pfn
-#undef clAmdBlasSrot
-//#define clAmdBlasSrot clAmdBlasSrot_pfn
-#undef clAmdBlasSrotg
-//#define clAmdBlasSrotg clAmdBlasSrotg_pfn
-#undef clAmdBlasSrotm
-//#define clAmdBlasSrotm clAmdBlasSrotm_pfn
-#undef clAmdBlasSrotmg
-//#define clAmdBlasSrotmg clAmdBlasSrotmg_pfn
-#undef clAmdBlasSsbmv
-//#define clAmdBlasSsbmv clAmdBlasSsbmv_pfn
-#undef clAmdBlasSscal
-//#define clAmdBlasSscal clAmdBlasSscal_pfn
-#undef clAmdBlasSspmv
-//#define clAmdBlasSspmv clAmdBlasSspmv_pfn
-#undef clAmdBlasSspr
-//#define clAmdBlasSspr clAmdBlasSspr_pfn
-#undef clAmdBlasSspr2
-//#define clAmdBlasSspr2 clAmdBlasSspr2_pfn
-#undef clAmdBlasSswap
-//#define clAmdBlasSswap clAmdBlasSswap_pfn
-#undef clAmdBlasSsymm
-//#define clAmdBlasSsymm clAmdBlasSsymm_pfn
-#undef clAmdBlasSsymv
-//#define clAmdBlasSsymv clAmdBlasSsymv_pfn
-#undef clAmdBlasSsymvEx
-//#define clAmdBlasSsymvEx clAmdBlasSsymvEx_pfn
-#undef clAmdBlasSsyr
-//#define clAmdBlasSsyr clAmdBlasSsyr_pfn
-#undef clAmdBlasSsyr2
-//#define clAmdBlasSsyr2 clAmdBlasSsyr2_pfn
-#undef clAmdBlasSsyr2k
-//#define clAmdBlasSsyr2k clAmdBlasSsyr2k_pfn
-#undef clAmdBlasSsyr2kEx
-//#define clAmdBlasSsyr2kEx clAmdBlasSsyr2kEx_pfn
-#undef clAmdBlasSsyrk
-//#define clAmdBlasSsyrk clAmdBlasSsyrk_pfn
-#undef clAmdBlasSsyrkEx
-//#define clAmdBlasSsyrkEx clAmdBlasSsyrkEx_pfn
-#undef clAmdBlasStbmv
-//#define clAmdBlasStbmv clAmdBlasStbmv_pfn
-#undef clAmdBlasStbsv
-//#define clAmdBlasStbsv clAmdBlasStbsv_pfn
-#undef clAmdBlasStpmv
-//#define clAmdBlasStpmv clAmdBlasStpmv_pfn
-#undef clAmdBlasStpsv
-//#define clAmdBlasStpsv clAmdBlasStpsv_pfn
-#undef clAmdBlasStrmm
-//#define clAmdBlasStrmm clAmdBlasStrmm_pfn
-#undef clAmdBlasStrmmEx
-//#define clAmdBlasStrmmEx clAmdBlasStrmmEx_pfn
-#undef clAmdBlasStrmv
-//#define clAmdBlasStrmv clAmdBlasStrmv_pfn
-#undef clAmdBlasStrsm
-//#define clAmdBlasStrsm clAmdBlasStrsm_pfn
-#undef clAmdBlasStrsmEx
-//#define clAmdBlasStrsmEx clAmdBlasStrsmEx_pfn
-#undef clAmdBlasStrsv
-//#define clAmdBlasStrsv clAmdBlasStrsv_pfn
-#undef clAmdBlasTeardown
-#define clAmdBlasTeardown clAmdBlasTeardown_pfn
-#undef clAmdBlasZaxpy
-//#define clAmdBlasZaxpy clAmdBlasZaxpy_pfn
-#undef clAmdBlasZcopy
-//#define clAmdBlasZcopy clAmdBlasZcopy_pfn
-#undef clAmdBlasZdotc
-//#define clAmdBlasZdotc clAmdBlasZdotc_pfn
-#undef clAmdBlasZdotu
-//#define clAmdBlasZdotu clAmdBlasZdotu_pfn
-#undef clAmdBlasZdrot
-//#define clAmdBlasZdrot clAmdBlasZdrot_pfn
-#undef clAmdBlasZdscal
-//#define clAmdBlasZdscal clAmdBlasZdscal_pfn
-#undef clAmdBlasZgbmv
-//#define clAmdBlasZgbmv clAmdBlasZgbmv_pfn
-#undef clAmdBlasZgemm
-//#define clAmdBlasZgemm clAmdBlasZgemm_pfn
-#undef clAmdBlasZgemmEx
-#define clAmdBlasZgemmEx clAmdBlasZgemmEx_pfn
-#undef clAmdBlasZgemv
-//#define clAmdBlasZgemv clAmdBlasZgemv_pfn
-#undef clAmdBlasZgemvEx
-//#define clAmdBlasZgemvEx clAmdBlasZgemvEx_pfn
-#undef clAmdBlasZgerc
-//#define clAmdBlasZgerc clAmdBlasZgerc_pfn
-#undef clAmdBlasZgeru
-//#define clAmdBlasZgeru clAmdBlasZgeru_pfn
-#undef clAmdBlasZhbmv
-//#define clAmdBlasZhbmv clAmdBlasZhbmv_pfn
-#undef clAmdBlasZhemm
-//#define clAmdBlasZhemm clAmdBlasZhemm_pfn
-#undef clAmdBlasZhemv
-//#define clAmdBlasZhemv clAmdBlasZhemv_pfn
-#undef clAmdBlasZher
-//#define clAmdBlasZher clAmdBlasZher_pfn
-#undef clAmdBlasZher2
-//#define clAmdBlasZher2 clAmdBlasZher2_pfn
-#undef clAmdBlasZher2k
-//#define clAmdBlasZher2k clAmdBlasZher2k_pfn
-#undef clAmdBlasZherk
-//#define clAmdBlasZherk clAmdBlasZherk_pfn
-#undef clAmdBlasZhpmv
-//#define clAmdBlasZhpmv clAmdBlasZhpmv_pfn
-#undef clAmdBlasZhpr
-//#define clAmdBlasZhpr clAmdBlasZhpr_pfn
-#undef clAmdBlasZhpr2
-//#define clAmdBlasZhpr2 clAmdBlasZhpr2_pfn
-#undef clAmdBlasZrotg
-//#define clAmdBlasZrotg clAmdBlasZrotg_pfn
-#undef clAmdBlasZscal
-//#define clAmdBlasZscal clAmdBlasZscal_pfn
-#undef clAmdBlasZswap
-//#define clAmdBlasZswap clAmdBlasZswap_pfn
-#undef clAmdBlasZsymm
-//#define clAmdBlasZsymm clAmdBlasZsymm_pfn
-#undef clAmdBlasZsyr2k
-//#define clAmdBlasZsyr2k clAmdBlasZsyr2k_pfn
-#undef clAmdBlasZsyr2kEx
-//#define clAmdBlasZsyr2kEx clAmdBlasZsyr2kEx_pfn
-#undef clAmdBlasZsyrk
-//#define clAmdBlasZsyrk clAmdBlasZsyrk_pfn
-#undef clAmdBlasZsyrkEx
-//#define clAmdBlasZsyrkEx clAmdBlasZsyrkEx_pfn
-#undef clAmdBlasZtbmv
-//#define clAmdBlasZtbmv clAmdBlasZtbmv_pfn
-#undef clAmdBlasZtbsv
-//#define clAmdBlasZtbsv clAmdBlasZtbsv_pfn
-#undef clAmdBlasZtpmv
-//#define clAmdBlasZtpmv clAmdBlasZtpmv_pfn
-#undef clAmdBlasZtpsv
-//#define clAmdBlasZtpsv clAmdBlasZtpsv_pfn
-#undef clAmdBlasZtrmm
-//#define clAmdBlasZtrmm clAmdBlasZtrmm_pfn
-#undef clAmdBlasZtrmmEx
-//#define clAmdBlasZtrmmEx clAmdBlasZtrmmEx_pfn
-#undef clAmdBlasZtrmv
-//#define clAmdBlasZtrmv clAmdBlasZtrmv_pfn
-#undef clAmdBlasZtrsm
-//#define clAmdBlasZtrsm clAmdBlasZtrsm_pfn
-#undef clAmdBlasZtrsmEx
-//#define clAmdBlasZtrsmEx clAmdBlasZtrsmEx_pfn
-#undef clAmdBlasZtrsv
-//#define clAmdBlasZtrsv clAmdBlasZtrsv_pfn
-#undef clAmdBlasiCamax
-//#define clAmdBlasiCamax clAmdBlasiCamax_pfn
-#undef clAmdBlasiDamax
-//#define clAmdBlasiDamax clAmdBlasiDamax_pfn
-#undef clAmdBlasiSamax
-//#define clAmdBlasiSamax clAmdBlasiSamax_pfn
-#undef clAmdBlasiZamax
-//#define clAmdBlasiZamax clAmdBlasiZamax_pfn
-
-// generated by parser_clamdblas.py
-//extern CL_RUNTIME_EXPORT cl_ulong (*clAmdBlasAddScratchImage)(cl_context context, size_t width, size_t height, clAmdBlasStatus* status);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCaxpy)(size_t N, cl_float2 alpha, const cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCcopy)(size_t N, const cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCdotc)(size_t N, cl_mem dotProduct, size_t offDP, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCdotu)(size_t N, cl_mem dotProduct, size_t offDP, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCgbmv)(clAmdBlasOrder order, clAmdBlasTranspose trans, size_t M, size_t N, size_t KL, size_t KU, cl_float2 alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem X, size_t offx, int incx, cl_float2 beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCgemm)(clAmdBlasOrder order, clAmdBlasTranspose transA, clAmdBlasTranspose transB, size_t M, size_t N, size_t K, FloatComplex alpha, const cl_mem A, size_t lda, const cl_mem B, size_t ldb, FloatComplex beta, cl_mem C, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCgemmEx)(clAmdBlasOrder order, clAmdBlasTranspose transA, clAmdBlasTranspose transB, size_t M, size_t N, size_t K, FloatComplex alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem B, size_t offB, size_t ldb, FloatComplex beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCgemv)(clAmdBlasOrder order, clAmdBlasTranspose transA, size_t M, size_t N, FloatComplex alpha, const cl_mem A, size_t lda, const cl_mem x, size_t offx, int incx, FloatComplex beta, cl_mem y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCgemvEx)(clAmdBlasOrder order, clAmdBlasTranspose transA, size_t M, size_t N, FloatComplex alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem x, size_t offx, int incx, FloatComplex beta, cl_mem y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCgerc)(clAmdBlasOrder order, size_t M, size_t N, cl_float2 alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCgeru)(clAmdBlasOrder order, size_t M, size_t N, cl_float2 alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasChbmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, size_t K, cl_float2 alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem X, size_t offx, int incx, cl_float2 beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasChemm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, size_t M, size_t N, cl_float2 alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem B, size_t offb, size_t ldb, cl_float2 beta, cl_mem C, size_t offc, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasChemv)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, FloatComplex alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem X, size_t offx, int incx, FloatComplex beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCher)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_float alpha, const cl_mem X, size_t offx, int incx, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCher2)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_float2 alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCher2k)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, size_t N, size_t K, FloatComplex alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem B, size_t offb, size_t ldb, cl_float beta, cl_mem C, size_t offc, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCherk)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transA, size_t N, size_t K, float alpha, const cl_mem A, size_t offa, size_t lda, float beta, cl_mem C, size_t offc, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasChpmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_float2 alpha, const cl_mem AP, size_t offa, const cl_mem X, size_t offx, int incx, cl_float2 beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasChpr)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_float alpha, const cl_mem X, size_t offx, int incx, cl_mem AP, size_t offa, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasChpr2)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_float2 alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem AP, size_t offa, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCrotg)(cl_mem CA, size_t offCA, cl_mem CB, size_t offCB, cl_mem C, size_t offC, cl_mem S, size_t offS, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCscal)(size_t N, cl_float2 alpha, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCsrot)(size_t N, cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_float C, cl_float S, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCsscal)(size_t N, cl_float alpha, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCswap)(size_t N, cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCsymm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, size_t M, size_t N, cl_float2 alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem B, size_t offb, size_t ldb, cl_float2 beta, cl_mem C, size_t offc, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCsyr2k)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transAB, size_t N, size_t K, FloatComplex alpha, const cl_mem A, size_t lda, const cl_mem B, size_t ldb, FloatComplex beta, cl_mem C, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCsyr2kEx)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transAB, size_t N, size_t K, FloatComplex alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem B, size_t offB, size_t ldb, FloatComplex beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCsyrk)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transA, size_t N, size_t K, FloatComplex alpha, const cl_mem A, size_t lda, FloatComplex beta, cl_mem C, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCsyrkEx)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transA, size_t N, size_t K, FloatComplex alpha, const cl_mem A, size_t offA, size_t lda, FloatComplex beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCtbmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, size_t K, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCtbsv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, size_t K, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCtpmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem AP, size_t offa, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCtpsv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem A, size_t offa, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCtrmm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, FloatComplex alpha, const cl_mem A, size_t lda, cl_mem B, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCtrmmEx)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, FloatComplex alpha, const cl_mem A, size_t offA, size_t lda, cl_mem B, size_t offB, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCtrmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCtrsm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, FloatComplex alpha, const cl_mem A, size_t lda, cl_mem B, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCtrsmEx)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, FloatComplex alpha, const cl_mem A, size_t offA, size_t lda, cl_mem B, size_t offB, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasCtrsv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDasum)(size_t N, cl_mem asum, size_t offAsum, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDaxpy)(size_t N, cl_double alpha, const cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDcopy)(size_t N, const cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDdot)(size_t N, cl_mem dotProduct, size_t offDP, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDgbmv)(clAmdBlasOrder order, clAmdBlasTranspose trans, size_t M, size_t N, size_t KL, size_t KU, cl_double alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem X, size_t offx, int incx, cl_double beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDgemm)(clAmdBlasOrder order, clAmdBlasTranspose transA, clAmdBlasTranspose transB, size_t M, size_t N, size_t K, cl_double alpha, const cl_mem A, size_t lda, const cl_mem B, size_t ldb, cl_double beta, cl_mem C, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDgemmEx)(clAmdBlasOrder order, clAmdBlasTranspose transA, clAmdBlasTranspose transB, size_t M, size_t N, size_t K, cl_double alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem B, size_t offB, size_t ldb, cl_double beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDgemv)(clAmdBlasOrder order, clAmdBlasTranspose transA, size_t M, size_t N, cl_double alpha, const cl_mem A, size_t lda, const cl_mem x, size_t offx, int incx, cl_double beta, cl_mem y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDgemvEx)(clAmdBlasOrder order, clAmdBlasTranspose transA, size_t M, size_t N, cl_double alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem x, size_t offx, int incx, cl_double beta, cl_mem y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDger)(clAmdBlasOrder order, size_t M, size_t N, cl_double alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDnrm2)(size_t N, cl_mem NRM2, size_t offNRM2, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDrot)(size_t N, cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_double C, cl_double S, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDrotg)(cl_mem DA, size_t offDA, cl_mem DB, size_t offDB, cl_mem C, size_t offC, cl_mem S, size_t offS, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDrotm)(size_t N, cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, const cl_mem DPARAM, size_t offDparam, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDrotmg)(cl_mem DD1, size_t offDD1, cl_mem DD2, size_t offDD2, cl_mem DX1, size_t offDX1, const cl_mem DY1, size_t offDY1, cl_mem DPARAM, size_t offDparam, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDsbmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, size_t K, cl_double alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem X, size_t offx, int incx, cl_double beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDscal)(size_t N, cl_double alpha, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDspmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_double alpha, const cl_mem AP, size_t offa, const cl_mem X, size_t offx, int incx, cl_double beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDspr)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_double alpha, const cl_mem X, size_t offx, int incx, cl_mem AP, size_t offa, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDspr2)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_double alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem AP, size_t offa, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDswap)(size_t N, cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDsymm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, size_t M, size_t N, cl_double alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem B, size_t offb, size_t ldb, cl_double beta, cl_mem C, size_t offc, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDsymv)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_double alpha, const cl_mem A, size_t lda, const cl_mem x, size_t offx, int incx, cl_double beta, cl_mem y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDsymvEx)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_double alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem x, size_t offx, int incx, cl_double beta, cl_mem y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDsyr)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_double alpha, const cl_mem X, size_t offx, int incx, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDsyr2)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_double alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDsyr2k)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transAB, size_t N, size_t K, cl_double alpha, const cl_mem A, size_t lda, const cl_mem B, size_t ldb, cl_double beta, cl_mem C, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDsyr2kEx)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transAB, size_t N, size_t K, cl_double alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem B, size_t offB, size_t ldb, cl_double beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDsyrk)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transA, size_t N, size_t K, cl_double alpha, const cl_mem A, size_t lda, cl_double beta, cl_mem C, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDsyrkEx)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transA, size_t N, size_t K, cl_double alpha, const cl_mem A, size_t offA, size_t lda, cl_double beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDtbmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, size_t K, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDtbsv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, size_t K, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDtpmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem AP, size_t offa, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDtpsv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem A, size_t offa, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDtrmm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, cl_double alpha, const cl_mem A, size_t lda, cl_mem B, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDtrmmEx)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, cl_double alpha, const cl_mem A, size_t offA, size_t lda, cl_mem B, size_t offB, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDtrmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDtrsm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, cl_double alpha, const cl_mem A, size_t lda, cl_mem B, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDtrsmEx)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, cl_double alpha, const cl_mem A, size_t offA, size_t lda, cl_mem B, size_t offB, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDtrsv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDzasum)(size_t N, cl_mem asum, size_t offAsum, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasDznrm2)(size_t N, cl_mem NRM2, size_t offNRM2, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasGetVersion)(cl_uint* major, cl_uint* minor, cl_uint* patch);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasRemoveScratchImage)(cl_ulong imageID);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSasum)(size_t N, cl_mem asum, size_t offAsum, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSaxpy)(size_t N, cl_float alpha, const cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasScasum)(size_t N, cl_mem asum, size_t offAsum, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasScnrm2)(size_t N, cl_mem NRM2, size_t offNRM2, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasScopy)(size_t N, const cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSdot)(size_t N, cl_mem dotProduct, size_t offDP, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSetup)();
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSgbmv)(clAmdBlasOrder order, clAmdBlasTranspose trans, size_t M, size_t N, size_t KL, size_t KU, cl_float alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem X, size_t offx, int incx, cl_float beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSgemm)(clAmdBlasOrder order, clAmdBlasTranspose transA, clAmdBlasTranspose transB, size_t M, size_t N, size_t K, cl_float alpha, const cl_mem A, size_t lda, const cl_mem B, size_t ldb, cl_float beta, cl_mem C, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSgemmEx)(clAmdBlasOrder order, clAmdBlasTranspose transA, clAmdBlasTranspose transB, size_t M, size_t N, size_t K, cl_float alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem B, size_t offB, size_t ldb, cl_float beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSgemv)(clAmdBlasOrder order, clAmdBlasTranspose transA, size_t M, size_t N, cl_float alpha, const cl_mem A, size_t lda, const cl_mem x, size_t offx, int incx, cl_float beta, cl_mem y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSgemvEx)(clAmdBlasOrder order, clAmdBlasTranspose transA, size_t M, size_t N, cl_float alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem x, size_t offx, int incx, cl_float beta, cl_mem y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSger)(clAmdBlasOrder order, size_t M, size_t N, cl_float alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSnrm2)(size_t N, cl_mem NRM2, size_t offNRM2, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSrot)(size_t N, cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_float C, cl_float S, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSrotg)(cl_mem SA, size_t offSA, cl_mem SB, size_t offSB, cl_mem C, size_t offC, cl_mem S, size_t offS, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSrotm)(size_t N, cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, const cl_mem SPARAM, size_t offSparam, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSrotmg)(cl_mem SD1, size_t offSD1, cl_mem SD2, size_t offSD2, cl_mem SX1, size_t offSX1, const cl_mem SY1, size_t offSY1, cl_mem SPARAM, size_t offSparam, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSsbmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, size_t K, cl_float alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem X, size_t offx, int incx, cl_float beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSscal)(size_t N, cl_float alpha, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSspmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_float alpha, const cl_mem AP, size_t offa, const cl_mem X, size_t offx, int incx, cl_float beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSspr)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_float alpha, const cl_mem X, size_t offx, int incx, cl_mem AP, size_t offa, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSspr2)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_float alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem AP, size_t offa, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSswap)(size_t N, cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSsymm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, size_t M, size_t N, cl_float alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem B, size_t offb, size_t ldb, cl_float beta, cl_mem C, size_t offc, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSsymv)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_float alpha, const cl_mem A, size_t lda, const cl_mem x, size_t offx, int incx, cl_float beta, cl_mem y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSsymvEx)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_float alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem x, size_t offx, int incx, cl_float beta, cl_mem y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSsyr)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_float alpha, const cl_mem X, size_t offx, int incx, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSsyr2)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_float alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSsyr2k)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transAB, size_t N, size_t K, cl_float alpha, const cl_mem A, size_t lda, const cl_mem B, size_t ldb, cl_float beta, cl_mem C, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSsyr2kEx)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transAB, size_t N, size_t K, cl_float alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem B, size_t offB, size_t ldb, cl_float beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSsyrk)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transA, size_t N, size_t K, cl_float alpha, const cl_mem A, size_t lda, cl_float beta, cl_mem C, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasSsyrkEx)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transA, size_t N, size_t K, cl_float alpha, const cl_mem A, size_t offA, size_t lda, cl_float beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasStbmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, size_t K, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasStbsv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, size_t K, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasStpmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem AP, size_t offa, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasStpsv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem A, size_t offa, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasStrmm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, cl_float alpha, const cl_mem A, size_t lda, cl_mem B, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasStrmmEx)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, cl_float alpha, const cl_mem A, size_t offA, size_t lda, cl_mem B, size_t offB, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasStrmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasStrsm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, cl_float alpha, const cl_mem A, size_t lda, cl_mem B, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasStrsmEx)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, cl_float alpha, const cl_mem A, size_t offA, size_t lda, cl_mem B, size_t offB, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasStrsv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-extern CL_RUNTIME_EXPORT void (*clAmdBlasTeardown)();
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZaxpy)(size_t N, cl_double2 alpha, const cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZcopy)(size_t N, const cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZdotc)(size_t N, cl_mem dotProduct, size_t offDP, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZdotu)(size_t N, cl_mem dotProduct, size_t offDP, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZdrot)(size_t N, cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_double C, cl_double S, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZdscal)(size_t N, cl_double alpha, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZgbmv)(clAmdBlasOrder order, clAmdBlasTranspose trans, size_t M, size_t N, size_t KL, size_t KU, cl_double2 alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem X, size_t offx, int incx, cl_double2 beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZgemm)(clAmdBlasOrder order, clAmdBlasTranspose transA, clAmdBlasTranspose transB, size_t M, size_t N, size_t K, DoubleComplex alpha, const cl_mem A, size_t lda, const cl_mem B, size_t ldb, DoubleComplex beta, cl_mem C, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZgemmEx)(clAmdBlasOrder order, clAmdBlasTranspose transA, clAmdBlasTranspose transB, size_t M, size_t N, size_t K, DoubleComplex alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem B, size_t offB, size_t ldb, DoubleComplex beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZgemv)(clAmdBlasOrder order, clAmdBlasTranspose transA, size_t M, size_t N, DoubleComplex alpha, const cl_mem A, size_t lda, const cl_mem x, size_t offx, int incx, DoubleComplex beta, cl_mem y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZgemvEx)(clAmdBlasOrder order, clAmdBlasTranspose transA, size_t M, size_t N, DoubleComplex alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem x, size_t offx, int incx, DoubleComplex beta, cl_mem y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZgerc)(clAmdBlasOrder order, size_t M, size_t N, cl_double2 alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZgeru)(clAmdBlasOrder order, size_t M, size_t N, cl_double2 alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZhbmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, size_t K, cl_double2 alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem X, size_t offx, int incx, cl_double2 beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZhemm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, size_t M, size_t N, cl_double2 alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem B, size_t offb, size_t ldb, cl_double2 beta, cl_mem C, size_t offc, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZhemv)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, DoubleComplex alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem X, size_t offx, int incx, DoubleComplex beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZher)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_double alpha, const cl_mem X, size_t offx, int incx, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZher2)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_double2 alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem A, size_t offa, size_t lda, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZher2k)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, size_t N, size_t K, DoubleComplex alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem B, size_t offb, size_t ldb, cl_double beta, cl_mem C, size_t offc, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZherk)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transA, size_t N, size_t K, double alpha, const cl_mem A, size_t offa, size_t lda, double beta, cl_mem C, size_t offc, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZhpmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_double2 alpha, const cl_mem AP, size_t offa, const cl_mem X, size_t offx, int incx, cl_double2 beta, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZhpr)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_double alpha, const cl_mem X, size_t offx, int incx, cl_mem AP, size_t offa, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZhpr2)(clAmdBlasOrder order, clAmdBlasUplo uplo, size_t N, cl_double2 alpha, const cl_mem X, size_t offx, int incx, const cl_mem Y, size_t offy, int incy, cl_mem AP, size_t offa, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZrotg)(cl_mem CA, size_t offCA, cl_mem CB, size_t offCB, cl_mem C, size_t offC, cl_mem S, size_t offS, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZscal)(size_t N, cl_double2 alpha, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZswap)(size_t N, cl_mem X, size_t offx, int incx, cl_mem Y, size_t offy, int incy, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZsymm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, size_t M, size_t N, cl_double2 alpha, const cl_mem A, size_t offa, size_t lda, const cl_mem B, size_t offb, size_t ldb, cl_double2 beta, cl_mem C, size_t offc, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZsyr2k)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transAB, size_t N, size_t K, DoubleComplex alpha, const cl_mem A, size_t lda, const cl_mem B, size_t ldb, DoubleComplex beta, cl_mem C, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZsyr2kEx)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transAB, size_t N, size_t K, DoubleComplex alpha, const cl_mem A, size_t offA, size_t lda, const cl_mem B, size_t offB, size_t ldb, DoubleComplex beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZsyrk)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transA, size_t N, size_t K, DoubleComplex alpha, const cl_mem A, size_t lda, DoubleComplex beta, cl_mem C, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZsyrkEx)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose transA, size_t N, size_t K, DoubleComplex alpha, const cl_mem A, size_t offA, size_t lda, DoubleComplex beta, cl_mem C, size_t offC, size_t ldc, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZtbmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, size_t K, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZtbsv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, size_t K, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZtpmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem AP, size_t offa, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZtpsv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem A, size_t offa, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZtrmm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, DoubleComplex alpha, const cl_mem A, size_t lda, cl_mem B, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZtrmmEx)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, DoubleComplex alpha, const cl_mem A, size_t offA, size_t lda, cl_mem B, size_t offB, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZtrmv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZtrsm)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, DoubleComplex alpha, const cl_mem A, size_t lda, cl_mem B, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZtrsmEx)(clAmdBlasOrder order, clAmdBlasSide side, clAmdBlasUplo uplo, clAmdBlasTranspose transA, clAmdBlasDiag diag, size_t M, size_t N, DoubleComplex alpha, const cl_mem A, size_t offA, size_t lda, cl_mem B, size_t offB, size_t ldb, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasZtrsv)(clAmdBlasOrder order, clAmdBlasUplo uplo, clAmdBlasTranspose trans, clAmdBlasDiag diag, size_t N, const cl_mem A, size_t offa, size_t lda, cl_mem X, size_t offx, int incx, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasiCamax)(size_t N, cl_mem iMax, size_t offiMax, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasiDamax)(size_t N, cl_mem iMax, size_t offiMax, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasiSamax)(size_t N, cl_mem iMax, size_t offiMax, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
-//extern CL_RUNTIME_EXPORT clAmdBlasStatus (*clAmdBlasiZamax)(size_t N, cl_mem iMax, size_t offiMax, const cl_mem X, size_t offx, int incx, cl_mem scratchBuff, cl_uint numCommandQueues, cl_command_queue* commandQueues, cl_uint numEventsInWaitList, const cl_event* eventWaitList, cl_event* events);
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/opencl/runtime/autogenerated/opencl_clamdfft.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/opencl/runtime/autogenerated/opencl_clamdfft.hpp
deleted file mode 100755
index 1457d7e..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/opencl/runtime/autogenerated/opencl_clamdfft.hpp
+++ /dev/null
@@ -1,142 +0,0 @@
-//
-// AUTOGENERATED, DO NOT EDIT
-//
-#ifndef OPENCV_CORE_OCL_RUNTIME_CLAMDFFT_HPP
-#error "Invalid usage"
-#endif
-
-// generated by parser_clamdfft.py
-#define clAmdFftBakePlan clAmdFftBakePlan_
-#define clAmdFftCopyPlan clAmdFftCopyPlan_
-#define clAmdFftCreateDefaultPlan clAmdFftCreateDefaultPlan_
-#define clAmdFftDestroyPlan clAmdFftDestroyPlan_
-#define clAmdFftEnqueueTransform clAmdFftEnqueueTransform_
-#define clAmdFftGetLayout clAmdFftGetLayout_
-#define clAmdFftGetPlanBatchSize clAmdFftGetPlanBatchSize_
-#define clAmdFftGetPlanContext clAmdFftGetPlanContext_
-#define clAmdFftGetPlanDim clAmdFftGetPlanDim_
-#define clAmdFftGetPlanDistance clAmdFftGetPlanDistance_
-#define clAmdFftGetPlanInStride clAmdFftGetPlanInStride_
-#define clAmdFftGetPlanLength clAmdFftGetPlanLength_
-#define clAmdFftGetPlanOutStride clAmdFftGetPlanOutStride_
-#define clAmdFftGetPlanPrecision clAmdFftGetPlanPrecision_
-#define clAmdFftGetPlanScale clAmdFftGetPlanScale_
-#define clAmdFftGetPlanTransposeResult clAmdFftGetPlanTransposeResult_
-#define clAmdFftGetResultLocation clAmdFftGetResultLocation_
-#define clAmdFftGetTmpBufSize clAmdFftGetTmpBufSize_
-#define clAmdFftGetVersion clAmdFftGetVersion_
-#define clAmdFftSetLayout clAmdFftSetLayout_
-#define clAmdFftSetPlanBatchSize clAmdFftSetPlanBatchSize_
-#define clAmdFftSetPlanDim clAmdFftSetPlanDim_
-#define clAmdFftSetPlanDistance clAmdFftSetPlanDistance_
-#define clAmdFftSetPlanInStride clAmdFftSetPlanInStride_
-#define clAmdFftSetPlanLength clAmdFftSetPlanLength_
-#define clAmdFftSetPlanOutStride clAmdFftSetPlanOutStride_
-#define clAmdFftSetPlanPrecision clAmdFftSetPlanPrecision_
-#define clAmdFftSetPlanScale clAmdFftSetPlanScale_
-#define clAmdFftSetPlanTransposeResult clAmdFftSetPlanTransposeResult_
-#define clAmdFftSetResultLocation clAmdFftSetResultLocation_
-#define clAmdFftSetup clAmdFftSetup_
-#define clAmdFftTeardown clAmdFftTeardown_
-
-#include <clAmdFft.h>
-
-// generated by parser_clamdfft.py
-#undef clAmdFftBakePlan
-#define clAmdFftBakePlan clAmdFftBakePlan_pfn
-#undef clAmdFftCopyPlan
-//#define clAmdFftCopyPlan clAmdFftCopyPlan_pfn
-#undef clAmdFftCreateDefaultPlan
-#define clAmdFftCreateDefaultPlan clAmdFftCreateDefaultPlan_pfn
-#undef clAmdFftDestroyPlan
-#define clAmdFftDestroyPlan clAmdFftDestroyPlan_pfn
-#undef clAmdFftEnqueueTransform
-#define clAmdFftEnqueueTransform clAmdFftEnqueueTransform_pfn
-#undef clAmdFftGetLayout
-//#define clAmdFftGetLayout clAmdFftGetLayout_pfn
-#undef clAmdFftGetPlanBatchSize
-//#define clAmdFftGetPlanBatchSize clAmdFftGetPlanBatchSize_pfn
-#undef clAmdFftGetPlanContext
-//#define clAmdFftGetPlanContext clAmdFftGetPlanContext_pfn
-#undef clAmdFftGetPlanDim
-//#define clAmdFftGetPlanDim clAmdFftGetPlanDim_pfn
-#undef clAmdFftGetPlanDistance
-//#define clAmdFftGetPlanDistance clAmdFftGetPlanDistance_pfn
-#undef clAmdFftGetPlanInStride
-//#define clAmdFftGetPlanInStride clAmdFftGetPlanInStride_pfn
-#undef clAmdFftGetPlanLength
-//#define clAmdFftGetPlanLength clAmdFftGetPlanLength_pfn
-#undef clAmdFftGetPlanOutStride
-//#define clAmdFftGetPlanOutStride clAmdFftGetPlanOutStride_pfn
-#undef clAmdFftGetPlanPrecision
-//#define clAmdFftGetPlanPrecision clAmdFftGetPlanPrecision_pfn
-#undef clAmdFftGetPlanScale
-//#define clAmdFftGetPlanScale clAmdFftGetPlanScale_pfn
-#undef clAmdFftGetPlanTransposeResult
-//#define clAmdFftGetPlanTransposeResult clAmdFftGetPlanTransposeResult_pfn
-#undef clAmdFftGetResultLocation
-//#define clAmdFftGetResultLocation clAmdFftGetResultLocation_pfn
-#undef clAmdFftGetTmpBufSize
-#define clAmdFftGetTmpBufSize clAmdFftGetTmpBufSize_pfn
-#undef clAmdFftGetVersion
-#define clAmdFftGetVersion clAmdFftGetVersion_pfn
-#undef clAmdFftSetLayout
-#define clAmdFftSetLayout clAmdFftSetLayout_pfn
-#undef clAmdFftSetPlanBatchSize
-#define clAmdFftSetPlanBatchSize clAmdFftSetPlanBatchSize_pfn
-#undef clAmdFftSetPlanDim
-//#define clAmdFftSetPlanDim clAmdFftSetPlanDim_pfn
-#undef clAmdFftSetPlanDistance
-#define clAmdFftSetPlanDistance clAmdFftSetPlanDistance_pfn
-#undef clAmdFftSetPlanInStride
-#define clAmdFftSetPlanInStride clAmdFftSetPlanInStride_pfn
-#undef clAmdFftSetPlanLength
-//#define clAmdFftSetPlanLength clAmdFftSetPlanLength_pfn
-#undef clAmdFftSetPlanOutStride
-#define clAmdFftSetPlanOutStride clAmdFftSetPlanOutStride_pfn
-#undef clAmdFftSetPlanPrecision
-#define clAmdFftSetPlanPrecision clAmdFftSetPlanPrecision_pfn
-#undef clAmdFftSetPlanScale
-#define clAmdFftSetPlanScale clAmdFftSetPlanScale_pfn
-#undef clAmdFftSetPlanTransposeResult
-//#define clAmdFftSetPlanTransposeResult clAmdFftSetPlanTransposeResult_pfn
-#undef clAmdFftSetResultLocation
-#define clAmdFftSetResultLocation clAmdFftSetResultLocation_pfn
-#undef clAmdFftSetup
-#define clAmdFftSetup clAmdFftSetup_pfn
-#undef clAmdFftTeardown
-#define clAmdFftTeardown clAmdFftTeardown_pfn
-
-// generated by parser_clamdfft.py
-extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftBakePlan)(clAmdFftPlanHandle plHandle, cl_uint numQueues, cl_command_queue* commQueueFFT, void (CL_CALLBACK* pfn_notify) (clAmdFftPlanHandle plHandle, void* user_data), void* user_data);
-//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftCopyPlan)(clAmdFftPlanHandle* out_plHandle, cl_context new_context, clAmdFftPlanHandle in_plHandle);
-extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftCreateDefaultPlan)(clAmdFftPlanHandle* plHandle, cl_context context, const clAmdFftDim dim, const size_t* clLengths);
-extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftDestroyPlan)(clAmdFftPlanHandle* plHandle);
-extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftEnqueueTransform)(clAmdFftPlanHandle plHandle, clAmdFftDirection dir, cl_uint numQueuesAndEvents, cl_command_queue* commQueues, cl_uint numWaitEvents, const cl_event* waitEvents, cl_event* outEvents, cl_mem* inputBuffers, cl_mem* outputBuffers, cl_mem tmpBuffer);
-//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetLayout)(const clAmdFftPlanHandle plHandle, clAmdFftLayout* iLayout, clAmdFftLayout* oLayout);
-//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetPlanBatchSize)(const clAmdFftPlanHandle plHandle, size_t* batchSize);
-//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetPlanContext)(const clAmdFftPlanHandle plHandle, cl_context* context);
-//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetPlanDim)(const clAmdFftPlanHandle plHandle, clAmdFftDim* dim, cl_uint* size);
-//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetPlanDistance)(const clAmdFftPlanHandle plHandle, size_t* iDist, size_t* oDist);
-//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetPlanInStride)(const clAmdFftPlanHandle plHandle, const clAmdFftDim dim, size_t* clStrides);
-//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetPlanLength)(const clAmdFftPlanHandle plHandle, const clAmdFftDim dim, size_t* clLengths);
-//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetPlanOutStride)(const clAmdFftPlanHandle plHandle, const clAmdFftDim dim, size_t* clStrides);
-//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetPlanPrecision)(const clAmdFftPlanHandle plHandle, clAmdFftPrecision* precision);
-//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetPlanScale)(const clAmdFftPlanHandle plHandle, clAmdFftDirection dir, cl_float* scale);
-//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetPlanTransposeResult)(const clAmdFftPlanHandle plHandle, clAmdFftResultTransposed* transposed);
-//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetResultLocation)(const clAmdFftPlanHandle plHandle, clAmdFftResultLocation* placeness);
-extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetTmpBufSize)(const clAmdFftPlanHandle plHandle, size_t* buffersize);
-extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftGetVersion)(cl_uint* major, cl_uint* minor, cl_uint* patch);
-extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftSetLayout)(clAmdFftPlanHandle plHandle, clAmdFftLayout iLayout, clAmdFftLayout oLayout);
-extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftSetPlanBatchSize)(clAmdFftPlanHandle plHandle, size_t batchSize);
-//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftSetPlanDim)(clAmdFftPlanHandle plHandle, const clAmdFftDim dim);
-extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftSetPlanDistance)(clAmdFftPlanHandle plHandle, size_t iDist, size_t oDist);
-extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftSetPlanInStride)(clAmdFftPlanHandle plHandle, const clAmdFftDim dim, size_t* clStrides);
-//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftSetPlanLength)(clAmdFftPlanHandle plHandle, const clAmdFftDim dim, const size_t* clLengths);
-extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftSetPlanOutStride)(clAmdFftPlanHandle plHandle, const clAmdFftDim dim, size_t* clStrides);
-extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftSetPlanPrecision)(clAmdFftPlanHandle plHandle, clAmdFftPrecision precision);
-extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftSetPlanScale)(clAmdFftPlanHandle plHandle, clAmdFftDirection dir, cl_float scale);
-//extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftSetPlanTransposeResult)(clAmdFftPlanHandle plHandle, clAmdFftResultTransposed transposed);
-extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftSetResultLocation)(clAmdFftPlanHandle plHandle, clAmdFftResultLocation placeness);
-extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftSetup)(const clAmdFftSetupData* setupData);
-extern CL_RUNTIME_EXPORT clAmdFftStatus (*clAmdFftTeardown)();
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/opencl/runtime/autogenerated/opencl_core.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/opencl/runtime/autogenerated/opencl_core.hpp
deleted file mode 100755
index fdaf469..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/opencl/runtime/autogenerated/opencl_core.hpp
+++ /dev/null
@@ -1,370 +0,0 @@
-//
-// AUTOGENERATED, DO NOT EDIT
-//
-#ifndef OPENCV_CORE_OCL_RUNTIME_OPENCL_CORE_HPP
-#error "Invalid usage"
-#endif
-
-// generated by parser_cl.py
-#define clBuildProgram clBuildProgram_
-#define clCompileProgram clCompileProgram_
-#define clCreateBuffer clCreateBuffer_
-#define clCreateCommandQueue clCreateCommandQueue_
-#define clCreateContext clCreateContext_
-#define clCreateContextFromType clCreateContextFromType_
-#define clCreateImage clCreateImage_
-#define clCreateImage2D clCreateImage2D_
-#define clCreateImage3D clCreateImage3D_
-#define clCreateKernel clCreateKernel_
-#define clCreateKernelsInProgram clCreateKernelsInProgram_
-#define clCreateProgramWithBinary clCreateProgramWithBinary_
-#define clCreateProgramWithBuiltInKernels clCreateProgramWithBuiltInKernels_
-#define clCreateProgramWithSource clCreateProgramWithSource_
-#define clCreateSampler clCreateSampler_
-#define clCreateSubBuffer clCreateSubBuffer_
-#define clCreateSubDevices clCreateSubDevices_
-#define clCreateUserEvent clCreateUserEvent_
-#define clEnqueueBarrier clEnqueueBarrier_
-#define clEnqueueBarrierWithWaitList clEnqueueBarrierWithWaitList_
-#define clEnqueueCopyBuffer clEnqueueCopyBuffer_
-#define clEnqueueCopyBufferRect clEnqueueCopyBufferRect_
-#define clEnqueueCopyBufferToImage clEnqueueCopyBufferToImage_
-#define clEnqueueCopyImage clEnqueueCopyImage_
-#define clEnqueueCopyImageToBuffer clEnqueueCopyImageToBuffer_
-#define clEnqueueFillBuffer clEnqueueFillBuffer_
-#define clEnqueueFillImage clEnqueueFillImage_
-#define clEnqueueMapBuffer clEnqueueMapBuffer_
-#define clEnqueueMapImage clEnqueueMapImage_
-#define clEnqueueMarker clEnqueueMarker_
-#define clEnqueueMarkerWithWaitList clEnqueueMarkerWithWaitList_
-#define clEnqueueMigrateMemObjects clEnqueueMigrateMemObjects_
-#define clEnqueueNDRangeKernel clEnqueueNDRangeKernel_
-#define clEnqueueNativeKernel clEnqueueNativeKernel_
-#define clEnqueueReadBuffer clEnqueueReadBuffer_
-#define clEnqueueReadBufferRect clEnqueueReadBufferRect_
-#define clEnqueueReadImage clEnqueueReadImage_
-#define clEnqueueTask clEnqueueTask_
-#define clEnqueueUnmapMemObject clEnqueueUnmapMemObject_
-#define clEnqueueWaitForEvents clEnqueueWaitForEvents_
-#define clEnqueueWriteBuffer clEnqueueWriteBuffer_
-#define clEnqueueWriteBufferRect clEnqueueWriteBufferRect_
-#define clEnqueueWriteImage clEnqueueWriteImage_
-#define clFinish clFinish_
-#define clFlush clFlush_
-#define clGetCommandQueueInfo clGetCommandQueueInfo_
-#define clGetContextInfo clGetContextInfo_
-#define clGetDeviceIDs clGetDeviceIDs_
-#define clGetDeviceInfo clGetDeviceInfo_
-#define clGetEventInfo clGetEventInfo_
-#define clGetEventProfilingInfo clGetEventProfilingInfo_
-#define clGetExtensionFunctionAddress clGetExtensionFunctionAddress_
-#define clGetExtensionFunctionAddressForPlatform clGetExtensionFunctionAddressForPlatform_
-#define clGetImageInfo clGetImageInfo_
-#define clGetKernelArgInfo clGetKernelArgInfo_
-#define clGetKernelInfo clGetKernelInfo_
-#define clGetKernelWorkGroupInfo clGetKernelWorkGroupInfo_
-#define clGetMemObjectInfo clGetMemObjectInfo_
-#define clGetPlatformIDs clGetPlatformIDs_
-#define clGetPlatformInfo clGetPlatformInfo_
-#define clGetProgramBuildInfo clGetProgramBuildInfo_
-#define clGetProgramInfo clGetProgramInfo_
-#define clGetSamplerInfo clGetSamplerInfo_
-#define clGetSupportedImageFormats clGetSupportedImageFormats_
-#define clLinkProgram clLinkProgram_
-#define clReleaseCommandQueue clReleaseCommandQueue_
-#define clReleaseContext clReleaseContext_
-#define clReleaseDevice clReleaseDevice_
-#define clReleaseEvent clReleaseEvent_
-#define clReleaseKernel clReleaseKernel_
-#define clReleaseMemObject clReleaseMemObject_
-#define clReleaseProgram clReleaseProgram_
-#define clReleaseSampler clReleaseSampler_
-#define clRetainCommandQueue clRetainCommandQueue_
-#define clRetainContext clRetainContext_
-#define clRetainDevice clRetainDevice_
-#define clRetainEvent clRetainEvent_
-#define clRetainKernel clRetainKernel_
-#define clRetainMemObject clRetainMemObject_
-#define clRetainProgram clRetainProgram_
-#define clRetainSampler clRetainSampler_
-#define clSetEventCallback clSetEventCallback_
-#define clSetKernelArg clSetKernelArg_
-#define clSetMemObjectDestructorCallback clSetMemObjectDestructorCallback_
-#define clSetUserEventStatus clSetUserEventStatus_
-#define clUnloadCompiler clUnloadCompiler_
-#define clUnloadPlatformCompiler clUnloadPlatformCompiler_
-#define clWaitForEvents clWaitForEvents_
-
-#if defined __APPLE__
-#include <OpenCL/cl.h>
-#else
-#include <CL/cl.h>
-#endif
-
-// generated by parser_cl.py
-#undef clBuildProgram
-#define clBuildProgram clBuildProgram_pfn
-#undef clCompileProgram
-#define clCompileProgram clCompileProgram_pfn
-#undef clCreateBuffer
-#define clCreateBuffer clCreateBuffer_pfn
-#undef clCreateCommandQueue
-#define clCreateCommandQueue clCreateCommandQueue_pfn
-#undef clCreateContext
-#define clCreateContext clCreateContext_pfn
-#undef clCreateContextFromType
-#define clCreateContextFromType clCreateContextFromType_pfn
-#undef clCreateImage
-#define clCreateImage clCreateImage_pfn
-#undef clCreateImage2D
-#define clCreateImage2D clCreateImage2D_pfn
-#undef clCreateImage3D
-#define clCreateImage3D clCreateImage3D_pfn
-#undef clCreateKernel
-#define clCreateKernel clCreateKernel_pfn
-#undef clCreateKernelsInProgram
-#define clCreateKernelsInProgram clCreateKernelsInProgram_pfn
-#undef clCreateProgramWithBinary
-#define clCreateProgramWithBinary clCreateProgramWithBinary_pfn
-#undef clCreateProgramWithBuiltInKernels
-#define clCreateProgramWithBuiltInKernels clCreateProgramWithBuiltInKernels_pfn
-#undef clCreateProgramWithSource
-#define clCreateProgramWithSource clCreateProgramWithSource_pfn
-#undef clCreateSampler
-#define clCreateSampler clCreateSampler_pfn
-#undef clCreateSubBuffer
-#define clCreateSubBuffer clCreateSubBuffer_pfn
-#undef clCreateSubDevices
-#define clCreateSubDevices clCreateSubDevices_pfn
-#undef clCreateUserEvent
-#define clCreateUserEvent clCreateUserEvent_pfn
-#undef clEnqueueBarrier
-#define clEnqueueBarrier clEnqueueBarrier_pfn
-#undef clEnqueueBarrierWithWaitList
-#define clEnqueueBarrierWithWaitList clEnqueueBarrierWithWaitList_pfn
-#undef clEnqueueCopyBuffer
-#define clEnqueueCopyBuffer clEnqueueCopyBuffer_pfn
-#undef clEnqueueCopyBufferRect
-#define clEnqueueCopyBufferRect clEnqueueCopyBufferRect_pfn
-#undef clEnqueueCopyBufferToImage
-#define clEnqueueCopyBufferToImage clEnqueueCopyBufferToImage_pfn
-#undef clEnqueueCopyImage
-#define clEnqueueCopyImage clEnqueueCopyImage_pfn
-#undef clEnqueueCopyImageToBuffer
-#define clEnqueueCopyImageToBuffer clEnqueueCopyImageToBuffer_pfn
-#undef clEnqueueFillBuffer
-#define clEnqueueFillBuffer clEnqueueFillBuffer_pfn
-#undef clEnqueueFillImage
-#define clEnqueueFillImage clEnqueueFillImage_pfn
-#undef clEnqueueMapBuffer
-#define clEnqueueMapBuffer clEnqueueMapBuffer_pfn
-#undef clEnqueueMapImage
-#define clEnqueueMapImage clEnqueueMapImage_pfn
-#undef clEnqueueMarker
-#define clEnqueueMarker clEnqueueMarker_pfn
-#undef clEnqueueMarkerWithWaitList
-#define clEnqueueMarkerWithWaitList clEnqueueMarkerWithWaitList_pfn
-#undef clEnqueueMigrateMemObjects
-#define clEnqueueMigrateMemObjects clEnqueueMigrateMemObjects_pfn
-#undef clEnqueueNDRangeKernel
-#define clEnqueueNDRangeKernel clEnqueueNDRangeKernel_pfn
-#undef clEnqueueNativeKernel
-#define clEnqueueNativeKernel clEnqueueNativeKernel_pfn
-#undef clEnqueueReadBuffer
-#define clEnqueueReadBuffer clEnqueueReadBuffer_pfn
-#undef clEnqueueReadBufferRect
-#define clEnqueueReadBufferRect clEnqueueReadBufferRect_pfn
-#undef clEnqueueReadImage
-#define clEnqueueReadImage clEnqueueReadImage_pfn
-#undef clEnqueueTask
-#define clEnqueueTask clEnqueueTask_pfn
-#undef clEnqueueUnmapMemObject
-#define clEnqueueUnmapMemObject clEnqueueUnmapMemObject_pfn
-#undef clEnqueueWaitForEvents
-#define clEnqueueWaitForEvents clEnqueueWaitForEvents_pfn
-#undef clEnqueueWriteBuffer
-#define clEnqueueWriteBuffer clEnqueueWriteBuffer_pfn
-#undef clEnqueueWriteBufferRect
-#define clEnqueueWriteBufferRect clEnqueueWriteBufferRect_pfn
-#undef clEnqueueWriteImage
-#define clEnqueueWriteImage clEnqueueWriteImage_pfn
-#undef clFinish
-#define clFinish clFinish_pfn
-#undef clFlush
-#define clFlush clFlush_pfn
-#undef clGetCommandQueueInfo
-#define clGetCommandQueueInfo clGetCommandQueueInfo_pfn
-#undef clGetContextInfo
-#define clGetContextInfo clGetContextInfo_pfn
-#undef clGetDeviceIDs
-#define clGetDeviceIDs clGetDeviceIDs_pfn
-#undef clGetDeviceInfo
-#define clGetDeviceInfo clGetDeviceInfo_pfn
-#undef clGetEventInfo
-#define clGetEventInfo clGetEventInfo_pfn
-#undef clGetEventProfilingInfo
-#define clGetEventProfilingInfo clGetEventProfilingInfo_pfn
-#undef clGetExtensionFunctionAddress
-#define clGetExtensionFunctionAddress clGetExtensionFunctionAddress_pfn
-#undef clGetExtensionFunctionAddressForPlatform
-#define clGetExtensionFunctionAddressForPlatform clGetExtensionFunctionAddressForPlatform_pfn
-#undef clGetImageInfo
-#define clGetImageInfo clGetImageInfo_pfn
-#undef clGetKernelArgInfo
-#define clGetKernelArgInfo clGetKernelArgInfo_pfn
-#undef clGetKernelInfo
-#define clGetKernelInfo clGetKernelInfo_pfn
-#undef clGetKernelWorkGroupInfo
-#define clGetKernelWorkGroupInfo clGetKernelWorkGroupInfo_pfn
-#undef clGetMemObjectInfo
-#define clGetMemObjectInfo clGetMemObjectInfo_pfn
-#undef clGetPlatformIDs
-#define clGetPlatformIDs clGetPlatformIDs_pfn
-#undef clGetPlatformInfo
-#define clGetPlatformInfo clGetPlatformInfo_pfn
-#undef clGetProgramBuildInfo
-#define clGetProgramBuildInfo clGetProgramBuildInfo_pfn
-#undef clGetProgramInfo
-#define clGetProgramInfo clGetProgramInfo_pfn
-#undef clGetSamplerInfo
-#define clGetSamplerInfo clGetSamplerInfo_pfn
-#undef clGetSupportedImageFormats
-#define clGetSupportedImageFormats clGetSupportedImageFormats_pfn
-#undef clLinkProgram
-#define clLinkProgram clLinkProgram_pfn
-#undef clReleaseCommandQueue
-#define clReleaseCommandQueue clReleaseCommandQueue_pfn
-#undef clReleaseContext
-#define clReleaseContext clReleaseContext_pfn
-#undef clReleaseDevice
-#define clReleaseDevice clReleaseDevice_pfn
-#undef clReleaseEvent
-#define clReleaseEvent clReleaseEvent_pfn
-#undef clReleaseKernel
-#define clReleaseKernel clReleaseKernel_pfn
-#undef clReleaseMemObject
-#define clReleaseMemObject clReleaseMemObject_pfn
-#undef clReleaseProgram
-#define clReleaseProgram clReleaseProgram_pfn
-#undef clReleaseSampler
-#define clReleaseSampler clReleaseSampler_pfn
-#undef clRetainCommandQueue
-#define clRetainCommandQueue clRetainCommandQueue_pfn
-#undef clRetainContext
-#define clRetainContext clRetainContext_pfn
-#undef clRetainDevice
-#define clRetainDevice clRetainDevice_pfn
-#undef clRetainEvent
-#define clRetainEvent clRetainEvent_pfn
-#undef clRetainKernel
-#define clRetainKernel clRetainKernel_pfn
-#undef clRetainMemObject
-#define clRetainMemObject clRetainMemObject_pfn
-#undef clRetainProgram
-#define clRetainProgram clRetainProgram_pfn
-#undef clRetainSampler
-#define clRetainSampler clRetainSampler_pfn
-#undef clSetEventCallback
-#define clSetEventCallback clSetEventCallback_pfn
-#undef clSetKernelArg
-#define clSetKernelArg clSetKernelArg_pfn
-#undef clSetMemObjectDestructorCallback
-#define clSetMemObjectDestructorCallback clSetMemObjectDestructorCallback_pfn
-#undef clSetUserEventStatus
-#define clSetUserEventStatus clSetUserEventStatus_pfn
-#undef clUnloadCompiler
-#define clUnloadCompiler clUnloadCompiler_pfn
-#undef clUnloadPlatformCompiler
-#define clUnloadPlatformCompiler clUnloadPlatformCompiler_pfn
-#undef clWaitForEvents
-#define clWaitForEvents clWaitForEvents_pfn
-
-// generated by parser_cl.py
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clBuildProgram)(cl_program, cl_uint, const cl_device_id*, const char*, void (CL_CALLBACK*) (cl_program, void*), void*);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clCompileProgram)(cl_program, cl_uint, const cl_device_id*, const char*, cl_uint, const cl_program*, const char**, void (CL_CALLBACK*) (cl_program, void*), void*);
-extern CL_RUNTIME_EXPORT cl_mem (CL_API_CALL*clCreateBuffer)(cl_context, cl_mem_flags, size_t, void*, cl_int*);
-extern CL_RUNTIME_EXPORT cl_command_queue (CL_API_CALL*clCreateCommandQueue)(cl_context, cl_device_id, cl_command_queue_properties, cl_int*);
-extern CL_RUNTIME_EXPORT cl_context (CL_API_CALL*clCreateContext)(const cl_context_properties*, cl_uint, const cl_device_id*, void (CL_CALLBACK*) (const char*, const void*, size_t, void*), void*, cl_int*);
-extern CL_RUNTIME_EXPORT cl_context (CL_API_CALL*clCreateContextFromType)(const cl_context_properties*, cl_device_type, void (CL_CALLBACK*) (const char*, const void*, size_t, void*), void*, cl_int*);
-extern CL_RUNTIME_EXPORT cl_mem (CL_API_CALL*clCreateImage)(cl_context, cl_mem_flags, const cl_image_format*, const cl_image_desc*, void*, cl_int*);
-extern CL_RUNTIME_EXPORT cl_mem (CL_API_CALL*clCreateImage2D)(cl_context, cl_mem_flags, const cl_image_format*, size_t, size_t, size_t, void*, cl_int*);
-extern CL_RUNTIME_EXPORT cl_mem (CL_API_CALL*clCreateImage3D)(cl_context, cl_mem_flags, const cl_image_format*, size_t, size_t, size_t, size_t, size_t, void*, cl_int*);
-extern CL_RUNTIME_EXPORT cl_kernel (CL_API_CALL*clCreateKernel)(cl_program, const char*, cl_int*);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clCreateKernelsInProgram)(cl_program, cl_uint, cl_kernel*, cl_uint*);
-extern CL_RUNTIME_EXPORT cl_program (CL_API_CALL*clCreateProgramWithBinary)(cl_context, cl_uint, const cl_device_id*, const size_t*, const unsigned char**, cl_int*, cl_int*);
-extern CL_RUNTIME_EXPORT cl_program (CL_API_CALL*clCreateProgramWithBuiltInKernels)(cl_context, cl_uint, const cl_device_id*, const char*, cl_int*);
-extern CL_RUNTIME_EXPORT cl_program (CL_API_CALL*clCreateProgramWithSource)(cl_context, cl_uint, const char**, const size_t*, cl_int*);
-extern CL_RUNTIME_EXPORT cl_sampler (CL_API_CALL*clCreateSampler)(cl_context, cl_bool, cl_addressing_mode, cl_filter_mode, cl_int*);
-extern CL_RUNTIME_EXPORT cl_mem (CL_API_CALL*clCreateSubBuffer)(cl_mem, cl_mem_flags, cl_buffer_create_type, const void*, cl_int*);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clCreateSubDevices)(cl_device_id, const cl_device_partition_property*, cl_uint, cl_device_id*, cl_uint*);
-extern CL_RUNTIME_EXPORT cl_event (CL_API_CALL*clCreateUserEvent)(cl_context, cl_int*);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueBarrier)(cl_command_queue);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueBarrierWithWaitList)(cl_command_queue, cl_uint, const cl_event*, cl_event*);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueCopyBuffer)(cl_command_queue, cl_mem, cl_mem, size_t, size_t, size_t, cl_uint, const cl_event*, cl_event*);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueCopyBufferRect)(cl_command_queue, cl_mem, cl_mem, const size_t*, const size_t*, const size_t*, size_t, size_t, size_t, size_t, cl_uint, const cl_event*, cl_event*);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueCopyBufferToImage)(cl_command_queue, cl_mem, cl_mem, size_t, const size_t*, const size_t*, cl_uint, const cl_event*, cl_event*);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueCopyImage)(cl_command_queue, cl_mem, cl_mem, const size_t*, const size_t*, const size_t*, cl_uint, const cl_event*, cl_event*);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueCopyImageToBuffer)(cl_command_queue, cl_mem, cl_mem, const size_t*, const size_t*, size_t, cl_uint, const cl_event*, cl_event*);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueFillBuffer)(cl_command_queue, cl_mem, const void*, size_t, size_t, size_t, cl_uint, const cl_event*, cl_event*);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueFillImage)(cl_command_queue, cl_mem, const void*, const size_t*, const size_t*, cl_uint, const cl_event*, cl_event*);
-extern CL_RUNTIME_EXPORT void* (CL_API_CALL*clEnqueueMapBuffer)(cl_command_queue, cl_mem, cl_bool, cl_map_flags, size_t, size_t, cl_uint, const cl_event*, cl_event*, cl_int*);
-extern CL_RUNTIME_EXPORT void* (CL_API_CALL*clEnqueueMapImage)(cl_command_queue, cl_mem, cl_bool, cl_map_flags, const size_t*, const size_t*, size_t*, size_t*, cl_uint, const cl_event*, cl_event*, cl_int*);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueMarker)(cl_command_queue, cl_event*);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueMarkerWithWaitList)(cl_command_queue, cl_uint, const cl_event*, cl_event*);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueMigrateMemObjects)(cl_command_queue, cl_uint, const cl_mem*, cl_mem_migration_flags, cl_uint, const cl_event*, cl_event*);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueNDRangeKernel)(cl_command_queue, cl_kernel, cl_uint, const size_t*, const size_t*, const size_t*, cl_uint, const cl_event*, cl_event*);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueNativeKernel)(cl_command_queue, void (CL_CALLBACK*) (void*), void*, size_t, cl_uint, const cl_mem*, const void**, cl_uint, const cl_event*, cl_event*);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueReadBuffer)(cl_command_queue, cl_mem, cl_bool, size_t, size_t, void*, cl_uint, const cl_event*, cl_event*);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueReadBufferRect)(cl_command_queue, cl_mem, cl_bool, const size_t*, const size_t*, const size_t*, size_t, size_t, size_t, size_t, void*, cl_uint, const cl_event*, cl_event*);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueReadImage)(cl_command_queue, cl_mem, cl_bool, const size_t*, const size_t*, size_t, size_t, void*, cl_uint, const cl_event*, cl_event*);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueTask)(cl_command_queue, cl_kernel, cl_uint, const cl_event*, cl_event*);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueUnmapMemObject)(cl_command_queue, cl_mem, void*, cl_uint, const cl_event*, cl_event*);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueWaitForEvents)(cl_command_queue, cl_uint, const cl_event*);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueWriteBuffer)(cl_command_queue, cl_mem, cl_bool, size_t, size_t, const void*, cl_uint, const cl_event*, cl_event*);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueWriteBufferRect)(cl_command_queue, cl_mem, cl_bool, const size_t*, const size_t*, const size_t*, size_t, size_t, size_t, size_t, const void*, cl_uint, const cl_event*, cl_event*);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueWriteImage)(cl_command_queue, cl_mem, cl_bool, const size_t*, const size_t*, size_t, size_t, const void*, cl_uint, const cl_event*, cl_event*);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clFinish)(cl_command_queue);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clFlush)(cl_command_queue);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetCommandQueueInfo)(cl_command_queue, cl_command_queue_info, size_t, void*, size_t*);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetContextInfo)(cl_context, cl_context_info, size_t, void*, size_t*);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetDeviceIDs)(cl_platform_id, cl_device_type, cl_uint, cl_device_id*, cl_uint*);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetDeviceInfo)(cl_device_id, cl_device_info, size_t, void*, size_t*);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetEventInfo)(cl_event, cl_event_info, size_t, void*, size_t*);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetEventProfilingInfo)(cl_event, cl_profiling_info, size_t, void*, size_t*);
-extern CL_RUNTIME_EXPORT void* (CL_API_CALL*clGetExtensionFunctionAddress)(const char*);
-extern CL_RUNTIME_EXPORT void* (CL_API_CALL*clGetExtensionFunctionAddressForPlatform)(cl_platform_id, const char*);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetImageInfo)(cl_mem, cl_image_info, size_t, void*, size_t*);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetKernelArgInfo)(cl_kernel, cl_uint, cl_kernel_arg_info, size_t, void*, size_t*);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetKernelInfo)(cl_kernel, cl_kernel_info, size_t, void*, size_t*);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetKernelWorkGroupInfo)(cl_kernel, cl_device_id, cl_kernel_work_group_info, size_t, void*, size_t*);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetMemObjectInfo)(cl_mem, cl_mem_info, size_t, void*, size_t*);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetPlatformIDs)(cl_uint, cl_platform_id*, cl_uint*);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetPlatformInfo)(cl_platform_id, cl_platform_info, size_t, void*, size_t*);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetProgramBuildInfo)(cl_program, cl_device_id, cl_program_build_info, size_t, void*, size_t*);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetProgramInfo)(cl_program, cl_program_info, size_t, void*, size_t*);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetSamplerInfo)(cl_sampler, cl_sampler_info, size_t, void*, size_t*);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetSupportedImageFormats)(cl_context, cl_mem_flags, cl_mem_object_type, cl_uint, cl_image_format*, cl_uint*);
-extern CL_RUNTIME_EXPORT cl_program (CL_API_CALL*clLinkProgram)(cl_context, cl_uint, const cl_device_id*, const char*, cl_uint, const cl_program*, void (CL_CALLBACK*) (cl_program, void*), void*, cl_int*);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clReleaseCommandQueue)(cl_command_queue);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clReleaseContext)(cl_context);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clReleaseDevice)(cl_device_id);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clReleaseEvent)(cl_event);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clReleaseKernel)(cl_kernel);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clReleaseMemObject)(cl_mem);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clReleaseProgram)(cl_program);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clReleaseSampler)(cl_sampler);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clRetainCommandQueue)(cl_command_queue);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clRetainContext)(cl_context);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clRetainDevice)(cl_device_id);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clRetainEvent)(cl_event);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clRetainKernel)(cl_kernel);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clRetainMemObject)(cl_mem);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clRetainProgram)(cl_program);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clRetainSampler)(cl_sampler);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clSetEventCallback)(cl_event, cl_int, void (CL_CALLBACK*) (cl_event, cl_int, void*), void*);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clSetKernelArg)(cl_kernel, cl_uint, size_t, const void*);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clSetMemObjectDestructorCallback)(cl_mem, void (CL_CALLBACK*) (cl_mem, void*), void*);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clSetUserEventStatus)(cl_event, cl_int);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clUnloadCompiler)();
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clUnloadPlatformCompiler)(cl_platform_id);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clWaitForEvents)(cl_uint, const cl_event*);
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/opencl/runtime/autogenerated/opencl_core_wrappers.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/opencl/runtime/autogenerated/opencl_core_wrappers.hpp
deleted file mode 100755
index 216b22b..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/opencl/runtime/autogenerated/opencl_core_wrappers.hpp
+++ /dev/null
@@ -1,272 +0,0 @@
-//
-// AUTOGENERATED, DO NOT EDIT
-//
-#ifndef OPENCV_CORE_OCL_RUNTIME_OPENCL_WRAPPERS_HPP
-#error "Invalid usage"
-#endif
-
-// generated by parser_cl.py
-#undef clBuildProgram
-#define clBuildProgram clBuildProgram_fn
-inline cl_int clBuildProgram(cl_program p0, cl_uint p1, const cl_device_id* p2, const char* p3, void (CL_CALLBACK*p4) (cl_program, void*), void* p5) { return clBuildProgram_pfn(p0, p1, p2, p3, p4, p5); }
-#undef clCompileProgram
-#define clCompileProgram clCompileProgram_fn
-inline cl_int clCompileProgram(cl_program p0, cl_uint p1, const cl_device_id* p2, const char* p3, cl_uint p4, const cl_program* p5, const char** p6, void (CL_CALLBACK*p7) (cl_program, void*), void* p8) { return clCompileProgram_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8); }
-#undef clCreateBuffer
-#define clCreateBuffer clCreateBuffer_fn
-inline cl_mem clCreateBuffer(cl_context p0, cl_mem_flags p1, size_t p2, void* p3, cl_int* p4) { return clCreateBuffer_pfn(p0, p1, p2, p3, p4); }
-#undef clCreateCommandQueue
-#define clCreateCommandQueue clCreateCommandQueue_fn
-inline cl_command_queue clCreateCommandQueue(cl_context p0, cl_device_id p1, cl_command_queue_properties p2, cl_int* p3) { return clCreateCommandQueue_pfn(p0, p1, p2, p3); }
-#undef clCreateContext
-#define clCreateContext clCreateContext_fn
-inline cl_context clCreateContext(const cl_context_properties* p0, cl_uint p1, const cl_device_id* p2, void (CL_CALLBACK*p3) (const char*, const void*, size_t, void*), void* p4, cl_int* p5) { return clCreateContext_pfn(p0, p1, p2, p3, p4, p5); }
-#undef clCreateContextFromType
-#define clCreateContextFromType clCreateContextFromType_fn
-inline cl_context clCreateContextFromType(const cl_context_properties* p0, cl_device_type p1, void (CL_CALLBACK*p2) (const char*, const void*, size_t, void*), void* p3, cl_int* p4) { return clCreateContextFromType_pfn(p0, p1, p2, p3, p4); }
-#undef clCreateImage
-#define clCreateImage clCreateImage_fn
-inline cl_mem clCreateImage(cl_context p0, cl_mem_flags p1, const cl_image_format* p2, const cl_image_desc* p3, void* p4, cl_int* p5) { return clCreateImage_pfn(p0, p1, p2, p3, p4, p5); }
-#undef clCreateImage2D
-#define clCreateImage2D clCreateImage2D_fn
-inline cl_mem clCreateImage2D(cl_context p0, cl_mem_flags p1, const cl_image_format* p2, size_t p3, size_t p4, size_t p5, void* p6, cl_int* p7) { return clCreateImage2D_pfn(p0, p1, p2, p3, p4, p5, p6, p7); }
-#undef clCreateImage3D
-#define clCreateImage3D clCreateImage3D_fn
-inline cl_mem clCreateImage3D(cl_context p0, cl_mem_flags p1, const cl_image_format* p2, size_t p3, size_t p4, size_t p5, size_t p6, size_t p7, void* p8, cl_int* p9) { return clCreateImage3D_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8, p9); }
-#undef clCreateKernel
-#define clCreateKernel clCreateKernel_fn
-inline cl_kernel clCreateKernel(cl_program p0, const char* p1, cl_int* p2) { return clCreateKernel_pfn(p0, p1, p2); }
-#undef clCreateKernelsInProgram
-#define clCreateKernelsInProgram clCreateKernelsInProgram_fn
-inline cl_int clCreateKernelsInProgram(cl_program p0, cl_uint p1, cl_kernel* p2, cl_uint* p3) { return clCreateKernelsInProgram_pfn(p0, p1, p2, p3); }
-#undef clCreateProgramWithBinary
-#define clCreateProgramWithBinary clCreateProgramWithBinary_fn
-inline cl_program clCreateProgramWithBinary(cl_context p0, cl_uint p1, const cl_device_id* p2, const size_t* p3, const unsigned char** p4, cl_int* p5, cl_int* p6) { return clCreateProgramWithBinary_pfn(p0, p1, p2, p3, p4, p5, p6); }
-#undef clCreateProgramWithBuiltInKernels
-#define clCreateProgramWithBuiltInKernels clCreateProgramWithBuiltInKernels_fn
-inline cl_program clCreateProgramWithBuiltInKernels(cl_context p0, cl_uint p1, const cl_device_id* p2, const char* p3, cl_int* p4) { return clCreateProgramWithBuiltInKernels_pfn(p0, p1, p2, p3, p4); }
-#undef clCreateProgramWithSource
-#define clCreateProgramWithSource clCreateProgramWithSource_fn
-inline cl_program clCreateProgramWithSource(cl_context p0, cl_uint p1, const char** p2, const size_t* p3, cl_int* p4) { return clCreateProgramWithSource_pfn(p0, p1, p2, p3, p4); }
-#undef clCreateSampler
-#define clCreateSampler clCreateSampler_fn
-inline cl_sampler clCreateSampler(cl_context p0, cl_bool p1, cl_addressing_mode p2, cl_filter_mode p3, cl_int* p4) { return clCreateSampler_pfn(p0, p1, p2, p3, p4); }
-#undef clCreateSubBuffer
-#define clCreateSubBuffer clCreateSubBuffer_fn
-inline cl_mem clCreateSubBuffer(cl_mem p0, cl_mem_flags p1, cl_buffer_create_type p2, const void* p3, cl_int* p4) { return clCreateSubBuffer_pfn(p0, p1, p2, p3, p4); }
-#undef clCreateSubDevices
-#define clCreateSubDevices clCreateSubDevices_fn
-inline cl_int clCreateSubDevices(cl_device_id p0, const cl_device_partition_property* p1, cl_uint p2, cl_device_id* p3, cl_uint* p4) { return clCreateSubDevices_pfn(p0, p1, p2, p3, p4); }
-#undef clCreateUserEvent
-#define clCreateUserEvent clCreateUserEvent_fn
-inline cl_event clCreateUserEvent(cl_context p0, cl_int* p1) { return clCreateUserEvent_pfn(p0, p1); }
-#undef clEnqueueBarrier
-#define clEnqueueBarrier clEnqueueBarrier_fn
-inline cl_int clEnqueueBarrier(cl_command_queue p0) { return clEnqueueBarrier_pfn(p0); }
-#undef clEnqueueBarrierWithWaitList
-#define clEnqueueBarrierWithWaitList clEnqueueBarrierWithWaitList_fn
-inline cl_int clEnqueueBarrierWithWaitList(cl_command_queue p0, cl_uint p1, const cl_event* p2, cl_event* p3) { return clEnqueueBarrierWithWaitList_pfn(p0, p1, p2, p3); }
-#undef clEnqueueCopyBuffer
-#define clEnqueueCopyBuffer clEnqueueCopyBuffer_fn
-inline cl_int clEnqueueCopyBuffer(cl_command_queue p0, cl_mem p1, cl_mem p2, size_t p3, size_t p4, size_t p5, cl_uint p6, const cl_event* p7, cl_event* p8) { return clEnqueueCopyBuffer_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8); }
-#undef clEnqueueCopyBufferRect
-#define clEnqueueCopyBufferRect clEnqueueCopyBufferRect_fn
-inline cl_int clEnqueueCopyBufferRect(cl_command_queue p0, cl_mem p1, cl_mem p2, const size_t* p3, const size_t* p4, const size_t* p5, size_t p6, size_t p7, size_t p8, size_t p9, cl_uint p10, const cl_event* p11, cl_event* p12) { return clEnqueueCopyBufferRect_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12); }
-#undef clEnqueueCopyBufferToImage
-#define clEnqueueCopyBufferToImage clEnqueueCopyBufferToImage_fn
-inline cl_int clEnqueueCopyBufferToImage(cl_command_queue p0, cl_mem p1, cl_mem p2, size_t p3, const size_t* p4, const size_t* p5, cl_uint p6, const cl_event* p7, cl_event* p8) { return clEnqueueCopyBufferToImage_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8); }
-#undef clEnqueueCopyImage
-#define clEnqueueCopyImage clEnqueueCopyImage_fn
-inline cl_int clEnqueueCopyImage(cl_command_queue p0, cl_mem p1, cl_mem p2, const size_t* p3, const size_t* p4, const size_t* p5, cl_uint p6, const cl_event* p7, cl_event* p8) { return clEnqueueCopyImage_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8); }
-#undef clEnqueueCopyImageToBuffer
-#define clEnqueueCopyImageToBuffer clEnqueueCopyImageToBuffer_fn
-inline cl_int clEnqueueCopyImageToBuffer(cl_command_queue p0, cl_mem p1, cl_mem p2, const size_t* p3, const size_t* p4, size_t p5, cl_uint p6, const cl_event* p7, cl_event* p8) { return clEnqueueCopyImageToBuffer_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8); }
-#undef clEnqueueFillBuffer
-#define clEnqueueFillBuffer clEnqueueFillBuffer_fn
-inline cl_int clEnqueueFillBuffer(cl_command_queue p0, cl_mem p1, const void* p2, size_t p3, size_t p4, size_t p5, cl_uint p6, const cl_event* p7, cl_event* p8) { return clEnqueueFillBuffer_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8); }
-#undef clEnqueueFillImage
-#define clEnqueueFillImage clEnqueueFillImage_fn
-inline cl_int clEnqueueFillImage(cl_command_queue p0, cl_mem p1, const void* p2, const size_t* p3, const size_t* p4, cl_uint p5, const cl_event* p6, cl_event* p7) { return clEnqueueFillImage_pfn(p0, p1, p2, p3, p4, p5, p6, p7); }
-#undef clEnqueueMapBuffer
-#define clEnqueueMapBuffer clEnqueueMapBuffer_fn
-inline void* clEnqueueMapBuffer(cl_command_queue p0, cl_mem p1, cl_bool p2, cl_map_flags p3, size_t p4, size_t p5, cl_uint p6, const cl_event* p7, cl_event* p8, cl_int* p9) { return clEnqueueMapBuffer_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8, p9); }
-#undef clEnqueueMapImage
-#define clEnqueueMapImage clEnqueueMapImage_fn
-inline void* clEnqueueMapImage(cl_command_queue p0, cl_mem p1, cl_bool p2, cl_map_flags p3, const size_t* p4, const size_t* p5, size_t* p6, size_t* p7, cl_uint p8, const cl_event* p9, cl_event* p10, cl_int* p11) { return clEnqueueMapImage_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11); }
-#undef clEnqueueMarker
-#define clEnqueueMarker clEnqueueMarker_fn
-inline cl_int clEnqueueMarker(cl_command_queue p0, cl_event* p1) { return clEnqueueMarker_pfn(p0, p1); }
-#undef clEnqueueMarkerWithWaitList
-#define clEnqueueMarkerWithWaitList clEnqueueMarkerWithWaitList_fn
-inline cl_int clEnqueueMarkerWithWaitList(cl_command_queue p0, cl_uint p1, const cl_event* p2, cl_event* p3) { return clEnqueueMarkerWithWaitList_pfn(p0, p1, p2, p3); }
-#undef clEnqueueMigrateMemObjects
-#define clEnqueueMigrateMemObjects clEnqueueMigrateMemObjects_fn
-inline cl_int clEnqueueMigrateMemObjects(cl_command_queue p0, cl_uint p1, const cl_mem* p2, cl_mem_migration_flags p3, cl_uint p4, const cl_event* p5, cl_event* p6) { return clEnqueueMigrateMemObjects_pfn(p0, p1, p2, p3, p4, p5, p6); }
-#undef clEnqueueNDRangeKernel
-#define clEnqueueNDRangeKernel clEnqueueNDRangeKernel_fn
-inline cl_int clEnqueueNDRangeKernel(cl_command_queue p0, cl_kernel p1, cl_uint p2, const size_t* p3, const size_t* p4, const size_t* p5, cl_uint p6, const cl_event* p7, cl_event* p8) { return clEnqueueNDRangeKernel_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8); }
-#undef clEnqueueNativeKernel
-#define clEnqueueNativeKernel clEnqueueNativeKernel_fn
-inline cl_int clEnqueueNativeKernel(cl_command_queue p0, void (CL_CALLBACK*p1) (void*), void* p2, size_t p3, cl_uint p4, const cl_mem* p5, const void** p6, cl_uint p7, const cl_event* p8, cl_event* p9) { return clEnqueueNativeKernel_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8, p9); }
-#undef clEnqueueReadBuffer
-#define clEnqueueReadBuffer clEnqueueReadBuffer_fn
-inline cl_int clEnqueueReadBuffer(cl_command_queue p0, cl_mem p1, cl_bool p2, size_t p3, size_t p4, void* p5, cl_uint p6, const cl_event* p7, cl_event* p8) { return clEnqueueReadBuffer_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8); }
-#undef clEnqueueReadBufferRect
-#define clEnqueueReadBufferRect clEnqueueReadBufferRect_fn
-inline cl_int clEnqueueReadBufferRect(cl_command_queue p0, cl_mem p1, cl_bool p2, const size_t* p3, const size_t* p4, const size_t* p5, size_t p6, size_t p7, size_t p8, size_t p9, void* p10, cl_uint p11, const cl_event* p12, cl_event* p13) { return clEnqueueReadBufferRect_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12, p13); }
-#undef clEnqueueReadImage
-#define clEnqueueReadImage clEnqueueReadImage_fn
-inline cl_int clEnqueueReadImage(cl_command_queue p0, cl_mem p1, cl_bool p2, const size_t* p3, const size_t* p4, size_t p5, size_t p6, void* p7, cl_uint p8, const cl_event* p9, cl_event* p10) { return clEnqueueReadImage_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8, p9, p10); }
-#undef clEnqueueTask
-#define clEnqueueTask clEnqueueTask_fn
-inline cl_int clEnqueueTask(cl_command_queue p0, cl_kernel p1, cl_uint p2, const cl_event* p3, cl_event* p4) { return clEnqueueTask_pfn(p0, p1, p2, p3, p4); }
-#undef clEnqueueUnmapMemObject
-#define clEnqueueUnmapMemObject clEnqueueUnmapMemObject_fn
-inline cl_int clEnqueueUnmapMemObject(cl_command_queue p0, cl_mem p1, void* p2, cl_uint p3, const cl_event* p4, cl_event* p5) { return clEnqueueUnmapMemObject_pfn(p0, p1, p2, p3, p4, p5); }
-#undef clEnqueueWaitForEvents
-#define clEnqueueWaitForEvents clEnqueueWaitForEvents_fn
-inline cl_int clEnqueueWaitForEvents(cl_command_queue p0, cl_uint p1, const cl_event* p2) { return clEnqueueWaitForEvents_pfn(p0, p1, p2); }
-#undef clEnqueueWriteBuffer
-#define clEnqueueWriteBuffer clEnqueueWriteBuffer_fn
-inline cl_int clEnqueueWriteBuffer(cl_command_queue p0, cl_mem p1, cl_bool p2, size_t p3, size_t p4, const void* p5, cl_uint p6, const cl_event* p7, cl_event* p8) { return clEnqueueWriteBuffer_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8); }
-#undef clEnqueueWriteBufferRect
-#define clEnqueueWriteBufferRect clEnqueueWriteBufferRect_fn
-inline cl_int clEnqueueWriteBufferRect(cl_command_queue p0, cl_mem p1, cl_bool p2, const size_t* p3, const size_t* p4, const size_t* p5, size_t p6, size_t p7, size_t p8, size_t p9, const void* p10, cl_uint p11, const cl_event* p12, cl_event* p13) { return clEnqueueWriteBufferRect_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12, p13); }
-#undef clEnqueueWriteImage
-#define clEnqueueWriteImage clEnqueueWriteImage_fn
-inline cl_int clEnqueueWriteImage(cl_command_queue p0, cl_mem p1, cl_bool p2, const size_t* p3, const size_t* p4, size_t p5, size_t p6, const void* p7, cl_uint p8, const cl_event* p9, cl_event* p10) { return clEnqueueWriteImage_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8, p9, p10); }
-#undef clFinish
-#define clFinish clFinish_fn
-inline cl_int clFinish(cl_command_queue p0) { return clFinish_pfn(p0); }
-#undef clFlush
-#define clFlush clFlush_fn
-inline cl_int clFlush(cl_command_queue p0) { return clFlush_pfn(p0); }
-#undef clGetCommandQueueInfo
-#define clGetCommandQueueInfo clGetCommandQueueInfo_fn
-inline cl_int clGetCommandQueueInfo(cl_command_queue p0, cl_command_queue_info p1, size_t p2, void* p3, size_t* p4) { return clGetCommandQueueInfo_pfn(p0, p1, p2, p3, p4); }
-#undef clGetContextInfo
-#define clGetContextInfo clGetContextInfo_fn
-inline cl_int clGetContextInfo(cl_context p0, cl_context_info p1, size_t p2, void* p3, size_t* p4) { return clGetContextInfo_pfn(p0, p1, p2, p3, p4); }
-#undef clGetDeviceIDs
-#define clGetDeviceIDs clGetDeviceIDs_fn
-inline cl_int clGetDeviceIDs(cl_platform_id p0, cl_device_type p1, cl_uint p2, cl_device_id* p3, cl_uint* p4) { return clGetDeviceIDs_pfn(p0, p1, p2, p3, p4); }
-#undef clGetDeviceInfo
-#define clGetDeviceInfo clGetDeviceInfo_fn
-inline cl_int clGetDeviceInfo(cl_device_id p0, cl_device_info p1, size_t p2, void* p3, size_t* p4) { return clGetDeviceInfo_pfn(p0, p1, p2, p3, p4); }
-#undef clGetEventInfo
-#define clGetEventInfo clGetEventInfo_fn
-inline cl_int clGetEventInfo(cl_event p0, cl_event_info p1, size_t p2, void* p3, size_t* p4) { return clGetEventInfo_pfn(p0, p1, p2, p3, p4); }
-#undef clGetEventProfilingInfo
-#define clGetEventProfilingInfo clGetEventProfilingInfo_fn
-inline cl_int clGetEventProfilingInfo(cl_event p0, cl_profiling_info p1, size_t p2, void* p3, size_t* p4) { return clGetEventProfilingInfo_pfn(p0, p1, p2, p3, p4); }
-#undef clGetExtensionFunctionAddress
-#define clGetExtensionFunctionAddress clGetExtensionFunctionAddress_fn
-inline void* clGetExtensionFunctionAddress(const char* p0) { return clGetExtensionFunctionAddress_pfn(p0); }
-#undef clGetExtensionFunctionAddressForPlatform
-#define clGetExtensionFunctionAddressForPlatform clGetExtensionFunctionAddressForPlatform_fn
-inline void* clGetExtensionFunctionAddressForPlatform(cl_platform_id p0, const char* p1) { return clGetExtensionFunctionAddressForPlatform_pfn(p0, p1); }
-#undef clGetImageInfo
-#define clGetImageInfo clGetImageInfo_fn
-inline cl_int clGetImageInfo(cl_mem p0, cl_image_info p1, size_t p2, void* p3, size_t* p4) { return clGetImageInfo_pfn(p0, p1, p2, p3, p4); }
-#undef clGetKernelArgInfo
-#define clGetKernelArgInfo clGetKernelArgInfo_fn
-inline cl_int clGetKernelArgInfo(cl_kernel p0, cl_uint p1, cl_kernel_arg_info p2, size_t p3, void* p4, size_t* p5) { return clGetKernelArgInfo_pfn(p0, p1, p2, p3, p4, p5); }
-#undef clGetKernelInfo
-#define clGetKernelInfo clGetKernelInfo_fn
-inline cl_int clGetKernelInfo(cl_kernel p0, cl_kernel_info p1, size_t p2, void* p3, size_t* p4) { return clGetKernelInfo_pfn(p0, p1, p2, p3, p4); }
-#undef clGetKernelWorkGroupInfo
-#define clGetKernelWorkGroupInfo clGetKernelWorkGroupInfo_fn
-inline cl_int clGetKernelWorkGroupInfo(cl_kernel p0, cl_device_id p1, cl_kernel_work_group_info p2, size_t p3, void* p4, size_t* p5) { return clGetKernelWorkGroupInfo_pfn(p0, p1, p2, p3, p4, p5); }
-#undef clGetMemObjectInfo
-#define clGetMemObjectInfo clGetMemObjectInfo_fn
-inline cl_int clGetMemObjectInfo(cl_mem p0, cl_mem_info p1, size_t p2, void* p3, size_t* p4) { return clGetMemObjectInfo_pfn(p0, p1, p2, p3, p4); }
-#undef clGetPlatformIDs
-#define clGetPlatformIDs clGetPlatformIDs_fn
-inline cl_int clGetPlatformIDs(cl_uint p0, cl_platform_id* p1, cl_uint* p2) { return clGetPlatformIDs_pfn(p0, p1, p2); }
-#undef clGetPlatformInfo
-#define clGetPlatformInfo clGetPlatformInfo_fn
-inline cl_int clGetPlatformInfo(cl_platform_id p0, cl_platform_info p1, size_t p2, void* p3, size_t* p4) { return clGetPlatformInfo_pfn(p0, p1, p2, p3, p4); }
-#undef clGetProgramBuildInfo
-#define clGetProgramBuildInfo clGetProgramBuildInfo_fn
-inline cl_int clGetProgramBuildInfo(cl_program p0, cl_device_id p1, cl_program_build_info p2, size_t p3, void* p4, size_t* p5) { return clGetProgramBuildInfo_pfn(p0, p1, p2, p3, p4, p5); }
-#undef clGetProgramInfo
-#define clGetProgramInfo clGetProgramInfo_fn
-inline cl_int clGetProgramInfo(cl_program p0, cl_program_info p1, size_t p2, void* p3, size_t* p4) { return clGetProgramInfo_pfn(p0, p1, p2, p3, p4); }
-#undef clGetSamplerInfo
-#define clGetSamplerInfo clGetSamplerInfo_fn
-inline cl_int clGetSamplerInfo(cl_sampler p0, cl_sampler_info p1, size_t p2, void* p3, size_t* p4) { return clGetSamplerInfo_pfn(p0, p1, p2, p3, p4); }
-#undef clGetSupportedImageFormats
-#define clGetSupportedImageFormats clGetSupportedImageFormats_fn
-inline cl_int clGetSupportedImageFormats(cl_context p0, cl_mem_flags p1, cl_mem_object_type p2, cl_uint p3, cl_image_format* p4, cl_uint* p5) { return clGetSupportedImageFormats_pfn(p0, p1, p2, p3, p4, p5); }
-#undef clLinkProgram
-#define clLinkProgram clLinkProgram_fn
-inline cl_program clLinkProgram(cl_context p0, cl_uint p1, const cl_device_id* p2, const char* p3, cl_uint p4, const cl_program* p5, void (CL_CALLBACK*p6) (cl_program, void*), void* p7, cl_int* p8) { return clLinkProgram_pfn(p0, p1, p2, p3, p4, p5, p6, p7, p8); }
-#undef clReleaseCommandQueue
-#define clReleaseCommandQueue clReleaseCommandQueue_fn
-inline cl_int clReleaseCommandQueue(cl_command_queue p0) { return clReleaseCommandQueue_pfn(p0); }
-#undef clReleaseContext
-#define clReleaseContext clReleaseContext_fn
-inline cl_int clReleaseContext(cl_context p0) { return clReleaseContext_pfn(p0); }
-#undef clReleaseDevice
-#define clReleaseDevice clReleaseDevice_fn
-inline cl_int clReleaseDevice(cl_device_id p0) { return clReleaseDevice_pfn(p0); }
-#undef clReleaseEvent
-#define clReleaseEvent clReleaseEvent_fn
-inline cl_int clReleaseEvent(cl_event p0) { return clReleaseEvent_pfn(p0); }
-#undef clReleaseKernel
-#define clReleaseKernel clReleaseKernel_fn
-inline cl_int clReleaseKernel(cl_kernel p0) { return clReleaseKernel_pfn(p0); }
-#undef clReleaseMemObject
-#define clReleaseMemObject clReleaseMemObject_fn
-inline cl_int clReleaseMemObject(cl_mem p0) { return clReleaseMemObject_pfn(p0); }
-#undef clReleaseProgram
-#define clReleaseProgram clReleaseProgram_fn
-inline cl_int clReleaseProgram(cl_program p0) { return clReleaseProgram_pfn(p0); }
-#undef clReleaseSampler
-#define clReleaseSampler clReleaseSampler_fn
-inline cl_int clReleaseSampler(cl_sampler p0) { return clReleaseSampler_pfn(p0); }
-#undef clRetainCommandQueue
-#define clRetainCommandQueue clRetainCommandQueue_fn
-inline cl_int clRetainCommandQueue(cl_command_queue p0) { return clRetainCommandQueue_pfn(p0); }
-#undef clRetainContext
-#define clRetainContext clRetainContext_fn
-inline cl_int clRetainContext(cl_context p0) { return clRetainContext_pfn(p0); }
-#undef clRetainDevice
-#define clRetainDevice clRetainDevice_fn
-inline cl_int clRetainDevice(cl_device_id p0) { return clRetainDevice_pfn(p0); }
-#undef clRetainEvent
-#define clRetainEvent clRetainEvent_fn
-inline cl_int clRetainEvent(cl_event p0) { return clRetainEvent_pfn(p0); }
-#undef clRetainKernel
-#define clRetainKernel clRetainKernel_fn
-inline cl_int clRetainKernel(cl_kernel p0) { return clRetainKernel_pfn(p0); }
-#undef clRetainMemObject
-#define clRetainMemObject clRetainMemObject_fn
-inline cl_int clRetainMemObject(cl_mem p0) { return clRetainMemObject_pfn(p0); }
-#undef clRetainProgram
-#define clRetainProgram clRetainProgram_fn
-inline cl_int clRetainProgram(cl_program p0) { return clRetainProgram_pfn(p0); }
-#undef clRetainSampler
-#define clRetainSampler clRetainSampler_fn
-inline cl_int clRetainSampler(cl_sampler p0) { return clRetainSampler_pfn(p0); }
-#undef clSetEventCallback
-#define clSetEventCallback clSetEventCallback_fn
-inline cl_int clSetEventCallback(cl_event p0, cl_int p1, void (CL_CALLBACK*p2) (cl_event, cl_int, void*), void* p3) { return clSetEventCallback_pfn(p0, p1, p2, p3); }
-#undef clSetKernelArg
-#define clSetKernelArg clSetKernelArg_fn
-inline cl_int clSetKernelArg(cl_kernel p0, cl_uint p1, size_t p2, const void* p3) { return clSetKernelArg_pfn(p0, p1, p2, p3); }
-#undef clSetMemObjectDestructorCallback
-#define clSetMemObjectDestructorCallback clSetMemObjectDestructorCallback_fn
-inline cl_int clSetMemObjectDestructorCallback(cl_mem p0, void (CL_CALLBACK*p1) (cl_mem, void*), void* p2) { return clSetMemObjectDestructorCallback_pfn(p0, p1, p2); }
-#undef clSetUserEventStatus
-#define clSetUserEventStatus clSetUserEventStatus_fn
-inline cl_int clSetUserEventStatus(cl_event p0, cl_int p1) { return clSetUserEventStatus_pfn(p0, p1); }
-#undef clUnloadCompiler
-#define clUnloadCompiler clUnloadCompiler_fn
-inline cl_int clUnloadCompiler() { return clUnloadCompiler_pfn(); }
-#undef clUnloadPlatformCompiler
-#define clUnloadPlatformCompiler clUnloadPlatformCompiler_fn
-inline cl_int clUnloadPlatformCompiler(cl_platform_id p0) { return clUnloadPlatformCompiler_pfn(p0); }
-#undef clWaitForEvents
-#define clWaitForEvents clWaitForEvents_fn
-inline cl_int clWaitForEvents(cl_uint p0, const cl_event* p1) { return clWaitForEvents_pfn(p0, p1); }
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/opencl/runtime/autogenerated/opencl_gl.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/opencl/runtime/autogenerated/opencl_gl.hpp
deleted file mode 100755
index 0b12aed..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/opencl/runtime/autogenerated/opencl_gl.hpp
+++ /dev/null
@@ -1,62 +0,0 @@
-//
-// AUTOGENERATED, DO NOT EDIT
-//
-#ifndef OPENCV_CORE_OCL_RUNTIME_OPENCL_GL_HPP
-#error "Invalid usage"
-#endif
-
-// generated by parser_cl.py
-#define clCreateFromGLBuffer clCreateFromGLBuffer_
-#define clCreateFromGLRenderbuffer clCreateFromGLRenderbuffer_
-#define clCreateFromGLTexture clCreateFromGLTexture_
-#define clCreateFromGLTexture2D clCreateFromGLTexture2D_
-#define clCreateFromGLTexture3D clCreateFromGLTexture3D_
-#define clEnqueueAcquireGLObjects clEnqueueAcquireGLObjects_
-#define clEnqueueReleaseGLObjects clEnqueueReleaseGLObjects_
-#define clGetGLContextInfoKHR clGetGLContextInfoKHR_
-#define clGetGLObjectInfo clGetGLObjectInfo_
-#define clGetGLTextureInfo clGetGLTextureInfo_
-
-#if defined __APPLE__
-#include <OpenCL/cl_gl.h>
-#else
-#include <CL/cl_gl.h>
-#endif
-
-// generated by parser_cl.py
-#undef clCreateFromGLBuffer
-#define clCreateFromGLBuffer clCreateFromGLBuffer_pfn
-#undef clCreateFromGLRenderbuffer
-#define clCreateFromGLRenderbuffer clCreateFromGLRenderbuffer_pfn
-#undef clCreateFromGLTexture
-#define clCreateFromGLTexture clCreateFromGLTexture_pfn
-#undef clCreateFromGLTexture2D
-#define clCreateFromGLTexture2D clCreateFromGLTexture2D_pfn
-#undef clCreateFromGLTexture3D
-#define clCreateFromGLTexture3D clCreateFromGLTexture3D_pfn
-#undef clEnqueueAcquireGLObjects
-#define clEnqueueAcquireGLObjects clEnqueueAcquireGLObjects_pfn
-#undef clEnqueueReleaseGLObjects
-#define clEnqueueReleaseGLObjects clEnqueueReleaseGLObjects_pfn
-#undef clGetGLContextInfoKHR
-#define clGetGLContextInfoKHR clGetGLContextInfoKHR_pfn
-#undef clGetGLObjectInfo
-#define clGetGLObjectInfo clGetGLObjectInfo_pfn
-#undef clGetGLTextureInfo
-#define clGetGLTextureInfo clGetGLTextureInfo_pfn
-
-#ifdef cl_khr_gl_sharing
-
-// generated by parser_cl.py
-extern CL_RUNTIME_EXPORT cl_mem (CL_API_CALL*clCreateFromGLBuffer)(cl_context, cl_mem_flags, cl_GLuint, int*);
-extern CL_RUNTIME_EXPORT cl_mem (CL_API_CALL*clCreateFromGLRenderbuffer)(cl_context, cl_mem_flags, cl_GLuint, cl_int*);
-extern CL_RUNTIME_EXPORT cl_mem (CL_API_CALL*clCreateFromGLTexture)(cl_context, cl_mem_flags, cl_GLenum, cl_GLint, cl_GLuint, cl_int*);
-extern CL_RUNTIME_EXPORT cl_mem (CL_API_CALL*clCreateFromGLTexture2D)(cl_context, cl_mem_flags, cl_GLenum, cl_GLint, cl_GLuint, cl_int*);
-extern CL_RUNTIME_EXPORT cl_mem (CL_API_CALL*clCreateFromGLTexture3D)(cl_context, cl_mem_flags, cl_GLenum, cl_GLint, cl_GLuint, cl_int*);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueAcquireGLObjects)(cl_command_queue, cl_uint, const cl_mem*, cl_uint, const cl_event*, cl_event*);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clEnqueueReleaseGLObjects)(cl_command_queue, cl_uint, const cl_mem*, cl_uint, const cl_event*, cl_event*);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetGLContextInfoKHR)(const cl_context_properties*, cl_gl_context_info, size_t, void*, size_t*);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetGLObjectInfo)(cl_mem, cl_gl_object_type*, cl_GLuint*);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL*clGetGLTextureInfo)(cl_mem, cl_gl_texture_info, size_t, void*, size_t*);
-
-#endif // cl_khr_gl_sharing
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/opencl/runtime/autogenerated/opencl_gl_wrappers.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/opencl/runtime/autogenerated/opencl_gl_wrappers.hpp
deleted file mode 100755
index 12f342b..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/opencl/runtime/autogenerated/opencl_gl_wrappers.hpp
+++ /dev/null
@@ -1,42 +0,0 @@
-//
-// AUTOGENERATED, DO NOT EDIT
-//
-#ifndef OPENCV_CORE_OCL_RUNTIME_OPENCL_GL_WRAPPERS_HPP
-#error "Invalid usage"
-#endif
-
-#ifdef cl_khr_gl_sharing
-
-// generated by parser_cl.py
-#undef clCreateFromGLBuffer
-#define clCreateFromGLBuffer clCreateFromGLBuffer_fn
-inline cl_mem clCreateFromGLBuffer(cl_context p0, cl_mem_flags p1, cl_GLuint p2, int* p3) { return clCreateFromGLBuffer_pfn(p0, p1, p2, p3); }
-#undef clCreateFromGLRenderbuffer
-#define clCreateFromGLRenderbuffer clCreateFromGLRenderbuffer_fn
-inline cl_mem clCreateFromGLRenderbuffer(cl_context p0, cl_mem_flags p1, cl_GLuint p2, cl_int* p3) { return clCreateFromGLRenderbuffer_pfn(p0, p1, p2, p3); }
-#undef clCreateFromGLTexture
-#define clCreateFromGLTexture clCreateFromGLTexture_fn
-inline cl_mem clCreateFromGLTexture(cl_context p0, cl_mem_flags p1, cl_GLenum p2, cl_GLint p3, cl_GLuint p4, cl_int* p5) { return clCreateFromGLTexture_pfn(p0, p1, p2, p3, p4, p5); }
-#undef clCreateFromGLTexture2D
-#define clCreateFromGLTexture2D clCreateFromGLTexture2D_fn
-inline cl_mem clCreateFromGLTexture2D(cl_context p0, cl_mem_flags p1, cl_GLenum p2, cl_GLint p3, cl_GLuint p4, cl_int* p5) { return clCreateFromGLTexture2D_pfn(p0, p1, p2, p3, p4, p5); }
-#undef clCreateFromGLTexture3D
-#define clCreateFromGLTexture3D clCreateFromGLTexture3D_fn
-inline cl_mem clCreateFromGLTexture3D(cl_context p0, cl_mem_flags p1, cl_GLenum p2, cl_GLint p3, cl_GLuint p4, cl_int* p5) { return clCreateFromGLTexture3D_pfn(p0, p1, p2, p3, p4, p5); }
-#undef clEnqueueAcquireGLObjects
-#define clEnqueueAcquireGLObjects clEnqueueAcquireGLObjects_fn
-inline cl_int clEnqueueAcquireGLObjects(cl_command_queue p0, cl_uint p1, const cl_mem* p2, cl_uint p3, const cl_event* p4, cl_event* p5) { return clEnqueueAcquireGLObjects_pfn(p0, p1, p2, p3, p4, p5); }
-#undef clEnqueueReleaseGLObjects
-#define clEnqueueReleaseGLObjects clEnqueueReleaseGLObjects_fn
-inline cl_int clEnqueueReleaseGLObjects(cl_command_queue p0, cl_uint p1, const cl_mem* p2, cl_uint p3, const cl_event* p4, cl_event* p5) { return clEnqueueReleaseGLObjects_pfn(p0, p1, p2, p3, p4, p5); }
-#undef clGetGLContextInfoKHR
-#define clGetGLContextInfoKHR clGetGLContextInfoKHR_fn
-inline cl_int clGetGLContextInfoKHR(const cl_context_properties* p0, cl_gl_context_info p1, size_t p2, void* p3, size_t* p4) { return clGetGLContextInfoKHR_pfn(p0, p1, p2, p3, p4); }
-#undef clGetGLObjectInfo
-#define clGetGLObjectInfo clGetGLObjectInfo_fn
-inline cl_int clGetGLObjectInfo(cl_mem p0, cl_gl_object_type* p1, cl_GLuint* p2) { return clGetGLObjectInfo_pfn(p0, p1, p2); }
-#undef clGetGLTextureInfo
-#define clGetGLTextureInfo clGetGLTextureInfo_fn
-inline cl_int clGetGLTextureInfo(cl_mem p0, cl_gl_texture_info p1, size_t p2, void* p3, size_t* p4) { return clGetGLTextureInfo_pfn(p0, p1, p2, p3, p4); }
-
-#endif // cl_khr_gl_sharing
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/opencl/runtime/opencl_clamdblas.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/opencl/runtime/opencl_clamdblas.hpp
deleted file mode 100755
index 2ad8ac0..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/opencl/runtime/opencl_clamdblas.hpp
+++ /dev/null
@@ -1,53 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2010-2013, Advanced Micro Devices, Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the OpenCV Foundation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_CORE_OCL_RUNTIME_CLAMDBLAS_HPP
-#define OPENCV_CORE_OCL_RUNTIME_CLAMDBLAS_HPP
-
-#ifdef HAVE_CLAMDBLAS
-
-#include "opencl_core.hpp"
-
-#include "autogenerated/opencl_clamdblas.hpp"
-
-#endif // HAVE_CLAMDBLAS
-
-#endif // OPENCV_CORE_OCL_RUNTIME_CLAMDBLAS_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/opencl/runtime/opencl_clamdfft.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/opencl/runtime/opencl_clamdfft.hpp
deleted file mode 100755
index a328f72..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/opencl/runtime/opencl_clamdfft.hpp
+++ /dev/null
@@ -1,53 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2010-2013, Advanced Micro Devices, Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the OpenCV Foundation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_CORE_OCL_RUNTIME_CLAMDFFT_HPP
-#define OPENCV_CORE_OCL_RUNTIME_CLAMDFFT_HPP
-
-#ifdef HAVE_CLAMDFFT
-
-#include "opencl_core.hpp"
-
-#include "autogenerated/opencl_clamdfft.hpp"
-
-#endif // HAVE_CLAMDFFT
-
-#endif // OPENCV_CORE_OCL_RUNTIME_CLAMDFFT_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/opencl/runtime/opencl_core.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/opencl/runtime/opencl_core.hpp
deleted file mode 100755
index 0404b31..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/opencl/runtime/opencl_core.hpp
+++ /dev/null
@@ -1,84 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2010-2013, Advanced Micro Devices, Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the OpenCV Foundation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_CORE_OCL_RUNTIME_OPENCL_CORE_HPP
-#define OPENCV_CORE_OCL_RUNTIME_OPENCL_CORE_HPP
-
-#ifdef HAVE_OPENCL
-
-#ifndef CL_RUNTIME_EXPORT
-#if (defined(BUILD_SHARED_LIBS) || defined(OPENCV_CORE_SHARED)) && (defined _WIN32 || defined WINCE) && \
-    !(defined(__OPENCV_BUILD) && defined(OPENCV_MODULE_IS_PART_OF_WORLD))
-#define CL_RUNTIME_EXPORT __declspec(dllimport)
-#else
-#define CL_RUNTIME_EXPORT
-#endif
-#endif
-
-#ifdef HAVE_OPENCL_SVM
-#define clSVMAlloc clSVMAlloc_
-#define clSVMFree clSVMFree_
-#define clSetKernelArgSVMPointer clSetKernelArgSVMPointer_
-#define clSetKernelExecInfo clSetKernelExecInfo_
-#define clEnqueueSVMFree clEnqueueSVMFree_
-#define clEnqueueSVMMemcpy clEnqueueSVMMemcpy_
-#define clEnqueueSVMMemFill clEnqueueSVMMemFill_
-#define clEnqueueSVMMap clEnqueueSVMMap_
-#define clEnqueueSVMUnmap clEnqueueSVMUnmap_
-#endif
-
-#include "autogenerated/opencl_core.hpp"
-
-#ifndef CL_DEVICE_DOUBLE_FP_CONFIG
-#define CL_DEVICE_DOUBLE_FP_CONFIG 0x1032
-#endif
-
-#ifndef CL_DEVICE_HALF_FP_CONFIG
-#define CL_DEVICE_HALF_FP_CONFIG 0x1033
-#endif
-
-#ifndef CL_VERSION_1_2
-#define CV_REQUIRE_OPENCL_1_2_ERROR CV_Error(cv::Error::OpenCLApiCallError, "OpenCV compiled without OpenCL v1.2 support, so we can't use functionality from OpenCL v1.2")
-#endif
-
-#endif // HAVE_OPENCL
-
-#endif // OPENCV_CORE_OCL_RUNTIME_OPENCL_CORE_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/opencl/runtime/opencl_core_wrappers.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/opencl/runtime/opencl_core_wrappers.hpp
deleted file mode 100755
index 38fcae9..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/opencl/runtime/opencl_core_wrappers.hpp
+++ /dev/null
@@ -1,47 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2010-2013, Advanced Micro Devices, Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the OpenCV Foundation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_CORE_OCL_RUNTIME_OPENCL_WRAPPERS_HPP
-#define OPENCV_CORE_OCL_RUNTIME_OPENCL_WRAPPERS_HPP
-
-#include "autogenerated/opencl_core_wrappers.hpp"
-
-#endif // OPENCV_CORE_OCL_RUNTIME_OPENCL_WRAPPERS_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/opencl/runtime/opencl_gl.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/opencl/runtime/opencl_gl.hpp
deleted file mode 100755
index 659c7d8..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/opencl/runtime/opencl_gl.hpp
+++ /dev/null
@@ -1,53 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2010-2013, Advanced Micro Devices, Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the OpenCV Foundation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_CORE_OCL_RUNTIME_OPENCL_GL_HPP
-#define OPENCV_CORE_OCL_RUNTIME_OPENCL_GL_HPP
-
-#if defined HAVE_OPENCL && defined HAVE_OPENGL
-
-#include "opencl_core.hpp"
-
-#include "autogenerated/opencl_gl.hpp"
-
-#endif // defined HAVE_OPENCL && defined HAVE_OPENGL
-
-#endif // OPENCV_CORE_OCL_RUNTIME_OPENCL_GL_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/opencl/runtime/opencl_gl_wrappers.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/opencl/runtime/opencl_gl_wrappers.hpp
deleted file mode 100755
index 9700004..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/opencl/runtime/opencl_gl_wrappers.hpp
+++ /dev/null
@@ -1,47 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2010-2013, Advanced Micro Devices, Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the OpenCV Foundation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_CORE_OCL_RUNTIME_OPENCL_GL_WRAPPERS_HPP
-#define OPENCV_CORE_OCL_RUNTIME_OPENCL_GL_WRAPPERS_HPP
-
-#include "autogenerated/opencl_gl_wrappers.hpp"
-
-#endif // OPENCV_CORE_OCL_RUNTIME_OPENCL_GL_WRAPPERS_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/opencl/runtime/opencl_svm_20.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/opencl/runtime/opencl_svm_20.hpp
deleted file mode 100755
index 9636b19..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/opencl/runtime/opencl_svm_20.hpp
+++ /dev/null
@@ -1,48 +0,0 @@
-/* See LICENSE file in the root OpenCV directory */
-
-#ifndef OPENCV_CORE_OCL_RUNTIME_OPENCL_SVM_2_0_HPP
-#define OPENCV_CORE_OCL_RUNTIME_OPENCL_SVM_2_0_HPP
-
-#if defined(HAVE_OPENCL_SVM)
-#include "opencl_core.hpp"
-
-#include "opencl_svm_definitions.hpp"
-
-#undef clSVMAlloc
-#define clSVMAlloc clSVMAlloc_pfn
-#undef clSVMFree
-#define clSVMFree clSVMFree_pfn
-#undef clSetKernelArgSVMPointer
-#define clSetKernelArgSVMPointer clSetKernelArgSVMPointer_pfn
-#undef clSetKernelExecInfo
-//#define clSetKernelExecInfo clSetKernelExecInfo_pfn
-#undef clEnqueueSVMFree
-//#define clEnqueueSVMFree clEnqueueSVMFree_pfn
-#undef clEnqueueSVMMemcpy
-#define clEnqueueSVMMemcpy clEnqueueSVMMemcpy_pfn
-#undef clEnqueueSVMMemFill
-#define clEnqueueSVMMemFill clEnqueueSVMMemFill_pfn
-#undef clEnqueueSVMMap
-#define clEnqueueSVMMap clEnqueueSVMMap_pfn
-#undef clEnqueueSVMUnmap
-#define clEnqueueSVMUnmap clEnqueueSVMUnmap_pfn
-
-extern CL_RUNTIME_EXPORT void* (CL_API_CALL *clSVMAlloc)(cl_context context, cl_svm_mem_flags flags, size_t size, unsigned int alignment);
-extern CL_RUNTIME_EXPORT void (CL_API_CALL *clSVMFree)(cl_context context, void* svm_pointer);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL *clSetKernelArgSVMPointer)(cl_kernel kernel, cl_uint arg_index, const void* arg_value);
-//extern CL_RUNTIME_EXPORT void* (CL_API_CALL *clSetKernelExecInfo)(cl_kernel kernel, cl_kernel_exec_info param_name, size_t param_value_size, const void* param_value);
-//extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL *clEnqueueSVMFree)(cl_command_queue command_queue, cl_uint num_svm_pointers, void* svm_pointers[],
-//        void (CL_CALLBACK *pfn_free_func)(cl_command_queue queue, cl_uint num_svm_pointers, void* svm_pointers[], void* user_data), void* user_data,
-//        cl_uint num_events_in_wait_list, const cl_event* event_wait_list, cl_event* event);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL *clEnqueueSVMMemcpy)(cl_command_queue command_queue, cl_bool blocking_copy, void* dst_ptr, const void* src_ptr, size_t size,
-        cl_uint num_events_in_wait_list, const cl_event* event_wait_list, cl_event* event);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL *clEnqueueSVMMemFill)(cl_command_queue command_queue, void* svm_ptr, const void* pattern, size_t pattern_size, size_t size,
-        cl_uint num_events_in_wait_list, const cl_event* event_wait_list, cl_event* event);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL *clEnqueueSVMMap)(cl_command_queue command_queue, cl_bool blocking_map, cl_map_flags map_flags, void* svm_ptr, size_t size,
-        cl_uint num_events_in_wait_list, const cl_event* event_wait_list, cl_event* event);
-extern CL_RUNTIME_EXPORT cl_int (CL_API_CALL *clEnqueueSVMUnmap)(cl_command_queue command_queue, void* svm_ptr,
-        cl_uint num_events_in_wait_list, const cl_event* event_wait_list, cl_event* event);
-
-#endif // HAVE_OPENCL_SVM
-
-#endif // OPENCV_CORE_OCL_RUNTIME_OPENCL_SVM_2_0_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/opencl/runtime/opencl_svm_definitions.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/opencl/runtime/opencl_svm_definitions.hpp
deleted file mode 100755
index 97c927b..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/opencl/runtime/opencl_svm_definitions.hpp
+++ /dev/null
@@ -1,42 +0,0 @@
-/* See LICENSE file in the root OpenCV directory */
-
-#ifndef OPENCV_CORE_OCL_RUNTIME_OPENCL_SVM_DEFINITIONS_HPP
-#define OPENCV_CORE_OCL_RUNTIME_OPENCL_SVM_DEFINITIONS_HPP
-
-#if defined(HAVE_OPENCL_SVM)
-#if defined(CL_VERSION_2_0)
-
-// OpenCL 2.0 contains SVM definitions
-
-#else
-
-typedef cl_bitfield cl_device_svm_capabilities;
-typedef cl_bitfield cl_svm_mem_flags;
-typedef cl_uint     cl_kernel_exec_info;
-
-//
-// TODO Add real values after OpenCL 2.0 release
-//
-
-#ifndef CL_DEVICE_SVM_CAPABILITIES
-#define CL_DEVICE_SVM_CAPABILITIES 0x1053
-
-#define CL_DEVICE_SVM_COARSE_GRAIN_BUFFER             (1 << 0)
-#define CL_DEVICE_SVM_FINE_GRAIN_BUFFER               (1 << 1)
-#define CL_DEVICE_SVM_FINE_GRAIN_SYSTEM               (1 << 2)
-#define CL_DEVICE_SVM_ATOMICS                         (1 << 3)
-#endif
-
-#ifndef CL_MEM_SVM_FINE_GRAIN_BUFFER
-#define CL_MEM_SVM_FINE_GRAIN_BUFFER (1 << 10)
-#endif
-
-#ifndef CL_MEM_SVM_ATOMICS
-#define CL_MEM_SVM_ATOMICS (1 << 11)
-#endif
-
-
-#endif // CL_VERSION_2_0
-#endif // HAVE_OPENCL_SVM
-
-#endif // OPENCV_CORE_OCL_RUNTIME_OPENCL_SVM_DEFINITIONS_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/opencl/runtime/opencl_svm_hsa_extension.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/opencl/runtime/opencl_svm_hsa_extension.hpp
deleted file mode 100755
index 497bc3d..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/opencl/runtime/opencl_svm_hsa_extension.hpp
+++ /dev/null
@@ -1,166 +0,0 @@
-/* See LICENSE file in the root OpenCV directory */
-
-#ifndef OPENCV_CORE_OCL_RUNTIME_OPENCL_SVM_HSA_EXTENSION_HPP
-#define OPENCV_CORE_OCL_RUNTIME_OPENCL_SVM_HSA_EXTENSION_HPP
-
-#if defined(HAVE_OPENCL_SVM)
-#include "opencl_core.hpp"
-
-#ifndef CL_DEVICE_SVM_CAPABILITIES_AMD
-//
-//  Part of the file is an extract from the cl_ext.h file from AMD APP SDK package.
-//  Below is the original copyright.
-//
-/*******************************************************************************
- * Copyright (c) 2008-2013 The Khronos Group Inc.
- *
- * Permission is hereby granted, free of charge, to any person obtaining a
- * copy of this software and/or associated documentation files (the
- * "Materials"), to deal in the Materials without restriction, including
- * without limitation the rights to use, copy, modify, merge, publish,
- * distribute, sublicense, and/or sell copies of the Materials, and to
- * permit persons to whom the Materials are furnished to do so, subject to
- * the following conditions:
- *
- * The above copyright notice and this permission notice shall be included
- * in all copies or substantial portions of the Materials.
- *
- * THE MATERIALS ARE PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
- * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
- * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
- * IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
- * CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
- * TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
- * MATERIALS OR THE USE OR OTHER DEALINGS IN THE MATERIALS.
- ******************************************************************************/
-
-/*******************************************
- * Shared Virtual Memory (SVM) extension
- *******************************************/
-typedef cl_bitfield                      cl_device_svm_capabilities_amd;
-typedef cl_bitfield                      cl_svm_mem_flags_amd;
-typedef cl_uint                          cl_kernel_exec_info_amd;
-
-/* cl_device_info */
-#define CL_DEVICE_SVM_CAPABILITIES_AMD                     0x1053
-#define CL_DEVICE_PREFERRED_PLATFORM_ATOMIC_ALIGNMENT_AMD  0x1054
-
-/* cl_device_svm_capabilities_amd */
-#define CL_DEVICE_SVM_COARSE_GRAIN_BUFFER_AMD             (1 << 0)
-#define CL_DEVICE_SVM_FINE_GRAIN_BUFFER_AMD               (1 << 1)
-#define CL_DEVICE_SVM_FINE_GRAIN_SYSTEM_AMD               (1 << 2)
-#define CL_DEVICE_SVM_ATOMICS_AMD                         (1 << 3)
-
-/* cl_svm_mem_flags_amd */
-#define CL_MEM_SVM_FINE_GRAIN_BUFFER_AMD                  (1 << 10)
-#define CL_MEM_SVM_ATOMICS_AMD                            (1 << 11)
-
-/* cl_mem_info */
-#define CL_MEM_USES_SVM_POINTER_AMD                       0x1109
-
-/* cl_kernel_exec_info_amd */
-#define CL_KERNEL_EXEC_INFO_SVM_PTRS_AMD                  0x11B6
-#define CL_KERNEL_EXEC_INFO_SVM_FINE_GRAIN_SYSTEM_AMD     0x11B7
-
-/* cl_command_type */
-#define CL_COMMAND_SVM_FREE_AMD                           0x1209
-#define CL_COMMAND_SVM_MEMCPY_AMD                         0x120A
-#define CL_COMMAND_SVM_MEMFILL_AMD                        0x120B
-#define CL_COMMAND_SVM_MAP_AMD                            0x120C
-#define CL_COMMAND_SVM_UNMAP_AMD                          0x120D
-
-typedef CL_API_ENTRY void*
-(CL_API_CALL * clSVMAllocAMD_fn)(
-    cl_context            /* context */,
-    cl_svm_mem_flags_amd  /* flags */,
-    size_t                /* size */,
-    unsigned int          /* alignment */
-) CL_EXT_SUFFIX__VERSION_1_2;
-
-typedef CL_API_ENTRY void
-(CL_API_CALL * clSVMFreeAMD_fn)(
-    cl_context  /* context */,
-    void*       /* svm_pointer */
-) CL_EXT_SUFFIX__VERSION_1_2;
-
-typedef CL_API_ENTRY cl_int
-(CL_API_CALL * clEnqueueSVMFreeAMD_fn)(
-    cl_command_queue /* command_queue */,
-    cl_uint          /* num_svm_pointers */,
-    void**           /* svm_pointers */,
-    void (CL_CALLBACK *)( /*pfn_free_func*/
-        cl_command_queue /* queue */,
-        cl_uint          /* num_svm_pointers */,
-        void**           /* svm_pointers */,
-        void*            /* user_data */),
-    void*             /* user_data */,
-    cl_uint           /* num_events_in_wait_list */,
-    const cl_event*   /* event_wait_list */,
-    cl_event*         /* event */
-) CL_EXT_SUFFIX__VERSION_1_2;
-
-typedef CL_API_ENTRY cl_int
-(CL_API_CALL * clEnqueueSVMMemcpyAMD_fn)(
-    cl_command_queue /* command_queue */,
-    cl_bool          /* blocking_copy */,
-    void*            /* dst_ptr */,
-    const void*      /* src_ptr */,
-    size_t           /* size */,
-    cl_uint          /* num_events_in_wait_list */,
-    const cl_event*  /* event_wait_list */,
-    cl_event*        /* event */
-) CL_EXT_SUFFIX__VERSION_1_2;
-
-typedef CL_API_ENTRY cl_int
-(CL_API_CALL * clEnqueueSVMMemFillAMD_fn)(
-    cl_command_queue /* command_queue */,
-    void*            /* svm_ptr */,
-    const void*      /* pattern */,
-    size_t           /* pattern_size */,
-    size_t           /* size */,
-    cl_uint          /* num_events_in_wait_list */,
-    const cl_event*  /* event_wait_list */,
-    cl_event*        /* event */
-) CL_EXT_SUFFIX__VERSION_1_2;
-
-typedef CL_API_ENTRY cl_int
-(CL_API_CALL * clEnqueueSVMMapAMD_fn)(
-    cl_command_queue /* command_queue */,
-    cl_bool          /* blocking_map */,
-    cl_map_flags     /* map_flags */,
-    void*            /* svm_ptr */,
-    size_t           /* size */,
-    cl_uint          /* num_events_in_wait_list */,
-    const cl_event*  /* event_wait_list */,
-    cl_event*        /* event */
-) CL_EXT_SUFFIX__VERSION_1_2;
-
-typedef CL_API_ENTRY cl_int
-(CL_API_CALL * clEnqueueSVMUnmapAMD_fn)(
-    cl_command_queue /* command_queue */,
-    void*            /* svm_ptr */,
-    cl_uint          /* num_events_in_wait_list */,
-    const cl_event*  /* event_wait_list */,
-    cl_event*        /* event */
-) CL_EXT_SUFFIX__VERSION_1_2;
-
-typedef CL_API_ENTRY cl_int
-(CL_API_CALL * clSetKernelArgSVMPointerAMD_fn)(
-    cl_kernel     /* kernel */,
-    cl_uint       /* arg_index */,
-    const void *  /* arg_value */
-) CL_EXT_SUFFIX__VERSION_1_2;
-
-typedef CL_API_ENTRY cl_int
-(CL_API_CALL * clSetKernelExecInfoAMD_fn)(
-     cl_kernel                /* kernel */,
-     cl_kernel_exec_info_amd  /* param_name */,
-     size_t                   /* param_value_size */,
-     const void *             /* param_value */
-) CL_EXT_SUFFIX__VERSION_1_2;
-
-#endif
-
-#endif // HAVE_OPENCL_SVM
-
-#endif // OPENCV_CORE_OCL_RUNTIME_OPENCL_SVM_HSA_EXTENSION_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/opengl.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/opengl.hpp
deleted file mode 100755
index 5e88cb8..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/opengl.hpp
+++ /dev/null
@@ -1,729 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_CORE_OPENGL_HPP
-#define OPENCV_CORE_OPENGL_HPP
-
-#ifndef __cplusplus
-#  error opengl.hpp header must be compiled as C++
-#endif
-
-#include "opencv2/core.hpp"
-#include "ocl.hpp"
-
-namespace cv { namespace ogl {
-
-/** @addtogroup core_opengl
-This section describes OpenGL interoperability.
-
-To enable OpenGL support, configure OpenCV using CMake with WITH_OPENGL=ON . Currently OpenGL is
-supported only with WIN32, GTK and Qt backends on Windows and Linux (MacOS and Android are not
-supported). For GTK backend gtkglext-1.0 library is required.
-
-To use OpenGL functionality you should first create OpenGL context (window or frame buffer). You can
-do this with namedWindow function or with other OpenGL toolkit (GLUT, for example).
-*/
-//! @{
-
-/////////////////// OpenGL Objects ///////////////////
-
-/** @brief Smart pointer for OpenGL buffer object with reference counting.
-
-Buffer Objects are OpenGL objects that store an array of unformatted memory allocated by the OpenGL
-context. These can be used to store vertex data, pixel data retrieved from images or the
-framebuffer, and a variety of other things.
-
-ogl::Buffer has interface similar with Mat interface and represents 2D array memory.
-
-ogl::Buffer supports memory transfers between host and device and also can be mapped to CUDA memory.
- */
-class CV_EXPORTS Buffer
-{
-public:
-    /** @brief The target defines how you intend to use the buffer object.
-    */
-    enum Target
-    {
-        ARRAY_BUFFER         = 0x8892, //!< The buffer will be used as a source for vertex data
-        ELEMENT_ARRAY_BUFFER = 0x8893, //!< The buffer will be used for indices (in glDrawElements, for example)
-        PIXEL_PACK_BUFFER    = 0x88EB, //!< The buffer will be used for reading from OpenGL textures
-        PIXEL_UNPACK_BUFFER  = 0x88EC  //!< The buffer will be used for writing to OpenGL textures
-    };
-
-    enum Access
-    {
-        READ_ONLY  = 0x88B8,
-        WRITE_ONLY = 0x88B9,
-        READ_WRITE = 0x88BA
-    };
-
-    /** @brief The constructors.
-
-    Creates empty ogl::Buffer object, creates ogl::Buffer object from existed buffer ( abufId
-    parameter), allocates memory for ogl::Buffer object or copies from host/device memory.
-     */
-    Buffer();
-
-    /** @overload
-    @param arows Number of rows in a 2D array.
-    @param acols Number of columns in a 2D array.
-    @param atype Array type ( CV_8UC1, ..., CV_64FC4 ). See Mat for details.
-    @param abufId Buffer object name.
-    @param autoRelease Auto release mode (if true, release will be called in object's destructor).
-    */
-    Buffer(int arows, int acols, int atype, unsigned int abufId, bool autoRelease = false);
-
-    /** @overload
-    @param asize 2D array size.
-    @param atype Array type ( CV_8UC1, ..., CV_64FC4 ). See Mat for details.
-    @param abufId Buffer object name.
-    @param autoRelease Auto release mode (if true, release will be called in object's destructor).
-    */
-    Buffer(Size asize, int atype, unsigned int abufId, bool autoRelease = false);
-
-    /** @overload
-    @param arows Number of rows in a 2D array.
-    @param acols Number of columns in a 2D array.
-    @param atype Array type ( CV_8UC1, ..., CV_64FC4 ). See Mat for details.
-    @param target Buffer usage. See cv::ogl::Buffer::Target .
-    @param autoRelease Auto release mode (if true, release will be called in object's destructor).
-    */
-    Buffer(int arows, int acols, int atype, Target target = ARRAY_BUFFER, bool autoRelease = false);
-
-    /** @overload
-    @param asize 2D array size.
-    @param atype Array type ( CV_8UC1, ..., CV_64FC4 ). See Mat for details.
-    @param target Buffer usage. See cv::ogl::Buffer::Target .
-    @param autoRelease Auto release mode (if true, release will be called in object's destructor).
-    */
-    Buffer(Size asize, int atype, Target target = ARRAY_BUFFER, bool autoRelease = false);
-
-    /** @overload
-    @param arr Input array (host or device memory, it can be Mat , cuda::GpuMat or std::vector ).
-    @param target Buffer usage. See cv::ogl::Buffer::Target .
-    @param autoRelease Auto release mode (if true, release will be called in object's destructor).
-    */
-    explicit Buffer(InputArray arr, Target target = ARRAY_BUFFER, bool autoRelease = false);
-
-    /** @brief Allocates memory for ogl::Buffer object.
-
-    @param arows Number of rows in a 2D array.
-    @param acols Number of columns in a 2D array.
-    @param atype Array type ( CV_8UC1, ..., CV_64FC4 ). See Mat for details.
-    @param target Buffer usage. See cv::ogl::Buffer::Target .
-    @param autoRelease Auto release mode (if true, release will be called in object's destructor).
-     */
-    void create(int arows, int acols, int atype, Target target = ARRAY_BUFFER, bool autoRelease = false);
-
-    /** @overload
-    @param asize 2D array size.
-    @param atype Array type ( CV_8UC1, ..., CV_64FC4 ). See Mat for details.
-    @param target Buffer usage. See cv::ogl::Buffer::Target .
-    @param autoRelease Auto release mode (if true, release will be called in object's destructor).
-    */
-    void create(Size asize, int atype, Target target = ARRAY_BUFFER, bool autoRelease = false);
-
-    /** @brief Decrements the reference counter and destroys the buffer object if needed.
-
-    The function will call setAutoRelease(true) .
-     */
-    void release();
-
-    /** @brief Sets auto release mode.
-
-    The lifetime of the OpenGL object is tied to the lifetime of the context. If OpenGL context was
-    bound to a window it could be released at any time (user can close a window). If object's destructor
-    is called after destruction of the context it will cause an error. Thus ogl::Buffer doesn't destroy
-    OpenGL object in destructor by default (all OpenGL resources will be released with OpenGL context).
-    This function can force ogl::Buffer destructor to destroy OpenGL object.
-    @param flag Auto release mode (if true, release will be called in object's destructor).
-     */
-    void setAutoRelease(bool flag);
-
-    /** @brief Copies from host/device memory to OpenGL buffer.
-    @param arr Input array (host or device memory, it can be Mat , cuda::GpuMat or std::vector ).
-    @param target Buffer usage. See cv::ogl::Buffer::Target .
-    @param autoRelease Auto release mode (if true, release will be called in object's destructor).
-     */
-    void copyFrom(InputArray arr, Target target = ARRAY_BUFFER, bool autoRelease = false);
-
-    /** @overload */
-    void copyFrom(InputArray arr, cuda::Stream& stream, Target target = ARRAY_BUFFER, bool autoRelease = false);
-
-    /** @brief Copies from OpenGL buffer to host/device memory or another OpenGL buffer object.
-
-    @param arr Destination array (host or device memory, can be Mat , cuda::GpuMat , std::vector or
-    ogl::Buffer ).
-     */
-    void copyTo(OutputArray arr) const;
-
-    /** @overload */
-    void copyTo(OutputArray arr, cuda::Stream& stream) const;
-
-    /** @brief Creates a full copy of the buffer object and the underlying data.
-
-    @param target Buffer usage for destination buffer.
-    @param autoRelease Auto release mode for destination buffer.
-     */
-    Buffer clone(Target target = ARRAY_BUFFER, bool autoRelease = false) const;
-
-    /** @brief Binds OpenGL buffer to the specified buffer binding point.
-
-    @param target Binding point. See cv::ogl::Buffer::Target .
-     */
-    void bind(Target target) const;
-
-    /** @brief Unbind any buffers from the specified binding point.
-
-    @param target Binding point. See cv::ogl::Buffer::Target .
-     */
-    static void unbind(Target target);
-
-    /** @brief Maps OpenGL buffer to host memory.
-
-    mapHost maps to the client's address space the entire data store of the buffer object. The data can
-    then be directly read and/or written relative to the returned pointer, depending on the specified
-    access policy.
-
-    A mapped data store must be unmapped with ogl::Buffer::unmapHost before its buffer object is used.
-
-    This operation can lead to memory transfers between host and device.
-
-    Only one buffer object can be mapped at a time.
-    @param access Access policy, indicating whether it will be possible to read from, write to, or both
-    read from and write to the buffer object's mapped data store. The symbolic constant must be
-    ogl::Buffer::READ_ONLY , ogl::Buffer::WRITE_ONLY or ogl::Buffer::READ_WRITE .
-     */
-    Mat mapHost(Access access);
-
-    /** @brief Unmaps OpenGL buffer.
-    */
-    void unmapHost();
-
-    //! map to device memory (blocking)
-    cuda::GpuMat mapDevice();
-    void unmapDevice();
-
-    /** @brief Maps OpenGL buffer to CUDA device memory.
-
-    This operation doesn't copy data. Several buffer objects can be mapped to CUDA memory at a time.
-
-    A mapped data store must be unmapped with ogl::Buffer::unmapDevice before its buffer object is used.
-     */
-    cuda::GpuMat mapDevice(cuda::Stream& stream);
-
-    /** @brief Unmaps OpenGL buffer.
-    */
-    void unmapDevice(cuda::Stream& stream);
-
-    int rows() const;
-    int cols() const;
-    Size size() const;
-    bool empty() const;
-
-    int type() const;
-    int depth() const;
-    int channels() const;
-    int elemSize() const;
-    int elemSize1() const;
-
-    //! get OpenGL opject id
-    unsigned int bufId() const;
-
-    class Impl;
-
-private:
-    Ptr<Impl> impl_;
-    int rows_;
-    int cols_;
-    int type_;
-};
-
-/** @brief Smart pointer for OpenGL 2D texture memory with reference counting.
- */
-class CV_EXPORTS Texture2D
-{
-public:
-    /** @brief An Image Format describes the way that the images in Textures store their data.
-    */
-    enum Format
-    {
-        NONE            = 0,
-        DEPTH_COMPONENT = 0x1902, //!< Depth
-        RGB             = 0x1907, //!< Red, Green, Blue
-        RGBA            = 0x1908  //!< Red, Green, Blue, Alpha
-    };
-
-    /** @brief The constructors.
-
-    Creates empty ogl::Texture2D object, allocates memory for ogl::Texture2D object or copies from
-    host/device memory.
-     */
-    Texture2D();
-
-    /** @overload */
-    Texture2D(int arows, int acols, Format aformat, unsigned int atexId, bool autoRelease = false);
-
-    /** @overload */
-    Texture2D(Size asize, Format aformat, unsigned int atexId, bool autoRelease = false);
-
-    /** @overload
-    @param arows Number of rows.
-    @param acols Number of columns.
-    @param aformat Image format. See cv::ogl::Texture2D::Format .
-    @param autoRelease Auto release mode (if true, release will be called in object's destructor).
-    */
-    Texture2D(int arows, int acols, Format aformat, bool autoRelease = false);
-
-    /** @overload
-    @param asize 2D array size.
-    @param aformat Image format. See cv::ogl::Texture2D::Format .
-    @param autoRelease Auto release mode (if true, release will be called in object's destructor).
-    */
-    Texture2D(Size asize, Format aformat, bool autoRelease = false);
-
-    /** @overload
-    @param arr Input array (host or device memory, it can be Mat , cuda::GpuMat or ogl::Buffer ).
-    @param autoRelease Auto release mode (if true, release will be called in object's destructor).
-    */
-    explicit Texture2D(InputArray arr, bool autoRelease = false);
-
-    /** @brief Allocates memory for ogl::Texture2D object.
-
-    @param arows Number of rows.
-    @param acols Number of columns.
-    @param aformat Image format. See cv::ogl::Texture2D::Format .
-    @param autoRelease Auto release mode (if true, release will be called in object's destructor).
-     */
-    void create(int arows, int acols, Format aformat, bool autoRelease = false);
-    /** @overload
-    @param asize 2D array size.
-    @param aformat Image format. See cv::ogl::Texture2D::Format .
-    @param autoRelease Auto release mode (if true, release will be called in object's destructor).
-    */
-    void create(Size asize, Format aformat, bool autoRelease = false);
-
-    /** @brief Decrements the reference counter and destroys the texture object if needed.
-
-    The function will call setAutoRelease(true) .
-     */
-    void release();
-
-    /** @brief Sets auto release mode.
-
-    @param flag Auto release mode (if true, release will be called in object's destructor).
-
-    The lifetime of the OpenGL object is tied to the lifetime of the context. If OpenGL context was
-    bound to a window it could be released at any time (user can close a window). If object's destructor
-    is called after destruction of the context it will cause an error. Thus ogl::Texture2D doesn't
-    destroy OpenGL object in destructor by default (all OpenGL resources will be released with OpenGL
-    context). This function can force ogl::Texture2D destructor to destroy OpenGL object.
-     */
-    void setAutoRelease(bool flag);
-
-    /** @brief Copies from host/device memory to OpenGL texture.
-
-    @param arr Input array (host or device memory, it can be Mat , cuda::GpuMat or ogl::Buffer ).
-    @param autoRelease Auto release mode (if true, release will be called in object's destructor).
-     */
-    void copyFrom(InputArray arr, bool autoRelease = false);
-
-    /** @brief Copies from OpenGL texture to host/device memory or another OpenGL texture object.
-
-    @param arr Destination array (host or device memory, can be Mat , cuda::GpuMat , ogl::Buffer or
-    ogl::Texture2D ).
-    @param ddepth Destination depth.
-    @param autoRelease Auto release mode for destination buffer (if arr is OpenGL buffer or texture).
-     */
-    void copyTo(OutputArray arr, int ddepth = CV_32F, bool autoRelease = false) const;
-
-    /** @brief Binds texture to current active texture unit for GL_TEXTURE_2D target.
-    */
-    void bind() const;
-
-    int rows() const;
-    int cols() const;
-    Size size() const;
-    bool empty() const;
-
-    Format format() const;
-
-    //! get OpenGL opject id
-    unsigned int texId() const;
-
-    class Impl;
-
-private:
-    Ptr<Impl> impl_;
-    int rows_;
-    int cols_;
-    Format format_;
-};
-
-/** @brief Wrapper for OpenGL Client-Side Vertex arrays.
-
-ogl::Arrays stores vertex data in ogl::Buffer objects.
- */
-class CV_EXPORTS Arrays
-{
-public:
-    /** @brief Default constructor
-     */
-    Arrays();
-
-    /** @brief Sets an array of vertex coordinates.
-    @param vertex array with vertex coordinates, can be both host and device memory.
-    */
-    void setVertexArray(InputArray vertex);
-
-    /** @brief Resets vertex coordinates.
-    */
-    void resetVertexArray();
-
-    /** @brief Sets an array of vertex colors.
-    @param color array with vertex colors, can be both host and device memory.
-     */
-    void setColorArray(InputArray color);
-
-    /** @brief Resets vertex colors.
-    */
-    void resetColorArray();
-
-    /** @brief Sets an array of vertex normals.
-    @param normal array with vertex normals, can be both host and device memory.
-     */
-    void setNormalArray(InputArray normal);
-
-    /** @brief Resets vertex normals.
-    */
-    void resetNormalArray();
-
-    /** @brief Sets an array of vertex texture coordinates.
-    @param texCoord array with vertex texture coordinates, can be both host and device memory.
-     */
-    void setTexCoordArray(InputArray texCoord);
-
-    /** @brief Resets vertex texture coordinates.
-    */
-    void resetTexCoordArray();
-
-    /** @brief Releases all inner buffers.
-    */
-    void release();
-
-    /** @brief Sets auto release mode all inner buffers.
-    @param flag Auto release mode.
-     */
-    void setAutoRelease(bool flag);
-
-    /** @brief Binds all vertex arrays.
-    */
-    void bind() const;
-
-    /** @brief Returns the vertex count.
-    */
-    int size() const;
-    bool empty() const;
-
-private:
-    int size_;
-    Buffer vertex_;
-    Buffer color_;
-    Buffer normal_;
-    Buffer texCoord_;
-};
-
-/////////////////// Render Functions ///////////////////
-
-//! render mode
-enum RenderModes {
-    POINTS         = 0x0000,
-    LINES          = 0x0001,
-    LINE_LOOP      = 0x0002,
-    LINE_STRIP     = 0x0003,
-    TRIANGLES      = 0x0004,
-    TRIANGLE_STRIP = 0x0005,
-    TRIANGLE_FAN   = 0x0006,
-    QUADS          = 0x0007,
-    QUAD_STRIP     = 0x0008,
-    POLYGON        = 0x0009
-};
-
-/** @brief Render OpenGL texture or primitives.
-@param tex Texture to draw.
-@param wndRect Region of window, where to draw a texture (normalized coordinates).
-@param texRect Region of texture to draw (normalized coordinates).
- */
-CV_EXPORTS void render(const Texture2D& tex,
-    Rect_<double> wndRect = Rect_<double>(0.0, 0.0, 1.0, 1.0),
-    Rect_<double> texRect = Rect_<double>(0.0, 0.0, 1.0, 1.0));
-
-/** @overload
-@param arr Array of privitives vertices.
-@param mode Render mode. One of cv::ogl::RenderModes
-@param color Color for all vertices. Will be used if arr doesn't contain color array.
-*/
-CV_EXPORTS void render(const Arrays& arr, int mode = POINTS, Scalar color = Scalar::all(255));
-
-/** @overload
-@param arr Array of privitives vertices.
-@param indices Array of vertices indices (host or device memory).
-@param mode Render mode. One of cv::ogl::RenderModes
-@param color Color for all vertices. Will be used if arr doesn't contain color array.
-*/
-CV_EXPORTS void render(const Arrays& arr, InputArray indices, int mode = POINTS, Scalar color = Scalar::all(255));
-
-/////////////////// CL-GL Interoperability Functions ///////////////////
-
-namespace ocl {
-using namespace cv::ocl;
-
-// TODO static functions in the Context class
-/** @brief Creates OpenCL context from GL.
-@return Returns reference to OpenCL Context
- */
-CV_EXPORTS Context& initializeContextFromGL();
-
-} // namespace cv::ogl::ocl
-
-/** @brief Converts InputArray to Texture2D object.
-@param src     - source InputArray.
-@param texture - destination Texture2D object.
- */
-CV_EXPORTS void convertToGLTexture2D(InputArray src, Texture2D& texture);
-
-/** @brief Converts Texture2D object to OutputArray.
-@param texture - source Texture2D object.
-@param dst     - destination OutputArray.
- */
-CV_EXPORTS void convertFromGLTexture2D(const Texture2D& texture, OutputArray dst);
-
-/** @brief Maps Buffer object to process on CL side (convert to UMat).
-
-Function creates CL buffer from GL one, and then constructs UMat that can be used
-to process buffer data with OpenCV functions. Note that in current implementation
-UMat constructed this way doesn't own corresponding GL buffer object, so it is
-the user responsibility to close down CL/GL buffers relationships by explicitly
-calling unmapGLBuffer() function.
-@param buffer      - source Buffer object.
-@param accessFlags - data access flags (ACCESS_READ|ACCESS_WRITE).
-@return Returns UMat object
- */
-CV_EXPORTS UMat mapGLBuffer(const Buffer& buffer, int accessFlags = ACCESS_READ|ACCESS_WRITE);
-
-/** @brief Unmaps Buffer object (releases UMat, previously mapped from Buffer).
-
-Function must be called explicitly by the user for each UMat previously constructed
-by the call to mapGLBuffer() function.
-@param u           - source UMat, created by mapGLBuffer().
- */
-CV_EXPORTS void unmapGLBuffer(UMat& u);
-
-}} // namespace cv::ogl
-
-namespace cv { namespace cuda {
-
-//! @addtogroup cuda
-//! @{
-
-/** @brief Sets a CUDA device and initializes it for the current thread with OpenGL interoperability.
-
-This function should be explicitly called after OpenGL context creation and before any CUDA calls.
-@param device System index of a CUDA device starting with 0.
-@ingroup core_opengl
- */
-CV_EXPORTS void setGlDevice(int device = 0);
-
-//! @}
-
-}}
-
-//! @cond IGNORED
-
-////////////////////////////////////////////////////////////////////////
-////////////////////////////////////////////////////////////////////////
-////////////////////////////////////////////////////////////////////////
-
-inline
-cv::ogl::Buffer::Buffer(int arows, int acols, int atype, Target target, bool autoRelease) : rows_(0), cols_(0), type_(0)
-{
-    create(arows, acols, atype, target, autoRelease);
-}
-
-inline
-cv::ogl::Buffer::Buffer(Size asize, int atype, Target target, bool autoRelease) : rows_(0), cols_(0), type_(0)
-{
-    create(asize, atype, target, autoRelease);
-}
-
-inline
-void cv::ogl::Buffer::create(Size asize, int atype, Target target, bool autoRelease)
-{
-    create(asize.height, asize.width, atype, target, autoRelease);
-}
-
-inline
-int cv::ogl::Buffer::rows() const
-{
-    return rows_;
-}
-
-inline
-int cv::ogl::Buffer::cols() const
-{
-    return cols_;
-}
-
-inline
-cv::Size cv::ogl::Buffer::size() const
-{
-    return Size(cols_, rows_);
-}
-
-inline
-bool cv::ogl::Buffer::empty() const
-{
-    return rows_ == 0 || cols_ == 0;
-}
-
-inline
-int cv::ogl::Buffer::type() const
-{
-    return type_;
-}
-
-inline
-int cv::ogl::Buffer::depth() const
-{
-    return CV_MAT_DEPTH(type_);
-}
-
-inline
-int cv::ogl::Buffer::channels() const
-{
-    return CV_MAT_CN(type_);
-}
-
-inline
-int cv::ogl::Buffer::elemSize() const
-{
-    return CV_ELEM_SIZE(type_);
-}
-
-inline
-int cv::ogl::Buffer::elemSize1() const
-{
-    return CV_ELEM_SIZE1(type_);
-}
-
-///////
-
-inline
-cv::ogl::Texture2D::Texture2D(int arows, int acols, Format aformat, bool autoRelease) : rows_(0), cols_(0), format_(NONE)
-{
-    create(arows, acols, aformat, autoRelease);
-}
-
-inline
-cv::ogl::Texture2D::Texture2D(Size asize, Format aformat, bool autoRelease) : rows_(0), cols_(0), format_(NONE)
-{
-    create(asize, aformat, autoRelease);
-}
-
-inline
-void cv::ogl::Texture2D::create(Size asize, Format aformat, bool autoRelease)
-{
-    create(asize.height, asize.width, aformat, autoRelease);
-}
-
-inline
-int cv::ogl::Texture2D::rows() const
-{
-    return rows_;
-}
-
-inline
-int cv::ogl::Texture2D::cols() const
-{
-    return cols_;
-}
-
-inline
-cv::Size cv::ogl::Texture2D::size() const
-{
-    return Size(cols_, rows_);
-}
-
-inline
-bool cv::ogl::Texture2D::empty() const
-{
-    return rows_ == 0 || cols_ == 0;
-}
-
-inline
-cv::ogl::Texture2D::Format cv::ogl::Texture2D::format() const
-{
-    return format_;
-}
-
-///////
-
-inline
-cv::ogl::Arrays::Arrays() : size_(0)
-{
-}
-
-inline
-int cv::ogl::Arrays::size() const
-{
-    return size_;
-}
-
-inline
-bool cv::ogl::Arrays::empty() const
-{
-    return size_ == 0;
-}
-
-//! @endcond
-
-#endif /* OPENCV_CORE_OPENGL_HPP */
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/operations.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/operations.hpp
deleted file mode 100755
index 75864ea..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/operations.hpp
+++ /dev/null
@@ -1,538 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
-// Copyright (C) 2015, Itseez Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_CORE_OPERATIONS_HPP
-#define OPENCV_CORE_OPERATIONS_HPP
-
-#ifndef __cplusplus
-#  error operations.hpp header must be compiled as C++
-#endif
-
-#include <cstdio>
-
-//! @cond IGNORED
-
-namespace cv
-{
-
-////////////////////////////// Matx methods depending on core API /////////////////////////////
-
-namespace internal
-{
-
-template<typename _Tp, int m> struct Matx_FastInvOp
-{
-    bool operator()(const Matx<_Tp, m, m>& a, Matx<_Tp, m, m>& b, int method) const
-    {
-        Matx<_Tp, m, m> temp = a;
-
-        // assume that b is all 0's on input => make it a unity matrix
-        for( int i = 0; i < m; i++ )
-            b(i, i) = (_Tp)1;
-
-        if( method == DECOMP_CHOLESKY )
-            return Cholesky(temp.val, m*sizeof(_Tp), m, b.val, m*sizeof(_Tp), m);
-
-        return LU(temp.val, m*sizeof(_Tp), m, b.val, m*sizeof(_Tp), m) != 0;
-    }
-};
-
-template<typename _Tp> struct Matx_FastInvOp<_Tp, 2>
-{
-    bool operator()(const Matx<_Tp, 2, 2>& a, Matx<_Tp, 2, 2>& b, int) const
-    {
-        _Tp d = (_Tp)determinant(a);
-        if( d == 0 )
-            return false;
-        d = 1/d;
-        b(1,1) = a(0,0)*d;
-        b(0,0) = a(1,1)*d;
-        b(0,1) = -a(0,1)*d;
-        b(1,0) = -a(1,0)*d;
-        return true;
-    }
-};
-
-template<typename _Tp> struct Matx_FastInvOp<_Tp, 3>
-{
-    bool operator()(const Matx<_Tp, 3, 3>& a, Matx<_Tp, 3, 3>& b, int) const
-    {
-        _Tp d = (_Tp)determinant(a);
-        if( d == 0 )
-            return false;
-        d = 1/d;
-        b(0,0) = (a(1,1) * a(2,2) - a(1,2) * a(2,1)) * d;
-        b(0,1) = (a(0,2) * a(2,1) - a(0,1) * a(2,2)) * d;
-        b(0,2) = (a(0,1) * a(1,2) - a(0,2) * a(1,1)) * d;
-
-        b(1,0) = (a(1,2) * a(2,0) - a(1,0) * a(2,2)) * d;
-        b(1,1) = (a(0,0) * a(2,2) - a(0,2) * a(2,0)) * d;
-        b(1,2) = (a(0,2) * a(1,0) - a(0,0) * a(1,2)) * d;
-
-        b(2,0) = (a(1,0) * a(2,1) - a(1,1) * a(2,0)) * d;
-        b(2,1) = (a(0,1) * a(2,0) - a(0,0) * a(2,1)) * d;
-        b(2,2) = (a(0,0) * a(1,1) - a(0,1) * a(1,0)) * d;
-        return true;
-    }
-};
-
-
-template<typename _Tp, int m, int n> struct Matx_FastSolveOp
-{
-    bool operator()(const Matx<_Tp, m, m>& a, const Matx<_Tp, m, n>& b,
-                    Matx<_Tp, m, n>& x, int method) const
-    {
-        Matx<_Tp, m, m> temp = a;
-        x = b;
-        if( method == DECOMP_CHOLESKY )
-            return Cholesky(temp.val, m*sizeof(_Tp), m, x.val, n*sizeof(_Tp), n);
-
-        return LU(temp.val, m*sizeof(_Tp), m, x.val, n*sizeof(_Tp), n) != 0;
-    }
-};
-
-template<typename _Tp> struct Matx_FastSolveOp<_Tp, 2, 1>
-{
-    bool operator()(const Matx<_Tp, 2, 2>& a, const Matx<_Tp, 2, 1>& b,
-                    Matx<_Tp, 2, 1>& x, int) const
-    {
-        _Tp d = (_Tp)determinant(a);
-        if( d == 0 )
-            return false;
-        d = 1/d;
-        x(0) = (b(0)*a(1,1) - b(1)*a(0,1))*d;
-        x(1) = (b(1)*a(0,0) - b(0)*a(1,0))*d;
-        return true;
-    }
-};
-
-template<typename _Tp> struct Matx_FastSolveOp<_Tp, 3, 1>
-{
-    bool operator()(const Matx<_Tp, 3, 3>& a, const Matx<_Tp, 3, 1>& b,
-                    Matx<_Tp, 3, 1>& x, int) const
-    {
-        _Tp d = (_Tp)determinant(a);
-        if( d == 0 )
-            return false;
-        d = 1/d;
-        x(0) = d*(b(0)*(a(1,1)*a(2,2) - a(1,2)*a(2,1)) -
-                a(0,1)*(b(1)*a(2,2) - a(1,2)*b(2)) +
-                a(0,2)*(b(1)*a(2,1) - a(1,1)*b(2)));
-
-        x(1) = d*(a(0,0)*(b(1)*a(2,2) - a(1,2)*b(2)) -
-                b(0)*(a(1,0)*a(2,2) - a(1,2)*a(2,0)) +
-                a(0,2)*(a(1,0)*b(2) - b(1)*a(2,0)));
-
-        x(2) = d*(a(0,0)*(a(1,1)*b(2) - b(1)*a(2,1)) -
-                a(0,1)*(a(1,0)*b(2) - b(1)*a(2,0)) +
-                b(0)*(a(1,0)*a(2,1) - a(1,1)*a(2,0)));
-        return true;
-    }
-};
-
-} // internal
-
-template<typename _Tp, int m, int n> inline
-Matx<_Tp,m,n> Matx<_Tp,m,n>::randu(_Tp a, _Tp b)
-{
-    Matx<_Tp,m,n> M;
-    cv::randu(M, Scalar(a), Scalar(b));
-    return M;
-}
-
-template<typename _Tp, int m, int n> inline
-Matx<_Tp,m,n> Matx<_Tp,m,n>::randn(_Tp a, _Tp b)
-{
-    Matx<_Tp,m,n> M;
-    cv::randn(M, Scalar(a), Scalar(b));
-    return M;
-}
-
-template<typename _Tp, int m, int n> inline
-Matx<_Tp, n, m> Matx<_Tp, m, n>::inv(int method, bool *p_is_ok /*= NULL*/) const
-{
-    Matx<_Tp, n, m> b;
-    bool ok;
-    if( m == n && (method == DECOMP_LU || method == DECOMP_CHOLESKY) )
-        ok = cv::internal::Matx_FastInvOp<_Tp, m>()(*reinterpret_cast<const Matx<_Tp, m, m>*>(this), reinterpret_cast<Matx<_Tp, m, m>&>(b), method);
-    else
-    {
-        Mat A(*this, false), B(b, false);
-        ok = (invert(A, B, method) != 0);
-    }
-    if( NULL != p_is_ok ) { *p_is_ok = ok; }
-    return ok ? b : Matx<_Tp, n, m>::zeros();
-}
-
-template<typename _Tp, int m, int n> template<int l> inline
-Matx<_Tp, n, l> Matx<_Tp, m, n>::solve(const Matx<_Tp, m, l>& rhs, int method) const
-{
-    Matx<_Tp, n, l> x;
-    bool ok;
-    if( method == DECOMP_LU || method == DECOMP_CHOLESKY )
-        ok = cv::internal::Matx_FastSolveOp<_Tp, m, l>()(*this, rhs, x, method);
-    else
-    {
-        Mat A(*this, false), B(rhs, false), X(x, false);
-        ok = cv::solve(A, B, X, method);
-    }
-
-    return ok ? x : Matx<_Tp, n, l>::zeros();
-}
-
-
-
-////////////////////////// Augmenting algebraic & logical operations //////////////////////////
-
-#define CV_MAT_AUG_OPERATOR1(op, cvop, A, B) \
-    static inline A& operator op (A& a, const B& b) { cvop; return a; }
-
-#define CV_MAT_AUG_OPERATOR(op, cvop, A, B)   \
-    CV_MAT_AUG_OPERATOR1(op, cvop, A, B)      \
-    CV_MAT_AUG_OPERATOR1(op, cvop, const A, B)
-
-#define CV_MAT_AUG_OPERATOR_T(op, cvop, A, B)                   \
-    template<typename _Tp> CV_MAT_AUG_OPERATOR1(op, cvop, A, B) \
-    template<typename _Tp> CV_MAT_AUG_OPERATOR1(op, cvop, const A, B)
-
-CV_MAT_AUG_OPERATOR  (+=, cv::add(a,b,a), Mat, Mat)
-CV_MAT_AUG_OPERATOR  (+=, cv::add(a,b,a), Mat, Scalar)
-CV_MAT_AUG_OPERATOR_T(+=, cv::add(a,b,a), Mat_<_Tp>, Mat)
-CV_MAT_AUG_OPERATOR_T(+=, cv::add(a,b,a), Mat_<_Tp>, Scalar)
-CV_MAT_AUG_OPERATOR_T(+=, cv::add(a,b,a), Mat_<_Tp>, Mat_<_Tp>)
-
-CV_MAT_AUG_OPERATOR  (-=, cv::subtract(a,b,a), Mat, Mat)
-CV_MAT_AUG_OPERATOR  (-=, cv::subtract(a,b,a), Mat, Scalar)
-CV_MAT_AUG_OPERATOR_T(-=, cv::subtract(a,b,a), Mat_<_Tp>, Mat)
-CV_MAT_AUG_OPERATOR_T(-=, cv::subtract(a,b,a), Mat_<_Tp>, Scalar)
-CV_MAT_AUG_OPERATOR_T(-=, cv::subtract(a,b,a), Mat_<_Tp>, Mat_<_Tp>)
-
-CV_MAT_AUG_OPERATOR  (*=, cv::gemm(a, b, 1, Mat(), 0, a, 0), Mat, Mat)
-CV_MAT_AUG_OPERATOR_T(*=, cv::gemm(a, b, 1, Mat(), 0, a, 0), Mat_<_Tp>, Mat)
-CV_MAT_AUG_OPERATOR_T(*=, cv::gemm(a, b, 1, Mat(), 0, a, 0), Mat_<_Tp>, Mat_<_Tp>)
-CV_MAT_AUG_OPERATOR  (*=, a.convertTo(a, -1, b), Mat, double)
-CV_MAT_AUG_OPERATOR_T(*=, a.convertTo(a, -1, b), Mat_<_Tp>, double)
-
-CV_MAT_AUG_OPERATOR  (/=, cv::divide(a,b,a), Mat, Mat)
-CV_MAT_AUG_OPERATOR_T(/=, cv::divide(a,b,a), Mat_<_Tp>, Mat)
-CV_MAT_AUG_OPERATOR_T(/=, cv::divide(a,b,a), Mat_<_Tp>, Mat_<_Tp>)
-CV_MAT_AUG_OPERATOR  (/=, a.convertTo((Mat&)a, -1, 1./b), Mat, double)
-CV_MAT_AUG_OPERATOR_T(/=, a.convertTo((Mat&)a, -1, 1./b), Mat_<_Tp>, double)
-
-CV_MAT_AUG_OPERATOR  (&=, cv::bitwise_and(a,b,a), Mat, Mat)
-CV_MAT_AUG_OPERATOR  (&=, cv::bitwise_and(a,b,a), Mat, Scalar)
-CV_MAT_AUG_OPERATOR_T(&=, cv::bitwise_and(a,b,a), Mat_<_Tp>, Mat)
-CV_MAT_AUG_OPERATOR_T(&=, cv::bitwise_and(a,b,a), Mat_<_Tp>, Scalar)
-CV_MAT_AUG_OPERATOR_T(&=, cv::bitwise_and(a,b,a), Mat_<_Tp>, Mat_<_Tp>)
-
-CV_MAT_AUG_OPERATOR  (|=, cv::bitwise_or(a,b,a), Mat, Mat)
-CV_MAT_AUG_OPERATOR  (|=, cv::bitwise_or(a,b,a), Mat, Scalar)
-CV_MAT_AUG_OPERATOR_T(|=, cv::bitwise_or(a,b,a), Mat_<_Tp>, Mat)
-CV_MAT_AUG_OPERATOR_T(|=, cv::bitwise_or(a,b,a), Mat_<_Tp>, Scalar)
-CV_MAT_AUG_OPERATOR_T(|=, cv::bitwise_or(a,b,a), Mat_<_Tp>, Mat_<_Tp>)
-
-CV_MAT_AUG_OPERATOR  (^=, cv::bitwise_xor(a,b,a), Mat, Mat)
-CV_MAT_AUG_OPERATOR  (^=, cv::bitwise_xor(a,b,a), Mat, Scalar)
-CV_MAT_AUG_OPERATOR_T(^=, cv::bitwise_xor(a,b,a), Mat_<_Tp>, Mat)
-CV_MAT_AUG_OPERATOR_T(^=, cv::bitwise_xor(a,b,a), Mat_<_Tp>, Scalar)
-CV_MAT_AUG_OPERATOR_T(^=, cv::bitwise_xor(a,b,a), Mat_<_Tp>, Mat_<_Tp>)
-
-#undef CV_MAT_AUG_OPERATOR_T
-#undef CV_MAT_AUG_OPERATOR
-#undef CV_MAT_AUG_OPERATOR1
-
-
-
-///////////////////////////////////////////// SVD /////////////////////////////////////////////
-
-inline SVD::SVD() {}
-inline SVD::SVD( InputArray m, int flags ) { operator ()(m, flags); }
-inline void SVD::solveZ( InputArray m, OutputArray _dst )
-{
-    Mat mtx = m.getMat();
-    SVD svd(mtx, (mtx.rows >= mtx.cols ? 0 : SVD::FULL_UV));
-    _dst.create(svd.vt.cols, 1, svd.vt.type());
-    Mat dst = _dst.getMat();
-    svd.vt.row(svd.vt.rows-1).reshape(1,svd.vt.cols).copyTo(dst);
-}
-
-template<typename _Tp, int m, int n, int nm> inline void
-    SVD::compute( const Matx<_Tp, m, n>& a, Matx<_Tp, nm, 1>& w, Matx<_Tp, m, nm>& u, Matx<_Tp, n, nm>& vt )
-{
-    CV_StaticAssert( nm == MIN(m, n), "Invalid size of output vector.");
-    Mat _a(a, false), _u(u, false), _w(w, false), _vt(vt, false);
-    SVD::compute(_a, _w, _u, _vt);
-    CV_Assert(_w.data == (uchar*)&w.val[0] && _u.data == (uchar*)&u.val[0] && _vt.data == (uchar*)&vt.val[0]);
-}
-
-template<typename _Tp, int m, int n, int nm> inline void
-SVD::compute( const Matx<_Tp, m, n>& a, Matx<_Tp, nm, 1>& w )
-{
-    CV_StaticAssert( nm == MIN(m, n), "Invalid size of output vector.");
-    Mat _a(a, false), _w(w, false);
-    SVD::compute(_a, _w);
-    CV_Assert(_w.data == (uchar*)&w.val[0]);
-}
-
-template<typename _Tp, int m, int n, int nm, int nb> inline void
-SVD::backSubst( const Matx<_Tp, nm, 1>& w, const Matx<_Tp, m, nm>& u,
-                const Matx<_Tp, n, nm>& vt, const Matx<_Tp, m, nb>& rhs,
-                Matx<_Tp, n, nb>& dst )
-{
-    CV_StaticAssert( nm == MIN(m, n), "Invalid size of output vector.");
-    Mat _u(u, false), _w(w, false), _vt(vt, false), _rhs(rhs, false), _dst(dst, false);
-    SVD::backSubst(_w, _u, _vt, _rhs, _dst);
-    CV_Assert(_dst.data == (uchar*)&dst.val[0]);
-}
-
-
-
-/////////////////////////////////// Multiply-with-Carry RNG ///////////////////////////////////
-
-inline RNG::RNG()              { state = 0xffffffff; }
-inline RNG::RNG(uint64 _state) { state = _state ? _state : 0xffffffff; }
-
-inline RNG::operator uchar()    { return (uchar)next(); }
-inline RNG::operator schar()    { return (schar)next(); }
-inline RNG::operator ushort()   { return (ushort)next(); }
-inline RNG::operator short()    { return (short)next(); }
-inline RNG::operator int()      { return (int)next(); }
-inline RNG::operator unsigned() { return next(); }
-inline RNG::operator float()    { return next()*2.3283064365386962890625e-10f; }
-inline RNG::operator double()   { unsigned t = next(); return (((uint64)t << 32) | next()) * 5.4210108624275221700372640043497e-20; }
-
-inline unsigned RNG::operator ()(unsigned N) { return (unsigned)uniform(0,N); }
-inline unsigned RNG::operator ()()           { return next(); }
-
-inline int    RNG::uniform(int a, int b)       { return a == b ? a : (int)(next() % (b - a) + a); }
-inline float  RNG::uniform(float a, float b)   { return ((float)*this)*(b - a) + a; }
-inline double RNG::uniform(double a, double b) { return ((double)*this)*(b - a) + a; }
-
-inline bool RNG::operator ==(const RNG& other) const { return state == other.state; }
-
-inline unsigned RNG::next()
-{
-    state = (uint64)(unsigned)state* /*CV_RNG_COEFF*/ 4164903690U + (unsigned)(state >> 32);
-    return (unsigned)state;
-}
-
-//! returns the next unifomly-distributed random number of the specified type
-template<typename _Tp> static inline _Tp randu()
-{
-  return (_Tp)theRNG();
-}
-
-///////////////////////////////// Formatted string generation /////////////////////////////////
-
-/** @brief Returns a text string formatted using the printf-like expression.
-
-The function acts like sprintf but forms and returns an STL string. It can be used to form an error
-message in the Exception constructor.
-@param fmt printf-compatible formatting specifiers.
- */
-CV_EXPORTS String format( const char* fmt, ... );
-
-///////////////////////////////// Formatted output of cv::Mat /////////////////////////////////
-
-static inline
-Ptr<Formatted> format(InputArray mtx, int fmt)
-{
-    return Formatter::get(fmt)->format(mtx.getMat());
-}
-
-static inline
-int print(Ptr<Formatted> fmtd, FILE* stream = stdout)
-{
-    int written = 0;
-    fmtd->reset();
-    for(const char* str = fmtd->next(); str; str = fmtd->next())
-        written += fputs(str, stream);
-
-    return written;
-}
-
-static inline
-int print(const Mat& mtx, FILE* stream = stdout)
-{
-    return print(Formatter::get()->format(mtx), stream);
-}
-
-static inline
-int print(const UMat& mtx, FILE* stream = stdout)
-{
-    return print(Formatter::get()->format(mtx.getMat(ACCESS_READ)), stream);
-}
-
-template<typename _Tp> static inline
-int print(const std::vector<Point_<_Tp> >& vec, FILE* stream = stdout)
-{
-    return print(Formatter::get()->format(Mat(vec)), stream);
-}
-
-template<typename _Tp> static inline
-int print(const std::vector<Point3_<_Tp> >& vec, FILE* stream = stdout)
-{
-    return print(Formatter::get()->format(Mat(vec)), stream);
-}
-
-template<typename _Tp, int m, int n> static inline
-int print(const Matx<_Tp, m, n>& matx, FILE* stream = stdout)
-{
-    return print(Formatter::get()->format(cv::Mat(matx)), stream);
-}
-
-//! @endcond
-
-/****************************************************************************************\
-*                                  Auxiliary algorithms                                  *
-\****************************************************************************************/
-
-/** @brief Splits an element set into equivalency classes.
-
-The generic function partition implements an \f$O(N^2)\f$ algorithm for splitting a set of \f$N\f$ elements
-into one or more equivalency classes, as described in
-<http://en.wikipedia.org/wiki/Disjoint-set_data_structure> . The function returns the number of
-equivalency classes.
-@param _vec Set of elements stored as a vector.
-@param labels Output vector of labels. It contains as many elements as vec. Each label labels[i] is
-a 0-based cluster index of `vec[i]`.
-@param predicate Equivalence predicate (pointer to a boolean function of two arguments or an
-instance of the class that has the method bool operator()(const _Tp& a, const _Tp& b) ). The
-predicate returns true when the elements are certainly in the same class, and returns false if they
-may or may not be in the same class.
-@ingroup core_cluster
-*/
-template<typename _Tp, class _EqPredicate> int
-partition( const std::vector<_Tp>& _vec, std::vector<int>& labels,
-          _EqPredicate predicate=_EqPredicate())
-{
-    int i, j, N = (int)_vec.size();
-    const _Tp* vec = &_vec[0];
-
-    const int PARENT=0;
-    const int RANK=1;
-
-    std::vector<int> _nodes(N*2);
-    int (*nodes)[2] = (int(*)[2])&_nodes[0];
-
-    // The first O(N) pass: create N single-vertex trees
-    for(i = 0; i < N; i++)
-    {
-        nodes[i][PARENT]=-1;
-        nodes[i][RANK] = 0;
-    }
-
-    // The main O(N^2) pass: merge connected components
-    for( i = 0; i < N; i++ )
-    {
-        int root = i;
-
-        // find root
-        while( nodes[root][PARENT] >= 0 )
-            root = nodes[root][PARENT];
-
-        for( j = 0; j < N; j++ )
-        {
-            if( i == j || !predicate(vec[i], vec[j]))
-                continue;
-            int root2 = j;
-
-            while( nodes[root2][PARENT] >= 0 )
-                root2 = nodes[root2][PARENT];
-
-            if( root2 != root )
-            {
-                // unite both trees
-                int rank = nodes[root][RANK], rank2 = nodes[root2][RANK];
-                if( rank > rank2 )
-                    nodes[root2][PARENT] = root;
-                else
-                {
-                    nodes[root][PARENT] = root2;
-                    nodes[root2][RANK] += rank == rank2;
-                    root = root2;
-                }
-                CV_Assert( nodes[root][PARENT] < 0 );
-
-                int k = j, parent;
-
-                // compress the path from node2 to root
-                while( (parent = nodes[k][PARENT]) >= 0 )
-                {
-                    nodes[k][PARENT] = root;
-                    k = parent;
-                }
-
-                // compress the path from node to root
-                k = i;
-                while( (parent = nodes[k][PARENT]) >= 0 )
-                {
-                    nodes[k][PARENT] = root;
-                    k = parent;
-                }
-            }
-        }
-    }
-
-    // Final O(N) pass: enumerate classes
-    labels.resize(N);
-    int nclasses = 0;
-
-    for( i = 0; i < N; i++ )
-    {
-        int root = i;
-        while( nodes[root][PARENT] >= 0 )
-            root = nodes[root][PARENT];
-        // re-use the rank as the class label
-        if( nodes[root][RANK] >= 0 )
-            nodes[root][RANK] = ~nclasses++;
-        labels[i] = ~nodes[root][RANK];
-    }
-
-    return nclasses;
-}
-
-} // cv
-
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/optim.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/optim.hpp
deleted file mode 100755
index c4729a9..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/optim.hpp
+++ /dev/null
@@ -1,302 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the OpenCV Foundation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_OPTIM_HPP
-#define OPENCV_OPTIM_HPP
-
-#include "opencv2/core.hpp"
-
-namespace cv
-{
-
-/** @addtogroup core_optim
-The algorithms in this section minimize or maximize function value within specified constraints or
-without any constraints.
-@{
-*/
-
-/** @brief Basic interface for all solvers
- */
-class CV_EXPORTS MinProblemSolver : public Algorithm
-{
-public:
-    /** @brief Represents function being optimized
-     */
-    class CV_EXPORTS Function
-    {
-    public:
-        virtual ~Function() {}
-        virtual int getDims() const = 0;
-        virtual double getGradientEps() const;
-        virtual double calc(const double* x) const = 0;
-        virtual void getGradient(const double* x,double* grad);
-    };
-
-    /** @brief Getter for the optimized function.
-
-    The optimized function is represented by Function interface, which requires derivatives to
-    implement the calc(double*) and getDim() methods to evaluate the function.
-
-    @return Smart-pointer to an object that implements Function interface - it represents the
-    function that is being optimized. It can be empty, if no function was given so far.
-     */
-    virtual Ptr<Function> getFunction() const = 0;
-
-    /** @brief Setter for the optimized function.
-
-    *It should be called at least once before the call to* minimize(), as default value is not usable.
-
-    @param f The new function to optimize.
-     */
-    virtual void setFunction(const Ptr<Function>& f) = 0;
-
-    /** @brief Getter for the previously set terminal criteria for this algorithm.
-
-    @return Deep copy of the terminal criteria used at the moment.
-     */
-    virtual TermCriteria getTermCriteria() const = 0;
-
-    /** @brief Set terminal criteria for solver.
-
-    This method *is not necessary* to be called before the first call to minimize(), as the default
-    value is sensible.
-
-    Algorithm stops when the number of function evaluations done exceeds termcrit.maxCount, when
-    the function values at the vertices of simplex are within termcrit.epsilon range or simplex
-    becomes so small that it can enclosed in a box with termcrit.epsilon sides, whatever comes
-    first.
-    @param termcrit Terminal criteria to be used, represented as cv::TermCriteria structure.
-     */
-    virtual void setTermCriteria(const TermCriteria& termcrit) = 0;
-
-    /** @brief actually runs the algorithm and performs the minimization.
-
-    The sole input parameter determines the centroid of the starting simplex (roughly, it tells
-    where to start), all the others (terminal criteria, initial step, function to be minimized) are
-    supposed to be set via the setters before the call to this method or the default values (not
-    always sensible) will be used.
-
-    @param x The initial point, that will become a centroid of an initial simplex. After the algorithm
-    will terminate, it will be set to the point where the algorithm stops, the point of possible
-    minimum.
-    @return The value of a function at the point found.
-     */
-    virtual double minimize(InputOutputArray x) = 0;
-};
-
-/** @brief This class is used to perform the non-linear non-constrained minimization of a function,
-
-defined on an `n`-dimensional Euclidean space, using the **Nelder-Mead method**, also known as
-**downhill simplex method**. The basic idea about the method can be obtained from
-<http://en.wikipedia.org/wiki/Nelder-Mead_method>.
-
-It should be noted, that this method, although deterministic, is rather a heuristic and therefore
-may converge to a local minima, not necessary a global one. It is iterative optimization technique,
-which at each step uses an information about the values of a function evaluated only at `n+1`
-points, arranged as a *simplex* in `n`-dimensional space (hence the second name of the method). At
-each step new point is chosen to evaluate function at, obtained value is compared with previous
-ones and based on this information simplex changes it's shape , slowly moving to the local minimum.
-Thus this method is using *only* function values to make decision, on contrary to, say, Nonlinear
-Conjugate Gradient method (which is also implemented in optim).
-
-Algorithm stops when the number of function evaluations done exceeds termcrit.maxCount, when the
-function values at the vertices of simplex are within termcrit.epsilon range or simplex becomes so
-small that it can enclosed in a box with termcrit.epsilon sides, whatever comes first, for some
-defined by user positive integer termcrit.maxCount and positive non-integer termcrit.epsilon.
-
-@note DownhillSolver is a derivative of the abstract interface
-cv::MinProblemSolver, which in turn is derived from the Algorithm interface and is used to
-encapsulate the functionality, common to all non-linear optimization algorithms in the optim
-module.
-
-@note term criteria should meet following condition:
-@code
-    termcrit.type == (TermCriteria::MAX_ITER + TermCriteria::EPS) && termcrit.epsilon > 0 && termcrit.maxCount > 0
-@endcode
- */
-class CV_EXPORTS DownhillSolver : public MinProblemSolver
-{
-public:
-    /** @brief Returns the initial step that will be used in downhill simplex algorithm.
-
-    @param step Initial step that will be used in algorithm. Note, that although corresponding setter
-    accepts column-vectors as well as row-vectors, this method will return a row-vector.
-    @see DownhillSolver::setInitStep
-     */
-    virtual void getInitStep(OutputArray step) const=0;
-
-    /** @brief Sets the initial step that will be used in downhill simplex algorithm.
-
-    Step, together with initial point (givin in DownhillSolver::minimize) are two `n`-dimensional
-    vectors that are used to determine the shape of initial simplex. Roughly said, initial point
-    determines the position of a simplex (it will become simplex's centroid), while step determines the
-    spread (size in each dimension) of a simplex. To be more precise, if \f$s,x_0\in\mathbb{R}^n\f$ are
-    the initial step and initial point respectively, the vertices of a simplex will be:
-    \f$v_0:=x_0-\frac{1}{2} s\f$ and \f$v_i:=x_0+s_i\f$ for \f$i=1,2,\dots,n\f$ where \f$s_i\f$ denotes
-    projections of the initial step of *n*-th coordinate (the result of projection is treated to be
-    vector given by \f$s_i:=e_i\cdot\left<e_i\cdot s\right>\f$, where \f$e_i\f$ form canonical basis)
-
-    @param step Initial step that will be used in algorithm. Roughly said, it determines the spread
-    (size in each dimension) of an initial simplex.
-     */
-    virtual void setInitStep(InputArray step)=0;
-
-    /** @brief This function returns the reference to the ready-to-use DownhillSolver object.
-
-    All the parameters are optional, so this procedure can be called even without parameters at
-    all. In this case, the default values will be used. As default value for terminal criteria are
-    the only sensible ones, MinProblemSolver::setFunction() and DownhillSolver::setInitStep()
-    should be called upon the obtained object, if the respective parameters were not given to
-    create(). Otherwise, the two ways (give parameters to createDownhillSolver() or miss them out
-    and call the MinProblemSolver::setFunction() and DownhillSolver::setInitStep()) are absolutely
-    equivalent (and will drop the same errors in the same way, should invalid input be detected).
-    @param f Pointer to the function that will be minimized, similarly to the one you submit via
-    MinProblemSolver::setFunction.
-    @param initStep Initial step, that will be used to construct the initial simplex, similarly to the one
-    you submit via MinProblemSolver::setInitStep.
-    @param termcrit Terminal criteria to the algorithm, similarly to the one you submit via
-    MinProblemSolver::setTermCriteria.
-     */
-    static Ptr<DownhillSolver> create(const Ptr<MinProblemSolver::Function>& f=Ptr<MinProblemSolver::Function>(),
-                                      InputArray initStep=Mat_<double>(1,1,0.0),
-                                      TermCriteria termcrit=TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,5000,0.000001));
-};
-
-/** @brief This class is used to perform the non-linear non-constrained minimization of a function
-with known gradient,
-
-defined on an *n*-dimensional Euclidean space, using the **Nonlinear Conjugate Gradient method**.
-The implementation was done based on the beautifully clear explanatory article [An Introduction to
-the Conjugate Gradient Method Without the Agonizing
-Pain](http://www.cs.cmu.edu/~quake-papers/painless-conjugate-gradient.pdf) by Jonathan Richard
-Shewchuk. The method can be seen as an adaptation of a standard Conjugate Gradient method (see, for
-example <http://en.wikipedia.org/wiki/Conjugate_gradient_method>) for numerically solving the
-systems of linear equations.
-
-It should be noted, that this method, although deterministic, is rather a heuristic method and
-therefore may converge to a local minima, not necessary a global one. What is even more disastrous,
-most of its behaviour is ruled by gradient, therefore it essentially cannot distinguish between
-local minima and maxima. Therefore, if it starts sufficiently near to the local maximum, it may
-converge to it. Another obvious restriction is that it should be possible to compute the gradient of
-a function at any point, thus it is preferable to have analytic expression for gradient and
-computational burden should be born by the user.
-
-The latter responsibility is accompilished via the getGradient method of a
-MinProblemSolver::Function interface (which represents function being optimized). This method takes
-point a point in *n*-dimensional space (first argument represents the array of coordinates of that
-point) and comput its gradient (it should be stored in the second argument as an array).
-
-@note class ConjGradSolver thus does not add any new methods to the basic MinProblemSolver interface.
-
-@note term criteria should meet following condition:
-@code
-    termcrit.type == (TermCriteria::MAX_ITER + TermCriteria::EPS) && termcrit.epsilon > 0 && termcrit.maxCount > 0
-    // or
-    termcrit.type == TermCriteria::MAX_ITER) && termcrit.maxCount > 0
-@endcode
- */
-class CV_EXPORTS ConjGradSolver : public MinProblemSolver
-{
-public:
-    /** @brief This function returns the reference to the ready-to-use ConjGradSolver object.
-
-    All the parameters are optional, so this procedure can be called even without parameters at
-    all. In this case, the default values will be used. As default value for terminal criteria are
-    the only sensible ones, MinProblemSolver::setFunction() should be called upon the obtained
-    object, if the function was not given to create(). Otherwise, the two ways (submit it to
-    create() or miss it out and call the MinProblemSolver::setFunction()) are absolutely equivalent
-    (and will drop the same errors in the same way, should invalid input be detected).
-    @param f Pointer to the function that will be minimized, similarly to the one you submit via
-    MinProblemSolver::setFunction.
-    @param termcrit Terminal criteria to the algorithm, similarly to the one you submit via
-    MinProblemSolver::setTermCriteria.
-    */
-    static Ptr<ConjGradSolver> create(const Ptr<MinProblemSolver::Function>& f=Ptr<ConjGradSolver::Function>(),
-                                      TermCriteria termcrit=TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,5000,0.000001));
-};
-
-//! return codes for cv::solveLP() function
-enum SolveLPResult
-{
-    SOLVELP_UNBOUNDED    = -2, //!< problem is unbounded (target function can achieve arbitrary high values)
-    SOLVELP_UNFEASIBLE    = -1, //!< problem is unfeasible (there are no points that satisfy all the constraints imposed)
-    SOLVELP_SINGLE    = 0, //!< there is only one maximum for target function
-    SOLVELP_MULTI    = 1 //!< there are multiple maxima for target function - the arbitrary one is returned
-};
-
-/** @brief Solve given (non-integer) linear programming problem using the Simplex Algorithm (Simplex Method).
-
-What we mean here by "linear programming problem" (or LP problem, for short) can be formulated as:
-
-\f[\mbox{Maximize } c\cdot x\\
- \mbox{Subject to:}\\
- Ax\leq b\\
- x\geq 0\f]
-
-Where \f$c\f$ is fixed `1`-by-`n` row-vector, \f$A\f$ is fixed `m`-by-`n` matrix, \f$b\f$ is fixed `m`-by-`1`
-column vector and \f$x\f$ is an arbitrary `n`-by-`1` column vector, which satisfies the constraints.
-
-Simplex algorithm is one of many algorithms that are designed to handle this sort of problems
-efficiently. Although it is not optimal in theoretical sense (there exist algorithms that can solve
-any problem written as above in polynomial time, while simplex method degenerates to exponential
-time for some special cases), it is well-studied, easy to implement and is shown to work well for
-real-life purposes.
-
-The particular implementation is taken almost verbatim from **Introduction to Algorithms, third
-edition** by T. H. Cormen, C. E. Leiserson, R. L. Rivest and Clifford Stein. In particular, the
-Bland's rule <http://en.wikipedia.org/wiki/Bland%27s_rule> is used to prevent cycling.
-
-@param Func This row-vector corresponds to \f$c\f$ in the LP problem formulation (see above). It should
-contain 32- or 64-bit floating point numbers. As a convenience, column-vector may be also submitted,
-in the latter case it is understood to correspond to \f$c^T\f$.
-@param Constr `m`-by-`n+1` matrix, whose rightmost column corresponds to \f$b\f$ in formulation above
-and the remaining to \f$A\f$. It should contain 32- or 64-bit floating point numbers.
-@param z The solution will be returned here as a column-vector - it corresponds to \f$c\f$ in the
-formulation above. It will contain 64-bit floating point numbers.
-@return One of cv::SolveLPResult
- */
-CV_EXPORTS_W int solveLP(const Mat& Func, const Mat& Constr, Mat& z);
-
-//! @}
-
-}// cv
-
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/ovx.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/ovx.hpp
deleted file mode 100755
index 8bb7d54..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/ovx.hpp
+++ /dev/null
@@ -1,28 +0,0 @@
-// This file is part of OpenCV project.
-// It is subject to the license terms in the LICENSE file found in the top-level directory
-// of this distribution and at http://opencv.org/license.html.
-
-// Copyright (C) 2016, Intel Corporation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-
-// OpenVX related definitions and declarations
-
-#pragma once
-#ifndef OPENCV_OVX_HPP
-#define OPENCV_OVX_HPP
-
-#include "cvdef.h"
-
-namespace cv
-{
-/// Check if use of OpenVX is possible
-CV_EXPORTS_W bool haveOpenVX();
-
-/// Check if use of OpenVX is enabled
-CV_EXPORTS_W bool useOpenVX();
-
-/// Enable/disable use of OpenVX
-CV_EXPORTS_W void setUseOpenVX(bool flag);
-} // namespace cv
-
-#endif // OPENCV_OVX_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/persistence.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/persistence.hpp
deleted file mode 100755
index 5103c7d..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/persistence.hpp
+++ /dev/null
@@ -1,1361 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_CORE_PERSISTENCE_HPP
-#define OPENCV_CORE_PERSISTENCE_HPP
-
-#ifndef CV_DOXYGEN
-/// Define to support persistence legacy formats
-#define CV__LEGACY_PERSISTENCE
-#endif
-
-#ifndef __cplusplus
-#  error persistence.hpp header must be compiled as C++
-#endif
-
-//! @addtogroup core_c
-//! @{
-
-/** @brief "black box" representation of the file storage associated with a file on disk.
-
-Several functions that are described below take CvFileStorage\* as inputs and allow the user to
-save or to load hierarchical collections that consist of scalar values, standard CXCore objects
-(such as matrices, sequences, graphs), and user-defined objects.
-
-OpenCV can read and write data in XML (<http://www.w3c.org/XML>), YAML (<http://www.yaml.org>) or
-JSON (<http://www.json.org/>) formats. Below is an example of 3x3 floating-point identity matrix A,
-stored in XML and YAML files
-using CXCore functions:
-XML:
-@code{.xml}
-    <?xml version="1.0">
-    <opencv_storage>
-    <A type_id="opencv-matrix">
-      <rows>3</rows>
-      <cols>3</cols>
-      <dt>f</dt>
-      <data>1. 0. 0. 0. 1. 0. 0. 0. 1.</data>
-    </A>
-    </opencv_storage>
-@endcode
-YAML:
-@code{.yaml}
-    %YAML:1.0
-    A: !!opencv-matrix
-      rows: 3
-      cols: 3
-      dt: f
-      data: [ 1., 0., 0., 0., 1., 0., 0., 0., 1.]
-@endcode
-As it can be seen from the examples, XML uses nested tags to represent hierarchy, while YAML uses
-indentation for that purpose (similar to the Python programming language).
-
-The same functions can read and write data in both formats; the particular format is determined by
-the extension of the opened file, ".xml" for XML files, ".yml" or ".yaml" for YAML and ".json" for
-JSON.
- */
-typedef struct CvFileStorage CvFileStorage;
-typedef struct CvFileNode CvFileNode;
-typedef struct CvMat CvMat;
-typedef struct CvMatND CvMatND;
-
-//! @} core_c
-
-#include "opencv2/core/types.hpp"
-#include "opencv2/core/mat.hpp"
-
-namespace cv {
-
-/** @addtogroup core_xml
-
-XML/YAML/JSON file storages.     {#xml_storage}
-=======================
-Writing to a file storage.
---------------------------
-You can store and then restore various OpenCV data structures to/from XML (<http://www.w3c.org/XML>),
-YAML (<http://www.yaml.org>) or JSON (<http://www.json.org/>) formats. Also, it is possible to store
-and load arbitrarily complex data structures, which include OpenCV data structures, as well as
-primitive data types (integer and floating-point numbers and text strings) as their elements.
-
-Use the following procedure to write something to XML, YAML or JSON:
--# Create new FileStorage and open it for writing. It can be done with a single call to
-FileStorage::FileStorage constructor that takes a filename, or you can use the default constructor
-and then call FileStorage::open. Format of the file (XML, YAML or JSON) is determined from the filename
-extension (".xml", ".yml"/".yaml" and ".json", respectively)
--# Write all the data you want using the streaming operator `<<`, just like in the case of STL
-streams.
--# Close the file using FileStorage::release. FileStorage destructor also closes the file.
-
-Here is an example:
-@code
-    #include "opencv2/opencv.hpp"
-    #include <time.h>
-
-    using namespace cv;
-
-    int main(int, char** argv)
-    {
-        FileStorage fs("test.yml", FileStorage::WRITE);
-
-        fs << "frameCount" << 5;
-        time_t rawtime; time(&rawtime);
-        fs << "calibrationDate" << asctime(localtime(&rawtime));
-        Mat cameraMatrix = (Mat_<double>(3,3) << 1000, 0, 320, 0, 1000, 240, 0, 0, 1);
-        Mat distCoeffs = (Mat_<double>(5,1) << 0.1, 0.01, -0.001, 0, 0);
-        fs << "cameraMatrix" << cameraMatrix << "distCoeffs" << distCoeffs;
-        fs << "features" << "[";
-        for( int i = 0; i < 3; i++ )
-        {
-            int x = rand() % 640;
-            int y = rand() % 480;
-            uchar lbp = rand() % 256;
-
-            fs << "{:" << "x" << x << "y" << y << "lbp" << "[:";
-            for( int j = 0; j < 8; j++ )
-                fs << ((lbp >> j) & 1);
-            fs << "]" << "}";
-        }
-        fs << "]";
-        fs.release();
-        return 0;
-    }
-@endcode
-The sample above stores to YML an integer, a text string (calibration date), 2 matrices, and a custom
-structure "feature", which includes feature coordinates and LBP (local binary pattern) value. Here
-is output of the sample:
-@code{.yaml}
-%YAML:1.0
-frameCount: 5
-calibrationDate: "Fri Jun 17 14:09:29 2011\n"
-cameraMatrix: !!opencv-matrix
-   rows: 3
-   cols: 3
-   dt: d
-   data: [ 1000., 0., 320., 0., 1000., 240., 0., 0., 1. ]
-distCoeffs: !!opencv-matrix
-   rows: 5
-   cols: 1
-   dt: d
-   data: [ 1.0000000000000001e-01, 1.0000000000000000e-02,
-       -1.0000000000000000e-03, 0., 0. ]
-features:
-   - { x:167, y:49, lbp:[ 1, 0, 0, 1, 1, 0, 1, 1 ] }
-   - { x:298, y:130, lbp:[ 0, 0, 0, 1, 0, 0, 1, 1 ] }
-   - { x:344, y:158, lbp:[ 1, 1, 0, 0, 0, 0, 1, 0 ] }
-@endcode
-
-As an exercise, you can replace ".yml" with ".xml" or ".json" in the sample above and see, how the
-corresponding XML file will look like.
-
-Several things can be noted by looking at the sample code and the output:
-
--   The produced YAML (and XML/JSON) consists of heterogeneous collections that can be nested. There are
-    2 types of collections: named collections (mappings) and unnamed collections (sequences). In mappings
-    each element has a name and is accessed by name. This is similar to structures and std::map in
-    C/C++ and dictionaries in Python. In sequences elements do not have names, they are accessed by
-    indices. This is similar to arrays and std::vector in C/C++ and lists, tuples in Python.
-    "Heterogeneous" means that elements of each single collection can have different types.
-
-    Top-level collection in YAML/XML/JSON is a mapping. Each matrix is stored as a mapping, and the matrix
-    elements are stored as a sequence. Then, there is a sequence of features, where each feature is
-    represented a mapping, and lbp value in a nested sequence.
-
--   When you write to a mapping (a structure), you write element name followed by its value. When you
-    write to a sequence, you simply write the elements one by one. OpenCV data structures (such as
-    cv::Mat) are written in absolutely the same way as simple C data structures - using `<<`
-    operator.
-
--   To write a mapping, you first write the special string `{` to the storage, then write the
-    elements as pairs (`fs << <element_name> << <element_value>`) and then write the closing
-    `}`.
-
--   To write a sequence, you first write the special string `[`, then write the elements, then
-    write the closing `]`.
-
--   In YAML/JSON (but not XML), mappings and sequences can be written in a compact Python-like inline
-    form. In the sample above matrix elements, as well as each feature, including its lbp value, is
-    stored in such inline form. To store a mapping/sequence in a compact form, put `:` after the
-    opening character, e.g. use `{:` instead of `{` and `[:` instead of `[`. When the
-    data is written to XML, those extra `:` are ignored.
-
-Reading data from a file storage.
----------------------------------
-To read the previously written XML, YAML or JSON file, do the following:
--#  Open the file storage using FileStorage::FileStorage constructor or FileStorage::open method.
-    In the current implementation the whole file is parsed and the whole representation of file
-    storage is built in memory as a hierarchy of file nodes (see FileNode)
-
--#  Read the data you are interested in. Use FileStorage::operator [], FileNode::operator []
-    and/or FileNodeIterator.
-
--#  Close the storage using FileStorage::release.
-
-Here is how to read the file created by the code sample above:
-@code
-    FileStorage fs2("test.yml", FileStorage::READ);
-
-    // first method: use (type) operator on FileNode.
-    int frameCount = (int)fs2["frameCount"];
-
-    String date;
-    // second method: use FileNode::operator >>
-    fs2["calibrationDate"] >> date;
-
-    Mat cameraMatrix2, distCoeffs2;
-    fs2["cameraMatrix"] >> cameraMatrix2;
-    fs2["distCoeffs"] >> distCoeffs2;
-
-    cout << "frameCount: " << frameCount << endl
-         << "calibration date: " << date << endl
-         << "camera matrix: " << cameraMatrix2 << endl
-         << "distortion coeffs: " << distCoeffs2 << endl;
-
-    FileNode features = fs2["features"];
-    FileNodeIterator it = features.begin(), it_end = features.end();
-    int idx = 0;
-    std::vector<uchar> lbpval;
-
-    // iterate through a sequence using FileNodeIterator
-    for( ; it != it_end; ++it, idx++ )
-    {
-        cout << "feature #" << idx << ": ";
-        cout << "x=" << (int)(*it)["x"] << ", y=" << (int)(*it)["y"] << ", lbp: (";
-        // you can also easily read numerical arrays using FileNode >> std::vector operator.
-        (*it)["lbp"] >> lbpval;
-        for( int i = 0; i < (int)lbpval.size(); i++ )
-            cout << " " << (int)lbpval[i];
-        cout << ")" << endl;
-    }
-    fs2.release();
-@endcode
-
-Format specification    {#format_spec}
---------------------
-`([count]{u|c|w|s|i|f|d})`... where the characters correspond to fundamental C++ types:
--   `u` 8-bit unsigned number
--   `c` 8-bit signed number
--   `w` 16-bit unsigned number
--   `s` 16-bit signed number
--   `i` 32-bit signed number
--   `f` single precision floating-point number
--   `d` double precision floating-point number
--   `r` pointer, 32 lower bits of which are written as a signed integer. The type can be used to
-    store structures with links between the elements.
-
-`count` is the optional counter of values of a given type. For example, `2if` means that each array
-element is a structure of 2 integers, followed by a single-precision floating-point number. The
-equivalent notations of the above specification are `iif`, `2i1f` and so forth. Other examples: `u`
-means that the array consists of bytes, and `2d` means the array consists of pairs of doubles.
-
-@see @ref filestorage.cpp
-*/
-
-//! @{
-
-/** @example filestorage.cpp
-A complete example using the FileStorage interface
-*/
-
-////////////////////////// XML & YAML I/O //////////////////////////
-
-class CV_EXPORTS FileNode;
-class CV_EXPORTS FileNodeIterator;
-
-/** @brief XML/YAML/JSON file storage class that encapsulates all the information necessary for writing or
-reading data to/from a file.
- */
-class CV_EXPORTS_W FileStorage
-{
-public:
-    //! file storage mode
-    enum Mode
-    {
-        READ        = 0, //!< value, open the file for reading
-        WRITE       = 1, //!< value, open the file for writing
-        APPEND      = 2, //!< value, open the file for appending
-        MEMORY      = 4, //!< flag, read data from source or write data to the internal buffer (which is
-                         //!< returned by FileStorage::release)
-        FORMAT_MASK = (7<<3), //!< mask for format flags
-        FORMAT_AUTO = 0,      //!< flag, auto format
-        FORMAT_XML  = (1<<3), //!< flag, XML format
-        FORMAT_YAML = (2<<3), //!< flag, YAML format
-        FORMAT_JSON = (3<<3), //!< flag, JSON format
-
-        BASE64      = 64,     //!< flag, write rawdata in Base64 by default. (consider using WRITE_BASE64)
-        WRITE_BASE64 = BASE64 | WRITE, //!< flag, enable both WRITE and BASE64
-    };
-    enum
-    {
-        UNDEFINED      = 0,
-        VALUE_EXPECTED = 1,
-        NAME_EXPECTED  = 2,
-        INSIDE_MAP     = 4
-    };
-
-    /** @brief The constructors.
-
-    The full constructor opens the file. Alternatively you can use the default constructor and then
-    call FileStorage::open.
-     */
-    CV_WRAP FileStorage();
-
-    /** @overload
-    @copydoc open()
-    */
-    CV_WRAP FileStorage(const String& filename, int flags, const String& encoding=String());
-
-    /** @overload */
-    FileStorage(CvFileStorage* fs, bool owning=true);
-
-    //! the destructor. calls release()
-    virtual ~FileStorage();
-
-    /** @brief Opens a file.
-
-    See description of parameters in FileStorage::FileStorage. The method calls FileStorage::release
-    before opening the file.
-    @param filename Name of the file to open or the text string to read the data from.
-       Extension of the file (.xml, .yml/.yaml or .json) determines its format (XML, YAML or JSON
-        respectively). Also you can append .gz to work with compressed files, for example myHugeMatrix.xml.gz. If both
-        FileStorage::WRITE and FileStorage::MEMORY flags are specified, source is used just to specify
-        the output file format (e.g. mydata.xml, .yml etc.). A file name can also contain parameters.
-        You can use this format, "*?base64" (e.g. "file.json?base64" (case sensitive)), as an alternative to
-        FileStorage::BASE64 flag.
-    @param flags Mode of operation. One of FileStorage::Mode
-    @param encoding Encoding of the file. Note that UTF-16 XML encoding is not supported currently and
-    you should use 8-bit encoding instead of it.
-     */
-    CV_WRAP virtual bool open(const String& filename, int flags, const String& encoding=String());
-
-    /** @brief Checks whether the file is opened.
-
-    @returns true if the object is associated with the current file and false otherwise. It is a
-    good practice to call this method after you tried to open a file.
-     */
-    CV_WRAP virtual bool isOpened() const;
-
-    /** @brief Closes the file and releases all the memory buffers.
-
-    Call this method after all I/O operations with the storage are finished.
-     */
-    CV_WRAP virtual void release();
-
-    /** @brief Closes the file and releases all the memory buffers.
-
-    Call this method after all I/O operations with the storage are finished. If the storage was
-    opened for writing data and FileStorage::WRITE was specified
-     */
-    CV_WRAP virtual String releaseAndGetString();
-
-    /** @brief Returns the first element of the top-level mapping.
-    @returns The first element of the top-level mapping.
-     */
-    CV_WRAP FileNode getFirstTopLevelNode() const;
-
-    /** @brief Returns the top-level mapping
-    @param streamidx Zero-based index of the stream. In most cases there is only one stream in the file.
-    However, YAML supports multiple streams and so there can be several.
-    @returns The top-level mapping.
-     */
-    CV_WRAP FileNode root(int streamidx=0) const;
-
-    /** @brief Returns the specified element of the top-level mapping.
-    @param nodename Name of the file node.
-    @returns Node with the given name.
-     */
-    FileNode operator[](const String& nodename) const;
-
-    /** @overload */
-    CV_WRAP_AS(getNode) FileNode operator[](const char* nodename) const;
-
-    /** @brief Returns the obsolete C FileStorage structure.
-    @returns Pointer to the underlying C FileStorage structure
-     */
-    CvFileStorage* operator *() { return fs.get(); }
-
-    /** @overload */
-    const CvFileStorage* operator *() const { return fs.get(); }
-
-    /** @brief Writes multiple numbers.
-
-    Writes one or more numbers of the specified format to the currently written structure. Usually it is
-    more convenient to use operator `<<` instead of this method.
-    @param fmt Specification of each array element, see @ref format_spec "format specification"
-    @param vec Pointer to the written array.
-    @param len Number of the uchar elements to write.
-     */
-    void writeRaw( const String& fmt, const uchar* vec, size_t len );
-
-    /** @brief Writes the registered C structure (CvMat, CvMatND, CvSeq).
-    @param name Name of the written object.
-    @param obj Pointer to the object.
-    @see ocvWrite for details.
-     */
-    void writeObj( const String& name, const void* obj );
-
-    /**
-     * @brief Simplified writing API to use with bindings.
-     * @param name Name of the written object
-     * @param val Value of the written object
-     */
-    CV_WRAP void write(const String& name, int val);
-    /// @overload
-    CV_WRAP void write(const String& name, double val);
-    /// @overload
-    CV_WRAP void write(const String& name, const String& val);
-    /// @overload
-    CV_WRAP void write(const String& name, InputArray val);
-
-    /** @brief Writes a comment.
-
-    The function writes a comment into file storage. The comments are skipped when the storage is read.
-    @param comment The written comment, single-line or multi-line
-    @param append If true, the function tries to put the comment at the end of current line.
-    Else if the comment is multi-line, or if it does not fit at the end of the current
-    line, the comment starts a new line.
-     */
-    CV_WRAP void writeComment(const String& comment, bool append = false);
-
-    /** @brief Returns the normalized object name for the specified name of a file.
-    @param filename Name of a file
-    @returns The normalized object name.
-     */
-    static String getDefaultObjectName(const String& filename);
-
-    /** @brief Returns the current format.
-     * @returns The current format, see FileStorage::Mode
-     */
-    CV_WRAP int getFormat() const;
-
-    Ptr<CvFileStorage> fs; //!< the underlying C FileStorage structure
-    String elname; //!< the currently written element
-    std::vector<char> structs; //!< the stack of written structures
-    int state; //!< the writer state
-};
-
-template<> CV_EXPORTS void DefaultDeleter<CvFileStorage>::operator ()(CvFileStorage* obj) const;
-
-/** @brief File Storage Node class.
-
-The node is used to store each and every element of the file storage opened for reading. When
-XML/YAML file is read, it is first parsed and stored in the memory as a hierarchical collection of
-nodes. Each node can be a "leaf" that is contain a single number or a string, or be a collection of
-other nodes. There can be named collections (mappings) where each element has a name and it is
-accessed by a name, and ordered collections (sequences) where elements do not have names but rather
-accessed by index. Type of the file node can be determined using FileNode::type method.
-
-Note that file nodes are only used for navigating file storages opened for reading. When a file
-storage is opened for writing, no data is stored in memory after it is written.
- */
-class CV_EXPORTS_W_SIMPLE FileNode
-{
-public:
-    //! type of the file storage node
-    enum Type
-    {
-        NONE      = 0, //!< empty node
-        INT       = 1, //!< an integer
-        REAL      = 2, //!< floating-point number
-        FLOAT     = REAL, //!< synonym or REAL
-        STR       = 3, //!< text string in UTF-8 encoding
-        STRING    = STR, //!< synonym for STR
-        REF       = 4, //!< integer of size size_t. Typically used for storing complex dynamic structures where some elements reference the others
-        SEQ       = 5, //!< sequence
-        MAP       = 6, //!< mapping
-        TYPE_MASK = 7,
-        FLOW      = 8,  //!< compact representation of a sequence or mapping. Used only by YAML writer
-        USER      = 16, //!< a registered object (e.g. a matrix)
-        EMPTY     = 32, //!< empty structure (sequence or mapping)
-        NAMED     = 64  //!< the node has a name (i.e. it is element of a mapping)
-    };
-    /** @brief The constructors.
-
-    These constructors are used to create a default file node, construct it from obsolete structures or
-    from the another file node.
-     */
-    CV_WRAP FileNode();
-
-    /** @overload
-    @param fs Pointer to the obsolete file storage structure.
-    @param node File node to be used as initialization for the created file node.
-    */
-    FileNode(const CvFileStorage* fs, const CvFileNode* node);
-
-    /** @overload
-    @param node File node to be used as initialization for the created file node.
-    */
-    FileNode(const FileNode& node);
-
-    /** @brief Returns element of a mapping node or a sequence node.
-    @param nodename Name of an element in the mapping node.
-    @returns Returns the element with the given identifier.
-     */
-    FileNode operator[](const String& nodename) const;
-
-    /** @overload
-    @param nodename Name of an element in the mapping node.
-    */
-    CV_WRAP_AS(getNode) FileNode operator[](const char* nodename) const;
-
-    /** @overload
-    @param i Index of an element in the sequence node.
-    */
-    CV_WRAP_AS(at) FileNode operator[](int i) const;
-
-    /** @brief Returns type of the node.
-    @returns Type of the node. See FileNode::Type
-     */
-    CV_WRAP int type() const;
-
-    //! returns true if the node is empty
-    CV_WRAP bool empty() const;
-    //! returns true if the node is a "none" object
-    CV_WRAP bool isNone() const;
-    //! returns true if the node is a sequence
-    CV_WRAP bool isSeq() const;
-    //! returns true if the node is a mapping
-    CV_WRAP bool isMap() const;
-    //! returns true if the node is an integer
-    CV_WRAP bool isInt() const;
-    //! returns true if the node is a floating-point number
-    CV_WRAP bool isReal() const;
-    //! returns true if the node is a text string
-    CV_WRAP bool isString() const;
-    //! returns true if the node has a name
-    CV_WRAP bool isNamed() const;
-    //! returns the node name or an empty string if the node is nameless
-    CV_WRAP String name() const;
-    //! returns the number of elements in the node, if it is a sequence or mapping, or 1 otherwise.
-    CV_WRAP size_t size() const;
-    //! returns the node content as an integer. If the node stores floating-point number, it is rounded.
-    operator int() const;
-    //! returns the node content as float
-    operator float() const;
-    //! returns the node content as double
-    operator double() const;
-    //! returns the node content as text string
-    operator String() const;
-    operator std::string() const;
-
-    //! returns pointer to the underlying file node
-    CvFileNode* operator *();
-    //! returns pointer to the underlying file node
-    const CvFileNode* operator* () const;
-
-    //! returns iterator pointing to the first node element
-    FileNodeIterator begin() const;
-    //! returns iterator pointing to the element following the last node element
-    FileNodeIterator end() const;
-
-    /** @brief Reads node elements to the buffer with the specified format.
-
-    Usually it is more convenient to use operator `>>` instead of this method.
-    @param fmt Specification of each array element. See @ref format_spec "format specification"
-    @param vec Pointer to the destination array.
-    @param len Number of elements to read. If it is greater than number of remaining elements then all
-    of them will be read.
-     */
-    void readRaw( const String& fmt, uchar* vec, size_t len ) const;
-
-    //! reads the registered object and returns pointer to it
-    void* readObj() const;
-
-    //! Simplified reading API to use with bindings.
-    CV_WRAP double real() const;
-    //! Simplified reading API to use with bindings.
-    CV_WRAP String string() const;
-    //! Simplified reading API to use with bindings.
-    CV_WRAP Mat mat() const;
-
-    // do not use wrapper pointer classes for better efficiency
-    const CvFileStorage* fs;
-    const CvFileNode* node;
-};
-
-
-/** @brief used to iterate through sequences and mappings.
-
-A standard STL notation, with node.begin(), node.end() denoting the beginning and the end of a
-sequence, stored in node. See the data reading sample in the beginning of the section.
- */
-class CV_EXPORTS FileNodeIterator
-{
-public:
-    /** @brief The constructors.
-
-    These constructors are used to create a default iterator, set it to specific element in a file node
-    or construct it from another iterator.
-     */
-    FileNodeIterator();
-
-    /** @overload
-    @param fs File storage for the iterator.
-    @param node File node for the iterator.
-    @param ofs Index of the element in the node. The created iterator will point to this element.
-    */
-    FileNodeIterator(const CvFileStorage* fs, const CvFileNode* node, size_t ofs=0);
-
-    /** @overload
-    @param it Iterator to be used as initialization for the created iterator.
-    */
-    FileNodeIterator(const FileNodeIterator& it);
-
-    //! returns the currently observed element
-    FileNode operator *() const;
-    //! accesses the currently observed element methods
-    FileNode operator ->() const;
-
-    //! moves iterator to the next node
-    FileNodeIterator& operator ++ ();
-    //! moves iterator to the next node
-    FileNodeIterator operator ++ (int);
-    //! moves iterator to the previous node
-    FileNodeIterator& operator -- ();
-    //! moves iterator to the previous node
-    FileNodeIterator operator -- (int);
-    //! moves iterator forward by the specified offset (possibly negative)
-    FileNodeIterator& operator += (int ofs);
-    //! moves iterator backward by the specified offset (possibly negative)
-    FileNodeIterator& operator -= (int ofs);
-
-    /** @brief Reads node elements to the buffer with the specified format.
-
-    Usually it is more convenient to use operator `>>` instead of this method.
-    @param fmt Specification of each array element. See @ref format_spec "format specification"
-    @param vec Pointer to the destination array.
-    @param maxCount Number of elements to read. If it is greater than number of remaining elements then
-    all of them will be read.
-     */
-    FileNodeIterator& readRaw( const String& fmt, uchar* vec,
-                               size_t maxCount=(size_t)INT_MAX );
-
-    struct SeqReader
-    {
-      int          header_size;
-      void*        seq;        /* sequence, beign read; CvSeq      */
-      void*        block;      /* current block;        CvSeqBlock */
-      schar*       ptr;        /* pointer to element be read next */
-      schar*       block_min;  /* pointer to the beginning of block */
-      schar*       block_max;  /* pointer to the end of block */
-      int          delta_index;/* = seq->first->start_index   */
-      schar*       prev_elem;  /* pointer to previous element */
-    };
-
-    const CvFileStorage* fs;
-    const CvFileNode* container;
-    SeqReader reader;
-    size_t remaining;
-};
-
-//! @} core_xml
-
-/////////////////// XML & YAML I/O implementation //////////////////
-
-//! @relates cv::FileStorage
-//! @{
-
-CV_EXPORTS void write( FileStorage& fs, const String& name, int value );
-CV_EXPORTS void write( FileStorage& fs, const String& name, float value );
-CV_EXPORTS void write( FileStorage& fs, const String& name, double value );
-CV_EXPORTS void write( FileStorage& fs, const String& name, const String& value );
-CV_EXPORTS void write( FileStorage& fs, const String& name, const Mat& value );
-CV_EXPORTS void write( FileStorage& fs, const String& name, const SparseMat& value );
-#ifdef CV__LEGACY_PERSISTENCE
-CV_EXPORTS void write( FileStorage& fs, const String& name, const std::vector<KeyPoint>& value);
-CV_EXPORTS void write( FileStorage& fs, const String& name, const std::vector<DMatch>& value);
-#endif
-
-CV_EXPORTS void writeScalar( FileStorage& fs, int value );
-CV_EXPORTS void writeScalar( FileStorage& fs, float value );
-CV_EXPORTS void writeScalar( FileStorage& fs, double value );
-CV_EXPORTS void writeScalar( FileStorage& fs, const String& value );
-
-//! @}
-
-//! @relates cv::FileNode
-//! @{
-
-CV_EXPORTS void read(const FileNode& node, int& value, int default_value);
-CV_EXPORTS void read(const FileNode& node, float& value, float default_value);
-CV_EXPORTS void read(const FileNode& node, double& value, double default_value);
-CV_EXPORTS void read(const FileNode& node, String& value, const String& default_value);
-CV_EXPORTS void read(const FileNode& node, std::string& value, const std::string& default_value);
-CV_EXPORTS void read(const FileNode& node, Mat& mat, const Mat& default_mat = Mat() );
-CV_EXPORTS void read(const FileNode& node, SparseMat& mat, const SparseMat& default_mat = SparseMat() );
-#ifdef CV__LEGACY_PERSISTENCE
-CV_EXPORTS void read(const FileNode& node, std::vector<KeyPoint>& keypoints);
-CV_EXPORTS void read(const FileNode& node, std::vector<DMatch>& matches);
-#endif
-CV_EXPORTS void read(const FileNode& node, KeyPoint& value, const KeyPoint& default_value);
-CV_EXPORTS void read(const FileNode& node, DMatch& value, const DMatch& default_value);
-
-template<typename _Tp> static inline void read(const FileNode& node, Point_<_Tp>& value, const Point_<_Tp>& default_value)
-{
-    std::vector<_Tp> temp; FileNodeIterator it = node.begin(); it >> temp;
-    value = temp.size() != 2 ? default_value : Point_<_Tp>(saturate_cast<_Tp>(temp[0]), saturate_cast<_Tp>(temp[1]));
-}
-
-template<typename _Tp> static inline void read(const FileNode& node, Point3_<_Tp>& value, const Point3_<_Tp>& default_value)
-{
-    std::vector<_Tp> temp; FileNodeIterator it = node.begin(); it >> temp;
-    value = temp.size() != 3 ? default_value : Point3_<_Tp>(saturate_cast<_Tp>(temp[0]), saturate_cast<_Tp>(temp[1]),
-                                                            saturate_cast<_Tp>(temp[2]));
-}
-
-template<typename _Tp> static inline void read(const FileNode& node, Size_<_Tp>& value, const Size_<_Tp>& default_value)
-{
-    std::vector<_Tp> temp; FileNodeIterator it = node.begin(); it >> temp;
-    value = temp.size() != 2 ? default_value : Size_<_Tp>(saturate_cast<_Tp>(temp[0]), saturate_cast<_Tp>(temp[1]));
-}
-
-template<typename _Tp> static inline void read(const FileNode& node, Complex<_Tp>& value, const Complex<_Tp>& default_value)
-{
-    std::vector<_Tp> temp; FileNodeIterator it = node.begin(); it >> temp;
-    value = temp.size() != 2 ? default_value : Complex<_Tp>(saturate_cast<_Tp>(temp[0]), saturate_cast<_Tp>(temp[1]));
-}
-
-template<typename _Tp> static inline void read(const FileNode& node, Rect_<_Tp>& value, const Rect_<_Tp>& default_value)
-{
-    std::vector<_Tp> temp; FileNodeIterator it = node.begin(); it >> temp;
-    value = temp.size() != 4 ? default_value : Rect_<_Tp>(saturate_cast<_Tp>(temp[0]), saturate_cast<_Tp>(temp[1]),
-                                                          saturate_cast<_Tp>(temp[2]), saturate_cast<_Tp>(temp[3]));
-}
-
-template<typename _Tp, int cn> static inline void read(const FileNode& node, Vec<_Tp, cn>& value, const Vec<_Tp, cn>& default_value)
-{
-    std::vector<_Tp> temp; FileNodeIterator it = node.begin(); it >> temp;
-    value = temp.size() != cn ? default_value : Vec<_Tp, cn>(&temp[0]);
-}
-
-template<typename _Tp> static inline void read(const FileNode& node, Scalar_<_Tp>& value, const Scalar_<_Tp>& default_value)
-{
-    std::vector<_Tp> temp; FileNodeIterator it = node.begin(); it >> temp;
-    value = temp.size() != 4 ? default_value : Scalar_<_Tp>(saturate_cast<_Tp>(temp[0]), saturate_cast<_Tp>(temp[1]),
-                                                            saturate_cast<_Tp>(temp[2]), saturate_cast<_Tp>(temp[3]));
-}
-
-static inline void read(const FileNode& node, Range& value, const Range& default_value)
-{
-    Point2i temp(value.start, value.end); const Point2i default_temp = Point2i(default_value.start, default_value.end);
-    read(node, temp, default_temp);
-    value.start = temp.x; value.end = temp.y;
-}
-
-//! @}
-
-/** @brief Writes string to a file storage.
-@relates cv::FileStorage
- */
-CV_EXPORTS FileStorage& operator << (FileStorage& fs, const String& str);
-
-//! @cond IGNORED
-
-namespace internal
-{
-    class CV_EXPORTS WriteStructContext
-    {
-    public:
-        WriteStructContext(FileStorage& _fs, const String& name, int flags, const String& typeName = String());
-        ~WriteStructContext();
-    private:
-        FileStorage* fs;
-    };
-
-    template<typename _Tp, int numflag> class VecWriterProxy
-    {
-    public:
-        VecWriterProxy( FileStorage* _fs ) : fs(_fs) {}
-        void operator()(const std::vector<_Tp>& vec) const
-        {
-            size_t count = vec.size();
-            for (size_t i = 0; i < count; i++)
-                write(*fs, vec[i]);
-        }
-    private:
-        FileStorage* fs;
-    };
-
-    template<typename _Tp> class VecWriterProxy<_Tp, 1>
-    {
-    public:
-        VecWriterProxy( FileStorage* _fs ) : fs(_fs) {}
-        void operator()(const std::vector<_Tp>& vec) const
-        {
-            int _fmt = traits::SafeFmt<_Tp>::fmt;
-            char fmt[] = { (char)((_fmt >> 8) + '1'), (char)_fmt, '\0' };
-            fs->writeRaw(fmt, !vec.empty() ? (uchar*)&vec[0] : 0, vec.size() * sizeof(_Tp));
-        }
-    private:
-        FileStorage* fs;
-    };
-
-    template<typename _Tp, int numflag> class VecReaderProxy
-    {
-    public:
-        VecReaderProxy( FileNodeIterator* _it ) : it(_it) {}
-        void operator()(std::vector<_Tp>& vec, size_t count) const
-        {
-            count = std::min(count, it->remaining);
-            vec.resize(count);
-            for (size_t i = 0; i < count; i++, ++(*it))
-                read(**it, vec[i], _Tp());
-        }
-    private:
-        FileNodeIterator* it;
-    };
-
-    template<typename _Tp> class VecReaderProxy<_Tp, 1>
-    {
-    public:
-        VecReaderProxy( FileNodeIterator* _it ) : it(_it) {}
-        void operator()(std::vector<_Tp>& vec, size_t count) const
-        {
-            size_t remaining = it->remaining;
-            size_t cn = DataType<_Tp>::channels;
-            int _fmt = traits::SafeFmt<_Tp>::fmt;
-            CV_Assert((_fmt >> 8) < 9);
-            char fmt[] = { (char)((_fmt >> 8)+'1'), (char)_fmt, '\0' };
-            CV_Assert((remaining % cn) == 0);
-            size_t remaining1 = remaining / cn;
-            count = count < remaining1 ? count : remaining1;
-            vec.resize(count);
-            it->readRaw(fmt, !vec.empty() ? (uchar*)&vec[0] : 0, count*sizeof(_Tp));
-        }
-    private:
-        FileNodeIterator* it;
-    };
-
-} // internal
-
-//! @endcond
-
-//! @relates cv::FileStorage
-//! @{
-
-template<typename _Tp> static inline
-void write(FileStorage& fs, const _Tp& value)
-{
-    write(fs, String(), value);
-}
-
-template<> inline
-void write( FileStorage& fs, const int& value )
-{
-    writeScalar(fs, value);
-}
-
-template<> inline
-void write( FileStorage& fs, const float& value )
-{
-    writeScalar(fs, value);
-}
-
-template<> inline
-void write( FileStorage& fs, const double& value )
-{
-    writeScalar(fs, value);
-}
-
-template<> inline
-void write( FileStorage& fs, const String& value )
-{
-    writeScalar(fs, value);
-}
-
-template<typename _Tp> static inline
-void write(FileStorage& fs, const Point_<_Tp>& pt )
-{
-    write(fs, pt.x);
-    write(fs, pt.y);
-}
-
-template<typename _Tp> static inline
-void write(FileStorage& fs, const Point3_<_Tp>& pt )
-{
-    write(fs, pt.x);
-    write(fs, pt.y);
-    write(fs, pt.z);
-}
-
-template<typename _Tp> static inline
-void write(FileStorage& fs, const Size_<_Tp>& sz )
-{
-    write(fs, sz.width);
-    write(fs, sz.height);
-}
-
-template<typename _Tp> static inline
-void write(FileStorage& fs, const Complex<_Tp>& c )
-{
-    write(fs, c.re);
-    write(fs, c.im);
-}
-
-template<typename _Tp> static inline
-void write(FileStorage& fs, const Rect_<_Tp>& r )
-{
-    write(fs, r.x);
-    write(fs, r.y);
-    write(fs, r.width);
-    write(fs, r.height);
-}
-
-template<typename _Tp, int cn> static inline
-void write(FileStorage& fs, const Vec<_Tp, cn>& v )
-{
-    for(int i = 0; i < cn; i++)
-        write(fs, v.val[i]);
-}
-
-template<typename _Tp> static inline
-void write(FileStorage& fs, const Scalar_<_Tp>& s )
-{
-    write(fs, s.val[0]);
-    write(fs, s.val[1]);
-    write(fs, s.val[2]);
-    write(fs, s.val[3]);
-}
-
-static inline
-void write(FileStorage& fs, const Range& r )
-{
-    write(fs, r.start);
-    write(fs, r.end);
-}
-
-template<typename _Tp> static inline
-void write( FileStorage& fs, const std::vector<_Tp>& vec )
-{
-    cv::internal::VecWriterProxy<_Tp, traits::SafeFmt<_Tp>::fmt != 0> w(&fs);
-    w(vec);
-}
-
-template<typename _Tp> static inline
-void write(FileStorage& fs, const String& name, const Point_<_Tp>& pt )
-{
-    cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW);
-    write(fs, pt);
-}
-
-template<typename _Tp> static inline
-void write(FileStorage& fs, const String& name, const Point3_<_Tp>& pt )
-{
-    cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW);
-    write(fs, pt);
-}
-
-template<typename _Tp> static inline
-void write(FileStorage& fs, const String& name, const Size_<_Tp>& sz )
-{
-    cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW);
-    write(fs, sz);
-}
-
-template<typename _Tp> static inline
-void write(FileStorage& fs, const String& name, const Complex<_Tp>& c )
-{
-    cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW);
-    write(fs, c);
-}
-
-template<typename _Tp> static inline
-void write(FileStorage& fs, const String& name, const Rect_<_Tp>& r )
-{
-    cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW);
-    write(fs, r);
-}
-
-template<typename _Tp, int cn> static inline
-void write(FileStorage& fs, const String& name, const Vec<_Tp, cn>& v )
-{
-    cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW);
-    write(fs, v);
-}
-
-template<typename _Tp> static inline
-void write(FileStorage& fs, const String& name, const Scalar_<_Tp>& s )
-{
-    cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW);
-    write(fs, s);
-}
-
-static inline
-void write(FileStorage& fs, const String& name, const Range& r )
-{
-    cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW);
-    write(fs, r);
-}
-
-static inline
-void write(FileStorage& fs, const String& name, const KeyPoint& kpt)
-{
-    cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW);
-    write(fs, kpt.pt.x);
-    write(fs, kpt.pt.y);
-    write(fs, kpt.size);
-    write(fs, kpt.angle);
-    write(fs, kpt.response);
-    write(fs, kpt.octave);
-    write(fs, kpt.class_id);
-}
-
-static inline
-void write(FileStorage& fs, const String& name, const DMatch& m)
-{
-    cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+FileNode::FLOW);
-    write(fs, m.queryIdx);
-    write(fs, m.trainIdx);
-    write(fs, m.imgIdx);
-    write(fs, m.distance);
-}
-
-template<typename _Tp> static inline
-void write( FileStorage& fs, const String& name, const std::vector<_Tp>& vec )
-{
-    cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ+(traits::SafeFmt<_Tp>::fmt != 0 ? FileNode::FLOW : 0));
-    write(fs, vec);
-}
-
-template<typename _Tp> static inline
-void write( FileStorage& fs, const String& name, const std::vector< std::vector<_Tp> >& vec )
-{
-    cv::internal::WriteStructContext ws(fs, name, FileNode::SEQ);
-    for(size_t i = 0; i < vec.size(); i++)
-    {
-        cv::internal::WriteStructContext ws_(fs, name, FileNode::SEQ+(traits::SafeFmt<_Tp>::fmt != 0 ? FileNode::FLOW : 0));
-        write(fs, vec[i]);
-    }
-}
-
-#ifdef CV__LEGACY_PERSISTENCE
-// This code is not needed anymore, but it is preserved here to keep source compatibility
-// Implementation is similar to templates instantiations
-static inline void write(FileStorage& fs, const KeyPoint& kpt) { write(fs, String(), kpt); }
-static inline void write(FileStorage& fs, const DMatch& m) { write(fs, String(), m); }
-static inline void write(FileStorage& fs, const std::vector<KeyPoint>& vec)
-{
-    cv::internal::VecWriterProxy<KeyPoint, 0> w(&fs);
-    w(vec);
-}
-static inline void write(FileStorage& fs, const std::vector<DMatch>& vec)
-{
-    cv::internal::VecWriterProxy<DMatch, 0> w(&fs);
-    w(vec);
-
-}
-#endif
-
-//! @} FileStorage
-
-//! @relates cv::FileNode
-//! @{
-
-static inline
-void read(const FileNode& node, bool& value, bool default_value)
-{
-    int temp;
-    read(node, temp, (int)default_value);
-    value = temp != 0;
-}
-
-static inline
-void read(const FileNode& node, uchar& value, uchar default_value)
-{
-    int temp;
-    read(node, temp, (int)default_value);
-    value = saturate_cast<uchar>(temp);
-}
-
-static inline
-void read(const FileNode& node, schar& value, schar default_value)
-{
-    int temp;
-    read(node, temp, (int)default_value);
-    value = saturate_cast<schar>(temp);
-}
-
-static inline
-void read(const FileNode& node, ushort& value, ushort default_value)
-{
-    int temp;
-    read(node, temp, (int)default_value);
-    value = saturate_cast<ushort>(temp);
-}
-
-static inline
-void read(const FileNode& node, short& value, short default_value)
-{
-    int temp;
-    read(node, temp, (int)default_value);
-    value = saturate_cast<short>(temp);
-}
-
-template<typename _Tp> static inline
-void read( FileNodeIterator& it, std::vector<_Tp>& vec, size_t maxCount = (size_t)INT_MAX )
-{
-    cv::internal::VecReaderProxy<_Tp, traits::SafeFmt<_Tp>::fmt != 0> r(&it);
-    r(vec, maxCount);
-}
-
-template<typename _Tp> static inline
-void read( const FileNode& node, std::vector<_Tp>& vec, const std::vector<_Tp>& default_value = std::vector<_Tp>() )
-{
-    if(!node.node)
-        vec = default_value;
-    else
-    {
-        FileNodeIterator it = node.begin();
-        read( it, vec );
-    }
-}
-
-static inline
-void read( const FileNode& node, std::vector<KeyPoint>& vec, const std::vector<KeyPoint>& default_value )
-{
-    if(!node.node)
-        vec = default_value;
-    else
-        read(node, vec);
-}
-
-static inline
-void read( const FileNode& node, std::vector<DMatch>& vec, const std::vector<DMatch>& default_value )
-{
-    if(!node.node)
-        vec = default_value;
-    else
-        read(node, vec);
-}
-
-//! @} FileNode
-
-//! @relates cv::FileStorage
-//! @{
-
-/** @brief Writes data to a file storage.
- */
-template<typename _Tp> static inline
-FileStorage& operator << (FileStorage& fs, const _Tp& value)
-{
-    if( !fs.isOpened() )
-        return fs;
-    if( fs.state == FileStorage::NAME_EXPECTED + FileStorage::INSIDE_MAP )
-        CV_Error( Error::StsError, "No element name has been given" );
-    write( fs, fs.elname, value );
-    if( fs.state & FileStorage::INSIDE_MAP )
-        fs.state = FileStorage::NAME_EXPECTED + FileStorage::INSIDE_MAP;
-    return fs;
-}
-
-/** @brief Writes data to a file storage.
- */
-static inline
-FileStorage& operator << (FileStorage& fs, const char* str)
-{
-    return (fs << String(str));
-}
-
-/** @brief Writes data to a file storage.
- */
-static inline
-FileStorage& operator << (FileStorage& fs, char* value)
-{
-    return (fs << String(value));
-}
-
-//! @} FileStorage
-
-//! @relates cv::FileNodeIterator
-//! @{
-
-/** @brief Reads data from a file storage.
- */
-template<typename _Tp> static inline
-FileNodeIterator& operator >> (FileNodeIterator& it, _Tp& value)
-{
-    read( *it, value, _Tp());
-    return ++it;
-}
-
-/** @brief Reads data from a file storage.
- */
-template<typename _Tp> static inline
-FileNodeIterator& operator >> (FileNodeIterator& it, std::vector<_Tp>& vec)
-{
-    cv::internal::VecReaderProxy<_Tp, traits::SafeFmt<_Tp>::fmt != 0> r(&it);
-    r(vec, (size_t)INT_MAX);
-    return it;
-}
-
-//! @} FileNodeIterator
-
-//! @relates cv::FileNode
-//! @{
-
-/** @brief Reads data from a file storage.
- */
-template<typename _Tp> static inline
-void operator >> (const FileNode& n, _Tp& value)
-{
-    read( n, value, _Tp());
-}
-
-/** @brief Reads data from a file storage.
- */
-template<typename _Tp> static inline
-void operator >> (const FileNode& n, std::vector<_Tp>& vec)
-{
-    FileNodeIterator it = n.begin();
-    it >> vec;
-}
-
-/** @brief Reads KeyPoint from a file storage.
-*/
-//It needs special handling because it contains two types of fields, int & float.
-static inline
-void operator >> (const FileNode& n, KeyPoint& kpt)
-{
-    FileNodeIterator it = n.begin();
-    it >> kpt.pt.x >> kpt.pt.y >> kpt.size >> kpt.angle >> kpt.response >> kpt.octave >> kpt.class_id;
-}
-
-#ifdef CV__LEGACY_PERSISTENCE
-static inline
-void operator >> (const FileNode& n, std::vector<KeyPoint>& vec)
-{
-    read(n, vec);
-}
-static inline
-void operator >> (const FileNode& n, std::vector<DMatch>& vec)
-{
-    read(n, vec);
-}
-#endif
-
-/** @brief Reads DMatch from a file storage.
-*/
-//It needs special handling because it contains two types of fields, int & float.
-static inline
-void operator >> (const FileNode& n, DMatch& m)
-{
-    FileNodeIterator it = n.begin();
-    it >> m.queryIdx >> m.trainIdx >> m.imgIdx >> m.distance;
-}
-
-//! @} FileNode
-
-//! @relates cv::FileNodeIterator
-//! @{
-
-static inline
-bool operator == (const FileNodeIterator& it1, const FileNodeIterator& it2)
-{
-    return it1.fs == it2.fs && it1.container == it2.container &&
-        it1.reader.ptr == it2.reader.ptr && it1.remaining == it2.remaining;
-}
-
-static inline
-bool operator != (const FileNodeIterator& it1, const FileNodeIterator& it2)
-{
-    return !(it1 == it2);
-}
-
-static inline
-ptrdiff_t operator - (const FileNodeIterator& it1, const FileNodeIterator& it2)
-{
-    return it2.remaining - it1.remaining;
-}
-
-static inline
-bool operator < (const FileNodeIterator& it1, const FileNodeIterator& it2)
-{
-    return it1.remaining > it2.remaining;
-}
-
-//! @} FileNodeIterator
-
-//! @cond IGNORED
-
-inline FileNode FileStorage::getFirstTopLevelNode() const { FileNode r = root(); FileNodeIterator it = r.begin(); return it != r.end() ? *it : FileNode(); }
-inline FileNode::FileNode() : fs(0), node(0) {}
-inline FileNode::FileNode(const CvFileStorage* _fs, const CvFileNode* _node) : fs(_fs), node(_node) {}
-inline FileNode::FileNode(const FileNode& _node) : fs(_node.fs), node(_node.node) {}
-inline bool FileNode::empty() const    { return node   == 0;    }
-inline bool FileNode::isNone() const   { return type() == NONE; }
-inline bool FileNode::isSeq() const    { return type() == SEQ;  }
-inline bool FileNode::isMap() const    { return type() == MAP;  }
-inline bool FileNode::isInt() const    { return type() == INT;  }
-inline bool FileNode::isReal() const   { return type() == REAL; }
-inline bool FileNode::isString() const { return type() == STR;  }
-inline CvFileNode* FileNode::operator *() { return (CvFileNode*)node; }
-inline const CvFileNode* FileNode::operator* () const { return node; }
-inline FileNode::operator int() const    { int value;    read(*this, value, 0);     return value; }
-inline FileNode::operator float() const  { float value;  read(*this, value, 0.f);   return value; }
-inline FileNode::operator double() const { double value; read(*this, value, 0.);    return value; }
-inline FileNode::operator String() const { String value; read(*this, value, value); return value; }
-inline double FileNode::real() const  { return double(*this); }
-inline String FileNode::string() const { return String(*this); }
-inline Mat FileNode::mat() const { Mat value; read(*this, value, value);    return value; }
-inline FileNodeIterator FileNode::begin() const { return FileNodeIterator(fs, node); }
-inline FileNodeIterator FileNode::end() const   { return FileNodeIterator(fs, node, size()); }
-inline void FileNode::readRaw( const String& fmt, uchar* vec, size_t len ) const { begin().readRaw( fmt, vec, len ); }
-inline FileNode FileNodeIterator::operator *() const  { return FileNode(fs, (const CvFileNode*)(const void*)reader.ptr); }
-inline FileNode FileNodeIterator::operator ->() const { return FileNode(fs, (const CvFileNode*)(const void*)reader.ptr); }
-inline String::String(const FileNode& fn): cstr_(0), len_(0) { read(fn, *this, *this); }
-
-//! @endcond
-
-
-CV_EXPORTS void cvStartWriteRawData_Base64(::CvFileStorage * fs, const char* name, int len, const char* dt);
-
-CV_EXPORTS void cvWriteRawData_Base64(::CvFileStorage * fs, const void* _data, int len);
-
-CV_EXPORTS void cvEndWriteRawData_Base64(::CvFileStorage * fs);
-
-CV_EXPORTS void cvWriteMat_Base64(::CvFileStorage* fs, const char* name, const ::CvMat* mat);
-
-CV_EXPORTS void cvWriteMatND_Base64(::CvFileStorage* fs, const char* name, const ::CvMatND* mat);
-
-} // cv
-
-#endif // OPENCV_CORE_PERSISTENCE_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/ptr.inl.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/ptr.inl.hpp
deleted file mode 100755
index 466f634..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/ptr.inl.hpp
+++ /dev/null
@@ -1,379 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2013, NVIDIA Corporation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the copyright holders or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_CORE_PTR_INL_HPP
-#define OPENCV_CORE_PTR_INL_HPP
-
-#include <algorithm>
-
-//! @cond IGNORED
-
-namespace cv {
-
-template<typename Y>
-void DefaultDeleter<Y>::operator () (Y* p) const
-{
-    delete p;
-}
-
-namespace detail
-{
-
-struct PtrOwner
-{
-    PtrOwner() : refCount(1)
-    {}
-
-    void incRef()
-    {
-        CV_XADD(&refCount, 1);
-    }
-
-    void decRef()
-    {
-        if (CV_XADD(&refCount, -1) == 1) deleteSelf();
-    }
-
-protected:
-    /* This doesn't really need to be virtual, since PtrOwner is never deleted
-       directly, but it doesn't hurt and it helps avoid warnings. */
-    virtual ~PtrOwner()
-    {}
-
-    virtual void deleteSelf() = 0;
-
-private:
-    unsigned int refCount;
-
-    // noncopyable
-    PtrOwner(const PtrOwner&);
-    PtrOwner& operator = (const PtrOwner&);
-};
-
-template<typename Y, typename D>
-struct PtrOwnerImpl CV_FINAL : PtrOwner
-{
-    PtrOwnerImpl(Y* p, D d) : owned(p), deleter(d)
-    {}
-
-    void deleteSelf() CV_OVERRIDE
-    {
-        deleter(owned);
-        delete this;
-    }
-
-private:
-    Y* owned;
-    D deleter;
-};
-
-
-}
-
-template<typename T>
-Ptr<T>::Ptr() : owner(NULL), stored(NULL)
-{}
-
-template<typename T>
-template<typename Y>
-Ptr<T>::Ptr(Y* p)
-  : owner(p
-      ? new detail::PtrOwnerImpl<Y, DefaultDeleter<Y> >(p, DefaultDeleter<Y>())
-      : NULL),
-    stored(p)
-{}
-
-template<typename T>
-template<typename Y, typename D>
-Ptr<T>::Ptr(Y* p, D d)
-  : owner(p
-      ? new detail::PtrOwnerImpl<Y, D>(p, d)
-      : NULL),
-    stored(p)
-{}
-
-template<typename T>
-Ptr<T>::Ptr(const Ptr& o) : owner(o.owner), stored(o.stored)
-{
-    if (owner) owner->incRef();
-}
-
-template<typename T>
-template<typename Y>
-Ptr<T>::Ptr(const Ptr<Y>& o) : owner(o.owner), stored(o.stored)
-{
-    if (owner) owner->incRef();
-}
-
-template<typename T>
-template<typename Y>
-Ptr<T>::Ptr(const Ptr<Y>& o, T* p) : owner(o.owner), stored(p)
-{
-    if (owner) owner->incRef();
-}
-
-template<typename T>
-Ptr<T>::~Ptr()
-{
-    release();
-}
-
-template<typename T>
-Ptr<T>& Ptr<T>::operator = (const Ptr<T>& o)
-{
-    Ptr(o).swap(*this);
-    return *this;
-}
-
-template<typename T>
-template<typename Y>
-Ptr<T>& Ptr<T>::operator = (const Ptr<Y>& o)
-{
-    Ptr(o).swap(*this);
-    return *this;
-}
-
-template<typename T>
-void Ptr<T>::release()
-{
-    if (owner) owner->decRef();
-    owner = NULL;
-    stored = NULL;
-}
-
-template<typename T>
-template<typename Y>
-void Ptr<T>::reset(Y* p)
-{
-    Ptr(p).swap(*this);
-}
-
-template<typename T>
-template<typename Y, typename D>
-void Ptr<T>::reset(Y* p, D d)
-{
-    Ptr(p, d).swap(*this);
-}
-
-template<typename T>
-void Ptr<T>::swap(Ptr<T>& o)
-{
-    std::swap(owner, o.owner);
-    std::swap(stored, o.stored);
-}
-
-template<typename T>
-T* Ptr<T>::get() const
-{
-    return stored;
-}
-
-template<typename T>
-typename detail::RefOrVoid<T>::type Ptr<T>::operator * () const
-{
-    return *stored;
-}
-
-template<typename T>
-T* Ptr<T>::operator -> () const
-{
-    return stored;
-}
-
-template<typename T>
-Ptr<T>::operator T* () const
-{
-    return stored;
-}
-
-
-template<typename T>
-bool Ptr<T>::empty() const
-{
-    return !stored;
-}
-
-template<typename T>
-template<typename Y>
-Ptr<Y> Ptr<T>::staticCast() const
-{
-    return Ptr<Y>(*this, static_cast<Y*>(stored));
-}
-
-template<typename T>
-template<typename Y>
-Ptr<Y> Ptr<T>::constCast() const
-{
-    return Ptr<Y>(*this, const_cast<Y*>(stored));
-}
-
-template<typename T>
-template<typename Y>
-Ptr<Y> Ptr<T>::dynamicCast() const
-{
-    return Ptr<Y>(*this, dynamic_cast<Y*>(stored));
-}
-
-#ifdef CV_CXX_MOVE_SEMANTICS
-
-template<typename T>
-Ptr<T>::Ptr(Ptr&& o) : owner(o.owner), stored(o.stored)
-{
-    o.owner = NULL;
-    o.stored = NULL;
-}
-
-template<typename T>
-Ptr<T>& Ptr<T>::operator = (Ptr<T>&& o)
-{
-    if (this == &o)
-        return *this;
-
-    release();
-    owner = o.owner;
-    stored = o.stored;
-    o.owner = NULL;
-    o.stored = NULL;
-    return *this;
-}
-
-#endif
-
-
-template<typename T>
-void swap(Ptr<T>& ptr1, Ptr<T>& ptr2){
-    ptr1.swap(ptr2);
-}
-
-template<typename T>
-bool operator == (const Ptr<T>& ptr1, const Ptr<T>& ptr2)
-{
-    return ptr1.get() == ptr2.get();
-}
-
-template<typename T>
-bool operator != (const Ptr<T>& ptr1, const Ptr<T>& ptr2)
-{
-    return ptr1.get() != ptr2.get();
-}
-
-template<typename T>
-Ptr<T> makePtr()
-{
-    return Ptr<T>(new T());
-}
-
-template<typename T, typename A1>
-Ptr<T> makePtr(const A1& a1)
-{
-    return Ptr<T>(new T(a1));
-}
-
-template<typename T, typename A1, typename A2>
-Ptr<T> makePtr(const A1& a1, const A2& a2)
-{
-    return Ptr<T>(new T(a1, a2));
-}
-
-template<typename T, typename A1, typename A2, typename A3>
-Ptr<T> makePtr(const A1& a1, const A2& a2, const A3& a3)
-{
-    return Ptr<T>(new T(a1, a2, a3));
-}
-
-template<typename T, typename A1, typename A2, typename A3, typename A4>
-Ptr<T> makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4)
-{
-    return Ptr<T>(new T(a1, a2, a3, a4));
-}
-
-template<typename T, typename A1, typename A2, typename A3, typename A4, typename A5>
-Ptr<T> makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5)
-{
-    return Ptr<T>(new T(a1, a2, a3, a4, a5));
-}
-
-template<typename T, typename A1, typename A2, typename A3, typename A4, typename A5, typename A6>
-Ptr<T> makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6)
-{
-    return Ptr<T>(new T(a1, a2, a3, a4, a5, a6));
-}
-
-template<typename T, typename A1, typename A2, typename A3, typename A4, typename A5, typename A6, typename A7>
-Ptr<T> makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6, const A7& a7)
-{
-    return Ptr<T>(new T(a1, a2, a3, a4, a5, a6, a7));
-}
-
-template<typename T, typename A1, typename A2, typename A3, typename A4, typename A5, typename A6, typename A7, typename A8>
-Ptr<T> makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6, const A7& a7, const A8& a8)
-{
-    return Ptr<T>(new T(a1, a2, a3, a4, a5, a6, a7, a8));
-}
-
-template<typename T, typename A1, typename A2, typename A3, typename A4, typename A5, typename A6, typename A7, typename A8, typename A9>
-Ptr<T> makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6, const A7& a7, const A8& a8, const A9& a9)
-{
-    return Ptr<T>(new T(a1, a2, a3, a4, a5, a6, a7, a8, a9));
-}
-
-template<typename T, typename A1, typename A2, typename A3, typename A4, typename A5, typename A6, typename A7, typename A8, typename A9, typename A10>
-Ptr<T> makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6, const A7& a7, const A8& a8, const A9& a9, const A10& a10)
-{
-    return Ptr<T>(new T(a1, a2, a3, a4, a5, a6, a7, a8, a9, a10));
-}
-
-template<typename T, typename A1, typename A2, typename A3, typename A4, typename A5, typename A6, typename A7, typename A8, typename A9, typename A10, typename A11>
-Ptr<T> makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6, const A7& a7, const A8& a8, const A9& a9, const A10& a10, const A11& a11)
-{
-    return Ptr<T>(new T(a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11));
-}
-
-template<typename T, typename A1, typename A2, typename A3, typename A4, typename A5, typename A6, typename A7, typename A8, typename A9, typename A10, typename A11, typename A12>
-Ptr<T> makePtr(const A1& a1, const A2& a2, const A3& a3, const A4& a4, const A5& a5, const A6& a6, const A7& a7, const A8& a8, const A9& a9, const A10& a10, const A11& a11, const A12& a12)
-{
-    return Ptr<T>(new T(a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12));
-}
-} // namespace cv
-
-//! @endcond
-
-#endif // OPENCV_CORE_PTR_INL_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/saturate.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/saturate.hpp
deleted file mode 100755
index 118599f..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/saturate.hpp
+++ /dev/null
@@ -1,165 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
-// Copyright (C) 2014, Itseez Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_CORE_SATURATE_HPP
-#define OPENCV_CORE_SATURATE_HPP
-
-#include "opencv2/core/cvdef.h"
-#include "opencv2/core/fast_math.hpp"
-
-namespace cv
-{
-
-//! @addtogroup core_utils
-//! @{
-
-/////////////// saturate_cast (used in image & signal processing) ///////////////////
-
-/** @brief Template function for accurate conversion from one primitive type to another.
-
- The function saturate_cast resembles the standard C++ cast operations, such as static_cast\<T\>()
- and others. It perform an efficient and accurate conversion from one primitive type to another
- (see the introduction chapter). saturate in the name means that when the input value v is out of the
- range of the target type, the result is not formed just by taking low bits of the input, but instead
- the value is clipped. For example:
- @code
- uchar a = saturate_cast<uchar>(-100); // a = 0 (UCHAR_MIN)
- short b = saturate_cast<short>(33333.33333); // b = 32767 (SHRT_MAX)
- @endcode
- Such clipping is done when the target type is unsigned char , signed char , unsigned short or
- signed short . For 32-bit integers, no clipping is done.
-
- When the parameter is a floating-point value and the target type is an integer (8-, 16- or 32-bit),
- the floating-point value is first rounded to the nearest integer and then clipped if needed (when
- the target type is 8- or 16-bit).
-
- This operation is used in the simplest or most complex image processing functions in OpenCV.
-
- @param v Function parameter.
- @sa add, subtract, multiply, divide, Mat::convertTo
- */
-template<typename _Tp> static inline _Tp saturate_cast(uchar v)    { return _Tp(v); }
-/** @overload */
-template<typename _Tp> static inline _Tp saturate_cast(schar v)    { return _Tp(v); }
-/** @overload */
-template<typename _Tp> static inline _Tp saturate_cast(ushort v)   { return _Tp(v); }
-/** @overload */
-template<typename _Tp> static inline _Tp saturate_cast(short v)    { return _Tp(v); }
-/** @overload */
-template<typename _Tp> static inline _Tp saturate_cast(unsigned v) { return _Tp(v); }
-/** @overload */
-template<typename _Tp> static inline _Tp saturate_cast(int v)      { return _Tp(v); }
-/** @overload */
-template<typename _Tp> static inline _Tp saturate_cast(float v)    { return _Tp(v); }
-/** @overload */
-template<typename _Tp> static inline _Tp saturate_cast(double v)   { return _Tp(v); }
-/** @overload */
-template<typename _Tp> static inline _Tp saturate_cast(int64 v)    { return _Tp(v); }
-/** @overload */
-template<typename _Tp> static inline _Tp saturate_cast(uint64 v)   { return _Tp(v); }
-
-template<> inline uchar saturate_cast<uchar>(schar v)        { return (uchar)std::max((int)v, 0); }
-template<> inline uchar saturate_cast<uchar>(ushort v)       { return (uchar)std::min((unsigned)v, (unsigned)UCHAR_MAX); }
-template<> inline uchar saturate_cast<uchar>(int v)          { return (uchar)((unsigned)v <= UCHAR_MAX ? v : v > 0 ? UCHAR_MAX : 0); }
-template<> inline uchar saturate_cast<uchar>(short v)        { return saturate_cast<uchar>((int)v); }
-template<> inline uchar saturate_cast<uchar>(unsigned v)     { return (uchar)std::min(v, (unsigned)UCHAR_MAX); }
-template<> inline uchar saturate_cast<uchar>(float v)        { int iv = cvRound(v); return saturate_cast<uchar>(iv); }
-template<> inline uchar saturate_cast<uchar>(double v)       { int iv = cvRound(v); return saturate_cast<uchar>(iv); }
-template<> inline uchar saturate_cast<uchar>(int64 v)        { return (uchar)((uint64)v <= (uint64)UCHAR_MAX ? v : v > 0 ? UCHAR_MAX : 0); }
-template<> inline uchar saturate_cast<uchar>(uint64 v)       { return (uchar)std::min(v, (uint64)UCHAR_MAX); }
-
-template<> inline schar saturate_cast<schar>(uchar v)        { return (schar)std::min((int)v, SCHAR_MAX); }
-template<> inline schar saturate_cast<schar>(ushort v)       { return (schar)std::min((unsigned)v, (unsigned)SCHAR_MAX); }
-template<> inline schar saturate_cast<schar>(int v)          { return (schar)((unsigned)(v-SCHAR_MIN) <= (unsigned)UCHAR_MAX ? v : v > 0 ? SCHAR_MAX : SCHAR_MIN); }
-template<> inline schar saturate_cast<schar>(short v)        { return saturate_cast<schar>((int)v); }
-template<> inline schar saturate_cast<schar>(unsigned v)     { return (schar)std::min(v, (unsigned)SCHAR_MAX); }
-template<> inline schar saturate_cast<schar>(float v)        { int iv = cvRound(v); return saturate_cast<schar>(iv); }
-template<> inline schar saturate_cast<schar>(double v)       { int iv = cvRound(v); return saturate_cast<schar>(iv); }
-template<> inline schar saturate_cast<schar>(int64 v)        { return (schar)((uint64)((int64)v-SCHAR_MIN) <= (uint64)UCHAR_MAX ? v : v > 0 ? SCHAR_MAX : SCHAR_MIN); }
-template<> inline schar saturate_cast<schar>(uint64 v)       { return (schar)std::min(v, (uint64)SCHAR_MAX); }
-
-template<> inline ushort saturate_cast<ushort>(schar v)      { return (ushort)std::max((int)v, 0); }
-template<> inline ushort saturate_cast<ushort>(short v)      { return (ushort)std::max((int)v, 0); }
-template<> inline ushort saturate_cast<ushort>(int v)        { return (ushort)((unsigned)v <= (unsigned)USHRT_MAX ? v : v > 0 ? USHRT_MAX : 0); }
-template<> inline ushort saturate_cast<ushort>(unsigned v)   { return (ushort)std::min(v, (unsigned)USHRT_MAX); }
-template<> inline ushort saturate_cast<ushort>(float v)      { int iv = cvRound(v); return saturate_cast<ushort>(iv); }
-template<> inline ushort saturate_cast<ushort>(double v)     { int iv = cvRound(v); return saturate_cast<ushort>(iv); }
-template<> inline ushort saturate_cast<ushort>(int64 v)      { return (ushort)((uint64)v <= (uint64)USHRT_MAX ? v : v > 0 ? USHRT_MAX : 0); }
-template<> inline ushort saturate_cast<ushort>(uint64 v)     { return (ushort)std::min(v, (uint64)USHRT_MAX); }
-
-template<> inline short saturate_cast<short>(ushort v)       { return (short)std::min((int)v, SHRT_MAX); }
-template<> inline short saturate_cast<short>(int v)          { return (short)((unsigned)(v - SHRT_MIN) <= (unsigned)USHRT_MAX ? v : v > 0 ? SHRT_MAX : SHRT_MIN); }
-template<> inline short saturate_cast<short>(unsigned v)     { return (short)std::min(v, (unsigned)SHRT_MAX); }
-template<> inline short saturate_cast<short>(float v)        { int iv = cvRound(v); return saturate_cast<short>(iv); }
-template<> inline short saturate_cast<short>(double v)       { int iv = cvRound(v); return saturate_cast<short>(iv); }
-template<> inline short saturate_cast<short>(int64 v)        { return (short)((uint64)((int64)v - SHRT_MIN) <= (uint64)USHRT_MAX ? v : v > 0 ? SHRT_MAX : SHRT_MIN); }
-template<> inline short saturate_cast<short>(uint64 v)       { return (short)std::min(v, (uint64)SHRT_MAX); }
-
-template<> inline int saturate_cast<int>(unsigned v)         { return (int)std::min(v, (unsigned)INT_MAX); }
-template<> inline int saturate_cast<int>(int64 v)            { return (int)((uint64)(v - INT_MIN) <= (uint64)UINT_MAX ? v : v > 0 ? INT_MAX : INT_MIN); }
-template<> inline int saturate_cast<int>(uint64 v)           { return (int)std::min(v, (uint64)INT_MAX); }
-template<> inline int saturate_cast<int>(float v)            { return cvRound(v); }
-template<> inline int saturate_cast<int>(double v)           { return cvRound(v); }
-
-template<> inline unsigned saturate_cast<unsigned>(schar v)  { return (unsigned)std::max(v, (schar)0); }
-template<> inline unsigned saturate_cast<unsigned>(short v)  { return (unsigned)std::max(v, (short)0); }
-template<> inline unsigned saturate_cast<unsigned>(int v)    { return (unsigned)std::max(v, (int)0); }
-template<> inline unsigned saturate_cast<unsigned>(int64 v)  { return (unsigned)((uint64)v <= (uint64)UINT_MAX ? v : v > 0 ? UINT_MAX : 0); }
-template<> inline unsigned saturate_cast<unsigned>(uint64 v) { return (unsigned)std::min(v, (uint64)UINT_MAX); }
-// we intentionally do not clip negative numbers, to make -1 become 0xffffffff etc.
-template<> inline unsigned saturate_cast<unsigned>(float v)  { return static_cast<unsigned>(cvRound(v)); }
-template<> inline unsigned saturate_cast<unsigned>(double v) { return static_cast<unsigned>(cvRound(v)); }
-
-template<> inline uint64 saturate_cast<uint64>(schar v)      { return (uint64)std::max(v, (schar)0); }
-template<> inline uint64 saturate_cast<uint64>(short v)      { return (uint64)std::max(v, (short)0); }
-template<> inline uint64 saturate_cast<uint64>(int v)        { return (uint64)std::max(v, (int)0); }
-template<> inline uint64 saturate_cast<uint64>(int64 v)      { return (uint64)std::max(v, (int64)0); }
-
-template<> inline int64 saturate_cast<int64>(uint64 v)       { return (int64)std::min(v, (uint64)LLONG_MAX); }
-
-//! @}
-
-} // cv
-
-#endif // OPENCV_CORE_SATURATE_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/softfloat.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/softfloat.hpp
deleted file mode 100755
index 5470980..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/softfloat.hpp
+++ /dev/null
@@ -1,514 +0,0 @@
-// This file is part of OpenCV project.
-// It is subject to the license terms in the LICENSE file found in the top-level directory
-// of this distribution and at http://opencv.org/license.html
-
-// This file is based on files from package issued with the following license:
-
-/*============================================================================
-
-This C header file is part of the SoftFloat IEEE Floating-Point Arithmetic
-Package, Release 3c, by John R. Hauser.
-
-Copyright 2011, 2012, 2013, 2014, 2015, 2016, 2017 The Regents of the
-University of California.  All rights reserved.
-
-Redistribution and use in source and binary forms, with or without
-modification, are permitted provided that the following conditions are met:
-
- 1. Redistributions of source code must retain the above copyright notice,
-    this list of conditions, and the following disclaimer.
-
- 2. Redistributions in binary form must reproduce the above copyright notice,
-    this list of conditions, and the following disclaimer in the documentation
-    and/or other materials provided with the distribution.
-
- 3. Neither the name of the University nor the names of its contributors may
-    be used to endorse or promote products derived from this software without
-    specific prior written permission.
-
-THIS SOFTWARE IS PROVIDED BY THE REGENTS AND CONTRIBUTORS "AS IS", AND ANY
-EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
-WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE, ARE
-DISCLAIMED.  IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE FOR ANY
-DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
-(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
-LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
-ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
-SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
-=============================================================================*/
-
-#pragma once
-#ifndef softfloat_h
-#define softfloat_h 1
-
-#include "cvdef.h"
-
-namespace cv
-{
-
-/** @addtogroup core_utils_softfloat
-
-  [SoftFloat](http://www.jhauser.us/arithmetic/SoftFloat.html) is a software implementation
-  of floating-point calculations according to IEEE 754 standard.
-  All calculations are done in integers, that's why they are machine-independent and bit-exact.
-  This library can be useful in accuracy-critical parts like look-up tables generation, tests, etc.
-  OpenCV contains a subset of SoftFloat partially rewritten to C++.
-
-  ### Types
-
-  There are two basic types: @ref softfloat and @ref softdouble.
-  These types are binary compatible with float and double types respectively
-  and support conversions to/from them.
-  Other types from original SoftFloat library like fp16 or fp128 were thrown away
-  as well as quiet/signaling NaN support, on-the-fly rounding mode switch
-  and exception flags (though exceptions can be implemented in the future).
-
-  ### Operations
-
-  Both types support the following:
-  - Construction from signed and unsigned 32-bit and 64 integers,
-  float/double or raw binary representation
-  - Conversions between each other, to float or double and to int
-  using @ref cvRound, @ref cvTrunc, @ref cvFloor, @ref cvCeil or a bunch of
-  saturate_cast functions
-  - Add, subtract, multiply, divide, remainder, square root, FMA with absolute precision
-  - Comparison operations
-  - Explicit sign, exponent and significand manipulation through get/set methods,
- number state indicators (isInf, isNan, isSubnormal)
-  - Type-specific constants like eps, minimum/maximum value, best pi approximation, etc.
-  - min(), max(), abs(), exp(), log() and pow() functions
-
-*/
-//! @{
-
-struct softfloat;
-struct softdouble;
-
-struct CV_EXPORTS softfloat
-{
-public:
-    /** @brief Default constructor */
-    softfloat() { v = 0; }
-    /** @brief Copy constructor */
-    softfloat( const softfloat& c) { v = c.v; }
-    /** @brief Assign constructor */
-    softfloat& operator=( const softfloat& c )
-    {
-        if(&c != this) v = c.v;
-        return *this;
-    }
-    /** @brief Construct from raw
-
-    Builds new value from raw binary representation
-    */
-    static const softfloat fromRaw( const uint32_t a ) { softfloat x; x.v = a; return x; }
-
-    /** @brief Construct from integer */
-    explicit softfloat( const uint32_t );
-    explicit softfloat( const uint64_t );
-    explicit softfloat( const int32_t );
-    explicit softfloat( const int64_t );
-
-#ifdef CV_INT32_T_IS_LONG_INT
-    // for platforms with int32_t = long int
-    explicit softfloat( const int a ) { *this = softfloat(static_cast<int32_t>(a)); }
-#endif
-
-    /** @brief Construct from float */
-    explicit softfloat( const float a ) { Cv32suf s; s.f = a; v = s.u; }
-
-    /** @brief Type casts  */
-    operator softdouble() const;
-    operator float() const { Cv32suf s; s.u = v; return s.f; }
-
-    /** @brief Basic arithmetics */
-    softfloat operator + (const softfloat&) const;
-    softfloat operator - (const softfloat&) const;
-    softfloat operator * (const softfloat&) const;
-    softfloat operator / (const softfloat&) const;
-    softfloat operator - () const { softfloat x; x.v = v ^ (1U << 31); return x; }
-
-    /** @brief Remainder operator
-
-    A quote from original SoftFloat manual:
-
-    > The IEEE Standard remainder operation computes the value
-    > a - n * b, where n is the integer closest to a / b.
-    > If a / b is exactly halfway between two integers, n is the even integer
-    > closest to a / b. The IEEE Standard’s remainder operation is always exact and so requires no rounding.
-    > Depending on the relative magnitudes of the operands, the remainder functions
-    > can take considerably longer to execute than the other SoftFloat functions.
-    > This is an inherent characteristic of the remainder operation itself and is not a flaw
-    > in the SoftFloat implementation.
-    */
-    softfloat operator % (const softfloat&) const;
-
-    softfloat& operator += (const softfloat& a) { *this = *this + a; return *this; }
-    softfloat& operator -= (const softfloat& a) { *this = *this - a; return *this; }
-    softfloat& operator *= (const softfloat& a) { *this = *this * a; return *this; }
-    softfloat& operator /= (const softfloat& a) { *this = *this / a; return *this; }
-    softfloat& operator %= (const softfloat& a) { *this = *this % a; return *this; }
-
-    /** @brief Comparison operations
-
-     - Any operation with NaN produces false
-       + The only exception is when x is NaN: x != y for any y.
-     - Positive and negative zeros are equal
-    */
-    bool operator == ( const softfloat& ) const;
-    bool operator != ( const softfloat& ) const;
-    bool operator >  ( const softfloat& ) const;
-    bool operator >= ( const softfloat& ) const;
-    bool operator <  ( const softfloat& ) const;
-    bool operator <= ( const softfloat& ) const;
-
-    /** @brief NaN state indicator */
-    inline bool isNaN() const { return (v & 0x7fffffff)  > 0x7f800000; }
-    /** @brief Inf state indicator */
-    inline bool isInf() const { return (v & 0x7fffffff) == 0x7f800000; }
-    /** @brief Subnormal number indicator */
-    inline bool isSubnormal() const { return ((v >> 23) & 0xFF) == 0; }
-
-    /** @brief Get sign bit */
-    inline bool getSign() const { return (v >> 31) != 0; }
-    /** @brief Construct a copy with new sign bit */
-    inline softfloat setSign(bool sign) const { softfloat x; x.v = (v & ((1U << 31) - 1)) | ((uint32_t)sign << 31); return x; }
-    /** @brief Get 0-based exponent */
-    inline int getExp() const { return ((v >> 23) & 0xFF) - 127; }
-    /** @brief Construct a copy with new 0-based exponent */
-    inline softfloat setExp(int e) const { softfloat x; x.v = (v & 0x807fffff) | (((e + 127) & 0xFF) << 23 ); return x; }
-
-    /** @brief Get a fraction part
-
-    Returns a number 1 <= x < 2 with the same significand
-    */
-    inline softfloat getFrac() const
-    {
-        uint_fast32_t vv = (v & 0x007fffff) | (127 << 23);
-        return softfloat::fromRaw(vv);
-    }
-    /** @brief Construct a copy with provided significand
-
-    Constructs a copy of a number with significand taken from parameter
-    */
-    inline softfloat setFrac(const softfloat& s) const
-    {
-        softfloat x;
-        x.v = (v & 0xff800000) | (s.v & 0x007fffff);
-        return x;
-    }
-
-    /** @brief Zero constant */
-    static softfloat zero() { return softfloat::fromRaw( 0 ); }
-    /** @brief Positive infinity constant */
-    static softfloat  inf() { return softfloat::fromRaw( 0xFF << 23 ); }
-    /** @brief Default NaN constant */
-    static softfloat  nan() { return softfloat::fromRaw( 0x7fffffff ); }
-    /** @brief One constant */
-    static softfloat  one() { return softfloat::fromRaw(  127 << 23 ); }
-    /** @brief Smallest normalized value */
-    static softfloat  min() { return softfloat::fromRaw( 0x01 << 23 ); }
-    /** @brief Difference between 1 and next representable value */
-    static softfloat  eps() { return softfloat::fromRaw( (127 - 23) << 23 ); }
-    /** @brief Biggest finite value */
-    static softfloat  max() { return softfloat::fromRaw( (0xFF << 23) - 1 ); }
-    /** @brief Correct pi approximation */
-    static softfloat   pi() { return softfloat::fromRaw( 0x40490fdb ); }
-
-    uint32_t v;
-};
-
-/*----------------------------------------------------------------------------
-*----------------------------------------------------------------------------*/
-
-struct CV_EXPORTS softdouble
-{
-public:
-    /** @brief Default constructor */
-    softdouble() : v(0) { }
-    /** @brief Copy constructor */
-    softdouble( const softdouble& c) { v = c.v; }
-    /** @brief Assign constructor */
-    softdouble& operator=( const softdouble& c )
-    {
-        if(&c != this) v = c.v;
-        return *this;
-    }
-    /** @brief Construct from raw
-
-    Builds new value from raw binary representation
-    */
-    static softdouble fromRaw( const uint64_t a ) { softdouble x; x.v = a; return x; }
-
-    /** @brief Construct from integer */
-    explicit softdouble( const uint32_t );
-    explicit softdouble( const uint64_t );
-    explicit softdouble( const  int32_t );
-    explicit softdouble( const  int64_t );
-
-#ifdef CV_INT32_T_IS_LONG_INT
-    // for platforms with int32_t = long int
-    explicit softdouble( const int a ) { *this = softdouble(static_cast<int32_t>(a)); }
-#endif
-
-    /** @brief Construct from double */
-    explicit softdouble( const double a ) { Cv64suf s; s.f = a; v = s.u; }
-
-    /** @brief Type casts  */
-    operator softfloat() const;
-    operator double() const { Cv64suf s; s.u = v; return s.f; }
-
-    /** @brief Basic arithmetics */
-    softdouble operator + (const softdouble&) const;
-    softdouble operator - (const softdouble&) const;
-    softdouble operator * (const softdouble&) const;
-    softdouble operator / (const softdouble&) const;
-    softdouble operator - () const { softdouble x; x.v = v ^ (1ULL << 63); return x; }
-
-    /** @brief Remainder operator
-
-    A quote from original SoftFloat manual:
-
-    > The IEEE Standard remainder operation computes the value
-    > a - n * b, where n is the integer closest to a / b.
-    > If a / b is exactly halfway between two integers, n is the even integer
-    > closest to a / b. The IEEE Standard’s remainder operation is always exact and so requires no rounding.
-    > Depending on the relative magnitudes of the operands, the remainder functions
-    > can take considerably longer to execute than the other SoftFloat functions.
-    > This is an inherent characteristic of the remainder operation itself and is not a flaw
-    > in the SoftFloat implementation.
-    */
-    softdouble operator % (const softdouble&) const;
-
-    softdouble& operator += (const softdouble& a) { *this = *this + a; return *this; }
-    softdouble& operator -= (const softdouble& a) { *this = *this - a; return *this; }
-    softdouble& operator *= (const softdouble& a) { *this = *this * a; return *this; }
-    softdouble& operator /= (const softdouble& a) { *this = *this / a; return *this; }
-    softdouble& operator %= (const softdouble& a) { *this = *this % a; return *this; }
-
-    /** @brief Comparison operations
-
-     - Any operation with NaN produces false
-       + The only exception is when x is NaN: x != y for any y.
-     - Positive and negative zeros are equal
-    */
-    bool operator == ( const softdouble& ) const;
-    bool operator != ( const softdouble& ) const;
-    bool operator >  ( const softdouble& ) const;
-    bool operator >= ( const softdouble& ) const;
-    bool operator <  ( const softdouble& ) const;
-    bool operator <= ( const softdouble& ) const;
-
-    /** @brief NaN state indicator */
-    inline bool isNaN() const { return (v & 0x7fffffffffffffff)  > 0x7ff0000000000000; }
-    /** @brief Inf state indicator */
-    inline bool isInf() const { return (v & 0x7fffffffffffffff) == 0x7ff0000000000000; }
-    /** @brief Subnormal number indicator */
-    inline bool isSubnormal() const { return ((v >> 52) & 0x7FF) == 0; }
-
-    /** @brief Get sign bit */
-    inline bool getSign() const { return (v >> 63) != 0; }
-    /** @brief Construct a copy with new sign bit */
-    softdouble setSign(bool sign) const { softdouble x; x.v = (v & ((1ULL << 63) - 1)) | ((uint_fast64_t)(sign) << 63); return x; }
-    /** @brief Get 0-based exponent */
-    inline int getExp() const { return ((v >> 52) & 0x7FF) - 1023; }
-    /** @brief Construct a copy with new 0-based exponent */
-    inline softdouble setExp(int e) const
-    {
-        softdouble x;
-        x.v = (v & 0x800FFFFFFFFFFFFF) | ((uint_fast64_t)((e + 1023) & 0x7FF) << 52);
-        return x;
-    }
-
-    /** @brief Get a fraction part
-
-    Returns a number 1 <= x < 2 with the same significand
-    */
-    inline softdouble getFrac() const
-    {
-        uint_fast64_t vv = (v & 0x000FFFFFFFFFFFFF) | ((uint_fast64_t)(1023) << 52);
-        return softdouble::fromRaw(vv);
-    }
-    /** @brief Construct a copy with provided significand
-
-    Constructs a copy of a number with significand taken from parameter
-    */
-    inline softdouble setFrac(const softdouble& s) const
-    {
-        softdouble x;
-        x.v = (v & 0xFFF0000000000000) | (s.v & 0x000FFFFFFFFFFFFF);
-        return x;
-    }
-
-    /** @brief Zero constant */
-    static softdouble zero() { return softdouble::fromRaw( 0 ); }
-    /** @brief Positive infinity constant */
-    static softdouble  inf() { return softdouble::fromRaw( (uint_fast64_t)(0x7FF) << 52 ); }
-    /** @brief Default NaN constant */
-    static softdouble  nan() { return softdouble::fromRaw( CV_BIG_INT(0x7FFFFFFFFFFFFFFF) ); }
-    /** @brief One constant */
-    static softdouble  one() { return softdouble::fromRaw( (uint_fast64_t)( 1023) << 52 ); }
-    /** @brief Smallest normalized value */
-    static softdouble  min() { return softdouble::fromRaw( (uint_fast64_t)( 0x01) << 52 ); }
-    /** @brief Difference between 1 and next representable value */
-    static softdouble  eps() { return softdouble::fromRaw( (uint_fast64_t)( 1023 - 52 ) << 52 ); }
-    /** @brief Biggest finite value */
-    static softdouble  max() { return softdouble::fromRaw( ((uint_fast64_t)(0x7FF) << 52) - 1 ); }
-    /** @brief Correct pi approximation */
-    static softdouble   pi() { return softdouble::fromRaw( CV_BIG_INT(0x400921FB54442D18) ); }
-
-    uint64_t v;
-};
-
-/*----------------------------------------------------------------------------
-*----------------------------------------------------------------------------*/
-
-/** @brief Fused Multiplication and Addition
-
-Computes (a*b)+c with single rounding
-*/
-CV_EXPORTS softfloat  mulAdd( const softfloat&  a, const softfloat&  b, const softfloat & c);
-CV_EXPORTS softdouble mulAdd( const softdouble& a, const softdouble& b, const softdouble& c);
-
-/** @brief Square root */
-CV_EXPORTS softfloat  sqrt( const softfloat&  a );
-CV_EXPORTS softdouble sqrt( const softdouble& a );
-}
-
-/*----------------------------------------------------------------------------
-| Ported from OpenCV and added for usability
-*----------------------------------------------------------------------------*/
-
-/** @brief Truncates number to integer with minimum magnitude */
-CV_EXPORTS int cvTrunc(const cv::softfloat&  a);
-CV_EXPORTS int cvTrunc(const cv::softdouble& a);
-
-/** @brief Rounds a number to nearest even integer */
-CV_EXPORTS int cvRound(const cv::softfloat&  a);
-CV_EXPORTS int cvRound(const cv::softdouble& a);
-
-/** @brief Rounds a number to nearest even long long integer */
-CV_EXPORTS int64_t cvRound64(const cv::softdouble& a);
-
-/** @brief Rounds a number down to integer */
-CV_EXPORTS int cvFloor(const cv::softfloat&  a);
-CV_EXPORTS int cvFloor(const cv::softdouble& a);
-
-/** @brief Rounds number up to integer */
-CV_EXPORTS int  cvCeil(const cv::softfloat&  a);
-CV_EXPORTS int  cvCeil(const cv::softdouble& a);
-
-namespace cv
-{
-/** @brief Saturate casts */
-template<typename _Tp> static inline _Tp saturate_cast(softfloat  a) { return _Tp(a); }
-template<typename _Tp> static inline _Tp saturate_cast(softdouble a) { return _Tp(a); }
-
-template<> inline uchar saturate_cast<uchar>(softfloat  a) { return (uchar)std::max(std::min(cvRound(a), (int)UCHAR_MAX), 0); }
-template<> inline uchar saturate_cast<uchar>(softdouble a) { return (uchar)std::max(std::min(cvRound(a), (int)UCHAR_MAX), 0); }
-
-template<> inline schar saturate_cast<schar>(softfloat  a) { return (schar)std::min(std::max(cvRound(a), (int)SCHAR_MIN), (int)SCHAR_MAX); }
-template<> inline schar saturate_cast<schar>(softdouble a) { return (schar)std::min(std::max(cvRound(a), (int)SCHAR_MIN), (int)SCHAR_MAX); }
-
-template<> inline ushort saturate_cast<ushort>(softfloat  a) { return (ushort)std::max(std::min(cvRound(a), (int)USHRT_MAX), 0); }
-template<> inline ushort saturate_cast<ushort>(softdouble a) { return (ushort)std::max(std::min(cvRound(a), (int)USHRT_MAX), 0); }
-
-template<> inline short saturate_cast<short>(softfloat  a) { return (short)std::min(std::max(cvRound(a), (int)SHRT_MIN), (int)SHRT_MAX); }
-template<> inline short saturate_cast<short>(softdouble a) { return (short)std::min(std::max(cvRound(a), (int)SHRT_MIN), (int)SHRT_MAX); }
-
-template<> inline int saturate_cast<int>(softfloat  a) { return cvRound(a); }
-template<> inline int saturate_cast<int>(softdouble a) { return cvRound(a); }
-
-template<> inline int64_t saturate_cast<int64_t>(softfloat  a) { return cvRound(a); }
-template<> inline int64_t saturate_cast<int64_t>(softdouble a) { return cvRound64(a); }
-
-/** @brief Saturate cast to unsigned integer and unsigned long long integer
-We intentionally do not clip negative numbers, to make -1 become 0xffffffff etc.
-*/
-template<> inline unsigned saturate_cast<unsigned>(softfloat  a) { return cvRound(a); }
-template<> inline unsigned saturate_cast<unsigned>(softdouble a) { return cvRound(a); }
-
-template<> inline uint64_t saturate_cast<uint64_t>(softfloat  a) { return cvRound(a); }
-template<> inline uint64_t saturate_cast<uint64_t>(softdouble a) { return cvRound64(a); }
-
-/** @brief Min and Max functions */
-inline softfloat  min(const softfloat&  a, const softfloat&  b) { return (a > b) ? b : a; }
-inline softdouble min(const softdouble& a, const softdouble& b) { return (a > b) ? b : a; }
-
-inline softfloat  max(const softfloat&  a, const softfloat&  b) { return (a > b) ? a : b; }
-inline softdouble max(const softdouble& a, const softdouble& b) { return (a > b) ? a : b; }
-
-/** @brief Absolute value */
-inline softfloat  abs( softfloat  a) { softfloat  x; x.v = a.v & ((1U   << 31) - 1); return x; }
-inline softdouble abs( softdouble a) { softdouble x; x.v = a.v & ((1ULL << 63) - 1); return x; }
-
-/** @brief Exponent
-
-Special cases:
-- exp(NaN) is NaN
-- exp(-Inf) == 0
-- exp(+Inf) == +Inf
-*/
-CV_EXPORTS softfloat  exp( const softfloat&  a);
-CV_EXPORTS softdouble exp( const softdouble& a);
-
-/** @brief Natural logarithm
-
-Special cases:
-- log(NaN), log(x < 0) are NaN
-- log(0) == -Inf
-*/
-CV_EXPORTS softfloat  log( const softfloat&  a );
-CV_EXPORTS softdouble log( const softdouble& a );
-
-/** @brief Raising to the power
-
-Special cases:
-- x**NaN is NaN for any x
-- ( |x| == 1 )**Inf is NaN
-- ( |x|  > 1 )**+Inf or ( |x| < 1 )**-Inf is +Inf
-- ( |x|  > 1 )**-Inf or ( |x| < 1 )**+Inf is 0
-- x ** 0 == 1 for any x
-- x ** 1 == 1 for any x
-- NaN ** y is NaN for any other y
-- Inf**(y < 0) == 0
-- Inf ** y is +Inf for any other y
-- (x < 0)**y is NaN for any other y if x can't be correctly rounded to integer
-- 0 ** 0 == 1
-- 0 ** (y < 0) is +Inf
-- 0 ** (y > 0) is 0
-*/
-CV_EXPORTS softfloat  pow( const softfloat&  a, const softfloat&  b);
-CV_EXPORTS softdouble pow( const softdouble& a, const softdouble& b);
-
-/** @brief Cube root
-
-Special cases:
-- cbrt(NaN) is NaN
-- cbrt(+/-Inf) is +/-Inf
-*/
-CV_EXPORTS softfloat cbrt( const softfloat& a );
-
-/** @brief Sine
-
-Special cases:
-- sin(Inf) or sin(NaN) is NaN
-- sin(x) == x when sin(x) is close to zero
-*/
-CV_EXPORTS softdouble sin( const softdouble& a );
-
-/** @brief Cosine
- *
-Special cases:
-- cos(Inf) or cos(NaN) is NaN
-- cos(x) == +/- 1 when cos(x) is close to +/- 1
-*/
-CV_EXPORTS softdouble cos( const softdouble& a );
-
-}
-
-//! @}
-
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/sse_utils.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/sse_utils.hpp
deleted file mode 100755
index 69efffe..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/sse_utils.hpp
+++ /dev/null
@@ -1,652 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2015, Itseez Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_CORE_SSE_UTILS_HPP
-#define OPENCV_CORE_SSE_UTILS_HPP
-
-#ifndef __cplusplus
-#  error sse_utils.hpp header must be compiled as C++
-#endif
-
-#include "opencv2/core/cvdef.h"
-
-//! @addtogroup core_utils_sse
-//! @{
-
-#if CV_SSE2
-
-inline void _mm_deinterleave_epi8(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, __m128i & v_g1)
-{
-    __m128i layer1_chunk0 = _mm_unpacklo_epi8(v_r0, v_g0);
-    __m128i layer1_chunk1 = _mm_unpackhi_epi8(v_r0, v_g0);
-    __m128i layer1_chunk2 = _mm_unpacklo_epi8(v_r1, v_g1);
-    __m128i layer1_chunk3 = _mm_unpackhi_epi8(v_r1, v_g1);
-
-    __m128i layer2_chunk0 = _mm_unpacklo_epi8(layer1_chunk0, layer1_chunk2);
-    __m128i layer2_chunk1 = _mm_unpackhi_epi8(layer1_chunk0, layer1_chunk2);
-    __m128i layer2_chunk2 = _mm_unpacklo_epi8(layer1_chunk1, layer1_chunk3);
-    __m128i layer2_chunk3 = _mm_unpackhi_epi8(layer1_chunk1, layer1_chunk3);
-
-    __m128i layer3_chunk0 = _mm_unpacklo_epi8(layer2_chunk0, layer2_chunk2);
-    __m128i layer3_chunk1 = _mm_unpackhi_epi8(layer2_chunk0, layer2_chunk2);
-    __m128i layer3_chunk2 = _mm_unpacklo_epi8(layer2_chunk1, layer2_chunk3);
-    __m128i layer3_chunk3 = _mm_unpackhi_epi8(layer2_chunk1, layer2_chunk3);
-
-    __m128i layer4_chunk0 = _mm_unpacklo_epi8(layer3_chunk0, layer3_chunk2);
-    __m128i layer4_chunk1 = _mm_unpackhi_epi8(layer3_chunk0, layer3_chunk2);
-    __m128i layer4_chunk2 = _mm_unpacklo_epi8(layer3_chunk1, layer3_chunk3);
-    __m128i layer4_chunk3 = _mm_unpackhi_epi8(layer3_chunk1, layer3_chunk3);
-
-    v_r0 = _mm_unpacklo_epi8(layer4_chunk0, layer4_chunk2);
-    v_r1 = _mm_unpackhi_epi8(layer4_chunk0, layer4_chunk2);
-    v_g0 = _mm_unpacklo_epi8(layer4_chunk1, layer4_chunk3);
-    v_g1 = _mm_unpackhi_epi8(layer4_chunk1, layer4_chunk3);
-}
-
-inline void _mm_deinterleave_epi8(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0,
-                                  __m128i & v_g1, __m128i & v_b0, __m128i & v_b1)
-{
-    __m128i layer1_chunk0 = _mm_unpacklo_epi8(v_r0, v_g1);
-    __m128i layer1_chunk1 = _mm_unpackhi_epi8(v_r0, v_g1);
-    __m128i layer1_chunk2 = _mm_unpacklo_epi8(v_r1, v_b0);
-    __m128i layer1_chunk3 = _mm_unpackhi_epi8(v_r1, v_b0);
-    __m128i layer1_chunk4 = _mm_unpacklo_epi8(v_g0, v_b1);
-    __m128i layer1_chunk5 = _mm_unpackhi_epi8(v_g0, v_b1);
-
-    __m128i layer2_chunk0 = _mm_unpacklo_epi8(layer1_chunk0, layer1_chunk3);
-    __m128i layer2_chunk1 = _mm_unpackhi_epi8(layer1_chunk0, layer1_chunk3);
-    __m128i layer2_chunk2 = _mm_unpacklo_epi8(layer1_chunk1, layer1_chunk4);
-    __m128i layer2_chunk3 = _mm_unpackhi_epi8(layer1_chunk1, layer1_chunk4);
-    __m128i layer2_chunk4 = _mm_unpacklo_epi8(layer1_chunk2, layer1_chunk5);
-    __m128i layer2_chunk5 = _mm_unpackhi_epi8(layer1_chunk2, layer1_chunk5);
-
-    __m128i layer3_chunk0 = _mm_unpacklo_epi8(layer2_chunk0, layer2_chunk3);
-    __m128i layer3_chunk1 = _mm_unpackhi_epi8(layer2_chunk0, layer2_chunk3);
-    __m128i layer3_chunk2 = _mm_unpacklo_epi8(layer2_chunk1, layer2_chunk4);
-    __m128i layer3_chunk3 = _mm_unpackhi_epi8(layer2_chunk1, layer2_chunk4);
-    __m128i layer3_chunk4 = _mm_unpacklo_epi8(layer2_chunk2, layer2_chunk5);
-    __m128i layer3_chunk5 = _mm_unpackhi_epi8(layer2_chunk2, layer2_chunk5);
-
-    __m128i layer4_chunk0 = _mm_unpacklo_epi8(layer3_chunk0, layer3_chunk3);
-    __m128i layer4_chunk1 = _mm_unpackhi_epi8(layer3_chunk0, layer3_chunk3);
-    __m128i layer4_chunk2 = _mm_unpacklo_epi8(layer3_chunk1, layer3_chunk4);
-    __m128i layer4_chunk3 = _mm_unpackhi_epi8(layer3_chunk1, layer3_chunk4);
-    __m128i layer4_chunk4 = _mm_unpacklo_epi8(layer3_chunk2, layer3_chunk5);
-    __m128i layer4_chunk5 = _mm_unpackhi_epi8(layer3_chunk2, layer3_chunk5);
-
-    v_r0 = _mm_unpacklo_epi8(layer4_chunk0, layer4_chunk3);
-    v_r1 = _mm_unpackhi_epi8(layer4_chunk0, layer4_chunk3);
-    v_g0 = _mm_unpacklo_epi8(layer4_chunk1, layer4_chunk4);
-    v_g1 = _mm_unpackhi_epi8(layer4_chunk1, layer4_chunk4);
-    v_b0 = _mm_unpacklo_epi8(layer4_chunk2, layer4_chunk5);
-    v_b1 = _mm_unpackhi_epi8(layer4_chunk2, layer4_chunk5);
-}
-
-inline void _mm_deinterleave_epi8(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, __m128i & v_g1,
-                                  __m128i & v_b0, __m128i & v_b1, __m128i & v_a0, __m128i & v_a1)
-{
-    __m128i layer1_chunk0 = _mm_unpacklo_epi8(v_r0, v_b0);
-    __m128i layer1_chunk1 = _mm_unpackhi_epi8(v_r0, v_b0);
-    __m128i layer1_chunk2 = _mm_unpacklo_epi8(v_r1, v_b1);
-    __m128i layer1_chunk3 = _mm_unpackhi_epi8(v_r1, v_b1);
-    __m128i layer1_chunk4 = _mm_unpacklo_epi8(v_g0, v_a0);
-    __m128i layer1_chunk5 = _mm_unpackhi_epi8(v_g0, v_a0);
-    __m128i layer1_chunk6 = _mm_unpacklo_epi8(v_g1, v_a1);
-    __m128i layer1_chunk7 = _mm_unpackhi_epi8(v_g1, v_a1);
-
-    __m128i layer2_chunk0 = _mm_unpacklo_epi8(layer1_chunk0, layer1_chunk4);
-    __m128i layer2_chunk1 = _mm_unpackhi_epi8(layer1_chunk0, layer1_chunk4);
-    __m128i layer2_chunk2 = _mm_unpacklo_epi8(layer1_chunk1, layer1_chunk5);
-    __m128i layer2_chunk3 = _mm_unpackhi_epi8(layer1_chunk1, layer1_chunk5);
-    __m128i layer2_chunk4 = _mm_unpacklo_epi8(layer1_chunk2, layer1_chunk6);
-    __m128i layer2_chunk5 = _mm_unpackhi_epi8(layer1_chunk2, layer1_chunk6);
-    __m128i layer2_chunk6 = _mm_unpacklo_epi8(layer1_chunk3, layer1_chunk7);
-    __m128i layer2_chunk7 = _mm_unpackhi_epi8(layer1_chunk3, layer1_chunk7);
-
-    __m128i layer3_chunk0 = _mm_unpacklo_epi8(layer2_chunk0, layer2_chunk4);
-    __m128i layer3_chunk1 = _mm_unpackhi_epi8(layer2_chunk0, layer2_chunk4);
-    __m128i layer3_chunk2 = _mm_unpacklo_epi8(layer2_chunk1, layer2_chunk5);
-    __m128i layer3_chunk3 = _mm_unpackhi_epi8(layer2_chunk1, layer2_chunk5);
-    __m128i layer3_chunk4 = _mm_unpacklo_epi8(layer2_chunk2, layer2_chunk6);
-    __m128i layer3_chunk5 = _mm_unpackhi_epi8(layer2_chunk2, layer2_chunk6);
-    __m128i layer3_chunk6 = _mm_unpacklo_epi8(layer2_chunk3, layer2_chunk7);
-    __m128i layer3_chunk7 = _mm_unpackhi_epi8(layer2_chunk3, layer2_chunk7);
-
-    __m128i layer4_chunk0 = _mm_unpacklo_epi8(layer3_chunk0, layer3_chunk4);
-    __m128i layer4_chunk1 = _mm_unpackhi_epi8(layer3_chunk0, layer3_chunk4);
-    __m128i layer4_chunk2 = _mm_unpacklo_epi8(layer3_chunk1, layer3_chunk5);
-    __m128i layer4_chunk3 = _mm_unpackhi_epi8(layer3_chunk1, layer3_chunk5);
-    __m128i layer4_chunk4 = _mm_unpacklo_epi8(layer3_chunk2, layer3_chunk6);
-    __m128i layer4_chunk5 = _mm_unpackhi_epi8(layer3_chunk2, layer3_chunk6);
-    __m128i layer4_chunk6 = _mm_unpacklo_epi8(layer3_chunk3, layer3_chunk7);
-    __m128i layer4_chunk7 = _mm_unpackhi_epi8(layer3_chunk3, layer3_chunk7);
-
-    v_r0 = _mm_unpacklo_epi8(layer4_chunk0, layer4_chunk4);
-    v_r1 = _mm_unpackhi_epi8(layer4_chunk0, layer4_chunk4);
-    v_g0 = _mm_unpacklo_epi8(layer4_chunk1, layer4_chunk5);
-    v_g1 = _mm_unpackhi_epi8(layer4_chunk1, layer4_chunk5);
-    v_b0 = _mm_unpacklo_epi8(layer4_chunk2, layer4_chunk6);
-    v_b1 = _mm_unpackhi_epi8(layer4_chunk2, layer4_chunk6);
-    v_a0 = _mm_unpacklo_epi8(layer4_chunk3, layer4_chunk7);
-    v_a1 = _mm_unpackhi_epi8(layer4_chunk3, layer4_chunk7);
-}
-
-inline void _mm_interleave_epi8(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, __m128i & v_g1)
-{
-    __m128i v_mask = _mm_set1_epi16(0x00ff);
-
-    __m128i layer4_chunk0 = _mm_packus_epi16(_mm_and_si128(v_r0, v_mask), _mm_and_si128(v_r1, v_mask));
-    __m128i layer4_chunk2 = _mm_packus_epi16(_mm_srli_epi16(v_r0, 8), _mm_srli_epi16(v_r1, 8));
-    __m128i layer4_chunk1 = _mm_packus_epi16(_mm_and_si128(v_g0, v_mask), _mm_and_si128(v_g1, v_mask));
-    __m128i layer4_chunk3 = _mm_packus_epi16(_mm_srli_epi16(v_g0, 8), _mm_srli_epi16(v_g1, 8));
-
-    __m128i layer3_chunk0 = _mm_packus_epi16(_mm_and_si128(layer4_chunk0, v_mask), _mm_and_si128(layer4_chunk1, v_mask));
-    __m128i layer3_chunk2 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk0, 8), _mm_srli_epi16(layer4_chunk1, 8));
-    __m128i layer3_chunk1 = _mm_packus_epi16(_mm_and_si128(layer4_chunk2, v_mask), _mm_and_si128(layer4_chunk3, v_mask));
-    __m128i layer3_chunk3 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk2, 8), _mm_srli_epi16(layer4_chunk3, 8));
-
-    __m128i layer2_chunk0 = _mm_packus_epi16(_mm_and_si128(layer3_chunk0, v_mask), _mm_and_si128(layer3_chunk1, v_mask));
-    __m128i layer2_chunk2 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk0, 8), _mm_srli_epi16(layer3_chunk1, 8));
-    __m128i layer2_chunk1 = _mm_packus_epi16(_mm_and_si128(layer3_chunk2, v_mask), _mm_and_si128(layer3_chunk3, v_mask));
-    __m128i layer2_chunk3 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk2, 8), _mm_srli_epi16(layer3_chunk3, 8));
-
-    __m128i layer1_chunk0 = _mm_packus_epi16(_mm_and_si128(layer2_chunk0, v_mask), _mm_and_si128(layer2_chunk1, v_mask));
-    __m128i layer1_chunk2 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk0, 8), _mm_srli_epi16(layer2_chunk1, 8));
-    __m128i layer1_chunk1 = _mm_packus_epi16(_mm_and_si128(layer2_chunk2, v_mask), _mm_and_si128(layer2_chunk3, v_mask));
-    __m128i layer1_chunk3 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk2, 8), _mm_srli_epi16(layer2_chunk3, 8));
-
-    v_r0 = _mm_packus_epi16(_mm_and_si128(layer1_chunk0, v_mask), _mm_and_si128(layer1_chunk1, v_mask));
-    v_g0 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk0, 8), _mm_srli_epi16(layer1_chunk1, 8));
-    v_r1 = _mm_packus_epi16(_mm_and_si128(layer1_chunk2, v_mask), _mm_and_si128(layer1_chunk3, v_mask));
-    v_g1 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk2, 8), _mm_srli_epi16(layer1_chunk3, 8));
-}
-
-inline void _mm_interleave_epi8(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0,
-                                __m128i & v_g1, __m128i & v_b0, __m128i & v_b1)
-{
-    __m128i v_mask = _mm_set1_epi16(0x00ff);
-
-    __m128i layer4_chunk0 = _mm_packus_epi16(_mm_and_si128(v_r0, v_mask), _mm_and_si128(v_r1, v_mask));
-    __m128i layer4_chunk3 = _mm_packus_epi16(_mm_srli_epi16(v_r0, 8), _mm_srli_epi16(v_r1, 8));
-    __m128i layer4_chunk1 = _mm_packus_epi16(_mm_and_si128(v_g0, v_mask), _mm_and_si128(v_g1, v_mask));
-    __m128i layer4_chunk4 = _mm_packus_epi16(_mm_srli_epi16(v_g0, 8), _mm_srli_epi16(v_g1, 8));
-    __m128i layer4_chunk2 = _mm_packus_epi16(_mm_and_si128(v_b0, v_mask), _mm_and_si128(v_b1, v_mask));
-    __m128i layer4_chunk5 = _mm_packus_epi16(_mm_srli_epi16(v_b0, 8), _mm_srli_epi16(v_b1, 8));
-
-    __m128i layer3_chunk0 = _mm_packus_epi16(_mm_and_si128(layer4_chunk0, v_mask), _mm_and_si128(layer4_chunk1, v_mask));
-    __m128i layer3_chunk3 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk0, 8), _mm_srli_epi16(layer4_chunk1, 8));
-    __m128i layer3_chunk1 = _mm_packus_epi16(_mm_and_si128(layer4_chunk2, v_mask), _mm_and_si128(layer4_chunk3, v_mask));
-    __m128i layer3_chunk4 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk2, 8), _mm_srli_epi16(layer4_chunk3, 8));
-    __m128i layer3_chunk2 = _mm_packus_epi16(_mm_and_si128(layer4_chunk4, v_mask), _mm_and_si128(layer4_chunk5, v_mask));
-    __m128i layer3_chunk5 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk4, 8), _mm_srli_epi16(layer4_chunk5, 8));
-
-    __m128i layer2_chunk0 = _mm_packus_epi16(_mm_and_si128(layer3_chunk0, v_mask), _mm_and_si128(layer3_chunk1, v_mask));
-    __m128i layer2_chunk3 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk0, 8), _mm_srli_epi16(layer3_chunk1, 8));
-    __m128i layer2_chunk1 = _mm_packus_epi16(_mm_and_si128(layer3_chunk2, v_mask), _mm_and_si128(layer3_chunk3, v_mask));
-    __m128i layer2_chunk4 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk2, 8), _mm_srli_epi16(layer3_chunk3, 8));
-    __m128i layer2_chunk2 = _mm_packus_epi16(_mm_and_si128(layer3_chunk4, v_mask), _mm_and_si128(layer3_chunk5, v_mask));
-    __m128i layer2_chunk5 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk4, 8), _mm_srli_epi16(layer3_chunk5, 8));
-
-    __m128i layer1_chunk0 = _mm_packus_epi16(_mm_and_si128(layer2_chunk0, v_mask), _mm_and_si128(layer2_chunk1, v_mask));
-    __m128i layer1_chunk3 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk0, 8), _mm_srli_epi16(layer2_chunk1, 8));
-    __m128i layer1_chunk1 = _mm_packus_epi16(_mm_and_si128(layer2_chunk2, v_mask), _mm_and_si128(layer2_chunk3, v_mask));
-    __m128i layer1_chunk4 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk2, 8), _mm_srli_epi16(layer2_chunk3, 8));
-    __m128i layer1_chunk2 = _mm_packus_epi16(_mm_and_si128(layer2_chunk4, v_mask), _mm_and_si128(layer2_chunk5, v_mask));
-    __m128i layer1_chunk5 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk4, 8), _mm_srli_epi16(layer2_chunk5, 8));
-
-    v_r0 = _mm_packus_epi16(_mm_and_si128(layer1_chunk0, v_mask), _mm_and_si128(layer1_chunk1, v_mask));
-    v_g1 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk0, 8), _mm_srli_epi16(layer1_chunk1, 8));
-    v_r1 = _mm_packus_epi16(_mm_and_si128(layer1_chunk2, v_mask), _mm_and_si128(layer1_chunk3, v_mask));
-    v_b0 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk2, 8), _mm_srli_epi16(layer1_chunk3, 8));
-    v_g0 = _mm_packus_epi16(_mm_and_si128(layer1_chunk4, v_mask), _mm_and_si128(layer1_chunk5, v_mask));
-    v_b1 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk4, 8), _mm_srli_epi16(layer1_chunk5, 8));
-}
-
-inline void _mm_interleave_epi8(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, __m128i & v_g1,
-                                __m128i & v_b0, __m128i & v_b1, __m128i & v_a0, __m128i & v_a1)
-{
-    __m128i v_mask = _mm_set1_epi16(0x00ff);
-
-    __m128i layer4_chunk0 = _mm_packus_epi16(_mm_and_si128(v_r0, v_mask), _mm_and_si128(v_r1, v_mask));
-    __m128i layer4_chunk4 = _mm_packus_epi16(_mm_srli_epi16(v_r0, 8), _mm_srli_epi16(v_r1, 8));
-    __m128i layer4_chunk1 = _mm_packus_epi16(_mm_and_si128(v_g0, v_mask), _mm_and_si128(v_g1, v_mask));
-    __m128i layer4_chunk5 = _mm_packus_epi16(_mm_srli_epi16(v_g0, 8), _mm_srli_epi16(v_g1, 8));
-    __m128i layer4_chunk2 = _mm_packus_epi16(_mm_and_si128(v_b0, v_mask), _mm_and_si128(v_b1, v_mask));
-    __m128i layer4_chunk6 = _mm_packus_epi16(_mm_srli_epi16(v_b0, 8), _mm_srli_epi16(v_b1, 8));
-    __m128i layer4_chunk3 = _mm_packus_epi16(_mm_and_si128(v_a0, v_mask), _mm_and_si128(v_a1, v_mask));
-    __m128i layer4_chunk7 = _mm_packus_epi16(_mm_srli_epi16(v_a0, 8), _mm_srli_epi16(v_a1, 8));
-
-    __m128i layer3_chunk0 = _mm_packus_epi16(_mm_and_si128(layer4_chunk0, v_mask), _mm_and_si128(layer4_chunk1, v_mask));
-    __m128i layer3_chunk4 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk0, 8), _mm_srli_epi16(layer4_chunk1, 8));
-    __m128i layer3_chunk1 = _mm_packus_epi16(_mm_and_si128(layer4_chunk2, v_mask), _mm_and_si128(layer4_chunk3, v_mask));
-    __m128i layer3_chunk5 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk2, 8), _mm_srli_epi16(layer4_chunk3, 8));
-    __m128i layer3_chunk2 = _mm_packus_epi16(_mm_and_si128(layer4_chunk4, v_mask), _mm_and_si128(layer4_chunk5, v_mask));
-    __m128i layer3_chunk6 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk4, 8), _mm_srli_epi16(layer4_chunk5, 8));
-    __m128i layer3_chunk3 = _mm_packus_epi16(_mm_and_si128(layer4_chunk6, v_mask), _mm_and_si128(layer4_chunk7, v_mask));
-    __m128i layer3_chunk7 = _mm_packus_epi16(_mm_srli_epi16(layer4_chunk6, 8), _mm_srli_epi16(layer4_chunk7, 8));
-
-    __m128i layer2_chunk0 = _mm_packus_epi16(_mm_and_si128(layer3_chunk0, v_mask), _mm_and_si128(layer3_chunk1, v_mask));
-    __m128i layer2_chunk4 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk0, 8), _mm_srli_epi16(layer3_chunk1, 8));
-    __m128i layer2_chunk1 = _mm_packus_epi16(_mm_and_si128(layer3_chunk2, v_mask), _mm_and_si128(layer3_chunk3, v_mask));
-    __m128i layer2_chunk5 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk2, 8), _mm_srli_epi16(layer3_chunk3, 8));
-    __m128i layer2_chunk2 = _mm_packus_epi16(_mm_and_si128(layer3_chunk4, v_mask), _mm_and_si128(layer3_chunk5, v_mask));
-    __m128i layer2_chunk6 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk4, 8), _mm_srli_epi16(layer3_chunk5, 8));
-    __m128i layer2_chunk3 = _mm_packus_epi16(_mm_and_si128(layer3_chunk6, v_mask), _mm_and_si128(layer3_chunk7, v_mask));
-    __m128i layer2_chunk7 = _mm_packus_epi16(_mm_srli_epi16(layer3_chunk6, 8), _mm_srli_epi16(layer3_chunk7, 8));
-
-    __m128i layer1_chunk0 = _mm_packus_epi16(_mm_and_si128(layer2_chunk0, v_mask), _mm_and_si128(layer2_chunk1, v_mask));
-    __m128i layer1_chunk4 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk0, 8), _mm_srli_epi16(layer2_chunk1, 8));
-    __m128i layer1_chunk1 = _mm_packus_epi16(_mm_and_si128(layer2_chunk2, v_mask), _mm_and_si128(layer2_chunk3, v_mask));
-    __m128i layer1_chunk5 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk2, 8), _mm_srli_epi16(layer2_chunk3, 8));
-    __m128i layer1_chunk2 = _mm_packus_epi16(_mm_and_si128(layer2_chunk4, v_mask), _mm_and_si128(layer2_chunk5, v_mask));
-    __m128i layer1_chunk6 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk4, 8), _mm_srli_epi16(layer2_chunk5, 8));
-    __m128i layer1_chunk3 = _mm_packus_epi16(_mm_and_si128(layer2_chunk6, v_mask), _mm_and_si128(layer2_chunk7, v_mask));
-    __m128i layer1_chunk7 = _mm_packus_epi16(_mm_srli_epi16(layer2_chunk6, 8), _mm_srli_epi16(layer2_chunk7, 8));
-
-    v_r0 = _mm_packus_epi16(_mm_and_si128(layer1_chunk0, v_mask), _mm_and_si128(layer1_chunk1, v_mask));
-    v_b0 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk0, 8), _mm_srli_epi16(layer1_chunk1, 8));
-    v_r1 = _mm_packus_epi16(_mm_and_si128(layer1_chunk2, v_mask), _mm_and_si128(layer1_chunk3, v_mask));
-    v_b1 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk2, 8), _mm_srli_epi16(layer1_chunk3, 8));
-    v_g0 = _mm_packus_epi16(_mm_and_si128(layer1_chunk4, v_mask), _mm_and_si128(layer1_chunk5, v_mask));
-    v_a0 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk4, 8), _mm_srli_epi16(layer1_chunk5, 8));
-    v_g1 = _mm_packus_epi16(_mm_and_si128(layer1_chunk6, v_mask), _mm_and_si128(layer1_chunk7, v_mask));
-    v_a1 = _mm_packus_epi16(_mm_srli_epi16(layer1_chunk6, 8), _mm_srli_epi16(layer1_chunk7, 8));
-}
-
-inline void _mm_deinterleave_epi16(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, __m128i & v_g1)
-{
-    __m128i layer1_chunk0 = _mm_unpacklo_epi16(v_r0, v_g0);
-    __m128i layer1_chunk1 = _mm_unpackhi_epi16(v_r0, v_g0);
-    __m128i layer1_chunk2 = _mm_unpacklo_epi16(v_r1, v_g1);
-    __m128i layer1_chunk3 = _mm_unpackhi_epi16(v_r1, v_g1);
-
-    __m128i layer2_chunk0 = _mm_unpacklo_epi16(layer1_chunk0, layer1_chunk2);
-    __m128i layer2_chunk1 = _mm_unpackhi_epi16(layer1_chunk0, layer1_chunk2);
-    __m128i layer2_chunk2 = _mm_unpacklo_epi16(layer1_chunk1, layer1_chunk3);
-    __m128i layer2_chunk3 = _mm_unpackhi_epi16(layer1_chunk1, layer1_chunk3);
-
-    __m128i layer3_chunk0 = _mm_unpacklo_epi16(layer2_chunk0, layer2_chunk2);
-    __m128i layer3_chunk1 = _mm_unpackhi_epi16(layer2_chunk0, layer2_chunk2);
-    __m128i layer3_chunk2 = _mm_unpacklo_epi16(layer2_chunk1, layer2_chunk3);
-    __m128i layer3_chunk3 = _mm_unpackhi_epi16(layer2_chunk1, layer2_chunk3);
-
-    v_r0 = _mm_unpacklo_epi16(layer3_chunk0, layer3_chunk2);
-    v_r1 = _mm_unpackhi_epi16(layer3_chunk0, layer3_chunk2);
-    v_g0 = _mm_unpacklo_epi16(layer3_chunk1, layer3_chunk3);
-    v_g1 = _mm_unpackhi_epi16(layer3_chunk1, layer3_chunk3);
-}
-
-inline void _mm_deinterleave_epi16(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0,
-                                   __m128i & v_g1, __m128i & v_b0, __m128i & v_b1)
-{
-    __m128i layer1_chunk0 = _mm_unpacklo_epi16(v_r0, v_g1);
-    __m128i layer1_chunk1 = _mm_unpackhi_epi16(v_r0, v_g1);
-    __m128i layer1_chunk2 = _mm_unpacklo_epi16(v_r1, v_b0);
-    __m128i layer1_chunk3 = _mm_unpackhi_epi16(v_r1, v_b0);
-    __m128i layer1_chunk4 = _mm_unpacklo_epi16(v_g0, v_b1);
-    __m128i layer1_chunk5 = _mm_unpackhi_epi16(v_g0, v_b1);
-
-    __m128i layer2_chunk0 = _mm_unpacklo_epi16(layer1_chunk0, layer1_chunk3);
-    __m128i layer2_chunk1 = _mm_unpackhi_epi16(layer1_chunk0, layer1_chunk3);
-    __m128i layer2_chunk2 = _mm_unpacklo_epi16(layer1_chunk1, layer1_chunk4);
-    __m128i layer2_chunk3 = _mm_unpackhi_epi16(layer1_chunk1, layer1_chunk4);
-    __m128i layer2_chunk4 = _mm_unpacklo_epi16(layer1_chunk2, layer1_chunk5);
-    __m128i layer2_chunk5 = _mm_unpackhi_epi16(layer1_chunk2, layer1_chunk5);
-
-    __m128i layer3_chunk0 = _mm_unpacklo_epi16(layer2_chunk0, layer2_chunk3);
-    __m128i layer3_chunk1 = _mm_unpackhi_epi16(layer2_chunk0, layer2_chunk3);
-    __m128i layer3_chunk2 = _mm_unpacklo_epi16(layer2_chunk1, layer2_chunk4);
-    __m128i layer3_chunk3 = _mm_unpackhi_epi16(layer2_chunk1, layer2_chunk4);
-    __m128i layer3_chunk4 = _mm_unpacklo_epi16(layer2_chunk2, layer2_chunk5);
-    __m128i layer3_chunk5 = _mm_unpackhi_epi16(layer2_chunk2, layer2_chunk5);
-
-    v_r0 = _mm_unpacklo_epi16(layer3_chunk0, layer3_chunk3);
-    v_r1 = _mm_unpackhi_epi16(layer3_chunk0, layer3_chunk3);
-    v_g0 = _mm_unpacklo_epi16(layer3_chunk1, layer3_chunk4);
-    v_g1 = _mm_unpackhi_epi16(layer3_chunk1, layer3_chunk4);
-    v_b0 = _mm_unpacklo_epi16(layer3_chunk2, layer3_chunk5);
-    v_b1 = _mm_unpackhi_epi16(layer3_chunk2, layer3_chunk5);
-}
-
-inline void _mm_deinterleave_epi16(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, __m128i & v_g1,
-                                   __m128i & v_b0, __m128i & v_b1, __m128i & v_a0, __m128i & v_a1)
-{
-    __m128i layer1_chunk0 = _mm_unpacklo_epi16(v_r0, v_b0);
-    __m128i layer1_chunk1 = _mm_unpackhi_epi16(v_r0, v_b0);
-    __m128i layer1_chunk2 = _mm_unpacklo_epi16(v_r1, v_b1);
-    __m128i layer1_chunk3 = _mm_unpackhi_epi16(v_r1, v_b1);
-    __m128i layer1_chunk4 = _mm_unpacklo_epi16(v_g0, v_a0);
-    __m128i layer1_chunk5 = _mm_unpackhi_epi16(v_g0, v_a0);
-    __m128i layer1_chunk6 = _mm_unpacklo_epi16(v_g1, v_a1);
-    __m128i layer1_chunk7 = _mm_unpackhi_epi16(v_g1, v_a1);
-
-    __m128i layer2_chunk0 = _mm_unpacklo_epi16(layer1_chunk0, layer1_chunk4);
-    __m128i layer2_chunk1 = _mm_unpackhi_epi16(layer1_chunk0, layer1_chunk4);
-    __m128i layer2_chunk2 = _mm_unpacklo_epi16(layer1_chunk1, layer1_chunk5);
-    __m128i layer2_chunk3 = _mm_unpackhi_epi16(layer1_chunk1, layer1_chunk5);
-    __m128i layer2_chunk4 = _mm_unpacklo_epi16(layer1_chunk2, layer1_chunk6);
-    __m128i layer2_chunk5 = _mm_unpackhi_epi16(layer1_chunk2, layer1_chunk6);
-    __m128i layer2_chunk6 = _mm_unpacklo_epi16(layer1_chunk3, layer1_chunk7);
-    __m128i layer2_chunk7 = _mm_unpackhi_epi16(layer1_chunk3, layer1_chunk7);
-
-    __m128i layer3_chunk0 = _mm_unpacklo_epi16(layer2_chunk0, layer2_chunk4);
-    __m128i layer3_chunk1 = _mm_unpackhi_epi16(layer2_chunk0, layer2_chunk4);
-    __m128i layer3_chunk2 = _mm_unpacklo_epi16(layer2_chunk1, layer2_chunk5);
-    __m128i layer3_chunk3 = _mm_unpackhi_epi16(layer2_chunk1, layer2_chunk5);
-    __m128i layer3_chunk4 = _mm_unpacklo_epi16(layer2_chunk2, layer2_chunk6);
-    __m128i layer3_chunk5 = _mm_unpackhi_epi16(layer2_chunk2, layer2_chunk6);
-    __m128i layer3_chunk6 = _mm_unpacklo_epi16(layer2_chunk3, layer2_chunk7);
-    __m128i layer3_chunk7 = _mm_unpackhi_epi16(layer2_chunk3, layer2_chunk7);
-
-    v_r0 = _mm_unpacklo_epi16(layer3_chunk0, layer3_chunk4);
-    v_r1 = _mm_unpackhi_epi16(layer3_chunk0, layer3_chunk4);
-    v_g0 = _mm_unpacklo_epi16(layer3_chunk1, layer3_chunk5);
-    v_g1 = _mm_unpackhi_epi16(layer3_chunk1, layer3_chunk5);
-    v_b0 = _mm_unpacklo_epi16(layer3_chunk2, layer3_chunk6);
-    v_b1 = _mm_unpackhi_epi16(layer3_chunk2, layer3_chunk6);
-    v_a0 = _mm_unpacklo_epi16(layer3_chunk3, layer3_chunk7);
-    v_a1 = _mm_unpackhi_epi16(layer3_chunk3, layer3_chunk7);
-}
-
-#if CV_SSE4_1
-
-inline void _mm_interleave_epi16(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, __m128i & v_g1)
-{
-    __m128i v_mask = _mm_set1_epi32(0x0000ffff);
-
-    __m128i layer3_chunk0 = _mm_packus_epi32(_mm_and_si128(v_r0, v_mask), _mm_and_si128(v_r1, v_mask));
-    __m128i layer3_chunk2 = _mm_packus_epi32(_mm_srli_epi32(v_r0, 16), _mm_srli_epi32(v_r1, 16));
-    __m128i layer3_chunk1 = _mm_packus_epi32(_mm_and_si128(v_g0, v_mask), _mm_and_si128(v_g1, v_mask));
-    __m128i layer3_chunk3 = _mm_packus_epi32(_mm_srli_epi32(v_g0, 16), _mm_srli_epi32(v_g1, 16));
-
-    __m128i layer2_chunk0 = _mm_packus_epi32(_mm_and_si128(layer3_chunk0, v_mask), _mm_and_si128(layer3_chunk1, v_mask));
-    __m128i layer2_chunk2 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk0, 16), _mm_srli_epi32(layer3_chunk1, 16));
-    __m128i layer2_chunk1 = _mm_packus_epi32(_mm_and_si128(layer3_chunk2, v_mask), _mm_and_si128(layer3_chunk3, v_mask));
-    __m128i layer2_chunk3 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk2, 16), _mm_srli_epi32(layer3_chunk3, 16));
-
-    __m128i layer1_chunk0 = _mm_packus_epi32(_mm_and_si128(layer2_chunk0, v_mask), _mm_and_si128(layer2_chunk1, v_mask));
-    __m128i layer1_chunk2 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk0, 16), _mm_srli_epi32(layer2_chunk1, 16));
-    __m128i layer1_chunk1 = _mm_packus_epi32(_mm_and_si128(layer2_chunk2, v_mask), _mm_and_si128(layer2_chunk3, v_mask));
-    __m128i layer1_chunk3 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk2, 16), _mm_srli_epi32(layer2_chunk3, 16));
-
-    v_r0 = _mm_packus_epi32(_mm_and_si128(layer1_chunk0, v_mask), _mm_and_si128(layer1_chunk1, v_mask));
-    v_g0 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk0, 16), _mm_srli_epi32(layer1_chunk1, 16));
-    v_r1 = _mm_packus_epi32(_mm_and_si128(layer1_chunk2, v_mask), _mm_and_si128(layer1_chunk3, v_mask));
-    v_g1 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk2, 16), _mm_srli_epi32(layer1_chunk3, 16));
-}
-
-inline void _mm_interleave_epi16(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0,
-                                 __m128i & v_g1, __m128i & v_b0, __m128i & v_b1)
-{
-    __m128i v_mask = _mm_set1_epi32(0x0000ffff);
-
-    __m128i layer3_chunk0 = _mm_packus_epi32(_mm_and_si128(v_r0, v_mask), _mm_and_si128(v_r1, v_mask));
-    __m128i layer3_chunk3 = _mm_packus_epi32(_mm_srli_epi32(v_r0, 16), _mm_srli_epi32(v_r1, 16));
-    __m128i layer3_chunk1 = _mm_packus_epi32(_mm_and_si128(v_g0, v_mask), _mm_and_si128(v_g1, v_mask));
-    __m128i layer3_chunk4 = _mm_packus_epi32(_mm_srli_epi32(v_g0, 16), _mm_srli_epi32(v_g1, 16));
-    __m128i layer3_chunk2 = _mm_packus_epi32(_mm_and_si128(v_b0, v_mask), _mm_and_si128(v_b1, v_mask));
-    __m128i layer3_chunk5 = _mm_packus_epi32(_mm_srli_epi32(v_b0, 16), _mm_srli_epi32(v_b1, 16));
-
-    __m128i layer2_chunk0 = _mm_packus_epi32(_mm_and_si128(layer3_chunk0, v_mask), _mm_and_si128(layer3_chunk1, v_mask));
-    __m128i layer2_chunk3 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk0, 16), _mm_srli_epi32(layer3_chunk1, 16));
-    __m128i layer2_chunk1 = _mm_packus_epi32(_mm_and_si128(layer3_chunk2, v_mask), _mm_and_si128(layer3_chunk3, v_mask));
-    __m128i layer2_chunk4 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk2, 16), _mm_srli_epi32(layer3_chunk3, 16));
-    __m128i layer2_chunk2 = _mm_packus_epi32(_mm_and_si128(layer3_chunk4, v_mask), _mm_and_si128(layer3_chunk5, v_mask));
-    __m128i layer2_chunk5 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk4, 16), _mm_srli_epi32(layer3_chunk5, 16));
-
-    __m128i layer1_chunk0 = _mm_packus_epi32(_mm_and_si128(layer2_chunk0, v_mask), _mm_and_si128(layer2_chunk1, v_mask));
-    __m128i layer1_chunk3 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk0, 16), _mm_srli_epi32(layer2_chunk1, 16));
-    __m128i layer1_chunk1 = _mm_packus_epi32(_mm_and_si128(layer2_chunk2, v_mask), _mm_and_si128(layer2_chunk3, v_mask));
-    __m128i layer1_chunk4 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk2, 16), _mm_srli_epi32(layer2_chunk3, 16));
-    __m128i layer1_chunk2 = _mm_packus_epi32(_mm_and_si128(layer2_chunk4, v_mask), _mm_and_si128(layer2_chunk5, v_mask));
-    __m128i layer1_chunk5 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk4, 16), _mm_srli_epi32(layer2_chunk5, 16));
-
-    v_r0 = _mm_packus_epi32(_mm_and_si128(layer1_chunk0, v_mask), _mm_and_si128(layer1_chunk1, v_mask));
-    v_g1 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk0, 16), _mm_srli_epi32(layer1_chunk1, 16));
-    v_r1 = _mm_packus_epi32(_mm_and_si128(layer1_chunk2, v_mask), _mm_and_si128(layer1_chunk3, v_mask));
-    v_b0 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk2, 16), _mm_srli_epi32(layer1_chunk3, 16));
-    v_g0 = _mm_packus_epi32(_mm_and_si128(layer1_chunk4, v_mask), _mm_and_si128(layer1_chunk5, v_mask));
-    v_b1 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk4, 16), _mm_srli_epi32(layer1_chunk5, 16));
-}
-
-inline void _mm_interleave_epi16(__m128i & v_r0, __m128i & v_r1, __m128i & v_g0, __m128i & v_g1,
-                                 __m128i & v_b0, __m128i & v_b1, __m128i & v_a0, __m128i & v_a1)
-{
-    __m128i v_mask = _mm_set1_epi32(0x0000ffff);
-
-    __m128i layer3_chunk0 = _mm_packus_epi32(_mm_and_si128(v_r0, v_mask), _mm_and_si128(v_r1, v_mask));
-    __m128i layer3_chunk4 = _mm_packus_epi32(_mm_srli_epi32(v_r0, 16), _mm_srli_epi32(v_r1, 16));
-    __m128i layer3_chunk1 = _mm_packus_epi32(_mm_and_si128(v_g0, v_mask), _mm_and_si128(v_g1, v_mask));
-    __m128i layer3_chunk5 = _mm_packus_epi32(_mm_srli_epi32(v_g0, 16), _mm_srli_epi32(v_g1, 16));
-    __m128i layer3_chunk2 = _mm_packus_epi32(_mm_and_si128(v_b0, v_mask), _mm_and_si128(v_b1, v_mask));
-    __m128i layer3_chunk6 = _mm_packus_epi32(_mm_srli_epi32(v_b0, 16), _mm_srli_epi32(v_b1, 16));
-    __m128i layer3_chunk3 = _mm_packus_epi32(_mm_and_si128(v_a0, v_mask), _mm_and_si128(v_a1, v_mask));
-    __m128i layer3_chunk7 = _mm_packus_epi32(_mm_srli_epi32(v_a0, 16), _mm_srli_epi32(v_a1, 16));
-
-    __m128i layer2_chunk0 = _mm_packus_epi32(_mm_and_si128(layer3_chunk0, v_mask), _mm_and_si128(layer3_chunk1, v_mask));
-    __m128i layer2_chunk4 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk0, 16), _mm_srli_epi32(layer3_chunk1, 16));
-    __m128i layer2_chunk1 = _mm_packus_epi32(_mm_and_si128(layer3_chunk2, v_mask), _mm_and_si128(layer3_chunk3, v_mask));
-    __m128i layer2_chunk5 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk2, 16), _mm_srli_epi32(layer3_chunk3, 16));
-    __m128i layer2_chunk2 = _mm_packus_epi32(_mm_and_si128(layer3_chunk4, v_mask), _mm_and_si128(layer3_chunk5, v_mask));
-    __m128i layer2_chunk6 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk4, 16), _mm_srli_epi32(layer3_chunk5, 16));
-    __m128i layer2_chunk3 = _mm_packus_epi32(_mm_and_si128(layer3_chunk6, v_mask), _mm_and_si128(layer3_chunk7, v_mask));
-    __m128i layer2_chunk7 = _mm_packus_epi32(_mm_srli_epi32(layer3_chunk6, 16), _mm_srli_epi32(layer3_chunk7, 16));
-
-    __m128i layer1_chunk0 = _mm_packus_epi32(_mm_and_si128(layer2_chunk0, v_mask), _mm_and_si128(layer2_chunk1, v_mask));
-    __m128i layer1_chunk4 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk0, 16), _mm_srli_epi32(layer2_chunk1, 16));
-    __m128i layer1_chunk1 = _mm_packus_epi32(_mm_and_si128(layer2_chunk2, v_mask), _mm_and_si128(layer2_chunk3, v_mask));
-    __m128i layer1_chunk5 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk2, 16), _mm_srli_epi32(layer2_chunk3, 16));
-    __m128i layer1_chunk2 = _mm_packus_epi32(_mm_and_si128(layer2_chunk4, v_mask), _mm_and_si128(layer2_chunk5, v_mask));
-    __m128i layer1_chunk6 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk4, 16), _mm_srli_epi32(layer2_chunk5, 16));
-    __m128i layer1_chunk3 = _mm_packus_epi32(_mm_and_si128(layer2_chunk6, v_mask), _mm_and_si128(layer2_chunk7, v_mask));
-    __m128i layer1_chunk7 = _mm_packus_epi32(_mm_srli_epi32(layer2_chunk6, 16), _mm_srli_epi32(layer2_chunk7, 16));
-
-    v_r0 = _mm_packus_epi32(_mm_and_si128(layer1_chunk0, v_mask), _mm_and_si128(layer1_chunk1, v_mask));
-    v_b0 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk0, 16), _mm_srli_epi32(layer1_chunk1, 16));
-    v_r1 = _mm_packus_epi32(_mm_and_si128(layer1_chunk2, v_mask), _mm_and_si128(layer1_chunk3, v_mask));
-    v_b1 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk2, 16), _mm_srli_epi32(layer1_chunk3, 16));
-    v_g0 = _mm_packus_epi32(_mm_and_si128(layer1_chunk4, v_mask), _mm_and_si128(layer1_chunk5, v_mask));
-    v_a0 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk4, 16), _mm_srli_epi32(layer1_chunk5, 16));
-    v_g1 = _mm_packus_epi32(_mm_and_si128(layer1_chunk6, v_mask), _mm_and_si128(layer1_chunk7, v_mask));
-    v_a1 = _mm_packus_epi32(_mm_srli_epi32(layer1_chunk6, 16), _mm_srli_epi32(layer1_chunk7, 16));
-}
-
-#endif // CV_SSE4_1
-
-inline void _mm_deinterleave_ps(__m128 & v_r0, __m128 & v_r1, __m128 & v_g0, __m128 & v_g1)
-{
-    __m128 layer1_chunk0 = _mm_unpacklo_ps(v_r0, v_g0);
-    __m128 layer1_chunk1 = _mm_unpackhi_ps(v_r0, v_g0);
-    __m128 layer1_chunk2 = _mm_unpacklo_ps(v_r1, v_g1);
-    __m128 layer1_chunk3 = _mm_unpackhi_ps(v_r1, v_g1);
-
-    __m128 layer2_chunk0 = _mm_unpacklo_ps(layer1_chunk0, layer1_chunk2);
-    __m128 layer2_chunk1 = _mm_unpackhi_ps(layer1_chunk0, layer1_chunk2);
-    __m128 layer2_chunk2 = _mm_unpacklo_ps(layer1_chunk1, layer1_chunk3);
-    __m128 layer2_chunk3 = _mm_unpackhi_ps(layer1_chunk1, layer1_chunk3);
-
-    v_r0 = _mm_unpacklo_ps(layer2_chunk0, layer2_chunk2);
-    v_r1 = _mm_unpackhi_ps(layer2_chunk0, layer2_chunk2);
-    v_g0 = _mm_unpacklo_ps(layer2_chunk1, layer2_chunk3);
-    v_g1 = _mm_unpackhi_ps(layer2_chunk1, layer2_chunk3);
-}
-
-inline void _mm_deinterleave_ps(__m128 & v_r0, __m128 & v_r1, __m128 & v_g0,
-                                __m128 & v_g1, __m128 & v_b0, __m128 & v_b1)
-{
-    __m128 layer1_chunk0 = _mm_unpacklo_ps(v_r0, v_g1);
-    __m128 layer1_chunk1 = _mm_unpackhi_ps(v_r0, v_g1);
-    __m128 layer1_chunk2 = _mm_unpacklo_ps(v_r1, v_b0);
-    __m128 layer1_chunk3 = _mm_unpackhi_ps(v_r1, v_b0);
-    __m128 layer1_chunk4 = _mm_unpacklo_ps(v_g0, v_b1);
-    __m128 layer1_chunk5 = _mm_unpackhi_ps(v_g0, v_b1);
-
-    __m128 layer2_chunk0 = _mm_unpacklo_ps(layer1_chunk0, layer1_chunk3);
-    __m128 layer2_chunk1 = _mm_unpackhi_ps(layer1_chunk0, layer1_chunk3);
-    __m128 layer2_chunk2 = _mm_unpacklo_ps(layer1_chunk1, layer1_chunk4);
-    __m128 layer2_chunk3 = _mm_unpackhi_ps(layer1_chunk1, layer1_chunk4);
-    __m128 layer2_chunk4 = _mm_unpacklo_ps(layer1_chunk2, layer1_chunk5);
-    __m128 layer2_chunk5 = _mm_unpackhi_ps(layer1_chunk2, layer1_chunk5);
-
-    v_r0 = _mm_unpacklo_ps(layer2_chunk0, layer2_chunk3);
-    v_r1 = _mm_unpackhi_ps(layer2_chunk0, layer2_chunk3);
-    v_g0 = _mm_unpacklo_ps(layer2_chunk1, layer2_chunk4);
-    v_g1 = _mm_unpackhi_ps(layer2_chunk1, layer2_chunk4);
-    v_b0 = _mm_unpacklo_ps(layer2_chunk2, layer2_chunk5);
-    v_b1 = _mm_unpackhi_ps(layer2_chunk2, layer2_chunk5);
-}
-
-inline void _mm_deinterleave_ps(__m128 & v_r0, __m128 & v_r1, __m128 & v_g0, __m128 & v_g1,
-                                __m128 & v_b0, __m128 & v_b1, __m128 & v_a0, __m128 & v_a1)
-{
-    __m128 layer1_chunk0 = _mm_unpacklo_ps(v_r0, v_b0);
-    __m128 layer1_chunk1 = _mm_unpackhi_ps(v_r0, v_b0);
-    __m128 layer1_chunk2 = _mm_unpacklo_ps(v_r1, v_b1);
-    __m128 layer1_chunk3 = _mm_unpackhi_ps(v_r1, v_b1);
-    __m128 layer1_chunk4 = _mm_unpacklo_ps(v_g0, v_a0);
-    __m128 layer1_chunk5 = _mm_unpackhi_ps(v_g0, v_a0);
-    __m128 layer1_chunk6 = _mm_unpacklo_ps(v_g1, v_a1);
-    __m128 layer1_chunk7 = _mm_unpackhi_ps(v_g1, v_a1);
-
-    __m128 layer2_chunk0 = _mm_unpacklo_ps(layer1_chunk0, layer1_chunk4);
-    __m128 layer2_chunk1 = _mm_unpackhi_ps(layer1_chunk0, layer1_chunk4);
-    __m128 layer2_chunk2 = _mm_unpacklo_ps(layer1_chunk1, layer1_chunk5);
-    __m128 layer2_chunk3 = _mm_unpackhi_ps(layer1_chunk1, layer1_chunk5);
-    __m128 layer2_chunk4 = _mm_unpacklo_ps(layer1_chunk2, layer1_chunk6);
-    __m128 layer2_chunk5 = _mm_unpackhi_ps(layer1_chunk2, layer1_chunk6);
-    __m128 layer2_chunk6 = _mm_unpacklo_ps(layer1_chunk3, layer1_chunk7);
-    __m128 layer2_chunk7 = _mm_unpackhi_ps(layer1_chunk3, layer1_chunk7);
-
-    v_r0 = _mm_unpacklo_ps(layer2_chunk0, layer2_chunk4);
-    v_r1 = _mm_unpackhi_ps(layer2_chunk0, layer2_chunk4);
-    v_g0 = _mm_unpacklo_ps(layer2_chunk1, layer2_chunk5);
-    v_g1 = _mm_unpackhi_ps(layer2_chunk1, layer2_chunk5);
-    v_b0 = _mm_unpacklo_ps(layer2_chunk2, layer2_chunk6);
-    v_b1 = _mm_unpackhi_ps(layer2_chunk2, layer2_chunk6);
-    v_a0 = _mm_unpacklo_ps(layer2_chunk3, layer2_chunk7);
-    v_a1 = _mm_unpackhi_ps(layer2_chunk3, layer2_chunk7);
-}
-
-inline void _mm_interleave_ps(__m128 & v_r0, __m128 & v_r1, __m128 & v_g0, __m128 & v_g1)
-{
-    const int mask_lo = _MM_SHUFFLE(2, 0, 2, 0), mask_hi = _MM_SHUFFLE(3, 1, 3, 1);
-
-    __m128 layer2_chunk0 = _mm_shuffle_ps(v_r0, v_r1, mask_lo);
-    __m128 layer2_chunk2 = _mm_shuffle_ps(v_r0, v_r1, mask_hi);
-    __m128 layer2_chunk1 = _mm_shuffle_ps(v_g0, v_g1, mask_lo);
-    __m128 layer2_chunk3 = _mm_shuffle_ps(v_g0, v_g1, mask_hi);
-
-    __m128 layer1_chunk0 = _mm_shuffle_ps(layer2_chunk0, layer2_chunk1, mask_lo);
-    __m128 layer1_chunk2 = _mm_shuffle_ps(layer2_chunk0, layer2_chunk1, mask_hi);
-    __m128 layer1_chunk1 = _mm_shuffle_ps(layer2_chunk2, layer2_chunk3, mask_lo);
-    __m128 layer1_chunk3 = _mm_shuffle_ps(layer2_chunk2, layer2_chunk3, mask_hi);
-
-    v_r0 = _mm_shuffle_ps(layer1_chunk0, layer1_chunk1, mask_lo);
-    v_g0 = _mm_shuffle_ps(layer1_chunk0, layer1_chunk1, mask_hi);
-    v_r1 = _mm_shuffle_ps(layer1_chunk2, layer1_chunk3, mask_lo);
-    v_g1 = _mm_shuffle_ps(layer1_chunk2, layer1_chunk3, mask_hi);
-}
-
-inline void _mm_interleave_ps(__m128 & v_r0, __m128 & v_r1, __m128 & v_g0,
-                              __m128 & v_g1, __m128 & v_b0, __m128 & v_b1)
-{
-    const int mask_lo = _MM_SHUFFLE(2, 0, 2, 0), mask_hi = _MM_SHUFFLE(3, 1, 3, 1);
-
-    __m128 layer2_chunk0 = _mm_shuffle_ps(v_r0, v_r1, mask_lo);
-    __m128 layer2_chunk3 = _mm_shuffle_ps(v_r0, v_r1, mask_hi);
-    __m128 layer2_chunk1 = _mm_shuffle_ps(v_g0, v_g1, mask_lo);
-    __m128 layer2_chunk4 = _mm_shuffle_ps(v_g0, v_g1, mask_hi);
-    __m128 layer2_chunk2 = _mm_shuffle_ps(v_b0, v_b1, mask_lo);
-    __m128 layer2_chunk5 = _mm_shuffle_ps(v_b0, v_b1, mask_hi);
-
-    __m128 layer1_chunk0 = _mm_shuffle_ps(layer2_chunk0, layer2_chunk1, mask_lo);
-    __m128 layer1_chunk3 = _mm_shuffle_ps(layer2_chunk0, layer2_chunk1, mask_hi);
-    __m128 layer1_chunk1 = _mm_shuffle_ps(layer2_chunk2, layer2_chunk3, mask_lo);
-    __m128 layer1_chunk4 = _mm_shuffle_ps(layer2_chunk2, layer2_chunk3, mask_hi);
-    __m128 layer1_chunk2 = _mm_shuffle_ps(layer2_chunk4, layer2_chunk5, mask_lo);
-    __m128 layer1_chunk5 = _mm_shuffle_ps(layer2_chunk4, layer2_chunk5, mask_hi);
-
-    v_r0 = _mm_shuffle_ps(layer1_chunk0, layer1_chunk1, mask_lo);
-    v_g1 = _mm_shuffle_ps(layer1_chunk0, layer1_chunk1, mask_hi);
-    v_r1 = _mm_shuffle_ps(layer1_chunk2, layer1_chunk3, mask_lo);
-    v_b0 = _mm_shuffle_ps(layer1_chunk2, layer1_chunk3, mask_hi);
-    v_g0 = _mm_shuffle_ps(layer1_chunk4, layer1_chunk5, mask_lo);
-    v_b1 = _mm_shuffle_ps(layer1_chunk4, layer1_chunk5, mask_hi);
-}
-
-inline void _mm_interleave_ps(__m128 & v_r0, __m128 & v_r1, __m128 & v_g0, __m128 & v_g1,
-                              __m128 & v_b0, __m128 & v_b1, __m128 & v_a0, __m128 & v_a1)
-{
-    const int mask_lo = _MM_SHUFFLE(2, 0, 2, 0), mask_hi = _MM_SHUFFLE(3, 1, 3, 1);
-
-    __m128 layer2_chunk0 = _mm_shuffle_ps(v_r0, v_r1, mask_lo);
-    __m128 layer2_chunk4 = _mm_shuffle_ps(v_r0, v_r1, mask_hi);
-    __m128 layer2_chunk1 = _mm_shuffle_ps(v_g0, v_g1, mask_lo);
-    __m128 layer2_chunk5 = _mm_shuffle_ps(v_g0, v_g1, mask_hi);
-    __m128 layer2_chunk2 = _mm_shuffle_ps(v_b0, v_b1, mask_lo);
-    __m128 layer2_chunk6 = _mm_shuffle_ps(v_b0, v_b1, mask_hi);
-    __m128 layer2_chunk3 = _mm_shuffle_ps(v_a0, v_a1, mask_lo);
-    __m128 layer2_chunk7 = _mm_shuffle_ps(v_a0, v_a1, mask_hi);
-
-    __m128 layer1_chunk0 = _mm_shuffle_ps(layer2_chunk0, layer2_chunk1, mask_lo);
-    __m128 layer1_chunk4 = _mm_shuffle_ps(layer2_chunk0, layer2_chunk1, mask_hi);
-    __m128 layer1_chunk1 = _mm_shuffle_ps(layer2_chunk2, layer2_chunk3, mask_lo);
-    __m128 layer1_chunk5 = _mm_shuffle_ps(layer2_chunk2, layer2_chunk3, mask_hi);
-    __m128 layer1_chunk2 = _mm_shuffle_ps(layer2_chunk4, layer2_chunk5, mask_lo);
-    __m128 layer1_chunk6 = _mm_shuffle_ps(layer2_chunk4, layer2_chunk5, mask_hi);
-    __m128 layer1_chunk3 = _mm_shuffle_ps(layer2_chunk6, layer2_chunk7, mask_lo);
-    __m128 layer1_chunk7 = _mm_shuffle_ps(layer2_chunk6, layer2_chunk7, mask_hi);
-
-    v_r0 = _mm_shuffle_ps(layer1_chunk0, layer1_chunk1, mask_lo);
-    v_b0 = _mm_shuffle_ps(layer1_chunk0, layer1_chunk1, mask_hi);
-    v_r1 = _mm_shuffle_ps(layer1_chunk2, layer1_chunk3, mask_lo);
-    v_b1 = _mm_shuffle_ps(layer1_chunk2, layer1_chunk3, mask_hi);
-    v_g0 = _mm_shuffle_ps(layer1_chunk4, layer1_chunk5, mask_lo);
-    v_a0 = _mm_shuffle_ps(layer1_chunk4, layer1_chunk5, mask_hi);
-    v_g1 = _mm_shuffle_ps(layer1_chunk6, layer1_chunk7, mask_lo);
-    v_a1 = _mm_shuffle_ps(layer1_chunk6, layer1_chunk7, mask_hi);
-}
-
-#endif // CV_SSE2
-
-//! @}
-
-#endif //OPENCV_CORE_SSE_UTILS_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/traits.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/traits.hpp
deleted file mode 100755
index 6cb10f4..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/traits.hpp
+++ /dev/null
@@ -1,397 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_CORE_TRAITS_HPP
-#define OPENCV_CORE_TRAITS_HPP
-
-#include "opencv2/core/cvdef.h"
-
-namespace cv
-{
-
-//#define OPENCV_TRAITS_ENABLE_DEPRECATED
-
-//! @addtogroup core_basic
-//! @{
-
-/** @brief Template "trait" class for OpenCV primitive data types.
-
-@note Deprecated. This is replaced by "single purpose" traits: traits::Type and traits::Depth
-
-A primitive OpenCV data type is one of unsigned char, bool, signed char, unsigned short, signed
-short, int, float, double, or a tuple of values of one of these types, where all the values in the
-tuple have the same type. Any primitive type from the list can be defined by an identifier in the
-form CV_\<bit-depth\>{U|S|F}C(\<number_of_channels\>), for example: uchar \~ CV_8UC1, 3-element
-floating-point tuple \~ CV_32FC3, and so on. A universal OpenCV structure that is able to store a
-single instance of such a primitive data type is Vec. Multiple instances of such a type can be
-stored in a std::vector, Mat, Mat_, SparseMat, SparseMat_, or any other container that is able to
-store Vec instances.
-
-The DataType class is basically used to provide a description of such primitive data types without
-adding any fields or methods to the corresponding classes (and it is actually impossible to add
-anything to primitive C/C++ data types). This technique is known in C++ as class traits. It is not
-DataType itself that is used but its specialized versions, such as:
-@code
-    template<> class DataType<uchar>
-    {
-        typedef uchar value_type;
-        typedef int work_type;
-        typedef uchar channel_type;
-        enum { channel_type = CV_8U, channels = 1, fmt='u', type = CV_8U };
-    };
-    ...
-    template<typename _Tp> DataType<std::complex<_Tp> >
-    {
-        typedef std::complex<_Tp> value_type;
-        typedef std::complex<_Tp> work_type;
-        typedef _Tp channel_type;
-        // DataDepth is another helper trait class
-        enum { depth = DataDepth<_Tp>::value, channels=2,
-            fmt=(channels-1)*256+DataDepth<_Tp>::fmt,
-            type=CV_MAKETYPE(depth, channels) };
-    };
-    ...
-@endcode
-The main purpose of this class is to convert compilation-time type information to an
-OpenCV-compatible data type identifier, for example:
-@code
-    // allocates a 30x40 floating-point matrix
-    Mat A(30, 40, DataType<float>::type);
-
-    Mat B = Mat_<std::complex<double> >(3, 3);
-    // the statement below will print 6, 2 , that is depth == CV_64F, channels == 2
-    cout << B.depth() << ", " << B.channels() << endl;
-@endcode
-So, such traits are used to tell OpenCV which data type you are working with, even if such a type is
-not native to OpenCV. For example, the matrix B initialization above is compiled because OpenCV
-defines the proper specialized template class DataType\<complex\<_Tp\> \> . This mechanism is also
-useful (and used in OpenCV this way) for generic algorithms implementations.
-
-@note Default values were dropped to stop confusing developers about using of unsupported types (see #7599)
-*/
-template<typename _Tp> class DataType
-{
-public:
-#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED
-    typedef _Tp         value_type;
-    typedef value_type  work_type;
-    typedef value_type  channel_type;
-    typedef value_type  vec_type;
-    enum { generic_type = 1,
-           depth        = -1,
-           channels     = 1,
-           fmt          = 0,
-           type = CV_MAKETYPE(depth, channels)
-         };
-#endif
-};
-
-template<> class DataType<bool>
-{
-public:
-    typedef bool        value_type;
-    typedef int         work_type;
-    typedef value_type  channel_type;
-    typedef value_type  vec_type;
-    enum { generic_type = 0,
-           depth        = CV_8U,
-           channels     = 1,
-           fmt          = (int)'u',
-           type         = CV_MAKETYPE(depth, channels)
-         };
-};
-
-template<> class DataType<uchar>
-{
-public:
-    typedef uchar       value_type;
-    typedef int         work_type;
-    typedef value_type  channel_type;
-    typedef value_type  vec_type;
-    enum { generic_type = 0,
-           depth        = CV_8U,
-           channels     = 1,
-           fmt          = (int)'u',
-           type         = CV_MAKETYPE(depth, channels)
-         };
-};
-
-template<> class DataType<schar>
-{
-public:
-    typedef schar       value_type;
-    typedef int         work_type;
-    typedef value_type  channel_type;
-    typedef value_type  vec_type;
-    enum { generic_type = 0,
-           depth        = CV_8S,
-           channels     = 1,
-           fmt          = (int)'c',
-           type         = CV_MAKETYPE(depth, channels)
-         };
-};
-
-template<> class DataType<char>
-{
-public:
-    typedef schar       value_type;
-    typedef int         work_type;
-    typedef value_type  channel_type;
-    typedef value_type  vec_type;
-    enum { generic_type = 0,
-           depth        = CV_8S,
-           channels     = 1,
-           fmt          = (int)'c',
-           type         = CV_MAKETYPE(depth, channels)
-         };
-};
-
-template<> class DataType<ushort>
-{
-public:
-    typedef ushort      value_type;
-    typedef int         work_type;
-    typedef value_type  channel_type;
-    typedef value_type  vec_type;
-    enum { generic_type = 0,
-           depth        = CV_16U,
-           channels     = 1,
-           fmt          = (int)'w',
-           type         = CV_MAKETYPE(depth, channels)
-         };
-};
-
-template<> class DataType<short>
-{
-public:
-    typedef short       value_type;
-    typedef int         work_type;
-    typedef value_type  channel_type;
-    typedef value_type  vec_type;
-    enum { generic_type = 0,
-           depth        = CV_16S,
-           channels     = 1,
-           fmt          = (int)'s',
-           type         = CV_MAKETYPE(depth, channels)
-         };
-};
-
-template<> class DataType<int>
-{
-public:
-    typedef int         value_type;
-    typedef value_type  work_type;
-    typedef value_type  channel_type;
-    typedef value_type  vec_type;
-    enum { generic_type = 0,
-           depth        = CV_32S,
-           channels     = 1,
-           fmt          = (int)'i',
-           type         = CV_MAKETYPE(depth, channels)
-         };
-};
-
-template<> class DataType<float>
-{
-public:
-    typedef float       value_type;
-    typedef value_type  work_type;
-    typedef value_type  channel_type;
-    typedef value_type  vec_type;
-    enum { generic_type = 0,
-           depth        = CV_32F,
-           channels     = 1,
-           fmt          = (int)'f',
-           type         = CV_MAKETYPE(depth, channels)
-         };
-};
-
-template<> class DataType<double>
-{
-public:
-    typedef double      value_type;
-    typedef value_type  work_type;
-    typedef value_type  channel_type;
-    typedef value_type  vec_type;
-    enum { generic_type = 0,
-           depth        = CV_64F,
-           channels     = 1,
-           fmt          = (int)'d',
-           type         = CV_MAKETYPE(depth, channels)
-         };
-};
-
-
-/** @brief A helper class for cv::DataType
-
-The class is specialized for each fundamental numerical data type supported by OpenCV. It provides
-DataDepth<T>::value constant.
-*/
-template<typename _Tp> class DataDepth
-{
-public:
-    enum
-    {
-        value = DataType<_Tp>::depth,
-        fmt   = DataType<_Tp>::fmt
-    };
-};
-
-
-#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED
-
-template<int _depth> class TypeDepth
-{
-#ifdef OPENCV_TRAITS_ENABLE_LEGACY_DEFAULTS
-    enum { depth = CV_USRTYPE1 };
-    typedef void value_type;
-#endif
-};
-
-template<> class TypeDepth<CV_8U>
-{
-    enum { depth = CV_8U };
-    typedef uchar value_type;
-};
-
-template<> class TypeDepth<CV_8S>
-{
-    enum { depth = CV_8S };
-    typedef schar value_type;
-};
-
-template<> class TypeDepth<CV_16U>
-{
-    enum { depth = CV_16U };
-    typedef ushort value_type;
-};
-
-template<> class TypeDepth<CV_16S>
-{
-    enum { depth = CV_16S };
-    typedef short value_type;
-};
-
-template<> class TypeDepth<CV_32S>
-{
-    enum { depth = CV_32S };
-    typedef int value_type;
-};
-
-template<> class TypeDepth<CV_32F>
-{
-    enum { depth = CV_32F };
-    typedef float value_type;
-};
-
-template<> class TypeDepth<CV_64F>
-{
-    enum { depth = CV_64F };
-    typedef double value_type;
-};
-
-#endif
-
-//! @}
-
-namespace traits {
-
-namespace internal {
-#define CV_CREATE_MEMBER_CHECK(X) \
-template<typename T> class CheckMember_##X { \
-    struct Fallback { int X; }; \
-    struct Derived : T, Fallback { }; \
-    template<typename U, U> struct Check; \
-    typedef char CV_NO[1]; \
-    typedef char CV_YES[2]; \
-    template<typename U> static CV_NO & func(Check<int Fallback::*, &U::X> *); \
-    template<typename U> static CV_YES & func(...); \
-public: \
-    typedef CheckMember_##X type; \
-    enum { value = sizeof(func<Derived>(0)) == sizeof(CV_YES) }; \
-};
-
-CV_CREATE_MEMBER_CHECK(fmt)
-CV_CREATE_MEMBER_CHECK(type)
-
-} // namespace internal
-
-
-template<typename T>
-struct Depth
-{ enum { value = DataType<T>::depth }; };
-
-template<typename T>
-struct Type
-{ enum { value = DataType<T>::type }; };
-
-/** Similar to traits::Type<T> but has value = -1 in case of unknown type (instead of compiler error) */
-template<typename T, bool available = internal::CheckMember_type< DataType<T> >::value >
-struct SafeType {};
-
-template<typename T>
-struct SafeType<T, false>
-{ enum { value = -1 }; };
-
-template<typename T>
-struct SafeType<T, true>
-{ enum { value = Type<T>::value }; };
-
-
-template<typename T, bool available = internal::CheckMember_fmt< DataType<T> >::value >
-struct SafeFmt {};
-
-template<typename T>
-struct SafeFmt<T, false>
-{ enum { fmt = 0 }; };
-
-template<typename T>
-struct SafeFmt<T, true>
-{ enum { fmt = DataType<T>::fmt }; };
-
-
-} // namespace
-
-} // cv
-
-#endif // OPENCV_CORE_TRAITS_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/types.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/types.hpp
deleted file mode 100755
index 5037434..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/types.hpp
+++ /dev/null
@@ -1,2372 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_CORE_TYPES_HPP
-#define OPENCV_CORE_TYPES_HPP
-
-#ifndef __cplusplus
-#  error types.hpp header must be compiled as C++
-#endif
-
-#include <climits>
-#include <cfloat>
-#include <vector>
-#include <limits>
-
-#include "opencv2/core/cvdef.h"
-#include "opencv2/core/cvstd.hpp"
-#include "opencv2/core/matx.hpp"
-
-namespace cv
-{
-
-//! @addtogroup core_basic
-//! @{
-
-//////////////////////////////// Complex //////////////////////////////
-
-/** @brief  A complex number class.
-
-  The template class is similar and compatible with std::complex, however it provides slightly
-  more convenient access to the real and imaginary parts using through the simple field access, as opposite
-  to std::complex::real() and std::complex::imag().
-*/
-template<typename _Tp> class Complex
-{
-public:
-
-    //! default constructor
-    Complex();
-    Complex( _Tp _re, _Tp _im = 0 );
-
-    //! conversion to another data type
-    template<typename T2> operator Complex<T2>() const;
-    //! conjugation
-    Complex conj() const;
-
-    _Tp re, im; //< the real and the imaginary parts
-};
-
-typedef Complex<float> Complexf;
-typedef Complex<double> Complexd;
-
-template<typename _Tp> class DataType< Complex<_Tp> >
-{
-public:
-    typedef Complex<_Tp> value_type;
-    typedef value_type   work_type;
-    typedef _Tp          channel_type;
-
-    enum { generic_type = 0,
-           channels     = 2,
-           fmt          = DataType<channel_type>::fmt + ((channels - 1) << 8)
-#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED
-           ,depth        = DataType<channel_type>::depth
-           ,type         = CV_MAKETYPE(depth, channels)
-#endif
-    };
-
-    typedef Vec<channel_type, channels> vec_type;
-};
-
-namespace traits {
-template<typename _Tp>
-struct Depth< Complex<_Tp> > { enum { value = Depth<_Tp>::value }; };
-template<typename _Tp>
-struct Type< Complex<_Tp> > { enum { value = CV_MAKETYPE(Depth<_Tp>::value, 2) }; };
-} // namespace
-
-
-//////////////////////////////// Point_ ////////////////////////////////
-
-/** @brief Template class for 2D points specified by its coordinates `x` and `y`.
-
-An instance of the class is interchangeable with C structures, CvPoint and CvPoint2D32f . There is
-also a cast operator to convert point coordinates to the specified type. The conversion from
-floating-point coordinates to integer coordinates is done by rounding. Commonly, the conversion
-uses this operation for each of the coordinates. Besides the class members listed in the
-declaration above, the following operations on points are implemented:
-@code
-    pt1 = pt2 + pt3;
-    pt1 = pt2 - pt3;
-    pt1 = pt2 * a;
-    pt1 = a * pt2;
-    pt1 = pt2 / a;
-    pt1 += pt2;
-    pt1 -= pt2;
-    pt1 *= a;
-    pt1 /= a;
-    double value = norm(pt); // L2 norm
-    pt1 == pt2;
-    pt1 != pt2;
-@endcode
-For your convenience, the following type aliases are defined:
-@code
-    typedef Point_<int> Point2i;
-    typedef Point2i Point;
-    typedef Point_<float> Point2f;
-    typedef Point_<double> Point2d;
-@endcode
-Example:
-@code
-    Point2f a(0.3f, 0.f), b(0.f, 0.4f);
-    Point pt = (a + b)*10.f;
-    cout << pt.x << ", " << pt.y << endl;
-@endcode
-*/
-template<typename _Tp> class Point_
-{
-public:
-    typedef _Tp value_type;
-
-    //! default constructor
-    Point_();
-    Point_(_Tp _x, _Tp _y);
-    Point_(const Point_& pt);
-    Point_(const Size_<_Tp>& sz);
-    Point_(const Vec<_Tp, 2>& v);
-
-    Point_& operator = (const Point_& pt);
-    //! conversion to another data type
-    template<typename _Tp2> operator Point_<_Tp2>() const;
-
-    //! conversion to the old-style C structures
-    operator Vec<_Tp, 2>() const;
-
-    //! dot product
-    _Tp dot(const Point_& pt) const;
-    //! dot product computed in double-precision arithmetics
-    double ddot(const Point_& pt) const;
-    //! cross-product
-    double cross(const Point_& pt) const;
-    //! checks whether the point is inside the specified rectangle
-    bool inside(const Rect_<_Tp>& r) const;
-    _Tp x; //!< x coordinate of the point
-    _Tp y; //!< y coordinate of the point
-};
-
-typedef Point_<int> Point2i;
-typedef Point_<int64> Point2l;
-typedef Point_<float> Point2f;
-typedef Point_<double> Point2d;
-typedef Point2i Point;
-
-template<typename _Tp> class DataType< Point_<_Tp> >
-{
-public:
-    typedef Point_<_Tp>                               value_type;
-    typedef Point_<typename DataType<_Tp>::work_type> work_type;
-    typedef _Tp                                       channel_type;
-
-    enum { generic_type = 0,
-           channels     = 2,
-           fmt          = traits::SafeFmt<channel_type>::fmt + ((channels - 1) << 8)
-#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED
-           ,depth        = DataType<channel_type>::depth
-           ,type         = CV_MAKETYPE(depth, channels)
-#endif
-         };
-
-    typedef Vec<channel_type, channels> vec_type;
-};
-
-namespace traits {
-template<typename _Tp>
-struct Depth< Point_<_Tp> > { enum { value = Depth<_Tp>::value }; };
-template<typename _Tp>
-struct Type< Point_<_Tp> > { enum { value = CV_MAKETYPE(Depth<_Tp>::value, 2) }; };
-} // namespace
-
-
-//////////////////////////////// Point3_ ////////////////////////////////
-
-/** @brief Template class for 3D points specified by its coordinates `x`, `y` and `z`.
-
-An instance of the class is interchangeable with the C structure CvPoint2D32f . Similarly to
-Point_ , the coordinates of 3D points can be converted to another type. The vector arithmetic and
-comparison operations are also supported.
-
-The following Point3_\<\> aliases are available:
-@code
-    typedef Point3_<int> Point3i;
-    typedef Point3_<float> Point3f;
-    typedef Point3_<double> Point3d;
-@endcode
-@see cv::Point3i, cv::Point3f and cv::Point3d
-*/
-template<typename _Tp> class Point3_
-{
-public:
-    typedef _Tp value_type;
-
-    //! default constructor
-    Point3_();
-    Point3_(_Tp _x, _Tp _y, _Tp _z);
-    Point3_(const Point3_& pt);
-    explicit Point3_(const Point_<_Tp>& pt);
-    Point3_(const Vec<_Tp, 3>& v);
-
-    Point3_& operator = (const Point3_& pt);
-    //! conversion to another data type
-    template<typename _Tp2> operator Point3_<_Tp2>() const;
-    //! conversion to cv::Vec<>
-#if OPENCV_ABI_COMPATIBILITY > 300
-    template<typename _Tp2> operator Vec<_Tp2, 3>() const;
-#else
-    operator Vec<_Tp, 3>() const;
-#endif
-
-    //! dot product
-    _Tp dot(const Point3_& pt) const;
-    //! dot product computed in double-precision arithmetics
-    double ddot(const Point3_& pt) const;
-    //! cross product of the 2 3D points
-    Point3_ cross(const Point3_& pt) const;
-    _Tp x; //!< x coordinate of the 3D point
-    _Tp y; //!< y coordinate of the 3D point
-    _Tp z; //!< z coordinate of the 3D point
-};
-
-typedef Point3_<int> Point3i;
-typedef Point3_<float> Point3f;
-typedef Point3_<double> Point3d;
-
-template<typename _Tp> class DataType< Point3_<_Tp> >
-{
-public:
-    typedef Point3_<_Tp>                               value_type;
-    typedef Point3_<typename DataType<_Tp>::work_type> work_type;
-    typedef _Tp                                        channel_type;
-
-    enum { generic_type = 0,
-           channels     = 3,
-           fmt          = traits::SafeFmt<channel_type>::fmt + ((channels - 1) << 8)
-#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED
-           ,depth        = DataType<channel_type>::depth
-           ,type         = CV_MAKETYPE(depth, channels)
-#endif
-         };
-
-    typedef Vec<channel_type, channels> vec_type;
-};
-
-namespace traits {
-template<typename _Tp>
-struct Depth< Point3_<_Tp> > { enum { value = Depth<_Tp>::value }; };
-template<typename _Tp>
-struct Type< Point3_<_Tp> > { enum { value = CV_MAKETYPE(Depth<_Tp>::value, 3) }; };
-} // namespace
-
-//////////////////////////////// Size_ ////////////////////////////////
-
-/** @brief Template class for specifying the size of an image or rectangle.
-
-The class includes two members called width and height. The structure can be converted to and from
-the old OpenCV structures CvSize and CvSize2D32f . The same set of arithmetic and comparison
-operations as for Point_ is available.
-
-OpenCV defines the following Size_\<\> aliases:
-@code
-    typedef Size_<int> Size2i;
-    typedef Size2i Size;
-    typedef Size_<float> Size2f;
-@endcode
-*/
-template<typename _Tp> class Size_
-{
-public:
-    typedef _Tp value_type;
-
-    //! default constructor
-    Size_();
-    Size_(_Tp _width, _Tp _height);
-    Size_(const Size_& sz);
-    Size_(const Point_<_Tp>& pt);
-
-    Size_& operator = (const Size_& sz);
-    //! the area (width*height)
-    _Tp area() const;
-    //! true if empty
-    bool empty() const;
-
-    //! conversion of another data type.
-    template<typename _Tp2> operator Size_<_Tp2>() const;
-
-    _Tp width; //!< the width
-    _Tp height; //!< the height
-};
-
-typedef Size_<int> Size2i;
-typedef Size_<int64> Size2l;
-typedef Size_<float> Size2f;
-typedef Size_<double> Size2d;
-typedef Size2i Size;
-
-template<typename _Tp> class DataType< Size_<_Tp> >
-{
-public:
-    typedef Size_<_Tp>                               value_type;
-    typedef Size_<typename DataType<_Tp>::work_type> work_type;
-    typedef _Tp                                      channel_type;
-
-    enum { generic_type = 0,
-           channels     = 2,
-           fmt          = DataType<channel_type>::fmt + ((channels - 1) << 8)
-#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED
-           ,depth        = DataType<channel_type>::depth
-           ,type         = CV_MAKETYPE(depth, channels)
-#endif
-         };
-
-    typedef Vec<channel_type, channels> vec_type;
-};
-
-namespace traits {
-template<typename _Tp>
-struct Depth< Size_<_Tp> > { enum { value = Depth<_Tp>::value }; };
-template<typename _Tp>
-struct Type< Size_<_Tp> > { enum { value = CV_MAKETYPE(Depth<_Tp>::value, 2) }; };
-} // namespace
-
-//////////////////////////////// Rect_ ////////////////////////////////
-
-/** @brief Template class for 2D rectangles
-
-described by the following parameters:
--   Coordinates of the top-left corner. This is a default interpretation of Rect_::x and Rect_::y
-    in OpenCV. Though, in your algorithms you may count x and y from the bottom-left corner.
--   Rectangle width and height.
-
-OpenCV typically assumes that the top and left boundary of the rectangle are inclusive, while the
-right and bottom boundaries are not. For example, the method Rect_::contains returns true if
-
-\f[x  \leq pt.x < x+width,
-      y  \leq pt.y < y+height\f]
-
-Virtually every loop over an image ROI in OpenCV (where ROI is specified by Rect_\<int\> ) is
-implemented as:
-@code
-    for(int y = roi.y; y < roi.y + roi.height; y++)
-        for(int x = roi.x; x < roi.x + roi.width; x++)
-        {
-            // ...
-        }
-@endcode
-In addition to the class members, the following operations on rectangles are implemented:
--   \f$\texttt{rect} = \texttt{rect} \pm \texttt{point}\f$ (shifting a rectangle by a certain offset)
--   \f$\texttt{rect} = \texttt{rect} \pm \texttt{size}\f$ (expanding or shrinking a rectangle by a
-    certain amount)
--   rect += point, rect -= point, rect += size, rect -= size (augmenting operations)
--   rect = rect1 & rect2 (rectangle intersection)
--   rect = rect1 | rect2 (minimum area rectangle containing rect1 and rect2 )
--   rect &= rect1, rect |= rect1 (and the corresponding augmenting operations)
--   rect == rect1, rect != rect1 (rectangle comparison)
-
-This is an example how the partial ordering on rectangles can be established (rect1 \f$\subseteq\f$
-rect2):
-@code
-    template<typename _Tp> inline bool
-    operator <= (const Rect_<_Tp>& r1, const Rect_<_Tp>& r2)
-    {
-        return (r1 & r2) == r1;
-    }
-@endcode
-For your convenience, the Rect_\<\> alias is available: cv::Rect
-*/
-template<typename _Tp> class Rect_
-{
-public:
-    typedef _Tp value_type;
-
-    //! default constructor
-    Rect_();
-    Rect_(_Tp _x, _Tp _y, _Tp _width, _Tp _height);
-    Rect_(const Rect_& r);
-    Rect_(const Point_<_Tp>& org, const Size_<_Tp>& sz);
-    Rect_(const Point_<_Tp>& pt1, const Point_<_Tp>& pt2);
-
-    Rect_& operator = ( const Rect_& r );
-    //! the top-left corner
-    Point_<_Tp> tl() const;
-    //! the bottom-right corner
-    Point_<_Tp> br() const;
-
-    //! size (width, height) of the rectangle
-    Size_<_Tp> size() const;
-    //! area (width*height) of the rectangle
-    _Tp area() const;
-    //! true if empty
-    bool empty() const;
-
-    //! conversion to another data type
-    template<typename _Tp2> operator Rect_<_Tp2>() const;
-
-    //! checks whether the rectangle contains the point
-    bool contains(const Point_<_Tp>& pt) const;
-
-    _Tp x; //!< x coordinate of the top-left corner
-    _Tp y; //!< y coordinate of the top-left corner
-    _Tp width; //!< width of the rectangle
-    _Tp height; //!< height of the rectangle
-};
-
-typedef Rect_<int> Rect2i;
-typedef Rect_<float> Rect2f;
-typedef Rect_<double> Rect2d;
-typedef Rect2i Rect;
-
-template<typename _Tp> class DataType< Rect_<_Tp> >
-{
-public:
-    typedef Rect_<_Tp>                               value_type;
-    typedef Rect_<typename DataType<_Tp>::work_type> work_type;
-    typedef _Tp                                      channel_type;
-
-    enum { generic_type = 0,
-           channels     = 4,
-           fmt          = traits::SafeFmt<channel_type>::fmt + ((channels - 1) << 8)
-#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED
-           ,depth        = DataType<channel_type>::depth
-           ,type         = CV_MAKETYPE(depth, channels)
-#endif
-         };
-
-    typedef Vec<channel_type, channels> vec_type;
-};
-
-namespace traits {
-template<typename _Tp>
-struct Depth< Rect_<_Tp> > { enum { value = Depth<_Tp>::value }; };
-template<typename _Tp>
-struct Type< Rect_<_Tp> > { enum { value = CV_MAKETYPE(Depth<_Tp>::value, 4) }; };
-} // namespace
-
-///////////////////////////// RotatedRect /////////////////////////////
-
-/** @brief The class represents rotated (i.e. not up-right) rectangles on a plane.
-
-Each rectangle is specified by the center point (mass center), length of each side (represented by
-#Size2f structure) and the rotation angle in degrees.
-
-The sample below demonstrates how to use RotatedRect:
-@snippet snippets/core_various.cpp RotatedRect_demo
-![image](pics/rotatedrect.png)
-
-@sa CamShift, fitEllipse, minAreaRect, CvBox2D
-*/
-class CV_EXPORTS RotatedRect
-{
-public:
-    //! default constructor
-    RotatedRect();
-    /** full constructor
-    @param center The rectangle mass center.
-    @param size Width and height of the rectangle.
-    @param angle The rotation angle in a clockwise direction. When the angle is 0, 90, 180, 270 etc.,
-    the rectangle becomes an up-right rectangle.
-    */
-    RotatedRect(const Point2f& center, const Size2f& size, float angle);
-    /**
-    Any 3 end points of the RotatedRect. They must be given in order (either clockwise or
-    anticlockwise).
-     */
-    RotatedRect(const Point2f& point1, const Point2f& point2, const Point2f& point3);
-
-    /** returns 4 vertices of the rectangle
-    @param pts The points array for storing rectangle vertices. The order is bottomLeft, topLeft, topRight, bottomRight.
-    */
-    void points(Point2f pts[]) const;
-    //! returns the minimal up-right integer rectangle containing the rotated rectangle
-    Rect boundingRect() const;
-    //! returns the minimal (exact) floating point rectangle containing the rotated rectangle, not intended for use with images
-    Rect_<float> boundingRect2f() const;
-    //! returns the rectangle mass center
-    Point2f center;
-    //! returns width and height of the rectangle
-    Size2f size;
-    //! returns the rotation angle. When the angle is 0, 90, 180, 270 etc., the rectangle becomes an up-right rectangle.
-    float angle;
-};
-
-template<> class DataType< RotatedRect >
-{
-public:
-    typedef RotatedRect  value_type;
-    typedef value_type   work_type;
-    typedef float        channel_type;
-
-    enum { generic_type = 0,
-           channels     = (int)sizeof(value_type)/sizeof(channel_type), // 5
-           fmt          = traits::SafeFmt<channel_type>::fmt + ((channels - 1) << 8)
-#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED
-           ,depth        = DataType<channel_type>::depth
-           ,type         = CV_MAKETYPE(depth, channels)
-#endif
-         };
-
-    typedef Vec<channel_type, channels> vec_type;
-};
-
-namespace traits {
-template<>
-struct Depth< RotatedRect > { enum { value = Depth<float>::value }; };
-template<>
-struct Type< RotatedRect > { enum { value = CV_MAKETYPE(Depth<float>::value, (int)sizeof(RotatedRect)/sizeof(float)) }; };
-} // namespace
-
-
-//////////////////////////////// Range /////////////////////////////////
-
-/** @brief Template class specifying a continuous subsequence (slice) of a sequence.
-
-The class is used to specify a row or a column span in a matrix ( Mat ) and for many other purposes.
-Range(a,b) is basically the same as a:b in Matlab or a..b in Python. As in Python, start is an
-inclusive left boundary of the range and end is an exclusive right boundary of the range. Such a
-half-opened interval is usually denoted as \f$[start,end)\f$ .
-
-The static method Range::all() returns a special variable that means "the whole sequence" or "the
-whole range", just like " : " in Matlab or " ... " in Python. All the methods and functions in
-OpenCV that take Range support this special Range::all() value. But, of course, in case of your own
-custom processing, you will probably have to check and handle it explicitly:
-@code
-    void my_function(..., const Range& r, ....)
-    {
-        if(r == Range::all()) {
-            // process all the data
-        }
-        else {
-            // process [r.start, r.end)
-        }
-    }
-@endcode
-*/
-class CV_EXPORTS Range
-{
-public:
-    Range();
-    Range(int _start, int _end);
-    int size() const;
-    bool empty() const;
-    static Range all();
-
-    int start, end;
-};
-
-template<> class DataType<Range>
-{
-public:
-    typedef Range      value_type;
-    typedef value_type work_type;
-    typedef int        channel_type;
-
-    enum { generic_type = 0,
-           channels     = 2,
-           fmt          = traits::SafeFmt<channel_type>::fmt + ((channels - 1) << 8)
-#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED
-           ,depth        = DataType<channel_type>::depth
-           ,type         = CV_MAKETYPE(depth, channels)
-#endif
-         };
-
-    typedef Vec<channel_type, channels> vec_type;
-};
-
-namespace traits {
-template<>
-struct Depth< Range > { enum { value = Depth<int>::value }; };
-template<>
-struct Type< Range > { enum { value = CV_MAKETYPE(Depth<int>::value, 2) }; };
-} // namespace
-
-
-//////////////////////////////// Scalar_ ///////////////////////////////
-
-/** @brief Template class for a 4-element vector derived from Vec.
-
-Being derived from Vec\<_Tp, 4\> , Scalar\_ and Scalar can be used just as typical 4-element
-vectors. In addition, they can be converted to/from CvScalar . The type Scalar is widely used in
-OpenCV to pass pixel values.
-*/
-template<typename _Tp> class Scalar_ : public Vec<_Tp, 4>
-{
-public:
-    //! default constructor
-    Scalar_();
-    Scalar_(_Tp v0, _Tp v1, _Tp v2=0, _Tp v3=0);
-    Scalar_(_Tp v0);
-
-    template<typename _Tp2, int cn>
-    Scalar_(const Vec<_Tp2, cn>& v);
-
-    //! returns a scalar with all elements set to v0
-    static Scalar_<_Tp> all(_Tp v0);
-
-    //! conversion to another data type
-    template<typename T2> operator Scalar_<T2>() const;
-
-    //! per-element product
-    Scalar_<_Tp> mul(const Scalar_<_Tp>& a, double scale=1 ) const;
-
-    //! returns (v0, -v1, -v2, -v3)
-    Scalar_<_Tp> conj() const;
-
-    //! returns true iff v1 == v2 == v3 == 0
-    bool isReal() const;
-};
-
-typedef Scalar_<double> Scalar;
-
-template<typename _Tp> class DataType< Scalar_<_Tp> >
-{
-public:
-    typedef Scalar_<_Tp>                               value_type;
-    typedef Scalar_<typename DataType<_Tp>::work_type> work_type;
-    typedef _Tp                                        channel_type;
-
-    enum { generic_type = 0,
-           channels     = 4,
-           fmt          = traits::SafeFmt<channel_type>::fmt + ((channels - 1) << 8)
-#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED
-           ,depth        = DataType<channel_type>::depth
-           ,type         = CV_MAKETYPE(depth, channels)
-#endif
-         };
-
-    typedef Vec<channel_type, channels> vec_type;
-};
-
-namespace traits {
-template<typename _Tp>
-struct Depth< Scalar_<_Tp> > { enum { value = Depth<_Tp>::value }; };
-template<typename _Tp>
-struct Type< Scalar_<_Tp> > { enum { value = CV_MAKETYPE(Depth<_Tp>::value, 4) }; };
-} // namespace
-
-
-/////////////////////////////// KeyPoint ////////////////////////////////
-
-/** @brief Data structure for salient point detectors.
-
-The class instance stores a keypoint, i.e. a point feature found by one of many available keypoint
-detectors, such as Harris corner detector, #FAST, %StarDetector, %SURF, %SIFT etc.
-
-The keypoint is characterized by the 2D position, scale (proportional to the diameter of the
-neighborhood that needs to be taken into account), orientation and some other parameters. The
-keypoint neighborhood is then analyzed by another algorithm that builds a descriptor (usually
-represented as a feature vector). The keypoints representing the same object in different images
-can then be matched using %KDTree or another method.
-*/
-class CV_EXPORTS_W_SIMPLE KeyPoint
-{
-public:
-    //! the default constructor
-    CV_WRAP KeyPoint();
-    /**
-    @param _pt x & y coordinates of the keypoint
-    @param _size keypoint diameter
-    @param _angle keypoint orientation
-    @param _response keypoint detector response on the keypoint (that is, strength of the keypoint)
-    @param _octave pyramid octave in which the keypoint has been detected
-    @param _class_id object id
-     */
-    KeyPoint(Point2f _pt, float _size, float _angle=-1, float _response=0, int _octave=0, int _class_id=-1);
-    /**
-    @param x x-coordinate of the keypoint
-    @param y y-coordinate of the keypoint
-    @param _size keypoint diameter
-    @param _angle keypoint orientation
-    @param _response keypoint detector response on the keypoint (that is, strength of the keypoint)
-    @param _octave pyramid octave in which the keypoint has been detected
-    @param _class_id object id
-     */
-    CV_WRAP KeyPoint(float x, float y, float _size, float _angle=-1, float _response=0, int _octave=0, int _class_id=-1);
-
-    size_t hash() const;
-
-    /**
-    This method converts vector of keypoints to vector of points or the reverse, where each keypoint is
-    assigned the same size and the same orientation.
-
-    @param keypoints Keypoints obtained from any feature detection algorithm like SIFT/SURF/ORB
-    @param points2f Array of (x,y) coordinates of each keypoint
-    @param keypointIndexes Array of indexes of keypoints to be converted to points. (Acts like a mask to
-    convert only specified keypoints)
-    */
-    CV_WRAP static void convert(const std::vector<KeyPoint>& keypoints,
-                                CV_OUT std::vector<Point2f>& points2f,
-                                const std::vector<int>& keypointIndexes=std::vector<int>());
-    /** @overload
-    @param points2f Array of (x,y) coordinates of each keypoint
-    @param keypoints Keypoints obtained from any feature detection algorithm like SIFT/SURF/ORB
-    @param size keypoint diameter
-    @param response keypoint detector response on the keypoint (that is, strength of the keypoint)
-    @param octave pyramid octave in which the keypoint has been detected
-    @param class_id object id
-    */
-    CV_WRAP static void convert(const std::vector<Point2f>& points2f,
-                                CV_OUT std::vector<KeyPoint>& keypoints,
-                                float size=1, float response=1, int octave=0, int class_id=-1);
-
-    /**
-    This method computes overlap for pair of keypoints. Overlap is the ratio between area of keypoint
-    regions' intersection and area of keypoint regions' union (considering keypoint region as circle).
-    If they don't overlap, we get zero. If they coincide at same location with same size, we get 1.
-    @param kp1 First keypoint
-    @param kp2 Second keypoint
-    */
-    CV_WRAP static float overlap(const KeyPoint& kp1, const KeyPoint& kp2);
-
-    CV_PROP_RW Point2f pt; //!< coordinates of the keypoints
-    CV_PROP_RW float size; //!< diameter of the meaningful keypoint neighborhood
-    CV_PROP_RW float angle; //!< computed orientation of the keypoint (-1 if not applicable);
-                            //!< it's in [0,360) degrees and measured relative to
-                            //!< image coordinate system, ie in clockwise.
-    CV_PROP_RW float response; //!< the response by which the most strong keypoints have been selected. Can be used for the further sorting or subsampling
-    CV_PROP_RW int octave; //!< octave (pyramid layer) from which the keypoint has been extracted
-    CV_PROP_RW int class_id; //!< object class (if the keypoints need to be clustered by an object they belong to)
-};
-
-#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED
-template<> class DataType<KeyPoint>
-{
-public:
-    typedef KeyPoint      value_type;
-    typedef float         work_type;
-    typedef float         channel_type;
-
-    enum { generic_type = 0,
-           depth        = DataType<channel_type>::depth,
-           channels     = (int)(sizeof(value_type)/sizeof(channel_type)), // 7
-           fmt          = DataType<channel_type>::fmt + ((channels - 1) << 8),
-           type         = CV_MAKETYPE(depth, channels)
-         };
-
-    typedef Vec<channel_type, channels> vec_type;
-};
-#endif
-
-
-//////////////////////////////// DMatch /////////////////////////////////
-
-/** @brief Class for matching keypoint descriptors
-
-query descriptor index, train descriptor index, train image index, and distance between
-descriptors.
-*/
-class CV_EXPORTS_W_SIMPLE DMatch
-{
-public:
-    CV_WRAP DMatch();
-    CV_WRAP DMatch(int _queryIdx, int _trainIdx, float _distance);
-    CV_WRAP DMatch(int _queryIdx, int _trainIdx, int _imgIdx, float _distance);
-
-    CV_PROP_RW int queryIdx; //!< query descriptor index
-    CV_PROP_RW int trainIdx; //!< train descriptor index
-    CV_PROP_RW int imgIdx;   //!< train image index
-
-    CV_PROP_RW float distance;
-
-    // less is better
-    bool operator<(const DMatch &m) const;
-};
-
-#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED
-template<> class DataType<DMatch>
-{
-public:
-    typedef DMatch      value_type;
-    typedef int         work_type;
-    typedef int         channel_type;
-
-    enum { generic_type = 0,
-           depth        = DataType<channel_type>::depth,
-           channels     = (int)(sizeof(value_type)/sizeof(channel_type)), // 4
-           fmt          = DataType<channel_type>::fmt + ((channels - 1) << 8),
-           type         = CV_MAKETYPE(depth, channels)
-         };
-
-    typedef Vec<channel_type, channels> vec_type;
-};
-#endif
-
-
-///////////////////////////// TermCriteria //////////////////////////////
-
-/** @brief The class defining termination criteria for iterative algorithms.
-
-You can initialize it by default constructor and then override any parameters, or the structure may
-be fully initialized using the advanced variant of the constructor.
-*/
-class CV_EXPORTS TermCriteria
-{
-public:
-    /**
-      Criteria type, can be one of: COUNT, EPS or COUNT + EPS
-    */
-    enum Type
-    {
-        COUNT=1, //!< the maximum number of iterations or elements to compute
-        MAX_ITER=COUNT, //!< ditto
-        EPS=2 //!< the desired accuracy or change in parameters at which the iterative algorithm stops
-    };
-
-    //! default constructor
-    TermCriteria();
-    /**
-    @param type The type of termination criteria, one of TermCriteria::Type
-    @param maxCount The maximum number of iterations or elements to compute.
-    @param epsilon The desired accuracy or change in parameters at which the iterative algorithm stops.
-    */
-    TermCriteria(int type, int maxCount, double epsilon);
-
-    int type; //!< the type of termination criteria: COUNT, EPS or COUNT + EPS
-    int maxCount; //!< the maximum number of iterations/elements
-    double epsilon; //!< the desired accuracy
-};
-
-
-//! @} core_basic
-
-///////////////////////// raster image moments //////////////////////////
-
-//! @addtogroup imgproc_shape
-//! @{
-
-/** @brief struct returned by cv::moments
-
-The spatial moments \f$\texttt{Moments::m}_{ji}\f$ are computed as:
-
-\f[\texttt{m} _{ji}= \sum _{x,y}  \left ( \texttt{array} (x,y)  \cdot x^j  \cdot y^i \right )\f]
-
-The central moments \f$\texttt{Moments::mu}_{ji}\f$ are computed as:
-
-\f[\texttt{mu} _{ji}= \sum _{x,y}  \left ( \texttt{array} (x,y)  \cdot (x -  \bar{x} )^j  \cdot (y -  \bar{y} )^i \right )\f]
-
-where \f$(\bar{x}, \bar{y})\f$ is the mass center:
-
-\f[\bar{x} = \frac{\texttt{m}_{10}}{\texttt{m}_{00}} , \; \bar{y} = \frac{\texttt{m}_{01}}{\texttt{m}_{00}}\f]
-
-The normalized central moments \f$\texttt{Moments::nu}_{ij}\f$ are computed as:
-
-\f[\texttt{nu} _{ji}= \frac{\texttt{mu}_{ji}}{\texttt{m}_{00}^{(i+j)/2+1}} .\f]
-
-@note
-\f$\texttt{mu}_{00}=\texttt{m}_{00}\f$, \f$\texttt{nu}_{00}=1\f$
-\f$\texttt{nu}_{10}=\texttt{mu}_{10}=\texttt{mu}_{01}=\texttt{mu}_{10}=0\f$ , hence the values are not
-stored.
-
-The moments of a contour are defined in the same way but computed using the Green's formula (see
-<http://en.wikipedia.org/wiki/Green_theorem>). So, due to a limited raster resolution, the moments
-computed for a contour are slightly different from the moments computed for the same rasterized
-contour.
-
-@note
-Since the contour moments are computed using Green formula, you may get seemingly odd results for
-contours with self-intersections, e.g. a zero area (m00) for butterfly-shaped contours.
- */
-class CV_EXPORTS_W_MAP Moments
-{
-public:
-    //! the default constructor
-    Moments();
-    //! the full constructor
-    Moments(double m00, double m10, double m01, double m20, double m11,
-            double m02, double m30, double m21, double m12, double m03 );
-    ////! the conversion from CvMoments
-    //Moments( const CvMoments& moments );
-    ////! the conversion to CvMoments
-    //operator CvMoments() const;
-
-    //! @name spatial moments
-    //! @{
-    CV_PROP_RW double  m00, m10, m01, m20, m11, m02, m30, m21, m12, m03;
-    //! @}
-
-    //! @name central moments
-    //! @{
-    CV_PROP_RW double  mu20, mu11, mu02, mu30, mu21, mu12, mu03;
-    //! @}
-
-    //! @name central normalized moments
-    //! @{
-    CV_PROP_RW double  nu20, nu11, nu02, nu30, nu21, nu12, nu03;
-    //! @}
-};
-
-template<> class DataType<Moments>
-{
-public:
-    typedef Moments     value_type;
-    typedef double      work_type;
-    typedef double      channel_type;
-
-    enum { generic_type = 0,
-           channels     = (int)(sizeof(value_type)/sizeof(channel_type)), // 24
-           fmt          = DataType<channel_type>::fmt + ((channels - 1) << 8)
-#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED
-           ,depth        = DataType<channel_type>::depth
-           ,type         = CV_MAKETYPE(depth, channels)
-#endif
-         };
-
-    typedef Vec<channel_type, channels> vec_type;
-};
-
-namespace traits {
-template<>
-struct Depth< Moments > { enum { value = Depth<double>::value }; };
-template<>
-struct Type< Moments > { enum { value = CV_MAKETYPE(Depth<double>::value, (int)(sizeof(Moments)/sizeof(double))) }; };
-} // namespace
-
-//! @} imgproc_shape
-
-//! @cond IGNORED
-
-/////////////////////////////////////////////////////////////////////////
-///////////////////////////// Implementation ////////////////////////////
-/////////////////////////////////////////////////////////////////////////
-
-//////////////////////////////// Complex ////////////////////////////////
-
-template<typename _Tp> inline
-Complex<_Tp>::Complex()
-    : re(0), im(0) {}
-
-template<typename _Tp> inline
-Complex<_Tp>::Complex( _Tp _re, _Tp _im )
-    : re(_re), im(_im) {}
-
-template<typename _Tp> template<typename T2> inline
-Complex<_Tp>::operator Complex<T2>() const
-{
-    return Complex<T2>(saturate_cast<T2>(re), saturate_cast<T2>(im));
-}
-
-template<typename _Tp> inline
-Complex<_Tp> Complex<_Tp>::conj() const
-{
-    return Complex<_Tp>(re, -im);
-}
-
-
-template<typename _Tp> static inline
-bool operator == (const Complex<_Tp>& a, const Complex<_Tp>& b)
-{
-    return a.re == b.re && a.im == b.im;
-}
-
-template<typename _Tp> static inline
-bool operator != (const Complex<_Tp>& a, const Complex<_Tp>& b)
-{
-    return a.re != b.re || a.im != b.im;
-}
-
-template<typename _Tp> static inline
-Complex<_Tp> operator + (const Complex<_Tp>& a, const Complex<_Tp>& b)
-{
-    return Complex<_Tp>( a.re + b.re, a.im + b.im );
-}
-
-template<typename _Tp> static inline
-Complex<_Tp>& operator += (Complex<_Tp>& a, const Complex<_Tp>& b)
-{
-    a.re += b.re; a.im += b.im;
-    return a;
-}
-
-template<typename _Tp> static inline
-Complex<_Tp> operator - (const Complex<_Tp>& a, const Complex<_Tp>& b)
-{
-    return Complex<_Tp>( a.re - b.re, a.im - b.im );
-}
-
-template<typename _Tp> static inline
-Complex<_Tp>& operator -= (Complex<_Tp>& a, const Complex<_Tp>& b)
-{
-    a.re -= b.re; a.im -= b.im;
-    return a;
-}
-
-template<typename _Tp> static inline
-Complex<_Tp> operator - (const Complex<_Tp>& a)
-{
-    return Complex<_Tp>(-a.re, -a.im);
-}
-
-template<typename _Tp> static inline
-Complex<_Tp> operator * (const Complex<_Tp>& a, const Complex<_Tp>& b)
-{
-    return Complex<_Tp>( a.re*b.re - a.im*b.im, a.re*b.im + a.im*b.re );
-}
-
-template<typename _Tp> static inline
-Complex<_Tp> operator * (const Complex<_Tp>& a, _Tp b)
-{
-    return Complex<_Tp>( a.re*b, a.im*b );
-}
-
-template<typename _Tp> static inline
-Complex<_Tp> operator * (_Tp b, const Complex<_Tp>& a)
-{
-    return Complex<_Tp>( a.re*b, a.im*b );
-}
-
-template<typename _Tp> static inline
-Complex<_Tp> operator + (const Complex<_Tp>& a, _Tp b)
-{
-    return Complex<_Tp>( a.re + b, a.im );
-}
-
-template<typename _Tp> static inline
-Complex<_Tp> operator - (const Complex<_Tp>& a, _Tp b)
-{ return Complex<_Tp>( a.re - b, a.im ); }
-
-template<typename _Tp> static inline
-Complex<_Tp> operator + (_Tp b, const Complex<_Tp>& a)
-{
-    return Complex<_Tp>( a.re + b, a.im );
-}
-
-template<typename _Tp> static inline
-Complex<_Tp> operator - (_Tp b, const Complex<_Tp>& a)
-{
-    return Complex<_Tp>( b - a.re, -a.im );
-}
-
-template<typename _Tp> static inline
-Complex<_Tp>& operator += (Complex<_Tp>& a, _Tp b)
-{
-    a.re += b; return a;
-}
-
-template<typename _Tp> static inline
-Complex<_Tp>& operator -= (Complex<_Tp>& a, _Tp b)
-{
-    a.re -= b; return a;
-}
-
-template<typename _Tp> static inline
-Complex<_Tp>& operator *= (Complex<_Tp>& a, _Tp b)
-{
-    a.re *= b; a.im *= b; return a;
-}
-
-template<typename _Tp> static inline
-double abs(const Complex<_Tp>& a)
-{
-    return std::sqrt( (double)a.re*a.re + (double)a.im*a.im);
-}
-
-template<typename _Tp> static inline
-Complex<_Tp> operator / (const Complex<_Tp>& a, const Complex<_Tp>& b)
-{
-    double t = 1./((double)b.re*b.re + (double)b.im*b.im);
-    return Complex<_Tp>( (_Tp)((a.re*b.re + a.im*b.im)*t),
-                        (_Tp)((-a.re*b.im + a.im*b.re)*t) );
-}
-
-template<typename _Tp> static inline
-Complex<_Tp>& operator /= (Complex<_Tp>& a, const Complex<_Tp>& b)
-{
-    a = a / b;
-    return a;
-}
-
-template<typename _Tp> static inline
-Complex<_Tp> operator / (const Complex<_Tp>& a, _Tp b)
-{
-    _Tp t = (_Tp)1/b;
-    return Complex<_Tp>( a.re*t, a.im*t );
-}
-
-template<typename _Tp> static inline
-Complex<_Tp> operator / (_Tp b, const Complex<_Tp>& a)
-{
-    return Complex<_Tp>(b)/a;
-}
-
-template<typename _Tp> static inline
-Complex<_Tp> operator /= (const Complex<_Tp>& a, _Tp b)
-{
-    _Tp t = (_Tp)1/b;
-    a.re *= t; a.im *= t; return a;
-}
-
-
-
-//////////////////////////////// 2D Point ///////////////////////////////
-
-template<typename _Tp> inline
-Point_<_Tp>::Point_()
-    : x(0), y(0) {}
-
-template<typename _Tp> inline
-Point_<_Tp>::Point_(_Tp _x, _Tp _y)
-    : x(_x), y(_y) {}
-
-template<typename _Tp> inline
-Point_<_Tp>::Point_(const Point_& pt)
-    : x(pt.x), y(pt.y) {}
-
-template<typename _Tp> inline
-Point_<_Tp>::Point_(const Size_<_Tp>& sz)
-    : x(sz.width), y(sz.height) {}
-
-template<typename _Tp> inline
-Point_<_Tp>::Point_(const Vec<_Tp,2>& v)
-    : x(v[0]), y(v[1]) {}
-
-template<typename _Tp> inline
-Point_<_Tp>& Point_<_Tp>::operator = (const Point_& pt)
-{
-    x = pt.x; y = pt.y;
-    return *this;
-}
-
-template<typename _Tp> template<typename _Tp2> inline
-Point_<_Tp>::operator Point_<_Tp2>() const
-{
-    return Point_<_Tp2>(saturate_cast<_Tp2>(x), saturate_cast<_Tp2>(y));
-}
-
-template<typename _Tp> inline
-Point_<_Tp>::operator Vec<_Tp, 2>() const
-{
-    return Vec<_Tp, 2>(x, y);
-}
-
-template<typename _Tp> inline
-_Tp Point_<_Tp>::dot(const Point_& pt) const
-{
-    return saturate_cast<_Tp>(x*pt.x + y*pt.y);
-}
-
-template<typename _Tp> inline
-double Point_<_Tp>::ddot(const Point_& pt) const
-{
-    return (double)x*pt.x + (double)y*pt.y;
-}
-
-template<typename _Tp> inline
-double Point_<_Tp>::cross(const Point_& pt) const
-{
-    return (double)x*pt.y - (double)y*pt.x;
-}
-
-template<typename _Tp> inline bool
-Point_<_Tp>::inside( const Rect_<_Tp>& r ) const
-{
-    return r.contains(*this);
-}
-
-
-template<typename _Tp> static inline
-Point_<_Tp>& operator += (Point_<_Tp>& a, const Point_<_Tp>& b)
-{
-    a.x += b.x;
-    a.y += b.y;
-    return a;
-}
-
-template<typename _Tp> static inline
-Point_<_Tp>& operator -= (Point_<_Tp>& a, const Point_<_Tp>& b)
-{
-    a.x -= b.x;
-    a.y -= b.y;
-    return a;
-}
-
-template<typename _Tp> static inline
-Point_<_Tp>& operator *= (Point_<_Tp>& a, int b)
-{
-    a.x = saturate_cast<_Tp>(a.x * b);
-    a.y = saturate_cast<_Tp>(a.y * b);
-    return a;
-}
-
-template<typename _Tp> static inline
-Point_<_Tp>& operator *= (Point_<_Tp>& a, float b)
-{
-    a.x = saturate_cast<_Tp>(a.x * b);
-    a.y = saturate_cast<_Tp>(a.y * b);
-    return a;
-}
-
-template<typename _Tp> static inline
-Point_<_Tp>& operator *= (Point_<_Tp>& a, double b)
-{
-    a.x = saturate_cast<_Tp>(a.x * b);
-    a.y = saturate_cast<_Tp>(a.y * b);
-    return a;
-}
-
-template<typename _Tp> static inline
-Point_<_Tp>& operator /= (Point_<_Tp>& a, int b)
-{
-    a.x = saturate_cast<_Tp>(a.x / b);
-    a.y = saturate_cast<_Tp>(a.y / b);
-    return a;
-}
-
-template<typename _Tp> static inline
-Point_<_Tp>& operator /= (Point_<_Tp>& a, float b)
-{
-    a.x = saturate_cast<_Tp>(a.x / b);
-    a.y = saturate_cast<_Tp>(a.y / b);
-    return a;
-}
-
-template<typename _Tp> static inline
-Point_<_Tp>& operator /= (Point_<_Tp>& a, double b)
-{
-    a.x = saturate_cast<_Tp>(a.x / b);
-    a.y = saturate_cast<_Tp>(a.y / b);
-    return a;
-}
-
-template<typename _Tp> static inline
-double norm(const Point_<_Tp>& pt)
-{
-    return std::sqrt((double)pt.x*pt.x + (double)pt.y*pt.y);
-}
-
-template<typename _Tp> static inline
-bool operator == (const Point_<_Tp>& a, const Point_<_Tp>& b)
-{
-    return a.x == b.x && a.y == b.y;
-}
-
-template<typename _Tp> static inline
-bool operator != (const Point_<_Tp>& a, const Point_<_Tp>& b)
-{
-    return a.x != b.x || a.y != b.y;
-}
-
-template<typename _Tp> static inline
-Point_<_Tp> operator + (const Point_<_Tp>& a, const Point_<_Tp>& b)
-{
-    return Point_<_Tp>( saturate_cast<_Tp>(a.x + b.x), saturate_cast<_Tp>(a.y + b.y) );
-}
-
-template<typename _Tp> static inline
-Point_<_Tp> operator - (const Point_<_Tp>& a, const Point_<_Tp>& b)
-{
-    return Point_<_Tp>( saturate_cast<_Tp>(a.x - b.x), saturate_cast<_Tp>(a.y - b.y) );
-}
-
-template<typename _Tp> static inline
-Point_<_Tp> operator - (const Point_<_Tp>& a)
-{
-    return Point_<_Tp>( saturate_cast<_Tp>(-a.x), saturate_cast<_Tp>(-a.y) );
-}
-
-template<typename _Tp> static inline
-Point_<_Tp> operator * (const Point_<_Tp>& a, int b)
-{
-    return Point_<_Tp>( saturate_cast<_Tp>(a.x*b), saturate_cast<_Tp>(a.y*b) );
-}
-
-template<typename _Tp> static inline
-Point_<_Tp> operator * (int a, const Point_<_Tp>& b)
-{
-    return Point_<_Tp>( saturate_cast<_Tp>(b.x*a), saturate_cast<_Tp>(b.y*a) );
-}
-
-template<typename _Tp> static inline
-Point_<_Tp> operator * (const Point_<_Tp>& a, float b)
-{
-    return Point_<_Tp>( saturate_cast<_Tp>(a.x*b), saturate_cast<_Tp>(a.y*b) );
-}
-
-template<typename _Tp> static inline
-Point_<_Tp> operator * (float a, const Point_<_Tp>& b)
-{
-    return Point_<_Tp>( saturate_cast<_Tp>(b.x*a), saturate_cast<_Tp>(b.y*a) );
-}
-
-template<typename _Tp> static inline
-Point_<_Tp> operator * (const Point_<_Tp>& a, double b)
-{
-    return Point_<_Tp>( saturate_cast<_Tp>(a.x*b), saturate_cast<_Tp>(a.y*b) );
-}
-
-template<typename _Tp> static inline
-Point_<_Tp> operator * (double a, const Point_<_Tp>& b)
-{
-    return Point_<_Tp>( saturate_cast<_Tp>(b.x*a), saturate_cast<_Tp>(b.y*a) );
-}
-
-template<typename _Tp> static inline
-Point_<_Tp> operator * (const Matx<_Tp, 2, 2>& a, const Point_<_Tp>& b)
-{
-    Matx<_Tp, 2, 1> tmp = a * Vec<_Tp,2>(b.x, b.y);
-    return Point_<_Tp>(tmp.val[0], tmp.val[1]);
-}
-
-template<typename _Tp> static inline
-Point3_<_Tp> operator * (const Matx<_Tp, 3, 3>& a, const Point_<_Tp>& b)
-{
-    Matx<_Tp, 3, 1> tmp = a * Vec<_Tp,3>(b.x, b.y, 1);
-    return Point3_<_Tp>(tmp.val[0], tmp.val[1], tmp.val[2]);
-}
-
-template<typename _Tp> static inline
-Point_<_Tp> operator / (const Point_<_Tp>& a, int b)
-{
-    Point_<_Tp> tmp(a);
-    tmp /= b;
-    return tmp;
-}
-
-template<typename _Tp> static inline
-Point_<_Tp> operator / (const Point_<_Tp>& a, float b)
-{
-    Point_<_Tp> tmp(a);
-    tmp /= b;
-    return tmp;
-}
-
-template<typename _Tp> static inline
-Point_<_Tp> operator / (const Point_<_Tp>& a, double b)
-{
-    Point_<_Tp> tmp(a);
-    tmp /= b;
-    return tmp;
-}
-
-
-template<typename _AccTp> static inline _AccTp normL2Sqr(const Point_<int>& pt);
-template<typename _AccTp> static inline _AccTp normL2Sqr(const Point_<int64>& pt);
-template<typename _AccTp> static inline _AccTp normL2Sqr(const Point_<float>& pt);
-template<typename _AccTp> static inline _AccTp normL2Sqr(const Point_<double>& pt);
-
-template<> inline int normL2Sqr<int>(const Point_<int>& pt) { return pt.dot(pt); }
-template<> inline int64 normL2Sqr<int64>(const Point_<int64>& pt) { return pt.dot(pt); }
-template<> inline float normL2Sqr<float>(const Point_<float>& pt) { return pt.dot(pt); }
-template<> inline double normL2Sqr<double>(const Point_<int>& pt) { return pt.dot(pt); }
-
-template<> inline double normL2Sqr<double>(const Point_<float>& pt) { return pt.ddot(pt); }
-template<> inline double normL2Sqr<double>(const Point_<double>& pt) { return pt.ddot(pt); }
-
-
-
-//////////////////////////////// 3D Point ///////////////////////////////
-
-template<typename _Tp> inline
-Point3_<_Tp>::Point3_()
-    : x(0), y(0), z(0) {}
-
-template<typename _Tp> inline
-Point3_<_Tp>::Point3_(_Tp _x, _Tp _y, _Tp _z)
-    : x(_x), y(_y), z(_z) {}
-
-template<typename _Tp> inline
-Point3_<_Tp>::Point3_(const Point3_& pt)
-    : x(pt.x), y(pt.y), z(pt.z) {}
-
-template<typename _Tp> inline
-Point3_<_Tp>::Point3_(const Point_<_Tp>& pt)
-    : x(pt.x), y(pt.y), z(_Tp()) {}
-
-template<typename _Tp> inline
-Point3_<_Tp>::Point3_(const Vec<_Tp, 3>& v)
-    : x(v[0]), y(v[1]), z(v[2]) {}
-
-template<typename _Tp> template<typename _Tp2> inline
-Point3_<_Tp>::operator Point3_<_Tp2>() const
-{
-    return Point3_<_Tp2>(saturate_cast<_Tp2>(x), saturate_cast<_Tp2>(y), saturate_cast<_Tp2>(z));
-}
-
-#if OPENCV_ABI_COMPATIBILITY > 300
-template<typename _Tp> template<typename _Tp2> inline
-Point3_<_Tp>::operator Vec<_Tp2, 3>() const
-{
-    return Vec<_Tp2, 3>(x, y, z);
-}
-#else
-template<typename _Tp> inline
-Point3_<_Tp>::operator Vec<_Tp, 3>() const
-{
-    return Vec<_Tp, 3>(x, y, z);
-}
-#endif
-
-template<typename _Tp> inline
-Point3_<_Tp>& Point3_<_Tp>::operator = (const Point3_& pt)
-{
-    x = pt.x; y = pt.y; z = pt.z;
-    return *this;
-}
-
-template<typename _Tp> inline
-_Tp Point3_<_Tp>::dot(const Point3_& pt) const
-{
-    return saturate_cast<_Tp>(x*pt.x + y*pt.y + z*pt.z);
-}
-
-template<typename _Tp> inline
-double Point3_<_Tp>::ddot(const Point3_& pt) const
-{
-    return (double)x*pt.x + (double)y*pt.y + (double)z*pt.z;
-}
-
-template<typename _Tp> inline
-Point3_<_Tp> Point3_<_Tp>::cross(const Point3_<_Tp>& pt) const
-{
-    return Point3_<_Tp>(y*pt.z - z*pt.y, z*pt.x - x*pt.z, x*pt.y - y*pt.x);
-}
-
-
-template<typename _Tp> static inline
-Point3_<_Tp>& operator += (Point3_<_Tp>& a, const Point3_<_Tp>& b)
-{
-    a.x += b.x;
-    a.y += b.y;
-    a.z += b.z;
-    return a;
-}
-
-template<typename _Tp> static inline
-Point3_<_Tp>& operator -= (Point3_<_Tp>& a, const Point3_<_Tp>& b)
-{
-    a.x -= b.x;
-    a.y -= b.y;
-    a.z -= b.z;
-    return a;
-}
-
-template<typename _Tp> static inline
-Point3_<_Tp>& operator *= (Point3_<_Tp>& a, int b)
-{
-    a.x = saturate_cast<_Tp>(a.x * b);
-    a.y = saturate_cast<_Tp>(a.y * b);
-    a.z = saturate_cast<_Tp>(a.z * b);
-    return a;
-}
-
-template<typename _Tp> static inline
-Point3_<_Tp>& operator *= (Point3_<_Tp>& a, float b)
-{
-    a.x = saturate_cast<_Tp>(a.x * b);
-    a.y = saturate_cast<_Tp>(a.y * b);
-    a.z = saturate_cast<_Tp>(a.z * b);
-    return a;
-}
-
-template<typename _Tp> static inline
-Point3_<_Tp>& operator *= (Point3_<_Tp>& a, double b)
-{
-    a.x = saturate_cast<_Tp>(a.x * b);
-    a.y = saturate_cast<_Tp>(a.y * b);
-    a.z = saturate_cast<_Tp>(a.z * b);
-    return a;
-}
-
-template<typename _Tp> static inline
-Point3_<_Tp>& operator /= (Point3_<_Tp>& a, int b)
-{
-    a.x = saturate_cast<_Tp>(a.x / b);
-    a.y = saturate_cast<_Tp>(a.y / b);
-    a.z = saturate_cast<_Tp>(a.z / b);
-    return a;
-}
-
-template<typename _Tp> static inline
-Point3_<_Tp>& operator /= (Point3_<_Tp>& a, float b)
-{
-    a.x = saturate_cast<_Tp>(a.x / b);
-    a.y = saturate_cast<_Tp>(a.y / b);
-    a.z = saturate_cast<_Tp>(a.z / b);
-    return a;
-}
-
-template<typename _Tp> static inline
-Point3_<_Tp>& operator /= (Point3_<_Tp>& a, double b)
-{
-    a.x = saturate_cast<_Tp>(a.x / b);
-    a.y = saturate_cast<_Tp>(a.y / b);
-    a.z = saturate_cast<_Tp>(a.z / b);
-    return a;
-}
-
-template<typename _Tp> static inline
-double norm(const Point3_<_Tp>& pt)
-{
-    return std::sqrt((double)pt.x*pt.x + (double)pt.y*pt.y + (double)pt.z*pt.z);
-}
-
-template<typename _Tp> static inline
-bool operator == (const Point3_<_Tp>& a, const Point3_<_Tp>& b)
-{
-    return a.x == b.x && a.y == b.y && a.z == b.z;
-}
-
-template<typename _Tp> static inline
-bool operator != (const Point3_<_Tp>& a, const Point3_<_Tp>& b)
-{
-    return a.x != b.x || a.y != b.y || a.z != b.z;
-}
-
-template<typename _Tp> static inline
-Point3_<_Tp> operator + (const Point3_<_Tp>& a, const Point3_<_Tp>& b)
-{
-    return Point3_<_Tp>( saturate_cast<_Tp>(a.x + b.x), saturate_cast<_Tp>(a.y + b.y), saturate_cast<_Tp>(a.z + b.z));
-}
-
-template<typename _Tp> static inline
-Point3_<_Tp> operator - (const Point3_<_Tp>& a, const Point3_<_Tp>& b)
-{
-    return Point3_<_Tp>( saturate_cast<_Tp>(a.x - b.x), saturate_cast<_Tp>(a.y - b.y), saturate_cast<_Tp>(a.z - b.z));
-}
-
-template<typename _Tp> static inline
-Point3_<_Tp> operator - (const Point3_<_Tp>& a)
-{
-    return Point3_<_Tp>( saturate_cast<_Tp>(-a.x), saturate_cast<_Tp>(-a.y), saturate_cast<_Tp>(-a.z) );
-}
-
-template<typename _Tp> static inline
-Point3_<_Tp> operator * (const Point3_<_Tp>& a, int b)
-{
-    return Point3_<_Tp>( saturate_cast<_Tp>(a.x*b), saturate_cast<_Tp>(a.y*b), saturate_cast<_Tp>(a.z*b) );
-}
-
-template<typename _Tp> static inline
-Point3_<_Tp> operator * (int a, const Point3_<_Tp>& b)
-{
-    return Point3_<_Tp>( saturate_cast<_Tp>(b.x * a), saturate_cast<_Tp>(b.y * a), saturate_cast<_Tp>(b.z * a) );
-}
-
-template<typename _Tp> static inline
-Point3_<_Tp> operator * (const Point3_<_Tp>& a, float b)
-{
-    return Point3_<_Tp>( saturate_cast<_Tp>(a.x * b), saturate_cast<_Tp>(a.y * b), saturate_cast<_Tp>(a.z * b) );
-}
-
-template<typename _Tp> static inline
-Point3_<_Tp> operator * (float a, const Point3_<_Tp>& b)
-{
-    return Point3_<_Tp>( saturate_cast<_Tp>(b.x * a), saturate_cast<_Tp>(b.y * a), saturate_cast<_Tp>(b.z * a) );
-}
-
-template<typename _Tp> static inline
-Point3_<_Tp> operator * (const Point3_<_Tp>& a, double b)
-{
-    return Point3_<_Tp>( saturate_cast<_Tp>(a.x * b), saturate_cast<_Tp>(a.y * b), saturate_cast<_Tp>(a.z * b) );
-}
-
-template<typename _Tp> static inline
-Point3_<_Tp> operator * (double a, const Point3_<_Tp>& b)
-{
-    return Point3_<_Tp>( saturate_cast<_Tp>(b.x * a), saturate_cast<_Tp>(b.y * a), saturate_cast<_Tp>(b.z * a) );
-}
-
-template<typename _Tp> static inline
-Point3_<_Tp> operator * (const Matx<_Tp, 3, 3>& a, const Point3_<_Tp>& b)
-{
-    Matx<_Tp, 3, 1> tmp = a * Vec<_Tp,3>(b.x, b.y, b.z);
-    return Point3_<_Tp>(tmp.val[0], tmp.val[1], tmp.val[2]);
-}
-
-template<typename _Tp> static inline
-Matx<_Tp, 4, 1> operator * (const Matx<_Tp, 4, 4>& a, const Point3_<_Tp>& b)
-{
-    return a * Matx<_Tp, 4, 1>(b.x, b.y, b.z, 1);
-}
-
-template<typename _Tp> static inline
-Point3_<_Tp> operator / (const Point3_<_Tp>& a, int b)
-{
-    Point3_<_Tp> tmp(a);
-    tmp /= b;
-    return tmp;
-}
-
-template<typename _Tp> static inline
-Point3_<_Tp> operator / (const Point3_<_Tp>& a, float b)
-{
-    Point3_<_Tp> tmp(a);
-    tmp /= b;
-    return tmp;
-}
-
-template<typename _Tp> static inline
-Point3_<_Tp> operator / (const Point3_<_Tp>& a, double b)
-{
-    Point3_<_Tp> tmp(a);
-    tmp /= b;
-    return tmp;
-}
-
-
-
-////////////////////////////////// Size /////////////////////////////////
-
-template<typename _Tp> inline
-Size_<_Tp>::Size_()
-    : width(0), height(0) {}
-
-template<typename _Tp> inline
-Size_<_Tp>::Size_(_Tp _width, _Tp _height)
-    : width(_width), height(_height) {}
-
-template<typename _Tp> inline
-Size_<_Tp>::Size_(const Size_& sz)
-    : width(sz.width), height(sz.height) {}
-
-template<typename _Tp> inline
-Size_<_Tp>::Size_(const Point_<_Tp>& pt)
-    : width(pt.x), height(pt.y) {}
-
-template<typename _Tp> template<typename _Tp2> inline
-Size_<_Tp>::operator Size_<_Tp2>() const
-{
-    return Size_<_Tp2>(saturate_cast<_Tp2>(width), saturate_cast<_Tp2>(height));
-}
-
-template<typename _Tp> inline
-Size_<_Tp>& Size_<_Tp>::operator = (const Size_<_Tp>& sz)
-{
-    width = sz.width; height = sz.height;
-    return *this;
-}
-
-template<typename _Tp> inline
-_Tp Size_<_Tp>::area() const
-{
-    const _Tp result = width * height;
-    CV_DbgAssert(!std::numeric_limits<_Tp>::is_integer
-        || width == 0 || result / width == height); // make sure the result fits in the return value
-    return result;
-}
-
-template<typename _Tp> inline
-bool Size_<_Tp>::empty() const
-{
-    return width <= 0 || height <= 0;
-}
-
-
-template<typename _Tp> static inline
-Size_<_Tp>& operator *= (Size_<_Tp>& a, _Tp b)
-{
-    a.width *= b;
-    a.height *= b;
-    return a;
-}
-
-template<typename _Tp> static inline
-Size_<_Tp> operator * (const Size_<_Tp>& a, _Tp b)
-{
-    Size_<_Tp> tmp(a);
-    tmp *= b;
-    return tmp;
-}
-
-template<typename _Tp> static inline
-Size_<_Tp>& operator /= (Size_<_Tp>& a, _Tp b)
-{
-    a.width /= b;
-    a.height /= b;
-    return a;
-}
-
-template<typename _Tp> static inline
-Size_<_Tp> operator / (const Size_<_Tp>& a, _Tp b)
-{
-    Size_<_Tp> tmp(a);
-    tmp /= b;
-    return tmp;
-}
-
-template<typename _Tp> static inline
-Size_<_Tp>& operator += (Size_<_Tp>& a, const Size_<_Tp>& b)
-{
-    a.width += b.width;
-    a.height += b.height;
-    return a;
-}
-
-template<typename _Tp> static inline
-Size_<_Tp> operator + (const Size_<_Tp>& a, const Size_<_Tp>& b)
-{
-    Size_<_Tp> tmp(a);
-    tmp += b;
-    return tmp;
-}
-
-template<typename _Tp> static inline
-Size_<_Tp>& operator -= (Size_<_Tp>& a, const Size_<_Tp>& b)
-{
-    a.width -= b.width;
-    a.height -= b.height;
-    return a;
-}
-
-template<typename _Tp> static inline
-Size_<_Tp> operator - (const Size_<_Tp>& a, const Size_<_Tp>& b)
-{
-    Size_<_Tp> tmp(a);
-    tmp -= b;
-    return tmp;
-}
-
-template<typename _Tp> static inline
-bool operator == (const Size_<_Tp>& a, const Size_<_Tp>& b)
-{
-    return a.width == b.width && a.height == b.height;
-}
-
-template<typename _Tp> static inline
-bool operator != (const Size_<_Tp>& a, const Size_<_Tp>& b)
-{
-    return !(a == b);
-}
-
-
-
-////////////////////////////////// Rect /////////////////////////////////
-
-template<typename _Tp> inline
-Rect_<_Tp>::Rect_()
-    : x(0), y(0), width(0), height(0) {}
-
-template<typename _Tp> inline
-Rect_<_Tp>::Rect_(_Tp _x, _Tp _y, _Tp _width, _Tp _height)
-    : x(_x), y(_y), width(_width), height(_height) {}
-
-template<typename _Tp> inline
-Rect_<_Tp>::Rect_(const Rect_<_Tp>& r)
-    : x(r.x), y(r.y), width(r.width), height(r.height) {}
-
-template<typename _Tp> inline
-Rect_<_Tp>::Rect_(const Point_<_Tp>& org, const Size_<_Tp>& sz)
-    : x(org.x), y(org.y), width(sz.width), height(sz.height) {}
-
-template<typename _Tp> inline
-Rect_<_Tp>::Rect_(const Point_<_Tp>& pt1, const Point_<_Tp>& pt2)
-{
-    x = std::min(pt1.x, pt2.x);
-    y = std::min(pt1.y, pt2.y);
-    width = std::max(pt1.x, pt2.x) - x;
-    height = std::max(pt1.y, pt2.y) - y;
-}
-
-template<typename _Tp> inline
-Rect_<_Tp>& Rect_<_Tp>::operator = ( const Rect_<_Tp>& r )
-{
-    x = r.x;
-    y = r.y;
-    width = r.width;
-    height = r.height;
-    return *this;
-}
-
-template<typename _Tp> inline
-Point_<_Tp> Rect_<_Tp>::tl() const
-{
-    return Point_<_Tp>(x,y);
-}
-
-template<typename _Tp> inline
-Point_<_Tp> Rect_<_Tp>::br() const
-{
-    return Point_<_Tp>(x + width, y + height);
-}
-
-template<typename _Tp> inline
-Size_<_Tp> Rect_<_Tp>::size() const
-{
-    return Size_<_Tp>(width, height);
-}
-
-template<typename _Tp> inline
-_Tp Rect_<_Tp>::area() const
-{
-    const _Tp result = width * height;
-    CV_DbgAssert(!std::numeric_limits<_Tp>::is_integer
-        || width == 0 || result / width == height); // make sure the result fits in the return value
-    return result;
-}
-
-template<typename _Tp> inline
-bool Rect_<_Tp>::empty() const
-{
-    return width <= 0 || height <= 0;
-}
-
-template<typename _Tp> template<typename _Tp2> inline
-Rect_<_Tp>::operator Rect_<_Tp2>() const
-{
-    return Rect_<_Tp2>(saturate_cast<_Tp2>(x), saturate_cast<_Tp2>(y), saturate_cast<_Tp2>(width), saturate_cast<_Tp2>(height));
-}
-
-template<typename _Tp> inline
-bool Rect_<_Tp>::contains(const Point_<_Tp>& pt) const
-{
-    return x <= pt.x && pt.x < x + width && y <= pt.y && pt.y < y + height;
-}
-
-
-template<typename _Tp> static inline
-Rect_<_Tp>& operator += ( Rect_<_Tp>& a, const Point_<_Tp>& b )
-{
-    a.x += b.x;
-    a.y += b.y;
-    return a;
-}
-
-template<typename _Tp> static inline
-Rect_<_Tp>& operator -= ( Rect_<_Tp>& a, const Point_<_Tp>& b )
-{
-    a.x -= b.x;
-    a.y -= b.y;
-    return a;
-}
-
-template<typename _Tp> static inline
-Rect_<_Tp>& operator += ( Rect_<_Tp>& a, const Size_<_Tp>& b )
-{
-    a.width += b.width;
-    a.height += b.height;
-    return a;
-}
-
-template<typename _Tp> static inline
-Rect_<_Tp>& operator -= ( Rect_<_Tp>& a, const Size_<_Tp>& b )
-{
-    a.width -= b.width;
-    a.height -= b.height;
-    return a;
-}
-
-template<typename _Tp> static inline
-Rect_<_Tp>& operator &= ( Rect_<_Tp>& a, const Rect_<_Tp>& b )
-{
-    _Tp x1 = std::max(a.x, b.x);
-    _Tp y1 = std::max(a.y, b.y);
-    a.width = std::min(a.x + a.width, b.x + b.width) - x1;
-    a.height = std::min(a.y + a.height, b.y + b.height) - y1;
-    a.x = x1;
-    a.y = y1;
-    if( a.width <= 0 || a.height <= 0 )
-        a = Rect();
-    return a;
-}
-
-template<typename _Tp> static inline
-Rect_<_Tp>& operator |= ( Rect_<_Tp>& a, const Rect_<_Tp>& b )
-{
-    if (a.empty()) {
-        a = b;
-    }
-    else if (!b.empty()) {
-        _Tp x1 = std::min(a.x, b.x);
-        _Tp y1 = std::min(a.y, b.y);
-        a.width = std::max(a.x + a.width, b.x + b.width) - x1;
-        a.height = std::max(a.y + a.height, b.y + b.height) - y1;
-        a.x = x1;
-        a.y = y1;
-    }
-    return a;
-}
-
-template<typename _Tp> static inline
-bool operator == (const Rect_<_Tp>& a, const Rect_<_Tp>& b)
-{
-    return a.x == b.x && a.y == b.y && a.width == b.width && a.height == b.height;
-}
-
-template<typename _Tp> static inline
-bool operator != (const Rect_<_Tp>& a, const Rect_<_Tp>& b)
-{
-    return a.x != b.x || a.y != b.y || a.width != b.width || a.height != b.height;
-}
-
-template<typename _Tp> static inline
-Rect_<_Tp> operator + (const Rect_<_Tp>& a, const Point_<_Tp>& b)
-{
-    return Rect_<_Tp>( a.x + b.x, a.y + b.y, a.width, a.height );
-}
-
-template<typename _Tp> static inline
-Rect_<_Tp> operator - (const Rect_<_Tp>& a, const Point_<_Tp>& b)
-{
-    return Rect_<_Tp>( a.x - b.x, a.y - b.y, a.width, a.height );
-}
-
-template<typename _Tp> static inline
-Rect_<_Tp> operator + (const Rect_<_Tp>& a, const Size_<_Tp>& b)
-{
-    return Rect_<_Tp>( a.x, a.y, a.width + b.width, a.height + b.height );
-}
-
-template<typename _Tp> static inline
-Rect_<_Tp> operator & (const Rect_<_Tp>& a, const Rect_<_Tp>& b)
-{
-    Rect_<_Tp> c = a;
-    return c &= b;
-}
-
-template<typename _Tp> static inline
-Rect_<_Tp> operator | (const Rect_<_Tp>& a, const Rect_<_Tp>& b)
-{
-    Rect_<_Tp> c = a;
-    return c |= b;
-}
-
-/**
- * @brief measure dissimilarity between two sample sets
- *
- * computes the complement of the Jaccard Index as described in <https://en.wikipedia.org/wiki/Jaccard_index>.
- * For rectangles this reduces to computing the intersection over the union.
- */
-template<typename _Tp> static inline
-double jaccardDistance(const Rect_<_Tp>& a, const Rect_<_Tp>& b) {
-    _Tp Aa = a.area();
-    _Tp Ab = b.area();
-
-    if ((Aa + Ab) <= std::numeric_limits<_Tp>::epsilon()) {
-        // jaccard_index = 1 -> distance = 0
-        return 0.0;
-    }
-
-    double Aab = (a & b).area();
-    // distance = 1 - jaccard_index
-    return 1.0 - Aab / (Aa + Ab - Aab);
-}
-
-////////////////////////////// RotatedRect //////////////////////////////
-
-inline
-RotatedRect::RotatedRect()
-    : center(), size(), angle(0) {}
-
-inline
-RotatedRect::RotatedRect(const Point2f& _center, const Size2f& _size, float _angle)
-    : center(_center), size(_size), angle(_angle) {}
-
-
-
-///////////////////////////////// Range /////////////////////////////////
-
-inline
-Range::Range()
-    : start(0), end(0) {}
-
-inline
-Range::Range(int _start, int _end)
-    : start(_start), end(_end) {}
-
-inline
-int Range::size() const
-{
-    return end - start;
-}
-
-inline
-bool Range::empty() const
-{
-    return start == end;
-}
-
-inline
-Range Range::all()
-{
-    return Range(INT_MIN, INT_MAX);
-}
-
-
-static inline
-bool operator == (const Range& r1, const Range& r2)
-{
-    return r1.start == r2.start && r1.end == r2.end;
-}
-
-static inline
-bool operator != (const Range& r1, const Range& r2)
-{
-    return !(r1 == r2);
-}
-
-static inline
-bool operator !(const Range& r)
-{
-    return r.start == r.end;
-}
-
-static inline
-Range operator & (const Range& r1, const Range& r2)
-{
-    Range r(std::max(r1.start, r2.start), std::min(r1.end, r2.end));
-    r.end = std::max(r.end, r.start);
-    return r;
-}
-
-static inline
-Range& operator &= (Range& r1, const Range& r2)
-{
-    r1 = r1 & r2;
-    return r1;
-}
-
-static inline
-Range operator + (const Range& r1, int delta)
-{
-    return Range(r1.start + delta, r1.end + delta);
-}
-
-static inline
-Range operator + (int delta, const Range& r1)
-{
-    return Range(r1.start + delta, r1.end + delta);
-}
-
-static inline
-Range operator - (const Range& r1, int delta)
-{
-    return r1 + (-delta);
-}
-
-
-
-///////////////////////////////// Scalar ////////////////////////////////
-
-template<typename _Tp> inline
-Scalar_<_Tp>::Scalar_()
-{
-    this->val[0] = this->val[1] = this->val[2] = this->val[3] = 0;
-}
-
-template<typename _Tp> inline
-Scalar_<_Tp>::Scalar_(_Tp v0, _Tp v1, _Tp v2, _Tp v3)
-{
-    this->val[0] = v0;
-    this->val[1] = v1;
-    this->val[2] = v2;
-    this->val[3] = v3;
-}
-
-template<typename _Tp> template<typename _Tp2, int cn> inline
-Scalar_<_Tp>::Scalar_(const Vec<_Tp2, cn>& v)
-{
-    int i;
-    for( i = 0; i < (cn < 4 ? cn : 4); i++ )
-        this->val[i] = cv::saturate_cast<_Tp>(v.val[i]);
-    for( ; i < 4; i++ )
-        this->val[i] = 0;
-}
-
-template<typename _Tp> inline
-Scalar_<_Tp>::Scalar_(_Tp v0)
-{
-    this->val[0] = v0;
-    this->val[1] = this->val[2] = this->val[3] = 0;
-}
-
-template<typename _Tp> inline
-Scalar_<_Tp> Scalar_<_Tp>::all(_Tp v0)
-{
-    return Scalar_<_Tp>(v0, v0, v0, v0);
-}
-
-
-template<typename _Tp> inline
-Scalar_<_Tp> Scalar_<_Tp>::mul(const Scalar_<_Tp>& a, double scale ) const
-{
-    return Scalar_<_Tp>(saturate_cast<_Tp>(this->val[0] * a.val[0] * scale),
-                        saturate_cast<_Tp>(this->val[1] * a.val[1] * scale),
-                        saturate_cast<_Tp>(this->val[2] * a.val[2] * scale),
-                        saturate_cast<_Tp>(this->val[3] * a.val[3] * scale));
-}
-
-template<typename _Tp> inline
-Scalar_<_Tp> Scalar_<_Tp>::conj() const
-{
-    return Scalar_<_Tp>(saturate_cast<_Tp>( this->val[0]),
-                        saturate_cast<_Tp>(-this->val[1]),
-                        saturate_cast<_Tp>(-this->val[2]),
-                        saturate_cast<_Tp>(-this->val[3]));
-}
-
-template<typename _Tp> inline
-bool Scalar_<_Tp>::isReal() const
-{
-    return this->val[1] == 0 && this->val[2] == 0 && this->val[3] == 0;
-}
-
-
-template<typename _Tp> template<typename T2> inline
-Scalar_<_Tp>::operator Scalar_<T2>() const
-{
-    return Scalar_<T2>(saturate_cast<T2>(this->val[0]),
-                       saturate_cast<T2>(this->val[1]),
-                       saturate_cast<T2>(this->val[2]),
-                       saturate_cast<T2>(this->val[3]));
-}
-
-
-template<typename _Tp> static inline
-Scalar_<_Tp>& operator += (Scalar_<_Tp>& a, const Scalar_<_Tp>& b)
-{
-    a.val[0] += b.val[0];
-    a.val[1] += b.val[1];
-    a.val[2] += b.val[2];
-    a.val[3] += b.val[3];
-    return a;
-}
-
-template<typename _Tp> static inline
-Scalar_<_Tp>& operator -= (Scalar_<_Tp>& a, const Scalar_<_Tp>& b)
-{
-    a.val[0] -= b.val[0];
-    a.val[1] -= b.val[1];
-    a.val[2] -= b.val[2];
-    a.val[3] -= b.val[3];
-    return a;
-}
-
-template<typename _Tp> static inline
-Scalar_<_Tp>& operator *= ( Scalar_<_Tp>& a, _Tp v )
-{
-    a.val[0] *= v;
-    a.val[1] *= v;
-    a.val[2] *= v;
-    a.val[3] *= v;
-    return a;
-}
-
-template<typename _Tp> static inline
-bool operator == ( const Scalar_<_Tp>& a, const Scalar_<_Tp>& b )
-{
-    return a.val[0] == b.val[0] && a.val[1] == b.val[1] &&
-           a.val[2] == b.val[2] && a.val[3] == b.val[3];
-}
-
-template<typename _Tp> static inline
-bool operator != ( const Scalar_<_Tp>& a, const Scalar_<_Tp>& b )
-{
-    return a.val[0] != b.val[0] || a.val[1] != b.val[1] ||
-           a.val[2] != b.val[2] || a.val[3] != b.val[3];
-}
-
-template<typename _Tp> static inline
-Scalar_<_Tp> operator + (const Scalar_<_Tp>& a, const Scalar_<_Tp>& b)
-{
-    return Scalar_<_Tp>(a.val[0] + b.val[0],
-                        a.val[1] + b.val[1],
-                        a.val[2] + b.val[2],
-                        a.val[3] + b.val[3]);
-}
-
-template<typename _Tp> static inline
-Scalar_<_Tp> operator - (const Scalar_<_Tp>& a, const Scalar_<_Tp>& b)
-{
-    return Scalar_<_Tp>(saturate_cast<_Tp>(a.val[0] - b.val[0]),
-                        saturate_cast<_Tp>(a.val[1] - b.val[1]),
-                        saturate_cast<_Tp>(a.val[2] - b.val[2]),
-                        saturate_cast<_Tp>(a.val[3] - b.val[3]));
-}
-
-template<typename _Tp> static inline
-Scalar_<_Tp> operator * (const Scalar_<_Tp>& a, _Tp alpha)
-{
-    return Scalar_<_Tp>(a.val[0] * alpha,
-                        a.val[1] * alpha,
-                        a.val[2] * alpha,
-                        a.val[3] * alpha);
-}
-
-template<typename _Tp> static inline
-Scalar_<_Tp> operator * (_Tp alpha, const Scalar_<_Tp>& a)
-{
-    return a*alpha;
-}
-
-template<typename _Tp> static inline
-Scalar_<_Tp> operator - (const Scalar_<_Tp>& a)
-{
-    return Scalar_<_Tp>(saturate_cast<_Tp>(-a.val[0]),
-                        saturate_cast<_Tp>(-a.val[1]),
-                        saturate_cast<_Tp>(-a.val[2]),
-                        saturate_cast<_Tp>(-a.val[3]));
-}
-
-
-template<typename _Tp> static inline
-Scalar_<_Tp> operator * (const Scalar_<_Tp>& a, const Scalar_<_Tp>& b)
-{
-    return Scalar_<_Tp>(saturate_cast<_Tp>(a[0]*b[0] - a[1]*b[1] - a[2]*b[2] - a[3]*b[3]),
-                        saturate_cast<_Tp>(a[0]*b[1] + a[1]*b[0] + a[2]*b[3] - a[3]*b[2]),
-                        saturate_cast<_Tp>(a[0]*b[2] - a[1]*b[3] + a[2]*b[0] + a[3]*b[1]),
-                        saturate_cast<_Tp>(a[0]*b[3] + a[1]*b[2] - a[2]*b[1] + a[3]*b[0]));
-}
-
-template<typename _Tp> static inline
-Scalar_<_Tp>& operator *= (Scalar_<_Tp>& a, const Scalar_<_Tp>& b)
-{
-    a = a * b;
-    return a;
-}
-
-template<typename _Tp> static inline
-Scalar_<_Tp> operator / (const Scalar_<_Tp>& a, _Tp alpha)
-{
-    return Scalar_<_Tp>(a.val[0] / alpha,
-                        a.val[1] / alpha,
-                        a.val[2] / alpha,
-                        a.val[3] / alpha);
-}
-
-template<typename _Tp> static inline
-Scalar_<float> operator / (const Scalar_<float>& a, float alpha)
-{
-    float s = 1 / alpha;
-    return Scalar_<float>(a.val[0] * s, a.val[1] * s, a.val[2] * s, a.val[3] * s);
-}
-
-template<typename _Tp> static inline
-Scalar_<double> operator / (const Scalar_<double>& a, double alpha)
-{
-    double s = 1 / alpha;
-    return Scalar_<double>(a.val[0] * s, a.val[1] * s, a.val[2] * s, a.val[3] * s);
-}
-
-template<typename _Tp> static inline
-Scalar_<_Tp>& operator /= (Scalar_<_Tp>& a, _Tp alpha)
-{
-    a = a / alpha;
-    return a;
-}
-
-template<typename _Tp> static inline
-Scalar_<_Tp> operator / (_Tp a, const Scalar_<_Tp>& b)
-{
-    _Tp s = a / (b[0]*b[0] + b[1]*b[1] + b[2]*b[2] + b[3]*b[3]);
-    return b.conj() * s;
-}
-
-template<typename _Tp> static inline
-Scalar_<_Tp> operator / (const Scalar_<_Tp>& a, const Scalar_<_Tp>& b)
-{
-    return a * ((_Tp)1 / b);
-}
-
-template<typename _Tp> static inline
-Scalar_<_Tp>& operator /= (Scalar_<_Tp>& a, const Scalar_<_Tp>& b)
-{
-    a = a / b;
-    return a;
-}
-
-template<typename _Tp> static inline
-Scalar operator * (const Matx<_Tp, 4, 4>& a, const Scalar& b)
-{
-    Matx<double, 4, 1> c((Matx<double, 4, 4>)a, b, Matx_MatMulOp());
-    return reinterpret_cast<const Scalar&>(c);
-}
-
-template<> inline
-Scalar operator * (const Matx<double, 4, 4>& a, const Scalar& b)
-{
-    Matx<double, 4, 1> c(a, b, Matx_MatMulOp());
-    return reinterpret_cast<const Scalar&>(c);
-}
-
-
-
-//////////////////////////////// KeyPoint ///////////////////////////////
-
-inline
-KeyPoint::KeyPoint()
-    : pt(0,0), size(0), angle(-1), response(0), octave(0), class_id(-1) {}
-
-inline
-KeyPoint::KeyPoint(Point2f _pt, float _size, float _angle, float _response, int _octave, int _class_id)
-    : pt(_pt), size(_size), angle(_angle), response(_response), octave(_octave), class_id(_class_id) {}
-
-inline
-KeyPoint::KeyPoint(float x, float y, float _size, float _angle, float _response, int _octave, int _class_id)
-    : pt(x, y), size(_size), angle(_angle), response(_response), octave(_octave), class_id(_class_id) {}
-
-
-
-///////////////////////////////// DMatch ////////////////////////////////
-
-inline
-DMatch::DMatch()
-    : queryIdx(-1), trainIdx(-1), imgIdx(-1), distance(FLT_MAX) {}
-
-inline
-DMatch::DMatch(int _queryIdx, int _trainIdx, float _distance)
-    : queryIdx(_queryIdx), trainIdx(_trainIdx), imgIdx(-1), distance(_distance) {}
-
-inline
-DMatch::DMatch(int _queryIdx, int _trainIdx, int _imgIdx, float _distance)
-    : queryIdx(_queryIdx), trainIdx(_trainIdx), imgIdx(_imgIdx), distance(_distance) {}
-
-inline
-bool DMatch::operator < (const DMatch &m) const
-{
-    return distance < m.distance;
-}
-
-
-
-////////////////////////////// TermCriteria /////////////////////////////
-
-inline
-TermCriteria::TermCriteria()
-    : type(0), maxCount(0), epsilon(0) {}
-
-inline
-TermCriteria::TermCriteria(int _type, int _maxCount, double _epsilon)
-    : type(_type), maxCount(_maxCount), epsilon(_epsilon) {}
-
-//! @endcond
-
-} // cv
-
-#endif //OPENCV_CORE_TYPES_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/types_c.h b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/types_c.h
deleted file mode 100755
index 65f354e..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/types_c.h
+++ /dev/null
@@ -1,1837 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_CORE_TYPES_H
-#define OPENCV_CORE_TYPES_H
-
-#ifdef HAVE_IPL
-#  ifndef __IPL_H__
-#    if defined _WIN32
-#      include <ipl.h>
-#    else
-#      include <ipl/ipl.h>
-#    endif
-#  endif
-#elif defined __IPL_H__
-#  define HAVE_IPL
-#endif
-
-#include "opencv2/core/cvdef.h"
-
-#ifndef SKIP_INCLUDES
-#include <assert.h>
-#include <stdlib.h>
-#include <string.h>
-#include <float.h>
-#endif // SKIP_INCLUDES
-
-#if defined _WIN32
-#  define CV_CDECL __cdecl
-#  define CV_STDCALL __stdcall
-#else
-#  define CV_CDECL
-#  define CV_STDCALL
-#endif
-
-#ifndef CV_DEFAULT
-#  ifdef __cplusplus
-#    define CV_DEFAULT(val) = val
-#  else
-#    define CV_DEFAULT(val)
-#  endif
-#endif
-
-#ifndef CV_EXTERN_C_FUNCPTR
-#  ifdef __cplusplus
-#    define CV_EXTERN_C_FUNCPTR(x) extern "C" { typedef x; }
-#  else
-#    define CV_EXTERN_C_FUNCPTR(x) typedef x
-#  endif
-#endif
-
-#ifndef CVAPI
-#  define CVAPI(rettype) CV_EXTERN_C CV_EXPORTS rettype CV_CDECL
-#endif
-
-#ifndef CV_IMPL
-#  define CV_IMPL CV_EXTERN_C
-#endif
-
-#ifdef __cplusplus
-#  include "opencv2/core.hpp"
-#endif
-
-/** @addtogroup core_c
-    @{
-*/
-
-/** @brief This is the "metatype" used *only* as a function parameter.
-
-It denotes that the function accepts arrays of multiple types, such as IplImage*, CvMat* or even
-CvSeq* sometimes. The particular array type is determined at runtime by analyzing the first 4
-bytes of the header. In C++ interface the role of CvArr is played by InputArray and OutputArray.
- */
-typedef void CvArr;
-
-typedef int CVStatus;
-
-/** @see cv::Error::Code */
-enum {
- CV_StsOk=                       0,  /**< everything is ok                */
- CV_StsBackTrace=               -1,  /**< pseudo error for back trace     */
- CV_StsError=                   -2,  /**< unknown /unspecified error      */
- CV_StsInternal=                -3,  /**< internal error (bad state)      */
- CV_StsNoMem=                   -4,  /**< insufficient memory             */
- CV_StsBadArg=                  -5,  /**< function arg/param is bad       */
- CV_StsBadFunc=                 -6,  /**< unsupported function            */
- CV_StsNoConv=                  -7,  /**< iter. didn't converge           */
- CV_StsAutoTrace=               -8,  /**< tracing                         */
- CV_HeaderIsNull=               -9,  /**< image header is NULL            */
- CV_BadImageSize=              -10,  /**< image size is invalid           */
- CV_BadOffset=                 -11,  /**< offset is invalid               */
- CV_BadDataPtr=                -12,  /**/
- CV_BadStep=                   -13,  /**< image step is wrong, this may happen for a non-continuous matrix */
- CV_BadModelOrChSeq=           -14,  /**/
- CV_BadNumChannels=            -15,  /**< bad number of channels, for example, some functions accept only single channel matrices */
- CV_BadNumChannel1U=           -16,  /**/
- CV_BadDepth=                  -17,  /**< input image depth is not supported by the function */
- CV_BadAlphaChannel=           -18,  /**/
- CV_BadOrder=                  -19,  /**< number of dimensions is out of range */
- CV_BadOrigin=                 -20,  /**< incorrect input origin               */
- CV_BadAlign=                  -21,  /**< incorrect input align                */
- CV_BadCallBack=               -22,  /**/
- CV_BadTileSize=               -23,  /**/
- CV_BadCOI=                    -24,  /**< input COI is not supported           */
- CV_BadROISize=                -25,  /**< incorrect input roi                  */
- CV_MaskIsTiled=               -26,  /**/
- CV_StsNullPtr=                -27,  /**< null pointer */
- CV_StsVecLengthErr=           -28,  /**< incorrect vector length */
- CV_StsFilterStructContentErr= -29,  /**< incorrect filter structure content */
- CV_StsKernelStructContentErr= -30,  /**< incorrect transform kernel content */
- CV_StsFilterOffsetErr=        -31,  /**< incorrect filter offset value */
- CV_StsBadSize=                -201, /**< the input/output structure size is incorrect  */
- CV_StsDivByZero=              -202, /**< division by zero */
- CV_StsInplaceNotSupported=    -203, /**< in-place operation is not supported */
- CV_StsObjectNotFound=         -204, /**< request can't be completed */
- CV_StsUnmatchedFormats=       -205, /**< formats of input/output arrays differ */
- CV_StsBadFlag=                -206, /**< flag is wrong or not supported */
- CV_StsBadPoint=               -207, /**< bad CvPoint */
- CV_StsBadMask=                -208, /**< bad format of mask (neither 8uC1 nor 8sC1)*/
- CV_StsUnmatchedSizes=         -209, /**< sizes of input/output structures do not match */
- CV_StsUnsupportedFormat=      -210, /**< the data format/type is not supported by the function*/
- CV_StsOutOfRange=             -211, /**< some of parameters are out of range */
- CV_StsParseError=             -212, /**< invalid syntax/structure of the parsed file */
- CV_StsNotImplemented=         -213, /**< the requested function/feature is not implemented */
- CV_StsBadMemBlock=            -214, /**< an allocated block has been corrupted */
- CV_StsAssert=                 -215, /**< assertion failed   */
- CV_GpuNotSupported=           -216, /**< no CUDA support    */
- CV_GpuApiCallError=           -217, /**< GPU API call error */
- CV_OpenGlNotSupported=        -218, /**< no OpenGL support  */
- CV_OpenGlApiCallError=        -219, /**< OpenGL API call error */
- CV_OpenCLApiCallError=        -220, /**< OpenCL API call error */
- CV_OpenCLDoubleNotSupported=  -221,
- CV_OpenCLInitError=           -222, /**< OpenCL initialization error */
- CV_OpenCLNoAMDBlasFft=        -223
-};
-
-/****************************************************************************************\
-*                             Common macros and inline functions                         *
-\****************************************************************************************/
-
-#define CV_SWAP(a,b,t) ((t) = (a), (a) = (b), (b) = (t))
-
-/** min & max without jumps */
-#define  CV_IMIN(a, b)  ((a) ^ (((a)^(b)) & (((a) < (b)) - 1)))
-
-#define  CV_IMAX(a, b)  ((a) ^ (((a)^(b)) & (((a) > (b)) - 1)))
-
-/** absolute value without jumps */
-#ifndef __cplusplus
-#  define  CV_IABS(a)     (((a) ^ ((a) < 0 ? -1 : 0)) - ((a) < 0 ? -1 : 0))
-#else
-#  define  CV_IABS(a)     abs(a)
-#endif
-#define  CV_CMP(a,b)    (((a) > (b)) - ((a) < (b)))
-#define  CV_SIGN(a)     CV_CMP((a),0)
-
-#define cvInvSqrt(value) ((float)(1./sqrt(value)))
-#define cvSqrt(value)  ((float)sqrt(value))
-
-
-/*************** Random number generation *******************/
-
-typedef uint64 CvRNG;
-
-#define CV_RNG_COEFF 4164903690U
-
-/** @brief Initializes a random number generator state.
-
-The function initializes a random number generator and returns the state. The pointer to the state
-can be then passed to the cvRandInt, cvRandReal and cvRandArr functions. In the current
-implementation a multiply-with-carry generator is used.
-@param seed 64-bit value used to initiate a random sequence
-@sa the C++ class RNG replaced CvRNG.
- */
-CV_INLINE CvRNG cvRNG( int64 seed CV_DEFAULT(-1))
-{
-    CvRNG rng = seed ? (uint64)seed : (uint64)(int64)-1;
-    return rng;
-}
-
-/** @brief Returns a 32-bit unsigned integer and updates RNG.
-
-The function returns a uniformly-distributed random 32-bit unsigned integer and updates the RNG
-state. It is similar to the rand() function from the C runtime library, except that OpenCV functions
-always generates a 32-bit random number, regardless of the platform.
-@param rng CvRNG state initialized by cvRNG.
- */
-CV_INLINE unsigned cvRandInt( CvRNG* rng )
-{
-    uint64 temp = *rng;
-    temp = (uint64)(unsigned)temp*CV_RNG_COEFF + (temp >> 32);
-    *rng = temp;
-    return (unsigned)temp;
-}
-
-/** @brief Returns a floating-point random number and updates RNG.
-
-The function returns a uniformly-distributed random floating-point number between 0 and 1 (1 is not
-included).
-@param rng RNG state initialized by cvRNG
- */
-CV_INLINE double cvRandReal( CvRNG* rng )
-{
-    return cvRandInt(rng)*2.3283064365386962890625e-10 /* 2^-32 */;
-}
-
-/****************************************************************************************\
-*                                  Image type (IplImage)                                 *
-\****************************************************************************************/
-
-#ifndef HAVE_IPL
-
-/*
- * The following definitions (until #endif)
- * is an extract from IPL headers.
- * Copyright (c) 1995 Intel Corporation.
- */
-#define IPL_DEPTH_SIGN 0x80000000
-
-#define IPL_DEPTH_1U     1
-#define IPL_DEPTH_8U     8
-#define IPL_DEPTH_16U   16
-#define IPL_DEPTH_32F   32
-
-#define IPL_DEPTH_8S  (IPL_DEPTH_SIGN| 8)
-#define IPL_DEPTH_16S (IPL_DEPTH_SIGN|16)
-#define IPL_DEPTH_32S (IPL_DEPTH_SIGN|32)
-
-#define IPL_DATA_ORDER_PIXEL  0
-#define IPL_DATA_ORDER_PLANE  1
-
-#define IPL_ORIGIN_TL 0
-#define IPL_ORIGIN_BL 1
-
-#define IPL_ALIGN_4BYTES   4
-#define IPL_ALIGN_8BYTES   8
-#define IPL_ALIGN_16BYTES 16
-#define IPL_ALIGN_32BYTES 32
-
-#define IPL_ALIGN_DWORD   IPL_ALIGN_4BYTES
-#define IPL_ALIGN_QWORD   IPL_ALIGN_8BYTES
-
-#define IPL_BORDER_CONSTANT   0
-#define IPL_BORDER_REPLICATE  1
-#define IPL_BORDER_REFLECT    2
-#define IPL_BORDER_WRAP       3
-
-/** The IplImage is taken from the Intel Image Processing Library, in which the format is native. OpenCV
-only supports a subset of possible IplImage formats, as outlined in the parameter list above.
-
-In addition to the above restrictions, OpenCV handles ROIs differently. OpenCV functions require
-that the image size or ROI size of all source and destination images match exactly. On the other
-hand, the Intel Image Processing Library processes the area of intersection between the source and
-destination images (or ROIs), allowing them to vary independently.
-*/
-typedef struct
-#ifdef __cplusplus
-  CV_EXPORTS
-#endif
-_IplImage
-{
-    int  nSize;             /**< sizeof(IplImage) */
-    int  ID;                /**< version (=0)*/
-    int  nChannels;         /**< Most of OpenCV functions support 1,2,3 or 4 channels */
-    int  alphaChannel;      /**< Ignored by OpenCV */
-    int  depth;             /**< Pixel depth in bits: IPL_DEPTH_8U, IPL_DEPTH_8S, IPL_DEPTH_16S,
-                               IPL_DEPTH_32S, IPL_DEPTH_32F and IPL_DEPTH_64F are supported.  */
-    char colorModel[4];     /**< Ignored by OpenCV */
-    char channelSeq[4];     /**< ditto */
-    int  dataOrder;         /**< 0 - interleaved color channels, 1 - separate color channels.
-                               cvCreateImage can only create interleaved images */
-    int  origin;            /**< 0 - top-left origin,
-                               1 - bottom-left origin (Windows bitmaps style).  */
-    int  align;             /**< Alignment of image rows (4 or 8).
-                               OpenCV ignores it and uses widthStep instead.    */
-    int  width;             /**< Image width in pixels.                           */
-    int  height;            /**< Image height in pixels.                          */
-    struct _IplROI *roi;    /**< Image ROI. If NULL, the whole image is selected. */
-    struct _IplImage *maskROI;      /**< Must be NULL. */
-    void  *imageId;                 /**< "           " */
-    struct _IplTileInfo *tileInfo;  /**< "           " */
-    int  imageSize;         /**< Image data size in bytes
-                               (==image->height*image->widthStep
-                               in case of interleaved data)*/
-    char *imageData;        /**< Pointer to aligned image data.         */
-    int  widthStep;         /**< Size of aligned image row in bytes.    */
-    int  BorderMode[4];     /**< Ignored by OpenCV.                     */
-    int  BorderConst[4];    /**< Ditto.                                 */
-    char *imageDataOrigin;  /**< Pointer to very origin of image data
-                               (not necessarily aligned) -
-                               needed for correct deallocation */
-
-#ifdef __cplusplus
-    _IplImage() {}
-    _IplImage(const cv::Mat& m);
-#endif
-}
-IplImage;
-
-typedef struct _IplTileInfo IplTileInfo;
-
-typedef struct _IplROI
-{
-    int  coi; /**< 0 - no COI (all channels are selected), 1 - 0th channel is selected ...*/
-    int  xOffset;
-    int  yOffset;
-    int  width;
-    int  height;
-}
-IplROI;
-
-typedef struct _IplConvKernel
-{
-    int  nCols;
-    int  nRows;
-    int  anchorX;
-    int  anchorY;
-    int *values;
-    int  nShiftR;
-}
-IplConvKernel;
-
-typedef struct _IplConvKernelFP
-{
-    int  nCols;
-    int  nRows;
-    int  anchorX;
-    int  anchorY;
-    float *values;
-}
-IplConvKernelFP;
-
-#define IPL_IMAGE_HEADER 1
-#define IPL_IMAGE_DATA   2
-#define IPL_IMAGE_ROI    4
-
-#endif/*HAVE_IPL*/
-
-/** extra border mode */
-#define IPL_BORDER_REFLECT_101    4
-#define IPL_BORDER_TRANSPARENT    5
-
-#define IPL_IMAGE_MAGIC_VAL  ((int)sizeof(IplImage))
-#define CV_TYPE_NAME_IMAGE "opencv-image"
-
-#define CV_IS_IMAGE_HDR(img) \
-    ((img) != NULL && ((const IplImage*)(img))->nSize == sizeof(IplImage))
-
-#define CV_IS_IMAGE(img) \
-    (CV_IS_IMAGE_HDR(img) && ((IplImage*)img)->imageData != NULL)
-
-/** for storing double-precision
-   floating point data in IplImage's */
-#define IPL_DEPTH_64F  64
-
-/** get reference to pixel at (col,row),
-   for multi-channel images (col) should be multiplied by number of channels */
-#define CV_IMAGE_ELEM( image, elemtype, row, col )       \
-    (((elemtype*)((image)->imageData + (image)->widthStep*(row)))[(col)])
-
-/****************************************************************************************\
-*                                  Matrix type (CvMat)                                   *
-\****************************************************************************************/
-
-#define CV_AUTO_STEP  0x7fffffff
-#define CV_WHOLE_ARR  cvSlice( 0, 0x3fffffff )
-
-#define CV_MAGIC_MASK       0xFFFF0000
-#define CV_MAT_MAGIC_VAL    0x42420000
-#define CV_TYPE_NAME_MAT    "opencv-matrix"
-
-/** Matrix elements are stored row by row. Element (i, j) (i - 0-based row index, j - 0-based column
-index) of a matrix can be retrieved or modified using CV_MAT_ELEM macro:
-
-    uchar pixval = CV_MAT_ELEM(grayimg, uchar, i, j)
-    CV_MAT_ELEM(cameraMatrix, float, 0, 2) = image.width*0.5f;
-
-To access multiple-channel matrices, you can use
-CV_MAT_ELEM(matrix, type, i, j\*nchannels + channel_idx).
-
-@deprecated CvMat is now obsolete; consider using Mat instead.
- */
-typedef struct CvMat
-{
-    int type;
-    int step;
-
-    /* for internal use only */
-    int* refcount;
-    int hdr_refcount;
-
-    union
-    {
-        uchar* ptr;
-        short* s;
-        int* i;
-        float* fl;
-        double* db;
-    } data;
-
-#ifdef __cplusplus
-    union
-    {
-        int rows;
-        int height;
-    };
-
-    union
-    {
-        int cols;
-        int width;
-    };
-#else
-    int rows;
-    int cols;
-#endif
-
-
-#ifdef __cplusplus
-    CvMat() {}
-    CvMat(const CvMat& m) { memcpy(this, &m, sizeof(CvMat));}
-    CvMat(const cv::Mat& m);
-#endif
-
-}
-CvMat;
-
-
-#define CV_IS_MAT_HDR(mat) \
-    ((mat) != NULL && \
-    (((const CvMat*)(mat))->type & CV_MAGIC_MASK) == CV_MAT_MAGIC_VAL && \
-    ((const CvMat*)(mat))->cols > 0 && ((const CvMat*)(mat))->rows > 0)
-
-#define CV_IS_MAT_HDR_Z(mat) \
-    ((mat) != NULL && \
-    (((const CvMat*)(mat))->type & CV_MAGIC_MASK) == CV_MAT_MAGIC_VAL && \
-    ((const CvMat*)(mat))->cols >= 0 && ((const CvMat*)(mat))->rows >= 0)
-
-#define CV_IS_MAT(mat) \
-    (CV_IS_MAT_HDR(mat) && ((const CvMat*)(mat))->data.ptr != NULL)
-
-#define CV_IS_MASK_ARR(mat) \
-    (((mat)->type & (CV_MAT_TYPE_MASK & ~CV_8SC1)) == 0)
-
-#define CV_ARE_TYPES_EQ(mat1, mat2) \
-    ((((mat1)->type ^ (mat2)->type) & CV_MAT_TYPE_MASK) == 0)
-
-#define CV_ARE_CNS_EQ(mat1, mat2) \
-    ((((mat1)->type ^ (mat2)->type) & CV_MAT_CN_MASK) == 0)
-
-#define CV_ARE_DEPTHS_EQ(mat1, mat2) \
-    ((((mat1)->type ^ (mat2)->type) & CV_MAT_DEPTH_MASK) == 0)
-
-#define CV_ARE_SIZES_EQ(mat1, mat2) \
-    ((mat1)->rows == (mat2)->rows && (mat1)->cols == (mat2)->cols)
-
-#define CV_IS_MAT_CONST(mat)  \
-    (((mat)->rows|(mat)->cols) == 1)
-
-#define IPL2CV_DEPTH(depth) \
-    ((((CV_8U)+(CV_16U<<4)+(CV_32F<<8)+(CV_64F<<16)+(CV_8S<<20)+ \
-    (CV_16S<<24)+(CV_32S<<28)) >> ((((depth) & 0xF0) >> 2) + \
-    (((depth) & IPL_DEPTH_SIGN) ? 20 : 0))) & 15)
-
-/** Inline constructor. No data is allocated internally!!!
- * (Use together with cvCreateData, or use cvCreateMat instead to
- * get a matrix with allocated data):
- */
-CV_INLINE CvMat cvMat( int rows, int cols, int type, void* data CV_DEFAULT(NULL))
-{
-    CvMat m;
-
-    assert( (unsigned)CV_MAT_DEPTH(type) <= CV_64F );
-    type = CV_MAT_TYPE(type);
-    m.type = CV_MAT_MAGIC_VAL | CV_MAT_CONT_FLAG | type;
-    m.cols = cols;
-    m.rows = rows;
-    m.step = m.cols*CV_ELEM_SIZE(type);
-    m.data.ptr = (uchar*)data;
-    m.refcount = NULL;
-    m.hdr_refcount = 0;
-
-    return m;
-}
-
-#ifdef __cplusplus
-inline CvMat::CvMat(const cv::Mat& m)
-{
-    CV_DbgAssert(m.dims <= 2);
-    *this = cvMat(m.rows, m.dims == 1 ? 1 : m.cols, m.type(), m.data);
-    step = (int)m.step[0];
-    type = (type & ~cv::Mat::CONTINUOUS_FLAG) | (m.flags & cv::Mat::CONTINUOUS_FLAG);
-}
-#endif
-
-
-#define CV_MAT_ELEM_PTR_FAST( mat, row, col, pix_size )  \
-    (assert( (unsigned)(row) < (unsigned)(mat).rows &&   \
-             (unsigned)(col) < (unsigned)(mat).cols ),   \
-     (mat).data.ptr + (size_t)(mat).step*(row) + (pix_size)*(col))
-
-#define CV_MAT_ELEM_PTR( mat, row, col )                 \
-    CV_MAT_ELEM_PTR_FAST( mat, row, col, CV_ELEM_SIZE((mat).type) )
-
-#define CV_MAT_ELEM( mat, elemtype, row, col )           \
-    (*(elemtype*)CV_MAT_ELEM_PTR_FAST( mat, row, col, sizeof(elemtype)))
-
-/** @brief Returns the particular element of single-channel floating-point matrix.
-
-The function is a fast replacement for cvGetReal2D in the case of single-channel floating-point
-matrices. It is faster because it is inline, it does fewer checks for array type and array element
-type, and it checks for the row and column ranges only in debug mode.
-@param mat Input matrix
-@param row The zero-based index of row
-@param col The zero-based index of column
- */
-CV_INLINE  double  cvmGet( const CvMat* mat, int row, int col )
-{
-    int type;
-
-    type = CV_MAT_TYPE(mat->type);
-    assert( (unsigned)row < (unsigned)mat->rows &&
-            (unsigned)col < (unsigned)mat->cols );
-
-    if( type == CV_32FC1 )
-        return ((float*)(void*)(mat->data.ptr + (size_t)mat->step*row))[col];
-    else
-    {
-        assert( type == CV_64FC1 );
-        return ((double*)(void*)(mat->data.ptr + (size_t)mat->step*row))[col];
-    }
-}
-
-/** @brief Sets a specific element of a single-channel floating-point matrix.
-
-The function is a fast replacement for cvSetReal2D in the case of single-channel floating-point
-matrices. It is faster because it is inline, it does fewer checks for array type and array element
-type, and it checks for the row and column ranges only in debug mode.
-@param mat The matrix
-@param row The zero-based index of row
-@param col The zero-based index of column
-@param value The new value of the matrix element
- */
-CV_INLINE  void  cvmSet( CvMat* mat, int row, int col, double value )
-{
-    int type;
-    type = CV_MAT_TYPE(mat->type);
-    assert( (unsigned)row < (unsigned)mat->rows &&
-            (unsigned)col < (unsigned)mat->cols );
-
-    if( type == CV_32FC1 )
-        ((float*)(void*)(mat->data.ptr + (size_t)mat->step*row))[col] = (float)value;
-    else
-    {
-        assert( type == CV_64FC1 );
-        ((double*)(void*)(mat->data.ptr + (size_t)mat->step*row))[col] = value;
-    }
-}
-
-
-CV_INLINE int cvIplDepth( int type )
-{
-    int depth = CV_MAT_DEPTH(type);
-    return CV_ELEM_SIZE1(depth)*8 | (depth == CV_8S || depth == CV_16S ||
-           depth == CV_32S ? IPL_DEPTH_SIGN : 0);
-}
-
-
-/****************************************************************************************\
-*                       Multi-dimensional dense array (CvMatND)                          *
-\****************************************************************************************/
-
-#define CV_MATND_MAGIC_VAL    0x42430000
-#define CV_TYPE_NAME_MATND    "opencv-nd-matrix"
-
-#define CV_MAX_DIM            32
-#define CV_MAX_DIM_HEAP       1024
-
-/**
-  @deprecated consider using cv::Mat instead
-  */
-typedef struct
-#ifdef __cplusplus
-  CV_EXPORTS
-#endif
-CvMatND
-{
-    int type;
-    int dims;
-
-    int* refcount;
-    int hdr_refcount;
-
-    union
-    {
-        uchar* ptr;
-        float* fl;
-        double* db;
-        int* i;
-        short* s;
-    } data;
-
-    struct
-    {
-        int size;
-        int step;
-    }
-    dim[CV_MAX_DIM];
-
-#ifdef __cplusplus
-    CvMatND() {}
-    CvMatND(const cv::Mat& m);
-#endif
-}
-CvMatND;
-
-#define CV_IS_MATND_HDR(mat) \
-    ((mat) != NULL && (((const CvMatND*)(mat))->type & CV_MAGIC_MASK) == CV_MATND_MAGIC_VAL)
-
-#define CV_IS_MATND(mat) \
-    (CV_IS_MATND_HDR(mat) && ((const CvMatND*)(mat))->data.ptr != NULL)
-
-
-/****************************************************************************************\
-*                      Multi-dimensional sparse array (CvSparseMat)                      *
-\****************************************************************************************/
-
-#define CV_SPARSE_MAT_MAGIC_VAL    0x42440000
-#define CV_TYPE_NAME_SPARSE_MAT    "opencv-sparse-matrix"
-
-struct CvSet;
-
-typedef struct
-#ifdef __cplusplus
-  CV_EXPORTS
-#endif
-CvSparseMat
-{
-    int type;
-    int dims;
-    int* refcount;
-    int hdr_refcount;
-
-    struct CvSet* heap;
-    void** hashtable;
-    int hashsize;
-    int valoffset;
-    int idxoffset;
-    int size[CV_MAX_DIM];
-
-#ifdef __cplusplus
-    void copyToSparseMat(cv::SparseMat& m) const;
-#endif
-}
-CvSparseMat;
-
-#ifdef __cplusplus
-    CV_EXPORTS CvSparseMat* cvCreateSparseMat(const cv::SparseMat& m);
-#endif
-
-#define CV_IS_SPARSE_MAT_HDR(mat) \
-    ((mat) != NULL && \
-    (((const CvSparseMat*)(mat))->type & CV_MAGIC_MASK) == CV_SPARSE_MAT_MAGIC_VAL)
-
-#define CV_IS_SPARSE_MAT(mat) \
-    CV_IS_SPARSE_MAT_HDR(mat)
-
-/**************** iteration through a sparse array *****************/
-
-typedef struct CvSparseNode
-{
-    unsigned hashval;
-    struct CvSparseNode* next;
-}
-CvSparseNode;
-
-typedef struct CvSparseMatIterator
-{
-    CvSparseMat* mat;
-    CvSparseNode* node;
-    int curidx;
-}
-CvSparseMatIterator;
-
-#define CV_NODE_VAL(mat,node)   ((void*)((uchar*)(node) + (mat)->valoffset))
-#define CV_NODE_IDX(mat,node)   ((int*)((uchar*)(node) + (mat)->idxoffset))
-
-/****************************************************************************************\
-*                                         Histogram                                      *
-\****************************************************************************************/
-
-typedef int CvHistType;
-
-#define CV_HIST_MAGIC_VAL     0x42450000
-#define CV_HIST_UNIFORM_FLAG  (1 << 10)
-
-/** indicates whether bin ranges are set already or not */
-#define CV_HIST_RANGES_FLAG   (1 << 11)
-
-#define CV_HIST_ARRAY         0
-#define CV_HIST_SPARSE        1
-#define CV_HIST_TREE          CV_HIST_SPARSE
-
-/** should be used as a parameter only,
-   it turns to CV_HIST_UNIFORM_FLAG of hist->type */
-#define CV_HIST_UNIFORM       1
-
-typedef struct CvHistogram
-{
-    int     type;
-    CvArr*  bins;
-    float   thresh[CV_MAX_DIM][2];  /**< For uniform histograms.                      */
-    float** thresh2;                /**< For non-uniform histograms.                  */
-    CvMatND mat;                    /**< Embedded matrix header for array histograms. */
-}
-CvHistogram;
-
-#define CV_IS_HIST( hist ) \
-    ((hist) != NULL  && \
-     (((CvHistogram*)(hist))->type & CV_MAGIC_MASK) == CV_HIST_MAGIC_VAL && \
-     (hist)->bins != NULL)
-
-#define CV_IS_UNIFORM_HIST( hist ) \
-    (((hist)->type & CV_HIST_UNIFORM_FLAG) != 0)
-
-#define CV_IS_SPARSE_HIST( hist ) \
-    CV_IS_SPARSE_MAT((hist)->bins)
-
-#define CV_HIST_HAS_RANGES( hist ) \
-    (((hist)->type & CV_HIST_RANGES_FLAG) != 0)
-
-/****************************************************************************************\
-*                      Other supplementary data type definitions                         *
-\****************************************************************************************/
-
-/*************************************** CvRect *****************************************/
-/** @sa Rect_ */
-typedef struct CvRect
-{
-    int x;
-    int y;
-    int width;
-    int height;
-
-#ifdef __cplusplus
-    CvRect(int _x = 0, int _y = 0, int w = 0, int h = 0): x(_x), y(_y), width(w), height(h) {}
-    template<typename _Tp>
-    CvRect(const cv::Rect_<_Tp>& r): x(cv::saturate_cast<int>(r.x)), y(cv::saturate_cast<int>(r.y)), width(cv::saturate_cast<int>(r.width)), height(cv::saturate_cast<int>(r.height)) {}
-    template<typename _Tp>
-    operator cv::Rect_<_Tp>() const { return cv::Rect_<_Tp>((_Tp)x, (_Tp)y, (_Tp)width, (_Tp)height); }
-#endif
-}
-CvRect;
-
-/** constructs CvRect structure. */
-CV_INLINE  CvRect  cvRect( int x, int y, int width, int height )
-{
-    CvRect r;
-
-    r.x = x;
-    r.y = y;
-    r.width = width;
-    r.height = height;
-
-    return r;
-}
-
-
-CV_INLINE  IplROI  cvRectToROI( CvRect rect, int coi )
-{
-    IplROI roi;
-    roi.xOffset = rect.x;
-    roi.yOffset = rect.y;
-    roi.width = rect.width;
-    roi.height = rect.height;
-    roi.coi = coi;
-
-    return roi;
-}
-
-
-CV_INLINE  CvRect  cvROIToRect( IplROI roi )
-{
-    return cvRect( roi.xOffset, roi.yOffset, roi.width, roi.height );
-}
-
-/*********************************** CvTermCriteria *************************************/
-
-#define CV_TERMCRIT_ITER    1
-#define CV_TERMCRIT_NUMBER  CV_TERMCRIT_ITER
-#define CV_TERMCRIT_EPS     2
-
-/** @sa TermCriteria
- */
-typedef struct CvTermCriteria
-{
-    int    type;  /**< may be combination of
-                     CV_TERMCRIT_ITER
-                     CV_TERMCRIT_EPS */
-    int    max_iter;
-    double epsilon;
-
-#ifdef __cplusplus
-    CvTermCriteria(int _type = 0, int _iter = 0, double _eps = 0) : type(_type), max_iter(_iter), epsilon(_eps)  {}
-    CvTermCriteria(const cv::TermCriteria& t) : type(t.type), max_iter(t.maxCount), epsilon(t.epsilon)  {}
-    operator cv::TermCriteria() const { return cv::TermCriteria(type, max_iter, epsilon); }
-#endif
-
-}
-CvTermCriteria;
-
-CV_INLINE  CvTermCriteria  cvTermCriteria( int type, int max_iter, double epsilon )
-{
-    CvTermCriteria t;
-
-    t.type = type;
-    t.max_iter = max_iter;
-    t.epsilon = (float)epsilon;
-
-    return t;
-}
-
-
-/******************************* CvPoint and variants ***********************************/
-
-typedef struct CvPoint
-{
-    int x;
-    int y;
-
-#ifdef __cplusplus
-    CvPoint(int _x = 0, int _y = 0): x(_x), y(_y) {}
-    template<typename _Tp>
-    CvPoint(const cv::Point_<_Tp>& pt): x((int)pt.x), y((int)pt.y) {}
-    template<typename _Tp>
-    operator cv::Point_<_Tp>() const { return cv::Point_<_Tp>(cv::saturate_cast<_Tp>(x), cv::saturate_cast<_Tp>(y)); }
-#endif
-}
-CvPoint;
-
-/** constructs CvPoint structure. */
-CV_INLINE  CvPoint  cvPoint( int x, int y )
-{
-    CvPoint p;
-
-    p.x = x;
-    p.y = y;
-
-    return p;
-}
-
-
-typedef struct CvPoint2D32f
-{
-    float x;
-    float y;
-
-#ifdef __cplusplus
-    CvPoint2D32f(float _x = 0, float _y = 0): x(_x), y(_y) {}
-    template<typename _Tp>
-    CvPoint2D32f(const cv::Point_<_Tp>& pt): x((float)pt.x), y((float)pt.y) {}
-    template<typename _Tp>
-    operator cv::Point_<_Tp>() const { return cv::Point_<_Tp>(cv::saturate_cast<_Tp>(x), cv::saturate_cast<_Tp>(y)); }
-#endif
-}
-CvPoint2D32f;
-
-/** constructs CvPoint2D32f structure. */
-CV_INLINE  CvPoint2D32f  cvPoint2D32f( double x, double y )
-{
-    CvPoint2D32f p;
-
-    p.x = (float)x;
-    p.y = (float)y;
-
-    return p;
-}
-
-/** converts CvPoint to CvPoint2D32f. */
-CV_INLINE  CvPoint2D32f  cvPointTo32f( CvPoint point )
-{
-    return cvPoint2D32f( (float)point.x, (float)point.y );
-}
-
-/** converts CvPoint2D32f to CvPoint. */
-CV_INLINE  CvPoint  cvPointFrom32f( CvPoint2D32f point )
-{
-    CvPoint ipt;
-    ipt.x = cvRound(point.x);
-    ipt.y = cvRound(point.y);
-
-    return ipt;
-}
-
-
-typedef struct CvPoint3D32f
-{
-    float x;
-    float y;
-    float z;
-
-#ifdef __cplusplus
-    CvPoint3D32f(float _x = 0, float _y = 0, float _z = 0): x(_x), y(_y), z(_z) {}
-    template<typename _Tp>
-    CvPoint3D32f(const cv::Point3_<_Tp>& pt): x((float)pt.x), y((float)pt.y), z((float)pt.z) {}
-    template<typename _Tp>
-    operator cv::Point3_<_Tp>() const { return cv::Point3_<_Tp>(cv::saturate_cast<_Tp>(x), cv::saturate_cast<_Tp>(y), cv::saturate_cast<_Tp>(z)); }
-#endif
-}
-CvPoint3D32f;
-
-/** constructs CvPoint3D32f structure. */
-CV_INLINE  CvPoint3D32f  cvPoint3D32f( double x, double y, double z )
-{
-    CvPoint3D32f p;
-
-    p.x = (float)x;
-    p.y = (float)y;
-    p.z = (float)z;
-
-    return p;
-}
-
-
-typedef struct CvPoint2D64f
-{
-    double x;
-    double y;
-}
-CvPoint2D64f;
-
-/** constructs CvPoint2D64f structure.*/
-CV_INLINE  CvPoint2D64f  cvPoint2D64f( double x, double y )
-{
-    CvPoint2D64f p;
-
-    p.x = x;
-    p.y = y;
-
-    return p;
-}
-
-
-typedef struct CvPoint3D64f
-{
-    double x;
-    double y;
-    double z;
-}
-CvPoint3D64f;
-
-/** constructs CvPoint3D64f structure. */
-CV_INLINE  CvPoint3D64f  cvPoint3D64f( double x, double y, double z )
-{
-    CvPoint3D64f p;
-
-    p.x = x;
-    p.y = y;
-    p.z = z;
-
-    return p;
-}
-
-
-/******************************** CvSize's & CvBox **************************************/
-
-typedef struct CvSize
-{
-    int width;
-    int height;
-
-#ifdef __cplusplus
-    CvSize(int w = 0, int h = 0): width(w), height(h) {}
-    template<typename _Tp>
-    CvSize(const cv::Size_<_Tp>& sz): width(cv::saturate_cast<int>(sz.width)), height(cv::saturate_cast<int>(sz.height)) {}
-    template<typename _Tp>
-    operator cv::Size_<_Tp>() const { return cv::Size_<_Tp>(cv::saturate_cast<_Tp>(width), cv::saturate_cast<_Tp>(height)); }
-#endif
-}
-CvSize;
-
-/** constructs CvSize structure. */
-CV_INLINE  CvSize  cvSize( int width, int height )
-{
-    CvSize s;
-
-    s.width = width;
-    s.height = height;
-
-    return s;
-}
-
-typedef struct CvSize2D32f
-{
-    float width;
-    float height;
-
-#ifdef __cplusplus
-    CvSize2D32f(float w = 0, float h = 0): width(w), height(h) {}
-    template<typename _Tp>
-    CvSize2D32f(const cv::Size_<_Tp>& sz): width(cv::saturate_cast<float>(sz.width)), height(cv::saturate_cast<float>(sz.height)) {}
-    template<typename _Tp>
-    operator cv::Size_<_Tp>() const { return cv::Size_<_Tp>(cv::saturate_cast<_Tp>(width), cv::saturate_cast<_Tp>(height)); }
-#endif
-}
-CvSize2D32f;
-
-/** constructs CvSize2D32f structure. */
-CV_INLINE  CvSize2D32f  cvSize2D32f( double width, double height )
-{
-    CvSize2D32f s;
-
-    s.width = (float)width;
-    s.height = (float)height;
-
-    return s;
-}
-
-/** @sa RotatedRect
- */
-typedef struct CvBox2D
-{
-    CvPoint2D32f center;  /**< Center of the box.                          */
-    CvSize2D32f  size;    /**< Box width and length.                       */
-    float angle;          /**< Angle between the horizontal axis           */
-                          /**< and the first side (i.e. length) in degrees */
-
-#ifdef __cplusplus
-    CvBox2D(CvPoint2D32f c = CvPoint2D32f(), CvSize2D32f s = CvSize2D32f(), float a = 0) : center(c), size(s), angle(a) {}
-    CvBox2D(const cv::RotatedRect& rr) : center(rr.center), size(rr.size), angle(rr.angle) {}
-    operator cv::RotatedRect() const { return cv::RotatedRect(center, size, angle); }
-#endif
-}
-CvBox2D;
-
-
-/** Line iterator state: */
-typedef struct CvLineIterator
-{
-    /** Pointer to the current point: */
-    uchar* ptr;
-
-    /* Bresenham algorithm state: */
-    int  err;
-    int  plus_delta;
-    int  minus_delta;
-    int  plus_step;
-    int  minus_step;
-}
-CvLineIterator;
-
-
-
-/************************************* CvSlice ******************************************/
-#define CV_WHOLE_SEQ_END_INDEX 0x3fffffff
-#define CV_WHOLE_SEQ  cvSlice(0, CV_WHOLE_SEQ_END_INDEX)
-
-typedef struct CvSlice
-{
-    int  start_index, end_index;
-
-#if defined(__cplusplus) && !defined(__CUDACC__)
-    CvSlice(int start = 0, int end = 0) : start_index(start), end_index(end) {}
-    CvSlice(const cv::Range& r) { *this = (r.start != INT_MIN && r.end != INT_MAX) ? CvSlice(r.start, r.end) : CvSlice(0, CV_WHOLE_SEQ_END_INDEX); }
-    operator cv::Range() const { return (start_index == 0 && end_index == CV_WHOLE_SEQ_END_INDEX ) ? cv::Range::all() : cv::Range(start_index, end_index); }
-#endif
-}
-CvSlice;
-
-CV_INLINE  CvSlice  cvSlice( int start, int end )
-{
-    CvSlice slice;
-    slice.start_index = start;
-    slice.end_index = end;
-
-    return slice;
-}
-
-
-
-/************************************* CvScalar *****************************************/
-/** @sa Scalar_
- */
-typedef struct CvScalar
-{
-    double val[4];
-
-#ifdef __cplusplus
-    CvScalar() {}
-    CvScalar(double d0, double d1 = 0, double d2 = 0, double d3 = 0) { val[0] = d0; val[1] = d1; val[2] = d2; val[3] = d3; }
-    template<typename _Tp>
-    CvScalar(const cv::Scalar_<_Tp>& s) { val[0] = s.val[0]; val[1] = s.val[1]; val[2] = s.val[2]; val[3] = s.val[3]; }
-    template<typename _Tp>
-    operator cv::Scalar_<_Tp>() const { return cv::Scalar_<_Tp>(cv::saturate_cast<_Tp>(val[0]), cv::saturate_cast<_Tp>(val[1]), cv::saturate_cast<_Tp>(val[2]), cv::saturate_cast<_Tp>(val[3])); }
-    template<typename _Tp, int cn>
-    CvScalar(const cv::Vec<_Tp, cn>& v)
-    {
-        int i;
-        for( i = 0; i < (cn < 4 ? cn : 4); i++ ) val[i] = v.val[i];
-        for( ; i < 4; i++ ) val[i] = 0;
-    }
-#endif
-}
-CvScalar;
-
-CV_INLINE  CvScalar  cvScalar( double val0, double val1 CV_DEFAULT(0),
-                               double val2 CV_DEFAULT(0), double val3 CV_DEFAULT(0))
-{
-    CvScalar scalar;
-    scalar.val[0] = val0; scalar.val[1] = val1;
-    scalar.val[2] = val2; scalar.val[3] = val3;
-    return scalar;
-}
-
-
-CV_INLINE  CvScalar  cvRealScalar( double val0 )
-{
-    CvScalar scalar;
-    scalar.val[0] = val0;
-    scalar.val[1] = scalar.val[2] = scalar.val[3] = 0;
-    return scalar;
-}
-
-CV_INLINE  CvScalar  cvScalarAll( double val0123 )
-{
-    CvScalar scalar;
-    scalar.val[0] = val0123;
-    scalar.val[1] = val0123;
-    scalar.val[2] = val0123;
-    scalar.val[3] = val0123;
-    return scalar;
-}
-
-/****************************************************************************************\
-*                                   Dynamic Data structures                              *
-\****************************************************************************************/
-
-/******************************** Memory storage ****************************************/
-
-typedef struct CvMemBlock
-{
-    struct CvMemBlock*  prev;
-    struct CvMemBlock*  next;
-}
-CvMemBlock;
-
-#define CV_STORAGE_MAGIC_VAL    0x42890000
-
-typedef struct CvMemStorage
-{
-    int signature;
-    CvMemBlock* bottom;           /**< First allocated block.                   */
-    CvMemBlock* top;              /**< Current memory block - top of the stack. */
-    struct  CvMemStorage* parent; /**< We get new blocks from parent as needed. */
-    int block_size;               /**< Block size.                              */
-    int free_space;               /**< Remaining free space in current block.   */
-}
-CvMemStorage;
-
-#define CV_IS_STORAGE(storage)  \
-    ((storage) != NULL &&       \
-    (((CvMemStorage*)(storage))->signature & CV_MAGIC_MASK) == CV_STORAGE_MAGIC_VAL)
-
-
-typedef struct CvMemStoragePos
-{
-    CvMemBlock* top;
-    int free_space;
-}
-CvMemStoragePos;
-
-
-/*********************************** Sequence *******************************************/
-
-typedef struct CvSeqBlock
-{
-    struct CvSeqBlock*  prev; /**< Previous sequence block.                   */
-    struct CvSeqBlock*  next; /**< Next sequence block.                       */
-  int    start_index;         /**< Index of the first element in the block +  */
-                              /**< sequence->first->start_index.              */
-    int    count;             /**< Number of elements in the block.           */
-    schar* data;              /**< Pointer to the first element of the block. */
-}
-CvSeqBlock;
-
-
-#define CV_TREE_NODE_FIELDS(node_type)                               \
-    int       flags;             /**< Miscellaneous flags.     */      \
-    int       header_size;       /**< Size of sequence header. */      \
-    struct    node_type* h_prev; /**< Previous sequence.       */      \
-    struct    node_type* h_next; /**< Next sequence.           */      \
-    struct    node_type* v_prev; /**< 2nd previous sequence.   */      \
-    struct    node_type* v_next  /**< 2nd next sequence.       */
-
-/**
-   Read/Write sequence.
-   Elements can be dynamically inserted to or deleted from the sequence.
-*/
-#define CV_SEQUENCE_FIELDS()                                              \
-    CV_TREE_NODE_FIELDS(CvSeq);                                           \
-    int       total;          /**< Total number of elements.            */  \
-    int       elem_size;      /**< Size of sequence element in bytes.   */  \
-    schar*    block_max;      /**< Maximal bound of the last block.     */  \
-    schar*    ptr;            /**< Current write pointer.               */  \
-    int       delta_elems;    /**< Grow seq this many at a time.        */  \
-    CvMemStorage* storage;    /**< Where the seq is stored.             */  \
-    CvSeqBlock* free_blocks;  /**< Free blocks list.                    */  \
-    CvSeqBlock* first;        /**< Pointer to the first sequence block. */
-
-typedef struct CvSeq
-{
-    CV_SEQUENCE_FIELDS()
-}
-CvSeq;
-
-#define CV_TYPE_NAME_SEQ             "opencv-sequence"
-#define CV_TYPE_NAME_SEQ_TREE        "opencv-sequence-tree"
-
-/*************************************** Set ********************************************/
-/** @brief Set
-  Order is not preserved. There can be gaps between sequence elements.
-  After the element has been inserted it stays in the same place all the time.
-  The MSB(most-significant or sign bit) of the first field (flags) is 0 iff the element exists.
-*/
-#define CV_SET_ELEM_FIELDS(elem_type)   \
-    int  flags;                         \
-    struct elem_type* next_free;
-
-typedef struct CvSetElem
-{
-    CV_SET_ELEM_FIELDS(CvSetElem)
-}
-CvSetElem;
-
-#define CV_SET_FIELDS()      \
-    CV_SEQUENCE_FIELDS()     \
-    CvSetElem* free_elems;   \
-    int active_count;
-
-typedef struct CvSet
-{
-    CV_SET_FIELDS()
-}
-CvSet;
-
-
-#define CV_SET_ELEM_IDX_MASK   ((1 << 26) - 1)
-#define CV_SET_ELEM_FREE_FLAG  (1 << (sizeof(int)*8-1))
-
-/** Checks whether the element pointed by ptr belongs to a set or not */
-#define CV_IS_SET_ELEM( ptr )  (((CvSetElem*)(ptr))->flags >= 0)
-
-/************************************* Graph ********************************************/
-
-/** @name Graph
-
-We represent a graph as a set of vertices. Vertices contain their adjacency lists (more exactly,
-pointers to first incoming or outcoming edge (or 0 if isolated vertex)). Edges are stored in
-another set. There is a singly-linked list of incoming/outcoming edges for each vertex.
-
-Each edge consists of:
-
-- Two pointers to the starting and ending vertices (vtx[0] and vtx[1] respectively).
-
-    A graph may be oriented or not. In the latter case, edges between vertex i to vertex j are not
-distinguished during search operations.
-
-- Two pointers to next edges for the starting and ending vertices, where next[0] points to the
-next edge in the vtx[0] adjacency list and next[1] points to the next edge in the vtx[1]
-adjacency list.
-
-@see CvGraphEdge, CvGraphVtx, CvGraphVtx2D, CvGraph
-@{
-*/
-#define CV_GRAPH_EDGE_FIELDS()      \
-    int flags;                      \
-    float weight;                   \
-    struct CvGraphEdge* next[2];    \
-    struct CvGraphVtx* vtx[2];
-
-
-#define CV_GRAPH_VERTEX_FIELDS()    \
-    int flags;                      \
-    struct CvGraphEdge* first;
-
-
-typedef struct CvGraphEdge
-{
-    CV_GRAPH_EDGE_FIELDS()
-}
-CvGraphEdge;
-
-typedef struct CvGraphVtx
-{
-    CV_GRAPH_VERTEX_FIELDS()
-}
-CvGraphVtx;
-
-typedef struct CvGraphVtx2D
-{
-    CV_GRAPH_VERTEX_FIELDS()
-    CvPoint2D32f* ptr;
-}
-CvGraphVtx2D;
-
-/**
-   Graph is "derived" from the set (this is set a of vertices)
-   and includes another set (edges)
-*/
-#define  CV_GRAPH_FIELDS()   \
-    CV_SET_FIELDS()          \
-    CvSet* edges;
-
-typedef struct CvGraph
-{
-    CV_GRAPH_FIELDS()
-}
-CvGraph;
-
-#define CV_TYPE_NAME_GRAPH "opencv-graph"
-
-/** @} */
-
-/*********************************** Chain/Contour *************************************/
-
-typedef struct CvChain
-{
-    CV_SEQUENCE_FIELDS()
-    CvPoint  origin;
-}
-CvChain;
-
-#define CV_CONTOUR_FIELDS()  \
-    CV_SEQUENCE_FIELDS()     \
-    CvRect rect;             \
-    int color;               \
-    int reserved[3];
-
-typedef struct CvContour
-{
-    CV_CONTOUR_FIELDS()
-}
-CvContour;
-
-typedef CvContour CvPoint2DSeq;
-
-/****************************************************************************************\
-*                                    Sequence types                                      *
-\****************************************************************************************/
-
-#define CV_SEQ_MAGIC_VAL             0x42990000
-
-#define CV_IS_SEQ(seq) \
-    ((seq) != NULL && (((CvSeq*)(seq))->flags & CV_MAGIC_MASK) == CV_SEQ_MAGIC_VAL)
-
-#define CV_SET_MAGIC_VAL             0x42980000
-#define CV_IS_SET(set) \
-    ((set) != NULL && (((CvSeq*)(set))->flags & CV_MAGIC_MASK) == CV_SET_MAGIC_VAL)
-
-#define CV_SEQ_ELTYPE_BITS           12
-#define CV_SEQ_ELTYPE_MASK           ((1 << CV_SEQ_ELTYPE_BITS) - 1)
-
-#define CV_SEQ_ELTYPE_POINT          CV_32SC2  /**< (x,y) */
-#define CV_SEQ_ELTYPE_CODE           CV_8UC1   /**< freeman code: 0..7 */
-#define CV_SEQ_ELTYPE_GENERIC        0
-#define CV_SEQ_ELTYPE_PTR            CV_USRTYPE1
-#define CV_SEQ_ELTYPE_PPOINT         CV_SEQ_ELTYPE_PTR  /**< &(x,y) */
-#define CV_SEQ_ELTYPE_INDEX          CV_32SC1  /**< #(x,y) */
-#define CV_SEQ_ELTYPE_GRAPH_EDGE     0  /**< &next_o, &next_d, &vtx_o, &vtx_d */
-#define CV_SEQ_ELTYPE_GRAPH_VERTEX   0  /**< first_edge, &(x,y) */
-#define CV_SEQ_ELTYPE_TRIAN_ATR      0  /**< vertex of the binary tree   */
-#define CV_SEQ_ELTYPE_CONNECTED_COMP 0  /**< connected component  */
-#define CV_SEQ_ELTYPE_POINT3D        CV_32FC3  /**< (x,y,z)  */
-
-#define CV_SEQ_KIND_BITS        2
-#define CV_SEQ_KIND_MASK        (((1 << CV_SEQ_KIND_BITS) - 1)<<CV_SEQ_ELTYPE_BITS)
-
-/** types of sequences */
-#define CV_SEQ_KIND_GENERIC     (0 << CV_SEQ_ELTYPE_BITS)
-#define CV_SEQ_KIND_CURVE       (1 << CV_SEQ_ELTYPE_BITS)
-#define CV_SEQ_KIND_BIN_TREE    (2 << CV_SEQ_ELTYPE_BITS)
-
-/** types of sparse sequences (sets) */
-#define CV_SEQ_KIND_GRAPH       (1 << CV_SEQ_ELTYPE_BITS)
-#define CV_SEQ_KIND_SUBDIV2D    (2 << CV_SEQ_ELTYPE_BITS)
-
-#define CV_SEQ_FLAG_SHIFT       (CV_SEQ_KIND_BITS + CV_SEQ_ELTYPE_BITS)
-
-/** flags for curves */
-#define CV_SEQ_FLAG_CLOSED     (1 << CV_SEQ_FLAG_SHIFT)
-#define CV_SEQ_FLAG_SIMPLE     (0 << CV_SEQ_FLAG_SHIFT)
-#define CV_SEQ_FLAG_CONVEX     (0 << CV_SEQ_FLAG_SHIFT)
-#define CV_SEQ_FLAG_HOLE       (2 << CV_SEQ_FLAG_SHIFT)
-
-/** flags for graphs */
-#define CV_GRAPH_FLAG_ORIENTED (1 << CV_SEQ_FLAG_SHIFT)
-
-#define CV_GRAPH               CV_SEQ_KIND_GRAPH
-#define CV_ORIENTED_GRAPH      (CV_SEQ_KIND_GRAPH|CV_GRAPH_FLAG_ORIENTED)
-
-/** point sets */
-#define CV_SEQ_POINT_SET       (CV_SEQ_KIND_GENERIC| CV_SEQ_ELTYPE_POINT)
-#define CV_SEQ_POINT3D_SET     (CV_SEQ_KIND_GENERIC| CV_SEQ_ELTYPE_POINT3D)
-#define CV_SEQ_POLYLINE        (CV_SEQ_KIND_CURVE  | CV_SEQ_ELTYPE_POINT)
-#define CV_SEQ_POLYGON         (CV_SEQ_FLAG_CLOSED | CV_SEQ_POLYLINE )
-#define CV_SEQ_CONTOUR         CV_SEQ_POLYGON
-#define CV_SEQ_SIMPLE_POLYGON  (CV_SEQ_FLAG_SIMPLE | CV_SEQ_POLYGON  )
-
-/** chain-coded curves */
-#define CV_SEQ_CHAIN           (CV_SEQ_KIND_CURVE  | CV_SEQ_ELTYPE_CODE)
-#define CV_SEQ_CHAIN_CONTOUR   (CV_SEQ_FLAG_CLOSED | CV_SEQ_CHAIN)
-
-/** binary tree for the contour */
-#define CV_SEQ_POLYGON_TREE    (CV_SEQ_KIND_BIN_TREE  | CV_SEQ_ELTYPE_TRIAN_ATR)
-
-/** sequence of the connected components */
-#define CV_SEQ_CONNECTED_COMP  (CV_SEQ_KIND_GENERIC  | CV_SEQ_ELTYPE_CONNECTED_COMP)
-
-/** sequence of the integer numbers */
-#define CV_SEQ_INDEX           (CV_SEQ_KIND_GENERIC  | CV_SEQ_ELTYPE_INDEX)
-
-#define CV_SEQ_ELTYPE( seq )   ((seq)->flags & CV_SEQ_ELTYPE_MASK)
-#define CV_SEQ_KIND( seq )     ((seq)->flags & CV_SEQ_KIND_MASK )
-
-/** flag checking */
-#define CV_IS_SEQ_INDEX( seq )      ((CV_SEQ_ELTYPE(seq) == CV_SEQ_ELTYPE_INDEX) && \
-                                     (CV_SEQ_KIND(seq) == CV_SEQ_KIND_GENERIC))
-
-#define CV_IS_SEQ_CURVE( seq )      (CV_SEQ_KIND(seq) == CV_SEQ_KIND_CURVE)
-#define CV_IS_SEQ_CLOSED( seq )     (((seq)->flags & CV_SEQ_FLAG_CLOSED) != 0)
-#define CV_IS_SEQ_CONVEX( seq )     0
-#define CV_IS_SEQ_HOLE( seq )       (((seq)->flags & CV_SEQ_FLAG_HOLE) != 0)
-#define CV_IS_SEQ_SIMPLE( seq )     1
-
-/** type checking macros */
-#define CV_IS_SEQ_POINT_SET( seq ) \
-    ((CV_SEQ_ELTYPE(seq) == CV_32SC2 || CV_SEQ_ELTYPE(seq) == CV_32FC2))
-
-#define CV_IS_SEQ_POINT_SUBSET( seq ) \
-    (CV_IS_SEQ_INDEX( seq ) || CV_SEQ_ELTYPE(seq) == CV_SEQ_ELTYPE_PPOINT)
-
-#define CV_IS_SEQ_POLYLINE( seq )   \
-    (CV_SEQ_KIND(seq) == CV_SEQ_KIND_CURVE && CV_IS_SEQ_POINT_SET(seq))
-
-#define CV_IS_SEQ_POLYGON( seq )   \
-    (CV_IS_SEQ_POLYLINE(seq) && CV_IS_SEQ_CLOSED(seq))
-
-#define CV_IS_SEQ_CHAIN( seq )   \
-    (CV_SEQ_KIND(seq) == CV_SEQ_KIND_CURVE && (seq)->elem_size == 1)
-
-#define CV_IS_SEQ_CONTOUR( seq )   \
-    (CV_IS_SEQ_CLOSED(seq) && (CV_IS_SEQ_POLYLINE(seq) || CV_IS_SEQ_CHAIN(seq)))
-
-#define CV_IS_SEQ_CHAIN_CONTOUR( seq ) \
-    (CV_IS_SEQ_CHAIN( seq ) && CV_IS_SEQ_CLOSED( seq ))
-
-#define CV_IS_SEQ_POLYGON_TREE( seq ) \
-    (CV_SEQ_ELTYPE (seq) ==  CV_SEQ_ELTYPE_TRIAN_ATR &&    \
-    CV_SEQ_KIND( seq ) ==  CV_SEQ_KIND_BIN_TREE )
-
-#define CV_IS_GRAPH( seq )    \
-    (CV_IS_SET(seq) && CV_SEQ_KIND((CvSet*)(seq)) == CV_SEQ_KIND_GRAPH)
-
-#define CV_IS_GRAPH_ORIENTED( seq )   \
-    (((seq)->flags & CV_GRAPH_FLAG_ORIENTED) != 0)
-
-#define CV_IS_SUBDIV2D( seq )  \
-    (CV_IS_SET(seq) && CV_SEQ_KIND((CvSet*)(seq)) == CV_SEQ_KIND_SUBDIV2D)
-
-/****************************************************************************************/
-/*                            Sequence writer & reader                                  */
-/****************************************************************************************/
-
-#define CV_SEQ_WRITER_FIELDS()                                     \
-    int          header_size;                                      \
-    CvSeq*       seq;        /**< the sequence written */            \
-    CvSeqBlock*  block;      /**< current block */                   \
-    schar*       ptr;        /**< pointer to free space */           \
-    schar*       block_min;  /**< pointer to the beginning of block*/\
-    schar*       block_max;  /**< pointer to the end of block */
-
-typedef struct CvSeqWriter
-{
-    CV_SEQ_WRITER_FIELDS()
-}
-CvSeqWriter;
-
-
-#define CV_SEQ_READER_FIELDS()                                      \
-    int          header_size;                                       \
-    CvSeq*       seq;        /**< sequence, beign read */             \
-    CvSeqBlock*  block;      /**< current block */                    \
-    schar*       ptr;        /**< pointer to element be read next */  \
-    schar*       block_min;  /**< pointer to the beginning of block */\
-    schar*       block_max;  /**< pointer to the end of block */      \
-    int          delta_index;/**< = seq->first->start_index   */      \
-    schar*       prev_elem;  /**< pointer to previous element */
-
-typedef struct CvSeqReader
-{
-    CV_SEQ_READER_FIELDS()
-}
-CvSeqReader;
-
-/****************************************************************************************/
-/*                                Operations on sequences                               */
-/****************************************************************************************/
-
-#define  CV_SEQ_ELEM( seq, elem_type, index )                    \
-/** assert gives some guarantee that <seq> parameter is valid */  \
-(   assert(sizeof((seq)->first[0]) == sizeof(CvSeqBlock) &&      \
-    (seq)->elem_size == sizeof(elem_type)),                      \
-    (elem_type*)((seq)->first && (unsigned)index <               \
-    (unsigned)((seq)->first->count) ?                            \
-    (seq)->first->data + (index) * sizeof(elem_type) :           \
-    cvGetSeqElem( (CvSeq*)(seq), (index) )))
-#define CV_GET_SEQ_ELEM( elem_type, seq, index ) CV_SEQ_ELEM( (seq), elem_type, (index) )
-
-/** Add element to sequence: */
-#define CV_WRITE_SEQ_ELEM_VAR( elem_ptr, writer )     \
-{                                                     \
-    if( (writer).ptr >= (writer).block_max )          \
-    {                                                 \
-        cvCreateSeqBlock( &writer);                   \
-    }                                                 \
-    memcpy((writer).ptr, elem_ptr, (writer).seq->elem_size);\
-    (writer).ptr += (writer).seq->elem_size;          \
-}
-
-#define CV_WRITE_SEQ_ELEM( elem, writer )             \
-{                                                     \
-    assert( (writer).seq->elem_size == sizeof(elem)); \
-    if( (writer).ptr >= (writer).block_max )          \
-    {                                                 \
-        cvCreateSeqBlock( &writer);                   \
-    }                                                 \
-    assert( (writer).ptr <= (writer).block_max - sizeof(elem));\
-    memcpy((writer).ptr, &(elem), sizeof(elem));      \
-    (writer).ptr += sizeof(elem);                     \
-}
-
-
-/** Move reader position forward: */
-#define CV_NEXT_SEQ_ELEM( elem_size, reader )                 \
-{                                                             \
-    if( ((reader).ptr += (elem_size)) >= (reader).block_max ) \
-    {                                                         \
-        cvChangeSeqBlock( &(reader), 1 );                     \
-    }                                                         \
-}
-
-
-/** Move reader position backward: */
-#define CV_PREV_SEQ_ELEM( elem_size, reader )                \
-{                                                            \
-    if( ((reader).ptr -= (elem_size)) < (reader).block_min ) \
-    {                                                        \
-        cvChangeSeqBlock( &(reader), -1 );                   \
-    }                                                        \
-}
-
-/** Read element and move read position forward: */
-#define CV_READ_SEQ_ELEM( elem, reader )                       \
-{                                                              \
-    assert( (reader).seq->elem_size == sizeof(elem));          \
-    memcpy( &(elem), (reader).ptr, sizeof((elem)));            \
-    CV_NEXT_SEQ_ELEM( sizeof(elem), reader )                   \
-}
-
-/** Read element and move read position backward: */
-#define CV_REV_READ_SEQ_ELEM( elem, reader )                     \
-{                                                                \
-    assert( (reader).seq->elem_size == sizeof(elem));            \
-    memcpy(&(elem), (reader).ptr, sizeof((elem)));               \
-    CV_PREV_SEQ_ELEM( sizeof(elem), reader )                     \
-}
-
-
-#define CV_READ_CHAIN_POINT( _pt, reader )                              \
-{                                                                       \
-    (_pt) = (reader).pt;                                                \
-    if( (reader).ptr )                                                  \
-    {                                                                   \
-        CV_READ_SEQ_ELEM( (reader).code, (reader));                     \
-        assert( ((reader).code & ~7) == 0 );                            \
-        (reader).pt.x += (reader).deltas[(int)(reader).code][0];        \
-        (reader).pt.y += (reader).deltas[(int)(reader).code][1];        \
-    }                                                                   \
-}
-
-#define CV_CURRENT_POINT( reader )  (*((CvPoint*)((reader).ptr)))
-#define CV_PREV_POINT( reader )     (*((CvPoint*)((reader).prev_elem)))
-
-#define CV_READ_EDGE( pt1, pt2, reader )               \
-{                                                      \
-    assert( sizeof(pt1) == sizeof(CvPoint) &&          \
-            sizeof(pt2) == sizeof(CvPoint) &&          \
-            reader.seq->elem_size == sizeof(CvPoint)); \
-    (pt1) = CV_PREV_POINT( reader );                   \
-    (pt2) = CV_CURRENT_POINT( reader );                \
-    (reader).prev_elem = (reader).ptr;                 \
-    CV_NEXT_SEQ_ELEM( sizeof(CvPoint), (reader));      \
-}
-
-/************ Graph macros ************/
-
-/** Return next graph edge for given vertex: */
-#define  CV_NEXT_GRAPH_EDGE( edge, vertex )                              \
-     (assert((edge)->vtx[0] == (vertex) || (edge)->vtx[1] == (vertex)),  \
-      (edge)->next[(edge)->vtx[1] == (vertex)])
-
-
-
-/****************************************************************************************\
-*             Data structures for persistence (a.k.a serialization) functionality        *
-\****************************************************************************************/
-
-/** "black box" file storage */
-typedef struct CvFileStorage CvFileStorage;
-
-/** Storage flags: */
-#define CV_STORAGE_READ          0
-#define CV_STORAGE_WRITE         1
-#define CV_STORAGE_WRITE_TEXT    CV_STORAGE_WRITE
-#define CV_STORAGE_WRITE_BINARY  CV_STORAGE_WRITE
-#define CV_STORAGE_APPEND        2
-#define CV_STORAGE_MEMORY        4
-#define CV_STORAGE_FORMAT_MASK   (7<<3)
-#define CV_STORAGE_FORMAT_AUTO   0
-#define CV_STORAGE_FORMAT_XML    8
-#define CV_STORAGE_FORMAT_YAML  16
-#define CV_STORAGE_FORMAT_JSON  24
-#define CV_STORAGE_BASE64       64
-#define CV_STORAGE_WRITE_BASE64  (CV_STORAGE_BASE64 | CV_STORAGE_WRITE)
-
-/** @brief List of attributes. :
-
-In the current implementation, attributes are used to pass extra parameters when writing user
-objects (see cvWrite). XML attributes inside tags are not supported, aside from the object type
-specification (type_id attribute).
-@see cvAttrList, cvAttrValue
- */
-typedef struct CvAttrList
-{
-    const char** attr;         /**< NULL-terminated array of (attribute_name,attribute_value) pairs. */
-    struct CvAttrList* next;   /**< Pointer to next chunk of the attributes list.                    */
-}
-CvAttrList;
-
-/** initializes CvAttrList structure */
-CV_INLINE CvAttrList cvAttrList( const char** attr CV_DEFAULT(NULL),
-                                 CvAttrList* next CV_DEFAULT(NULL) )
-{
-    CvAttrList l;
-    l.attr = attr;
-    l.next = next;
-
-    return l;
-}
-
-struct CvTypeInfo;
-
-#define CV_NODE_NONE        0
-#define CV_NODE_INT         1
-#define CV_NODE_INTEGER     CV_NODE_INT
-#define CV_NODE_REAL        2
-#define CV_NODE_FLOAT       CV_NODE_REAL
-#define CV_NODE_STR         3
-#define CV_NODE_STRING      CV_NODE_STR
-#define CV_NODE_REF         4 /**< not used */
-#define CV_NODE_SEQ         5
-#define CV_NODE_MAP         6
-#define CV_NODE_TYPE_MASK   7
-
-#define CV_NODE_TYPE(flags)  ((flags) & CV_NODE_TYPE_MASK)
-
-/** file node flags */
-#define CV_NODE_FLOW        8 /**<Used only for writing structures in YAML format. */
-#define CV_NODE_USER        16
-#define CV_NODE_EMPTY       32
-#define CV_NODE_NAMED       64
-
-#define CV_NODE_IS_INT(flags)        (CV_NODE_TYPE(flags) == CV_NODE_INT)
-#define CV_NODE_IS_REAL(flags)       (CV_NODE_TYPE(flags) == CV_NODE_REAL)
-#define CV_NODE_IS_STRING(flags)     (CV_NODE_TYPE(flags) == CV_NODE_STRING)
-#define CV_NODE_IS_SEQ(flags)        (CV_NODE_TYPE(flags) == CV_NODE_SEQ)
-#define CV_NODE_IS_MAP(flags)        (CV_NODE_TYPE(flags) == CV_NODE_MAP)
-#define CV_NODE_IS_COLLECTION(flags) (CV_NODE_TYPE(flags) >= CV_NODE_SEQ)
-#define CV_NODE_IS_FLOW(flags)       (((flags) & CV_NODE_FLOW) != 0)
-#define CV_NODE_IS_EMPTY(flags)      (((flags) & CV_NODE_EMPTY) != 0)
-#define CV_NODE_IS_USER(flags)       (((flags) & CV_NODE_USER) != 0)
-#define CV_NODE_HAS_NAME(flags)      (((flags) & CV_NODE_NAMED) != 0)
-
-#define CV_NODE_SEQ_SIMPLE 256
-#define CV_NODE_SEQ_IS_SIMPLE(seq) (((seq)->flags & CV_NODE_SEQ_SIMPLE) != 0)
-
-typedef struct CvString
-{
-    int len;
-    char* ptr;
-}
-CvString;
-
-/** All the keys (names) of elements in the readed file storage
-   are stored in the hash to speed up the lookup operations: */
-typedef struct CvStringHashNode
-{
-    unsigned hashval;
-    CvString str;
-    struct CvStringHashNode* next;
-}
-CvStringHashNode;
-
-typedef struct CvGenericHash CvFileNodeHash;
-
-/** Basic element of the file storage - scalar or collection: */
-typedef struct CvFileNode
-{
-    int tag;
-    struct CvTypeInfo* info; /**< type information
-            (only for user-defined object, for others it is 0) */
-    union
-    {
-        double f; /**< scalar floating-point number */
-        int i;    /**< scalar integer number */
-        CvString str; /**< text string */
-        CvSeq* seq; /**< sequence (ordered collection of file nodes) */
-        CvFileNodeHash* map; /**< map (collection of named file nodes) */
-    } data;
-}
-CvFileNode;
-
-#ifdef __cplusplus
-extern "C" {
-#endif
-typedef int (CV_CDECL *CvIsInstanceFunc)( const void* struct_ptr );
-typedef void (CV_CDECL *CvReleaseFunc)( void** struct_dblptr );
-typedef void* (CV_CDECL *CvReadFunc)( CvFileStorage* storage, CvFileNode* node );
-typedef void (CV_CDECL *CvWriteFunc)( CvFileStorage* storage, const char* name,
-                                      const void* struct_ptr, CvAttrList attributes );
-typedef void* (CV_CDECL *CvCloneFunc)( const void* struct_ptr );
-#ifdef __cplusplus
-}
-#endif
-
-/** @brief Type information
-
-The structure contains information about one of the standard or user-defined types. Instances of the
-type may or may not contain a pointer to the corresponding CvTypeInfo structure. In any case, there
-is a way to find the type info structure for a given object using the cvTypeOf function.
-Alternatively, type info can be found by type name using cvFindType, which is used when an object
-is read from file storage. The user can register a new type with cvRegisterType that adds the type
-information structure into the beginning of the type list. Thus, it is possible to create
-specialized types from generic standard types and override the basic methods.
- */
-typedef struct CvTypeInfo
-{
-    int flags; /**< not used */
-    int header_size; /**< sizeof(CvTypeInfo) */
-    struct CvTypeInfo* prev; /**< previous registered type in the list */
-    struct CvTypeInfo* next; /**< next registered type in the list */
-    const char* type_name; /**< type name, written to file storage */
-    CvIsInstanceFunc is_instance; /**< checks if the passed object belongs to the type */
-    CvReleaseFunc release; /**< releases object (memory etc.) */
-    CvReadFunc read; /**< reads object from file storage */
-    CvWriteFunc write; /**< writes object to file storage */
-    CvCloneFunc clone; /**< creates a copy of the object */
-}
-CvTypeInfo;
-
-
-/**** System data types ******/
-
-typedef struct CvPluginFuncInfo
-{
-    void** func_addr;
-    void* default_func_addr;
-    const char* func_names;
-    int search_modules;
-    int loaded_from;
-}
-CvPluginFuncInfo;
-
-typedef struct CvModuleInfo
-{
-    struct CvModuleInfo* next;
-    const char* name;
-    const char* version;
-    CvPluginFuncInfo* func_tab;
-}
-CvModuleInfo;
-
-/** @} */
-
-#endif /*OPENCV_CORE_TYPES_H*/
-
-/* End of file. */
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/utility.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/utility.hpp
deleted file mode 100755
index 8ee82b8..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/utility.hpp
+++ /dev/null
@@ -1,1258 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
-// Copyright (C) 2015, Itseez Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_CORE_UTILITY_H
-#define OPENCV_CORE_UTILITY_H
-
-#ifndef __cplusplus
-#  error utility.hpp header must be compiled as C++
-#endif
-
-#if defined(check)
-#  warning Detected Apple 'check' macro definition, it can cause build conflicts. Please, include this header before any Apple headers.
-#endif
-
-#include "opencv2/core.hpp"
-#include <ostream>
-
-#ifdef CV_CXX11
-#include <functional>
-#endif
-
-namespace cv
-{
-
-#ifdef CV_COLLECT_IMPL_DATA
-CV_EXPORTS void setImpl(int flags); // set implementation flags and reset storage arrays
-CV_EXPORTS void addImpl(int flag, const char* func = 0); // add implementation and function name to storage arrays
-// Get stored implementation flags and functions names arrays
-// Each implementation entry correspond to function name entry, so you can find which implementation was executed in which function
-CV_EXPORTS int getImpl(std::vector<int> &impl, std::vector<String> &funName);
-
-CV_EXPORTS bool useCollection(); // return implementation collection state
-CV_EXPORTS void setUseCollection(bool flag); // set implementation collection state
-
-#define CV_IMPL_PLAIN  0x01 // native CPU OpenCV implementation
-#define CV_IMPL_OCL    0x02 // OpenCL implementation
-#define CV_IMPL_IPP    0x04 // IPP implementation
-#define CV_IMPL_MT     0x10 // multithreaded implementation
-
-#define CV_IMPL_ADD(impl)                                                   \
-    if(cv::useCollection())                                                 \
-    {                                                                       \
-        cv::addImpl(impl, CV_Func);                                         \
-    }
-#else
-#define CV_IMPL_ADD(impl)
-#endif
-
-//! @addtogroup core_utils
-//! @{
-
-/** @brief  Automatically Allocated Buffer Class
-
- The class is used for temporary buffers in functions and methods.
- If a temporary buffer is usually small (a few K's of memory),
- but its size depends on the parameters, it makes sense to create a small
- fixed-size array on stack and use it if it's large enough. If the required buffer size
- is larger than the fixed size, another buffer of sufficient size is allocated dynamically
- and released after the processing. Therefore, in typical cases, when the buffer size is small,
- there is no overhead associated with malloc()/free().
- At the same time, there is no limit on the size of processed data.
-
- This is what AutoBuffer does. The template takes 2 parameters - type of the buffer elements and
- the number of stack-allocated elements. Here is how the class is used:
-
- \code
- void my_func(const cv::Mat& m)
- {
-    cv::AutoBuffer<float> buf(1000); // create automatic buffer containing 1000 floats
-
-    buf.allocate(m.rows); // if m.rows <= 1000, the pre-allocated buffer is used,
-                          // otherwise the buffer of "m.rows" floats will be allocated
-                          // dynamically and deallocated in cv::AutoBuffer destructor
-    ...
- }
- \endcode
-*/
-template<typename _Tp, size_t fixed_size = 1024/sizeof(_Tp)+8> class AutoBuffer
-{
-public:
-    typedef _Tp value_type;
-
-    //! the default constructor
-    AutoBuffer();
-    //! constructor taking the real buffer size
-    explicit AutoBuffer(size_t _size);
-
-    //! the copy constructor
-    AutoBuffer(const AutoBuffer<_Tp, fixed_size>& buf);
-    //! the assignment operator
-    AutoBuffer<_Tp, fixed_size>& operator = (const AutoBuffer<_Tp, fixed_size>& buf);
-
-    //! destructor. calls deallocate()
-    ~AutoBuffer();
-
-    //! allocates the new buffer of size _size. if the _size is small enough, stack-allocated buffer is used
-    void allocate(size_t _size);
-    //! deallocates the buffer if it was dynamically allocated
-    void deallocate();
-    //! resizes the buffer and preserves the content
-    void resize(size_t _size);
-    //! returns the current buffer size
-    size_t size() const;
-    //! returns pointer to the real buffer, stack-allocated or heap-allocated
-    operator _Tp* ();
-    //! returns read-only pointer to the real buffer, stack-allocated or heap-allocated
-    operator const _Tp* () const;
-
-protected:
-    //! pointer to the real buffer, can point to buf if the buffer is small enough
-    _Tp* ptr;
-    //! size of the real buffer
-    size_t sz;
-    //! pre-allocated buffer. At least 1 element to confirm C++ standard requirements
-    _Tp buf[(fixed_size > 0) ? fixed_size : 1];
-};
-
-/**  @brief Sets/resets the break-on-error mode.
-
-When the break-on-error mode is set, the default error handler issues a hardware exception, which
-can make debugging more convenient.
-
-\return the previous state
- */
-CV_EXPORTS bool setBreakOnError(bool flag);
-
-extern "C" typedef int (*ErrorCallback)( int status, const char* func_name,
-                                       const char* err_msg, const char* file_name,
-                                       int line, void* userdata );
-
-
-/** @brief Sets the new error handler and the optional user data.
-
-  The function sets the new error handler, called from cv::error().
-
-  \param errCallback the new error handler. If NULL, the default error handler is used.
-  \param userdata the optional user data pointer, passed to the callback.
-  \param prevUserdata the optional output parameter where the previous user data pointer is stored
-
-  \return the previous error handler
-*/
-CV_EXPORTS ErrorCallback redirectError( ErrorCallback errCallback, void* userdata=0, void** prevUserdata=0);
-
-CV_EXPORTS String tempfile( const char* suffix = 0);
-CV_EXPORTS void glob(String pattern, std::vector<String>& result, bool recursive = false);
-
-/** @brief OpenCV will try to set the number of threads for the next parallel region.
-
-If threads == 0, OpenCV will disable threading optimizations and run all it's functions
-sequentially. Passing threads \< 0 will reset threads number to system default. This function must
-be called outside of parallel region.
-
-OpenCV will try to run its functions with specified threads number, but some behaviour differs from
-framework:
--   `TBB` - User-defined parallel constructions will run with the same threads number, if
-    another is not specified. If later on user creates his own scheduler, OpenCV will use it.
--   `OpenMP` - No special defined behaviour.
--   `Concurrency` - If threads == 1, OpenCV will disable threading optimizations and run its
-    functions sequentially.
--   `GCD` - Supports only values \<= 0.
--   `C=` - No special defined behaviour.
-@param nthreads Number of threads used by OpenCV.
-@sa getNumThreads, getThreadNum
- */
-CV_EXPORTS_W void setNumThreads(int nthreads);
-
-/** @brief Returns the number of threads used by OpenCV for parallel regions.
-
-Always returns 1 if OpenCV is built without threading support.
-
-The exact meaning of return value depends on the threading framework used by OpenCV library:
-- `TBB` - The number of threads, that OpenCV will try to use for parallel regions. If there is
-  any tbb::thread_scheduler_init in user code conflicting with OpenCV, then function returns
-  default number of threads used by TBB library.
-- `OpenMP` - An upper bound on the number of threads that could be used to form a new team.
-- `Concurrency` - The number of threads, that OpenCV will try to use for parallel regions.
-- `GCD` - Unsupported; returns the GCD thread pool limit (512) for compatibility.
-- `C=` - The number of threads, that OpenCV will try to use for parallel regions, if before
-  called setNumThreads with threads \> 0, otherwise returns the number of logical CPUs,
-  available for the process.
-@sa setNumThreads, getThreadNum
- */
-CV_EXPORTS_W int getNumThreads();
-
-/** @brief Returns the index of the currently executed thread within the current parallel region. Always
-returns 0 if called outside of parallel region.
-
-@deprecated Current implementation doesn't corresponding to this documentation.
-
-The exact meaning of the return value depends on the threading framework used by OpenCV library:
-- `TBB` - Unsupported with current 4.1 TBB release. Maybe will be supported in future.
-- `OpenMP` - The thread number, within the current team, of the calling thread.
-- `Concurrency` - An ID for the virtual processor that the current context is executing on (0
-  for master thread and unique number for others, but not necessary 1,2,3,...).
-- `GCD` - System calling thread's ID. Never returns 0 inside parallel region.
-- `C=` - The index of the current parallel task.
-@sa setNumThreads, getNumThreads
- */
-CV_EXPORTS_W int getThreadNum();
-
-/** @brief Returns full configuration time cmake output.
-
-Returned value is raw cmake output including version control system revision, compiler version,
-compiler flags, enabled modules and third party libraries, etc. Output format depends on target
-architecture.
- */
-CV_EXPORTS_W const String& getBuildInformation();
-
-/** @brief Returns library version string
-
-For example "3.4.1-dev".
-
-@sa getMajorVersion, getMinorVersion, getRevisionVersion
-*/
-CV_EXPORTS_W String getVersionString();
-
-/** @brief Returns major library version */
-CV_EXPORTS_W int getVersionMajor();
-
-/** @brief Returns minor library version */
-CV_EXPORTS_W int getVersionMinor();
-
-/** @brief Returns revision field of the library version */
-CV_EXPORTS_W int getVersionRevision();
-
-/** @brief Returns the number of ticks.
-
-The function returns the number of ticks after the certain event (for example, when the machine was
-turned on). It can be used to initialize RNG or to measure a function execution time by reading the
-tick count before and after the function call.
-@sa getTickFrequency, TickMeter
- */
-CV_EXPORTS_W int64 getTickCount();
-
-/** @brief Returns the number of ticks per second.
-
-The function returns the number of ticks per second. That is, the following code computes the
-execution time in seconds:
-@code
-    double t = (double)getTickCount();
-    // do something ...
-    t = ((double)getTickCount() - t)/getTickFrequency();
-@endcode
-@sa getTickCount, TickMeter
- */
-CV_EXPORTS_W double getTickFrequency();
-
-/** @brief a Class to measure passing time.
-
-The class computes passing time by counting the number of ticks per second. That is, the following code computes the
-execution time in seconds:
-@code
-TickMeter tm;
-tm.start();
-// do something ...
-tm.stop();
-std::cout << tm.getTimeSec();
-@endcode
-
-It is also possible to compute the average time over multiple runs:
-@code
-TickMeter tm;
-for (int i = 0; i < 100; i++)
-{
-    tm.start();
-    // do something ...
-    tm.stop();
-}
-double average_time = tm.getTimeSec() / tm.getCounter();
-std::cout << "Average time in second per iteration is: " << average_time << std::endl;
-@endcode
-@sa getTickCount, getTickFrequency
-*/
-
-class CV_EXPORTS_W TickMeter
-{
-public:
-    //! the default constructor
-    CV_WRAP TickMeter()
-    {
-    reset();
-    }
-
-    /**
-    starts counting ticks.
-    */
-    CV_WRAP void start()
-    {
-    startTime = cv::getTickCount();
-    }
-
-    /**
-    stops counting ticks.
-    */
-    CV_WRAP void stop()
-    {
-    int64 time = cv::getTickCount();
-    if (startTime == 0)
-    return;
-    ++counter;
-    sumTime += (time - startTime);
-    startTime = 0;
-    }
-
-    /**
-    returns counted ticks.
-    */
-    CV_WRAP int64 getTimeTicks() const
-    {
-    return sumTime;
-    }
-
-    /**
-    returns passed time in microseconds.
-    */
-    CV_WRAP double getTimeMicro() const
-    {
-    return getTimeMilli()*1e3;
-    }
-
-    /**
-    returns passed time in milliseconds.
-    */
-    CV_WRAP double getTimeMilli() const
-    {
-    return getTimeSec()*1e3;
-    }
-
-    /**
-    returns passed time in seconds.
-    */
-    CV_WRAP double getTimeSec()   const
-    {
-    return (double)getTimeTicks() / getTickFrequency();
-    }
-
-    /**
-    returns internal counter value.
-    */
-    CV_WRAP int64 getCounter() const
-    {
-    return counter;
-    }
-
-    /**
-    resets internal values.
-    */
-    CV_WRAP void reset()
-    {
-    startTime = 0;
-    sumTime = 0;
-    counter = 0;
-    }
-
-private:
-    int64 counter;
-    int64 sumTime;
-    int64 startTime;
-};
-
-/** @brief output operator
-@code
-TickMeter tm;
-tm.start();
-// do something ...
-tm.stop();
-std::cout << tm;
-@endcode
-*/
-
-static inline
-std::ostream& operator << (std::ostream& out, const TickMeter& tm)
-{
-    return out << tm.getTimeSec() << "sec";
-}
-
-/** @brief Returns the number of CPU ticks.
-
-The function returns the current number of CPU ticks on some architectures (such as x86, x64,
-PowerPC). On other platforms the function is equivalent to getTickCount. It can also be used for
-very accurate time measurements, as well as for RNG initialization. Note that in case of multi-CPU
-systems a thread, from which getCPUTickCount is called, can be suspended and resumed at another CPU
-with its own counter. So, theoretically (and practically) the subsequent calls to the function do
-not necessary return the monotonously increasing values. Also, since a modern CPU varies the CPU
-frequency depending on the load, the number of CPU clocks spent in some code cannot be directly
-converted to time units. Therefore, getTickCount is generally a preferable solution for measuring
-execution time.
- */
-CV_EXPORTS_W int64 getCPUTickCount();
-
-/** @brief Returns true if the specified feature is supported by the host hardware.
-
-The function returns true if the host hardware supports the specified feature. When user calls
-setUseOptimized(false), the subsequent calls to checkHardwareSupport() will return false until
-setUseOptimized(true) is called. This way user can dynamically switch on and off the optimized code
-in OpenCV.
-@param feature The feature of interest, one of cv::CpuFeatures
- */
-CV_EXPORTS_W bool checkHardwareSupport(int feature);
-
-/** @brief Returns feature name by ID
-
-Returns empty string if feature is not defined
-*/
-CV_EXPORTS_W String getHardwareFeatureName(int feature);
-
-/** @brief Returns the number of logical CPUs available for the process.
- */
-CV_EXPORTS_W int getNumberOfCPUs();
-
-
-/** @brief Aligns a pointer to the specified number of bytes.
-
-The function returns the aligned pointer of the same type as the input pointer:
-\f[\texttt{(_Tp*)(((size_t)ptr + n-1) & -n)}\f]
-@param ptr Aligned pointer.
-@param n Alignment size that must be a power of two.
- */
-template<typename _Tp> static inline _Tp* alignPtr(_Tp* ptr, int n=(int)sizeof(_Tp))
-{
-    CV_DbgAssert((n & (n - 1)) == 0); // n is a power of 2
-    return (_Tp*)(((size_t)ptr + n-1) & -n);
-}
-
-/** @brief Aligns a buffer size to the specified number of bytes.
-
-The function returns the minimum number that is greater than or equal to sz and is divisible by n :
-\f[\texttt{(sz + n-1) & -n}\f]
-@param sz Buffer size to align.
-@param n Alignment size that must be a power of two.
- */
-static inline size_t alignSize(size_t sz, int n)
-{
-    CV_DbgAssert((n & (n - 1)) == 0); // n is a power of 2
-    return (sz + n-1) & -n;
-}
-
-/** @brief Integer division with result round up.
-
-Use this function instead of `ceil((float)a / b)` expressions.
-
-@sa alignSize
-*/
-static inline int divUp(int a, unsigned int b)
-{
-    CV_DbgAssert(a >= 0);
-    return (a + b - 1) / b;
-}
-/** @overload */
-static inline size_t divUp(size_t a, unsigned int b)
-{
-    return (a + b - 1) / b;
-}
-
-/** @brief Enables or disables the optimized code.
-
-The function can be used to dynamically turn on and off optimized code (code that uses SSE2, AVX,
-and other instructions on the platforms that support it). It sets a global flag that is further
-checked by OpenCV functions. Since the flag is not checked in the inner OpenCV loops, it is only
-safe to call the function on the very top level in your application where you can be sure that no
-other OpenCV function is currently executed.
-
-By default, the optimized code is enabled unless you disable it in CMake. The current status can be
-retrieved using useOptimized.
-@param onoff The boolean flag specifying whether the optimized code should be used (onoff=true)
-or not (onoff=false).
- */
-CV_EXPORTS_W void setUseOptimized(bool onoff);
-
-/** @brief Returns the status of optimized code usage.
-
-The function returns true if the optimized code is enabled. Otherwise, it returns false.
- */
-CV_EXPORTS_W bool useOptimized();
-
-static inline size_t getElemSize(int type) { return (size_t)CV_ELEM_SIZE(type); }
-
-/////////////////////////////// Parallel Primitives //////////////////////////////////
-
-/** @brief Base class for parallel data processors
-*/
-class CV_EXPORTS ParallelLoopBody
-{
-public:
-    virtual ~ParallelLoopBody();
-    virtual void operator() (const Range& range) const = 0;
-};
-
-/** @brief Parallel data processor
-*/
-CV_EXPORTS void parallel_for_(const Range& range, const ParallelLoopBody& body, double nstripes=-1.);
-
-#ifdef CV_CXX11
-class ParallelLoopBodyLambdaWrapper : public ParallelLoopBody
-{
-private:
-    std::function<void(const Range&)> m_functor;
-public:
-    ParallelLoopBodyLambdaWrapper(std::function<void(const Range&)> functor) :
-        m_functor(functor)
-    { }
-
-    virtual void operator() (const cv::Range& range) const CV_OVERRIDE
-    {
-        m_functor(range);
-    }
-};
-
-inline void parallel_for_(const Range& range, std::function<void(const Range&)> functor, double nstripes=-1.)
-{
-    parallel_for_(range, ParallelLoopBodyLambdaWrapper(functor), nstripes);
-}
-#endif
-
-/////////////////////////////// forEach method of cv::Mat ////////////////////////////
-template<typename _Tp, typename Functor> inline
-void Mat::forEach_impl(const Functor& operation) {
-    if (false) {
-        operation(*reinterpret_cast<_Tp*>(0), reinterpret_cast<int*>(0));
-        // If your compiler fails in this line.
-        // Please check that your functor signature is
-        //     (_Tp&, const int*)   <- multi-dimensional
-        //  or (_Tp&, void*)        <- in case you don't need current idx.
-    }
-
-    CV_Assert(this->total() / this->size[this->dims - 1] <= INT_MAX);
-    const int LINES = static_cast<int>(this->total() / this->size[this->dims - 1]);
-
-    class PixelOperationWrapper :public ParallelLoopBody
-    {
-    public:
-        PixelOperationWrapper(Mat_<_Tp>* const frame, const Functor& _operation)
-            : mat(frame), op(_operation) {}
-        virtual ~PixelOperationWrapper(){}
-        // ! Overloaded virtual operator
-        // convert range call to row call.
-        virtual void operator()(const Range &range) const CV_OVERRIDE
-        {
-            const int DIMS = mat->dims;
-            const int COLS = mat->size[DIMS - 1];
-            if (DIMS <= 2) {
-                for (int row = range.start; row < range.end; ++row) {
-                    this->rowCall2(row, COLS);
-                }
-            } else {
-                std::vector<int> idx(DIMS); /// idx is modified in this->rowCall
-                idx[DIMS - 2] = range.start - 1;
-
-                for (int line_num = range.start; line_num < range.end; ++line_num) {
-                    idx[DIMS - 2]++;
-                    for (int i = DIMS - 2; i >= 0; --i) {
-                        if (idx[i] >= mat->size[i]) {
-                            idx[i - 1] += idx[i] / mat->size[i];
-                            idx[i] %= mat->size[i];
-                            continue; // carry-over;
-                        }
-                        else {
-                            break;
-                        }
-                    }
-                    this->rowCall(&idx[0], COLS, DIMS);
-                }
-            }
-        }
-    private:
-        Mat_<_Tp>* const mat;
-        const Functor op;
-        // ! Call operator for each elements in this row.
-        inline void rowCall(int* const idx, const int COLS, const int DIMS) const {
-            int &col = idx[DIMS - 1];
-            col = 0;
-            _Tp* pixel = &(mat->template at<_Tp>(idx));
-
-            while (col < COLS) {
-                op(*pixel, const_cast<const int*>(idx));
-                pixel++; col++;
-            }
-            col = 0;
-        }
-        // ! Call operator for each elements in this row. 2d mat special version.
-        inline void rowCall2(const int row, const int COLS) const {
-            union Index{
-                int body[2];
-                operator const int*() const {
-                    return reinterpret_cast<const int*>(this);
-                }
-                int& operator[](const int i) {
-                    return body[i];
-                }
-            } idx = {{row, 0}};
-            // Special union is needed to avoid
-            // "error: array subscript is above array bounds [-Werror=array-bounds]"
-            // when call the functor `op` such that access idx[3].
-
-            _Tp* pixel = &(mat->template at<_Tp>(idx));
-            const _Tp* const pixel_end = pixel + COLS;
-            while(pixel < pixel_end) {
-                op(*pixel++, static_cast<const int*>(idx));
-                idx[1]++;
-            }
-        }
-        PixelOperationWrapper& operator=(const PixelOperationWrapper &) {
-            CV_Assert(false);
-            // We can not remove this implementation because Visual Studio warning C4822.
-            return *this;
-        }
-    };
-
-    parallel_for_(cv::Range(0, LINES), PixelOperationWrapper(reinterpret_cast<Mat_<_Tp>*>(this), operation));
-}
-
-/////////////////////////// Synchronization Primitives ///////////////////////////////
-
-class CV_EXPORTS Mutex
-{
-public:
-    Mutex();
-    ~Mutex();
-    Mutex(const Mutex& m);
-    Mutex& operator = (const Mutex& m);
-
-    void lock();
-    bool trylock();
-    void unlock();
-
-    struct Impl;
-protected:
-    Impl* impl;
-};
-
-class CV_EXPORTS AutoLock
-{
-public:
-    AutoLock(Mutex& m) : mutex(&m) { mutex->lock(); }
-    ~AutoLock() { mutex->unlock(); }
-protected:
-    Mutex* mutex;
-private:
-    AutoLock(const AutoLock&);
-    AutoLock& operator = (const AutoLock&);
-};
-
-// TLS interface
-class CV_EXPORTS TLSDataContainer
-{
-protected:
-    TLSDataContainer();
-    virtual ~TLSDataContainer();
-
-    void  gatherData(std::vector<void*> &data) const;
-#if OPENCV_ABI_COMPATIBILITY > 300
-    void* getData() const;
-    void  release();
-
-private:
-#else
-    void  release();
-
-public:
-    void* getData() const;
-#endif
-    virtual void* createDataInstance() const = 0;
-    virtual void  deleteDataInstance(void* pData) const = 0;
-
-    int key_;
-
-public:
-    void cleanup(); //! Release created TLS data container objects. It is similar to release() call, but it keeps TLS container valid.
-};
-
-// Main TLS data class
-template <typename T>
-class TLSData : protected TLSDataContainer
-{
-public:
-    inline TLSData()        {}
-    inline ~TLSData()       { release();            } // Release key and delete associated data
-    inline T* get() const   { return (T*)getData(); } // Get data associated with key
-    inline T& getRef() const { T* ptr = (T*)getData(); CV_Assert(ptr); return *ptr; } // Get data associated with key
-
-    // Get data from all threads
-    inline void gather(std::vector<T*> &data) const
-    {
-        std::vector<void*> &dataVoid = reinterpret_cast<std::vector<void*>&>(data);
-        gatherData(dataVoid);
-    }
-
-    inline void cleanup() { TLSDataContainer::cleanup(); }
-
-private:
-    virtual void* createDataInstance() const CV_OVERRIDE {return new T;}                // Wrapper to allocate data by template
-    virtual void  deleteDataInstance(void* pData) const CV_OVERRIDE {delete (T*)pData;} // Wrapper to release data by template
-
-    // Disable TLS copy operations
-    TLSData(TLSData &) {}
-    TLSData& operator =(const TLSData &) {return *this;}
-};
-
-/** @brief Designed for command line parsing
-
-The sample below demonstrates how to use CommandLineParser:
-@code
-    CommandLineParser parser(argc, argv, keys);
-    parser.about("Application name v1.0.0");
-
-    if (parser.has("help"))
-    {
-        parser.printMessage();
-        return 0;
-    }
-
-    int N = parser.get<int>("N");
-    double fps = parser.get<double>("fps");
-    String path = parser.get<String>("path");
-
-    use_time_stamp = parser.has("timestamp");
-
-    String img1 = parser.get<String>(0);
-    String img2 = parser.get<String>(1);
-
-    int repeat = parser.get<int>(2);
-
-    if (!parser.check())
-    {
-        parser.printErrors();
-        return 0;
-    }
-@endcode
-
-### Keys syntax
-
-The keys parameter is a string containing several blocks, each one is enclosed in curly braces and
-describes one argument. Each argument contains three parts separated by the `|` symbol:
-
--# argument names is a space-separated list of option synonyms (to mark argument as positional, prefix it with the `@` symbol)
--# default value will be used if the argument was not provided (can be empty)
--# help message (can be empty)
-
-For example:
-
-@code{.cpp}
-    const String keys =
-        "{help h usage ? |      | print this message   }"
-        "{@image1        |      | image1 for compare   }"
-        "{@image2        |<none>| image2 for compare   }"
-        "{@repeat        |1     | number               }"
-        "{path           |.     | path to file         }"
-        "{fps            | -1.0 | fps for output video }"
-        "{N count        |100   | count of objects     }"
-        "{ts timestamp   |      | use time stamp       }"
-        ;
-}
-@endcode
-
-Note that there are no default values for `help` and `timestamp` so we can check their presence using the `has()` method.
-Arguments with default values are considered to be always present. Use the `get()` method in these cases to check their
-actual value instead.
-
-String keys like `get<String>("@image1")` return the empty string `""` by default - even with an empty default value.
-Use the special `<none>` default value to enforce that the returned string must not be empty. (like in `get<String>("@image2")`)
-
-### Usage
-
-For the described keys:
-
-@code{.sh}
-    # Good call (3 positional parameters: image1, image2 and repeat; N is 200, ts is true)
-    $ ./app -N=200 1.png 2.jpg 19 -ts
-
-    # Bad call
-    $ ./app -fps=aaa
-    ERRORS:
-    Parameter 'fps': can not convert: [aaa] to [double]
-@endcode
- */
-class CV_EXPORTS CommandLineParser
-{
-public:
-
-    /** @brief Constructor
-
-    Initializes command line parser object
-
-    @param argc number of command line arguments (from main())
-    @param argv array of command line arguments (from main())
-    @param keys string describing acceptable command line parameters (see class description for syntax)
-    */
-    CommandLineParser(int argc, const char* const argv[], const String& keys);
-
-    /** @brief Copy constructor */
-    CommandLineParser(const CommandLineParser& parser);
-
-    /** @brief Assignment operator */
-    CommandLineParser& operator = (const CommandLineParser& parser);
-
-    /** @brief Destructor */
-    ~CommandLineParser();
-
-    /** @brief Returns application path
-
-    This method returns the path to the executable from the command line (`argv[0]`).
-
-    For example, if the application has been started with such a command:
-    @code{.sh}
-    $ ./bin/my-executable
-    @endcode
-    this method will return `./bin`.
-    */
-    String getPathToApplication() const;
-
-    /** @brief Access arguments by name
-
-    Returns argument converted to selected type. If the argument is not known or can not be
-    converted to selected type, the error flag is set (can be checked with @ref check).
-
-    For example, define:
-    @code{.cpp}
-    String keys = "{N count||}";
-    @endcode
-
-    Call:
-    @code{.sh}
-    $ ./my-app -N=20
-    # or
-    $ ./my-app --count=20
-    @endcode
-
-    Access:
-    @code{.cpp}
-    int N = parser.get<int>("N");
-    @endcode
-
-    @param name name of the argument
-    @param space_delete remove spaces from the left and right of the string
-    @tparam T the argument will be converted to this type if possible
-
-    @note You can access positional arguments by their `@`-prefixed name:
-    @code{.cpp}
-    parser.get<String>("@image");
-    @endcode
-     */
-    template <typename T>
-    T get(const String& name, bool space_delete = true) const
-    {
-        T val = T();
-        getByName(name, space_delete, ParamType<T>::type, (void*)&val);
-        return val;
-    }
-
-    /** @brief Access positional arguments by index
-
-    Returns argument converted to selected type. Indexes are counted from zero.
-
-    For example, define:
-    @code{.cpp}
-    String keys = "{@arg1||}{@arg2||}"
-    @endcode
-
-    Call:
-    @code{.sh}
-    ./my-app abc qwe
-    @endcode
-
-    Access arguments:
-    @code{.cpp}
-    String val_1 = parser.get<String>(0); // returns "abc", arg1
-    String val_2 = parser.get<String>(1); // returns "qwe", arg2
-    @endcode
-
-    @param index index of the argument
-    @param space_delete remove spaces from the left and right of the string
-    @tparam T the argument will be converted to this type if possible
-     */
-    template <typename T>
-    T get(int index, bool space_delete = true) const
-    {
-        T val = T();
-        getByIndex(index, space_delete, ParamType<T>::type, (void*)&val);
-        return val;
-    }
-
-    /** @brief Check if field was provided in the command line
-
-    @param name argument name to check
-    */
-    bool has(const String& name) const;
-
-    /** @brief Check for parsing errors
-
-    Returns false if error occurred while accessing the parameters (bad conversion, missing arguments,
-    etc.). Call @ref printErrors to print error messages list.
-     */
-    bool check() const;
-
-    /** @brief Set the about message
-
-    The about message will be shown when @ref printMessage is called, right before arguments table.
-     */
-    void about(const String& message);
-
-    /** @brief Print help message
-
-    This method will print standard help message containing the about message and arguments description.
-
-    @sa about
-    */
-    void printMessage() const;
-
-    /** @brief Print list of errors occurred
-
-    @sa check
-    */
-    void printErrors() const;
-
-protected:
-    void getByName(const String& name, bool space_delete, int type, void* dst) const;
-    void getByIndex(int index, bool space_delete, int type, void* dst) const;
-
-    struct Impl;
-    Impl* impl;
-};
-
-//! @} core_utils
-
-//! @cond IGNORED
-
-/////////////////////////////// AutoBuffer implementation ////////////////////////////////////////
-
-template<typename _Tp, size_t fixed_size> inline
-AutoBuffer<_Tp, fixed_size>::AutoBuffer()
-{
-    ptr = buf;
-    sz = fixed_size;
-}
-
-template<typename _Tp, size_t fixed_size> inline
-AutoBuffer<_Tp, fixed_size>::AutoBuffer(size_t _size)
-{
-    ptr = buf;
-    sz = fixed_size;
-    allocate(_size);
-}
-
-template<typename _Tp, size_t fixed_size> inline
-AutoBuffer<_Tp, fixed_size>::AutoBuffer(const AutoBuffer<_Tp, fixed_size>& abuf )
-{
-    ptr = buf;
-    sz = fixed_size;
-    allocate(abuf.size());
-    for( size_t i = 0; i < sz; i++ )
-        ptr[i] = abuf.ptr[i];
-}
-
-template<typename _Tp, size_t fixed_size> inline AutoBuffer<_Tp, fixed_size>&
-AutoBuffer<_Tp, fixed_size>::operator = (const AutoBuffer<_Tp, fixed_size>& abuf)
-{
-    if( this != &abuf )
-    {
-        deallocate();
-        allocate(abuf.size());
-        for( size_t i = 0; i < sz; i++ )
-            ptr[i] = abuf.ptr[i];
-    }
-    return *this;
-}
-
-template<typename _Tp, size_t fixed_size> inline
-AutoBuffer<_Tp, fixed_size>::~AutoBuffer()
-{ deallocate(); }
-
-template<typename _Tp, size_t fixed_size> inline void
-AutoBuffer<_Tp, fixed_size>::allocate(size_t _size)
-{
-    if(_size <= sz)
-    {
-        sz = _size;
-        return;
-    }
-    deallocate();
-    sz = _size;
-    if(_size > fixed_size)
-    {
-        ptr = new _Tp[_size];
-    }
-}
-
-template<typename _Tp, size_t fixed_size> inline void
-AutoBuffer<_Tp, fixed_size>::deallocate()
-{
-    if( ptr != buf )
-    {
-        delete[] ptr;
-        ptr = buf;
-        sz = fixed_size;
-    }
-}
-
-template<typename _Tp, size_t fixed_size> inline void
-AutoBuffer<_Tp, fixed_size>::resize(size_t _size)
-{
-    if(_size <= sz)
-    {
-        sz = _size;
-        return;
-    }
-    size_t i, prevsize = sz, minsize = MIN(prevsize, _size);
-    _Tp* prevptr = ptr;
-
-    ptr = _size > fixed_size ? new _Tp[_size] : buf;
-    sz = _size;
-
-    if( ptr != prevptr )
-        for( i = 0; i < minsize; i++ )
-            ptr[i] = prevptr[i];
-    for( i = prevsize; i < _size; i++ )
-        ptr[i] = _Tp();
-
-    if( prevptr != buf )
-        delete[] prevptr;
-}
-
-template<typename _Tp, size_t fixed_size> inline size_t
-AutoBuffer<_Tp, fixed_size>::size() const
-{ return sz; }
-
-template<typename _Tp, size_t fixed_size> inline
-AutoBuffer<_Tp, fixed_size>::operator _Tp* ()
-{ return ptr; }
-
-template<typename _Tp, size_t fixed_size> inline
-AutoBuffer<_Tp, fixed_size>::operator const _Tp* () const
-{ return ptr; }
-
-template<> inline std::string CommandLineParser::get<std::string>(int index, bool space_delete) const
-{
-    return get<String>(index, space_delete);
-}
-template<> inline std::string CommandLineParser::get<std::string>(const String& name, bool space_delete) const
-{
-    return get<String>(name, space_delete);
-}
-
-//! @endcond
-
-
-// Basic Node class for tree building
-template<class OBJECT>
-class CV_EXPORTS Node
-{
-public:
-    Node()
-    {
-        m_pParent  = 0;
-    }
-    Node(OBJECT& payload) : m_payload(payload)
-    {
-        m_pParent  = 0;
-    }
-    ~Node()
-    {
-        removeChilds();
-        if (m_pParent)
-        {
-            int idx = m_pParent->findChild(this);
-            if (idx >= 0)
-                m_pParent->m_childs.erase(m_pParent->m_childs.begin() + idx);
-        }
-    }
-
-    Node<OBJECT>* findChild(OBJECT& payload) const
-    {
-        for(size_t i = 0; i < this->m_childs.size(); i++)
-        {
-            if(this->m_childs[i]->m_payload == payload)
-                return this->m_childs[i];
-        }
-        return NULL;
-    }
-
-    int findChild(Node<OBJECT> *pNode) const
-    {
-        for (size_t i = 0; i < this->m_childs.size(); i++)
-        {
-            if(this->m_childs[i] == pNode)
-                return (int)i;
-        }
-        return -1;
-    }
-
-    void addChild(Node<OBJECT> *pNode)
-    {
-        if(!pNode)
-            return;
-
-        CV_Assert(pNode->m_pParent == 0);
-        pNode->m_pParent = this;
-        this->m_childs.push_back(pNode);
-    }
-
-    void removeChilds()
-    {
-        for(size_t i = 0; i < m_childs.size(); i++)
-        {
-            m_childs[i]->m_pParent = 0; // avoid excessive parent vector trimming
-            delete m_childs[i];
-        }
-        m_childs.clear();
-    }
-
-    int getDepth()
-    {
-        int   count   = 0;
-        Node *pParent = m_pParent;
-        while(pParent) count++, pParent = pParent->m_pParent;
-        return count;
-    }
-
-public:
-    OBJECT                     m_payload;
-    Node<OBJECT>*              m_pParent;
-    std::vector<Node<OBJECT>*> m_childs;
-};
-
-// Instrumentation external interface
-namespace instr
-{
-
-#if !defined OPENCV_ABI_CHECK
-
-enum TYPE
-{
-    TYPE_GENERAL = 0,   // OpenCV API function, e.g. exported function
-    TYPE_MARKER,        // Information marker
-    TYPE_WRAPPER,       // Wrapper function for implementation
-    TYPE_FUN,           // Simple function call
-};
-
-enum IMPL
-{
-    IMPL_PLAIN = 0,
-    IMPL_IPP,
-    IMPL_OPENCL,
-};
-
-struct NodeDataTls
-{
-    NodeDataTls()
-    {
-        m_ticksTotal = 0;
-    }
-    uint64      m_ticksTotal;
-};
-
-class CV_EXPORTS NodeData
-{
-public:
-    NodeData(const char* funName = 0, const char* fileName = NULL, int lineNum = 0, void* retAddress = NULL, bool alwaysExpand = false, cv::instr::TYPE instrType = TYPE_GENERAL, cv::instr::IMPL implType = IMPL_PLAIN);
-    NodeData(NodeData &ref);
-    ~NodeData();
-    NodeData& operator=(const NodeData&);
-
-    cv::String          m_funName;
-    cv::instr::TYPE     m_instrType;
-    cv::instr::IMPL     m_implType;
-    const char*         m_fileName;
-    int                 m_lineNum;
-    void*               m_retAddress;
-    bool                m_alwaysExpand;
-    bool                m_funError;
-
-    volatile int         m_counter;
-    volatile uint64      m_ticksTotal;
-    TLSData<NodeDataTls> m_tls;
-    int                  m_threads;
-
-    // No synchronization
-    double getTotalMs()   const { return ((double)m_ticksTotal / cv::getTickFrequency()) * 1000; }
-    double getMeanMs()    const { return (((double)m_ticksTotal/m_counter) / cv::getTickFrequency()) * 1000; }
-};
-bool operator==(const NodeData& lhs, const NodeData& rhs);
-
-typedef Node<NodeData> InstrNode;
-
-CV_EXPORTS InstrNode* getTrace();
-
-#endif // !defined OPENCV_ABI_CHECK
-
-
-CV_EXPORTS bool       useInstrumentation();
-CV_EXPORTS void       setUseInstrumentation(bool flag);
-CV_EXPORTS void       resetTrace();
-
-enum FLAGS
-{
-    FLAGS_NONE              = 0,
-    FLAGS_MAPPING           = 0x01,
-    FLAGS_EXPAND_SAME_NAMES = 0x02,
-};
-
-CV_EXPORTS void       setFlags(FLAGS modeFlags);
-static inline void    setFlags(int modeFlags) { setFlags((FLAGS)modeFlags); }
-CV_EXPORTS FLAGS      getFlags();
-}
-
-namespace utils {
-
-CV_EXPORTS int getThreadID();
-
-} // namespace
-
-} //namespace cv
-
-#ifndef DISABLE_OPENCV_24_COMPATIBILITY
-#include "opencv2/core/core_c.h"
-#endif
-
-#endif //OPENCV_CORE_UTILITY_H
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/utils/filesystem.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/utils/filesystem.hpp
deleted file mode 100755
index 12b10a7..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/utils/filesystem.hpp
+++ /dev/null
@@ -1,71 +0,0 @@
-// This file is part of OpenCV project.
-// It is subject to the license terms in the LICENSE file found in the top-level directory
-// of this distribution and at http://opencv.org/license.html.
-
-#ifndef OPENCV_UTILS_FILESYSTEM_HPP
-#define OPENCV_UTILS_FILESYSTEM_HPP
-
-namespace cv { namespace utils { namespace fs {
-
-
-CV_EXPORTS bool exists(const cv::String& path);
-CV_EXPORTS bool isDirectory(const cv::String& path);
-
-CV_EXPORTS void remove_all(const cv::String& path);
-
-
-CV_EXPORTS cv::String getcwd();
-
-/** Join path components */
-CV_EXPORTS cv::String join(const cv::String& base, const cv::String& path);
-
-/**
- * Generate a list of all files that match the globbing pattern.
- *
- * Result entries are prefixed by base directory path.
- *
- * @param directory base directory
- * @param pattern filter pattern (based on '*'/'?' symbols). Use empty string to disable filtering and return all results
- * @param[out] result result of globing.
- * @param recursive scan nested directories too
- * @param includeDirectories include directories into results list
- */
-CV_EXPORTS void glob(const cv::String& directory, const cv::String& pattern,
-        CV_OUT std::vector<cv::String>& result,
-        bool recursive = false, bool includeDirectories = false);
-
-/**
- * Generate a list of all files that match the globbing pattern.
- *
- * @param directory base directory
- * @param pattern filter pattern (based on '*'/'?' symbols). Use empty string to disable filtering and return all results
- * @param[out] result globbing result with relative paths from base directory
- * @param recursive scan nested directories too
- * @param includeDirectories include directories into results list
- */
-CV_EXPORTS void glob_relative(const cv::String& directory, const cv::String& pattern,
-        CV_OUT std::vector<cv::String>& result,
-        bool recursive = false, bool includeDirectories = false);
-
-
-CV_EXPORTS bool createDirectory(const cv::String& path);
-CV_EXPORTS bool createDirectories(const cv::String& path);
-
-#ifdef __OPENCV_BUILD
-// TODO
-//CV_EXPORTS cv::String getTempDirectory();
-
-/**
- * @brief Returns directory to store OpenCV cache files
- * Create sub-directory in common OpenCV cache directory if it doesn't exist.
- * @param sub_directory_name name of sub-directory. NULL or "" value asks to return root cache directory.
- * @param configuration_name optional name of configuration parameter name which overrides default behavior.
- * @return Path to cache directory. Returns empty string if cache directories support is not available. Returns "disabled" if cache disabled by user.
- */
-CV_EXPORTS cv::String getCacheDirectory(const char* sub_directory_name, const char* configuration_name = NULL);
-
-#endif
-
-}}} // namespace
-
-#endif // OPENCV_UTILS_FILESYSTEM_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/utils/logger.defines.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/utils/logger.defines.hpp
deleted file mode 100755
index b2dfc41..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/utils/logger.defines.hpp
+++ /dev/null
@@ -1,22 +0,0 @@
-// This file is part of OpenCV project.
-// It is subject to the license terms in the LICENSE file found in the top-level directory
-// of this distribution and at http://opencv.org/license.html.
-
-#ifndef OPENCV_LOGGER_DEFINES_HPP
-#define OPENCV_LOGGER_DEFINES_HPP
-
-//! @addtogroup core_logging
-//! @{
-
-// Supported logging levels and their semantic
-#define CV_LOG_LEVEL_SILENT 0          //!< for using in setLogLevel() call
-#define CV_LOG_LEVEL_FATAL 1           //!< Fatal (critical) error (unrecoverable internal error)
-#define CV_LOG_LEVEL_ERROR 2           //!< Error message
-#define CV_LOG_LEVEL_WARN 3            //!< Warning message
-#define CV_LOG_LEVEL_INFO 4            //!< Info message
-#define CV_LOG_LEVEL_DEBUG 5           //!< Debug message. Disabled in the "Release" build.
-#define CV_LOG_LEVEL_VERBOSE 6         //!< Verbose (trace) messages. Requires verbosity level. Disabled in the "Release" build.
-
-//! @}
-
-#endif // OPENCV_LOGGER_DEFINES_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/utils/logger.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/utils/logger.hpp
deleted file mode 100755
index 47094f9..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/utils/logger.hpp
+++ /dev/null
@@ -1,87 +0,0 @@
-// This file is part of OpenCV project.
-// It is subject to the license terms in the LICENSE file found in the top-level directory
-// of this distribution and at http://opencv.org/license.html.
-
-#ifndef OPENCV_LOGGER_HPP
-#define OPENCV_LOGGER_HPP
-
-#include <iostream>
-#include <sstream>
-#include <limits.h> // INT_MAX
-
-#include "logger.defines.hpp"
-
-//! @addtogroup core_logging
-// This section describes OpenCV logging utilities.
-//
-//! @{
-
-namespace cv {
-namespace utils {
-namespace logging {
-
-//! Supported logging levels and their semantic
-enum LogLevel {
-    LOG_LEVEL_SILENT = 0,              //!< for using in setLogVevel() call
-    LOG_LEVEL_FATAL = 1,               //!< Fatal (critical) error (unrecoverable internal error)
-    LOG_LEVEL_ERROR = 2,               //!< Error message
-    LOG_LEVEL_WARNING = 3,             //!< Warning message
-    LOG_LEVEL_INFO = 4,                //!< Info message
-    LOG_LEVEL_DEBUG = 5,               //!< Debug message. Disabled in the "Release" build.
-    LOG_LEVEL_VERBOSE = 6,             //!< Verbose (trace) messages. Requires verbosity level. Disabled in the "Release" build.
-#ifndef CV_DOXYGEN
-    ENUM_LOG_LEVEL_FORCE_INT = INT_MAX
-#endif
-};
-
-/** Set global logging level
-@return previous logging level
-*/
-CV_EXPORTS LogLevel setLogLevel(LogLevel logLevel);
-/** Get global logging level */
-CV_EXPORTS LogLevel getLogLevel();
-
-namespace internal {
-/** Write log message */
-CV_EXPORTS void writeLogMessage(LogLevel logLevel, const char* message);
-} // namespace
-
-/**
- * \def CV_LOG_STRIP_LEVEL
- *
- * Define CV_LOG_STRIP_LEVEL=CV_LOG_LEVEL_[DEBUG|INFO|WARN|ERROR|FATAL|DISABLED] to compile out anything at that and before that logging level
- */
-#ifndef CV_LOG_STRIP_LEVEL
-# if defined NDEBUG
-#   define CV_LOG_STRIP_LEVEL CV_LOG_LEVEL_DEBUG
-# else
-#   define CV_LOG_STRIP_LEVEL CV_LOG_LEVEL_VERBOSE
-# endif
-#endif
-
-
-#define CV_LOG_FATAL(tag, ...)   for(;;) { if (cv::utils::logging::getLogLevel() < cv::utils::logging::LOG_LEVEL_FATAL) break; std::stringstream ss; ss << __VA_ARGS__; cv::utils::logging::internal::writeLogMessage(cv::utils::logging::LOG_LEVEL_FATAL, ss.str().c_str()); break; }
-#define CV_LOG_ERROR(tag, ...)   for(;;) { if (cv::utils::logging::getLogLevel() < cv::utils::logging::LOG_LEVEL_ERROR) break; std::stringstream ss; ss << __VA_ARGS__; cv::utils::logging::internal::writeLogMessage(cv::utils::logging::LOG_LEVEL_ERROR, ss.str().c_str()); break; }
-#define CV_LOG_WARNING(tag, ...) for(;;) { if (cv::utils::logging::getLogLevel() < cv::utils::logging::LOG_LEVEL_WARNING) break; std::stringstream ss; ss << __VA_ARGS__; cv::utils::logging::internal::writeLogMessage(cv::utils::logging::LOG_LEVEL_WARNING, ss.str().c_str()); break; }
-#if CV_LOG_STRIP_LEVEL <= CV_LOG_LEVEL_INFO
-#define CV_LOG_INFO(tag, ...)
-#else
-#define CV_LOG_INFO(tag, ...)    for(;;) { if (cv::utils::logging::getLogLevel() < cv::utils::logging::LOG_LEVEL_INFO) break; std::stringstream ss; ss << __VA_ARGS__; cv::utils::logging::internal::writeLogMessage(cv::utils::logging::LOG_LEVEL_INFO, ss.str().c_str()); break; }
-#endif
-#if CV_LOG_STRIP_LEVEL <= CV_LOG_LEVEL_DEBUG
-#define CV_LOG_DEBUG(tag, ...)
-#else
-#define CV_LOG_DEBUG(tag, ...)   for(;;) { if (cv::utils::logging::getLogLevel() < cv::utils::logging::LOG_LEVEL_DEBUG) break; std::stringstream ss; ss << __VA_ARGS__; cv::utils::logging::internal::writeLogMessage(cv::utils::logging::LOG_LEVEL_DEBUG, ss.str().c_str()); break; }
-#endif
-#if CV_LOG_STRIP_LEVEL <= CV_LOG_LEVEL_VERBOSE
-#define CV_LOG_VERBOSE(tag, v, ...)
-#else
-#define CV_LOG_VERBOSE(tag, v, ...) for(;;) { if (cv::utils::logging::getLogLevel() < cv::utils::logging::LOG_LEVEL_VERBOSE) break; std::stringstream ss; ss << "[VERB" << v << ":" << cv::utils::getThreadID() << "] " << __VA_ARGS__; cv::utils::logging::internal::writeLogMessage(cv::utils::logging::LOG_LEVEL_VERBOSE, ss.str().c_str()); break; }
-#endif
-
-
-}}} // namespace
-
-//! @}
-
-#endif // OPENCV_LOGGER_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/utils/trace.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/utils/trace.hpp
deleted file mode 100755
index 1539fb9..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/utils/trace.hpp
+++ /dev/null
@@ -1,250 +0,0 @@
-// This file is part of OpenCV project.
-// It is subject to the license terms in the LICENSE file found in the top-level directory
-// of this distribution and at http://opencv.org/license.html.
-
-#ifndef OPENCV_TRACE_HPP
-#define OPENCV_TRACE_HPP
-
-#include <opencv2/core/cvdef.h>
-
-//! @addtogroup core_logging
-// This section describes OpenCV tracing utilities.
-//
-//! @{
-
-namespace cv {
-namespace utils {
-namespace trace {
-
-//! Macro to trace function
-#define CV_TRACE_FUNCTION()
-
-#define CV_TRACE_FUNCTION_SKIP_NESTED()
-
-//! Trace code scope.
-//! @note Dynamic names are not supported in this macro (on stack or heap). Use string literals here only, like "initialize".
-#define CV_TRACE_REGION(name_as_static_string_literal)
-//! mark completed of the current opened region and create new one
-//! @note Dynamic names are not supported in this macro (on stack or heap). Use string literals here only, like "step1".
-#define CV_TRACE_REGION_NEXT(name_as_static_string_literal)
-
-//! Macro to trace argument value
-#define CV_TRACE_ARG(arg_id)
-
-//! Macro to trace argument value (expanded version)
-#define CV_TRACE_ARG_VALUE(arg_id, arg_name, value)
-
-//! @cond IGNORED
-#define CV_TRACE_NS cv::utils::trace
-
-namespace details {
-
-#ifndef __OPENCV_TRACE
-# if defined __OPENCV_BUILD && !defined __OPENCV_TESTS && !defined __OPENCV_APPS
-#   define __OPENCV_TRACE 1
-# else
-#   define __OPENCV_TRACE 0
-# endif
-#endif
-
-#ifndef CV_TRACE_FILENAME
-# define CV_TRACE_FILENAME __FILE__
-#endif
-
-#ifndef CV__TRACE_FUNCTION
-# if defined _MSC_VER
-#   define CV__TRACE_FUNCTION __FUNCSIG__
-# elif defined __GNUC__
-#   define CV__TRACE_FUNCTION __PRETTY_FUNCTION__
-# else
-#   define CV__TRACE_FUNCTION "<unknown>"
-# endif
-#endif
-
-//! Thread-local instance (usually allocated on stack)
-class CV_EXPORTS Region
-{
-public:
-    struct LocationExtraData;
-    struct LocationStaticStorage
-    {
-        LocationExtraData** ppExtra;   //< implementation specific data
-        const char* name;              //< region name (function name or other custom name)
-        const char* filename;          //< source code filename
-        int line;                      //< source code line
-        int flags;                     //< flags (implementation code path: Plain, IPP, OpenCL)
-    };
-
-    Region(const LocationStaticStorage& location);
-    inline ~Region()
-    {
-        if (implFlags != 0)
-            destroy();
-        CV_DbgAssert(implFlags == 0);
-        CV_DbgAssert(pImpl == NULL);
-    }
-
-    class Impl;
-    Impl* pImpl; // NULL if current region is not active
-    int implFlags; // see RegionFlag, 0 if region is ignored
-
-    bool isActive() const { return pImpl != NULL; }
-
-    void destroy();
-private:
-    Region(const Region&); // disabled
-    Region& operator= (const Region&); // disabled
-};
-
-//! Specify region flags
-enum RegionLocationFlag {
-    REGION_FLAG_FUNCTION = (1 << 0),             //< region is function (=1) / nested named region (=0)
-    REGION_FLAG_APP_CODE = (1 << 1),             //< region is Application code (=1) / OpenCV library code (=0)
-    REGION_FLAG_SKIP_NESTED = (1 << 2),          //< avoid processing of nested regions
-
-    REGION_FLAG_IMPL_IPP = (1 << 16),            //< region is part of IPP code path
-    REGION_FLAG_IMPL_OPENCL = (2 << 16),         //< region is part of OpenCL code path
-    REGION_FLAG_IMPL_OPENVX = (3 << 16),         //< region is part of OpenVX code path
-
-    REGION_FLAG_IMPL_MASK = (15 << 16),
-
-    REGION_FLAG_REGION_FORCE = (1 << 30),
-    REGION_FLAG_REGION_NEXT = (1 << 31),         //< close previous region (see #CV_TRACE_REGION_NEXT macro)
-
-    ENUM_REGION_FLAG_FORCE_INT = INT_MAX
-};
-
-struct CV_EXPORTS TraceArg {
-public:
-    struct ExtraData;
-    ExtraData** ppExtra;
-    const char* name;
-    int flags;
-};
-/** @brief Add meta information to current region (function)
- * See CV_TRACE_ARG macro
- * @param arg argument information structure (global static cache)
- * @param value argument value (can by dynamic string literal in case of string, static allocation is not required)
- */
-CV_EXPORTS void traceArg(const TraceArg& arg, const char* value);
-//! @overload
-CV_EXPORTS void traceArg(const TraceArg& arg, int value);
-//! @overload
-CV_EXPORTS void traceArg(const TraceArg& arg, int64 value);
-//! @overload
-CV_EXPORTS void traceArg(const TraceArg& arg, double value);
-
-#define CV__TRACE_LOCATION_VARNAME(loc_id) CVAUX_CONCAT(CVAUX_CONCAT(__cv_trace_location_, loc_id), __LINE__)
-#define CV__TRACE_LOCATION_EXTRA_VARNAME(loc_id) CVAUX_CONCAT(CVAUX_CONCAT(__cv_trace_location_extra_, loc_id) , __LINE__)
-
-#define CV__TRACE_DEFINE_LOCATION_(loc_id, name, flags) \
-    static CV_TRACE_NS::details::Region::LocationExtraData* CV__TRACE_LOCATION_EXTRA_VARNAME(loc_id) = 0; \
-    static const CV_TRACE_NS::details::Region::LocationStaticStorage \
-        CV__TRACE_LOCATION_VARNAME(loc_id) = { &(CV__TRACE_LOCATION_EXTRA_VARNAME(loc_id)), name, CV_TRACE_FILENAME, __LINE__, flags};
-
-#define CV__TRACE_DEFINE_LOCATION_FN(name, flags) CV__TRACE_DEFINE_LOCATION_(fn, name, (flags | CV_TRACE_NS::details::REGION_FLAG_FUNCTION))
-
-
-#define CV__TRACE_OPENCV_FUNCTION() \
-    CV__TRACE_DEFINE_LOCATION_FN(CV__TRACE_FUNCTION, 0); \
-    const CV_TRACE_NS::details::Region __region_fn(CV__TRACE_LOCATION_VARNAME(fn));
-
-#define CV__TRACE_OPENCV_FUNCTION_NAME(name) \
-    CV__TRACE_DEFINE_LOCATION_FN(name, 0); \
-    const CV_TRACE_NS::details::Region __region_fn(CV__TRACE_LOCATION_VARNAME(fn));
-
-#define CV__TRACE_APP_FUNCTION() \
-    CV__TRACE_DEFINE_LOCATION_FN(CV__TRACE_FUNCTION, CV_TRACE_NS::details::REGION_FLAG_APP_CODE); \
-    const CV_TRACE_NS::details::Region __region_fn(CV__TRACE_LOCATION_VARNAME(fn));
-
-#define CV__TRACE_APP_FUNCTION_NAME(name) \
-    CV__TRACE_DEFINE_LOCATION_FN(name, CV_TRACE_NS::details::REGION_FLAG_APP_CODE); \
-    const CV_TRACE_NS::details::Region __region_fn(CV__TRACE_LOCATION_VARNAME(fn));
-
-
-#define CV__TRACE_OPENCV_FUNCTION_SKIP_NESTED() \
-    CV__TRACE_DEFINE_LOCATION_FN(CV__TRACE_FUNCTION, CV_TRACE_NS::details::REGION_FLAG_SKIP_NESTED); \
-    const CV_TRACE_NS::details::Region __region_fn(CV__TRACE_LOCATION_VARNAME(fn));
-
-#define CV__TRACE_OPENCV_FUNCTION_NAME_SKIP_NESTED(name) \
-    CV__TRACE_DEFINE_LOCATION_FN(name, CV_TRACE_NS::details::REGION_FLAG_SKIP_NESTED); \
-    const CV_TRACE_NS::details::Region __region_fn(CV__TRACE_LOCATION_VARNAME(fn));
-
-#define CV__TRACE_APP_FUNCTION_SKIP_NESTED() \
-    CV__TRACE_DEFINE_LOCATION_FN(CV__TRACE_FUNCTION, CV_TRACE_NS::details::REGION_FLAG_SKIP_NESTED | CV_TRACE_NS::details::REGION_FLAG_APP_CODE); \
-    const CV_TRACE_NS::details::Region __region_fn(CV__TRACE_LOCATION_VARNAME(fn));
-
-
-#define CV__TRACE_REGION_(name_as_static_string_literal, flags) \
-    CV__TRACE_DEFINE_LOCATION_(region, name_as_static_string_literal, flags); \
-    CV_TRACE_NS::details::Region CVAUX_CONCAT(__region_, __LINE__)(CV__TRACE_LOCATION_VARNAME(region));
-
-#define CV__TRACE_REGION(name_as_static_string_literal) CV__TRACE_REGION_(name_as_static_string_literal, 0)
-#define CV__TRACE_REGION_NEXT(name_as_static_string_literal) CV__TRACE_REGION_(name_as_static_string_literal, CV_TRACE_NS::details::REGION_FLAG_REGION_NEXT)
-
-#define CV__TRACE_ARG_VARNAME(arg_id) CVAUX_CONCAT(__cv_trace_arg_ ## arg_id, __LINE__)
-#define CV__TRACE_ARG_EXTRA_VARNAME(arg_id) CVAUX_CONCAT(__cv_trace_arg_extra_ ## arg_id, __LINE__)
-
-#define CV__TRACE_DEFINE_ARG_(arg_id, name, flags) \
-    static CV_TRACE_NS::details::TraceArg::ExtraData* CV__TRACE_ARG_EXTRA_VARNAME(arg_id) = 0; \
-    static const CV_TRACE_NS::details::TraceArg \
-        CV__TRACE_ARG_VARNAME(arg_id) = { &(CV__TRACE_ARG_EXTRA_VARNAME(arg_id)), name, flags };
-
-#define CV__TRACE_ARG_VALUE(arg_id, arg_name, value) \
-        CV__TRACE_DEFINE_ARG_(arg_id, arg_name, 0); \
-        CV_TRACE_NS::details::traceArg((CV__TRACE_ARG_VARNAME(arg_id)), value);
-
-#define CV__TRACE_ARG(arg_id) CV_TRACE_ARG_VALUE(arg_id, #arg_id, (arg_id))
-
-} // namespace
-
-#ifndef OPENCV_DISABLE_TRACE
-#undef CV_TRACE_FUNCTION
-#undef CV_TRACE_FUNCTION_SKIP_NESTED
-#if __OPENCV_TRACE
-#define CV_TRACE_FUNCTION CV__TRACE_OPENCV_FUNCTION
-#define CV_TRACE_FUNCTION_SKIP_NESTED CV__TRACE_OPENCV_FUNCTION_SKIP_NESTED
-#else
-#define CV_TRACE_FUNCTION CV__TRACE_APP_FUNCTION
-#define CV_TRACE_FUNCTION_SKIP_NESTED CV__TRACE_APP_FUNCTION_SKIP_NESTED
-#endif
-
-#undef CV_TRACE_REGION
-#define CV_TRACE_REGION CV__TRACE_REGION
-
-#undef CV_TRACE_REGION_NEXT
-#define CV_TRACE_REGION_NEXT CV__TRACE_REGION_NEXT
-
-#undef CV_TRACE_ARG_VALUE
-#define CV_TRACE_ARG_VALUE(arg_id, arg_name, value) \
-        if (__region_fn.isActive()) \
-        { \
-            CV__TRACE_ARG_VALUE(arg_id, arg_name, value); \
-        }
-
-#undef CV_TRACE_ARG
-#define CV_TRACE_ARG CV__TRACE_ARG
-
-#endif // OPENCV_DISABLE_TRACE
-
-#ifdef OPENCV_TRACE_VERBOSE
-#define CV_TRACE_FUNCTION_VERBOSE CV_TRACE_FUNCTION
-#define CV_TRACE_REGION_VERBOSE CV_TRACE_REGION
-#define CV_TRACE_REGION_NEXT_VERBOSE CV_TRACE_REGION_NEXT
-#define CV_TRACE_ARG_VALUE_VERBOSE CV_TRACE_ARG_VALUE
-#define CV_TRACE_ARG_VERBOSE CV_TRACE_ARG
-#else
-#define CV_TRACE_FUNCTION_VERBOSE(...)
-#define CV_TRACE_REGION_VERBOSE(...)
-#define CV_TRACE_REGION_NEXT_VERBOSE(...)
-#define CV_TRACE_ARG_VALUE_VERBOSE(...)
-#define CV_TRACE_ARG_VERBOSE(...)
-#endif
-
-//! @endcond
-
-}}} // namespace
-
-//! @}
-
-#endif // OPENCV_TRACE_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/va_intel.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/va_intel.hpp
deleted file mode 100755
index 3325848..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/va_intel.hpp
+++ /dev/null
@@ -1,77 +0,0 @@
-// This file is part of OpenCV project.
-// It is subject to the license terms in the LICENSE file found in the top-level directory
-// of this distribution and at http://opencv.org/license.html.
-
-// Copyright (C) 2015, Itseez, Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-
-#ifndef OPENCV_CORE_VA_INTEL_HPP
-#define OPENCV_CORE_VA_INTEL_HPP
-
-#ifndef __cplusplus
-#  error va_intel.hpp header must be compiled as C++
-#endif
-
-#include "opencv2/core.hpp"
-#include "ocl.hpp"
-
-#if defined(HAVE_VA)
-# include "va/va.h"
-#else  // HAVE_VA
-# if !defined(_VA_H_)
-    typedef void* VADisplay;
-    typedef unsigned int VASurfaceID;
-# endif // !_VA_H_
-#endif // HAVE_VA
-
-namespace cv { namespace va_intel {
-
-/** @addtogroup core_va_intel
-This section describes Intel VA-API/OpenCL (CL-VA) interoperability.
-
-To enable CL-VA interoperability support, configure OpenCV using CMake with WITH_VA_INTEL=ON . Currently VA-API is
-supported on Linux only. You should also install Intel Media Server Studio (MSS) to use this feature. You may
-have to specify the path(s) to MSS components for cmake in environment variables: VA_INTEL_MSDK_ROOT for Media SDK
-(default is "/opt/intel/mediasdk"), and VA_INTEL_IOCL_ROOT for Intel OpenCL (default is "/opt/intel/opencl").
-
-To use CL-VA interoperability you should first create VADisplay (libva), and then call initializeContextFromVA()
-function to create OpenCL context and set up interoperability.
-*/
-//! @{
-
-/////////////////// CL-VA Interoperability Functions ///////////////////
-
-namespace ocl {
-using namespace cv::ocl;
-
-// TODO static functions in the Context class
-/** @brief Creates OpenCL context from VA.
-@param display    - VADisplay for which CL interop should be established.
-@param tryInterop - try to set up for interoperability, if true; set up for use slow copy if false.
-@return Returns reference to OpenCL Context
- */
-CV_EXPORTS Context& initializeContextFromVA(VADisplay display, bool tryInterop = true);
-
-} // namespace cv::va_intel::ocl
-
-/** @brief Converts InputArray to VASurfaceID object.
-@param display - VADisplay object.
-@param src     - source InputArray.
-@param surface - destination VASurfaceID object.
-@param size    - size of image represented by VASurfaceID object.
- */
-CV_EXPORTS void convertToVASurface(VADisplay display, InputArray src, VASurfaceID surface, Size size);
-
-/** @brief Converts VASurfaceID object to OutputArray.
-@param display - VADisplay object.
-@param surface - source VASurfaceID object.
-@param size    - size of image represented by VASurfaceID object.
-@param dst     - destination OutputArray.
- */
-CV_EXPORTS void convertFromVASurface(VADisplay display, VASurfaceID surface, Size size, OutputArray dst);
-
-//! @}
-
-}} // namespace cv::va_intel
-
-#endif /* OPENCV_CORE_VA_INTEL_HPP */
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/version.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/version.hpp
deleted file mode 100755
index 28a25ee..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/version.hpp
+++ /dev/null
@@ -1,26 +0,0 @@
-// This file is part of OpenCV project.
-// It is subject to the license terms in the LICENSE file found in the top-level directory
-// of this distribution and at http://opencv.org/license.html.
-
-#ifndef OPENCV_VERSION_HPP
-#define OPENCV_VERSION_HPP
-
-#define CV_VERSION_MAJOR    3
-#define CV_VERSION_MINOR    4
-#define CV_VERSION_REVISION 2
-#define CV_VERSION_STATUS   ""
-
-#define CVAUX_STR_EXP(__A)  #__A
-#define CVAUX_STR(__A)      CVAUX_STR_EXP(__A)
-
-#define CVAUX_STRW_EXP(__A)  L ## #__A
-#define CVAUX_STRW(__A)      CVAUX_STRW_EXP(__A)
-
-#define CV_VERSION          CVAUX_STR(CV_VERSION_MAJOR) "." CVAUX_STR(CV_VERSION_MINOR) "." CVAUX_STR(CV_VERSION_REVISION) CV_VERSION_STATUS
-
-/* old  style version constants*/
-#define CV_MAJOR_VERSION    CV_VERSION_MAJOR
-#define CV_MINOR_VERSION    CV_VERSION_MINOR
-#define CV_SUBMINOR_VERSION CV_VERSION_REVISION
-
-#endif // OPENCV_VERSION_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/vsx_utils.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/vsx_utils.hpp
deleted file mode 100755
index 21b50e8..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/vsx_utils.hpp
+++ /dev/null
@@ -1,1009 +0,0 @@
-// This file is part of OpenCV project.
-// It is subject to the license terms in the LICENSE file found in the top-level directory
-// of this distribution and at http://opencv.org/license.html
-
-#ifndef OPENCV_HAL_VSX_UTILS_HPP
-#define OPENCV_HAL_VSX_UTILS_HPP
-
-#include "opencv2/core/cvdef.h"
-
-#ifndef SKIP_INCLUDES
-#   include <assert.h>
-#endif
-
-//! @addtogroup core_utils_vsx
-//! @{
-#if CV_VSX
-
-#define __VSX_S16__(c, v) (c){v, v, v, v, v, v, v, v, v, v, v, v, v, v, v, v}
-#define __VSX_S8__(c, v)  (c){v, v, v, v, v, v, v, v}
-#define __VSX_S4__(c, v)  (c){v, v, v, v}
-#define __VSX_S2__(c, v)  (c){v, v}
-
-typedef __vector unsigned char vec_uchar16;
-#define vec_uchar16_set(...) (vec_uchar16){__VA_ARGS__}
-#define vec_uchar16_sp(c)    (__VSX_S16__(vec_uchar16, c))
-#define vec_uchar16_c(v)     ((vec_uchar16)(v))
-#define vec_uchar16_z        vec_uchar16_sp(0)
-
-typedef __vector signed char vec_char16;
-#define vec_char16_set(...) (vec_char16){__VA_ARGS__}
-#define vec_char16_sp(c)    (__VSX_S16__(vec_char16, c))
-#define vec_char16_c(v)     ((vec_char16)(v))
-#define vec_char16_z        vec_char16_sp(0)
-
-typedef __vector unsigned short vec_ushort8;
-#define vec_ushort8_set(...) (vec_ushort8){__VA_ARGS__}
-#define vec_ushort8_sp(c)    (__VSX_S8__(vec_ushort8, c))
-#define vec_ushort8_c(v)     ((vec_ushort8)(v))
-#define vec_ushort8_z        vec_ushort8_sp(0)
-
-typedef __vector signed short vec_short8;
-#define vec_short8_set(...) (vec_short8){__VA_ARGS__}
-#define vec_short8_sp(c)    (__VSX_S8__(vec_short8, c))
-#define vec_short8_c(v)     ((vec_short8)(v))
-#define vec_short8_z        vec_short8_sp(0)
-
-typedef __vector unsigned int vec_uint4;
-#define vec_uint4_set(...) (vec_uint4){__VA_ARGS__}
-#define vec_uint4_sp(c)    (__VSX_S4__(vec_uint4, c))
-#define vec_uint4_c(v)     ((vec_uint4)(v))
-#define vec_uint4_z        vec_uint4_sp(0)
-
-typedef __vector signed int vec_int4;
-#define vec_int4_set(...)  (vec_int4){__VA_ARGS__}
-#define vec_int4_sp(c)     (__VSX_S4__(vec_int4, c))
-#define vec_int4_c(v)      ((vec_int4)(v))
-#define vec_int4_z         vec_int4_sp(0)
-
-typedef __vector float vec_float4;
-#define vec_float4_set(...)  (vec_float4){__VA_ARGS__}
-#define vec_float4_sp(c)     (__VSX_S4__(vec_float4, c))
-#define vec_float4_c(v)      ((vec_float4)(v))
-#define vec_float4_z         vec_float4_sp(0)
-
-typedef __vector unsigned long long vec_udword2;
-#define vec_udword2_set(...) (vec_udword2){__VA_ARGS__}
-#define vec_udword2_sp(c)    (__VSX_S2__(vec_udword2, c))
-#define vec_udword2_c(v)     ((vec_udword2)(v))
-#define vec_udword2_z        vec_udword2_sp(0)
-
-typedef __vector signed long long vec_dword2;
-#define vec_dword2_set(...) (vec_dword2){__VA_ARGS__}
-#define vec_dword2_sp(c)    (__VSX_S2__(vec_dword2, c))
-#define vec_dword2_c(v)     ((vec_dword2)(v))
-#define vec_dword2_z        vec_dword2_sp(0)
-
-typedef  __vector double vec_double2;
-#define vec_double2_set(...) (vec_double2){__VA_ARGS__}
-#define vec_double2_c(v)     ((vec_double2)(v))
-#define vec_double2_sp(c)    (__VSX_S2__(vec_double2, c))
-#define vec_double2_z        vec_double2_sp(0)
-
-#define vec_bchar16           __vector __bool char
-#define vec_bchar16_set(...) (vec_bchar16){__VA_ARGS__}
-#define vec_bchar16_c(v)     ((vec_bchar16)(v))
-
-#define vec_bshort8           __vector __bool short
-#define vec_bshort8_set(...) (vec_bshort8){__VA_ARGS__}
-#define vec_bshort8_c(v)     ((vec_bshort8)(v))
-
-#define vec_bint4             __vector __bool int
-#define vec_bint4_set(...)   (vec_bint4){__VA_ARGS__}
-#define vec_bint4_c(v)       ((vec_bint4)(v))
-
-#define vec_bdword2            __vector __bool long long
-#define vec_bdword2_set(...)  (vec_bdword2){__VA_ARGS__}
-#define vec_bdword2_c(v)      ((vec_bdword2)(v))
-
-#define VSX_FINLINE(tp) extern inline tp __attribute__((always_inline))
-
-#define VSX_REDIRECT_1RG(rt, rg, fnm, fn2)   \
-VSX_FINLINE(rt) fnm(const rg& a) { return fn2(a); }
-
-#define VSX_REDIRECT_2RG(rt, rg, fnm, fn2)   \
-VSX_FINLINE(rt) fnm(const rg& a, const rg& b) { return fn2(a, b); }
-
-/*
- * GCC VSX compatibility
-**/
-#if defined(__GNUG__) && !defined(__clang__)
-
-// inline asm helper
-#define VSX_IMPL_1RG(rt, rto, rg, rgo, opc, fnm) \
-VSX_FINLINE(rt) fnm(const rg& a)                 \
-{ rt rs; __asm__ __volatile__(#opc" %x0,%x1" : "="#rto (rs) : #rgo (a)); return rs; }
-
-#define VSX_IMPL_1VRG(rt, rg, opc, fnm) \
-VSX_FINLINE(rt) fnm(const rg& a)        \
-{ rt rs; __asm__ __volatile__(#opc" %0,%1" : "=v" (rs) : "v" (a)); return rs; }
-
-#define VSX_IMPL_2VRG_F(rt, rg, fopc, fnm)     \
-VSX_FINLINE(rt) fnm(const rg& a, const rg& b)  \
-{ rt rs; __asm__ __volatile__(fopc : "=v" (rs) : "v" (a), "v" (b)); return rs; }
-
-#define VSX_IMPL_2VRG(rt, rg, opc, fnm) VSX_IMPL_2VRG_F(rt, rg, #opc" %0,%1,%2", fnm)
-
-#if __GNUG__ < 7
-// up to GCC 6 vec_mul only supports precisions and llong
-#   ifdef vec_mul
-#       undef vec_mul
-#   endif
-/*
- * there's no a direct instruction for supporting 16-bit multiplication in ISA 2.07,
- * XLC Implement it by using instruction "multiply even", "multiply odd" and "permute"
- * todo: Do I need to support 8-bit ?
-**/
-#   define VSX_IMPL_MULH(Tvec, Tcast)                                               \
-    VSX_FINLINE(Tvec) vec_mul(const Tvec& a, const Tvec& b)                         \
-    {                                                                               \
-        static const vec_uchar16 even_perm = {0, 1, 16, 17, 4, 5, 20, 21,           \
-                                              8, 9, 24, 25, 12, 13, 28, 29};        \
-        return vec_perm(Tcast(vec_mule(a, b)), Tcast(vec_mulo(a, b)), even_perm);   \
-    }
-    VSX_IMPL_MULH(vec_short8,  vec_short8_c)
-    VSX_IMPL_MULH(vec_ushort8, vec_ushort8_c)
-    // vmuluwm can be used for unsigned or signed integers, that's what they said
-    VSX_IMPL_2VRG(vec_int4,  vec_int4,  vmuluwm, vec_mul)
-    VSX_IMPL_2VRG(vec_uint4, vec_uint4, vmuluwm, vec_mul)
-    // redirect to GCC builtin vec_mul, since it already supports precisions and llong
-    VSX_REDIRECT_2RG(vec_float4,  vec_float4,  vec_mul, __builtin_vec_mul)
-    VSX_REDIRECT_2RG(vec_double2, vec_double2, vec_mul, __builtin_vec_mul)
-    VSX_REDIRECT_2RG(vec_dword2,  vec_dword2,  vec_mul, __builtin_vec_mul)
-    VSX_REDIRECT_2RG(vec_udword2, vec_udword2, vec_mul, __builtin_vec_mul)
-#endif // __GNUG__ < 7
-
-#if __GNUG__ < 6
-/*
- * Instruction "compare greater than or equal" in ISA 2.07 only supports single
- * and double precision.
- * In XLC and new versions of GCC implement integers by using instruction "greater than" and NOR.
-**/
-#   ifdef vec_cmpge
-#       undef vec_cmpge
-#   endif
-#   ifdef vec_cmple
-#       undef vec_cmple
-#   endif
-#   define vec_cmple(a, b) vec_cmpge(b, a)
-#   define VSX_IMPL_CMPGE(rt, rg, opc, fnm) \
-    VSX_IMPL_2VRG_F(rt, rg, #opc" %0,%2,%1\n\t xxlnor %x0,%x0,%x0", fnm)
-
-    VSX_IMPL_CMPGE(vec_bchar16, vec_char16,  vcmpgtsb, vec_cmpge)
-    VSX_IMPL_CMPGE(vec_bchar16, vec_uchar16, vcmpgtub, vec_cmpge)
-    VSX_IMPL_CMPGE(vec_bshort8, vec_short8,  vcmpgtsh, vec_cmpge)
-    VSX_IMPL_CMPGE(vec_bshort8, vec_ushort8, vcmpgtuh, vec_cmpge)
-    VSX_IMPL_CMPGE(vec_bint4,   vec_int4,    vcmpgtsw, vec_cmpge)
-    VSX_IMPL_CMPGE(vec_bint4,   vec_uint4,   vcmpgtuw, vec_cmpge)
-    VSX_IMPL_CMPGE(vec_bdword2, vec_dword2,  vcmpgtsd, vec_cmpge)
-    VSX_IMPL_CMPGE(vec_bdword2, vec_udword2, vcmpgtud, vec_cmpge)
-
-// redirect to GCC builtin cmpge, since it already supports precisions
-    VSX_REDIRECT_2RG(vec_bint4,   vec_float4,  vec_cmpge, __builtin_vec_cmpge)
-    VSX_REDIRECT_2RG(vec_bdword2, vec_double2, vec_cmpge, __builtin_vec_cmpge)
-
-// up to gcc5 vec_nor doesn't support bool long long
-#   undef vec_nor
-    template<typename T>
-    VSX_REDIRECT_2RG(T, T, vec_nor, __builtin_vec_nor)
-
-    VSX_FINLINE(vec_bdword2) vec_nor(const vec_bdword2& a, const vec_bdword2& b)
-    { return vec_bdword2_c(__builtin_vec_nor(vec_dword2_c(a), vec_dword2_c(b))); }
-
-// vec_packs doesn't support double words in gcc4 and old versions of gcc5
-#   undef vec_packs
-    VSX_REDIRECT_2RG(vec_char16,  vec_short8,  vec_packs, __builtin_vec_packs)
-    VSX_REDIRECT_2RG(vec_uchar16, vec_ushort8, vec_packs, __builtin_vec_packs)
-    VSX_REDIRECT_2RG(vec_short8,  vec_int4,    vec_packs, __builtin_vec_packs)
-    VSX_REDIRECT_2RG(vec_ushort8, vec_uint4,   vec_packs, __builtin_vec_packs)
-
-    VSX_IMPL_2VRG_F(vec_int4,  vec_dword2,  "vpksdss %0,%2,%1", vec_packs)
-    VSX_IMPL_2VRG_F(vec_uint4, vec_udword2, "vpkudus %0,%2,%1", vec_packs)
-#endif // __GNUG__ < 6
-
-#if __GNUG__ < 5
-// vec_xxpermdi in gcc4 missing little-endian supports just like clang
-#   define vec_permi(a, b, c) vec_xxpermdi(b, a, (3 ^ ((c & 1) << 1 | c >> 1)))
-#else
-#   define vec_permi vec_xxpermdi
-#endif // __GNUG__ < 5
-
-// shift left double by word immediate
-#ifndef vec_sldw
-#   define vec_sldw __builtin_vsx_xxsldwi
-#endif
-
-// vector population count
-VSX_IMPL_1VRG(vec_uchar16, vec_uchar16, vpopcntb, vec_popcntu)
-VSX_IMPL_1VRG(vec_uchar16, vec_char16,  vpopcntb, vec_popcntu)
-VSX_IMPL_1VRG(vec_ushort8, vec_ushort8, vpopcnth, vec_popcntu)
-VSX_IMPL_1VRG(vec_ushort8, vec_short8,  vpopcnth, vec_popcntu)
-VSX_IMPL_1VRG(vec_uint4,   vec_uint4,   vpopcntw, vec_popcntu)
-VSX_IMPL_1VRG(vec_uint4,   vec_int4,    vpopcntw, vec_popcntu)
-VSX_IMPL_1VRG(vec_udword2, vec_udword2, vpopcntd, vec_popcntu)
-VSX_IMPL_1VRG(vec_udword2, vec_dword2,  vpopcntd, vec_popcntu)
-
-// converts between single and double-precision
-VSX_REDIRECT_1RG(vec_float4,  vec_double2, vec_cvfo, __builtin_vsx_xvcvdpsp)
-VSX_REDIRECT_1RG(vec_double2, vec_float4,  vec_cvfo, __builtin_vsx_xvcvspdp)
-
-// converts word and doubleword to double-precision
-#ifdef vec_ctd
-#   undef vec_ctd
-#endif
-VSX_IMPL_1RG(vec_double2, wd, vec_int4,    wa, xvcvsxwdp, vec_ctdo)
-VSX_IMPL_1RG(vec_double2, wd, vec_uint4,   wa, xvcvuxwdp, vec_ctdo)
-VSX_IMPL_1RG(vec_double2, wd, vec_dword2,  wi, xvcvsxddp, vec_ctd)
-VSX_IMPL_1RG(vec_double2, wd, vec_udword2, wi, xvcvuxddp, vec_ctd)
-
-// converts word and doubleword to single-precision
-#undef vec_ctf
-VSX_IMPL_1RG(vec_float4, wf, vec_int4,    wa, xvcvsxwsp, vec_ctf)
-VSX_IMPL_1RG(vec_float4, wf, vec_uint4,   wa, xvcvuxwsp, vec_ctf)
-VSX_IMPL_1RG(vec_float4, wf, vec_dword2,  wi, xvcvsxdsp, vec_ctfo)
-VSX_IMPL_1RG(vec_float4, wf, vec_udword2, wi, xvcvuxdsp, vec_ctfo)
-
-// converts single and double precision to signed word
-#undef vec_cts
-VSX_IMPL_1RG(vec_int4,  wa, vec_double2, wd, xvcvdpsxws, vec_ctso)
-VSX_IMPL_1RG(vec_int4,  wa, vec_float4,  wf, xvcvspsxws, vec_cts)
-
-// converts single and double precision to unsigned word
-#undef vec_ctu
-VSX_IMPL_1RG(vec_uint4, wa, vec_double2, wd, xvcvdpuxws, vec_ctuo)
-VSX_IMPL_1RG(vec_uint4, wa, vec_float4,  wf, xvcvspuxws, vec_ctu)
-
-// converts single and double precision to signed doubleword
-#ifdef vec_ctsl
-#   undef vec_ctsl
-#endif
-VSX_IMPL_1RG(vec_dword2, wi, vec_double2, wd, xvcvdpsxds, vec_ctsl)
-VSX_IMPL_1RG(vec_dword2, wi, vec_float4,  wf, xvcvspsxds, vec_ctslo)
-
-// converts single and double precision to unsigned doubleword
-#ifdef vec_ctul
-#   undef vec_ctul
-#endif
-VSX_IMPL_1RG(vec_udword2, wi, vec_double2, wd, xvcvdpuxds, vec_ctul)
-VSX_IMPL_1RG(vec_udword2, wi, vec_float4,  wf, xvcvspuxds, vec_ctulo)
-
-// just in case if GCC doesn't define it
-#ifndef vec_xl
-#   define vec_xl vec_vsx_ld
-#   define vec_xst vec_vsx_st
-#endif
-
-#endif // GCC VSX compatibility
-
-/*
- * CLANG VSX compatibility
-**/
-#if defined(__clang__) && !defined(__IBMCPP__)
-
-/*
- * CLANG doesn't support %x<n> in the inline asm template which fixes register number
- * when using any of the register constraints wa, wd, wf
- *
- * For more explanation checkout PowerPC and IBM RS6000 in https://gcc.gnu.org/onlinedocs/gcc/Machine-Constraints.html
- * Also there's already an open bug https://bugs.llvm.org/show_bug.cgi?id=31837
- *
- * So we're not able to use inline asm and only use built-in functions that CLANG supports
- * and use __builtin_convertvector if clang missng any of vector conversions built-in functions
-*/
-
-// convert vector helper
-#define VSX_IMPL_CONVERT(rt, rg, fnm) \
-VSX_FINLINE(rt) fnm(const rg& a) { return __builtin_convertvector(a, rt); }
-
-#if __clang_major__ < 5
-// implement vec_permi in a dirty way
-#   define VSX_IMPL_CLANG_4_PERMI(Tvec)                                                 \
-    VSX_FINLINE(Tvec) vec_permi(const Tvec& a, const Tvec& b, unsigned const char c)    \
-    {                                                                                   \
-        switch (c)                                                                      \
-        {                                                                               \
-        case 0:                                                                         \
-            return vec_mergeh(a, b);                                                    \
-        case 1:                                                                         \
-            return vec_mergel(vec_mergeh(a, a), b);                                     \
-        case 2:                                                                         \
-            return vec_mergeh(vec_mergel(a, a), b);                                     \
-        default:                                                                        \
-            return vec_mergel(a, b);                                                    \
-        }                                                                               \
-    }
-    VSX_IMPL_CLANG_4_PERMI(vec_udword2)
-    VSX_IMPL_CLANG_4_PERMI(vec_dword2)
-    VSX_IMPL_CLANG_4_PERMI(vec_double2)
-
-// vec_xxsldwi is missing in clang 4
-#   define vec_xxsldwi(a, b, c) vec_sld(a, b, (c) * 4)
-#else
-// vec_xxpermdi is missing little-endian supports in clang 4 just like gcc4
-#   define vec_permi(a, b, c) vec_xxpermdi(b, a, (3 ^ ((c & 1) << 1 | c >> 1)))
-#endif // __clang_major__ < 5
-
-// shift left double by word immediate
-#ifndef vec_sldw
-#   define vec_sldw vec_xxsldwi
-#endif
-
-// Implement vec_rsqrt since clang only supports vec_rsqrte
-#ifndef vec_rsqrt
-    VSX_FINLINE(vec_float4) vec_rsqrt(const vec_float4& a)
-    { return vec_div(vec_float4_sp(1), vec_sqrt(a)); }
-
-    VSX_FINLINE(vec_double2) vec_rsqrt(const vec_double2& a)
-    { return vec_div(vec_double2_sp(1), vec_sqrt(a)); }
-#endif
-
-// vec_promote missing support for doubleword
-VSX_FINLINE(vec_dword2) vec_promote(long long a, int b)
-{
-    vec_dword2 ret = vec_dword2_z;
-    ret[b & 1] = a;
-    return ret;
-}
-
-VSX_FINLINE(vec_udword2) vec_promote(unsigned long long a, int b)
-{
-    vec_udword2 ret = vec_udword2_z;
-    ret[b & 1] = a;
-    return ret;
-}
-
-// vec_popcnt should return unsigned but clang has different thought just like gcc in vec_vpopcnt
-#define VSX_IMPL_POPCNTU(Tvec, Tvec2, ucast)   \
-VSX_FINLINE(Tvec) vec_popcntu(const Tvec2& a)  \
-{ return ucast(vec_popcnt(a)); }
-VSX_IMPL_POPCNTU(vec_uchar16, vec_char16, vec_uchar16_c);
-VSX_IMPL_POPCNTU(vec_ushort8, vec_short8, vec_ushort8_c);
-VSX_IMPL_POPCNTU(vec_uint4,   vec_int4,   vec_uint4_c);
-// redirect unsigned types
-VSX_REDIRECT_1RG(vec_uchar16, vec_uchar16, vec_popcntu, vec_popcnt)
-VSX_REDIRECT_1RG(vec_ushort8, vec_ushort8, vec_popcntu, vec_popcnt)
-VSX_REDIRECT_1RG(vec_uint4,   vec_uint4,   vec_popcntu, vec_popcnt)
-
-// converts between single and double precision
-VSX_REDIRECT_1RG(vec_float4,  vec_double2, vec_cvfo, __builtin_vsx_xvcvdpsp)
-VSX_REDIRECT_1RG(vec_double2, vec_float4,  vec_cvfo, __builtin_vsx_xvcvspdp)
-
-// converts word and doubleword to double-precision
-#ifdef vec_ctd
-#   undef vec_ctd
-#endif
-VSX_REDIRECT_1RG(vec_double2, vec_int4,  vec_ctdo, __builtin_vsx_xvcvsxwdp)
-VSX_REDIRECT_1RG(vec_double2, vec_uint4, vec_ctdo, __builtin_vsx_xvcvuxwdp)
-
-VSX_IMPL_CONVERT(vec_double2, vec_dword2,  vec_ctd)
-VSX_IMPL_CONVERT(vec_double2, vec_udword2, vec_ctd)
-
-// converts word and doubleword to single-precision
-#if __clang_major__ > 4
-#   undef vec_ctf
-#endif
-VSX_IMPL_CONVERT(vec_float4, vec_int4,    vec_ctf)
-VSX_IMPL_CONVERT(vec_float4, vec_uint4,   vec_ctf)
-VSX_REDIRECT_1RG(vec_float4, vec_dword2,  vec_ctfo, __builtin_vsx_xvcvsxdsp)
-VSX_REDIRECT_1RG(vec_float4, vec_udword2, vec_ctfo, __builtin_vsx_xvcvuxdsp)
-
-// converts single and double precision to signed word
-#if __clang_major__ > 4
-#   undef vec_cts
-#endif
-VSX_REDIRECT_1RG(vec_int4,  vec_double2, vec_ctso, __builtin_vsx_xvcvdpsxws)
-VSX_IMPL_CONVERT(vec_int4,  vec_float4,  vec_cts)
-
-// converts single and double precision to unsigned word
-#if __clang_major__ > 4
-#   undef vec_ctu
-#endif
-VSX_REDIRECT_1RG(vec_uint4, vec_double2, vec_ctuo, __builtin_vsx_xvcvdpuxws)
-VSX_IMPL_CONVERT(vec_uint4, vec_float4,  vec_ctu)
-
-// converts single and double precision to signed doubleword
-#ifdef vec_ctsl
-#   undef vec_ctsl
-#endif
-VSX_IMPL_CONVERT(vec_dword2, vec_double2, vec_ctsl)
-// __builtin_convertvector unable to convert, xvcvspsxds is missing on it
-VSX_FINLINE(vec_dword2) vec_ctslo(const vec_float4& a)
-{ return vec_ctsl(vec_cvfo(a)); }
-
-// converts single and double precision to unsigned doubleword
-#ifdef vec_ctul
-#   undef vec_ctul
-#endif
-VSX_IMPL_CONVERT(vec_udword2, vec_double2, vec_ctul)
-// __builtin_convertvector unable to convert, xvcvspuxds is missing on it
-VSX_FINLINE(vec_udword2) vec_ctulo(const vec_float4& a)
-{ return vec_ctul(vec_cvfo(a)); }
-
-#endif // CLANG VSX compatibility
-
-/*
- * Common GCC, CLANG compatibility
-**/
-#if defined(__GNUG__) && !defined(__IBMCPP__)
-
-#ifdef vec_cvf
-#   undef vec_cvf
-#endif
-
-#define VSX_IMPL_CONV_EVEN_4_2(rt, rg, fnm, fn2) \
-VSX_FINLINE(rt) fnm(const rg& a)                 \
-{ return fn2(vec_sldw(a, a, 1)); }
-
-VSX_IMPL_CONV_EVEN_4_2(vec_double2, vec_float4, vec_cvf,  vec_cvfo)
-VSX_IMPL_CONV_EVEN_4_2(vec_double2, vec_int4,   vec_ctd,  vec_ctdo)
-VSX_IMPL_CONV_EVEN_4_2(vec_double2, vec_uint4,  vec_ctd,  vec_ctdo)
-
-VSX_IMPL_CONV_EVEN_4_2(vec_dword2,  vec_float4, vec_ctsl, vec_ctslo)
-VSX_IMPL_CONV_EVEN_4_2(vec_udword2, vec_float4, vec_ctul, vec_ctulo)
-
-#define VSX_IMPL_CONV_EVEN_2_4(rt, rg, fnm, fn2) \
-VSX_FINLINE(rt) fnm(const rg& a)                 \
-{                                                \
-    rt v4 = fn2(a);                              \
-    return vec_sldw(v4, v4, 3);                  \
-}
-
-VSX_IMPL_CONV_EVEN_2_4(vec_float4, vec_double2, vec_cvf, vec_cvfo)
-VSX_IMPL_CONV_EVEN_2_4(vec_float4, vec_dword2,  vec_ctf, vec_ctfo)
-VSX_IMPL_CONV_EVEN_2_4(vec_float4, vec_udword2, vec_ctf, vec_ctfo)
-
-VSX_IMPL_CONV_EVEN_2_4(vec_int4,   vec_double2, vec_cts, vec_ctso)
-VSX_IMPL_CONV_EVEN_2_4(vec_uint4,  vec_double2, vec_ctu, vec_ctuo)
-
-// Only for Eigen!
-/*
- * changing behavior of conversion intrinsics for gcc has effect on Eigen
- * so we redfine old behavior again only on gcc, clang
-*/
-#if !defined(__clang__) || __clang_major__ > 4
-    // ignoring second arg since Eigen only truncates toward zero
-#   define VSX_IMPL_CONV_2VARIANT(rt, rg, fnm, fn2)     \
-    VSX_FINLINE(rt) fnm(const rg& a, int only_truncate) \
-    {                                                   \
-        assert(only_truncate == 0);                     \
-        (void)only_truncate;                            \
-        return fn2(a);                                  \
-    }
-    VSX_IMPL_CONV_2VARIANT(vec_int4,   vec_float4,  vec_cts, vec_cts)
-    VSX_IMPL_CONV_2VARIANT(vec_float4, vec_int4,    vec_ctf, vec_ctf)
-    // define vec_cts for converting double precision to signed doubleword
-    // which isn't combitable with xlc but its okay since Eigen only use it for gcc
-    VSX_IMPL_CONV_2VARIANT(vec_dword2, vec_double2, vec_cts, vec_ctsl)
-#endif // Eigen
-
-#endif // Common GCC, CLANG compatibility
-
-/*
- * XLC VSX compatibility
-**/
-#if defined(__IBMCPP__)
-
-// vector population count
-#define vec_popcntu vec_popcnt
-
-// overload and redirect with setting second arg to zero
-// since we only support conversions without the second arg
-#define VSX_IMPL_OVERLOAD_Z2(rt, rg, fnm) \
-VSX_FINLINE(rt) fnm(const rg& a) { return fnm(a, 0); }
-
-VSX_IMPL_OVERLOAD_Z2(vec_double2, vec_int4,    vec_ctd)
-VSX_IMPL_OVERLOAD_Z2(vec_double2, vec_uint4,   vec_ctd)
-VSX_IMPL_OVERLOAD_Z2(vec_double2, vec_dword2,  vec_ctd)
-VSX_IMPL_OVERLOAD_Z2(vec_double2, vec_udword2, vec_ctd)
-
-VSX_IMPL_OVERLOAD_Z2(vec_float4,  vec_int4,    vec_ctf)
-VSX_IMPL_OVERLOAD_Z2(vec_float4,  vec_uint4,   vec_ctf)
-VSX_IMPL_OVERLOAD_Z2(vec_float4,  vec_dword2,  vec_ctf)
-VSX_IMPL_OVERLOAD_Z2(vec_float4,  vec_udword2, vec_ctf)
-
-VSX_IMPL_OVERLOAD_Z2(vec_int4,    vec_double2, vec_cts)
-VSX_IMPL_OVERLOAD_Z2(vec_int4,    vec_float4,  vec_cts)
-
-VSX_IMPL_OVERLOAD_Z2(vec_uint4,   vec_double2, vec_ctu)
-VSX_IMPL_OVERLOAD_Z2(vec_uint4,   vec_float4,  vec_ctu)
-
-VSX_IMPL_OVERLOAD_Z2(vec_dword2,  vec_double2, vec_ctsl)
-VSX_IMPL_OVERLOAD_Z2(vec_dword2,  vec_float4,  vec_ctsl)
-
-VSX_IMPL_OVERLOAD_Z2(vec_udword2, vec_double2, vec_ctul)
-VSX_IMPL_OVERLOAD_Z2(vec_udword2, vec_float4,  vec_ctul)
-
-// fixme: implement conversions of odd-numbered elements in a dirty way
-// since xlc doesn't support VSX registers operand in inline asm.
-#define VSX_IMPL_CONV_ODD_4_2(rt, rg, fnm, fn2) \
-VSX_FINLINE(rt) fnm(const rg& a) { return fn2(vec_sldw(a, a, 3)); }
-
-VSX_IMPL_CONV_ODD_4_2(vec_double2, vec_float4, vec_cvfo,  vec_cvf)
-VSX_IMPL_CONV_ODD_4_2(vec_double2, vec_int4,   vec_ctdo,  vec_ctd)
-VSX_IMPL_CONV_ODD_4_2(vec_double2, vec_uint4,  vec_ctdo,  vec_ctd)
-
-VSX_IMPL_CONV_ODD_4_2(vec_dword2,  vec_float4, vec_ctslo, vec_ctsl)
-VSX_IMPL_CONV_ODD_4_2(vec_udword2, vec_float4, vec_ctulo, vec_ctul)
-
-#define VSX_IMPL_CONV_ODD_2_4(rt, rg, fnm, fn2)  \
-VSX_FINLINE(rt) fnm(const rg& a)                 \
-{                                                \
-    rt v4 = fn2(a);                              \
-    return vec_sldw(v4, v4, 1);                  \
-}
-
-VSX_IMPL_CONV_ODD_2_4(vec_float4, vec_double2, vec_cvfo, vec_cvf)
-VSX_IMPL_CONV_ODD_2_4(vec_float4, vec_dword2,  vec_ctfo, vec_ctf)
-VSX_IMPL_CONV_ODD_2_4(vec_float4, vec_udword2, vec_ctfo, vec_ctf)
-
-VSX_IMPL_CONV_ODD_2_4(vec_int4,   vec_double2, vec_ctso, vec_cts)
-VSX_IMPL_CONV_ODD_2_4(vec_uint4,  vec_double2, vec_ctuo, vec_ctu)
-
-#endif // XLC VSX compatibility
-
-// ignore GCC warning that caused by -Wunused-but-set-variable in rare cases
-#if defined(__GNUG__) && !defined(__clang__)
-#   define VSX_UNUSED(Tvec) Tvec __attribute__((__unused__))
-#else // CLANG, XLC
-#   define VSX_UNUSED(Tvec) Tvec
-#endif
-
-// gcc can find his way in casting log int and XLC, CLANG ambiguous
-#if defined(__clang__) || defined(__IBMCPP__)
-    VSX_FINLINE(vec_udword2) vec_splats(uint64 v)
-    { return vec_splats((unsigned long long) v); }
-
-    VSX_FINLINE(vec_dword2) vec_splats(int64 v)
-    { return vec_splats((long long) v); }
-
-    VSX_FINLINE(vec_udword2) vec_promote(uint64 a, int b)
-    { return vec_promote((unsigned long long) a, b); }
-
-    VSX_FINLINE(vec_dword2) vec_promote(int64 a, int b)
-    { return vec_promote((long long) a, b); }
-#endif
-
-/*
- * implement vsx_ld(offset, pointer), vsx_st(vector, offset, pointer)
- * load and set using offset depend on the pointer type
- *
- * implement vsx_ldf(offset, pointer), vsx_stf(vector, offset, pointer)
- * load and set using offset depend on fixed bytes size
- *
- * Note: In clang vec_xl and vec_xst fails to load unaligned addresses
- * so we are using vec_vsx_ld, vec_vsx_st instead
-*/
-
-#if defined(__clang__) && !defined(__IBMCPP__)
-#   define vsx_ldf  vec_vsx_ld
-#   define vsx_stf  vec_vsx_st
-#else // GCC , XLC
-#   define vsx_ldf  vec_xl
-#   define vsx_stf  vec_xst
-#endif
-
-#define VSX_OFFSET(o, p) ((o) * sizeof(*(p)))
-#define vsx_ld(o, p) vsx_ldf(VSX_OFFSET(o, p), p)
-#define vsx_st(v, o, p) vsx_stf(v, VSX_OFFSET(o, p), p)
-
-/*
- * implement vsx_ld2(offset, pointer), vsx_st2(vector, offset, pointer) to load and store double words
- * In GCC vec_xl and vec_xst it maps to vec_vsx_ld, vec_vsx_st which doesn't support long long
- * and in CLANG we are using vec_vsx_ld, vec_vsx_st because vec_xl, vec_xst fails to load unaligned addresses
- *
- * In XLC vec_xl and vec_xst fail to cast int64(long int) to long long
-*/
-#if (defined(__GNUG__) || defined(__clang__)) && !defined(__IBMCPP__)
-    VSX_FINLINE(vec_udword2) vsx_ld2(long o, const uint64* p)
-    { return vec_udword2_c(vsx_ldf(VSX_OFFSET(o, p), (unsigned int*)p)); }
-
-    VSX_FINLINE(vec_dword2) vsx_ld2(long o, const int64* p)
-    { return vec_dword2_c(vsx_ldf(VSX_OFFSET(o, p), (int*)p)); }
-
-    VSX_FINLINE(void) vsx_st2(const vec_udword2& vec, long o, uint64* p)
-    { vsx_stf(vec_uint4_c(vec), VSX_OFFSET(o, p), (unsigned int*)p); }
-
-    VSX_FINLINE(void) vsx_st2(const vec_dword2& vec, long o, int64* p)
-    { vsx_stf(vec_int4_c(vec), VSX_OFFSET(o, p), (int*)p); }
-#else // XLC
-    VSX_FINLINE(vec_udword2) vsx_ld2(long o, const uint64* p)
-    { return vsx_ldf(VSX_OFFSET(o, p), (unsigned long long*)p); }
-
-    VSX_FINLINE(vec_dword2) vsx_ld2(long o, const int64* p)
-    { return vsx_ldf(VSX_OFFSET(o, p), (long long*)p); }
-
-    VSX_FINLINE(void) vsx_st2(const vec_udword2& vec, long o, uint64* p)
-    { vsx_stf(vec, VSX_OFFSET(o, p), (unsigned long long*)p); }
-
-    VSX_FINLINE(void) vsx_st2(const vec_dword2& vec, long o, int64* p)
-    { vsx_stf(vec, VSX_OFFSET(o, p), (long long*)p); }
-#endif
-
-// Store lower 8 byte
-#define vec_st_l8(v, p) *((uint64*)(p)) = vec_extract(vec_udword2_c(v), 0)
-
-// Store higher 8 byte
-#define vec_st_h8(v, p) *((uint64*)(p)) = vec_extract(vec_udword2_c(v), 1)
-
-// Load 64-bits of integer data to lower part
-#define VSX_IMPL_LOAD_L8(Tvec, Tp)                  \
-VSX_FINLINE(Tvec) vec_ld_l8(const Tp *p)            \
-{ return ((Tvec)vec_promote(*((uint64*)p), 0)); }
-
-VSX_IMPL_LOAD_L8(vec_uchar16, uchar)
-VSX_IMPL_LOAD_L8(vec_char16,  schar)
-VSX_IMPL_LOAD_L8(vec_ushort8, ushort)
-VSX_IMPL_LOAD_L8(vec_short8,  short)
-VSX_IMPL_LOAD_L8(vec_uint4,   uint)
-VSX_IMPL_LOAD_L8(vec_int4,    int)
-VSX_IMPL_LOAD_L8(vec_float4,  float)
-VSX_IMPL_LOAD_L8(vec_udword2, uint64)
-VSX_IMPL_LOAD_L8(vec_dword2,  int64)
-VSX_IMPL_LOAD_L8(vec_double2, double)
-
-// logical not
-#define vec_not(a) vec_nor(a, a)
-
-// power9 yaya
-// not equal
-#ifndef vec_cmpne
-#   define vec_cmpne(a, b) vec_not(vec_cmpeq(a, b))
-#endif
-
-// absolute difference
-#ifndef vec_absd
-#   define vec_absd(a, b) vec_sub(vec_max(a, b), vec_min(a, b))
-#endif
-
-/*
- * Implement vec_unpacklu and vec_unpackhu
- * since vec_unpackl, vec_unpackh only support signed integers
-**/
-#define VSX_IMPL_UNPACKU(rt, rg, zero)      \
-VSX_FINLINE(rt) vec_unpacklu(const rg& a)   \
-{ return (rt)(vec_mergel(a, zero)); }       \
-VSX_FINLINE(rt) vec_unpackhu(const rg& a)   \
-{ return (rt)(vec_mergeh(a, zero));  }
-
-VSX_IMPL_UNPACKU(vec_ushort8, vec_uchar16, vec_uchar16_z)
-VSX_IMPL_UNPACKU(vec_uint4,   vec_ushort8, vec_ushort8_z)
-VSX_IMPL_UNPACKU(vec_udword2, vec_uint4,   vec_uint4_z)
-
-/*
- * Implement vec_mergesqe and vec_mergesqo
- * Merges the sequence values of even and odd elements of two vectors
-*/
-#define VSX_IMPL_PERM(rt, fnm, ...)            \
-VSX_FINLINE(rt) fnm(const rt& a, const rt& b)  \
-{ static const vec_uchar16 perm = {__VA_ARGS__}; return vec_perm(a, b, perm); }
-
-// 16
-#define perm16_mergesqe 0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30
-#define perm16_mergesqo 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31
-VSX_IMPL_PERM(vec_uchar16, vec_mergesqe, perm16_mergesqe)
-VSX_IMPL_PERM(vec_uchar16, vec_mergesqo, perm16_mergesqo)
-VSX_IMPL_PERM(vec_char16,  vec_mergesqe, perm16_mergesqe)
-VSX_IMPL_PERM(vec_char16,  vec_mergesqo, perm16_mergesqo)
-// 8
-#define perm8_mergesqe 0, 1, 4, 5, 8, 9, 12, 13, 16, 17, 20, 21, 24, 25, 28, 29
-#define perm8_mergesqo 2, 3, 6, 7, 10, 11, 14, 15, 18, 19, 22, 23, 26, 27, 30, 31
-VSX_IMPL_PERM(vec_ushort8, vec_mergesqe, perm8_mergesqe)
-VSX_IMPL_PERM(vec_ushort8, vec_mergesqo, perm8_mergesqo)
-VSX_IMPL_PERM(vec_short8,  vec_mergesqe, perm8_mergesqe)
-VSX_IMPL_PERM(vec_short8,  vec_mergesqo, perm8_mergesqo)
-// 4
-#define perm4_mergesqe 0, 1, 2, 3, 8, 9, 10, 11, 16, 17, 18, 19, 24, 25, 26, 27
-#define perm4_mergesqo 4, 5, 6, 7, 12, 13, 14, 15, 20, 21, 22, 23, 28, 29, 30, 31
-VSX_IMPL_PERM(vec_uint4,  vec_mergesqe, perm4_mergesqe)
-VSX_IMPL_PERM(vec_uint4,  vec_mergesqo, perm4_mergesqo)
-VSX_IMPL_PERM(vec_int4,   vec_mergesqe, perm4_mergesqe)
-VSX_IMPL_PERM(vec_int4,   vec_mergesqo, perm4_mergesqo)
-VSX_IMPL_PERM(vec_float4, vec_mergesqe, perm4_mergesqe)
-VSX_IMPL_PERM(vec_float4, vec_mergesqo, perm4_mergesqo)
-// 2
-VSX_REDIRECT_2RG(vec_double2, vec_double2, vec_mergesqe, vec_mergeh)
-VSX_REDIRECT_2RG(vec_double2, vec_double2, vec_mergesqo, vec_mergel)
-VSX_REDIRECT_2RG(vec_dword2,  vec_dword2,  vec_mergesqe, vec_mergeh)
-VSX_REDIRECT_2RG(vec_dword2,  vec_dword2,  vec_mergesqo, vec_mergel)
-VSX_REDIRECT_2RG(vec_udword2, vec_udword2, vec_mergesqe, vec_mergeh)
-VSX_REDIRECT_2RG(vec_udword2, vec_udword2, vec_mergesqo, vec_mergel)
-
-/*
- * Implement vec_mergesqh and vec_mergesql
- * Merges the sequence most and least significant halves of two vectors
-*/
-#define VSX_IMPL_MERGESQHL(Tvec)                                    \
-VSX_FINLINE(Tvec) vec_mergesqh(const Tvec& a, const Tvec& b)        \
-{ return (Tvec)vec_mergeh(vec_udword2_c(a), vec_udword2_c(b)); }    \
-VSX_FINLINE(Tvec) vec_mergesql(const Tvec& a, const Tvec& b)        \
-{ return (Tvec)vec_mergel(vec_udword2_c(a), vec_udword2_c(b)); }
-VSX_IMPL_MERGESQHL(vec_uchar16)
-VSX_IMPL_MERGESQHL(vec_char16)
-VSX_IMPL_MERGESQHL(vec_ushort8)
-VSX_IMPL_MERGESQHL(vec_short8)
-VSX_IMPL_MERGESQHL(vec_uint4)
-VSX_IMPL_MERGESQHL(vec_int4)
-VSX_IMPL_MERGESQHL(vec_float4)
-VSX_REDIRECT_2RG(vec_udword2, vec_udword2, vec_mergesqh, vec_mergeh)
-VSX_REDIRECT_2RG(vec_udword2, vec_udword2, vec_mergesql, vec_mergel)
-VSX_REDIRECT_2RG(vec_dword2,  vec_dword2,  vec_mergesqh, vec_mergeh)
-VSX_REDIRECT_2RG(vec_dword2,  vec_dword2,  vec_mergesql, vec_mergel)
-VSX_REDIRECT_2RG(vec_double2, vec_double2, vec_mergesqh, vec_mergeh)
-VSX_REDIRECT_2RG(vec_double2, vec_double2, vec_mergesql, vec_mergel)
-
-
-// 2 and 4 channels interleave for all types except 2 lanes
-#define VSX_IMPL_ST_INTERLEAVE(Tp, Tvec)                                    \
-VSX_FINLINE(void) vec_st_interleave(const Tvec& a, const Tvec& b, Tp* ptr)  \
-{                                                                           \
-    vsx_stf(vec_mergeh(a, b), 0, ptr);                                      \
-    vsx_stf(vec_mergel(a, b), 16, ptr);                                     \
-}                                                                           \
-VSX_FINLINE(void) vec_st_interleave(const Tvec& a, const Tvec& b,           \
-                                     const Tvec& c, const Tvec& d, Tp* ptr) \
-{                                                                           \
-    Tvec ac = vec_mergeh(a, c);                                             \
-    Tvec bd = vec_mergeh(b, d);                                             \
-    vsx_stf(vec_mergeh(ac, bd), 0, ptr);                                    \
-    vsx_stf(vec_mergel(ac, bd), 16, ptr);                                   \
-    ac = vec_mergel(a, c);                                                  \
-    bd = vec_mergel(b, d);                                                  \
-    vsx_stf(vec_mergeh(ac, bd), 32, ptr);                                   \
-    vsx_stf(vec_mergel(ac, bd), 48, ptr);                                   \
-}
-VSX_IMPL_ST_INTERLEAVE(uchar,  vec_uchar16)
-VSX_IMPL_ST_INTERLEAVE(schar,  vec_char16)
-VSX_IMPL_ST_INTERLEAVE(ushort, vec_ushort8)
-VSX_IMPL_ST_INTERLEAVE(short,  vec_short8)
-VSX_IMPL_ST_INTERLEAVE(uint,   vec_uint4)
-VSX_IMPL_ST_INTERLEAVE(int,    vec_int4)
-VSX_IMPL_ST_INTERLEAVE(float,  vec_float4)
-
-// 2 and 4 channels deinterleave for 16 lanes
-#define VSX_IMPL_ST_DINTERLEAVE_8(Tp, Tvec)                                 \
-VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, Tvec& b)      \
-{                                                                           \
-    Tvec v0 = vsx_ld(0, ptr);                                               \
-    Tvec v1 = vsx_ld(16, ptr);                                              \
-    a = vec_mergesqe(v0, v1);                                               \
-    b = vec_mergesqo(v0, v1);                                               \
-}                                                                           \
-VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, Tvec& b,      \
-                                       Tvec& c, Tvec& d)                    \
-{                                                                           \
-    Tvec v0 = vsx_ld(0, ptr);                                               \
-    Tvec v1 = vsx_ld(16, ptr);                                              \
-    Tvec v2 = vsx_ld(32, ptr);                                              \
-    Tvec v3 = vsx_ld(48, ptr);                                              \
-    Tvec m0 = vec_mergesqe(v0, v1);                                         \
-    Tvec m1 = vec_mergesqe(v2, v3);                                         \
-    a = vec_mergesqe(m0, m1);                                               \
-    c = vec_mergesqo(m0, m1);                                               \
-    m0 = vec_mergesqo(v0, v1);                                              \
-    m1 = vec_mergesqo(v2, v3);                                              \
-    b = vec_mergesqe(m0, m1);                                               \
-    d = vec_mergesqo(m0, m1);                                               \
-}
-VSX_IMPL_ST_DINTERLEAVE_8(uchar, vec_uchar16)
-VSX_IMPL_ST_DINTERLEAVE_8(schar, vec_char16)
-
-// 2 and 4 channels deinterleave for 8 lanes
-#define VSX_IMPL_ST_DINTERLEAVE_16(Tp, Tvec)                                \
-VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, Tvec& b)      \
-{                                                                           \
-    Tvec v0 = vsx_ld(0, ptr);                                               \
-    Tvec v1 = vsx_ld(8, ptr);                                               \
-    a = vec_mergesqe(v0, v1);                                               \
-    b = vec_mergesqo(v0, v1);                                               \
-}                                                                           \
-VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, Tvec& b,      \
-                                       Tvec& c, Tvec& d)                    \
-{                                                                           \
-    Tvec v0 = vsx_ld(0, ptr);                                               \
-    Tvec v1 = vsx_ld(8, ptr);                                               \
-    Tvec m0 = vec_mergeh(v0, v1);                                           \
-    Tvec m1 = vec_mergel(v0, v1);                                           \
-    Tvec ab0 = vec_mergeh(m0, m1);                                          \
-    Tvec cd0 = vec_mergel(m0, m1);                                          \
-    v0 = vsx_ld(16, ptr);                                                   \
-    v1 = vsx_ld(24, ptr);                                                   \
-    m0 = vec_mergeh(v0, v1);                                                \
-    m1 = vec_mergel(v0, v1);                                                \
-    Tvec ab1 = vec_mergeh(m0, m1);                                          \
-    Tvec cd1 = vec_mergel(m0, m1);                                          \
-    a = vec_mergesqh(ab0, ab1);                                             \
-    b = vec_mergesql(ab0, ab1);                                             \
-    c = vec_mergesqh(cd0, cd1);                                             \
-    d = vec_mergesql(cd0, cd1);                                             \
-}
-VSX_IMPL_ST_DINTERLEAVE_16(ushort, vec_ushort8)
-VSX_IMPL_ST_DINTERLEAVE_16(short,  vec_short8)
-
-// 2 and 4 channels deinterleave for 4 lanes
-#define VSX_IMPL_ST_DINTERLEAVE_32(Tp, Tvec)                                \
-VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, Tvec& b)      \
-{                                                                           \
-    a = vsx_ld(0, ptr);                                                     \
-    b = vsx_ld(4, ptr);                                                     \
-    Tvec m0 = vec_mergeh(a, b);                                             \
-    Tvec m1 = vec_mergel(a, b);                                             \
-    a = vec_mergeh(m0, m1);                                                 \
-    b = vec_mergel(m0, m1);                                                 \
-}                                                                           \
-VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, Tvec& b,      \
-                                       Tvec& c, Tvec& d)                    \
-{                                                                           \
-    Tvec v0 = vsx_ld(0, ptr);                                               \
-    Tvec v1 = vsx_ld(4, ptr);                                               \
-    Tvec v2 = vsx_ld(8, ptr);                                               \
-    Tvec v3 = vsx_ld(12, ptr);                                              \
-    Tvec m0 = vec_mergeh(v0, v2);                                           \
-    Tvec m1 = vec_mergeh(v1, v3);                                           \
-    a = vec_mergeh(m0, m1);                                                 \
-    b = vec_mergel(m0, m1);                                                 \
-    m0 = vec_mergel(v0, v2);                                                \
-    m1 = vec_mergel(v1, v3);                                                \
-    c = vec_mergeh(m0, m1);                                                 \
-    d = vec_mergel(m0, m1);                                                 \
-}
-VSX_IMPL_ST_DINTERLEAVE_32(uint,  vec_uint4)
-VSX_IMPL_ST_DINTERLEAVE_32(int,   vec_int4)
-VSX_IMPL_ST_DINTERLEAVE_32(float, vec_float4)
-
-// 2 and 4 channels interleave and deinterleave for 2 lanes
-#define VSX_IMPL_ST_D_INTERLEAVE_64(Tp, Tvec, ld_func, st_func)             \
-VSX_FINLINE(void) vec_st_interleave(const Tvec& a, const Tvec& b, Tp* ptr)  \
-{                                                                           \
-    st_func(vec_mergeh(a, b), 0, ptr);                                      \
-    st_func(vec_mergel(a, b), 2, ptr);                                      \
-}                                                                           \
-VSX_FINLINE(void) vec_st_interleave(const Tvec& a, const Tvec& b,           \
-                                     const Tvec& c, const Tvec& d, Tp* ptr) \
-{                                                                           \
-    st_func(vec_mergeh(a, b), 0, ptr);                                      \
-    st_func(vec_mergeh(c, d), 2, ptr);                                      \
-    st_func(vec_mergel(a, b), 4, ptr);                                      \
-    st_func(vec_mergel(c, d), 6, ptr);                                      \
-}                                                                           \
-VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, Tvec& b)      \
-{                                                                           \
-    Tvec m0 = ld_func(0, ptr);                                              \
-    Tvec m1 = ld_func(2, ptr);                                              \
-    a = vec_mergeh(m0, m1);                                                 \
-    b = vec_mergel(m0, m1);                                                 \
-}                                                                           \
-VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, Tvec& b,      \
-                                       Tvec& c, Tvec& d)                    \
-{                                                                           \
-    Tvec v0 = ld_func(0, ptr);                                              \
-    Tvec v1 = ld_func(2, ptr);                                              \
-    Tvec v2 = ld_func(4, ptr);                                              \
-    Tvec v3 = ld_func(6, ptr);                                              \
-    a = vec_mergeh(v0, v2);                                                 \
-    b = vec_mergel(v0, v2);                                                 \
-    c = vec_mergeh(v1, v3);                                                 \
-    d = vec_mergel(v1, v3);                                                 \
-}
-VSX_IMPL_ST_D_INTERLEAVE_64(int64,  vec_dword2,  vsx_ld2, vsx_st2)
-VSX_IMPL_ST_D_INTERLEAVE_64(uint64, vec_udword2, vsx_ld2, vsx_st2)
-VSX_IMPL_ST_D_INTERLEAVE_64(double, vec_double2, vsx_ld,  vsx_st)
-
-/* 3 channels */
-#define VSX_IMPL_ST_INTERLEAVE_3CH_16(Tp, Tvec)                                                   \
-VSX_FINLINE(void) vec_st_interleave(const Tvec& a, const Tvec& b,                                 \
-                                     const Tvec& c, Tp* ptr)                                      \
-{                                                                                                 \
-    static const vec_uchar16 a12 = {0, 16, 0, 1, 17, 0, 2, 18, 0, 3, 19, 0, 4, 20, 0, 5};         \
-    static const vec_uchar16 a123 = {0, 1, 16, 3, 4, 17, 6, 7, 18, 9, 10, 19, 12, 13, 20, 15};    \
-    vsx_st(vec_perm(vec_perm(a, b, a12), c, a123), 0, ptr);                                       \
-    static const vec_uchar16 b12 = {21, 0, 6, 22, 0, 7, 23, 0, 8, 24, 0, 9, 25, 0, 10, 26};       \
-    static const vec_uchar16 b123 = {0, 21, 2, 3, 22, 5, 6, 23, 8, 9, 24, 11, 12, 25, 14, 15};    \
-    vsx_st(vec_perm(vec_perm(a, b, b12), c, b123), 16, ptr);                                      \
-    static const vec_uchar16 c12 = {0, 11, 27, 0, 12, 28, 0, 13, 29, 0, 14, 30, 0, 15, 31, 0};    \
-    static const vec_uchar16 c123 = {26, 1, 2, 27, 4, 5, 28, 7, 8, 29, 10, 11, 30, 13, 14, 31};   \
-    vsx_st(vec_perm(vec_perm(a, b, c12), c, c123), 32, ptr);                                      \
-}                                                                                                 \
-VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, Tvec& b, Tvec& c)                   \
-{                                                                                                 \
-    Tvec v1 = vsx_ld(0, ptr);                                                                     \
-    Tvec v2 = vsx_ld(16, ptr);                                                                    \
-    Tvec v3 = vsx_ld(32, ptr);                                                                    \
-    static const vec_uchar16 a12_perm = {0, 3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 0, 0, 0, 0, 0};  \
-    static const vec_uchar16 a123_perm = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 17, 20, 23, 26, 29};  \
-    a = vec_perm(vec_perm(v1, v2, a12_perm), v3, a123_perm);                                      \
-    static const vec_uchar16 b12_perm = {1, 4, 7, 10, 13, 16, 19, 22, 25, 28, 31, 0, 0, 0, 0, 0}; \
-    static const vec_uchar16 b123_perm = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 18, 21, 24, 27, 30};  \
-    b = vec_perm(vec_perm(v1, v2, b12_perm), v3, b123_perm);                                      \
-    static const vec_uchar16 c12_perm = {2, 5, 8, 11, 14, 17, 20, 23, 26, 29, 0, 0, 0, 0, 0, 0};  \
-    static const vec_uchar16 c123_perm = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 16, 19, 22, 25, 28, 31};  \
-    c = vec_perm(vec_perm(v1, v2, c12_perm), v3, c123_perm);                                      \
-}
-VSX_IMPL_ST_INTERLEAVE_3CH_16(uchar, vec_uchar16)
-VSX_IMPL_ST_INTERLEAVE_3CH_16(schar, vec_char16)
-
-#define VSX_IMPL_ST_INTERLEAVE_3CH_8(Tp, Tvec)                                                    \
-VSX_FINLINE(void) vec_st_interleave(const Tvec& a, const Tvec& b,                                 \
-                                     const Tvec& c, Tp* ptr)                                      \
-{                                                                                                 \
-    static const vec_uchar16 a12 = {0, 1, 16, 17, 0, 0, 2, 3, 18, 19, 0, 0, 4, 5, 20, 21};        \
-    static const vec_uchar16 a123 = {0, 1, 2, 3, 16, 17, 6, 7, 8, 9, 18, 19, 12, 13, 14, 15};     \
-    vsx_st(vec_perm(vec_perm(a, b, a12), c, a123), 0, ptr);                                       \
-    static const vec_uchar16 b12 = {0, 0, 6, 7, 22, 23, 0, 0, 8, 9, 24, 25, 0, 0, 10, 11};        \
-    static const vec_uchar16 b123 = {20, 21, 2, 3, 4, 5, 22, 23, 8, 9, 10, 11, 24, 25, 14, 15};   \
-    vsx_st(vec_perm(vec_perm(a, b, b12), c, b123), 8, ptr);                                       \
-    static const vec_uchar16 c12 = {26, 27, 0, 0, 12, 13, 28, 29, 0, 0, 14, 15, 30, 31, 0, 0};    \
-    static const vec_uchar16 c123 = {0, 1, 26, 27, 4, 5, 6, 7, 28, 29, 10, 11, 12, 13, 30, 31};   \
-    vsx_st(vec_perm(vec_perm(a, b, c12), c, c123), 16, ptr);                                      \
-}                                                                                                 \
-VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, Tvec& b, Tvec& c)                   \
-{                                                                                                 \
-    Tvec v1 = vsx_ld(0, ptr);                                                                     \
-    Tvec v2 = vsx_ld(8, ptr);                                                                     \
-    Tvec v3 = vsx_ld(16, ptr);                                                                    \
-    static const vec_uchar16 a12_perm = {0, 1, 6, 7, 12, 13, 18, 19, 24, 25, 30, 31, 0, 0, 0, 0}; \
-    static const vec_uchar16 a123_perm = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 20, 21, 26, 27};  \
-    a = vec_perm(vec_perm(v1, v2, a12_perm), v3, a123_perm);                                      \
-    static const vec_uchar16 b12_perm = {2, 3, 8, 9, 14, 15, 20, 21, 26, 27, 0, 0, 0, 0, 0, 0};   \
-    static const vec_uchar16 b123_perm = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 16, 17, 22, 23, 28, 29};  \
-    b = vec_perm(vec_perm(v1, v2, b12_perm), v3, b123_perm);                                      \
-    static const vec_uchar16 c12_perm = {4, 5, 10, 11, 16, 17, 22, 23, 28, 29, 0, 0, 0, 0, 0, 0}; \
-    static const vec_uchar16 c123_perm = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 18, 19, 24, 25, 30, 31};  \
-    c = vec_perm(vec_perm(v1, v2, c12_perm), v3, c123_perm);                                      \
-}
-VSX_IMPL_ST_INTERLEAVE_3CH_8(ushort, vec_ushort8)
-VSX_IMPL_ST_INTERLEAVE_3CH_8(short,  vec_short8)
-
-#define VSX_IMPL_ST_INTERLEAVE_3CH_4(Tp, Tvec)                                                     \
-VSX_FINLINE(void) vec_st_interleave(const Tvec& a, const Tvec& b,                                  \
-                                     const Tvec& c, Tp* ptr)                                       \
-{                                                                                                  \
-    Tvec hbc = vec_mergeh(b, c);                                                                   \
-    static const vec_uchar16 ahbc = {0, 1, 2, 3, 16, 17, 18, 19, 20, 21, 22, 23, 4, 5, 6, 7};      \
-    vsx_st(vec_perm(a, hbc, ahbc), 0, ptr);                                                        \
-    Tvec lab = vec_mergel(a, b);                                                                   \
-    vsx_st(vec_sld(lab, hbc, 8), 4, ptr);                                                          \
-    static const vec_uchar16 clab = {8, 9, 10, 11, 24, 25, 26, 27, 28, 29, 30, 31, 12, 13, 14, 15};\
-    vsx_st(vec_perm(c, lab, clab), 8, ptr);                                                        \
-}                                                                                                  \
-VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a, Tvec& b, Tvec& c)                    \
-{                                                                                                  \
-    Tvec v1 = vsx_ld(0, ptr);                                                                      \
-    Tvec v2 = vsx_ld(4, ptr);                                                                      \
-    Tvec v3 = vsx_ld(8, ptr);                                                                      \
-    static const vec_uchar16 flp = {0, 1, 2, 3, 12, 13, 14, 15, 16, 17, 18, 19, 28, 29, 30, 31};   \
-    a = vec_perm(v1, vec_sld(v3, v2, 8), flp);                                                     \
-    static const vec_uchar16 flp2 = {28, 29, 30, 31, 0, 1, 2, 3, 12, 13, 14, 15, 16, 17, 18, 19};  \
-    b = vec_perm(v2, vec_sld(v1, v3, 8), flp2);                                                    \
-    c = vec_perm(vec_sld(v2, v1, 8), v3, flp);                                                     \
-}
-VSX_IMPL_ST_INTERLEAVE_3CH_4(uint,  vec_uint4)
-VSX_IMPL_ST_INTERLEAVE_3CH_4(int,   vec_int4)
-VSX_IMPL_ST_INTERLEAVE_3CH_4(float, vec_float4)
-
-#define VSX_IMPL_ST_INTERLEAVE_3CH_2(Tp, Tvec, ld_func, st_func)     \
-VSX_FINLINE(void) vec_st_interleave(const Tvec& a, const Tvec& b,    \
-                                     const Tvec& c, Tp* ptr)         \
-{                                                                    \
-    st_func(vec_mergeh(a, b), 0, ptr);                               \
-    st_func(vec_permi(c, a, 1), 2, ptr);                             \
-    st_func(vec_mergel(b, c), 4, ptr);                               \
-}                                                                    \
-VSX_FINLINE(void) vec_ld_deinterleave(const Tp* ptr, Tvec& a,        \
-                                       Tvec& b, Tvec& c)             \
-{                                                                    \
-    Tvec v1 = ld_func(0, ptr);                                       \
-    Tvec v2 = ld_func(2, ptr);                                       \
-    Tvec v3 = ld_func(4, ptr);                                       \
-    a = vec_permi(v1, v2, 1);                                        \
-    b = vec_permi(v1, v3, 2);                                        \
-    c = vec_permi(v2, v3, 1);                                        \
-}
-VSX_IMPL_ST_INTERLEAVE_3CH_2(int64,  vec_dword2,  vsx_ld2, vsx_st2)
-VSX_IMPL_ST_INTERLEAVE_3CH_2(uint64, vec_udword2, vsx_ld2, vsx_st2)
-VSX_IMPL_ST_INTERLEAVE_3CH_2(double, vec_double2, vsx_ld,  vsx_st)
-
-#endif // CV_VSX
-
-//! @}
-
-#endif // OPENCV_HAL_VSX_UTILS_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/wimage.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/wimage.hpp
deleted file mode 100755
index c7b6efa..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/core/wimage.hpp
+++ /dev/null
@@ -1,603 +0,0 @@
-/*M//////////////////////////////////////////////////////////////////////////////
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to
-//  this license.  If you do not agree to this license, do not download,
-//  install, copy or use the software.
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2008, Google, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without
-// modification, are permitted provided that the following conditions are met:
-//
-//  * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//  * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//  * The name of Intel Corporation or contributors may not be used to endorse
-//     or promote products derived from this software without specific
-//     prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is"
-// and any express or implied warranties, including, but not limited to, the
-// implied warranties of merchantability and fitness for a particular purpose
-// are disclaimed. In no event shall the Intel Corporation or contributors be
-// liable for any direct, indirect, incidental, special, exemplary, or
-// consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-/////////////////////////////////////////////////////////////////////////////////
-//M*/
-
-#ifndef OPENCV_CORE_WIMAGE_HPP
-#define OPENCV_CORE_WIMAGE_HPP
-
-#include "opencv2/core/core_c.h"
-
-#ifdef __cplusplus
-
-namespace cv {
-
-//! @addtogroup core
-//! @{
-
-template <typename T> class WImage;
-template <typename T> class WImageBuffer;
-template <typename T> class WImageView;
-
-template<typename T, int C> class WImageC;
-template<typename T, int C> class WImageBufferC;
-template<typename T, int C> class WImageViewC;
-
-// Commonly used typedefs.
-typedef WImage<uchar>            WImage_b;
-typedef WImageView<uchar>        WImageView_b;
-typedef WImageBuffer<uchar>      WImageBuffer_b;
-
-typedef WImageC<uchar, 1>        WImage1_b;
-typedef WImageViewC<uchar, 1>    WImageView1_b;
-typedef WImageBufferC<uchar, 1>  WImageBuffer1_b;
-
-typedef WImageC<uchar, 3>        WImage3_b;
-typedef WImageViewC<uchar, 3>    WImageView3_b;
-typedef WImageBufferC<uchar, 3>  WImageBuffer3_b;
-
-typedef WImage<float>            WImage_f;
-typedef WImageView<float>        WImageView_f;
-typedef WImageBuffer<float>      WImageBuffer_f;
-
-typedef WImageC<float, 1>        WImage1_f;
-typedef WImageViewC<float, 1>    WImageView1_f;
-typedef WImageBufferC<float, 1>  WImageBuffer1_f;
-
-typedef WImageC<float, 3>        WImage3_f;
-typedef WImageViewC<float, 3>    WImageView3_f;
-typedef WImageBufferC<float, 3>  WImageBuffer3_f;
-
-// There isn't a standard for signed and unsigned short so be more
-// explicit in the typename for these cases.
-typedef WImage<short>            WImage_16s;
-typedef WImageView<short>        WImageView_16s;
-typedef WImageBuffer<short>      WImageBuffer_16s;
-
-typedef WImageC<short, 1>        WImage1_16s;
-typedef WImageViewC<short, 1>    WImageView1_16s;
-typedef WImageBufferC<short, 1>  WImageBuffer1_16s;
-
-typedef WImageC<short, 3>        WImage3_16s;
-typedef WImageViewC<short, 3>    WImageView3_16s;
-typedef WImageBufferC<short, 3>  WImageBuffer3_16s;
-
-typedef WImage<ushort>            WImage_16u;
-typedef WImageView<ushort>        WImageView_16u;
-typedef WImageBuffer<ushort>      WImageBuffer_16u;
-
-typedef WImageC<ushort, 1>        WImage1_16u;
-typedef WImageViewC<ushort, 1>    WImageView1_16u;
-typedef WImageBufferC<ushort, 1>  WImageBuffer1_16u;
-
-typedef WImageC<ushort, 3>        WImage3_16u;
-typedef WImageViewC<ushort, 3>    WImageView3_16u;
-typedef WImageBufferC<ushort, 3>  WImageBuffer3_16u;
-
-/** @brief Image class which provides a thin layer around an IplImage.
-
-The goals of the class design are:
-
-    -# All the data has explicit ownership to avoid memory leaks
-    -# No hidden allocations or copies for performance.
-    -# Easy access to OpenCV methods (which will access IPP if available)
-    -# Can easily treat external data as an image
-    -# Easy to create images which are subsets of other images
-    -# Fast pixel access which can take advantage of number of channels if known at compile time.
-
-The WImage class is the image class which provides the data accessors. The 'W' comes from the fact
-that it is also a wrapper around the popular but inconvenient IplImage class. A WImage can be
-constructed either using a WImageBuffer class which allocates and frees the data, or using a
-WImageView class which constructs a subimage or a view into external data. The view class does no
-memory management. Each class actually has two versions, one when the number of channels is known
-at compile time and one when it isn't. Using the one with the number of channels specified can
-provide some compile time optimizations by using the fact that the number of channels is a
-constant.
-
-We use the convention (c,r) to refer to column c and row r with (0,0) being the upper left corner.
-This is similar to standard Euclidean coordinates with the first coordinate varying in the
-horizontal direction and the second coordinate varying in the vertical direction. Thus (c,r) is
-usually in the domain [0, width) X [0, height)
-
-Example usage:
-@code
-WImageBuffer3_b  im(5,7);  // Make a 5X7 3 channel image of type uchar
-WImageView3_b  sub_im(im, 2,2, 3,3); // 3X3 submatrix
-vector<float> vec(10, 3.0f);
-WImageView1_f user_im(&vec[0], 2, 5);  // 2X5 image w/ supplied data
-
-im.SetZero();  // same as cvSetZero(im.Ipl())
-*im(2, 3) = 15;  // Modify the element at column 2, row 3
-MySetRand(&sub_im);
-
-// Copy the second row into the first.  This can be done with no memory
-// allocation and will use SSE if IPP is available.
-int w = im.Width();
-im.View(0,0, w,1).CopyFrom(im.View(0,1, w,1));
-
-// Doesn't care about source of data since using WImage
-void MySetRand(WImage_b* im) { // Works with any number of channels
-for (int r = 0; r < im->Height(); ++r) {
- float* row = im->Row(r);
- for (int c = 0; c < im->Width(); ++c) {
-    for (int ch = 0; ch < im->Channels(); ++ch, ++row) {
-      *row = uchar(rand() & 255);
-    }
- }
-}
-}
-@endcode
-
-Functions that are not part of the basic image allocation, viewing, and access should come from
-OpenCV, except some useful functions that are not part of OpenCV can be found in wimage_util.h
-*/
-template<typename T>
-class WImage
-{
-public:
-    typedef T BaseType;
-
-    // WImage is an abstract class with no other virtual methods so make the
-    // destructor virtual.
-    virtual ~WImage() = 0;
-
-    // Accessors
-    IplImage* Ipl() {return image_; }
-    const IplImage* Ipl() const {return image_; }
-    T* ImageData() { return reinterpret_cast<T*>(image_->imageData); }
-    const T* ImageData() const {
-        return reinterpret_cast<const T*>(image_->imageData);
-    }
-
-    int Width() const {return image_->width; }
-    int Height() const {return image_->height; }
-
-    // WidthStep is the number of bytes to go to the pixel with the next y coord
-    int WidthStep() const {return image_->widthStep; }
-
-    int Channels() const {return image_->nChannels; }
-    int ChannelSize() const {return sizeof(T); }  // number of bytes per channel
-
-    // Number of bytes per pixel
-    int PixelSize() const {return Channels() * ChannelSize(); }
-
-    // Return depth type (e.g. IPL_DEPTH_8U, IPL_DEPTH_32F) which is the number
-    // of bits per channel and with the signed bit set.
-    // This is known at compile time using specializations.
-    int Depth() const;
-
-    inline const T* Row(int r) const {
-        return reinterpret_cast<T*>(image_->imageData + r*image_->widthStep);
-    }
-
-    inline T* Row(int r) {
-        return reinterpret_cast<T*>(image_->imageData + r*image_->widthStep);
-    }
-
-    // Pixel accessors which returns a pointer to the start of the channel
-    inline T* operator() (int c, int r)  {
-        return reinterpret_cast<T*>(image_->imageData + r*image_->widthStep) +
-            c*Channels();
-    }
-
-    inline const T* operator() (int c, int r) const  {
-        return reinterpret_cast<T*>(image_->imageData + r*image_->widthStep) +
-            c*Channels();
-    }
-
-    // Copy the contents from another image which is just a convenience to cvCopy
-    void CopyFrom(const WImage<T>& src) { cvCopy(src.Ipl(), image_); }
-
-    // Set contents to zero which is just a convenient to cvSetZero
-    void SetZero() { cvSetZero(image_); }
-
-    // Construct a view into a region of this image
-    WImageView<T> View(int c, int r, int width, int height);
-
-protected:
-    // Disallow copy and assignment
-    WImage(const WImage&);
-    void operator=(const WImage&);
-
-    explicit WImage(IplImage* img) : image_(img) {
-        assert(!img || img->depth == Depth());
-    }
-
-    void SetIpl(IplImage* image) {
-        assert(!image || image->depth == Depth());
-        image_ = image;
-    }
-
-    IplImage* image_;
-};
-
-
-/** Image class when both the pixel type and number of channels
-are known at compile time.  This wrapper will speed up some of the operations
-like accessing individual pixels using the () operator.
-*/
-template<typename T, int C>
-class WImageC : public WImage<T>
-{
-public:
-    typedef typename WImage<T>::BaseType BaseType;
-    enum { kChannels = C };
-
-    explicit WImageC(IplImage* img) : WImage<T>(img) {
-        assert(!img || img->nChannels == Channels());
-    }
-
-    // Construct a view into a region of this image
-    WImageViewC<T, C> View(int c, int r, int width, int height);
-
-    // Copy the contents from another image which is just a convenience to cvCopy
-    void CopyFrom(const WImageC<T, C>& src) {
-        cvCopy(src.Ipl(), WImage<T>::image_);
-    }
-
-    // WImageC is an abstract class with no other virtual methods so make the
-    // destructor virtual.
-    virtual ~WImageC() = 0;
-
-    int Channels() const {return C; }
-
-protected:
-    // Disallow copy and assignment
-    WImageC(const WImageC&);
-    void operator=(const WImageC&);
-
-    void SetIpl(IplImage* image) {
-        assert(!image || image->depth == WImage<T>::Depth());
-        WImage<T>::SetIpl(image);
-    }
-};
-
-/** Image class which owns the data, so it can be allocated and is always
-freed.  It cannot be copied but can be explicitly cloned.
-*/
-template<typename T>
-class WImageBuffer : public WImage<T>
-{
-public:
-    typedef typename WImage<T>::BaseType BaseType;
-
-    // Default constructor which creates an object that can be
-    WImageBuffer() : WImage<T>(0) {}
-
-    WImageBuffer(int width, int height, int nchannels) : WImage<T>(0) {
-        Allocate(width, height, nchannels);
-    }
-
-    // Constructor which takes ownership of a given IplImage so releases
-    // the image on destruction.
-    explicit WImageBuffer(IplImage* img) : WImage<T>(img) {}
-
-    // Allocate an image.  Does nothing if current size is the same as
-    // the new size.
-    void Allocate(int width, int height, int nchannels);
-
-    // Set the data to point to an image, releasing the old data
-    void SetIpl(IplImage* img) {
-        ReleaseImage();
-        WImage<T>::SetIpl(img);
-    }
-
-    // Clone an image which reallocates the image if of a different dimension.
-    void CloneFrom(const WImage<T>& src) {
-        Allocate(src.Width(), src.Height(), src.Channels());
-        CopyFrom(src);
-    }
-
-    ~WImageBuffer() {
-        ReleaseImage();
-    }
-
-    // Release the image if it isn't null.
-    void ReleaseImage() {
-        if (WImage<T>::image_) {
-            IplImage* image = WImage<T>::image_;
-            cvReleaseImage(&image);
-            WImage<T>::SetIpl(0);
-        }
-    }
-
-    bool IsNull() const {return WImage<T>::image_ == NULL; }
-
-private:
-    // Disallow copy and assignment
-    WImageBuffer(const WImageBuffer&);
-    void operator=(const WImageBuffer&);
-};
-
-/** Like a WImageBuffer class but when the number of channels is known at compile time.
-*/
-template<typename T, int C>
-class WImageBufferC : public WImageC<T, C>
-{
-public:
-    typedef typename WImage<T>::BaseType BaseType;
-    enum { kChannels = C };
-
-    // Default constructor which creates an object that can be
-    WImageBufferC() : WImageC<T, C>(0) {}
-
-    WImageBufferC(int width, int height) : WImageC<T, C>(0) {
-        Allocate(width, height);
-    }
-
-    // Constructor which takes ownership of a given IplImage so releases
-    // the image on destruction.
-    explicit WImageBufferC(IplImage* img) : WImageC<T, C>(img) {}
-
-    // Allocate an image.  Does nothing if current size is the same as
-    // the new size.
-    void Allocate(int width, int height);
-
-    // Set the data to point to an image, releasing the old data
-    void SetIpl(IplImage* img) {
-        ReleaseImage();
-        WImageC<T, C>::SetIpl(img);
-    }
-
-    // Clone an image which reallocates the image if of a different dimension.
-    void CloneFrom(const WImageC<T, C>& src) {
-        Allocate(src.Width(), src.Height());
-        CopyFrom(src);
-    }
-
-    ~WImageBufferC() {
-        ReleaseImage();
-    }
-
-    // Release the image if it isn't null.
-    void ReleaseImage() {
-        if (WImage<T>::image_) {
-            IplImage* image = WImage<T>::image_;
-            cvReleaseImage(&image);
-            WImageC<T, C>::SetIpl(0);
-        }
-    }
-
-    bool IsNull() const {return WImage<T>::image_ == NULL; }
-
-private:
-    // Disallow copy and assignment
-    WImageBufferC(const WImageBufferC&);
-    void operator=(const WImageBufferC&);
-};
-
-/** View into an image class which allows treating a subimage as an image or treating external data
-as an image
-*/
-template<typename T> class WImageView : public WImage<T>
-{
-public:
-    typedef typename WImage<T>::BaseType BaseType;
-
-    // Construct a subimage.  No checks are done that the subimage lies
-    // completely inside the original image.
-    WImageView(WImage<T>* img, int c, int r, int width, int height);
-
-    // Refer to external data.
-    // If not given width_step assumed to be same as width.
-    WImageView(T* data, int width, int height, int channels, int width_step = -1);
-
-    // Refer to external data.  This does NOT take ownership
-    // of the supplied IplImage.
-    WImageView(IplImage* img) : WImage<T>(img) {}
-
-    // Copy constructor
-    WImageView(const WImage<T>& img) : WImage<T>(0) {
-        header_ = *(img.Ipl());
-        WImage<T>::SetIpl(&header_);
-    }
-
-    WImageView& operator=(const WImage<T>& img) {
-        header_ = *(img.Ipl());
-        WImage<T>::SetIpl(&header_);
-        return *this;
-    }
-
-protected:
-    IplImage header_;
-};
-
-
-template<typename T, int C>
-class WImageViewC : public WImageC<T, C>
-{
-public:
-    typedef typename WImage<T>::BaseType BaseType;
-    enum { kChannels = C };
-
-    // Default constructor needed for vectors of views.
-    WImageViewC();
-
-    virtual ~WImageViewC() {}
-
-    // Construct a subimage.  No checks are done that the subimage lies
-    // completely inside the original image.
-    WImageViewC(WImageC<T, C>* img,
-        int c, int r, int width, int height);
-
-    // Refer to external data
-    WImageViewC(T* data, int width, int height, int width_step = -1);
-
-    // Refer to external data.  This does NOT take ownership
-    // of the supplied IplImage.
-    WImageViewC(IplImage* img) : WImageC<T, C>(img) {}
-
-    // Copy constructor which does a shallow copy to allow multiple views
-    // of same data.  gcc-4.1.1 gets confused if both versions of
-    // the constructor and assignment operator are not provided.
-    WImageViewC(const WImageC<T, C>& img) : WImageC<T, C>(0) {
-        header_ = *(img.Ipl());
-        WImageC<T, C>::SetIpl(&header_);
-    }
-    WImageViewC(const WImageViewC<T, C>& img) : WImageC<T, C>(0) {
-        header_ = *(img.Ipl());
-        WImageC<T, C>::SetIpl(&header_);
-    }
-
-    WImageViewC& operator=(const WImageC<T, C>& img) {
-        header_ = *(img.Ipl());
-        WImageC<T, C>::SetIpl(&header_);
-        return *this;
-    }
-    WImageViewC& operator=(const WImageViewC<T, C>& img) {
-        header_ = *(img.Ipl());
-        WImageC<T, C>::SetIpl(&header_);
-        return *this;
-    }
-
-protected:
-    IplImage header_;
-};
-
-
-// Specializations for depth
-template<>
-inline int WImage<uchar>::Depth() const {return IPL_DEPTH_8U; }
-template<>
-inline int WImage<signed char>::Depth() const {return IPL_DEPTH_8S; }
-template<>
-inline int WImage<short>::Depth() const {return IPL_DEPTH_16S; }
-template<>
-inline int WImage<ushort>::Depth() const {return IPL_DEPTH_16U; }
-template<>
-inline int WImage<int>::Depth() const {return IPL_DEPTH_32S; }
-template<>
-inline int WImage<float>::Depth() const {return IPL_DEPTH_32F; }
-template<>
-inline int WImage<double>::Depth() const {return IPL_DEPTH_64F; }
-
-template<typename T> inline WImage<T>::~WImage() {}
-template<typename T, int C> inline WImageC<T, C>::~WImageC() {}
-
-template<typename T>
-inline void WImageBuffer<T>::Allocate(int width, int height, int nchannels)
-{
-    if (IsNull() || WImage<T>::Width() != width ||
-        WImage<T>::Height() != height || WImage<T>::Channels() != nchannels) {
-        ReleaseImage();
-        WImage<T>::image_ = cvCreateImage(cvSize(width, height),
-            WImage<T>::Depth(), nchannels);
-    }
-}
-
-template<typename T, int C>
-inline void WImageBufferC<T, C>::Allocate(int width, int height)
-{
-    if (IsNull() || WImage<T>::Width() != width || WImage<T>::Height() != height) {
-        ReleaseImage();
-        WImageC<T, C>::SetIpl(cvCreateImage(cvSize(width, height),WImage<T>::Depth(), C));
-    }
-}
-
-template<typename T>
-WImageView<T>::WImageView(WImage<T>* img, int c, int r, int width, int height)
-        : WImage<T>(0)
-{
-    header_ = *(img->Ipl());
-    header_.imageData = reinterpret_cast<char*>((*img)(c, r));
-    header_.width = width;
-    header_.height = height;
-    WImage<T>::SetIpl(&header_);
-}
-
-template<typename T>
-WImageView<T>::WImageView(T* data, int width, int height, int nchannels, int width_step)
-          : WImage<T>(0)
-{
-    cvInitImageHeader(&header_, cvSize(width, height), WImage<T>::Depth(), nchannels);
-    header_.imageData = reinterpret_cast<char*>(data);
-    if (width_step > 0) {
-        header_.widthStep = width_step;
-    }
-    WImage<T>::SetIpl(&header_);
-}
-
-template<typename T, int C>
-WImageViewC<T, C>::WImageViewC(WImageC<T, C>* img, int c, int r, int width, int height)
-        : WImageC<T, C>(0)
-{
-    header_ = *(img->Ipl());
-    header_.imageData = reinterpret_cast<char*>((*img)(c, r));
-    header_.width = width;
-    header_.height = height;
-    WImageC<T, C>::SetIpl(&header_);
-}
-
-template<typename T, int C>
-WImageViewC<T, C>::WImageViewC() : WImageC<T, C>(0) {
-    cvInitImageHeader(&header_, cvSize(0, 0), WImage<T>::Depth(), C);
-    header_.imageData = reinterpret_cast<char*>(0);
-    WImageC<T, C>::SetIpl(&header_);
-}
-
-template<typename T, int C>
-WImageViewC<T, C>::WImageViewC(T* data, int width, int height, int width_step)
-    : WImageC<T, C>(0)
-{
-    cvInitImageHeader(&header_, cvSize(width, height), WImage<T>::Depth(), C);
-    header_.imageData = reinterpret_cast<char*>(data);
-    if (width_step > 0) {
-        header_.widthStep = width_step;
-    }
-    WImageC<T, C>::SetIpl(&header_);
-}
-
-// Construct a view into a region of an image
-template<typename T>
-WImageView<T> WImage<T>::View(int c, int r, int width, int height) {
-    return WImageView<T>(this, c, r, width, height);
-}
-
-template<typename T, int C>
-WImageViewC<T, C> WImageC<T, C>::View(int c, int r, int width, int height) {
-    return WImageViewC<T, C>(this, c, r, width, height);
-}
-
-//! @} core
-
-}  // end of namespace
-
-#endif // __cplusplus
-
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/cvconfig.h b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/cvconfig.h
deleted file mode 100755
index cb3c49d..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/cvconfig.h
+++ /dev/null
@@ -1,248 +0,0 @@
-#ifndef OPENCV_CVCONFIG_H_INCLUDED
-#define OPENCV_CVCONFIG_H_INCLUDED
-
-/* OpenCV compiled as static or dynamic libs */
-/* #undef BUILD_SHARED_LIBS */
-
-/* OpenCV intrinsics optimized code */
-#define CV_ENABLE_INTRINSICS
-
-/* OpenCV additional optimized code */
-/* #undef CV_DISABLE_OPTIMIZATION */
-
-/* Compile for 'real' NVIDIA GPU architectures */
-#define CUDA_ARCH_BIN ""
-
-/* Create PTX or BIN for 1.0 compute capability */
-/* #undef CUDA_ARCH_BIN_OR_PTX_10 */
-
-/* NVIDIA GPU features are used */
-#define CUDA_ARCH_FEATURES ""
-
-/* Compile for 'virtual' NVIDIA PTX architectures */
-#define CUDA_ARCH_PTX ""
-
-/* AVFoundation video libraries */
-/* #undef HAVE_AVFOUNDATION */
-
-/* V4L capturing support */
-/* #undef HAVE_CAMV4L */
-
-/* V4L2 capturing support */
-/* #undef HAVE_CAMV4L2 */
-
-/* Carbon windowing environment */
-/* #undef HAVE_CARBON */
-
-/* AMD's Basic Linear Algebra Subprograms Library*/
-/* #undef HAVE_CLAMDBLAS */
-
-/* AMD's OpenCL Fast Fourier Transform Library*/
-/* #undef HAVE_CLAMDFFT */
-
-/* Clp support */
-/* #undef HAVE_CLP */
-
-/* Cocoa API */
-/* #undef HAVE_COCOA */
-
-/* C= */
-/* #undef HAVE_CSTRIPES */
-
-/* NVIDIA CUDA Basic Linear Algebra Subprograms (BLAS) API*/
-/* #undef HAVE_CUBLAS */
-
-/* NVIDIA CUDA Runtime API*/
-/* #undef HAVE_CUDA */
-
-/* NVIDIA CUDA Fast Fourier Transform (FFT) API*/
-/* #undef HAVE_CUFFT */
-
-/* IEEE1394 capturing support */
-/* #undef HAVE_DC1394 */
-
-/* IEEE1394 capturing support - libdc1394 v2.x */
-/* #undef HAVE_DC1394_2 */
-
-/* DirectX */
-/* #undef HAVE_DIRECTX */
-/* #undef HAVE_DIRECTX_NV12 */
-/* #undef HAVE_D3D11 */
-/* #undef HAVE_D3D10 */
-/* #undef HAVE_D3D9 */
-
-/* DirectShow Video Capture library */
-/* #undef HAVE_DSHOW */
-
-/* Eigen Matrix & Linear Algebra Library */
-/* #undef HAVE_EIGEN */
-
-/* FFMpeg video library */
-/* #undef HAVE_FFMPEG */
-
-/* Geospatial Data Abstraction Library */
-/* #undef HAVE_GDAL */
-
-/* GStreamer multimedia framework */
-/* #undef HAVE_GSTREAMER */
-
-/* GTK+ 2.0 Thread support */
-/* #undef HAVE_GTHREAD */
-
-/* GTK+ 2.x toolkit */
-/* #undef HAVE_GTK */
-
-/* Halide support */
-/* #undef HAVE_HALIDE */
-
-/* Define to 1 if you have the <inttypes.h> header file. */
-#define HAVE_INTTYPES_H 1
-
-/* Intel Perceptual Computing SDK library */
-/* #undef HAVE_INTELPERC */
-
-/* Intel Integrated Performance Primitives */
-/* #undef HAVE_IPP */
-/* #undef HAVE_IPP_ICV */
-/* #undef HAVE_IPP_IW */
-
-/* Intel IPP Async */
-/* #undef HAVE_IPP_A */
-
-/* JPEG-2000 codec */
-#define HAVE_JASPER
-
-/* IJG JPEG codec */
-#define HAVE_JPEG
-
-/* libpng/png.h needs to be included */
-/* #undef HAVE_LIBPNG_PNG_H */
-
-/* GDCM DICOM codec */
-/* #undef HAVE_GDCM */
-
-/* V4L/V4L2 capturing support via libv4l */
-/* #undef HAVE_LIBV4L */
-
-/* Microsoft Media Foundation Capture library */
-/* #undef HAVE_MSMF */
-
-/* NVIDIA Video Decoding API*/
-/* #undef HAVE_NVCUVID */
-
-/* NVIDIA Video Encoding API*/
-/* #undef HAVE_NVCUVENC */
-
-/* OpenCL Support */
-/* #undef HAVE_OPENCL */
-/* #undef HAVE_OPENCL_STATIC */
-/* #undef HAVE_OPENCL_SVM */
-
-/* OpenEXR codec */
-#define HAVE_OPENEXR
-
-/* OpenGL support*/
-/* #undef HAVE_OPENGL */
-
-/* OpenNI library */
-/* #undef HAVE_OPENNI */
-
-/* OpenNI library */
-/* #undef HAVE_OPENNI2 */
-
-/* PNG codec */
-#define HAVE_PNG
-
-/* Posix threads (pthreads) */
-#define HAVE_PTHREAD
-
-/* parallel_for with pthreads */
-#define HAVE_PTHREADS_PF
-
-/* Qt support */
-/* #undef HAVE_QT */
-
-/* Qt OpenGL support */
-/* #undef HAVE_QT_OPENGL */
-
-/* QuickTime video libraries */
-/* #undef HAVE_QUICKTIME */
-
-/* QTKit video libraries */
-/* #undef HAVE_QTKIT */
-
-/* Intel Threading Building Blocks */
-#define HAVE_TBB
-
-/* TIFF codec */
-#define HAVE_TIFF
-
-/* Unicap video capture library */
-/* #undef HAVE_UNICAP */
-
-/* Video for Windows support */
-/* #undef HAVE_VFW */
-
-/* V4L2 capturing support in videoio.h */
-/* #undef HAVE_VIDEOIO */
-
-/* Win32 UI */
-/* #undef HAVE_WIN32UI */
-
-/* XIMEA camera support */
-/* #undef HAVE_XIMEA */
-
-/* Xine video library */
-/* #undef HAVE_XINE */
-
-/* Define if your processor stores words with the most significant byte
-   first (like Motorola and SPARC, unlike Intel and VAX). */
-/* #undef WORDS_BIGENDIAN */
-
-/* gPhoto2 library */
-/* #undef HAVE_GPHOTO2 */
-
-/* VA library (libva) */
-/* #undef HAVE_VA */
-
-/* Intel VA-API/OpenCL */
-/* #undef HAVE_VA_INTEL */
-
-/* Intel Media SDK */
-/* #undef HAVE_MFX */
-
-/* Lapack */
-/* #undef HAVE_LAPACK */
-
-/* Library was compiled with functions instrumentation */
-/* #undef ENABLE_INSTRUMENTATION */
-
-/* OpenVX */
-/* #undef HAVE_OPENVX */
-
-#if defined(HAVE_XINE)         || \
-    defined(HAVE_GSTREAMER)    || \
-    defined(HAVE_QUICKTIME)    || \
-    defined(HAVE_QTKIT)        || \
-    defined(HAVE_AVFOUNDATION) || \
-    /*defined(HAVE_OPENNI)     || too specialized */ \
-    defined(HAVE_FFMPEG)       || \
-    defined(HAVE_MSMF)
-#define HAVE_VIDEO_INPUT
-#endif
-
-#if /*defined(HAVE_XINE)       || */\
-    defined(HAVE_GSTREAMER)    || \
-    defined(HAVE_QUICKTIME)    || \
-    defined(HAVE_QTKIT)        || \
-    defined(HAVE_AVFOUNDATION) || \
-    defined(HAVE_FFMPEG)       || \
-    defined(HAVE_MSMF)
-#define HAVE_VIDEO_OUTPUT
-#endif
-
-/* OpenCV trace utilities */
-#define OPENCV_TRACE
-
-
-#endif // OPENCV_CVCONFIG_H_INCLUDED
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/dnn.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/dnn.hpp
deleted file mode 100755
index af91900..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/dnn.hpp
+++ /dev/null
@@ -1,64 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_DNN_HPP
-#define OPENCV_DNN_HPP
-
-// This is an umbrella header to include into you project.
-// We are free to change headers layout in dnn subfolder, so please include
-// this header for future compatibility
-
-
-/** @defgroup dnn Deep Neural Network module
-  @{
-    This module contains:
-        - API for new layers creation, layers are building bricks of neural networks;
-        - set of built-in most-useful Layers;
-        - API to construct and modify comprehensive neural networks from layers;
-        - functionality for loading serialized networks models from different frameworks.
-
-    Functionality of this module is designed only for forward pass computations (i.e. network testing).
-    A network training is in principle not supported.
-  @}
-*/
-#include <opencv2/dnn/dnn.hpp>
-
-#endif /* OPENCV_DNN_HPP */
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/dnn/all_layers.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/dnn/all_layers.hpp
deleted file mode 100755
index 9ba180c..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/dnn/all_layers.hpp
+++ /dev/null
@@ -1,623 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_DNN_DNN_ALL_LAYERS_HPP
-#define OPENCV_DNN_DNN_ALL_LAYERS_HPP
-#include <opencv2/dnn.hpp>
-
-namespace cv {
-namespace dnn {
-CV__DNN_EXPERIMENTAL_NS_BEGIN
-//! @addtogroup dnn
-//! @{
-
-/** @defgroup dnnLayerList Partial List of Implemented Layers
-  @{
-  This subsection of dnn module contains information about built-in layers and their descriptions.
-
-  Classes listed here, in fact, provides C++ API for creating instances of built-in layers.
-  In addition to this way of layers instantiation, there is a more common factory API (see @ref dnnLayerFactory), it allows to create layers dynamically (by name) and register new ones.
-  You can use both API, but factory API is less convenient for native C++ programming and basically designed for use inside importers (see @ref readNetFromCaffe(), @ref readNetFromTorch(), @ref readNetFromTensorflow()).
-
-  Built-in layers partially reproduce functionality of corresponding Caffe and Torch7 layers.
-  In particular, the following layers and Caffe importer were tested to reproduce <a href="http://caffe.berkeleyvision.org/tutorial/layers.html">Caffe</a> functionality:
-  - Convolution
-  - Deconvolution
-  - Pooling
-  - InnerProduct
-  - TanH, ReLU, Sigmoid, BNLL, Power, AbsVal
-  - Softmax
-  - Reshape, Flatten, Slice, Split
-  - LRN
-  - MVN
-  - Dropout (since it does nothing on forward pass -))
-*/
-
-    class CV_EXPORTS BlankLayer : public Layer
-    {
-    public:
-        static Ptr<Layer> create(const LayerParams &params);
-    };
-
-    //! LSTM recurrent layer
-    class CV_EXPORTS LSTMLayer : public Layer
-    {
-    public:
-        /** Creates instance of LSTM layer */
-        static Ptr<LSTMLayer> create(const LayerParams& params);
-
-        /** @deprecated Use LayerParams::blobs instead.
-        @brief Set trained weights for LSTM layer.
-
-        LSTM behavior on each step is defined by current input, previous output, previous cell state and learned weights.
-
-        Let @f$x_t@f$ be current input, @f$h_t@f$ be current output, @f$c_t@f$ be current state.
-        Than current output and current cell state is computed as follows:
-        @f{eqnarray*}{
-        h_t &= o_t \odot tanh(c_t),               \\
-        c_t &= f_t \odot c_{t-1} + i_t \odot g_t, \\
-        @f}
-        where @f$\odot@f$ is per-element multiply operation and @f$i_t, f_t, o_t, g_t@f$ is internal gates that are computed using learned wights.
-
-        Gates are computed as follows:
-        @f{eqnarray*}{
-        i_t &= sigmoid&(W_{xi} x_t + W_{hi} h_{t-1} + b_i), \\
-        f_t &= sigmoid&(W_{xf} x_t + W_{hf} h_{t-1} + b_f), \\
-        o_t &= sigmoid&(W_{xo} x_t + W_{ho} h_{t-1} + b_o), \\
-        g_t &= tanh   &(W_{xg} x_t + W_{hg} h_{t-1} + b_g), \\
-        @f}
-        where @f$W_{x?}@f$, @f$W_{h?}@f$ and @f$b_{?}@f$ are learned weights represented as matrices:
-        @f$W_{x?} \in R^{N_h \times N_x}@f$, @f$W_{h?} \in R^{N_h \times N_h}@f$, @f$b_? \in R^{N_h}@f$.
-
-        For simplicity and performance purposes we use @f$ W_x = [W_{xi}; W_{xf}; W_{xo}, W_{xg}] @f$
-        (i.e. @f$W_x@f$ is vertical concatenation of @f$ W_{x?} @f$), @f$ W_x \in R^{4N_h \times N_x} @f$.
-        The same for @f$ W_h = [W_{hi}; W_{hf}; W_{ho}, W_{hg}], W_h \in R^{4N_h \times N_h} @f$
-        and for @f$ b = [b_i; b_f, b_o, b_g]@f$, @f$b \in R^{4N_h} @f$.
-
-        @param Wh is matrix defining how previous output is transformed to internal gates (i.e. according to above mentioned notation is @f$ W_h @f$)
-        @param Wx is matrix defining how current input is transformed to internal gates (i.e. according to above mentioned notation is @f$ W_x @f$)
-        @param b  is bias vector (i.e. according to above mentioned notation is @f$ b @f$)
-        */
-        CV_DEPRECATED virtual void setWeights(const Mat &Wh, const Mat &Wx, const Mat &b) = 0;
-
-        /** @brief Specifies shape of output blob which will be [[`T`], `N`] + @p outTailShape.
-          * @details If this parameter is empty or unset then @p outTailShape = [`Wh`.size(0)] will be used,
-          * where `Wh` is parameter from setWeights().
-          */
-        virtual void setOutShape(const MatShape &outTailShape = MatShape()) = 0;
-
-        /** @deprecated Use flag `produce_cell_output` in LayerParams.
-          * @brief Specifies either interpret first dimension of input blob as timestamp dimenion either as sample.
-          *
-          * If flag is set to true then shape of input blob will be interpreted as [`T`, `N`, `[data dims]`] where `T` specifies number of timestamps, `N` is number of independent streams.
-          * In this case each forward() call will iterate through `T` timestamps and update layer's state `T` times.
-          *
-          * If flag is set to false then shape of input blob will be interpreted as [`N`, `[data dims]`].
-          * In this case each forward() call will make one iteration and produce one timestamp with shape [`N`, `[out dims]`].
-          */
-        CV_DEPRECATED virtual void setUseTimstampsDim(bool use = true) = 0;
-
-        /** @deprecated Use flag `use_timestamp_dim` in LayerParams.
-         * @brief If this flag is set to true then layer will produce @f$ c_t @f$ as second output.
-         * @details Shape of the second output is the same as first output.
-         */
-        CV_DEPRECATED virtual void setProduceCellOutput(bool produce = false) = 0;
-
-        /* In common case it use single input with @f$x_t@f$ values to compute output(s) @f$h_t@f$ (and @f$c_t@f$).
-         * @param input should contain packed values @f$x_t@f$
-         * @param output contains computed outputs: @f$h_t@f$ (and @f$c_t@f$ if setProduceCellOutput() flag was set to true).
-         *
-         * If setUseTimstampsDim() is set to true then @p input[0] should has at least two dimensions with the following shape: [`T`, `N`, `[data dims]`],
-         * where `T` specifies number of timestamps, `N` is number of independent streams (i.e. @f$ x_{t_0 + t}^{stream} @f$ is stored inside @p input[0][t, stream, ...]).
-         *
-         * If setUseTimstampsDim() is set to false then @p input[0] should contain single timestamp, its shape should has form [`N`, `[data dims]`] with at least one dimension.
-         * (i.e. @f$ x_{t}^{stream} @f$ is stored inside @p input[0][stream, ...]).
-        */
-
-        int inputNameToIndex(String inputName) CV_OVERRIDE;
-        int outputNameToIndex(const String& outputName) CV_OVERRIDE;
-    };
-
-    /** @brief Classical recurrent layer
-
-    Accepts two inputs @f$x_t@f$ and @f$h_{t-1}@f$ and compute two outputs @f$o_t@f$ and @f$h_t@f$.
-
-    - input: should contain packed input @f$x_t@f$.
-    - output: should contain output @f$o_t@f$ (and @f$h_t@f$ if setProduceHiddenOutput() is set to true).
-
-    input[0] should have shape [`T`, `N`, `data_dims`] where `T` and `N` is number of timestamps and number of independent samples of @f$x_t@f$ respectively.
-
-    output[0] will have shape [`T`, `N`, @f$N_o@f$], where @f$N_o@f$ is number of rows in @f$ W_{xo} @f$ matrix.
-
-    If setProduceHiddenOutput() is set to true then @p output[1] will contain a Mat with shape [`T`, `N`, @f$N_h@f$], where @f$N_h@f$ is number of rows in @f$ W_{hh} @f$ matrix.
-    */
-    class CV_EXPORTS RNNLayer : public Layer
-    {
-    public:
-        /** Creates instance of RNNLayer */
-        static Ptr<RNNLayer> create(const LayerParams& params);
-
-        /** Setups learned weights.
-
-        Recurrent-layer behavior on each step is defined by current input @f$ x_t @f$, previous state @f$ h_t @f$ and learned weights as follows:
-        @f{eqnarray*}{
-        h_t &= tanh&(W_{hh} h_{t-1} + W_{xh} x_t + b_h),  \\
-        o_t &= tanh&(W_{ho} h_t + b_o),
-        @f}
-
-        @param Wxh is @f$ W_{xh} @f$ matrix
-        @param bh  is @f$ b_{h}  @f$ vector
-        @param Whh is @f$ W_{hh} @f$ matrix
-        @param Who is @f$ W_{xo} @f$ matrix
-        @param bo  is @f$ b_{o}  @f$ vector
-        */
-        virtual void setWeights(const Mat &Wxh, const Mat &bh, const Mat &Whh, const Mat &Who, const Mat &bo) = 0;
-
-        /** @brief If this flag is set to true then layer will produce @f$ h_t @f$ as second output.
-         * @details Shape of the second output is the same as first output.
-         */
-        virtual void setProduceHiddenOutput(bool produce = false) = 0;
-
-    };
-
-    class CV_EXPORTS BaseConvolutionLayer : public Layer
-    {
-    public:
-        Size kernel, stride, pad, dilation, adjustPad;
-        String padMode;
-        int numOutput;
-    };
-
-    class CV_EXPORTS ConvolutionLayer : public BaseConvolutionLayer
-    {
-    public:
-        static Ptr<BaseConvolutionLayer> create(const LayerParams& params);
-    };
-
-    class CV_EXPORTS DeconvolutionLayer : public BaseConvolutionLayer
-    {
-    public:
-        static Ptr<BaseConvolutionLayer> create(const LayerParams& params);
-    };
-
-    class CV_EXPORTS LRNLayer : public Layer
-    {
-    public:
-        int type;
-
-        int size;
-        float alpha, beta, bias;
-        bool normBySize;
-
-        static Ptr<LRNLayer> create(const LayerParams& params);
-    };
-
-    class CV_EXPORTS PoolingLayer : public Layer
-    {
-    public:
-        int type;
-        Size kernel, stride, pad;
-        bool globalPooling;
-        bool computeMaxIdx;
-        String padMode;
-        bool ceilMode;
-        // If true for average pooling with padding, divide an every output region
-        // by a whole kernel area. Otherwise exclude zero padded values and divide
-        // by number of real values.
-        bool avePoolPaddedArea;
-        // ROIPooling parameters.
-        Size pooledSize;
-        float spatialScale;
-        // PSROIPooling parameters.
-        int psRoiOutChannels;
-
-        static Ptr<PoolingLayer> create(const LayerParams& params);
-    };
-
-    class CV_EXPORTS SoftmaxLayer : public Layer
-    {
-    public:
-        bool logSoftMax;
-
-        static Ptr<SoftmaxLayer> create(const LayerParams& params);
-    };
-
-    class CV_EXPORTS InnerProductLayer : public Layer
-    {
-    public:
-        int axis;
-        static Ptr<InnerProductLayer> create(const LayerParams& params);
-    };
-
-    class CV_EXPORTS MVNLayer : public Layer
-    {
-    public:
-        float eps;
-        bool normVariance, acrossChannels;
-
-        static Ptr<MVNLayer> create(const LayerParams& params);
-    };
-
-    /* Reshaping */
-
-    class CV_EXPORTS ReshapeLayer : public Layer
-    {
-    public:
-        MatShape newShapeDesc;
-        Range newShapeRange;
-
-        static Ptr<ReshapeLayer> create(const LayerParams& params);
-    };
-
-    class CV_EXPORTS FlattenLayer : public Layer
-    {
-    public:
-        static Ptr<FlattenLayer> create(const LayerParams &params);
-    };
-
-    class CV_EXPORTS ConcatLayer : public Layer
-    {
-    public:
-        int axis;
-        /**
-         * @brief Add zero padding in case of concatenation of blobs with different
-         * spatial sizes.
-         *
-         * Details: https://github.com/torch/nn/blob/master/doc/containers.md#depthconcat
-         */
-        bool padding;
-
-        static Ptr<ConcatLayer> create(const LayerParams &params);
-    };
-
-    class CV_EXPORTS SplitLayer : public Layer
-    {
-    public:
-        int outputsCount; //!< Number of copies that will be produced (is ignored when negative).
-
-        static Ptr<SplitLayer> create(const LayerParams &params);
-    };
-
-    /**
-     * Slice layer has several modes:
-     * 1. Caffe mode
-     * @param[in] axis Axis of split operation
-     * @param[in] slice_point Array of split points
-     *
-     * Number of output blobs equals to number of split points plus one. The
-     * first blob is a slice on input from 0 to @p slice_point[0] - 1 by @p axis,
-     * the second output blob is a slice of input from @p slice_point[0] to
-     * @p slice_point[1] - 1 by @p axis and the last output blob is a slice of
-     * input from @p slice_point[-1] up to the end of @p axis size.
-     *
-     * 2. TensorFlow mode
-     * @param begin Vector of start indices
-     * @param size Vector of sizes
-     *
-     * More convenient numpy-like slice. One and only output blob
-     * is a slice `input[begin[0]:begin[0]+size[0], begin[1]:begin[1]+size[1], ...]`
-     *
-     * 3. Torch mode
-     * @param axis Axis of split operation
-     *
-     * Split input blob on the equal parts by @p axis.
-     */
-    class CV_EXPORTS SliceLayer : public Layer
-    {
-    public:
-        /**
-         * @brief Vector of slice ranges.
-         *
-         * The first dimension equals number of output blobs.
-         * Inner vector has slice ranges for the first number of input dimensions.
-         */
-        std::vector<std::vector<Range> > sliceRanges;
-        int axis;
-
-        static Ptr<SliceLayer> create(const LayerParams &params);
-    };
-
-    class CV_EXPORTS PermuteLayer : public Layer
-    {
-    public:
-        static Ptr<PermuteLayer> create(const LayerParams& params);
-    };
-
-    /**
-     * Permute channels of 4-dimensional input blob.
-     * @param group Number of groups to split input channels and pick in turns
-     *              into output blob.
-     *
-     * \f[ groupSize = \frac{number\ of\ channels}{group} \f]
-     * \f[ output(n, c, h, w) = input(n, groupSize \times (c \% group) + \lfloor \frac{c}{group} \rfloor, h, w) \f]
-     * Read more at https://arxiv.org/pdf/1707.01083.pdf
-     */
-    class CV_EXPORTS ShuffleChannelLayer : public Layer
-    {
-    public:
-        static Ptr<Layer> create(const LayerParams& params);
-
-        int group;
-    };
-
-    /**
-     * @brief Adds extra values for specific axes.
-     * @param paddings Vector of paddings in format
-     *                 @code
-     *                 [ pad_before, pad_after,  // [0]th dimension
-     *                   pad_before, pad_after,  // [1]st dimension
-     *                   ...
-     *                   pad_before, pad_after ] // [n]th dimension
-     *                 @endcode
-     *                 that represents number of padded values at every dimension
-     *                 starting from the first one. The rest of dimensions won't
-     *                 be padded.
-     * @param value Value to be padded. Defaults to zero.
-     * @param type Padding type: 'constant', 'reflect'
-     * @param input_dims Torch's parameter. If @p input_dims is not equal to the
-     *                   actual input dimensionality then the `[0]th` dimension
-     *                   is considered as a batch dimension and @p paddings are shifted
-     *                   to a one dimension. Defaults to `-1` that means padding
-     *                   corresponding to @p paddings.
-     */
-    class CV_EXPORTS PaddingLayer : public Layer
-    {
-    public:
-        static Ptr<PaddingLayer> create(const LayerParams& params);
-    };
-
-    /* Activations */
-    class CV_EXPORTS ActivationLayer : public Layer
-    {
-    public:
-        virtual void forwardSlice(const float* src, float* dst, int len,
-                                  size_t outPlaneSize, int cn0, int cn1) const = 0;
-    };
-
-    class CV_EXPORTS ReLULayer : public ActivationLayer
-    {
-    public:
-        float negativeSlope;
-
-        static Ptr<ReLULayer> create(const LayerParams &params);
-    };
-
-    class CV_EXPORTS ReLU6Layer : public ActivationLayer
-    {
-    public:
-        float minValue, maxValue;
-
-        static Ptr<ReLU6Layer> create(const LayerParams &params);
-    };
-
-    class CV_EXPORTS ChannelsPReLULayer : public ActivationLayer
-    {
-    public:
-        static Ptr<Layer> create(const LayerParams& params);
-    };
-
-    class CV_EXPORTS ELULayer : public ActivationLayer
-    {
-    public:
-        static Ptr<ELULayer> create(const LayerParams &params);
-    };
-
-    class CV_EXPORTS TanHLayer : public ActivationLayer
-    {
-    public:
-        static Ptr<TanHLayer> create(const LayerParams &params);
-    };
-
-    class CV_EXPORTS SigmoidLayer : public ActivationLayer
-    {
-    public:
-        static Ptr<SigmoidLayer> create(const LayerParams &params);
-    };
-
-    class CV_EXPORTS BNLLLayer : public ActivationLayer
-    {
-    public:
-        static Ptr<BNLLLayer> create(const LayerParams &params);
-    };
-
-    class CV_EXPORTS AbsLayer : public ActivationLayer
-    {
-    public:
-        static Ptr<AbsLayer> create(const LayerParams &params);
-    };
-
-    class CV_EXPORTS PowerLayer : public ActivationLayer
-    {
-    public:
-        float power, scale, shift;
-
-        static Ptr<PowerLayer> create(const LayerParams &params);
-    };
-
-    /* Layers used in semantic segmentation */
-
-    class CV_EXPORTS CropLayer : public Layer
-    {
-    public:
-        int startAxis;
-        std::vector<int> offset;
-
-        static Ptr<CropLayer> create(const LayerParams &params);
-    };
-
-    class CV_EXPORTS EltwiseLayer : public Layer
-    {
-    public:
-        static Ptr<EltwiseLayer> create(const LayerParams &params);
-    };
-
-    class CV_EXPORTS BatchNormLayer : public Layer
-    {
-    public:
-        bool hasWeights, hasBias;
-        float epsilon;
-
-        static Ptr<BatchNormLayer> create(const LayerParams &params);
-    };
-
-    class CV_EXPORTS MaxUnpoolLayer : public Layer
-    {
-    public:
-        Size poolKernel;
-        Size poolPad;
-        Size poolStride;
-
-        static Ptr<MaxUnpoolLayer> create(const LayerParams &params);
-    };
-
-    class CV_EXPORTS ScaleLayer : public Layer
-    {
-    public:
-        bool hasBias;
-        int axis;
-
-        static Ptr<ScaleLayer> create(const LayerParams& params);
-    };
-
-    class CV_EXPORTS ShiftLayer : public Layer
-    {
-    public:
-        static Ptr<Layer> create(const LayerParams& params);
-    };
-
-    class CV_EXPORTS PriorBoxLayer : public Layer
-    {
-    public:
-        static Ptr<PriorBoxLayer> create(const LayerParams& params);
-    };
-
-    class CV_EXPORTS ReorgLayer : public Layer
-    {
-    public:
-        static Ptr<ReorgLayer> create(const LayerParams& params);
-    };
-
-    class CV_EXPORTS RegionLayer : public Layer
-    {
-    public:
-        static Ptr<RegionLayer> create(const LayerParams& params);
-    };
-
-    class CV_EXPORTS DetectionOutputLayer : public Layer
-    {
-    public:
-        static Ptr<DetectionOutputLayer> create(const LayerParams& params);
-    };
-
-    /**
-     * @brief \f$ L_p \f$ - normalization layer.
-     * @param p Normalization factor. The most common `p = 1` for \f$ L_1 \f$ -
-     *          normalization or `p = 2` for \f$ L_2 \f$ - normalization or a custom one.
-     * @param eps Parameter \f$ \epsilon \f$ to prevent a division by zero.
-     * @param across_spatial If true, normalize an input across all non-batch dimensions.
-     *                       Otherwise normalize an every channel separately.
-     *
-     * Across spatial:
-     * @f[
-     * norm = \sqrt[p]{\epsilon + \sum_{x, y, c} |src(x, y, c)|^p } \\
-     * dst(x, y, c) = \frac{ src(x, y, c) }{norm}
-     * @f]
-     *
-     * Channel wise normalization:
-     * @f[
-     * norm(c) = \sqrt[p]{\epsilon + \sum_{x, y} |src(x, y, c)|^p } \\
-     * dst(x, y, c) = \frac{ src(x, y, c) }{norm(c)}
-     * @f]
-     *
-     * Where `x, y` - spatial coordinates, `c` - channel.
-     *
-     * An every sample in the batch is normalized separately. Optionally,
-     * output is scaled by the trained parameters.
-     */
-    class CV_EXPORTS NormalizeBBoxLayer : public Layer
-    {
-    public:
-        float pnorm, epsilon;
-        CV_DEPRECATED bool acrossSpatial;
-
-        static Ptr<NormalizeBBoxLayer> create(const LayerParams& params);
-    };
-
-    /**
-     * @brief Resize input 4-dimensional blob by nearest neighbor or bilinear strategy.
-     *
-     * Layer is used to support TensorFlow's resize_nearest_neighbor and resize_bilinear ops.
-     */
-    class CV_EXPORTS ResizeLayer : public Layer
-    {
-    public:
-        static Ptr<ResizeLayer> create(const LayerParams& params);
-    };
-
-    /**
-     * @brief Bilinear resize layer from https://github.com/cdmh/deeplab-public
-     *
-     * It differs from @ref ResizeLayer in output shape and resize scales computations.
-     */
-    class CV_EXPORTS InterpLayer : public Layer
-    {
-    public:
-        static Ptr<Layer> create(const LayerParams& params);
-    };
-
-    class CV_EXPORTS ProposalLayer : public Layer
-    {
-    public:
-        static Ptr<ProposalLayer> create(const LayerParams& params);
-    };
-
-    class CV_EXPORTS CropAndResizeLayer : public Layer
-    {
-    public:
-        static Ptr<Layer> create(const LayerParams& params);
-    };
-
-//! @}
-//! @}
-CV__DNN_EXPERIMENTAL_NS_END
-}
-}
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/dnn/dict.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/dnn/dict.hpp
deleted file mode 100755
index 69287dc..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/dnn/dict.hpp
+++ /dev/null
@@ -1,156 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#include <opencv2/core.hpp>
-#include <map>
-#include <ostream>
-
-#include <opencv2/dnn/dnn.hpp>
-
-#ifndef OPENCV_DNN_DNN_DICT_HPP
-#define OPENCV_DNN_DNN_DICT_HPP
-
-namespace cv {
-namespace dnn {
-CV__DNN_EXPERIMENTAL_NS_BEGIN
-//! @addtogroup dnn
-//! @{
-
-/** @brief This struct stores the scalar value (or array) of one of the following type: double, cv::String or int64.
- *  @todo Maybe int64 is useless because double type exactly stores at least 2^52 integers.
- */
-struct CV_EXPORTS_W DictValue
-{
-    DictValue(const DictValue &r);
-    DictValue(int64 i = 0)      : type(Param::INT), pi(new AutoBuffer<int64,1>) { (*pi)[0] = i; }       //!< Constructs integer scalar
-    CV_WRAP DictValue(int i)            : type(Param::INT), pi(new AutoBuffer<int64,1>) { (*pi)[0] = i; }       //!< Constructs integer scalar
-    DictValue(unsigned p)       : type(Param::INT), pi(new AutoBuffer<int64,1>) { (*pi)[0] = p; }       //!< Constructs integer scalar
-    CV_WRAP DictValue(double p)         : type(Param::REAL), pd(new AutoBuffer<double,1>) { (*pd)[0] = p; }     //!< Constructs floating point scalar
-    CV_WRAP DictValue(const String &s)  : type(Param::STRING), ps(new AutoBuffer<String,1>) { (*ps)[0] = s; }   //!< Constructs string scalar
-    DictValue(const char *s)    : type(Param::STRING), ps(new AutoBuffer<String,1>) { (*ps)[0] = s; }   //!< @overload
-
-    template<typename TypeIter>
-    static DictValue arrayInt(TypeIter begin, int size);    //!< Constructs integer array
-    template<typename TypeIter>
-    static DictValue arrayReal(TypeIter begin, int size);   //!< Constructs floating point array
-    template<typename TypeIter>
-    static DictValue arrayString(TypeIter begin, int size); //!< Constructs array of strings
-
-    template<typename T>
-    T get(int idx = -1) const; //!< Tries to convert array element with specified index to requested type and returns its.
-
-    int size() const;
-
-    CV_WRAP bool isInt() const;
-    CV_WRAP bool isString() const;
-    CV_WRAP bool isReal() const;
-
-    CV_WRAP int getIntValue(int idx = -1) const;
-    CV_WRAP double getRealValue(int idx = -1) const;
-    CV_WRAP String getStringValue(int idx = -1) const;
-
-    DictValue &operator=(const DictValue &r);
-
-    friend std::ostream &operator<<(std::ostream &stream, const DictValue &dictv);
-
-    ~DictValue();
-
-private:
-
-    int type;
-
-    union
-    {
-        AutoBuffer<int64, 1> *pi;
-        AutoBuffer<double, 1> *pd;
-        AutoBuffer<String, 1> *ps;
-        void *pv;
-    };
-
-    DictValue(int _type, void *_p) : type(_type), pv(_p) {}
-    void release();
-};
-
-/** @brief This class implements name-value dictionary, values are instances of DictValue. */
-class CV_EXPORTS Dict
-{
-    typedef std::map<String, DictValue> _Dict;
-    _Dict dict;
-
-public:
-
-    //! Checks a presence of the @p key in the dictionary.
-    bool has(const String &key) const;
-
-    //! If the @p key in the dictionary then returns pointer to its value, else returns NULL.
-    DictValue *ptr(const String &key);
-
-    /** @overload */
-    const DictValue *ptr(const String &key) const;
-
-    //! If the @p key in the dictionary then returns its value, else an error will be generated.
-    const DictValue &get(const String &key) const;
-
-    /** @overload */
-    template <typename T>
-    T get(const String &key) const;
-
-    //! If the @p key in the dictionary then returns its value, else returns @p defaultValue.
-    template <typename T>
-    T get(const String &key, const T &defaultValue) const;
-
-    //! Sets new @p value for the @p key, or adds new key-value pair into the dictionary.
-    template<typename T>
-    const T &set(const String &key, const T &value);
-
-    friend std::ostream &operator<<(std::ostream &stream, const Dict &dict);
-
-    std::map<String, DictValue>::const_iterator begin() const;
-
-    std::map<String, DictValue>::const_iterator end() const;
-};
-
-//! @}
-CV__DNN_EXPERIMENTAL_NS_END
-}
-}
-
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/dnn/dnn.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/dnn/dnn.hpp
deleted file mode 100755
index f65f503..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/dnn/dnn.hpp
+++ /dev/null
@@ -1,860 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_DNN_DNN_HPP
-#define OPENCV_DNN_DNN_HPP
-
-#include <vector>
-#include <opencv2/core.hpp>
-
-#if !defined CV_DOXYGEN && !defined CV_DNN_DONT_ADD_EXPERIMENTAL_NS
-#define CV__DNN_EXPERIMENTAL_NS_BEGIN namespace experimental_dnn_v5 {
-#define CV__DNN_EXPERIMENTAL_NS_END }
-namespace cv { namespace dnn { namespace experimental_dnn_v5 { } using namespace experimental_dnn_v5; }}
-#else
-#define CV__DNN_EXPERIMENTAL_NS_BEGIN
-#define CV__DNN_EXPERIMENTAL_NS_END
-#endif
-
-#include <opencv2/dnn/dict.hpp>
-
-namespace cv {
-namespace dnn {
-CV__DNN_EXPERIMENTAL_NS_BEGIN
-//! @addtogroup dnn
-//! @{
-
-    typedef std::vector<int> MatShape;
-
-    /**
-     * @brief Enum of computation backends supported by layers.
-     * @see Net::setPreferableBackend
-     */
-    enum Backend
-    {
-        //! DNN_BACKEND_DEFAULT equals to DNN_BACKEND_INFERENCE_ENGINE if
-        //! OpenCV is built with Intel's Inference Engine library or
-        //! DNN_BACKEND_OPENCV otherwise.
-        DNN_BACKEND_DEFAULT,
-        DNN_BACKEND_HALIDE,
-        DNN_BACKEND_INFERENCE_ENGINE,
-        DNN_BACKEND_OPENCV
-    };
-
-    /**
-     * @brief Enum of target devices for computations.
-     * @see Net::setPreferableTarget
-     */
-    enum Target
-    {
-        DNN_TARGET_CPU,
-        DNN_TARGET_OPENCL,
-        DNN_TARGET_OPENCL_FP16,
-        DNN_TARGET_MYRIAD
-    };
-
-    /** @brief This class provides all data needed to initialize layer.
-     *
-     * It includes dictionary with scalar params (which can be read by using Dict interface),
-     * blob params #blobs and optional meta information: #name and #type of layer instance.
-    */
-    class CV_EXPORTS LayerParams : public Dict
-    {
-    public:
-        //TODO: Add ability to name blob params
-        std::vector<Mat> blobs; //!< List of learned parameters stored as blobs.
-
-        String name; //!< Name of the layer instance (optional, can be used internal purposes).
-        String type; //!< Type name which was used for creating layer by layer factory (optional).
-    };
-
-   /**
-    * @brief Derivatives of this class encapsulates functions of certain backends.
-    */
-    class BackendNode
-    {
-    public:
-        BackendNode(int backendId);
-
-        virtual ~BackendNode(); //!< Virtual destructor to make polymorphism.
-
-        int backendId; //!< Backend identifier.
-    };
-
-    /**
-     * @brief Derivatives of this class wraps cv::Mat for different backends and targets.
-     */
-    class BackendWrapper
-    {
-    public:
-        BackendWrapper(int backendId, int targetId);
-
-        /**
-         * @brief Wrap cv::Mat for specific backend and target.
-         * @param[in] targetId Target identifier.
-         * @param[in] m cv::Mat for wrapping.
-         *
-         * Make CPU->GPU data transfer if it's require for the target.
-         */
-        BackendWrapper(int targetId, const cv::Mat& m);
-
-        /**
-         * @brief Make wrapper for reused cv::Mat.
-         * @param[in] base Wrapper of cv::Mat that will be reused.
-         * @param[in] shape Specific shape.
-         *
-         * Initialize wrapper from another one. It'll wrap the same host CPU
-         * memory and mustn't allocate memory on device(i.e. GPU). It might
-         * has different shape. Use in case of CPU memory reusing for reuse
-         * associated memory on device too.
-         */
-        BackendWrapper(const Ptr<BackendWrapper>& base, const MatShape& shape);
-
-        virtual ~BackendWrapper(); //!< Virtual destructor to make polymorphism.
-
-        /**
-         * @brief Transfer data to CPU host memory.
-         */
-        virtual void copyToHost() = 0;
-
-        /**
-         * @brief Indicate that an actual data is on CPU.
-         */
-        virtual void setHostDirty() = 0;
-
-        int backendId;  //!< Backend identifier.
-        int targetId;   //!< Target identifier.
-    };
-
-    class CV_EXPORTS ActivationLayer;
-    class CV_EXPORTS BatchNormLayer;
-    class CV_EXPORTS ScaleLayer;
-
-    /** @brief This interface class allows to build new Layers - are building blocks of networks.
-     *
-     * Each class, derived from Layer, must implement allocate() methods to declare own outputs and forward() to compute outputs.
-     * Also before using the new layer into networks you must register your layer by using one of @ref dnnLayerFactory "LayerFactory" macros.
-     */
-    class CV_EXPORTS_W Layer : public Algorithm
-    {
-    public:
-
-        //! List of learned parameters must be stored here to allow read them by using Net::getParam().
-        CV_PROP_RW std::vector<Mat> blobs;
-
-        /** @brief Computes and sets internal parameters according to inputs, outputs and blobs.
-         *  @param[in]  input  vector of already allocated input blobs
-         *  @param[out] output vector of already allocated output blobs
-         *
-         * If this method is called after network has allocated all memory for input and output blobs
-         * and before inferencing.
-         */
-        virtual void finalize(const std::vector<Mat*> &input, std::vector<Mat> &output);
-
-        /** @brief Given the @p input blobs, computes the output @p blobs.
-         *  @param[in]  input  the input blobs.
-         *  @param[out] output allocated output blobs, which will store results of the computation.
-         *  @param[out] internals allocated internal blobs
-         */
-        virtual void forward(std::vector<Mat*> &input, std::vector<Mat> &output, std::vector<Mat> &internals) = 0;
-
-        /** @brief Given the @p input blobs, computes the output @p blobs.
-         *  @param[in]  inputs  the input blobs.
-         *  @param[out] outputs allocated output blobs, which will store results of the computation.
-         *  @param[out] internals allocated internal blobs
-         */
-        virtual void forward(InputArrayOfArrays inputs, OutputArrayOfArrays outputs, OutputArrayOfArrays internals) = 0;
-
-        /** @brief Given the @p input blobs, computes the output @p blobs.
-         *  @param[in]  inputs  the input blobs.
-         *  @param[out] outputs allocated output blobs, which will store results of the computation.
-         *  @param[out] internals allocated internal blobs
-         */
-        void forward_fallback(InputArrayOfArrays inputs, OutputArrayOfArrays outputs, OutputArrayOfArrays internals);
-
-        /** @brief @overload */
-        CV_WRAP void finalize(const std::vector<Mat> &inputs, CV_OUT std::vector<Mat> &outputs);
-
-        /** @brief @overload */
-        CV_WRAP std::vector<Mat> finalize(const std::vector<Mat> &inputs);
-
-        /** @brief Allocates layer and computes output. */
-        CV_WRAP void run(const std::vector<Mat> &inputs, CV_OUT std::vector<Mat> &outputs,
-                         CV_IN_OUT std::vector<Mat> &internals);
-
-        /** @brief Returns index of input blob into the input array.
-         *  @param inputName label of input blob
-         *
-         * Each layer input and output can be labeled to easily identify them using "%<layer_name%>[.output_name]" notation.
-         * This method maps label of input blob to its index into input vector.
-         */
-        virtual int inputNameToIndex(String inputName);
-        /** @brief Returns index of output blob in output array.
-         *  @see inputNameToIndex()
-         */
-        CV_WRAP virtual int outputNameToIndex(const String& outputName);
-
-        /**
-         * @brief Ask layer if it support specific backend for doing computations.
-         * @param[in] backendId computation backend identifier.
-         * @see Backend
-         */
-        virtual bool supportBackend(int backendId);
-
-        /**
-         * @brief Returns Halide backend node.
-         * @param[in] inputs Input Halide buffers.
-         * @see BackendNode, BackendWrapper
-         *
-         * Input buffers should be exactly the same that will be used in forward invocations.
-         * Despite we can use Halide::ImageParam based on input shape only,
-         * it helps prevent some memory management issues (if something wrong,
-         * Halide tests will be failed).
-         */
-        virtual Ptr<BackendNode> initHalide(const std::vector<Ptr<BackendWrapper> > &inputs);
-
-        virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> > &inputs);
-
-       /**
-        * @brief Automatic Halide scheduling based on layer hyper-parameters.
-        * @param[in] node Backend node with Halide functions.
-        * @param[in] inputs Blobs that will be used in forward invocations.
-        * @param[in] outputs Blobs that will be used in forward invocations.
-        * @param[in] targetId Target identifier
-        * @see BackendNode, Target
-        *
-        * Layer don't use own Halide::Func members because we can have applied
-        * layers fusing. In this way the fused function should be scheduled.
-        */
-        virtual void applyHalideScheduler(Ptr<BackendNode>& node,
-                                          const std::vector<Mat*> &inputs,
-                                          const std::vector<Mat> &outputs,
-                                          int targetId) const;
-
-        /**
-         * @brief Implement layers fusing.
-         * @param[in] node Backend node of bottom layer.
-         * @see BackendNode
-         *
-         * Actual for graph-based backends. If layer attached successfully,
-         * returns non-empty cv::Ptr to node of the same backend.
-         * Fuse only over the last function.
-         */
-        virtual Ptr<BackendNode> tryAttach(const Ptr<BackendNode>& node);
-
-        /**
-         * @brief Tries to attach to the layer the subsequent activation layer, i.e. do the layer fusion in a partial case.
-         * @param[in] layer The subsequent activation layer.
-         *
-         * Returns true if the activation layer has been attached successfully.
-         */
-        virtual bool setActivation(const Ptr<ActivationLayer>& layer);
-
-        /**
-         * @brief Try to fuse current layer with a next one
-         * @param[in] top Next layer to be fused.
-         * @returns True if fusion was performed.
-         */
-        virtual bool tryFuse(Ptr<Layer>& top);
-
-        /**
-         * @brief Returns parameters of layers with channel-wise multiplication and addition.
-         * @param[out] scale Channel-wise multipliers. Total number of values should
-         *                   be equal to number of channels.
-         * @param[out] shift Channel-wise offsets. Total number of values should
-         *                   be equal to number of channels.
-         *
-         * Some layers can fuse their transformations with further layers.
-         * In example, convolution + batch normalization. This way base layer
-         * use weights from layer after it. Fused layer is skipped.
-         * By default, @p scale and @p shift are empty that means layer has no
-         * element-wise multiplications or additions.
-         */
-        virtual void getScaleShift(Mat& scale, Mat& shift) const;
-
-        /**
-         * @brief "Deattaches" all the layers, attached to particular layer.
-         */
-        virtual void unsetAttached();
-
-        virtual bool getMemoryShapes(const std::vector<MatShape> &inputs,
-                                     const int requiredOutputs,
-                                     std::vector<MatShape> &outputs,
-                                     std::vector<MatShape> &internals) const;
-        virtual int64 getFLOPS(const std::vector<MatShape> &inputs,
-                               const std::vector<MatShape> &outputs) const {(void)inputs; (void)outputs; return 0;}
-
-        CV_PROP String name; //!< Name of the layer instance, can be used for logging or other internal purposes.
-        CV_PROP String type; //!< Type name which was used for creating layer by layer factory.
-        CV_PROP int preferableTarget; //!< prefer target for layer forwarding
-
-        Layer();
-        explicit Layer(const LayerParams &params);      //!< Initializes only #name, #type and #blobs fields.
-        void setParamsFrom(const LayerParams &params);  //!< Initializes only #name, #type and #blobs fields.
-        virtual ~Layer();
-    };
-
-    /** @brief This class allows to create and manipulate comprehensive artificial neural networks.
-     *
-     * Neural network is presented as directed acyclic graph (DAG), where vertices are Layer instances,
-     * and edges specify relationships between layers inputs and outputs.
-     *
-     * Each network layer has unique integer id and unique string name inside its network.
-     * LayerId can store either layer name or layer id.
-     *
-     * This class supports reference counting of its instances, i. e. copies point to the same instance.
-     */
-    class CV_EXPORTS_W_SIMPLE Net
-    {
-    public:
-
-        CV_WRAP Net();  //!< Default constructor.
-        CV_WRAP ~Net(); //!< Destructor frees the net only if there aren't references to the net anymore.
-
-        /** @brief Create a network from Intel's Model Optimizer intermediate representation.
-         *  @param[in] xml XML configuration file with network's topology.
-         *  @param[in] bin Binary file with trained weights.
-         *  Networks imported from Intel's Model Optimizer are launched in Intel's Inference Engine
-         *  backend.
-         */
-        CV_WRAP static Net readFromModelOptimizer(const String& xml, const String& bin);
-
-        /** Returns true if there are no layers in the network. */
-        CV_WRAP bool empty() const;
-
-        /** @brief Adds new layer to the net.
-         *  @param name   unique name of the adding layer.
-         *  @param type   typename of the adding layer (type must be registered in LayerRegister).
-         *  @param params parameters which will be used to initialize the creating layer.
-         *  @returns unique identifier of created layer, or -1 if a failure will happen.
-         */
-        int addLayer(const String &name, const String &type, LayerParams &params);
-        /** @brief Adds new layer and connects its first input to the first output of previously added layer.
-         *  @see addLayer()
-         */
-        int addLayerToPrev(const String &name, const String &type, LayerParams &params);
-
-        /** @brief Converts string name of the layer to the integer identifier.
-         *  @returns id of the layer, or -1 if the layer wasn't found.
-         */
-        CV_WRAP int getLayerId(const String &layer);
-
-        CV_WRAP std::vector<String> getLayerNames() const;
-
-        /** @brief Container for strings and integers. */
-        typedef DictValue LayerId;
-
-        /** @brief Returns pointer to layer with specified id or name which the network use. */
-        CV_WRAP Ptr<Layer> getLayer(LayerId layerId);
-
-        /** @brief Returns pointers to input layers of specific layer. */
-        std::vector<Ptr<Layer> > getLayerInputs(LayerId layerId); // FIXIT: CV_WRAP
-
-        /** @brief Delete layer for the network (not implemented yet) */
-        CV_WRAP void deleteLayer(LayerId layer);
-
-        /** @brief Connects output of the first layer to input of the second layer.
-         *  @param outPin descriptor of the first layer output.
-         *  @param inpPin descriptor of the second layer input.
-         *
-         * Descriptors have the following template <DFN>&lt;layer_name&gt;[.input_number]</DFN>:
-         * - the first part of the template <DFN>layer_name</DFN> is sting name of the added layer.
-         *   If this part is empty then the network input pseudo layer will be used;
-         * - the second optional part of the template <DFN>input_number</DFN>
-         *   is either number of the layer input, either label one.
-         *   If this part is omitted then the first layer input will be used.
-         *
-         *  @see setNetInputs(), Layer::inputNameToIndex(), Layer::outputNameToIndex()
-         */
-        CV_WRAP void connect(String outPin, String inpPin);
-
-        /** @brief Connects #@p outNum output of the first layer to #@p inNum input of the second layer.
-         *  @param outLayerId identifier of the first layer
-         *  @param outNum number of the first layer output
-         *  @param inpLayerId identifier of the second layer
-         *  @param inpNum number of the second layer input
-         */
-        void connect(int outLayerId, int outNum, int inpLayerId, int inpNum);
-
-        /** @brief Sets outputs names of the network input pseudo layer.
-         *
-         * Each net always has special own the network input pseudo layer with id=0.
-         * This layer stores the user blobs only and don't make any computations.
-         * In fact, this layer provides the only way to pass user data into the network.
-         * As any other layer, this layer can label its outputs and this function provides an easy way to do this.
-         */
-        CV_WRAP void setInputsNames(const std::vector<String> &inputBlobNames);
-
-        /** @brief Runs forward pass to compute output of layer with name @p outputName.
-         *  @param outputName name for layer which output is needed to get
-         *  @return blob for first output of specified layer.
-         *  @details By default runs forward pass for the whole network.
-         */
-        CV_WRAP Mat forward(const String& outputName = String());
-
-        /** @brief Runs forward pass to compute output of layer with name @p outputName.
-         *  @param outputBlobs contains all output blobs for specified layer.
-         *  @param outputName name for layer which output is needed to get
-         *  @details If @p outputName is empty, runs forward pass for the whole network.
-         */
-        CV_WRAP void forward(OutputArrayOfArrays outputBlobs, const String& outputName = String());
-
-        /** @brief Runs forward pass to compute outputs of layers listed in @p outBlobNames.
-         *  @param outputBlobs contains blobs for first outputs of specified layers.
-         *  @param outBlobNames names for layers which outputs are needed to get
-         */
-        CV_WRAP void forward(OutputArrayOfArrays outputBlobs,
-                             const std::vector<String>& outBlobNames);
-
-        /** @brief Runs forward pass to compute outputs of layers listed in @p outBlobNames.
-         *  @param outputBlobs contains all output blobs for each layer specified in @p outBlobNames.
-         *  @param outBlobNames names for layers which outputs are needed to get
-         */
-        CV_WRAP_AS(forwardAndRetrieve) void forward(CV_OUT std::vector<std::vector<Mat> >& outputBlobs,
-                                                    const std::vector<String>& outBlobNames);
-
-        /**
-         * @brief Compile Halide layers.
-         * @param[in] scheduler Path to YAML file with scheduling directives.
-         * @see setPreferableBackend
-         *
-         * Schedule layers that support Halide backend. Then compile them for
-         * specific target. For layers that not represented in scheduling file
-         * or if no manual scheduling used at all, automatic scheduling will be applied.
-         */
-        CV_WRAP void setHalideScheduler(const String& scheduler);
-
-        /**
-         * @brief Ask network to use specific computation backend where it supported.
-         * @param[in] backendId backend identifier.
-         * @see Backend
-         *
-         * If OpenCV is compiled with Intel's Inference Engine library, DNN_BACKEND_DEFAULT
-         * means DNN_BACKEND_INFERENCE_ENGINE. Otherwise it equals to DNN_BACKEND_OPENCV.
-         */
-        CV_WRAP void setPreferableBackend(int backendId);
-
-        /**
-         * @brief Ask network to make computations on specific target device.
-         * @param[in] targetId target identifier.
-         * @see Target
-         *
-         * List of supported combinations backend / target:
-         * |                        | DNN_BACKEND_OPENCV | DNN_BACKEND_INFERENCE_ENGINE | DNN_BACKEND_HALIDE |
-         * |------------------------|--------------------|------------------------------|--------------------|
-         * | DNN_TARGET_CPU         |                  + |                            + |                  + |
-         * | DNN_TARGET_OPENCL      |                  + |                            + |                  + |
-         * | DNN_TARGET_OPENCL_FP16 |                  + |                            + |                    |
-         * | DNN_TARGET_MYRIAD      |                    |                            + |                    |
-         */
-        CV_WRAP void setPreferableTarget(int targetId);
-
-        /** @brief Sets the new value for the layer output blob
-         *  @param name descriptor of the updating layer output blob.
-         *  @param blob new blob.
-         *  @see connect(String, String) to know format of the descriptor.
-         *  @note If updating blob is not empty then @p blob must have the same shape,
-         *  because network reshaping is not implemented yet.
-         */
-        CV_WRAP void setInput(InputArray blob, const String& name = "");
-
-        /** @brief Sets the new value for the learned param of the layer.
-         *  @param layer name or id of the layer.
-         *  @param numParam index of the layer parameter in the Layer::blobs array.
-         *  @param blob the new value.
-         *  @see Layer::blobs
-         *  @note If shape of the new blob differs from the previous shape,
-         *  then the following forward pass may fail.
-        */
-        CV_WRAP void setParam(LayerId layer, int numParam, const Mat &blob);
-
-        /** @brief Returns parameter blob of the layer.
-         *  @param layer name or id of the layer.
-         *  @param numParam index of the layer parameter in the Layer::blobs array.
-         *  @see Layer::blobs
-         */
-        CV_WRAP Mat getParam(LayerId layer, int numParam = 0);
-
-        /** @brief Returns indexes of layers with unconnected outputs.
-         */
-        CV_WRAP std::vector<int> getUnconnectedOutLayers() const;
-        /** @brief Returns input and output shapes for all layers in loaded model;
-         *  preliminary inferencing isn't necessary.
-         *  @param netInputShapes shapes for all input blobs in net input layer.
-         *  @param layersIds output parameter for layer IDs.
-         *  @param inLayersShapes output parameter for input layers shapes;
-         * order is the same as in layersIds
-         *  @param outLayersShapes output parameter for output layers shapes;
-         * order is the same as in layersIds
-         */
-        CV_WRAP void getLayersShapes(const std::vector<MatShape>& netInputShapes,
-                                     CV_OUT std::vector<int>& layersIds,
-                                     CV_OUT std::vector<std::vector<MatShape> >& inLayersShapes,
-                                     CV_OUT std::vector<std::vector<MatShape> >& outLayersShapes) const;
-
-        /** @overload */
-        CV_WRAP void getLayersShapes(const MatShape& netInputShape,
-                                     CV_OUT std::vector<int>& layersIds,
-                                     CV_OUT std::vector<std::vector<MatShape> >& inLayersShapes,
-                                     CV_OUT std::vector<std::vector<MatShape> >& outLayersShapes) const;
-
-        /** @brief Returns input and output shapes for layer with specified
-         * id in loaded model; preliminary inferencing isn't necessary.
-         *  @param netInputShape shape input blob in net input layer.
-         *  @param layerId id for layer.
-         *  @param inLayerShapes output parameter for input layers shapes;
-         * order is the same as in layersIds
-         *  @param outLayerShapes output parameter for output layers shapes;
-         * order is the same as in layersIds
-         */
-        void getLayerShapes(const MatShape& netInputShape,
-                                    const int layerId,
-                                    CV_OUT std::vector<MatShape>& inLayerShapes,
-                                    CV_OUT std::vector<MatShape>& outLayerShapes) const; // FIXIT: CV_WRAP
-
-        /** @overload */
-        void getLayerShapes(const std::vector<MatShape>& netInputShapes,
-                                    const int layerId,
-                                    CV_OUT std::vector<MatShape>& inLayerShapes,
-                                    CV_OUT std::vector<MatShape>& outLayerShapes) const; // FIXIT: CV_WRAP
-
-        /** @brief Computes FLOP for whole loaded model with specified input shapes.
-         * @param netInputShapes vector of shapes for all net inputs.
-         * @returns computed FLOP.
-         */
-        CV_WRAP int64 getFLOPS(const std::vector<MatShape>& netInputShapes) const;
-        /** @overload */
-        CV_WRAP int64 getFLOPS(const MatShape& netInputShape) const;
-        /** @overload */
-        CV_WRAP int64 getFLOPS(const int layerId,
-                               const std::vector<MatShape>& netInputShapes) const;
-        /** @overload */
-        CV_WRAP int64 getFLOPS(const int layerId,
-                               const MatShape& netInputShape) const;
-
-        /** @brief Returns list of types for layer used in model.
-         * @param layersTypes output parameter for returning types.
-         */
-        CV_WRAP void getLayerTypes(CV_OUT std::vector<String>& layersTypes) const;
-
-        /** @brief Returns count of layers of specified type.
-         * @param layerType type.
-         * @returns count of layers
-         */
-        CV_WRAP int getLayersCount(const String& layerType) const;
-
-        /** @brief Computes bytes number which are required to store
-         * all weights and intermediate blobs for model.
-         * @param netInputShapes vector of shapes for all net inputs.
-         * @param weights output parameter to store resulting bytes for weights.
-         * @param blobs output parameter to store resulting bytes for intermediate blobs.
-         */
-        void getMemoryConsumption(const std::vector<MatShape>& netInputShapes,
-                                          CV_OUT size_t& weights, CV_OUT size_t& blobs) const; // FIXIT: CV_WRAP
-        /** @overload */
-        CV_WRAP void getMemoryConsumption(const MatShape& netInputShape,
-                                          CV_OUT size_t& weights, CV_OUT size_t& blobs) const;
-        /** @overload */
-        CV_WRAP void getMemoryConsumption(const int layerId,
-                                          const std::vector<MatShape>& netInputShapes,
-                                          CV_OUT size_t& weights, CV_OUT size_t& blobs) const;
-        /** @overload */
-        CV_WRAP void getMemoryConsumption(const int layerId,
-                                          const MatShape& netInputShape,
-                                          CV_OUT size_t& weights, CV_OUT size_t& blobs) const;
-
-        /** @brief Computes bytes number which are required to store
-         * all weights and intermediate blobs for each layer.
-         * @param netInputShapes vector of shapes for all net inputs.
-         * @param layerIds output vector to save layer IDs.
-         * @param weights output parameter to store resulting bytes for weights.
-         * @param blobs output parameter to store resulting bytes for intermediate blobs.
-         */
-        void getMemoryConsumption(const std::vector<MatShape>& netInputShapes,
-                                          CV_OUT std::vector<int>& layerIds,
-                                          CV_OUT std::vector<size_t>& weights,
-                                          CV_OUT std::vector<size_t>& blobs) const; // FIXIT: CV_WRAP
-        /** @overload */
-        void getMemoryConsumption(const MatShape& netInputShape,
-                                          CV_OUT std::vector<int>& layerIds,
-                                          CV_OUT std::vector<size_t>& weights,
-                                          CV_OUT std::vector<size_t>& blobs) const; // FIXIT: CV_WRAP
-
-        /** @brief Enables or disables layer fusion in the network.
-         * @param fusion true to enable the fusion, false to disable. The fusion is enabled by default.
-         */
-        CV_WRAP void enableFusion(bool fusion);
-
-        /** @brief Returns overall time for inference and timings (in ticks) for layers.
-         * Indexes in returned vector correspond to layers ids. Some layers can be fused with others,
-         * in this case zero ticks count will be return for that skipped layers.
-         * @param timings vector for tick timings for all layers.
-         * @return overall ticks for model inference.
-         */
-        CV_WRAP int64 getPerfProfile(CV_OUT std::vector<double>& timings);
-
-    private:
-        struct Impl;
-        Ptr<Impl> impl;
-    };
-
-    /** @brief Reads a network model stored in <a href="https://pjreddie.com/darknet/">Darknet</a> model files.
-    *  @param cfgFile      path to the .cfg file with text description of the network architecture.
-    *  @param darknetModel path to the .weights file with learned network.
-    *  @returns Network object that ready to do forward, throw an exception in failure cases.
-    *  @returns Net object.
-    */
-    CV_EXPORTS_W Net readNetFromDarknet(const String &cfgFile, const String &darknetModel = String());
-
-    /** @brief Reads a network model stored in <a href="http://caffe.berkeleyvision.org">Caffe</a> framework's format.
-      * @param prototxt   path to the .prototxt file with text description of the network architecture.
-      * @param caffeModel path to the .caffemodel file with learned network.
-      * @returns Net object.
-      */
-    CV_EXPORTS_W Net readNetFromCaffe(const String &prototxt, const String &caffeModel = String());
-
-    /** @brief Reads a network model stored in Caffe model in memory.
-      * @details This is an overloaded member function, provided for convenience.
-      * It differs from the above function only in what argument(s) it accepts.
-      * @param bufferProto buffer containing the content of the .prototxt file
-      * @param lenProto length of bufferProto
-      * @param bufferModel buffer containing the content of the .caffemodel file
-      * @param lenModel length of bufferModel
-      * @returns Net object.
-      */
-    CV_EXPORTS Net readNetFromCaffe(const char *bufferProto, size_t lenProto,
-                                    const char *bufferModel = NULL, size_t lenModel = 0);
-
-    /** @brief Reads a network model stored in <a href="https://www.tensorflow.org/">TensorFlow</a> framework's format.
-      * @param model  path to the .pb file with binary protobuf description of the network architecture
-      * @param config path to the .pbtxt file that contains text graph definition in protobuf format.
-      *               Resulting Net object is built by text graph using weights from a binary one that
-      *               let us make it more flexible.
-      * @returns Net object.
-      */
-    CV_EXPORTS_W Net readNetFromTensorflow(const String &model, const String &config = String());
-
-    /** @brief Reads a network model stored in <a href="https://www.tensorflow.org/">TensorFlow</a> framework's format.
-      * @details This is an overloaded member function, provided for convenience.
-      * It differs from the above function only in what argument(s) it accepts.
-      * @param bufferModel buffer containing the content of the pb file
-      * @param lenModel length of bufferModel
-      * @param bufferConfig buffer containing the content of the pbtxt file
-      * @param lenConfig length of bufferConfig
-      */
-    CV_EXPORTS Net readNetFromTensorflow(const char *bufferModel, size_t lenModel,
-                                         const char *bufferConfig = NULL, size_t lenConfig = 0);
-
-    /**
-     *  @brief Reads a network model stored in <a href="http://torch.ch">Torch7</a> framework's format.
-     *  @param model    path to the file, dumped from Torch by using torch.save() function.
-     *  @param isBinary specifies whether the network was serialized in ascii mode or binary.
-     *  @returns Net object.
-     *
-     *  @note Ascii mode of Torch serializer is more preferable, because binary mode extensively use `long` type of C language,
-     *  which has various bit-length on different systems.
-     *
-     * The loading file must contain serialized <a href="https://github.com/torch/nn/blob/master/doc/module.md">nn.Module</a> object
-     * with importing network. Try to eliminate a custom objects from serialazing data to avoid importing errors.
-     *
-     * List of supported layers (i.e. object instances derived from Torch nn.Module class):
-     * - nn.Sequential
-     * - nn.Parallel
-     * - nn.Concat
-     * - nn.Linear
-     * - nn.SpatialConvolution
-     * - nn.SpatialMaxPooling, nn.SpatialAveragePooling
-     * - nn.ReLU, nn.TanH, nn.Sigmoid
-     * - nn.Reshape
-     * - nn.SoftMax, nn.LogSoftMax
-     *
-     * Also some equivalents of these classes from cunn, cudnn, and fbcunn may be successfully imported.
-     */
-     CV_EXPORTS_W Net readNetFromTorch(const String &model, bool isBinary = true);
-
-     /**
-      * @brief Read deep learning network represented in one of the supported formats.
-      * @param[in] model Binary file contains trained weights. The following file
-      *                  extensions are expected for models from different frameworks:
-      *                  * `*.caffemodel` (Caffe, http://caffe.berkeleyvision.org/)
-      *                  * `*.pb` (TensorFlow, https://www.tensorflow.org/)
-      *                  * `*.t7` | `*.net` (Torch, http://torch.ch/)
-      *                  * `*.weights` (Darknet, https://pjreddie.com/darknet/)
-      *                  * `*.bin` (DLDT, https://software.intel.com/openvino-toolkit)
-      * @param[in] config Text file contains network configuration. It could be a
-      *                   file with the following extensions:
-      *                  * `*.prototxt` (Caffe, http://caffe.berkeleyvision.org/)
-      *                  * `*.pbtxt` (TensorFlow, https://www.tensorflow.org/)
-      *                  * `*.cfg` (Darknet, https://pjreddie.com/darknet/)
-      *                  * `*.xml` (DLDT, https://software.intel.com/openvino-toolkit)
-      * @param[in] framework Explicit framework name tag to determine a format.
-      * @returns Net object.
-      *
-      * This function automatically detects an origin framework of trained model
-      * and calls an appropriate function such @ref readNetFromCaffe, @ref readNetFromTensorflow,
-      * @ref readNetFromTorch or @ref readNetFromDarknet. An order of @p model and @p config
-      * arguments does not matter.
-      */
-     CV_EXPORTS_W Net readNet(const String& model, const String& config = "", const String& framework = "");
-
-    /** @brief Loads blob which was serialized as torch.Tensor object of Torch7 framework.
-     *  @warning This function has the same limitations as readNetFromTorch().
-     */
-    CV_EXPORTS_W Mat readTorchBlob(const String &filename, bool isBinary = true);
-
-    /** @brief Load a network from Intel's Model Optimizer intermediate representation.
-     *  @param[in] xml XML configuration file with network's topology.
-     *  @param[in] bin Binary file with trained weights.
-     *  @returns Net object.
-     *  Networks imported from Intel's Model Optimizer are launched in Intel's Inference Engine
-     *  backend.
-     */
-    CV_EXPORTS_W Net readNetFromModelOptimizer(const String &xml, const String &bin);
-
-    /** @brief Creates 4-dimensional blob from image. Optionally resizes and crops @p image from center,
-     *  subtract @p mean values, scales values by @p scalefactor, swap Blue and Red channels.
-     *  @param image input image (with 1-, 3- or 4-channels).
-     *  @param size spatial size for output image
-     *  @param mean scalar with mean values which are subtracted from channels. Values are intended
-     *  to be in (mean-R, mean-G, mean-B) order if @p image has BGR ordering and @p swapRB is true.
-     *  @param scalefactor multiplier for @p image values.
-     *  @param swapRB flag which indicates that swap first and last channels
-     *  in 3-channel image is necessary.
-     *  @param crop flag which indicates whether image will be cropped after resize or not
-     *  @details if @p crop is true, input image is resized so one side after resize is equal to corresponding
-     *  dimension in @p size and another one is equal or larger. Then, crop from the center is performed.
-     *  If @p crop is false, direct resize without cropping and preserving aspect ratio is performed.
-     *  @returns 4-dimensional Mat with NCHW dimensions order.
-     */
-    CV_EXPORTS_W Mat blobFromImage(InputArray image, double scalefactor=1.0, const Size& size = Size(),
-                                   const Scalar& mean = Scalar(), bool swapRB=true, bool crop=true);
-
-    /** @brief Creates 4-dimensional blob from image.
-     *  @details This is an overloaded member function, provided for convenience.
-     *           It differs from the above function only in what argument(s) it accepts.
-     */
-    CV_EXPORTS void blobFromImage(InputArray image, OutputArray blob, double scalefactor=1.0,
-                                  const Size& size = Size(), const Scalar& mean = Scalar(),
-                                  bool swapRB=true, bool crop=true);
-
-
-    /** @brief Creates 4-dimensional blob from series of images. Optionally resizes and
-     *  crops @p images from center, subtract @p mean values, scales values by @p scalefactor,
-     *  swap Blue and Red channels.
-     *  @param images input images (all with 1-, 3- or 4-channels).
-     *  @param size spatial size for output image
-     *  @param mean scalar with mean values which are subtracted from channels. Values are intended
-     *  to be in (mean-R, mean-G, mean-B) order if @p image has BGR ordering and @p swapRB is true.
-     *  @param scalefactor multiplier for @p images values.
-     *  @param swapRB flag which indicates that swap first and last channels
-     *  in 3-channel image is necessary.
-     *  @param crop flag which indicates whether image will be cropped after resize or not
-     *  @details if @p crop is true, input image is resized so one side after resize is equal to corresponding
-     *  dimension in @p size and another one is equal or larger. Then, crop from the center is performed.
-     *  If @p crop is false, direct resize without cropping and preserving aspect ratio is performed.
-     *  @returns 4-dimansional Mat with NCHW dimensions order.
-     */
-    CV_EXPORTS_W Mat blobFromImages(InputArrayOfArrays images, double scalefactor=1.0,
-                                    Size size = Size(), const Scalar& mean = Scalar(), bool swapRB=true, bool crop=true);
-
-    /** @brief Creates 4-dimensional blob from series of images.
-     *  @details This is an overloaded member function, provided for convenience.
-     *           It differs from the above function only in what argument(s) it accepts.
-     */
-    CV_EXPORTS void blobFromImages(InputArrayOfArrays images, OutputArray blob,
-                                   double scalefactor=1.0, Size size = Size(),
-                                   const Scalar& mean = Scalar(), bool swapRB=true, bool crop=true);
-
-    /** @brief Parse a 4D blob and output the images it contains as 2D arrays through a simpler data structure
-     *  (std::vector<cv::Mat>).
-     *  @param[in] blob_ 4 dimensional array (images, channels, height, width) in floating point precision (CV_32F) from
-     *  which you would like to extract the images.
-     *  @param[out] images_ array of 2D Mat containing the images extracted from the blob in floating point precision
-     *  (CV_32F). They are non normalized neither mean added. The number of returned images equals the first dimension
-     *  of the blob (batch size). Every image has a number of channels equals to the second dimension of the blob (depth).
-     */
-    CV_EXPORTS_W void imagesFromBlob(const cv::Mat& blob_, OutputArrayOfArrays images_);
-
-    /** @brief Convert all weights of Caffe network to half precision floating point.
-     * @param src Path to origin model from Caffe framework contains single
-     *            precision floating point weights (usually has `.caffemodel` extension).
-     * @param dst Path to destination model with updated weights.
-     * @param layersTypes Set of layers types which parameters will be converted.
-     *                    By default, converts only Convolutional and Fully-Connected layers'
-     *                    weights.
-     *
-     * @note Shrinked model has no origin float32 weights so it can't be used
-     *       in origin Caffe framework anymore. However the structure of data
-     *       is taken from NVidia's Caffe fork: https://github.com/NVIDIA/caffe.
-     *       So the resulting model may be used there.
-     */
-    CV_EXPORTS_W void shrinkCaffeModel(const String& src, const String& dst,
-                                       const std::vector<String>& layersTypes = std::vector<String>());
-
-    /** @brief Performs non maximum suppression given boxes and corresponding scores.
-
-     * @param bboxes a set of bounding boxes to apply NMS.
-     * @param scores a set of corresponding confidences.
-     * @param score_threshold a threshold used to filter boxes by score.
-     * @param nms_threshold a threshold used in non maximum suppression.
-     * @param indices the kept indices of bboxes after NMS.
-     * @param eta a coefficient in adaptive threshold formula: \f$nms\_threshold_{i+1}=eta\cdot nms\_threshold_i\f$.
-     * @param top_k if `>0`, keep at most @p top_k picked indices.
-     */
-    CV_EXPORTS_W void NMSBoxes(const std::vector<Rect>& bboxes, const std::vector<float>& scores,
-                               const float score_threshold, const float nms_threshold,
-                               CV_OUT std::vector<int>& indices,
-                               const float eta = 1.f, const int top_k = 0);
-
-    CV_EXPORTS void NMSBoxes(const std::vector<RotatedRect>& bboxes, const std::vector<float>& scores,
-                             const float score_threshold, const float nms_threshold,
-                             CV_OUT std::vector<int>& indices,
-                             const float eta = 1.f, const int top_k = 0);
-
-//! @}
-CV__DNN_EXPERIMENTAL_NS_END
-}
-}
-
-#include <opencv2/dnn/layer.hpp>
-#include <opencv2/dnn/dnn.inl.hpp>
-
-#endif  /* OPENCV_DNN_DNN_HPP */
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/dnn/dnn.inl.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/dnn/dnn.inl.hpp
deleted file mode 100755
index 4231896..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/dnn/dnn.inl.hpp
+++ /dev/null
@@ -1,390 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_DNN_DNN_INL_HPP
-#define OPENCV_DNN_DNN_INL_HPP
-
-#include <opencv2/dnn.hpp>
-
-namespace cv {
-namespace dnn {
-CV__DNN_EXPERIMENTAL_NS_BEGIN
-
-template<typename TypeIter>
-DictValue DictValue::arrayInt(TypeIter begin, int size)
-{
-    DictValue res(Param::INT, new AutoBuffer<int64, 1>(size));
-    for (int j = 0; j < size; begin++, j++)
-        (*res.pi)[j] = *begin;
-    return res;
-}
-
-template<typename TypeIter>
-DictValue DictValue::arrayReal(TypeIter begin, int size)
-{
-    DictValue res(Param::REAL, new AutoBuffer<double, 1>(size));
-    for (int j = 0; j < size; begin++, j++)
-        (*res.pd)[j] = *begin;
-    return res;
-}
-
-template<typename TypeIter>
-DictValue DictValue::arrayString(TypeIter begin, int size)
-{
-    DictValue res(Param::STRING, new AutoBuffer<String, 1>(size));
-    for (int j = 0; j < size; begin++, j++)
-        (*res.ps)[j] = *begin;
-    return res;
-}
-
-template<>
-inline DictValue DictValue::get<DictValue>(int idx) const
-{
-    CV_Assert(idx == -1);
-    return *this;
-}
-
-template<>
-inline int64 DictValue::get<int64>(int idx) const
-{
-    CV_Assert((idx == -1 && size() == 1) || (idx >= 0 && idx < size()));
-    idx = (idx == -1) ? 0 : idx;
-
-    if (type == Param::INT)
-    {
-        return (*pi)[idx];
-    }
-    else if (type == Param::REAL)
-    {
-        double doubleValue = (*pd)[idx];
-
-        double fracpart, intpart;
-        fracpart = std::modf(doubleValue, &intpart);
-        CV_Assert(fracpart == 0.0);
-
-        return (int64)doubleValue;
-    }
-    else if (type == Param::STRING)
-    {
-        return std::atoi((*ps)[idx].c_str());
-    }
-    else
-    {
-        CV_Assert(isInt() || isReal() || isString());
-        return 0;
-    }
-}
-
-template<>
-inline int DictValue::get<int>(int idx) const
-{
-    return (int)get<int64>(idx);
-}
-
-inline int DictValue::getIntValue(int idx) const
-{
-    return (int)get<int64>(idx);
-}
-
-template<>
-inline unsigned DictValue::get<unsigned>(int idx) const
-{
-    return (unsigned)get<int64>(idx);
-}
-
-template<>
-inline bool DictValue::get<bool>(int idx) const
-{
-    return (get<int64>(idx) != 0);
-}
-
-template<>
-inline double DictValue::get<double>(int idx) const
-{
-    CV_Assert((idx == -1 && size() == 1) || (idx >= 0 && idx < size()));
-    idx = (idx == -1) ? 0 : idx;
-
-    if (type == Param::REAL)
-    {
-        return (*pd)[idx];
-    }
-    else if (type == Param::INT)
-    {
-        return (double)(*pi)[idx];
-    }
-    else if (type == Param::STRING)
-    {
-        return std::atof((*ps)[idx].c_str());
-    }
-    else
-    {
-        CV_Assert(isReal() || isInt() || isString());
-        return 0;
-    }
-}
-
-inline double DictValue::getRealValue(int idx) const
-{
-    return get<double>(idx);
-}
-
-template<>
-inline float DictValue::get<float>(int idx) const
-{
-    return (float)get<double>(idx);
-}
-
-template<>
-inline String DictValue::get<String>(int idx) const
-{
-    CV_Assert(isString());
-    CV_Assert((idx == -1 && ps->size() == 1) || (idx >= 0 && idx < (int)ps->size()));
-    return (*ps)[(idx == -1) ? 0 : idx];
-}
-
-
-inline String DictValue::getStringValue(int idx) const
-{
-    return get<String>(idx);
-}
-
-inline void DictValue::release()
-{
-    switch (type)
-    {
-    case Param::INT:
-        delete pi;
-        break;
-    case Param::STRING:
-        delete ps;
-        break;
-    case Param::REAL:
-        delete pd;
-        break;
-    }
-}
-
-inline DictValue::~DictValue()
-{
-    release();
-}
-
-inline DictValue & DictValue::operator=(const DictValue &r)
-{
-    if (&r == this)
-        return *this;
-
-    if (r.type == Param::INT)
-    {
-        AutoBuffer<int64, 1> *tmp = new AutoBuffer<int64, 1>(*r.pi);
-        release();
-        pi = tmp;
-    }
-    else if (r.type == Param::STRING)
-    {
-        AutoBuffer<String, 1> *tmp = new AutoBuffer<String, 1>(*r.ps);
-        release();
-        ps = tmp;
-    }
-    else if (r.type == Param::REAL)
-    {
-        AutoBuffer<double, 1> *tmp = new AutoBuffer<double, 1>(*r.pd);
-        release();
-        pd = tmp;
-    }
-
-    type = r.type;
-
-    return *this;
-}
-
-inline DictValue::DictValue(const DictValue &r)
-{
-    type = r.type;
-
-    if (r.type == Param::INT)
-        pi = new AutoBuffer<int64, 1>(*r.pi);
-    else if (r.type == Param::STRING)
-        ps = new AutoBuffer<String, 1>(*r.ps);
-    else if (r.type == Param::REAL)
-        pd = new AutoBuffer<double, 1>(*r.pd);
-}
-
-inline bool DictValue::isString() const
-{
-    return (type == Param::STRING);
-}
-
-inline bool DictValue::isInt() const
-{
-    return (type == Param::INT);
-}
-
-inline bool DictValue::isReal() const
-{
-    return (type == Param::REAL || type == Param::INT);
-}
-
-inline int DictValue::size() const
-{
-    switch (type)
-    {
-    case Param::INT:
-        return (int)pi->size();
-    case Param::STRING:
-        return (int)ps->size();
-    case Param::REAL:
-        return (int)pd->size();
-    }
-#ifdef __OPENCV_BUILD
-    CV_Error(Error::StsInternal, "");
-#else
-    CV_ErrorNoReturn(Error::StsInternal, "");
-#endif
-}
-
-inline std::ostream &operator<<(std::ostream &stream, const DictValue &dictv)
-{
-    int i;
-
-    if (dictv.isInt())
-    {
-        for (i = 0; i < dictv.size() - 1; i++)
-            stream << dictv.get<int64>(i) << ", ";
-        stream << dictv.get<int64>(i);
-    }
-    else if (dictv.isReal())
-    {
-        for (i = 0; i < dictv.size() - 1; i++)
-            stream << dictv.get<double>(i) << ", ";
-        stream << dictv.get<double>(i);
-    }
-    else if (dictv.isString())
-    {
-        for (i = 0; i < dictv.size() - 1; i++)
-            stream << "\"" << dictv.get<String>(i) << "\", ";
-        stream << dictv.get<String>(i);
-    }
-
-    return stream;
-}
-
-/////////////////////////////////////////////////////////////////
-
-inline bool Dict::has(const String &key) const
-{
-    return dict.count(key) != 0;
-}
-
-inline DictValue *Dict::ptr(const String &key)
-{
-    _Dict::iterator i = dict.find(key);
-    return (i == dict.end()) ? NULL : &i->second;
-}
-
-inline const DictValue *Dict::ptr(const String &key) const
-{
-    _Dict::const_iterator i = dict.find(key);
-    return (i == dict.end()) ? NULL : &i->second;
-}
-
-inline const DictValue &Dict::get(const String &key) const
-{
-    _Dict::const_iterator i = dict.find(key);
-    if (i == dict.end())
-        CV_Error(Error::StsObjectNotFound, "Required argument \"" + key + "\" not found into dictionary");
-    return i->second;
-}
-
-template <typename T>
-inline T Dict::get(const String &key) const
-{
-    return this->get(key).get<T>();
-}
-
-template <typename T>
-inline T Dict::get(const String &key, const T &defaultValue) const
-{
-    _Dict::const_iterator i = dict.find(key);
-
-    if (i != dict.end())
-        return i->second.get<T>();
-    else
-        return defaultValue;
-}
-
-template<typename T>
-inline const T &Dict::set(const String &key, const T &value)
-{
-    _Dict::iterator i = dict.find(key);
-
-    if (i != dict.end())
-        i->second = DictValue(value);
-    else
-        dict.insert(std::make_pair(key, DictValue(value)));
-
-    return value;
-}
-
-inline std::ostream &operator<<(std::ostream &stream, const Dict &dict)
-{
-    Dict::_Dict::const_iterator it;
-    for (it = dict.dict.begin(); it != dict.dict.end(); it++)
-        stream << it->first << " : " << it->second << "\n";
-
-    return stream;
-}
-
-inline std::map<String, DictValue>::const_iterator Dict::begin() const
-{
-    return dict.begin();
-}
-
-inline std::map<String, DictValue>::const_iterator Dict::end() const
-{
-    return dict.end();
-}
-
-CV__DNN_EXPERIMENTAL_NS_END
-}
-}
-
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/dnn/layer.details.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/dnn/layer.details.hpp
deleted file mode 100755
index 619514e..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/dnn/layer.details.hpp
+++ /dev/null
@@ -1,78 +0,0 @@
-// This file is part of OpenCV project.
-// It is subject to the license terms in the LICENSE file found in the top-level directory
-// of this distribution and at http://opencv.org/license.html.
-//
-#ifndef OPENCV_DNN_LAYER_DETAILS_HPP
-#define OPENCV_DNN_LAYER_DETAILS_HPP
-
-#include <opencv2/dnn/layer.hpp>
-
-namespace cv {
-namespace dnn {
-CV__DNN_EXPERIMENTAL_NS_BEGIN
-
-/** @brief Registers layer constructor in runtime.
-*   @param type string, containing type name of the layer.
-*   @param constructorFunc pointer to the function of type LayerRegister::Constructor, which creates the layer.
-*   @details This macros must be placed inside the function code.
-*/
-#define CV_DNN_REGISTER_LAYER_FUNC(type, constructorFunc) \
-    cv::dnn::LayerFactory::registerLayer(#type, constructorFunc);
-
-/** @brief Registers layer class in runtime.
- *  @param type string, containing type name of the layer.
- *  @param class C++ class, derived from Layer.
- *  @details This macros must be placed inside the function code.
- */
-#define CV_DNN_REGISTER_LAYER_CLASS(type, class) \
-    cv::dnn::LayerFactory::registerLayer(#type, cv::dnn::details::_layerDynamicRegisterer<class>);
-
-/** @brief Registers layer constructor on module load time.
-*   @param type string, containing type name of the layer.
-*   @param constructorFunc pointer to the function of type LayerRegister::Constructor, which creates the layer.
-*   @details This macros must be placed outside the function code.
-*/
-#define CV_DNN_REGISTER_LAYER_FUNC_STATIC(type, constructorFunc) \
-static cv::dnn::details::_LayerStaticRegisterer __LayerStaticRegisterer_##type(#type, constructorFunc);
-
-/** @brief Registers layer class on module load time.
- *  @param type string, containing type name of the layer.
- *  @param class C++ class, derived from Layer.
- *  @details This macros must be placed outside the function code.
- */
-#define CV_DNN_REGISTER_LAYER_CLASS_STATIC(type, class)                         \
-Ptr<Layer> __LayerStaticRegisterer_func_##type(LayerParams &params) \
-    { return Ptr<Layer>(new class(params)); }                       \
-static cv::dnn::details::_LayerStaticRegisterer __LayerStaticRegisterer_##type(#type, __LayerStaticRegisterer_func_##type);
-
-namespace details {
-
-template<typename LayerClass>
-Ptr<Layer> _layerDynamicRegisterer(LayerParams &params)
-{
-    return Ptr<Layer>(LayerClass::create(params));
-}
-
-//allows automatically register created layer on module load time
-class _LayerStaticRegisterer
-{
-    String type;
-public:
-
-    _LayerStaticRegisterer(const String &layerType, LayerFactory::Constructor layerConstructor)
-    {
-        this->type = layerType;
-        LayerFactory::registerLayer(layerType, layerConstructor);
-    }
-
-    ~_LayerStaticRegisterer()
-    {
-        LayerFactory::unregisterLayer(type);
-    }
-};
-
-} // namespace
-CV__DNN_EXPERIMENTAL_NS_END
-}} // namespace
-
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/dnn/layer.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/dnn/layer.hpp
deleted file mode 100755
index c4712b8..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/dnn/layer.hpp
+++ /dev/null
@@ -1,85 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_DNN_LAYER_HPP
-#define OPENCV_DNN_LAYER_HPP
-#include <opencv2/dnn.hpp>
-
-namespace cv {
-namespace dnn {
-CV__DNN_EXPERIMENTAL_NS_BEGIN
-//! @addtogroup dnn
-//! @{
-//!
-//! @defgroup dnnLayerFactory Utilities for New Layers Registration
-//! @{
-
-/** @brief %Layer factory allows to create instances of registered layers. */
-class CV_EXPORTS LayerFactory
-{
-public:
-
-    //! Each Layer class must provide this function to the factory
-    typedef Ptr<Layer>(*Constructor)(LayerParams &params);
-
-    //! Registers the layer class with typename @p type and specified @p constructor. Thread-safe.
-    static void registerLayer(const String &type, Constructor constructor);
-
-    //! Unregisters registered layer with specified type name. Thread-safe.
-    static void unregisterLayer(const String &type);
-
-    /** @brief Creates instance of registered layer.
-     *  @param type type name of creating layer.
-     *  @param params parameters which will be used for layer initialization.
-     *  @note Thread-safe.
-     */
-    static Ptr<Layer> createLayerInstance(const String &type, LayerParams& params);
-
-private:
-    LayerFactory();
-};
-
-//! @}
-//! @}
-CV__DNN_EXPERIMENTAL_NS_END
-}
-}
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/dnn/shape_utils.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/dnn/shape_utils.hpp
deleted file mode 100755
index 1e2332c..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/dnn/shape_utils.hpp
+++ /dev/null
@@ -1,211 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_DNN_DNN_SHAPE_UTILS_HPP
-#define OPENCV_DNN_DNN_SHAPE_UTILS_HPP
-
-#include <opencv2/core.hpp>
-#include <opencv2/core/types_c.h>
-#include <ostream>
-
-namespace cv {
-namespace dnn {
-CV__DNN_EXPERIMENTAL_NS_BEGIN
-
-//Useful shortcut
-inline std::ostream &operator<< (std::ostream &s, cv::Range &r)
-{
-    return s << "[" << r.start << ", " << r.end << ")";
-}
-
-//Slicing
-
-struct _Range : public cv::Range
-{
-    _Range(const Range &r) : cv::Range(r) {}
-    _Range(int start_, int size_ = 1) : cv::Range(start_, start_ + size_) {}
-};
-
-static inline Mat slice(const Mat &m, const _Range &r0)
-{
-    Range ranges[CV_MAX_DIM];
-    for (int i = 1; i < m.dims; i++)
-        ranges[i] = Range::all();
-    ranges[0] = r0;
-    return m(&ranges[0]);
-}
-
-static inline Mat slice(const Mat &m, const _Range &r0, const _Range &r1)
-{
-    CV_Assert(m.dims >= 2);
-    Range ranges[CV_MAX_DIM];
-    for (int i = 2; i < m.dims; i++)
-        ranges[i] = Range::all();
-    ranges[0] = r0;
-    ranges[1] = r1;
-    return m(&ranges[0]);
-}
-
-static inline Mat slice(const Mat &m, const _Range &r0, const _Range &r1, const _Range &r2)
-{
-    CV_Assert(m.dims >= 3);
-    Range ranges[CV_MAX_DIM];
-    for (int i = 3; i < m.dims; i++)
-        ranges[i] = Range::all();
-    ranges[0] = r0;
-    ranges[1] = r1;
-    ranges[2] = r2;
-    return m(&ranges[0]);
-}
-
-static inline Mat slice(const Mat &m, const _Range &r0, const _Range &r1, const _Range &r2, const _Range &r3)
-{
-    CV_Assert(m.dims >= 4);
-    Range ranges[CV_MAX_DIM];
-    for (int i = 4; i < m.dims; i++)
-        ranges[i] = Range::all();
-    ranges[0] = r0;
-    ranges[1] = r1;
-    ranges[2] = r2;
-    ranges[3] = r3;
-    return m(&ranges[0]);
-}
-
-static inline Mat getPlane(const Mat &m, int n, int cn)
-{
-    CV_Assert(m.dims > 2);
-    int sz[CV_MAX_DIM];
-    for(int i = 2; i < m.dims; i++)
-    {
-        sz[i-2] = m.size.p[i];
-    }
-    return Mat(m.dims - 2, sz, m.type(), (void*)m.ptr<float>(n, cn));
-}
-
-static inline MatShape shape(const int* dims, const int n)
-{
-    MatShape shape;
-    shape.assign(dims, dims + n);
-    return shape;
-}
-
-static inline MatShape shape(const Mat& mat)
-{
-    return shape(mat.size.p, mat.dims);
-}
-
-static inline MatShape shape(const MatSize& sz)
-{
-    return shape(sz.p, sz.dims());
-}
-
-static inline MatShape shape(const UMat& mat)
-{
-    return shape(mat.size.p, mat.dims);
-}
-
-namespace {inline bool is_neg(int i) { return i < 0; }}
-
-static inline MatShape shape(int a0, int a1=-1, int a2=-1, int a3=-1)
-{
-    int dims[] = {a0, a1, a2, a3};
-    MatShape s = shape(dims, 4);
-    s.erase(std::remove_if(s.begin(), s.end(), is_neg), s.end());
-    return s;
-}
-
-static inline int total(const MatShape& shape, int start = -1, int end = -1)
-{
-    if (start == -1) start = 0;
-    if (end == -1) end = (int)shape.size();
-
-    if (shape.empty())
-        return 0;
-
-    int elems = 1;
-    CV_Assert(start <= (int)shape.size() && end <= (int)shape.size() &&
-              start <= end);
-    for(int i = start; i < end; i++)
-    {
-        elems *= shape[i];
-    }
-    return elems;
-}
-
-static inline MatShape concat(const MatShape& a, const MatShape& b)
-{
-    MatShape c = a;
-    c.insert(c.end(), b.begin(), b.end());
-
-    return c;
-}
-
-inline void print(const MatShape& shape, const String& name = "")
-{
-    printf("%s: [", name.c_str());
-    size_t i, n = shape.size();
-    for( i = 0; i < n; i++ )
-        printf(" %d", shape[i]);
-    printf(" ]\n");
-}
-
-inline int clamp(int ax, int dims)
-{
-    return ax < 0 ? ax + dims : ax;
-}
-
-inline int clamp(int ax, const MatShape& shape)
-{
-    return clamp(ax, (int)shape.size());
-}
-
-inline Range clamp(const Range& r, int axisSize)
-{
-    Range clamped(std::max(r.start, 0),
-                  r.end > 0 ? std::min(r.end, axisSize) : axisSize + r.end + 1);
-    CV_Assert(clamped.start < clamped.end, clamped.end <= axisSize);
-    return clamped;
-}
-
-CV__DNN_EXPERIMENTAL_NS_END
-}
-}
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/features2d.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/features2d.hpp
deleted file mode 100755
index 1ba4f0d..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/features2d.hpp
+++ /dev/null
@@ -1,1424 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_FEATURES_2D_HPP
-#define OPENCV_FEATURES_2D_HPP
-
-#include "opencv2/opencv_modules.hpp"
-#include "opencv2/core.hpp"
-
-#ifdef HAVE_OPENCV_FLANN
-#include "opencv2/flann/miniflann.hpp"
-#endif
-
-/**
-  @defgroup features2d 2D Features Framework
-  @{
-    @defgroup features2d_main Feature Detection and Description
-    @defgroup features2d_match Descriptor Matchers
-
-Matchers of keypoint descriptors in OpenCV have wrappers with a common interface that enables you to
-easily switch between different algorithms solving the same problem. This section is devoted to
-matching descriptors that are represented as vectors in a multidimensional space. All objects that
-implement vector descriptor matchers inherit the DescriptorMatcher interface.
-
-@note
-   -   An example explaining keypoint matching can be found at
-        opencv_source_code/samples/cpp/descriptor_extractor_matcher.cpp
-    -   An example on descriptor matching evaluation can be found at
-        opencv_source_code/samples/cpp/detector_descriptor_matcher_evaluation.cpp
-    -   An example on one to many image matching can be found at
-        opencv_source_code/samples/cpp/matching_to_many_images.cpp
-
-    @defgroup features2d_draw Drawing Function of Keypoints and Matches
-    @defgroup features2d_category Object Categorization
-
-This section describes approaches based on local 2D features and used to categorize objects.
-
-@note
-   -   A complete Bag-Of-Words sample can be found at
-        opencv_source_code/samples/cpp/bagofwords_classification.cpp
-    -   (Python) An example using the features2D framework to perform object categorization can be
-        found at opencv_source_code/samples/python/find_obj.py
-
-  @}
- */
-
-namespace cv
-{
-
-//! @addtogroup features2d
-//! @{
-
-// //! writes vector of keypoints to the file storage
-// CV_EXPORTS void write(FileStorage& fs, const String& name, const std::vector<KeyPoint>& keypoints);
-// //! reads vector of keypoints from the specified file storage node
-// CV_EXPORTS void read(const FileNode& node, CV_OUT std::vector<KeyPoint>& keypoints);
-
-/** @brief A class filters a vector of keypoints.
-
- Because now it is difficult to provide a convenient interface for all usage scenarios of the
- keypoints filter class, it has only several needed by now static methods.
- */
-class CV_EXPORTS KeyPointsFilter
-{
-public:
-    KeyPointsFilter(){}
-
-    /*
-     * Remove keypoints within borderPixels of an image edge.
-     */
-    static void runByImageBorder( std::vector<KeyPoint>& keypoints, Size imageSize, int borderSize );
-    /*
-     * Remove keypoints of sizes out of range.
-     */
-    static void runByKeypointSize( std::vector<KeyPoint>& keypoints, float minSize,
-                                   float maxSize=FLT_MAX );
-    /*
-     * Remove keypoints from some image by mask for pixels of this image.
-     */
-    static void runByPixelsMask( std::vector<KeyPoint>& keypoints, const Mat& mask );
-    /*
-     * Remove duplicated keypoints.
-     */
-    static void removeDuplicated( std::vector<KeyPoint>& keypoints );
-    /*
-     * Remove duplicated keypoints and sort the remaining keypoints
-     */
-    static void removeDuplicatedSorted( std::vector<KeyPoint>& keypoints );
-
-    /*
-     * Retain the specified number of the best keypoints (according to the response)
-     */
-    static void retainBest( std::vector<KeyPoint>& keypoints, int npoints );
-};
-
-
-/************************************ Base Classes ************************************/
-
-/** @brief Abstract base class for 2D image feature detectors and descriptor extractors
-*/
-class CV_EXPORTS_W Feature2D : public virtual Algorithm
-{
-public:
-    virtual ~Feature2D();
-
-    /** @brief Detects keypoints in an image (first variant) or image set (second variant).
-
-    @param image Image.
-    @param keypoints The detected keypoints. In the second variant of the method keypoints[i] is a set
-    of keypoints detected in images[i] .
-    @param mask Mask specifying where to look for keypoints (optional). It must be a 8-bit integer
-    matrix with non-zero values in the region of interest.
-     */
-    CV_WRAP virtual void detect( InputArray image,
-                                 CV_OUT std::vector<KeyPoint>& keypoints,
-                                 InputArray mask=noArray() );
-
-    /** @overload
-    @param images Image set.
-    @param keypoints The detected keypoints. In the second variant of the method keypoints[i] is a set
-    of keypoints detected in images[i] .
-    @param masks Masks for each input image specifying where to look for keypoints (optional).
-    masks[i] is a mask for images[i].
-    */
-    CV_WRAP virtual void detect( InputArrayOfArrays images,
-                         CV_OUT std::vector<std::vector<KeyPoint> >& keypoints,
-                         InputArrayOfArrays masks=noArray() );
-
-    /** @brief Computes the descriptors for a set of keypoints detected in an image (first variant) or image set
-    (second variant).
-
-    @param image Image.
-    @param keypoints Input collection of keypoints. Keypoints for which a descriptor cannot be
-    computed are removed. Sometimes new keypoints can be added, for example: SIFT duplicates keypoint
-    with several dominant orientations (for each orientation).
-    @param descriptors Computed descriptors. In the second variant of the method descriptors[i] are
-    descriptors computed for a keypoints[i]. Row j is the keypoints (or keypoints[i]) is the
-    descriptor for keypoint j-th keypoint.
-     */
-    CV_WRAP virtual void compute( InputArray image,
-                                  CV_OUT CV_IN_OUT std::vector<KeyPoint>& keypoints,
-                                  OutputArray descriptors );
-
-    /** @overload
-
-    @param images Image set.
-    @param keypoints Input collection of keypoints. Keypoints for which a descriptor cannot be
-    computed are removed. Sometimes new keypoints can be added, for example: SIFT duplicates keypoint
-    with several dominant orientations (for each orientation).
-    @param descriptors Computed descriptors. In the second variant of the method descriptors[i] are
-    descriptors computed for a keypoints[i]. Row j is the keypoints (or keypoints[i]) is the
-    descriptor for keypoint j-th keypoint.
-    */
-    CV_WRAP virtual void compute( InputArrayOfArrays images,
-                          CV_OUT CV_IN_OUT std::vector<std::vector<KeyPoint> >& keypoints,
-                          OutputArrayOfArrays descriptors );
-
-    /** Detects keypoints and computes the descriptors */
-    CV_WRAP virtual void detectAndCompute( InputArray image, InputArray mask,
-                                           CV_OUT std::vector<KeyPoint>& keypoints,
-                                           OutputArray descriptors,
-                                           bool useProvidedKeypoints=false );
-
-    CV_WRAP virtual int descriptorSize() const;
-    CV_WRAP virtual int descriptorType() const;
-    CV_WRAP virtual int defaultNorm() const;
-
-    CV_WRAP void write( const String& fileName ) const;
-
-    CV_WRAP void read( const String& fileName );
-
-    virtual void write( FileStorage&) const CV_OVERRIDE;
-
-    // see corresponding cv::Algorithm method
-    CV_WRAP virtual void read( const FileNode&) CV_OVERRIDE;
-
-    //! Return true if detector object is empty
-    CV_WRAP virtual bool empty() const CV_OVERRIDE;
-    CV_WRAP virtual String getDefaultName() const CV_OVERRIDE;
-
-    // see corresponding cv::Algorithm method
-    CV_WRAP inline void write(const Ptr<FileStorage>& fs, const String& name = String()) const { Algorithm::write(fs, name); }
-};
-
-/** Feature detectors in OpenCV have wrappers with a common interface that enables you to easily switch
-between different algorithms solving the same problem. All objects that implement keypoint detectors
-inherit the FeatureDetector interface. */
-typedef Feature2D FeatureDetector;
-
-/** Extractors of keypoint descriptors in OpenCV have wrappers with a common interface that enables you
-to easily switch between different algorithms solving the same problem. This section is devoted to
-computing descriptors represented as vectors in a multidimensional space. All objects that implement
-the vector descriptor extractors inherit the DescriptorExtractor interface.
- */
-typedef Feature2D DescriptorExtractor;
-
-//! @addtogroup features2d_main
-//! @{
-
-/** @brief Class implementing the BRISK keypoint detector and descriptor extractor, described in @cite LCS11 .
- */
-class CV_EXPORTS_W BRISK : public Feature2D
-{
-public:
-    /** @brief The BRISK constructor
-
-    @param thresh AGAST detection threshold score.
-    @param octaves detection octaves. Use 0 to do single scale.
-    @param patternScale apply this scale to the pattern used for sampling the neighbourhood of a
-    keypoint.
-     */
-    CV_WRAP static Ptr<BRISK> create(int thresh=30, int octaves=3, float patternScale=1.0f);
-
-    /** @brief The BRISK constructor for a custom pattern
-
-    @param radiusList defines the radii (in pixels) where the samples around a keypoint are taken (for
-    keypoint scale 1).
-    @param numberList defines the number of sampling points on the sampling circle. Must be the same
-    size as radiusList..
-    @param dMax threshold for the short pairings used for descriptor formation (in pixels for keypoint
-    scale 1).
-    @param dMin threshold for the long pairings used for orientation determination (in pixels for
-    keypoint scale 1).
-    @param indexChange index remapping of the bits. */
-    CV_WRAP static Ptr<BRISK> create(const std::vector<float> &radiusList, const std::vector<int> &numberList,
-        float dMax=5.85f, float dMin=8.2f, const std::vector<int>& indexChange=std::vector<int>());
-
-    /** @brief The BRISK constructor for a custom pattern, detection threshold and octaves
-
-    @param thresh AGAST detection threshold score.
-    @param octaves detection octaves. Use 0 to do single scale.
-    @param radiusList defines the radii (in pixels) where the samples around a keypoint are taken (for
-    keypoint scale 1).
-    @param numberList defines the number of sampling points on the sampling circle. Must be the same
-    size as radiusList..
-    @param dMax threshold for the short pairings used for descriptor formation (in pixels for keypoint
-    scale 1).
-    @param dMin threshold for the long pairings used for orientation determination (in pixels for
-    keypoint scale 1).
-    @param indexChange index remapping of the bits. */
-    CV_WRAP static Ptr<BRISK> create(int thresh, int octaves, const std::vector<float> &radiusList,
-        const std::vector<int> &numberList, float dMax=5.85f, float dMin=8.2f,
-        const std::vector<int>& indexChange=std::vector<int>());
-    CV_WRAP virtual String getDefaultName() const CV_OVERRIDE;
-};
-
-/** @brief Class implementing the ORB (*oriented BRIEF*) keypoint detector and descriptor extractor
-
-described in @cite RRKB11 . The algorithm uses FAST in pyramids to detect stable keypoints, selects
-the strongest features using FAST or Harris response, finds their orientation using first-order
-moments and computes the descriptors using BRIEF (where the coordinates of random point pairs (or
-k-tuples) are rotated according to the measured orientation).
- */
-class CV_EXPORTS_W ORB : public Feature2D
-{
-public:
-    enum { kBytes = 32, HARRIS_SCORE=0, FAST_SCORE=1 };
-
-    /** @brief The ORB constructor
-
-    @param nfeatures The maximum number of features to retain.
-    @param scaleFactor Pyramid decimation ratio, greater than 1. scaleFactor==2 means the classical
-    pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor
-    will degrade feature matching scores dramatically. On the other hand, too close to 1 scale factor
-    will mean that to cover certain scale range you will need more pyramid levels and so the speed
-    will suffer.
-    @param nlevels The number of pyramid levels. The smallest level will have linear size equal to
-    input_image_linear_size/pow(scaleFactor, nlevels - firstLevel).
-    @param edgeThreshold This is size of the border where the features are not detected. It should
-    roughly match the patchSize parameter.
-    @param firstLevel The level of pyramid to put source image to. Previous layers are filled
-    with upscaled source image.
-    @param WTA_K The number of points that produce each element of the oriented BRIEF descriptor. The
-    default value 2 means the BRIEF where we take a random point pair and compare their brightnesses,
-    so we get 0/1 response. Other possible values are 3 and 4. For example, 3 means that we take 3
-    random points (of course, those point coordinates are random, but they are generated from the
-    pre-defined seed, so each element of BRIEF descriptor is computed deterministically from the pixel
-    rectangle), find point of maximum brightness and output index of the winner (0, 1 or 2). Such
-    output will occupy 2 bits, and therefore it will need a special variant of Hamming distance,
-    denoted as NORM_HAMMING2 (2 bits per bin). When WTA_K=4, we take 4 random points to compute each
-    bin (that will also occupy 2 bits with possible values 0, 1, 2 or 3).
-    @param scoreType The default HARRIS_SCORE means that Harris algorithm is used to rank features
-    (the score is written to KeyPoint::score and is used to retain best nfeatures features);
-    FAST_SCORE is alternative value of the parameter that produces slightly less stable keypoints,
-    but it is a little faster to compute.
-    @param patchSize size of the patch used by the oriented BRIEF descriptor. Of course, on smaller
-    pyramid layers the perceived image area covered by a feature will be larger.
-    @param fastThreshold
-     */
-    CV_WRAP static Ptr<ORB> create(int nfeatures=500, float scaleFactor=1.2f, int nlevels=8, int edgeThreshold=31,
-        int firstLevel=0, int WTA_K=2, int scoreType=ORB::HARRIS_SCORE, int patchSize=31, int fastThreshold=20);
-
-    CV_WRAP virtual void setMaxFeatures(int maxFeatures) = 0;
-    CV_WRAP virtual int getMaxFeatures() const = 0;
-
-    CV_WRAP virtual void setScaleFactor(double scaleFactor) = 0;
-    CV_WRAP virtual double getScaleFactor() const = 0;
-
-    CV_WRAP virtual void setNLevels(int nlevels) = 0;
-    CV_WRAP virtual int getNLevels() const = 0;
-
-    CV_WRAP virtual void setEdgeThreshold(int edgeThreshold) = 0;
-    CV_WRAP virtual int getEdgeThreshold() const = 0;
-
-    CV_WRAP virtual void setFirstLevel(int firstLevel) = 0;
-    CV_WRAP virtual int getFirstLevel() const = 0;
-
-    CV_WRAP virtual void setWTA_K(int wta_k) = 0;
-    CV_WRAP virtual int getWTA_K() const = 0;
-
-    CV_WRAP virtual void setScoreType(int scoreType) = 0;
-    CV_WRAP virtual int getScoreType() const = 0;
-
-    CV_WRAP virtual void setPatchSize(int patchSize) = 0;
-    CV_WRAP virtual int getPatchSize() const = 0;
-
-    CV_WRAP virtual void setFastThreshold(int fastThreshold) = 0;
-    CV_WRAP virtual int getFastThreshold() const = 0;
-    CV_WRAP virtual String getDefaultName() const CV_OVERRIDE;
-};
-
-/** @brief Maximally stable extremal region extractor
-
-The class encapsulates all the parameters of the %MSER extraction algorithm (see [wiki
-article](http://en.wikipedia.org/wiki/Maximally_stable_extremal_regions)).
-
-- there are two different implementation of %MSER: one for grey image, one for color image
-
-- the grey image algorithm is taken from: @cite nister2008linear ;  the paper claims to be faster
-than union-find method; it actually get 1.5~2m/s on my centrino L7200 1.2GHz laptop.
-
-- the color image algorithm is taken from: @cite forssen2007maximally ; it should be much slower
-than grey image method ( 3~4 times ); the chi_table.h file is taken directly from paper's source
-code which is distributed under GPL.
-
-- (Python) A complete example showing the use of the %MSER detector can be found at samples/python/mser.py
-*/
-class CV_EXPORTS_W MSER : public Feature2D
-{
-public:
-    /** @brief Full consturctor for %MSER detector
-
-    @param _delta it compares \f$(size_{i}-size_{i-delta})/size_{i-delta}\f$
-    @param _min_area prune the area which smaller than minArea
-    @param _max_area prune the area which bigger than maxArea
-    @param _max_variation prune the area have similar size to its children
-    @param _min_diversity for color image, trace back to cut off mser with diversity less than min_diversity
-    @param _max_evolution  for color image, the evolution steps
-    @param _area_threshold for color image, the area threshold to cause re-initialize
-    @param _min_margin for color image, ignore too small margin
-    @param _edge_blur_size for color image, the aperture size for edge blur
-     */
-    CV_WRAP static Ptr<MSER> create( int _delta=5, int _min_area=60, int _max_area=14400,
-          double _max_variation=0.25, double _min_diversity=.2,
-          int _max_evolution=200, double _area_threshold=1.01,
-          double _min_margin=0.003, int _edge_blur_size=5 );
-
-    /** @brief Detect %MSER regions
-
-    @param image input image (8UC1, 8UC3 or 8UC4, must be greater or equal than 3x3)
-    @param msers resulting list of point sets
-    @param bboxes resulting bounding boxes
-    */
-    CV_WRAP virtual void detectRegions( InputArray image,
-                                        CV_OUT std::vector<std::vector<Point> >& msers,
-                                        CV_OUT std::vector<Rect>& bboxes ) = 0;
-
-    CV_WRAP virtual void setDelta(int delta) = 0;
-    CV_WRAP virtual int getDelta() const = 0;
-
-    CV_WRAP virtual void setMinArea(int minArea) = 0;
-    CV_WRAP virtual int getMinArea() const = 0;
-
-    CV_WRAP virtual void setMaxArea(int maxArea) = 0;
-    CV_WRAP virtual int getMaxArea() const = 0;
-
-    CV_WRAP virtual void setPass2Only(bool f) = 0;
-    CV_WRAP virtual bool getPass2Only() const = 0;
-    CV_WRAP virtual String getDefaultName() const CV_OVERRIDE;
-};
-
-/** @overload */
-CV_EXPORTS void FAST( InputArray image, CV_OUT std::vector<KeyPoint>& keypoints,
-                      int threshold, bool nonmaxSuppression=true );
-
-/** @brief Detects corners using the FAST algorithm
-
-@param image grayscale image where keypoints (corners) are detected.
-@param keypoints keypoints detected on the image.
-@param threshold threshold on difference between intensity of the central pixel and pixels of a
-circle around this pixel.
-@param nonmaxSuppression if true, non-maximum suppression is applied to detected corners
-(keypoints).
-@param type one of the three neighborhoods as defined in the paper:
-FastFeatureDetector::TYPE_9_16, FastFeatureDetector::TYPE_7_12,
-FastFeatureDetector::TYPE_5_8
-
-Detects corners using the FAST algorithm by @cite Rosten06 .
-
-@note In Python API, types are given as cv2.FAST_FEATURE_DETECTOR_TYPE_5_8,
-cv2.FAST_FEATURE_DETECTOR_TYPE_7_12 and cv2.FAST_FEATURE_DETECTOR_TYPE_9_16. For corner
-detection, use cv2.FAST.detect() method.
- */
-CV_EXPORTS void FAST( InputArray image, CV_OUT std::vector<KeyPoint>& keypoints,
-                      int threshold, bool nonmaxSuppression, int type );
-
-//! @} features2d_main
-
-//! @addtogroup features2d_main
-//! @{
-
-/** @brief Wrapping class for feature detection using the FAST method. :
- */
-class CV_EXPORTS_W FastFeatureDetector : public Feature2D
-{
-public:
-    enum
-    {
-        TYPE_5_8 = 0, TYPE_7_12 = 1, TYPE_9_16 = 2,
-        THRESHOLD = 10000, NONMAX_SUPPRESSION=10001, FAST_N=10002,
-    };
-
-    CV_WRAP static Ptr<FastFeatureDetector> create( int threshold=10,
-                                                    bool nonmaxSuppression=true,
-                                                    int type=FastFeatureDetector::TYPE_9_16 );
-
-    CV_WRAP virtual void setThreshold(int threshold) = 0;
-    CV_WRAP virtual int getThreshold() const = 0;
-
-    CV_WRAP virtual void setNonmaxSuppression(bool f) = 0;
-    CV_WRAP virtual bool getNonmaxSuppression() const = 0;
-
-    CV_WRAP virtual void setType(int type) = 0;
-    CV_WRAP virtual int getType() const = 0;
-    CV_WRAP virtual String getDefaultName() const CV_OVERRIDE;
-};
-
-/** @overload */
-CV_EXPORTS void AGAST( InputArray image, CV_OUT std::vector<KeyPoint>& keypoints,
-                      int threshold, bool nonmaxSuppression=true );
-
-/** @brief Detects corners using the AGAST algorithm
-
-@param image grayscale image where keypoints (corners) are detected.
-@param keypoints keypoints detected on the image.
-@param threshold threshold on difference between intensity of the central pixel and pixels of a
-circle around this pixel.
-@param nonmaxSuppression if true, non-maximum suppression is applied to detected corners
-(keypoints).
-@param type one of the four neighborhoods as defined in the paper:
-AgastFeatureDetector::AGAST_5_8, AgastFeatureDetector::AGAST_7_12d,
-AgastFeatureDetector::AGAST_7_12s, AgastFeatureDetector::OAST_9_16
-
-For non-Intel platforms, there is a tree optimised variant of AGAST with same numerical results.
-The 32-bit binary tree tables were generated automatically from original code using perl script.
-The perl script and examples of tree generation are placed in features2d/doc folder.
-Detects corners using the AGAST algorithm by @cite mair2010_agast .
-
- */
-CV_EXPORTS void AGAST( InputArray image, CV_OUT std::vector<KeyPoint>& keypoints,
-                      int threshold, bool nonmaxSuppression, int type );
-//! @} features2d_main
-
-//! @addtogroup features2d_main
-//! @{
-
-/** @brief Wrapping class for feature detection using the AGAST method. :
- */
-class CV_EXPORTS_W AgastFeatureDetector : public Feature2D
-{
-public:
-    enum
-    {
-        AGAST_5_8 = 0, AGAST_7_12d = 1, AGAST_7_12s = 2, OAST_9_16 = 3,
-        THRESHOLD = 10000, NONMAX_SUPPRESSION = 10001,
-    };
-
-    CV_WRAP static Ptr<AgastFeatureDetector> create( int threshold=10,
-                                                     bool nonmaxSuppression=true,
-                                                     int type=AgastFeatureDetector::OAST_9_16 );
-
-    CV_WRAP virtual void setThreshold(int threshold) = 0;
-    CV_WRAP virtual int getThreshold() const = 0;
-
-    CV_WRAP virtual void setNonmaxSuppression(bool f) = 0;
-    CV_WRAP virtual bool getNonmaxSuppression() const = 0;
-
-    CV_WRAP virtual void setType(int type) = 0;
-    CV_WRAP virtual int getType() const = 0;
-    CV_WRAP virtual String getDefaultName() const CV_OVERRIDE;
-};
-
-/** @brief Wrapping class for feature detection using the goodFeaturesToTrack function. :
- */
-class CV_EXPORTS_W GFTTDetector : public Feature2D
-{
-public:
-    CV_WRAP static Ptr<GFTTDetector> create( int maxCorners=1000, double qualityLevel=0.01, double minDistance=1,
-                                             int blockSize=3, bool useHarrisDetector=false, double k=0.04 );
-    CV_WRAP static Ptr<GFTTDetector> create( int maxCorners, double qualityLevel, double minDistance,
-                                             int blockSize, int gradiantSize, bool useHarrisDetector=false, double k=0.04 );
-    CV_WRAP virtual void setMaxFeatures(int maxFeatures) = 0;
-    CV_WRAP virtual int getMaxFeatures() const = 0;
-
-    CV_WRAP virtual void setQualityLevel(double qlevel) = 0;
-    CV_WRAP virtual double getQualityLevel() const = 0;
-
-    CV_WRAP virtual void setMinDistance(double minDistance) = 0;
-    CV_WRAP virtual double getMinDistance() const = 0;
-
-    CV_WRAP virtual void setBlockSize(int blockSize) = 0;
-    CV_WRAP virtual int getBlockSize() const = 0;
-
-    CV_WRAP virtual void setHarrisDetector(bool val) = 0;
-    CV_WRAP virtual bool getHarrisDetector() const = 0;
-
-    CV_WRAP virtual void setK(double k) = 0;
-    CV_WRAP virtual double getK() const = 0;
-    CV_WRAP virtual String getDefaultName() const CV_OVERRIDE;
-};
-
-/** @brief Class for extracting blobs from an image. :
-
-The class implements a simple algorithm for extracting blobs from an image:
-
-1.  Convert the source image to binary images by applying thresholding with several thresholds from
-    minThreshold (inclusive) to maxThreshold (exclusive) with distance thresholdStep between
-    neighboring thresholds.
-2.  Extract connected components from every binary image by findContours and calculate their
-    centers.
-3.  Group centers from several binary images by their coordinates. Close centers form one group that
-    corresponds to one blob, which is controlled by the minDistBetweenBlobs parameter.
-4.  From the groups, estimate final centers of blobs and their radiuses and return as locations and
-    sizes of keypoints.
-
-This class performs several filtrations of returned blobs. You should set filterBy\* to true/false
-to turn on/off corresponding filtration. Available filtrations:
-
--   **By color**. This filter compares the intensity of a binary image at the center of a blob to
-blobColor. If they differ, the blob is filtered out. Use blobColor = 0 to extract dark blobs
-and blobColor = 255 to extract light blobs.
--   **By area**. Extracted blobs have an area between minArea (inclusive) and maxArea (exclusive).
--   **By circularity**. Extracted blobs have circularity
-(\f$\frac{4*\pi*Area}{perimeter * perimeter}\f$) between minCircularity (inclusive) and
-maxCircularity (exclusive).
--   **By ratio of the minimum inertia to maximum inertia**. Extracted blobs have this ratio
-between minInertiaRatio (inclusive) and maxInertiaRatio (exclusive).
--   **By convexity**. Extracted blobs have convexity (area / area of blob convex hull) between
-minConvexity (inclusive) and maxConvexity (exclusive).
-
-Default values of parameters are tuned to extract dark circular blobs.
- */
-class CV_EXPORTS_W SimpleBlobDetector : public Feature2D
-{
-public:
-  struct CV_EXPORTS_W_SIMPLE Params
-  {
-      CV_WRAP Params();
-      CV_PROP_RW float thresholdStep;
-      CV_PROP_RW float minThreshold;
-      CV_PROP_RW float maxThreshold;
-      CV_PROP_RW size_t minRepeatability;
-      CV_PROP_RW float minDistBetweenBlobs;
-
-      CV_PROP_RW bool filterByColor;
-      CV_PROP_RW uchar blobColor;
-
-      CV_PROP_RW bool filterByArea;
-      CV_PROP_RW float minArea, maxArea;
-
-      CV_PROP_RW bool filterByCircularity;
-      CV_PROP_RW float minCircularity, maxCircularity;
-
-      CV_PROP_RW bool filterByInertia;
-      CV_PROP_RW float minInertiaRatio, maxInertiaRatio;
-
-      CV_PROP_RW bool filterByConvexity;
-      CV_PROP_RW float minConvexity, maxConvexity;
-
-      void read( const FileNode& fn );
-      void write( FileStorage& fs ) const;
-  };
-
-  CV_WRAP static Ptr<SimpleBlobDetector>
-    create(const SimpleBlobDetector::Params &parameters = SimpleBlobDetector::Params());
-  CV_WRAP virtual String getDefaultName() const CV_OVERRIDE;
-};
-
-//! @} features2d_main
-
-//! @addtogroup features2d_main
-//! @{
-
-/** @brief Class implementing the KAZE keypoint detector and descriptor extractor, described in @cite ABD12 .
-
-@note AKAZE descriptor can only be used with KAZE or AKAZE keypoints .. [ABD12] KAZE Features. Pablo
-F. Alcantarilla, Adrien Bartoli and Andrew J. Davison. In European Conference on Computer Vision
-(ECCV), Fiorenze, Italy, October 2012.
-*/
-class CV_EXPORTS_W KAZE : public Feature2D
-{
-public:
-    enum
-    {
-        DIFF_PM_G1 = 0,
-        DIFF_PM_G2 = 1,
-        DIFF_WEICKERT = 2,
-        DIFF_CHARBONNIER = 3
-    };
-
-    /** @brief The KAZE constructor
-
-    @param extended Set to enable extraction of extended (128-byte) descriptor.
-    @param upright Set to enable use of upright descriptors (non rotation-invariant).
-    @param threshold Detector response threshold to accept point
-    @param nOctaves Maximum octave evolution of the image
-    @param nOctaveLayers Default number of sublevels per scale level
-    @param diffusivity Diffusivity type. DIFF_PM_G1, DIFF_PM_G2, DIFF_WEICKERT or
-    DIFF_CHARBONNIER
-     */
-    CV_WRAP static Ptr<KAZE> create(bool extended=false, bool upright=false,
-                                    float threshold = 0.001f,
-                                    int nOctaves = 4, int nOctaveLayers = 4,
-                                    int diffusivity = KAZE::DIFF_PM_G2);
-
-    CV_WRAP virtual void setExtended(bool extended) = 0;
-    CV_WRAP virtual bool getExtended() const = 0;
-
-    CV_WRAP virtual void setUpright(bool upright) = 0;
-    CV_WRAP virtual bool getUpright() const = 0;
-
-    CV_WRAP virtual void setThreshold(double threshold) = 0;
-    CV_WRAP virtual double getThreshold() const = 0;
-
-    CV_WRAP virtual void setNOctaves(int octaves) = 0;
-    CV_WRAP virtual int getNOctaves() const = 0;
-
-    CV_WRAP virtual void setNOctaveLayers(int octaveLayers) = 0;
-    CV_WRAP virtual int getNOctaveLayers() const = 0;
-
-    CV_WRAP virtual void setDiffusivity(int diff) = 0;
-    CV_WRAP virtual int getDiffusivity() const = 0;
-    CV_WRAP virtual String getDefaultName() const CV_OVERRIDE;
-};
-
-/** @brief Class implementing the AKAZE keypoint detector and descriptor extractor, described in @cite ANB13.
-
-@details AKAZE descriptors can only be used with KAZE or AKAZE keypoints. This class is thread-safe.
-
-@note When you need descriptors use Feature2D::detectAndCompute, which
-provides better performance. When using Feature2D::detect followed by
-Feature2D::compute scale space pyramid is computed twice.
-
-@note AKAZE implements T-API. When image is passed as UMat some parts of the algorithm
-will use OpenCL.
-
-@note [ANB13] Fast Explicit Diffusion for Accelerated Features in Nonlinear
-Scale Spaces. Pablo F. Alcantarilla, Jesús Nuevo and Adrien Bartoli. In
-British Machine Vision Conference (BMVC), Bristol, UK, September 2013.
-
-*/
-class CV_EXPORTS_W AKAZE : public Feature2D
-{
-public:
-    // AKAZE descriptor type
-    enum
-    {
-        DESCRIPTOR_KAZE_UPRIGHT = 2, ///< Upright descriptors, not invariant to rotation
-        DESCRIPTOR_KAZE = 3,
-        DESCRIPTOR_MLDB_UPRIGHT = 4, ///< Upright descriptors, not invariant to rotation
-        DESCRIPTOR_MLDB = 5
-    };
-
-    /** @brief The AKAZE constructor
-
-    @param descriptor_type Type of the extracted descriptor: DESCRIPTOR_KAZE,
-    DESCRIPTOR_KAZE_UPRIGHT, DESCRIPTOR_MLDB or DESCRIPTOR_MLDB_UPRIGHT.
-    @param descriptor_size Size of the descriptor in bits. 0 -\> Full size
-    @param descriptor_channels Number of channels in the descriptor (1, 2, 3)
-    @param threshold Detector response threshold to accept point
-    @param nOctaves Maximum octave evolution of the image
-    @param nOctaveLayers Default number of sublevels per scale level
-    @param diffusivity Diffusivity type. DIFF_PM_G1, DIFF_PM_G2, DIFF_WEICKERT or
-    DIFF_CHARBONNIER
-     */
-    CV_WRAP static Ptr<AKAZE> create(int descriptor_type=AKAZE::DESCRIPTOR_MLDB,
-                                     int descriptor_size = 0, int descriptor_channels = 3,
-                                     float threshold = 0.001f, int nOctaves = 4,
-                                     int nOctaveLayers = 4, int diffusivity = KAZE::DIFF_PM_G2);
-
-    CV_WRAP virtual void setDescriptorType(int dtype) = 0;
-    CV_WRAP virtual int getDescriptorType() const = 0;
-
-    CV_WRAP virtual void setDescriptorSize(int dsize) = 0;
-    CV_WRAP virtual int getDescriptorSize() const = 0;
-
-    CV_WRAP virtual void setDescriptorChannels(int dch) = 0;
-    CV_WRAP virtual int getDescriptorChannels() const = 0;
-
-    CV_WRAP virtual void setThreshold(double threshold) = 0;
-    CV_WRAP virtual double getThreshold() const = 0;
-
-    CV_WRAP virtual void setNOctaves(int octaves) = 0;
-    CV_WRAP virtual int getNOctaves() const = 0;
-
-    CV_WRAP virtual void setNOctaveLayers(int octaveLayers) = 0;
-    CV_WRAP virtual int getNOctaveLayers() const = 0;
-
-    CV_WRAP virtual void setDiffusivity(int diff) = 0;
-    CV_WRAP virtual int getDiffusivity() const = 0;
-    CV_WRAP virtual String getDefaultName() const CV_OVERRIDE;
-};
-
-//! @} features2d_main
-
-/****************************************************************************************\
-*                                      Distance                                          *
-\****************************************************************************************/
-
-template<typename T>
-struct CV_EXPORTS Accumulator
-{
-    typedef T Type;
-};
-
-template<> struct Accumulator<unsigned char>  { typedef float Type; };
-template<> struct Accumulator<unsigned short> { typedef float Type; };
-template<> struct Accumulator<char>   { typedef float Type; };
-template<> struct Accumulator<short>  { typedef float Type; };
-
-/*
- * Squared Euclidean distance functor
- */
-template<class T>
-struct CV_EXPORTS SL2
-{
-    enum { normType = NORM_L2SQR };
-    typedef T ValueType;
-    typedef typename Accumulator<T>::Type ResultType;
-
-    ResultType operator()( const T* a, const T* b, int size ) const
-    {
-        return normL2Sqr<ValueType, ResultType>(a, b, size);
-    }
-};
-
-/*
- * Euclidean distance functor
- */
-template<class T>
-struct L2
-{
-    enum { normType = NORM_L2 };
-    typedef T ValueType;
-    typedef typename Accumulator<T>::Type ResultType;
-
-    ResultType operator()( const T* a, const T* b, int size ) const
-    {
-        return (ResultType)std::sqrt((double)normL2Sqr<ValueType, ResultType>(a, b, size));
-    }
-};
-
-/*
- * Manhattan distance (city block distance) functor
- */
-template<class T>
-struct L1
-{
-    enum { normType = NORM_L1 };
-    typedef T ValueType;
-    typedef typename Accumulator<T>::Type ResultType;
-
-    ResultType operator()( const T* a, const T* b, int size ) const
-    {
-        return normL1<ValueType, ResultType>(a, b, size);
-    }
-};
-
-/****************************************************************************************\
-*                                  DescriptorMatcher                                     *
-\****************************************************************************************/
-
-//! @addtogroup features2d_match
-//! @{
-
-/** @brief Abstract base class for matching keypoint descriptors.
-
-It has two groups of match methods: for matching descriptors of an image with another image or with
-an image set.
- */
-class CV_EXPORTS_W DescriptorMatcher : public Algorithm
-{
-public:
-   enum
-    {
-        FLANNBASED            = 1,
-        BRUTEFORCE            = 2,
-        BRUTEFORCE_L1         = 3,
-        BRUTEFORCE_HAMMING    = 4,
-        BRUTEFORCE_HAMMINGLUT = 5,
-        BRUTEFORCE_SL2        = 6
-    };
-    virtual ~DescriptorMatcher();
-
-    /** @brief Adds descriptors to train a CPU(trainDescCollectionis) or GPU(utrainDescCollectionis) descriptor
-    collection.
-
-    If the collection is not empty, the new descriptors are added to existing train descriptors.
-
-    @param descriptors Descriptors to add. Each descriptors[i] is a set of descriptors from the same
-    train image.
-     */
-    CV_WRAP virtual void add( InputArrayOfArrays descriptors );
-
-    /** @brief Returns a constant link to the train descriptor collection trainDescCollection .
-     */
-    CV_WRAP const std::vector<Mat>& getTrainDescriptors() const;
-
-    /** @brief Clears the train descriptor collections.
-     */
-    CV_WRAP virtual void clear() CV_OVERRIDE;
-
-    /** @brief Returns true if there are no train descriptors in the both collections.
-     */
-    CV_WRAP virtual bool empty() const CV_OVERRIDE;
-
-    /** @brief Returns true if the descriptor matcher supports masking permissible matches.
-     */
-    CV_WRAP virtual bool isMaskSupported() const = 0;
-
-    /** @brief Trains a descriptor matcher
-
-    Trains a descriptor matcher (for example, the flann index). In all methods to match, the method
-    train() is run every time before matching. Some descriptor matchers (for example, BruteForceMatcher)
-    have an empty implementation of this method. Other matchers really train their inner structures (for
-    example, FlannBasedMatcher trains flann::Index ).
-     */
-    CV_WRAP virtual void train();
-
-    /** @brief Finds the best match for each descriptor from a query set.
-
-    @param queryDescriptors Query set of descriptors.
-    @param trainDescriptors Train set of descriptors. This set is not added to the train descriptors
-    collection stored in the class object.
-    @param matches Matches. If a query descriptor is masked out in mask , no match is added for this
-    descriptor. So, matches size may be smaller than the query descriptors count.
-    @param mask Mask specifying permissible matches between an input query and train matrices of
-    descriptors.
-
-    In the first variant of this method, the train descriptors are passed as an input argument. In the
-    second variant of the method, train descriptors collection that was set by DescriptorMatcher::add is
-    used. Optional mask (or masks) can be passed to specify which query and training descriptors can be
-    matched. Namely, queryDescriptors[i] can be matched with trainDescriptors[j] only if
-    mask.at\<uchar\>(i,j) is non-zero.
-     */
-    CV_WRAP void match( InputArray queryDescriptors, InputArray trainDescriptors,
-                CV_OUT std::vector<DMatch>& matches, InputArray mask=noArray() ) const;
-
-    /** @brief Finds the k best matches for each descriptor from a query set.
-
-    @param queryDescriptors Query set of descriptors.
-    @param trainDescriptors Train set of descriptors. This set is not added to the train descriptors
-    collection stored in the class object.
-    @param mask Mask specifying permissible matches between an input query and train matrices of
-    descriptors.
-    @param matches Matches. Each matches[i] is k or less matches for the same query descriptor.
-    @param k Count of best matches found per each query descriptor or less if a query descriptor has
-    less than k possible matches in total.
-    @param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is
-    false, the matches vector has the same size as queryDescriptors rows. If compactResult is true,
-    the matches vector does not contain matches for fully masked-out query descriptors.
-
-    These extended variants of DescriptorMatcher::match methods find several best matches for each query
-    descriptor. The matches are returned in the distance increasing order. See DescriptorMatcher::match
-    for the details about query and train descriptors.
-     */
-    CV_WRAP void knnMatch( InputArray queryDescriptors, InputArray trainDescriptors,
-                   CV_OUT std::vector<std::vector<DMatch> >& matches, int k,
-                   InputArray mask=noArray(), bool compactResult=false ) const;
-
-    /** @brief For each query descriptor, finds the training descriptors not farther than the specified distance.
-
-    @param queryDescriptors Query set of descriptors.
-    @param trainDescriptors Train set of descriptors. This set is not added to the train descriptors
-    collection stored in the class object.
-    @param matches Found matches.
-    @param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is
-    false, the matches vector has the same size as queryDescriptors rows. If compactResult is true,
-    the matches vector does not contain matches for fully masked-out query descriptors.
-    @param maxDistance Threshold for the distance between matched descriptors. Distance means here
-    metric distance (e.g. Hamming distance), not the distance between coordinates (which is measured
-    in Pixels)!
-    @param mask Mask specifying permissible matches between an input query and train matrices of
-    descriptors.
-
-    For each query descriptor, the methods find such training descriptors that the distance between the
-    query descriptor and the training descriptor is equal or smaller than maxDistance. Found matches are
-    returned in the distance increasing order.
-     */
-    CV_WRAP void radiusMatch( InputArray queryDescriptors, InputArray trainDescriptors,
-                      CV_OUT std::vector<std::vector<DMatch> >& matches, float maxDistance,
-                      InputArray mask=noArray(), bool compactResult=false ) const;
-
-    /** @overload
-    @param queryDescriptors Query set of descriptors.
-    @param matches Matches. If a query descriptor is masked out in mask , no match is added for this
-    descriptor. So, matches size may be smaller than the query descriptors count.
-    @param masks Set of masks. Each masks[i] specifies permissible matches between the input query
-    descriptors and stored train descriptors from the i-th image trainDescCollection[i].
-    */
-    CV_WRAP void match( InputArray queryDescriptors, CV_OUT std::vector<DMatch>& matches,
-                        InputArrayOfArrays masks=noArray() );
-    /** @overload
-    @param queryDescriptors Query set of descriptors.
-    @param matches Matches. Each matches[i] is k or less matches for the same query descriptor.
-    @param k Count of best matches found per each query descriptor or less if a query descriptor has
-    less than k possible matches in total.
-    @param masks Set of masks. Each masks[i] specifies permissible matches between the input query
-    descriptors and stored train descriptors from the i-th image trainDescCollection[i].
-    @param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is
-    false, the matches vector has the same size as queryDescriptors rows. If compactResult is true,
-    the matches vector does not contain matches for fully masked-out query descriptors.
-    */
-    CV_WRAP void knnMatch( InputArray queryDescriptors, CV_OUT std::vector<std::vector<DMatch> >& matches, int k,
-                           InputArrayOfArrays masks=noArray(), bool compactResult=false );
-    /** @overload
-    @param queryDescriptors Query set of descriptors.
-    @param matches Found matches.
-    @param maxDistance Threshold for the distance between matched descriptors. Distance means here
-    metric distance (e.g. Hamming distance), not the distance between coordinates (which is measured
-    in Pixels)!
-    @param masks Set of masks. Each masks[i] specifies permissible matches between the input query
-    descriptors and stored train descriptors from the i-th image trainDescCollection[i].
-    @param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is
-    false, the matches vector has the same size as queryDescriptors rows. If compactResult is true,
-    the matches vector does not contain matches for fully masked-out query descriptors.
-    */
-    CV_WRAP void radiusMatch( InputArray queryDescriptors, CV_OUT std::vector<std::vector<DMatch> >& matches, float maxDistance,
-                      InputArrayOfArrays masks=noArray(), bool compactResult=false );
-
-
-    CV_WRAP void write( const String& fileName ) const
-    {
-        FileStorage fs(fileName, FileStorage::WRITE);
-        write(fs);
-    }
-
-    CV_WRAP void read( const String& fileName )
-    {
-        FileStorage fs(fileName, FileStorage::READ);
-        read(fs.root());
-    }
-    // Reads matcher object from a file node
-    // see corresponding cv::Algorithm method
-    CV_WRAP virtual void read( const FileNode& ) CV_OVERRIDE;
-    // Writes matcher object to a file storage
-    virtual void write( FileStorage& ) const CV_OVERRIDE;
-
-    /** @brief Clones the matcher.
-
-    @param emptyTrainData If emptyTrainData is false, the method creates a deep copy of the object,
-    that is, copies both parameters and train data. If emptyTrainData is true, the method creates an
-    object copy with the current parameters but with empty train data.
-     */
-    CV_WRAP virtual Ptr<DescriptorMatcher> clone( bool emptyTrainData=false ) const = 0;
-
-    /** @brief Creates a descriptor matcher of a given type with the default parameters (using default
-    constructor).
-
-    @param descriptorMatcherType Descriptor matcher type. Now the following matcher types are
-    supported:
-    -   `BruteForce` (it uses L2 )
-    -   `BruteForce-L1`
-    -   `BruteForce-Hamming`
-    -   `BruteForce-Hamming(2)`
-    -   `FlannBased`
-     */
-    CV_WRAP static Ptr<DescriptorMatcher> create( const String& descriptorMatcherType );
-
-    CV_WRAP static Ptr<DescriptorMatcher> create( int matcherType );
-
-
-    // see corresponding cv::Algorithm method
-    CV_WRAP inline void write(const Ptr<FileStorage>& fs, const String& name = String()) const { Algorithm::write(fs, name); }
-
-protected:
-    /**
-     * Class to work with descriptors from several images as with one merged matrix.
-     * It is used e.g. in FlannBasedMatcher.
-     */
-    class CV_EXPORTS DescriptorCollection
-    {
-    public:
-        DescriptorCollection();
-        DescriptorCollection( const DescriptorCollection& collection );
-        virtual ~DescriptorCollection();
-
-        // Vector of matrices "descriptors" will be merged to one matrix "mergedDescriptors" here.
-        void set( const std::vector<Mat>& descriptors );
-        virtual void clear();
-
-        const Mat& getDescriptors() const;
-        const Mat getDescriptor( int imgIdx, int localDescIdx ) const;
-        const Mat getDescriptor( int globalDescIdx ) const;
-        void getLocalIdx( int globalDescIdx, int& imgIdx, int& localDescIdx ) const;
-
-        int size() const;
-
-    protected:
-        Mat mergedDescriptors;
-        std::vector<int> startIdxs;
-    };
-
-    //! In fact the matching is implemented only by the following two methods. These methods suppose
-    //! that the class object has been trained already. Public match methods call these methods
-    //! after calling train().
-    virtual void knnMatchImpl( InputArray queryDescriptors, std::vector<std::vector<DMatch> >& matches, int k,
-        InputArrayOfArrays masks=noArray(), bool compactResult=false ) = 0;
-    virtual void radiusMatchImpl( InputArray queryDescriptors, std::vector<std::vector<DMatch> >& matches, float maxDistance,
-        InputArrayOfArrays masks=noArray(), bool compactResult=false ) = 0;
-
-    static bool isPossibleMatch( InputArray mask, int queryIdx, int trainIdx );
-    static bool isMaskedOut( InputArrayOfArrays masks, int queryIdx );
-
-    static Mat clone_op( Mat m ) { return m.clone(); }
-    void checkMasks( InputArrayOfArrays masks, int queryDescriptorsCount ) const;
-
-    //! Collection of descriptors from train images.
-    std::vector<Mat> trainDescCollection;
-    std::vector<UMat> utrainDescCollection;
-};
-
-/** @brief Brute-force descriptor matcher.
-
-For each descriptor in the first set, this matcher finds the closest descriptor in the second set
-by trying each one. This descriptor matcher supports masking permissible matches of descriptor
-sets.
- */
-class CV_EXPORTS_W BFMatcher : public DescriptorMatcher
-{
-public:
-    /** @brief Brute-force matcher constructor (obsolete). Please use BFMatcher.create()
-     *
-     *
-    */
-    CV_WRAP BFMatcher( int normType=NORM_L2, bool crossCheck=false );
-
-    virtual ~BFMatcher() {}
-
-    virtual bool isMaskSupported() const CV_OVERRIDE { return true; }
-
-    /** @brief Brute-force matcher create method.
-    @param normType One of NORM_L1, NORM_L2, NORM_HAMMING, NORM_HAMMING2. L1 and L2 norms are
-    preferable choices for SIFT and SURF descriptors, NORM_HAMMING should be used with ORB, BRISK and
-    BRIEF, NORM_HAMMING2 should be used with ORB when WTA_K==3 or 4 (see ORB::ORB constructor
-    description).
-    @param crossCheck If it is false, this is will be default BFMatcher behaviour when it finds the k
-    nearest neighbors for each query descriptor. If crossCheck==true, then the knnMatch() method with
-    k=1 will only return pairs (i,j) such that for i-th query descriptor the j-th descriptor in the
-    matcher's collection is the nearest and vice versa, i.e. the BFMatcher will only return consistent
-    pairs. Such technique usually produces best results with minimal number of outliers when there are
-    enough matches. This is alternative to the ratio test, used by D. Lowe in SIFT paper.
-     */
-    CV_WRAP static Ptr<BFMatcher> create( int normType=NORM_L2, bool crossCheck=false ) ;
-
-    virtual Ptr<DescriptorMatcher> clone( bool emptyTrainData=false ) const CV_OVERRIDE;
-protected:
-    virtual void knnMatchImpl( InputArray queryDescriptors, std::vector<std::vector<DMatch> >& matches, int k,
-        InputArrayOfArrays masks=noArray(), bool compactResult=false ) CV_OVERRIDE;
-    virtual void radiusMatchImpl( InputArray queryDescriptors, std::vector<std::vector<DMatch> >& matches, float maxDistance,
-        InputArrayOfArrays masks=noArray(), bool compactResult=false ) CV_OVERRIDE;
-
-    int normType;
-    bool crossCheck;
-};
-
-#if defined(HAVE_OPENCV_FLANN) || defined(CV_DOXYGEN)
-
-/** @brief Flann-based descriptor matcher.
-
-This matcher trains cv::flann::Index on a train descriptor collection and calls its nearest search
-methods to find the best matches. So, this matcher may be faster when matching a large train
-collection than the brute force matcher. FlannBasedMatcher does not support masking permissible
-matches of descriptor sets because flann::Index does not support this. :
- */
-class CV_EXPORTS_W FlannBasedMatcher : public DescriptorMatcher
-{
-public:
-    CV_WRAP FlannBasedMatcher( const Ptr<flann::IndexParams>& indexParams=makePtr<flann::KDTreeIndexParams>(),
-                       const Ptr<flann::SearchParams>& searchParams=makePtr<flann::SearchParams>() );
-
-    virtual void add( InputArrayOfArrays descriptors ) CV_OVERRIDE;
-    virtual void clear() CV_OVERRIDE;
-
-    // Reads matcher object from a file node
-    virtual void read( const FileNode& ) CV_OVERRIDE;
-    // Writes matcher object to a file storage
-    virtual void write( FileStorage& ) const CV_OVERRIDE;
-
-    virtual void train() CV_OVERRIDE;
-    virtual bool isMaskSupported() const CV_OVERRIDE;
-
-    CV_WRAP static Ptr<FlannBasedMatcher> create();
-
-    virtual Ptr<DescriptorMatcher> clone( bool emptyTrainData=false ) const CV_OVERRIDE;
-protected:
-    static void convertToDMatches( const DescriptorCollection& descriptors,
-                                   const Mat& indices, const Mat& distances,
-                                   std::vector<std::vector<DMatch> >& matches );
-
-    virtual void knnMatchImpl( InputArray queryDescriptors, std::vector<std::vector<DMatch> >& matches, int k,
-        InputArrayOfArrays masks=noArray(), bool compactResult=false ) CV_OVERRIDE;
-    virtual void radiusMatchImpl( InputArray queryDescriptors, std::vector<std::vector<DMatch> >& matches, float maxDistance,
-        InputArrayOfArrays masks=noArray(), bool compactResult=false ) CV_OVERRIDE;
-
-    Ptr<flann::IndexParams> indexParams;
-    Ptr<flann::SearchParams> searchParams;
-    Ptr<flann::Index> flannIndex;
-
-    DescriptorCollection mergedDescriptors;
-    int addedDescCount;
-};
-
-#endif
-
-//! @} features2d_match
-
-/****************************************************************************************\
-*                                   Drawing functions                                    *
-\****************************************************************************************/
-
-//! @addtogroup features2d_draw
-//! @{
-
-struct CV_EXPORTS DrawMatchesFlags
-{
-    enum{ DEFAULT = 0, //!< Output image matrix will be created (Mat::create),
-                       //!< i.e. existing memory of output image may be reused.
-                       //!< Two source image, matches and single keypoints will be drawn.
-                       //!< For each keypoint only the center point will be drawn (without
-                       //!< the circle around keypoint with keypoint size and orientation).
-          DRAW_OVER_OUTIMG = 1, //!< Output image matrix will not be created (Mat::create).
-                                //!< Matches will be drawn on existing content of output image.
-          NOT_DRAW_SINGLE_POINTS = 2, //!< Single keypoints will not be drawn.
-          DRAW_RICH_KEYPOINTS = 4 //!< For each keypoint the circle around keypoint with keypoint size and
-                                  //!< orientation will be drawn.
-        };
-};
-
-/** @brief Draws keypoints.
-
-@param image Source image.
-@param keypoints Keypoints from the source image.
-@param outImage Output image. Its content depends on the flags value defining what is drawn in the
-output image. See possible flags bit values below.
-@param color Color of keypoints.
-@param flags Flags setting drawing features. Possible flags bit values are defined by
-DrawMatchesFlags. See details above in drawMatches .
-
-@note
-For Python API, flags are modified as cv2.DRAW_MATCHES_FLAGS_DEFAULT,
-cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS, cv2.DRAW_MATCHES_FLAGS_DRAW_OVER_OUTIMG,
-cv2.DRAW_MATCHES_FLAGS_NOT_DRAW_SINGLE_POINTS
- */
-CV_EXPORTS_W void drawKeypoints( InputArray image, const std::vector<KeyPoint>& keypoints, InputOutputArray outImage,
-                               const Scalar& color=Scalar::all(-1), int flags=DrawMatchesFlags::DEFAULT );
-
-/** @brief Draws the found matches of keypoints from two images.
-
-@param img1 First source image.
-@param keypoints1 Keypoints from the first source image.
-@param img2 Second source image.
-@param keypoints2 Keypoints from the second source image.
-@param matches1to2 Matches from the first image to the second one, which means that keypoints1[i]
-has a corresponding point in keypoints2[matches[i]] .
-@param outImg Output image. Its content depends on the flags value defining what is drawn in the
-output image. See possible flags bit values below.
-@param matchColor Color of matches (lines and connected keypoints). If matchColor==Scalar::all(-1)
-, the color is generated randomly.
-@param singlePointColor Color of single keypoints (circles), which means that keypoints do not
-have the matches. If singlePointColor==Scalar::all(-1) , the color is generated randomly.
-@param matchesMask Mask determining which matches are drawn. If the mask is empty, all matches are
-drawn.
-@param flags Flags setting drawing features. Possible flags bit values are defined by
-DrawMatchesFlags.
-
-This function draws matches of keypoints from two images in the output image. Match is a line
-connecting two keypoints (circles). See cv::DrawMatchesFlags.
- */
-CV_EXPORTS_W void drawMatches( InputArray img1, const std::vector<KeyPoint>& keypoints1,
-                             InputArray img2, const std::vector<KeyPoint>& keypoints2,
-                             const std::vector<DMatch>& matches1to2, InputOutputArray outImg,
-                             const Scalar& matchColor=Scalar::all(-1), const Scalar& singlePointColor=Scalar::all(-1),
-                             const std::vector<char>& matchesMask=std::vector<char>(), int flags=DrawMatchesFlags::DEFAULT );
-
-/** @overload */
-CV_EXPORTS_AS(drawMatchesKnn) void drawMatches( InputArray img1, const std::vector<KeyPoint>& keypoints1,
-                             InputArray img2, const std::vector<KeyPoint>& keypoints2,
-                             const std::vector<std::vector<DMatch> >& matches1to2, InputOutputArray outImg,
-                             const Scalar& matchColor=Scalar::all(-1), const Scalar& singlePointColor=Scalar::all(-1),
-                             const std::vector<std::vector<char> >& matchesMask=std::vector<std::vector<char> >(), int flags=DrawMatchesFlags::DEFAULT );
-
-//! @} features2d_draw
-
-/****************************************************************************************\
-*   Functions to evaluate the feature detectors and [generic] descriptor extractors      *
-\****************************************************************************************/
-
-CV_EXPORTS void evaluateFeatureDetector( const Mat& img1, const Mat& img2, const Mat& H1to2,
-                                         std::vector<KeyPoint>* keypoints1, std::vector<KeyPoint>* keypoints2,
-                                         float& repeatability, int& correspCount,
-                                         const Ptr<FeatureDetector>& fdetector=Ptr<FeatureDetector>() );
-
-CV_EXPORTS void computeRecallPrecisionCurve( const std::vector<std::vector<DMatch> >& matches1to2,
-                                             const std::vector<std::vector<uchar> >& correctMatches1to2Mask,
-                                             std::vector<Point2f>& recallPrecisionCurve );
-
-CV_EXPORTS float getRecall( const std::vector<Point2f>& recallPrecisionCurve, float l_precision );
-CV_EXPORTS int getNearestPoint( const std::vector<Point2f>& recallPrecisionCurve, float l_precision );
-
-/****************************************************************************************\
-*                                     Bag of visual words                                *
-\****************************************************************************************/
-
-//! @addtogroup features2d_category
-//! @{
-
-/** @brief Abstract base class for training the *bag of visual words* vocabulary from a set of descriptors.
-
-For details, see, for example, *Visual Categorization with Bags of Keypoints* by Gabriella Csurka,
-Christopher R. Dance, Lixin Fan, Jutta Willamowski, Cedric Bray, 2004. :
- */
-class CV_EXPORTS_W BOWTrainer
-{
-public:
-    BOWTrainer();
-    virtual ~BOWTrainer();
-
-    /** @brief Adds descriptors to a training set.
-
-    @param descriptors Descriptors to add to a training set. Each row of the descriptors matrix is a
-    descriptor.
-
-    The training set is clustered using clustermethod to construct the vocabulary.
-     */
-    CV_WRAP void add( const Mat& descriptors );
-
-    /** @brief Returns a training set of descriptors.
-    */
-    CV_WRAP const std::vector<Mat>& getDescriptors() const;
-
-    /** @brief Returns the count of all descriptors stored in the training set.
-    */
-    CV_WRAP int descriptorsCount() const;
-
-    CV_WRAP virtual void clear();
-
-    /** @overload */
-    CV_WRAP virtual Mat cluster() const = 0;
-
-    /** @brief Clusters train descriptors.
-
-    @param descriptors Descriptors to cluster. Each row of the descriptors matrix is a descriptor.
-    Descriptors are not added to the inner train descriptor set.
-
-    The vocabulary consists of cluster centers. So, this method returns the vocabulary. In the first
-    variant of the method, train descriptors stored in the object are clustered. In the second variant,
-    input descriptors are clustered.
-     */
-    CV_WRAP virtual Mat cluster( const Mat& descriptors ) const = 0;
-
-protected:
-    std::vector<Mat> descriptors;
-    int size;
-};
-
-/** @brief kmeans -based class to train visual vocabulary using the *bag of visual words* approach. :
- */
-class CV_EXPORTS_W BOWKMeansTrainer : public BOWTrainer
-{
-public:
-    /** @brief The constructor.
-
-    @see cv::kmeans
-    */
-    CV_WRAP BOWKMeansTrainer( int clusterCount, const TermCriteria& termcrit=TermCriteria(),
-                      int attempts=3, int flags=KMEANS_PP_CENTERS );
-    virtual ~BOWKMeansTrainer();
-
-    // Returns trained vocabulary (i.e. cluster centers).
-    CV_WRAP virtual Mat cluster() const CV_OVERRIDE;
-    CV_WRAP virtual Mat cluster( const Mat& descriptors ) const CV_OVERRIDE;
-
-protected:
-
-    int clusterCount;
-    TermCriteria termcrit;
-    int attempts;
-    int flags;
-};
-
-/** @brief Class to compute an image descriptor using the *bag of visual words*.
-
-Such a computation consists of the following steps:
-
-1.  Compute descriptors for a given image and its keypoints set.
-2.  Find the nearest visual words from the vocabulary for each keypoint descriptor.
-3.  Compute the bag-of-words image descriptor as is a normalized histogram of vocabulary words
-encountered in the image. The i-th bin of the histogram is a frequency of i-th word of the
-vocabulary in the given image.
- */
-class CV_EXPORTS_W BOWImgDescriptorExtractor
-{
-public:
-    /** @brief The constructor.
-
-    @param dextractor Descriptor extractor that is used to compute descriptors for an input image and
-    its keypoints.
-    @param dmatcher Descriptor matcher that is used to find the nearest word of the trained vocabulary
-    for each keypoint descriptor of the image.
-     */
-    CV_WRAP BOWImgDescriptorExtractor( const Ptr<DescriptorExtractor>& dextractor,
-                               const Ptr<DescriptorMatcher>& dmatcher );
-    /** @overload */
-    BOWImgDescriptorExtractor( const Ptr<DescriptorMatcher>& dmatcher );
-    virtual ~BOWImgDescriptorExtractor();
-
-    /** @brief Sets a visual vocabulary.
-
-    @param vocabulary Vocabulary (can be trained using the inheritor of BOWTrainer ). Each row of the
-    vocabulary is a visual word (cluster center).
-     */
-    CV_WRAP void setVocabulary( const Mat& vocabulary );
-
-    /** @brief Returns the set vocabulary.
-    */
-    CV_WRAP const Mat& getVocabulary() const;
-
-    /** @brief Computes an image descriptor using the set visual vocabulary.
-
-    @param image Image, for which the descriptor is computed.
-    @param keypoints Keypoints detected in the input image.
-    @param imgDescriptor Computed output image descriptor.
-    @param pointIdxsOfClusters Indices of keypoints that belong to the cluster. This means that
-    pointIdxsOfClusters[i] are keypoint indices that belong to the i -th cluster (word of vocabulary)
-    returned if it is non-zero.
-    @param descriptors Descriptors of the image keypoints that are returned if they are non-zero.
-     */
-    void compute( InputArray image, std::vector<KeyPoint>& keypoints, OutputArray imgDescriptor,
-                  std::vector<std::vector<int> >* pointIdxsOfClusters=0, Mat* descriptors=0 );
-    /** @overload
-    @param keypointDescriptors Computed descriptors to match with vocabulary.
-    @param imgDescriptor Computed output image descriptor.
-    @param pointIdxsOfClusters Indices of keypoints that belong to the cluster. This means that
-    pointIdxsOfClusters[i] are keypoint indices that belong to the i -th cluster (word of vocabulary)
-    returned if it is non-zero.
-    */
-    void compute( InputArray keypointDescriptors, OutputArray imgDescriptor,
-                  std::vector<std::vector<int> >* pointIdxsOfClusters=0 );
-    // compute() is not constant because DescriptorMatcher::match is not constant
-
-    CV_WRAP_AS(compute) void compute2( const Mat& image, std::vector<KeyPoint>& keypoints, CV_OUT Mat& imgDescriptor )
-    { compute(image,keypoints,imgDescriptor); }
-
-    /** @brief Returns an image descriptor size if the vocabulary is set. Otherwise, it returns 0.
-    */
-    CV_WRAP int descriptorSize() const;
-
-    /** @brief Returns an image descriptor type.
-     */
-    CV_WRAP int descriptorType() const;
-
-protected:
-    Mat vocabulary;
-    Ptr<DescriptorExtractor> dextractor;
-    Ptr<DescriptorMatcher> dmatcher;
-};
-
-//! @} features2d_category
-
-//! @} features2d
-
-} /* namespace cv */
-
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/features2d/features2d.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/features2d/features2d.hpp
deleted file mode 100755
index e81df0a..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/features2d/features2d.hpp
+++ /dev/null
@@ -1,48 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifdef __OPENCV_BUILD
-#error this is a compatibility header which should not be used inside the OpenCV library
-#endif
-
-#include "opencv2/features2d.hpp"
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/features2d/hal/interface.h b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/features2d/hal/interface.h
deleted file mode 100755
index bcc6577..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/features2d/hal/interface.h
+++ /dev/null
@@ -1,33 +0,0 @@
-#ifndef OPENCV_FEATURE2D_HAL_INTERFACE_H
-#define OPENCV_FEATURE2D_HAL_INTERFACE_H
-
-#include "opencv2/core/cvdef.h"
-//! @addtogroup featrure2d_hal_interface
-//! @{
-
-//! @name Fast feature detector types
-//! @sa cv::FastFeatureDetector
-//! @{
-#define CV_HAL_TYPE_5_8  0
-#define CV_HAL_TYPE_7_12 1
-#define CV_HAL_TYPE_9_16 2
-//! @}
-
-//! @name Key point
-//! @sa cv::KeyPoint
-//! @{
-struct CV_EXPORTS cvhalKeyPoint
-{
-    float x;
-    float y;
-    float size;
-    float angle;
-    float response;
-    int octave;
-    int class_id;
-};
-//! @}
-
-//! @}
-
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann.hpp
deleted file mode 100755
index 22c6ffc..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann.hpp
+++ /dev/null
@@ -1,531 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_FLANN_HPP
-#define OPENCV_FLANN_HPP
-
-#include "opencv2/core.hpp"
-#include "opencv2/flann/miniflann.hpp"
-#include "opencv2/flann/flann_base.hpp"
-
-/**
-@defgroup flann Clustering and Search in Multi-Dimensional Spaces
-
-This section documents OpenCV's interface to the FLANN library. FLANN (Fast Library for Approximate
-Nearest Neighbors) is a library that contains a collection of algorithms optimized for fast nearest
-neighbor search in large datasets and for high dimensional features. More information about FLANN
-can be found in @cite Muja2009 .
-*/
-
-namespace cvflann
-{
-    CV_EXPORTS flann_distance_t flann_distance_type();
-    CV_DEPRECATED CV_EXPORTS void set_distance_type(flann_distance_t distance_type, int order);
-}
-
-
-namespace cv
-{
-namespace flann
-{
-
-
-//! @addtogroup flann
-//! @{
-
-template <typename T> struct CvType {};
-template <> struct CvType<unsigned char> { static int type() { return CV_8U; } };
-template <> struct CvType<char> { static int type() { return CV_8S; } };
-template <> struct CvType<unsigned short> { static int type() { return CV_16U; } };
-template <> struct CvType<short> { static int type() { return CV_16S; } };
-template <> struct CvType<int> { static int type() { return CV_32S; } };
-template <> struct CvType<float> { static int type() { return CV_32F; } };
-template <> struct CvType<double> { static int type() { return CV_64F; } };
-
-
-// bring the flann parameters into this namespace
-using ::cvflann::get_param;
-using ::cvflann::print_params;
-
-// bring the flann distances into this namespace
-using ::cvflann::L2_Simple;
-using ::cvflann::L2;
-using ::cvflann::L1;
-using ::cvflann::MinkowskiDistance;
-using ::cvflann::MaxDistance;
-using ::cvflann::HammingLUT;
-using ::cvflann::Hamming;
-using ::cvflann::Hamming2;
-using ::cvflann::HistIntersectionDistance;
-using ::cvflann::HellingerDistance;
-using ::cvflann::ChiSquareDistance;
-using ::cvflann::KL_Divergence;
-
-
-/** @brief The FLANN nearest neighbor index class. This class is templated with the type of elements for which
-the index is built.
- */
-template <typename Distance>
-class GenericIndex
-{
-public:
-        typedef typename Distance::ElementType ElementType;
-        typedef typename Distance::ResultType DistanceType;
-
-        /** @brief Constructs a nearest neighbor search index for a given dataset.
-
-        @param features Matrix of containing the features(points) to index. The size of the matrix is
-        num_features x feature_dimensionality and the data type of the elements in the matrix must
-        coincide with the type of the index.
-        @param params Structure containing the index parameters. The type of index that will be
-        constructed depends on the type of this parameter. See the description.
-        @param distance
-
-        The method constructs a fast search structure from a set of features using the specified algorithm
-        with specified parameters, as defined by params. params is a reference to one of the following class
-        IndexParams descendants:
-
-        - **LinearIndexParams** When passing an object of this type, the index will perform a linear,
-        brute-force search. :
-        @code
-        struct LinearIndexParams : public IndexParams
-        {
-        };
-        @endcode
-        - **KDTreeIndexParams** When passing an object of this type the index constructed will consist of
-        a set of randomized kd-trees which will be searched in parallel. :
-        @code
-        struct KDTreeIndexParams : public IndexParams
-        {
-            KDTreeIndexParams( int trees = 4 );
-        };
-        @endcode
-        - **KMeansIndexParams** When passing an object of this type the index constructed will be a
-        hierarchical k-means tree. :
-        @code
-        struct KMeansIndexParams : public IndexParams
-        {
-            KMeansIndexParams(
-                int branching = 32,
-                int iterations = 11,
-                flann_centers_init_t centers_init = CENTERS_RANDOM,
-                float cb_index = 0.2 );
-        };
-        @endcode
-        - **CompositeIndexParams** When using a parameters object of this type the index created
-        combines the randomized kd-trees and the hierarchical k-means tree. :
-        @code
-        struct CompositeIndexParams : public IndexParams
-        {
-            CompositeIndexParams(
-                int trees = 4,
-                int branching = 32,
-                int iterations = 11,
-                flann_centers_init_t centers_init = CENTERS_RANDOM,
-                float cb_index = 0.2 );
-        };
-        @endcode
-        - **LshIndexParams** When using a parameters object of this type the index created uses
-        multi-probe LSH (by Multi-Probe LSH: Efficient Indexing for High-Dimensional Similarity Search
-        by Qin Lv, William Josephson, Zhe Wang, Moses Charikar, Kai Li., Proceedings of the 33rd
-        International Conference on Very Large Data Bases (VLDB). Vienna, Austria. September 2007) :
-        @code
-        struct LshIndexParams : public IndexParams
-        {
-            LshIndexParams(
-                unsigned int table_number,
-                unsigned int key_size,
-                unsigned int multi_probe_level );
-        };
-        @endcode
-        - **AutotunedIndexParams** When passing an object of this type the index created is
-        automatically tuned to offer the best performance, by choosing the optimal index type
-        (randomized kd-trees, hierarchical kmeans, linear) and parameters for the dataset provided. :
-        @code
-        struct AutotunedIndexParams : public IndexParams
-        {
-            AutotunedIndexParams(
-                float target_precision = 0.9,
-                float build_weight = 0.01,
-                float memory_weight = 0,
-                float sample_fraction = 0.1 );
-        };
-        @endcode
-        - **SavedIndexParams** This object type is used for loading a previously saved index from the
-        disk. :
-        @code
-        struct SavedIndexParams : public IndexParams
-        {
-            SavedIndexParams( String filename );
-        };
-        @endcode
-         */
-        GenericIndex(const Mat& features, const ::cvflann::IndexParams& params, Distance distance = Distance());
-
-        ~GenericIndex();
-
-        /** @brief Performs a K-nearest neighbor search for a given query point using the index.
-
-        @param query The query point
-        @param indices Vector that will contain the indices of the K-nearest neighbors found. It must have
-        at least knn size.
-        @param dists Vector that will contain the distances to the K-nearest neighbors found. It must have
-        at least knn size.
-        @param knn Number of nearest neighbors to search for.
-        @param params SearchParams
-         */
-        void knnSearch(const std::vector<ElementType>& query, std::vector<int>& indices,
-                       std::vector<DistanceType>& dists, int knn, const ::cvflann::SearchParams& params);
-        void knnSearch(const Mat& queries, Mat& indices, Mat& dists, int knn, const ::cvflann::SearchParams& params);
-
-        int radiusSearch(const std::vector<ElementType>& query, std::vector<int>& indices,
-                         std::vector<DistanceType>& dists, DistanceType radius, const ::cvflann::SearchParams& params);
-        int radiusSearch(const Mat& query, Mat& indices, Mat& dists,
-                         DistanceType radius, const ::cvflann::SearchParams& params);
-
-        void save(String filename) { nnIndex->save(filename); }
-
-        int veclen() const { return nnIndex->veclen(); }
-
-        int size() const { return nnIndex->size(); }
-
-        ::cvflann::IndexParams getParameters() { return nnIndex->getParameters(); }
-
-        CV_DEPRECATED const ::cvflann::IndexParams* getIndexParameters() { return nnIndex->getIndexParameters(); }
-
-private:
-        ::cvflann::Index<Distance>* nnIndex;
-};
-
-//! @cond IGNORED
-
-#define FLANN_DISTANCE_CHECK \
-    if ( ::cvflann::flann_distance_type() != cvflann::FLANN_DIST_L2) { \
-        printf("[WARNING] You are using cv::flann::Index (or cv::flann::GenericIndex) and have also changed "\
-        "the distance using cvflann::set_distance_type. This is no longer working as expected "\
-        "(cv::flann::Index always uses L2). You should create the index templated on the distance, "\
-        "for example for L1 distance use: GenericIndex< L1<float> > \n"); \
-    }
-
-
-template <typename Distance>
-GenericIndex<Distance>::GenericIndex(const Mat& dataset, const ::cvflann::IndexParams& params, Distance distance)
-{
-    CV_Assert(dataset.type() == CvType<ElementType>::type());
-    CV_Assert(dataset.isContinuous());
-    ::cvflann::Matrix<ElementType> m_dataset((ElementType*)dataset.ptr<ElementType>(0), dataset.rows, dataset.cols);
-
-    nnIndex = new ::cvflann::Index<Distance>(m_dataset, params, distance);
-
-    FLANN_DISTANCE_CHECK
-
-    nnIndex->buildIndex();
-}
-
-template <typename Distance>
-GenericIndex<Distance>::~GenericIndex()
-{
-    delete nnIndex;
-}
-
-template <typename Distance>
-void GenericIndex<Distance>::knnSearch(const std::vector<ElementType>& query, std::vector<int>& indices, std::vector<DistanceType>& dists, int knn, const ::cvflann::SearchParams& searchParams)
-{
-    ::cvflann::Matrix<ElementType> m_query((ElementType*)&query[0], 1, query.size());
-    ::cvflann::Matrix<int> m_indices(&indices[0], 1, indices.size());
-    ::cvflann::Matrix<DistanceType> m_dists(&dists[0], 1, dists.size());
-
-    FLANN_DISTANCE_CHECK
-
-    nnIndex->knnSearch(m_query,m_indices,m_dists,knn,searchParams);
-}
-
-
-template <typename Distance>
-void GenericIndex<Distance>::knnSearch(const Mat& queries, Mat& indices, Mat& dists, int knn, const ::cvflann::SearchParams& searchParams)
-{
-    CV_Assert(queries.type() == CvType<ElementType>::type());
-    CV_Assert(queries.isContinuous());
-    ::cvflann::Matrix<ElementType> m_queries((ElementType*)queries.ptr<ElementType>(0), queries.rows, queries.cols);
-
-    CV_Assert(indices.type() == CV_32S);
-    CV_Assert(indices.isContinuous());
-    ::cvflann::Matrix<int> m_indices((int*)indices.ptr<int>(0), indices.rows, indices.cols);
-
-    CV_Assert(dists.type() == CvType<DistanceType>::type());
-    CV_Assert(dists.isContinuous());
-    ::cvflann::Matrix<DistanceType> m_dists((DistanceType*)dists.ptr<DistanceType>(0), dists.rows, dists.cols);
-
-    FLANN_DISTANCE_CHECK
-
-    nnIndex->knnSearch(m_queries,m_indices,m_dists,knn, searchParams);
-}
-
-template <typename Distance>
-int GenericIndex<Distance>::radiusSearch(const std::vector<ElementType>& query, std::vector<int>& indices, std::vector<DistanceType>& dists, DistanceType radius, const ::cvflann::SearchParams& searchParams)
-{
-    ::cvflann::Matrix<ElementType> m_query((ElementType*)&query[0], 1, query.size());
-    ::cvflann::Matrix<int> m_indices(&indices[0], 1, indices.size());
-    ::cvflann::Matrix<DistanceType> m_dists(&dists[0], 1, dists.size());
-
-    FLANN_DISTANCE_CHECK
-
-    return nnIndex->radiusSearch(m_query,m_indices,m_dists,radius,searchParams);
-}
-
-template <typename Distance>
-int GenericIndex<Distance>::radiusSearch(const Mat& query, Mat& indices, Mat& dists, DistanceType radius, const ::cvflann::SearchParams& searchParams)
-{
-    CV_Assert(query.type() == CvType<ElementType>::type());
-    CV_Assert(query.isContinuous());
-    ::cvflann::Matrix<ElementType> m_query((ElementType*)query.ptr<ElementType>(0), query.rows, query.cols);
-
-    CV_Assert(indices.type() == CV_32S);
-    CV_Assert(indices.isContinuous());
-    ::cvflann::Matrix<int> m_indices((int*)indices.ptr<int>(0), indices.rows, indices.cols);
-
-    CV_Assert(dists.type() == CvType<DistanceType>::type());
-    CV_Assert(dists.isContinuous());
-    ::cvflann::Matrix<DistanceType> m_dists((DistanceType*)dists.ptr<DistanceType>(0), dists.rows, dists.cols);
-
-    FLANN_DISTANCE_CHECK
-
-    return nnIndex->radiusSearch(m_query,m_indices,m_dists,radius,searchParams);
-}
-
-//! @endcond
-
-/**
- * @deprecated Use GenericIndex class instead
- */
-template <typename T>
-class Index_
-{
-public:
-    typedef typename L2<T>::ElementType ElementType;
-    typedef typename L2<T>::ResultType DistanceType;
-
-    CV_DEPRECATED Index_(const Mat& dataset, const ::cvflann::IndexParams& params)
-    {
-        printf("[WARNING] The cv::flann::Index_<T> class is deperecated, use cv::flann::GenericIndex<Distance> instead\n");
-
-        CV_Assert(dataset.type() == CvType<ElementType>::type());
-        CV_Assert(dataset.isContinuous());
-        ::cvflann::Matrix<ElementType> m_dataset((ElementType*)dataset.ptr<ElementType>(0), dataset.rows, dataset.cols);
-
-        if ( ::cvflann::flann_distance_type() == cvflann::FLANN_DIST_L2 ) {
-            nnIndex_L1 = NULL;
-            nnIndex_L2 = new ::cvflann::Index< L2<ElementType> >(m_dataset, params);
-        }
-        else if ( ::cvflann::flann_distance_type() == cvflann::FLANN_DIST_L1 ) {
-            nnIndex_L1 = new ::cvflann::Index< L1<ElementType> >(m_dataset, params);
-            nnIndex_L2 = NULL;
-        }
-        else {
-            printf("[ERROR] cv::flann::Index_<T> only provides backwards compatibility for the L1 and L2 distances. "
-                   "For other distance types you must use cv::flann::GenericIndex<Distance>\n");
-            CV_Assert(0);
-        }
-        if (nnIndex_L1) nnIndex_L1->buildIndex();
-        if (nnIndex_L2) nnIndex_L2->buildIndex();
-    }
-    CV_DEPRECATED ~Index_()
-    {
-        if (nnIndex_L1) delete nnIndex_L1;
-        if (nnIndex_L2) delete nnIndex_L2;
-    }
-
-    CV_DEPRECATED void knnSearch(const std::vector<ElementType>& query, std::vector<int>& indices, std::vector<DistanceType>& dists, int knn, const ::cvflann::SearchParams& searchParams)
-    {
-        ::cvflann::Matrix<ElementType> m_query((ElementType*)&query[0], 1, query.size());
-        ::cvflann::Matrix<int> m_indices(&indices[0], 1, indices.size());
-        ::cvflann::Matrix<DistanceType> m_dists(&dists[0], 1, dists.size());
-
-        if (nnIndex_L1) nnIndex_L1->knnSearch(m_query,m_indices,m_dists,knn,searchParams);
-        if (nnIndex_L2) nnIndex_L2->knnSearch(m_query,m_indices,m_dists,knn,searchParams);
-    }
-    CV_DEPRECATED void knnSearch(const Mat& queries, Mat& indices, Mat& dists, int knn, const ::cvflann::SearchParams& searchParams)
-    {
-        CV_Assert(queries.type() == CvType<ElementType>::type());
-        CV_Assert(queries.isContinuous());
-        ::cvflann::Matrix<ElementType> m_queries((ElementType*)queries.ptr<ElementType>(0), queries.rows, queries.cols);
-
-        CV_Assert(indices.type() == CV_32S);
-        CV_Assert(indices.isContinuous());
-        ::cvflann::Matrix<int> m_indices((int*)indices.ptr<int>(0), indices.rows, indices.cols);
-
-        CV_Assert(dists.type() == CvType<DistanceType>::type());
-        CV_Assert(dists.isContinuous());
-        ::cvflann::Matrix<DistanceType> m_dists((DistanceType*)dists.ptr<DistanceType>(0), dists.rows, dists.cols);
-
-        if (nnIndex_L1) nnIndex_L1->knnSearch(m_queries,m_indices,m_dists,knn, searchParams);
-        if (nnIndex_L2) nnIndex_L2->knnSearch(m_queries,m_indices,m_dists,knn, searchParams);
-    }
-
-    CV_DEPRECATED int radiusSearch(const std::vector<ElementType>& query, std::vector<int>& indices, std::vector<DistanceType>& dists, DistanceType radius, const ::cvflann::SearchParams& searchParams)
-    {
-        ::cvflann::Matrix<ElementType> m_query((ElementType*)&query[0], 1, query.size());
-        ::cvflann::Matrix<int> m_indices(&indices[0], 1, indices.size());
-        ::cvflann::Matrix<DistanceType> m_dists(&dists[0], 1, dists.size());
-
-        if (nnIndex_L1) return nnIndex_L1->radiusSearch(m_query,m_indices,m_dists,radius,searchParams);
-        if (nnIndex_L2) return nnIndex_L2->radiusSearch(m_query,m_indices,m_dists,radius,searchParams);
-    }
-
-    CV_DEPRECATED int radiusSearch(const Mat& query, Mat& indices, Mat& dists, DistanceType radius, const ::cvflann::SearchParams& searchParams)
-    {
-        CV_Assert(query.type() == CvType<ElementType>::type());
-        CV_Assert(query.isContinuous());
-        ::cvflann::Matrix<ElementType> m_query((ElementType*)query.ptr<ElementType>(0), query.rows, query.cols);
-
-        CV_Assert(indices.type() == CV_32S);
-        CV_Assert(indices.isContinuous());
-        ::cvflann::Matrix<int> m_indices((int*)indices.ptr<int>(0), indices.rows, indices.cols);
-
-        CV_Assert(dists.type() == CvType<DistanceType>::type());
-        CV_Assert(dists.isContinuous());
-        ::cvflann::Matrix<DistanceType> m_dists((DistanceType*)dists.ptr<DistanceType>(0), dists.rows, dists.cols);
-
-        if (nnIndex_L1) return nnIndex_L1->radiusSearch(m_query,m_indices,m_dists,radius,searchParams);
-        if (nnIndex_L2) return nnIndex_L2->radiusSearch(m_query,m_indices,m_dists,radius,searchParams);
-    }
-
-    CV_DEPRECATED void save(String filename)
-    {
-        if (nnIndex_L1) nnIndex_L1->save(filename);
-        if (nnIndex_L2) nnIndex_L2->save(filename);
-    }
-
-    CV_DEPRECATED int veclen() const
-    {
-        if (nnIndex_L1) return nnIndex_L1->veclen();
-        if (nnIndex_L2) return nnIndex_L2->veclen();
-    }
-
-    CV_DEPRECATED int size() const
-    {
-        if (nnIndex_L1) return nnIndex_L1->size();
-        if (nnIndex_L2) return nnIndex_L2->size();
-    }
-
-    CV_DEPRECATED ::cvflann::IndexParams getParameters()
-    {
-        if (nnIndex_L1) return nnIndex_L1->getParameters();
-        if (nnIndex_L2) return nnIndex_L2->getParameters();
-
-    }
-
-    CV_DEPRECATED const ::cvflann::IndexParams* getIndexParameters()
-    {
-        if (nnIndex_L1) return nnIndex_L1->getIndexParameters();
-        if (nnIndex_L2) return nnIndex_L2->getIndexParameters();
-    }
-
-private:
-    // providing backwards compatibility for L2 and L1 distances (most common)
-    ::cvflann::Index< L2<ElementType> >* nnIndex_L2;
-    ::cvflann::Index< L1<ElementType> >* nnIndex_L1;
-};
-
-
-/** @brief Clusters features using hierarchical k-means algorithm.
-
-@param features The points to be clustered. The matrix must have elements of type
-Distance::ElementType.
-@param centers The centers of the clusters obtained. The matrix must have type
-Distance::ResultType. The number of rows in this matrix represents the number of clusters desired,
-however, because of the way the cut in the hierarchical tree is chosen, the number of clusters
-computed will be the highest number of the form (branching-1)\*k+1 that's lower than the number of
-clusters desired, where branching is the tree's branching factor (see description of the
-KMeansIndexParams).
-@param params Parameters used in the construction of the hierarchical k-means tree.
-@param d Distance to be used for clustering.
-
-The method clusters the given feature vectors by constructing a hierarchical k-means tree and
-choosing a cut in the tree that minimizes the cluster's variance. It returns the number of clusters
-found.
- */
-template <typename Distance>
-int hierarchicalClustering(const Mat& features, Mat& centers, const ::cvflann::KMeansIndexParams& params,
-                           Distance d = Distance())
-{
-    typedef typename Distance::ElementType ElementType;
-    typedef typename Distance::ResultType DistanceType;
-
-    CV_Assert(features.type() == CvType<ElementType>::type());
-    CV_Assert(features.isContinuous());
-    ::cvflann::Matrix<ElementType> m_features((ElementType*)features.ptr<ElementType>(0), features.rows, features.cols);
-
-    CV_Assert(centers.type() == CvType<DistanceType>::type());
-    CV_Assert(centers.isContinuous());
-    ::cvflann::Matrix<DistanceType> m_centers((DistanceType*)centers.ptr<DistanceType>(0), centers.rows, centers.cols);
-
-    return ::cvflann::hierarchicalClustering<Distance>(m_features, m_centers, params, d);
-}
-
-/** @deprecated
-*/
-template <typename ELEM_TYPE, typename DIST_TYPE>
-CV_DEPRECATED int hierarchicalClustering(const Mat& features, Mat& centers, const ::cvflann::KMeansIndexParams& params)
-{
-    printf("[WARNING] cv::flann::hierarchicalClustering<ELEM_TYPE,DIST_TYPE> is deprecated, use "
-        "cv::flann::hierarchicalClustering<Distance> instead\n");
-
-    if ( ::cvflann::flann_distance_type() == cvflann::FLANN_DIST_L2 ) {
-        return hierarchicalClustering< L2<ELEM_TYPE> >(features, centers, params);
-    }
-    else if ( ::cvflann::flann_distance_type() == cvflann::FLANN_DIST_L1 ) {
-        return hierarchicalClustering< L1<ELEM_TYPE> >(features, centers, params);
-    }
-    else {
-        printf("[ERROR] cv::flann::hierarchicalClustering<ELEM_TYPE,DIST_TYPE> only provides backwards "
-        "compatibility for the L1 and L2 distances. "
-        "For other distance types you must use cv::flann::hierarchicalClustering<Distance>\n");
-        CV_Assert(0);
-    }
-}
-
-//! @} flann
-
-} } // namespace cv::flann
-
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/all_indices.h b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/all_indices.h
deleted file mode 100755
index ff53fd8..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/all_indices.h
+++ /dev/null
@@ -1,155 +0,0 @@
-/***********************************************************************
- * Software License Agreement (BSD License)
- *
- * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
- * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions
- * are met:
- *
- * 1. Redistributions of source code must retain the above copyright
- *    notice, this list of conditions and the following disclaimer.
- * 2. Redistributions in binary form must reproduce the above copyright
- *    notice, this list of conditions and the following disclaimer in the
- *    documentation and/or other materials provided with the distribution.
- *
- * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
- * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
- * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
- * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
- * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
- * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
- * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
- * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *************************************************************************/
-
-
-#ifndef OPENCV_FLANN_ALL_INDICES_H_
-#define OPENCV_FLANN_ALL_INDICES_H_
-
-#include "general.h"
-
-#include "nn_index.h"
-#include "kdtree_index.h"
-#include "kdtree_single_index.h"
-#include "kmeans_index.h"
-#include "composite_index.h"
-#include "linear_index.h"
-#include "hierarchical_clustering_index.h"
-#include "lsh_index.h"
-#include "autotuned_index.h"
-
-
-namespace cvflann
-{
-
-template<typename KDTreeCapability, typename VectorSpace, typename Distance>
-struct index_creator
-{
-    static NNIndex<Distance>* create(const Matrix<typename Distance::ElementType>& dataset, const IndexParams& params, const Distance& distance)
-    {
-        flann_algorithm_t index_type = get_param<flann_algorithm_t>(params, "algorithm");
-
-        NNIndex<Distance>* nnIndex;
-        switch (index_type) {
-        case FLANN_INDEX_LINEAR:
-            nnIndex = new LinearIndex<Distance>(dataset, params, distance);
-            break;
-        case FLANN_INDEX_KDTREE_SINGLE:
-            nnIndex = new KDTreeSingleIndex<Distance>(dataset, params, distance);
-            break;
-        case FLANN_INDEX_KDTREE:
-            nnIndex = new KDTreeIndex<Distance>(dataset, params, distance);
-            break;
-        case FLANN_INDEX_KMEANS:
-            nnIndex = new KMeansIndex<Distance>(dataset, params, distance);
-            break;
-        case FLANN_INDEX_COMPOSITE:
-            nnIndex = new CompositeIndex<Distance>(dataset, params, distance);
-            break;
-        case FLANN_INDEX_AUTOTUNED:
-            nnIndex = new AutotunedIndex<Distance>(dataset, params, distance);
-            break;
-        case FLANN_INDEX_HIERARCHICAL:
-            nnIndex = new HierarchicalClusteringIndex<Distance>(dataset, params, distance);
-            break;
-        case FLANN_INDEX_LSH:
-            nnIndex = new LshIndex<Distance>(dataset, params, distance);
-            break;
-        default:
-            throw FLANNException("Unknown index type");
-        }
-
-        return nnIndex;
-    }
-};
-
-template<typename VectorSpace, typename Distance>
-struct index_creator<False,VectorSpace,Distance>
-{
-    static NNIndex<Distance>* create(const Matrix<typename Distance::ElementType>& dataset, const IndexParams& params, const Distance& distance)
-    {
-        flann_algorithm_t index_type = get_param<flann_algorithm_t>(params, "algorithm");
-
-        NNIndex<Distance>* nnIndex;
-        switch (index_type) {
-        case FLANN_INDEX_LINEAR:
-            nnIndex = new LinearIndex<Distance>(dataset, params, distance);
-            break;
-        case FLANN_INDEX_KMEANS:
-            nnIndex = new KMeansIndex<Distance>(dataset, params, distance);
-            break;
-        case FLANN_INDEX_HIERARCHICAL:
-            nnIndex = new HierarchicalClusteringIndex<Distance>(dataset, params, distance);
-            break;
-        case FLANN_INDEX_LSH:
-            nnIndex = new LshIndex<Distance>(dataset, params, distance);
-            break;
-        default:
-            throw FLANNException("Unknown index type");
-        }
-
-        return nnIndex;
-    }
-};
-
-template<typename Distance>
-struct index_creator<False,False,Distance>
-{
-    static NNIndex<Distance>* create(const Matrix<typename Distance::ElementType>& dataset, const IndexParams& params, const Distance& distance)
-    {
-        flann_algorithm_t index_type = get_param<flann_algorithm_t>(params, "algorithm");
-
-        NNIndex<Distance>* nnIndex;
-        switch (index_type) {
-        case FLANN_INDEX_LINEAR:
-            nnIndex = new LinearIndex<Distance>(dataset, params, distance);
-            break;
-        case FLANN_INDEX_HIERARCHICAL:
-            nnIndex = new HierarchicalClusteringIndex<Distance>(dataset, params, distance);
-            break;
-        case FLANN_INDEX_LSH:
-            nnIndex = new LshIndex<Distance>(dataset, params, distance);
-            break;
-        default:
-            throw FLANNException("Unknown index type");
-        }
-
-        return nnIndex;
-    }
-};
-
-template<typename Distance>
-NNIndex<Distance>* create_index_by_type(const Matrix<typename Distance::ElementType>& dataset, const IndexParams& params, const Distance& distance)
-{
-    return index_creator<typename Distance::is_kdtree_distance,
-                         typename Distance::is_vector_space_distance,
-                         Distance>::create(dataset, params,distance);
-}
-
-}
-
-#endif /* OPENCV_FLANN_ALL_INDICES_H_ */
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/allocator.h b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/allocator.h
deleted file mode 100755
index f347f88..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/allocator.h
+++ /dev/null
@@ -1,192 +0,0 @@
-/***********************************************************************
- * Software License Agreement (BSD License)
- *
- * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
- * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
- *
- * THE BSD LICENSE
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions
- * are met:
- *
- * 1. Redistributions of source code must retain the above copyright
- *    notice, this list of conditions and the following disclaimer.
- * 2. Redistributions in binary form must reproduce the above copyright
- *    notice, this list of conditions and the following disclaimer in the
- *    documentation and/or other materials provided with the distribution.
- *
- * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
- * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
- * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
- * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
- * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
- * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
- * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
- * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *************************************************************************/
-
-#ifndef OPENCV_FLANN_ALLOCATOR_H_
-#define OPENCV_FLANN_ALLOCATOR_H_
-
-#include <stdlib.h>
-#include <stdio.h>
-
-
-namespace cvflann
-{
-
-/**
- * Allocates (using C's malloc) a generic type T.
- *
- * Params:
- *     count = number of instances to allocate.
- * Returns: pointer (of type T*) to memory buffer
- */
-template <typename T>
-T* allocate(size_t count = 1)
-{
-    T* mem = (T*) ::malloc(sizeof(T)*count);
-    return mem;
-}
-
-
-/**
- * Pooled storage allocator
- *
- * The following routines allow for the efficient allocation of storage in
- * small chunks from a specified pool.  Rather than allowing each structure
- * to be freed individually, an entire pool of storage is freed at once.
- * This method has two advantages over just using malloc() and free().  First,
- * it is far more efficient for allocating small objects, as there is
- * no overhead for remembering all the information needed to free each
- * object or consolidating fragmented memory.  Second, the decision about
- * how long to keep an object is made at the time of allocation, and there
- * is no need to track down all the objects to free them.
- *
- */
-
-const size_t     WORDSIZE=16;
-const  size_t     BLOCKSIZE=8192;
-
-class PooledAllocator
-{
-    /* We maintain memory alignment to word boundaries by requiring that all
-        allocations be in multiples of the machine wordsize.  */
-    /* Size of machine word in bytes.  Must be power of 2. */
-    /* Minimum number of bytes requested at a time from	the system.  Must be multiple of WORDSIZE. */
-
-
-    int     remaining;  /* Number of bytes left in current block of storage. */
-    void*   base;     /* Pointer to base of current block of storage. */
-    void*   loc;      /* Current location in block to next allocate memory. */
-    int     blocksize;
-
-
-public:
-    int     usedMemory;
-    int     wastedMemory;
-
-    /**
-        Default constructor. Initializes a new pool.
-     */
-    PooledAllocator(int blockSize = BLOCKSIZE)
-    {
-        blocksize = blockSize;
-        remaining = 0;
-        base = NULL;
-        loc = NULL;
-
-        usedMemory = 0;
-        wastedMemory = 0;
-    }
-
-    /**
-     * Destructor. Frees all the memory allocated in this pool.
-     */
-    ~PooledAllocator()
-    {
-        void* prev;
-
-        while (base != NULL) {
-            prev = *((void**) base); /* Get pointer to prev block. */
-            ::free(base);
-            base = prev;
-        }
-    }
-
-    /**
-     * Returns a pointer to a piece of new memory of the given size in bytes
-     * allocated from the pool.
-     */
-    void* allocateMemory(int size)
-    {
-        int blockSize;
-
-        /* Round size up to a multiple of wordsize.  The following expression
-            only works for WORDSIZE that is a power of 2, by masking last bits of
-            incremented size to zero.
-         */
-        size = (size + (WORDSIZE - 1)) & ~(WORDSIZE - 1);
-
-        /* Check whether a new block must be allocated.  Note that the first word
-            of a block is reserved for a pointer to the previous block.
-         */
-        if (size > remaining) {
-
-            wastedMemory += remaining;
-
-            /* Allocate new storage. */
-            blockSize = (size + sizeof(void*) + (WORDSIZE-1) > BLOCKSIZE) ?
-                        size + sizeof(void*) + (WORDSIZE-1) : BLOCKSIZE;
-
-            // use the standard C malloc to allocate memory
-            void* m = ::malloc(blockSize);
-            if (!m) {
-                fprintf(stderr,"Failed to allocate memory.\n");
-                return NULL;
-            }
-
-            /* Fill first word of new block with pointer to previous block. */
-            ((void**) m)[0] = base;
-            base = m;
-
-            int shift = 0;
-            //int shift = (WORDSIZE - ( (((size_t)m) + sizeof(void*)) & (WORDSIZE-1))) & (WORDSIZE-1);
-
-            remaining = blockSize - sizeof(void*) - shift;
-            loc = ((char*)m + sizeof(void*) + shift);
-        }
-        void* rloc = loc;
-        loc = (char*)loc + size;
-        remaining -= size;
-
-        usedMemory += size;
-
-        return rloc;
-    }
-
-    /**
-     * Allocates (using this pool) a generic type T.
-     *
-     * Params:
-     *     count = number of instances to allocate.
-     * Returns: pointer (of type T*) to memory buffer
-     */
-    template <typename T>
-    T* allocate(size_t count = 1)
-    {
-        T* mem = (T*) this->allocateMemory((int)(sizeof(T)*count));
-        return mem;
-    }
-
-private:
-    PooledAllocator(const PooledAllocator &); // copy disabled
-    PooledAllocator& operator=(const PooledAllocator &); // assign disabled
-};
-
-}
-
-#endif //OPENCV_FLANN_ALLOCATOR_H_
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/any.h b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/any.h
deleted file mode 100755
index 5b57aa3..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/any.h
+++ /dev/null
@@ -1,330 +0,0 @@
-#ifndef OPENCV_FLANN_ANY_H_
-#define OPENCV_FLANN_ANY_H_
-/*
- * (C) Copyright Christopher Diggins 2005-2011
- * (C) Copyright Pablo Aguilar 2005
- * (C) Copyright Kevlin Henney 2001
- *
- * Distributed under the Boost Software License, Version 1.0. (See
- * accompanying file LICENSE_1_0.txt or copy at
- * http://www.boost.org/LICENSE_1_0.txt
- *
- * Adapted for FLANN by Marius Muja
- */
-
-#include "defines.h"
-#include <stdexcept>
-#include <ostream>
-#include <typeinfo>
-
-namespace cvflann
-{
-
-namespace anyimpl
-{
-
-struct bad_any_cast
-{
-};
-
-struct empty_any
-{
-};
-
-inline std::ostream& operator <<(std::ostream& out, const empty_any&)
-{
-    out << "[empty_any]";
-    return out;
-}
-
-struct base_any_policy
-{
-    virtual void static_delete(void** x) = 0;
-    virtual void copy_from_value(void const* src, void** dest) = 0;
-    virtual void clone(void* const* src, void** dest) = 0;
-    virtual void move(void* const* src, void** dest) = 0;
-    virtual void* get_value(void** src) = 0;
-    virtual const void* get_value(void* const * src) = 0;
-    virtual ::size_t get_size() = 0;
-    virtual const std::type_info& type() = 0;
-    virtual void print(std::ostream& out, void* const* src) = 0;
-    virtual ~base_any_policy() {}
-};
-
-template<typename T>
-struct typed_base_any_policy : base_any_policy
-{
-    virtual ::size_t get_size() CV_OVERRIDE { return sizeof(T); }
-    virtual const std::type_info& type() CV_OVERRIDE { return typeid(T); }
-
-};
-
-template<typename T>
-struct small_any_policy CV_FINAL : typed_base_any_policy<T>
-{
-    virtual void static_delete(void**) CV_OVERRIDE { }
-    virtual void copy_from_value(void const* src, void** dest) CV_OVERRIDE
-    {
-        new (dest) T(* reinterpret_cast<T const*>(src));
-    }
-    virtual void clone(void* const* src, void** dest) CV_OVERRIDE { *dest = *src; }
-    virtual void move(void* const* src, void** dest) CV_OVERRIDE { *dest = *src; }
-    virtual void* get_value(void** src) CV_OVERRIDE { return reinterpret_cast<void*>(src); }
-    virtual const void* get_value(void* const * src) CV_OVERRIDE { return reinterpret_cast<const void*>(src); }
-    virtual void print(std::ostream& out, void* const* src) CV_OVERRIDE { out << *reinterpret_cast<T const*>(src); }
-};
-
-template<typename T>
-struct big_any_policy CV_FINAL : typed_base_any_policy<T>
-{
-    virtual void static_delete(void** x) CV_OVERRIDE
-    {
-        if (* x) delete (* reinterpret_cast<T**>(x));
-        *x = NULL;
-    }
-    virtual void copy_from_value(void const* src, void** dest) CV_OVERRIDE
-    {
-        *dest = new T(*reinterpret_cast<T const*>(src));
-    }
-    virtual void clone(void* const* src, void** dest) CV_OVERRIDE
-    {
-        *dest = new T(**reinterpret_cast<T* const*>(src));
-    }
-    virtual void move(void* const* src, void** dest) CV_OVERRIDE
-    {
-        (*reinterpret_cast<T**>(dest))->~T();
-        **reinterpret_cast<T**>(dest) = **reinterpret_cast<T* const*>(src);
-    }
-    virtual void* get_value(void** src) CV_OVERRIDE { return *src; }
-    virtual const void* get_value(void* const * src) CV_OVERRIDE { return *src; }
-    virtual void print(std::ostream& out, void* const* src) CV_OVERRIDE { out << *reinterpret_cast<T const*>(*src); }
-};
-
-template<> inline void big_any_policy<flann_centers_init_t>::print(std::ostream& out, void* const* src)
-{
-    out << int(*reinterpret_cast<flann_centers_init_t const*>(*src));
-}
-
-template<> inline void big_any_policy<flann_algorithm_t>::print(std::ostream& out, void* const* src)
-{
-    out << int(*reinterpret_cast<flann_algorithm_t const*>(*src));
-}
-
-template<> inline void big_any_policy<cv::String>::print(std::ostream& out, void* const* src)
-{
-    out << (*reinterpret_cast<cv::String const*>(*src)).c_str();
-}
-
-template<typename T>
-struct choose_policy
-{
-    typedef big_any_policy<T> type;
-};
-
-template<typename T>
-struct choose_policy<T*>
-{
-    typedef small_any_policy<T*> type;
-};
-
-struct any;
-
-/// Choosing the policy for an any type is illegal, but should never happen.
-/// This is designed to throw a compiler error.
-template<>
-struct choose_policy<any>
-{
-    typedef void type;
-};
-
-/// Specializations for small types.
-#define SMALL_POLICY(TYPE) \
-    template<> \
-    struct choose_policy<TYPE> { typedef small_any_policy<TYPE> type; \
-    }
-
-SMALL_POLICY(signed char);
-SMALL_POLICY(unsigned char);
-SMALL_POLICY(signed short);
-SMALL_POLICY(unsigned short);
-SMALL_POLICY(signed int);
-SMALL_POLICY(unsigned int);
-SMALL_POLICY(signed long);
-SMALL_POLICY(unsigned long);
-SMALL_POLICY(float);
-SMALL_POLICY(bool);
-
-#undef SMALL_POLICY
-
-template <typename T>
-class SinglePolicy
-{
-    SinglePolicy();
-    SinglePolicy(const SinglePolicy& other);
-    SinglePolicy& operator=(const SinglePolicy& other);
-
-public:
-    static base_any_policy* get_policy();
-
-private:
-    static typename choose_policy<T>::type policy;
-};
-
-template <typename T>
-typename choose_policy<T>::type SinglePolicy<T>::policy;
-
-/// This function will return a different policy for each type.
-template <typename T>
-inline base_any_policy* SinglePolicy<T>::get_policy() { return &policy; }
-
-} // namespace anyimpl
-
-struct any
-{
-private:
-    // fields
-    anyimpl::base_any_policy* policy;
-    void* object;
-
-public:
-    /// Initializing constructor.
-    template <typename T>
-    any(const T& x)
-        : policy(anyimpl::SinglePolicy<anyimpl::empty_any>::get_policy()), object(NULL)
-    {
-        assign(x);
-    }
-
-    /// Empty constructor.
-    any()
-        : policy(anyimpl::SinglePolicy<anyimpl::empty_any>::get_policy()), object(NULL)
-    { }
-
-    /// Special initializing constructor for string literals.
-    any(const char* x)
-        : policy(anyimpl::SinglePolicy<anyimpl::empty_any>::get_policy()), object(NULL)
-    {
-        assign(x);
-    }
-
-    /// Copy constructor.
-    any(const any& x)
-        : policy(anyimpl::SinglePolicy<anyimpl::empty_any>::get_policy()), object(NULL)
-    {
-        assign(x);
-    }
-
-    /// Destructor.
-    ~any()
-    {
-        policy->static_delete(&object);
-    }
-
-    /// Assignment function from another any.
-    any& assign(const any& x)
-    {
-        reset();
-        policy = x.policy;
-        policy->clone(&x.object, &object);
-        return *this;
-    }
-
-    /// Assignment function.
-    template <typename T>
-    any& assign(const T& x)
-    {
-        reset();
-        policy = anyimpl::SinglePolicy<T>::get_policy();
-        policy->copy_from_value(&x, &object);
-        return *this;
-    }
-
-    /// Assignment operator.
-    template<typename T>
-    any& operator=(const T& x)
-    {
-        return assign(x);
-    }
-
-    /// Assignment operator. Template-based version above doesn't work as expected. We need regular assignment operator here.
-    any& operator=(const any& x)
-    {
-        return assign(x);
-    }
-
-    /// Assignment operator, specialed for literal strings.
-    /// They have types like const char [6] which don't work as expected.
-    any& operator=(const char* x)
-    {
-        return assign(x);
-    }
-
-    /// Utility functions
-    any& swap(any& x)
-    {
-        std::swap(policy, x.policy);
-        std::swap(object, x.object);
-        return *this;
-    }
-
-    /// Cast operator. You can only cast to the original type.
-    template<typename T>
-    T& cast()
-    {
-        if (policy->type() != typeid(T)) throw anyimpl::bad_any_cast();
-        T* r = reinterpret_cast<T*>(policy->get_value(&object));
-        return *r;
-    }
-
-    /// Cast operator. You can only cast to the original type.
-    template<typename T>
-    const T& cast() const
-    {
-        if (policy->type() != typeid(T)) throw anyimpl::bad_any_cast();
-        const T* r = reinterpret_cast<const T*>(policy->get_value(&object));
-        return *r;
-    }
-
-    /// Returns true if the any contains no value.
-    bool empty() const
-    {
-        return policy->type() == typeid(anyimpl::empty_any);
-    }
-
-    /// Frees any allocated memory, and sets the value to NULL.
-    void reset()
-    {
-        policy->static_delete(&object);
-        policy = anyimpl::SinglePolicy<anyimpl::empty_any>::get_policy();
-    }
-
-    /// Returns true if the two types are the same.
-    bool compatible(const any& x) const
-    {
-        return policy->type() == x.policy->type();
-    }
-
-    /// Returns if the type is compatible with the policy
-    template<typename T>
-    bool has_type()
-    {
-        return policy->type() == typeid(T);
-    }
-
-    const std::type_info& type() const
-    {
-        return policy->type();
-    }
-
-    friend std::ostream& operator <<(std::ostream& out, const any& any_val);
-};
-
-inline std::ostream& operator <<(std::ostream& out, const any& any_val)
-{
-    any_val.policy->print(out,&any_val.object);
-    return out;
-}
-
-}
-
-#endif // OPENCV_FLANN_ANY_H_
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/autotuned_index.h b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/autotuned_index.h
deleted file mode 100755
index 2fbc6c9..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/autotuned_index.h
+++ /dev/null
@@ -1,591 +0,0 @@
-/***********************************************************************
- * Software License Agreement (BSD License)
- *
- * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
- * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
- *
- * THE BSD LICENSE
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions
- * are met:
- *
- * 1. Redistributions of source code must retain the above copyright
- *    notice, this list of conditions and the following disclaimer.
- * 2. Redistributions in binary form must reproduce the above copyright
- *    notice, this list of conditions and the following disclaimer in the
- *    documentation and/or other materials provided with the distribution.
- *
- * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
- * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
- * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
- * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
- * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
- * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
- * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
- * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *************************************************************************/
-#ifndef OPENCV_FLANN_AUTOTUNED_INDEX_H_
-#define OPENCV_FLANN_AUTOTUNED_INDEX_H_
-
-#include <sstream>
-
-#include "general.h"
-#include "nn_index.h"
-#include "ground_truth.h"
-#include "index_testing.h"
-#include "sampling.h"
-#include "kdtree_index.h"
-#include "kdtree_single_index.h"
-#include "kmeans_index.h"
-#include "composite_index.h"
-#include "linear_index.h"
-#include "logger.h"
-
-namespace cvflann
-{
-
-template<typename Distance>
-NNIndex<Distance>* create_index_by_type(const Matrix<typename Distance::ElementType>& dataset, const IndexParams& params, const Distance& distance);
-
-
-struct AutotunedIndexParams : public IndexParams
-{
-    AutotunedIndexParams(float target_precision = 0.8, float build_weight = 0.01, float memory_weight = 0, float sample_fraction = 0.1)
-    {
-        (*this)["algorithm"] = FLANN_INDEX_AUTOTUNED;
-        // precision desired (used for autotuning, -1 otherwise)
-        (*this)["target_precision"] = target_precision;
-        // build tree time weighting factor
-        (*this)["build_weight"] = build_weight;
-        // index memory weighting factor
-        (*this)["memory_weight"] = memory_weight;
-        // what fraction of the dataset to use for autotuning
-        (*this)["sample_fraction"] = sample_fraction;
-    }
-};
-
-
-template <typename Distance>
-class AutotunedIndex : public NNIndex<Distance>
-{
-public:
-    typedef typename Distance::ElementType ElementType;
-    typedef typename Distance::ResultType DistanceType;
-
-    AutotunedIndex(const Matrix<ElementType>& inputData, const IndexParams& params = AutotunedIndexParams(), Distance d = Distance()) :
-        dataset_(inputData), distance_(d)
-    {
-        target_precision_ = get_param(params, "target_precision",0.8f);
-        build_weight_ =  get_param(params,"build_weight", 0.01f);
-        memory_weight_ = get_param(params, "memory_weight", 0.0f);
-        sample_fraction_ = get_param(params,"sample_fraction", 0.1f);
-        bestIndex_ = NULL;
-        speedup_ = 0;
-    }
-
-    AutotunedIndex(const AutotunedIndex&);
-    AutotunedIndex& operator=(const AutotunedIndex&);
-
-    virtual ~AutotunedIndex()
-    {
-        if (bestIndex_ != NULL) {
-            delete bestIndex_;
-            bestIndex_ = NULL;
-        }
-    }
-
-    /**
-     *          Method responsible with building the index.
-     */
-    virtual void buildIndex() CV_OVERRIDE
-    {
-        std::ostringstream stream;
-        bestParams_ = estimateBuildParams();
-        print_params(bestParams_, stream);
-        Logger::info("----------------------------------------------------\n");
-        Logger::info("Autotuned parameters:\n");
-        Logger::info("%s", stream.str().c_str());
-        Logger::info("----------------------------------------------------\n");
-
-        bestIndex_ = create_index_by_type(dataset_, bestParams_, distance_);
-        bestIndex_->buildIndex();
-        speedup_ = estimateSearchParams(bestSearchParams_);
-        stream.str(std::string());
-        print_params(bestSearchParams_, stream);
-        Logger::info("----------------------------------------------------\n");
-        Logger::info("Search parameters:\n");
-        Logger::info("%s", stream.str().c_str());
-        Logger::info("----------------------------------------------------\n");
-    }
-
-    /**
-     *  Saves the index to a stream
-     */
-    virtual void saveIndex(FILE* stream) CV_OVERRIDE
-    {
-        save_value(stream, (int)bestIndex_->getType());
-        bestIndex_->saveIndex(stream);
-        save_value(stream, get_param<int>(bestSearchParams_, "checks"));
-    }
-
-    /**
-     *  Loads the index from a stream
-     */
-    virtual void loadIndex(FILE* stream) CV_OVERRIDE
-    {
-        int index_type;
-
-        load_value(stream, index_type);
-        IndexParams params;
-        params["algorithm"] = (flann_algorithm_t)index_type;
-        bestIndex_ = create_index_by_type<Distance>(dataset_, params, distance_);
-        bestIndex_->loadIndex(stream);
-        int checks;
-        load_value(stream, checks);
-        bestSearchParams_["checks"] = checks;
-    }
-
-    /**
-     *      Method that searches for nearest-neighbors
-     */
-    virtual void findNeighbors(ResultSet<DistanceType>& result, const ElementType* vec, const SearchParams& searchParams) CV_OVERRIDE
-    {
-        int checks = get_param<int>(searchParams,"checks",FLANN_CHECKS_AUTOTUNED);
-        if (checks == FLANN_CHECKS_AUTOTUNED) {
-            bestIndex_->findNeighbors(result, vec, bestSearchParams_);
-        }
-        else {
-            bestIndex_->findNeighbors(result, vec, searchParams);
-        }
-    }
-
-
-    IndexParams getParameters() const CV_OVERRIDE
-    {
-        return bestIndex_->getParameters();
-    }
-
-    SearchParams getSearchParameters() const
-    {
-        return bestSearchParams_;
-    }
-
-    float getSpeedup() const
-    {
-        return speedup_;
-    }
-
-
-    /**
-     *      Number of features in this index.
-     */
-    virtual size_t size() const CV_OVERRIDE
-    {
-        return bestIndex_->size();
-    }
-
-    /**
-     *  The length of each vector in this index.
-     */
-    virtual size_t veclen() const CV_OVERRIDE
-    {
-        return bestIndex_->veclen();
-    }
-
-    /**
-     * The amount of memory (in bytes) this index uses.
-     */
-    virtual int usedMemory() const CV_OVERRIDE
-    {
-        return bestIndex_->usedMemory();
-    }
-
-    /**
-     * Algorithm name
-     */
-    virtual flann_algorithm_t getType() const CV_OVERRIDE
-    {
-        return FLANN_INDEX_AUTOTUNED;
-    }
-
-private:
-
-    struct CostData
-    {
-        float searchTimeCost;
-        float buildTimeCost;
-        float memoryCost;
-        float totalCost;
-        IndexParams params;
-    };
-
-    void evaluate_kmeans(CostData& cost)
-    {
-        StartStopTimer t;
-        int checks;
-        const int nn = 1;
-
-        Logger::info("KMeansTree using params: max_iterations=%d, branching=%d\n",
-                     get_param<int>(cost.params,"iterations"),
-                     get_param<int>(cost.params,"branching"));
-        KMeansIndex<Distance> kmeans(sampledDataset_, cost.params, distance_);
-        // measure index build time
-        t.start();
-        kmeans.buildIndex();
-        t.stop();
-        float buildTime = (float)t.value;
-
-        // measure search time
-        float searchTime = test_index_precision(kmeans, sampledDataset_, testDataset_, gt_matches_, target_precision_, checks, distance_, nn);
-
-        float datasetMemory = float(sampledDataset_.rows * sampledDataset_.cols * sizeof(float));
-        cost.memoryCost = (kmeans.usedMemory() + datasetMemory) / datasetMemory;
-        cost.searchTimeCost = searchTime;
-        cost.buildTimeCost = buildTime;
-        Logger::info("KMeansTree buildTime=%g, searchTime=%g, build_weight=%g\n", buildTime, searchTime, build_weight_);
-    }
-
-
-    void evaluate_kdtree(CostData& cost)
-    {
-        StartStopTimer t;
-        int checks;
-        const int nn = 1;
-
-        Logger::info("KDTree using params: trees=%d\n", get_param<int>(cost.params,"trees"));
-        KDTreeIndex<Distance> kdtree(sampledDataset_, cost.params, distance_);
-
-        t.start();
-        kdtree.buildIndex();
-        t.stop();
-        float buildTime = (float)t.value;
-
-        //measure search time
-        float searchTime = test_index_precision(kdtree, sampledDataset_, testDataset_, gt_matches_, target_precision_, checks, distance_, nn);
-
-        float datasetMemory = float(sampledDataset_.rows * sampledDataset_.cols * sizeof(float));
-        cost.memoryCost = (kdtree.usedMemory() + datasetMemory) / datasetMemory;
-        cost.searchTimeCost = searchTime;
-        cost.buildTimeCost = buildTime;
-        Logger::info("KDTree buildTime=%g, searchTime=%g\n", buildTime, searchTime);
-    }
-
-
-    //    struct KMeansSimpleDownhillFunctor {
-    //
-    //        Autotune& autotuner;
-    //        KMeansSimpleDownhillFunctor(Autotune& autotuner_) : autotuner(autotuner_) {}
-    //
-    //        float operator()(int* params) {
-    //
-    //            float maxFloat = numeric_limits<float>::max();
-    //
-    //            if (params[0]<2) return maxFloat;
-    //            if (params[1]<0) return maxFloat;
-    //
-    //            CostData c;
-    //            c.params["algorithm"] = KMEANS;
-    //            c.params["centers-init"] = CENTERS_RANDOM;
-    //            c.params["branching"] = params[0];
-    //            c.params["max-iterations"] = params[1];
-    //
-    //            autotuner.evaluate_kmeans(c);
-    //
-    //            return c.timeCost;
-    //
-    //        }
-    //    };
-    //
-    //    struct KDTreeSimpleDownhillFunctor {
-    //
-    //        Autotune& autotuner;
-    //        KDTreeSimpleDownhillFunctor(Autotune& autotuner_) : autotuner(autotuner_) {}
-    //
-    //        float operator()(int* params) {
-    //            float maxFloat = numeric_limits<float>::max();
-    //
-    //            if (params[0]<1) return maxFloat;
-    //
-    //            CostData c;
-    //            c.params["algorithm"] = KDTREE;
-    //            c.params["trees"] = params[0];
-    //
-    //            autotuner.evaluate_kdtree(c);
-    //
-    //            return c.timeCost;
-    //
-    //        }
-    //    };
-
-
-
-    void optimizeKMeans(std::vector<CostData>& costs)
-    {
-        Logger::info("KMEANS, Step 1: Exploring parameter space\n");
-
-        // explore kmeans parameters space using combinations of the parameters below
-        int maxIterations[] = { 1, 5, 10, 15 };
-        int branchingFactors[] = { 16, 32, 64, 128, 256 };
-
-        int kmeansParamSpaceSize = FLANN_ARRAY_LEN(maxIterations) * FLANN_ARRAY_LEN(branchingFactors);
-        costs.reserve(costs.size() + kmeansParamSpaceSize);
-
-        // evaluate kmeans for all parameter combinations
-        for (size_t i = 0; i < FLANN_ARRAY_LEN(maxIterations); ++i) {
-            for (size_t j = 0; j < FLANN_ARRAY_LEN(branchingFactors); ++j) {
-                CostData cost;
-                cost.params["algorithm"] = FLANN_INDEX_KMEANS;
-                cost.params["centers_init"] = FLANN_CENTERS_RANDOM;
-                cost.params["iterations"] = maxIterations[i];
-                cost.params["branching"] = branchingFactors[j];
-
-                evaluate_kmeans(cost);
-                costs.push_back(cost);
-            }
-        }
-
-        //         Logger::info("KMEANS, Step 2: simplex-downhill optimization\n");
-        //
-        //         const int n = 2;
-        //         // choose initial simplex points as the best parameters so far
-        //         int kmeansNMPoints[n*(n+1)];
-        //         float kmeansVals[n+1];
-        //         for (int i=0;i<n+1;++i) {
-        //             kmeansNMPoints[i*n] = (int)kmeansCosts[i].params["branching"];
-        //             kmeansNMPoints[i*n+1] = (int)kmeansCosts[i].params["max-iterations"];
-        //             kmeansVals[i] = kmeansCosts[i].timeCost;
-        //         }
-        //         KMeansSimpleDownhillFunctor kmeans_cost_func(*this);
-        //         // run optimization
-        //         optimizeSimplexDownhill(kmeansNMPoints,n,kmeans_cost_func,kmeansVals);
-        //         // store results
-        //         for (int i=0;i<n+1;++i) {
-        //             kmeansCosts[i].params["branching"] = kmeansNMPoints[i*2];
-        //             kmeansCosts[i].params["max-iterations"] = kmeansNMPoints[i*2+1];
-        //             kmeansCosts[i].timeCost = kmeansVals[i];
-        //         }
-    }
-
-
-    void optimizeKDTree(std::vector<CostData>& costs)
-    {
-        Logger::info("KD-TREE, Step 1: Exploring parameter space\n");
-
-        // explore kd-tree parameters space using the parameters below
-        int testTrees[] = { 1, 4, 8, 16, 32 };
-
-        // evaluate kdtree for all parameter combinations
-        for (size_t i = 0; i < FLANN_ARRAY_LEN(testTrees); ++i) {
-            CostData cost;
-            cost.params["algorithm"] = FLANN_INDEX_KDTREE;
-            cost.params["trees"] = testTrees[i];
-
-            evaluate_kdtree(cost);
-            costs.push_back(cost);
-        }
-
-        //         Logger::info("KD-TREE, Step 2: simplex-downhill optimization\n");
-        //
-        //         const int n = 1;
-        //         // choose initial simplex points as the best parameters so far
-        //         int kdtreeNMPoints[n*(n+1)];
-        //         float kdtreeVals[n+1];
-        //         for (int i=0;i<n+1;++i) {
-        //             kdtreeNMPoints[i] = (int)kdtreeCosts[i].params["trees"];
-        //             kdtreeVals[i] = kdtreeCosts[i].timeCost;
-        //         }
-        //         KDTreeSimpleDownhillFunctor kdtree_cost_func(*this);
-        //         // run optimization
-        //         optimizeSimplexDownhill(kdtreeNMPoints,n,kdtree_cost_func,kdtreeVals);
-        //         // store results
-        //         for (int i=0;i<n+1;++i) {
-        //             kdtreeCosts[i].params["trees"] = kdtreeNMPoints[i];
-        //             kdtreeCosts[i].timeCost = kdtreeVals[i];
-        //         }
-    }
-
-    /**
-     *  Chooses the best nearest-neighbor algorithm and estimates the optimal
-     *  parameters to use when building the index (for a given precision).
-     *  Returns a dictionary with the optimal parameters.
-     */
-    IndexParams estimateBuildParams()
-    {
-        std::vector<CostData> costs;
-
-        int sampleSize = int(sample_fraction_ * dataset_.rows);
-        int testSampleSize = std::min(sampleSize / 10, 1000);
-
-        Logger::info("Entering autotuning, dataset size: %d, sampleSize: %d, testSampleSize: %d, target precision: %g\n", dataset_.rows, sampleSize, testSampleSize, target_precision_);
-
-        // For a very small dataset, it makes no sense to build any fancy index, just
-        // use linear search
-        if (testSampleSize < 10) {
-            Logger::info("Choosing linear, dataset too small\n");
-            return LinearIndexParams();
-        }
-
-        // We use a fraction of the original dataset to speedup the autotune algorithm
-        sampledDataset_ = random_sample(dataset_, sampleSize);
-        // We use a cross-validation approach, first we sample a testset from the dataset
-        testDataset_ = random_sample(sampledDataset_, testSampleSize, true);
-
-        // We compute the ground truth using linear search
-        Logger::info("Computing ground truth... \n");
-        gt_matches_ = Matrix<int>(new int[testDataset_.rows], testDataset_.rows, 1);
-        StartStopTimer t;
-        t.start();
-        compute_ground_truth<Distance>(sampledDataset_, testDataset_, gt_matches_, 0, distance_);
-        t.stop();
-
-        CostData linear_cost;
-        linear_cost.searchTimeCost = (float)t.value;
-        linear_cost.buildTimeCost = 0;
-        linear_cost.memoryCost = 0;
-        linear_cost.params["algorithm"] = FLANN_INDEX_LINEAR;
-
-        costs.push_back(linear_cost);
-
-        // Start parameter autotune process
-        Logger::info("Autotuning parameters...\n");
-
-        optimizeKMeans(costs);
-        optimizeKDTree(costs);
-
-        float bestTimeCost = costs[0].searchTimeCost;
-        for (size_t i = 0; i < costs.size(); ++i) {
-            float timeCost = costs[i].buildTimeCost * build_weight_ + costs[i].searchTimeCost;
-            if (timeCost < bestTimeCost) {
-                bestTimeCost = timeCost;
-            }
-        }
-
-        float bestCost = costs[0].searchTimeCost / bestTimeCost;
-        IndexParams bestParams = costs[0].params;
-        if (bestTimeCost > 0) {
-            for (size_t i = 0; i < costs.size(); ++i) {
-                float crtCost = (costs[i].buildTimeCost * build_weight_ + costs[i].searchTimeCost) / bestTimeCost +
-                                memory_weight_ * costs[i].memoryCost;
-                if (crtCost < bestCost) {
-                    bestCost = crtCost;
-                    bestParams = costs[i].params;
-                }
-            }
-        }
-
-        delete[] gt_matches_.data;
-        delete[] testDataset_.data;
-        delete[] sampledDataset_.data;
-
-        return bestParams;
-    }
-
-
-
-    /**
-     *  Estimates the search time parameters needed to get the desired precision.
-     *  Precondition: the index is built
-     *  Postcondition: the searchParams will have the optimum params set, also the speedup obtained over linear search.
-     */
-    float estimateSearchParams(SearchParams& searchParams)
-    {
-        const int nn = 1;
-        const size_t SAMPLE_COUNT = 1000;
-
-        assert(bestIndex_ != NULL); // must have a valid index
-
-        float speedup = 0;
-
-        int samples = (int)std::min(dataset_.rows / 10, SAMPLE_COUNT);
-        if (samples > 0) {
-            Matrix<ElementType> testDataset = random_sample(dataset_, samples);
-
-            Logger::info("Computing ground truth\n");
-
-            // we need to compute the ground truth first
-            Matrix<int> gt_matches(new int[testDataset.rows], testDataset.rows, 1);
-            StartStopTimer t;
-            t.start();
-            compute_ground_truth<Distance>(dataset_, testDataset, gt_matches, 1, distance_);
-            t.stop();
-            float linear = (float)t.value;
-
-            int checks;
-            Logger::info("Estimating number of checks\n");
-
-            float searchTime;
-            float cb_index;
-            if (bestIndex_->getType() == FLANN_INDEX_KMEANS) {
-                Logger::info("KMeans algorithm, estimating cluster border factor\n");
-                KMeansIndex<Distance>* kmeans = (KMeansIndex<Distance>*)bestIndex_;
-                float bestSearchTime = -1;
-                float best_cb_index = -1;
-                int best_checks = -1;
-                for (cb_index = 0; cb_index < 1.1f; cb_index += 0.2f) {
-                    kmeans->set_cb_index(cb_index);
-                    searchTime = test_index_precision(*kmeans, dataset_, testDataset, gt_matches, target_precision_, checks, distance_, nn, 1);
-                    if ((searchTime < bestSearchTime) || (bestSearchTime == -1)) {
-                        bestSearchTime = searchTime;
-                        best_cb_index = cb_index;
-                        best_checks = checks;
-                    }
-                }
-                searchTime = bestSearchTime;
-                cb_index = best_cb_index;
-                checks = best_checks;
-
-                kmeans->set_cb_index(best_cb_index);
-                Logger::info("Optimum cb_index: %g\n", cb_index);
-                bestParams_["cb_index"] = cb_index;
-            }
-            else {
-                searchTime = test_index_precision(*bestIndex_, dataset_, testDataset, gt_matches, target_precision_, checks, distance_, nn, 1);
-            }
-
-            Logger::info("Required number of checks: %d \n", checks);
-            searchParams["checks"] = checks;
-
-            speedup = linear / searchTime;
-
-            delete[] gt_matches.data;
-            delete[] testDataset.data;
-        }
-
-        return speedup;
-    }
-
-private:
-    NNIndex<Distance>* bestIndex_;
-
-    IndexParams bestParams_;
-    SearchParams bestSearchParams_;
-
-    Matrix<ElementType> sampledDataset_;
-    Matrix<ElementType> testDataset_;
-    Matrix<int> gt_matches_;
-
-    float speedup_;
-
-    /**
-     * The dataset used by this index
-     */
-    const Matrix<ElementType> dataset_;
-
-    /**
-     * Index parameters
-     */
-    float target_precision_;
-    float build_weight_;
-    float memory_weight_;
-    float sample_fraction_;
-
-    Distance distance_;
-
-
-};
-}
-
-#endif /* OPENCV_FLANN_AUTOTUNED_INDEX_H_ */
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/composite_index.h b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/composite_index.h
deleted file mode 100755
index 5e12a17..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/composite_index.h
+++ /dev/null
@@ -1,194 +0,0 @@
-/***********************************************************************
- * Software License Agreement (BSD License)
- *
- * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
- * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
- *
- * THE BSD LICENSE
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions
- * are met:
- *
- * 1. Redistributions of source code must retain the above copyright
- *    notice, this list of conditions and the following disclaimer.
- * 2. Redistributions in binary form must reproduce the above copyright
- *    notice, this list of conditions and the following disclaimer in the
- *    documentation and/or other materials provided with the distribution.
- *
- * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
- * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
- * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
- * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
- * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
- * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
- * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
- * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *************************************************************************/
-
-#ifndef OPENCV_FLANN_COMPOSITE_INDEX_H_
-#define OPENCV_FLANN_COMPOSITE_INDEX_H_
-
-#include "general.h"
-#include "nn_index.h"
-#include "kdtree_index.h"
-#include "kmeans_index.h"
-
-namespace cvflann
-{
-
-/**
- * Index parameters for the CompositeIndex.
- */
-struct CompositeIndexParams : public IndexParams
-{
-    CompositeIndexParams(int trees = 4, int branching = 32, int iterations = 11,
-                         flann_centers_init_t centers_init = FLANN_CENTERS_RANDOM, float cb_index = 0.2 )
-    {
-        (*this)["algorithm"] = FLANN_INDEX_KMEANS;
-        // number of randomized trees to use (for kdtree)
-        (*this)["trees"] = trees;
-        // branching factor
-        (*this)["branching"] = branching;
-        // max iterations to perform in one kmeans clustering (kmeans tree)
-        (*this)["iterations"] = iterations;
-        // algorithm used for picking the initial cluster centers for kmeans tree
-        (*this)["centers_init"] = centers_init;
-        // cluster boundary index. Used when searching the kmeans tree
-        (*this)["cb_index"] = cb_index;
-    }
-};
-
-
-/**
- * This index builds a kd-tree index and a k-means index and performs nearest
- * neighbour search both indexes. This gives a slight boost in search performance
- * as some of the neighbours that are missed by one index are found by the other.
- */
-template <typename Distance>
-class CompositeIndex : public NNIndex<Distance>
-{
-public:
-    typedef typename Distance::ElementType ElementType;
-    typedef typename Distance::ResultType DistanceType;
-
-    /**
-     * Index constructor
-     * @param inputData dataset containing the points to index
-     * @param params Index parameters
-     * @param d Distance functor
-     * @return
-     */
-    CompositeIndex(const Matrix<ElementType>& inputData, const IndexParams& params = CompositeIndexParams(),
-                   Distance d = Distance()) : index_params_(params)
-    {
-        kdtree_index_ = new KDTreeIndex<Distance>(inputData, params, d);
-        kmeans_index_ = new KMeansIndex<Distance>(inputData, params, d);
-
-    }
-
-    CompositeIndex(const CompositeIndex&);
-    CompositeIndex& operator=(const CompositeIndex&);
-
-    virtual ~CompositeIndex()
-    {
-        delete kdtree_index_;
-        delete kmeans_index_;
-    }
-
-    /**
-     * @return The index type
-     */
-    flann_algorithm_t getType() const CV_OVERRIDE
-    {
-        return FLANN_INDEX_COMPOSITE;
-    }
-
-    /**
-     * @return Size of the index
-     */
-    size_t size() const CV_OVERRIDE
-    {
-        return kdtree_index_->size();
-    }
-
-    /**
-     * \returns The dimensionality of the features in this index.
-     */
-    size_t veclen() const CV_OVERRIDE
-    {
-        return kdtree_index_->veclen();
-    }
-
-    /**
-     * \returns The amount of memory (in bytes) used by the index.
-     */
-    int usedMemory() const CV_OVERRIDE
-    {
-        return kmeans_index_->usedMemory() + kdtree_index_->usedMemory();
-    }
-
-    /**
-     * \brief Builds the index
-     */
-    void buildIndex() CV_OVERRIDE
-    {
-        Logger::info("Building kmeans tree...\n");
-        kmeans_index_->buildIndex();
-        Logger::info("Building kdtree tree...\n");
-        kdtree_index_->buildIndex();
-    }
-
-    /**
-     * \brief Saves the index to a stream
-     * \param stream The stream to save the index to
-     */
-    void saveIndex(FILE* stream) CV_OVERRIDE
-    {
-        kmeans_index_->saveIndex(stream);
-        kdtree_index_->saveIndex(stream);
-    }
-
-    /**
-     * \brief Loads the index from a stream
-     * \param stream The stream from which the index is loaded
-     */
-    void loadIndex(FILE* stream) CV_OVERRIDE
-    {
-        kmeans_index_->loadIndex(stream);
-        kdtree_index_->loadIndex(stream);
-    }
-
-    /**
-     * \returns The index parameters
-     */
-    IndexParams getParameters() const CV_OVERRIDE
-    {
-        return index_params_;
-    }
-
-    /**
-     * \brief Method that searches for nearest-neighbours
-     */
-    void findNeighbors(ResultSet<DistanceType>& result, const ElementType* vec, const SearchParams& searchParams) CV_OVERRIDE
-    {
-        kmeans_index_->findNeighbors(result, vec, searchParams);
-        kdtree_index_->findNeighbors(result, vec, searchParams);
-    }
-
-private:
-    /** The k-means index */
-    KMeansIndex<Distance>* kmeans_index_;
-
-    /** The kd-tree index */
-    KDTreeIndex<Distance>* kdtree_index_;
-
-    /** The index parameters */
-    const IndexParams index_params_;
-};
-
-}
-
-#endif //OPENCV_FLANN_COMPOSITE_INDEX_H_
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/config.h b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/config.h
deleted file mode 100755
index 56832fd..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/config.h
+++ /dev/null
@@ -1,38 +0,0 @@
-/***********************************************************************
- * Software License Agreement (BSD License)
- *
- * Copyright 2008-2011  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
- * Copyright 2008-2011  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions
- * are met:
- *
- * 1. Redistributions of source code must retain the above copyright
- *    notice, this list of conditions and the following disclaimer.
- * 2. Redistributions in binary form must reproduce the above copyright
- *    notice, this list of conditions and the following disclaimer in the
- *    documentation and/or other materials provided with the distribution.
- *
- * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
- * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
- * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
- * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
- * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
- * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
- * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
- * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *************************************************************************/
-
-
-#ifndef OPENCV_FLANN_CONFIG_H_
-#define OPENCV_FLANN_CONFIG_H_
-
-#ifdef FLANN_VERSION_
-#undef FLANN_VERSION_
-#endif
-#define FLANN_VERSION_ "1.6.10"
-
-#endif /* OPENCV_FLANN_CONFIG_H_ */
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/defines.h b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/defines.h
deleted file mode 100755
index 6fd53c2..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/defines.h
+++ /dev/null
@@ -1,164 +0,0 @@
-/***********************************************************************
- * Software License Agreement (BSD License)
- *
- * Copyright 2008-2011  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
- * Copyright 2008-2011  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions
- * are met:
- *
- * 1. Redistributions of source code must retain the above copyright
- *    notice, this list of conditions and the following disclaimer.
- * 2. Redistributions in binary form must reproduce the above copyright
- *    notice, this list of conditions and the following disclaimer in the
- *    documentation and/or other materials provided with the distribution.
- *
- * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
- * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
- * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
- * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
- * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
- * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
- * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
- * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *************************************************************************/
-
-
-#ifndef OPENCV_FLANN_DEFINES_H_
-#define OPENCV_FLANN_DEFINES_H_
-
-#include "config.h"
-
-#ifdef FLANN_EXPORT
-#undef FLANN_EXPORT
-#endif
-#ifdef _WIN32
-/* win32 dll export/import directives */
- #ifdef FLANN_EXPORTS
-  #define FLANN_EXPORT __declspec(dllexport)
- #elif defined(FLANN_STATIC)
-  #define FLANN_EXPORT
- #else
-  #define FLANN_EXPORT __declspec(dllimport)
- #endif
-#else
-/* unix needs nothing */
- #define FLANN_EXPORT
-#endif
-
-
-#undef FLANN_PLATFORM_32_BIT
-#undef FLANN_PLATFORM_64_BIT
-#if defined __amd64__ || defined __x86_64__ || defined _WIN64 || defined _M_X64
-#define FLANN_PLATFORM_64_BIT
-#else
-#define FLANN_PLATFORM_32_BIT
-#endif
-
-
-#undef FLANN_ARRAY_LEN
-#define FLANN_ARRAY_LEN(a) (sizeof(a)/sizeof(a[0]))
-
-namespace cvflann {
-
-/* Nearest neighbour index algorithms */
-enum flann_algorithm_t
-{
-    FLANN_INDEX_LINEAR = 0,
-    FLANN_INDEX_KDTREE = 1,
-    FLANN_INDEX_KMEANS = 2,
-    FLANN_INDEX_COMPOSITE = 3,
-    FLANN_INDEX_KDTREE_SINGLE = 4,
-    FLANN_INDEX_HIERARCHICAL = 5,
-    FLANN_INDEX_LSH = 6,
-    FLANN_INDEX_SAVED = 254,
-    FLANN_INDEX_AUTOTUNED = 255,
-
-    // deprecated constants, should use the FLANN_INDEX_* ones instead
-    LINEAR = 0,
-    KDTREE = 1,
-    KMEANS = 2,
-    COMPOSITE = 3,
-    KDTREE_SINGLE = 4,
-    SAVED = 254,
-    AUTOTUNED = 255
-};
-
-
-
-enum flann_centers_init_t
-{
-    FLANN_CENTERS_RANDOM = 0,
-    FLANN_CENTERS_GONZALES = 1,
-    FLANN_CENTERS_KMEANSPP = 2,
-    FLANN_CENTERS_GROUPWISE = 3,
-
-    // deprecated constants, should use the FLANN_CENTERS_* ones instead
-    CENTERS_RANDOM = 0,
-    CENTERS_GONZALES = 1,
-    CENTERS_KMEANSPP = 2
-};
-
-enum flann_log_level_t
-{
-    FLANN_LOG_NONE = 0,
-    FLANN_LOG_FATAL = 1,
-    FLANN_LOG_ERROR = 2,
-    FLANN_LOG_WARN = 3,
-    FLANN_LOG_INFO = 4
-};
-
-enum flann_distance_t
-{
-    FLANN_DIST_EUCLIDEAN = 1,
-    FLANN_DIST_L2 = 1,
-    FLANN_DIST_MANHATTAN = 2,
-    FLANN_DIST_L1 = 2,
-    FLANN_DIST_MINKOWSKI = 3,
-    FLANN_DIST_MAX   = 4,
-    FLANN_DIST_HIST_INTERSECT   = 5,
-    FLANN_DIST_HELLINGER = 6,
-    FLANN_DIST_CHI_SQUARE = 7,
-    FLANN_DIST_CS         = 7,
-    FLANN_DIST_KULLBACK_LEIBLER  = 8,
-    FLANN_DIST_KL                = 8,
-    FLANN_DIST_HAMMING          = 9,
-
-    // deprecated constants, should use the FLANN_DIST_* ones instead
-    EUCLIDEAN = 1,
-    MANHATTAN = 2,
-    MINKOWSKI = 3,
-    MAX_DIST   = 4,
-    HIST_INTERSECT   = 5,
-    HELLINGER = 6,
-    CS         = 7,
-    KL         = 8,
-    KULLBACK_LEIBLER  = 8
-};
-
-enum flann_datatype_t
-{
-    FLANN_INT8 = 0,
-    FLANN_INT16 = 1,
-    FLANN_INT32 = 2,
-    FLANN_INT64 = 3,
-    FLANN_UINT8 = 4,
-    FLANN_UINT16 = 5,
-    FLANN_UINT32 = 6,
-    FLANN_UINT64 = 7,
-    FLANN_FLOAT32 = 8,
-    FLANN_FLOAT64 = 9
-};
-
-enum
-{
-    FLANN_CHECKS_UNLIMITED = -1,
-    FLANN_CHECKS_AUTOTUNED = -2
-};
-
-}
-
-#endif /* OPENCV_FLANN_DEFINES_H_ */
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/dist.h b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/dist.h
deleted file mode 100755
index eedaeff..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/dist.h
+++ /dev/null
@@ -1,905 +0,0 @@
-/***********************************************************************
- * Software License Agreement (BSD License)
- *
- * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
- * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
- *
- * THE BSD LICENSE
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions
- * are met:
- *
- * 1. Redistributions of source code must retain the above copyright
- *    notice, this list of conditions and the following disclaimer.
- * 2. Redistributions in binary form must reproduce the above copyright
- *    notice, this list of conditions and the following disclaimer in the
- *    documentation and/or other materials provided with the distribution.
- *
- * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
- * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
- * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
- * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
- * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
- * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
- * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
- * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *************************************************************************/
-
-#ifndef OPENCV_FLANN_DIST_H_
-#define OPENCV_FLANN_DIST_H_
-
-#include <cmath>
-#include <cstdlib>
-#include <string.h>
-#ifdef _MSC_VER
-typedef unsigned __int32 uint32_t;
-typedef unsigned __int64 uint64_t;
-#else
-#include <stdint.h>
-#endif
-
-#include "defines.h"
-
-#if defined _WIN32 && defined(_M_ARM)
-# include <Intrin.h>
-#endif
-
-#ifdef __ARM_NEON__
-# include "arm_neon.h"
-#endif
-
-namespace cvflann
-{
-
-template<typename T>
-inline T abs(T x) { return (x<0) ? -x : x; }
-
-template<>
-inline int abs<int>(int x) { return ::abs(x); }
-
-template<>
-inline float abs<float>(float x) { return fabsf(x); }
-
-template<>
-inline double abs<double>(double x) { return fabs(x); }
-
-template<typename T>
-struct Accumulator { typedef T Type; };
-template<>
-struct Accumulator<unsigned char>  { typedef float Type; };
-template<>
-struct Accumulator<unsigned short> { typedef float Type; };
-template<>
-struct Accumulator<unsigned int> { typedef float Type; };
-template<>
-struct Accumulator<char>   { typedef float Type; };
-template<>
-struct Accumulator<short>  { typedef float Type; };
-template<>
-struct Accumulator<int> { typedef float Type; };
-
-#undef True
-#undef False
-
-class True
-{
-};
-
-class False
-{
-};
-
-
-/**
- * Squared Euclidean distance functor.
- *
- * This is the simpler, unrolled version. This is preferable for
- * very low dimensionality data (eg 3D points)
- */
-template<class T>
-struct L2_Simple
-{
-    typedef True is_kdtree_distance;
-    typedef True is_vector_space_distance;
-
-    typedef T ElementType;
-    typedef typename Accumulator<T>::Type ResultType;
-
-    template <typename Iterator1, typename Iterator2>
-    ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType /*worst_dist*/ = -1) const
-    {
-        ResultType result = ResultType();
-        ResultType diff;
-        for(size_t i = 0; i < size; ++i ) {
-            diff = *a++ - *b++;
-            result += diff*diff;
-        }
-        return result;
-    }
-
-    template <typename U, typename V>
-    inline ResultType accum_dist(const U& a, const V& b, int) const
-    {
-        return (a-b)*(a-b);
-    }
-};
-
-
-
-/**
- * Squared Euclidean distance functor, optimized version
- */
-template<class T>
-struct L2
-{
-    typedef True is_kdtree_distance;
-    typedef True is_vector_space_distance;
-
-    typedef T ElementType;
-    typedef typename Accumulator<T>::Type ResultType;
-
-    /**
-     *  Compute the squared Euclidean distance between two vectors.
-     *
-     *	This is highly optimised, with loop unrolling, as it is one
-     *	of the most expensive inner loops.
-     *
-     *	The computation of squared root at the end is omitted for
-     *	efficiency.
-     */
-    template <typename Iterator1, typename Iterator2>
-    ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType worst_dist = -1) const
-    {
-        ResultType result = ResultType();
-        ResultType diff0, diff1, diff2, diff3;
-        Iterator1 last = a + size;
-        Iterator1 lastgroup = last - 3;
-
-        /* Process 4 items with each loop for efficiency. */
-        while (a < lastgroup) {
-            diff0 = (ResultType)(a[0] - b[0]);
-            diff1 = (ResultType)(a[1] - b[1]);
-            diff2 = (ResultType)(a[2] - b[2]);
-            diff3 = (ResultType)(a[3] - b[3]);
-            result += diff0 * diff0 + diff1 * diff1 + diff2 * diff2 + diff3 * diff3;
-            a += 4;
-            b += 4;
-
-            if ((worst_dist>0)&&(result>worst_dist)) {
-                return result;
-            }
-        }
-        /* Process last 0-3 pixels.  Not needed for standard vector lengths. */
-        while (a < last) {
-            diff0 = (ResultType)(*a++ - *b++);
-            result += diff0 * diff0;
-        }
-        return result;
-    }
-
-    /**
-     *	Partial euclidean distance, using just one dimension. This is used by the
-     *	kd-tree when computing partial distances while traversing the tree.
-     *
-     *	Squared root is omitted for efficiency.
-     */
-    template <typename U, typename V>
-    inline ResultType accum_dist(const U& a, const V& b, int) const
-    {
-        return (a-b)*(a-b);
-    }
-};
-
-
-/*
- * Manhattan distance functor, optimized version
- */
-template<class T>
-struct L1
-{
-    typedef True is_kdtree_distance;
-    typedef True is_vector_space_distance;
-
-    typedef T ElementType;
-    typedef typename Accumulator<T>::Type ResultType;
-
-    /**
-     *  Compute the Manhattan (L_1) distance between two vectors.
-     *
-     *	This is highly optimised, with loop unrolling, as it is one
-     *	of the most expensive inner loops.
-     */
-    template <typename Iterator1, typename Iterator2>
-    ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType worst_dist = -1) const
-    {
-        ResultType result = ResultType();
-        ResultType diff0, diff1, diff2, diff3;
-        Iterator1 last = a + size;
-        Iterator1 lastgroup = last - 3;
-
-        /* Process 4 items with each loop for efficiency. */
-        while (a < lastgroup) {
-            diff0 = (ResultType)abs(a[0] - b[0]);
-            diff1 = (ResultType)abs(a[1] - b[1]);
-            diff2 = (ResultType)abs(a[2] - b[2]);
-            diff3 = (ResultType)abs(a[3] - b[3]);
-            result += diff0 + diff1 + diff2 + diff3;
-            a += 4;
-            b += 4;
-
-            if ((worst_dist>0)&&(result>worst_dist)) {
-                return result;
-            }
-        }
-        /* Process last 0-3 pixels.  Not needed for standard vector lengths. */
-        while (a < last) {
-            diff0 = (ResultType)abs(*a++ - *b++);
-            result += diff0;
-        }
-        return result;
-    }
-
-    /**
-     * Partial distance, used by the kd-tree.
-     */
-    template <typename U, typename V>
-    inline ResultType accum_dist(const U& a, const V& b, int) const
-    {
-        return abs(a-b);
-    }
-};
-
-
-
-template<class T>
-struct MinkowskiDistance
-{
-    typedef True is_kdtree_distance;
-    typedef True is_vector_space_distance;
-
-    typedef T ElementType;
-    typedef typename Accumulator<T>::Type ResultType;
-
-    int order;
-
-    MinkowskiDistance(int order_) : order(order_) {}
-
-    /**
-     *  Compute the Minkowsky (L_p) distance between two vectors.
-     *
-     *	This is highly optimised, with loop unrolling, as it is one
-     *	of the most expensive inner loops.
-     *
-     *	The computation of squared root at the end is omitted for
-     *	efficiency.
-     */
-    template <typename Iterator1, typename Iterator2>
-    ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType worst_dist = -1) const
-    {
-        ResultType result = ResultType();
-        ResultType diff0, diff1, diff2, diff3;
-        Iterator1 last = a + size;
-        Iterator1 lastgroup = last - 3;
-
-        /* Process 4 items with each loop for efficiency. */
-        while (a < lastgroup) {
-            diff0 = (ResultType)abs(a[0] - b[0]);
-            diff1 = (ResultType)abs(a[1] - b[1]);
-            diff2 = (ResultType)abs(a[2] - b[2]);
-            diff3 = (ResultType)abs(a[3] - b[3]);
-            result += pow(diff0,order) + pow(diff1,order) + pow(diff2,order) + pow(diff3,order);
-            a += 4;
-            b += 4;
-
-            if ((worst_dist>0)&&(result>worst_dist)) {
-                return result;
-            }
-        }
-        /* Process last 0-3 pixels.  Not needed for standard vector lengths. */
-        while (a < last) {
-            diff0 = (ResultType)abs(*a++ - *b++);
-            result += pow(diff0,order);
-        }
-        return result;
-    }
-
-    /**
-     * Partial distance, used by the kd-tree.
-     */
-    template <typename U, typename V>
-    inline ResultType accum_dist(const U& a, const V& b, int) const
-    {
-        return pow(static_cast<ResultType>(abs(a-b)),order);
-    }
-};
-
-
-
-template<class T>
-struct MaxDistance
-{
-    typedef False is_kdtree_distance;
-    typedef True is_vector_space_distance;
-
-    typedef T ElementType;
-    typedef typename Accumulator<T>::Type ResultType;
-
-    /**
-     *  Compute the max distance (L_infinity) between two vectors.
-     *
-     *  This distance is not a valid kdtree distance, it's not dimensionwise additive.
-     */
-    template <typename Iterator1, typename Iterator2>
-    ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType worst_dist = -1) const
-    {
-        ResultType result = ResultType();
-        ResultType diff0, diff1, diff2, diff3;
-        Iterator1 last = a + size;
-        Iterator1 lastgroup = last - 3;
-
-        /* Process 4 items with each loop for efficiency. */
-        while (a < lastgroup) {
-            diff0 = abs(a[0] - b[0]);
-            diff1 = abs(a[1] - b[1]);
-            diff2 = abs(a[2] - b[2]);
-            diff3 = abs(a[3] - b[3]);
-            if (diff0>result) {result = diff0; }
-            if (diff1>result) {result = diff1; }
-            if (diff2>result) {result = diff2; }
-            if (diff3>result) {result = diff3; }
-            a += 4;
-            b += 4;
-
-            if ((worst_dist>0)&&(result>worst_dist)) {
-                return result;
-            }
-        }
-        /* Process last 0-3 pixels.  Not needed for standard vector lengths. */
-        while (a < last) {
-            diff0 = abs(*a++ - *b++);
-            result = (diff0>result) ? diff0 : result;
-        }
-        return result;
-    }
-
-    /* This distance functor is not dimension-wise additive, which
-     * makes it an invalid kd-tree distance, not implementing the accum_dist method */
-
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
-
-/**
- * Hamming distance functor - counts the bit differences between two strings - useful for the Brief descriptor
- * bit count of A exclusive XOR'ed with B
- */
-struct HammingLUT
-{
-    typedef False is_kdtree_distance;
-    typedef False is_vector_space_distance;
-
-    typedef unsigned char ElementType;
-    typedef int ResultType;
-
-    /** this will count the bits in a ^ b
-     */
-    ResultType operator()(const unsigned char* a, const unsigned char* b, size_t size) const
-    {
-        static const uchar popCountTable[] =
-        {
-            0, 1, 1, 2, 1, 2, 2, 3, 1, 2, 2, 3, 2, 3, 3, 4, 1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5,
-            1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6,
-            1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6,
-            2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7,
-            1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6,
-            2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7,
-            2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7,
-            3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7, 4, 5, 5, 6, 5, 6, 6, 7, 5, 6, 6, 7, 6, 7, 7, 8
-        };
-        ResultType result = 0;
-        for (size_t i = 0; i < size; i++) {
-            result += popCountTable[a[i] ^ b[i]];
-        }
-        return result;
-    }
-};
-
-/**
- * Hamming distance functor (pop count between two binary vectors, i.e. xor them and count the number of bits set)
- * That code was taken from brief.cpp in OpenCV
- */
-template<class T>
-struct Hamming
-{
-    typedef False is_kdtree_distance;
-    typedef False is_vector_space_distance;
-
-
-    typedef T ElementType;
-    typedef int ResultType;
-
-    template<typename Iterator1, typename Iterator2>
-    ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType /*worst_dist*/ = -1) const
-    {
-        ResultType result = 0;
-#ifdef __ARM_NEON__
-        {
-            uint32x4_t bits = vmovq_n_u32(0);
-            for (size_t i = 0; i < size; i += 16) {
-                uint8x16_t A_vec = vld1q_u8 (a + i);
-                uint8x16_t B_vec = vld1q_u8 (b + i);
-                uint8x16_t AxorB = veorq_u8 (A_vec, B_vec);
-                uint8x16_t bitsSet = vcntq_u8 (AxorB);
-                uint16x8_t bitSet8 = vpaddlq_u8 (bitsSet);
-                uint32x4_t bitSet4 = vpaddlq_u16 (bitSet8);
-                bits = vaddq_u32(bits, bitSet4);
-            }
-            uint64x2_t bitSet2 = vpaddlq_u32 (bits);
-            result = vgetq_lane_s32 (vreinterpretq_s32_u64(bitSet2),0);
-            result += vgetq_lane_s32 (vreinterpretq_s32_u64(bitSet2),2);
-        }
-#elif __GNUC__
-        {
-            //for portability just use unsigned long -- and use the __builtin_popcountll (see docs for __builtin_popcountll)
-            typedef unsigned long long pop_t;
-            const size_t modulo = size % sizeof(pop_t);
-            const pop_t* a2 = reinterpret_cast<const pop_t*> (a);
-            const pop_t* b2 = reinterpret_cast<const pop_t*> (b);
-            const pop_t* a2_end = a2 + (size / sizeof(pop_t));
-
-            for (; a2 != a2_end; ++a2, ++b2) result += __builtin_popcountll((*a2) ^ (*b2));
-
-            if (modulo) {
-                //in the case where size is not dividable by sizeof(size_t)
-                //need to mask off the bits at the end
-                pop_t a_final = 0, b_final = 0;
-                memcpy(&a_final, a2, modulo);
-                memcpy(&b_final, b2, modulo);
-                result += __builtin_popcountll(a_final ^ b_final);
-            }
-        }
-#else // NO NEON and NOT GNUC
-        typedef unsigned long long pop_t;
-        HammingLUT lut;
-        result = lut(reinterpret_cast<const unsigned char*> (a),
-                     reinterpret_cast<const unsigned char*> (b), size * sizeof(pop_t));
-#endif
-        return result;
-    }
-};
-
-template<typename T>
-struct Hamming2
-{
-    typedef False is_kdtree_distance;
-    typedef False is_vector_space_distance;
-
-    typedef T ElementType;
-    typedef int ResultType;
-
-    /** This is popcount_3() from:
-     * http://en.wikipedia.org/wiki/Hamming_weight */
-    unsigned int popcnt32(uint32_t n) const
-    {
-        n -= ((n >> 1) & 0x55555555);
-        n = (n & 0x33333333) + ((n >> 2) & 0x33333333);
-        return (((n + (n >> 4))& 0xF0F0F0F)* 0x1010101) >> 24;
-    }
-
-#ifdef FLANN_PLATFORM_64_BIT
-    unsigned int popcnt64(uint64_t n) const
-    {
-        n -= ((n >> 1) & 0x5555555555555555);
-        n = (n & 0x3333333333333333) + ((n >> 2) & 0x3333333333333333);
-        return (((n + (n >> 4))& 0x0f0f0f0f0f0f0f0f)* 0x0101010101010101) >> 56;
-    }
-#endif
-
-    template <typename Iterator1, typename Iterator2>
-    ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType /*worst_dist*/ = -1) const
-    {
-#ifdef FLANN_PLATFORM_64_BIT
-        const uint64_t* pa = reinterpret_cast<const uint64_t*>(a);
-        const uint64_t* pb = reinterpret_cast<const uint64_t*>(b);
-        ResultType result = 0;
-        size /= (sizeof(uint64_t)/sizeof(unsigned char));
-        for(size_t i = 0; i < size; ++i ) {
-            result += popcnt64(*pa ^ *pb);
-            ++pa;
-            ++pb;
-        }
-#else
-        const uint32_t* pa = reinterpret_cast<const uint32_t*>(a);
-        const uint32_t* pb = reinterpret_cast<const uint32_t*>(b);
-        ResultType result = 0;
-        size /= (sizeof(uint32_t)/sizeof(unsigned char));
-        for(size_t i = 0; i < size; ++i ) {
-            result += popcnt32(*pa ^ *pb);
-            ++pa;
-            ++pb;
-        }
-#endif
-        return result;
-    }
-};
-
-
-
-////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template<class T>
-struct HistIntersectionDistance
-{
-    typedef True is_kdtree_distance;
-    typedef True is_vector_space_distance;
-
-    typedef T ElementType;
-    typedef typename Accumulator<T>::Type ResultType;
-
-    /**
-     *  Compute the histogram intersection distance
-     */
-    template <typename Iterator1, typename Iterator2>
-    ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType worst_dist = -1) const
-    {
-        ResultType result = ResultType();
-        ResultType min0, min1, min2, min3;
-        Iterator1 last = a + size;
-        Iterator1 lastgroup = last - 3;
-
-        /* Process 4 items with each loop for efficiency. */
-        while (a < lastgroup) {
-            min0 = (ResultType)(a[0] < b[0] ? a[0] : b[0]);
-            min1 = (ResultType)(a[1] < b[1] ? a[1] : b[1]);
-            min2 = (ResultType)(a[2] < b[2] ? a[2] : b[2]);
-            min3 = (ResultType)(a[3] < b[3] ? a[3] : b[3]);
-            result += min0 + min1 + min2 + min3;
-            a += 4;
-            b += 4;
-            if ((worst_dist>0)&&(result>worst_dist)) {
-                return result;
-            }
-        }
-        /* Process last 0-3 pixels.  Not needed for standard vector lengths. */
-        while (a < last) {
-            min0 = (ResultType)(*a < *b ? *a : *b);
-            result += min0;
-            ++a;
-            ++b;
-        }
-        return result;
-    }
-
-    /**
-     * Partial distance, used by the kd-tree.
-     */
-    template <typename U, typename V>
-    inline ResultType accum_dist(const U& a, const V& b, int) const
-    {
-        return a<b ? a : b;
-    }
-};
-
-
-
-template<class T>
-struct HellingerDistance
-{
-    typedef True is_kdtree_distance;
-    typedef True is_vector_space_distance;
-
-    typedef T ElementType;
-    typedef typename Accumulator<T>::Type ResultType;
-
-    /**
-     *  Compute the Hellinger distance
-     */
-    template <typename Iterator1, typename Iterator2>
-    ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType /*worst_dist*/ = -1) const
-    {
-        ResultType result = ResultType();
-        ResultType diff0, diff1, diff2, diff3;
-        Iterator1 last = a + size;
-        Iterator1 lastgroup = last - 3;
-
-        /* Process 4 items with each loop for efficiency. */
-        while (a < lastgroup) {
-            diff0 = sqrt(static_cast<ResultType>(a[0])) - sqrt(static_cast<ResultType>(b[0]));
-            diff1 = sqrt(static_cast<ResultType>(a[1])) - sqrt(static_cast<ResultType>(b[1]));
-            diff2 = sqrt(static_cast<ResultType>(a[2])) - sqrt(static_cast<ResultType>(b[2]));
-            diff3 = sqrt(static_cast<ResultType>(a[3])) - sqrt(static_cast<ResultType>(b[3]));
-            result += diff0 * diff0 + diff1 * diff1 + diff2 * diff2 + diff3 * diff3;
-            a += 4;
-            b += 4;
-        }
-        while (a < last) {
-            diff0 = sqrt(static_cast<ResultType>(*a++)) - sqrt(static_cast<ResultType>(*b++));
-            result += diff0 * diff0;
-        }
-        return result;
-    }
-
-    /**
-     * Partial distance, used by the kd-tree.
-     */
-    template <typename U, typename V>
-    inline ResultType accum_dist(const U& a, const V& b, int) const
-    {
-        ResultType diff = sqrt(static_cast<ResultType>(a)) - sqrt(static_cast<ResultType>(b));
-        return diff * diff;
-    }
-};
-
-
-template<class T>
-struct ChiSquareDistance
-{
-    typedef True is_kdtree_distance;
-    typedef True is_vector_space_distance;
-
-    typedef T ElementType;
-    typedef typename Accumulator<T>::Type ResultType;
-
-    /**
-     *  Compute the chi-square distance
-     */
-    template <typename Iterator1, typename Iterator2>
-    ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType worst_dist = -1) const
-    {
-        ResultType result = ResultType();
-        ResultType sum, diff;
-        Iterator1 last = a + size;
-
-        while (a < last) {
-            sum = (ResultType)(*a + *b);
-            if (sum>0) {
-                diff = (ResultType)(*a - *b);
-                result += diff*diff/sum;
-            }
-            ++a;
-            ++b;
-
-            if ((worst_dist>0)&&(result>worst_dist)) {
-                return result;
-            }
-        }
-        return result;
-    }
-
-    /**
-     * Partial distance, used by the kd-tree.
-     */
-    template <typename U, typename V>
-    inline ResultType accum_dist(const U& a, const V& b, int) const
-    {
-        ResultType result = ResultType();
-        ResultType sum, diff;
-
-        sum = (ResultType)(a+b);
-        if (sum>0) {
-            diff = (ResultType)(a-b);
-            result = diff*diff/sum;
-        }
-        return result;
-    }
-};
-
-
-template<class T>
-struct KL_Divergence
-{
-    typedef True is_kdtree_distance;
-    typedef True is_vector_space_distance;
-
-    typedef T ElementType;
-    typedef typename Accumulator<T>::Type ResultType;
-
-    /**
-     *  Compute the Kullback-Leibler divergence
-     */
-    template <typename Iterator1, typename Iterator2>
-    ResultType operator()(Iterator1 a, Iterator2 b, size_t size, ResultType worst_dist = -1) const
-    {
-        ResultType result = ResultType();
-        Iterator1 last = a + size;
-
-        while (a < last) {
-            if (* b != 0) {
-                ResultType ratio = (ResultType)(*a / *b);
-                if (ratio>0) {
-                    result += *a * log(ratio);
-                }
-            }
-            ++a;
-            ++b;
-
-            if ((worst_dist>0)&&(result>worst_dist)) {
-                return result;
-            }
-        }
-        return result;
-    }
-
-    /**
-     * Partial distance, used by the kd-tree.
-     */
-    template <typename U, typename V>
-    inline ResultType accum_dist(const U& a, const V& b, int) const
-    {
-        ResultType result = ResultType();
-        if( *b != 0 ) {
-            ResultType ratio = (ResultType)(a / b);
-            if (ratio>0) {
-                result = a * log(ratio);
-            }
-        }
-        return result;
-    }
-};
-
-
-
-/*
- * This is a "zero iterator". It basically behaves like a zero filled
- * array to all algorithms that use arrays as iterators (STL style).
- * It's useful when there's a need to compute the distance between feature
- * and origin it and allows for better compiler optimisation than using a
- * zero-filled array.
- */
-template <typename T>
-struct ZeroIterator
-{
-
-    T operator*()
-    {
-        return 0;
-    }
-
-    T operator[](int)
-    {
-        return 0;
-    }
-
-    const ZeroIterator<T>& operator ++()
-    {
-        return *this;
-    }
-
-    ZeroIterator<T> operator ++(int)
-    {
-        return *this;
-    }
-
-    ZeroIterator<T>& operator+=(int)
-    {
-        return *this;
-    }
-
-};
-
-
-/*
- * Depending on processed distances, some of them are already squared (e.g. L2)
- * and some are not (e.g.Hamming). In KMeans++ for instance we want to be sure
- * we are working on ^2 distances, thus following templates to ensure that.
- */
-template <typename Distance, typename ElementType>
-struct squareDistance
-{
-    typedef typename Distance::ResultType ResultType;
-    ResultType operator()( ResultType dist ) { return dist*dist; }
-};
-
-
-template <typename ElementType>
-struct squareDistance<L2_Simple<ElementType>, ElementType>
-{
-    typedef typename L2_Simple<ElementType>::ResultType ResultType;
-    ResultType operator()( ResultType dist ) { return dist; }
-};
-
-template <typename ElementType>
-struct squareDistance<L2<ElementType>, ElementType>
-{
-    typedef typename L2<ElementType>::ResultType ResultType;
-    ResultType operator()( ResultType dist ) { return dist; }
-};
-
-
-template <typename ElementType>
-struct squareDistance<MinkowskiDistance<ElementType>, ElementType>
-{
-    typedef typename MinkowskiDistance<ElementType>::ResultType ResultType;
-    ResultType operator()( ResultType dist ) { return dist; }
-};
-
-template <typename ElementType>
-struct squareDistance<HellingerDistance<ElementType>, ElementType>
-{
-    typedef typename HellingerDistance<ElementType>::ResultType ResultType;
-    ResultType operator()( ResultType dist ) { return dist; }
-};
-
-template <typename ElementType>
-struct squareDistance<ChiSquareDistance<ElementType>, ElementType>
-{
-    typedef typename ChiSquareDistance<ElementType>::ResultType ResultType;
-    ResultType operator()( ResultType dist ) { return dist; }
-};
-
-
-template <typename Distance>
-typename Distance::ResultType ensureSquareDistance( typename Distance::ResultType dist )
-{
-    typedef typename Distance::ElementType ElementType;
-
-    squareDistance<Distance, ElementType> dummy;
-    return dummy( dist );
-}
-
-
-/*
- * ...and a template to ensure the user that he will process the normal distance,
- * and not squared distance, without losing processing time calling sqrt(ensureSquareDistance)
- * that will result in doing actually sqrt(dist*dist) for L1 distance for instance.
- */
-template <typename Distance, typename ElementType>
-struct simpleDistance
-{
-    typedef typename Distance::ResultType ResultType;
-    ResultType operator()( ResultType dist ) { return dist; }
-};
-
-
-template <typename ElementType>
-struct simpleDistance<L2_Simple<ElementType>, ElementType>
-{
-    typedef typename L2_Simple<ElementType>::ResultType ResultType;
-    ResultType operator()( ResultType dist ) { return sqrt(dist); }
-};
-
-template <typename ElementType>
-struct simpleDistance<L2<ElementType>, ElementType>
-{
-    typedef typename L2<ElementType>::ResultType ResultType;
-    ResultType operator()( ResultType dist ) { return sqrt(dist); }
-};
-
-
-template <typename ElementType>
-struct simpleDistance<MinkowskiDistance<ElementType>, ElementType>
-{
-    typedef typename MinkowskiDistance<ElementType>::ResultType ResultType;
-    ResultType operator()( ResultType dist ) { return sqrt(dist); }
-};
-
-template <typename ElementType>
-struct simpleDistance<HellingerDistance<ElementType>, ElementType>
-{
-    typedef typename HellingerDistance<ElementType>::ResultType ResultType;
-    ResultType operator()( ResultType dist ) { return sqrt(dist); }
-};
-
-template <typename ElementType>
-struct simpleDistance<ChiSquareDistance<ElementType>, ElementType>
-{
-    typedef typename ChiSquareDistance<ElementType>::ResultType ResultType;
-    ResultType operator()( ResultType dist ) { return sqrt(dist); }
-};
-
-
-template <typename Distance>
-typename Distance::ResultType ensureSimpleDistance( typename Distance::ResultType dist )
-{
-    typedef typename Distance::ElementType ElementType;
-
-    simpleDistance<Distance, ElementType> dummy;
-    return dummy( dist );
-}
-
-}
-
-#endif //OPENCV_FLANN_DIST_H_
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/dummy.h b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/dummy.h
deleted file mode 100755
index d6837e5..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/dummy.h
+++ /dev/null
@@ -1,13 +0,0 @@
-
-#ifndef OPENCV_FLANN_DUMMY_H_
-#define OPENCV_FLANN_DUMMY_H_
-
-namespace cvflann
-{
-
-CV_DEPRECATED inline void dummyfunc() {}
-
-}
-
-
-#endif  /* OPENCV_FLANN_DUMMY_H_ */
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/dynamic_bitset.h b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/dynamic_bitset.h
deleted file mode 100755
index 923b658..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/dynamic_bitset.h
+++ /dev/null
@@ -1,159 +0,0 @@
-/***********************************************************************
- * Software License Agreement (BSD License)
- *
- * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
- * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
- *
- * THE BSD LICENSE
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions
- * are met:
- *
- * 1. Redistributions of source code must retain the above copyright
- *    notice, this list of conditions and the following disclaimer.
- * 2. Redistributions in binary form must reproduce the above copyright
- *    notice, this list of conditions and the following disclaimer in the
- *    documentation and/or other materials provided with the distribution.
- *
- * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
- * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
- * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
- * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
- * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
- * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
- * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
- * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *************************************************************************/
-
-/***********************************************************************
- * Author: Vincent Rabaud
- *************************************************************************/
-
-#ifndef OPENCV_FLANN_DYNAMIC_BITSET_H_
-#define OPENCV_FLANN_DYNAMIC_BITSET_H_
-
-#ifndef FLANN_USE_BOOST
-#  define FLANN_USE_BOOST 0
-#endif
-//#define FLANN_USE_BOOST 1
-#if FLANN_USE_BOOST
-#include <boost/dynamic_bitset.hpp>
-typedef boost::dynamic_bitset<> DynamicBitset;
-#else
-
-#include <limits.h>
-
-#include "dist.h"
-
-namespace cvflann {
-
-/** Class re-implementing the boost version of it
- * This helps not depending on boost, it also does not do the bound checks
- * and has a way to reset a block for speed
- */
-class DynamicBitset
-{
-public:
-    /** default constructor
-     */
-    DynamicBitset() : size_(0)
-    {
-    }
-
-    /** only constructor we use in our code
-     * @param sz the size of the bitset (in bits)
-     */
-    DynamicBitset(size_t sz)
-    {
-        resize(sz);
-        reset();
-    }
-
-    /** Sets all the bits to 0
-     */
-    void clear()
-    {
-        std::fill(bitset_.begin(), bitset_.end(), 0);
-    }
-
-    /** @brief checks if the bitset is empty
-     * @return true if the bitset is empty
-     */
-    bool empty() const
-    {
-        return bitset_.empty();
-    }
-
-    /** set all the bits to 0
-     */
-    void reset()
-    {
-        std::fill(bitset_.begin(), bitset_.end(), 0);
-    }
-
-    /** @brief set one bit to 0
-     * @param index
-     */
-    void reset(size_t index)
-    {
-        bitset_[index / cell_bit_size_] &= ~(size_t(1) << (index % cell_bit_size_));
-    }
-
-    /** @brief sets a specific bit to 0, and more bits too
-     * This function is useful when resetting a given set of bits so that the
-     * whole bitset ends up being 0: if that's the case, we don't care about setting
-     * other bits to 0
-     * @param index
-     */
-    void reset_block(size_t index)
-    {
-        bitset_[index / cell_bit_size_] = 0;
-    }
-
-    /** resize the bitset so that it contains at least sz bits
-     * @param sz
-     */
-    void resize(size_t sz)
-    {
-        size_ = sz;
-        bitset_.resize(sz / cell_bit_size_ + 1);
-    }
-
-    /** set a bit to true
-     * @param index the index of the bit to set to 1
-     */
-    void set(size_t index)
-    {
-        bitset_[index / cell_bit_size_] |= size_t(1) << (index % cell_bit_size_);
-    }
-
-    /** gives the number of contained bits
-     */
-    size_t size() const
-    {
-        return size_;
-    }
-
-    /** check if a bit is set
-     * @param index the index of the bit to check
-     * @return true if the bit is set
-     */
-    bool test(size_t index) const
-    {
-        return (bitset_[index / cell_bit_size_] & (size_t(1) << (index % cell_bit_size_))) != 0;
-    }
-
-private:
-    std::vector<size_t> bitset_;
-    size_t size_;
-    static const unsigned int cell_bit_size_ = CHAR_BIT * sizeof(size_t);
-};
-
-} // namespace cvflann
-
-#endif
-
-#endif // OPENCV_FLANN_DYNAMIC_BITSET_H_
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/flann.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/flann.hpp
deleted file mode 100755
index 227683f..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/flann.hpp
+++ /dev/null
@@ -1,48 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifdef __OPENCV_BUILD
-#error this is a compatibility header which should not be used inside the OpenCV library
-#endif
-
-#include "opencv2/flann.hpp"
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/flann_base.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/flann_base.hpp
deleted file mode 100755
index 0ffb857..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/flann_base.hpp
+++ /dev/null
@@ -1,295 +0,0 @@
-/***********************************************************************
- * Software License Agreement (BSD License)
- *
- * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
- * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
- *
- * THE BSD LICENSE
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions
- * are met:
- *
- * 1. Redistributions of source code must retain the above copyright
- *    notice, this list of conditions and the following disclaimer.
- * 2. Redistributions in binary form must reproduce the above copyright
- *    notice, this list of conditions and the following disclaimer in the
- *    documentation and/or other materials provided with the distribution.
- *
- * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
- * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
- * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
- * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
- * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
- * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
- * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
- * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *************************************************************************/
-
-#ifndef OPENCV_FLANN_BASE_HPP_
-#define OPENCV_FLANN_BASE_HPP_
-
-#include <vector>
-#include <cassert>
-#include <cstdio>
-
-#include "general.h"
-#include "matrix.h"
-#include "params.h"
-#include "saving.h"
-
-#include "all_indices.h"
-
-namespace cvflann
-{
-
-/**
- * Sets the log level used for all flann functions
- * @param level Verbosity level
- */
-inline void log_verbosity(int level)
-{
-    if (level >= 0) {
-        Logger::setLevel(level);
-    }
-}
-
-/**
- * (Deprecated) Index parameters for creating a saved index.
- */
-struct SavedIndexParams : public IndexParams
-{
-    SavedIndexParams(cv::String filename)
-    {
-        (* this)["algorithm"] = FLANN_INDEX_SAVED;
-        (*this)["filename"] = filename;
-    }
-};
-
-
-template<typename Distance>
-NNIndex<Distance>* load_saved_index(const Matrix<typename Distance::ElementType>& dataset, const cv::String& filename, Distance distance)
-{
-    typedef typename Distance::ElementType ElementType;
-
-    FILE* fin = fopen(filename.c_str(), "rb");
-    if (fin == NULL) {
-        return NULL;
-    }
-    IndexHeader header = load_header(fin);
-    if (header.data_type != Datatype<ElementType>::type()) {
-        fclose(fin);
-        throw FLANNException("Datatype of saved index is different than of the one to be created.");
-    }
-    if ((size_t(header.rows) != dataset.rows)||(size_t(header.cols) != dataset.cols)) {
-        fclose(fin);
-        throw FLANNException("The index saved belongs to a different dataset");
-    }
-
-    IndexParams params;
-    params["algorithm"] = header.index_type;
-    NNIndex<Distance>* nnIndex = create_index_by_type<Distance>(dataset, params, distance);
-    nnIndex->loadIndex(fin);
-    fclose(fin);
-
-    return nnIndex;
-}
-
-
-template<typename Distance>
-class Index : public NNIndex<Distance>
-{
-public:
-    typedef typename Distance::ElementType ElementType;
-    typedef typename Distance::ResultType DistanceType;
-
-    Index(const Matrix<ElementType>& features, const IndexParams& params, Distance distance = Distance() )
-        : index_params_(params)
-    {
-        flann_algorithm_t index_type = get_param<flann_algorithm_t>(params,"algorithm");
-        loaded_ = false;
-
-        if (index_type == FLANN_INDEX_SAVED) {
-            nnIndex_ = load_saved_index<Distance>(features, get_param<cv::String>(params,"filename"), distance);
-            loaded_ = true;
-        }
-        else {
-            nnIndex_ = create_index_by_type<Distance>(features, params, distance);
-        }
-    }
-
-    ~Index()
-    {
-        delete nnIndex_;
-    }
-
-    /**
-     * Builds the index.
-     */
-    void buildIndex() CV_OVERRIDE
-    {
-        if (!loaded_) {
-            nnIndex_->buildIndex();
-        }
-    }
-
-    void save(cv::String filename)
-    {
-        FILE* fout = fopen(filename.c_str(), "wb");
-        if (fout == NULL) {
-            throw FLANNException("Cannot open file");
-        }
-        save_header(fout, *nnIndex_);
-        saveIndex(fout);
-        fclose(fout);
-    }
-
-    /**
-     * \brief Saves the index to a stream
-     * \param stream The stream to save the index to
-     */
-    virtual void saveIndex(FILE* stream) CV_OVERRIDE
-    {
-        nnIndex_->saveIndex(stream);
-    }
-
-    /**
-     * \brief Loads the index from a stream
-     * \param stream The stream from which the index is loaded
-     */
-    virtual void loadIndex(FILE* stream) CV_OVERRIDE
-    {
-        nnIndex_->loadIndex(stream);
-    }
-
-    /**
-     * \returns number of features in this index.
-     */
-    size_t veclen() const CV_OVERRIDE
-    {
-        return nnIndex_->veclen();
-    }
-
-    /**
-     * \returns The dimensionality of the features in this index.
-     */
-    size_t size() const CV_OVERRIDE
-    {
-        return nnIndex_->size();
-    }
-
-    /**
-     * \returns The index type (kdtree, kmeans,...)
-     */
-    flann_algorithm_t getType() const CV_OVERRIDE
-    {
-        return nnIndex_->getType();
-    }
-
-    /**
-     * \returns The amount of memory (in bytes) used by the index.
-     */
-    virtual int usedMemory() const CV_OVERRIDE
-    {
-        return nnIndex_->usedMemory();
-    }
-
-
-    /**
-     * \returns The index parameters
-     */
-    IndexParams getParameters() const CV_OVERRIDE
-    {
-        return nnIndex_->getParameters();
-    }
-
-    /**
-     * \brief Perform k-nearest neighbor search
-     * \param[in] queries The query points for which to find the nearest neighbors
-     * \param[out] indices The indices of the nearest neighbors found
-     * \param[out] dists Distances to the nearest neighbors found
-     * \param[in] knn Number of nearest neighbors to return
-     * \param[in] params Search parameters
-     */
-    void knnSearch(const Matrix<ElementType>& queries, Matrix<int>& indices, Matrix<DistanceType>& dists, int knn, const SearchParams& params) CV_OVERRIDE
-    {
-        nnIndex_->knnSearch(queries, indices, dists, knn, params);
-    }
-
-    /**
-     * \brief Perform radius search
-     * \param[in] query The query point
-     * \param[out] indices The indinces of the neighbors found within the given radius
-     * \param[out] dists The distances to the nearest neighbors found
-     * \param[in] radius The radius used for search
-     * \param[in] params Search parameters
-     * \returns Number of neighbors found
-     */
-    int radiusSearch(const Matrix<ElementType>& query, Matrix<int>& indices, Matrix<DistanceType>& dists, float radius, const SearchParams& params) CV_OVERRIDE
-    {
-        return nnIndex_->radiusSearch(query, indices, dists, radius, params);
-    }
-
-    /**
-     * \brief Method that searches for nearest-neighbours
-     */
-    void findNeighbors(ResultSet<DistanceType>& result, const ElementType* vec, const SearchParams& searchParams) CV_OVERRIDE
-    {
-        nnIndex_->findNeighbors(result, vec, searchParams);
-    }
-
-    /**
-     * \brief Returns actual index
-     */
-    CV_DEPRECATED NNIndex<Distance>* getIndex()
-    {
-        return nnIndex_;
-    }
-
-    /**
-     * \brief Returns index parameters.
-     * \deprecated use getParameters() instead.
-     */
-    CV_DEPRECATED  const IndexParams* getIndexParameters()
-    {
-        return &index_params_;
-    }
-
-private:
-    /** Pointer to actual index class */
-    NNIndex<Distance>* nnIndex_;
-    /** Indices if the index was loaded from a file */
-    bool loaded_;
-    /** Parameters passed to the index */
-    IndexParams index_params_;
-
-    Index(const Index &); // copy disabled
-    Index& operator=(const Index &); // assign disabled
-};
-
-/**
- * Performs a hierarchical clustering of the points passed as argument and then takes a cut in the
- * the clustering tree to return a flat clustering.
- * @param[in] points Points to be clustered
- * @param centers The computed cluster centres. Matrix should be preallocated and centers.rows is the
- *  number of clusters requested.
- * @param params Clustering parameters (The same as for cvflann::KMeansIndex)
- * @param d Distance to be used for clustering (eg: cvflann::L2)
- * @return number of clusters computed (can be different than clusters.rows and is the highest number
- * of the form (branching-1)*K+1 smaller than clusters.rows).
- */
-template <typename Distance>
-int hierarchicalClustering(const Matrix<typename Distance::ElementType>& points, Matrix<typename Distance::ResultType>& centers,
-                           const KMeansIndexParams& params, Distance d = Distance())
-{
-    KMeansIndex<Distance> kmeans(points, params, d);
-    kmeans.buildIndex();
-
-    int clusterNum = kmeans.getClusterCenters(centers);
-    return clusterNum;
-}
-
-}
-#endif /* OPENCV_FLANN_BASE_HPP_ */
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/general.h b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/general.h
deleted file mode 100755
index 9d5402a..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/general.h
+++ /dev/null
@@ -1,50 +0,0 @@
-/***********************************************************************
- * Software License Agreement (BSD License)
- *
- * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
- * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
- *
- * THE BSD LICENSE
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions
- * are met:
- *
- * 1. Redistributions of source code must retain the above copyright
- *    notice, this list of conditions and the following disclaimer.
- * 2. Redistributions in binary form must reproduce the above copyright
- *    notice, this list of conditions and the following disclaimer in the
- *    documentation and/or other materials provided with the distribution.
- *
- * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
- * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
- * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
- * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
- * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
- * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
- * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
- * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *************************************************************************/
-
-#ifndef OPENCV_FLANN_GENERAL_H_
-#define OPENCV_FLANN_GENERAL_H_
-
-#include "opencv2/core.hpp"
-
-namespace cvflann
-{
-
-class FLANNException : public cv::Exception
-{
-public:
-    FLANNException(const char* message) : cv::Exception(0, message, "", __FILE__, __LINE__) { }
-
-    FLANNException(const cv::String& message) : cv::Exception(0, message, "", __FILE__, __LINE__) { }
-};
-
-}
-
-
-#endif  /* OPENCV_FLANN_GENERAL_H_ */
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/ground_truth.h b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/ground_truth.h
deleted file mode 100755
index fd8f3ae..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/ground_truth.h
+++ /dev/null
@@ -1,94 +0,0 @@
-/***********************************************************************
- * Software License Agreement (BSD License)
- *
- * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
- * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
- *
- * THE BSD LICENSE
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions
- * are met:
- *
- * 1. Redistributions of source code must retain the above copyright
- *    notice, this list of conditions and the following disclaimer.
- * 2. Redistributions in binary form must reproduce the above copyright
- *    notice, this list of conditions and the following disclaimer in the
- *    documentation and/or other materials provided with the distribution.
- *
- * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
- * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
- * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
- * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
- * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
- * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
- * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
- * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *************************************************************************/
-
-#ifndef OPENCV_FLANN_GROUND_TRUTH_H_
-#define OPENCV_FLANN_GROUND_TRUTH_H_
-
-#include "dist.h"
-#include "matrix.h"
-
-
-namespace cvflann
-{
-
-template <typename Distance>
-void find_nearest(const Matrix<typename Distance::ElementType>& dataset, typename Distance::ElementType* query, int* matches, int nn,
-                  int skip = 0, Distance distance = Distance())
-{
-    typedef typename Distance::ResultType DistanceType;
-    int n = nn + skip;
-
-    std::vector<int> match(n);
-    std::vector<DistanceType> dists(n);
-
-    dists[0] = distance(dataset[0], query, dataset.cols);
-    match[0] = 0;
-    int dcnt = 1;
-
-    for (size_t i=1; i<dataset.rows; ++i) {
-        DistanceType tmp = distance(dataset[i], query, dataset.cols);
-
-        if (dcnt<n) {
-            match[dcnt] = (int)i;
-            dists[dcnt++] = tmp;
-        }
-        else if (tmp < dists[dcnt-1]) {
-            dists[dcnt-1] = tmp;
-            match[dcnt-1] = (int)i;
-        }
-
-        int j = dcnt-1;
-        // bubble up
-        while (j>=1 && dists[j]<dists[j-1]) {
-            std::swap(dists[j],dists[j-1]);
-            std::swap(match[j],match[j-1]);
-            j--;
-        }
-    }
-
-    for (int i=0; i<nn; ++i) {
-        matches[i] = match[i+skip];
-    }
-}
-
-
-template <typename Distance>
-void compute_ground_truth(const Matrix<typename Distance::ElementType>& dataset, const Matrix<typename Distance::ElementType>& testset, Matrix<int>& matches,
-                          int skip=0, Distance d = Distance())
-{
-    for (size_t i=0; i<testset.rows; ++i) {
-        find_nearest<Distance>(dataset, testset[i], matches[i], (int)matches.cols, skip, d);
-    }
-}
-
-
-}
-
-#endif //OPENCV_FLANN_GROUND_TRUTH_H_
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/hdf5.h b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/hdf5.h
deleted file mode 100755
index 80d23b9..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/hdf5.h
+++ /dev/null
@@ -1,231 +0,0 @@
-/***********************************************************************
- * Software License Agreement (BSD License)
- *
- * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
- * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions
- * are met:
- *
- * 1. Redistributions of source code must retain the above copyright
- *    notice, this list of conditions and the following disclaimer.
- * 2. Redistributions in binary form must reproduce the above copyright
- *    notice, this list of conditions and the following disclaimer in the
- *    documentation and/or other materials provided with the distribution.
- *
- * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
- * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
- * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
- * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
- * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
- * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
- * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
- * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *************************************************************************/
-
-
-#ifndef OPENCV_FLANN_HDF5_H_
-#define OPENCV_FLANN_HDF5_H_
-
-#include <hdf5.h>
-
-#include "matrix.h"
-
-
-namespace cvflann
-{
-
-namespace
-{
-
-template<typename T>
-hid_t get_hdf5_type()
-{
-    throw FLANNException("Unsupported type for IO operations");
-}
-
-template<>
-hid_t get_hdf5_type<char>() { return H5T_NATIVE_CHAR; }
-template<>
-hid_t get_hdf5_type<unsigned char>() { return H5T_NATIVE_UCHAR; }
-template<>
-hid_t get_hdf5_type<short int>() { return H5T_NATIVE_SHORT; }
-template<>
-hid_t get_hdf5_type<unsigned short int>() { return H5T_NATIVE_USHORT; }
-template<>
-hid_t get_hdf5_type<int>() { return H5T_NATIVE_INT; }
-template<>
-hid_t get_hdf5_type<unsigned int>() { return H5T_NATIVE_UINT; }
-template<>
-hid_t get_hdf5_type<long>() { return H5T_NATIVE_LONG; }
-template<>
-hid_t get_hdf5_type<unsigned long>() { return H5T_NATIVE_ULONG; }
-template<>
-hid_t get_hdf5_type<float>() { return H5T_NATIVE_FLOAT; }
-template<>
-hid_t get_hdf5_type<double>() { return H5T_NATIVE_DOUBLE; }
-}
-
-
-#define CHECK_ERROR(x,y) if ((x)<0) throw FLANNException((y));
-
-template<typename T>
-void save_to_file(const cvflann::Matrix<T>& dataset, const String& filename, const String& name)
-{
-
-#if H5Eset_auto_vers == 2
-    H5Eset_auto( H5E_DEFAULT, NULL, NULL );
-#else
-    H5Eset_auto( NULL, NULL );
-#endif
-
-    herr_t status;
-    hid_t file_id;
-    file_id = H5Fopen(filename.c_str(), H5F_ACC_RDWR, H5P_DEFAULT);
-    if (file_id < 0) {
-        file_id = H5Fcreate(filename.c_str(), H5F_ACC_EXCL, H5P_DEFAULT, H5P_DEFAULT);
-    }
-    CHECK_ERROR(file_id,"Error creating hdf5 file.");
-
-    hsize_t     dimsf[2];              // dataset dimensions
-    dimsf[0] = dataset.rows;
-    dimsf[1] = dataset.cols;
-
-    hid_t space_id = H5Screate_simple(2, dimsf, NULL);
-    hid_t memspace_id = H5Screate_simple(2, dimsf, NULL);
-
-    hid_t dataset_id;
-#if H5Dcreate_vers == 2
-    dataset_id = H5Dcreate2(file_id, name.c_str(), get_hdf5_type<T>(), space_id, H5P_DEFAULT, H5P_DEFAULT, H5P_DEFAULT);
-#else
-    dataset_id = H5Dcreate(file_id, name.c_str(), get_hdf5_type<T>(), space_id, H5P_DEFAULT);
-#endif
-
-    if (dataset_id<0) {
-#if H5Dopen_vers == 2
-        dataset_id = H5Dopen2(file_id, name.c_str(), H5P_DEFAULT);
-#else
-        dataset_id = H5Dopen(file_id, name.c_str());
-#endif
-    }
-    CHECK_ERROR(dataset_id,"Error creating or opening dataset in file.");
-
-    status = H5Dwrite(dataset_id, get_hdf5_type<T>(), memspace_id, space_id, H5P_DEFAULT, dataset.data );
-    CHECK_ERROR(status, "Error writing to dataset");
-
-    H5Sclose(memspace_id);
-    H5Sclose(space_id);
-    H5Dclose(dataset_id);
-    H5Fclose(file_id);
-
-}
-
-
-template<typename T>
-void load_from_file(cvflann::Matrix<T>& dataset, const String& filename, const String& name)
-{
-    herr_t status;
-    hid_t file_id = H5Fopen(filename.c_str(), H5F_ACC_RDWR, H5P_DEFAULT);
-    CHECK_ERROR(file_id,"Error opening hdf5 file.");
-
-    hid_t dataset_id;
-#if H5Dopen_vers == 2
-    dataset_id = H5Dopen2(file_id, name.c_str(), H5P_DEFAULT);
-#else
-    dataset_id = H5Dopen(file_id, name.c_str());
-#endif
-    CHECK_ERROR(dataset_id,"Error opening dataset in file.");
-
-    hid_t space_id = H5Dget_space(dataset_id);
-
-    hsize_t dims_out[2];
-    H5Sget_simple_extent_dims(space_id, dims_out, NULL);
-
-    dataset = cvflann::Matrix<T>(new T[dims_out[0]*dims_out[1]], dims_out[0], dims_out[1]);
-
-    status = H5Dread(dataset_id, get_hdf5_type<T>(), H5S_ALL, H5S_ALL, H5P_DEFAULT, dataset[0]);
-    CHECK_ERROR(status, "Error reading dataset");
-
-    H5Sclose(space_id);
-    H5Dclose(dataset_id);
-    H5Fclose(file_id);
-}
-
-
-#ifdef HAVE_MPI
-
-namespace mpi
-{
-/**
- * Loads a the hyperslice corresponding to this processor from a hdf5 file.
- * @param flann_dataset Dataset where the data is loaded
- * @param filename HDF5 file name
- * @param name Name of dataset inside file
- */
-template<typename T>
-void load_from_file(cvflann::Matrix<T>& dataset, const String& filename, const String& name)
-{
-    MPI_Comm comm  = MPI_COMM_WORLD;
-    MPI_Info info  = MPI_INFO_NULL;
-
-    int mpi_size, mpi_rank;
-    MPI_Comm_size(comm, &mpi_size);
-    MPI_Comm_rank(comm, &mpi_rank);
-
-    herr_t status;
-
-    hid_t plist_id = H5Pcreate(H5P_FILE_ACCESS);
-    H5Pset_fapl_mpio(plist_id, comm, info);
-    hid_t file_id = H5Fopen(filename.c_str(), H5F_ACC_RDWR, plist_id);
-    CHECK_ERROR(file_id,"Error opening hdf5 file.");
-    H5Pclose(plist_id);
-    hid_t dataset_id;
-#if H5Dopen_vers == 2
-    dataset_id = H5Dopen2(file_id, name.c_str(), H5P_DEFAULT);
-#else
-    dataset_id = H5Dopen(file_id, name.c_str());
-#endif
-    CHECK_ERROR(dataset_id,"Error opening dataset in file.");
-
-    hid_t space_id = H5Dget_space(dataset_id);
-    hsize_t dims[2];
-    H5Sget_simple_extent_dims(space_id, dims, NULL);
-
-    hsize_t count[2];
-    hsize_t offset[2];
-
-    hsize_t item_cnt = dims[0]/mpi_size+(dims[0]%mpi_size==0 ? 0 : 1);
-    hsize_t cnt = (mpi_rank<mpi_size-1 ? item_cnt : dims[0]-item_cnt*(mpi_size-1));
-
-    count[0] = cnt;
-    count[1] = dims[1];
-    offset[0] = mpi_rank*item_cnt;
-    offset[1] = 0;
-
-    hid_t memspace_id = H5Screate_simple(2,count,NULL);
-
-    H5Sselect_hyperslab(space_id, H5S_SELECT_SET, offset, NULL, count, NULL);
-
-    dataset.rows = count[0];
-    dataset.cols = count[1];
-    dataset.data = new T[dataset.rows*dataset.cols];
-
-    plist_id = H5Pcreate(H5P_DATASET_XFER);
-    H5Pset_dxpl_mpio(plist_id, H5FD_MPIO_COLLECTIVE);
-    status = H5Dread(dataset_id, get_hdf5_type<T>(), memspace_id, space_id, plist_id, dataset.data);
-    CHECK_ERROR(status, "Error reading dataset");
-
-    H5Pclose(plist_id);
-    H5Sclose(space_id);
-    H5Sclose(memspace_id);
-    H5Dclose(dataset_id);
-    H5Fclose(file_id);
-}
-}
-#endif // HAVE_MPI
-} // namespace cvflann::mpi
-
-#endif /* OPENCV_FLANN_HDF5_H_ */
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/heap.h b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/heap.h
deleted file mode 100755
index 92a6ea6..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/heap.h
+++ /dev/null
@@ -1,165 +0,0 @@
-/***********************************************************************
- * Software License Agreement (BSD License)
- *
- * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
- * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
- *
- * THE BSD LICENSE
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions
- * are met:
- *
- * 1. Redistributions of source code must retain the above copyright
- *    notice, this list of conditions and the following disclaimer.
- * 2. Redistributions in binary form must reproduce the above copyright
- *    notice, this list of conditions and the following disclaimer in the
- *    documentation and/or other materials provided with the distribution.
- *
- * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
- * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
- * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
- * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
- * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
- * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
- * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
- * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *************************************************************************/
-
-#ifndef OPENCV_FLANN_HEAP_H_
-#define OPENCV_FLANN_HEAP_H_
-
-#include <algorithm>
-#include <vector>
-
-namespace cvflann
-{
-
-/**
- * Priority Queue Implementation
- *
- * The priority queue is implemented with a heap.  A heap is a complete
- * (full) binary tree in which each parent is less than both of its
- * children, but the order of the children is unspecified.
- */
-template <typename T>
-class Heap
-{
-
-    /**
-     * Storage array for the heap.
-     * Type T must be comparable.
-     */
-    std::vector<T> heap;
-    int length;
-
-    /**
-     * Number of element in the heap
-     */
-    int count;
-
-
-
-public:
-    /**
-     * Constructor.
-     *
-     * Params:
-     *     sz = heap size
-     */
-
-    Heap(int sz)
-    {
-        length = sz;
-        heap.reserve(length);
-        count = 0;
-    }
-
-    /**
-     *
-     * Returns: heap size
-     */
-    int size()
-    {
-        return count;
-    }
-
-    /**
-     * Tests if the heap is empty
-     *
-     * Returns: true is heap empty, false otherwise
-     */
-    bool empty()
-    {
-        return size()==0;
-    }
-
-    /**
-     * Clears the heap.
-     */
-    void clear()
-    {
-        heap.clear();
-        count = 0;
-    }
-
-    struct CompareT
-    {
-        bool operator()(const T& t_1, const T& t_2) const
-        {
-            return t_2 < t_1;
-        }
-    };
-
-    /**
-     * Insert a new element in the heap.
-     *
-     * We select the next empty leaf node, and then keep moving any larger
-     * parents down until the right location is found to store this element.
-     *
-     * Params:
-     *     value = the new element to be inserted in the heap
-     */
-    void insert(T value)
-    {
-        /* If heap is full, then return without adding this element. */
-        if (count == length) {
-            return;
-        }
-
-        heap.push_back(value);
-        static CompareT compareT;
-        std::push_heap(heap.begin(), heap.end(), compareT);
-        ++count;
-    }
-
-
-
-    /**
-     * Returns the node of minimum value from the heap (top of the heap).
-     *
-     * Params:
-     *     value = out parameter used to return the min element
-     * Returns: false if heap empty
-     */
-    bool popMin(T& value)
-    {
-        if (count == 0) {
-            return false;
-        }
-
-        value = heap[0];
-        static CompareT compareT;
-        std::pop_heap(heap.begin(), heap.end(), compareT);
-        heap.pop_back();
-        --count;
-
-        return true;  /* Return old last node. */
-    }
-};
-
-}
-
-#endif //OPENCV_FLANN_HEAP_H_
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/hierarchical_clustering_index.h b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/hierarchical_clustering_index.h
deleted file mode 100755
index 2a947da..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/hierarchical_clustering_index.h
+++ /dev/null
@@ -1,848 +0,0 @@
-/***********************************************************************
- * Software License Agreement (BSD License)
- *
- * Copyright 2008-2011  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
- * Copyright 2008-2011  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
- *
- * THE BSD LICENSE
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions
- * are met:
- *
- * 1. Redistributions of source code must retain the above copyright
- *    notice, this list of conditions and the following disclaimer.
- * 2. Redistributions in binary form must reproduce the above copyright
- *    notice, this list of conditions and the following disclaimer in the
- *    documentation and/or other materials provided with the distribution.
- *
- * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
- * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
- * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
- * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
- * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
- * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
- * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
- * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *************************************************************************/
-
-#ifndef OPENCV_FLANN_HIERARCHICAL_CLUSTERING_INDEX_H_
-#define OPENCV_FLANN_HIERARCHICAL_CLUSTERING_INDEX_H_
-
-#include <algorithm>
-#include <map>
-#include <cassert>
-#include <limits>
-#include <cmath>
-
-#include "general.h"
-#include "nn_index.h"
-#include "dist.h"
-#include "matrix.h"
-#include "result_set.h"
-#include "heap.h"
-#include "allocator.h"
-#include "random.h"
-#include "saving.h"
-
-
-namespace cvflann
-{
-
-struct HierarchicalClusteringIndexParams : public IndexParams
-{
-    HierarchicalClusteringIndexParams(int branching = 32,
-                                      flann_centers_init_t centers_init = FLANN_CENTERS_RANDOM,
-                                      int trees = 4, int leaf_size = 100)
-    {
-        (*this)["algorithm"] = FLANN_INDEX_HIERARCHICAL;
-        // The branching factor used in the hierarchical clustering
-        (*this)["branching"] = branching;
-        // Algorithm used for picking the initial cluster centers
-        (*this)["centers_init"] = centers_init;
-        // number of parallel trees to build
-        (*this)["trees"] = trees;
-        // maximum leaf size
-        (*this)["leaf_size"] = leaf_size;
-    }
-};
-
-
-/**
- * Hierarchical index
- *
- * Contains a tree constructed through a hierarchical clustering
- * and other information for indexing a set of points for nearest-neighbour matching.
- */
-template <typename Distance>
-class HierarchicalClusteringIndex : public NNIndex<Distance>
-{
-public:
-    typedef typename Distance::ElementType ElementType;
-    typedef typename Distance::ResultType DistanceType;
-
-private:
-
-
-    typedef void (HierarchicalClusteringIndex::* centersAlgFunction)(int, int*, int, int*, int&);
-
-    /**
-     * The function used for choosing the cluster centers.
-     */
-    centersAlgFunction chooseCenters;
-
-
-
-    /**
-     * Chooses the initial centers in the k-means clustering in a random manner.
-     *
-     * Params:
-     *     k = number of centers
-     *     vecs = the dataset of points
-     *     indices = indices in the dataset
-     *     indices_length = length of indices vector
-     *
-     */
-    void chooseCentersRandom(int k, int* dsindices, int indices_length, int* centers, int& centers_length)
-    {
-        UniqueRandom r(indices_length);
-
-        int index;
-        for (index=0; index<k; ++index) {
-            bool duplicate = true;
-            int rnd;
-            while (duplicate) {
-                duplicate = false;
-                rnd = r.next();
-                if (rnd<0) {
-                    centers_length = index;
-                    return;
-                }
-
-                centers[index] = dsindices[rnd];
-
-                for (int j=0; j<index; ++j) {
-                    DistanceType sq = distance(dataset[centers[index]], dataset[centers[j]], dataset.cols);
-                    if (sq<1e-16) {
-                        duplicate = true;
-                    }
-                }
-            }
-        }
-
-        centers_length = index;
-    }
-
-
-    /**
-     * Chooses the initial centers in the k-means using Gonzales' algorithm
-     * so that the centers are spaced apart from each other.
-     *
-     * Params:
-     *     k = number of centers
-     *     vecs = the dataset of points
-     *     indices = indices in the dataset
-     * Returns:
-     */
-    void chooseCentersGonzales(int k, int* dsindices, int indices_length, int* centers, int& centers_length)
-    {
-        int n = indices_length;
-
-        int rnd = rand_int(n);
-        assert(rnd >=0 && rnd < n);
-
-        centers[0] = dsindices[rnd];
-
-        int index;
-        for (index=1; index<k; ++index) {
-
-            int best_index = -1;
-            DistanceType best_val = 0;
-            for (int j=0; j<n; ++j) {
-                DistanceType dist = distance(dataset[centers[0]],dataset[dsindices[j]],dataset.cols);
-                for (int i=1; i<index; ++i) {
-                    DistanceType tmp_dist = distance(dataset[centers[i]],dataset[dsindices[j]],dataset.cols);
-                    if (tmp_dist<dist) {
-                        dist = tmp_dist;
-                    }
-                }
-                if (dist>best_val) {
-                    best_val = dist;
-                    best_index = j;
-                }
-            }
-            if (best_index!=-1) {
-                centers[index] = dsindices[best_index];
-            }
-            else {
-                break;
-            }
-        }
-        centers_length = index;
-    }
-
-
-    /**
-     * Chooses the initial centers in the k-means using the algorithm
-     * proposed in the KMeans++ paper:
-     * Arthur, David; Vassilvitskii, Sergei - k-means++: The Advantages of Careful Seeding
-     *
-     * Implementation of this function was converted from the one provided in Arthur's code.
-     *
-     * Params:
-     *     k = number of centers
-     *     vecs = the dataset of points
-     *     indices = indices in the dataset
-     * Returns:
-     */
-    void chooseCentersKMeanspp(int k, int* dsindices, int indices_length, int* centers, int& centers_length)
-    {
-        int n = indices_length;
-
-        double currentPot = 0;
-        DistanceType* closestDistSq = new DistanceType[n];
-
-        // Choose one random center and set the closestDistSq values
-        int index = rand_int(n);
-        assert(index >=0 && index < n);
-        centers[0] = dsindices[index];
-
-        // Computing distance^2 will have the advantage of even higher probability further to pick new centers
-        // far from previous centers (and this complies to "k-means++: the advantages of careful seeding" article)
-        for (int i = 0; i < n; i++) {
-            closestDistSq[i] = distance(dataset[dsindices[i]], dataset[dsindices[index]], dataset.cols);
-            closestDistSq[i] = ensureSquareDistance<Distance>( closestDistSq[i] );
-            currentPot += closestDistSq[i];
-        }
-
-
-        const int numLocalTries = 1;
-
-        // Choose each center
-        int centerCount;
-        for (centerCount = 1; centerCount < k; centerCount++) {
-
-            // Repeat several trials
-            double bestNewPot = -1;
-            int bestNewIndex = 0;
-            for (int localTrial = 0; localTrial < numLocalTries; localTrial++) {
-
-                // Choose our center - have to be slightly careful to return a valid answer even accounting
-                // for possible rounding errors
-                double randVal = rand_double(currentPot);
-                for (index = 0; index < n-1; index++) {
-                    if (randVal <= closestDistSq[index]) break;
-                    else randVal -= closestDistSq[index];
-                }
-
-                // Compute the new potential
-                double newPot = 0;
-                for (int i = 0; i < n; i++) {
-                    DistanceType dist = distance(dataset[dsindices[i]], dataset[dsindices[index]], dataset.cols);
-                    newPot += std::min( ensureSquareDistance<Distance>(dist), closestDistSq[i] );
-                }
-
-                // Store the best result
-                if ((bestNewPot < 0)||(newPot < bestNewPot)) {
-                    bestNewPot = newPot;
-                    bestNewIndex = index;
-                }
-            }
-
-            // Add the appropriate center
-            centers[centerCount] = dsindices[bestNewIndex];
-            currentPot = bestNewPot;
-            for (int i = 0; i < n; i++) {
-                DistanceType dist = distance(dataset[dsindices[i]], dataset[dsindices[bestNewIndex]], dataset.cols);
-                closestDistSq[i] = std::min( ensureSquareDistance<Distance>(dist), closestDistSq[i] );
-            }
-        }
-
-        centers_length = centerCount;
-
-        delete[] closestDistSq;
-    }
-
-
-    /**
-     * Chooses the initial centers in a way inspired by Gonzales (by Pierre-Emmanuel Viel):
-     * select the first point of the list as a candidate, then parse the points list. If another
-     * point is further than current candidate from the other centers, test if it is a good center
-     * of a local aggregation. If it is, replace current candidate by this point. And so on...
-     *
-     * Used with KMeansIndex that computes centers coordinates by averaging positions of clusters points,
-     * this doesn't make a real difference with previous methods. But used with HierarchicalClusteringIndex
-     * class that pick centers among existing points instead of computing the barycenters, there is a real
-     * improvement.
-     *
-     * Params:
-     *     k = number of centers
-     *     vecs = the dataset of points
-     *     indices = indices in the dataset
-     * Returns:
-     */
-    void GroupWiseCenterChooser(int k, int* dsindices, int indices_length, int* centers, int& centers_length)
-    {
-        const float kSpeedUpFactor = 1.3f;
-
-        int n = indices_length;
-
-        DistanceType* closestDistSq = new DistanceType[n];
-
-        // Choose one random center and set the closestDistSq values
-        int index = rand_int(n);
-        assert(index >=0 && index < n);
-        centers[0] = dsindices[index];
-
-        for (int i = 0; i < n; i++) {
-            closestDistSq[i] = distance(dataset[dsindices[i]], dataset[dsindices[index]], dataset.cols);
-        }
-
-
-        // Choose each center
-        int centerCount;
-        for (centerCount = 1; centerCount < k; centerCount++) {
-
-            // Repeat several trials
-            double bestNewPot = -1;
-            int bestNewIndex = 0;
-            DistanceType furthest = 0;
-            for (index = 0; index < n; index++) {
-
-                // We will test only the potential of the points further than current candidate
-                if( closestDistSq[index] > kSpeedUpFactor * (float)furthest ) {
-
-                    // Compute the new potential
-                    double newPot = 0;
-                    for (int i = 0; i < n; i++) {
-                        newPot += std::min( distance(dataset[dsindices[i]], dataset[dsindices[index]], dataset.cols)
-                                            , closestDistSq[i] );
-                    }
-
-                    // Store the best result
-                    if ((bestNewPot < 0)||(newPot <= bestNewPot)) {
-                        bestNewPot = newPot;
-                        bestNewIndex = index;
-                        furthest = closestDistSq[index];
-                    }
-                }
-            }
-
-            // Add the appropriate center
-            centers[centerCount] = dsindices[bestNewIndex];
-            for (int i = 0; i < n; i++) {
-                closestDistSq[i] = std::min( distance(dataset[dsindices[i]], dataset[dsindices[bestNewIndex]], dataset.cols)
-                                             , closestDistSq[i] );
-            }
-        }
-
-        centers_length = centerCount;
-
-        delete[] closestDistSq;
-    }
-
-
-public:
-
-
-    /**
-     * Index constructor
-     *
-     * Params:
-     *          inputData = dataset with the input features
-     *          params = parameters passed to the hierarchical k-means algorithm
-     */
-    HierarchicalClusteringIndex(const Matrix<ElementType>& inputData, const IndexParams& index_params = HierarchicalClusteringIndexParams(),
-                                Distance d = Distance())
-        : dataset(inputData), params(index_params), root(NULL), indices(NULL), distance(d)
-    {
-        memoryCounter = 0;
-
-        size_ = dataset.rows;
-        veclen_ = dataset.cols;
-
-        branching_ = get_param(params,"branching",32);
-        centers_init_ = get_param(params,"centers_init", FLANN_CENTERS_RANDOM);
-        trees_ = get_param(params,"trees",4);
-        leaf_size_ = get_param(params,"leaf_size",100);
-
-        if (centers_init_==FLANN_CENTERS_RANDOM) {
-            chooseCenters = &HierarchicalClusteringIndex::chooseCentersRandom;
-        }
-        else if (centers_init_==FLANN_CENTERS_GONZALES) {
-            chooseCenters = &HierarchicalClusteringIndex::chooseCentersGonzales;
-        }
-        else if (centers_init_==FLANN_CENTERS_KMEANSPP) {
-            chooseCenters = &HierarchicalClusteringIndex::chooseCentersKMeanspp;
-        }
-        else if (centers_init_==FLANN_CENTERS_GROUPWISE) {
-            chooseCenters = &HierarchicalClusteringIndex::GroupWiseCenterChooser;
-        }
-        else {
-            throw FLANNException("Unknown algorithm for choosing initial centers.");
-        }
-
-        trees_ = get_param(params,"trees",4);
-        root = new NodePtr[trees_];
-        indices = new int*[trees_];
-
-        for (int i=0; i<trees_; ++i) {
-            root[i] = NULL;
-            indices[i] = NULL;
-        }
-    }
-
-    HierarchicalClusteringIndex(const HierarchicalClusteringIndex&);
-    HierarchicalClusteringIndex& operator=(const HierarchicalClusteringIndex&);
-
-    /**
-     * Index destructor.
-     *
-     * Release the memory used by the index.
-     */
-    virtual ~HierarchicalClusteringIndex()
-    {
-        free_elements();
-
-        if (root!=NULL) {
-            delete[] root;
-        }
-
-        if (indices!=NULL) {
-            delete[] indices;
-        }
-    }
-
-
-    /**
-     * Release the inner elements of indices[]
-     */
-    void free_elements()
-    {
-        if (indices!=NULL) {
-            for(int i=0; i<trees_; ++i) {
-                if (indices[i]!=NULL) {
-                    delete[] indices[i];
-                    indices[i] = NULL;
-                }
-            }
-        }
-    }
-
-
-    /**
-     *  Returns size of index.
-     */
-    size_t size() const CV_OVERRIDE
-    {
-        return size_;
-    }
-
-    /**
-     * Returns the length of an index feature.
-     */
-    size_t veclen() const CV_OVERRIDE
-    {
-        return veclen_;
-    }
-
-
-    /**
-     * Computes the inde memory usage
-     * Returns: memory used by the index
-     */
-    int usedMemory() const CV_OVERRIDE
-    {
-        return pool.usedMemory+pool.wastedMemory+memoryCounter;
-    }
-
-    /**
-     * Builds the index
-     */
-    void buildIndex() CV_OVERRIDE
-    {
-        if (branching_<2) {
-            throw FLANNException("Branching factor must be at least 2");
-        }
-
-        free_elements();
-
-        for (int i=0; i<trees_; ++i) {
-            indices[i] = new int[size_];
-            for (size_t j=0; j<size_; ++j) {
-                indices[i][j] = (int)j;
-            }
-            root[i] = pool.allocate<Node>();
-            computeClustering(root[i], indices[i], (int)size_, branching_,0);
-        }
-    }
-
-
-    flann_algorithm_t getType() const CV_OVERRIDE
-    {
-        return FLANN_INDEX_HIERARCHICAL;
-    }
-
-
-    void saveIndex(FILE* stream) CV_OVERRIDE
-    {
-        save_value(stream, branching_);
-        save_value(stream, trees_);
-        save_value(stream, centers_init_);
-        save_value(stream, leaf_size_);
-        save_value(stream, memoryCounter);
-        for (int i=0; i<trees_; ++i) {
-            save_value(stream, *indices[i], size_);
-            save_tree(stream, root[i], i);
-        }
-
-    }
-
-
-    void loadIndex(FILE* stream) CV_OVERRIDE
-    {
-        free_elements();
-
-        if (root!=NULL) {
-            delete[] root;
-        }
-
-        if (indices!=NULL) {
-            delete[] indices;
-        }
-
-        load_value(stream, branching_);
-        load_value(stream, trees_);
-        load_value(stream, centers_init_);
-        load_value(stream, leaf_size_);
-        load_value(stream, memoryCounter);
-
-        indices = new int*[trees_];
-        root = new NodePtr[trees_];
-        for (int i=0; i<trees_; ++i) {
-            indices[i] = new int[size_];
-            load_value(stream, *indices[i], size_);
-            load_tree(stream, root[i], i);
-        }
-
-        params["algorithm"] = getType();
-        params["branching"] = branching_;
-        params["trees"] = trees_;
-        params["centers_init"] = centers_init_;
-        params["leaf_size"] = leaf_size_;
-    }
-
-
-    /**
-     * Find set of nearest neighbors to vec. Their indices are stored inside
-     * the result object.
-     *
-     * Params:
-     *     result = the result object in which the indices of the nearest-neighbors are stored
-     *     vec = the vector for which to search the nearest neighbors
-     *     searchParams = parameters that influence the search algorithm (checks)
-     */
-    void findNeighbors(ResultSet<DistanceType>& result, const ElementType* vec, const SearchParams& searchParams) CV_OVERRIDE
-    {
-
-        int maxChecks = get_param(searchParams,"checks",32);
-
-        // Priority queue storing intermediate branches in the best-bin-first search
-        Heap<BranchSt>* heap = new Heap<BranchSt>((int)size_);
-
-        std::vector<bool> checked(size_,false);
-        int checks = 0;
-        for (int i=0; i<trees_; ++i) {
-            findNN(root[i], result, vec, checks, maxChecks, heap, checked);
-        }
-
-        BranchSt branch;
-        while (heap->popMin(branch) && (checks<maxChecks || !result.full())) {
-            NodePtr node = branch.node;
-            findNN(node, result, vec, checks, maxChecks, heap, checked);
-        }
-        assert(result.full());
-
-        delete heap;
-
-    }
-
-    IndexParams getParameters() const CV_OVERRIDE
-    {
-        return params;
-    }
-
-
-private:
-
-    /**
-     * Struture representing a node in the hierarchical k-means tree.
-     */
-    struct Node
-    {
-        /**
-         * The cluster center index
-         */
-        int pivot;
-        /**
-         * The cluster size (number of points in the cluster)
-         */
-        int size;
-        /**
-         * Child nodes (only for non-terminal nodes)
-         */
-        Node** childs;
-        /**
-         * Node points (only for terminal nodes)
-         */
-        int* indices;
-        /**
-         * Level
-         */
-        int level;
-    };
-    typedef Node* NodePtr;
-
-
-
-    /**
-     * Alias definition for a nicer syntax.
-     */
-    typedef BranchStruct<NodePtr, DistanceType> BranchSt;
-
-
-
-    void save_tree(FILE* stream, NodePtr node, int num)
-    {
-        save_value(stream, *node);
-        if (node->childs==NULL) {
-            int indices_offset = (int)(node->indices - indices[num]);
-            save_value(stream, indices_offset);
-        }
-        else {
-            for(int i=0; i<branching_; ++i) {
-                save_tree(stream, node->childs[i], num);
-            }
-        }
-    }
-
-
-    void load_tree(FILE* stream, NodePtr& node, int num)
-    {
-        node = pool.allocate<Node>();
-        load_value(stream, *node);
-        if (node->childs==NULL) {
-            int indices_offset;
-            load_value(stream, indices_offset);
-            node->indices = indices[num] + indices_offset;
-        }
-        else {
-            node->childs = pool.allocate<NodePtr>(branching_);
-            for(int i=0; i<branching_; ++i) {
-                load_tree(stream, node->childs[i], num);
-            }
-        }
-    }
-
-
-
-
-    void computeLabels(int* dsindices, int indices_length,  int* centers, int centers_length, int* labels, DistanceType& cost)
-    {
-        cost = 0;
-        for (int i=0; i<indices_length; ++i) {
-            ElementType* point = dataset[dsindices[i]];
-            DistanceType dist = distance(point, dataset[centers[0]], veclen_);
-            labels[i] = 0;
-            for (int j=1; j<centers_length; ++j) {
-                DistanceType new_dist = distance(point, dataset[centers[j]], veclen_);
-                if (dist>new_dist) {
-                    labels[i] = j;
-                    dist = new_dist;
-                }
-            }
-            cost += dist;
-        }
-    }
-
-    /**
-     * The method responsible with actually doing the recursive hierarchical
-     * clustering
-     *
-     * Params:
-     *     node = the node to cluster
-     *     indices = indices of the points belonging to the current node
-     *     branching = the branching factor to use in the clustering
-     *
-     * TODO: for 1-sized clusters don't store a cluster center (it's the same as the single cluster point)
-     */
-    void computeClustering(NodePtr node, int* dsindices, int indices_length, int branching, int level)
-    {
-        node->size = indices_length;
-        node->level = level;
-
-        if (indices_length < leaf_size_) { // leaf node
-            node->indices = dsindices;
-            std::sort(node->indices,node->indices+indices_length);
-            node->childs = NULL;
-            return;
-        }
-
-        std::vector<int> centers(branching);
-        std::vector<int> labels(indices_length);
-
-        int centers_length;
-        (this->*chooseCenters)(branching, dsindices, indices_length, &centers[0], centers_length);
-
-        if (centers_length<branching) {
-            node->indices = dsindices;
-            std::sort(node->indices,node->indices+indices_length);
-            node->childs = NULL;
-            return;
-        }
-
-
-        //	assign points to clusters
-        DistanceType cost;
-        computeLabels(dsindices, indices_length, &centers[0], centers_length, &labels[0], cost);
-
-        node->childs = pool.allocate<NodePtr>(branching);
-        int start = 0;
-        int end = start;
-        for (int i=0; i<branching; ++i) {
-            for (int j=0; j<indices_length; ++j) {
-                if (labels[j]==i) {
-                    std::swap(dsindices[j],dsindices[end]);
-                    std::swap(labels[j],labels[end]);
-                    end++;
-                }
-            }
-
-            node->childs[i] = pool.allocate<Node>();
-            node->childs[i]->pivot = centers[i];
-            node->childs[i]->indices = NULL;
-            computeClustering(node->childs[i],dsindices+start, end-start, branching, level+1);
-            start=end;
-        }
-    }
-
-
-
-    /**
-     * Performs one descent in the hierarchical k-means tree. The branches not
-     * visited are stored in a priority queue.
-     *
-     * Params:
-     *      node = node to explore
-     *      result = container for the k-nearest neighbors found
-     *      vec = query points
-     *      checks = how many points in the dataset have been checked so far
-     *      maxChecks = maximum dataset points to checks
-     */
-
-
-    void findNN(NodePtr node, ResultSet<DistanceType>& result, const ElementType* vec, int& checks, int maxChecks,
-                Heap<BranchSt>* heap, std::vector<bool>& checked)
-    {
-        if (node->childs==NULL) {
-            if (checks>=maxChecks) {
-                if (result.full()) return;
-            }
-            for (int i=0; i<node->size; ++i) {
-                int index = node->indices[i];
-                if (!checked[index]) {
-                    DistanceType dist = distance(dataset[index], vec, veclen_);
-                    result.addPoint(dist, index);
-                    checked[index] = true;
-                    ++checks;
-                }
-            }
-        }
-        else {
-            DistanceType* domain_distances = new DistanceType[branching_];
-            int best_index = 0;
-            domain_distances[best_index] = distance(vec, dataset[node->childs[best_index]->pivot], veclen_);
-            for (int i=1; i<branching_; ++i) {
-                domain_distances[i] = distance(vec, dataset[node->childs[i]->pivot], veclen_);
-                if (domain_distances[i]<domain_distances[best_index]) {
-                    best_index = i;
-                }
-            }
-            for (int i=0; i<branching_; ++i) {
-                if (i!=best_index) {
-                    heap->insert(BranchSt(node->childs[i],domain_distances[i]));
-                }
-            }
-            delete[] domain_distances;
-            findNN(node->childs[best_index],result,vec, checks, maxChecks, heap, checked);
-        }
-    }
-
-private:
-
-
-    /**
-     * The dataset used by this index
-     */
-    const Matrix<ElementType> dataset;
-
-    /**
-     * Parameters used by this index
-     */
-    IndexParams params;
-
-
-    /**
-     * Number of features in the dataset.
-     */
-    size_t size_;
-
-    /**
-     * Length of each feature.
-     */
-    size_t veclen_;
-
-    /**
-     * The root node in the tree.
-     */
-    NodePtr* root;
-
-    /**
-     *  Array of indices to vectors in the dataset.
-     */
-    int** indices;
-
-
-    /**
-     * The distance
-     */
-    Distance distance;
-
-    /**
-     * Pooled memory allocator.
-     *
-     * Using a pooled memory allocator is more efficient
-     * than allocating memory directly when there is a large
-     * number small of memory allocations.
-     */
-    PooledAllocator pool;
-
-    /**
-     * Memory occupied by the index.
-     */
-    int memoryCounter;
-
-    /** index parameters */
-    int branching_;
-    int trees_;
-    flann_centers_init_t centers_init_;
-    int leaf_size_;
-
-
-};
-
-}
-
-#endif /* OPENCV_FLANN_HIERARCHICAL_CLUSTERING_INDEX_H_ */
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/index_testing.h b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/index_testing.h
deleted file mode 100755
index d764004..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/index_testing.h
+++ /dev/null
@@ -1,318 +0,0 @@
-/***********************************************************************
- * Software License Agreement (BSD License)
- *
- * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
- * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
- *
- * THE BSD LICENSE
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions
- * are met:
- *
- * 1. Redistributions of source code must retain the above copyright
- *    notice, this list of conditions and the following disclaimer.
- * 2. Redistributions in binary form must reproduce the above copyright
- *    notice, this list of conditions and the following disclaimer in the
- *    documentation and/or other materials provided with the distribution.
- *
- * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
- * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
- * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
- * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
- * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
- * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
- * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
- * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *************************************************************************/
-
-#ifndef OPENCV_FLANN_INDEX_TESTING_H_
-#define OPENCV_FLANN_INDEX_TESTING_H_
-
-#include <cstring>
-#include <cassert>
-#include <cmath>
-
-#include "matrix.h"
-#include "nn_index.h"
-#include "result_set.h"
-#include "logger.h"
-#include "timer.h"
-
-
-namespace cvflann
-{
-
-inline int countCorrectMatches(int* neighbors, int* groundTruth, int n)
-{
-    int count = 0;
-    for (int i=0; i<n; ++i) {
-        for (int k=0; k<n; ++k) {
-            if (neighbors[i]==groundTruth[k]) {
-                count++;
-                break;
-            }
-        }
-    }
-    return count;
-}
-
-
-template <typename Distance>
-typename Distance::ResultType computeDistanceRaport(const Matrix<typename Distance::ElementType>& inputData, typename Distance::ElementType* target,
-                                                    int* neighbors, int* groundTruth, int veclen, int n, const Distance& distance)
-{
-    typedef typename Distance::ResultType DistanceType;
-
-    DistanceType ret = 0;
-    for (int i=0; i<n; ++i) {
-        DistanceType den = distance(inputData[groundTruth[i]], target, veclen);
-        DistanceType num = distance(inputData[neighbors[i]], target, veclen);
-
-        if ((den==0)&&(num==0)) {
-            ret += 1;
-        }
-        else {
-            ret += num/den;
-        }
-    }
-
-    return ret;
-}
-
-template <typename Distance>
-float search_with_ground_truth(NNIndex<Distance>& index, const Matrix<typename Distance::ElementType>& inputData,
-                               const Matrix<typename Distance::ElementType>& testData, const Matrix<int>& matches, int nn, int checks,
-                               float& time, typename Distance::ResultType& dist, const Distance& distance, int skipMatches)
-{
-    typedef typename Distance::ResultType DistanceType;
-
-    if (matches.cols<size_t(nn)) {
-        Logger::info("matches.cols=%d, nn=%d\n",matches.cols,nn);
-
-        throw FLANNException("Ground truth is not computed for as many neighbors as requested");
-    }
-
-    KNNResultSet<DistanceType> resultSet(nn+skipMatches);
-    SearchParams searchParams(checks);
-
-    std::vector<int> indices(nn+skipMatches);
-    std::vector<DistanceType> dists(nn+skipMatches);
-    int* neighbors = &indices[skipMatches];
-
-    int correct = 0;
-    DistanceType distR = 0;
-    StartStopTimer t;
-    int repeats = 0;
-    while (t.value<0.2) {
-        repeats++;
-        t.start();
-        correct = 0;
-        distR = 0;
-        for (size_t i = 0; i < testData.rows; i++) {
-            resultSet.init(&indices[0], &dists[0]);
-            index.findNeighbors(resultSet, testData[i], searchParams);
-
-            correct += countCorrectMatches(neighbors,matches[i], nn);
-            distR += computeDistanceRaport<Distance>(inputData, testData[i], neighbors, matches[i], (int)testData.cols, nn, distance);
-        }
-        t.stop();
-    }
-    time = float(t.value/repeats);
-
-    float precicion = (float)correct/(nn*testData.rows);
-
-    dist = distR/(testData.rows*nn);
-
-    Logger::info("%8d %10.4g %10.5g %10.5g %10.5g\n",
-                 checks, precicion, time, 1000.0 * time / testData.rows, dist);
-
-    return precicion;
-}
-
-
-template <typename Distance>
-float test_index_checks(NNIndex<Distance>& index, const Matrix<typename Distance::ElementType>& inputData,
-                        const Matrix<typename Distance::ElementType>& testData, const Matrix<int>& matches,
-                        int checks, float& precision, const Distance& distance, int nn = 1, int skipMatches = 0)
-{
-    typedef typename Distance::ResultType DistanceType;
-
-    Logger::info("  Nodes  Precision(%)   Time(s)   Time/vec(ms)  Mean dist\n");
-    Logger::info("---------------------------------------------------------\n");
-
-    float time = 0;
-    DistanceType dist = 0;
-    precision = search_with_ground_truth(index, inputData, testData, matches, nn, checks, time, dist, distance, skipMatches);
-
-    return time;
-}
-
-template <typename Distance>
-float test_index_precision(NNIndex<Distance>& index, const Matrix<typename Distance::ElementType>& inputData,
-                           const Matrix<typename Distance::ElementType>& testData, const Matrix<int>& matches,
-                           float precision, int& checks, const Distance& distance, int nn = 1, int skipMatches = 0)
-{
-    typedef typename Distance::ResultType DistanceType;
-    const float SEARCH_EPS = 0.001f;
-
-    Logger::info("  Nodes  Precision(%)   Time(s)   Time/vec(ms)  Mean dist\n");
-    Logger::info("---------------------------------------------------------\n");
-
-    int c2 = 1;
-    float p2;
-    int c1 = 1;
-    //float p1;
-    float time;
-    DistanceType dist;
-
-    p2 = search_with_ground_truth(index, inputData, testData, matches, nn, c2, time, dist, distance, skipMatches);
-
-    if (p2>precision) {
-        Logger::info("Got as close as I can\n");
-        checks = c2;
-        return time;
-    }
-
-    while (p2<precision) {
-        c1 = c2;
-        //p1 = p2;
-        c2 *=2;
-        p2 = search_with_ground_truth(index, inputData, testData, matches, nn, c2, time, dist, distance, skipMatches);
-    }
-
-    int cx;
-    float realPrecision;
-    if (fabs(p2-precision)>SEARCH_EPS) {
-        Logger::info("Start linear estimation\n");
-        // after we got to values in the vecinity of the desired precision
-        // use linear approximation get a better estimation
-
-        cx = (c1+c2)/2;
-        realPrecision = search_with_ground_truth(index, inputData, testData, matches, nn, cx, time, dist, distance, skipMatches);
-        while (fabs(realPrecision-precision)>SEARCH_EPS) {
-
-            if (realPrecision<precision) {
-                c1 = cx;
-            }
-            else {
-                c2 = cx;
-            }
-            cx = (c1+c2)/2;
-            if (cx==c1) {
-                Logger::info("Got as close as I can\n");
-                break;
-            }
-            realPrecision = search_with_ground_truth(index, inputData, testData, matches, nn, cx, time, dist, distance, skipMatches);
-        }
-
-        c2 = cx;
-        p2 = realPrecision;
-
-    }
-    else {
-        Logger::info("No need for linear estimation\n");
-        cx = c2;
-        realPrecision = p2;
-    }
-
-    checks = cx;
-    return time;
-}
-
-
-template <typename Distance>
-void test_index_precisions(NNIndex<Distance>& index, const Matrix<typename Distance::ElementType>& inputData,
-                           const Matrix<typename Distance::ElementType>& testData, const Matrix<int>& matches,
-                           float* precisions, int precisions_length, const Distance& distance, int nn = 1, int skipMatches = 0, float maxTime = 0)
-{
-    typedef typename Distance::ResultType DistanceType;
-
-    const float SEARCH_EPS = 0.001;
-
-    // make sure precisions array is sorted
-    std::sort(precisions, precisions+precisions_length);
-
-    int pindex = 0;
-    float precision = precisions[pindex];
-
-    Logger::info("  Nodes  Precision(%)   Time(s)   Time/vec(ms)  Mean dist\n");
-    Logger::info("---------------------------------------------------------\n");
-
-    int c2 = 1;
-    float p2;
-
-    int c1 = 1;
-    float p1;
-
-    float time;
-    DistanceType dist;
-
-    p2 = search_with_ground_truth(index, inputData, testData, matches, nn, c2, time, dist, distance, skipMatches);
-
-    // if precision for 1 run down the tree is already
-    // better then some of the requested precisions, then
-    // skip those
-    while (precisions[pindex]<p2 && pindex<precisions_length) {
-        pindex++;
-    }
-
-    if (pindex==precisions_length) {
-        Logger::info("Got as close as I can\n");
-        return;
-    }
-
-    for (int i=pindex; i<precisions_length; ++i) {
-
-        precision = precisions[i];
-        while (p2<precision) {
-            c1 = c2;
-            p1 = p2;
-            c2 *=2;
-            p2 = search_with_ground_truth(index, inputData, testData, matches, nn, c2, time, dist, distance, skipMatches);
-            if ((maxTime> 0)&&(time > maxTime)&&(p2<precision)) return;
-        }
-
-        int cx;
-        float realPrecision;
-        if (fabs(p2-precision)>SEARCH_EPS) {
-            Logger::info("Start linear estimation\n");
-            // after we got to values in the vecinity of the desired precision
-            // use linear approximation get a better estimation
-
-            cx = (c1+c2)/2;
-            realPrecision = search_with_ground_truth(index, inputData, testData, matches, nn, cx, time, dist, distance, skipMatches);
-            while (fabs(realPrecision-precision)>SEARCH_EPS) {
-
-                if (realPrecision<precision) {
-                    c1 = cx;
-                }
-                else {
-                    c2 = cx;
-                }
-                cx = (c1+c2)/2;
-                if (cx==c1) {
-                    Logger::info("Got as close as I can\n");
-                    break;
-                }
-                realPrecision = search_with_ground_truth(index, inputData, testData, matches, nn, cx, time, dist, distance, skipMatches);
-            }
-
-            c2 = cx;
-            p2 = realPrecision;
-
-        }
-        else {
-            Logger::info("No need for linear estimation\n");
-            cx = c2;
-            realPrecision = p2;
-        }
-
-    }
-}
-
-}
-
-#endif //OPENCV_FLANN_INDEX_TESTING_H_
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/kdtree_index.h b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/kdtree_index.h
deleted file mode 100755
index c233515..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/kdtree_index.h
+++ /dev/null
@@ -1,626 +0,0 @@
-/***********************************************************************
- * Software License Agreement (BSD License)
- *
- * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
- * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
- *
- * THE BSD LICENSE
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions
- * are met:
- *
- * 1. Redistributions of source code must retain the above copyright
- *    notice, this list of conditions and the following disclaimer.
- * 2. Redistributions in binary form must reproduce the above copyright
- *    notice, this list of conditions and the following disclaimer in the
- *    documentation and/or other materials provided with the distribution.
- *
- * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
- * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
- * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
- * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
- * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
- * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
- * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
- * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *************************************************************************/
-
-#ifndef OPENCV_FLANN_KDTREE_INDEX_H_
-#define OPENCV_FLANN_KDTREE_INDEX_H_
-
-#include <algorithm>
-#include <map>
-#include <cassert>
-#include <cstring>
-
-#include "general.h"
-#include "nn_index.h"
-#include "dynamic_bitset.h"
-#include "matrix.h"
-#include "result_set.h"
-#include "heap.h"
-#include "allocator.h"
-#include "random.h"
-#include "saving.h"
-
-
-namespace cvflann
-{
-
-struct KDTreeIndexParams : public IndexParams
-{
-    KDTreeIndexParams(int trees = 4)
-    {
-        (*this)["algorithm"] = FLANN_INDEX_KDTREE;
-        (*this)["trees"] = trees;
-    }
-};
-
-
-/**
- * Randomized kd-tree index
- *
- * Contains the k-d trees and other information for indexing a set of points
- * for nearest-neighbor matching.
- */
-template <typename Distance>
-class KDTreeIndex : public NNIndex<Distance>
-{
-public:
-    typedef typename Distance::ElementType ElementType;
-    typedef typename Distance::ResultType DistanceType;
-
-
-    /**
-     * KDTree constructor
-     *
-     * Params:
-     *          inputData = dataset with the input features
-     *          params = parameters passed to the kdtree algorithm
-     */
-    KDTreeIndex(const Matrix<ElementType>& inputData, const IndexParams& params = KDTreeIndexParams(),
-                Distance d = Distance() ) :
-        dataset_(inputData), index_params_(params), distance_(d)
-    {
-        size_ = dataset_.rows;
-        veclen_ = dataset_.cols;
-
-        trees_ = get_param(index_params_,"trees",4);
-        tree_roots_ = new NodePtr[trees_];
-
-        // Create a permutable array of indices to the input vectors.
-        vind_.resize(size_);
-        for (size_t i = 0; i < size_; ++i) {
-            vind_[i] = int(i);
-        }
-
-        mean_ = new DistanceType[veclen_];
-        var_ = new DistanceType[veclen_];
-    }
-
-
-    KDTreeIndex(const KDTreeIndex&);
-    KDTreeIndex& operator=(const KDTreeIndex&);
-
-    /**
-     * Standard destructor
-     */
-    ~KDTreeIndex()
-    {
-        if (tree_roots_!=NULL) {
-            delete[] tree_roots_;
-        }
-        delete[] mean_;
-        delete[] var_;
-    }
-
-    /**
-     * Builds the index
-     */
-    void buildIndex() CV_OVERRIDE
-    {
-        /* Construct the randomized trees. */
-        for (int i = 0; i < trees_; i++) {
-            /* Randomize the order of vectors to allow for unbiased sampling. */
-#ifndef OPENCV_FLANN_USE_STD_RAND
-            cv::randShuffle(vind_);
-#else
-            std::random_shuffle(vind_.begin(), vind_.end());
-#endif
-
-            tree_roots_[i] = divideTree(&vind_[0], int(size_) );
-        }
-    }
-
-
-    flann_algorithm_t getType() const CV_OVERRIDE
-    {
-        return FLANN_INDEX_KDTREE;
-    }
-
-
-    void saveIndex(FILE* stream) CV_OVERRIDE
-    {
-        save_value(stream, trees_);
-        for (int i=0; i<trees_; ++i) {
-            save_tree(stream, tree_roots_[i]);
-        }
-    }
-
-
-
-    void loadIndex(FILE* stream) CV_OVERRIDE
-    {
-        load_value(stream, trees_);
-        if (tree_roots_!=NULL) {
-            delete[] tree_roots_;
-        }
-        tree_roots_ = new NodePtr[trees_];
-        for (int i=0; i<trees_; ++i) {
-            load_tree(stream,tree_roots_[i]);
-        }
-
-        index_params_["algorithm"] = getType();
-        index_params_["trees"] = tree_roots_;
-    }
-
-    /**
-     *  Returns size of index.
-     */
-    size_t size() const CV_OVERRIDE
-    {
-        return size_;
-    }
-
-    /**
-     * Returns the length of an index feature.
-     */
-    size_t veclen() const CV_OVERRIDE
-    {
-        return veclen_;
-    }
-
-    /**
-     * Computes the inde memory usage
-     * Returns: memory used by the index
-     */
-    int usedMemory() const CV_OVERRIDE
-    {
-        return int(pool_.usedMemory+pool_.wastedMemory+dataset_.rows*sizeof(int));  // pool memory and vind array memory
-    }
-
-    /**
-     * Find set of nearest neighbors to vec. Their indices are stored inside
-     * the result object.
-     *
-     * Params:
-     *     result = the result object in which the indices of the nearest-neighbors are stored
-     *     vec = the vector for which to search the nearest neighbors
-     *     maxCheck = the maximum number of restarts (in a best-bin-first manner)
-     */
-    void findNeighbors(ResultSet<DistanceType>& result, const ElementType* vec, const SearchParams& searchParams) CV_OVERRIDE
-    {
-        int maxChecks = get_param(searchParams,"checks", 32);
-        float epsError = 1+get_param(searchParams,"eps",0.0f);
-
-        if (maxChecks==FLANN_CHECKS_UNLIMITED) {
-            getExactNeighbors(result, vec, epsError);
-        }
-        else {
-            getNeighbors(result, vec, maxChecks, epsError);
-        }
-    }
-
-    IndexParams getParameters() const CV_OVERRIDE
-    {
-        return index_params_;
-    }
-
-private:
-
-
-    /*--------------------- Internal Data Structures --------------------------*/
-    struct Node
-    {
-        /**
-         * Dimension used for subdivision.
-         */
-        int divfeat;
-        /**
-         * The values used for subdivision.
-         */
-        DistanceType divval;
-        /**
-         * The child nodes.
-         */
-        Node* child1, * child2;
-    };
-    typedef Node* NodePtr;
-    typedef BranchStruct<NodePtr, DistanceType> BranchSt;
-    typedef BranchSt* Branch;
-
-
-
-    void save_tree(FILE* stream, NodePtr tree)
-    {
-        save_value(stream, *tree);
-        if (tree->child1!=NULL) {
-            save_tree(stream, tree->child1);
-        }
-        if (tree->child2!=NULL) {
-            save_tree(stream, tree->child2);
-        }
-    }
-
-
-    void load_tree(FILE* stream, NodePtr& tree)
-    {
-        tree = pool_.allocate<Node>();
-        load_value(stream, *tree);
-        if (tree->child1!=NULL) {
-            load_tree(stream, tree->child1);
-        }
-        if (tree->child2!=NULL) {
-            load_tree(stream, tree->child2);
-        }
-    }
-
-
-    /**
-     * Create a tree node that subdivides the list of vecs from vind[first]
-     * to vind[last].  The routine is called recursively on each sublist.
-     * Place a pointer to this new tree node in the location pTree.
-     *
-     * Params: pTree = the new node to create
-     *                  first = index of the first vector
-     *                  last = index of the last vector
-     */
-    NodePtr divideTree(int* ind, int count)
-    {
-        NodePtr node = pool_.allocate<Node>(); // allocate memory
-
-        /* If too few exemplars remain, then make this a leaf node. */
-        if ( count == 1) {
-            node->child1 = node->child2 = NULL;    /* Mark as leaf node. */
-            node->divfeat = *ind;    /* Store index of this vec. */
-        }
-        else {
-            int idx;
-            int cutfeat;
-            DistanceType cutval;
-            meanSplit(ind, count, idx, cutfeat, cutval);
-
-            node->divfeat = cutfeat;
-            node->divval = cutval;
-            node->child1 = divideTree(ind, idx);
-            node->child2 = divideTree(ind+idx, count-idx);
-        }
-
-        return node;
-    }
-
-
-    /**
-     * Choose which feature to use in order to subdivide this set of vectors.
-     * Make a random choice among those with the highest variance, and use
-     * its variance as the threshold value.
-     */
-    void meanSplit(int* ind, int count, int& index, int& cutfeat, DistanceType& cutval)
-    {
-        memset(mean_,0,veclen_*sizeof(DistanceType));
-        memset(var_,0,veclen_*sizeof(DistanceType));
-
-        /* Compute mean values.  Only the first SAMPLE_MEAN values need to be
-            sampled to get a good estimate.
-         */
-        int cnt = std::min((int)SAMPLE_MEAN+1, count);
-        for (int j = 0; j < cnt; ++j) {
-            ElementType* v = dataset_[ind[j]];
-            for (size_t k=0; k<veclen_; ++k) {
-                mean_[k] += v[k];
-            }
-        }
-        for (size_t k=0; k<veclen_; ++k) {
-            mean_[k] /= cnt;
-        }
-
-        /* Compute variances (no need to divide by count). */
-        for (int j = 0; j < cnt; ++j) {
-            ElementType* v = dataset_[ind[j]];
-            for (size_t k=0; k<veclen_; ++k) {
-                DistanceType dist = v[k] - mean_[k];
-                var_[k] += dist * dist;
-            }
-        }
-        /* Select one of the highest variance indices at random. */
-        cutfeat = selectDivision(var_);
-        cutval = mean_[cutfeat];
-
-        int lim1, lim2;
-        planeSplit(ind, count, cutfeat, cutval, lim1, lim2);
-
-        if (lim1>count/2) index = lim1;
-        else if (lim2<count/2) index = lim2;
-        else index = count/2;
-
-        /* If either list is empty, it means that all remaining features
-         * are identical. Split in the middle to maintain a balanced tree.
-         */
-        if ((lim1==count)||(lim2==0)) index = count/2;
-    }
-
-
-    /**
-     * Select the top RAND_DIM largest values from v and return the index of
-     * one of these selected at random.
-     */
-    int selectDivision(DistanceType* v)
-    {
-        int num = 0;
-        size_t topind[RAND_DIM];
-
-        /* Create a list of the indices of the top RAND_DIM values. */
-        for (size_t i = 0; i < veclen_; ++i) {
-            if ((num < RAND_DIM)||(v[i] > v[topind[num-1]])) {
-                /* Put this element at end of topind. */
-                if (num < RAND_DIM) {
-                    topind[num++] = i;            /* Add to list. */
-                }
-                else {
-                    topind[num-1] = i;         /* Replace last element. */
-                }
-                /* Bubble end value down to right location by repeated swapping. */
-                int j = num - 1;
-                while (j > 0  &&  v[topind[j]] > v[topind[j-1]]) {
-                    std::swap(topind[j], topind[j-1]);
-                    --j;
-                }
-            }
-        }
-        /* Select a random integer in range [0,num-1], and return that index. */
-        int rnd = rand_int(num);
-        return (int)topind[rnd];
-    }
-
-
-    /**
-     *  Subdivide the list of points by a plane perpendicular on axe corresponding
-     *  to the 'cutfeat' dimension at 'cutval' position.
-     *
-     *  On return:
-     *  dataset[ind[0..lim1-1]][cutfeat]<cutval
-     *  dataset[ind[lim1..lim2-1]][cutfeat]==cutval
-     *  dataset[ind[lim2..count]][cutfeat]>cutval
-     */
-    void planeSplit(int* ind, int count, int cutfeat, DistanceType cutval, int& lim1, int& lim2)
-    {
-        /* Move vector indices for left subtree to front of list. */
-        int left = 0;
-        int right = count-1;
-        for (;; ) {
-            while (left<=right && dataset_[ind[left]][cutfeat]<cutval) ++left;
-            while (left<=right && dataset_[ind[right]][cutfeat]>=cutval) --right;
-            if (left>right) break;
-            std::swap(ind[left], ind[right]); ++left; --right;
-        }
-        lim1 = left;
-        right = count-1;
-        for (;; ) {
-            while (left<=right && dataset_[ind[left]][cutfeat]<=cutval) ++left;
-            while (left<=right && dataset_[ind[right]][cutfeat]>cutval) --right;
-            if (left>right) break;
-            std::swap(ind[left], ind[right]); ++left; --right;
-        }
-        lim2 = left;
-    }
-
-    /**
-     * Performs an exact nearest neighbor search. The exact search performs a full
-     * traversal of the tree.
-     */
-    void getExactNeighbors(ResultSet<DistanceType>& result, const ElementType* vec, float epsError)
-    {
-        //		checkID -= 1;  /* Set a different unique ID for each search. */
-
-        if (trees_ > 1) {
-            fprintf(stderr,"It doesn't make any sense to use more than one tree for exact search");
-        }
-        if (trees_>0) {
-            searchLevelExact(result, vec, tree_roots_[0], 0.0, epsError);
-        }
-        assert(result.full());
-    }
-
-    /**
-     * Performs the approximate nearest-neighbor search. The search is approximate
-     * because the tree traversal is abandoned after a given number of descends in
-     * the tree.
-     */
-    void getNeighbors(ResultSet<DistanceType>& result, const ElementType* vec, int maxCheck, float epsError)
-    {
-        int i;
-        BranchSt branch;
-
-        int checkCount = 0;
-        Heap<BranchSt>* heap = new Heap<BranchSt>((int)size_);
-        DynamicBitset checked(size_);
-
-        /* Search once through each tree down to root. */
-        for (i = 0; i < trees_; ++i) {
-            searchLevel(result, vec, tree_roots_[i], 0, checkCount, maxCheck, epsError, heap, checked);
-        }
-
-        /* Keep searching other branches from heap until finished. */
-        while ( heap->popMin(branch) && (checkCount < maxCheck || !result.full() )) {
-            searchLevel(result, vec, branch.node, branch.mindist, checkCount, maxCheck, epsError, heap, checked);
-        }
-
-        delete heap;
-
-        assert(result.full());
-    }
-
-
-    /**
-     *  Search starting from a given node of the tree.  Based on any mismatches at
-     *  higher levels, all exemplars below this level must have a distance of
-     *  at least "mindistsq".
-     */
-    void searchLevel(ResultSet<DistanceType>& result_set, const ElementType* vec, NodePtr node, DistanceType mindist, int& checkCount, int maxCheck,
-                     float epsError, Heap<BranchSt>* heap, DynamicBitset& checked)
-    {
-        if (result_set.worstDist()<mindist) {
-            //			printf("Ignoring branch, too far\n");
-            return;
-        }
-
-        /* If this is a leaf node, then do check and return. */
-        if ((node->child1 == NULL)&&(node->child2 == NULL)) {
-            /*  Do not check same node more than once when searching multiple trees.
-                Once a vector is checked, we set its location in vind to the
-                current checkID.
-             */
-            int index = node->divfeat;
-            if ( checked.test(index) || ((checkCount>=maxCheck)&& result_set.full()) ) return;
-            checked.set(index);
-            checkCount++;
-
-            DistanceType dist = distance_(dataset_[index], vec, veclen_);
-            result_set.addPoint(dist,index);
-
-            return;
-        }
-
-        /* Which child branch should be taken first? */
-        ElementType val = vec[node->divfeat];
-        DistanceType diff = val - node->divval;
-        NodePtr bestChild = (diff < 0) ? node->child1 : node->child2;
-        NodePtr otherChild = (diff < 0) ? node->child2 : node->child1;
-
-        /* Create a branch record for the branch not taken.  Add distance
-            of this feature boundary (we don't attempt to correct for any
-            use of this feature in a parent node, which is unlikely to
-            happen and would have only a small effect).  Don't bother
-            adding more branches to heap after halfway point, as cost of
-            adding exceeds their value.
-         */
-
-        DistanceType new_distsq = mindist + distance_.accum_dist(val, node->divval, node->divfeat);
-        //		if (2 * checkCount < maxCheck  ||  !result.full()) {
-        if ((new_distsq*epsError < result_set.worstDist())||  !result_set.full()) {
-            heap->insert( BranchSt(otherChild, new_distsq) );
-        }
-
-        /* Call recursively to search next level down. */
-        searchLevel(result_set, vec, bestChild, mindist, checkCount, maxCheck, epsError, heap, checked);
-    }
-
-    /**
-     * Performs an exact search in the tree starting from a node.
-     */
-    void searchLevelExact(ResultSet<DistanceType>& result_set, const ElementType* vec, const NodePtr node, DistanceType mindist, const float epsError)
-    {
-        /* If this is a leaf node, then do check and return. */
-        if ((node->child1 == NULL)&&(node->child2 == NULL)) {
-            int index = node->divfeat;
-            DistanceType dist = distance_(dataset_[index], vec, veclen_);
-            result_set.addPoint(dist,index);
-            return;
-        }
-
-        /* Which child branch should be taken first? */
-        ElementType val = vec[node->divfeat];
-        DistanceType diff = val - node->divval;
-        NodePtr bestChild = (diff < 0) ? node->child1 : node->child2;
-        NodePtr otherChild = (diff < 0) ? node->child2 : node->child1;
-
-        /* Create a branch record for the branch not taken.  Add distance
-            of this feature boundary (we don't attempt to correct for any
-            use of this feature in a parent node, which is unlikely to
-            happen and would have only a small effect).  Don't bother
-            adding more branches to heap after halfway point, as cost of
-            adding exceeds their value.
-         */
-
-        DistanceType new_distsq = mindist + distance_.accum_dist(val, node->divval, node->divfeat);
-
-        /* Call recursively to search next level down. */
-        searchLevelExact(result_set, vec, bestChild, mindist, epsError);
-
-        if (new_distsq*epsError<=result_set.worstDist()) {
-            searchLevelExact(result_set, vec, otherChild, new_distsq, epsError);
-        }
-    }
-
-
-private:
-
-    enum
-    {
-        /**
-         * To improve efficiency, only SAMPLE_MEAN random values are used to
-         * compute the mean and variance at each level when building a tree.
-         * A value of 100 seems to perform as well as using all values.
-         */
-        SAMPLE_MEAN = 100,
-        /**
-         * Top random dimensions to consider
-         *
-         * When creating random trees, the dimension on which to subdivide is
-         * selected at random from among the top RAND_DIM dimensions with the
-         * highest variance.  A value of 5 works well.
-         */
-        RAND_DIM=5
-    };
-
-
-    /**
-     * Number of randomized trees that are used
-     */
-    int trees_;
-
-    /**
-     *  Array of indices to vectors in the dataset.
-     */
-    std::vector<int> vind_;
-
-    /**
-     * The dataset used by this index
-     */
-    const Matrix<ElementType> dataset_;
-
-    IndexParams index_params_;
-
-    size_t size_;
-    size_t veclen_;
-
-
-    DistanceType* mean_;
-    DistanceType* var_;
-
-
-    /**
-     * Array of k-d trees used to find neighbours.
-     */
-    NodePtr* tree_roots_;
-
-    /**
-     * Pooled memory allocator.
-     *
-     * Using a pooled memory allocator is more efficient
-     * than allocating memory directly when there is a large
-     * number small of memory allocations.
-     */
-    PooledAllocator pool_;
-
-    Distance distance_;
-
-
-};   // class KDTreeForest
-
-}
-
-#endif //OPENCV_FLANN_KDTREE_INDEX_H_
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/kdtree_single_index.h b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/kdtree_single_index.h
deleted file mode 100755
index 22a28d0..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/kdtree_single_index.h
+++ /dev/null
@@ -1,635 +0,0 @@
-/***********************************************************************
- * Software License Agreement (BSD License)
- *
- * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
- * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
- *
- * THE BSD LICENSE
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions
- * are met:
- *
- * 1. Redistributions of source code must retain the above copyright
- *    notice, this list of conditions and the following disclaimer.
- * 2. Redistributions in binary form must reproduce the above copyright
- *    notice, this list of conditions and the following disclaimer in the
- *    documentation and/or other materials provided with the distribution.
- *
- * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
- * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
- * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
- * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
- * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
- * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
- * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
- * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *************************************************************************/
-
-#ifndef OPENCV_FLANN_KDTREE_SINGLE_INDEX_H_
-#define OPENCV_FLANN_KDTREE_SINGLE_INDEX_H_
-
-#include <algorithm>
-#include <map>
-#include <cassert>
-#include <cstring>
-
-#include "general.h"
-#include "nn_index.h"
-#include "matrix.h"
-#include "result_set.h"
-#include "heap.h"
-#include "allocator.h"
-#include "random.h"
-#include "saving.h"
-
-namespace cvflann
-{
-
-struct KDTreeSingleIndexParams : public IndexParams
-{
-    KDTreeSingleIndexParams(int leaf_max_size = 10, bool reorder = true, int dim = -1)
-    {
-        (*this)["algorithm"] = FLANN_INDEX_KDTREE_SINGLE;
-        (*this)["leaf_max_size"] = leaf_max_size;
-        (*this)["reorder"] = reorder;
-        (*this)["dim"] = dim;
-    }
-};
-
-
-/**
- * Randomized kd-tree index
- *
- * Contains the k-d trees and other information for indexing a set of points
- * for nearest-neighbor matching.
- */
-template <typename Distance>
-class KDTreeSingleIndex : public NNIndex<Distance>
-{
-public:
-    typedef typename Distance::ElementType ElementType;
-    typedef typename Distance::ResultType DistanceType;
-
-
-    /**
-     * KDTree constructor
-     *
-     * Params:
-     *          inputData = dataset with the input features
-     *          params = parameters passed to the kdtree algorithm
-     */
-    KDTreeSingleIndex(const Matrix<ElementType>& inputData, const IndexParams& params = KDTreeSingleIndexParams(),
-                      Distance d = Distance() ) :
-        dataset_(inputData), index_params_(params), distance_(d)
-    {
-        size_ = dataset_.rows;
-        dim_ = dataset_.cols;
-        root_node_ = 0;
-        int dim_param = get_param(params,"dim",-1);
-        if (dim_param>0) dim_ = dim_param;
-        leaf_max_size_ = get_param(params,"leaf_max_size",10);
-        reorder_ = get_param(params,"reorder",true);
-
-        // Create a permutable array of indices to the input vectors.
-        vind_.resize(size_);
-        for (size_t i = 0; i < size_; i++) {
-            vind_[i] = (int)i;
-        }
-    }
-
-    KDTreeSingleIndex(const KDTreeSingleIndex&);
-    KDTreeSingleIndex& operator=(const KDTreeSingleIndex&);
-
-    /**
-     * Standard destructor
-     */
-    ~KDTreeSingleIndex()
-    {
-        if (reorder_) delete[] data_.data;
-    }
-
-    /**
-     * Builds the index
-     */
-    void buildIndex() CV_OVERRIDE
-    {
-        computeBoundingBox(root_bbox_);
-        root_node_ = divideTree(0, (int)size_, root_bbox_ );   // construct the tree
-
-        if (reorder_) {
-            delete[] data_.data;
-            data_ = cvflann::Matrix<ElementType>(new ElementType[size_*dim_], size_, dim_);
-            for (size_t i=0; i<size_; ++i) {
-                for (size_t j=0; j<dim_; ++j) {
-                    data_[i][j] = dataset_[vind_[i]][j];
-                }
-            }
-        }
-        else {
-            data_ = dataset_;
-        }
-    }
-
-    flann_algorithm_t getType() const CV_OVERRIDE
-    {
-        return FLANN_INDEX_KDTREE_SINGLE;
-    }
-
-
-    void saveIndex(FILE* stream) CV_OVERRIDE
-    {
-        save_value(stream, size_);
-        save_value(stream, dim_);
-        save_value(stream, root_bbox_);
-        save_value(stream, reorder_);
-        save_value(stream, leaf_max_size_);
-        save_value(stream, vind_);
-        if (reorder_) {
-            save_value(stream, data_);
-        }
-        save_tree(stream, root_node_);
-    }
-
-
-    void loadIndex(FILE* stream) CV_OVERRIDE
-    {
-        load_value(stream, size_);
-        load_value(stream, dim_);
-        load_value(stream, root_bbox_);
-        load_value(stream, reorder_);
-        load_value(stream, leaf_max_size_);
-        load_value(stream, vind_);
-        if (reorder_) {
-            load_value(stream, data_);
-        }
-        else {
-            data_ = dataset_;
-        }
-        load_tree(stream, root_node_);
-
-
-        index_params_["algorithm"] = getType();
-        index_params_["leaf_max_size"] = leaf_max_size_;
-        index_params_["reorder"] = reorder_;
-    }
-
-    /**
-     *  Returns size of index.
-     */
-    size_t size() const CV_OVERRIDE
-    {
-        return size_;
-    }
-
-    /**
-     * Returns the length of an index feature.
-     */
-    size_t veclen() const CV_OVERRIDE
-    {
-        return dim_;
-    }
-
-    /**
-     * Computes the inde memory usage
-     * Returns: memory used by the index
-     */
-    int usedMemory() const CV_OVERRIDE
-    {
-        return (int)(pool_.usedMemory+pool_.wastedMemory+dataset_.rows*sizeof(int));  // pool memory and vind array memory
-    }
-
-
-    /**
-     * \brief Perform k-nearest neighbor search
-     * \param[in] queries The query points for which to find the nearest neighbors
-     * \param[out] indices The indices of the nearest neighbors found
-     * \param[out] dists Distances to the nearest neighbors found
-     * \param[in] knn Number of nearest neighbors to return
-     * \param[in] params Search parameters
-     */
-    void knnSearch(const Matrix<ElementType>& queries, Matrix<int>& indices, Matrix<DistanceType>& dists, int knn, const SearchParams& params) CV_OVERRIDE
-    {
-        assert(queries.cols == veclen());
-        assert(indices.rows >= queries.rows);
-        assert(dists.rows >= queries.rows);
-        assert(int(indices.cols) >= knn);
-        assert(int(dists.cols) >= knn);
-
-        KNNSimpleResultSet<DistanceType> resultSet(knn);
-        for (size_t i = 0; i < queries.rows; i++) {
-            resultSet.init(indices[i], dists[i]);
-            findNeighbors(resultSet, queries[i], params);
-        }
-    }
-
-    IndexParams getParameters() const CV_OVERRIDE
-    {
-        return index_params_;
-    }
-
-    /**
-     * Find set of nearest neighbors to vec. Their indices are stored inside
-     * the result object.
-     *
-     * Params:
-     *     result = the result object in which the indices of the nearest-neighbors are stored
-     *     vec = the vector for which to search the nearest neighbors
-     *     maxCheck = the maximum number of restarts (in a best-bin-first manner)
-     */
-    void findNeighbors(ResultSet<DistanceType>& result, const ElementType* vec, const SearchParams& searchParams) CV_OVERRIDE
-    {
-        float epsError = 1+get_param(searchParams,"eps",0.0f);
-
-        std::vector<DistanceType> dists(dim_,0);
-        DistanceType distsq = computeInitialDistances(vec, dists);
-        searchLevel(result, vec, root_node_, distsq, dists, epsError);
-    }
-
-private:
-
-
-    /*--------------------- Internal Data Structures --------------------------*/
-    struct Node
-    {
-        /**
-         * Indices of points in leaf node
-         */
-        int left, right;
-        /**
-         * Dimension used for subdivision.
-         */
-        int divfeat;
-        /**
-         * The values used for subdivision.
-         */
-        DistanceType divlow, divhigh;
-        /**
-         * The child nodes.
-         */
-        Node* child1, * child2;
-    };
-    typedef Node* NodePtr;
-
-
-    struct Interval
-    {
-        DistanceType low, high;
-    };
-
-    typedef std::vector<Interval> BoundingBox;
-
-    typedef BranchStruct<NodePtr, DistanceType> BranchSt;
-    typedef BranchSt* Branch;
-
-
-
-
-    void save_tree(FILE* stream, NodePtr tree)
-    {
-        save_value(stream, *tree);
-        if (tree->child1!=NULL) {
-            save_tree(stream, tree->child1);
-        }
-        if (tree->child2!=NULL) {
-            save_tree(stream, tree->child2);
-        }
-    }
-
-
-    void load_tree(FILE* stream, NodePtr& tree)
-    {
-        tree = pool_.allocate<Node>();
-        load_value(stream, *tree);
-        if (tree->child1!=NULL) {
-            load_tree(stream, tree->child1);
-        }
-        if (tree->child2!=NULL) {
-            load_tree(stream, tree->child2);
-        }
-    }
-
-
-    void computeBoundingBox(BoundingBox& bbox)
-    {
-        bbox.resize(dim_);
-        for (size_t i=0; i<dim_; ++i) {
-            bbox[i].low = (DistanceType)dataset_[0][i];
-            bbox[i].high = (DistanceType)dataset_[0][i];
-        }
-        for (size_t k=1; k<dataset_.rows; ++k) {
-            for (size_t i=0; i<dim_; ++i) {
-                if (dataset_[k][i]<bbox[i].low) bbox[i].low = (DistanceType)dataset_[k][i];
-                if (dataset_[k][i]>bbox[i].high) bbox[i].high = (DistanceType)dataset_[k][i];
-            }
-        }
-    }
-
-
-    /**
-     * Create a tree node that subdivides the list of vecs from vind[first]
-     * to vind[last].  The routine is called recursively on each sublist.
-     * Place a pointer to this new tree node in the location pTree.
-     *
-     * Params: pTree = the new node to create
-     *                  first = index of the first vector
-     *                  last = index of the last vector
-     */
-    NodePtr divideTree(int left, int right, BoundingBox& bbox)
-    {
-        NodePtr node = pool_.allocate<Node>(); // allocate memory
-
-        /* If too few exemplars remain, then make this a leaf node. */
-        if ( (right-left) <= leaf_max_size_) {
-            node->child1 = node->child2 = NULL;    /* Mark as leaf node. */
-            node->left = left;
-            node->right = right;
-
-            // compute bounding-box of leaf points
-            for (size_t i=0; i<dim_; ++i) {
-                bbox[i].low = (DistanceType)dataset_[vind_[left]][i];
-                bbox[i].high = (DistanceType)dataset_[vind_[left]][i];
-            }
-            for (int k=left+1; k<right; ++k) {
-                for (size_t i=0; i<dim_; ++i) {
-                    if (bbox[i].low>dataset_[vind_[k]][i]) bbox[i].low=(DistanceType)dataset_[vind_[k]][i];
-                    if (bbox[i].high<dataset_[vind_[k]][i]) bbox[i].high=(DistanceType)dataset_[vind_[k]][i];
-                }
-            }
-        }
-        else {
-            int idx;
-            int cutfeat;
-            DistanceType cutval;
-            middleSplit_(&vind_[0]+left, right-left, idx, cutfeat, cutval, bbox);
-
-            node->divfeat = cutfeat;
-
-            BoundingBox left_bbox(bbox);
-            left_bbox[cutfeat].high = cutval;
-            node->child1 = divideTree(left, left+idx, left_bbox);
-
-            BoundingBox right_bbox(bbox);
-            right_bbox[cutfeat].low = cutval;
-            node->child2 = divideTree(left+idx, right, right_bbox);
-
-            node->divlow = left_bbox[cutfeat].high;
-            node->divhigh = right_bbox[cutfeat].low;
-
-            for (size_t i=0; i<dim_; ++i) {
-                bbox[i].low = std::min(left_bbox[i].low, right_bbox[i].low);
-                bbox[i].high = std::max(left_bbox[i].high, right_bbox[i].high);
-            }
-        }
-
-        return node;
-    }
-
-    void computeMinMax(int* ind, int count, int dim, ElementType& min_elem, ElementType& max_elem)
-    {
-        min_elem = dataset_[ind[0]][dim];
-        max_elem = dataset_[ind[0]][dim];
-        for (int i=1; i<count; ++i) {
-            ElementType val = dataset_[ind[i]][dim];
-            if (val<min_elem) min_elem = val;
-            if (val>max_elem) max_elem = val;
-        }
-    }
-
-    void middleSplit(int* ind, int count, int& index, int& cutfeat, DistanceType& cutval, const BoundingBox& bbox)
-    {
-        // find the largest span from the approximate bounding box
-        ElementType max_span = bbox[0].high-bbox[0].low;
-        cutfeat = 0;
-        cutval = (bbox[0].high+bbox[0].low)/2;
-        for (size_t i=1; i<dim_; ++i) {
-            ElementType span = bbox[i].high-bbox[i].low;
-            if (span>max_span) {
-                max_span = span;
-                cutfeat = i;
-                cutval = (bbox[i].high+bbox[i].low)/2;
-            }
-        }
-
-        // compute exact span on the found dimension
-        ElementType min_elem, max_elem;
-        computeMinMax(ind, count, cutfeat, min_elem, max_elem);
-        cutval = (min_elem+max_elem)/2;
-        max_span = max_elem - min_elem;
-
-        // check if a dimension of a largest span exists
-        size_t k = cutfeat;
-        for (size_t i=0; i<dim_; ++i) {
-            if (i==k) continue;
-            ElementType span = bbox[i].high-bbox[i].low;
-            if (span>max_span) {
-                computeMinMax(ind, count, i, min_elem, max_elem);
-                span = max_elem - min_elem;
-                if (span>max_span) {
-                    max_span = span;
-                    cutfeat = i;
-                    cutval = (min_elem+max_elem)/2;
-                }
-            }
-        }
-        int lim1, lim2;
-        planeSplit(ind, count, cutfeat, cutval, lim1, lim2);
-
-        if (lim1>count/2) index = lim1;
-        else if (lim2<count/2) index = lim2;
-        else index = count/2;
-    }
-
-
-    void middleSplit_(int* ind, int count, int& index, int& cutfeat, DistanceType& cutval, const BoundingBox& bbox)
-    {
-        const float EPS=0.00001f;
-        DistanceType max_span = bbox[0].high-bbox[0].low;
-        for (size_t i=1; i<dim_; ++i) {
-            DistanceType span = bbox[i].high-bbox[i].low;
-            if (span>max_span) {
-                max_span = span;
-            }
-        }
-        DistanceType max_spread = -1;
-        cutfeat = 0;
-        for (size_t i=0; i<dim_; ++i) {
-            DistanceType span = bbox[i].high-bbox[i].low;
-            if (span>(DistanceType)((1-EPS)*max_span)) {
-                ElementType min_elem, max_elem;
-                computeMinMax(ind, count, cutfeat, min_elem, max_elem);
-                DistanceType spread = (DistanceType)(max_elem-min_elem);
-                if (spread>max_spread) {
-                    cutfeat = (int)i;
-                    max_spread = spread;
-                }
-            }
-        }
-        // split in the middle
-        DistanceType split_val = (bbox[cutfeat].low+bbox[cutfeat].high)/2;
-        ElementType min_elem, max_elem;
-        computeMinMax(ind, count, cutfeat, min_elem, max_elem);
-
-        if (split_val<min_elem) cutval = (DistanceType)min_elem;
-        else if (split_val>max_elem) cutval = (DistanceType)max_elem;
-        else cutval = split_val;
-
-        int lim1, lim2;
-        planeSplit(ind, count, cutfeat, cutval, lim1, lim2);
-
-        if (lim1>count/2) index = lim1;
-        else if (lim2<count/2) index = lim2;
-        else index = count/2;
-    }
-
-
-    /**
-     *  Subdivide the list of points by a plane perpendicular on axe corresponding
-     *  to the 'cutfeat' dimension at 'cutval' position.
-     *
-     *  On return:
-     *  dataset[ind[0..lim1-1]][cutfeat]<cutval
-     *  dataset[ind[lim1..lim2-1]][cutfeat]==cutval
-     *  dataset[ind[lim2..count]][cutfeat]>cutval
-     */
-    void planeSplit(int* ind, int count, int cutfeat, DistanceType cutval, int& lim1, int& lim2)
-    {
-        /* Move vector indices for left subtree to front of list. */
-        int left = 0;
-        int right = count-1;
-        for (;; ) {
-            while (left<=right && dataset_[ind[left]][cutfeat]<cutval) ++left;
-            while (left<=right && dataset_[ind[right]][cutfeat]>=cutval) --right;
-            if (left>right) break;
-            std::swap(ind[left], ind[right]); ++left; --right;
-        }
-        /* If either list is empty, it means that all remaining features
-         * are identical. Split in the middle to maintain a balanced tree.
-         */
-        lim1 = left;
-        right = count-1;
-        for (;; ) {
-            while (left<=right && dataset_[ind[left]][cutfeat]<=cutval) ++left;
-            while (left<=right && dataset_[ind[right]][cutfeat]>cutval) --right;
-            if (left>right) break;
-            std::swap(ind[left], ind[right]); ++left; --right;
-        }
-        lim2 = left;
-    }
-
-    DistanceType computeInitialDistances(const ElementType* vec, std::vector<DistanceType>& dists)
-    {
-        DistanceType distsq = 0.0;
-
-        for (size_t i = 0; i < dim_; ++i) {
-            if (vec[i] < root_bbox_[i].low) {
-                dists[i] = distance_.accum_dist(vec[i], root_bbox_[i].low, (int)i);
-                distsq += dists[i];
-            }
-            if (vec[i] > root_bbox_[i].high) {
-                dists[i] = distance_.accum_dist(vec[i], root_bbox_[i].high, (int)i);
-                distsq += dists[i];
-            }
-        }
-
-        return distsq;
-    }
-
-    /**
-     * Performs an exact search in the tree starting from a node.
-     */
-    void searchLevel(ResultSet<DistanceType>& result_set, const ElementType* vec, const NodePtr node, DistanceType mindistsq,
-                     std::vector<DistanceType>& dists, const float epsError)
-    {
-        /* If this is a leaf node, then do check and return. */
-        if ((node->child1 == NULL)&&(node->child2 == NULL)) {
-            DistanceType worst_dist = result_set.worstDist();
-            for (int i=node->left; i<node->right; ++i) {
-                int index = reorder_ ? i : vind_[i];
-                DistanceType dist = distance_(vec, data_[index], dim_, worst_dist);
-                if (dist<worst_dist) {
-                    result_set.addPoint(dist,vind_[i]);
-                }
-            }
-            return;
-        }
-
-        /* Which child branch should be taken first? */
-        int idx = node->divfeat;
-        ElementType val = vec[idx];
-        DistanceType diff1 = val - node->divlow;
-        DistanceType diff2 = val - node->divhigh;
-
-        NodePtr bestChild;
-        NodePtr otherChild;
-        DistanceType cut_dist;
-        if ((diff1+diff2)<0) {
-            bestChild = node->child1;
-            otherChild = node->child2;
-            cut_dist = distance_.accum_dist(val, node->divhigh, idx);
-        }
-        else {
-            bestChild = node->child2;
-            otherChild = node->child1;
-            cut_dist = distance_.accum_dist( val, node->divlow, idx);
-        }
-
-        /* Call recursively to search next level down. */
-        searchLevel(result_set, vec, bestChild, mindistsq, dists, epsError);
-
-        DistanceType dst = dists[idx];
-        mindistsq = mindistsq + cut_dist - dst;
-        dists[idx] = cut_dist;
-        if (mindistsq*epsError<=result_set.worstDist()) {
-            searchLevel(result_set, vec, otherChild, mindistsq, dists, epsError);
-        }
-        dists[idx] = dst;
-    }
-
-private:
-
-    /**
-     * The dataset used by this index
-     */
-    const Matrix<ElementType> dataset_;
-
-    IndexParams index_params_;
-
-    int leaf_max_size_;
-    bool reorder_;
-
-
-    /**
-     *  Array of indices to vectors in the dataset.
-     */
-    std::vector<int> vind_;
-
-    Matrix<ElementType> data_;
-
-    size_t size_;
-    size_t dim_;
-
-    /**
-     * Array of k-d trees used to find neighbours.
-     */
-    NodePtr root_node_;
-
-    BoundingBox root_bbox_;
-
-    /**
-     * Pooled memory allocator.
-     *
-     * Using a pooled memory allocator is more efficient
-     * than allocating memory directly when there is a large
-     * number small of memory allocations.
-     */
-    PooledAllocator pool_;
-
-    Distance distance_;
-};   // class KDTree
-
-}
-
-#endif //OPENCV_FLANN_KDTREE_SINGLE_INDEX_H_
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/kmeans_index.h b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/kmeans_index.h
deleted file mode 100755
index 2f48e40..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/kmeans_index.h
+++ /dev/null
@@ -1,1171 +0,0 @@
-/***********************************************************************
- * Software License Agreement (BSD License)
- *
- * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
- * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
- *
- * THE BSD LICENSE
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions
- * are met:
- *
- * 1. Redistributions of source code must retain the above copyright
- *    notice, this list of conditions and the following disclaimer.
- * 2. Redistributions in binary form must reproduce the above copyright
- *    notice, this list of conditions and the following disclaimer in the
- *    documentation and/or other materials provided with the distribution.
- *
- * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
- * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
- * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
- * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
- * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
- * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
- * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
- * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *************************************************************************/
-
-#ifndef OPENCV_FLANN_KMEANS_INDEX_H_
-#define OPENCV_FLANN_KMEANS_INDEX_H_
-
-#include <algorithm>
-#include <map>
-#include <cassert>
-#include <limits>
-#include <cmath>
-
-#include "general.h"
-#include "nn_index.h"
-#include "dist.h"
-#include "matrix.h"
-#include "result_set.h"
-#include "heap.h"
-#include "allocator.h"
-#include "random.h"
-#include "saving.h"
-#include "logger.h"
-
-
-namespace cvflann
-{
-
-struct KMeansIndexParams : public IndexParams
-{
-    KMeansIndexParams(int branching = 32, int iterations = 11,
-                      flann_centers_init_t centers_init = FLANN_CENTERS_RANDOM, float cb_index = 0.2 )
-    {
-        (*this)["algorithm"] = FLANN_INDEX_KMEANS;
-        // branching factor
-        (*this)["branching"] = branching;
-        // max iterations to perform in one kmeans clustering (kmeans tree)
-        (*this)["iterations"] = iterations;
-        // algorithm used for picking the initial cluster centers for kmeans tree
-        (*this)["centers_init"] = centers_init;
-        // cluster boundary index. Used when searching the kmeans tree
-        (*this)["cb_index"] = cb_index;
-    }
-};
-
-
-/**
- * Hierarchical kmeans index
- *
- * Contains a tree constructed through a hierarchical kmeans clustering
- * and other information for indexing a set of points for nearest-neighbour matching.
- */
-template <typename Distance>
-class KMeansIndex : public NNIndex<Distance>
-{
-public:
-    typedef typename Distance::ElementType ElementType;
-    typedef typename Distance::ResultType DistanceType;
-
-
-
-    typedef void (KMeansIndex::* centersAlgFunction)(int, int*, int, int*, int&);
-
-    /**
-     * The function used for choosing the cluster centers.
-     */
-    centersAlgFunction chooseCenters;
-
-
-
-    /**
-     * Chooses the initial centers in the k-means clustering in a random manner.
-     *
-     * Params:
-     *     k = number of centers
-     *     vecs = the dataset of points
-     *     indices = indices in the dataset
-     *     indices_length = length of indices vector
-     *
-     */
-    void chooseCentersRandom(int k, int* indices, int indices_length, int* centers, int& centers_length)
-    {
-        UniqueRandom r(indices_length);
-
-        int index;
-        for (index=0; index<k; ++index) {
-            bool duplicate = true;
-            int rnd;
-            while (duplicate) {
-                duplicate = false;
-                rnd = r.next();
-                if (rnd<0) {
-                    centers_length = index;
-                    return;
-                }
-
-                centers[index] = indices[rnd];
-
-                for (int j=0; j<index; ++j) {
-                    DistanceType sq = distance_(dataset_[centers[index]], dataset_[centers[j]], dataset_.cols);
-                    if (sq<1e-16) {
-                        duplicate = true;
-                    }
-                }
-            }
-        }
-
-        centers_length = index;
-    }
-
-
-    /**
-     * Chooses the initial centers in the k-means using Gonzales' algorithm
-     * so that the centers are spaced apart from each other.
-     *
-     * Params:
-     *     k = number of centers
-     *     vecs = the dataset of points
-     *     indices = indices in the dataset
-     * Returns:
-     */
-    void chooseCentersGonzales(int k, int* indices, int indices_length, int* centers, int& centers_length)
-    {
-        int n = indices_length;
-
-        int rnd = rand_int(n);
-        assert(rnd >=0 && rnd < n);
-
-        centers[0] = indices[rnd];
-
-        int index;
-        for (index=1; index<k; ++index) {
-
-            int best_index = -1;
-            DistanceType best_val = 0;
-            for (int j=0; j<n; ++j) {
-                DistanceType dist = distance_(dataset_[centers[0]],dataset_[indices[j]],dataset_.cols);
-                for (int i=1; i<index; ++i) {
-                    DistanceType tmp_dist = distance_(dataset_[centers[i]],dataset_[indices[j]],dataset_.cols);
-                    if (tmp_dist<dist) {
-                        dist = tmp_dist;
-                    }
-                }
-                if (dist>best_val) {
-                    best_val = dist;
-                    best_index = j;
-                }
-            }
-            if (best_index!=-1) {
-                centers[index] = indices[best_index];
-            }
-            else {
-                break;
-            }
-        }
-        centers_length = index;
-    }
-
-
-    /**
-     * Chooses the initial centers in the k-means using the algorithm
-     * proposed in the KMeans++ paper:
-     * Arthur, David; Vassilvitskii, Sergei - k-means++: The Advantages of Careful Seeding
-     *
-     * Implementation of this function was converted from the one provided in Arthur's code.
-     *
-     * Params:
-     *     k = number of centers
-     *     vecs = the dataset of points
-     *     indices = indices in the dataset
-     * Returns:
-     */
-    void chooseCentersKMeanspp(int k, int* indices, int indices_length, int* centers, int& centers_length)
-    {
-        int n = indices_length;
-
-        double currentPot = 0;
-        DistanceType* closestDistSq = new DistanceType[n];
-
-        // Choose one random center and set the closestDistSq values
-        int index = rand_int(n);
-        assert(index >=0 && index < n);
-        centers[0] = indices[index];
-
-        for (int i = 0; i < n; i++) {
-            closestDistSq[i] = distance_(dataset_[indices[i]], dataset_[indices[index]], dataset_.cols);
-            closestDistSq[i] = ensureSquareDistance<Distance>( closestDistSq[i] );
-            currentPot += closestDistSq[i];
-        }
-
-
-        const int numLocalTries = 1;
-
-        // Choose each center
-        int centerCount;
-        for (centerCount = 1; centerCount < k; centerCount++) {
-
-            // Repeat several trials
-            double bestNewPot = -1;
-            int bestNewIndex = -1;
-            for (int localTrial = 0; localTrial < numLocalTries; localTrial++) {
-
-                // Choose our center - have to be slightly careful to return a valid answer even accounting
-                // for possible rounding errors
-                double randVal = rand_double(currentPot);
-                for (index = 0; index < n-1; index++) {
-                    if (randVal <= closestDistSq[index]) break;
-                    else randVal -= closestDistSq[index];
-                }
-
-                // Compute the new potential
-                double newPot = 0;
-                for (int i = 0; i < n; i++) {
-                    DistanceType dist = distance_(dataset_[indices[i]], dataset_[indices[index]], dataset_.cols);
-                    newPot += std::min( ensureSquareDistance<Distance>(dist), closestDistSq[i] );
-                }
-
-                // Store the best result
-                if ((bestNewPot < 0)||(newPot < bestNewPot)) {
-                    bestNewPot = newPot;
-                    bestNewIndex = index;
-                }
-            }
-
-            // Add the appropriate center
-            centers[centerCount] = indices[bestNewIndex];
-            currentPot = bestNewPot;
-            for (int i = 0; i < n; i++) {
-                DistanceType dist = distance_(dataset_[indices[i]], dataset_[indices[bestNewIndex]], dataset_.cols);
-                closestDistSq[i] = std::min( ensureSquareDistance<Distance>(dist), closestDistSq[i] );
-            }
-        }
-
-        centers_length = centerCount;
-
-        delete[] closestDistSq;
-    }
-
-
-
-public:
-
-    flann_algorithm_t getType() const CV_OVERRIDE
-    {
-        return FLANN_INDEX_KMEANS;
-    }
-
-    class KMeansDistanceComputer : public cv::ParallelLoopBody
-    {
-    public:
-        KMeansDistanceComputer(Distance _distance, const Matrix<ElementType>& _dataset,
-            const int _branching, const int* _indices, const Matrix<double>& _dcenters, const size_t _veclen,
-            int* _count, int* _belongs_to, std::vector<DistanceType>& _radiuses, bool& _converged, cv::Mutex& _mtx)
-            : distance(_distance)
-            , dataset(_dataset)
-            , branching(_branching)
-            , indices(_indices)
-            , dcenters(_dcenters)
-            , veclen(_veclen)
-            , count(_count)
-            , belongs_to(_belongs_to)
-            , radiuses(_radiuses)
-            , converged(_converged)
-            , mtx(_mtx)
-        {
-        }
-
-        void operator()(const cv::Range& range) const CV_OVERRIDE
-        {
-            const int begin = range.start;
-            const int end = range.end;
-
-            for( int i = begin; i<end; ++i)
-            {
-                DistanceType sq_dist = distance(dataset[indices[i]], dcenters[0], veclen);
-                int new_centroid = 0;
-                for (int j=1; j<branching; ++j) {
-                    DistanceType new_sq_dist = distance(dataset[indices[i]], dcenters[j], veclen);
-                    if (sq_dist>new_sq_dist) {
-                        new_centroid = j;
-                        sq_dist = new_sq_dist;
-                    }
-                }
-                if (sq_dist > radiuses[new_centroid]) {
-                    radiuses[new_centroid] = sq_dist;
-                }
-                if (new_centroid != belongs_to[i]) {
-                    count[belongs_to[i]]--;
-                    count[new_centroid]++;
-                    belongs_to[i] = new_centroid;
-                    mtx.lock();
-                    converged = false;
-                    mtx.unlock();
-                }
-            }
-        }
-
-    private:
-        Distance distance;
-        const Matrix<ElementType>& dataset;
-        const int branching;
-        const int* indices;
-        const Matrix<double>& dcenters;
-        const size_t veclen;
-        int* count;
-        int* belongs_to;
-        std::vector<DistanceType>& radiuses;
-        bool& converged;
-        cv::Mutex& mtx;
-        KMeansDistanceComputer& operator=( const KMeansDistanceComputer & ) { return *this; }
-    };
-
-    /**
-     * Index constructor
-     *
-     * Params:
-     *          inputData = dataset with the input features
-     *          params = parameters passed to the hierarchical k-means algorithm
-     */
-    KMeansIndex(const Matrix<ElementType>& inputData, const IndexParams& params = KMeansIndexParams(),
-                Distance d = Distance())
-        : dataset_(inputData), index_params_(params), root_(NULL), indices_(NULL), distance_(d)
-    {
-        memoryCounter_ = 0;
-
-        size_ = dataset_.rows;
-        veclen_ = dataset_.cols;
-
-        branching_ = get_param(params,"branching",32);
-        iterations_ = get_param(params,"iterations",11);
-        if (iterations_<0) {
-            iterations_ = (std::numeric_limits<int>::max)();
-        }
-        centers_init_  = get_param(params,"centers_init",FLANN_CENTERS_RANDOM);
-
-        if (centers_init_==FLANN_CENTERS_RANDOM) {
-            chooseCenters = &KMeansIndex::chooseCentersRandom;
-        }
-        else if (centers_init_==FLANN_CENTERS_GONZALES) {
-            chooseCenters = &KMeansIndex::chooseCentersGonzales;
-        }
-        else if (centers_init_==FLANN_CENTERS_KMEANSPP) {
-            chooseCenters = &KMeansIndex::chooseCentersKMeanspp;
-        }
-        else {
-            throw FLANNException("Unknown algorithm for choosing initial centers.");
-        }
-        cb_index_ = 0.4f;
-
-    }
-
-
-    KMeansIndex(const KMeansIndex&);
-    KMeansIndex& operator=(const KMeansIndex&);
-
-
-    /**
-     * Index destructor.
-     *
-     * Release the memory used by the index.
-     */
-    virtual ~KMeansIndex()
-    {
-        if (root_ != NULL) {
-            free_centers(root_);
-        }
-        if (indices_!=NULL) {
-            delete[] indices_;
-        }
-    }
-
-    /**
-     *  Returns size of index.
-     */
-    size_t size() const CV_OVERRIDE
-    {
-        return size_;
-    }
-
-    /**
-     * Returns the length of an index feature.
-     */
-    size_t veclen() const CV_OVERRIDE
-    {
-        return veclen_;
-    }
-
-
-    void set_cb_index( float index)
-    {
-        cb_index_ = index;
-    }
-
-    /**
-     * Computes the inde memory usage
-     * Returns: memory used by the index
-     */
-    int usedMemory() const CV_OVERRIDE
-    {
-        return pool_.usedMemory+pool_.wastedMemory+memoryCounter_;
-    }
-
-    /**
-     * Builds the index
-     */
-    void buildIndex() CV_OVERRIDE
-    {
-        if (branching_<2) {
-            throw FLANNException("Branching factor must be at least 2");
-        }
-
-        indices_ = new int[size_];
-        for (size_t i=0; i<size_; ++i) {
-            indices_[i] = int(i);
-        }
-
-        root_ = pool_.allocate<KMeansNode>();
-        std::memset(root_, 0, sizeof(KMeansNode));
-
-        computeNodeStatistics(root_, indices_, (int)size_);
-        computeClustering(root_, indices_, (int)size_, branching_,0);
-    }
-
-
-    void saveIndex(FILE* stream) CV_OVERRIDE
-    {
-        save_value(stream, branching_);
-        save_value(stream, iterations_);
-        save_value(stream, memoryCounter_);
-        save_value(stream, cb_index_);
-        save_value(stream, *indices_, (int)size_);
-
-        save_tree(stream, root_);
-    }
-
-
-    void loadIndex(FILE* stream) CV_OVERRIDE
-    {
-        load_value(stream, branching_);
-        load_value(stream, iterations_);
-        load_value(stream, memoryCounter_);
-        load_value(stream, cb_index_);
-        if (indices_!=NULL) {
-            delete[] indices_;
-        }
-        indices_ = new int[size_];
-        load_value(stream, *indices_, size_);
-
-        if (root_!=NULL) {
-            free_centers(root_);
-        }
-        load_tree(stream, root_);
-
-        index_params_["algorithm"] = getType();
-        index_params_["branching"] = branching_;
-        index_params_["iterations"] = iterations_;
-        index_params_["centers_init"] = centers_init_;
-        index_params_["cb_index"] = cb_index_;
-
-    }
-
-
-    /**
-     * Find set of nearest neighbors to vec. Their indices are stored inside
-     * the result object.
-     *
-     * Params:
-     *     result = the result object in which the indices of the nearest-neighbors are stored
-     *     vec = the vector for which to search the nearest neighbors
-     *     searchParams = parameters that influence the search algorithm (checks, cb_index)
-     */
-    void findNeighbors(ResultSet<DistanceType>& result, const ElementType* vec, const SearchParams& searchParams) CV_OVERRIDE
-    {
-
-        int maxChecks = get_param(searchParams,"checks",32);
-
-        if (maxChecks==FLANN_CHECKS_UNLIMITED) {
-            findExactNN(root_, result, vec);
-        }
-        else {
-            // Priority queue storing intermediate branches in the best-bin-first search
-            Heap<BranchSt>* heap = new Heap<BranchSt>((int)size_);
-
-            int checks = 0;
-            findNN(root_, result, vec, checks, maxChecks, heap);
-
-            BranchSt branch;
-            while (heap->popMin(branch) && (checks<maxChecks || !result.full())) {
-                KMeansNodePtr node = branch.node;
-                findNN(node, result, vec, checks, maxChecks, heap);
-            }
-            assert(result.full());
-
-            delete heap;
-        }
-
-    }
-
-    /**
-     * Clustering function that takes a cut in the hierarchical k-means
-     * tree and return the clusters centers of that clustering.
-     * Params:
-     *     numClusters = number of clusters to have in the clustering computed
-     * Returns: number of cluster centers
-     */
-    int getClusterCenters(Matrix<DistanceType>& centers)
-    {
-        int numClusters = centers.rows;
-        if (numClusters<1) {
-            throw FLANNException("Number of clusters must be at least 1");
-        }
-
-        DistanceType variance;
-        KMeansNodePtr* clusters = new KMeansNodePtr[numClusters];
-
-        int clusterCount = getMinVarianceClusters(root_, clusters, numClusters, variance);
-
-        Logger::info("Clusters requested: %d, returning %d\n",numClusters, clusterCount);
-
-        for (int i=0; i<clusterCount; ++i) {
-            DistanceType* center = clusters[i]->pivot;
-            for (size_t j=0; j<veclen_; ++j) {
-                centers[i][j] = center[j];
-            }
-        }
-        delete[] clusters;
-
-        return clusterCount;
-    }
-
-    IndexParams getParameters() const CV_OVERRIDE
-    {
-        return index_params_;
-    }
-
-
-private:
-    /**
-     * Struture representing a node in the hierarchical k-means tree.
-     */
-    struct KMeansNode
-    {
-        /**
-         * The cluster center.
-         */
-        DistanceType* pivot;
-        /**
-         * The cluster radius.
-         */
-        DistanceType radius;
-        /**
-         * The cluster mean radius.
-         */
-        DistanceType mean_radius;
-        /**
-         * The cluster variance.
-         */
-        DistanceType variance;
-        /**
-         * The cluster size (number of points in the cluster)
-         */
-        int size;
-        /**
-         * Child nodes (only for non-terminal nodes)
-         */
-        KMeansNode** childs;
-        /**
-         * Node points (only for terminal nodes)
-         */
-        int* indices;
-        /**
-         * Level
-         */
-        int level;
-    };
-    typedef KMeansNode* KMeansNodePtr;
-
-    /**
-     * Alias definition for a nicer syntax.
-     */
-    typedef BranchStruct<KMeansNodePtr, DistanceType> BranchSt;
-
-
-
-
-    void save_tree(FILE* stream, KMeansNodePtr node)
-    {
-        save_value(stream, *node);
-        save_value(stream, *(node->pivot), (int)veclen_);
-        if (node->childs==NULL) {
-            int indices_offset = (int)(node->indices - indices_);
-            save_value(stream, indices_offset);
-        }
-        else {
-            for(int i=0; i<branching_; ++i) {
-                save_tree(stream, node->childs[i]);
-            }
-        }
-    }
-
-
-    void load_tree(FILE* stream, KMeansNodePtr& node)
-    {
-        node = pool_.allocate<KMeansNode>();
-        load_value(stream, *node);
-        node->pivot = new DistanceType[veclen_];
-        load_value(stream, *(node->pivot), (int)veclen_);
-        if (node->childs==NULL) {
-            int indices_offset;
-            load_value(stream, indices_offset);
-            node->indices = indices_ + indices_offset;
-        }
-        else {
-            node->childs = pool_.allocate<KMeansNodePtr>(branching_);
-            for(int i=0; i<branching_; ++i) {
-                load_tree(stream, node->childs[i]);
-            }
-        }
-    }
-
-
-    /**
-     * Helper function
-     */
-    void free_centers(KMeansNodePtr node)
-    {
-        delete[] node->pivot;
-        if (node->childs!=NULL) {
-            for (int k=0; k<branching_; ++k) {
-                free_centers(node->childs[k]);
-            }
-        }
-    }
-
-    /**
-     * Computes the statistics of a node (mean, radius, variance).
-     *
-     * Params:
-     *     node = the node to use
-     *     indices = the indices of the points belonging to the node
-     */
-    void computeNodeStatistics(KMeansNodePtr node, int* indices, int indices_length)
-    {
-
-        DistanceType radius = 0;
-        DistanceType variance = 0;
-        DistanceType* mean = new DistanceType[veclen_];
-        memoryCounter_ += int(veclen_*sizeof(DistanceType));
-
-        memset(mean,0,veclen_*sizeof(DistanceType));
-
-        for (size_t i=0; i<size_; ++i) {
-            ElementType* vec = dataset_[indices[i]];
-            for (size_t j=0; j<veclen_; ++j) {
-                mean[j] += vec[j];
-            }
-            variance += distance_(vec, ZeroIterator<ElementType>(), veclen_);
-        }
-        for (size_t j=0; j<veclen_; ++j) {
-            mean[j] /= size_;
-        }
-        variance /= size_;
-        variance -= distance_(mean, ZeroIterator<ElementType>(), veclen_);
-
-        DistanceType tmp = 0;
-        for (int i=0; i<indices_length; ++i) {
-            tmp = distance_(mean, dataset_[indices[i]], veclen_);
-            if (tmp>radius) {
-                radius = tmp;
-            }
-        }
-
-        node->variance = variance;
-        node->radius = radius;
-        node->pivot = mean;
-    }
-
-
-    /**
-     * The method responsible with actually doing the recursive hierarchical
-     * clustering
-     *
-     * Params:
-     *     node = the node to cluster
-     *     indices = indices of the points belonging to the current node
-     *     branching = the branching factor to use in the clustering
-     *
-     * TODO: for 1-sized clusters don't store a cluster center (it's the same as the single cluster point)
-     */
-    void computeClustering(KMeansNodePtr node, int* indices, int indices_length, int branching, int level)
-    {
-        node->size = indices_length;
-        node->level = level;
-
-        if (indices_length < branching) {
-            node->indices = indices;
-            std::sort(node->indices,node->indices+indices_length);
-            node->childs = NULL;
-            return;
-        }
-
-        cv::AutoBuffer<int> centers_idx_buf(branching);
-        int* centers_idx = (int*)centers_idx_buf;
-        int centers_length;
-        (this->*chooseCenters)(branching, indices, indices_length, centers_idx, centers_length);
-
-        if (centers_length<branching) {
-            node->indices = indices;
-            std::sort(node->indices,node->indices+indices_length);
-            node->childs = NULL;
-            return;
-        }
-
-
-        cv::AutoBuffer<double> dcenters_buf(branching*veclen_);
-        Matrix<double> dcenters((double*)dcenters_buf,branching,veclen_);
-        for (int i=0; i<centers_length; ++i) {
-            ElementType* vec = dataset_[centers_idx[i]];
-            for (size_t k=0; k<veclen_; ++k) {
-                dcenters[i][k] = double(vec[k]);
-            }
-        }
-
-        std::vector<DistanceType> radiuses(branching);
-        cv::AutoBuffer<int> count_buf(branching);
-        int* count = (int*)count_buf;
-        for (int i=0; i<branching; ++i) {
-            radiuses[i] = 0;
-            count[i] = 0;
-        }
-
-        //	assign points to clusters
-        cv::AutoBuffer<int> belongs_to_buf(indices_length);
-        int* belongs_to = (int*)belongs_to_buf;
-        for (int i=0; i<indices_length; ++i) {
-
-            DistanceType sq_dist = distance_(dataset_[indices[i]], dcenters[0], veclen_);
-            belongs_to[i] = 0;
-            for (int j=1; j<branching; ++j) {
-                DistanceType new_sq_dist = distance_(dataset_[indices[i]], dcenters[j], veclen_);
-                if (sq_dist>new_sq_dist) {
-                    belongs_to[i] = j;
-                    sq_dist = new_sq_dist;
-                }
-            }
-            if (sq_dist>radiuses[belongs_to[i]]) {
-                radiuses[belongs_to[i]] = sq_dist;
-            }
-            count[belongs_to[i]]++;
-        }
-
-        bool converged = false;
-        int iteration = 0;
-        while (!converged && iteration<iterations_) {
-            converged = true;
-            iteration++;
-
-            // compute the new cluster centers
-            for (int i=0; i<branching; ++i) {
-                memset(dcenters[i],0,sizeof(double)*veclen_);
-                radiuses[i] = 0;
-            }
-            for (int i=0; i<indices_length; ++i) {
-                ElementType* vec = dataset_[indices[i]];
-                double* center = dcenters[belongs_to[i]];
-                for (size_t k=0; k<veclen_; ++k) {
-                    center[k] += vec[k];
-                }
-            }
-            for (int i=0; i<branching; ++i) {
-                int cnt = count[i];
-                for (size_t k=0; k<veclen_; ++k) {
-                    dcenters[i][k] /= cnt;
-                }
-            }
-
-            // reassign points to clusters
-            cv::Mutex mtx;
-            KMeansDistanceComputer invoker(distance_, dataset_, branching, indices, dcenters, veclen_, count, belongs_to, radiuses, converged, mtx);
-            parallel_for_(cv::Range(0, (int)indices_length), invoker);
-
-            for (int i=0; i<branching; ++i) {
-                // if one cluster converges to an empty cluster,
-                // move an element into that cluster
-                if (count[i]==0) {
-                    int j = (i+1)%branching;
-                    while (count[j]<=1) {
-                        j = (j+1)%branching;
-                    }
-
-                    for (int k=0; k<indices_length; ++k) {
-                        if (belongs_to[k]==j) {
-                            // for cluster j, we move the furthest element from the center to the empty cluster i
-                            if ( distance_(dataset_[indices[k]], dcenters[j], veclen_) == radiuses[j] ) {
-                                belongs_to[k] = i;
-                                count[j]--;
-                                count[i]++;
-                                break;
-                            }
-                        }
-                    }
-                    converged = false;
-                }
-            }
-
-        }
-
-        DistanceType** centers = new DistanceType*[branching];
-
-        for (int i=0; i<branching; ++i) {
-            centers[i] = new DistanceType[veclen_];
-            memoryCounter_ += (int)(veclen_*sizeof(DistanceType));
-            for (size_t k=0; k<veclen_; ++k) {
-                centers[i][k] = (DistanceType)dcenters[i][k];
-            }
-        }
-
-
-        // compute kmeans clustering for each of the resulting clusters
-        node->childs = pool_.allocate<KMeansNodePtr>(branching);
-        int start = 0;
-        int end = start;
-        for (int c=0; c<branching; ++c) {
-            int s = count[c];
-
-            DistanceType variance = 0;
-            DistanceType mean_radius =0;
-            for (int i=0; i<indices_length; ++i) {
-                if (belongs_to[i]==c) {
-                    DistanceType d = distance_(dataset_[indices[i]], ZeroIterator<ElementType>(), veclen_);
-                    variance += d;
-                    mean_radius += sqrt(d);
-                    std::swap(indices[i],indices[end]);
-                    std::swap(belongs_to[i],belongs_to[end]);
-                    end++;
-                }
-            }
-            variance /= s;
-            mean_radius /= s;
-            variance -= distance_(centers[c], ZeroIterator<ElementType>(), veclen_);
-
-            node->childs[c] = pool_.allocate<KMeansNode>();
-            std::memset(node->childs[c], 0, sizeof(KMeansNode));
-            node->childs[c]->radius = radiuses[c];
-            node->childs[c]->pivot = centers[c];
-            node->childs[c]->variance = variance;
-            node->childs[c]->mean_radius = mean_radius;
-            computeClustering(node->childs[c],indices+start, end-start, branching, level+1);
-            start=end;
-        }
-
-        delete[] centers;
-    }
-
-
-
-    /**
-     * Performs one descent in the hierarchical k-means tree. The branches not
-     * visited are stored in a priority queue.
-     *
-     * Params:
-     *      node = node to explore
-     *      result = container for the k-nearest neighbors found
-     *      vec = query points
-     *      checks = how many points in the dataset have been checked so far
-     *      maxChecks = maximum dataset points to checks
-     */
-
-
-    void findNN(KMeansNodePtr node, ResultSet<DistanceType>& result, const ElementType* vec, int& checks, int maxChecks,
-                Heap<BranchSt>* heap)
-    {
-        // Ignore those clusters that are too far away
-        {
-            DistanceType bsq = distance_(vec, node->pivot, veclen_);
-            DistanceType rsq = node->radius;
-            DistanceType wsq = result.worstDist();
-
-            DistanceType val = bsq-rsq-wsq;
-            DistanceType val2 = val*val-4*rsq*wsq;
-
-            //if (val>0) {
-            if ((val>0)&&(val2>0)) {
-                return;
-            }
-        }
-
-        if (node->childs==NULL) {
-            if (checks>=maxChecks) {
-                if (result.full()) return;
-            }
-            checks += node->size;
-            for (int i=0; i<node->size; ++i) {
-                int index = node->indices[i];
-                DistanceType dist = distance_(dataset_[index], vec, veclen_);
-                result.addPoint(dist, index);
-            }
-        }
-        else {
-            DistanceType* domain_distances = new DistanceType[branching_];
-            int closest_center = exploreNodeBranches(node, vec, domain_distances, heap);
-            delete[] domain_distances;
-            findNN(node->childs[closest_center],result,vec, checks, maxChecks, heap);
-        }
-    }
-
-    /**
-     * Helper function that computes the nearest childs of a node to a given query point.
-     * Params:
-     *     node = the node
-     *     q = the query point
-     *     distances = array with the distances to each child node.
-     * Returns:
-     */
-    int exploreNodeBranches(KMeansNodePtr node, const ElementType* q, DistanceType* domain_distances, Heap<BranchSt>* heap)
-    {
-
-        int best_index = 0;
-        domain_distances[best_index] = distance_(q, node->childs[best_index]->pivot, veclen_);
-        for (int i=1; i<branching_; ++i) {
-            domain_distances[i] = distance_(q, node->childs[i]->pivot, veclen_);
-            if (domain_distances[i]<domain_distances[best_index]) {
-                best_index = i;
-            }
-        }
-
-        //		float* best_center = node->childs[best_index]->pivot;
-        for (int i=0; i<branching_; ++i) {
-            if (i != best_index) {
-                domain_distances[i] -= cb_index_*node->childs[i]->variance;
-
-                //				float dist_to_border = getDistanceToBorder(node.childs[i].pivot,best_center,q);
-                //				if (domain_distances[i]<dist_to_border) {
-                //					domain_distances[i] = dist_to_border;
-                //				}
-                heap->insert(BranchSt(node->childs[i],domain_distances[i]));
-            }
-        }
-
-        return best_index;
-    }
-
-
-    /**
-     * Function the performs exact nearest neighbor search by traversing the entire tree.
-     */
-    void findExactNN(KMeansNodePtr node, ResultSet<DistanceType>& result, const ElementType* vec)
-    {
-        // Ignore those clusters that are too far away
-        {
-            DistanceType bsq = distance_(vec, node->pivot, veclen_);
-            DistanceType rsq = node->radius;
-            DistanceType wsq = result.worstDist();
-
-            DistanceType val = bsq-rsq-wsq;
-            DistanceType val2 = val*val-4*rsq*wsq;
-
-            //                  if (val>0) {
-            if ((val>0)&&(val2>0)) {
-                return;
-            }
-        }
-
-
-        if (node->childs==NULL) {
-            for (int i=0; i<node->size; ++i) {
-                int index = node->indices[i];
-                DistanceType dist = distance_(dataset_[index], vec, veclen_);
-                result.addPoint(dist, index);
-            }
-        }
-        else {
-            int* sort_indices = new int[branching_];
-
-            getCenterOrdering(node, vec, sort_indices);
-
-            for (int i=0; i<branching_; ++i) {
-                findExactNN(node->childs[sort_indices[i]],result,vec);
-            }
-
-            delete[] sort_indices;
-        }
-    }
-
-
-    /**
-     * Helper function.
-     *
-     * I computes the order in which to traverse the child nodes of a particular node.
-     */
-    void getCenterOrdering(KMeansNodePtr node, const ElementType* q, int* sort_indices)
-    {
-        DistanceType* domain_distances = new DistanceType[branching_];
-        for (int i=0; i<branching_; ++i) {
-            DistanceType dist = distance_(q, node->childs[i]->pivot, veclen_);
-
-            int j=0;
-            while (domain_distances[j]<dist && j<i) j++;
-            for (int k=i; k>j; --k) {
-                domain_distances[k] = domain_distances[k-1];
-                sort_indices[k] = sort_indices[k-1];
-            }
-            domain_distances[j] = dist;
-            sort_indices[j] = i;
-        }
-        delete[] domain_distances;
-    }
-
-    /**
-     * Method that computes the squared distance from the query point q
-     * from inside region with center c to the border between this
-     * region and the region with center p
-     */
-    DistanceType getDistanceToBorder(DistanceType* p, DistanceType* c, DistanceType* q)
-    {
-        DistanceType sum = 0;
-        DistanceType sum2 = 0;
-
-        for (int i=0; i<veclen_; ++i) {
-            DistanceType t = c[i]-p[i];
-            sum += t*(q[i]-(c[i]+p[i])/2);
-            sum2 += t*t;
-        }
-
-        return sum*sum/sum2;
-    }
-
-
-    /**
-     * Helper function the descends in the hierarchical k-means tree by splitting those clusters that minimize
-     * the overall variance of the clustering.
-     * Params:
-     *     root = root node
-     *     clusters = array with clusters centers (return value)
-     *     varianceValue = variance of the clustering (return value)
-     * Returns:
-     */
-    int getMinVarianceClusters(KMeansNodePtr root, KMeansNodePtr* clusters, int clusters_length, DistanceType& varianceValue)
-    {
-        int clusterCount = 1;
-        clusters[0] = root;
-
-        DistanceType meanVariance = root->variance*root->size;
-
-        while (clusterCount<clusters_length) {
-            DistanceType minVariance = (std::numeric_limits<DistanceType>::max)();
-            int splitIndex = -1;
-
-            for (int i=0; i<clusterCount; ++i) {
-                if (clusters[i]->childs != NULL) {
-
-                    DistanceType variance = meanVariance - clusters[i]->variance*clusters[i]->size;
-
-                    for (int j=0; j<branching_; ++j) {
-                        variance += clusters[i]->childs[j]->variance*clusters[i]->childs[j]->size;
-                    }
-                    if (variance<minVariance) {
-                        minVariance = variance;
-                        splitIndex = i;
-                    }
-                }
-            }
-
-            if (splitIndex==-1) break;
-            if ( (branching_+clusterCount-1) > clusters_length) break;
-
-            meanVariance = minVariance;
-
-            // split node
-            KMeansNodePtr toSplit = clusters[splitIndex];
-            clusters[splitIndex] = toSplit->childs[0];
-            for (int i=1; i<branching_; ++i) {
-                clusters[clusterCount++] = toSplit->childs[i];
-            }
-        }
-
-        varianceValue = meanVariance/root->size;
-        return clusterCount;
-    }
-
-private:
-    /** The branching factor used in the hierarchical k-means clustering */
-    int branching_;
-
-    /** Maximum number of iterations to use when performing k-means clustering */
-    int iterations_;
-
-    /** Algorithm for choosing the cluster centers */
-    flann_centers_init_t centers_init_;
-
-    /**
-     * Cluster border index. This is used in the tree search phase when determining
-     * the closest cluster to explore next. A zero value takes into account only
-     * the cluster centres, a value greater then zero also take into account the size
-     * of the cluster.
-     */
-    float cb_index_;
-
-    /**
-     * The dataset used by this index
-     */
-    const Matrix<ElementType> dataset_;
-
-    /** Index parameters */
-    IndexParams index_params_;
-
-    /**
-     * Number of features in the dataset.
-     */
-    size_t size_;
-
-    /**
-     * Length of each feature.
-     */
-    size_t veclen_;
-
-    /**
-     * The root node in the tree.
-     */
-    KMeansNodePtr root_;
-
-    /**
-     *  Array of indices to vectors in the dataset.
-     */
-    int* indices_;
-
-    /**
-     * The distance
-     */
-    Distance distance_;
-
-    /**
-     * Pooled memory allocator.
-     */
-    PooledAllocator pool_;
-
-    /**
-     * Memory occupied by the index.
-     */
-    int memoryCounter_;
-};
-
-}
-
-#endif //OPENCV_FLANN_KMEANS_INDEX_H_
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/linear_index.h b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/linear_index.h
deleted file mode 100755
index ca3f44d..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/linear_index.h
+++ /dev/null
@@ -1,132 +0,0 @@
-/***********************************************************************
- * Software License Agreement (BSD License)
- *
- * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
- * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
- *
- * THE BSD LICENSE
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions
- * are met:
- *
- * 1. Redistributions of source code must retain the above copyright
- *    notice, this list of conditions and the following disclaimer.
- * 2. Redistributions in binary form must reproduce the above copyright
- *    notice, this list of conditions and the following disclaimer in the
- *    documentation and/or other materials provided with the distribution.
- *
- * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
- * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
- * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
- * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
- * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
- * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
- * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
- * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *************************************************************************/
-
-#ifndef OPENCV_FLANN_LINEAR_INDEX_H_
-#define OPENCV_FLANN_LINEAR_INDEX_H_
-
-#include "general.h"
-#include "nn_index.h"
-
-namespace cvflann
-{
-
-struct LinearIndexParams : public IndexParams
-{
-    LinearIndexParams()
-    {
-        (* this)["algorithm"] = FLANN_INDEX_LINEAR;
-    }
-};
-
-template <typename Distance>
-class LinearIndex : public NNIndex<Distance>
-{
-public:
-
-    typedef typename Distance::ElementType ElementType;
-    typedef typename Distance::ResultType DistanceType;
-
-
-    LinearIndex(const Matrix<ElementType>& inputData, const IndexParams& params = LinearIndexParams(),
-                Distance d = Distance()) :
-        dataset_(inputData), index_params_(params), distance_(d)
-    {
-    }
-
-    LinearIndex(const LinearIndex&);
-    LinearIndex& operator=(const LinearIndex&);
-
-    flann_algorithm_t getType() const CV_OVERRIDE
-    {
-        return FLANN_INDEX_LINEAR;
-    }
-
-
-    size_t size() const CV_OVERRIDE
-    {
-        return dataset_.rows;
-    }
-
-    size_t veclen() const CV_OVERRIDE
-    {
-        return dataset_.cols;
-    }
-
-
-    int usedMemory() const CV_OVERRIDE
-    {
-        return 0;
-    }
-
-    void buildIndex() CV_OVERRIDE
-    {
-        /* nothing to do here for linear search */
-    }
-
-    void saveIndex(FILE*) CV_OVERRIDE
-    {
-        /* nothing to do here for linear search */
-    }
-
-
-    void loadIndex(FILE*) CV_OVERRIDE
-    {
-        /* nothing to do here for linear search */
-
-        index_params_["algorithm"] = getType();
-    }
-
-    void findNeighbors(ResultSet<DistanceType>& resultSet, const ElementType* vec, const SearchParams& /*searchParams*/) CV_OVERRIDE
-    {
-        ElementType* data = dataset_.data;
-        for (size_t i = 0; i < dataset_.rows; ++i, data += dataset_.cols) {
-            DistanceType dist = distance_(data, vec, dataset_.cols);
-            resultSet.addPoint(dist, (int)i);
-        }
-    }
-
-    IndexParams getParameters() const CV_OVERRIDE
-    {
-        return index_params_;
-    }
-
-private:
-    /** The dataset */
-    const Matrix<ElementType> dataset_;
-    /** Index parameters */
-    IndexParams index_params_;
-    /** Index distance */
-    Distance distance_;
-
-};
-
-}
-
-#endif // OPENCV_FLANN_LINEAR_INDEX_H_
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/logger.h b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/logger.h
deleted file mode 100755
index 32618db..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/logger.h
+++ /dev/null
@@ -1,135 +0,0 @@
-/***********************************************************************
- * Software License Agreement (BSD License)
- *
- * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
- * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
- *
- * THE BSD LICENSE
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions
- * are met:
- *
- * 1. Redistributions of source code must retain the above copyright
- *    notice, this list of conditions and the following disclaimer.
- * 2. Redistributions in binary form must reproduce the above copyright
- *    notice, this list of conditions and the following disclaimer in the
- *    documentation and/or other materials provided with the distribution.
- *
- * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
- * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
- * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
- * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
- * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
- * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
- * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
- * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *************************************************************************/
-
-#ifndef OPENCV_FLANN_LOGGER_H
-#define OPENCV_FLANN_LOGGER_H
-
-#include <stdio.h>
-#include <stdarg.h>
-
-#include "defines.h"
-
-
-namespace cvflann
-{
-
-class Logger
-{
-    Logger() : stream(stdout), logLevel(FLANN_LOG_WARN) {}
-
-    ~Logger()
-    {
-        if ((stream!=NULL)&&(stream!=stdout)) {
-            fclose(stream);
-        }
-    }
-
-    static Logger& instance()
-    {
-        static Logger logger;
-        return logger;
-    }
-
-    void _setDestination(const char* name)
-    {
-        if (name==NULL) {
-            stream = stdout;
-        }
-        else {
-#ifdef _MSC_VER
-            if (fopen_s(&stream, name, "w") != 0)
-                stream = NULL;
-#else
-            stream = fopen(name,"w");
-#endif
-            if (stream == NULL) {
-                stream = stdout;
-            }
-        }
-    }
-
-    int _log(int level, const char* fmt, va_list arglist)
-    {
-        if (level > logLevel ) return -1;
-        int ret = vfprintf(stream, fmt, arglist);
-        return ret;
-    }
-
-public:
-    /**
-     * Sets the logging level. All messages with lower priority will be ignored.
-     * @param level Logging level
-     */
-    static void setLevel(int level) { instance().logLevel = level; }
-
-    /**
-     * Sets the logging destination
-     * @param name Filename or NULL for console
-     */
-    static void setDestination(const char* name) { instance()._setDestination(name); }
-
-    /**
-     * Print log message
-     * @param level Log level
-     * @param fmt Message format
-     * @return
-     */
-    static int log(int level, const char* fmt, ...)
-    {
-        va_list arglist;
-        va_start(arglist, fmt);
-        int ret = instance()._log(level,fmt,arglist);
-        va_end(arglist);
-        return ret;
-    }
-
-#define LOG_METHOD(NAME,LEVEL) \
-    static int NAME(const char* fmt, ...) \
-    { \
-        va_list ap; \
-        va_start(ap, fmt); \
-        int ret = instance()._log(LEVEL, fmt, ap); \
-        va_end(ap); \
-        return ret; \
-    }
-
-    LOG_METHOD(fatal, FLANN_LOG_FATAL)
-    LOG_METHOD(error, FLANN_LOG_ERROR)
-    LOG_METHOD(warn, FLANN_LOG_WARN)
-    LOG_METHOD(info, FLANN_LOG_INFO)
-
-private:
-    FILE* stream;
-    int logLevel;
-};
-
-}
-
-#endif //OPENCV_FLANN_LOGGER_H
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/lsh_index.h b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/lsh_index.h
deleted file mode 100755
index 42afe89..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/lsh_index.h
+++ /dev/null
@@ -1,392 +0,0 @@
-/***********************************************************************
- * Software License Agreement (BSD License)
- *
- * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
- * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
- *
- * THE BSD LICENSE
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions
- * are met:
- *
- * 1. Redistributions of source code must retain the above copyright
- *    notice, this list of conditions and the following disclaimer.
- * 2. Redistributions in binary form must reproduce the above copyright
- *    notice, this list of conditions and the following disclaimer in the
- *    documentation and/or other materials provided with the distribution.
- *
- * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
- * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
- * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
- * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
- * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
- * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
- * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
- * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *************************************************************************/
-
-/***********************************************************************
- * Author: Vincent Rabaud
- *************************************************************************/
-
-#ifndef OPENCV_FLANN_LSH_INDEX_H_
-#define OPENCV_FLANN_LSH_INDEX_H_
-
-#include <algorithm>
-#include <cassert>
-#include <cstring>
-#include <map>
-#include <vector>
-
-#include "general.h"
-#include "nn_index.h"
-#include "matrix.h"
-#include "result_set.h"
-#include "heap.h"
-#include "lsh_table.h"
-#include "allocator.h"
-#include "random.h"
-#include "saving.h"
-
-namespace cvflann
-{
-
-struct LshIndexParams : public IndexParams
-{
-    LshIndexParams(unsigned int table_number = 12, unsigned int key_size = 20, unsigned int multi_probe_level = 2)
-    {
-        (* this)["algorithm"] = FLANN_INDEX_LSH;
-        // The number of hash tables to use
-        (*this)["table_number"] = table_number;
-        // The length of the key in the hash tables
-        (*this)["key_size"] = key_size;
-        // Number of levels to use in multi-probe (0 for standard LSH)
-        (*this)["multi_probe_level"] = multi_probe_level;
-    }
-};
-
-/**
- * Randomized kd-tree index
- *
- * Contains the k-d trees and other information for indexing a set of points
- * for nearest-neighbor matching.
- */
-template<typename Distance>
-class LshIndex : public NNIndex<Distance>
-{
-public:
-    typedef typename Distance::ElementType ElementType;
-    typedef typename Distance::ResultType DistanceType;
-
-    /** Constructor
-     * @param input_data dataset with the input features
-     * @param params parameters passed to the LSH algorithm
-     * @param d the distance used
-     */
-    LshIndex(const Matrix<ElementType>& input_data, const IndexParams& params = LshIndexParams(),
-             Distance d = Distance()) :
-        dataset_(input_data), index_params_(params), distance_(d)
-    {
-        // cv::flann::IndexParams sets integer params as 'int', so it is used with get_param
-        // in place of 'unsigned int'
-        table_number_ = (unsigned int)get_param<int>(index_params_,"table_number",12);
-        key_size_ = (unsigned int)get_param<int>(index_params_,"key_size",20);
-        multi_probe_level_ = (unsigned int)get_param<int>(index_params_,"multi_probe_level",2);
-
-        feature_size_ = (unsigned)dataset_.cols;
-        fill_xor_mask(0, key_size_, multi_probe_level_, xor_masks_);
-    }
-
-
-    LshIndex(const LshIndex&);
-    LshIndex& operator=(const LshIndex&);
-
-    /**
-     * Builds the index
-     */
-    void buildIndex() CV_OVERRIDE
-    {
-        tables_.resize(table_number_);
-        for (unsigned int i = 0; i < table_number_; ++i) {
-            lsh::LshTable<ElementType>& table = tables_[i];
-            table = lsh::LshTable<ElementType>(feature_size_, key_size_);
-
-            // Add the features to the table
-            table.add(dataset_);
-        }
-    }
-
-    flann_algorithm_t getType() const CV_OVERRIDE
-    {
-        return FLANN_INDEX_LSH;
-    }
-
-
-    void saveIndex(FILE* stream) CV_OVERRIDE
-    {
-        save_value(stream,table_number_);
-        save_value(stream,key_size_);
-        save_value(stream,multi_probe_level_);
-        save_value(stream, dataset_);
-    }
-
-    void loadIndex(FILE* stream) CV_OVERRIDE
-    {
-        load_value(stream, table_number_);
-        load_value(stream, key_size_);
-        load_value(stream, multi_probe_level_);
-        load_value(stream, dataset_);
-        // Building the index is so fast we can afford not storing it
-        buildIndex();
-
-        index_params_["algorithm"] = getType();
-        index_params_["table_number"] = table_number_;
-        index_params_["key_size"] = key_size_;
-        index_params_["multi_probe_level"] = multi_probe_level_;
-    }
-
-    /**
-     *  Returns size of index.
-     */
-    size_t size() const CV_OVERRIDE
-    {
-        return dataset_.rows;
-    }
-
-    /**
-     * Returns the length of an index feature.
-     */
-    size_t veclen() const CV_OVERRIDE
-    {
-        return feature_size_;
-    }
-
-    /**
-     * Computes the index memory usage
-     * Returns: memory used by the index
-     */
-    int usedMemory() const CV_OVERRIDE
-    {
-        return (int)(dataset_.rows * sizeof(int));
-    }
-
-
-    IndexParams getParameters() const CV_OVERRIDE
-    {
-        return index_params_;
-    }
-
-    /**
-     * \brief Perform k-nearest neighbor search
-     * \param[in] queries The query points for which to find the nearest neighbors
-     * \param[out] indices The indices of the nearest neighbors found
-     * \param[out] dists Distances to the nearest neighbors found
-     * \param[in] knn Number of nearest neighbors to return
-     * \param[in] params Search parameters
-     */
-    virtual void knnSearch(const Matrix<ElementType>& queries, Matrix<int>& indices, Matrix<DistanceType>& dists, int knn, const SearchParams& params) CV_OVERRIDE
-    {
-        assert(queries.cols == veclen());
-        assert(indices.rows >= queries.rows);
-        assert(dists.rows >= queries.rows);
-        assert(int(indices.cols) >= knn);
-        assert(int(dists.cols) >= knn);
-
-
-        KNNUniqueResultSet<DistanceType> resultSet(knn);
-        for (size_t i = 0; i < queries.rows; i++) {
-            resultSet.clear();
-            std::fill_n(indices[i], knn, -1);
-            std::fill_n(dists[i], knn, std::numeric_limits<DistanceType>::max());
-            findNeighbors(resultSet, queries[i], params);
-            if (get_param(params,"sorted",true)) resultSet.sortAndCopy(indices[i], dists[i], knn);
-            else resultSet.copy(indices[i], dists[i], knn);
-        }
-    }
-
-
-    /**
-     * Find set of nearest neighbors to vec. Their indices are stored inside
-     * the result object.
-     *
-     * Params:
-     *     result = the result object in which the indices of the nearest-neighbors are stored
-     *     vec = the vector for which to search the nearest neighbors
-     *     maxCheck = the maximum number of restarts (in a best-bin-first manner)
-     */
-    void findNeighbors(ResultSet<DistanceType>& result, const ElementType* vec, const SearchParams& /*searchParams*/) CV_OVERRIDE
-    {
-        getNeighbors(vec, result);
-    }
-
-private:
-    /** Defines the comparator on score and index
-     */
-    typedef std::pair<float, unsigned int> ScoreIndexPair;
-    struct SortScoreIndexPairOnSecond
-    {
-        bool operator()(const ScoreIndexPair& left, const ScoreIndexPair& right) const
-        {
-            return left.second < right.second;
-        }
-    };
-
-    /** Fills the different xor masks to use when getting the neighbors in multi-probe LSH
-     * @param key the key we build neighbors from
-     * @param lowest_index the lowest index of the bit set
-     * @param level the multi-probe level we are at
-     * @param xor_masks all the xor mask
-     */
-    void fill_xor_mask(lsh::BucketKey key, int lowest_index, unsigned int level,
-                       std::vector<lsh::BucketKey>& xor_masks)
-    {
-        xor_masks.push_back(key);
-        if (level == 0) return;
-        for (int index = lowest_index - 1; index >= 0; --index) {
-            // Create a new key
-            lsh::BucketKey new_key = key | (1 << index);
-            fill_xor_mask(new_key, index, level - 1, xor_masks);
-        }
-    }
-
-    /** Performs the approximate nearest-neighbor search.
-     * @param vec the feature to analyze
-     * @param do_radius flag indicating if we check the radius too
-     * @param radius the radius if it is a radius search
-     * @param do_k flag indicating if we limit the number of nn
-     * @param k_nn the number of nearest neighbors
-     * @param checked_average used for debugging
-     */
-    void getNeighbors(const ElementType* vec, bool /*do_radius*/, float radius, bool do_k, unsigned int k_nn,
-                      float& /*checked_average*/)
-    {
-        static std::vector<ScoreIndexPair> score_index_heap;
-
-        if (do_k) {
-            unsigned int worst_score = std::numeric_limits<unsigned int>::max();
-            typename std::vector<lsh::LshTable<ElementType> >::const_iterator table = tables_.begin();
-            typename std::vector<lsh::LshTable<ElementType> >::const_iterator table_end = tables_.end();
-            for (; table != table_end; ++table) {
-                size_t key = table->getKey(vec);
-                std::vector<lsh::BucketKey>::const_iterator xor_mask = xor_masks_.begin();
-                std::vector<lsh::BucketKey>::const_iterator xor_mask_end = xor_masks_.end();
-                for (; xor_mask != xor_mask_end; ++xor_mask) {
-                    size_t sub_key = key ^ (*xor_mask);
-                    const lsh::Bucket* bucket = table->getBucketFromKey(sub_key);
-                    if (bucket == 0) continue;
-
-                    // Go over each descriptor index
-                    std::vector<lsh::FeatureIndex>::const_iterator training_index = bucket->begin();
-                    std::vector<lsh::FeatureIndex>::const_iterator last_training_index = bucket->end();
-                    DistanceType hamming_distance;
-
-                    // Process the rest of the candidates
-                    for (; training_index < last_training_index; ++training_index) {
-                        hamming_distance = distance_(vec, dataset_[*training_index], dataset_.cols);
-
-                        if (hamming_distance < worst_score) {
-                            // Insert the new element
-                            score_index_heap.push_back(ScoreIndexPair(hamming_distance, training_index));
-                            std::push_heap(score_index_heap.begin(), score_index_heap.end());
-
-                            if (score_index_heap.size() > (unsigned int)k_nn) {
-                                // Remove the highest distance value as we have too many elements
-                                std::pop_heap(score_index_heap.begin(), score_index_heap.end());
-                                score_index_heap.pop_back();
-                                // Keep track of the worst score
-                                worst_score = score_index_heap.front().first;
-                            }
-                        }
-                    }
-                }
-            }
-        }
-        else {
-            typename std::vector<lsh::LshTable<ElementType> >::const_iterator table = tables_.begin();
-            typename std::vector<lsh::LshTable<ElementType> >::const_iterator table_end = tables_.end();
-            for (; table != table_end; ++table) {
-                size_t key = table->getKey(vec);
-                std::vector<lsh::BucketKey>::const_iterator xor_mask = xor_masks_.begin();
-                std::vector<lsh::BucketKey>::const_iterator xor_mask_end = xor_masks_.end();
-                for (; xor_mask != xor_mask_end; ++xor_mask) {
-                    size_t sub_key = key ^ (*xor_mask);
-                    const lsh::Bucket* bucket = table->getBucketFromKey(sub_key);
-                    if (bucket == 0) continue;
-
-                    // Go over each descriptor index
-                    std::vector<lsh::FeatureIndex>::const_iterator training_index = bucket->begin();
-                    std::vector<lsh::FeatureIndex>::const_iterator last_training_index = bucket->end();
-                    DistanceType hamming_distance;
-
-                    // Process the rest of the candidates
-                    for (; training_index < last_training_index; ++training_index) {
-                        // Compute the Hamming distance
-                        hamming_distance = distance_(vec, dataset_[*training_index], dataset_.cols);
-                        if (hamming_distance < radius) score_index_heap.push_back(ScoreIndexPair(hamming_distance, training_index));
-                    }
-                }
-            }
-        }
-    }
-
-    /** Performs the approximate nearest-neighbor search.
-     * This is a slower version than the above as it uses the ResultSet
-     * @param vec the feature to analyze
-     */
-    void getNeighbors(const ElementType* vec, ResultSet<DistanceType>& result)
-    {
-        typename std::vector<lsh::LshTable<ElementType> >::const_iterator table = tables_.begin();
-        typename std::vector<lsh::LshTable<ElementType> >::const_iterator table_end = tables_.end();
-        for (; table != table_end; ++table) {
-            size_t key = table->getKey(vec);
-            std::vector<lsh::BucketKey>::const_iterator xor_mask = xor_masks_.begin();
-            std::vector<lsh::BucketKey>::const_iterator xor_mask_end = xor_masks_.end();
-            for (; xor_mask != xor_mask_end; ++xor_mask) {
-                size_t sub_key = key ^ (*xor_mask);
-                const lsh::Bucket* bucket = table->getBucketFromKey((lsh::BucketKey)sub_key);
-                if (bucket == 0) continue;
-
-                // Go over each descriptor index
-                std::vector<lsh::FeatureIndex>::const_iterator training_index = bucket->begin();
-                std::vector<lsh::FeatureIndex>::const_iterator last_training_index = bucket->end();
-                DistanceType hamming_distance;
-
-                // Process the rest of the candidates
-                for (; training_index < last_training_index; ++training_index) {
-                    // Compute the Hamming distance
-                    hamming_distance = distance_(vec, dataset_[*training_index], (int)dataset_.cols);
-                    result.addPoint(hamming_distance, *training_index);
-                }
-            }
-        }
-    }
-
-    /** The different hash tables */
-    std::vector<lsh::LshTable<ElementType> > tables_;
-
-    /** The data the LSH tables where built from */
-    Matrix<ElementType> dataset_;
-
-    /** The size of the features (as ElementType[]) */
-    unsigned int feature_size_;
-
-    IndexParams index_params_;
-
-    /** table number */
-    unsigned int table_number_;
-    /** key size */
-    unsigned int key_size_;
-    /** How far should we look for neighbors in multi-probe LSH */
-    unsigned int multi_probe_level_;
-
-    /** The XOR masks to apply to a key to get the neighboring buckets */
-    std::vector<lsh::BucketKey> xor_masks_;
-
-    Distance distance_;
-};
-}
-
-#endif //OPENCV_FLANN_LSH_INDEX_H_
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/lsh_table.h b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/lsh_table.h
deleted file mode 100755
index 1db9960..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/lsh_table.h
+++ /dev/null
@@ -1,513 +0,0 @@
-/***********************************************************************
- * Software License Agreement (BSD License)
- *
- * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
- * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
- *
- * THE BSD LICENSE
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions
- * are met:
- *
- * 1. Redistributions of source code must retain the above copyright
- *    notice, this list of conditions and the following disclaimer.
- * 2. Redistributions in binary form must reproduce the above copyright
- *    notice, this list of conditions and the following disclaimer in the
- *    documentation and/or other materials provided with the distribution.
- *
- * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
- * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
- * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
- * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
- * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
- * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
- * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
- * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *************************************************************************/
-
-/***********************************************************************
- * Author: Vincent Rabaud
- *************************************************************************/
-
-#ifndef OPENCV_FLANN_LSH_TABLE_H_
-#define OPENCV_FLANN_LSH_TABLE_H_
-
-#include <algorithm>
-#include <iostream>
-#include <iomanip>
-#include <limits.h>
-// TODO as soon as we use C++0x, use the code in USE_UNORDERED_MAP
-#ifdef __GXX_EXPERIMENTAL_CXX0X__
-#  define USE_UNORDERED_MAP 1
-#else
-#  define USE_UNORDERED_MAP 0
-#endif
-#if USE_UNORDERED_MAP
-#include <unordered_map>
-#else
-#include <map>
-#endif
-#include <math.h>
-#include <stddef.h>
-
-#include "dynamic_bitset.h"
-#include "matrix.h"
-
-namespace cvflann
-{
-
-namespace lsh
-{
-
-////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
-
-/** What is stored in an LSH bucket
- */
-typedef uint32_t FeatureIndex;
-/** The id from which we can get a bucket back in an LSH table
- */
-typedef unsigned int BucketKey;
-
-/** A bucket in an LSH table
- */
-typedef std::vector<FeatureIndex> Bucket;
-
-////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
-
-/** POD for stats about an LSH table
- */
-struct LshStats
-{
-    std::vector<unsigned int> bucket_sizes_;
-    size_t n_buckets_;
-    size_t bucket_size_mean_;
-    size_t bucket_size_median_;
-    size_t bucket_size_min_;
-    size_t bucket_size_max_;
-    size_t bucket_size_std_dev;
-    /** Each contained vector contains three value: beginning/end for interval, number of elements in the bin
-     */
-    std::vector<std::vector<unsigned int> > size_histogram_;
-};
-
-/** Overload the << operator for LshStats
- * @param out the streams
- * @param stats the stats to display
- * @return the streams
- */
-inline std::ostream& operator <<(std::ostream& out, const LshStats& stats)
-{
-    int w = 20;
-    out << "Lsh Table Stats:\n" << std::setw(w) << std::setiosflags(std::ios::right) << "N buckets : "
-    << stats.n_buckets_ << "\n" << std::setw(w) << std::setiosflags(std::ios::right) << "mean size : "
-    << std::setiosflags(std::ios::left) << stats.bucket_size_mean_ << "\n" << std::setw(w)
-    << std::setiosflags(std::ios::right) << "median size : " << stats.bucket_size_median_ << "\n" << std::setw(w)
-    << std::setiosflags(std::ios::right) << "min size : " << std::setiosflags(std::ios::left)
-    << stats.bucket_size_min_ << "\n" << std::setw(w) << std::setiosflags(std::ios::right) << "max size : "
-    << std::setiosflags(std::ios::left) << stats.bucket_size_max_;
-
-    // Display the histogram
-    out << std::endl << std::setw(w) << std::setiosflags(std::ios::right) << "histogram : "
-    << std::setiosflags(std::ios::left);
-    for (std::vector<std::vector<unsigned int> >::const_iterator iterator = stats.size_histogram_.begin(), end =
-             stats.size_histogram_.end(); iterator != end; ++iterator) out << (*iterator)[0] << "-" << (*iterator)[1] << ": " << (*iterator)[2] << ",  ";
-
-    return out;
-}
-
-
-////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
-
-/** Lsh hash table. As its key is a sub-feature, and as usually
- * the size of it is pretty small, we keep it as a continuous memory array.
- * The value is an index in the corpus of features (we keep it as an unsigned
- * int for pure memory reasons, it could be a size_t)
- */
-template<typename ElementType>
-class LshTable
-{
-public:
-    /** A container of all the feature indices. Optimized for space
-     */
-#if USE_UNORDERED_MAP
-    typedef std::unordered_map<BucketKey, Bucket> BucketsSpace;
-#else
-    typedef std::map<BucketKey, Bucket> BucketsSpace;
-#endif
-
-    /** A container of all the feature indices. Optimized for speed
-     */
-    typedef std::vector<Bucket> BucketsSpeed;
-
-    /** Default constructor
-     */
-    LshTable()
-    {
-        key_size_ = 0;
-        feature_size_ = 0;
-        speed_level_ = kArray;
-    }
-
-    /** Default constructor
-     * Create the mask and allocate the memory
-     * @param feature_size is the size of the feature (considered as a ElementType[])
-     * @param key_size is the number of bits that are turned on in the feature
-     */
-    LshTable(unsigned int feature_size, unsigned int key_size)
-    {
-        feature_size_ = feature_size;
-        (void)key_size;
-        std::cerr << "LSH is not implemented for that type" << std::endl;
-        assert(0);
-    }
-
-    /** Add a feature to the table
-     * @param value the value to store for that feature
-     * @param feature the feature itself
-     */
-    void add(unsigned int value, const ElementType* feature)
-    {
-        // Add the value to the corresponding bucket
-        BucketKey key = (lsh::BucketKey)getKey(feature);
-
-        switch (speed_level_) {
-        case kArray:
-            // That means we get the buckets from an array
-            buckets_speed_[key].push_back(value);
-            break;
-        case kBitsetHash:
-            // That means we can check the bitset for the presence of a key
-            key_bitset_.set(key);
-            buckets_space_[key].push_back(value);
-            break;
-        case kHash:
-        {
-            // That means we have to check for the hash table for the presence of a key
-            buckets_space_[key].push_back(value);
-            break;
-        }
-        }
-    }
-
-    /** Add a set of features to the table
-     * @param dataset the values to store
-     */
-    void add(Matrix<ElementType> dataset)
-    {
-#if USE_UNORDERED_MAP
-        buckets_space_.rehash((buckets_space_.size() + dataset.rows) * 1.2);
-#endif
-        // Add the features to the table
-        for (unsigned int i = 0; i < dataset.rows; ++i) add(i, dataset[i]);
-        // Now that the table is full, optimize it for speed/space
-        optimize();
-    }
-
-    /** Get a bucket given the key
-     * @param key
-     * @return
-     */
-    inline const Bucket* getBucketFromKey(BucketKey key) const
-    {
-        // Generate other buckets
-        switch (speed_level_) {
-        case kArray:
-            // That means we get the buckets from an array
-            return &buckets_speed_[key];
-            break;
-        case kBitsetHash:
-            // That means we can check the bitset for the presence of a key
-            if (key_bitset_.test(key)) return &buckets_space_.find(key)->second;
-            else return 0;
-            break;
-        case kHash:
-        {
-            // That means we have to check for the hash table for the presence of a key
-            BucketsSpace::const_iterator bucket_it, bucket_end = buckets_space_.end();
-            bucket_it = buckets_space_.find(key);
-            // Stop here if that bucket does not exist
-            if (bucket_it == bucket_end) return 0;
-            else return &bucket_it->second;
-            break;
-        }
-        }
-        return 0;
-    }
-
-    /** Compute the sub-signature of a feature
-     */
-    size_t getKey(const ElementType* /*feature*/) const
-    {
-        std::cerr << "LSH is not implemented for that type" << std::endl;
-        assert(0);
-        return 1;
-    }
-
-    /** Get statistics about the table
-     * @return
-     */
-    LshStats getStats() const;
-
-private:
-    /** defines the speed fo the implementation
-     * kArray uses a vector for storing data
-     * kBitsetHash uses a hash map but checks for the validity of a key with a bitset
-     * kHash uses a hash map only
-     */
-    enum SpeedLevel
-    {
-        kArray, kBitsetHash, kHash
-    };
-
-    /** Initialize some variables
-     */
-    void initialize(size_t key_size)
-    {
-        const size_t key_size_lower_bound = 1;
-        //a value (size_t(1) << key_size) must fit the size_t type so key_size has to be strictly less than size of size_t
-        const size_t key_size_upper_bound = (std::min)(sizeof(BucketKey) * CHAR_BIT + 1, sizeof(size_t) * CHAR_BIT);
-        if (key_size < key_size_lower_bound || key_size >= key_size_upper_bound)
-        {
-            CV_Error(cv::Error::StsBadArg, cv::format("Invalid key_size (=%d). Valid values for your system are %d <= key_size < %d.", (int)key_size, (int)key_size_lower_bound, (int)key_size_upper_bound));
-        }
-
-        speed_level_ = kHash;
-        key_size_ = (unsigned)key_size;
-    }
-
-    /** Optimize the table for speed/space
-     */
-    void optimize()
-    {
-        // If we are already using the fast storage, no need to do anything
-        if (speed_level_ == kArray) return;
-
-        // Use an array if it will be more than half full
-        if (buckets_space_.size() > ((size_t(1) << key_size_) / 2)) {
-            speed_level_ = kArray;
-            // Fill the array version of it
-            buckets_speed_.resize(size_t(1) << key_size_);
-            for (BucketsSpace::const_iterator key_bucket = buckets_space_.begin(); key_bucket != buckets_space_.end(); ++key_bucket) buckets_speed_[key_bucket->first] = key_bucket->second;
-
-            // Empty the hash table
-            buckets_space_.clear();
-            return;
-        }
-
-        // If the bitset is going to use less than 10% of the RAM of the hash map (at least 1 size_t for the key and two
-        // for the vector) or less than 512MB (key_size_ <= 30)
-        if (((std::max(buckets_space_.size(), buckets_speed_.size()) * CHAR_BIT * 3 * sizeof(BucketKey)) / 10
-             >= (size_t(1) << key_size_)) || (key_size_ <= 32)) {
-            speed_level_ = kBitsetHash;
-            key_bitset_.resize(size_t(1) << key_size_);
-            key_bitset_.reset();
-            // Try with the BucketsSpace
-            for (BucketsSpace::const_iterator key_bucket = buckets_space_.begin(); key_bucket != buckets_space_.end(); ++key_bucket) key_bitset_.set(key_bucket->first);
-        }
-        else {
-            speed_level_ = kHash;
-            key_bitset_.clear();
-        }
-    }
-
-    /** The vector of all the buckets if they are held for speed
-     */
-    BucketsSpeed buckets_speed_;
-
-    /** The hash table of all the buckets in case we cannot use the speed version
-     */
-    BucketsSpace buckets_space_;
-
-    /** What is used to store the data */
-    SpeedLevel speed_level_;
-
-    /** If the subkey is small enough, it will keep track of which subkeys are set through that bitset
-     * That is just a speedup so that we don't look in the hash table (which can be mush slower that checking a bitset)
-     */
-    DynamicBitset key_bitset_;
-
-    /** The size of the sub-signature in bits
-     */
-    unsigned int key_size_;
-
-    unsigned int feature_size_;
-
-    // Members only used for the unsigned char specialization
-    /** The mask to apply to a feature to get the hash key
-     * Only used in the unsigned char case
-     */
-    std::vector<size_t> mask_;
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
-// Specialization for unsigned char
-
-template<>
-inline LshTable<unsigned char>::LshTable(unsigned int feature_size, unsigned int subsignature_size)
-{
-    feature_size_ = feature_size;
-    initialize(subsignature_size);
-    // Allocate the mask
-    mask_ = std::vector<size_t>((feature_size * sizeof(char) + sizeof(size_t) - 1) / sizeof(size_t), 0);
-
-    // A bit brutal but fast to code
-    std::vector<int> indices(feature_size * CHAR_BIT);
-    for (size_t i = 0; i < feature_size * CHAR_BIT; ++i) indices[i] = (int)i;
-#ifndef OPENCV_FLANN_USE_STD_RAND
-    cv::randShuffle(indices);
-#else
-    std::random_shuffle(indices.begin(), indices.end());
-#endif
-
-    // Generate a random set of order of subsignature_size_ bits
-    for (unsigned int i = 0; i < key_size_; ++i) {
-        size_t index = indices[i];
-
-        // Set that bit in the mask
-        size_t divisor = CHAR_BIT * sizeof(size_t);
-        size_t idx = index / divisor; //pick the right size_t index
-        mask_[idx] |= size_t(1) << (index % divisor); //use modulo to find the bit offset
-    }
-
-    // Set to 1 if you want to display the mask for debug
-#if 0
-    {
-        size_t bcount = 0;
-        BOOST_FOREACH(size_t mask_block, mask_){
-            out << std::setw(sizeof(size_t) * CHAR_BIT / 4) << std::setfill('0') << std::hex << mask_block
-                << std::endl;
-            bcount += __builtin_popcountll(mask_block);
-        }
-        out << "bit count : " << std::dec << bcount << std::endl;
-        out << "mask size : " << mask_.size() << std::endl;
-        return out;
-    }
-#endif
-}
-
-/** Return the Subsignature of a feature
- * @param feature the feature to analyze
- */
-template<>
-inline size_t LshTable<unsigned char>::getKey(const unsigned char* feature) const
-{
-    // no need to check if T is dividable by sizeof(size_t) like in the Hamming
-    // distance computation as we have a mask
-    // FIXIT: This is bad assumption, because we reading tail bytes after of the allocated features buffer
-    const size_t* feature_block_ptr = reinterpret_cast<const size_t*> ((const void*)feature);
-
-    // Figure out the subsignature of the feature
-    // Given the feature ABCDEF, and the mask 001011, the output will be
-    // 000CEF
-    size_t subsignature = 0;
-    size_t bit_index = 1;
-
-    for (unsigned i = 0; i < feature_size_; i += sizeof(size_t)) {
-        // get the mask and signature blocks
-        size_t feature_block;
-        if (i <= feature_size_ - sizeof(size_t))
-        {
-            feature_block = *feature_block_ptr;
-        }
-        else
-        {
-            size_t tmp = 0;
-            memcpy(&tmp, feature_block_ptr, feature_size_ - i); // preserve bytes order
-            feature_block = tmp;
-        }
-        size_t mask_block = mask_[i / sizeof(size_t)];
-        while (mask_block) {
-            // Get the lowest set bit in the mask block
-            size_t lowest_bit = mask_block & (-(ptrdiff_t)mask_block);
-            // Add it to the current subsignature if necessary
-            subsignature += (feature_block & lowest_bit) ? bit_index : 0;
-            // Reset the bit in the mask block
-            mask_block ^= lowest_bit;
-            // increment the bit index for the subsignature
-            bit_index <<= 1;
-        }
-        // Check the next feature block
-        ++feature_block_ptr;
-    }
-    return subsignature;
-}
-
-template<>
-inline LshStats LshTable<unsigned char>::getStats() const
-{
-    LshStats stats;
-    stats.bucket_size_mean_ = 0;
-    if ((buckets_speed_.empty()) && (buckets_space_.empty())) {
-        stats.n_buckets_ = 0;
-        stats.bucket_size_median_ = 0;
-        stats.bucket_size_min_ = 0;
-        stats.bucket_size_max_ = 0;
-        return stats;
-    }
-
-    if (!buckets_speed_.empty()) {
-        for (BucketsSpeed::const_iterator pbucket = buckets_speed_.begin(); pbucket != buckets_speed_.end(); ++pbucket) {
-            stats.bucket_sizes_.push_back((lsh::FeatureIndex)pbucket->size());
-            stats.bucket_size_mean_ += pbucket->size();
-        }
-        stats.bucket_size_mean_ /= buckets_speed_.size();
-        stats.n_buckets_ = buckets_speed_.size();
-    }
-    else {
-        for (BucketsSpace::const_iterator x = buckets_space_.begin(); x != buckets_space_.end(); ++x) {
-            stats.bucket_sizes_.push_back((lsh::FeatureIndex)x->second.size());
-            stats.bucket_size_mean_ += x->second.size();
-        }
-        stats.bucket_size_mean_ /= buckets_space_.size();
-        stats.n_buckets_ = buckets_space_.size();
-    }
-
-    std::sort(stats.bucket_sizes_.begin(), stats.bucket_sizes_.end());
-
-    //  BOOST_FOREACH(int size, stats.bucket_sizes_)
-    //          std::cout << size << " ";
-    //  std::cout << std::endl;
-    stats.bucket_size_median_ = stats.bucket_sizes_[stats.bucket_sizes_.size() / 2];
-    stats.bucket_size_min_ = stats.bucket_sizes_.front();
-    stats.bucket_size_max_ = stats.bucket_sizes_.back();
-
-    // TODO compute mean and std
-    /*float mean, stddev;
-       stats.bucket_size_mean_ = mean;
-       stats.bucket_size_std_dev = stddev;*/
-
-    // Include a histogram of the buckets
-    unsigned int bin_start = 0;
-    unsigned int bin_end = 20;
-    bool is_new_bin = true;
-    for (std::vector<unsigned int>::iterator iterator = stats.bucket_sizes_.begin(), end = stats.bucket_sizes_.end(); iterator
-         != end; )
-        if (*iterator < bin_end) {
-            if (is_new_bin) {
-                stats.size_histogram_.push_back(std::vector<unsigned int>(3, 0));
-                stats.size_histogram_.back()[0] = bin_start;
-                stats.size_histogram_.back()[1] = bin_end - 1;
-                is_new_bin = false;
-            }
-            ++stats.size_histogram_.back()[2];
-            ++iterator;
-        }
-        else {
-            bin_start += 20;
-            bin_end += 20;
-            is_new_bin = true;
-        }
-
-    return stats;
-}
-
-// End the two namespaces
-}
-}
-
-////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
-
-#endif /* OPENCV_FLANN_LSH_TABLE_H_ */
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/matrix.h b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/matrix.h
deleted file mode 100755
index f6092d1..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/matrix.h
+++ /dev/null
@@ -1,116 +0,0 @@
-/***********************************************************************
- * Software License Agreement (BSD License)
- *
- * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
- * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
- *
- * THE BSD LICENSE
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions
- * are met:
- *
- * 1. Redistributions of source code must retain the above copyright
- *    notice, this list of conditions and the following disclaimer.
- * 2. Redistributions in binary form must reproduce the above copyright
- *    notice, this list of conditions and the following disclaimer in the
- *    documentation and/or other materials provided with the distribution.
- *
- * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
- * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
- * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
- * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
- * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
- * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
- * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
- * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *************************************************************************/
-
-#ifndef OPENCV_FLANN_DATASET_H_
-#define OPENCV_FLANN_DATASET_H_
-
-#include <stdio.h>
-
-#include "general.h"
-
-namespace cvflann
-{
-
-/**
- * Class that implements a simple rectangular matrix stored in a memory buffer and
- * provides convenient matrix-like access using the [] operators.
- */
-template <typename T>
-class Matrix
-{
-public:
-    typedef T type;
-
-    size_t rows;
-    size_t cols;
-    size_t stride;
-    T* data;
-
-    Matrix() : rows(0), cols(0), stride(0), data(NULL)
-    {
-    }
-
-    Matrix(T* data_, size_t rows_, size_t cols_, size_t stride_ = 0) :
-        rows(rows_), cols(cols_),  stride(stride_), data(data_)
-    {
-        if (stride==0) stride = cols;
-    }
-
-    /**
-     * Convenience function for deallocating the storage data.
-     */
-    CV_DEPRECATED void free()
-    {
-        fprintf(stderr, "The cvflann::Matrix<T>::free() method is deprecated "
-                "and it does not do any memory deallocation any more.  You are"
-                "responsible for deallocating the matrix memory (by doing"
-                "'delete[] matrix.data' for example)");
-    }
-
-    /**
-     * Operator that return a (pointer to a) row of the data.
-     */
-    T* operator[](size_t index) const
-    {
-        return data+index*stride;
-    }
-};
-
-
-class UntypedMatrix
-{
-public:
-    size_t rows;
-    size_t cols;
-    void* data;
-    flann_datatype_t type;
-
-    UntypedMatrix(void* data_, long rows_, long cols_) :
-        rows(rows_), cols(cols_), data(data_)
-    {
-    }
-
-    ~UntypedMatrix()
-    {
-    }
-
-
-    template<typename T>
-    Matrix<T> as()
-    {
-        return Matrix<T>((T*)data, rows, cols);
-    }
-};
-
-
-
-}
-
-#endif //OPENCV_FLANN_DATASET_H_
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/miniflann.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/miniflann.hpp
deleted file mode 100755
index bda2ed4..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/miniflann.hpp
+++ /dev/null
@@ -1,162 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_MINIFLANN_HPP
-#define OPENCV_MINIFLANN_HPP
-
-#include "opencv2/core.hpp"
-#include "opencv2/flann/defines.h"
-
-namespace cv
-{
-
-namespace flann
-{
-
-struct CV_EXPORTS IndexParams
-{
-    IndexParams();
-    ~IndexParams();
-
-    String getString(const String& key, const String& defaultVal=String()) const;
-    int getInt(const String& key, int defaultVal=-1) const;
-    double getDouble(const String& key, double defaultVal=-1) const;
-
-    void setString(const String& key, const String& value);
-    void setInt(const String& key, int value);
-    void setDouble(const String& key, double value);
-    void setFloat(const String& key, float value);
-    void setBool(const String& key, bool value);
-    void setAlgorithm(int value);
-
-    void getAll(std::vector<String>& names,
-                std::vector<int>& types,
-                std::vector<String>& strValues,
-                std::vector<double>& numValues) const;
-
-    void* params;
-
-private:
-    IndexParams(const IndexParams &); // copy disabled
-    IndexParams& operator=(const IndexParams &); // assign disabled
-};
-
-struct CV_EXPORTS KDTreeIndexParams : public IndexParams
-{
-    KDTreeIndexParams(int trees=4);
-};
-
-struct CV_EXPORTS LinearIndexParams : public IndexParams
-{
-    LinearIndexParams();
-};
-
-struct CV_EXPORTS CompositeIndexParams : public IndexParams
-{
-    CompositeIndexParams(int trees = 4, int branching = 32, int iterations = 11,
-                         cvflann::flann_centers_init_t centers_init = cvflann::FLANN_CENTERS_RANDOM, float cb_index = 0.2f );
-};
-
-struct CV_EXPORTS AutotunedIndexParams : public IndexParams
-{
-    AutotunedIndexParams(float target_precision = 0.8f, float build_weight = 0.01f,
-                         float memory_weight = 0, float sample_fraction = 0.1f);
-};
-
-struct CV_EXPORTS HierarchicalClusteringIndexParams : public IndexParams
-{
-    HierarchicalClusteringIndexParams(int branching = 32,
-                      cvflann::flann_centers_init_t centers_init = cvflann::FLANN_CENTERS_RANDOM, int trees = 4, int leaf_size = 100 );
-};
-
-struct CV_EXPORTS KMeansIndexParams : public IndexParams
-{
-    KMeansIndexParams(int branching = 32, int iterations = 11,
-                      cvflann::flann_centers_init_t centers_init = cvflann::FLANN_CENTERS_RANDOM, float cb_index = 0.2f );
-};
-
-struct CV_EXPORTS LshIndexParams : public IndexParams
-{
-    LshIndexParams(int table_number, int key_size, int multi_probe_level);
-};
-
-struct CV_EXPORTS SavedIndexParams : public IndexParams
-{
-    SavedIndexParams(const String& filename);
-};
-
-struct CV_EXPORTS SearchParams : public IndexParams
-{
-    SearchParams( int checks = 32, float eps = 0, bool sorted = true );
-};
-
-class CV_EXPORTS_W Index
-{
-public:
-    CV_WRAP Index();
-    CV_WRAP Index(InputArray features, const IndexParams& params, cvflann::flann_distance_t distType=cvflann::FLANN_DIST_L2);
-    virtual ~Index();
-
-    CV_WRAP virtual void build(InputArray features, const IndexParams& params, cvflann::flann_distance_t distType=cvflann::FLANN_DIST_L2);
-    CV_WRAP virtual void knnSearch(InputArray query, OutputArray indices,
-                   OutputArray dists, int knn, const SearchParams& params=SearchParams());
-
-    CV_WRAP virtual int radiusSearch(InputArray query, OutputArray indices,
-                             OutputArray dists, double radius, int maxResults,
-                             const SearchParams& params=SearchParams());
-
-    CV_WRAP virtual void save(const String& filename) const;
-    CV_WRAP virtual bool load(InputArray features, const String& filename);
-    CV_WRAP virtual void release();
-    CV_WRAP cvflann::flann_distance_t getDistance() const;
-    CV_WRAP cvflann::flann_algorithm_t getAlgorithm() const;
-
-protected:
-    cvflann::flann_distance_t distType;
-    cvflann::flann_algorithm_t algo;
-    int featureType;
-    void* index;
-};
-
-} } // namespace cv::flann
-
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/nn_index.h b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/nn_index.h
deleted file mode 100755
index 381d4bc..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/nn_index.h
+++ /dev/null
@@ -1,177 +0,0 @@
-/***********************************************************************
- * Software License Agreement (BSD License)
- *
- * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
- * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
- *
- * THE BSD LICENSE
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions
- * are met:
- *
- * 1. Redistributions of source code must retain the above copyright
- *    notice, this list of conditions and the following disclaimer.
- * 2. Redistributions in binary form must reproduce the above copyright
- *    notice, this list of conditions and the following disclaimer in the
- *    documentation and/or other materials provided with the distribution.
- *
- * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
- * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
- * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
- * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
- * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
- * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
- * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
- * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *************************************************************************/
-
-#ifndef OPENCV_FLANN_NNINDEX_H
-#define OPENCV_FLANN_NNINDEX_H
-
-#include "general.h"
-#include "matrix.h"
-#include "result_set.h"
-#include "params.h"
-
-namespace cvflann
-{
-
-/**
- * Nearest-neighbour index base class
- */
-template <typename Distance>
-class NNIndex
-{
-    typedef typename Distance::ElementType ElementType;
-    typedef typename Distance::ResultType DistanceType;
-
-public:
-
-    virtual ~NNIndex() {}
-
-    /**
-     * \brief Builds the index
-     */
-    virtual void buildIndex() = 0;
-
-    /**
-     * \brief Perform k-nearest neighbor search
-     * \param[in] queries The query points for which to find the nearest neighbors
-     * \param[out] indices The indices of the nearest neighbors found
-     * \param[out] dists Distances to the nearest neighbors found
-     * \param[in] knn Number of nearest neighbors to return
-     * \param[in] params Search parameters
-     */
-    virtual void knnSearch(const Matrix<ElementType>& queries, Matrix<int>& indices, Matrix<DistanceType>& dists, int knn, const SearchParams& params)
-    {
-        assert(queries.cols == veclen());
-        assert(indices.rows >= queries.rows);
-        assert(dists.rows >= queries.rows);
-        assert(int(indices.cols) >= knn);
-        assert(int(dists.cols) >= knn);
-
-#if 0
-        KNNResultSet<DistanceType> resultSet(knn);
-        for (size_t i = 0; i < queries.rows; i++) {
-            resultSet.init(indices[i], dists[i]);
-            findNeighbors(resultSet, queries[i], params);
-        }
-#else
-        KNNUniqueResultSet<DistanceType> resultSet(knn);
-        for (size_t i = 0; i < queries.rows; i++) {
-            resultSet.clear();
-            findNeighbors(resultSet, queries[i], params);
-            if (get_param(params,"sorted",true)) resultSet.sortAndCopy(indices[i], dists[i], knn);
-            else resultSet.copy(indices[i], dists[i], knn);
-        }
-#endif
-    }
-
-    /**
-     * \brief Perform radius search
-     * \param[in] query The query point
-     * \param[out] indices The indinces of the neighbors found within the given radius
-     * \param[out] dists The distances to the nearest neighbors found
-     * \param[in] radius The radius used for search
-     * \param[in] params Search parameters
-     * \returns Number of neighbors found
-     */
-    virtual int radiusSearch(const Matrix<ElementType>& query, Matrix<int>& indices, Matrix<DistanceType>& dists, float radius, const SearchParams& params)
-    {
-        if (query.rows != 1) {
-            fprintf(stderr, "I can only search one feature at a time for range search\n");
-            return -1;
-        }
-        assert(query.cols == veclen());
-        assert(indices.cols == dists.cols);
-
-        int n = 0;
-        int* indices_ptr = NULL;
-        DistanceType* dists_ptr = NULL;
-        if (indices.cols > 0) {
-            n = (int)indices.cols;
-            indices_ptr = indices[0];
-            dists_ptr = dists[0];
-        }
-
-        RadiusUniqueResultSet<DistanceType> resultSet((DistanceType)radius);
-        resultSet.clear();
-        findNeighbors(resultSet, query[0], params);
-        if (n>0) {
-            if (get_param(params,"sorted",true)) resultSet.sortAndCopy(indices_ptr, dists_ptr, n);
-            else resultSet.copy(indices_ptr, dists_ptr, n);
-        }
-
-        return (int)resultSet.size();
-    }
-
-    /**
-     * \brief Saves the index to a stream
-     * \param stream The stream to save the index to
-     */
-    virtual void saveIndex(FILE* stream) = 0;
-
-    /**
-     * \brief Loads the index from a stream
-     * \param stream The stream from which the index is loaded
-     */
-    virtual void loadIndex(FILE* stream) = 0;
-
-    /**
-     * \returns number of features in this index.
-     */
-    virtual size_t size() const = 0;
-
-    /**
-     * \returns The dimensionality of the features in this index.
-     */
-    virtual size_t veclen() const = 0;
-
-    /**
-     * \returns The amount of memory (in bytes) used by the index.
-     */
-    virtual int usedMemory() const = 0;
-
-    /**
-     * \returns The index type (kdtree, kmeans,...)
-     */
-    virtual flann_algorithm_t getType() const = 0;
-
-    /**
-     * \returns The index parameters
-     */
-    virtual IndexParams getParameters() const = 0;
-
-
-    /**
-     * \brief Method that searches for nearest-neighbours
-     */
-    virtual void findNeighbors(ResultSet<DistanceType>& result, const ElementType* vec, const SearchParams& searchParams) = 0;
-};
-
-}
-
-#endif //OPENCV_FLANN_NNINDEX_H
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/object_factory.h b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/object_factory.h
deleted file mode 100755
index 7f971c5..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/object_factory.h
+++ /dev/null
@@ -1,91 +0,0 @@
-/***********************************************************************
- * Software License Agreement (BSD License)
- *
- * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
- * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
- *
- * THE BSD LICENSE
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions
- * are met:
- *
- * 1. Redistributions of source code must retain the above copyright
- *    notice, this list of conditions and the following disclaimer.
- * 2. Redistributions in binary form must reproduce the above copyright
- *    notice, this list of conditions and the following disclaimer in the
- *    documentation and/or other materials provided with the distribution.
- *
- * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
- * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
- * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
- * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
- * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
- * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
- * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
- * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *************************************************************************/
-
-#ifndef OPENCV_FLANN_OBJECT_FACTORY_H_
-#define OPENCV_FLANN_OBJECT_FACTORY_H_
-
-#include <map>
-
-namespace cvflann
-{
-
-class CreatorNotFound
-{
-};
-
-template<typename BaseClass,
-         typename UniqueIdType,
-         typename ObjectCreator = BaseClass* (*)()>
-class ObjectFactory
-{
-    typedef ObjectFactory<BaseClass,UniqueIdType,ObjectCreator> ThisClass;
-    typedef std::map<UniqueIdType, ObjectCreator> ObjectRegistry;
-
-    // singleton class, private constructor
-    ObjectFactory() {}
-
-public:
-
-    bool subscribe(UniqueIdType id, ObjectCreator creator)
-    {
-        if (object_registry.find(id) != object_registry.end()) return false;
-
-        object_registry[id] = creator;
-        return true;
-    }
-
-    bool unregister(UniqueIdType id)
-    {
-        return object_registry.erase(id) == 1;
-    }
-
-    ObjectCreator create(UniqueIdType id)
-    {
-        typename ObjectRegistry::const_iterator iter = object_registry.find(id);
-
-        if (iter == object_registry.end()) {
-            throw CreatorNotFound();
-        }
-
-        return iter->second;
-    }
-
-    static ThisClass& instance()
-    {
-        static ThisClass the_factory;
-        return the_factory;
-    }
-private:
-    ObjectRegistry object_registry;
-};
-
-}
-
-#endif /* OPENCV_FLANN_OBJECT_FACTORY_H_ */
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/params.h b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/params.h
deleted file mode 100755
index 95ef4cd..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/params.h
+++ /dev/null
@@ -1,99 +0,0 @@
-/***********************************************************************
- * Software License Agreement (BSD License)
- *
- * Copyright 2008-2011  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
- * Copyright 2008-2011  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions
- * are met:
- *
- * 1. Redistributions of source code must retain the above copyright
- *    notice, this list of conditions and the following disclaimer.
- * 2. Redistributions in binary form must reproduce the above copyright
- *    notice, this list of conditions and the following disclaimer in the
- *    documentation and/or other materials provided with the distribution.
- *
- * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
- * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
- * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
- * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
- * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
- * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
- * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
- * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *************************************************************************/
-
-
-#ifndef OPENCV_FLANN_PARAMS_H_
-#define OPENCV_FLANN_PARAMS_H_
-
-#include "any.h"
-#include "general.h"
-#include <iostream>
-#include <map>
-
-
-namespace cvflann
-{
-
-typedef std::map<cv::String, any> IndexParams;
-
-struct SearchParams : public IndexParams
-{
-    SearchParams(int checks = 32, float eps = 0, bool sorted = true )
-    {
-        // how many leafs to visit when searching for neighbours (-1 for unlimited)
-        (*this)["checks"] = checks;
-        // search for eps-approximate neighbours (default: 0)
-        (*this)["eps"] = eps;
-        // only for radius search, require neighbours sorted by distance (default: true)
-        (*this)["sorted"] = sorted;
-    }
-};
-
-
-template<typename T>
-T get_param(const IndexParams& params, cv::String name, const T& default_value)
-{
-    IndexParams::const_iterator it = params.find(name);
-    if (it != params.end()) {
-        return it->second.cast<T>();
-    }
-    else {
-        return default_value;
-    }
-}
-
-template<typename T>
-T get_param(const IndexParams& params, cv::String name)
-{
-    IndexParams::const_iterator it = params.find(name);
-    if (it != params.end()) {
-        return it->second.cast<T>();
-    }
-    else {
-        throw FLANNException(cv::String("Missing parameter '")+name+cv::String("' in the parameters given"));
-    }
-}
-
-inline void print_params(const IndexParams& params, std::ostream& stream)
-{
-    IndexParams::const_iterator it;
-
-    for(it=params.begin(); it!=params.end(); ++it) {
-        stream << it->first << " : " << it->second << std::endl;
-    }
-}
-
-inline void print_params(const IndexParams& params)
-{
-    print_params(params, std::cout);
-}
-
-}
-
-
-#endif /* OPENCV_FLANN_PARAMS_H_ */
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/random.h b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/random.h
deleted file mode 100755
index d678474..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/random.h
+++ /dev/null
@@ -1,155 +0,0 @@
-/***********************************************************************
- * Software License Agreement (BSD License)
- *
- * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
- * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
- *
- * THE BSD LICENSE
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions
- * are met:
- *
- * 1. Redistributions of source code must retain the above copyright
- *    notice, this list of conditions and the following disclaimer.
- * 2. Redistributions in binary form must reproduce the above copyright
- *    notice, this list of conditions and the following disclaimer in the
- *    documentation and/or other materials provided with the distribution.
- *
- * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
- * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
- * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
- * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
- * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
- * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
- * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
- * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *************************************************************************/
-
-#ifndef OPENCV_FLANN_RANDOM_H
-#define OPENCV_FLANN_RANDOM_H
-
-#include <algorithm>
-#include <cstdlib>
-#include <vector>
-
-#include "general.h"
-
-namespace cvflann
-{
-
-inline int rand()
-{
-#ifndef OPENCV_FLANN_USE_STD_RAND
-#   if INT_MAX == RAND_MAX
-    int v = cv::theRNG().next() & INT_MAX;
-#   else
-    int v = cv::theRNG().uniform(0, RAND_MAX + 1);
-#   endif
-#else
-    int v = std::rand();
-#endif // OPENCV_FLANN_USE_STD_RAND
-    return v;
-}
-
-/**
- * Seeds the random number generator
- *  @param seed Random seed
- */
-inline void seed_random(unsigned int seed)
-{
-#ifndef OPENCV_FLANN_USE_STD_RAND
-    cv::theRNG() = cv::RNG(seed);
-#else
-    std::srand(seed);
-#endif
-}
-
-/*
- * Generates a random double value.
- */
-/**
- * Generates a random double value.
- * @param high Upper limit
- * @param low Lower limit
- * @return Random double value
- */
-inline double rand_double(double high = 1.0, double low = 0)
-{
-    return low + ((high-low) * (rand() / (RAND_MAX + 1.0)));
-}
-
-/**
- * Generates a random integer value.
- * @param high Upper limit
- * @param low Lower limit
- * @return Random integer value
- */
-inline int rand_int(int high = RAND_MAX, int low = 0)
-{
-    return low + (int) ( double(high-low) * (rand() / (RAND_MAX + 1.0)));
-}
-
-/**
- * Random number generator that returns a distinct number from
- * the [0,n) interval each time.
- */
-class UniqueRandom
-{
-    std::vector<int> vals_;
-    int size_;
-    int counter_;
-
-public:
-    /**
-     * Constructor.
-     * @param n Size of the interval from which to generate
-     * @return
-     */
-    UniqueRandom(int n)
-    {
-        init(n);
-    }
-
-    /**
-     * Initializes the number generator.
-     * @param n the size of the interval from which to generate random numbers.
-     */
-    void init(int n)
-    {
-        // create and initialize an array of size n
-        vals_.resize(n);
-        size_ = n;
-        for (int i = 0; i < size_; ++i) vals_[i] = i;
-
-        // shuffle the elements in the array
-#ifndef OPENCV_FLANN_USE_STD_RAND
-        cv::randShuffle(vals_);
-#else
-        std::random_shuffle(vals_.begin(), vals_.end());
-#endif
-
-        counter_ = 0;
-    }
-
-    /**
-     * Return a distinct random integer in greater or equal to 0 and less
-     * than 'n' on each call. It should be called maximum 'n' times.
-     * Returns: a random integer
-     */
-    int next()
-    {
-        if (counter_ == size_) {
-            return -1;
-        }
-        else {
-            return vals_[counter_++];
-        }
-    }
-};
-
-}
-
-#endif //OPENCV_FLANN_RANDOM_H
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/result_set.h b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/result_set.h
deleted file mode 100755
index 5c69ac2..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/result_set.h
+++ /dev/null
@@ -1,543 +0,0 @@
-/***********************************************************************
- * Software License Agreement (BSD License)
- *
- * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
- * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
- *
- * THE BSD LICENSE
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions
- * are met:
- *
- * 1. Redistributions of source code must retain the above copyright
- *    notice, this list of conditions and the following disclaimer.
- * 2. Redistributions in binary form must reproduce the above copyright
- *    notice, this list of conditions and the following disclaimer in the
- *    documentation and/or other materials provided with the distribution.
- *
- * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
- * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
- * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
- * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
- * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
- * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
- * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
- * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *************************************************************************/
-
-#ifndef OPENCV_FLANN_RESULTSET_H
-#define OPENCV_FLANN_RESULTSET_H
-
-#include <algorithm>
-#include <cstring>
-#include <iostream>
-#include <limits>
-#include <set>
-#include <vector>
-
-namespace cvflann
-{
-
-/* This record represents a branch point when finding neighbors in
-    the tree.  It contains a record of the minimum distance to the query
-    point, as well as the node at which the search resumes.
- */
-
-template <typename T, typename DistanceType>
-struct BranchStruct
-{
-    T node;           /* Tree node at which search resumes */
-    DistanceType mindist;     /* Minimum distance to query for all nodes below. */
-
-    BranchStruct() {}
-    BranchStruct(const T& aNode, DistanceType dist) : node(aNode), mindist(dist) {}
-
-    bool operator<(const BranchStruct<T, DistanceType>& rhs) const
-    {
-        return mindist<rhs.mindist;
-    }
-};
-
-
-template <typename DistanceType>
-class ResultSet
-{
-public:
-    virtual ~ResultSet() {}
-
-    virtual bool full() const = 0;
-
-    virtual void addPoint(DistanceType dist, int index) = 0;
-
-    virtual DistanceType worstDist() const = 0;
-
-};
-
-/**
- * KNNSimpleResultSet does not ensure that the element it holds are unique.
- * Is used in those cases where the nearest neighbour algorithm used does not
- * attempt to insert the same element multiple times.
- */
-template <typename DistanceType>
-class KNNSimpleResultSet : public ResultSet<DistanceType>
-{
-    int* indices;
-    DistanceType* dists;
-    int capacity;
-    int count;
-    DistanceType worst_distance_;
-
-public:
-    KNNSimpleResultSet(int capacity_) : capacity(capacity_), count(0)
-    {
-    }
-
-    void init(int* indices_, DistanceType* dists_)
-    {
-        indices = indices_;
-        dists = dists_;
-        count = 0;
-        worst_distance_ = (std::numeric_limits<DistanceType>::max)();
-        dists[capacity-1] = worst_distance_;
-    }
-
-    size_t size() const
-    {
-        return count;
-    }
-
-    bool full() const CV_OVERRIDE
-    {
-        return count == capacity;
-    }
-
-
-    void addPoint(DistanceType dist, int index) CV_OVERRIDE
-    {
-        if (dist >= worst_distance_) return;
-        int i;
-        for (i=count; i>0; --i) {
-#ifdef FLANN_FIRST_MATCH
-            if ( (dists[i-1]>dist) || ((dist==dists[i-1])&&(indices[i-1]>index)) )
-#else
-            if (dists[i-1]>dist)
-#endif
-            {
-                if (i<capacity) {
-                    dists[i] = dists[i-1];
-                    indices[i] = indices[i-1];
-                }
-            }
-            else break;
-        }
-        if (count < capacity) ++count;
-        dists[i] = dist;
-        indices[i] = index;
-        worst_distance_ = dists[capacity-1];
-    }
-
-    DistanceType worstDist() const CV_OVERRIDE
-    {
-        return worst_distance_;
-    }
-};
-
-/**
- * K-Nearest neighbour result set. Ensures that the elements inserted are unique
- */
-template <typename DistanceType>
-class KNNResultSet : public ResultSet<DistanceType>
-{
-    int* indices;
-    DistanceType* dists;
-    int capacity;
-    int count;
-    DistanceType worst_distance_;
-
-public:
-    KNNResultSet(int capacity_) : capacity(capacity_), count(0)
-    {
-    }
-
-    void init(int* indices_, DistanceType* dists_)
-    {
-        indices = indices_;
-        dists = dists_;
-        count = 0;
-        worst_distance_ = (std::numeric_limits<DistanceType>::max)();
-        dists[capacity-1] = worst_distance_;
-    }
-
-    size_t size() const
-    {
-        return count;
-    }
-
-    bool full() const CV_OVERRIDE
-    {
-        return count == capacity;
-    }
-
-
-    void addPoint(DistanceType dist, int index) CV_OVERRIDE
-    {
-        if (dist >= worst_distance_) return;
-        int i;
-        for (i = count; i > 0; --i) {
-#ifdef FLANN_FIRST_MATCH
-            if ( (dists[i-1]<=dist) && ((dist!=dists[i-1])||(indices[i-1]<=index)) )
-#else
-            if (dists[i-1]<=dist)
-#endif
-            {
-                // Check for duplicate indices
-                int j = i - 1;
-                while ((j >= 0) && (dists[j] == dist)) {
-                    if (indices[j] == index) {
-                        return;
-                    }
-                    --j;
-                }
-                break;
-            }
-        }
-
-        if (count < capacity) ++count;
-        for (int j = count-1; j > i; --j) {
-            dists[j] = dists[j-1];
-            indices[j] = indices[j-1];
-        }
-        dists[i] = dist;
-        indices[i] = index;
-        worst_distance_ = dists[capacity-1];
-    }
-
-    DistanceType worstDist() const CV_OVERRIDE
-    {
-        return worst_distance_;
-    }
-};
-
-
-/**
- * A result-set class used when performing a radius based search.
- */
-template <typename DistanceType>
-class RadiusResultSet : public ResultSet<DistanceType>
-{
-    DistanceType radius;
-    int* indices;
-    DistanceType* dists;
-    size_t capacity;
-    size_t count;
-
-public:
-    RadiusResultSet(DistanceType radius_, int* indices_, DistanceType* dists_, int capacity_) :
-        radius(radius_), indices(indices_), dists(dists_), capacity(capacity_)
-    {
-        init();
-    }
-
-    ~RadiusResultSet()
-    {
-    }
-
-    void init()
-    {
-        count = 0;
-    }
-
-    size_t size() const
-    {
-        return count;
-    }
-
-    bool full() const
-    {
-        return true;
-    }
-
-    void addPoint(DistanceType dist, int index)
-    {
-        if (dist<radius) {
-            if ((capacity>0)&&(count < capacity)) {
-                dists[count] = dist;
-                indices[count] = index;
-            }
-            count++;
-        }
-    }
-
-    DistanceType worstDist() const
-    {
-        return radius;
-    }
-
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
-
-/** Class that holds the k NN neighbors
- * Faster than KNNResultSet as it uses a binary heap and does not maintain two arrays
- */
-template<typename DistanceType>
-class UniqueResultSet : public ResultSet<DistanceType>
-{
-public:
-    struct DistIndex
-    {
-        DistIndex(DistanceType dist, unsigned int index) :
-            dist_(dist), index_(index)
-        {
-        }
-        bool operator<(const DistIndex dist_index) const
-        {
-            return (dist_ < dist_index.dist_) || ((dist_ == dist_index.dist_) && index_ < dist_index.index_);
-        }
-        DistanceType dist_;
-        unsigned int index_;
-    };
-
-    /** Default cosntructor */
-    UniqueResultSet() :
-        is_full_(false), worst_distance_(std::numeric_limits<DistanceType>::max())
-    {
-    }
-
-    /** Check the status of the set
-     * @return true if we have k NN
-     */
-    inline bool full() const CV_OVERRIDE
-    {
-        return is_full_;
-    }
-
-    /** Remove all elements in the set
-     */
-    virtual void clear() = 0;
-
-    /** Copy the set to two C arrays
-     * @param indices pointer to a C array of indices
-     * @param dist pointer to a C array of distances
-     * @param n_neighbors the number of neighbors to copy
-     */
-    virtual void copy(int* indices, DistanceType* dist, int n_neighbors = -1) const
-    {
-        if (n_neighbors < 0) {
-            for (typename std::set<DistIndex>::const_iterator dist_index = dist_indices_.begin(), dist_index_end =
-                     dist_indices_.end(); dist_index != dist_index_end; ++dist_index, ++indices, ++dist) {
-                *indices = dist_index->index_;
-                *dist = dist_index->dist_;
-            }
-        }
-        else {
-            int i = 0;
-            for (typename std::set<DistIndex>::const_iterator dist_index = dist_indices_.begin(), dist_index_end =
-                     dist_indices_.end(); (dist_index != dist_index_end) && (i < n_neighbors); ++dist_index, ++indices, ++dist, ++i) {
-                *indices = dist_index->index_;
-                *dist = dist_index->dist_;
-            }
-        }
-    }
-
-    /** Copy the set to two C arrays but sort it according to the distance first
-     * @param indices pointer to a C array of indices
-     * @param dist pointer to a C array of distances
-     * @param n_neighbors the number of neighbors to copy
-     */
-    virtual void sortAndCopy(int* indices, DistanceType* dist, int n_neighbors = -1) const
-    {
-        copy(indices, dist, n_neighbors);
-    }
-
-    /** The number of neighbors in the set
-     * @return
-     */
-    size_t size() const
-    {
-        return dist_indices_.size();
-    }
-
-    /** The distance of the furthest neighbor
-     * If we don't have enough neighbors, it returns the max possible value
-     * @return
-     */
-    inline DistanceType worstDist() const CV_OVERRIDE
-    {
-        return worst_distance_;
-    }
-protected:
-    /** Flag to say if the set is full */
-    bool is_full_;
-
-    /** The worst distance found so far */
-    DistanceType worst_distance_;
-
-    /** The best candidates so far */
-    std::set<DistIndex> dist_indices_;
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
-
-/** Class that holds the k NN neighbors
- * Faster than KNNResultSet as it uses a binary heap and does not maintain two arrays
- */
-template<typename DistanceType>
-class KNNUniqueResultSet : public UniqueResultSet<DistanceType>
-{
-public:
-    /** Constructor
-     * @param capacity the number of neighbors to store at max
-     */
-    KNNUniqueResultSet(unsigned int capacity) : capacity_(capacity)
-    {
-        this->is_full_ = false;
-        this->clear();
-    }
-
-    /** Add a possible candidate to the best neighbors
-     * @param dist distance for that neighbor
-     * @param index index of that neighbor
-     */
-    inline void addPoint(DistanceType dist, int index) CV_OVERRIDE
-    {
-        // Don't do anything if we are worse than the worst
-        if (dist >= worst_distance_) return;
-        dist_indices_.insert(DistIndex(dist, index));
-
-        if (is_full_) {
-            if (dist_indices_.size() > capacity_) {
-                dist_indices_.erase(*dist_indices_.rbegin());
-                worst_distance_ = dist_indices_.rbegin()->dist_;
-            }
-        }
-        else if (dist_indices_.size() == capacity_) {
-            is_full_ = true;
-            worst_distance_ = dist_indices_.rbegin()->dist_;
-        }
-    }
-
-    /** Remove all elements in the set
-     */
-    void clear() CV_OVERRIDE
-    {
-        dist_indices_.clear();
-        worst_distance_ = std::numeric_limits<DistanceType>::max();
-        is_full_ = false;
-    }
-
-protected:
-    typedef typename UniqueResultSet<DistanceType>::DistIndex DistIndex;
-    using UniqueResultSet<DistanceType>::is_full_;
-    using UniqueResultSet<DistanceType>::worst_distance_;
-    using UniqueResultSet<DistanceType>::dist_indices_;
-
-    /** The number of neighbors to keep */
-    unsigned int capacity_;
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
-
-/** Class that holds the radius nearest neighbors
- * It is more accurate than RadiusResult as it is not limited in the number of neighbors
- */
-template<typename DistanceType>
-class RadiusUniqueResultSet : public UniqueResultSet<DistanceType>
-{
-public:
-    /** Constructor
-     * @param radius the maximum distance of a neighbor
-     */
-    RadiusUniqueResultSet(DistanceType radius) :
-        radius_(radius)
-    {
-        is_full_ = true;
-    }
-
-    /** Add a possible candidate to the best neighbors
-     * @param dist distance for that neighbor
-     * @param index index of that neighbor
-     */
-    void addPoint(DistanceType dist, int index) CV_OVERRIDE
-    {
-        if (dist <= radius_) dist_indices_.insert(DistIndex(dist, index));
-    }
-
-    /** Remove all elements in the set
-     */
-    inline void clear() CV_OVERRIDE
-    {
-        dist_indices_.clear();
-    }
-
-
-    /** Check the status of the set
-     * @return alwys false
-     */
-    inline bool full() const CV_OVERRIDE
-    {
-        return true;
-    }
-
-    /** The distance of the furthest neighbor
-     * If we don't have enough neighbors, it returns the max possible value
-     * @return
-     */
-    inline DistanceType worstDist() const CV_OVERRIDE
-    {
-        return radius_;
-    }
-private:
-    typedef typename UniqueResultSet<DistanceType>::DistIndex DistIndex;
-    using UniqueResultSet<DistanceType>::dist_indices_;
-    using UniqueResultSet<DistanceType>::is_full_;
-
-    /** The furthest distance a neighbor can be */
-    DistanceType radius_;
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
-
-/** Class that holds the k NN neighbors within a radius distance
- */
-template<typename DistanceType>
-class KNNRadiusUniqueResultSet : public KNNUniqueResultSet<DistanceType>
-{
-public:
-    /** Constructor
-     * @param capacity the number of neighbors to store at max
-     * @param radius the maximum distance of a neighbor
-     */
-    KNNRadiusUniqueResultSet(unsigned int capacity, DistanceType radius)
-    {
-        this->capacity_ = capacity;
-        this->radius_ = radius;
-        this->dist_indices_.reserve(capacity_);
-        this->clear();
-    }
-
-    /** Remove all elements in the set
-     */
-    void clear()
-    {
-        dist_indices_.clear();
-        worst_distance_ = radius_;
-        is_full_ = false;
-    }
-private:
-    using KNNUniqueResultSet<DistanceType>::dist_indices_;
-    using KNNUniqueResultSet<DistanceType>::is_full_;
-    using KNNUniqueResultSet<DistanceType>::worst_distance_;
-
-    /** The maximum number of neighbors to consider */
-    unsigned int capacity_;
-
-    /** The maximum distance of a neighbor */
-    DistanceType radius_;
-};
-}
-
-#endif //OPENCV_FLANN_RESULTSET_H
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/sampling.h b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/sampling.h
deleted file mode 100755
index 396f177..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/sampling.h
+++ /dev/null
@@ -1,81 +0,0 @@
-/***********************************************************************
- * Software License Agreement (BSD License)
- *
- * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
- * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions
- * are met:
- *
- * 1. Redistributions of source code must retain the above copyright
- *    notice, this list of conditions and the following disclaimer.
- * 2. Redistributions in binary form must reproduce the above copyright
- *    notice, this list of conditions and the following disclaimer in the
- *    documentation and/or other materials provided with the distribution.
- *
- * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
- * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
- * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
- * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
- * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
- * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
- * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
- * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *************************************************************************/
-
-
-#ifndef OPENCV_FLANN_SAMPLING_H_
-#define OPENCV_FLANN_SAMPLING_H_
-
-#include "matrix.h"
-#include "random.h"
-
-namespace cvflann
-{
-
-template<typename T>
-Matrix<T> random_sample(Matrix<T>& srcMatrix, long size, bool remove = false)
-{
-    Matrix<T> newSet(new T[size * srcMatrix.cols], size,srcMatrix.cols);
-
-    T* src,* dest;
-    for (long i=0; i<size; ++i) {
-        long r = rand_int((int)(srcMatrix.rows-i));
-        dest = newSet[i];
-        src = srcMatrix[r];
-        std::copy(src, src+srcMatrix.cols, dest);
-        if (remove) {
-            src = srcMatrix[srcMatrix.rows-i-1];
-            dest = srcMatrix[r];
-            std::copy(src, src+srcMatrix.cols, dest);
-        }
-    }
-    if (remove) {
-        srcMatrix.rows -= size;
-    }
-    return newSet;
-}
-
-template<typename T>
-Matrix<T> random_sample(const Matrix<T>& srcMatrix, size_t size)
-{
-    UniqueRandom rand((int)srcMatrix.rows);
-    Matrix<T> newSet(new T[size * srcMatrix.cols], size,srcMatrix.cols);
-
-    T* src,* dest;
-    for (size_t i=0; i<size; ++i) {
-        long r = rand.next();
-        dest = newSet[i];
-        src = srcMatrix[r];
-        std::copy(src, src+srcMatrix.cols, dest);
-    }
-    return newSet;
-}
-
-} // namespace
-
-
-#endif /* OPENCV_FLANN_SAMPLING_H_ */
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/saving.h b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/saving.h
deleted file mode 100755
index 7e3bea5..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/saving.h
+++ /dev/null
@@ -1,187 +0,0 @@
-/***********************************************************************
- * Software License Agreement (BSD License)
- *
- * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
- * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions
- * are met:
- *
- * 1. Redistributions of source code must retain the above copyright
- *    notice, this list of conditions and the following disclaimer.
- * 2. Redistributions in binary form must reproduce the above copyright
- *    notice, this list of conditions and the following disclaimer in the
- *    documentation and/or other materials provided with the distribution.
- *
- * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
- * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
- * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
- * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
- * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE NNIndexGOODS OR SERVICES; LOSS OF USE,
- * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
- * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
- * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *************************************************************************/
-
-#ifndef OPENCV_FLANN_SAVING_H_
-#define OPENCV_FLANN_SAVING_H_
-
-#include <cstring>
-#include <vector>
-
-#include "general.h"
-#include "nn_index.h"
-
-#ifdef FLANN_SIGNATURE_
-#undef FLANN_SIGNATURE_
-#endif
-#define FLANN_SIGNATURE_ "FLANN_INDEX"
-
-namespace cvflann
-{
-
-template <typename T>
-struct Datatype {};
-template<>
-struct Datatype<char> { static flann_datatype_t type() { return FLANN_INT8; } };
-template<>
-struct Datatype<short> { static flann_datatype_t type() { return FLANN_INT16; } };
-template<>
-struct Datatype<int> { static flann_datatype_t type() { return FLANN_INT32; } };
-template<>
-struct Datatype<unsigned char> { static flann_datatype_t type() { return FLANN_UINT8; } };
-template<>
-struct Datatype<unsigned short> { static flann_datatype_t type() { return FLANN_UINT16; } };
-template<>
-struct Datatype<unsigned int> { static flann_datatype_t type() { return FLANN_UINT32; } };
-template<>
-struct Datatype<float> { static flann_datatype_t type() { return FLANN_FLOAT32; } };
-template<>
-struct Datatype<double> { static flann_datatype_t type() { return FLANN_FLOAT64; } };
-
-
-/**
- * Structure representing the index header.
- */
-struct IndexHeader
-{
-    char signature[16];
-    char version[16];
-    flann_datatype_t data_type;
-    flann_algorithm_t index_type;
-    size_t rows;
-    size_t cols;
-};
-
-/**
- * Saves index header to stream
- *
- * @param stream - Stream to save to
- * @param index - The index to save
- */
-template<typename Distance>
-void save_header(FILE* stream, const NNIndex<Distance>& index)
-{
-    IndexHeader header;
-    memset(header.signature, 0, sizeof(header.signature));
-    strcpy(header.signature, FLANN_SIGNATURE_);
-    memset(header.version, 0, sizeof(header.version));
-    strcpy(header.version, FLANN_VERSION_);
-    header.data_type = Datatype<typename Distance::ElementType>::type();
-    header.index_type = index.getType();
-    header.rows = index.size();
-    header.cols = index.veclen();
-
-    std::fwrite(&header, sizeof(header),1,stream);
-}
-
-
-/**
- *
- * @param stream - Stream to load from
- * @return Index header
- */
-inline IndexHeader load_header(FILE* stream)
-{
-    IndexHeader header;
-    size_t read_size = fread(&header,sizeof(header),1,stream);
-
-    if (read_size!=(size_t)1) {
-        throw FLANNException("Invalid index file, cannot read");
-    }
-
-    if (strcmp(header.signature,FLANN_SIGNATURE_)!=0) {
-        throw FLANNException("Invalid index file, wrong signature");
-    }
-
-    return header;
-
-}
-
-
-template<typename T>
-void save_value(FILE* stream, const T& value, size_t count = 1)
-{
-    fwrite(&value, sizeof(value),count, stream);
-}
-
-template<typename T>
-void save_value(FILE* stream, const cvflann::Matrix<T>& value)
-{
-    fwrite(&value, sizeof(value),1, stream);
-    fwrite(value.data, sizeof(T),value.rows*value.cols, stream);
-}
-
-template<typename T>
-void save_value(FILE* stream, const std::vector<T>& value)
-{
-    size_t size = value.size();
-    fwrite(&size, sizeof(size_t), 1, stream);
-    fwrite(&value[0], sizeof(T), size, stream);
-}
-
-template<typename T>
-void load_value(FILE* stream, T& value, size_t count = 1)
-{
-    size_t read_cnt = fread(&value, sizeof(value), count, stream);
-    if (read_cnt != count) {
-        throw FLANNException("Cannot read from file");
-    }
-}
-
-template<typename T>
-void load_value(FILE* stream, cvflann::Matrix<T>& value)
-{
-    size_t read_cnt = fread(&value, sizeof(value), 1, stream);
-    if (read_cnt != 1) {
-        throw FLANNException("Cannot read from file");
-    }
-    value.data = new T[value.rows*value.cols];
-    read_cnt = fread(value.data, sizeof(T), value.rows*value.cols, stream);
-    if (read_cnt != (size_t)(value.rows*value.cols)) {
-        throw FLANNException("Cannot read from file");
-    }
-}
-
-
-template<typename T>
-void load_value(FILE* stream, std::vector<T>& value)
-{
-    size_t size;
-    size_t read_cnt = fread(&size, sizeof(size_t), 1, stream);
-    if (read_cnt!=1) {
-        throw FLANNException("Cannot read from file");
-    }
-    value.resize(size);
-    read_cnt = fread(&value[0], sizeof(T), size, stream);
-    if (read_cnt != size) {
-        throw FLANNException("Cannot read from file");
-    }
-}
-
-}
-
-#endif /* OPENCV_FLANN_SAVING_H_ */
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/simplex_downhill.h b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/simplex_downhill.h
deleted file mode 100755
index 145901a..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/simplex_downhill.h
+++ /dev/null
@@ -1,186 +0,0 @@
-/***********************************************************************
- * Software License Agreement (BSD License)
- *
- * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
- * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
- *
- * THE BSD LICENSE
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions
- * are met:
- *
- * 1. Redistributions of source code must retain the above copyright
- *    notice, this list of conditions and the following disclaimer.
- * 2. Redistributions in binary form must reproduce the above copyright
- *    notice, this list of conditions and the following disclaimer in the
- *    documentation and/or other materials provided with the distribution.
- *
- * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
- * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
- * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
- * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
- * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
- * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
- * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
- * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *************************************************************************/
-
-#ifndef OPENCV_FLANN_SIMPLEX_DOWNHILL_H_
-#define OPENCV_FLANN_SIMPLEX_DOWNHILL_H_
-
-namespace cvflann
-{
-
-/**
-    Adds val to array vals (and point to array points) and keeping the arrays sorted by vals.
- */
-template <typename T>
-void addValue(int pos, float val, float* vals, T* point, T* points, int n)
-{
-    vals[pos] = val;
-    for (int i=0; i<n; ++i) {
-        points[pos*n+i] = point[i];
-    }
-
-    // bubble down
-    int j=pos;
-    while (j>0 && vals[j]<vals[j-1]) {
-        swap(vals[j],vals[j-1]);
-        for (int i=0; i<n; ++i) {
-            swap(points[j*n+i],points[(j-1)*n+i]);
-        }
-        --j;
-    }
-}
-
-
-/**
-    Simplex downhill optimization function.
-    Preconditions: points is a 2D mattrix of size (n+1) x n
-                    func is the cost function taking n an array of n params and returning float
-                    vals is the cost function in the n+1 simplex points, if NULL it will be computed
-
-    Postcondition: returns optimum value and points[0..n] are the optimum parameters
- */
-template <typename T, typename F>
-float optimizeSimplexDownhill(T* points, int n, F func, float* vals = NULL )
-{
-    const int MAX_ITERATIONS = 10;
-
-    assert(n>0);
-
-    T* p_o = new T[n];
-    T* p_r = new T[n];
-    T* p_e = new T[n];
-
-    int alpha = 1;
-
-    int iterations = 0;
-
-    bool ownVals = false;
-    if (vals == NULL) {
-        ownVals = true;
-        vals = new float[n+1];
-        for (int i=0; i<n+1; ++i) {
-            float val = func(points+i*n);
-            addValue(i, val, vals, points+i*n, points, n);
-        }
-    }
-    int nn = n*n;
-
-    while (true) {
-
-        if (iterations++ > MAX_ITERATIONS) break;
-
-        // compute average of simplex points (except the highest point)
-        for (int j=0; j<n; ++j) {
-            p_o[j] = 0;
-            for (int i=0; i<n; ++i) {
-                p_o[i] += points[j*n+i];
-            }
-        }
-        for (int i=0; i<n; ++i) {
-            p_o[i] /= n;
-        }
-
-        bool converged = true;
-        for (int i=0; i<n; ++i) {
-            if (p_o[i] != points[nn+i]) {
-                converged = false;
-            }
-        }
-        if (converged) break;
-
-        // trying a reflection
-        for (int i=0; i<n; ++i) {
-            p_r[i] = p_o[i] + alpha*(p_o[i]-points[nn+i]);
-        }
-        float val_r = func(p_r);
-
-        if ((val_r>=vals[0])&&(val_r<vals[n])) {
-            // reflection between second highest and lowest
-            // add it to the simplex
-            Logger::info("Choosing reflection\n");
-            addValue(n, val_r,vals, p_r, points, n);
-            continue;
-        }
-
-        if (val_r<vals[0]) {
-            // value is smaller than smalest in simplex
-
-            // expand some more to see if it drops further
-            for (int i=0; i<n; ++i) {
-                p_e[i] = 2*p_r[i]-p_o[i];
-            }
-            float val_e = func(p_e);
-
-            if (val_e<val_r) {
-                Logger::info("Choosing reflection and expansion\n");
-                addValue(n, val_e,vals,p_e,points,n);
-            }
-            else {
-                Logger::info("Choosing reflection\n");
-                addValue(n, val_r,vals,p_r,points,n);
-            }
-            continue;
-        }
-        if (val_r>=vals[n]) {
-            for (int i=0; i<n; ++i) {
-                p_e[i] = (p_o[i]+points[nn+i])/2;
-            }
-            float val_e = func(p_e);
-
-            if (val_e<vals[n]) {
-                Logger::info("Choosing contraction\n");
-                addValue(n,val_e,vals,p_e,points,n);
-                continue;
-            }
-        }
-        {
-            Logger::info("Full contraction\n");
-            for (int j=1; j<=n; ++j) {
-                for (int i=0; i<n; ++i) {
-                    points[j*n+i] = (points[j*n+i]+points[i])/2;
-                }
-                float val = func(points+j*n);
-                addValue(j,val,vals,points+j*n,points,n);
-            }
-        }
-    }
-
-    float bestVal = vals[0];
-
-    delete[] p_r;
-    delete[] p_o;
-    delete[] p_e;
-    if (ownVals) delete[] vals;
-
-    return bestVal;
-}
-
-}
-
-#endif //OPENCV_FLANN_SIMPLEX_DOWNHILL_H_
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/timer.h b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/timer.h
deleted file mode 100755
index f771a34..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/flann/timer.h
+++ /dev/null
@@ -1,94 +0,0 @@
-/***********************************************************************
- * Software License Agreement (BSD License)
- *
- * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
- * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
- *
- * THE BSD LICENSE
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions
- * are met:
- *
- * 1. Redistributions of source code must retain the above copyright
- *    notice, this list of conditions and the following disclaimer.
- * 2. Redistributions in binary form must reproduce the above copyright
- *    notice, this list of conditions and the following disclaimer in the
- *    documentation and/or other materials provided with the distribution.
- *
- * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
- * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
- * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
- * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
- * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
- * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
- * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
- * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *************************************************************************/
-
-#ifndef OPENCV_FLANN_TIMER_H
-#define OPENCV_FLANN_TIMER_H
-
-#include <time.h>
-#include "opencv2/core.hpp"
-#include "opencv2/core/utility.hpp"
-
-namespace cvflann
-{
-
-/**
- * A start-stop timer class.
- *
- * Can be used to time portions of code.
- */
-class StartStopTimer
-{
-    int64 startTime;
-
-public:
-    /**
-     * Value of the timer.
-     */
-    double value;
-
-
-    /**
-     * Constructor.
-     */
-    StartStopTimer()
-    {
-        reset();
-    }
-
-    /**
-     * Starts the timer.
-     */
-    void start()
-    {
-        startTime = cv::getTickCount();
-    }
-
-    /**
-     * Stops the timer and updates timer value.
-     */
-    void stop()
-    {
-        int64 stopTime = cv::getTickCount();
-        value += ( (double)stopTime - startTime) / cv::getTickFrequency();
-    }
-
-    /**
-     * Resets the timer value to 0.
-     */
-    void reset()
-    {
-        value = 0;
-    }
-
-};
-
-}
-
-#endif // FLANN_TIMER_H
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/highgui.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/highgui.hpp
deleted file mode 100755
index 06cfd80..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/highgui.hpp
+++ /dev/null
@@ -1,844 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_HIGHGUI_HPP
-#define OPENCV_HIGHGUI_HPP
-
-#include "opencv2/core.hpp"
-#ifdef HAVE_OPENCV_IMGCODECS
-#include "opencv2/imgcodecs.hpp"
-#endif
-#ifdef HAVE_OPENCV_VIDEOIO
-#include "opencv2/videoio.hpp"
-#endif
-
-/**
-@defgroup highgui High-level GUI
-
-While OpenCV was designed for use in full-scale applications and can be used within functionally
-rich UI frameworks (such as Qt\*, WinForms\*, or Cocoa\*) or without any UI at all, sometimes there
-it is required to try functionality quickly and visualize the results. This is what the HighGUI
-module has been designed for.
-
-It provides easy interface to:
-
--   Create and manipulate windows that can display images and "remember" their content (no need to
-    handle repaint events from OS).
--   Add trackbars to the windows, handle simple mouse events as well as keyboard commands.
-
-@{
-    @defgroup highgui_opengl OpenGL support
-    @defgroup highgui_qt Qt New Functions
-
-    ![image](pics/qtgui.png)
-
-    This figure explains new functionality implemented with Qt\* GUI. The new GUI provides a statusbar,
-    a toolbar, and a control panel. The control panel can have trackbars and buttonbars attached to it.
-    If you cannot see the control panel, press Ctrl+P or right-click any Qt window and select **Display
-    properties window**.
-
-    -   To attach a trackbar, the window name parameter must be NULL.
-
-    -   To attach a buttonbar, a button must be created. If the last bar attached to the control panel
-        is a buttonbar, the new button is added to the right of the last button. If the last bar
-        attached to the control panel is a trackbar, or the control panel is empty, a new buttonbar is
-        created. Then, a new button is attached to it.
-
-    See below the example used to generate the figure:
-    @code
-        int main(int argc, char *argv[])
-        {
-
-            int value = 50;
-            int value2 = 0;
-
-
-            namedWindow("main1",WINDOW_NORMAL);
-            namedWindow("main2",WINDOW_AUTOSIZE | CV_GUI_NORMAL);
-            createTrackbar( "track1", "main1", &value, 255,  NULL);
-
-            String nameb1 = "button1";
-            String nameb2 = "button2";
-
-            createButton(nameb1,callbackButton,&nameb1,QT_CHECKBOX,1);
-            createButton(nameb2,callbackButton,NULL,QT_CHECKBOX,0);
-            createTrackbar( "track2", NULL, &value2, 255, NULL);
-            createButton("button5",callbackButton1,NULL,QT_RADIOBOX,0);
-            createButton("button6",callbackButton2,NULL,QT_RADIOBOX,1);
-
-            setMouseCallback( "main2",on_mouse,NULL );
-
-            Mat img1 = imread("files/flower.jpg");
-            VideoCapture video;
-            video.open("files/hockey.avi");
-
-            Mat img2,img3;
-
-            while( waitKey(33) != 27 )
-            {
-                img1.convertTo(img2,-1,1,value);
-                video >> img3;
-
-                imshow("main1",img2);
-                imshow("main2",img3);
-            }
-
-            destroyAllWindows();
-
-            return 0;
-        }
-    @endcode
-
-
-    @defgroup highgui_winrt WinRT support
-
-    This figure explains new functionality implemented with WinRT GUI. The new GUI provides an Image control,
-    and a slider panel. Slider panel holds trackbars attached to it.
-
-    Sliders are attached below the image control. Every new slider is added below the previous one.
-
-    See below the example used to generate the figure:
-    @code
-        void sample_app::MainPage::ShowWindow()
-        {
-            static cv::String windowName("sample");
-            cv::winrt_initContainer(this->cvContainer);
-            cv::namedWindow(windowName); // not required
-
-            cv::Mat image = cv::imread("Assets/sample.jpg");
-            cv::Mat converted = cv::Mat(image.rows, image.cols, CV_8UC4);
-            cv::cvtColor(image, converted, COLOR_BGR2BGRA);
-            cv::imshow(windowName, converted); // this will create window if it hasn't been created before
-
-            int state = 42;
-            cv::TrackbarCallback callback = [](int pos, void* userdata)
-            {
-                if (pos == 0) {
-                    cv::destroyWindow(windowName);
-                }
-            };
-            cv::TrackbarCallback callbackTwin = [](int pos, void* userdata)
-            {
-                if (pos >= 70) {
-                    cv::destroyAllWindows();
-                }
-            };
-            cv::createTrackbar("Sample trackbar", windowName, &state, 100, callback);
-            cv::createTrackbar("Twin brother", windowName, &state, 100, callbackTwin);
-        }
-    @endcode
-
-    @defgroup highgui_c C API
-@}
-*/
-
-///////////////////////// graphical user interface //////////////////////////
-namespace cv
-{
-
-//! @addtogroup highgui
-//! @{
-
-//! Flags for cv::namedWindow
-enum WindowFlags {
-       WINDOW_NORMAL     = 0x00000000, //!< the user can resize the window (no constraint) / also use to switch a fullscreen window to a normal size.
-       WINDOW_AUTOSIZE   = 0x00000001, //!< the user cannot resize the window, the size is constrainted by the image displayed.
-       WINDOW_OPENGL     = 0x00001000, //!< window with opengl support.
-
-       WINDOW_FULLSCREEN = 1,          //!< change the window to fullscreen.
-       WINDOW_FREERATIO  = 0x00000100, //!< the image expends as much as it can (no ratio constraint).
-       WINDOW_KEEPRATIO  = 0x00000000, //!< the ratio of the image is respected.
-       WINDOW_GUI_EXPANDED=0x00000000, //!< status bar and tool bar
-       WINDOW_GUI_NORMAL = 0x00000010, //!< old fashious way
-    };
-
-//! Flags for cv::setWindowProperty / cv::getWindowProperty
-enum WindowPropertyFlags {
-       WND_PROP_FULLSCREEN   = 0, //!< fullscreen property    (can be WINDOW_NORMAL or WINDOW_FULLSCREEN).
-       WND_PROP_AUTOSIZE     = 1, //!< autosize property      (can be WINDOW_NORMAL or WINDOW_AUTOSIZE).
-       WND_PROP_ASPECT_RATIO = 2, //!< window's aspect ration (can be set to WINDOW_FREERATIO or WINDOW_KEEPRATIO).
-       WND_PROP_OPENGL       = 3, //!< opengl support.
-       WND_PROP_VISIBLE      = 4  //!< checks whether the window exists and is visible
-     };
-
-//! Mouse Events see cv::MouseCallback
-enum MouseEventTypes {
-       EVENT_MOUSEMOVE      = 0, //!< indicates that the mouse pointer has moved over the window.
-       EVENT_LBUTTONDOWN    = 1, //!< indicates that the left mouse button is pressed.
-       EVENT_RBUTTONDOWN    = 2, //!< indicates that the right mouse button is pressed.
-       EVENT_MBUTTONDOWN    = 3, //!< indicates that the middle mouse button is pressed.
-       EVENT_LBUTTONUP      = 4, //!< indicates that left mouse button is released.
-       EVENT_RBUTTONUP      = 5, //!< indicates that right mouse button is released.
-       EVENT_MBUTTONUP      = 6, //!< indicates that middle mouse button is released.
-       EVENT_LBUTTONDBLCLK  = 7, //!< indicates that left mouse button is double clicked.
-       EVENT_RBUTTONDBLCLK  = 8, //!< indicates that right mouse button is double clicked.
-       EVENT_MBUTTONDBLCLK  = 9, //!< indicates that middle mouse button is double clicked.
-       EVENT_MOUSEWHEEL     = 10,//!< positive and negative values mean forward and backward scrolling, respectively.
-       EVENT_MOUSEHWHEEL    = 11 //!< positive and negative values mean right and left scrolling, respectively.
-     };
-
-//! Mouse Event Flags see cv::MouseCallback
-enum MouseEventFlags {
-       EVENT_FLAG_LBUTTON   = 1, //!< indicates that the left mouse button is down.
-       EVENT_FLAG_RBUTTON   = 2, //!< indicates that the right mouse button is down.
-       EVENT_FLAG_MBUTTON   = 4, //!< indicates that the middle mouse button is down.
-       EVENT_FLAG_CTRLKEY   = 8, //!< indicates that CTRL Key is pressed.
-       EVENT_FLAG_SHIFTKEY  = 16,//!< indicates that SHIFT Key is pressed.
-       EVENT_FLAG_ALTKEY    = 32 //!< indicates that ALT Key is pressed.
-     };
-
-//! Qt font weight
-enum QtFontWeights {
-        QT_FONT_LIGHT           = 25, //!< Weight of 25
-        QT_FONT_NORMAL          = 50, //!< Weight of 50
-        QT_FONT_DEMIBOLD        = 63, //!< Weight of 63
-        QT_FONT_BOLD            = 75, //!< Weight of 75
-        QT_FONT_BLACK           = 87  //!< Weight of 87
-     };
-
-//! Qt font style
-enum QtFontStyles {
-        QT_STYLE_NORMAL         = 0, //!< Normal font.
-        QT_STYLE_ITALIC         = 1, //!< Italic font.
-        QT_STYLE_OBLIQUE        = 2  //!< Oblique font.
-     };
-
-//! Qt "button" type
-enum QtButtonTypes {
-       QT_PUSH_BUTTON   = 0,    //!< Push button.
-       QT_CHECKBOX      = 1,    //!< Checkbox button.
-       QT_RADIOBOX      = 2,    //!< Radiobox button.
-       QT_NEW_BUTTONBAR = 1024  //!< Button should create a new buttonbar
-     };
-
-/** @brief Callback function for mouse events. see cv::setMouseCallback
-@param event one of the cv::MouseEventTypes constants.
-@param x The x-coordinate of the mouse event.
-@param y The y-coordinate of the mouse event.
-@param flags one of the cv::MouseEventFlags constants.
-@param userdata The optional parameter.
- */
-typedef void (*MouseCallback)(int event, int x, int y, int flags, void* userdata);
-
-/** @brief Callback function for Trackbar see cv::createTrackbar
-@param pos current position of the specified trackbar.
-@param userdata The optional parameter.
- */
-typedef void (*TrackbarCallback)(int pos, void* userdata);
-
-/** @brief Callback function defined to be called every frame. See cv::setOpenGlDrawCallback
-@param userdata The optional parameter.
- */
-typedef void (*OpenGlDrawCallback)(void* userdata);
-
-/** @brief Callback function for a button created by cv::createButton
-@param state current state of the button. It could be -1 for a push button, 0 or 1 for a check/radio box button.
-@param userdata The optional parameter.
- */
-typedef void (*ButtonCallback)(int state, void* userdata);
-
-/** @brief Creates a window.
-
-The function namedWindow creates a window that can be used as a placeholder for images and
-trackbars. Created windows are referred to by their names.
-
-If a window with the same name already exists, the function does nothing.
-
-You can call cv::destroyWindow or cv::destroyAllWindows to close the window and de-allocate any associated
-memory usage. For a simple program, you do not really have to call these functions because all the
-resources and windows of the application are closed automatically by the operating system upon exit.
-
-@note
-
-Qt backend supports additional flags:
- -   **WINDOW_NORMAL or WINDOW_AUTOSIZE:** WINDOW_NORMAL enables you to resize the
-     window, whereas WINDOW_AUTOSIZE adjusts automatically the window size to fit the
-     displayed image (see imshow ), and you cannot change the window size manually.
- -   **WINDOW_FREERATIO or WINDOW_KEEPRATIO:** WINDOW_FREERATIO adjusts the image
-     with no respect to its ratio, whereas WINDOW_KEEPRATIO keeps the image ratio.
- -   **WINDOW_GUI_NORMAL or WINDOW_GUI_EXPANDED:** WINDOW_GUI_NORMAL is the old way to draw the window
-     without statusbar and toolbar, whereas WINDOW_GUI_EXPANDED is a new enhanced GUI.
-By default, flags == WINDOW_AUTOSIZE | WINDOW_KEEPRATIO | WINDOW_GUI_EXPANDED
-
-@param winname Name of the window in the window caption that may be used as a window identifier.
-@param flags Flags of the window. The supported flags are: (cv::WindowFlags)
- */
-CV_EXPORTS_W void namedWindow(const String& winname, int flags = WINDOW_AUTOSIZE);
-
-/** @brief Destroys the specified window.
-
-The function destroyWindow destroys the window with the given name.
-
-@param winname Name of the window to be destroyed.
- */
-CV_EXPORTS_W void destroyWindow(const String& winname);
-
-/** @brief Destroys all of the HighGUI windows.
-
-The function destroyAllWindows destroys all of the opened HighGUI windows.
- */
-CV_EXPORTS_W void destroyAllWindows();
-
-CV_EXPORTS_W int startWindowThread();
-
-/** @brief Similar to #waitKey, but returns full key code.
-
-@note
-
-Key code is implementation specific and depends on used backend: QT/GTK/Win32/etc
-
-*/
-CV_EXPORTS_W int waitKeyEx(int delay = 0);
-
-/** @brief Waits for a pressed key.
-
-The function waitKey waits for a key event infinitely (when \f$\texttt{delay}\leq 0\f$ ) or for delay
-milliseconds, when it is positive. Since the OS has a minimum time between switching threads, the
-function will not wait exactly delay ms, it will wait at least delay ms, depending on what else is
-running on your computer at that time. It returns the code of the pressed key or -1 if no key was
-pressed before the specified time had elapsed.
-
-@note
-
-This function is the only method in HighGUI that can fetch and handle events, so it needs to be
-called periodically for normal event processing unless HighGUI is used within an environment that
-takes care of event processing.
-
-@note
-
-The function only works if there is at least one HighGUI window created and the window is active.
-If there are several HighGUI windows, any of them can be active.
-
-@param delay Delay in milliseconds. 0 is the special value that means "forever".
- */
-CV_EXPORTS_W int waitKey(int delay = 0);
-
-/** @brief Displays an image in the specified window.
-
-The function imshow displays an image in the specified window. If the window was created with the
-cv::WINDOW_AUTOSIZE flag, the image is shown with its original size, however it is still limited by the screen resolution.
-Otherwise, the image is scaled to fit the window. The function may scale the image, depending on its depth:
-
--   If the image is 8-bit unsigned, it is displayed as is.
--   If the image is 16-bit unsigned or 32-bit integer, the pixels are divided by 256. That is, the
-    value range [0,255\*256] is mapped to [0,255].
--   If the image is 32-bit or 64-bit floating-point, the pixel values are multiplied by 255. That is, the
-    value range [0,1] is mapped to [0,255].
-
-If window was created with OpenGL support, cv::imshow also support ogl::Buffer , ogl::Texture2D and
-cuda::GpuMat as input.
-
-If the window was not created before this function, it is assumed creating a window with cv::WINDOW_AUTOSIZE.
-
-If you need to show an image that is bigger than the screen resolution, you will need to call namedWindow("", WINDOW_NORMAL) before the imshow.
-
-@note This function should be followed by cv::waitKey function which displays the image for specified
-milliseconds. Otherwise, it won't display the image. For example, **waitKey(0)** will display the window
-infinitely until any keypress (it is suitable for image display). **waitKey(25)** will display a frame
-for 25 ms, after which display will be automatically closed. (If you put it in a loop to read
-videos, it will display the video frame-by-frame)
-
-@note
-
-[__Windows Backend Only__] Pressing Ctrl+C will copy the image to the clipboard.
-
-[__Windows Backend Only__] Pressing Ctrl+S will show a dialog to save the image.
-
-@param winname Name of the window.
-@param mat Image to be shown.
- */
-CV_EXPORTS_W void imshow(const String& winname, InputArray mat);
-
-/** @brief Resizes window to the specified size
-
-@note
-
--   The specified window size is for the image area. Toolbars are not counted.
--   Only windows created without cv::WINDOW_AUTOSIZE flag can be resized.
-
-@param winname Window name.
-@param width The new window width.
-@param height The new window height.
- */
-CV_EXPORTS_W void resizeWindow(const String& winname, int width, int height);
-
-/** @overload
-@param winname Window name.
-@param size The new window size.
-*/
-CV_EXPORTS_W void resizeWindow(const String& winname, const cv::Size& size);
-
-/** @brief Moves window to the specified position
-
-@param winname Name of the window.
-@param x The new x-coordinate of the window.
-@param y The new y-coordinate of the window.
- */
-CV_EXPORTS_W void moveWindow(const String& winname, int x, int y);
-
-/** @brief Changes parameters of a window dynamically.
-
-The function setWindowProperty enables changing properties of a window.
-
-@param winname Name of the window.
-@param prop_id Window property to edit. The supported operation flags are: (cv::WindowPropertyFlags)
-@param prop_value New value of the window property. The supported flags are: (cv::WindowFlags)
- */
-CV_EXPORTS_W void setWindowProperty(const String& winname, int prop_id, double prop_value);
-
-/** @brief Updates window title
-@param winname Name of the window.
-@param title New title.
-*/
-CV_EXPORTS_W void setWindowTitle(const String& winname, const String& title);
-
-/** @brief Provides parameters of a window.
-
-The function getWindowProperty returns properties of a window.
-
-@param winname Name of the window.
-@param prop_id Window property to retrieve. The following operation flags are available: (cv::WindowPropertyFlags)
-
-@sa setWindowProperty
- */
-CV_EXPORTS_W double getWindowProperty(const String& winname, int prop_id);
-
-/** @brief Provides rectangle of image in the window.
-
-The function getWindowImageRect returns the client screen coordinates, width and height of the image rendering area.
-
-@param winname Name of the window.
-
-@sa resizeWindow moveWindow
- */
-CV_EXPORTS_W Rect getWindowImageRect(const String& winname);
-
-/** @brief Sets mouse handler for the specified window
-
-@param winname Name of the window.
-@param onMouse Mouse callback. See OpenCV samples, such as
-<https://github.com/opencv/opencv/tree/3.4/samples/cpp/ffilldemo.cpp>, on how to specify and
-use the callback.
-@param userdata The optional parameter passed to the callback.
- */
-CV_EXPORTS void setMouseCallback(const String& winname, MouseCallback onMouse, void* userdata = 0);
-
-/** @brief Gets the mouse-wheel motion delta, when handling mouse-wheel events cv::EVENT_MOUSEWHEEL and
-cv::EVENT_MOUSEHWHEEL.
-
-For regular mice with a scroll-wheel, delta will be a multiple of 120. The value 120 corresponds to
-a one notch rotation of the wheel or the threshold for action to be taken and one such action should
-occur for each delta. Some high-precision mice with higher-resolution freely-rotating wheels may
-generate smaller values.
-
-For cv::EVENT_MOUSEWHEEL positive and negative values mean forward and backward scrolling,
-respectively. For cv::EVENT_MOUSEHWHEEL, where available, positive and negative values mean right and
-left scrolling, respectively.
-
-With the C API, the macro CV_GET_WHEEL_DELTA(flags) can be used alternatively.
-
-@note
-
-Mouse-wheel events are currently supported only on Windows.
-
-@param flags The mouse callback flags parameter.
- */
-CV_EXPORTS int getMouseWheelDelta(int flags);
-
-/** @brief Selects ROI on the given image.
-Function creates a window and allows user to select a ROI using mouse.
-Controls: use `space` or `enter` to finish selection, use key `c` to cancel selection (function will return the zero cv::Rect).
-
-@param windowName name of the window where selection process will be shown.
-@param img image to select a ROI.
-@param showCrosshair if true crosshair of selection rectangle will be shown.
-@param fromCenter if true center of selection will match initial mouse position. In opposite case a corner of
-selection rectangle will correspont to the initial mouse position.
-@return selected ROI or empty rect if selection canceled.
-
-@note The function sets it's own mouse callback for specified window using cv::setMouseCallback(windowName, ...).
-After finish of work an empty callback will be set for the used window.
- */
-CV_EXPORTS_W Rect selectROI(const String& windowName, InputArray img, bool showCrosshair = true, bool fromCenter = false);
-
-/** @overload
- */
-CV_EXPORTS_W Rect selectROI(InputArray img, bool showCrosshair = true, bool fromCenter = false);
-
-/** @brief Selects ROIs on the given image.
-Function creates a window and allows user to select a ROIs using mouse.
-Controls: use `space` or `enter` to finish current selection and start a new one,
-use `esc` to terminate multiple ROI selection process.
-
-@param windowName name of the window where selection process will be shown.
-@param img image to select a ROI.
-@param boundingBoxes selected ROIs.
-@param showCrosshair if true crosshair of selection rectangle will be shown.
-@param fromCenter if true center of selection will match initial mouse position. In opposite case a corner of
-selection rectangle will correspont to the initial mouse position.
-
-@note The function sets it's own mouse callback for specified window using cv::setMouseCallback(windowName, ...).
-After finish of work an empty callback will be set for the used window.
- */
-CV_EXPORTS_W void selectROIs(const String& windowName, InputArray img,
-                             CV_OUT std::vector<Rect>& boundingBoxes, bool showCrosshair = true, bool fromCenter = false);
-
-/** @brief Creates a trackbar and attaches it to the specified window.
-
-The function createTrackbar creates a trackbar (a slider or range control) with the specified name
-and range, assigns a variable value to be a position synchronized with the trackbar and specifies
-the callback function onChange to be called on the trackbar position change. The created trackbar is
-displayed in the specified window winname.
-
-@note
-
-[__Qt Backend Only__] winname can be empty (or NULL) if the trackbar should be attached to the
-control panel.
-
-Clicking the label of each trackbar enables editing the trackbar values manually.
-
-@param trackbarname Name of the created trackbar.
-@param winname Name of the window that will be used as a parent of the created trackbar.
-@param value Optional pointer to an integer variable whose value reflects the position of the
-slider. Upon creation, the slider position is defined by this variable.
-@param count Maximal position of the slider. The minimal position is always 0.
-@param onChange Pointer to the function to be called every time the slider changes position. This
-function should be prototyped as void Foo(int,void\*); , where the first parameter is the trackbar
-position and the second parameter is the user data (see the next parameter). If the callback is
-the NULL pointer, no callbacks are called, but only value is updated.
-@param userdata User data that is passed as is to the callback. It can be used to handle trackbar
-events without using global variables.
- */
-CV_EXPORTS int createTrackbar(const String& trackbarname, const String& winname,
-                              int* value, int count,
-                              TrackbarCallback onChange = 0,
-                              void* userdata = 0);
-
-/** @brief Returns the trackbar position.
-
-The function returns the current position of the specified trackbar.
-
-@note
-
-[__Qt Backend Only__] winname can be empty (or NULL) if the trackbar is attached to the control
-panel.
-
-@param trackbarname Name of the trackbar.
-@param winname Name of the window that is the parent of the trackbar.
- */
-CV_EXPORTS_W int getTrackbarPos(const String& trackbarname, const String& winname);
-
-/** @brief Sets the trackbar position.
-
-The function sets the position of the specified trackbar in the specified window.
-
-@note
-
-[__Qt Backend Only__] winname can be empty (or NULL) if the trackbar is attached to the control
-panel.
-
-@param trackbarname Name of the trackbar.
-@param winname Name of the window that is the parent of trackbar.
-@param pos New position.
- */
-CV_EXPORTS_W void setTrackbarPos(const String& trackbarname, const String& winname, int pos);
-
-/** @brief Sets the trackbar maximum position.
-
-The function sets the maximum position of the specified trackbar in the specified window.
-
-@note
-
-[__Qt Backend Only__] winname can be empty (or NULL) if the trackbar is attached to the control
-panel.
-
-@param trackbarname Name of the trackbar.
-@param winname Name of the window that is the parent of trackbar.
-@param maxval New maximum position.
- */
-CV_EXPORTS_W void setTrackbarMax(const String& trackbarname, const String& winname, int maxval);
-
-/** @brief Sets the trackbar minimum position.
-
-The function sets the minimum position of the specified trackbar in the specified window.
-
-@note
-
-[__Qt Backend Only__] winname can be empty (or NULL) if the trackbar is attached to the control
-panel.
-
-@param trackbarname Name of the trackbar.
-@param winname Name of the window that is the parent of trackbar.
-@param minval New minimum position.
- */
-CV_EXPORTS_W void setTrackbarMin(const String& trackbarname, const String& winname, int minval);
-
-//! @addtogroup highgui_opengl OpenGL support
-//! @{
-
-/** @brief Displays OpenGL 2D texture in the specified window.
-
-@param winname Name of the window.
-@param tex OpenGL 2D texture data.
- */
-CV_EXPORTS void imshow(const String& winname, const ogl::Texture2D& tex);
-
-/** @brief Sets a callback function to be called to draw on top of displayed image.
-
-The function setOpenGlDrawCallback can be used to draw 3D data on the window. See the example of
-callback function below:
-@code
-    void on_opengl(void* param)
-    {
-        glLoadIdentity();
-
-        glTranslated(0.0, 0.0, -1.0);
-
-        glRotatef( 55, 1, 0, 0 );
-        glRotatef( 45, 0, 1, 0 );
-        glRotatef( 0, 0, 0, 1 );
-
-        static const int coords[6][4][3] = {
-            { { +1, -1, -1 }, { -1, -1, -1 }, { -1, +1, -1 }, { +1, +1, -1 } },
-            { { +1, +1, -1 }, { -1, +1, -1 }, { -1, +1, +1 }, { +1, +1, +1 } },
-            { { +1, -1, +1 }, { +1, -1, -1 }, { +1, +1, -1 }, { +1, +1, +1 } },
-            { { -1, -1, -1 }, { -1, -1, +1 }, { -1, +1, +1 }, { -1, +1, -1 } },
-            { { +1, -1, +1 }, { -1, -1, +1 }, { -1, -1, -1 }, { +1, -1, -1 } },
-            { { -1, -1, +1 }, { +1, -1, +1 }, { +1, +1, +1 }, { -1, +1, +1 } }
-        };
-
-        for (int i = 0; i < 6; ++i) {
-                    glColor3ub( i*20, 100+i*10, i*42 );
-                    glBegin(GL_QUADS);
-                    for (int j = 0; j < 4; ++j) {
-                            glVertex3d(0.2 * coords[i][j][0], 0.2 * coords[i][j][1], 0.2 * coords[i][j][2]);
-                    }
-                    glEnd();
-        }
-    }
-@endcode
-
-@param winname Name of the window.
-@param onOpenGlDraw Pointer to the function to be called every frame. This function should be
-prototyped as void Foo(void\*) .
-@param userdata Pointer passed to the callback function.(__Optional__)
- */
-CV_EXPORTS void setOpenGlDrawCallback(const String& winname, OpenGlDrawCallback onOpenGlDraw, void* userdata = 0);
-
-/** @brief Sets the specified window as current OpenGL context.
-
-@param winname Name of the window.
- */
-CV_EXPORTS void setOpenGlContext(const String& winname);
-
-/** @brief Force window to redraw its context and call draw callback ( See cv::setOpenGlDrawCallback ).
-
-@param winname Name of the window.
- */
-CV_EXPORTS void updateWindow(const String& winname);
-
-//! @} highgui_opengl
-
-//! @addtogroup highgui_qt
-//! @{
-
-/** @brief QtFont available only for Qt. See cv::fontQt
- */
-struct QtFont
-{
-    const char* nameFont;  //!< Name of the font
-    Scalar      color;     //!< Color of the font. Scalar(blue_component, green_component, red_component[, alpha_component])
-    int         font_face; //!< See cv::QtFontStyles
-    const int*  ascii;     //!< font data and metrics
-    const int*  greek;
-    const int*  cyrillic;
-    float       hscale, vscale;
-    float       shear;     //!< slope coefficient: 0 - normal, >0 - italic
-    int         thickness; //!< See cv::QtFontWeights
-    float       dx;        //!< horizontal interval between letters
-    int         line_type; //!< PointSize
-};
-
-/** @brief Creates the font to draw a text on an image.
-
-The function fontQt creates a cv::QtFont object. This cv::QtFont is not compatible with putText .
-
-A basic usage of this function is the following: :
-@code
-    QtFont font = fontQt("Times");
-    addText( img1, "Hello World !", Point(50,50), font);
-@endcode
-
-@param nameFont Name of the font. The name should match the name of a system font (such as
-*Times*). If the font is not found, a default one is used.
-@param pointSize Size of the font. If not specified, equal zero or negative, the point size of the
-font is set to a system-dependent default value. Generally, this is 12 points.
-@param color Color of the font in BGRA where A = 255 is fully transparent. Use the macro CV_RGB
-for simplicity.
-@param weight Font weight. Available operation flags are : cv::QtFontWeights You can also specify a positive integer for better control.
-@param style Font style. Available operation flags are : cv::QtFontStyles
-@param spacing Spacing between characters. It can be negative or positive.
- */
-CV_EXPORTS QtFont fontQt(const String& nameFont, int pointSize = -1,
-                         Scalar color = Scalar::all(0), int weight = QT_FONT_NORMAL,
-                         int style = QT_STYLE_NORMAL, int spacing = 0);
-
-/** @brief Draws a text on the image.
-
-The function addText draws *text* on the image *img* using a specific font *font* (see example cv::fontQt
-)
-
-@param img 8-bit 3-channel image where the text should be drawn.
-@param text Text to write on an image.
-@param org Point(x,y) where the text should start on an image.
-@param font Font to use to draw a text.
- */
-CV_EXPORTS void addText( const Mat& img, const String& text, Point org, const QtFont& font);
-
-/** @brief Draws a text on the image.
-
-@param img 8-bit 3-channel image where the text should be drawn.
-@param text Text to write on an image.
-@param org Point(x,y) where the text should start on an image.
-@param nameFont Name of the font. The name should match the name of a system font (such as
-*Times*). If the font is not found, a default one is used.
-@param pointSize Size of the font. If not specified, equal zero or negative, the point size of the
-font is set to a system-dependent default value. Generally, this is 12 points.
-@param color Color of the font in BGRA where A = 255 is fully transparent.
-@param weight Font weight. Available operation flags are : cv::QtFontWeights You can also specify a positive integer for better control.
-@param style Font style. Available operation flags are : cv::QtFontStyles
-@param spacing Spacing between characters. It can be negative or positive.
- */
-CV_EXPORTS_W void addText(const Mat& img, const String& text, Point org, const String& nameFont, int pointSize = -1, Scalar color = Scalar::all(0),
-        int weight = QT_FONT_NORMAL, int style = QT_STYLE_NORMAL, int spacing = 0);
-
-/** @brief Displays a text on a window image as an overlay for a specified duration.
-
-The function displayOverlay displays useful information/tips on top of the window for a certain
-amount of time *delayms*. The function does not modify the image, displayed in the window, that is,
-after the specified delay the original content of the window is restored.
-
-@param winname Name of the window.
-@param text Overlay text to write on a window image.
-@param delayms The period (in milliseconds), during which the overlay text is displayed. If this
-function is called before the previous overlay text timed out, the timer is restarted and the text
-is updated. If this value is zero, the text never disappears.
- */
-CV_EXPORTS_W void displayOverlay(const String& winname, const String& text, int delayms = 0);
-
-/** @brief Displays a text on the window statusbar during the specified period of time.
-
-The function displayStatusBar displays useful information/tips on top of the window for a certain
-amount of time *delayms* . This information is displayed on the window statusbar (the window must be
-created with the CV_GUI_EXPANDED flags).
-
-@param winname Name of the window.
-@param text Text to write on the window statusbar.
-@param delayms Duration (in milliseconds) to display the text. If this function is called before
-the previous text timed out, the timer is restarted and the text is updated. If this value is
-zero, the text never disappears.
- */
-CV_EXPORTS_W void displayStatusBar(const String& winname, const String& text, int delayms = 0);
-
-/** @brief Saves parameters of the specified window.
-
-The function saveWindowParameters saves size, location, flags, trackbars value, zoom and panning
-location of the window windowName.
-
-@param windowName Name of the window.
- */
-CV_EXPORTS void saveWindowParameters(const String& windowName);
-
-/** @brief Loads parameters of the specified window.
-
-The function loadWindowParameters loads size, location, flags, trackbars value, zoom and panning
-location of the window windowName.
-
-@param windowName Name of the window.
- */
-CV_EXPORTS void loadWindowParameters(const String& windowName);
-
-CV_EXPORTS  int startLoop(int (*pt2Func)(int argc, char *argv[]), int argc, char* argv[]);
-
-CV_EXPORTS  void stopLoop();
-
-/** @brief Attaches a button to the control panel.
-
-The function createButton attaches a button to the control panel. Each button is added to a
-buttonbar to the right of the last button. A new buttonbar is created if nothing was attached to the
-control panel before, or if the last element attached to the control panel was a trackbar or if the
-QT_NEW_BUTTONBAR flag is added to the type.
-
-See below various examples of the cv::createButton function call: :
-@code
-    createButton(NULL,callbackButton);//create a push button "button 0", that will call callbackButton.
-    createButton("button2",callbackButton,NULL,QT_CHECKBOX,0);
-    createButton("button3",callbackButton,&value);
-    createButton("button5",callbackButton1,NULL,QT_RADIOBOX);
-    createButton("button6",callbackButton2,NULL,QT_PUSH_BUTTON,1);
-    createButton("button6",callbackButton2,NULL,QT_PUSH_BUTTON|QT_NEW_BUTTONBAR);// create a push button in a new row
-@endcode
-
-@param  bar_name Name of the button.
-@param on_change Pointer to the function to be called every time the button changes its state.
-This function should be prototyped as void Foo(int state,\*void); . *state* is the current state
-of the button. It could be -1 for a push button, 0 or 1 for a check/radio box button.
-@param userdata Pointer passed to the callback function.
-@param type Optional type of the button. Available types are: (cv::QtButtonTypes)
-@param initial_button_state Default state of the button. Use for checkbox and radiobox. Its
-value could be 0 or 1. (__Optional__)
-*/
-CV_EXPORTS int createButton( const String& bar_name, ButtonCallback on_change,
-                             void* userdata = 0, int type = QT_PUSH_BUTTON,
-                             bool initial_button_state = false);
-
-//! @} highgui_qt
-
-//! @} highgui
-
-} // cv
-
-#ifndef DISABLE_OPENCV_24_COMPATIBILITY
-#include "opencv2/highgui/highgui_c.h"
-#endif
-
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/highgui/highgui.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/highgui/highgui.hpp
deleted file mode 100755
index 160c9cf..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/highgui/highgui.hpp
+++ /dev/null
@@ -1,48 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifdef __OPENCV_BUILD
-#error this is a compatibility header which should not be used inside the OpenCV library
-#endif
-
-#include "opencv2/highgui.hpp"
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/highgui/highgui_c.h b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/highgui/highgui_c.h
deleted file mode 100755
index 3541313..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/highgui/highgui_c.h
+++ /dev/null
@@ -1,262 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                        Intel License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000, Intel Corporation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of Intel Corporation may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_HIGHGUI_H
-#define OPENCV_HIGHGUI_H
-
-#include "opencv2/core/core_c.h"
-#include "opencv2/imgproc/imgproc_c.h"
-#ifdef HAVE_OPENCV_IMGCODECS
-#include "opencv2/imgcodecs/imgcodecs_c.h"
-#endif
-#ifdef HAVE_OPENCV_VIDEOIO
-#include "opencv2/videoio/videoio_c.h"
-#endif
-
-#ifdef __cplusplus
-extern "C" {
-#endif /* __cplusplus */
-
-/** @addtogroup highgui_c
-  @{
-  */
-
-/****************************************************************************************\
-*                                  Basic GUI functions                                   *
-\****************************************************************************************/
-//YV
-//-----------New for Qt
-/* For font */
-enum {  CV_FONT_LIGHT           = 25,//QFont::Light,
-        CV_FONT_NORMAL          = 50,//QFont::Normal,
-        CV_FONT_DEMIBOLD        = 63,//QFont::DemiBold,
-        CV_FONT_BOLD            = 75,//QFont::Bold,
-        CV_FONT_BLACK           = 87 //QFont::Black
-};
-
-enum {  CV_STYLE_NORMAL         = 0,//QFont::StyleNormal,
-        CV_STYLE_ITALIC         = 1,//QFont::StyleItalic,
-        CV_STYLE_OBLIQUE        = 2 //QFont::StyleOblique
-};
-/* ---------*/
-
-//for color cvScalar(blue_component, green_component, red_component[, alpha_component])
-//and alpha= 0 <-> 0xFF (not transparent <-> transparent)
-CVAPI(CvFont) cvFontQt(const char* nameFont, int pointSize CV_DEFAULT(-1), CvScalar color CV_DEFAULT(cvScalarAll(0)), int weight CV_DEFAULT(CV_FONT_NORMAL),  int style CV_DEFAULT(CV_STYLE_NORMAL), int spacing CV_DEFAULT(0));
-
-CVAPI(void) cvAddText(const CvArr* img, const char* text, CvPoint org, CvFont *arg2);
-
-CVAPI(void) cvDisplayOverlay(const char* name, const char* text, int delayms CV_DEFAULT(0));
-CVAPI(void) cvDisplayStatusBar(const char* name, const char* text, int delayms CV_DEFAULT(0));
-
-CVAPI(void) cvSaveWindowParameters(const char* name);
-CVAPI(void) cvLoadWindowParameters(const char* name);
-CVAPI(int) cvStartLoop(int (*pt2Func)(int argc, char *argv[]), int argc, char* argv[]);
-CVAPI(void) cvStopLoop( void );
-
-typedef void (CV_CDECL *CvButtonCallback)(int state, void* userdata);
-enum {CV_PUSH_BUTTON = 0, CV_CHECKBOX = 1, CV_RADIOBOX = 2};
-CVAPI(int) cvCreateButton( const char* button_name CV_DEFAULT(NULL),CvButtonCallback on_change CV_DEFAULT(NULL), void* userdata CV_DEFAULT(NULL) , int button_type CV_DEFAULT(CV_PUSH_BUTTON), int initial_button_state CV_DEFAULT(0));
-//----------------------
-
-
-/* this function is used to set some external parameters in case of X Window */
-CVAPI(int) cvInitSystem( int argc, char** argv );
-
-CVAPI(int) cvStartWindowThread( void );
-
-// ---------  YV ---------
-enum
-{
-    //These 3 flags are used by cvSet/GetWindowProperty
-    CV_WND_PROP_FULLSCREEN = 0, //to change/get window's fullscreen property
-    CV_WND_PROP_AUTOSIZE   = 1, //to change/get window's autosize property
-    CV_WND_PROP_ASPECTRATIO= 2, //to change/get window's aspectratio property
-    CV_WND_PROP_OPENGL     = 3, //to change/get window's opengl support
-    CV_WND_PROP_VISIBLE    = 4,
-
-    //These 2 flags are used by cvNamedWindow and cvSet/GetWindowProperty
-    CV_WINDOW_NORMAL       = 0x00000000, //the user can resize the window (no constraint)  / also use to switch a fullscreen window to a normal size
-    CV_WINDOW_AUTOSIZE     = 0x00000001, //the user cannot resize the window, the size is constrainted by the image displayed
-    CV_WINDOW_OPENGL       = 0x00001000, //window with opengl support
-
-    //Those flags are only for Qt
-    CV_GUI_EXPANDED         = 0x00000000, //status bar and tool bar
-    CV_GUI_NORMAL           = 0x00000010, //old fashious way
-
-    //These 3 flags are used by cvNamedWindow and cvSet/GetWindowProperty
-    CV_WINDOW_FULLSCREEN   = 1,//change the window to fullscreen
-    CV_WINDOW_FREERATIO    = 0x00000100,//the image expends as much as it can (no ratio constraint)
-    CV_WINDOW_KEEPRATIO    = 0x00000000//the ration image is respected.
-};
-
-/* create window */
-CVAPI(int) cvNamedWindow( const char* name, int flags CV_DEFAULT(CV_WINDOW_AUTOSIZE) );
-
-/* Set and Get Property of the window */
-CVAPI(void) cvSetWindowProperty(const char* name, int prop_id, double prop_value);
-CVAPI(double) cvGetWindowProperty(const char* name, int prop_id);
-
-#ifdef __cplusplus  // FIXIT remove in OpenCV 4.0
-/* Get window image rectangle coordinates, width and height */
-CVAPI(cv::Rect)cvGetWindowImageRect(const char* name);
-#endif
-
-/* display image within window (highgui windows remember their content) */
-CVAPI(void) cvShowImage( const char* name, const CvArr* image );
-
-/* resize/move window */
-CVAPI(void) cvResizeWindow( const char* name, int width, int height );
-CVAPI(void) cvMoveWindow( const char* name, int x, int y );
-
-
-/* destroy window and all the trackers associated with it */
-CVAPI(void) cvDestroyWindow( const char* name );
-
-CVAPI(void) cvDestroyAllWindows(void);
-
-/* get native window handle (HWND in case of Win32 and Widget in case of X Window) */
-CVAPI(void*) cvGetWindowHandle( const char* name );
-
-/* get name of highgui window given its native handle */
-CVAPI(const char*) cvGetWindowName( void* window_handle );
-
-
-typedef void (CV_CDECL *CvTrackbarCallback)(int pos);
-
-/* create trackbar and display it on top of given window, set callback */
-CVAPI(int) cvCreateTrackbar( const char* trackbar_name, const char* window_name,
-                             int* value, int count, CvTrackbarCallback on_change CV_DEFAULT(NULL));
-
-typedef void (CV_CDECL *CvTrackbarCallback2)(int pos, void* userdata);
-
-CVAPI(int) cvCreateTrackbar2( const char* trackbar_name, const char* window_name,
-                              int* value, int count, CvTrackbarCallback2 on_change,
-                              void* userdata CV_DEFAULT(0));
-
-/* retrieve or set trackbar position */
-CVAPI(int) cvGetTrackbarPos( const char* trackbar_name, const char* window_name );
-CVAPI(void) cvSetTrackbarPos( const char* trackbar_name, const char* window_name, int pos );
-CVAPI(void) cvSetTrackbarMax(const char* trackbar_name, const char* window_name, int maxval);
-CVAPI(void) cvSetTrackbarMin(const char* trackbar_name, const char* window_name, int minval);
-
-enum
-{
-    CV_EVENT_MOUSEMOVE      =0,
-    CV_EVENT_LBUTTONDOWN    =1,
-    CV_EVENT_RBUTTONDOWN    =2,
-    CV_EVENT_MBUTTONDOWN    =3,
-    CV_EVENT_LBUTTONUP      =4,
-    CV_EVENT_RBUTTONUP      =5,
-    CV_EVENT_MBUTTONUP      =6,
-    CV_EVENT_LBUTTONDBLCLK  =7,
-    CV_EVENT_RBUTTONDBLCLK  =8,
-    CV_EVENT_MBUTTONDBLCLK  =9,
-    CV_EVENT_MOUSEWHEEL     =10,
-    CV_EVENT_MOUSEHWHEEL    =11
-};
-
-enum
-{
-    CV_EVENT_FLAG_LBUTTON   =1,
-    CV_EVENT_FLAG_RBUTTON   =2,
-    CV_EVENT_FLAG_MBUTTON   =4,
-    CV_EVENT_FLAG_CTRLKEY   =8,
-    CV_EVENT_FLAG_SHIFTKEY  =16,
-    CV_EVENT_FLAG_ALTKEY    =32
-};
-
-
-#define CV_GET_WHEEL_DELTA(flags) ((short)((flags >> 16) & 0xffff)) // upper 16 bits
-
-typedef void (CV_CDECL *CvMouseCallback )(int event, int x, int y, int flags, void* param);
-
-/* assign callback for mouse events */
-CVAPI(void) cvSetMouseCallback( const char* window_name, CvMouseCallback on_mouse,
-                                void* param CV_DEFAULT(NULL));
-
-/* wait for key event infinitely (delay<=0) or for "delay" milliseconds */
-CVAPI(int) cvWaitKey(int delay CV_DEFAULT(0));
-
-// OpenGL support
-
-typedef void (CV_CDECL *CvOpenGlDrawCallback)(void* userdata);
-CVAPI(void) cvSetOpenGlDrawCallback(const char* window_name, CvOpenGlDrawCallback callback, void* userdata CV_DEFAULT(NULL));
-
-CVAPI(void) cvSetOpenGlContext(const char* window_name);
-CVAPI(void) cvUpdateWindow(const char* window_name);
-
-
-/****************************************************************************************\
-
-*                              Obsolete functions/synonyms                               *
-\****************************************************************************************/
-
-#define cvAddSearchPath(path)
-#define cvvInitSystem cvInitSystem
-#define cvvNamedWindow cvNamedWindow
-#define cvvShowImage cvShowImage
-#define cvvResizeWindow cvResizeWindow
-#define cvvDestroyWindow cvDestroyWindow
-#define cvvCreateTrackbar cvCreateTrackbar
-#define cvvAddSearchPath cvAddSearchPath
-#define cvvWaitKey(name) cvWaitKey(0)
-#define cvvWaitKeyEx(name,delay) cvWaitKey(delay)
-#define HG_AUTOSIZE CV_WINDOW_AUTOSIZE
-#define set_preprocess_func cvSetPreprocessFuncWin32
-#define set_postprocess_func cvSetPostprocessFuncWin32
-
-#if defined _WIN32
-
-CVAPI(void) cvSetPreprocessFuncWin32_(const void* callback);
-CVAPI(void) cvSetPostprocessFuncWin32_(const void* callback);
-#define cvSetPreprocessFuncWin32(callback) cvSetPreprocessFuncWin32_((const void*)(callback))
-#define cvSetPostprocessFuncWin32(callback) cvSetPostprocessFuncWin32_((const void*)(callback))
-
-#endif
-
-/** @} highgui_c */
-
-#ifdef __cplusplus
-}
-#endif
-
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/imgcodecs.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/imgcodecs.hpp
deleted file mode 100755
index 5839c58..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/imgcodecs.hpp
+++ /dev/null
@@ -1,250 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_IMGCODECS_HPP
-#define OPENCV_IMGCODECS_HPP
-
-#include "opencv2/core.hpp"
-
-/**
-  @defgroup imgcodecs Image file reading and writing
-  @{
-    @defgroup imgcodecs_c C API
-    @defgroup imgcodecs_ios iOS glue
-  @}
-*/
-
-//////////////////////////////// image codec ////////////////////////////////
-namespace cv
-{
-
-//! @addtogroup imgcodecs
-//! @{
-
-//! Imread flags
-enum ImreadModes {
-       IMREAD_UNCHANGED            = -1, //!< If set, return the loaded image as is (with alpha channel, otherwise it gets cropped).
-       IMREAD_GRAYSCALE            = 0,  //!< If set, always convert image to the single channel grayscale image.
-       IMREAD_COLOR                = 1,  //!< If set, always convert image to the 3 channel BGR color image.
-       IMREAD_ANYDEPTH             = 2,  //!< If set, return 16-bit/32-bit image when the input has the corresponding depth, otherwise convert it to 8-bit.
-       IMREAD_ANYCOLOR             = 4,  //!< If set, the image is read in any possible color format.
-       IMREAD_LOAD_GDAL            = 8,  //!< If set, use the gdal driver for loading the image.
-       IMREAD_REDUCED_GRAYSCALE_2  = 16, //!< If set, always convert image to the single channel grayscale image and the image size reduced 1/2.
-       IMREAD_REDUCED_COLOR_2      = 17, //!< If set, always convert image to the 3 channel BGR color image and the image size reduced 1/2.
-       IMREAD_REDUCED_GRAYSCALE_4  = 32, //!< If set, always convert image to the single channel grayscale image and the image size reduced 1/4.
-       IMREAD_REDUCED_COLOR_4      = 33, //!< If set, always convert image to the 3 channel BGR color image and the image size reduced 1/4.
-       IMREAD_REDUCED_GRAYSCALE_8  = 64, //!< If set, always convert image to the single channel grayscale image and the image size reduced 1/8.
-       IMREAD_REDUCED_COLOR_8      = 65, //!< If set, always convert image to the 3 channel BGR color image and the image size reduced 1/8.
-       IMREAD_IGNORE_ORIENTATION   = 128 //!< If set, do not rotate the image according to EXIF's orientation flag.
-     };
-
-//! Imwrite flags
-enum ImwriteFlags {
-       IMWRITE_JPEG_QUALITY        = 1,  //!< For JPEG, it can be a quality from 0 to 100 (the higher is the better). Default value is 95.
-       IMWRITE_JPEG_PROGRESSIVE    = 2,  //!< Enable JPEG features, 0 or 1, default is False.
-       IMWRITE_JPEG_OPTIMIZE       = 3,  //!< Enable JPEG features, 0 or 1, default is False.
-       IMWRITE_JPEG_RST_INTERVAL   = 4,  //!< JPEG restart interval, 0 - 65535, default is 0 - no restart.
-       IMWRITE_JPEG_LUMA_QUALITY   = 5,  //!< Separate luma quality level, 0 - 100, default is 0 - don't use.
-       IMWRITE_JPEG_CHROMA_QUALITY = 6,  //!< Separate chroma quality level, 0 - 100, default is 0 - don't use.
-       IMWRITE_PNG_COMPRESSION     = 16, //!< For PNG, it can be the compression level from 0 to 9. A higher value means a smaller size and longer compression time. If specified, strategy is changed to IMWRITE_PNG_STRATEGY_DEFAULT (Z_DEFAULT_STRATEGY). Default value is 1 (best speed setting).
-       IMWRITE_PNG_STRATEGY        = 17, //!< One of cv::ImwritePNGFlags, default is IMWRITE_PNG_STRATEGY_RLE.
-       IMWRITE_PNG_BILEVEL         = 18, //!< Binary level PNG, 0 or 1, default is 0.
-       IMWRITE_PXM_BINARY          = 32, //!< For PPM, PGM, or PBM, it can be a binary format flag, 0 or 1. Default value is 1.
-       IMWRITE_EXR_TYPE            = (3 << 4) + 0, /* 48 */ //!< override EXR storage type (FLOAT (FP32) is default)
-       IMWRITE_WEBP_QUALITY        = 64, //!< For WEBP, it can be a quality from 1 to 100 (the higher is the better). By default (without any parameter) and for quality above 100 the lossless compression is used.
-       IMWRITE_PAM_TUPLETYPE       = 128,//!< For PAM, sets the TUPLETYPE field to the corresponding string value that is defined for the format
-       IMWRITE_TIFF_RESUNIT = 256,//!< For TIFF, use to specify which DPI resolution unit to set; see libtiff documentation for valid values
-       IMWRITE_TIFF_XDPI = 257,//!< For TIFF, use to specify the X direction DPI
-       IMWRITE_TIFF_YDPI = 258 //!< For TIFF, use to specify the Y direction DPI
-     };
-
-enum ImwriteEXRTypeFlags {
-       /*IMWRITE_EXR_TYPE_UNIT = 0, //!< not supported */
-       IMWRITE_EXR_TYPE_HALF = 1,   //!< store as HALF (FP16)
-       IMWRITE_EXR_TYPE_FLOAT = 2   //!< store as FP32 (default)
-     };
-
-//! Imwrite PNG specific flags used to tune the compression algorithm.
-/** These flags will be modify the way of PNG image compression and will be passed to the underlying zlib processing stage.
-
--   The effect of IMWRITE_PNG_STRATEGY_FILTERED is to force more Huffman coding and less string matching; it is somewhat intermediate between IMWRITE_PNG_STRATEGY_DEFAULT and IMWRITE_PNG_STRATEGY_HUFFMAN_ONLY.
--   IMWRITE_PNG_STRATEGY_RLE is designed to be almost as fast as IMWRITE_PNG_STRATEGY_HUFFMAN_ONLY, but give better compression for PNG image data.
--   The strategy parameter only affects the compression ratio but not the correctness of the compressed output even if it is not set appropriately.
--   IMWRITE_PNG_STRATEGY_FIXED prevents the use of dynamic Huffman codes, allowing for a simpler decoder for special applications.
-*/
-enum ImwritePNGFlags {
-       IMWRITE_PNG_STRATEGY_DEFAULT      = 0, //!< Use this value for normal data.
-       IMWRITE_PNG_STRATEGY_FILTERED     = 1, //!< Use this value for data produced by a filter (or predictor).Filtered data consists mostly of small values with a somewhat random distribution. In this case, the compression algorithm is tuned to compress them better.
-       IMWRITE_PNG_STRATEGY_HUFFMAN_ONLY = 2, //!< Use this value to force Huffman encoding only (no string match).
-       IMWRITE_PNG_STRATEGY_RLE          = 3, //!< Use this value to limit match distances to one (run-length encoding).
-       IMWRITE_PNG_STRATEGY_FIXED        = 4  //!< Using this value prevents the use of dynamic Huffman codes, allowing for a simpler decoder for special applications.
-     };
-
-//! Imwrite PAM specific tupletype flags used to define the 'TUPETYPE' field of a PAM file.
-enum ImwritePAMFlags {
-       IMWRITE_PAM_FORMAT_NULL = 0,
-       IMWRITE_PAM_FORMAT_BLACKANDWHITE = 1,
-       IMWRITE_PAM_FORMAT_GRAYSCALE = 2,
-       IMWRITE_PAM_FORMAT_GRAYSCALE_ALPHA = 3,
-       IMWRITE_PAM_FORMAT_RGB = 4,
-       IMWRITE_PAM_FORMAT_RGB_ALPHA = 5,
-     };
-
-/** @brief Loads an image from a file.
-
-@anchor imread
-
-The function imread loads an image from the specified file and returns it. If the image cannot be
-read (because of missing file, improper permissions, unsupported or invalid format), the function
-returns an empty matrix ( Mat::data==NULL ).
-
-Currently, the following file formats are supported:
-
--   Windows bitmaps - \*.bmp, \*.dib (always supported)
--   JPEG files - \*.jpeg, \*.jpg, \*.jpe (see the *Notes* section)
--   JPEG 2000 files - \*.jp2 (see the *Notes* section)
--   Portable Network Graphics - \*.png (see the *Notes* section)
--   WebP - \*.webp (see the *Notes* section)
--   Portable image format - \*.pbm, \*.pgm, \*.ppm \*.pxm, \*.pnm (always supported)
--   Sun rasters - \*.sr, \*.ras (always supported)
--   TIFF files - \*.tiff, \*.tif (see the *Notes* section)
--   OpenEXR Image files - \*.exr (see the *Notes* section)
--   Radiance HDR - \*.hdr, \*.pic (always supported)
--   Raster and Vector geospatial data supported by Gdal (see the *Notes* section)
-
-@note
-
--   The function determines the type of an image by the content, not by the file extension.
--   In the case of color images, the decoded images will have the channels stored in **B G R** order.
--   On Microsoft Windows\* OS and MacOSX\*, the codecs shipped with an OpenCV image (libjpeg,
-    libpng, libtiff, and libjasper) are used by default. So, OpenCV can always read JPEGs, PNGs,
-    and TIFFs. On MacOSX, there is also an option to use native MacOSX image readers. But beware
-    that currently these native image loaders give images with different pixel values because of
-    the color management embedded into MacOSX.
--   On Linux\*, BSD flavors and other Unix-like open-source operating systems, OpenCV looks for
-    codecs supplied with an OS image. Install the relevant packages (do not forget the development
-    files, for example, "libjpeg-dev", in Debian\* and Ubuntu\*) to get the codec support or turn
-    on the OPENCV_BUILD_3RDPARTY_LIBS flag in CMake.
--   In the case you set *WITH_GDAL* flag to true in CMake and @ref IMREAD_LOAD_GDAL to load the image,
-    then [GDAL](http://www.gdal.org) driver will be used in order to decode the image by supporting
-    the following formats: [Raster](http://www.gdal.org/formats_list.html),
-    [Vector](http://www.gdal.org/ogr_formats.html).
--   If EXIF information are embedded in the image file, the EXIF orientation will be taken into account
-    and thus the image will be rotated accordingly except if the flag @ref IMREAD_IGNORE_ORIENTATION is passed.
-@param filename Name of file to be loaded.
-@param flags Flag that can take values of cv::ImreadModes
-*/
-CV_EXPORTS_W Mat imread( const String& filename, int flags = IMREAD_COLOR );
-
-/** @brief Loads a multi-page image from a file.
-
-The function imreadmulti loads a multi-page image from the specified file into a vector of Mat objects.
-@param filename Name of file to be loaded.
-@param flags Flag that can take values of cv::ImreadModes, default with cv::IMREAD_ANYCOLOR.
-@param mats A vector of Mat objects holding each page, if more than one.
-@sa cv::imread
-*/
-CV_EXPORTS_W bool imreadmulti(const String& filename, CV_OUT std::vector<Mat>& mats, int flags = IMREAD_ANYCOLOR);
-
-/** @brief Saves an image to a specified file.
-
-The function imwrite saves the image to the specified file. The image format is chosen based on the
-filename extension (see cv::imread for the list of extensions). Only 8-bit (or 16-bit unsigned (CV_16U)
-in case of PNG, JPEG 2000, and TIFF) single-channel or 3-channel (with 'BGR' channel order) images
-can be saved using this function. If the format, depth or channel order is different, use
-Mat::convertTo , and cv::cvtColor to convert it before saving. Or, use the universal FileStorage I/O
-functions to save the image to XML or YAML format.
-
-It is possible to store PNG images with an alpha channel using this function. To do this, create
-8-bit (or 16-bit) 4-channel image BGRA, where the alpha channel goes last. Fully transparent pixels
-should have alpha set to 0, fully opaque pixels should have alpha set to 255/65535.
-
-The sample below shows how to create such a BGRA image and store to PNG file. It also demonstrates how to set custom
-compression parameters :
-@include snippets/imgcodecs_imwrite.cpp
-@param filename Name of the file.
-@param img Image to be saved.
-@param params Format-specific parameters encoded as pairs (paramId_1, paramValue_1, paramId_2, paramValue_2, ... .) see cv::ImwriteFlags
-*/
-CV_EXPORTS_W bool imwrite( const String& filename, InputArray img,
-              const std::vector<int>& params = std::vector<int>());
-
-/** @brief Reads an image from a buffer in memory.
-
-The function imdecode reads an image from the specified buffer in the memory. If the buffer is too short or
-contains invalid data, the function returns an empty matrix ( Mat::data==NULL ).
-
-See cv::imread for the list of supported formats and flags description.
-
-@note In the case of color images, the decoded images will have the channels stored in **B G R** order.
-@param buf Input array or vector of bytes.
-@param flags The same flags as in cv::imread, see cv::ImreadModes.
-*/
-CV_EXPORTS_W Mat imdecode( InputArray buf, int flags );
-
-/** @overload
-@param buf
-@param flags
-@param dst The optional output placeholder for the decoded matrix. It can save the image
-reallocations when the function is called repeatedly for images of the same size.
-*/
-CV_EXPORTS Mat imdecode( InputArray buf, int flags, Mat* dst);
-
-/** @brief Encodes an image into a memory buffer.
-
-The function imencode compresses the image and stores it in the memory buffer that is resized to fit the
-result. See cv::imwrite for the list of supported formats and flags description.
-
-@param ext File extension that defines the output format.
-@param img Image to be written.
-@param buf Output buffer resized to fit the compressed image.
-@param params Format-specific parameters. See cv::imwrite and cv::ImwriteFlags.
-*/
-CV_EXPORTS_W bool imencode( const String& ext, InputArray img,
-                            CV_OUT std::vector<uchar>& buf,
-                            const std::vector<int>& params = std::vector<int>());
-
-//! @} imgcodecs
-
-} // cv
-
-#endif //OPENCV_IMGCODECS_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/imgcodecs/imgcodecs.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/imgcodecs/imgcodecs.hpp
deleted file mode 100755
index a3cd232..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/imgcodecs/imgcodecs.hpp
+++ /dev/null
@@ -1,48 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifdef __OPENCV_BUILD
-#error this is a compatibility header which should not be used inside the OpenCV library
-#endif
-
-#include "opencv2/imgcodecs.hpp"
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/imgcodecs/imgcodecs_c.h b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/imgcodecs/imgcodecs_c.h
deleted file mode 100755
index c36dac3..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/imgcodecs/imgcodecs_c.h
+++ /dev/null
@@ -1,149 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                        Intel License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000, Intel Corporation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of Intel Corporation may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_IMGCODECS_H
-#define OPENCV_IMGCODECS_H
-
-#include "opencv2/core/core_c.h"
-
-#ifdef __cplusplus
-extern "C" {
-#endif /* __cplusplus */
-
-/** @addtogroup imgcodecs_c
-  @{
-  */
-
-enum
-{
-/* 8bit, color or not */
-    CV_LOAD_IMAGE_UNCHANGED  =-1,
-/* 8bit, gray */
-    CV_LOAD_IMAGE_GRAYSCALE  =0,
-/* ?, color */
-    CV_LOAD_IMAGE_COLOR      =1,
-/* any depth, ? */
-    CV_LOAD_IMAGE_ANYDEPTH   =2,
-/* ?, any color */
-    CV_LOAD_IMAGE_ANYCOLOR   =4,
-/* ?, no rotate */
-    CV_LOAD_IMAGE_IGNORE_ORIENTATION  =128
-};
-
-/* load image from file
-  iscolor can be a combination of above flags where CV_LOAD_IMAGE_UNCHANGED
-  overrides the other flags
-  using CV_LOAD_IMAGE_ANYCOLOR alone is equivalent to CV_LOAD_IMAGE_UNCHANGED
-  unless CV_LOAD_IMAGE_ANYDEPTH is specified images are converted to 8bit
-*/
-CVAPI(IplImage*) cvLoadImage( const char* filename, int iscolor CV_DEFAULT(CV_LOAD_IMAGE_COLOR));
-CVAPI(CvMat*) cvLoadImageM( const char* filename, int iscolor CV_DEFAULT(CV_LOAD_IMAGE_COLOR));
-
-enum
-{
-    CV_IMWRITE_JPEG_QUALITY =1,
-    CV_IMWRITE_JPEG_PROGRESSIVE =2,
-    CV_IMWRITE_JPEG_OPTIMIZE =3,
-    CV_IMWRITE_JPEG_RST_INTERVAL =4,
-    CV_IMWRITE_JPEG_LUMA_QUALITY =5,
-    CV_IMWRITE_JPEG_CHROMA_QUALITY =6,
-    CV_IMWRITE_PNG_COMPRESSION =16,
-    CV_IMWRITE_PNG_STRATEGY =17,
-    CV_IMWRITE_PNG_BILEVEL =18,
-    CV_IMWRITE_PNG_STRATEGY_DEFAULT =0,
-    CV_IMWRITE_PNG_STRATEGY_FILTERED =1,
-    CV_IMWRITE_PNG_STRATEGY_HUFFMAN_ONLY =2,
-    CV_IMWRITE_PNG_STRATEGY_RLE =3,
-    CV_IMWRITE_PNG_STRATEGY_FIXED =4,
-    CV_IMWRITE_PXM_BINARY =32,
-    CV_IMWRITE_EXR_TYPE = 48,
-    CV_IMWRITE_WEBP_QUALITY =64,
-    CV_IMWRITE_PAM_TUPLETYPE = 128,
-    CV_IMWRITE_PAM_FORMAT_NULL = 0,
-    CV_IMWRITE_PAM_FORMAT_BLACKANDWHITE = 1,
-    CV_IMWRITE_PAM_FORMAT_GRAYSCALE = 2,
-    CV_IMWRITE_PAM_FORMAT_GRAYSCALE_ALPHA = 3,
-    CV_IMWRITE_PAM_FORMAT_RGB = 4,
-    CV_IMWRITE_PAM_FORMAT_RGB_ALPHA = 5,
-};
-
-
-
-/* save image to file */
-CVAPI(int) cvSaveImage( const char* filename, const CvArr* image,
-                        const int* params CV_DEFAULT(0) );
-
-/* decode image stored in the buffer */
-CVAPI(IplImage*) cvDecodeImage( const CvMat* buf, int iscolor CV_DEFAULT(CV_LOAD_IMAGE_COLOR));
-CVAPI(CvMat*) cvDecodeImageM( const CvMat* buf, int iscolor CV_DEFAULT(CV_LOAD_IMAGE_COLOR));
-
-/* encode image and store the result as a byte vector (single-row 8uC1 matrix) */
-CVAPI(CvMat*) cvEncodeImage( const char* ext, const CvArr* image,
-                             const int* params CV_DEFAULT(0) );
-
-enum
-{
-    CV_CVTIMG_FLIP      =1,
-    CV_CVTIMG_SWAP_RB   =2
-};
-
-/* utility function: convert one image to another with optional vertical flip */
-CVAPI(void) cvConvertImage( const CvArr* src, CvArr* dst, int flags CV_DEFAULT(0));
-
-CVAPI(int) cvHaveImageReader(const char* filename);
-CVAPI(int) cvHaveImageWriter(const char* filename);
-
-
-/****************************************************************************************\
-*                              Obsolete functions/synonyms                               *
-\****************************************************************************************/
-
-#define cvvLoadImage(name) cvLoadImage((name),1)
-#define cvvSaveImage cvSaveImage
-#define cvvConvertImage cvConvertImage
-
-/** @} imgcodecs_c */
-
-#ifdef __cplusplus
-}
-#endif
-
-#endif // OPENCV_IMGCODECS_H
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/imgcodecs/ios.h b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/imgcodecs/ios.h
deleted file mode 100755
index fbd6371..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/imgcodecs/ios.h
+++ /dev/null
@@ -1,57 +0,0 @@
-
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#import <UIKit/UIKit.h>
-#import <Accelerate/Accelerate.h>
-#import <AVFoundation/AVFoundation.h>
-#import <ImageIO/ImageIO.h>
-#include "opencv2/core/core.hpp"
-
-//! @addtogroup imgcodecs_ios
-//! @{
-
-UIImage* MatToUIImage(const cv::Mat& image);
-void UIImageToMat(const UIImage* image,
-                         cv::Mat& m, bool alphaExist = false);
-
-//! @}
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/imgproc.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/imgproc.hpp
deleted file mode 100755
index c760a54..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/imgproc.hpp
+++ /dev/null
@@ -1,4812 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_IMGPROC_HPP
-#define OPENCV_IMGPROC_HPP
-
-#include "opencv2/core.hpp"
-
-/**
-  @defgroup imgproc Image processing
-  @{
-    @defgroup imgproc_filter Image Filtering
-
-Functions and classes described in this section are used to perform various linear or non-linear
-filtering operations on 2D images (represented as Mat's). It means that for each pixel location
-\f$(x,y)\f$ in the source image (normally, rectangular), its neighborhood is considered and used to
-compute the response. In case of a linear filter, it is a weighted sum of pixel values. In case of
-morphological operations, it is the minimum or maximum values, and so on. The computed response is
-stored in the destination image at the same location \f$(x,y)\f$. It means that the output image
-will be of the same size as the input image. Normally, the functions support multi-channel arrays,
-in which case every channel is processed independently. Therefore, the output image will also have
-the same number of channels as the input one.
-
-Another common feature of the functions and classes described in this section is that, unlike
-simple arithmetic functions, they need to extrapolate values of some non-existing pixels. For
-example, if you want to smooth an image using a Gaussian \f$3 \times 3\f$ filter, then, when
-processing the left-most pixels in each row, you need pixels to the left of them, that is, outside
-of the image. You can let these pixels be the same as the left-most image pixels ("replicated
-border" extrapolation method), or assume that all the non-existing pixels are zeros ("constant
-border" extrapolation method), and so on. OpenCV enables you to specify the extrapolation method.
-For details, see #BorderTypes
-
-@anchor filter_depths
-### Depth combinations
-Input depth (src.depth()) | Output depth (ddepth)
---------------------------|----------------------
-CV_8U                     | -1/CV_16S/CV_32F/CV_64F
-CV_16U/CV_16S             | -1/CV_32F/CV_64F
-CV_32F                    | -1/CV_32F/CV_64F
-CV_64F                    | -1/CV_64F
-
-@note when ddepth=-1, the output image will have the same depth as the source.
-
-    @defgroup imgproc_transform Geometric Image Transformations
-
-The functions in this section perform various geometrical transformations of 2D images. They do not
-change the image content but deform the pixel grid and map this deformed grid to the destination
-image. In fact, to avoid sampling artifacts, the mapping is done in the reverse order, from
-destination to the source. That is, for each pixel \f$(x, y)\f$ of the destination image, the
-functions compute coordinates of the corresponding "donor" pixel in the source image and copy the
-pixel value:
-
-\f[\texttt{dst} (x,y)= \texttt{src} (f_x(x,y), f_y(x,y))\f]
-
-In case when you specify the forward mapping \f$\left<g_x, g_y\right>: \texttt{src} \rightarrow
-\texttt{dst}\f$, the OpenCV functions first compute the corresponding inverse mapping
-\f$\left<f_x, f_y\right>: \texttt{dst} \rightarrow \texttt{src}\f$ and then use the above formula.
-
-The actual implementations of the geometrical transformations, from the most generic remap and to
-the simplest and the fastest resize, need to solve two main problems with the above formula:
-
-- Extrapolation of non-existing pixels. Similarly to the filtering functions described in the
-previous section, for some \f$(x,y)\f$, either one of \f$f_x(x,y)\f$, or \f$f_y(x,y)\f$, or both
-of them may fall outside of the image. In this case, an extrapolation method needs to be used.
-OpenCV provides the same selection of extrapolation methods as in the filtering functions. In
-addition, it provides the method #BORDER_TRANSPARENT. This means that the corresponding pixels in
-the destination image will not be modified at all.
-
-- Interpolation of pixel values. Usually \f$f_x(x,y)\f$ and \f$f_y(x,y)\f$ are floating-point
-numbers. This means that \f$\left<f_x, f_y\right>\f$ can be either an affine or perspective
-transformation, or radial lens distortion correction, and so on. So, a pixel value at fractional
-coordinates needs to be retrieved. In the simplest case, the coordinates can be just rounded to the
-nearest integer coordinates and the corresponding pixel can be used. This is called a
-nearest-neighbor interpolation. However, a better result can be achieved by using more
-sophisticated [interpolation methods](http://en.wikipedia.org/wiki/Multivariate_interpolation) ,
-where a polynomial function is fit into some neighborhood of the computed pixel \f$(f_x(x,y),
-f_y(x,y))\f$, and then the value of the polynomial at \f$(f_x(x,y), f_y(x,y))\f$ is taken as the
-interpolated pixel value. In OpenCV, you can choose between several interpolation methods. See
-resize for details.
-
-@note The geometrical transformations do not work with `CV_8S` or `CV_32S` images.
-
-    @defgroup imgproc_misc Miscellaneous Image Transformations
-    @defgroup imgproc_draw Drawing Functions
-
-Drawing functions work with matrices/images of arbitrary depth. The boundaries of the shapes can be
-rendered with antialiasing (implemented only for 8-bit images for now). All the functions include
-the parameter color that uses an RGB value (that may be constructed with the Scalar constructor )
-for color images and brightness for grayscale images. For color images, the channel ordering is
-normally *Blue, Green, Red*. This is what imshow, imread, and imwrite expect. So, if you form a
-color using the Scalar constructor, it should look like:
-
-\f[\texttt{Scalar} (blue \_ component, green \_ component, red \_ component[, alpha \_ component])\f]
-
-If you are using your own image rendering and I/O functions, you can use any channel ordering. The
-drawing functions process each channel independently and do not depend on the channel order or even
-on the used color space. The whole image can be converted from BGR to RGB or to a different color
-space using cvtColor .
-
-If a drawn figure is partially or completely outside the image, the drawing functions clip it. Also,
-many drawing functions can handle pixel coordinates specified with sub-pixel accuracy. This means
-that the coordinates can be passed as fixed-point numbers encoded as integers. The number of
-fractional bits is specified by the shift parameter and the real point coordinates are calculated as
-\f$\texttt{Point}(x,y)\rightarrow\texttt{Point2f}(x*2^{-shift},y*2^{-shift})\f$ . This feature is
-especially effective when rendering antialiased shapes.
-
-@note The functions do not support alpha-transparency when the target image is 4-channel. In this
-case, the color[3] is simply copied to the repainted pixels. Thus, if you want to paint
-semi-transparent shapes, you can paint them in a separate buffer and then blend it with the main
-image.
-
-    @defgroup imgproc_colormap ColorMaps in OpenCV
-
-The human perception isn't built for observing fine changes in grayscale images. Human eyes are more
-sensitive to observing changes between colors, so you often need to recolor your grayscale images to
-get a clue about them. OpenCV now comes with various colormaps to enhance the visualization in your
-computer vision application.
-
-In OpenCV you only need applyColorMap to apply a colormap on a given image. The following sample
-code reads the path to an image from command line, applies a Jet colormap on it and shows the
-result:
-
-@include snippets/imgproc_applyColorMap.cpp
-
-@see #ColormapTypes
-
-    @defgroup imgproc_subdiv2d Planar Subdivision
-
-The Subdiv2D class described in this section is used to perform various planar subdivision on
-a set of 2D points (represented as vector of Point2f). OpenCV subdivides a plane into triangles
-using the Delaunay's algorithm, which corresponds to the dual graph of the Voronoi diagram.
-In the figure below, the Delaunay's triangulation is marked with black lines and the Voronoi
-diagram with red lines.
-
-![Delaunay triangulation (black) and Voronoi (red)](pics/delaunay_voronoi.png)
-
-The subdivisions can be used for the 3D piece-wise transformation of a plane, morphing, fast
-location of points on the plane, building special graphs (such as NNG,RNG), and so forth.
-
-    @defgroup imgproc_hist Histograms
-    @defgroup imgproc_shape Structural Analysis and Shape Descriptors
-    @defgroup imgproc_motion Motion Analysis and Object Tracking
-    @defgroup imgproc_feature Feature Detection
-    @defgroup imgproc_object Object Detection
-    @defgroup imgproc_c C API
-    @defgroup imgproc_hal Hardware Acceleration Layer
-    @{
-        @defgroup imgproc_hal_functions Functions
-        @defgroup imgproc_hal_interface Interface
-    @}
-  @}
-*/
-
-namespace cv
-{
-
-/** @addtogroup imgproc
-@{
-*/
-
-//! @addtogroup imgproc_filter
-//! @{
-
-//! type of morphological operation
-enum MorphTypes{
-    MORPH_ERODE    = 0, //!< see #erode
-    MORPH_DILATE   = 1, //!< see #dilate
-    MORPH_OPEN     = 2, //!< an opening operation
-                        //!< \f[\texttt{dst} = \mathrm{open} ( \texttt{src} , \texttt{element} )= \mathrm{dilate} ( \mathrm{erode} ( \texttt{src} , \texttt{element} ))\f]
-    MORPH_CLOSE    = 3, //!< a closing operation
-                        //!< \f[\texttt{dst} = \mathrm{close} ( \texttt{src} , \texttt{element} )= \mathrm{erode} ( \mathrm{dilate} ( \texttt{src} , \texttt{element} ))\f]
-    MORPH_GRADIENT = 4, //!< a morphological gradient
-                        //!< \f[\texttt{dst} = \mathrm{morph\_grad} ( \texttt{src} , \texttt{element} )= \mathrm{dilate} ( \texttt{src} , \texttt{element} )- \mathrm{erode} ( \texttt{src} , \texttt{element} )\f]
-    MORPH_TOPHAT   = 5, //!< "top hat"
-                        //!< \f[\texttt{dst} = \mathrm{tophat} ( \texttt{src} , \texttt{element} )= \texttt{src} - \mathrm{open} ( \texttt{src} , \texttt{element} )\f]
-    MORPH_BLACKHAT = 6, //!< "black hat"
-                        //!< \f[\texttt{dst} = \mathrm{blackhat} ( \texttt{src} , \texttt{element} )= \mathrm{close} ( \texttt{src} , \texttt{element} )- \texttt{src}\f]
-    MORPH_HITMISS  = 7  //!< "hit or miss"
-                        //!<   .- Only supported for CV_8UC1 binary images. A tutorial can be found in the documentation
-};
-
-//! shape of the structuring element
-enum MorphShapes {
-    MORPH_RECT    = 0, //!< a rectangular structuring element:  \f[E_{ij}=1\f]
-    MORPH_CROSS   = 1, //!< a cross-shaped structuring element:
-                       //!< \f[E_{ij} =  \fork{1}{if i=\texttt{anchor.y} or j=\texttt{anchor.x}}{0}{otherwise}\f]
-    MORPH_ELLIPSE = 2 //!< an elliptic structuring element, that is, a filled ellipse inscribed
-                      //!< into the rectangle Rect(0, 0, esize.width, 0.esize.height)
-};
-
-//! @} imgproc_filter
-
-//! @addtogroup imgproc_transform
-//! @{
-
-//! interpolation algorithm
-enum InterpolationFlags{
-    /** nearest neighbor interpolation */
-    INTER_NEAREST        = 0,
-    /** bilinear interpolation */
-    INTER_LINEAR         = 1,
-    /** bicubic interpolation */
-    INTER_CUBIC          = 2,
-    /** resampling using pixel area relation. It may be a preferred method for image decimation, as
-    it gives moire'-free results. But when the image is zoomed, it is similar to the INTER_NEAREST
-    method. */
-    INTER_AREA           = 3,
-    /** Lanczos interpolation over 8x8 neighborhood */
-    INTER_LANCZOS4       = 4,
-    /** Bit exact bilinear interpolation */
-    INTER_LINEAR_EXACT = 5,
-    /** mask for interpolation codes */
-    INTER_MAX            = 7,
-    /** flag, fills all of the destination image pixels. If some of them correspond to outliers in the
-    source image, they are set to zero */
-    WARP_FILL_OUTLIERS   = 8,
-    /** flag, inverse transformation
-
-    For example, #linearPolar or #logPolar transforms:
-    - flag is __not__ set: \f$dst( \rho , \phi ) = src(x,y)\f$
-    - flag is set: \f$dst(x,y) = src( \rho , \phi )\f$
-    */
-    WARP_INVERSE_MAP     = 16
-};
-
-/** \brief Specify the polar mapping mode
-@sa warpPolar
-*/
-enum WarpPolarMode
-{
-    WARP_POLAR_LINEAR = 0, ///< Remaps an image to/from polar space.
-    WARP_POLAR_LOG = 256   ///< Remaps an image to/from semilog-polar space.
-};
-
-enum InterpolationMasks {
-       INTER_BITS      = 5,
-       INTER_BITS2     = INTER_BITS * 2,
-       INTER_TAB_SIZE  = 1 << INTER_BITS,
-       INTER_TAB_SIZE2 = INTER_TAB_SIZE * INTER_TAB_SIZE
-     };
-
-//! @} imgproc_transform
-
-//! @addtogroup imgproc_misc
-//! @{
-
-//! Distance types for Distance Transform and M-estimators
-//! @see distanceTransform, fitLine
-enum DistanceTypes {
-    DIST_USER    = -1,  //!< User defined distance
-    DIST_L1      = 1,   //!< distance = |x1-x2| + |y1-y2|
-    DIST_L2      = 2,   //!< the simple euclidean distance
-    DIST_C       = 3,   //!< distance = max(|x1-x2|,|y1-y2|)
-    DIST_L12     = 4,   //!< L1-L2 metric: distance = 2(sqrt(1+x*x/2) - 1))
-    DIST_FAIR    = 5,   //!< distance = c^2(|x|/c-log(1+|x|/c)), c = 1.3998
-    DIST_WELSCH  = 6,   //!< distance = c^2/2(1-exp(-(x/c)^2)), c = 2.9846
-    DIST_HUBER   = 7    //!< distance = |x|<c ? x^2/2 : c(|x|-c/2), c=1.345
-};
-
-//! Mask size for distance transform
-enum DistanceTransformMasks {
-    DIST_MASK_3       = 3, //!< mask=3
-    DIST_MASK_5       = 5, //!< mask=5
-    DIST_MASK_PRECISE = 0  //!<
-};
-
-//! type of the threshold operation
-//! ![threshold types](pics/threshold.png)
-enum ThresholdTypes {
-    THRESH_BINARY     = 0, //!< \f[\texttt{dst} (x,y) =  \fork{\texttt{maxval}}{if \(\texttt{src}(x,y) > \texttt{thresh}\)}{0}{otherwise}\f]
-    THRESH_BINARY_INV = 1, //!< \f[\texttt{dst} (x,y) =  \fork{0}{if \(\texttt{src}(x,y) > \texttt{thresh}\)}{\texttt{maxval}}{otherwise}\f]
-    THRESH_TRUNC      = 2, //!< \f[\texttt{dst} (x,y) =  \fork{\texttt{threshold}}{if \(\texttt{src}(x,y) > \texttt{thresh}\)}{\texttt{src}(x,y)}{otherwise}\f]
-    THRESH_TOZERO     = 3, //!< \f[\texttt{dst} (x,y) =  \fork{\texttt{src}(x,y)}{if \(\texttt{src}(x,y) > \texttt{thresh}\)}{0}{otherwise}\f]
-    THRESH_TOZERO_INV = 4, //!< \f[\texttt{dst} (x,y) =  \fork{0}{if \(\texttt{src}(x,y) > \texttt{thresh}\)}{\texttt{src}(x,y)}{otherwise}\f]
-    THRESH_MASK       = 7,
-    THRESH_OTSU       = 8, //!< flag, use Otsu algorithm to choose the optimal threshold value
-    THRESH_TRIANGLE   = 16 //!< flag, use Triangle algorithm to choose the optimal threshold value
-};
-
-//! adaptive threshold algorithm
-//! @see adaptiveThreshold
-enum AdaptiveThresholdTypes {
-    /** the threshold value \f$T(x,y)\f$ is a mean of the \f$\texttt{blockSize} \times
-    \texttt{blockSize}\f$ neighborhood of \f$(x, y)\f$ minus C */
-    ADAPTIVE_THRESH_MEAN_C     = 0,
-    /** the threshold value \f$T(x, y)\f$ is a weighted sum (cross-correlation with a Gaussian
-    window) of the \f$\texttt{blockSize} \times \texttt{blockSize}\f$ neighborhood of \f$(x, y)\f$
-    minus C . The default sigma (standard deviation) is used for the specified blockSize . See
-    #getGaussianKernel*/
-    ADAPTIVE_THRESH_GAUSSIAN_C = 1
-};
-
-//! cv::undistort mode
-enum UndistortTypes {
-       PROJ_SPHERICAL_ORTHO  = 0,
-       PROJ_SPHERICAL_EQRECT = 1
-     };
-
-//! class of the pixel in GrabCut algorithm
-enum GrabCutClasses {
-    GC_BGD    = 0,  //!< an obvious background pixels
-    GC_FGD    = 1,  //!< an obvious foreground (object) pixel
-    GC_PR_BGD = 2,  //!< a possible background pixel
-    GC_PR_FGD = 3   //!< a possible foreground pixel
-};
-
-//! GrabCut algorithm flags
-enum GrabCutModes {
-    /** The function initializes the state and the mask using the provided rectangle. After that it
-    runs iterCount iterations of the algorithm. */
-    GC_INIT_WITH_RECT  = 0,
-    /** The function initializes the state using the provided mask. Note that GC_INIT_WITH_RECT
-    and GC_INIT_WITH_MASK can be combined. Then, all the pixels outside of the ROI are
-    automatically initialized with GC_BGD .*/
-    GC_INIT_WITH_MASK  = 1,
-    /** The value means that the algorithm should just resume. */
-    GC_EVAL            = 2,
-    /** The value means that the algorithm should just run the grabCut algorithm (a single iteration) with the fixed model */
-    GC_EVAL_FREEZE_MODEL = 3
-};
-
-//! distanceTransform algorithm flags
-enum DistanceTransformLabelTypes {
-    /** each connected component of zeros in src (as well as all the non-zero pixels closest to the
-    connected component) will be assigned the same label */
-    DIST_LABEL_CCOMP = 0,
-    /** each zero pixel (and all the non-zero pixels closest to it) gets its own label. */
-    DIST_LABEL_PIXEL = 1
-};
-
-//! floodfill algorithm flags
-enum FloodFillFlags {
-    /** If set, the difference between the current pixel and seed pixel is considered. Otherwise,
-    the difference between neighbor pixels is considered (that is, the range is floating). */
-    FLOODFILL_FIXED_RANGE = 1 << 16,
-    /** If set, the function does not change the image ( newVal is ignored), and only fills the
-    mask with the value specified in bits 8-16 of flags as described above. This option only make
-    sense in function variants that have the mask parameter. */
-    FLOODFILL_MASK_ONLY   = 1 << 17
-};
-
-//! @} imgproc_misc
-
-//! @addtogroup imgproc_shape
-//! @{
-
-//! connected components algorithm output formats
-enum ConnectedComponentsTypes {
-    CC_STAT_LEFT   = 0, //!< The leftmost (x) coordinate which is the inclusive start of the bounding
-                        //!< box in the horizontal direction.
-    CC_STAT_TOP    = 1, //!< The topmost (y) coordinate which is the inclusive start of the bounding
-                        //!< box in the vertical direction.
-    CC_STAT_WIDTH  = 2, //!< The horizontal size of the bounding box
-    CC_STAT_HEIGHT = 3, //!< The vertical size of the bounding box
-    CC_STAT_AREA   = 4, //!< The total area (in pixels) of the connected component
-    CC_STAT_MAX    = 5
-};
-
-//! connected components algorithm
-enum ConnectedComponentsAlgorithmsTypes {
-    CCL_WU      = 0,  //!< SAUF algorithm for 8-way connectivity, SAUF algorithm for 4-way connectivity
-    CCL_DEFAULT = -1, //!< BBDT algorithm for 8-way connectivity, SAUF algorithm for 4-way connectivity
-    CCL_GRANA   = 1   //!< BBDT algorithm for 8-way connectivity, SAUF algorithm for 4-way connectivity
-};
-
-//! mode of the contour retrieval algorithm
-enum RetrievalModes {
-    /** retrieves only the extreme outer contours. It sets `hierarchy[i][2]=hierarchy[i][3]=-1` for
-    all the contours. */
-    RETR_EXTERNAL  = 0,
-    /** retrieves all of the contours without establishing any hierarchical relationships. */
-    RETR_LIST      = 1,
-    /** retrieves all of the contours and organizes them into a two-level hierarchy. At the top
-    level, there are external boundaries of the components. At the second level, there are
-    boundaries of the holes. If there is another contour inside a hole of a connected component, it
-    is still put at the top level. */
-    RETR_CCOMP     = 2,
-    /** retrieves all of the contours and reconstructs a full hierarchy of nested contours.*/
-    RETR_TREE      = 3,
-    RETR_FLOODFILL = 4 //!<
-};
-
-//! the contour approximation algorithm
-enum ContourApproximationModes {
-    /** stores absolutely all the contour points. That is, any 2 subsequent points (x1,y1) and
-    (x2,y2) of the contour will be either horizontal, vertical or diagonal neighbors, that is,
-    max(abs(x1-x2),abs(y2-y1))==1. */
-    CHAIN_APPROX_NONE      = 1,
-    /** compresses horizontal, vertical, and diagonal segments and leaves only their end points.
-    For example, an up-right rectangular contour is encoded with 4 points. */
-    CHAIN_APPROX_SIMPLE    = 2,
-    /** applies one of the flavors of the Teh-Chin chain approximation algorithm @cite TehChin89 */
-    CHAIN_APPROX_TC89_L1   = 3,
-    /** applies one of the flavors of the Teh-Chin chain approximation algorithm @cite TehChin89 */
-    CHAIN_APPROX_TC89_KCOS = 4
-};
-
-/** @brief Shape matching methods
-
-\f$A\f$ denotes object1,\f$B\f$ denotes object2
-
-\f$\begin{array}{l} m^A_i =  \mathrm{sign} (h^A_i)  \cdot \log{h^A_i} \\ m^B_i =  \mathrm{sign} (h^B_i)  \cdot \log{h^B_i} \end{array}\f$
-
-and \f$h^A_i, h^B_i\f$ are the Hu moments of \f$A\f$ and \f$B\f$ , respectively.
-*/
-enum ShapeMatchModes {
-    CONTOURS_MATCH_I1  =1, //!< \f[I_1(A,B) =  \sum _{i=1...7}  \left |  \frac{1}{m^A_i} -  \frac{1}{m^B_i} \right |\f]
-    CONTOURS_MATCH_I2  =2, //!< \f[I_2(A,B) =  \sum _{i=1...7}  \left | m^A_i - m^B_i  \right |\f]
-    CONTOURS_MATCH_I3  =3  //!< \f[I_3(A,B) =  \max _{i=1...7}  \frac{ \left| m^A_i - m^B_i \right| }{ \left| m^A_i \right| }\f]
-};
-
-//! @} imgproc_shape
-
-//! Variants of a Hough transform
-enum HoughModes {
-
-    /** classical or standard Hough transform. Every line is represented by two floating-point
-    numbers \f$(\rho, \theta)\f$ , where \f$\rho\f$ is a distance between (0,0) point and the line,
-    and \f$\theta\f$ is the angle between x-axis and the normal to the line. Thus, the matrix must
-    be (the created sequence will be) of CV_32FC2 type */
-    HOUGH_STANDARD      = 0,
-    /** probabilistic Hough transform (more efficient in case if the picture contains a few long
-    linear segments). It returns line segments rather than the whole line. Each segment is
-    represented by starting and ending points, and the matrix must be (the created sequence will
-    be) of the CV_32SC4 type. */
-    HOUGH_PROBABILISTIC = 1,
-    /** multi-scale variant of the classical Hough transform. The lines are encoded the same way as
-    HOUGH_STANDARD. */
-    HOUGH_MULTI_SCALE   = 2,
-    HOUGH_GRADIENT      = 3 //!< basically *21HT*, described in @cite Yuen90
-};
-
-//! Variants of Line Segment %Detector
-//! @ingroup imgproc_feature
-enum LineSegmentDetectorModes {
-    LSD_REFINE_NONE = 0, //!< No refinement applied
-    LSD_REFINE_STD  = 1, //!< Standard refinement is applied. E.g. breaking arches into smaller straighter line approximations.
-    LSD_REFINE_ADV  = 2  //!< Advanced refinement. Number of false alarms is calculated, lines are
-                         //!< refined through increase of precision, decrement in size, etc.
-};
-
-/** Histogram comparison methods
-  @ingroup imgproc_hist
-*/
-enum HistCompMethods {
-    /** Correlation
-    \f[d(H_1,H_2) =  \frac{\sum_I (H_1(I) - \bar{H_1}) (H_2(I) - \bar{H_2})}{\sqrt{\sum_I(H_1(I) - \bar{H_1})^2 \sum_I(H_2(I) - \bar{H_2})^2}}\f]
-    where
-    \f[\bar{H_k} =  \frac{1}{N} \sum _J H_k(J)\f]
-    and \f$N\f$ is a total number of histogram bins. */
-    HISTCMP_CORREL        = 0,
-    /** Chi-Square
-    \f[d(H_1,H_2) =  \sum _I  \frac{\left(H_1(I)-H_2(I)\right)^2}{H_1(I)}\f] */
-    HISTCMP_CHISQR        = 1,
-    /** Intersection
-    \f[d(H_1,H_2) =  \sum _I  \min (H_1(I), H_2(I))\f] */
-    HISTCMP_INTERSECT     = 2,
-    /** Bhattacharyya distance
-    (In fact, OpenCV computes Hellinger distance, which is related to Bhattacharyya coefficient.)
-    \f[d(H_1,H_2) =  \sqrt{1 - \frac{1}{\sqrt{\bar{H_1} \bar{H_2} N^2}} \sum_I \sqrt{H_1(I) \cdot H_2(I)}}\f] */
-    HISTCMP_BHATTACHARYYA = 3,
-    HISTCMP_HELLINGER     = HISTCMP_BHATTACHARYYA, //!< Synonym for HISTCMP_BHATTACHARYYA
-    /** Alternative Chi-Square
-    \f[d(H_1,H_2) =  2 * \sum _I  \frac{\left(H_1(I)-H_2(I)\right)^2}{H_1(I)+H_2(I)}\f]
-    This alternative formula is regularly used for texture comparison. See e.g. @cite Puzicha1997 */
-    HISTCMP_CHISQR_ALT    = 4,
-    /** Kullback-Leibler divergence
-    \f[d(H_1,H_2) = \sum _I H_1(I) \log \left(\frac{H_1(I)}{H_2(I)}\right)\f] */
-    HISTCMP_KL_DIV        = 5
-};
-
-/** the color conversion code
-@see @ref imgproc_color_conversions
-@ingroup imgproc_misc
- */
-enum ColorConversionCodes {
-    COLOR_BGR2BGRA     = 0, //!< add alpha channel to RGB or BGR image
-    COLOR_RGB2RGBA     = COLOR_BGR2BGRA,
-
-    COLOR_BGRA2BGR     = 1, //!< remove alpha channel from RGB or BGR image
-    COLOR_RGBA2RGB     = COLOR_BGRA2BGR,
-
-    COLOR_BGR2RGBA     = 2, //!< convert between RGB and BGR color spaces (with or without alpha channel)
-    COLOR_RGB2BGRA     = COLOR_BGR2RGBA,
-
-    COLOR_RGBA2BGR     = 3,
-    COLOR_BGRA2RGB     = COLOR_RGBA2BGR,
-
-    COLOR_BGR2RGB      = 4,
-    COLOR_RGB2BGR      = COLOR_BGR2RGB,
-
-    COLOR_BGRA2RGBA    = 5,
-    COLOR_RGBA2BGRA    = COLOR_BGRA2RGBA,
-
-    COLOR_BGR2GRAY     = 6, //!< convert between RGB/BGR and grayscale, @ref color_convert_rgb_gray "color conversions"
-    COLOR_RGB2GRAY     = 7,
-    COLOR_GRAY2BGR     = 8,
-    COLOR_GRAY2RGB     = COLOR_GRAY2BGR,
-    COLOR_GRAY2BGRA    = 9,
-    COLOR_GRAY2RGBA    = COLOR_GRAY2BGRA,
-    COLOR_BGRA2GRAY    = 10,
-    COLOR_RGBA2GRAY    = 11,
-
-    COLOR_BGR2BGR565   = 12, //!< convert between RGB/BGR and BGR565 (16-bit images)
-    COLOR_RGB2BGR565   = 13,
-    COLOR_BGR5652BGR   = 14,
-    COLOR_BGR5652RGB   = 15,
-    COLOR_BGRA2BGR565  = 16,
-    COLOR_RGBA2BGR565  = 17,
-    COLOR_BGR5652BGRA  = 18,
-    COLOR_BGR5652RGBA  = 19,
-
-    COLOR_GRAY2BGR565  = 20, //!< convert between grayscale to BGR565 (16-bit images)
-    COLOR_BGR5652GRAY  = 21,
-
-    COLOR_BGR2BGR555   = 22,  //!< convert between RGB/BGR and BGR555 (16-bit images)
-    COLOR_RGB2BGR555   = 23,
-    COLOR_BGR5552BGR   = 24,
-    COLOR_BGR5552RGB   = 25,
-    COLOR_BGRA2BGR555  = 26,
-    COLOR_RGBA2BGR555  = 27,
-    COLOR_BGR5552BGRA  = 28,
-    COLOR_BGR5552RGBA  = 29,
-
-    COLOR_GRAY2BGR555  = 30, //!< convert between grayscale and BGR555 (16-bit images)
-    COLOR_BGR5552GRAY  = 31,
-
-    COLOR_BGR2XYZ      = 32, //!< convert RGB/BGR to CIE XYZ, @ref color_convert_rgb_xyz "color conversions"
-    COLOR_RGB2XYZ      = 33,
-    COLOR_XYZ2BGR      = 34,
-    COLOR_XYZ2RGB      = 35,
-
-    COLOR_BGR2YCrCb    = 36, //!< convert RGB/BGR to luma-chroma (aka YCC), @ref color_convert_rgb_ycrcb "color conversions"
-    COLOR_RGB2YCrCb    = 37,
-    COLOR_YCrCb2BGR    = 38,
-    COLOR_YCrCb2RGB    = 39,
-
-    COLOR_BGR2HSV      = 40, //!< convert RGB/BGR to HSV (hue saturation value), @ref color_convert_rgb_hsv "color conversions"
-    COLOR_RGB2HSV      = 41,
-
-    COLOR_BGR2Lab      = 44, //!< convert RGB/BGR to CIE Lab, @ref color_convert_rgb_lab "color conversions"
-    COLOR_RGB2Lab      = 45,
-
-    COLOR_BGR2Luv      = 50, //!< convert RGB/BGR to CIE Luv, @ref color_convert_rgb_luv "color conversions"
-    COLOR_RGB2Luv      = 51,
-    COLOR_BGR2HLS      = 52, //!< convert RGB/BGR to HLS (hue lightness saturation), @ref color_convert_rgb_hls "color conversions"
-    COLOR_RGB2HLS      = 53,
-
-    COLOR_HSV2BGR      = 54, //!< backward conversions to RGB/BGR
-    COLOR_HSV2RGB      = 55,
-
-    COLOR_Lab2BGR      = 56,
-    COLOR_Lab2RGB      = 57,
-    COLOR_Luv2BGR      = 58,
-    COLOR_Luv2RGB      = 59,
-    COLOR_HLS2BGR      = 60,
-    COLOR_HLS2RGB      = 61,
-
-    COLOR_BGR2HSV_FULL = 66, //!<
-    COLOR_RGB2HSV_FULL = 67,
-    COLOR_BGR2HLS_FULL = 68,
-    COLOR_RGB2HLS_FULL = 69,
-
-    COLOR_HSV2BGR_FULL = 70,
-    COLOR_HSV2RGB_FULL = 71,
-    COLOR_HLS2BGR_FULL = 72,
-    COLOR_HLS2RGB_FULL = 73,
-
-    COLOR_LBGR2Lab     = 74,
-    COLOR_LRGB2Lab     = 75,
-    COLOR_LBGR2Luv     = 76,
-    COLOR_LRGB2Luv     = 77,
-
-    COLOR_Lab2LBGR     = 78,
-    COLOR_Lab2LRGB     = 79,
-    COLOR_Luv2LBGR     = 80,
-    COLOR_Luv2LRGB     = 81,
-
-    COLOR_BGR2YUV      = 82, //!< convert between RGB/BGR and YUV
-    COLOR_RGB2YUV      = 83,
-    COLOR_YUV2BGR      = 84,
-    COLOR_YUV2RGB      = 85,
-
-    //! YUV 4:2:0 family to RGB
-    COLOR_YUV2RGB_NV12  = 90,
-    COLOR_YUV2BGR_NV12  = 91,
-    COLOR_YUV2RGB_NV21  = 92,
-    COLOR_YUV2BGR_NV21  = 93,
-    COLOR_YUV420sp2RGB  = COLOR_YUV2RGB_NV21,
-    COLOR_YUV420sp2BGR  = COLOR_YUV2BGR_NV21,
-
-    COLOR_YUV2RGBA_NV12 = 94,
-    COLOR_YUV2BGRA_NV12 = 95,
-    COLOR_YUV2RGBA_NV21 = 96,
-    COLOR_YUV2BGRA_NV21 = 97,
-    COLOR_YUV420sp2RGBA = COLOR_YUV2RGBA_NV21,
-    COLOR_YUV420sp2BGRA = COLOR_YUV2BGRA_NV21,
-
-    COLOR_YUV2RGB_YV12  = 98,
-    COLOR_YUV2BGR_YV12  = 99,
-    COLOR_YUV2RGB_IYUV  = 100,
-    COLOR_YUV2BGR_IYUV  = 101,
-    COLOR_YUV2RGB_I420  = COLOR_YUV2RGB_IYUV,
-    COLOR_YUV2BGR_I420  = COLOR_YUV2BGR_IYUV,
-    COLOR_YUV420p2RGB   = COLOR_YUV2RGB_YV12,
-    COLOR_YUV420p2BGR   = COLOR_YUV2BGR_YV12,
-
-    COLOR_YUV2RGBA_YV12 = 102,
-    COLOR_YUV2BGRA_YV12 = 103,
-    COLOR_YUV2RGBA_IYUV = 104,
-    COLOR_YUV2BGRA_IYUV = 105,
-    COLOR_YUV2RGBA_I420 = COLOR_YUV2RGBA_IYUV,
-    COLOR_YUV2BGRA_I420 = COLOR_YUV2BGRA_IYUV,
-    COLOR_YUV420p2RGBA  = COLOR_YUV2RGBA_YV12,
-    COLOR_YUV420p2BGRA  = COLOR_YUV2BGRA_YV12,
-
-    COLOR_YUV2GRAY_420  = 106,
-    COLOR_YUV2GRAY_NV21 = COLOR_YUV2GRAY_420,
-    COLOR_YUV2GRAY_NV12 = COLOR_YUV2GRAY_420,
-    COLOR_YUV2GRAY_YV12 = COLOR_YUV2GRAY_420,
-    COLOR_YUV2GRAY_IYUV = COLOR_YUV2GRAY_420,
-    COLOR_YUV2GRAY_I420 = COLOR_YUV2GRAY_420,
-    COLOR_YUV420sp2GRAY = COLOR_YUV2GRAY_420,
-    COLOR_YUV420p2GRAY  = COLOR_YUV2GRAY_420,
-
-    //! YUV 4:2:2 family to RGB
-    COLOR_YUV2RGB_UYVY = 107,
-    COLOR_YUV2BGR_UYVY = 108,
-    //COLOR_YUV2RGB_VYUY = 109,
-    //COLOR_YUV2BGR_VYUY = 110,
-    COLOR_YUV2RGB_Y422 = COLOR_YUV2RGB_UYVY,
-    COLOR_YUV2BGR_Y422 = COLOR_YUV2BGR_UYVY,
-    COLOR_YUV2RGB_UYNV = COLOR_YUV2RGB_UYVY,
-    COLOR_YUV2BGR_UYNV = COLOR_YUV2BGR_UYVY,
-
-    COLOR_YUV2RGBA_UYVY = 111,
-    COLOR_YUV2BGRA_UYVY = 112,
-    //COLOR_YUV2RGBA_VYUY = 113,
-    //COLOR_YUV2BGRA_VYUY = 114,
-    COLOR_YUV2RGBA_Y422 = COLOR_YUV2RGBA_UYVY,
-    COLOR_YUV2BGRA_Y422 = COLOR_YUV2BGRA_UYVY,
-    COLOR_YUV2RGBA_UYNV = COLOR_YUV2RGBA_UYVY,
-    COLOR_YUV2BGRA_UYNV = COLOR_YUV2BGRA_UYVY,
-
-    COLOR_YUV2RGB_YUY2 = 115,
-    COLOR_YUV2BGR_YUY2 = 116,
-    COLOR_YUV2RGB_YVYU = 117,
-    COLOR_YUV2BGR_YVYU = 118,
-    COLOR_YUV2RGB_YUYV = COLOR_YUV2RGB_YUY2,
-    COLOR_YUV2BGR_YUYV = COLOR_YUV2BGR_YUY2,
-    COLOR_YUV2RGB_YUNV = COLOR_YUV2RGB_YUY2,
-    COLOR_YUV2BGR_YUNV = COLOR_YUV2BGR_YUY2,
-
-    COLOR_YUV2RGBA_YUY2 = 119,
-    COLOR_YUV2BGRA_YUY2 = 120,
-    COLOR_YUV2RGBA_YVYU = 121,
-    COLOR_YUV2BGRA_YVYU = 122,
-    COLOR_YUV2RGBA_YUYV = COLOR_YUV2RGBA_YUY2,
-    COLOR_YUV2BGRA_YUYV = COLOR_YUV2BGRA_YUY2,
-    COLOR_YUV2RGBA_YUNV = COLOR_YUV2RGBA_YUY2,
-    COLOR_YUV2BGRA_YUNV = COLOR_YUV2BGRA_YUY2,
-
-    COLOR_YUV2GRAY_UYVY = 123,
-    COLOR_YUV2GRAY_YUY2 = 124,
-    //CV_YUV2GRAY_VYUY    = CV_YUV2GRAY_UYVY,
-    COLOR_YUV2GRAY_Y422 = COLOR_YUV2GRAY_UYVY,
-    COLOR_YUV2GRAY_UYNV = COLOR_YUV2GRAY_UYVY,
-    COLOR_YUV2GRAY_YVYU = COLOR_YUV2GRAY_YUY2,
-    COLOR_YUV2GRAY_YUYV = COLOR_YUV2GRAY_YUY2,
-    COLOR_YUV2GRAY_YUNV = COLOR_YUV2GRAY_YUY2,
-
-    //! alpha premultiplication
-    COLOR_RGBA2mRGBA    = 125,
-    COLOR_mRGBA2RGBA    = 126,
-
-    //! RGB to YUV 4:2:0 family
-    COLOR_RGB2YUV_I420  = 127,
-    COLOR_BGR2YUV_I420  = 128,
-    COLOR_RGB2YUV_IYUV  = COLOR_RGB2YUV_I420,
-    COLOR_BGR2YUV_IYUV  = COLOR_BGR2YUV_I420,
-
-    COLOR_RGBA2YUV_I420 = 129,
-    COLOR_BGRA2YUV_I420 = 130,
-    COLOR_RGBA2YUV_IYUV = COLOR_RGBA2YUV_I420,
-    COLOR_BGRA2YUV_IYUV = COLOR_BGRA2YUV_I420,
-    COLOR_RGB2YUV_YV12  = 131,
-    COLOR_BGR2YUV_YV12  = 132,
-    COLOR_RGBA2YUV_YV12 = 133,
-    COLOR_BGRA2YUV_YV12 = 134,
-
-    //! Demosaicing
-    COLOR_BayerBG2BGR = 46,
-    COLOR_BayerGB2BGR = 47,
-    COLOR_BayerRG2BGR = 48,
-    COLOR_BayerGR2BGR = 49,
-
-    COLOR_BayerBG2RGB = COLOR_BayerRG2BGR,
-    COLOR_BayerGB2RGB = COLOR_BayerGR2BGR,
-    COLOR_BayerRG2RGB = COLOR_BayerBG2BGR,
-    COLOR_BayerGR2RGB = COLOR_BayerGB2BGR,
-
-    COLOR_BayerBG2GRAY = 86,
-    COLOR_BayerGB2GRAY = 87,
-    COLOR_BayerRG2GRAY = 88,
-    COLOR_BayerGR2GRAY = 89,
-
-    //! Demosaicing using Variable Number of Gradients
-    COLOR_BayerBG2BGR_VNG = 62,
-    COLOR_BayerGB2BGR_VNG = 63,
-    COLOR_BayerRG2BGR_VNG = 64,
-    COLOR_BayerGR2BGR_VNG = 65,
-
-    COLOR_BayerBG2RGB_VNG = COLOR_BayerRG2BGR_VNG,
-    COLOR_BayerGB2RGB_VNG = COLOR_BayerGR2BGR_VNG,
-    COLOR_BayerRG2RGB_VNG = COLOR_BayerBG2BGR_VNG,
-    COLOR_BayerGR2RGB_VNG = COLOR_BayerGB2BGR_VNG,
-
-    //! Edge-Aware Demosaicing
-    COLOR_BayerBG2BGR_EA  = 135,
-    COLOR_BayerGB2BGR_EA  = 136,
-    COLOR_BayerRG2BGR_EA  = 137,
-    COLOR_BayerGR2BGR_EA  = 138,
-
-    COLOR_BayerBG2RGB_EA  = COLOR_BayerRG2BGR_EA,
-    COLOR_BayerGB2RGB_EA  = COLOR_BayerGR2BGR_EA,
-    COLOR_BayerRG2RGB_EA  = COLOR_BayerBG2BGR_EA,
-    COLOR_BayerGR2RGB_EA  = COLOR_BayerGB2BGR_EA,
-
-    //! Demosaicing with alpha channel
-    COLOR_BayerBG2BGRA = 139,
-    COLOR_BayerGB2BGRA = 140,
-    COLOR_BayerRG2BGRA = 141,
-    COLOR_BayerGR2BGRA = 142,
-
-    COLOR_BayerBG2RGBA = COLOR_BayerRG2BGRA,
-    COLOR_BayerGB2RGBA = COLOR_BayerGR2BGRA,
-    COLOR_BayerRG2RGBA = COLOR_BayerBG2BGRA,
-    COLOR_BayerGR2RGBA = COLOR_BayerGB2BGRA,
-
-    COLOR_COLORCVT_MAX  = 143
-};
-
-/** types of intersection between rectangles
-@ingroup imgproc_shape
-*/
-enum RectanglesIntersectTypes {
-    INTERSECT_NONE = 0, //!< No intersection
-    INTERSECT_PARTIAL  = 1, //!< There is a partial intersection
-    INTERSECT_FULL  = 2 //!< One of the rectangle is fully enclosed in the other
-};
-
-//! finds arbitrary template in the grayscale image using Generalized Hough Transform
-class CV_EXPORTS GeneralizedHough : public Algorithm
-{
-public:
-    //! set template to search
-    virtual void setTemplate(InputArray templ, Point templCenter = Point(-1, -1)) = 0;
-    virtual void setTemplate(InputArray edges, InputArray dx, InputArray dy, Point templCenter = Point(-1, -1)) = 0;
-
-    //! find template on image
-    virtual void detect(InputArray image, OutputArray positions, OutputArray votes = noArray()) = 0;
-    virtual void detect(InputArray edges, InputArray dx, InputArray dy, OutputArray positions, OutputArray votes = noArray()) = 0;
-
-    //! Canny low threshold.
-    virtual void setCannyLowThresh(int cannyLowThresh) = 0;
-    virtual int getCannyLowThresh() const = 0;
-
-    //! Canny high threshold.
-    virtual void setCannyHighThresh(int cannyHighThresh) = 0;
-    virtual int getCannyHighThresh() const = 0;
-
-    //! Minimum distance between the centers of the detected objects.
-    virtual void setMinDist(double minDist) = 0;
-    virtual double getMinDist() const = 0;
-
-    //! Inverse ratio of the accumulator resolution to the image resolution.
-    virtual void setDp(double dp) = 0;
-    virtual double getDp() const = 0;
-
-    //! Maximal size of inner buffers.
-    virtual void setMaxBufferSize(int maxBufferSize) = 0;
-    virtual int getMaxBufferSize() const = 0;
-};
-
-//! Ballard, D.H. (1981). Generalizing the Hough transform to detect arbitrary shapes. Pattern Recognition 13 (2): 111-122.
-//! Detects position only without translation and rotation
-class CV_EXPORTS GeneralizedHoughBallard : public GeneralizedHough
-{
-public:
-    //! R-Table levels.
-    virtual void setLevels(int levels) = 0;
-    virtual int getLevels() const = 0;
-
-    //! The accumulator threshold for the template centers at the detection stage. The smaller it is, the more false positions may be detected.
-    virtual void setVotesThreshold(int votesThreshold) = 0;
-    virtual int getVotesThreshold() const = 0;
-};
-
-//! Guil, N., González-Linares, J.M. and Zapata, E.L. (1999). Bidimensional shape detection using an invariant approach. Pattern Recognition 32 (6): 1025-1038.
-//! Detects position, translation and rotation
-class CV_EXPORTS GeneralizedHoughGuil : public GeneralizedHough
-{
-public:
-    //! Angle difference in degrees between two points in feature.
-    virtual void setXi(double xi) = 0;
-    virtual double getXi() const = 0;
-
-    //! Feature table levels.
-    virtual void setLevels(int levels) = 0;
-    virtual int getLevels() const = 0;
-
-    //! Maximal difference between angles that treated as equal.
-    virtual void setAngleEpsilon(double angleEpsilon) = 0;
-    virtual double getAngleEpsilon() const = 0;
-
-    //! Minimal rotation angle to detect in degrees.
-    virtual void setMinAngle(double minAngle) = 0;
-    virtual double getMinAngle() const = 0;
-
-    //! Maximal rotation angle to detect in degrees.
-    virtual void setMaxAngle(double maxAngle) = 0;
-    virtual double getMaxAngle() const = 0;
-
-    //! Angle step in degrees.
-    virtual void setAngleStep(double angleStep) = 0;
-    virtual double getAngleStep() const = 0;
-
-    //! Angle votes threshold.
-    virtual void setAngleThresh(int angleThresh) = 0;
-    virtual int getAngleThresh() const = 0;
-
-    //! Minimal scale to detect.
-    virtual void setMinScale(double minScale) = 0;
-    virtual double getMinScale() const = 0;
-
-    //! Maximal scale to detect.
-    virtual void setMaxScale(double maxScale) = 0;
-    virtual double getMaxScale() const = 0;
-
-    //! Scale step.
-    virtual void setScaleStep(double scaleStep) = 0;
-    virtual double getScaleStep() const = 0;
-
-    //! Scale votes threshold.
-    virtual void setScaleThresh(int scaleThresh) = 0;
-    virtual int getScaleThresh() const = 0;
-
-    //! Position votes threshold.
-    virtual void setPosThresh(int posThresh) = 0;
-    virtual int getPosThresh() const = 0;
-};
-
-/** @brief Base class for Contrast Limited Adaptive Histogram Equalization. :
- */
-class CV_EXPORTS_W CLAHE : public Algorithm
-{
-public:
-    /** @brief Equalizes the histogram of a grayscale image using Contrast Limited Adaptive Histogram Equalization.
-
-    @param src Source image with CV_8UC1 type.
-    @param dst Destination image.
-     */
-    CV_WRAP virtual void apply(InputArray src, OutputArray dst) = 0;
-
-    /** @brief Sets threshold for contrast limiting.
-
-    @param clipLimit threshold value.
-    */
-    CV_WRAP virtual void setClipLimit(double clipLimit) = 0;
-
-    //! Returns threshold value for contrast limiting.
-    CV_WRAP virtual double getClipLimit() const = 0;
-
-    /** @brief Sets size of grid for histogram equalization. Input image will be divided into
-    equally sized rectangular tiles.
-
-    @param tileGridSize defines the number of tiles in row and column.
-    */
-    CV_WRAP virtual void setTilesGridSize(Size tileGridSize) = 0;
-
-    //!@brief Returns Size defines the number of tiles in row and column.
-    CV_WRAP virtual Size getTilesGridSize() const = 0;
-
-    CV_WRAP virtual void collectGarbage() = 0;
-};
-
-
-//! @addtogroup imgproc_subdiv2d
-//! @{
-
-class CV_EXPORTS_W Subdiv2D
-{
-public:
-    /** Subdiv2D point location cases */
-    enum { PTLOC_ERROR        = -2, //!< Point location error
-           PTLOC_OUTSIDE_RECT = -1, //!< Point outside the subdivision bounding rect
-           PTLOC_INSIDE       = 0, //!< Point inside some facet
-           PTLOC_VERTEX       = 1, //!< Point coincides with one of the subdivision vertices
-           PTLOC_ON_EDGE      = 2  //!< Point on some edge
-         };
-
-    /** Subdiv2D edge type navigation (see: getEdge()) */
-    enum { NEXT_AROUND_ORG   = 0x00,
-           NEXT_AROUND_DST   = 0x22,
-           PREV_AROUND_ORG   = 0x11,
-           PREV_AROUND_DST   = 0x33,
-           NEXT_AROUND_LEFT  = 0x13,
-           NEXT_AROUND_RIGHT = 0x31,
-           PREV_AROUND_LEFT  = 0x20,
-           PREV_AROUND_RIGHT = 0x02
-         };
-
-    /** creates an empty Subdiv2D object.
-    To create a new empty Delaunay subdivision you need to use the #initDelaunay function.
-     */
-    CV_WRAP Subdiv2D();
-
-    /** @overload
-
-    @param rect Rectangle that includes all of the 2D points that are to be added to the subdivision.
-
-    The function creates an empty Delaunay subdivision where 2D points can be added using the function
-    insert() . All of the points to be added must be within the specified rectangle, otherwise a runtime
-    error is raised.
-     */
-    CV_WRAP Subdiv2D(Rect rect);
-
-    /** @brief Creates a new empty Delaunay subdivision
-
-    @param rect Rectangle that includes all of the 2D points that are to be added to the subdivision.
-
-     */
-    CV_WRAP void initDelaunay(Rect rect);
-
-    /** @brief Insert a single point into a Delaunay triangulation.
-
-    @param pt Point to insert.
-
-    The function inserts a single point into a subdivision and modifies the subdivision topology
-    appropriately. If a point with the same coordinates exists already, no new point is added.
-    @returns the ID of the point.
-
-    @note If the point is outside of the triangulation specified rect a runtime error is raised.
-     */
-    CV_WRAP int insert(Point2f pt);
-
-    /** @brief Insert multiple points into a Delaunay triangulation.
-
-    @param ptvec Points to insert.
-
-    The function inserts a vector of points into a subdivision and modifies the subdivision topology
-    appropriately.
-     */
-    CV_WRAP void insert(const std::vector<Point2f>& ptvec);
-
-    /** @brief Returns the location of a point within a Delaunay triangulation.
-
-    @param pt Point to locate.
-    @param edge Output edge that the point belongs to or is located to the right of it.
-    @param vertex Optional output vertex the input point coincides with.
-
-    The function locates the input point within the subdivision and gives one of the triangle edges
-    or vertices.
-
-    @returns an integer which specify one of the following five cases for point location:
-    -  The point falls into some facet. The function returns #PTLOC_INSIDE and edge will contain one of
-       edges of the facet.
-    -  The point falls onto the edge. The function returns #PTLOC_ON_EDGE and edge will contain this edge.
-    -  The point coincides with one of the subdivision vertices. The function returns #PTLOC_VERTEX and
-       vertex will contain a pointer to the vertex.
-    -  The point is outside the subdivision reference rectangle. The function returns #PTLOC_OUTSIDE_RECT
-       and no pointers are filled.
-    -  One of input arguments is invalid. A runtime error is raised or, if silent or "parent" error
-       processing mode is selected, #PTLOC_ERROR is returned.
-     */
-    CV_WRAP int locate(Point2f pt, CV_OUT int& edge, CV_OUT int& vertex);
-
-    /** @brief Finds the subdivision vertex closest to the given point.
-
-    @param pt Input point.
-    @param nearestPt Output subdivision vertex point.
-
-    The function is another function that locates the input point within the subdivision. It finds the
-    subdivision vertex that is the closest to the input point. It is not necessarily one of vertices
-    of the facet containing the input point, though the facet (located using locate() ) is used as a
-    starting point.
-
-    @returns vertex ID.
-     */
-    CV_WRAP int findNearest(Point2f pt, CV_OUT Point2f* nearestPt = 0);
-
-    /** @brief Returns a list of all edges.
-
-    @param edgeList Output vector.
-
-    The function gives each edge as a 4 numbers vector, where each two are one of the edge
-    vertices. i.e. org_x = v[0], org_y = v[1], dst_x = v[2], dst_y = v[3].
-     */
-    CV_WRAP void getEdgeList(CV_OUT std::vector<Vec4f>& edgeList) const;
-
-    /** @brief Returns a list of the leading edge ID connected to each triangle.
-
-    @param leadingEdgeList Output vector.
-
-    The function gives one edge ID for each triangle.
-     */
-    CV_WRAP void getLeadingEdgeList(CV_OUT std::vector<int>& leadingEdgeList) const;
-
-    /** @brief Returns a list of all triangles.
-
-    @param triangleList Output vector.
-
-    The function gives each triangle as a 6 numbers vector, where each two are one of the triangle
-    vertices. i.e. p1_x = v[0], p1_y = v[1], p2_x = v[2], p2_y = v[3], p3_x = v[4], p3_y = v[5].
-     */
-    CV_WRAP void getTriangleList(CV_OUT std::vector<Vec6f>& triangleList) const;
-
-    /** @brief Returns a list of all Voroni facets.
-
-    @param idx Vector of vertices IDs to consider. For all vertices you can pass empty vector.
-    @param facetList Output vector of the Voroni facets.
-    @param facetCenters Output vector of the Voroni facets center points.
-
-     */
-    CV_WRAP void getVoronoiFacetList(const std::vector<int>& idx, CV_OUT std::vector<std::vector<Point2f> >& facetList,
-                                     CV_OUT std::vector<Point2f>& facetCenters);
-
-    /** @brief Returns vertex location from vertex ID.
-
-    @param vertex vertex ID.
-    @param firstEdge Optional. The first edge ID which is connected to the vertex.
-    @returns vertex (x,y)
-
-     */
-    CV_WRAP Point2f getVertex(int vertex, CV_OUT int* firstEdge = 0) const;
-
-    /** @brief Returns one of the edges related to the given edge.
-
-    @param edge Subdivision edge ID.
-    @param nextEdgeType Parameter specifying which of the related edges to return.
-    The following values are possible:
-    -   NEXT_AROUND_ORG next around the edge origin ( eOnext on the picture below if e is the input edge)
-    -   NEXT_AROUND_DST next around the edge vertex ( eDnext )
-    -   PREV_AROUND_ORG previous around the edge origin (reversed eRnext )
-    -   PREV_AROUND_DST previous around the edge destination (reversed eLnext )
-    -   NEXT_AROUND_LEFT next around the left facet ( eLnext )
-    -   NEXT_AROUND_RIGHT next around the right facet ( eRnext )
-    -   PREV_AROUND_LEFT previous around the left facet (reversed eOnext )
-    -   PREV_AROUND_RIGHT previous around the right facet (reversed eDnext )
-
-    ![sample output](pics/quadedge.png)
-
-    @returns edge ID related to the input edge.
-     */
-    CV_WRAP int getEdge( int edge, int nextEdgeType ) const;
-
-    /** @brief Returns next edge around the edge origin.
-
-    @param edge Subdivision edge ID.
-
-    @returns an integer which is next edge ID around the edge origin: eOnext on the
-    picture above if e is the input edge).
-     */
-    CV_WRAP int nextEdge(int edge) const;
-
-    /** @brief Returns another edge of the same quad-edge.
-
-    @param edge Subdivision edge ID.
-    @param rotate Parameter specifying which of the edges of the same quad-edge as the input
-    one to return. The following values are possible:
-    -   0 - the input edge ( e on the picture below if e is the input edge)
-    -   1 - the rotated edge ( eRot )
-    -   2 - the reversed edge (reversed e (in green))
-    -   3 - the reversed rotated edge (reversed eRot (in green))
-
-    @returns one of the edges ID of the same quad-edge as the input edge.
-     */
-    CV_WRAP int rotateEdge(int edge, int rotate) const;
-    CV_WRAP int symEdge(int edge) const;
-
-    /** @brief Returns the edge origin.
-
-    @param edge Subdivision edge ID.
-    @param orgpt Output vertex location.
-
-    @returns vertex ID.
-     */
-    CV_WRAP int edgeOrg(int edge, CV_OUT Point2f* orgpt = 0) const;
-
-    /** @brief Returns the edge destination.
-
-    @param edge Subdivision edge ID.
-    @param dstpt Output vertex location.
-
-    @returns vertex ID.
-     */
-    CV_WRAP int edgeDst(int edge, CV_OUT Point2f* dstpt = 0) const;
-
-protected:
-    int newEdge();
-    void deleteEdge(int edge);
-    int newPoint(Point2f pt, bool isvirtual, int firstEdge = 0);
-    void deletePoint(int vtx);
-    void setEdgePoints( int edge, int orgPt, int dstPt );
-    void splice( int edgeA, int edgeB );
-    int connectEdges( int edgeA, int edgeB );
-    void swapEdges( int edge );
-    int isRightOf(Point2f pt, int edge) const;
-    void calcVoronoi();
-    void clearVoronoi();
-    void checkSubdiv() const;
-
-    struct CV_EXPORTS Vertex
-    {
-        Vertex();
-        Vertex(Point2f pt, bool _isvirtual, int _firstEdge=0);
-        bool isvirtual() const;
-        bool isfree() const;
-
-        int firstEdge;
-        int type;
-        Point2f pt;
-    };
-
-    struct CV_EXPORTS QuadEdge
-    {
-        QuadEdge();
-        QuadEdge(int edgeidx);
-        bool isfree() const;
-
-        int next[4];
-        int pt[4];
-    };
-
-    //! All of the vertices
-    std::vector<Vertex> vtx;
-    //! All of the edges
-    std::vector<QuadEdge> qedges;
-    int freeQEdge;
-    int freePoint;
-    bool validGeometry;
-
-    int recentEdge;
-    //! Top left corner of the bounding rect
-    Point2f topLeft;
-    //! Bottom right corner of the bounding rect
-    Point2f bottomRight;
-};
-
-//! @} imgproc_subdiv2d
-
-//! @addtogroup imgproc_feature
-//! @{
-
-/** @example lsd_lines.cpp
-An example using the LineSegmentDetector
-\image html building_lsd.png "Sample output image" width=434 height=300
-*/
-
-/** @brief Line segment detector class
-
-following the algorithm described at @cite Rafael12 .
-*/
-class CV_EXPORTS_W LineSegmentDetector : public Algorithm
-{
-public:
-
-    /** @brief Finds lines in the input image.
-
-    This is the output of the default parameters of the algorithm on the above shown image.
-
-    ![image](pics/building_lsd.png)
-
-    @param _image A grayscale (CV_8UC1) input image. If only a roi needs to be selected, use:
-    `lsd_ptr-\>detect(image(roi), lines, ...); lines += Scalar(roi.x, roi.y, roi.x, roi.y);`
-    @param _lines A vector of Vec4i or Vec4f elements specifying the beginning and ending point of a line. Where
-    Vec4i/Vec4f is (x1, y1, x2, y2), point 1 is the start, point 2 - end. Returned lines are strictly
-    oriented depending on the gradient.
-    @param width Vector of widths of the regions, where the lines are found. E.g. Width of line.
-    @param prec Vector of precisions with which the lines are found.
-    @param nfa Vector containing number of false alarms in the line region, with precision of 10%. The
-    bigger the value, logarithmically better the detection.
-    - -1 corresponds to 10 mean false alarms
-    - 0 corresponds to 1 mean false alarm
-    - 1 corresponds to 0.1 mean false alarms
-    This vector will be calculated only when the objects type is #LSD_REFINE_ADV.
-    */
-    CV_WRAP virtual void detect(InputArray _image, OutputArray _lines,
-                        OutputArray width = noArray(), OutputArray prec = noArray(),
-                        OutputArray nfa = noArray()) = 0;
-
-    /** @brief Draws the line segments on a given image.
-    @param _image The image, where the lines will be drawn. Should be bigger or equal to the image,
-    where the lines were found.
-    @param lines A vector of the lines that needed to be drawn.
-     */
-    CV_WRAP virtual void drawSegments(InputOutputArray _image, InputArray lines) = 0;
-
-    /** @brief Draws two groups of lines in blue and red, counting the non overlapping (mismatching) pixels.
-
-    @param size The size of the image, where lines1 and lines2 were found.
-    @param lines1 The first group of lines that needs to be drawn. It is visualized in blue color.
-    @param lines2 The second group of lines. They visualized in red color.
-    @param _image Optional image, where the lines will be drawn. The image should be color(3-channel)
-    in order for lines1 and lines2 to be drawn in the above mentioned colors.
-     */
-    CV_WRAP virtual int compareSegments(const Size& size, InputArray lines1, InputArray lines2, InputOutputArray _image = noArray()) = 0;
-
-    virtual ~LineSegmentDetector() { }
-};
-
-/** @brief Creates a smart pointer to a LineSegmentDetector object and initializes it.
-
-The LineSegmentDetector algorithm is defined using the standard values. Only advanced users may want
-to edit those, as to tailor it for their own application.
-
-@param _refine The way found lines will be refined, see #LineSegmentDetectorModes
-@param _scale The scale of the image that will be used to find the lines. Range (0..1].
-@param _sigma_scale Sigma for Gaussian filter. It is computed as sigma = _sigma_scale/_scale.
-@param _quant Bound to the quantization error on the gradient norm.
-@param _ang_th Gradient angle tolerance in degrees.
-@param _log_eps Detection threshold: -log10(NFA) \> log_eps. Used only when advance refinement
-is chosen.
-@param _density_th Minimal density of aligned region points in the enclosing rectangle.
-@param _n_bins Number of bins in pseudo-ordering of gradient modulus.
- */
-CV_EXPORTS_W Ptr<LineSegmentDetector> createLineSegmentDetector(
-    int _refine = LSD_REFINE_STD, double _scale = 0.8,
-    double _sigma_scale = 0.6, double _quant = 2.0, double _ang_th = 22.5,
-    double _log_eps = 0, double _density_th = 0.7, int _n_bins = 1024);
-
-//! @} imgproc_feature
-
-//! @addtogroup imgproc_filter
-//! @{
-
-/** @brief Returns Gaussian filter coefficients.
-
-The function computes and returns the \f$\texttt{ksize} \times 1\f$ matrix of Gaussian filter
-coefficients:
-
-\f[G_i= \alpha *e^{-(i-( \texttt{ksize} -1)/2)^2/(2* \texttt{sigma}^2)},\f]
-
-where \f$i=0..\texttt{ksize}-1\f$ and \f$\alpha\f$ is the scale factor chosen so that \f$\sum_i G_i=1\f$.
-
-Two of such generated kernels can be passed to sepFilter2D. Those functions automatically recognize
-smoothing kernels (a symmetrical kernel with sum of weights equal to 1) and handle them accordingly.
-You may also use the higher-level GaussianBlur.
-@param ksize Aperture size. It should be odd ( \f$\texttt{ksize} \mod 2 = 1\f$ ) and positive.
-@param sigma Gaussian standard deviation. If it is non-positive, it is computed from ksize as
-`sigma = 0.3*((ksize-1)*0.5 - 1) + 0.8`.
-@param ktype Type of filter coefficients. It can be CV_32F or CV_64F .
-@sa  sepFilter2D, getDerivKernels, getStructuringElement, GaussianBlur
- */
-CV_EXPORTS_W Mat getGaussianKernel( int ksize, double sigma, int ktype = CV_64F );
-
-/** @brief Returns filter coefficients for computing spatial image derivatives.
-
-The function computes and returns the filter coefficients for spatial image derivatives. When
-`ksize=CV_SCHARR`, the Scharr \f$3 \times 3\f$ kernels are generated (see #Scharr). Otherwise, Sobel
-kernels are generated (see #Sobel). The filters are normally passed to #sepFilter2D or to
-
-@param kx Output matrix of row filter coefficients. It has the type ktype .
-@param ky Output matrix of column filter coefficients. It has the type ktype .
-@param dx Derivative order in respect of x.
-@param dy Derivative order in respect of y.
-@param ksize Aperture size. It can be CV_SCHARR, 1, 3, 5, or 7.
-@param normalize Flag indicating whether to normalize (scale down) the filter coefficients or not.
-Theoretically, the coefficients should have the denominator \f$=2^{ksize*2-dx-dy-2}\f$. If you are
-going to filter floating-point images, you are likely to use the normalized kernels. But if you
-compute derivatives of an 8-bit image, store the results in a 16-bit image, and wish to preserve
-all the fractional bits, you may want to set normalize=false .
-@param ktype Type of filter coefficients. It can be CV_32f or CV_64F .
- */
-CV_EXPORTS_W void getDerivKernels( OutputArray kx, OutputArray ky,
-                                   int dx, int dy, int ksize,
-                                   bool normalize = false, int ktype = CV_32F );
-
-/** @brief Returns Gabor filter coefficients.
-
-For more details about gabor filter equations and parameters, see: [Gabor
-Filter](http://en.wikipedia.org/wiki/Gabor_filter).
-
-@param ksize Size of the filter returned.
-@param sigma Standard deviation of the gaussian envelope.
-@param theta Orientation of the normal to the parallel stripes of a Gabor function.
-@param lambd Wavelength of the sinusoidal factor.
-@param gamma Spatial aspect ratio.
-@param psi Phase offset.
-@param ktype Type of filter coefficients. It can be CV_32F or CV_64F .
- */
-CV_EXPORTS_W Mat getGaborKernel( Size ksize, double sigma, double theta, double lambd,
-                                 double gamma, double psi = CV_PI*0.5, int ktype = CV_64F );
-
-//! returns "magic" border value for erosion and dilation. It is automatically transformed to Scalar::all(-DBL_MAX) for dilation.
-static inline Scalar morphologyDefaultBorderValue() { return Scalar::all(DBL_MAX); }
-
-/** @brief Returns a structuring element of the specified size and shape for morphological operations.
-
-The function constructs and returns the structuring element that can be further passed to #erode,
-#dilate or #morphologyEx. But you can also construct an arbitrary binary mask yourself and use it as
-the structuring element.
-
-@param shape Element shape that could be one of #MorphShapes
-@param ksize Size of the structuring element.
-@param anchor Anchor position within the element. The default value \f$(-1, -1)\f$ means that the
-anchor is at the center. Note that only the shape of a cross-shaped element depends on the anchor
-position. In other cases the anchor just regulates how much the result of the morphological
-operation is shifted.
- */
-CV_EXPORTS_W Mat getStructuringElement(int shape, Size ksize, Point anchor = Point(-1,-1));
-
-/** @example Smoothing.cpp
-Sample code for simple filters
-![Sample screenshot](Smoothing_Tutorial_Result_Median_Filter.jpg)
-Check @ref tutorial_gausian_median_blur_bilateral_filter "the corresponding tutorial" for more details
- */
-/** @brief Blurs an image using the median filter.
-
-The function smoothes an image using the median filter with the \f$\texttt{ksize} \times
-\texttt{ksize}\f$ aperture. Each channel of a multi-channel image is processed independently.
-In-place operation is supported.
-
-@note The median filter uses #BORDER_REPLICATE internally to cope with border pixels, see #BorderTypes
-
-@param src input 1-, 3-, or 4-channel image; when ksize is 3 or 5, the image depth should be
-CV_8U, CV_16U, or CV_32F, for larger aperture sizes, it can only be CV_8U.
-@param dst destination array of the same size and type as src.
-@param ksize aperture linear size; it must be odd and greater than 1, for example: 3, 5, 7 ...
-@sa  bilateralFilter, blur, boxFilter, GaussianBlur
- */
-CV_EXPORTS_W void medianBlur( InputArray src, OutputArray dst, int ksize );
-
-/** @brief Blurs an image using a Gaussian filter.
-
-The function convolves the source image with the specified Gaussian kernel. In-place filtering is
-supported.
-
-@param src input image; the image can have any number of channels, which are processed
-independently, but the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
-@param dst output image of the same size and type as src.
-@param ksize Gaussian kernel size. ksize.width and ksize.height can differ but they both must be
-positive and odd. Or, they can be zero's and then they are computed from sigma.
-@param sigmaX Gaussian kernel standard deviation in X direction.
-@param sigmaY Gaussian kernel standard deviation in Y direction; if sigmaY is zero, it is set to be
-equal to sigmaX, if both sigmas are zeros, they are computed from ksize.width and ksize.height,
-respectively (see #getGaussianKernel for details); to fully control the result regardless of
-possible future modifications of all this semantics, it is recommended to specify all of ksize,
-sigmaX, and sigmaY.
-@param borderType pixel extrapolation method, see #BorderTypes
-
-@sa  sepFilter2D, filter2D, blur, boxFilter, bilateralFilter, medianBlur
- */
-CV_EXPORTS_W void GaussianBlur( InputArray src, OutputArray dst, Size ksize,
-                                double sigmaX, double sigmaY = 0,
-                                int borderType = BORDER_DEFAULT );
-
-/** @brief Applies the bilateral filter to an image.
-
-The function applies bilateral filtering to the input image, as described in
-http://www.dai.ed.ac.uk/CVonline/LOCAL_COPIES/MANDUCHI1/Bilateral_Filtering.html
-bilateralFilter can reduce unwanted noise very well while keeping edges fairly sharp. However, it is
-very slow compared to most filters.
-
-_Sigma values_: For simplicity, you can set the 2 sigma values to be the same. If they are small (\<
-10), the filter will not have much effect, whereas if they are large (\> 150), they will have a very
-strong effect, making the image look "cartoonish".
-
-_Filter size_: Large filters (d \> 5) are very slow, so it is recommended to use d=5 for real-time
-applications, and perhaps d=9 for offline applications that need heavy noise filtering.
-
-This filter does not work inplace.
-@param src Source 8-bit or floating-point, 1-channel or 3-channel image.
-@param dst Destination image of the same size and type as src .
-@param d Diameter of each pixel neighborhood that is used during filtering. If it is non-positive,
-it is computed from sigmaSpace.
-@param sigmaColor Filter sigma in the color space. A larger value of the parameter means that
-farther colors within the pixel neighborhood (see sigmaSpace) will be mixed together, resulting
-in larger areas of semi-equal color.
-@param sigmaSpace Filter sigma in the coordinate space. A larger value of the parameter means that
-farther pixels will influence each other as long as their colors are close enough (see sigmaColor
-). When d\>0, it specifies the neighborhood size regardless of sigmaSpace. Otherwise, d is
-proportional to sigmaSpace.
-@param borderType border mode used to extrapolate pixels outside of the image, see #BorderTypes
- */
-CV_EXPORTS_W void bilateralFilter( InputArray src, OutputArray dst, int d,
-                                   double sigmaColor, double sigmaSpace,
-                                   int borderType = BORDER_DEFAULT );
-
-/** @brief Blurs an image using the box filter.
-
-The function smooths an image using the kernel:
-
-\f[\texttt{K} =  \alpha \begin{bmatrix} 1 & 1 & 1 &  \cdots & 1 & 1  \\ 1 & 1 & 1 &  \cdots & 1 & 1  \\ \hdotsfor{6} \\ 1 & 1 & 1 &  \cdots & 1 & 1 \end{bmatrix}\f]
-
-where
-
-\f[\alpha = \fork{\frac{1}{\texttt{ksize.width*ksize.height}}}{when \texttt{normalize=true}}{1}{otherwise}\f]
-
-Unnormalized box filter is useful for computing various integral characteristics over each pixel
-neighborhood, such as covariance matrices of image derivatives (used in dense optical flow
-algorithms, and so on). If you need to compute pixel sums over variable-size windows, use #integral.
-
-@param src input image.
-@param dst output image of the same size and type as src.
-@param ddepth the output image depth (-1 to use src.depth()).
-@param ksize blurring kernel size.
-@param anchor anchor point; default value Point(-1,-1) means that the anchor is at the kernel
-center.
-@param normalize flag, specifying whether the kernel is normalized by its area or not.
-@param borderType border mode used to extrapolate pixels outside of the image, see #BorderTypes
-@sa  blur, bilateralFilter, GaussianBlur, medianBlur, integral
- */
-CV_EXPORTS_W void boxFilter( InputArray src, OutputArray dst, int ddepth,
-                             Size ksize, Point anchor = Point(-1,-1),
-                             bool normalize = true,
-                             int borderType = BORDER_DEFAULT );
-
-/** @brief Calculates the normalized sum of squares of the pixel values overlapping the filter.
-
-For every pixel \f$ (x, y) \f$ in the source image, the function calculates the sum of squares of those neighboring
-pixel values which overlap the filter placed over the pixel \f$ (x, y) \f$.
-
-The unnormalized square box filter can be useful in computing local image statistics such as the the local
-variance and standard deviation around the neighborhood of a pixel.
-
-@param _src input image
-@param _dst output image of the same size and type as _src
-@param ddepth the output image depth (-1 to use src.depth())
-@param ksize kernel size
-@param anchor kernel anchor point. The default value of Point(-1, -1) denotes that the anchor is at the kernel
-center.
-@param normalize flag, specifying whether the kernel is to be normalized by it's area or not.
-@param borderType border mode used to extrapolate pixels outside of the image, see #BorderTypes
-@sa boxFilter
-*/
-CV_EXPORTS_W void sqrBoxFilter( InputArray _src, OutputArray _dst, int ddepth,
-                                Size ksize, Point anchor = Point(-1, -1),
-                                bool normalize = true,
-                                int borderType = BORDER_DEFAULT );
-
-/** @brief Blurs an image using the normalized box filter.
-
-The function smooths an image using the kernel:
-
-\f[\texttt{K} =  \frac{1}{\texttt{ksize.width*ksize.height}} \begin{bmatrix} 1 & 1 & 1 &  \cdots & 1 & 1  \\ 1 & 1 & 1 &  \cdots & 1 & 1  \\ \hdotsfor{6} \\ 1 & 1 & 1 &  \cdots & 1 & 1  \\ \end{bmatrix}\f]
-
-The call `blur(src, dst, ksize, anchor, borderType)` is equivalent to `boxFilter(src, dst, src.type(),
-anchor, true, borderType)`.
-
-@param src input image; it can have any number of channels, which are processed independently, but
-the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
-@param dst output image of the same size and type as src.
-@param ksize blurring kernel size.
-@param anchor anchor point; default value Point(-1,-1) means that the anchor is at the kernel
-center.
-@param borderType border mode used to extrapolate pixels outside of the image, see #BorderTypes
-@sa  boxFilter, bilateralFilter, GaussianBlur, medianBlur
- */
-CV_EXPORTS_W void blur( InputArray src, OutputArray dst,
-                        Size ksize, Point anchor = Point(-1,-1),
-                        int borderType = BORDER_DEFAULT );
-
-/** @brief Convolves an image with the kernel.
-
-The function applies an arbitrary linear filter to an image. In-place operation is supported. When
-the aperture is partially outside the image, the function interpolates outlier pixel values
-according to the specified border mode.
-
-The function does actually compute correlation, not the convolution:
-
-\f[\texttt{dst} (x,y) =  \sum _{ \stackrel{0\leq x' < \texttt{kernel.cols},}{0\leq y' < \texttt{kernel.rows}} }  \texttt{kernel} (x',y')* \texttt{src} (x+x'- \texttt{anchor.x} ,y+y'- \texttt{anchor.y} )\f]
-
-That is, the kernel is not mirrored around the anchor point. If you need a real convolution, flip
-the kernel using #flip and set the new anchor to `(kernel.cols - anchor.x - 1, kernel.rows -
-anchor.y - 1)`.
-
-The function uses the DFT-based algorithm in case of sufficiently large kernels (~`11 x 11` or
-larger) and the direct algorithm for small kernels.
-
-@param src input image.
-@param dst output image of the same size and the same number of channels as src.
-@param ddepth desired depth of the destination image, see @ref filter_depths "combinations"
-@param kernel convolution kernel (or rather a correlation kernel), a single-channel floating point
-matrix; if you want to apply different kernels to different channels, split the image into
-separate color planes using split and process them individually.
-@param anchor anchor of the kernel that indicates the relative position of a filtered point within
-the kernel; the anchor should lie within the kernel; default value (-1,-1) means that the anchor
-is at the kernel center.
-@param delta optional value added to the filtered pixels before storing them in dst.
-@param borderType pixel extrapolation method, see #BorderTypes
-@sa  sepFilter2D, dft, matchTemplate
- */
-CV_EXPORTS_W void filter2D( InputArray src, OutputArray dst, int ddepth,
-                            InputArray kernel, Point anchor = Point(-1,-1),
-                            double delta = 0, int borderType = BORDER_DEFAULT );
-
-/** @brief Applies a separable linear filter to an image.
-
-The function applies a separable linear filter to the image. That is, first, every row of src is
-filtered with the 1D kernel kernelX. Then, every column of the result is filtered with the 1D
-kernel kernelY. The final result shifted by delta is stored in dst .
-
-@param src Source image.
-@param dst Destination image of the same size and the same number of channels as src .
-@param ddepth Destination image depth, see @ref filter_depths "combinations"
-@param kernelX Coefficients for filtering each row.
-@param kernelY Coefficients for filtering each column.
-@param anchor Anchor position within the kernel. The default value \f$(-1,-1)\f$ means that the anchor
-is at the kernel center.
-@param delta Value added to the filtered results before storing them.
-@param borderType Pixel extrapolation method, see #BorderTypes
-@sa  filter2D, Sobel, GaussianBlur, boxFilter, blur
- */
-CV_EXPORTS_W void sepFilter2D( InputArray src, OutputArray dst, int ddepth,
-                               InputArray kernelX, InputArray kernelY,
-                               Point anchor = Point(-1,-1),
-                               double delta = 0, int borderType = BORDER_DEFAULT );
-
-/** @example Sobel_Demo.cpp
-Sample code using Sobel and/or Scharr OpenCV functions to make a simple Edge Detector
-![Sample screenshot](Sobel_Derivatives_Tutorial_Result.jpg)
-Check @ref tutorial_sobel_derivatives "the corresponding tutorial" for more details
- */
-/** @brief Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator.
-
-In all cases except one, the \f$\texttt{ksize} \times \texttt{ksize}\f$ separable kernel is used to
-calculate the derivative. When \f$\texttt{ksize = 1}\f$, the \f$3 \times 1\f$ or \f$1 \times 3\f$
-kernel is used (that is, no Gaussian smoothing is done). `ksize = 1` can only be used for the first
-or the second x- or y- derivatives.
-
-There is also the special value `ksize = #CV_SCHARR (-1)` that corresponds to the \f$3\times3\f$ Scharr
-filter that may give more accurate results than the \f$3\times3\f$ Sobel. The Scharr aperture is
-
-\f[\vecthreethree{-3}{0}{3}{-10}{0}{10}{-3}{0}{3}\f]
-
-for the x-derivative, or transposed for the y-derivative.
-
-The function calculates an image derivative by convolving the image with the appropriate kernel:
-
-\f[\texttt{dst} =  \frac{\partial^{xorder+yorder} \texttt{src}}{\partial x^{xorder} \partial y^{yorder}}\f]
-
-The Sobel operators combine Gaussian smoothing and differentiation, so the result is more or less
-resistant to the noise. Most often, the function is called with ( xorder = 1, yorder = 0, ksize = 3)
-or ( xorder = 0, yorder = 1, ksize = 3) to calculate the first x- or y- image derivative. The first
-case corresponds to a kernel of:
-
-\f[\vecthreethree{-1}{0}{1}{-2}{0}{2}{-1}{0}{1}\f]
-
-The second case corresponds to a kernel of:
-
-\f[\vecthreethree{-1}{-2}{-1}{0}{0}{0}{1}{2}{1}\f]
-
-@param src input image.
-@param dst output image of the same size and the same number of channels as src .
-@param ddepth output image depth, see @ref filter_depths "combinations"; in the case of
-    8-bit input images it will result in truncated derivatives.
-@param dx order of the derivative x.
-@param dy order of the derivative y.
-@param ksize size of the extended Sobel kernel; it must be 1, 3, 5, or 7.
-@param scale optional scale factor for the computed derivative values; by default, no scaling is
-applied (see #getDerivKernels for details).
-@param delta optional delta value that is added to the results prior to storing them in dst.
-@param borderType pixel extrapolation method, see #BorderTypes
-@sa  Scharr, Laplacian, sepFilter2D, filter2D, GaussianBlur, cartToPolar
- */
-CV_EXPORTS_W void Sobel( InputArray src, OutputArray dst, int ddepth,
-                         int dx, int dy, int ksize = 3,
-                         double scale = 1, double delta = 0,
-                         int borderType = BORDER_DEFAULT );
-
-/** @brief Calculates the first order image derivative in both x and y using a Sobel operator
-
-Equivalent to calling:
-
-@code
-Sobel( src, dx, CV_16SC1, 1, 0, 3 );
-Sobel( src, dy, CV_16SC1, 0, 1, 3 );
-@endcode
-
-@param src input image.
-@param dx output image with first-order derivative in x.
-@param dy output image with first-order derivative in y.
-@param ksize size of Sobel kernel. It must be 3.
-@param borderType pixel extrapolation method, see #BorderTypes
-
-@sa Sobel
- */
-
-CV_EXPORTS_W void spatialGradient( InputArray src, OutputArray dx,
-                                   OutputArray dy, int ksize = 3,
-                                   int borderType = BORDER_DEFAULT );
-
-/** @brief Calculates the first x- or y- image derivative using Scharr operator.
-
-The function computes the first x- or y- spatial image derivative using the Scharr operator. The
-call
-
-\f[\texttt{Scharr(src, dst, ddepth, dx, dy, scale, delta, borderType)}\f]
-
-is equivalent to
-
-\f[\texttt{Sobel(src, dst, ddepth, dx, dy, CV_SCHARR, scale, delta, borderType)} .\f]
-
-@param src input image.
-@param dst output image of the same size and the same number of channels as src.
-@param ddepth output image depth, see @ref filter_depths "combinations"
-@param dx order of the derivative x.
-@param dy order of the derivative y.
-@param scale optional scale factor for the computed derivative values; by default, no scaling is
-applied (see #getDerivKernels for details).
-@param delta optional delta value that is added to the results prior to storing them in dst.
-@param borderType pixel extrapolation method, see #BorderTypes
-@sa  cartToPolar
- */
-CV_EXPORTS_W void Scharr( InputArray src, OutputArray dst, int ddepth,
-                          int dx, int dy, double scale = 1, double delta = 0,
-                          int borderType = BORDER_DEFAULT );
-
-/** @example laplace.cpp
-  An example using Laplace transformations for edge detection
-*/
-
-/** @brief Calculates the Laplacian of an image.
-
-The function calculates the Laplacian of the source image by adding up the second x and y
-derivatives calculated using the Sobel operator:
-
-\f[\texttt{dst} =  \Delta \texttt{src} =  \frac{\partial^2 \texttt{src}}{\partial x^2} +  \frac{\partial^2 \texttt{src}}{\partial y^2}\f]
-
-This is done when `ksize > 1`. When `ksize == 1`, the Laplacian is computed by filtering the image
-with the following \f$3 \times 3\f$ aperture:
-
-\f[\vecthreethree {0}{1}{0}{1}{-4}{1}{0}{1}{0}\f]
-
-@param src Source image.
-@param dst Destination image of the same size and the same number of channels as src .
-@param ddepth Desired depth of the destination image.
-@param ksize Aperture size used to compute the second-derivative filters. See #getDerivKernels for
-details. The size must be positive and odd.
-@param scale Optional scale factor for the computed Laplacian values. By default, no scaling is
-applied. See #getDerivKernels for details.
-@param delta Optional delta value that is added to the results prior to storing them in dst .
-@param borderType Pixel extrapolation method, see #BorderTypes
-@sa  Sobel, Scharr
- */
-CV_EXPORTS_W void Laplacian( InputArray src, OutputArray dst, int ddepth,
-                             int ksize = 1, double scale = 1, double delta = 0,
-                             int borderType = BORDER_DEFAULT );
-
-//! @} imgproc_filter
-
-//! @addtogroup imgproc_feature
-//! @{
-
-/** @example edge.cpp
-  This program demonstrates usage of the Canny edge detector
-
-  Check @ref tutorial_canny_detector "the corresponding tutorial" for more details
-*/
-
-/** @brief Finds edges in an image using the Canny algorithm @cite Canny86 .
-
-The function finds edges in the input image and marks them in the output map edges using the
-Canny algorithm. The smallest value between threshold1 and threshold2 is used for edge linking. The
-largest value is used to find initial segments of strong edges. See
-<http://en.wikipedia.org/wiki/Canny_edge_detector>
-
-@param image 8-bit input image.
-@param edges output edge map; single channels 8-bit image, which has the same size as image .
-@param threshold1 first threshold for the hysteresis procedure.
-@param threshold2 second threshold for the hysteresis procedure.
-@param apertureSize aperture size for the Sobel operator.
-@param L2gradient a flag, indicating whether a more accurate \f$L_2\f$ norm
-\f$=\sqrt{(dI/dx)^2 + (dI/dy)^2}\f$ should be used to calculate the image gradient magnitude (
-L2gradient=true ), or whether the default \f$L_1\f$ norm \f$=|dI/dx|+|dI/dy|\f$ is enough (
-L2gradient=false ).
- */
-CV_EXPORTS_W void Canny( InputArray image, OutputArray edges,
-                         double threshold1, double threshold2,
-                         int apertureSize = 3, bool L2gradient = false );
-
-/** \overload
-
-Finds edges in an image using the Canny algorithm with custom image gradient.
-
-@param dx 16-bit x derivative of input image (CV_16SC1 or CV_16SC3).
-@param dy 16-bit y derivative of input image (same type as dx).
-@param edges output edge map; single channels 8-bit image, which has the same size as image .
-@param threshold1 first threshold for the hysteresis procedure.
-@param threshold2 second threshold for the hysteresis procedure.
-@param L2gradient a flag, indicating whether a more accurate \f$L_2\f$ norm
-\f$=\sqrt{(dI/dx)^2 + (dI/dy)^2}\f$ should be used to calculate the image gradient magnitude (
-L2gradient=true ), or whether the default \f$L_1\f$ norm \f$=|dI/dx|+|dI/dy|\f$ is enough (
-L2gradient=false ).
- */
-CV_EXPORTS_W void Canny( InputArray dx, InputArray dy,
-                         OutputArray edges,
-                         double threshold1, double threshold2,
-                         bool L2gradient = false );
-
-/** @brief Calculates the minimal eigenvalue of gradient matrices for corner detection.
-
-The function is similar to cornerEigenValsAndVecs but it calculates and stores only the minimal
-eigenvalue of the covariance matrix of derivatives, that is, \f$\min(\lambda_1, \lambda_2)\f$ in terms
-of the formulae in the cornerEigenValsAndVecs description.
-
-@param src Input single-channel 8-bit or floating-point image.
-@param dst Image to store the minimal eigenvalues. It has the type CV_32FC1 and the same size as
-src .
-@param blockSize Neighborhood size (see the details on #cornerEigenValsAndVecs ).
-@param ksize Aperture parameter for the Sobel operator.
-@param borderType Pixel extrapolation method. See #BorderTypes.
- */
-CV_EXPORTS_W void cornerMinEigenVal( InputArray src, OutputArray dst,
-                                     int blockSize, int ksize = 3,
-                                     int borderType = BORDER_DEFAULT );
-
-/** @brief Harris corner detector.
-
-The function runs the Harris corner detector on the image. Similarly to cornerMinEigenVal and
-cornerEigenValsAndVecs , for each pixel \f$(x, y)\f$ it calculates a \f$2\times2\f$ gradient covariance
-matrix \f$M^{(x,y)}\f$ over a \f$\texttt{blockSize} \times \texttt{blockSize}\f$ neighborhood. Then, it
-computes the following characteristic:
-
-\f[\texttt{dst} (x,y) =  \mathrm{det} M^{(x,y)} - k  \cdot \left ( \mathrm{tr} M^{(x,y)} \right )^2\f]
-
-Corners in the image can be found as the local maxima of this response map.
-
-@param src Input single-channel 8-bit or floating-point image.
-@param dst Image to store the Harris detector responses. It has the type CV_32FC1 and the same
-size as src .
-@param blockSize Neighborhood size (see the details on #cornerEigenValsAndVecs ).
-@param ksize Aperture parameter for the Sobel operator.
-@param k Harris detector free parameter. See the formula below.
-@param borderType Pixel extrapolation method. See #BorderTypes.
- */
-CV_EXPORTS_W void cornerHarris( InputArray src, OutputArray dst, int blockSize,
-                                int ksize, double k,
-                                int borderType = BORDER_DEFAULT );
-
-/** @brief Calculates eigenvalues and eigenvectors of image blocks for corner detection.
-
-For every pixel \f$p\f$ , the function cornerEigenValsAndVecs considers a blockSize \f$\times\f$ blockSize
-neighborhood \f$S(p)\f$ . It calculates the covariation matrix of derivatives over the neighborhood as:
-
-\f[M =  \begin{bmatrix} \sum _{S(p)}(dI/dx)^2 &  \sum _{S(p)}dI/dx dI/dy  \\ \sum _{S(p)}dI/dx dI/dy &  \sum _{S(p)}(dI/dy)^2 \end{bmatrix}\f]
-
-where the derivatives are computed using the Sobel operator.
-
-After that, it finds eigenvectors and eigenvalues of \f$M\f$ and stores them in the destination image as
-\f$(\lambda_1, \lambda_2, x_1, y_1, x_2, y_2)\f$ where
-
--   \f$\lambda_1, \lambda_2\f$ are the non-sorted eigenvalues of \f$M\f$
--   \f$x_1, y_1\f$ are the eigenvectors corresponding to \f$\lambda_1\f$
--   \f$x_2, y_2\f$ are the eigenvectors corresponding to \f$\lambda_2\f$
-
-The output of the function can be used for robust edge or corner detection.
-
-@param src Input single-channel 8-bit or floating-point image.
-@param dst Image to store the results. It has the same size as src and the type CV_32FC(6) .
-@param blockSize Neighborhood size (see details below).
-@param ksize Aperture parameter for the Sobel operator.
-@param borderType Pixel extrapolation method. See #BorderTypes.
-
-@sa  cornerMinEigenVal, cornerHarris, preCornerDetect
- */
-CV_EXPORTS_W void cornerEigenValsAndVecs( InputArray src, OutputArray dst,
-                                          int blockSize, int ksize,
-                                          int borderType = BORDER_DEFAULT );
-
-/** @brief Calculates a feature map for corner detection.
-
-The function calculates the complex spatial derivative-based function of the source image
-
-\f[\texttt{dst} = (D_x  \texttt{src} )^2  \cdot D_{yy}  \texttt{src} + (D_y  \texttt{src} )^2  \cdot D_{xx}  \texttt{src} - 2 D_x  \texttt{src} \cdot D_y  \texttt{src} \cdot D_{xy}  \texttt{src}\f]
-
-where \f$D_x\f$,\f$D_y\f$ are the first image derivatives, \f$D_{xx}\f$,\f$D_{yy}\f$ are the second image
-derivatives, and \f$D_{xy}\f$ is the mixed derivative.
-
-The corners can be found as local maximums of the functions, as shown below:
-@code
-    Mat corners, dilated_corners;
-    preCornerDetect(image, corners, 3);
-    // dilation with 3x3 rectangular structuring element
-    dilate(corners, dilated_corners, Mat(), 1);
-    Mat corner_mask = corners == dilated_corners;
-@endcode
-
-@param src Source single-channel 8-bit of floating-point image.
-@param dst Output image that has the type CV_32F and the same size as src .
-@param ksize %Aperture size of the Sobel .
-@param borderType Pixel extrapolation method. See #BorderTypes.
- */
-CV_EXPORTS_W void preCornerDetect( InputArray src, OutputArray dst, int ksize,
-                                   int borderType = BORDER_DEFAULT );
-
-/** @brief Refines the corner locations.
-
-The function iterates to find the sub-pixel accurate location of corners or radial saddle points, as
-shown on the figure below.
-
-![image](pics/cornersubpix.png)
-
-Sub-pixel accurate corner locator is based on the observation that every vector from the center \f$q\f$
-to a point \f$p\f$ located within a neighborhood of \f$q\f$ is orthogonal to the image gradient at \f$p\f$
-subject to image and measurement noise. Consider the expression:
-
-\f[\epsilon _i = {DI_{p_i}}^T  \cdot (q - p_i)\f]
-
-where \f${DI_{p_i}}\f$ is an image gradient at one of the points \f$p_i\f$ in a neighborhood of \f$q\f$ . The
-value of \f$q\f$ is to be found so that \f$\epsilon_i\f$ is minimized. A system of equations may be set up
-with \f$\epsilon_i\f$ set to zero:
-
-\f[\sum _i(DI_{p_i}  \cdot {DI_{p_i}}^T) -  \sum _i(DI_{p_i}  \cdot {DI_{p_i}}^T  \cdot p_i)\f]
-
-where the gradients are summed within a neighborhood ("search window") of \f$q\f$ . Calling the first
-gradient term \f$G\f$ and the second gradient term \f$b\f$ gives:
-
-\f[q = G^{-1}  \cdot b\f]
-
-The algorithm sets the center of the neighborhood window at this new center \f$q\f$ and then iterates
-until the center stays within a set threshold.
-
-@param image Input image.
-@param corners Initial coordinates of the input corners and refined coordinates provided for
-output.
-@param winSize Half of the side length of the search window. For example, if winSize=Size(5,5) ,
-then a \f$5*2+1 \times 5*2+1 = 11 \times 11\f$ search window is used.
-@param zeroZone Half of the size of the dead region in the middle of the search zone over which
-the summation in the formula below is not done. It is used sometimes to avoid possible
-singularities of the autocorrelation matrix. The value of (-1,-1) indicates that there is no such
-a size.
-@param criteria Criteria for termination of the iterative process of corner refinement. That is,
-the process of corner position refinement stops either after criteria.maxCount iterations or when
-the corner position moves by less than criteria.epsilon on some iteration.
- */
-CV_EXPORTS_W void cornerSubPix( InputArray image, InputOutputArray corners,
-                                Size winSize, Size zeroZone,
-                                TermCriteria criteria );
-
-/** @brief Determines strong corners on an image.
-
-The function finds the most prominent corners in the image or in the specified image region, as
-described in @cite Shi94
-
--   Function calculates the corner quality measure at every source image pixel using the
-    #cornerMinEigenVal or #cornerHarris .
--   Function performs a non-maximum suppression (the local maximums in *3 x 3* neighborhood are
-    retained).
--   The corners with the minimal eigenvalue less than
-    \f$\texttt{qualityLevel} \cdot \max_{x,y} qualityMeasureMap(x,y)\f$ are rejected.
--   The remaining corners are sorted by the quality measure in the descending order.
--   Function throws away each corner for which there is a stronger corner at a distance less than
-    maxDistance.
-
-The function can be used to initialize a point-based tracker of an object.
-
-@note If the function is called with different values A and B of the parameter qualityLevel , and
-A \> B, the vector of returned corners with qualityLevel=A will be the prefix of the output vector
-with qualityLevel=B .
-
-@param image Input 8-bit or floating-point 32-bit, single-channel image.
-@param corners Output vector of detected corners.
-@param maxCorners Maximum number of corners to return. If there are more corners than are found,
-the strongest of them is returned. `maxCorners <= 0` implies that no limit on the maximum is set
-and all detected corners are returned.
-@param qualityLevel Parameter characterizing the minimal accepted quality of image corners. The
-parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue
-(see #cornerMinEigenVal ) or the Harris function response (see #cornerHarris ). The corners with the
-quality measure less than the product are rejected. For example, if the best corner has the
-quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure
-less than 15 are rejected.
-@param minDistance Minimum possible Euclidean distance between the returned corners.
-@param mask Optional region of interest. If the image is not empty (it needs to have the type
-CV_8UC1 and the same size as image ), it specifies the region in which the corners are detected.
-@param blockSize Size of an average block for computing a derivative covariation matrix over each
-pixel neighborhood. See cornerEigenValsAndVecs .
-@param useHarrisDetector Parameter indicating whether to use a Harris detector (see #cornerHarris)
-or #cornerMinEigenVal.
-@param k Free parameter of the Harris detector.
-
-@sa  cornerMinEigenVal, cornerHarris, calcOpticalFlowPyrLK, estimateRigidTransform,
- */
-
-CV_EXPORTS_W void goodFeaturesToTrack( InputArray image, OutputArray corners,
-                                     int maxCorners, double qualityLevel, double minDistance,
-                                     InputArray mask = noArray(), int blockSize = 3,
-                                     bool useHarrisDetector = false, double k = 0.04 );
-
-CV_EXPORTS_W void goodFeaturesToTrack( InputArray image, OutputArray corners,
-                                     int maxCorners, double qualityLevel, double minDistance,
-                                     InputArray mask, int blockSize,
-                                     int gradientSize, bool useHarrisDetector = false,
-                                     double k = 0.04 );
-/** @example houghlines.cpp
-An example using the Hough line detector
-![Sample input image](Hough_Lines_Tutorial_Original_Image.jpg) ![Output image](Hough_Lines_Tutorial_Result.jpg)
-*/
-
-/** @brief Finds lines in a binary image using the standard Hough transform.
-
-The function implements the standard or standard multi-scale Hough transform algorithm for line
-detection. See <http://homepages.inf.ed.ac.uk/rbf/HIPR2/hough.htm> for a good explanation of Hough
-transform.
-
-@param image 8-bit, single-channel binary source image. The image may be modified by the function.
-@param lines Output vector of lines. Each line is represented by a 2 or 3 element vector
-\f$(\rho, \theta)\f$ or \f$(\rho, \theta, \votes)\f$ . \f$\rho\f$ is the distance from the coordinate origin \f$(0,0)\f$ (top-left corner of
-the image). \f$\theta\f$ is the line rotation angle in radians (
-\f$0 \sim \textrm{vertical line}, \pi/2 \sim \textrm{horizontal line}\f$ ).
-\f$\votes\f$ is the value of accumulator.
-@param rho Distance resolution of the accumulator in pixels.
-@param theta Angle resolution of the accumulator in radians.
-@param threshold Accumulator threshold parameter. Only those lines are returned that get enough
-votes ( \f$>\texttt{threshold}\f$ ).
-@param srn For the multi-scale Hough transform, it is a divisor for the distance resolution rho .
-The coarse accumulator distance resolution is rho and the accurate accumulator resolution is
-rho/srn . If both srn=0 and stn=0 , the classical Hough transform is used. Otherwise, both these
-parameters should be positive.
-@param stn For the multi-scale Hough transform, it is a divisor for the distance resolution theta.
-@param min_theta For standard and multi-scale Hough transform, minimum angle to check for lines.
-Must fall between 0 and max_theta.
-@param max_theta For standard and multi-scale Hough transform, maximum angle to check for lines.
-Must fall between min_theta and CV_PI.
- */
-CV_EXPORTS_W void HoughLines( InputArray image, OutputArray lines,
-                              double rho, double theta, int threshold,
-                              double srn = 0, double stn = 0,
-                              double min_theta = 0, double max_theta = CV_PI );
-
-/** @brief Finds line segments in a binary image using the probabilistic Hough transform.
-
-The function implements the probabilistic Hough transform algorithm for line detection, described
-in @cite Matas00
-
-See the line detection example below:
-@include snippets/imgproc_HoughLinesP.cpp
-This is a sample picture the function parameters have been tuned for:
-
-![image](pics/building.jpg)
-
-And this is the output of the above program in case of the probabilistic Hough transform:
-
-![image](pics/houghp.png)
-
-@param image 8-bit, single-channel binary source image. The image may be modified by the function.
-@param lines Output vector of lines. Each line is represented by a 4-element vector
-\f$(x_1, y_1, x_2, y_2)\f$ , where \f$(x_1,y_1)\f$ and \f$(x_2, y_2)\f$ are the ending points of each detected
-line segment.
-@param rho Distance resolution of the accumulator in pixels.
-@param theta Angle resolution of the accumulator in radians.
-@param threshold Accumulator threshold parameter. Only those lines are returned that get enough
-votes ( \f$>\texttt{threshold}\f$ ).
-@param minLineLength Minimum line length. Line segments shorter than that are rejected.
-@param maxLineGap Maximum allowed gap between points on the same line to link them.
-
-@sa LineSegmentDetector
- */
-CV_EXPORTS_W void HoughLinesP( InputArray image, OutputArray lines,
-                               double rho, double theta, int threshold,
-                               double minLineLength = 0, double maxLineGap = 0 );
-
-/** @brief Finds lines in a set of points using the standard Hough transform.
-
-The function finds lines in a set of points using a modification of the Hough transform.
-@include snippets/imgproc_HoughLinesPointSet.cpp
-@param _point Input vector of points. Each vector must be encoded as a Point vector \f$(x,y)\f$. Type must be CV_32FC2 or CV_32SC2.
-@param _lines Output vector of found lines. Each vector is encoded as a vector<Vec3d> \f$(votes, rho, theta)\f$.
-The larger the value of 'votes', the higher the reliability of the Hough line.
-@param lines_max Max count of hough lines.
-@param threshold Accumulator threshold parameter. Only those lines are returned that get enough
-votes ( \f$>\texttt{threshold}\f$ )
-@param min_rho Minimum Distance value of the accumulator in pixels.
-@param max_rho Maximum Distance value of the accumulator in pixels.
-@param rho_step Distance resolution of the accumulator in pixels.
-@param min_theta Minimum angle value of the accumulator in radians.
-@param max_theta Maximum angle value of the accumulator in radians.
-@param theta_step Angle resolution of the accumulator in radians.
- */
-CV_EXPORTS_W void HoughLinesPointSet( InputArray _point, OutputArray _lines, int lines_max, int threshold,
-                                      double min_rho, double max_rho, double rho_step,
-                                      double min_theta, double max_theta, double theta_step );
-
-/** @example houghcircles.cpp
-An example using the Hough circle detector
-*/
-
-/** @brief Finds circles in a grayscale image using the Hough transform.
-
-The function finds circles in a grayscale image using a modification of the Hough transform.
-
-Example: :
-@include snippets/imgproc_HoughLinesCircles.cpp
-
-@note Usually the function detects the centers of circles well. However, it may fail to find correct
-radii. You can assist to the function by specifying the radius range ( minRadius and maxRadius ) if
-you know it. Or, you may set maxRadius to a negative number to return centers only without radius
-search, and find the correct radius using an additional procedure.
-
-@param image 8-bit, single-channel, grayscale input image.
-@param circles Output vector of found circles. Each vector is encoded as  3 or 4 element
-floating-point vector \f$(x, y, radius)\f$ or \f$(x, y, radius, votes)\f$ .
-@param method Detection method, see #HoughModes. Currently, the only implemented method is #HOUGH_GRADIENT
-@param dp Inverse ratio of the accumulator resolution to the image resolution. For example, if
-dp=1 , the accumulator has the same resolution as the input image. If dp=2 , the accumulator has
-half as big width and height.
-@param minDist Minimum distance between the centers of the detected circles. If the parameter is
-too small, multiple neighbor circles may be falsely detected in addition to a true one. If it is
-too large, some circles may be missed.
-@param param1 First method-specific parameter. In case of #HOUGH_GRADIENT , it is the higher
-threshold of the two passed to the Canny edge detector (the lower one is twice smaller).
-@param param2 Second method-specific parameter. In case of #HOUGH_GRADIENT , it is the
-accumulator threshold for the circle centers at the detection stage. The smaller it is, the more
-false circles may be detected. Circles, corresponding to the larger accumulator values, will be
-returned first.
-@param minRadius Minimum circle radius.
-@param maxRadius Maximum circle radius. If <= 0, uses the maximum image dimension. If < 0, returns
-centers without finding the radius.
-
-@sa fitEllipse, minEnclosingCircle
- */
-CV_EXPORTS_W void HoughCircles( InputArray image, OutputArray circles,
-                               int method, double dp, double minDist,
-                               double param1 = 100, double param2 = 100,
-                               int minRadius = 0, int maxRadius = 0 );
-
-//! @} imgproc_feature
-
-//! @addtogroup imgproc_filter
-//! @{
-
-/** @example morphology2.cpp
-Advanced morphology Transformations sample code
-![Sample screenshot](Morphology_2_Tutorial_Result.jpg)
-Check @ref tutorial_opening_closing_hats "the corresponding tutorial" for more details
-*/
-
-/** @brief Erodes an image by using a specific structuring element.
-
-The function erodes the source image using the specified structuring element that determines the
-shape of a pixel neighborhood over which the minimum is taken:
-
-\f[\texttt{dst} (x,y) =  \min _{(x',y'):  \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\f]
-
-The function supports the in-place mode. Erosion can be applied several ( iterations ) times. In
-case of multi-channel images, each channel is processed independently.
-
-@param src input image; the number of channels can be arbitrary, but the depth should be one of
-CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
-@param dst output image of the same size and type as src.
-@param kernel structuring element used for erosion; if `element=Mat()`, a `3 x 3` rectangular
-structuring element is used. Kernel can be created using #getStructuringElement.
-@param anchor position of the anchor within the element; default value (-1, -1) means that the
-anchor is at the element center.
-@param iterations number of times erosion is applied.
-@param borderType pixel extrapolation method, see #BorderTypes
-@param borderValue border value in case of a constant border
-@sa  dilate, morphologyEx, getStructuringElement
- */
-CV_EXPORTS_W void erode( InputArray src, OutputArray dst, InputArray kernel,
-                         Point anchor = Point(-1,-1), int iterations = 1,
-                         int borderType = BORDER_CONSTANT,
-                         const Scalar& borderValue = morphologyDefaultBorderValue() );
-
-/** @example Morphology_1.cpp
-Erosion and Dilation sample code
-![Sample Screenshot-Erosion](Morphology_1_Tutorial_Erosion_Result.jpg)![Sample Screenshot-Dilation](Morphology_1_Tutorial_Dilation_Result.jpg)
-Check @ref tutorial_erosion_dilatation "the corresponding tutorial" for more details
- */
-/** @brief Dilates an image by using a specific structuring element.
-
-The function dilates the source image using the specified structuring element that determines the
-shape of a pixel neighborhood over which the maximum is taken:
-\f[\texttt{dst} (x,y) =  \max _{(x',y'):  \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\f]
-
-The function supports the in-place mode. Dilation can be applied several ( iterations ) times. In
-case of multi-channel images, each channel is processed independently.
-
-@param src input image; the number of channels can be arbitrary, but the depth should be one of
-CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
-@param dst output image of the same size and type as src.
-@param kernel structuring element used for dilation; if elemenat=Mat(), a 3 x 3 rectangular
-structuring element is used. Kernel can be created using #getStructuringElement
-@param anchor position of the anchor within the element; default value (-1, -1) means that the
-anchor is at the element center.
-@param iterations number of times dilation is applied.
-@param borderType pixel extrapolation method, see #BorderTypes
-@param borderValue border value in case of a constant border
-@sa  erode, morphologyEx, getStructuringElement
- */
-CV_EXPORTS_W void dilate( InputArray src, OutputArray dst, InputArray kernel,
-                          Point anchor = Point(-1,-1), int iterations = 1,
-                          int borderType = BORDER_CONSTANT,
-                          const Scalar& borderValue = morphologyDefaultBorderValue() );
-
-/** @brief Performs advanced morphological transformations.
-
-The function cv::morphologyEx can perform advanced morphological transformations using an erosion and dilation as
-basic operations.
-
-Any of the operations can be done in-place. In case of multi-channel images, each channel is
-processed independently.
-
-@param src Source image. The number of channels can be arbitrary. The depth should be one of
-CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
-@param dst Destination image of the same size and type as source image.
-@param op Type of a morphological operation, see #MorphTypes
-@param kernel Structuring element. It can be created using #getStructuringElement.
-@param anchor Anchor position with the kernel. Negative values mean that the anchor is at the
-kernel center.
-@param iterations Number of times erosion and dilation are applied.
-@param borderType Pixel extrapolation method, see #BorderTypes
-@param borderValue Border value in case of a constant border. The default value has a special
-meaning.
-@sa  dilate, erode, getStructuringElement
-@note The number of iterations is the number of times erosion or dilatation operation will be applied.
-For instance, an opening operation (#MORPH_OPEN) with two iterations is equivalent to apply
-successively: erode -> erode -> dilate -> dilate (and not erode -> dilate -> erode -> dilate).
- */
-CV_EXPORTS_W void morphologyEx( InputArray src, OutputArray dst,
-                                int op, InputArray kernel,
-                                Point anchor = Point(-1,-1), int iterations = 1,
-                                int borderType = BORDER_CONSTANT,
-                                const Scalar& borderValue = morphologyDefaultBorderValue() );
-
-//! @} imgproc_filter
-
-//! @addtogroup imgproc_transform
-//! @{
-
-/** @brief Resizes an image.
-
-The function resize resizes the image src down to or up to the specified size. Note that the
-initial dst type or size are not taken into account. Instead, the size and type are derived from
-the `src`,`dsize`,`fx`, and `fy`. If you want to resize src so that it fits the pre-created dst,
-you may call the function as follows:
-@code
-    // explicitly specify dsize=dst.size(); fx and fy will be computed from that.
-    resize(src, dst, dst.size(), 0, 0, interpolation);
-@endcode
-If you want to decimate the image by factor of 2 in each direction, you can call the function this
-way:
-@code
-    // specify fx and fy and let the function compute the destination image size.
-    resize(src, dst, Size(), 0.5, 0.5, interpolation);
-@endcode
-To shrink an image, it will generally look best with #INTER_AREA interpolation, whereas to
-enlarge an image, it will generally look best with c#INTER_CUBIC (slow) or #INTER_LINEAR
-(faster but still looks OK).
-
-@param src input image.
-@param dst output image; it has the size dsize (when it is non-zero) or the size computed from
-src.size(), fx, and fy; the type of dst is the same as of src.
-@param dsize output image size; if it equals zero, it is computed as:
- \f[\texttt{dsize = Size(round(fx*src.cols), round(fy*src.rows))}\f]
- Either dsize or both fx and fy must be non-zero.
-@param fx scale factor along the horizontal axis; when it equals 0, it is computed as
-\f[\texttt{(double)dsize.width/src.cols}\f]
-@param fy scale factor along the vertical axis; when it equals 0, it is computed as
-\f[\texttt{(double)dsize.height/src.rows}\f]
-@param interpolation interpolation method, see #InterpolationFlags
-
-@sa  warpAffine, warpPerspective, remap
- */
-CV_EXPORTS_W void resize( InputArray src, OutputArray dst,
-                          Size dsize, double fx = 0, double fy = 0,
-                          int interpolation = INTER_LINEAR );
-
-/** @brief Applies an affine transformation to an image.
-
-The function warpAffine transforms the source image using the specified matrix:
-
-\f[\texttt{dst} (x,y) =  \texttt{src} ( \texttt{M} _{11} x +  \texttt{M} _{12} y +  \texttt{M} _{13}, \texttt{M} _{21} x +  \texttt{M} _{22} y +  \texttt{M} _{23})\f]
-
-when the flag #WARP_INVERSE_MAP is set. Otherwise, the transformation is first inverted
-with #invertAffineTransform and then put in the formula above instead of M. The function cannot
-operate in-place.
-
-@param src input image.
-@param dst output image that has the size dsize and the same type as src .
-@param M \f$2\times 3\f$ transformation matrix.
-@param dsize size of the output image.
-@param flags combination of interpolation methods (see #InterpolationFlags) and the optional
-flag #WARP_INVERSE_MAP that means that M is the inverse transformation (
-\f$\texttt{dst}\rightarrow\texttt{src}\f$ ).
-@param borderMode pixel extrapolation method (see #BorderTypes); when
-borderMode=#BORDER_TRANSPARENT, it means that the pixels in the destination image corresponding to
-the "outliers" in the source image are not modified by the function.
-@param borderValue value used in case of a constant border; by default, it is 0.
-
-@sa  warpPerspective, resize, remap, getRectSubPix, transform
- */
-CV_EXPORTS_W void warpAffine( InputArray src, OutputArray dst,
-                              InputArray M, Size dsize,
-                              int flags = INTER_LINEAR,
-                              int borderMode = BORDER_CONSTANT,
-                              const Scalar& borderValue = Scalar());
-
-/** @example warpPerspective_demo.cpp
-An example program shows using cv::findHomography and cv::warpPerspective for image warping
- */
-/** @brief Applies a perspective transformation to an image.
-
-The function warpPerspective transforms the source image using the specified matrix:
-
-\f[\texttt{dst} (x,y) =  \texttt{src} \left ( \frac{M_{11} x + M_{12} y + M_{13}}{M_{31} x + M_{32} y + M_{33}} ,
-     \frac{M_{21} x + M_{22} y + M_{23}}{M_{31} x + M_{32} y + M_{33}} \right )\f]
-
-when the flag #WARP_INVERSE_MAP is set. Otherwise, the transformation is first inverted with invert
-and then put in the formula above instead of M. The function cannot operate in-place.
-
-@param src input image.
-@param dst output image that has the size dsize and the same type as src .
-@param M \f$3\times 3\f$ transformation matrix.
-@param dsize size of the output image.
-@param flags combination of interpolation methods (#INTER_LINEAR or #INTER_NEAREST) and the
-optional flag #WARP_INVERSE_MAP, that sets M as the inverse transformation (
-\f$\texttt{dst}\rightarrow\texttt{src}\f$ ).
-@param borderMode pixel extrapolation method (#BORDER_CONSTANT or #BORDER_REPLICATE).
-@param borderValue value used in case of a constant border; by default, it equals 0.
-
-@sa  warpAffine, resize, remap, getRectSubPix, perspectiveTransform
- */
-CV_EXPORTS_W void warpPerspective( InputArray src, OutputArray dst,
-                                   InputArray M, Size dsize,
-                                   int flags = INTER_LINEAR,
-                                   int borderMode = BORDER_CONSTANT,
-                                   const Scalar& borderValue = Scalar());
-
-/** @brief Applies a generic geometrical transformation to an image.
-
-The function remap transforms the source image using the specified map:
-
-\f[\texttt{dst} (x,y) =  \texttt{src} (map_x(x,y),map_y(x,y))\f]
-
-where values of pixels with non-integer coordinates are computed using one of available
-interpolation methods. \f$map_x\f$ and \f$map_y\f$ can be encoded as separate floating-point maps
-in \f$map_1\f$ and \f$map_2\f$ respectively, or interleaved floating-point maps of \f$(x,y)\f$ in
-\f$map_1\f$, or fixed-point maps created by using convertMaps. The reason you might want to
-convert from floating to fixed-point representations of a map is that they can yield much faster
-(\~2x) remapping operations. In the converted case, \f$map_1\f$ contains pairs (cvFloor(x),
-cvFloor(y)) and \f$map_2\f$ contains indices in a table of interpolation coefficients.
-
-This function cannot operate in-place.
-
-@param src Source image.
-@param dst Destination image. It has the same size as map1 and the same type as src .
-@param map1 The first map of either (x,y) points or just x values having the type CV_16SC2 ,
-CV_32FC1, or CV_32FC2. See convertMaps for details on converting a floating point
-representation to fixed-point for speed.
-@param map2 The second map of y values having the type CV_16UC1, CV_32FC1, or none (empty map
-if map1 is (x,y) points), respectively.
-@param interpolation Interpolation method (see #InterpolationFlags). The method #INTER_AREA is
-not supported by this function.
-@param borderMode Pixel extrapolation method (see #BorderTypes). When
-borderMode=#BORDER_TRANSPARENT, it means that the pixels in the destination image that
-corresponds to the "outliers" in the source image are not modified by the function.
-@param borderValue Value used in case of a constant border. By default, it is 0.
-@note
-Due to current implementation limitations the size of an input and output images should be less than 32767x32767.
- */
-CV_EXPORTS_W void remap( InputArray src, OutputArray dst,
-                         InputArray map1, InputArray map2,
-                         int interpolation, int borderMode = BORDER_CONSTANT,
-                         const Scalar& borderValue = Scalar());
-
-/** @brief Converts image transformation maps from one representation to another.
-
-The function converts a pair of maps for remap from one representation to another. The following
-options ( (map1.type(), map2.type()) \f$\rightarrow\f$ (dstmap1.type(), dstmap2.type()) ) are
-supported:
-
-- \f$\texttt{(CV_32FC1, CV_32FC1)} \rightarrow \texttt{(CV_16SC2, CV_16UC1)}\f$. This is the
-most frequently used conversion operation, in which the original floating-point maps (see remap )
-are converted to a more compact and much faster fixed-point representation. The first output array
-contains the rounded coordinates and the second array (created only when nninterpolation=false )
-contains indices in the interpolation tables.
-
-- \f$\texttt{(CV_32FC2)} \rightarrow \texttt{(CV_16SC2, CV_16UC1)}\f$. The same as above but
-the original maps are stored in one 2-channel matrix.
-
-- Reverse conversion. Obviously, the reconstructed floating-point maps will not be exactly the same
-as the originals.
-
-@param map1 The first input map of type CV_16SC2, CV_32FC1, or CV_32FC2 .
-@param map2 The second input map of type CV_16UC1, CV_32FC1, or none (empty matrix),
-respectively.
-@param dstmap1 The first output map that has the type dstmap1type and the same size as src .
-@param dstmap2 The second output map.
-@param dstmap1type Type of the first output map that should be CV_16SC2, CV_32FC1, or
-CV_32FC2 .
-@param nninterpolation Flag indicating whether the fixed-point maps are used for the
-nearest-neighbor or for a more complex interpolation.
-
-@sa  remap, undistort, initUndistortRectifyMap
- */
-CV_EXPORTS_W void convertMaps( InputArray map1, InputArray map2,
-                               OutputArray dstmap1, OutputArray dstmap2,
-                               int dstmap1type, bool nninterpolation = false );
-
-/** @brief Calculates an affine matrix of 2D rotation.
-
-The function calculates the following matrix:
-
-\f[\begin{bmatrix} \alpha &  \beta & (1- \alpha )  \cdot \texttt{center.x} -  \beta \cdot \texttt{center.y} \\ - \beta &  \alpha &  \beta \cdot \texttt{center.x} + (1- \alpha )  \cdot \texttt{center.y} \end{bmatrix}\f]
-
-where
-
-\f[\begin{array}{l} \alpha =  \texttt{scale} \cdot \cos \texttt{angle} , \\ \beta =  \texttt{scale} \cdot \sin \texttt{angle} \end{array}\f]
-
-The transformation maps the rotation center to itself. If this is not the target, adjust the shift.
-
-@param center Center of the rotation in the source image.
-@param angle Rotation angle in degrees. Positive values mean counter-clockwise rotation (the
-coordinate origin is assumed to be the top-left corner).
-@param scale Isotropic scale factor.
-
-@sa  getAffineTransform, warpAffine, transform
- */
-CV_EXPORTS_W Mat getRotationMatrix2D( Point2f center, double angle, double scale );
-
-//! returns 3x3 perspective transformation for the corresponding 4 point pairs.
-CV_EXPORTS Mat getPerspectiveTransform( const Point2f src[], const Point2f dst[] );
-
-/** @brief Calculates an affine transform from three pairs of the corresponding points.
-
-The function calculates the \f$2 \times 3\f$ matrix of an affine transform so that:
-
-\f[\begin{bmatrix} x'_i \\ y'_i \end{bmatrix} = \texttt{map_matrix} \cdot \begin{bmatrix} x_i \\ y_i \\ 1 \end{bmatrix}\f]
-
-where
-
-\f[dst(i)=(x'_i,y'_i), src(i)=(x_i, y_i), i=0,1,2\f]
-
-@param src Coordinates of triangle vertices in the source image.
-@param dst Coordinates of the corresponding triangle vertices in the destination image.
-
-@sa  warpAffine, transform
- */
-CV_EXPORTS Mat getAffineTransform( const Point2f src[], const Point2f dst[] );
-
-/** @brief Inverts an affine transformation.
-
-The function computes an inverse affine transformation represented by \f$2 \times 3\f$ matrix M:
-
-\f[\begin{bmatrix} a_{11} & a_{12} & b_1  \\ a_{21} & a_{22} & b_2 \end{bmatrix}\f]
-
-The result is also a \f$2 \times 3\f$ matrix of the same type as M.
-
-@param M Original affine transformation.
-@param iM Output reverse affine transformation.
- */
-CV_EXPORTS_W void invertAffineTransform( InputArray M, OutputArray iM );
-
-/** @brief Calculates a perspective transform from four pairs of the corresponding points.
-
-The function calculates the \f$3 \times 3\f$ matrix of a perspective transform so that:
-
-\f[\begin{bmatrix} t_i x'_i \\ t_i y'_i \\ t_i \end{bmatrix} = \texttt{map_matrix} \cdot \begin{bmatrix} x_i \\ y_i \\ 1 \end{bmatrix}\f]
-
-where
-
-\f[dst(i)=(x'_i,y'_i), src(i)=(x_i, y_i), i=0,1,2,3\f]
-
-@param src Coordinates of quadrangle vertices in the source image.
-@param dst Coordinates of the corresponding quadrangle vertices in the destination image.
-
-@sa  findHomography, warpPerspective, perspectiveTransform
- */
-CV_EXPORTS_W Mat getPerspectiveTransform( InputArray src, InputArray dst );
-
-CV_EXPORTS_W Mat getAffineTransform( InputArray src, InputArray dst );
-
-/** @brief Retrieves a pixel rectangle from an image with sub-pixel accuracy.
-
-The function getRectSubPix extracts pixels from src:
-
-\f[patch(x, y) = src(x +  \texttt{center.x} - ( \texttt{dst.cols} -1)*0.5, y +  \texttt{center.y} - ( \texttt{dst.rows} -1)*0.5)\f]
-
-where the values of the pixels at non-integer coordinates are retrieved using bilinear
-interpolation. Every channel of multi-channel images is processed independently. Also
-the image should be a single channel or three channel image. While the center of the
-rectangle must be inside the image, parts of the rectangle may be outside.
-
-@param image Source image.
-@param patchSize Size of the extracted patch.
-@param center Floating point coordinates of the center of the extracted rectangle within the
-source image. The center must be inside the image.
-@param patch Extracted patch that has the size patchSize and the same number of channels as src .
-@param patchType Depth of the extracted pixels. By default, they have the same depth as src .
-
-@sa  warpAffine, warpPerspective
- */
-CV_EXPORTS_W void getRectSubPix( InputArray image, Size patchSize,
-                                 Point2f center, OutputArray patch, int patchType = -1 );
-
-/** @example polar_transforms.cpp
-An example using the cv::linearPolar and cv::logPolar operations
-*/
-
-/** @brief Remaps an image to semilog-polar coordinates space.
-
-@deprecated This function produces same result as cv::warpPolar(src, dst, src.size(), center, maxRadius, flags+WARP_POLAR_LOG);
-
-@internal
-Transform the source image using the following transformation (See @ref polar_remaps_reference_image "Polar remaps reference image d)"):
-\f[\begin{array}{l}
-  dst( \rho , \phi ) = src(x,y) \\
-  dst.size() \leftarrow src.size()
-\end{array}\f]
-
-where
-\f[\begin{array}{l}
-  I = (dx,dy) = (x - center.x,y - center.y) \\
-  \rho = M \cdot log_e(\texttt{magnitude} (I)) ,\\
-  \phi = Kangle \cdot \texttt{angle} (I) \\
-\end{array}\f]
-
-and
-\f[\begin{array}{l}
-  M = src.cols / log_e(maxRadius) \\
-  Kangle = src.rows / 2\Pi \\
-\end{array}\f]
-
-The function emulates the human "foveal" vision and can be used for fast scale and
-rotation-invariant template matching, for object tracking and so forth.
-@param src Source image
-@param dst Destination image. It will have same size and type as src.
-@param center The transformation center; where the output precision is maximal
-@param M Magnitude scale parameter. It determines the radius of the bounding circle to transform too.
-@param flags A combination of interpolation methods, see #InterpolationFlags
-
-@note
--   The function can not operate in-place.
--   To calculate magnitude and angle in degrees #cartToPolar is used internally thus angles are measured from 0 to 360 with accuracy about 0.3 degrees.
-
-@sa cv::linearPolar
-@endinternal
-*/
-CV_EXPORTS_W void logPolar( InputArray src, OutputArray dst,
-                            Point2f center, double M, int flags );
-
-/** @brief Remaps an image to polar coordinates space.
-
-@deprecated This function produces same result as cv::warpPolar(src, dst, src.size(), center, maxRadius, flags)
-
-@internal
-Transform the source image using the following transformation (See @ref polar_remaps_reference_image "Polar remaps reference image c)"):
-\f[\begin{array}{l}
-  dst( \rho , \phi ) = src(x,y) \\
-  dst.size() \leftarrow src.size()
-\end{array}\f]
-
-where
-\f[\begin{array}{l}
-  I = (dx,dy) = (x - center.x,y - center.y) \\
-  \rho = Kmag \cdot \texttt{magnitude} (I) ,\\
-  \phi = angle \cdot \texttt{angle} (I)
-\end{array}\f]
-
-and
-\f[\begin{array}{l}
-  Kx = src.cols / maxRadius \\
-  Ky = src.rows / 2\Pi
-\end{array}\f]
-
-
-@param src Source image
-@param dst Destination image. It will have same size and type as src.
-@param center The transformation center;
-@param maxRadius The radius of the bounding circle to transform. It determines the inverse magnitude scale parameter too.
-@param flags A combination of interpolation methods, see #InterpolationFlags
-
-@note
--   The function can not operate in-place.
--   To calculate magnitude and angle in degrees #cartToPolar is used internally thus angles are measured from 0 to 360 with accuracy about 0.3 degrees.
-
-@sa cv::logPolar
-@endinternal
-*/
-CV_EXPORTS_W void linearPolar( InputArray src, OutputArray dst,
-                               Point2f center, double maxRadius, int flags );
-
-
-/** \brief Remaps an image to polar or semilog-polar coordinates space
-
-@anchor polar_remaps_reference_image
-![Polar remaps reference](pics/polar_remap_doc.png)
-
-Transform the source image using the following transformation:
-\f[
-dst(\rho , \phi ) = src(x,y)
-\f]
-
-where
-\f[
-\begin{array}{l}
-\vec{I} = (x - center.x, \;y - center.y) \\
-\phi = Kangle \cdot \texttt{angle} (\vec{I}) \\
-\rho = \left\{\begin{matrix}
-Klin \cdot \texttt{magnitude} (\vec{I}) & default \\
-Klog \cdot log_e(\texttt{magnitude} (\vec{I})) & if \; semilog \\
-\end{matrix}\right.
-\end{array}
-\f]
-
-and
-\f[
-\begin{array}{l}
-Kangle = dsize.height / 2\Pi \\
-Klin = dsize.width / maxRadius \\
-Klog = dsize.width / log_e(maxRadius) \\
-\end{array}
-\f]
-
-
-\par Linear vs semilog mapping
-
-Polar mapping can be linear or semi-log. Add one of #WarpPolarMode to `flags` to specify the polar mapping mode.
-
-Linear is the default mode.
-
-The semilog mapping emulates the human "foveal" vision that permit very high acuity on the line of sight (central vision)
-in contrast to peripheral vision where acuity is minor.
-
-\par Option on `dsize`:
-
-- if both values in `dsize <=0 ` (default),
-the destination image will have (almost) same area of source bounding circle:
-\f[\begin{array}{l}
-dsize.area  \leftarrow (maxRadius^2 \cdot \Pi) \\
-dsize.width = \texttt{cvRound}(maxRadius) \\
-dsize.height = \texttt{cvRound}(maxRadius \cdot \Pi) \\
-\end{array}\f]
-
-
-- if only `dsize.height <= 0`,
-the destination image area will be proportional to the bounding circle area but scaled by `Kx * Kx`:
-\f[\begin{array}{l}
-dsize.height = \texttt{cvRound}(dsize.width \cdot \Pi) \\
-\end{array}
-\f]
-
-- if both values in `dsize > 0 `,
-the destination image will have the given size therefore the area of the bounding circle will be scaled to `dsize`.
-
-
-\par Reverse mapping
-
-You can get reverse mapping adding #WARP_INVERSE_MAP to `flags`
-\snippet polar_transforms.cpp InverseMap
-
-In addiction, to calculate the original coordinate from a polar mapped coordinate \f$(rho, phi)->(x, y)\f$:
-\snippet polar_transforms.cpp InverseCoordinate
-
-@param src Source image.
-@param dst Destination image. It will have same type as src.
-@param dsize The destination image size (see description for valid options).
-@param center The transformation center.
-@param maxRadius The radius of the bounding circle to transform. It determines the inverse magnitude scale parameter too.
-@param flags A combination of interpolation methods, #InterpolationFlags + #WarpPolarMode.
-            - Add #WARP_POLAR_LINEAR to select linear polar mapping (default)
-            - Add #WARP_POLAR_LOG to select semilog polar mapping
-            - Add #WARP_INVERSE_MAP for reverse mapping.
-@note
--  The function can not operate in-place.
--  To calculate magnitude and angle in degrees #cartToPolar is used internally thus angles are measured from 0 to 360 with accuracy about 0.3 degrees.
--  This function uses #remap. Due to current implementation limitations the size of an input and output images should be less than 32767x32767.
-
-@sa cv::remap
-*/
-CV_EXPORTS_W void warpPolar(InputArray src, OutputArray dst, Size dsize,
-                            Point2f center, double maxRadius, int flags);
-
-
-//! @} imgproc_transform
-
-//! @addtogroup imgproc_misc
-//! @{
-
-/** @overload */
-CV_EXPORTS_W void integral( InputArray src, OutputArray sum, int sdepth = -1 );
-
-/** @overload */
-CV_EXPORTS_AS(integral2) void integral( InputArray src, OutputArray sum,
-                                        OutputArray sqsum, int sdepth = -1, int sqdepth = -1 );
-
-/** @brief Calculates the integral of an image.
-
-The function calculates one or more integral images for the source image as follows:
-
-\f[\texttt{sum} (X,Y) =  \sum _{x<X,y<Y}  \texttt{image} (x,y)\f]
-
-\f[\texttt{sqsum} (X,Y) =  \sum _{x<X,y<Y}  \texttt{image} (x,y)^2\f]
-
-\f[\texttt{tilted} (X,Y) =  \sum _{y<Y,abs(x-X+1) \leq Y-y-1}  \texttt{image} (x,y)\f]
-
-Using these integral images, you can calculate sum, mean, and standard deviation over a specific
-up-right or rotated rectangular region of the image in a constant time, for example:
-
-\f[\sum _{x_1 \leq x < x_2,  \, y_1  \leq y < y_2}  \texttt{image} (x,y) =  \texttt{sum} (x_2,y_2)- \texttt{sum} (x_1,y_2)- \texttt{sum} (x_2,y_1)+ \texttt{sum} (x_1,y_1)\f]
-
-It makes possible to do a fast blurring or fast block correlation with a variable window size, for
-example. In case of multi-channel images, sums for each channel are accumulated independently.
-
-As a practical example, the next figure shows the calculation of the integral of a straight
-rectangle Rect(3,3,3,2) and of a tilted rectangle Rect(5,1,2,3) . The selected pixels in the
-original image are shown, as well as the relative pixels in the integral images sum and tilted .
-
-![integral calculation example](pics/integral.png)
-
-@param src input image as \f$W \times H\f$, 8-bit or floating-point (32f or 64f).
-@param sum integral image as \f$(W+1)\times (H+1)\f$ , 32-bit integer or floating-point (32f or 64f).
-@param sqsum integral image for squared pixel values; it is \f$(W+1)\times (H+1)\f$, double-precision
-floating-point (64f) array.
-@param tilted integral for the image rotated by 45 degrees; it is \f$(W+1)\times (H+1)\f$ array with
-the same data type as sum.
-@param sdepth desired depth of the integral and the tilted integral images, CV_32S, CV_32F, or
-CV_64F.
-@param sqdepth desired depth of the integral image of squared pixel values, CV_32F or CV_64F.
- */
-CV_EXPORTS_AS(integral3) void integral( InputArray src, OutputArray sum,
-                                        OutputArray sqsum, OutputArray tilted,
-                                        int sdepth = -1, int sqdepth = -1 );
-
-//! @} imgproc_misc
-
-//! @addtogroup imgproc_motion
-//! @{
-
-/** @brief Adds an image to the accumulator image.
-
-The function adds src or some of its elements to dst :
-
-\f[\texttt{dst} (x,y)  \leftarrow \texttt{dst} (x,y) +  \texttt{src} (x,y)  \quad \text{if} \quad \texttt{mask} (x,y)  \ne 0\f]
-
-The function supports multi-channel images. Each channel is processed independently.
-
-The function cv::accumulate can be used, for example, to collect statistics of a scene background
-viewed by a still camera and for the further foreground-background segmentation.
-
-@param src Input image of type CV_8UC(n), CV_16UC(n), CV_32FC(n) or CV_64FC(n), where n is a positive integer.
-@param dst %Accumulator image with the same number of channels as input image, and a depth of CV_32F or CV_64F.
-@param mask Optional operation mask.
-
-@sa  accumulateSquare, accumulateProduct, accumulateWeighted
- */
-CV_EXPORTS_W void accumulate( InputArray src, InputOutputArray dst,
-                              InputArray mask = noArray() );
-
-/** @brief Adds the square of a source image to the accumulator image.
-
-The function adds the input image src or its selected region, raised to a power of 2, to the
-accumulator dst :
-
-\f[\texttt{dst} (x,y)  \leftarrow \texttt{dst} (x,y) +  \texttt{src} (x,y)^2  \quad \text{if} \quad \texttt{mask} (x,y)  \ne 0\f]
-
-The function supports multi-channel images. Each channel is processed independently.
-
-@param src Input image as 1- or 3-channel, 8-bit or 32-bit floating point.
-@param dst %Accumulator image with the same number of channels as input image, 32-bit or 64-bit
-floating-point.
-@param mask Optional operation mask.
-
-@sa  accumulateSquare, accumulateProduct, accumulateWeighted
- */
-CV_EXPORTS_W void accumulateSquare( InputArray src, InputOutputArray dst,
-                                    InputArray mask = noArray() );
-
-/** @brief Adds the per-element product of two input images to the accumulator image.
-
-The function adds the product of two images or their selected regions to the accumulator dst :
-
-\f[\texttt{dst} (x,y)  \leftarrow \texttt{dst} (x,y) +  \texttt{src1} (x,y)  \cdot \texttt{src2} (x,y)  \quad \text{if} \quad \texttt{mask} (x,y)  \ne 0\f]
-
-The function supports multi-channel images. Each channel is processed independently.
-
-@param src1 First input image, 1- or 3-channel, 8-bit or 32-bit floating point.
-@param src2 Second input image of the same type and the same size as src1 .
-@param dst %Accumulator image with the same number of channels as input images, 32-bit or 64-bit
-floating-point.
-@param mask Optional operation mask.
-
-@sa  accumulate, accumulateSquare, accumulateWeighted
- */
-CV_EXPORTS_W void accumulateProduct( InputArray src1, InputArray src2,
-                                     InputOutputArray dst, InputArray mask=noArray() );
-
-/** @brief Updates a running average.
-
-The function calculates the weighted sum of the input image src and the accumulator dst so that dst
-becomes a running average of a frame sequence:
-
-\f[\texttt{dst} (x,y)  \leftarrow (1- \texttt{alpha} )  \cdot \texttt{dst} (x,y) +  \texttt{alpha} \cdot \texttt{src} (x,y)  \quad \text{if} \quad \texttt{mask} (x,y)  \ne 0\f]
-
-That is, alpha regulates the update speed (how fast the accumulator "forgets" about earlier images).
-The function supports multi-channel images. Each channel is processed independently.
-
-@param src Input image as 1- or 3-channel, 8-bit or 32-bit floating point.
-@param dst %Accumulator image with the same number of channels as input image, 32-bit or 64-bit
-floating-point.
-@param alpha Weight of the input image.
-@param mask Optional operation mask.
-
-@sa  accumulate, accumulateSquare, accumulateProduct
- */
-CV_EXPORTS_W void accumulateWeighted( InputArray src, InputOutputArray dst,
-                                      double alpha, InputArray mask = noArray() );
-
-/** @brief The function is used to detect translational shifts that occur between two images.
-
-The operation takes advantage of the Fourier shift theorem for detecting the translational shift in
-the frequency domain. It can be used for fast image registration as well as motion estimation. For
-more information please see <http://en.wikipedia.org/wiki/Phase_correlation>
-
-Calculates the cross-power spectrum of two supplied source arrays. The arrays are padded if needed
-with getOptimalDFTSize.
-
-The function performs the following equations:
-- First it applies a Hanning window (see <http://en.wikipedia.org/wiki/Hann_function>) to each
-image to remove possible edge effects. This window is cached until the array size changes to speed
-up processing time.
-- Next it computes the forward DFTs of each source array:
-\f[\mathbf{G}_a = \mathcal{F}\{src_1\}, \; \mathbf{G}_b = \mathcal{F}\{src_2\}\f]
-where \f$\mathcal{F}\f$ is the forward DFT.
-- It then computes the cross-power spectrum of each frequency domain array:
-\f[R = \frac{ \mathbf{G}_a \mathbf{G}_b^*}{|\mathbf{G}_a \mathbf{G}_b^*|}\f]
-- Next the cross-correlation is converted back into the time domain via the inverse DFT:
-\f[r = \mathcal{F}^{-1}\{R\}\f]
-- Finally, it computes the peak location and computes a 5x5 weighted centroid around the peak to
-achieve sub-pixel accuracy.
-\f[(\Delta x, \Delta y) = \texttt{weightedCentroid} \{\arg \max_{(x, y)}\{r\}\}\f]
-- If non-zero, the response parameter is computed as the sum of the elements of r within the 5x5
-centroid around the peak location. It is normalized to a maximum of 1 (meaning there is a single
-peak) and will be smaller when there are multiple peaks.
-
-@param src1 Source floating point array (CV_32FC1 or CV_64FC1)
-@param src2 Source floating point array (CV_32FC1 or CV_64FC1)
-@param window Floating point array with windowing coefficients to reduce edge effects (optional).
-@param response Signal power within the 5x5 centroid around the peak, between 0 and 1 (optional).
-@returns detected phase shift (sub-pixel) between the two arrays.
-
-@sa dft, getOptimalDFTSize, idft, mulSpectrums createHanningWindow
- */
-CV_EXPORTS_W Point2d phaseCorrelate(InputArray src1, InputArray src2,
-                                    InputArray window = noArray(), CV_OUT double* response = 0);
-
-/** @brief This function computes a Hanning window coefficients in two dimensions.
-
-See (http://en.wikipedia.org/wiki/Hann_function) and (http://en.wikipedia.org/wiki/Window_function)
-for more information.
-
-An example is shown below:
-@code
-    // create hanning window of size 100x100 and type CV_32F
-    Mat hann;
-    createHanningWindow(hann, Size(100, 100), CV_32F);
-@endcode
-@param dst Destination array to place Hann coefficients in
-@param winSize The window size specifications (both width and height must be > 1)
-@param type Created array type
- */
-CV_EXPORTS_W void createHanningWindow(OutputArray dst, Size winSize, int type);
-
-//! @} imgproc_motion
-
-//! @addtogroup imgproc_misc
-//! @{
-
-/** @brief Applies a fixed-level threshold to each array element.
-
-The function applies fixed-level thresholding to a multiple-channel array. The function is typically
-used to get a bi-level (binary) image out of a grayscale image ( #compare could be also used for
-this purpose) or for removing a noise, that is, filtering out pixels with too small or too large
-values. There are several types of thresholding supported by the function. They are determined by
-type parameter.
-
-Also, the special values #THRESH_OTSU or #THRESH_TRIANGLE may be combined with one of the
-above values. In these cases, the function determines the optimal threshold value using the Otsu's
-or Triangle algorithm and uses it instead of the specified thresh.
-
-@note Currently, the Otsu's and Triangle methods are implemented only for 8-bit single-channel images.
-
-@param src input array (multiple-channel, 8-bit or 32-bit floating point).
-@param dst output array of the same size  and type and the same number of channels as src.
-@param thresh threshold value.
-@param maxval maximum value to use with the #THRESH_BINARY and #THRESH_BINARY_INV thresholding
-types.
-@param type thresholding type (see #ThresholdTypes).
-@return the computed threshold value if Otsu's or Triangle methods used.
-
-@sa  adaptiveThreshold, findContours, compare, min, max
- */
-CV_EXPORTS_W double threshold( InputArray src, OutputArray dst,
-                               double thresh, double maxval, int type );
-
-
-/** @brief Applies an adaptive threshold to an array.
-
-The function transforms a grayscale image to a binary image according to the formulae:
--   **THRESH_BINARY**
-    \f[dst(x,y) =  \fork{\texttt{maxValue}}{if \(src(x,y) > T(x,y)\)}{0}{otherwise}\f]
--   **THRESH_BINARY_INV**
-    \f[dst(x,y) =  \fork{0}{if \(src(x,y) > T(x,y)\)}{\texttt{maxValue}}{otherwise}\f]
-where \f$T(x,y)\f$ is a threshold calculated individually for each pixel (see adaptiveMethod parameter).
-
-The function can process the image in-place.
-
-@param src Source 8-bit single-channel image.
-@param dst Destination image of the same size and the same type as src.
-@param maxValue Non-zero value assigned to the pixels for which the condition is satisfied
-@param adaptiveMethod Adaptive thresholding algorithm to use, see #AdaptiveThresholdTypes.
-The #BORDER_REPLICATE | #BORDER_ISOLATED is used to process boundaries.
-@param thresholdType Thresholding type that must be either #THRESH_BINARY or #THRESH_BINARY_INV,
-see #ThresholdTypes.
-@param blockSize Size of a pixel neighborhood that is used to calculate a threshold value for the
-pixel: 3, 5, 7, and so on.
-@param C Constant subtracted from the mean or weighted mean (see the details below). Normally, it
-is positive but may be zero or negative as well.
-
-@sa  threshold, blur, GaussianBlur
- */
-CV_EXPORTS_W void adaptiveThreshold( InputArray src, OutputArray dst,
-                                     double maxValue, int adaptiveMethod,
-                                     int thresholdType, int blockSize, double C );
-
-//! @} imgproc_misc
-
-//! @addtogroup imgproc_filter
-//! @{
-
-/** @example Pyramids.cpp
-An example using pyrDown and pyrUp functions
- */
-/** @brief Blurs an image and downsamples it.
-
-By default, size of the output image is computed as `Size((src.cols+1)/2, (src.rows+1)/2)`, but in
-any case, the following conditions should be satisfied:
-
-\f[\begin{array}{l} | \texttt{dstsize.width} *2-src.cols| \leq 2 \\ | \texttt{dstsize.height} *2-src.rows| \leq 2 \end{array}\f]
-
-The function performs the downsampling step of the Gaussian pyramid construction. First, it
-convolves the source image with the kernel:
-
-\f[\frac{1}{256} \begin{bmatrix} 1 & 4 & 6 & 4 & 1  \\ 4 & 16 & 24 & 16 & 4  \\ 6 & 24 & 36 & 24 & 6  \\ 4 & 16 & 24 & 16 & 4  \\ 1 & 4 & 6 & 4 & 1 \end{bmatrix}\f]
-
-Then, it downsamples the image by rejecting even rows and columns.
-
-@param src input image.
-@param dst output image; it has the specified size and the same type as src.
-@param dstsize size of the output image.
-@param borderType Pixel extrapolation method, see #BorderTypes (#BORDER_CONSTANT isn't supported)
- */
-CV_EXPORTS_W void pyrDown( InputArray src, OutputArray dst,
-                           const Size& dstsize = Size(), int borderType = BORDER_DEFAULT );
-
-/** @brief Upsamples an image and then blurs it.
-
-By default, size of the output image is computed as `Size(src.cols\*2, (src.rows\*2)`, but in any
-case, the following conditions should be satisfied:
-
-\f[\begin{array}{l} | \texttt{dstsize.width} -src.cols*2| \leq  ( \texttt{dstsize.width}   \mod  2)  \\ | \texttt{dstsize.height} -src.rows*2| \leq  ( \texttt{dstsize.height}   \mod  2) \end{array}\f]
-
-The function performs the upsampling step of the Gaussian pyramid construction, though it can
-actually be used to construct the Laplacian pyramid. First, it upsamples the source image by
-injecting even zero rows and columns and then convolves the result with the same kernel as in
-pyrDown multiplied by 4.
-
-@param src input image.
-@param dst output image. It has the specified size and the same type as src .
-@param dstsize size of the output image.
-@param borderType Pixel extrapolation method, see #BorderTypes (only #BORDER_DEFAULT is supported)
- */
-CV_EXPORTS_W void pyrUp( InputArray src, OutputArray dst,
-                         const Size& dstsize = Size(), int borderType = BORDER_DEFAULT );
-
-/** @brief Constructs the Gaussian pyramid for an image.
-
-The function constructs a vector of images and builds the Gaussian pyramid by recursively applying
-pyrDown to the previously built pyramid layers, starting from `dst[0]==src`.
-
-@param src Source image. Check pyrDown for the list of supported types.
-@param dst Destination vector of maxlevel+1 images of the same type as src. dst[0] will be the
-same as src. dst[1] is the next pyramid layer, a smoothed and down-sized src, and so on.
-@param maxlevel 0-based index of the last (the smallest) pyramid layer. It must be non-negative.
-@param borderType Pixel extrapolation method, see #BorderTypes (#BORDER_CONSTANT isn't supported)
- */
-CV_EXPORTS void buildPyramid( InputArray src, OutputArrayOfArrays dst,
-                              int maxlevel, int borderType = BORDER_DEFAULT );
-
-//! @} imgproc_filter
-
-//! @addtogroup imgproc_transform
-//! @{
-
-/** @brief Transforms an image to compensate for lens distortion.
-
-The function transforms an image to compensate radial and tangential lens distortion.
-
-The function is simply a combination of #initUndistortRectifyMap (with unity R ) and #remap
-(with bilinear interpolation). See the former function for details of the transformation being
-performed.
-
-Those pixels in the destination image, for which there is no correspondent pixels in the source
-image, are filled with zeros (black color).
-
-A particular subset of the source image that will be visible in the corrected image can be regulated
-by newCameraMatrix. You can use #getOptimalNewCameraMatrix to compute the appropriate
-newCameraMatrix depending on your requirements.
-
-The camera matrix and the distortion parameters can be determined using #calibrateCamera. If
-the resolution of images is different from the resolution used at the calibration stage, \f$f_x,
-f_y, c_x\f$ and \f$c_y\f$ need to be scaled accordingly, while the distortion coefficients remain
-the same.
-
-@param src Input (distorted) image.
-@param dst Output (corrected) image that has the same size and type as src .
-@param cameraMatrix Input camera matrix \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ .
-@param distCoeffs Input vector of distortion coefficients
-\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$
-of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
-@param newCameraMatrix Camera matrix of the distorted image. By default, it is the same as
-cameraMatrix but you may additionally scale and shift the result by using a different matrix.
- */
-CV_EXPORTS_W void undistort( InputArray src, OutputArray dst,
-                             InputArray cameraMatrix,
-                             InputArray distCoeffs,
-                             InputArray newCameraMatrix = noArray() );
-
-/** @brief Computes the undistortion and rectification transformation map.
-
-The function computes the joint undistortion and rectification transformation and represents the
-result in the form of maps for remap. The undistorted image looks like original, as if it is
-captured with a camera using the camera matrix =newCameraMatrix and zero distortion. In case of a
-monocular camera, newCameraMatrix is usually equal to cameraMatrix, or it can be computed by
-#getOptimalNewCameraMatrix for a better control over scaling. In case of a stereo camera,
-newCameraMatrix is normally set to P1 or P2 computed by #stereoRectify .
-
-Also, this new camera is oriented differently in the coordinate space, according to R. That, for
-example, helps to align two heads of a stereo camera so that the epipolar lines on both images
-become horizontal and have the same y- coordinate (in case of a horizontally aligned stereo camera).
-
-The function actually builds the maps for the inverse mapping algorithm that is used by remap. That
-is, for each pixel \f$(u, v)\f$ in the destination (corrected and rectified) image, the function
-computes the corresponding coordinates in the source image (that is, in the original image from
-camera). The following process is applied:
-\f[
-\begin{array}{l}
-x  \leftarrow (u - {c'}_x)/{f'}_x  \\
-y  \leftarrow (v - {c'}_y)/{f'}_y  \\
-{[X\,Y\,W]} ^T  \leftarrow R^{-1}*[x \, y \, 1]^T  \\
-x'  \leftarrow X/W  \\
-y'  \leftarrow Y/W  \\
-r^2  \leftarrow x'^2 + y'^2 \\
-x''  \leftarrow x' \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6}
-+ 2p_1 x' y' + p_2(r^2 + 2 x'^2)  + s_1 r^2 + s_2 r^4\\
-y''  \leftarrow y' \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6}
-+ p_1 (r^2 + 2 y'^2) + 2 p_2 x' y' + s_3 r^2 + s_4 r^4 \\
-s\vecthree{x'''}{y'''}{1} =
-\vecthreethree{R_{33}(\tau_x, \tau_y)}{0}{-R_{13}((\tau_x, \tau_y)}
-{0}{R_{33}(\tau_x, \tau_y)}{-R_{23}(\tau_x, \tau_y)}
-{0}{0}{1} R(\tau_x, \tau_y) \vecthree{x''}{y''}{1}\\
-map_x(u,v)  \leftarrow x''' f_x + c_x  \\
-map_y(u,v)  \leftarrow y''' f_y + c_y
-\end{array}
-\f]
-where \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$
-are the distortion coefficients.
-
-In case of a stereo camera, this function is called twice: once for each camera head, after
-stereoRectify, which in its turn is called after #stereoCalibrate. But if the stereo camera
-was not calibrated, it is still possible to compute the rectification transformations directly from
-the fundamental matrix using #stereoRectifyUncalibrated. For each camera, the function computes
-homography H as the rectification transformation in a pixel domain, not a rotation matrix R in 3D
-space. R can be computed from H as
-\f[\texttt{R} = \texttt{cameraMatrix} ^{-1} \cdot \texttt{H} \cdot \texttt{cameraMatrix}\f]
-where cameraMatrix can be chosen arbitrarily.
-
-@param cameraMatrix Input camera matrix \f$A=\vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ .
-@param distCoeffs Input vector of distortion coefficients
-\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$
-of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
-@param R Optional rectification transformation in the object space (3x3 matrix). R1 or R2 ,
-computed by #stereoRectify can be passed here. If the matrix is empty, the identity transformation
-is assumed. In cvInitUndistortMap R assumed to be an identity matrix.
-@param newCameraMatrix New camera matrix \f$A'=\vecthreethree{f_x'}{0}{c_x'}{0}{f_y'}{c_y'}{0}{0}{1}\f$.
-@param size Undistorted image size.
-@param m1type Type of the first output map that can be CV_32FC1, CV_32FC2 or CV_16SC2, see #convertMaps
-@param map1 The first output map.
-@param map2 The second output map.
- */
-CV_EXPORTS_W void initUndistortRectifyMap( InputArray cameraMatrix, InputArray distCoeffs,
-                           InputArray R, InputArray newCameraMatrix,
-                           Size size, int m1type, OutputArray map1, OutputArray map2 );
-
-//! initializes maps for #remap for wide-angle
-CV_EXPORTS_W float initWideAngleProjMap( InputArray cameraMatrix, InputArray distCoeffs,
-                                         Size imageSize, int destImageWidth,
-                                         int m1type, OutputArray map1, OutputArray map2,
-                                         int projType = PROJ_SPHERICAL_EQRECT, double alpha = 0);
-
-/** @brief Returns the default new camera matrix.
-
-The function returns the camera matrix that is either an exact copy of the input cameraMatrix (when
-centerPrinicipalPoint=false ), or the modified one (when centerPrincipalPoint=true).
-
-In the latter case, the new camera matrix will be:
-
-\f[\begin{bmatrix} f_x && 0 && ( \texttt{imgSize.width} -1)*0.5  \\ 0 && f_y && ( \texttt{imgSize.height} -1)*0.5  \\ 0 && 0 && 1 \end{bmatrix} ,\f]
-
-where \f$f_x\f$ and \f$f_y\f$ are \f$(0,0)\f$ and \f$(1,1)\f$ elements of cameraMatrix, respectively.
-
-By default, the undistortion functions in OpenCV (see #initUndistortRectifyMap, #undistort) do not
-move the principal point. However, when you work with stereo, it is important to move the principal
-points in both views to the same y-coordinate (which is required by most of stereo correspondence
-algorithms), and may be to the same x-coordinate too. So, you can form the new camera matrix for
-each view where the principal points are located at the center.
-
-@param cameraMatrix Input camera matrix.
-@param imgsize Camera view image size in pixels.
-@param centerPrincipalPoint Location of the principal point in the new camera matrix. The
-parameter indicates whether this location should be at the image center or not.
- */
-CV_EXPORTS_W Mat getDefaultNewCameraMatrix( InputArray cameraMatrix, Size imgsize = Size(),
-                                            bool centerPrincipalPoint = false );
-
-/** @brief Computes the ideal point coordinates from the observed point coordinates.
-
-The function is similar to #undistort and #initUndistortRectifyMap but it operates on a
-sparse set of points instead of a raster image. Also the function performs a reverse transformation
-to projectPoints. In case of a 3D object, it does not reconstruct its 3D coordinates, but for a
-planar object, it does, up to a translation vector, if the proper R is specified.
-
-For each observed point coordinate \f$(u, v)\f$ the function computes:
-\f[
-\begin{array}{l}
-x^{"}  \leftarrow (u - c_x)/f_x  \\
-y^{"}  \leftarrow (v - c_y)/f_y  \\
-(x',y') = undistort(x^{"},y^{"}, \texttt{distCoeffs}) \\
-{[X\,Y\,W]} ^T  \leftarrow R*[x' \, y' \, 1]^T  \\
-x  \leftarrow X/W  \\
-y  \leftarrow Y/W  \\
-\text{only performed if P is specified:} \\
-u'  \leftarrow x {f'}_x + {c'}_x  \\
-v'  \leftarrow y {f'}_y + {c'}_y
-\end{array}
-\f]
-
-where *undistort* is an approximate iterative algorithm that estimates the normalized original
-point coordinates out of the normalized distorted point coordinates ("normalized" means that the
-coordinates do not depend on the camera matrix).
-
-The function can be used for both a stereo camera head or a monocular camera (when R is empty).
-
-@param src Observed point coordinates, 1xN or Nx1 2-channel (CV_32FC2 or CV_64FC2).
-@param dst Output ideal point coordinates after undistortion and reverse perspective
-transformation. If matrix P is identity or omitted, dst will contain normalized point coordinates.
-@param cameraMatrix Camera matrix \f$\vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ .
-@param distCoeffs Input vector of distortion coefficients
-\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$
-of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
-@param R Rectification transformation in the object space (3x3 matrix). R1 or R2 computed by
-#stereoRectify can be passed here. If the matrix is empty, the identity transformation is used.
-@param P New camera matrix (3x3) or new projection matrix (3x4) \f$\begin{bmatrix} {f'}_x & 0 & {c'}_x & t_x \\ 0 & {f'}_y & {c'}_y & t_y \\ 0 & 0 & 1 & t_z \end{bmatrix}\f$. P1 or P2 computed by
-#stereoRectify can be passed here. If the matrix is empty, the identity new camera matrix is used.
- */
-CV_EXPORTS_W void undistortPoints( InputArray src, OutputArray dst,
-                                   InputArray cameraMatrix, InputArray distCoeffs,
-                                   InputArray R = noArray(), InputArray P = noArray());
-/** @overload
-    @note Default version of #undistortPoints does 5 iterations to compute undistorted points.
-
- */
-CV_EXPORTS_AS(undistortPointsIter) void undistortPoints( InputArray src, OutputArray dst,
-                                   InputArray cameraMatrix, InputArray distCoeffs,
-                                   InputArray R, InputArray P, TermCriteria criteria);
-
-//! @} imgproc_transform
-
-//! @addtogroup imgproc_hist
-//! @{
-
-/** @example demhist.cpp
-An example for creating histograms of an image
-*/
-
-/** @brief Calculates a histogram of a set of arrays.
-
-The function cv::calcHist calculates the histogram of one or more arrays. The elements of a tuple used
-to increment a histogram bin are taken from the corresponding input arrays at the same location. The
-sample below shows how to compute a 2D Hue-Saturation histogram for a color image. :
-@include snippets/imgproc_calcHist.cpp
-
-@param images Source arrays. They all should have the same depth, CV_8U, CV_16U or CV_32F , and the same
-size. Each of them can have an arbitrary number of channels.
-@param nimages Number of source images.
-@param channels List of the dims channels used to compute the histogram. The first array channels
-are numerated from 0 to images[0].channels()-1 , the second array channels are counted from
-images[0].channels() to images[0].channels() + images[1].channels()-1, and so on.
-@param mask Optional mask. If the matrix is not empty, it must be an 8-bit array of the same size
-as images[i] . The non-zero mask elements mark the array elements counted in the histogram.
-@param hist Output histogram, which is a dense or sparse dims -dimensional array.
-@param dims Histogram dimensionality that must be positive and not greater than CV_MAX_DIMS
-(equal to 32 in the current OpenCV version).
-@param histSize Array of histogram sizes in each dimension.
-@param ranges Array of the dims arrays of the histogram bin boundaries in each dimension. When the
-histogram is uniform ( uniform =true), then for each dimension i it is enough to specify the lower
-(inclusive) boundary \f$L_0\f$ of the 0-th histogram bin and the upper (exclusive) boundary
-\f$U_{\texttt{histSize}[i]-1}\f$ for the last histogram bin histSize[i]-1 . That is, in case of a
-uniform histogram each of ranges[i] is an array of 2 elements. When the histogram is not uniform (
-uniform=false ), then each of ranges[i] contains histSize[i]+1 elements:
-\f$L_0, U_0=L_1, U_1=L_2, ..., U_{\texttt{histSize[i]}-2}=L_{\texttt{histSize[i]}-1}, U_{\texttt{histSize[i]}-1}\f$
-. The array elements, that are not between \f$L_0\f$ and \f$U_{\texttt{histSize[i]}-1}\f$ , are not
-counted in the histogram.
-@param uniform Flag indicating whether the histogram is uniform or not (see above).
-@param accumulate Accumulation flag. If it is set, the histogram is not cleared in the beginning
-when it is allocated. This feature enables you to compute a single histogram from several sets of
-arrays, or to update the histogram in time.
-*/
-CV_EXPORTS void calcHist( const Mat* images, int nimages,
-                          const int* channels, InputArray mask,
-                          OutputArray hist, int dims, const int* histSize,
-                          const float** ranges, bool uniform = true, bool accumulate = false );
-
-/** @overload
-
-this variant uses %SparseMat for output
-*/
-CV_EXPORTS void calcHist( const Mat* images, int nimages,
-                          const int* channels, InputArray mask,
-                          SparseMat& hist, int dims,
-                          const int* histSize, const float** ranges,
-                          bool uniform = true, bool accumulate = false );
-
-/** @overload */
-CV_EXPORTS_W void calcHist( InputArrayOfArrays images,
-                            const std::vector<int>& channels,
-                            InputArray mask, OutputArray hist,
-                            const std::vector<int>& histSize,
-                            const std::vector<float>& ranges,
-                            bool accumulate = false );
-
-/** @brief Calculates the back projection of a histogram.
-
-The function cv::calcBackProject calculates the back project of the histogram. That is, similarly to
-#calcHist , at each location (x, y) the function collects the values from the selected channels
-in the input images and finds the corresponding histogram bin. But instead of incrementing it, the
-function reads the bin value, scales it by scale , and stores in backProject(x,y) . In terms of
-statistics, the function computes probability of each element value in respect with the empirical
-probability distribution represented by the histogram. See how, for example, you can find and track
-a bright-colored object in a scene:
-
-- Before tracking, show the object to the camera so that it covers almost the whole frame.
-Calculate a hue histogram. The histogram may have strong maximums, corresponding to the dominant
-colors in the object.
-
-- When tracking, calculate a back projection of a hue plane of each input video frame using that
-pre-computed histogram. Threshold the back projection to suppress weak colors. It may also make
-sense to suppress pixels with non-sufficient color saturation and too dark or too bright pixels.
-
-- Find connected components in the resulting picture and choose, for example, the largest
-component.
-
-This is an approximate algorithm of the CamShift color object tracker.
-
-@param images Source arrays. They all should have the same depth, CV_8U, CV_16U or CV_32F , and the same
-size. Each of them can have an arbitrary number of channels.
-@param nimages Number of source images.
-@param channels The list of channels used to compute the back projection. The number of channels
-must match the histogram dimensionality. The first array channels are numerated from 0 to
-images[0].channels()-1 , the second array channels are counted from images[0].channels() to
-images[0].channels() + images[1].channels()-1, and so on.
-@param hist Input histogram that can be dense or sparse.
-@param backProject Destination back projection array that is a single-channel array of the same
-size and depth as images[0] .
-@param ranges Array of arrays of the histogram bin boundaries in each dimension. See #calcHist .
-@param scale Optional scale factor for the output back projection.
-@param uniform Flag indicating whether the histogram is uniform or not (see above).
-
-@sa calcHist, compareHist
- */
-CV_EXPORTS void calcBackProject( const Mat* images, int nimages,
-                                 const int* channels, InputArray hist,
-                                 OutputArray backProject, const float** ranges,
-                                 double scale = 1, bool uniform = true );
-
-/** @overload */
-CV_EXPORTS void calcBackProject( const Mat* images, int nimages,
-                                 const int* channels, const SparseMat& hist,
-                                 OutputArray backProject, const float** ranges,
-                                 double scale = 1, bool uniform = true );
-
-/** @overload */
-CV_EXPORTS_W void calcBackProject( InputArrayOfArrays images, const std::vector<int>& channels,
-                                   InputArray hist, OutputArray dst,
-                                   const std::vector<float>& ranges,
-                                   double scale );
-
-/** @brief Compares two histograms.
-
-The function cv::compareHist compares two dense or two sparse histograms using the specified method.
-
-The function returns \f$d(H_1, H_2)\f$ .
-
-While the function works well with 1-, 2-, 3-dimensional dense histograms, it may not be suitable
-for high-dimensional sparse histograms. In such histograms, because of aliasing and sampling
-problems, the coordinates of non-zero histogram bins can slightly shift. To compare such histograms
-or more general sparse configurations of weighted points, consider using the #EMD function.
-
-@param H1 First compared histogram.
-@param H2 Second compared histogram of the same size as H1 .
-@param method Comparison method, see #HistCompMethods
- */
-CV_EXPORTS_W double compareHist( InputArray H1, InputArray H2, int method );
-
-/** @overload */
-CV_EXPORTS double compareHist( const SparseMat& H1, const SparseMat& H2, int method );
-
-/** @brief Equalizes the histogram of a grayscale image.
-
-The function equalizes the histogram of the input image using the following algorithm:
-
-- Calculate the histogram \f$H\f$ for src .
-- Normalize the histogram so that the sum of histogram bins is 255.
-- Compute the integral of the histogram:
-\f[H'_i =  \sum _{0  \le j < i} H(j)\f]
-- Transform the image using \f$H'\f$ as a look-up table: \f$\texttt{dst}(x,y) = H'(\texttt{src}(x,y))\f$
-
-The algorithm normalizes the brightness and increases the contrast of the image.
-
-@param src Source 8-bit single channel image.
-@param dst Destination image of the same size and type as src .
- */
-CV_EXPORTS_W void equalizeHist( InputArray src, OutputArray dst );
-
-/** @brief Computes the "minimal work" distance between two weighted point configurations.
-
-The function computes the earth mover distance and/or a lower boundary of the distance between the
-two weighted point configurations. One of the applications described in @cite RubnerSept98,
-@cite Rubner2000 is multi-dimensional histogram comparison for image retrieval. EMD is a transportation
-problem that is solved using some modification of a simplex algorithm, thus the complexity is
-exponential in the worst case, though, on average it is much faster. In the case of a real metric
-the lower boundary can be calculated even faster (using linear-time algorithm) and it can be used
-to determine roughly whether the two signatures are far enough so that they cannot relate to the
-same object.
-
-@param signature1 First signature, a \f$\texttt{size1}\times \texttt{dims}+1\f$ floating-point matrix.
-Each row stores the point weight followed by the point coordinates. The matrix is allowed to have
-a single column (weights only) if the user-defined cost matrix is used. The weights must be
-non-negative and have at least one non-zero value.
-@param signature2 Second signature of the same format as signature1 , though the number of rows
-may be different. The total weights may be different. In this case an extra "dummy" point is added
-to either signature1 or signature2. The weights must be non-negative and have at least one non-zero
-value.
-@param distType Used metric. See #DistanceTypes.
-@param cost User-defined \f$\texttt{size1}\times \texttt{size2}\f$ cost matrix. Also, if a cost matrix
-is used, lower boundary lowerBound cannot be calculated because it needs a metric function.
-@param lowerBound Optional input/output parameter: lower boundary of a distance between the two
-signatures that is a distance between mass centers. The lower boundary may not be calculated if
-the user-defined cost matrix is used, the total weights of point configurations are not equal, or
-if the signatures consist of weights only (the signature matrices have a single column). You
-**must** initialize \*lowerBound . If the calculated distance between mass centers is greater or
-equal to \*lowerBound (it means that the signatures are far enough), the function does not
-calculate EMD. In any case \*lowerBound is set to the calculated distance between mass centers on
-return. Thus, if you want to calculate both distance between mass centers and EMD, \*lowerBound
-should be set to 0.
-@param flow Resultant \f$\texttt{size1} \times \texttt{size2}\f$ flow matrix: \f$\texttt{flow}_{i,j}\f$ is
-a flow from \f$i\f$ -th point of signature1 to \f$j\f$ -th point of signature2 .
- */
-CV_EXPORTS float EMD( InputArray signature1, InputArray signature2,
-                      int distType, InputArray cost=noArray(),
-                      float* lowerBound = 0, OutputArray flow = noArray() );
-
-CV_EXPORTS_AS(EMD) float wrapperEMD( InputArray signature1, InputArray signature2,
-                      int distType, InputArray cost=noArray(),
-                      CV_IN_OUT Ptr<float> lowerBound = Ptr<float>(), OutputArray flow = noArray() );
-
-//! @} imgproc_hist
-
-/** @example watershed.cpp
-An example using the watershed algorithm
- */
-
-/** @brief Performs a marker-based image segmentation using the watershed algorithm.
-
-The function implements one of the variants of watershed, non-parametric marker-based segmentation
-algorithm, described in @cite Meyer92 .
-
-Before passing the image to the function, you have to roughly outline the desired regions in the
-image markers with positive (\>0) indices. So, every region is represented as one or more connected
-components with the pixel values 1, 2, 3, and so on. Such markers can be retrieved from a binary
-mask using #findContours and #drawContours (see the watershed.cpp demo). The markers are "seeds" of
-the future image regions. All the other pixels in markers , whose relation to the outlined regions
-is not known and should be defined by the algorithm, should be set to 0's. In the function output,
-each pixel in markers is set to a value of the "seed" components or to -1 at boundaries between the
-regions.
-
-@note Any two neighbor connected components are not necessarily separated by a watershed boundary
-(-1's pixels); for example, they can touch each other in the initial marker image passed to the
-function.
-
-@param image Input 8-bit 3-channel image.
-@param markers Input/output 32-bit single-channel image (map) of markers. It should have the same
-size as image .
-
-@sa findContours
-
-@ingroup imgproc_misc
- */
-CV_EXPORTS_W void watershed( InputArray image, InputOutputArray markers );
-
-//! @addtogroup imgproc_filter
-//! @{
-
-/** @brief Performs initial step of meanshift segmentation of an image.
-
-The function implements the filtering stage of meanshift segmentation, that is, the output of the
-function is the filtered "posterized" image with color gradients and fine-grain texture flattened.
-At every pixel (X,Y) of the input image (or down-sized input image, see below) the function executes
-meanshift iterations, that is, the pixel (X,Y) neighborhood in the joint space-color hyperspace is
-considered:
-
-\f[(x,y): X- \texttt{sp} \le x  \le X+ \texttt{sp} , Y- \texttt{sp} \le y  \le Y+ \texttt{sp} , ||(R,G,B)-(r,g,b)||   \le \texttt{sr}\f]
-
-where (R,G,B) and (r,g,b) are the vectors of color components at (X,Y) and (x,y), respectively
-(though, the algorithm does not depend on the color space used, so any 3-component color space can
-be used instead). Over the neighborhood the average spatial value (X',Y') and average color vector
-(R',G',B') are found and they act as the neighborhood center on the next iteration:
-
-\f[(X,Y)~(X',Y'), (R,G,B)~(R',G',B').\f]
-
-After the iterations over, the color components of the initial pixel (that is, the pixel from where
-the iterations started) are set to the final value (average color at the last iteration):
-
-\f[I(X,Y) <- (R*,G*,B*)\f]
-
-When maxLevel \> 0, the gaussian pyramid of maxLevel+1 levels is built, and the above procedure is
-run on the smallest layer first. After that, the results are propagated to the larger layer and the
-iterations are run again only on those pixels where the layer colors differ by more than sr from the
-lower-resolution layer of the pyramid. That makes boundaries of color regions sharper. Note that the
-results will be actually different from the ones obtained by running the meanshift procedure on the
-whole original image (i.e. when maxLevel==0).
-
-@param src The source 8-bit, 3-channel image.
-@param dst The destination image of the same format and the same size as the source.
-@param sp The spatial window radius.
-@param sr The color window radius.
-@param maxLevel Maximum level of the pyramid for the segmentation.
-@param termcrit Termination criteria: when to stop meanshift iterations.
- */
-CV_EXPORTS_W void pyrMeanShiftFiltering( InputArray src, OutputArray dst,
-                                         double sp, double sr, int maxLevel = 1,
-                                         TermCriteria termcrit=TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,5,1) );
-
-//! @}
-
-//! @addtogroup imgproc_misc
-//! @{
-
-/** @example grabcut.cpp
-An example using the GrabCut algorithm
-![Sample Screenshot](grabcut_output1.jpg)
- */
-
-/** @brief Runs the GrabCut algorithm.
-
-The function implements the [GrabCut image segmentation algorithm](http://en.wikipedia.org/wiki/GrabCut).
-
-@param img Input 8-bit 3-channel image.
-@param mask Input/output 8-bit single-channel mask. The mask is initialized by the function when
-mode is set to #GC_INIT_WITH_RECT. Its elements may have one of the #GrabCutClasses.
-@param rect ROI containing a segmented object. The pixels outside of the ROI are marked as
-"obvious background". The parameter is only used when mode==#GC_INIT_WITH_RECT .
-@param bgdModel Temporary array for the background model. Do not modify it while you are
-processing the same image.
-@param fgdModel Temporary arrays for the foreground model. Do not modify it while you are
-processing the same image.
-@param iterCount Number of iterations the algorithm should make before returning the result. Note
-that the result can be refined with further calls with mode==#GC_INIT_WITH_MASK or
-mode==GC_EVAL .
-@param mode Operation mode that could be one of the #GrabCutModes
- */
-CV_EXPORTS_W void grabCut( InputArray img, InputOutputArray mask, Rect rect,
-                           InputOutputArray bgdModel, InputOutputArray fgdModel,
-                           int iterCount, int mode = GC_EVAL );
-
-/** @example distrans.cpp
-An example on using the distance transform\
-*/
-
-
-/** @brief Calculates the distance to the closest zero pixel for each pixel of the source image.
-
-The function cv::distanceTransform calculates the approximate or precise distance from every binary
-image pixel to the nearest zero pixel. For zero image pixels, the distance will obviously be zero.
-
-When maskSize == #DIST_MASK_PRECISE and distanceType == #DIST_L2 , the function runs the
-algorithm described in @cite Felzenszwalb04 . This algorithm is parallelized with the TBB library.
-
-In other cases, the algorithm @cite Borgefors86 is used. This means that for a pixel the function
-finds the shortest path to the nearest zero pixel consisting of basic shifts: horizontal, vertical,
-diagonal, or knight's move (the latest is available for a \f$5\times 5\f$ mask). The overall
-distance is calculated as a sum of these basic distances. Since the distance function should be
-symmetric, all of the horizontal and vertical shifts must have the same cost (denoted as a ), all
-the diagonal shifts must have the same cost (denoted as `b`), and all knight's moves must have the
-same cost (denoted as `c`). For the #DIST_C and #DIST_L1 types, the distance is calculated
-precisely, whereas for #DIST_L2 (Euclidean distance) the distance can be calculated only with a
-relative error (a \f$5\times 5\f$ mask gives more accurate results). For `a`,`b`, and `c`, OpenCV
-uses the values suggested in the original paper:
-- DIST_L1: `a = 1, b = 2`
-- DIST_L2:
-    - `3 x 3`: `a=0.955, b=1.3693`
-    - `5 x 5`: `a=1, b=1.4, c=2.1969`
-- DIST_C: `a = 1, b = 1`
-
-Typically, for a fast, coarse distance estimation #DIST_L2, a \f$3\times 3\f$ mask is used. For a
-more accurate distance estimation #DIST_L2, a \f$5\times 5\f$ mask or the precise algorithm is used.
-Note that both the precise and the approximate algorithms are linear on the number of pixels.
-
-This variant of the function does not only compute the minimum distance for each pixel \f$(x, y)\f$
-but also identifies the nearest connected component consisting of zero pixels
-(labelType==#DIST_LABEL_CCOMP) or the nearest zero pixel (labelType==#DIST_LABEL_PIXEL). Index of the
-component/pixel is stored in `labels(x, y)`. When labelType==#DIST_LABEL_CCOMP, the function
-automatically finds connected components of zero pixels in the input image and marks them with
-distinct labels. When labelType==#DIST_LABEL_CCOMP, the function scans through the input image and
-marks all the zero pixels with distinct labels.
-
-In this mode, the complexity is still linear. That is, the function provides a very fast way to
-compute the Voronoi diagram for a binary image. Currently, the second variant can use only the
-approximate distance transform algorithm, i.e. maskSize=#DIST_MASK_PRECISE is not supported
-yet.
-
-@param src 8-bit, single-channel (binary) source image.
-@param dst Output image with calculated distances. It is a 8-bit or 32-bit floating-point,
-single-channel image of the same size as src.
-@param labels Output 2D array of labels (the discrete Voronoi diagram). It has the type
-CV_32SC1 and the same size as src.
-@param distanceType Type of distance, see #DistanceTypes
-@param maskSize Size of the distance transform mask, see #DistanceTransformMasks.
-#DIST_MASK_PRECISE is not supported by this variant. In case of the #DIST_L1 or #DIST_C distance type,
-the parameter is forced to 3 because a \f$3\times 3\f$ mask gives the same result as \f$5\times
-5\f$ or any larger aperture.
-@param labelType Type of the label array to build, see #DistanceTransformLabelTypes.
- */
-CV_EXPORTS_AS(distanceTransformWithLabels) void distanceTransform( InputArray src, OutputArray dst,
-                                     OutputArray labels, int distanceType, int maskSize,
-                                     int labelType = DIST_LABEL_CCOMP );
-
-/** @overload
-@param src 8-bit, single-channel (binary) source image.
-@param dst Output image with calculated distances. It is a 8-bit or 32-bit floating-point,
-single-channel image of the same size as src .
-@param distanceType Type of distance, see #DistanceTypes
-@param maskSize Size of the distance transform mask, see #DistanceTransformMasks. In case of the
-#DIST_L1 or #DIST_C distance type, the parameter is forced to 3 because a \f$3\times 3\f$ mask gives
-the same result as \f$5\times 5\f$ or any larger aperture.
-@param dstType Type of output image. It can be CV_8U or CV_32F. Type CV_8U can be used only for
-the first variant of the function and distanceType == #DIST_L1.
-*/
-CV_EXPORTS_W void distanceTransform( InputArray src, OutputArray dst,
-                                     int distanceType, int maskSize, int dstType=CV_32F);
-
-/** @example ffilldemo.cpp
-  An example using the FloodFill technique
-*/
-
-/** @overload
-
-variant without `mask` parameter
-*/
-CV_EXPORTS int floodFill( InputOutputArray image,
-                          Point seedPoint, Scalar newVal, CV_OUT Rect* rect = 0,
-                          Scalar loDiff = Scalar(), Scalar upDiff = Scalar(),
-                          int flags = 4 );
-
-/** @brief Fills a connected component with the given color.
-
-The function cv::floodFill fills a connected component starting from the seed point with the specified
-color. The connectivity is determined by the color/brightness closeness of the neighbor pixels. The
-pixel at \f$(x,y)\f$ is considered to belong to the repainted domain if:
-
-- in case of a grayscale image and floating range
-\f[\texttt{src} (x',y')- \texttt{loDiff} \leq \texttt{src} (x,y)  \leq \texttt{src} (x',y')+ \texttt{upDiff}\f]
-
-
-- in case of a grayscale image and fixed range
-\f[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)- \texttt{loDiff} \leq \texttt{src} (x,y)  \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)+ \texttt{upDiff}\f]
-
-
-- in case of a color image and floating range
-\f[\texttt{src} (x',y')_r- \texttt{loDiff} _r \leq \texttt{src} (x,y)_r \leq \texttt{src} (x',y')_r+ \texttt{upDiff} _r,\f]
-\f[\texttt{src} (x',y')_g- \texttt{loDiff} _g \leq \texttt{src} (x,y)_g \leq \texttt{src} (x',y')_g+ \texttt{upDiff} _g\f]
-and
-\f[\texttt{src} (x',y')_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} (x',y')_b+ \texttt{upDiff} _b\f]
-
-
-- in case of a color image and fixed range
-\f[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_r- \texttt{loDiff} _r \leq \texttt{src} (x,y)_r \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_r+ \texttt{upDiff} _r,\f]
-\f[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_g- \texttt{loDiff} _g \leq \texttt{src} (x,y)_g \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_g+ \texttt{upDiff} _g\f]
-and
-\f[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b+ \texttt{upDiff} _b\f]
-
-
-where \f$src(x',y')\f$ is the value of one of pixel neighbors that is already known to belong to the
-component. That is, to be added to the connected component, a color/brightness of the pixel should
-be close enough to:
-- Color/brightness of one of its neighbors that already belong to the connected component in case
-of a floating range.
-- Color/brightness of the seed point in case of a fixed range.
-
-Use these functions to either mark a connected component with the specified color in-place, or build
-a mask and then extract the contour, or copy the region to another image, and so on.
-
-@param image Input/output 1- or 3-channel, 8-bit, or floating-point image. It is modified by the
-function unless the #FLOODFILL_MASK_ONLY flag is set in the second variant of the function. See
-the details below.
-@param mask Operation mask that should be a single-channel 8-bit image, 2 pixels wider and 2 pixels
-taller than image. Since this is both an input and output parameter, you must take responsibility
-of initializing it. Flood-filling cannot go across non-zero pixels in the input mask. For example,
-an edge detector output can be used as a mask to stop filling at edges. On output, pixels in the
-mask corresponding to filled pixels in the image are set to 1 or to the a value specified in flags
-as described below. Additionally, the function fills the border of the mask with ones to simplify
-internal processing. It is therefore possible to use the same mask in multiple calls to the function
-to make sure the filled areas do not overlap.
-@param seedPoint Starting point.
-@param newVal New value of the repainted domain pixels.
-@param loDiff Maximal lower brightness/color difference between the currently observed pixel and
-one of its neighbors belonging to the component, or a seed pixel being added to the component.
-@param upDiff Maximal upper brightness/color difference between the currently observed pixel and
-one of its neighbors belonging to the component, or a seed pixel being added to the component.
-@param rect Optional output parameter set by the function to the minimum bounding rectangle of the
-repainted domain.
-@param flags Operation flags. The first 8 bits contain a connectivity value. The default value of
-4 means that only the four nearest neighbor pixels (those that share an edge) are considered. A
-connectivity value of 8 means that the eight nearest neighbor pixels (those that share a corner)
-will be considered. The next 8 bits (8-16) contain a value between 1 and 255 with which to fill
-the mask (the default value is 1). For example, 4 | ( 255 \<\< 8 ) will consider 4 nearest
-neighbours and fill the mask with a value of 255. The following additional options occupy higher
-bits and therefore may be further combined with the connectivity and mask fill values using
-bit-wise or (|), see #FloodFillFlags.
-
-@note Since the mask is larger than the filled image, a pixel \f$(x, y)\f$ in image corresponds to the
-pixel \f$(x+1, y+1)\f$ in the mask .
-
-@sa findContours
- */
-CV_EXPORTS_W int floodFill( InputOutputArray image, InputOutputArray mask,
-                            Point seedPoint, Scalar newVal, CV_OUT Rect* rect=0,
-                            Scalar loDiff = Scalar(), Scalar upDiff = Scalar(),
-                            int flags = 4 );
-
-/** @brief Converts an image from one color space to another.
-
-The function converts an input image from one color space to another. In case of a transformation
-to-from RGB color space, the order of the channels should be specified explicitly (RGB or BGR). Note
-that the default color format in OpenCV is often referred to as RGB but it is actually BGR (the
-bytes are reversed). So the first byte in a standard (24-bit) color image will be an 8-bit Blue
-component, the second byte will be Green, and the third byte will be Red. The fourth, fifth, and
-sixth bytes would then be the second pixel (Blue, then Green, then Red), and so on.
-
-The conventional ranges for R, G, and B channel values are:
--   0 to 255 for CV_8U images
--   0 to 65535 for CV_16U images
--   0 to 1 for CV_32F images
-
-In case of linear transformations, the range does not matter. But in case of a non-linear
-transformation, an input RGB image should be normalized to the proper value range to get the correct
-results, for example, for RGB \f$\rightarrow\f$ L\*u\*v\* transformation. For example, if you have a
-32-bit floating-point image directly converted from an 8-bit image without any scaling, then it will
-have the 0..255 value range instead of 0..1 assumed by the function. So, before calling #cvtColor ,
-you need first to scale the image down:
-@code
-    img *= 1./255;
-    cvtColor(img, img, COLOR_BGR2Luv);
-@endcode
-If you use #cvtColor with 8-bit images, the conversion will have some information lost. For many
-applications, this will not be noticeable but it is recommended to use 32-bit images in applications
-that need the full range of colors or that convert an image before an operation and then convert
-back.
-
-If conversion adds the alpha channel, its value will set to the maximum of corresponding channel
-range: 255 for CV_8U, 65535 for CV_16U, 1 for CV_32F.
-
-@param src input image: 8-bit unsigned, 16-bit unsigned ( CV_16UC... ), or single-precision
-floating-point.
-@param dst output image of the same size and depth as src.
-@param code color space conversion code (see #ColorConversionCodes).
-@param dstCn number of channels in the destination image; if the parameter is 0, the number of the
-channels is derived automatically from src and code.
-
-@see @ref imgproc_color_conversions
- */
-CV_EXPORTS_W void cvtColor( InputArray src, OutputArray dst, int code, int dstCn = 0 );
-
-CV_EXPORTS_W void cvtColorTwoPlane( InputArray src1, InputArray src2, OutputArray dst, int code );
-
-//! @} imgproc_misc
-
-// main function for all demosaicing processes
-CV_EXPORTS_W void demosaicing(InputArray _src, OutputArray _dst, int code, int dcn = 0);
-
-//! @addtogroup imgproc_shape
-//! @{
-
-/** @brief Calculates all of the moments up to the third order of a polygon or rasterized shape.
-
-The function computes moments, up to the 3rd order, of a vector shape or a rasterized shape. The
-results are returned in the structure cv::Moments.
-
-@param array Raster image (single-channel, 8-bit or floating-point 2D array) or an array (
-\f$1 \times N\f$ or \f$N \times 1\f$ ) of 2D points (Point or Point2f ).
-@param binaryImage If it is true, all non-zero image pixels are treated as 1's. The parameter is
-used for images only.
-@returns moments.
-
-@note Only applicable to contour moments calculations from Python bindings: Note that the numpy
-type for the input array should be either np.int32 or np.float32.
-
-@sa  contourArea, arcLength
- */
-CV_EXPORTS_W Moments moments( InputArray array, bool binaryImage = false );
-
-/** @brief Calculates seven Hu invariants.
-
-The function calculates seven Hu invariants (introduced in @cite Hu62; see also
-<http://en.wikipedia.org/wiki/Image_moment>) defined as:
-
-\f[\begin{array}{l} hu[0]= \eta _{20}+ \eta _{02} \\ hu[1]=( \eta _{20}- \eta _{02})^{2}+4 \eta _{11}^{2} \\ hu[2]=( \eta _{30}-3 \eta _{12})^{2}+ (3 \eta _{21}- \eta _{03})^{2} \\ hu[3]=( \eta _{30}+ \eta _{12})^{2}+ ( \eta _{21}+ \eta _{03})^{2} \\ hu[4]=( \eta _{30}-3 \eta _{12})( \eta _{30}+ \eta _{12})[( \eta _{30}+ \eta _{12})^{2}-3( \eta _{21}+ \eta _{03})^{2}]+(3 \eta _{21}- \eta _{03})( \eta _{21}+ \eta _{03})[3( \eta _{30}+ \eta _{12})^{2}-( \eta _{21}+ \eta _{03})^{2}] \\ hu[5]=( \eta _{20}- \eta _{02})[( \eta _{30}+ \eta _{12})^{2}- ( \eta _{21}+ \eta _{03})^{2}]+4 \eta _{11}( \eta _{30}+ \eta _{12})( \eta _{21}+ \eta _{03}) \\ hu[6]=(3 \eta _{21}- \eta _{03})( \eta _{21}+ \eta _{03})[3( \eta _{30}+ \eta _{12})^{2}-( \eta _{21}+ \eta _{03})^{2}]-( \eta _{30}-3 \eta _{12})( \eta _{21}+ \eta _{03})[3( \eta _{30}+ \eta _{12})^{2}-( \eta _{21}+ \eta _{03})^{2}] \\ \end{array}\f]
-
-where \f$\eta_{ji}\f$ stands for \f$\texttt{Moments::nu}_{ji}\f$ .
-
-These values are proved to be invariants to the image scale, rotation, and reflection except the
-seventh one, whose sign is changed by reflection. This invariance is proved with the assumption of
-infinite image resolution. In case of raster images, the computed Hu invariants for the original and
-transformed images are a bit different.
-
-@param moments Input moments computed with moments .
-@param hu Output Hu invariants.
-
-@sa matchShapes
- */
-CV_EXPORTS void HuMoments( const Moments& moments, double hu[7] );
-
-/** @overload */
-CV_EXPORTS_W void HuMoments( const Moments& m, OutputArray hu );
-
-//! @} imgproc_shape
-
-//! @addtogroup imgproc_object
-//! @{
-
-//! type of the template matching operation
-enum TemplateMatchModes {
-    TM_SQDIFF        = 0, //!< \f[R(x,y)= \sum _{x',y'} (T(x',y')-I(x+x',y+y'))^2\f]
-    TM_SQDIFF_NORMED = 1, //!< \f[R(x,y)= \frac{\sum_{x',y'} (T(x',y')-I(x+x',y+y'))^2}{\sqrt{\sum_{x',y'}T(x',y')^2 \cdot \sum_{x',y'} I(x+x',y+y')^2}}\f]
-    TM_CCORR         = 2, //!< \f[R(x,y)= \sum _{x',y'} (T(x',y')  \cdot I(x+x',y+y'))\f]
-    TM_CCORR_NORMED  = 3, //!< \f[R(x,y)= \frac{\sum_{x',y'} (T(x',y') \cdot I(x+x',y+y'))}{\sqrt{\sum_{x',y'}T(x',y')^2 \cdot \sum_{x',y'} I(x+x',y+y')^2}}\f]
-    TM_CCOEFF        = 4, //!< \f[R(x,y)= \sum _{x',y'} (T'(x',y')  \cdot I'(x+x',y+y'))\f]
-                          //!< where
-                          //!< \f[\begin{array}{l} T'(x',y')=T(x',y') - 1/(w  \cdot h)  \cdot \sum _{x'',y''} T(x'',y'') \\ I'(x+x',y+y')=I(x+x',y+y') - 1/(w  \cdot h)  \cdot \sum _{x'',y''} I(x+x'',y+y'') \end{array}\f]
-    TM_CCOEFF_NORMED = 5  //!< \f[R(x,y)= \frac{ \sum_{x',y'} (T'(x',y') \cdot I'(x+x',y+y')) }{ \sqrt{\sum_{x',y'}T'(x',y')^2 \cdot \sum_{x',y'} I'(x+x',y+y')^2} }\f]
-};
-
-/** @example MatchTemplate_Demo.cpp
-An example using Template Matching algorithm
- */
-/** @brief Compares a template against overlapped image regions.
-
-The function slides through image , compares the overlapped patches of size \f$w \times h\f$ against
-templ using the specified method and stores the comparison results in result . Here are the formulae
-for the available comparison methods ( \f$I\f$ denotes image, \f$T\f$ template, \f$R\f$ result ). The summation
-is done over template and/or the image patch: \f$x' = 0...w-1, y' = 0...h-1\f$
-
-After the function finishes the comparison, the best matches can be found as global minimums (when
-#TM_SQDIFF was used) or maximums (when #TM_CCORR or #TM_CCOEFF was used) using the
-#minMaxLoc function. In case of a color image, template summation in the numerator and each sum in
-the denominator is done over all of the channels and separate mean values are used for each channel.
-That is, the function can take a color template and a color image. The result will still be a
-single-channel image, which is easier to analyze.
-
-@param image Image where the search is running. It must be 8-bit or 32-bit floating-point.
-@param templ Searched template. It must be not greater than the source image and have the same
-data type.
-@param result Map of comparison results. It must be single-channel 32-bit floating-point. If image
-is \f$W \times H\f$ and templ is \f$w \times h\f$ , then result is \f$(W-w+1) \times (H-h+1)\f$ .
-@param method Parameter specifying the comparison method, see #TemplateMatchModes
-@param mask Mask of searched template. It must have the same datatype and size with templ. It is
-not set by default. Currently, only the #TM_SQDIFF and #TM_CCORR_NORMED methods are supported.
- */
-CV_EXPORTS_W void matchTemplate( InputArray image, InputArray templ,
-                                 OutputArray result, int method, InputArray mask = noArray() );
-
-//! @}
-
-//! @addtogroup imgproc_shape
-//! @{
-
-/** @brief computes the connected components labeled image of boolean image
-
-image with 4 or 8 way connectivity - returns N, the total number of labels [0, N-1] where 0
-represents the background label. ltype specifies the output label image type, an important
-consideration based on the total number of labels or alternatively the total number of pixels in
-the source image. ccltype specifies the connected components labeling algorithm to use, currently
-Grana (BBDT) and Wu's (SAUF) algorithms are supported, see the #ConnectedComponentsAlgorithmsTypes
-for details. Note that SAUF algorithm forces a row major ordering of labels while BBDT does not.
-This function uses parallel version of both Grana and Wu's algorithms if at least one allowed
-parallel framework is enabled and if the rows of the image are at least twice the number returned by #getNumberOfCPUs.
-
-@param image the 8-bit single-channel image to be labeled
-@param labels destination labeled image
-@param connectivity 8 or 4 for 8-way or 4-way connectivity respectively
-@param ltype output image label type. Currently CV_32S and CV_16U are supported.
-@param ccltype connected components algorithm type (see the #ConnectedComponentsAlgorithmsTypes).
-*/
-CV_EXPORTS_AS(connectedComponentsWithAlgorithm) int connectedComponents(InputArray image, OutputArray labels,
-                                                                        int connectivity, int ltype, int ccltype);
-
-
-/** @overload
-
-@param image the 8-bit single-channel image to be labeled
-@param labels destination labeled image
-@param connectivity 8 or 4 for 8-way or 4-way connectivity respectively
-@param ltype output image label type. Currently CV_32S and CV_16U are supported.
-*/
-CV_EXPORTS_W int connectedComponents(InputArray image, OutputArray labels,
-                                     int connectivity = 8, int ltype = CV_32S);
-
-
-/** @brief computes the connected components labeled image of boolean image and also produces a statistics output for each label
-
-image with 4 or 8 way connectivity - returns N, the total number of labels [0, N-1] where 0
-represents the background label. ltype specifies the output label image type, an important
-consideration based on the total number of labels or alternatively the total number of pixels in
-the source image. ccltype specifies the connected components labeling algorithm to use, currently
-Grana's (BBDT) and Wu's (SAUF) algorithms are supported, see the #ConnectedComponentsAlgorithmsTypes
-for details. Note that SAUF algorithm forces a row major ordering of labels while BBDT does not.
-This function uses parallel version of both Grana and Wu's algorithms (statistics included) if at least one allowed
-parallel framework is enabled and if the rows of the image are at least twice the number returned by #getNumberOfCPUs.
-
-@param image the 8-bit single-channel image to be labeled
-@param labels destination labeled image
-@param stats statistics output for each label, including the background label, see below for
-available statistics. Statistics are accessed via stats(label, COLUMN) where COLUMN is one of
-#ConnectedComponentsTypes. The data type is CV_32S.
-@param centroids centroid output for each label, including the background label. Centroids are
-accessed via centroids(label, 0) for x and centroids(label, 1) for y. The data type CV_64F.
-@param connectivity 8 or 4 for 8-way or 4-way connectivity respectively
-@param ltype output image label type. Currently CV_32S and CV_16U are supported.
-@param ccltype connected components algorithm type (see #ConnectedComponentsAlgorithmsTypes).
-*/
-CV_EXPORTS_AS(connectedComponentsWithStatsWithAlgorithm) int connectedComponentsWithStats(InputArray image, OutputArray labels,
-                                                                                          OutputArray stats, OutputArray centroids,
-                                                                                          int connectivity, int ltype, int ccltype);
-
-/** @overload
-@param image the 8-bit single-channel image to be labeled
-@param labels destination labeled image
-@param stats statistics output for each label, including the background label, see below for
-available statistics. Statistics are accessed via stats(label, COLUMN) where COLUMN is one of
-#ConnectedComponentsTypes. The data type is CV_32S.
-@param centroids centroid output for each label, including the background label. Centroids are
-accessed via centroids(label, 0) for x and centroids(label, 1) for y. The data type CV_64F.
-@param connectivity 8 or 4 for 8-way or 4-way connectivity respectively
-@param ltype output image label type. Currently CV_32S and CV_16U are supported.
-*/
-CV_EXPORTS_W int connectedComponentsWithStats(InputArray image, OutputArray labels,
-                                              OutputArray stats, OutputArray centroids,
-                                              int connectivity = 8, int ltype = CV_32S);
-
-
-/** @brief Finds contours in a binary image.
-
-The function retrieves contours from the binary image using the algorithm @cite Suzuki85 . The contours
-are a useful tool for shape analysis and object detection and recognition. See squares.cpp in the
-OpenCV sample directory.
-@note Since opencv 3.2 source image is not modified by this function.
-
-@param image Source, an 8-bit single-channel image. Non-zero pixels are treated as 1's. Zero
-pixels remain 0's, so the image is treated as binary . You can use #compare, #inRange, #threshold ,
-#adaptiveThreshold, #Canny, and others to create a binary image out of a grayscale or color one.
-If mode equals to #RETR_CCOMP or #RETR_FLOODFILL, the input can also be a 32-bit integer image of labels (CV_32SC1).
-@param contours Detected contours. Each contour is stored as a vector of points (e.g.
-std::vector<std::vector<cv::Point> >).
-@param hierarchy Optional output vector (e.g. std::vector<cv::Vec4i>), containing information about the image topology. It has
-as many elements as the number of contours. For each i-th contour contours[i], the elements
-hierarchy[i][0] , hierarchy[i][1] , hierarchy[i][2] , and hierarchy[i][3] are set to 0-based indices
-in contours of the next and previous contours at the same hierarchical level, the first child
-contour and the parent contour, respectively. If for the contour i there are no next, previous,
-parent, or nested contours, the corresponding elements of hierarchy[i] will be negative.
-@param mode Contour retrieval mode, see #RetrievalModes
-@param method Contour approximation method, see #ContourApproximationModes
-@param offset Optional offset by which every contour point is shifted. This is useful if the
-contours are extracted from the image ROI and then they should be analyzed in the whole image
-context.
- */
-CV_EXPORTS_W void findContours( InputOutputArray image, OutputArrayOfArrays contours,
-                              OutputArray hierarchy, int mode,
-                              int method, Point offset = Point());
-
-/** @overload */
-CV_EXPORTS void findContours( InputOutputArray image, OutputArrayOfArrays contours,
-                              int mode, int method, Point offset = Point());
-
-/** @brief Approximates a polygonal curve(s) with the specified precision.
-
-The function cv::approxPolyDP approximates a curve or a polygon with another curve/polygon with less
-vertices so that the distance between them is less or equal to the specified precision. It uses the
-Douglas-Peucker algorithm <http://en.wikipedia.org/wiki/Ramer-Douglas-Peucker_algorithm>
-
-@param curve Input vector of a 2D point stored in std::vector or Mat
-@param approxCurve Result of the approximation. The type should match the type of the input curve.
-@param epsilon Parameter specifying the approximation accuracy. This is the maximum distance
-between the original curve and its approximation.
-@param closed If true, the approximated curve is closed (its first and last vertices are
-connected). Otherwise, it is not closed.
- */
-CV_EXPORTS_W void approxPolyDP( InputArray curve,
-                                OutputArray approxCurve,
-                                double epsilon, bool closed );
-
-/** @brief Calculates a contour perimeter or a curve length.
-
-The function computes a curve length or a closed contour perimeter.
-
-@param curve Input vector of 2D points, stored in std::vector or Mat.
-@param closed Flag indicating whether the curve is closed or not.
- */
-CV_EXPORTS_W double arcLength( InputArray curve, bool closed );
-
-/** @brief Calculates the up-right bounding rectangle of a point set.
-
-The function calculates and returns the minimal up-right bounding rectangle for the specified point set.
-
-@param points Input 2D point set, stored in std::vector or Mat.
- */
-CV_EXPORTS_W Rect boundingRect( InputArray points );
-
-/** @brief Calculates a contour area.
-
-The function computes a contour area. Similarly to moments , the area is computed using the Green
-formula. Thus, the returned area and the number of non-zero pixels, if you draw the contour using
-#drawContours or #fillPoly , can be different. Also, the function will most certainly give a wrong
-results for contours with self-intersections.
-
-Example:
-@code
-    vector<Point> contour;
-    contour.push_back(Point2f(0, 0));
-    contour.push_back(Point2f(10, 0));
-    contour.push_back(Point2f(10, 10));
-    contour.push_back(Point2f(5, 4));
-
-    double area0 = contourArea(contour);
-    vector<Point> approx;
-    approxPolyDP(contour, approx, 5, true);
-    double area1 = contourArea(approx);
-
-    cout << "area0 =" << area0 << endl <<
-            "area1 =" << area1 << endl <<
-            "approx poly vertices" << approx.size() << endl;
-@endcode
-@param contour Input vector of 2D points (contour vertices), stored in std::vector or Mat.
-@param oriented Oriented area flag. If it is true, the function returns a signed area value,
-depending on the contour orientation (clockwise or counter-clockwise). Using this feature you can
-determine orientation of a contour by taking the sign of an area. By default, the parameter is
-false, which means that the absolute value is returned.
- */
-CV_EXPORTS_W double contourArea( InputArray contour, bool oriented = false );
-
-/** @brief Finds a rotated rectangle of the minimum area enclosing the input 2D point set.
-
-The function calculates and returns the minimum-area bounding rectangle (possibly rotated) for a
-specified point set. Developer should keep in mind that the returned RotatedRect can contain negative
-indices when data is close to the containing Mat element boundary.
-
-@param points Input vector of 2D points, stored in std::vector\<\> or Mat
- */
-CV_EXPORTS_W RotatedRect minAreaRect( InputArray points );
-
-/** @brief Finds the four vertices of a rotated rect. Useful to draw the rotated rectangle.
-
-The function finds the four vertices of a rotated rectangle. This function is useful to draw the
-rectangle. In C++, instead of using this function, you can directly use RotatedRect::points method. Please
-visit the @ref tutorial_bounding_rotated_ellipses "tutorial on Creating Bounding rotated boxes and ellipses for contours" for more information.
-
-@param box The input rotated rectangle. It may be the output of
-@param points The output array of four vertices of rectangles.
- */
-CV_EXPORTS_W void boxPoints(RotatedRect box, OutputArray points);
-
-/** @brief Finds a circle of the minimum area enclosing a 2D point set.
-
-The function finds the minimal enclosing circle of a 2D point set using an iterative algorithm.
-
-@param points Input vector of 2D points, stored in std::vector\<\> or Mat
-@param center Output center of the circle.
-@param radius Output radius of the circle.
- */
-CV_EXPORTS_W void minEnclosingCircle( InputArray points,
-                                      CV_OUT Point2f& center, CV_OUT float& radius );
-
-/** @example minarea.cpp
-  */
-
-/** @brief Finds a triangle of minimum area enclosing a 2D point set and returns its area.
-
-The function finds a triangle of minimum area enclosing the given set of 2D points and returns its
-area. The output for a given 2D point set is shown in the image below. 2D points are depicted in
-*red* and the enclosing triangle in *yellow*.
-
-![Sample output of the minimum enclosing triangle function](pics/minenclosingtriangle.png)
-
-The implementation of the algorithm is based on O'Rourke's @cite ORourke86 and Klee and Laskowski's
-@cite KleeLaskowski85 papers. O'Rourke provides a \f$\theta(n)\f$ algorithm for finding the minimal
-enclosing triangle of a 2D convex polygon with n vertices. Since the #minEnclosingTriangle function
-takes a 2D point set as input an additional preprocessing step of computing the convex hull of the
-2D point set is required. The complexity of the #convexHull function is \f$O(n log(n))\f$ which is higher
-than \f$\theta(n)\f$. Thus the overall complexity of the function is \f$O(n log(n))\f$.
-
-@param points Input vector of 2D points with depth CV_32S or CV_32F, stored in std::vector\<\> or Mat
-@param triangle Output vector of three 2D points defining the vertices of the triangle. The depth
-of the OutputArray must be CV_32F.
- */
-CV_EXPORTS_W double minEnclosingTriangle( InputArray points, CV_OUT OutputArray triangle );
-
-/** @brief Compares two shapes.
-
-The function compares two shapes. All three implemented methods use the Hu invariants (see #HuMoments)
-
-@param contour1 First contour or grayscale image.
-@param contour2 Second contour or grayscale image.
-@param method Comparison method, see #ShapeMatchModes
-@param parameter Method-specific parameter (not supported now).
- */
-CV_EXPORTS_W double matchShapes( InputArray contour1, InputArray contour2,
-                                 int method, double parameter );
-
-/** @example convexhull.cpp
-An example using the convexHull functionality
-*/
-
-/** @brief Finds the convex hull of a point set.
-
-The function cv::convexHull finds the convex hull of a 2D point set using the Sklansky's algorithm @cite Sklansky82
-that has *O(N logN)* complexity in the current implementation.
-
-@param points Input 2D point set, stored in std::vector or Mat.
-@param hull Output convex hull. It is either an integer vector of indices or vector of points. In
-the first case, the hull elements are 0-based indices of the convex hull points in the original
-array (since the set of convex hull points is a subset of the original point set). In the second
-case, hull elements are the convex hull points themselves.
-@param clockwise Orientation flag. If it is true, the output convex hull is oriented clockwise.
-Otherwise, it is oriented counter-clockwise. The assumed coordinate system has its X axis pointing
-to the right, and its Y axis pointing upwards.
-@param returnPoints Operation flag. In case of a matrix, when the flag is true, the function
-returns convex hull points. Otherwise, it returns indices of the convex hull points. When the
-output array is std::vector, the flag is ignored, and the output depends on the type of the
-vector: std::vector\<int\> implies returnPoints=false, std::vector\<Point\> implies
-returnPoints=true.
-
-@note `points` and `hull` should be different arrays, inplace processing isn't supported.
- */
-CV_EXPORTS_W void convexHull( InputArray points, OutputArray hull,
-                              bool clockwise = false, bool returnPoints = true );
-
-/** @brief Finds the convexity defects of a contour.
-
-The figure below displays convexity defects of a hand contour:
-
-![image](pics/defects.png)
-
-@param contour Input contour.
-@param convexhull Convex hull obtained using convexHull that should contain indices of the contour
-points that make the hull.
-@param convexityDefects The output vector of convexity defects. In C++ and the new Python/Java
-interface each convexity defect is represented as 4-element integer vector (a.k.a. #Vec4i):
-(start_index, end_index, farthest_pt_index, fixpt_depth), where indices are 0-based indices
-in the original contour of the convexity defect beginning, end and the farthest point, and
-fixpt_depth is fixed-point approximation (with 8 fractional bits) of the distance between the
-farthest contour point and the hull. That is, to get the floating-point value of the depth will be
-fixpt_depth/256.0.
- */
-CV_EXPORTS_W void convexityDefects( InputArray contour, InputArray convexhull, OutputArray convexityDefects );
-
-/** @brief Tests a contour convexity.
-
-The function tests whether the input contour is convex or not. The contour must be simple, that is,
-without self-intersections. Otherwise, the function output is undefined.
-
-@param contour Input vector of 2D points, stored in std::vector\<\> or Mat
- */
-CV_EXPORTS_W bool isContourConvex( InputArray contour );
-
-//! finds intersection of two convex polygons
-CV_EXPORTS_W float intersectConvexConvex( InputArray _p1, InputArray _p2,
-                                          OutputArray _p12, bool handleNested = true );
-
-/** @example fitellipse.cpp
-  An example using the fitEllipse technique
-*/
-
-/** @brief Fits an ellipse around a set of 2D points.
-
-The function calculates the ellipse that fits (in a least-squares sense) a set of 2D points best of
-all. It returns the rotated rectangle in which the ellipse is inscribed. The first algorithm described by @cite Fitzgibbon95
-is used. Developer should keep in mind that it is possible that the returned
-ellipse/rotatedRect data contains negative indices, due to the data points being close to the
-border of the containing Mat element.
-
-@param points Input 2D point set, stored in std::vector\<\> or Mat
- */
-CV_EXPORTS_W RotatedRect fitEllipse( InputArray points );
-
-/** @brief Fits an ellipse around a set of 2D points.
-
- The function calculates the ellipse that fits a set of 2D points.
- It returns the rotated rectangle in which the ellipse is inscribed.
- The Approximate Mean Square (AMS) proposed by @cite Taubin1991 is used.
-
- For an ellipse, this basis set is \f$ \chi= \left(x^2, x y, y^2, x, y, 1\right) \f$,
- which is a set of six free coefficients \f$ A^T=\left\{A_{\text{xx}},A_{\text{xy}},A_{\text{yy}},A_x,A_y,A_0\right\} \f$.
- However, to specify an ellipse, all that is needed is five numbers; the major and minor axes lengths \f$ (a,b) \f$,
- the position \f$ (x_0,y_0) \f$, and the orientation \f$ \theta \f$. This is because the basis set includes lines,
- quadratics, parabolic and hyperbolic functions as well as elliptical functions as possible fits.
- If the fit is found to be a parabolic or hyperbolic function then the standard #fitEllipse method is used.
- The AMS method restricts the fit to parabolic, hyperbolic and elliptical curves
- by imposing the condition that \f$ A^T ( D_x^T D_x  +   D_y^T D_y) A = 1 \f$ where
- the matrices \f$ Dx \f$ and \f$ Dy \f$ are the partial derivatives of the design matrix \f$ D \f$ with
- respect to x and y. The matrices are formed row by row applying the following to
- each of the points in the set:
- \f{align*}{
- D(i,:)&=\left\{x_i^2, x_i y_i, y_i^2, x_i, y_i, 1\right\} &
- D_x(i,:)&=\left\{2 x_i,y_i,0,1,0,0\right\} &
- D_y(i,:)&=\left\{0,x_i,2 y_i,0,1,0\right\}
- \f}
- The AMS method minimizes the cost function
- \f{equation*}{
- \epsilon ^2=\frac{ A^T D^T D A }{ A^T (D_x^T D_x +  D_y^T D_y) A^T }
- \f}
-
- The minimum cost is found by solving the generalized eigenvalue problem.
-
- \f{equation*}{
- D^T D A = \lambda  \left( D_x^T D_x +  D_y^T D_y\right) A
- \f}
-
- @param points Input 2D point set, stored in std::vector\<\> or Mat
- */
-CV_EXPORTS_W RotatedRect fitEllipseAMS( InputArray points );
-
-
-/** @brief Fits an ellipse around a set of 2D points.
-
- The function calculates the ellipse that fits a set of 2D points.
- It returns the rotated rectangle in which the ellipse is inscribed.
- The Direct least square (Direct) method by @cite Fitzgibbon1999 is used.
-
- For an ellipse, this basis set is \f$ \chi= \left(x^2, x y, y^2, x, y, 1\right) \f$,
- which is a set of six free coefficients \f$ A^T=\left\{A_{\text{xx}},A_{\text{xy}},A_{\text{yy}},A_x,A_y,A_0\right\} \f$.
- However, to specify an ellipse, all that is needed is five numbers; the major and minor axes lengths \f$ (a,b) \f$,
- the position \f$ (x_0,y_0) \f$, and the orientation \f$ \theta \f$. This is because the basis set includes lines,
- quadratics, parabolic and hyperbolic functions as well as elliptical functions as possible fits.
- The Direct method confines the fit to ellipses by ensuring that \f$ 4 A_{xx} A_{yy}- A_{xy}^2 > 0 \f$.
- The condition imposed is that \f$ 4 A_{xx} A_{yy}- A_{xy}^2=1 \f$ which satisfies the inequality
- and as the coefficients can be arbitrarily scaled is not overly restrictive.
-
- \f{equation*}{
- \epsilon ^2= A^T D^T D A \quad \text{with} \quad A^T C A =1 \quad \text{and} \quad C=\left(\begin{matrix}
- 0 & 0  & 2  & 0  & 0  &  0  \\
- 0 & -1  & 0  & 0  & 0  &  0 \\
- 2 & 0  & 0  & 0  & 0  &  0 \\
- 0 & 0  & 0  & 0  & 0  &  0 \\
- 0 & 0  & 0  & 0  & 0  &  0 \\
- 0 & 0  & 0  & 0  & 0  &  0
- \end{matrix} \right)
- \f}
-
- The minimum cost is found by solving the generalized eigenvalue problem.
-
- \f{equation*}{
- D^T D A = \lambda  \left( C\right) A
- \f}
-
- The system produces only one positive eigenvalue \f$ \lambda\f$ which is chosen as the solution
- with its eigenvector \f$\mathbf{u}\f$. These are used to find the coefficients
-
- \f{equation*}{
- A = \sqrt{\frac{1}{\mathbf{u}^T C \mathbf{u}}}  \mathbf{u}
- \f}
- The scaling factor guarantees that  \f$A^T C A =1\f$.
-
- @param points Input 2D point set, stored in std::vector\<\> or Mat
- */
-CV_EXPORTS_W RotatedRect fitEllipseDirect( InputArray points );
-
-/** @brief Fits a line to a 2D or 3D point set.
-
-The function fitLine fits a line to a 2D or 3D point set by minimizing \f$\sum_i \rho(r_i)\f$ where
-\f$r_i\f$ is a distance between the \f$i^{th}\f$ point, the line and \f$\rho(r)\f$ is a distance function, one
-of the following:
--  DIST_L2
-\f[\rho (r) = r^2/2  \quad \text{(the simplest and the fastest least-squares method)}\f]
-- DIST_L1
-\f[\rho (r) = r\f]
-- DIST_L12
-\f[\rho (r) = 2  \cdot ( \sqrt{1 + \frac{r^2}{2}} - 1)\f]
-- DIST_FAIR
-\f[\rho \left (r \right ) = C^2  \cdot \left (  \frac{r}{C} -  \log{\left(1 + \frac{r}{C}\right)} \right )  \quad \text{where} \quad C=1.3998\f]
-- DIST_WELSCH
-\f[\rho \left (r \right ) =  \frac{C^2}{2} \cdot \left ( 1 -  \exp{\left(-\left(\frac{r}{C}\right)^2\right)} \right )  \quad \text{where} \quad C=2.9846\f]
-- DIST_HUBER
-\f[\rho (r) =  \fork{r^2/2}{if \(r < C\)}{C \cdot (r-C/2)}{otherwise} \quad \text{where} \quad C=1.345\f]
-
-The algorithm is based on the M-estimator ( <http://en.wikipedia.org/wiki/M-estimator> ) technique
-that iteratively fits the line using the weighted least-squares algorithm. After each iteration the
-weights \f$w_i\f$ are adjusted to be inversely proportional to \f$\rho(r_i)\f$ .
-
-@param points Input vector of 2D or 3D points, stored in std::vector\<\> or Mat.
-@param line Output line parameters. In case of 2D fitting, it should be a vector of 4 elements
-(like Vec4f) - (vx, vy, x0, y0), where (vx, vy) is a normalized vector collinear to the line and
-(x0, y0) is a point on the line. In case of 3D fitting, it should be a vector of 6 elements (like
-Vec6f) - (vx, vy, vz, x0, y0, z0), where (vx, vy, vz) is a normalized vector collinear to the line
-and (x0, y0, z0) is a point on the line.
-@param distType Distance used by the M-estimator, see #DistanceTypes
-@param param Numerical parameter ( C ) for some types of distances. If it is 0, an optimal value
-is chosen.
-@param reps Sufficient accuracy for the radius (distance between the coordinate origin and the line).
-@param aeps Sufficient accuracy for the angle. 0.01 would be a good default value for reps and aeps.
- */
-CV_EXPORTS_W void fitLine( InputArray points, OutputArray line, int distType,
-                           double param, double reps, double aeps );
-
-/** @brief Performs a point-in-contour test.
-
-The function determines whether the point is inside a contour, outside, or lies on an edge (or
-coincides with a vertex). It returns positive (inside), negative (outside), or zero (on an edge)
-value, correspondingly. When measureDist=false , the return value is +1, -1, and 0, respectively.
-Otherwise, the return value is a signed distance between the point and the nearest contour edge.
-
-See below a sample output of the function where each image pixel is tested against the contour:
-
-![sample output](pics/pointpolygon.png)
-
-@param contour Input contour.
-@param pt Point tested against the contour.
-@param measureDist If true, the function estimates the signed distance from the point to the
-nearest contour edge. Otherwise, the function only checks if the point is inside a contour or not.
- */
-CV_EXPORTS_W double pointPolygonTest( InputArray contour, Point2f pt, bool measureDist );
-
-/** @brief Finds out if there is any intersection between two rotated rectangles.
-
-If there is then the vertices of the intersecting region are returned as well.
-
-Below are some examples of intersection configurations. The hatched pattern indicates the
-intersecting region and the red vertices are returned by the function.
-
-![intersection examples](pics/intersection.png)
-
-@param rect1 First rectangle
-@param rect2 Second rectangle
-@param intersectingRegion The output array of the vertices of the intersecting region. It returns
-at most 8 vertices. Stored as std::vector\<cv::Point2f\> or cv::Mat as Mx1 of type CV_32FC2.
-@returns One of #RectanglesIntersectTypes
- */
-CV_EXPORTS_W int rotatedRectangleIntersection( const RotatedRect& rect1, const RotatedRect& rect2, OutputArray intersectingRegion  );
-
-//! @} imgproc_shape
-/** @brief Creates implementation for cv::CLAHE .
-
-@param clipLimit Threshold for contrast limiting.
-@param tileGridSize Size of grid for histogram equalization. Input image will be divided into
-equally sized rectangular tiles. tileGridSize defines the number of tiles in row and column.
- */
-CV_EXPORTS_W Ptr<CLAHE> createCLAHE(double clipLimit = 40.0, Size tileGridSize = Size(8, 8));
-
-//! Ballard, D.H. (1981). Generalizing the Hough transform to detect arbitrary shapes. Pattern Recognition 13 (2): 111-122.
-//! Detects position only without translation and rotation
-CV_EXPORTS Ptr<GeneralizedHoughBallard> createGeneralizedHoughBallard();
-
-//! Guil, N., González-Linares, J.M. and Zapata, E.L. (1999). Bidimensional shape detection using an invariant approach. Pattern Recognition 32 (6): 1025-1038.
-//! Detects position, translation and rotation
-CV_EXPORTS Ptr<GeneralizedHoughGuil> createGeneralizedHoughGuil();
-
-//! Performs linear blending of two images:
-//! \f[ \texttt{dst}(i,j) = \texttt{weights1}(i,j)*\texttt{src1}(i,j) + \texttt{weights2}(i,j)*\texttt{src2}(i,j) \f]
-//! @param src1 It has a type of CV_8UC(n) or CV_32FC(n), where n is a positive integer.
-//! @param src2 It has the same type and size as src1.
-//! @param weights1 It has a type of CV_32FC1 and the same size with src1.
-//! @param weights2 It has a type of CV_32FC1 and the same size with src1.
-//! @param dst It is created if it does not have the same size and type with src1.
-CV_EXPORTS void blendLinear(InputArray src1, InputArray src2, InputArray weights1, InputArray weights2, OutputArray dst);
-
-//! @addtogroup imgproc_colormap
-//! @{
-
-//! GNU Octave/MATLAB equivalent colormaps
-enum ColormapTypes
-{
-    COLORMAP_AUTUMN = 0, //!< ![autumn](pics/colormaps/colorscale_autumn.jpg)
-    COLORMAP_BONE = 1, //!< ![bone](pics/colormaps/colorscale_bone.jpg)
-    COLORMAP_JET = 2, //!< ![jet](pics/colormaps/colorscale_jet.jpg)
-    COLORMAP_WINTER = 3, //!< ![winter](pics/colormaps/colorscale_winter.jpg)
-    COLORMAP_RAINBOW = 4, //!< ![rainbow](pics/colormaps/colorscale_rainbow.jpg)
-    COLORMAP_OCEAN = 5, //!< ![ocean](pics/colormaps/colorscale_ocean.jpg)
-    COLORMAP_SUMMER = 6, //!< ![summer](pics/colormaps/colorscale_summer.jpg)
-    COLORMAP_SPRING = 7, //!< ![spring](pics/colormaps/colorscale_spring.jpg)
-    COLORMAP_COOL = 8, //!< ![cool](pics/colormaps/colorscale_cool.jpg)
-    COLORMAP_HSV = 9, //!< ![HSV](pics/colormaps/colorscale_hsv.jpg)
-    COLORMAP_PINK = 10, //!< ![pink](pics/colormaps/colorscale_pink.jpg)
-    COLORMAP_HOT = 11, //!< ![hot](pics/colormaps/colorscale_hot.jpg)
-    COLORMAP_PARULA = 12 //!< ![parula](pics/colormaps/colorscale_parula.jpg)
-};
-
-/** @example falsecolor.cpp
-An example using applyColorMap function
-*/
-/** @brief Applies a GNU Octave/MATLAB equivalent colormap on a given image.
-
-@param src The source image, grayscale or colored of type CV_8UC1 or CV_8UC3.
-@param dst The result is the colormapped source image. Note: Mat::create is called on dst.
-@param colormap The colormap to apply, see #ColormapTypes
-*/
-CV_EXPORTS_W void applyColorMap(InputArray src, OutputArray dst, int colormap);
-
-/** @brief Applies a user colormap on a given image.
-
-@param src The source image, grayscale or colored of type CV_8UC1 or CV_8UC3.
-@param dst The result is the colormapped source image. Note: Mat::create is called on dst.
-@param userColor The colormap to apply of type CV_8UC1 or CV_8UC3 and size 256
-*/
-CV_EXPORTS_W void applyColorMap(InputArray src, OutputArray dst, InputArray userColor);
-
-//! @} imgproc_colormap
-
-//! @addtogroup imgproc_draw
-//! @{
-
-
-/** OpenCV color channel order is BGR[A] */
-#define CV_RGB(r, g, b)  cv::Scalar((b), (g), (r), 0)
-
-/** @brief Draws a line segment connecting two points.
-
-The function line draws the line segment between pt1 and pt2 points in the image. The line is
-clipped by the image boundaries. For non-antialiased lines with integer coordinates, the 8-connected
-or 4-connected Bresenham algorithm is used. Thick lines are drawn with rounding endings. Antialiased
-lines are drawn using Gaussian filtering.
-
-@param img Image.
-@param pt1 First point of the line segment.
-@param pt2 Second point of the line segment.
-@param color Line color.
-@param thickness Line thickness.
-@param lineType Type of the line. See #LineTypes.
-@param shift Number of fractional bits in the point coordinates.
- */
-CV_EXPORTS_W void line(InputOutputArray img, Point pt1, Point pt2, const Scalar& color,
-                     int thickness = 1, int lineType = LINE_8, int shift = 0);
-
-/** @brief Draws a arrow segment pointing from the first point to the second one.
-
-The function cv::arrowedLine draws an arrow between pt1 and pt2 points in the image. See also #line.
-
-@param img Image.
-@param pt1 The point the arrow starts from.
-@param pt2 The point the arrow points to.
-@param color Line color.
-@param thickness Line thickness.
-@param line_type Type of the line. See #LineTypes
-@param shift Number of fractional bits in the point coordinates.
-@param tipLength The length of the arrow tip in relation to the arrow length
- */
-CV_EXPORTS_W void arrowedLine(InputOutputArray img, Point pt1, Point pt2, const Scalar& color,
-                     int thickness=1, int line_type=8, int shift=0, double tipLength=0.1);
-
-/** @brief Draws a simple, thick, or filled up-right rectangle.
-
-The function cv::rectangle draws a rectangle outline or a filled rectangle whose two opposite corners
-are pt1 and pt2.
-
-@param img Image.
-@param pt1 Vertex of the rectangle.
-@param pt2 Vertex of the rectangle opposite to pt1 .
-@param color Rectangle color or brightness (grayscale image).
-@param thickness Thickness of lines that make up the rectangle. Negative values, like #FILLED,
-mean that the function has to draw a filled rectangle.
-@param lineType Type of the line. See #LineTypes
-@param shift Number of fractional bits in the point coordinates.
- */
-CV_EXPORTS_W void rectangle(InputOutputArray img, Point pt1, Point pt2,
-                          const Scalar& color, int thickness = 1,
-                          int lineType = LINE_8, int shift = 0);
-
-/** @overload
-
-use `rec` parameter as alternative specification of the drawn rectangle: `r.tl() and
-r.br()-Point(1,1)` are opposite corners
-*/
-CV_EXPORTS void rectangle(CV_IN_OUT Mat& img, Rect rec,
-                          const Scalar& color, int thickness = 1,
-                          int lineType = LINE_8, int shift = 0);
-
-/** @example Drawing_2.cpp
-An example using drawing functions
- */
-/** @brief Draws a circle.
-
-The function cv::circle draws a simple or filled circle with a given center and radius.
-@param img Image where the circle is drawn.
-@param center Center of the circle.
-@param radius Radius of the circle.
-@param color Circle color.
-@param thickness Thickness of the circle outline, if positive. Negative values, like #FILLED,
-mean that a filled circle is to be drawn.
-@param lineType Type of the circle boundary. See #LineTypes
-@param shift Number of fractional bits in the coordinates of the center and in the radius value.
- */
-CV_EXPORTS_W void circle(InputOutputArray img, Point center, int radius,
-                       const Scalar& color, int thickness = 1,
-                       int lineType = LINE_8, int shift = 0);
-
-/** @brief Draws a simple or thick elliptic arc or fills an ellipse sector.
-
-The function cv::ellipse with more parameters draws an ellipse outline, a filled ellipse, an elliptic
-arc, or a filled ellipse sector. The drawing code uses general parametric form.
-A piecewise-linear curve is used to approximate the elliptic arc
-boundary. If you need more control of the ellipse rendering, you can retrieve the curve using
-#ellipse2Poly and then render it with #polylines or fill it with #fillPoly. If you use the first
-variant of the function and want to draw the whole ellipse, not an arc, pass `startAngle=0` and
-`endAngle=360`. If `startAngle` is greater than `endAngle`, they are swapped. The figure below explains
-the meaning of the parameters to draw the blue arc.
-
-![Parameters of Elliptic Arc](pics/ellipse.svg)
-
-@param img Image.
-@param center Center of the ellipse.
-@param axes Half of the size of the ellipse main axes.
-@param angle Ellipse rotation angle in degrees.
-@param startAngle Starting angle of the elliptic arc in degrees.
-@param endAngle Ending angle of the elliptic arc in degrees.
-@param color Ellipse color.
-@param thickness Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that
-a filled ellipse sector is to be drawn.
-@param lineType Type of the ellipse boundary. See #LineTypes
-@param shift Number of fractional bits in the coordinates of the center and values of axes.
- */
-CV_EXPORTS_W void ellipse(InputOutputArray img, Point center, Size axes,
-                        double angle, double startAngle, double endAngle,
-                        const Scalar& color, int thickness = 1,
-                        int lineType = LINE_8, int shift = 0);
-
-/** @overload
-@param img Image.
-@param box Alternative ellipse representation via RotatedRect. This means that the function draws
-an ellipse inscribed in the rotated rectangle.
-@param color Ellipse color.
-@param thickness Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that
-a filled ellipse sector is to be drawn.
-@param lineType Type of the ellipse boundary. See #LineTypes
-*/
-CV_EXPORTS_W void ellipse(InputOutputArray img, const RotatedRect& box, const Scalar& color,
-                        int thickness = 1, int lineType = LINE_8);
-
-/* ----------------------------------------------------------------------------------------- */
-/* ADDING A SET OF PREDEFINED MARKERS WHICH COULD BE USED TO HIGHLIGHT POSITIONS IN AN IMAGE */
-/* ----------------------------------------------------------------------------------------- */
-
-//! Possible set of marker types used for the cv::drawMarker function
-enum MarkerTypes
-{
-    MARKER_CROSS = 0,           //!< A crosshair marker shape
-    MARKER_TILTED_CROSS = 1,    //!< A 45 degree tilted crosshair marker shape
-    MARKER_STAR = 2,            //!< A star marker shape, combination of cross and tilted cross
-    MARKER_DIAMOND = 3,         //!< A diamond marker shape
-    MARKER_SQUARE = 4,          //!< A square marker shape
-    MARKER_TRIANGLE_UP = 5,     //!< An upwards pointing triangle marker shape
-    MARKER_TRIANGLE_DOWN = 6    //!< A downwards pointing triangle marker shape
-};
-
-/** @brief Draws a marker on a predefined position in an image.
-
-The function cv::drawMarker draws a marker on a given position in the image. For the moment several
-marker types are supported, see #MarkerTypes for more information.
-
-@param img Image.
-@param position The point where the crosshair is positioned.
-@param color Line color.
-@param markerType The specific type of marker you want to use, see #MarkerTypes
-@param thickness Line thickness.
-@param line_type Type of the line, See #LineTypes
-@param markerSize The length of the marker axis [default = 20 pixels]
- */
-CV_EXPORTS_W void drawMarker(CV_IN_OUT Mat& img, Point position, const Scalar& color,
-                             int markerType = MARKER_CROSS, int markerSize=20, int thickness=1,
-                             int line_type=8);
-
-/* ----------------------------------------------------------------------------------------- */
-/* END OF MARKER SECTION */
-/* ----------------------------------------------------------------------------------------- */
-
-/** @overload */
-CV_EXPORTS void fillConvexPoly(Mat& img, const Point* pts, int npts,
-                               const Scalar& color, int lineType = LINE_8,
-                               int shift = 0);
-
-/** @brief Fills a convex polygon.
-
-The function cv::fillConvexPoly draws a filled convex polygon. This function is much faster than the
-function #fillPoly . It can fill not only convex polygons but any monotonic polygon without
-self-intersections, that is, a polygon whose contour intersects every horizontal line (scan line)
-twice at the most (though, its top-most and/or the bottom edge could be horizontal).
-
-@param img Image.
-@param points Polygon vertices.
-@param color Polygon color.
-@param lineType Type of the polygon boundaries. See #LineTypes
-@param shift Number of fractional bits in the vertex coordinates.
- */
-CV_EXPORTS_W void fillConvexPoly(InputOutputArray img, InputArray points,
-                                 const Scalar& color, int lineType = LINE_8,
-                                 int shift = 0);
-
-/** @overload */
-CV_EXPORTS void fillPoly(Mat& img, const Point** pts,
-                         const int* npts, int ncontours,
-                         const Scalar& color, int lineType = LINE_8, int shift = 0,
-                         Point offset = Point() );
-
-/** @example Drawing_1.cpp
-An example using drawing functions
- */
-/** @brief Fills the area bounded by one or more polygons.
-
-The function cv::fillPoly fills an area bounded by several polygonal contours. The function can fill
-complex areas, for example, areas with holes, contours with self-intersections (some of their
-parts), and so forth.
-
-@param img Image.
-@param pts Array of polygons where each polygon is represented as an array of points.
-@param color Polygon color.
-@param lineType Type of the polygon boundaries. See #LineTypes
-@param shift Number of fractional bits in the vertex coordinates.
-@param offset Optional offset of all points of the contours.
- */
-CV_EXPORTS_W void fillPoly(InputOutputArray img, InputArrayOfArrays pts,
-                           const Scalar& color, int lineType = LINE_8, int shift = 0,
-                           Point offset = Point() );
-
-/** @overload */
-CV_EXPORTS void polylines(Mat& img, const Point* const* pts, const int* npts,
-                          int ncontours, bool isClosed, const Scalar& color,
-                          int thickness = 1, int lineType = LINE_8, int shift = 0 );
-
-/** @brief Draws several polygonal curves.
-
-@param img Image.
-@param pts Array of polygonal curves.
-@param isClosed Flag indicating whether the drawn polylines are closed or not. If they are closed,
-the function draws a line from the last vertex of each curve to its first vertex.
-@param color Polyline color.
-@param thickness Thickness of the polyline edges.
-@param lineType Type of the line segments. See #LineTypes
-@param shift Number of fractional bits in the vertex coordinates.
-
-The function cv::polylines draws one or more polygonal curves.
- */
-CV_EXPORTS_W void polylines(InputOutputArray img, InputArrayOfArrays pts,
-                            bool isClosed, const Scalar& color,
-                            int thickness = 1, int lineType = LINE_8, int shift = 0 );
-
-/** @example contours2.cpp
-  An example program illustrates the use of cv::findContours and cv::drawContours
-  \image html WindowsQtContoursOutput.png "Screenshot of the program"
-*/
-
-/** @example segment_objects.cpp
-An example using drawContours to clean up a background segmentation result
- */
-
-/** @brief Draws contours outlines or filled contours.
-
-The function draws contour outlines in the image if \f$\texttt{thickness} \ge 0\f$ or fills the area
-bounded by the contours if \f$\texttt{thickness}<0\f$ . The example below shows how to retrieve
-connected components from the binary image and label them: :
-@include snippets/imgproc_drawContours.cpp
-
-@param image Destination image.
-@param contours All the input contours. Each contour is stored as a point vector.
-@param contourIdx Parameter indicating a contour to draw. If it is negative, all the contours are drawn.
-@param color Color of the contours.
-@param thickness Thickness of lines the contours are drawn with. If it is negative (for example,
-thickness=#FILLED ), the contour interiors are drawn.
-@param lineType Line connectivity. See #LineTypes
-@param hierarchy Optional information about hierarchy. It is only needed if you want to draw only
-some of the contours (see maxLevel ).
-@param maxLevel Maximal level for drawn contours. If it is 0, only the specified contour is drawn.
-If it is 1, the function draws the contour(s) and all the nested contours. If it is 2, the function
-draws the contours, all the nested contours, all the nested-to-nested contours, and so on. This
-parameter is only taken into account when there is hierarchy available.
-@param offset Optional contour shift parameter. Shift all the drawn contours by the specified
-\f$\texttt{offset}=(dx,dy)\f$ .
-@note When thickness=#FILLED, the function is designed to handle connected components with holes correctly
-even when no hierarchy date is provided. This is done by analyzing all the outlines together
-using even-odd rule. This may give incorrect results if you have a joint collection of separately retrieved
-contours. In order to solve this problem, you need to call #drawContours separately for each sub-group
-of contours, or iterate over the collection using contourIdx parameter.
- */
-CV_EXPORTS_W void drawContours( InputOutputArray image, InputArrayOfArrays contours,
-                              int contourIdx, const Scalar& color,
-                              int thickness = 1, int lineType = LINE_8,
-                              InputArray hierarchy = noArray(),
-                              int maxLevel = INT_MAX, Point offset = Point() );
-
-/** @brief Clips the line against the image rectangle.
-
-The function cv::clipLine calculates a part of the line segment that is entirely within the specified
-rectangle. it returns false if the line segment is completely outside the rectangle. Otherwise,
-it returns true .
-@param imgSize Image size. The image rectangle is Rect(0, 0, imgSize.width, imgSize.height) .
-@param pt1 First line point.
-@param pt2 Second line point.
- */
-CV_EXPORTS bool clipLine(Size imgSize, CV_IN_OUT Point& pt1, CV_IN_OUT Point& pt2);
-
-/** @overload
-@param imgSize Image size. The image rectangle is Rect(0, 0, imgSize.width, imgSize.height) .
-@param pt1 First line point.
-@param pt2 Second line point.
-*/
-CV_EXPORTS bool clipLine(Size2l imgSize, CV_IN_OUT Point2l& pt1, CV_IN_OUT Point2l& pt2);
-
-/** @overload
-@param imgRect Image rectangle.
-@param pt1 First line point.
-@param pt2 Second line point.
-*/
-CV_EXPORTS_W bool clipLine(Rect imgRect, CV_OUT CV_IN_OUT Point& pt1, CV_OUT CV_IN_OUT Point& pt2);
-
-/** @brief Approximates an elliptic arc with a polyline.
-
-The function ellipse2Poly computes the vertices of a polyline that approximates the specified
-elliptic arc. It is used by #ellipse. If `arcStart` is greater than `arcEnd`, they are swapped.
-
-@param center Center of the arc.
-@param axes Half of the size of the ellipse main axes. See #ellipse for details.
-@param angle Rotation angle of the ellipse in degrees. See #ellipse for details.
-@param arcStart Starting angle of the elliptic arc in degrees.
-@param arcEnd Ending angle of the elliptic arc in degrees.
-@param delta Angle between the subsequent polyline vertices. It defines the approximation
-accuracy.
-@param pts Output vector of polyline vertices.
- */
-CV_EXPORTS_W void ellipse2Poly( Point center, Size axes, int angle,
-                                int arcStart, int arcEnd, int delta,
-                                CV_OUT std::vector<Point>& pts );
-
-/** @overload
-@param center Center of the arc.
-@param axes Half of the size of the ellipse main axes. See #ellipse for details.
-@param angle Rotation angle of the ellipse in degrees. See #ellipse for details.
-@param arcStart Starting angle of the elliptic arc in degrees.
-@param arcEnd Ending angle of the elliptic arc in degrees.
-@param delta Angle between the subsequent polyline vertices. It defines the approximation accuracy.
-@param pts Output vector of polyline vertices.
-*/
-CV_EXPORTS void ellipse2Poly(Point2d center, Size2d axes, int angle,
-                             int arcStart, int arcEnd, int delta,
-                             CV_OUT std::vector<Point2d>& pts);
-
-/** @brief Draws a text string.
-
-The function cv::putText renders the specified text string in the image. Symbols that cannot be rendered
-using the specified font are replaced by question marks. See #getTextSize for a text rendering code
-example.
-
-@param img Image.
-@param text Text string to be drawn.
-@param org Bottom-left corner of the text string in the image.
-@param fontFace Font type, see #HersheyFonts.
-@param fontScale Font scale factor that is multiplied by the font-specific base size.
-@param color Text color.
-@param thickness Thickness of the lines used to draw a text.
-@param lineType Line type. See #LineTypes
-@param bottomLeftOrigin When true, the image data origin is at the bottom-left corner. Otherwise,
-it is at the top-left corner.
- */
-CV_EXPORTS_W void putText( InputOutputArray img, const String& text, Point org,
-                         int fontFace, double fontScale, Scalar color,
-                         int thickness = 1, int lineType = LINE_8,
-                         bool bottomLeftOrigin = false );
-
-/** @brief Calculates the width and height of a text string.
-
-The function cv::getTextSize calculates and returns the size of a box that contains the specified text.
-That is, the following code renders some text, the tight box surrounding it, and the baseline: :
-@code
-    String text = "Funny text inside the box";
-    int fontFace = FONT_HERSHEY_SCRIPT_SIMPLEX;
-    double fontScale = 2;
-    int thickness = 3;
-
-    Mat img(600, 800, CV_8UC3, Scalar::all(0));
-
-    int baseline=0;
-    Size textSize = getTextSize(text, fontFace,
-                                fontScale, thickness, &baseline);
-    baseline += thickness;
-
-    // center the text
-    Point textOrg((img.cols - textSize.width)/2,
-                  (img.rows + textSize.height)/2);
-
-    // draw the box
-    rectangle(img, textOrg + Point(0, baseline),
-              textOrg + Point(textSize.width, -textSize.height),
-              Scalar(0,0,255));
-    // ... and the baseline first
-    line(img, textOrg + Point(0, thickness),
-         textOrg + Point(textSize.width, thickness),
-         Scalar(0, 0, 255));
-
-    // then put the text itself
-    putText(img, text, textOrg, fontFace, fontScale,
-            Scalar::all(255), thickness, 8);
-@endcode
-
-@param text Input text string.
-@param fontFace Font to use, see #HersheyFonts.
-@param fontScale Font scale factor that is multiplied by the font-specific base size.
-@param thickness Thickness of lines used to render the text. See #putText for details.
-@param[out] baseLine y-coordinate of the baseline relative to the bottom-most text
-point.
-@return The size of a box that contains the specified text.
-
-@see putText
- */
-CV_EXPORTS_W Size getTextSize(const String& text, int fontFace,
-                            double fontScale, int thickness,
-                            CV_OUT int* baseLine);
-
-
-/** @brief Calculates the font-specific size to use to achieve a given height in pixels.
-
-@param fontFace Font to use, see cv::HersheyFonts.
-@param pixelHeight Pixel height to compute the fontScale for
-@param thickness Thickness of lines used to render the text.See putText for details.
-@return The fontSize to use for cv::putText
-
-@see cv::putText
-*/
-CV_EXPORTS_W double getFontScaleFromHeight(const int fontFace,
-                                           const int pixelHeight,
-                                           const int thickness = 1);
-
-/** @brief Line iterator
-
-The class is used to iterate over all the pixels on the raster line
-segment connecting two specified points.
-
-The class LineIterator is used to get each pixel of a raster line. It
-can be treated as versatile implementation of the Bresenham algorithm
-where you can stop at each pixel and do some extra processing, for
-example, grab pixel values along the line or draw a line with an effect
-(for example, with XOR operation).
-
-The number of pixels along the line is stored in LineIterator::count.
-The method LineIterator::pos returns the current position in the image:
-
-@code{.cpp}
-// grabs pixels along the line (pt1, pt2)
-// from 8-bit 3-channel image to the buffer
-LineIterator it(img, pt1, pt2, 8);
-LineIterator it2 = it;
-vector<Vec3b> buf(it.count);
-
-for(int i = 0; i < it.count; i++, ++it)
-    buf[i] = *(const Vec3b)*it;
-
-// alternative way of iterating through the line
-for(int i = 0; i < it2.count; i++, ++it2)
-{
-    Vec3b val = img.at<Vec3b>(it2.pos());
-    CV_Assert(buf[i] == val);
-}
-@endcode
-*/
-class CV_EXPORTS LineIterator
-{
-public:
-    /** @brief initializes the iterator
-
-    creates iterators for the line connecting pt1 and pt2
-    the line will be clipped on the image boundaries
-    the line is 8-connected or 4-connected
-    If leftToRight=true, then the iteration is always done
-    from the left-most point to the right most,
-    not to depend on the ordering of pt1 and pt2 parameters
-    */
-    LineIterator( const Mat& img, Point pt1, Point pt2,
-                  int connectivity = 8, bool leftToRight = false );
-    /** @brief returns pointer to the current pixel
-    */
-    uchar* operator *();
-    /** @brief prefix increment operator (++it). shifts iterator to the next pixel
-    */
-    LineIterator& operator ++();
-    /** @brief postfix increment operator (it++). shifts iterator to the next pixel
-    */
-    LineIterator operator ++(int);
-    /** @brief returns coordinates of the current pixel
-    */
-    Point pos() const;
-
-    uchar* ptr;
-    const uchar* ptr0;
-    int step, elemSize;
-    int err, count;
-    int minusDelta, plusDelta;
-    int minusStep, plusStep;
-};
-
-//! @cond IGNORED
-
-// === LineIterator implementation ===
-
-inline
-uchar* LineIterator::operator *()
-{
-    return ptr;
-}
-
-inline
-LineIterator& LineIterator::operator ++()
-{
-    int mask = err < 0 ? -1 : 0;
-    err += minusDelta + (plusDelta & mask);
-    ptr += minusStep + (plusStep & mask);
-    return *this;
-}
-
-inline
-LineIterator LineIterator::operator ++(int)
-{
-    LineIterator it = *this;
-    ++(*this);
-    return it;
-}
-
-inline
-Point LineIterator::pos() const
-{
-    Point p;
-    p.y = (int)((ptr - ptr0)/step);
-    p.x = (int)(((ptr - ptr0) - p.y*step)/elemSize);
-    return p;
-}
-
-//! @endcond
-
-//! @} imgproc_draw
-
-//! @} imgproc
-
-} // cv
-
-#ifndef DISABLE_OPENCV_24_COMPATIBILITY
-#include "opencv2/imgproc/imgproc_c.h"
-#endif
-
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/imgproc/detail/distortion_model.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/imgproc/detail/distortion_model.hpp
deleted file mode 100755
index a9c3dde..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/imgproc/detail/distortion_model.hpp
+++ /dev/null
@@ -1,123 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_IMGPROC_DETAIL_DISTORTION_MODEL_HPP
-#define OPENCV_IMGPROC_DETAIL_DISTORTION_MODEL_HPP
-
-//! @cond IGNORED
-
-namespace cv { namespace detail {
-/**
-Computes the matrix for the projection onto a tilted image sensor
-\param tauX angular parameter rotation around x-axis
-\param tauY angular parameter rotation around y-axis
-\param matTilt if not NULL returns the matrix
-\f[
-\vecthreethree{R_{33}(\tau_x, \tau_y)}{0}{-R_{13}((\tau_x, \tau_y)}
-{0}{R_{33}(\tau_x, \tau_y)}{-R_{23}(\tau_x, \tau_y)}
-{0}{0}{1} R(\tau_x, \tau_y)
-\f]
-where
-\f[
-R(\tau_x, \tau_y) =
-\vecthreethree{\cos(\tau_y)}{0}{-\sin(\tau_y)}{0}{1}{0}{\sin(\tau_y)}{0}{\cos(\tau_y)}
-\vecthreethree{1}{0}{0}{0}{\cos(\tau_x)}{\sin(\tau_x)}{0}{-\sin(\tau_x)}{\cos(\tau_x)} =
-\vecthreethree{\cos(\tau_y)}{\sin(\tau_y)\sin(\tau_x)}{-\sin(\tau_y)\cos(\tau_x)}
-{0}{\cos(\tau_x)}{\sin(\tau_x)}
-{\sin(\tau_y)}{-\cos(\tau_y)\sin(\tau_x)}{\cos(\tau_y)\cos(\tau_x)}.
-\f]
-\param dMatTiltdTauX if not NULL it returns the derivative of matTilt with
-respect to \f$\tau_x\f$.
-\param dMatTiltdTauY if not NULL it returns the derivative of matTilt with
-respect to \f$\tau_y\f$.
-\param invMatTilt if not NULL it returns the inverse of matTilt
-**/
-template <typename FLOAT>
-void computeTiltProjectionMatrix(FLOAT tauX,
-    FLOAT tauY,
-    Matx<FLOAT, 3, 3>* matTilt = 0,
-    Matx<FLOAT, 3, 3>* dMatTiltdTauX = 0,
-    Matx<FLOAT, 3, 3>* dMatTiltdTauY = 0,
-    Matx<FLOAT, 3, 3>* invMatTilt = 0)
-{
-    FLOAT cTauX = cos(tauX);
-    FLOAT sTauX = sin(tauX);
-    FLOAT cTauY = cos(tauY);
-    FLOAT sTauY = sin(tauY);
-    Matx<FLOAT, 3, 3> matRotX = Matx<FLOAT, 3, 3>(1,0,0,0,cTauX,sTauX,0,-sTauX,cTauX);
-    Matx<FLOAT, 3, 3> matRotY = Matx<FLOAT, 3, 3>(cTauY,0,-sTauY,0,1,0,sTauY,0,cTauY);
-    Matx<FLOAT, 3, 3> matRotXY = matRotY * matRotX;
-    Matx<FLOAT, 3, 3> matProjZ = Matx<FLOAT, 3, 3>(matRotXY(2,2),0,-matRotXY(0,2),0,matRotXY(2,2),-matRotXY(1,2),0,0,1);
-    if (matTilt)
-    {
-        // Matrix for trapezoidal distortion of tilted image sensor
-        *matTilt = matProjZ * matRotXY;
-    }
-    if (dMatTiltdTauX)
-    {
-        // Derivative with respect to tauX
-        Matx<FLOAT, 3, 3> dMatRotXYdTauX = matRotY * Matx<FLOAT, 3, 3>(0,0,0,0,-sTauX,cTauX,0,-cTauX,-sTauX);
-        Matx<FLOAT, 3, 3> dMatProjZdTauX = Matx<FLOAT, 3, 3>(dMatRotXYdTauX(2,2),0,-dMatRotXYdTauX(0,2),
-          0,dMatRotXYdTauX(2,2),-dMatRotXYdTauX(1,2),0,0,0);
-        *dMatTiltdTauX = (matProjZ * dMatRotXYdTauX) + (dMatProjZdTauX * matRotXY);
-    }
-    if (dMatTiltdTauY)
-    {
-        // Derivative with respect to tauY
-        Matx<FLOAT, 3, 3> dMatRotXYdTauY = Matx<FLOAT, 3, 3>(-sTauY,0,-cTauY,0,0,0,cTauY,0,-sTauY) * matRotX;
-        Matx<FLOAT, 3, 3> dMatProjZdTauY = Matx<FLOAT, 3, 3>(dMatRotXYdTauY(2,2),0,-dMatRotXYdTauY(0,2),
-          0,dMatRotXYdTauY(2,2),-dMatRotXYdTauY(1,2),0,0,0);
-        *dMatTiltdTauY = (matProjZ * dMatRotXYdTauY) + (dMatProjZdTauY * matRotXY);
-    }
-    if (invMatTilt)
-    {
-        FLOAT inv = 1./matRotXY(2,2);
-        Matx<FLOAT, 3, 3> invMatProjZ = Matx<FLOAT, 3, 3>(inv,0,inv*matRotXY(0,2),0,inv,inv*matRotXY(1,2),0,0,1);
-        *invMatTilt = matRotXY.t()*invMatProjZ;
-    }
-}
-}} // namespace detail, cv
-
-
-//! @endcond
-
-#endif // OPENCV_IMGPROC_DETAIL_DISTORTION_MODEL_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/imgproc/hal/hal.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/imgproc/hal/hal.hpp
deleted file mode 100755
index a435fd6..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/imgproc/hal/hal.hpp
+++ /dev/null
@@ -1,241 +0,0 @@
-#ifndef CV_IMGPROC_HAL_HPP
-#define CV_IMGPROC_HAL_HPP
-
-#include "opencv2/core/cvdef.h"
-#include "opencv2/core/cvstd.hpp"
-#include "opencv2/core/hal/interface.h"
-
-namespace cv { namespace hal {
-
-//! @addtogroup imgproc_hal_functions
-//! @{
-
-//---------------------------
-//! @cond IGNORED
-
-struct CV_EXPORTS Filter2D
-{
-    CV_DEPRECATED static Ptr<hal::Filter2D> create(uchar * , size_t , int ,
-                                     int , int ,
-                                     int , int ,
-                                     int , int ,
-                                     int , double ,
-                                     int , int ,
-                                     bool , bool );
-    virtual void apply(uchar * , size_t ,
-                       uchar * , size_t ,
-                       int , int ,
-                       int , int ,
-                       int , int ) = 0;
-    virtual ~Filter2D() {}
-};
-
-struct CV_EXPORTS SepFilter2D
-{
-    CV_DEPRECATED static Ptr<hal::SepFilter2D> create(int , int , int ,
-                                        uchar * , int ,
-                                        uchar * , int ,
-                                        int , int ,
-                                        double , int );
-    virtual void apply(uchar * , size_t ,
-                       uchar * , size_t ,
-                       int , int ,
-                       int , int ,
-                       int , int ) = 0;
-    virtual ~SepFilter2D() {}
-};
-
-
-struct CV_EXPORTS Morph
-{
-    CV_DEPRECATED static Ptr<hal::Morph> create(int , int , int , int , int ,
-                                    int , uchar * , size_t ,
-                                    int , int ,
-                                    int , int ,
-                                    int , const double *,
-                                    int , bool , bool );
-    virtual void apply(uchar * , size_t , uchar * , size_t , int , int ,
-                       int , int , int , int ,
-                       int , int , int , int ) = 0;
-    virtual ~Morph() {}
-};
-
-//! @endcond
-//---------------------------
-
-CV_EXPORTS void filter2D(int stype, int dtype, int kernel_type,
-                         uchar * src_data, size_t src_step,
-                         uchar * dst_data, size_t dst_step,
-                         int width, int height,
-                         int full_width, int full_height,
-                         int offset_x, int offset_y,
-                         uchar * kernel_data, size_t kernel_step,
-                         int kernel_width, int kernel_height,
-                         int anchor_x, int anchor_y,
-                         double delta, int borderType,
-                         bool isSubmatrix);
-
-CV_EXPORTS void sepFilter2D(int stype, int dtype, int ktype,
-                            uchar * src_data, size_t src_step,
-                            uchar * dst_data, size_t dst_step,
-                            int width, int height,
-                            int full_width, int full_height,
-                            int offset_x, int offset_y,
-                            uchar * kernelx_data, int kernelx_len,
-                            uchar * kernely_data, int kernely_len,
-                            int anchor_x, int anchor_y,
-                            double delta, int borderType);
-
-CV_EXPORTS void morph(int op, int src_type, int dst_type,
-                      uchar * src_data, size_t src_step,
-                      uchar * dst_data, size_t dst_step,
-                      int width, int height,
-                      int roi_width, int roi_height, int roi_x, int roi_y,
-                      int roi_width2, int roi_height2, int roi_x2, int roi_y2,
-                      int kernel_type, uchar * kernel_data, size_t kernel_step,
-                      int kernel_width, int kernel_height, int anchor_x, int anchor_y,
-                      int borderType, const double borderValue[4],
-                      int iterations, bool isSubmatrix);
-
-
-CV_EXPORTS void resize(int src_type,
-                       const uchar * src_data, size_t src_step, int src_width, int src_height,
-                       uchar * dst_data, size_t dst_step, int dst_width, int dst_height,
-                       double inv_scale_x, double inv_scale_y, int interpolation);
-
-CV_EXPORTS void warpAffine(int src_type,
-                           const uchar * src_data, size_t src_step, int src_width, int src_height,
-                           uchar * dst_data, size_t dst_step, int dst_width, int dst_height,
-                           const double M[6], int interpolation, int borderType, const double borderValue[4]);
-
-CV_EXPORTS void warpPerspectve(int src_type,
-                               const uchar * src_data, size_t src_step, int src_width, int src_height,
-                               uchar * dst_data, size_t dst_step, int dst_width, int dst_height,
-                               const double M[9], int interpolation, int borderType, const double borderValue[4]);
-
-CV_EXPORTS void cvtBGRtoBGR(const uchar * src_data, size_t src_step,
-                            uchar * dst_data, size_t dst_step,
-                            int width, int height,
-                            int depth, int scn, int dcn, bool swapBlue);
-
-CV_EXPORTS void cvtBGRtoBGR5x5(const uchar * src_data, size_t src_step,
-                               uchar * dst_data, size_t dst_step,
-                               int width, int height,
-                               int scn, bool swapBlue, int greenBits);
-
-CV_EXPORTS void cvtBGR5x5toBGR(const uchar * src_data, size_t src_step,
-                               uchar * dst_data, size_t dst_step,
-                               int width, int height,
-                               int dcn, bool swapBlue, int greenBits);
-
-CV_EXPORTS void cvtBGRtoGray(const uchar * src_data, size_t src_step,
-                             uchar * dst_data, size_t dst_step,
-                             int width, int height,
-                             int depth, int scn, bool swapBlue);
-
-CV_EXPORTS void cvtGraytoBGR(const uchar * src_data, size_t src_step,
-                             uchar * dst_data, size_t dst_step,
-                             int width, int height,
-                             int depth, int dcn);
-
-CV_EXPORTS void cvtBGR5x5toGray(const uchar * src_data, size_t src_step,
-                                uchar * dst_data, size_t dst_step,
-                                int width, int height,
-                                int greenBits);
-
-CV_EXPORTS void cvtGraytoBGR5x5(const uchar * src_data, size_t src_step,
-                                uchar * dst_data, size_t dst_step,
-                                int width, int height,
-                                int greenBits);
-CV_EXPORTS void cvtBGRtoYUV(const uchar * src_data, size_t src_step,
-                            uchar * dst_data, size_t dst_step,
-                            int width, int height,
-                            int depth, int scn, bool swapBlue, bool isCbCr);
-
-CV_EXPORTS void cvtYUVtoBGR(const uchar * src_data, size_t src_step,
-                            uchar * dst_data, size_t dst_step,
-                            int width, int height,
-                            int depth, int dcn, bool swapBlue, bool isCbCr);
-
-CV_EXPORTS void cvtBGRtoXYZ(const uchar * src_data, size_t src_step,
-                            uchar * dst_data, size_t dst_step,
-                            int width, int height,
-                            int depth, int scn, bool swapBlue);
-
-CV_EXPORTS void cvtXYZtoBGR(const uchar * src_data, size_t src_step,
-                            uchar * dst_data, size_t dst_step,
-                            int width, int height,
-                            int depth, int dcn, bool swapBlue);
-
-CV_EXPORTS void cvtBGRtoHSV(const uchar * src_data, size_t src_step,
-                            uchar * dst_data, size_t dst_step,
-                            int width, int height,
-                            int depth, int scn, bool swapBlue, bool isFullRange, bool isHSV);
-
-CV_EXPORTS void cvtHSVtoBGR(const uchar * src_data, size_t src_step,
-                            uchar * dst_data, size_t dst_step,
-                            int width, int height,
-                            int depth, int dcn, bool swapBlue, bool isFullRange, bool isHSV);
-
-CV_EXPORTS void cvtBGRtoLab(const uchar * src_data, size_t src_step,
-                            uchar * dst_data, size_t dst_step,
-                            int width, int height,
-                            int depth, int scn, bool swapBlue, bool isLab, bool srgb);
-
-CV_EXPORTS void cvtLabtoBGR(const uchar * src_data, size_t src_step,
-                            uchar * dst_data, size_t dst_step,
-                            int width, int height,
-                            int depth, int dcn, bool swapBlue, bool isLab, bool srgb);
-
-CV_EXPORTS void cvtTwoPlaneYUVtoBGR(const uchar * src_data, size_t src_step,
-                                    uchar * dst_data, size_t dst_step,
-                                    int dst_width, int dst_height,
-                                    int dcn, bool swapBlue, int uIdx);
-
-//! Separate Y and UV planes
-CV_EXPORTS void cvtTwoPlaneYUVtoBGR(const uchar * y_data, const uchar * uv_data, size_t src_step,
-                                    uchar * dst_data, size_t dst_step,
-                                    int dst_width, int dst_height,
-                                    int dcn, bool swapBlue, int uIdx);
-
-CV_EXPORTS void cvtThreePlaneYUVtoBGR(const uchar * src_data, size_t src_step,
-                                      uchar * dst_data, size_t dst_step,
-                                      int dst_width, int dst_height,
-                                      int dcn, bool swapBlue, int uIdx);
-
-CV_EXPORTS void cvtBGRtoThreePlaneYUV(const uchar * src_data, size_t src_step,
-                                      uchar * dst_data, size_t dst_step,
-                                      int width, int height,
-                                      int scn, bool swapBlue, int uIdx);
-
-//! Separate Y and UV planes
-CV_EXPORTS void cvtBGRtoTwoPlaneYUV(const uchar * src_data, size_t src_step,
-                                    uchar * y_data, uchar * uv_data, size_t dst_step,
-                                    int width, int height,
-                                    int scn, bool swapBlue, int uIdx);
-
-CV_EXPORTS void cvtOnePlaneYUVtoBGR(const uchar * src_data, size_t src_step,
-                                    uchar * dst_data, size_t dst_step,
-                                    int width, int height,
-                                    int dcn, bool swapBlue, int uIdx, int ycn);
-
-CV_EXPORTS void cvtRGBAtoMultipliedRGBA(const uchar * src_data, size_t src_step,
-                                        uchar * dst_data, size_t dst_step,
-                                        int width, int height);
-
-CV_EXPORTS void cvtMultipliedRGBAtoRGBA(const uchar * src_data, size_t src_step,
-                                        uchar * dst_data, size_t dst_step,
-                                        int width, int height);
-
-CV_EXPORTS void integral(int depth, int sdepth, int sqdepth,
-                         const uchar* src, size_t srcstep,
-                         uchar* sum, size_t sumstep,
-                         uchar* sqsum, size_t sqsumstep,
-                         uchar* tilted, size_t tstep,
-                         int width, int height, int cn);
-
-//! @}
-
-}}
-
-#endif // CV_IMGPROC_HAL_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/imgproc/hal/interface.h b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/imgproc/hal/interface.h
deleted file mode 100755
index f8dbcfe..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/imgproc/hal/interface.h
+++ /dev/null
@@ -1,46 +0,0 @@
-#ifndef OPENCV_IMGPROC_HAL_INTERFACE_H
-#define OPENCV_IMGPROC_HAL_INTERFACE_H
-
-//! @addtogroup imgproc_hal_interface
-//! @{
-
-//! @name Interpolation modes
-//! @sa cv::InterpolationFlags
-//! @{
-#define CV_HAL_INTER_NEAREST 0
-#define CV_HAL_INTER_LINEAR 1
-#define CV_HAL_INTER_CUBIC 2
-#define CV_HAL_INTER_AREA 3
-#define CV_HAL_INTER_LANCZOS4 4
-//! @}
-
-//! @name Morphology operations
-//! @sa cv::MorphTypes
-//! @{
-#define CV_HAL_MORPH_ERODE 0
-#define CV_HAL_MORPH_DILATE 1
-//! @}
-
-//! @name Threshold types
-//! @sa cv::ThresholdTypes
-//! @{
-#define CV_HAL_THRESH_BINARY      0
-#define CV_HAL_THRESH_BINARY_INV  1
-#define CV_HAL_THRESH_TRUNC       2
-#define CV_HAL_THRESH_TOZERO      3
-#define CV_HAL_THRESH_TOZERO_INV  4
-#define CV_HAL_THRESH_MASK        7
-#define CV_HAL_THRESH_OTSU        8
-#define CV_HAL_THRESH_TRIANGLE    16
-//! @}
-
-//! @name Adaptive threshold algorithm
-//! @sa cv::AdaptiveThresholdTypes
-//! @{
-#define CV_HAL_ADAPTIVE_THRESH_MEAN_C     0
-#define CV_HAL_ADAPTIVE_THRESH_GAUSSIAN_C 1
-//! @}
-
-//! @}
-
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/imgproc/imgproc.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/imgproc/imgproc.hpp
deleted file mode 100755
index 4175bd0..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/imgproc/imgproc.hpp
+++ /dev/null
@@ -1,48 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifdef __OPENCV_BUILD
-#error this is a compatibility header which should not be used inside the OpenCV library
-#endif
-
-#include "opencv2/imgproc.hpp"
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/imgproc/imgproc_c.h b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/imgproc/imgproc_c.h
deleted file mode 100755
index 78032b7..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/imgproc/imgproc_c.h
+++ /dev/null
@@ -1,1209 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_IMGPROC_IMGPROC_C_H
-#define OPENCV_IMGPROC_IMGPROC_C_H
-
-#include "opencv2/imgproc/types_c.h"
-
-#ifdef __cplusplus
-extern "C" {
-#endif
-
-/** @addtogroup imgproc_c
-@{
-*/
-
-/*********************** Background statistics accumulation *****************************/
-
-/** @brief Adds image to accumulator
-@see cv::accumulate
-*/
-CVAPI(void)  cvAcc( const CvArr* image, CvArr* sum,
-                   const CvArr* mask CV_DEFAULT(NULL) );
-
-/** @brief Adds squared image to accumulator
-@see cv::accumulateSquare
-*/
-CVAPI(void)  cvSquareAcc( const CvArr* image, CvArr* sqsum,
-                         const CvArr* mask CV_DEFAULT(NULL) );
-
-/** @brief Adds a product of two images to accumulator
-@see cv::accumulateProduct
-*/
-CVAPI(void)  cvMultiplyAcc( const CvArr* image1, const CvArr* image2, CvArr* acc,
-                           const CvArr* mask CV_DEFAULT(NULL) );
-
-/** @brief Adds image to accumulator with weights: acc = acc*(1-alpha) + image*alpha
-@see cv::accumulateWeighted
-*/
-CVAPI(void)  cvRunningAvg( const CvArr* image, CvArr* acc, double alpha,
-                          const CvArr* mask CV_DEFAULT(NULL) );
-
-/****************************************************************************************\
-*                                    Image Processing                                    *
-\****************************************************************************************/
-
-/** Copies source 2D array inside of the larger destination array and
-   makes a border of the specified type (IPL_BORDER_*) around the copied area. */
-CVAPI(void) cvCopyMakeBorder( const CvArr* src, CvArr* dst, CvPoint offset,
-                              int bordertype, CvScalar value CV_DEFAULT(cvScalarAll(0)));
-
-/** @brief Smooths the image in one of several ways.
-
-@param src The source image
-@param dst The destination image
-@param smoothtype Type of the smoothing, see SmoothMethod_c
-@param size1 The first parameter of the smoothing operation, the aperture width. Must be a
-positive odd number (1, 3, 5, ...)
-@param size2 The second parameter of the smoothing operation, the aperture height. Ignored by
-CV_MEDIAN and CV_BILATERAL methods. In the case of simple scaled/non-scaled and Gaussian blur if
-size2 is zero, it is set to size1. Otherwise it must be a positive odd number.
-@param sigma1 In the case of a Gaussian parameter this parameter may specify Gaussian \f$\sigma\f$
-(standard deviation). If it is zero, it is calculated from the kernel size:
-\f[\sigma  = 0.3 (n/2 - 1) + 0.8  \quad   \text{where}   \quad  n= \begin{array}{l l} \mbox{\texttt{size1} for horizontal kernel} \\ \mbox{\texttt{size2} for vertical kernel} \end{array}\f]
-Using standard sigma for small kernels ( \f$3\times 3\f$ to \f$7\times 7\f$ ) gives better speed. If
-sigma1 is not zero, while size1 and size2 are zeros, the kernel size is calculated from the
-sigma (to provide accurate enough operation).
-@param sigma2 additional parameter for bilateral filtering
-
-@see cv::GaussianBlur, cv::blur, cv::medianBlur, cv::bilateralFilter.
- */
-CVAPI(void) cvSmooth( const CvArr* src, CvArr* dst,
-                      int smoothtype CV_DEFAULT(CV_GAUSSIAN),
-                      int size1 CV_DEFAULT(3),
-                      int size2 CV_DEFAULT(0),
-                      double sigma1 CV_DEFAULT(0),
-                      double sigma2 CV_DEFAULT(0));
-
-/** @brief Convolves an image with the kernel.
-
-@param src input image.
-@param dst output image of the same size and the same number of channels as src.
-@param kernel convolution kernel (or rather a correlation kernel), a single-channel floating point
-matrix; if you want to apply different kernels to different channels, split the image into
-separate color planes using split and process them individually.
-@param anchor anchor of the kernel that indicates the relative position of a filtered point within
-the kernel; the anchor should lie within the kernel; default value (-1,-1) means that the anchor
-is at the kernel center.
-
-@see cv::filter2D
- */
-CVAPI(void) cvFilter2D( const CvArr* src, CvArr* dst, const CvMat* kernel,
-                        CvPoint anchor CV_DEFAULT(cvPoint(-1,-1)));
-
-/** @brief Finds integral image: SUM(X,Y) = sum(x<X,y<Y)I(x,y)
-@see cv::integral
-*/
-CVAPI(void) cvIntegral( const CvArr* image, CvArr* sum,
-                       CvArr* sqsum CV_DEFAULT(NULL),
-                       CvArr* tilted_sum CV_DEFAULT(NULL));
-
-/** @brief Smoothes the input image with gaussian kernel and then down-samples it.
-
-   dst_width = floor(src_width/2)[+1],
-   dst_height = floor(src_height/2)[+1]
-   @see cv::pyrDown
-*/
-CVAPI(void)  cvPyrDown( const CvArr* src, CvArr* dst,
-                        int filter CV_DEFAULT(CV_GAUSSIAN_5x5) );
-
-/** @brief Up-samples image and smoothes the result with gaussian kernel.
-
-   dst_width = src_width*2,
-   dst_height = src_height*2
-   @see cv::pyrUp
-*/
-CVAPI(void)  cvPyrUp( const CvArr* src, CvArr* dst,
-                      int filter CV_DEFAULT(CV_GAUSSIAN_5x5) );
-
-/** @brief Builds pyramid for an image
-@see buildPyramid
-*/
-CVAPI(CvMat**) cvCreatePyramid( const CvArr* img, int extra_layers, double rate,
-                                const CvSize* layer_sizes CV_DEFAULT(0),
-                                CvArr* bufarr CV_DEFAULT(0),
-                                int calc CV_DEFAULT(1),
-                                int filter CV_DEFAULT(CV_GAUSSIAN_5x5) );
-
-/** @brief Releases pyramid */
-CVAPI(void)  cvReleasePyramid( CvMat*** pyramid, int extra_layers );
-
-
-/** @brief Filters image using meanshift algorithm
-@see cv::pyrMeanShiftFiltering
-*/
-CVAPI(void) cvPyrMeanShiftFiltering( const CvArr* src, CvArr* dst,
-    double sp, double sr, int max_level CV_DEFAULT(1),
-    CvTermCriteria termcrit CV_DEFAULT(cvTermCriteria(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS,5,1)));
-
-/** @brief Segments image using seed "markers"
-@see cv::watershed
-*/
-CVAPI(void) cvWatershed( const CvArr* image, CvArr* markers );
-
-/** @brief Calculates an image derivative using generalized Sobel
-
-   (aperture_size = 1,3,5,7) or Scharr (aperture_size = -1) operator.
-   Scharr can be used only for the first dx or dy derivative
-@see cv::Sobel
-*/
-CVAPI(void) cvSobel( const CvArr* src, CvArr* dst,
-                    int xorder, int yorder,
-                    int aperture_size CV_DEFAULT(3));
-
-/** @brief Calculates the image Laplacian: (d2/dx + d2/dy)I
-@see cv::Laplacian
-*/
-CVAPI(void) cvLaplace( const CvArr* src, CvArr* dst,
-                      int aperture_size CV_DEFAULT(3) );
-
-/** @brief Converts input array pixels from one color space to another
-@see cv::cvtColor
-*/
-CVAPI(void)  cvCvtColor( const CvArr* src, CvArr* dst, int code );
-
-
-/** @brief Resizes image (input array is resized to fit the destination array)
-@see cv::resize
-*/
-CVAPI(void)  cvResize( const CvArr* src, CvArr* dst,
-                       int interpolation CV_DEFAULT( CV_INTER_LINEAR ));
-
-/** @brief Warps image with affine transform
-@note ::cvGetQuadrangleSubPix is similar to ::cvWarpAffine, but the outliers are extrapolated using
-replication border mode.
-@see cv::warpAffine
-*/
-CVAPI(void)  cvWarpAffine( const CvArr* src, CvArr* dst, const CvMat* map_matrix,
-                           int flags CV_DEFAULT(CV_INTER_LINEAR+CV_WARP_FILL_OUTLIERS),
-                           CvScalar fillval CV_DEFAULT(cvScalarAll(0)) );
-
-/** @brief Computes affine transform matrix for mapping src[i] to dst[i] (i=0,1,2)
-@see cv::getAffineTransform
-*/
-CVAPI(CvMat*) cvGetAffineTransform( const CvPoint2D32f * src,
-                                    const CvPoint2D32f * dst,
-                                    CvMat * map_matrix );
-
-/** @brief Computes rotation_matrix matrix
-@see cv::getRotationMatrix2D
-*/
-CVAPI(CvMat*)  cv2DRotationMatrix( CvPoint2D32f center, double angle,
-                                   double scale, CvMat* map_matrix );
-
-/** @brief Warps image with perspective (projective) transform
-@see cv::warpPerspective
-*/
-CVAPI(void)  cvWarpPerspective( const CvArr* src, CvArr* dst, const CvMat* map_matrix,
-                                int flags CV_DEFAULT(CV_INTER_LINEAR+CV_WARP_FILL_OUTLIERS),
-                                CvScalar fillval CV_DEFAULT(cvScalarAll(0)) );
-
-/** @brief Computes perspective transform matrix for mapping src[i] to dst[i] (i=0,1,2,3)
-@see cv::getPerspectiveTransform
-*/
-CVAPI(CvMat*) cvGetPerspectiveTransform( const CvPoint2D32f* src,
-                                         const CvPoint2D32f* dst,
-                                         CvMat* map_matrix );
-
-/** @brief Performs generic geometric transformation using the specified coordinate maps
-@see cv::remap
-*/
-CVAPI(void)  cvRemap( const CvArr* src, CvArr* dst,
-                      const CvArr* mapx, const CvArr* mapy,
-                      int flags CV_DEFAULT(CV_INTER_LINEAR+CV_WARP_FILL_OUTLIERS),
-                      CvScalar fillval CV_DEFAULT(cvScalarAll(0)) );
-
-/** @brief Converts mapx & mapy from floating-point to integer formats for cvRemap
-@see cv::convertMaps
-*/
-CVAPI(void)  cvConvertMaps( const CvArr* mapx, const CvArr* mapy,
-                            CvArr* mapxy, CvArr* mapalpha );
-
-/** @brief Performs forward or inverse log-polar image transform
-@see cv::warpPolar
-*/
-CVAPI(void)  cvLogPolar( const CvArr* src, CvArr* dst,
-                         CvPoint2D32f center, double M,
-                         int flags CV_DEFAULT(CV_INTER_LINEAR+CV_WARP_FILL_OUTLIERS));
-
-/** Performs forward or inverse linear-polar image transform
-@see cv::warpPolar
-*/
-CVAPI(void)  cvLinearPolar( const CvArr* src, CvArr* dst,
-                         CvPoint2D32f center, double maxRadius,
-                         int flags CV_DEFAULT(CV_INTER_LINEAR+CV_WARP_FILL_OUTLIERS));
-
-/** @brief Transforms the input image to compensate lens distortion
-@see cv::undistort
-*/
-CVAPI(void) cvUndistort2( const CvArr* src, CvArr* dst,
-                          const CvMat* camera_matrix,
-                          const CvMat* distortion_coeffs,
-                          const CvMat* new_camera_matrix CV_DEFAULT(0) );
-
-/** @brief Computes transformation map from intrinsic camera parameters
-   that can used by cvRemap
-*/
-CVAPI(void) cvInitUndistortMap( const CvMat* camera_matrix,
-                                const CvMat* distortion_coeffs,
-                                CvArr* mapx, CvArr* mapy );
-
-/** @brief Computes undistortion+rectification map for a head of stereo camera
-@see cv::initUndistortRectifyMap
-*/
-CVAPI(void) cvInitUndistortRectifyMap( const CvMat* camera_matrix,
-                                       const CvMat* dist_coeffs,
-                                       const CvMat *R, const CvMat* new_camera_matrix,
-                                       CvArr* mapx, CvArr* mapy );
-
-/** @brief Computes the original (undistorted) feature coordinates
-   from the observed (distorted) coordinates
-@see cv::undistortPoints
-*/
-CVAPI(void) cvUndistortPoints( const CvMat* src, CvMat* dst,
-                               const CvMat* camera_matrix,
-                               const CvMat* dist_coeffs,
-                               const CvMat* R CV_DEFAULT(0),
-                               const CvMat* P CV_DEFAULT(0));
-
-/** @brief Returns a structuring element of the specified size and shape for morphological operations.
-
-@note the created structuring element IplConvKernel\* element must be released in the end using
-`cvReleaseStructuringElement(&element)`.
-
-@param cols Width of the structuring element
-@param rows Height of the structuring element
-@param anchor_x x-coordinate of the anchor
-@param anchor_y y-coordinate of the anchor
-@param shape element shape that could be one of the cv::MorphShapes_c
-@param values integer array of cols*rows elements that specifies the custom shape of the
-structuring element, when shape=CV_SHAPE_CUSTOM.
-
-@see cv::getStructuringElement
- */
- CVAPI(IplConvKernel*)  cvCreateStructuringElementEx(
-            int cols, int  rows, int  anchor_x, int  anchor_y,
-            int shape, int* values CV_DEFAULT(NULL) );
-
-/** @brief releases structuring element
-@see cvCreateStructuringElementEx
-*/
-CVAPI(void)  cvReleaseStructuringElement( IplConvKernel** element );
-
-/** @brief erodes input image (applies minimum filter) one or more times.
-   If element pointer is NULL, 3x3 rectangular element is used
-@see cv::erode
-*/
-CVAPI(void)  cvErode( const CvArr* src, CvArr* dst,
-                      IplConvKernel* element CV_DEFAULT(NULL),
-                      int iterations CV_DEFAULT(1) );
-
-/** @brief dilates input image (applies maximum filter) one or more times.
-
-   If element pointer is NULL, 3x3 rectangular element is used
-@see cv::dilate
-*/
-CVAPI(void)  cvDilate( const CvArr* src, CvArr* dst,
-                       IplConvKernel* element CV_DEFAULT(NULL),
-                       int iterations CV_DEFAULT(1) );
-
-/** @brief Performs complex morphological transformation
-@see cv::morphologyEx
-*/
-CVAPI(void)  cvMorphologyEx( const CvArr* src, CvArr* dst,
-                             CvArr* temp, IplConvKernel* element,
-                             int operation, int iterations CV_DEFAULT(1) );
-
-/** @brief Calculates all spatial and central moments up to the 3rd order
-@see cv::moments
-*/
-CVAPI(void) cvMoments( const CvArr* arr, CvMoments* moments, int binary CV_DEFAULT(0));
-
-/** @brief Retrieve spatial moments */
-CVAPI(double)  cvGetSpatialMoment( CvMoments* moments, int x_order, int y_order );
-/** @brief Retrieve central moments */
-CVAPI(double)  cvGetCentralMoment( CvMoments* moments, int x_order, int y_order );
-/** @brief Retrieve normalized central moments */
-CVAPI(double)  cvGetNormalizedCentralMoment( CvMoments* moments,
-                                             int x_order, int y_order );
-
-/** @brief Calculates 7 Hu's invariants from precalculated spatial and central moments
-@see cv::HuMoments
-*/
-CVAPI(void) cvGetHuMoments( CvMoments*  moments, CvHuMoments*  hu_moments );
-
-/*********************************** data sampling **************************************/
-
-/** @brief Fetches pixels that belong to the specified line segment and stores them to the buffer.
-
-   Returns the number of retrieved points.
-@see cv::LineSegmentDetector
-*/
-CVAPI(int)  cvSampleLine( const CvArr* image, CvPoint pt1, CvPoint pt2, void* buffer,
-                          int connectivity CV_DEFAULT(8));
-
-/** @brief Retrieves the rectangular image region with specified center from the input array.
-
- dst(x,y) <- src(x + center.x - dst_width/2, y + center.y - dst_height/2).
- Values of pixels with fractional coordinates are retrieved using bilinear interpolation
-@see cv::getRectSubPix
-*/
-CVAPI(void)  cvGetRectSubPix( const CvArr* src, CvArr* dst, CvPoint2D32f center );
-
-
-/** @brief Retrieves quadrangle from the input array.
-
-    matrixarr = ( a11  a12 | b1 )   dst(x,y) <- src(A[x y]' + b)
-                ( a21  a22 | b2 )   (bilinear interpolation is used to retrieve pixels
-                                     with fractional coordinates)
-@see cvWarpAffine
-*/
-CVAPI(void)  cvGetQuadrangleSubPix( const CvArr* src, CvArr* dst,
-                                    const CvMat* map_matrix );
-
-/** @brief Measures similarity between template and overlapped windows in the source image
-   and fills the resultant image with the measurements
-@see cv::matchTemplate
-*/
-CVAPI(void)  cvMatchTemplate( const CvArr* image, const CvArr* templ,
-                              CvArr* result, int method );
-
-/** @brief Computes earth mover distance between
-   two weighted point sets (called signatures)
-@see cv::EMD
-*/
-CVAPI(float)  cvCalcEMD2( const CvArr* signature1,
-                          const CvArr* signature2,
-                          int distance_type,
-                          CvDistanceFunction distance_func CV_DEFAULT(NULL),
-                          const CvArr* cost_matrix CV_DEFAULT(NULL),
-                          CvArr* flow CV_DEFAULT(NULL),
-                          float* lower_bound CV_DEFAULT(NULL),
-                          void* userdata CV_DEFAULT(NULL));
-
-/****************************************************************************************\
-*                              Contours retrieving                                       *
-\****************************************************************************************/
-
-/** @brief Retrieves outer and optionally inner boundaries of white (non-zero) connected
-   components in the black (zero) background
-@see cv::findContours, cvStartFindContours, cvFindNextContour, cvSubstituteContour, cvEndFindContours
-*/
-CVAPI(int)  cvFindContours( CvArr* image, CvMemStorage* storage, CvSeq** first_contour,
-                            int header_size CV_DEFAULT(sizeof(CvContour)),
-                            int mode CV_DEFAULT(CV_RETR_LIST),
-                            int method CV_DEFAULT(CV_CHAIN_APPROX_SIMPLE),
-                            CvPoint offset CV_DEFAULT(cvPoint(0,0)));
-
-/** @brief Initializes contour retrieving process.
-
-   Calls cvStartFindContours.
-   Calls cvFindNextContour until null pointer is returned
-   or some other condition becomes true.
-   Calls cvEndFindContours at the end.
-@see cvFindContours
-*/
-CVAPI(CvContourScanner)  cvStartFindContours( CvArr* image, CvMemStorage* storage,
-                            int header_size CV_DEFAULT(sizeof(CvContour)),
-                            int mode CV_DEFAULT(CV_RETR_LIST),
-                            int method CV_DEFAULT(CV_CHAIN_APPROX_SIMPLE),
-                            CvPoint offset CV_DEFAULT(cvPoint(0,0)));
-
-/** @brief Retrieves next contour
-@see cvFindContours
-*/
-CVAPI(CvSeq*)  cvFindNextContour( CvContourScanner scanner );
-
-
-/** @brief Substitutes the last retrieved contour with the new one
-
-   (if the substitutor is null, the last retrieved contour is removed from the tree)
-@see cvFindContours
-*/
-CVAPI(void)   cvSubstituteContour( CvContourScanner scanner, CvSeq* new_contour );
-
-
-/** @brief Releases contour scanner and returns pointer to the first outer contour
-@see cvFindContours
-*/
-CVAPI(CvSeq*)  cvEndFindContours( CvContourScanner* scanner );
-
-/** @brief Approximates Freeman chain(s) with a polygonal curve.
-
-This is a standalone contour approximation routine, not represented in the new interface. When
-cvFindContours retrieves contours as Freeman chains, it calls the function to get approximated
-contours, represented as polygons.
-
-@param src_seq Pointer to the approximated Freeman chain that can refer to other chains.
-@param storage Storage location for the resulting polylines.
-@param method Approximation method (see the description of the function :ocvFindContours ).
-@param parameter Method parameter (not used now).
-@param minimal_perimeter Approximates only those contours whose perimeters are not less than
-minimal_perimeter . Other chains are removed from the resulting structure.
-@param recursive Recursion flag. If it is non-zero, the function approximates all chains that can
-be obtained from chain by using the h_next or v_next links. Otherwise, the single input chain is
-approximated.
-@see cvStartReadChainPoints, cvReadChainPoint
- */
-CVAPI(CvSeq*) cvApproxChains( CvSeq* src_seq, CvMemStorage* storage,
-                            int method CV_DEFAULT(CV_CHAIN_APPROX_SIMPLE),
-                            double parameter CV_DEFAULT(0),
-                            int  minimal_perimeter CV_DEFAULT(0),
-                            int  recursive CV_DEFAULT(0));
-
-/** @brief Initializes Freeman chain reader.
-
-   The reader is used to iteratively get coordinates of all the chain points.
-   If the Freeman codes should be read as is, a simple sequence reader should be used
-@see cvApproxChains
-*/
-CVAPI(void) cvStartReadChainPoints( CvChain* chain, CvChainPtReader* reader );
-
-/** @brief Retrieves the next chain point
-@see cvApproxChains
-*/
-CVAPI(CvPoint) cvReadChainPoint( CvChainPtReader* reader );
-
-
-/****************************************************************************************\
-*                            Contour Processing and Shape Analysis                       *
-\****************************************************************************************/
-
-/** @brief Approximates a single polygonal curve (contour) or
-   a tree of polygonal curves (contours)
-@see cv::approxPolyDP
-*/
-CVAPI(CvSeq*)  cvApproxPoly( const void* src_seq,
-                             int header_size, CvMemStorage* storage,
-                             int method, double eps,
-                             int recursive CV_DEFAULT(0));
-
-/** @brief Calculates perimeter of a contour or length of a part of contour
-@see cv::arcLength
-*/
-CVAPI(double)  cvArcLength( const void* curve,
-                            CvSlice slice CV_DEFAULT(CV_WHOLE_SEQ),
-                            int is_closed CV_DEFAULT(-1));
-
-/** same as cvArcLength for closed contour
-*/
-CV_INLINE double cvContourPerimeter( const void* contour )
-{
-    return cvArcLength( contour, CV_WHOLE_SEQ, 1 );
-}
-
-
-/** @brief Calculates contour bounding rectangle (update=1) or
-   just retrieves pre-calculated rectangle (update=0)
-@see cv::boundingRect
-*/
-CVAPI(CvRect)  cvBoundingRect( CvArr* points, int update CV_DEFAULT(0) );
-
-/** @brief Calculates area of a contour or contour segment
-@see cv::contourArea
-*/
-CVAPI(double)  cvContourArea( const CvArr* contour,
-                              CvSlice slice CV_DEFAULT(CV_WHOLE_SEQ),
-                              int oriented CV_DEFAULT(0));
-
-/** @brief Finds minimum area rotated rectangle bounding a set of points
-@see cv::minAreaRect
-*/
-CVAPI(CvBox2D)  cvMinAreaRect2( const CvArr* points,
-                                CvMemStorage* storage CV_DEFAULT(NULL));
-
-/** @brief Finds minimum enclosing circle for a set of points
-@see cv::minEnclosingCircle
-*/
-CVAPI(int)  cvMinEnclosingCircle( const CvArr* points,
-                                  CvPoint2D32f* center, float* radius );
-
-/** @brief Compares two contours by matching their moments
-@see cv::matchShapes
-*/
-CVAPI(double)  cvMatchShapes( const void* object1, const void* object2,
-                              int method, double parameter CV_DEFAULT(0));
-
-/** @brief Calculates exact convex hull of 2d point set
-@see cv::convexHull
-*/
-CVAPI(CvSeq*) cvConvexHull2( const CvArr* input,
-                             void* hull_storage CV_DEFAULT(NULL),
-                             int orientation CV_DEFAULT(CV_CLOCKWISE),
-                             int return_points CV_DEFAULT(0));
-
-/** @brief Checks whether the contour is convex or not (returns 1 if convex, 0 if not)
-@see cv::isContourConvex
-*/
-CVAPI(int)  cvCheckContourConvexity( const CvArr* contour );
-
-
-/** @brief Finds convexity defects for the contour
-@see cv::convexityDefects
-*/
-CVAPI(CvSeq*)  cvConvexityDefects( const CvArr* contour, const CvArr* convexhull,
-                                   CvMemStorage* storage CV_DEFAULT(NULL));
-
-/** @brief Fits ellipse into a set of 2d points
-@see cv::fitEllipse
-*/
-CVAPI(CvBox2D) cvFitEllipse2( const CvArr* points );
-
-/** @brief Finds minimum rectangle containing two given rectangles */
-CVAPI(CvRect)  cvMaxRect( const CvRect* rect1, const CvRect* rect2 );
-
-/** @brief Finds coordinates of the box vertices */
-CVAPI(void) cvBoxPoints( CvBox2D box, CvPoint2D32f pt[4] );
-
-/** @brief Initializes sequence header for a matrix (column or row vector) of points
-
-   a wrapper for cvMakeSeqHeaderForArray (it does not initialize bounding rectangle!!!) */
-CVAPI(CvSeq*) cvPointSeqFromMat( int seq_kind, const CvArr* mat,
-                                 CvContour* contour_header,
-                                 CvSeqBlock* block );
-
-/** @brief Checks whether the point is inside polygon, outside, on an edge (at a vertex).
-
-   Returns positive, negative or zero value, correspondingly.
-   Optionally, measures a signed distance between
-   the point and the nearest polygon edge (measure_dist=1)
-@see cv::pointPolygonTest
-*/
-CVAPI(double) cvPointPolygonTest( const CvArr* contour,
-                                  CvPoint2D32f pt, int measure_dist );
-
-/****************************************************************************************\
-*                                  Histogram functions                                   *
-\****************************************************************************************/
-
-/** @brief Creates a histogram.
-
-The function creates a histogram of the specified size and returns a pointer to the created
-histogram. If the array ranges is 0, the histogram bin ranges must be specified later via the
-function cvSetHistBinRanges. Though cvCalcHist and cvCalcBackProject may process 8-bit images
-without setting bin ranges, they assume they are equally spaced in 0 to 255 bins.
-
-@param dims Number of histogram dimensions.
-@param sizes Array of the histogram dimension sizes.
-@param type Histogram representation format. CV_HIST_ARRAY means that the histogram data is
-represented as a multi-dimensional dense array CvMatND. CV_HIST_SPARSE means that histogram data
-is represented as a multi-dimensional sparse array CvSparseMat.
-@param ranges Array of ranges for the histogram bins. Its meaning depends on the uniform parameter
-value. The ranges are used when the histogram is calculated or backprojected to determine which
-histogram bin corresponds to which value/tuple of values from the input image(s).
-@param uniform Uniformity flag. If not zero, the histogram has evenly spaced bins and for every
-\f$0<=i<cDims\f$ ranges[i] is an array of two numbers: lower and upper boundaries for the i-th
-histogram dimension. The whole range [lower,upper] is then split into dims[i] equal parts to
-determine the i-th input tuple value ranges for every histogram bin. And if uniform=0 , then the
-i-th element of the ranges array contains dims[i]+1 elements: \f$\texttt{lower}_0,
-\texttt{upper}_0, \texttt{lower}_1, \texttt{upper}_1 = \texttt{lower}_2,
-...
-\texttt{upper}_{dims[i]-1}\f$ where \f$\texttt{lower}_j\f$ and \f$\texttt{upper}_j\f$ are lower
-and upper boundaries of the i-th input tuple value for the j-th bin, respectively. In either
-case, the input values that are beyond the specified range for a histogram bin are not counted
-by cvCalcHist and filled with 0 by cvCalcBackProject.
- */
-CVAPI(CvHistogram*)  cvCreateHist( int dims, int* sizes, int type,
-                                   float** ranges CV_DEFAULT(NULL),
-                                   int uniform CV_DEFAULT(1));
-
-/** @brief Sets the bounds of the histogram bins.
-
-This is a standalone function for setting bin ranges in the histogram. For a more detailed
-description of the parameters ranges and uniform, see the :ocvCalcHist function that can initialize
-the ranges as well. Ranges for the histogram bins must be set before the histogram is calculated or
-the backproject of the histogram is calculated.
-
-@param hist Histogram.
-@param ranges Array of bin ranges arrays. See :ocvCreateHist for details.
-@param uniform Uniformity flag. See :ocvCreateHist for details.
- */
-CVAPI(void)  cvSetHistBinRanges( CvHistogram* hist, float** ranges,
-                                int uniform CV_DEFAULT(1));
-
-/** @brief Makes a histogram out of an array.
-
-The function initializes the histogram, whose header and bins are allocated by the user.
-cvReleaseHist does not need to be called afterwards. Only dense histograms can be initialized this
-way. The function returns hist.
-
-@param dims Number of the histogram dimensions.
-@param sizes Array of the histogram dimension sizes.
-@param hist Histogram header initialized by the function.
-@param data Array used to store histogram bins.
-@param ranges Histogram bin ranges. See cvCreateHist for details.
-@param uniform Uniformity flag. See cvCreateHist for details.
- */
-CVAPI(CvHistogram*)  cvMakeHistHeaderForArray(
-                            int  dims, int* sizes, CvHistogram* hist,
-                            float* data, float** ranges CV_DEFAULT(NULL),
-                            int uniform CV_DEFAULT(1));
-
-/** @brief Releases the histogram.
-
-The function releases the histogram (header and the data). The pointer to the histogram is cleared
-by the function. If \*hist pointer is already NULL, the function does nothing.
-
-@param hist Double pointer to the released histogram.
- */
-CVAPI(void)  cvReleaseHist( CvHistogram** hist );
-
-/** @brief Clears the histogram.
-
-The function sets all of the histogram bins to 0 in case of a dense histogram and removes all
-histogram bins in case of a sparse array.
-
-@param hist Histogram.
- */
-CVAPI(void)  cvClearHist( CvHistogram* hist );
-
-/** @brief Finds the minimum and maximum histogram bins.
-
-The function finds the minimum and maximum histogram bins and their positions. All of output
-arguments are optional. Among several extremas with the same value the ones with the minimum index
-(in the lexicographical order) are returned. In case of several maximums or minimums, the earliest
-in the lexicographical order (extrema locations) is returned.
-
-@param hist Histogram.
-@param min_value Pointer to the minimum value of the histogram.
-@param max_value Pointer to the maximum value of the histogram.
-@param min_idx Pointer to the array of coordinates for the minimum.
-@param max_idx Pointer to the array of coordinates for the maximum.
- */
-CVAPI(void)  cvGetMinMaxHistValue( const CvHistogram* hist,
-                                   float* min_value, float* max_value,
-                                   int* min_idx CV_DEFAULT(NULL),
-                                   int* max_idx CV_DEFAULT(NULL));
-
-
-/** @brief Normalizes the histogram.
-
-The function normalizes the histogram bins by scaling them so that the sum of the bins becomes equal
-to factor.
-
-@param hist Pointer to the histogram.
-@param factor Normalization factor.
- */
-CVAPI(void)  cvNormalizeHist( CvHistogram* hist, double factor );
-
-
-/** @brief Thresholds the histogram.
-
-The function clears histogram bins that are below the specified threshold.
-
-@param hist Pointer to the histogram.
-@param threshold Threshold level.
- */
-CVAPI(void)  cvThreshHist( CvHistogram* hist, double threshold );
-
-
-/** Compares two histogram */
-CVAPI(double)  cvCompareHist( const CvHistogram* hist1,
-                              const CvHistogram* hist2,
-                              int method);
-
-/** @brief Copies a histogram.
-
-The function makes a copy of the histogram. If the second histogram pointer \*dst is NULL, a new
-histogram of the same size as src is created. Otherwise, both histograms must have equal types and
-sizes. Then the function copies the bin values of the source histogram to the destination histogram
-and sets the same bin value ranges as in src.
-
-@param src Source histogram.
-@param dst Pointer to the destination histogram.
- */
-CVAPI(void)  cvCopyHist( const CvHistogram* src, CvHistogram** dst );
-
-
-/** @brief Calculates bayesian probabilistic histograms
-   (each or src and dst is an array of _number_ histograms */
-CVAPI(void)  cvCalcBayesianProb( CvHistogram** src, int number,
-                                CvHistogram** dst);
-
-/** @brief Calculates array histogram
-@see cv::calcHist
-*/
-CVAPI(void)  cvCalcArrHist( CvArr** arr, CvHistogram* hist,
-                            int accumulate CV_DEFAULT(0),
-                            const CvArr* mask CV_DEFAULT(NULL) );
-
-/** @overload */
-CV_INLINE  void  cvCalcHist( IplImage** image, CvHistogram* hist,
-                             int accumulate CV_DEFAULT(0),
-                             const CvArr* mask CV_DEFAULT(NULL) )
-{
-    cvCalcArrHist( (CvArr**)image, hist, accumulate, mask );
-}
-
-/** @brief Calculates back project
-@see cvCalcBackProject, cv::calcBackProject
-*/
-CVAPI(void)  cvCalcArrBackProject( CvArr** image, CvArr* dst,
-                                   const CvHistogram* hist );
-
-#define  cvCalcBackProject(image, dst, hist) cvCalcArrBackProject((CvArr**)image, dst, hist)
-
-
-/** @brief Locates a template within an image by using a histogram comparison.
-
-The function calculates the back projection by comparing histograms of the source image patches with
-the given histogram. The function is similar to matchTemplate, but instead of comparing the raster
-patch with all its possible positions within the search window, the function CalcBackProjectPatch
-compares histograms. See the algorithm diagram below:
-
-![image](pics/backprojectpatch.png)
-
-@param image Source images (though, you may pass CvMat\*\* as well).
-@param dst Destination image.
-@param range
-@param hist Histogram.
-@param method Comparison method passed to cvCompareHist (see the function description).
-@param factor Normalization factor for histograms that affects the normalization scale of the
-destination image. Pass 1 if not sure.
-
-@see cvCalcBackProjectPatch
- */
-CVAPI(void)  cvCalcArrBackProjectPatch( CvArr** image, CvArr* dst, CvSize range,
-                                        CvHistogram* hist, int method,
-                                        double factor );
-
-#define  cvCalcBackProjectPatch( image, dst, range, hist, method, factor ) \
-     cvCalcArrBackProjectPatch( (CvArr**)image, dst, range, hist, method, factor )
-
-
-/** @brief Divides one histogram by another.
-
-The function calculates the object probability density from two histograms as:
-
-\f[\texttt{disthist} (I)= \forkthree{0}{if \(\texttt{hist1}(I)=0\)}{\texttt{scale}}{if \(\texttt{hist1}(I) \ne 0\) and \(\texttt{hist2}(I) > \texttt{hist1}(I)\)}{\frac{\texttt{hist2}(I) \cdot \texttt{scale}}{\texttt{hist1}(I)}}{if \(\texttt{hist1}(I) \ne 0\) and \(\texttt{hist2}(I) \le \texttt{hist1}(I)\)}\f]
-
-@param hist1 First histogram (the divisor).
-@param hist2 Second histogram.
-@param dst_hist Destination histogram.
-@param scale Scale factor for the destination histogram.
- */
-CVAPI(void)  cvCalcProbDensity( const CvHistogram* hist1, const CvHistogram* hist2,
-                                CvHistogram* dst_hist, double scale CV_DEFAULT(255) );
-
-/** @brief equalizes histogram of 8-bit single-channel image
-@see cv::equalizeHist
-*/
-CVAPI(void)  cvEqualizeHist( const CvArr* src, CvArr* dst );
-
-
-/** @brief Applies distance transform to binary image
-@see cv::distanceTransform
-*/
-CVAPI(void)  cvDistTransform( const CvArr* src, CvArr* dst,
-                              int distance_type CV_DEFAULT(CV_DIST_L2),
-                              int mask_size CV_DEFAULT(3),
-                              const float* mask CV_DEFAULT(NULL),
-                              CvArr* labels CV_DEFAULT(NULL),
-                              int labelType CV_DEFAULT(CV_DIST_LABEL_CCOMP));
-
-
-/** @brief Applies fixed-level threshold to grayscale image.
-
-   This is a basic operation applied before retrieving contours
-@see cv::threshold
-*/
-CVAPI(double)  cvThreshold( const CvArr*  src, CvArr*  dst,
-                            double  threshold, double  max_value,
-                            int threshold_type );
-
-/** @brief Applies adaptive threshold to grayscale image.
-
-   The two parameters for methods CV_ADAPTIVE_THRESH_MEAN_C and
-   CV_ADAPTIVE_THRESH_GAUSSIAN_C are:
-   neighborhood size (3, 5, 7 etc.),
-   and a constant subtracted from mean (...,-3,-2,-1,0,1,2,3,...)
-@see cv::adaptiveThreshold
-*/
-CVAPI(void)  cvAdaptiveThreshold( const CvArr* src, CvArr* dst, double max_value,
-                                  int adaptive_method CV_DEFAULT(CV_ADAPTIVE_THRESH_MEAN_C),
-                                  int threshold_type CV_DEFAULT(CV_THRESH_BINARY),
-                                  int block_size CV_DEFAULT(3),
-                                  double param1 CV_DEFAULT(5));
-
-/** @brief Fills the connected component until the color difference gets large enough
-@see cv::floodFill
-*/
-CVAPI(void)  cvFloodFill( CvArr* image, CvPoint seed_point,
-                          CvScalar new_val, CvScalar lo_diff CV_DEFAULT(cvScalarAll(0)),
-                          CvScalar up_diff CV_DEFAULT(cvScalarAll(0)),
-                          CvConnectedComp* comp CV_DEFAULT(NULL),
-                          int flags CV_DEFAULT(4),
-                          CvArr* mask CV_DEFAULT(NULL));
-
-/****************************************************************************************\
-*                                  Feature detection                                     *
-\****************************************************************************************/
-
-/** @brief Runs canny edge detector
-@see cv::Canny
-*/
-CVAPI(void)  cvCanny( const CvArr* image, CvArr* edges, double threshold1,
-                      double threshold2, int  aperture_size CV_DEFAULT(3) );
-
-/** @brief Calculates constraint image for corner detection
-
-   Dx^2 * Dyy + Dxx * Dy^2 - 2 * Dx * Dy * Dxy.
-   Applying threshold to the result gives coordinates of corners
-@see cv::preCornerDetect
-*/
-CVAPI(void) cvPreCornerDetect( const CvArr* image, CvArr* corners,
-                               int aperture_size CV_DEFAULT(3) );
-
-/** @brief Calculates eigen values and vectors of 2x2
-   gradient covariation matrix at every image pixel
-@see cv::cornerEigenValsAndVecs
-*/
-CVAPI(void)  cvCornerEigenValsAndVecs( const CvArr* image, CvArr* eigenvv,
-                                       int block_size, int aperture_size CV_DEFAULT(3) );
-
-/** @brief Calculates minimal eigenvalue for 2x2 gradient covariation matrix at
-   every image pixel
-@see cv::cornerMinEigenVal
-*/
-CVAPI(void)  cvCornerMinEigenVal( const CvArr* image, CvArr* eigenval,
-                                  int block_size, int aperture_size CV_DEFAULT(3) );
-
-/** @brief Harris corner detector:
-
-   Calculates det(M) - k*(trace(M)^2), where M is 2x2 gradient covariation matrix for each pixel
-@see cv::cornerHarris
-*/
-CVAPI(void)  cvCornerHarris( const CvArr* image, CvArr* harris_response,
-                             int block_size, int aperture_size CV_DEFAULT(3),
-                             double k CV_DEFAULT(0.04) );
-
-/** @brief Adjust corner position using some sort of gradient search
-@see cv::cornerSubPix
-*/
-CVAPI(void)  cvFindCornerSubPix( const CvArr* image, CvPoint2D32f* corners,
-                                 int count, CvSize win, CvSize zero_zone,
-                                 CvTermCriteria  criteria );
-
-/** @brief Finds a sparse set of points within the selected region
-   that seem to be easy to track
-@see cv::goodFeaturesToTrack
-*/
-CVAPI(void)  cvGoodFeaturesToTrack( const CvArr* image, CvArr* eig_image,
-                                    CvArr* temp_image, CvPoint2D32f* corners,
-                                    int* corner_count, double  quality_level,
-                                    double  min_distance,
-                                    const CvArr* mask CV_DEFAULT(NULL),
-                                    int block_size CV_DEFAULT(3),
-                                    int use_harris CV_DEFAULT(0),
-                                    double k CV_DEFAULT(0.04) );
-
-/** @brief Finds lines on binary image using one of several methods.
-
-   line_storage is either memory storage or 1 x _max number of lines_ CvMat, its
-   number of columns is changed by the function.
-   method is one of CV_HOUGH_*;
-   rho, theta and threshold are used for each of those methods;
-   param1 ~ line length, param2 ~ line gap - for probabilistic,
-   param1 ~ srn, param2 ~ stn - for multi-scale
-@see cv::HoughLines
-*/
-CVAPI(CvSeq*)  cvHoughLines2( CvArr* image, void* line_storage, int method,
-                              double rho, double theta, int threshold,
-                              double param1 CV_DEFAULT(0), double param2 CV_DEFAULT(0),
-                              double min_theta CV_DEFAULT(0), double max_theta CV_DEFAULT(CV_PI));
-
-/** @brief Finds circles in the image
-@see cv::HoughCircles
-*/
-CVAPI(CvSeq*) cvHoughCircles( CvArr* image, void* circle_storage,
-                              int method, double dp, double min_dist,
-                              double param1 CV_DEFAULT(100),
-                              double param2 CV_DEFAULT(100),
-                              int min_radius CV_DEFAULT(0),
-                              int max_radius CV_DEFAULT(0));
-
-/** @brief Fits a line into set of 2d or 3d points in a robust way (M-estimator technique)
-@see cv::fitLine
-*/
-CVAPI(void)  cvFitLine( const CvArr* points, int dist_type, double param,
-                        double reps, double aeps, float* line );
-
-/****************************************************************************************\
-*                                     Drawing                                            *
-\****************************************************************************************/
-
-/****************************************************************************************\
-*       Drawing functions work with images/matrices of arbitrary type.                   *
-*       For color images the channel order is BGR[A]                                     *
-*       Antialiasing is supported only for 8-bit image now.                              *
-*       All the functions include parameter color that means rgb value (that may be      *
-*       constructed with CV_RGB macro) for color images and brightness                   *
-*       for grayscale images.                                                            *
-*       If a drawn figure is partially or completely outside of the image, it is clipped.*
-\****************************************************************************************/
-
-#define CV_FILLED -1
-
-#define CV_AA 16
-
-/** @brief Draws 4-connected, 8-connected or antialiased line segment connecting two points
-@see cv::line
-*/
-CVAPI(void)  cvLine( CvArr* img, CvPoint pt1, CvPoint pt2,
-                     CvScalar color, int thickness CV_DEFAULT(1),
-                     int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0) );
-
-/** @brief Draws a rectangle given two opposite corners of the rectangle (pt1 & pt2)
-
-   if thickness<0 (e.g. thickness == CV_FILLED), the filled box is drawn
-@see cv::rectangle
-*/
-CVAPI(void)  cvRectangle( CvArr* img, CvPoint pt1, CvPoint pt2,
-                          CvScalar color, int thickness CV_DEFAULT(1),
-                          int line_type CV_DEFAULT(8),
-                          int shift CV_DEFAULT(0));
-
-/** @brief Draws a rectangle specified by a CvRect structure
-@see cv::rectangle
-*/
-CVAPI(void)  cvRectangleR( CvArr* img, CvRect r,
-                           CvScalar color, int thickness CV_DEFAULT(1),
-                           int line_type CV_DEFAULT(8),
-                           int shift CV_DEFAULT(0));
-
-
-/** @brief Draws a circle with specified center and radius.
-
-   Thickness works in the same way as with cvRectangle
-@see cv::circle
-*/
-CVAPI(void)  cvCircle( CvArr* img, CvPoint center, int radius,
-                       CvScalar color, int thickness CV_DEFAULT(1),
-                       int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0));
-
-/** @brief Draws ellipse outline, filled ellipse, elliptic arc or filled elliptic sector
-
-   depending on _thickness_, _start_angle_ and _end_angle_ parameters. The resultant figure
-   is rotated by _angle_. All the angles are in degrees
-@see cv::ellipse
-*/
-CVAPI(void)  cvEllipse( CvArr* img, CvPoint center, CvSize axes,
-                        double angle, double start_angle, double end_angle,
-                        CvScalar color, int thickness CV_DEFAULT(1),
-                        int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0));
-
-CV_INLINE  void  cvEllipseBox( CvArr* img, CvBox2D box, CvScalar color,
-                               int thickness CV_DEFAULT(1),
-                               int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0) )
-{
-    CvSize axes;
-    axes.width = cvRound(box.size.width*0.5);
-    axes.height = cvRound(box.size.height*0.5);
-
-    cvEllipse( img, cvPointFrom32f( box.center ), axes, box.angle,
-               0, 360, color, thickness, line_type, shift );
-}
-
-/** @brief Fills convex or monotonous polygon.
-@see cv::fillConvexPoly
-*/
-CVAPI(void)  cvFillConvexPoly( CvArr* img, const CvPoint* pts, int npts, CvScalar color,
-                               int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0));
-
-/** @brief Fills an area bounded by one or more arbitrary polygons
-@see cv::fillPoly
-*/
-CVAPI(void)  cvFillPoly( CvArr* img, CvPoint** pts, const int* npts,
-                         int contours, CvScalar color,
-                         int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0) );
-
-/** @brief Draws one or more polygonal curves
-@see cv::polylines
-*/
-CVAPI(void)  cvPolyLine( CvArr* img, CvPoint** pts, const int* npts, int contours,
-                         int is_closed, CvScalar color, int thickness CV_DEFAULT(1),
-                         int line_type CV_DEFAULT(8), int shift CV_DEFAULT(0) );
-
-#define cvDrawRect cvRectangle
-#define cvDrawLine cvLine
-#define cvDrawCircle cvCircle
-#define cvDrawEllipse cvEllipse
-#define cvDrawPolyLine cvPolyLine
-
-/** @brief Clips the line segment connecting *pt1 and *pt2
-   by the rectangular window
-
-   (0<=x<img_size.width, 0<=y<img_size.height).
-@see cv::clipLine
-*/
-CVAPI(int) cvClipLine( CvSize img_size, CvPoint* pt1, CvPoint* pt2 );
-
-/** @brief Initializes line iterator.
-
-Initially, line_iterator->ptr will point to pt1 (or pt2, see left_to_right description) location in
-the image. Returns the number of pixels on the line between the ending points.
-@see cv::LineIterator
-*/
-CVAPI(int)  cvInitLineIterator( const CvArr* image, CvPoint pt1, CvPoint pt2,
-                                CvLineIterator* line_iterator,
-                                int connectivity CV_DEFAULT(8),
-                                int left_to_right CV_DEFAULT(0));
-
-#define CV_NEXT_LINE_POINT( line_iterator )                     \
-{                                                               \
-    int _line_iterator_mask = (line_iterator).err < 0 ? -1 : 0; \
-    (line_iterator).err += (line_iterator).minus_delta +        \
-        ((line_iterator).plus_delta & _line_iterator_mask);     \
-    (line_iterator).ptr += (line_iterator).minus_step +         \
-        ((line_iterator).plus_step & _line_iterator_mask);      \
-}
-
-
-#define CV_FONT_HERSHEY_SIMPLEX         0
-#define CV_FONT_HERSHEY_PLAIN           1
-#define CV_FONT_HERSHEY_DUPLEX          2
-#define CV_FONT_HERSHEY_COMPLEX         3
-#define CV_FONT_HERSHEY_TRIPLEX         4
-#define CV_FONT_HERSHEY_COMPLEX_SMALL   5
-#define CV_FONT_HERSHEY_SCRIPT_SIMPLEX  6
-#define CV_FONT_HERSHEY_SCRIPT_COMPLEX  7
-
-#define CV_FONT_ITALIC                 16
-
-#define CV_FONT_VECTOR0    CV_FONT_HERSHEY_SIMPLEX
-
-
-/** Font structure */
-typedef struct CvFont
-{
-  const char* nameFont;   //Qt:nameFont
-  CvScalar color;       //Qt:ColorFont -> cvScalar(blue_component, green_component, red_component[, alpha_component])
-    int         font_face;    //Qt: bool italic         /** =CV_FONT_* */
-    const int*  ascii;      //!< font data and metrics
-    const int*  greek;
-    const int*  cyrillic;
-    float       hscale, vscale;
-    float       shear;      //!< slope coefficient: 0 - normal, >0 - italic
-    int         thickness;    //!< Qt: weight               /** letters thickness */
-    float       dx;       //!< horizontal interval between letters
-    int         line_type;    //!< Qt: PointSize
-}
-CvFont;
-
-/** @brief Initializes font structure (OpenCV 1.x API).
-
-The function initializes the font structure that can be passed to text rendering functions.
-
-@param font Pointer to the font structure initialized by the function
-@param font_face Font name identifier. See cv::HersheyFonts and corresponding old CV_* identifiers.
-@param hscale Horizontal scale. If equal to 1.0f , the characters have the original width
-depending on the font type. If equal to 0.5f , the characters are of half the original width.
-@param vscale Vertical scale. If equal to 1.0f , the characters have the original height depending
-on the font type. If equal to 0.5f , the characters are of half the original height.
-@param shear Approximate tangent of the character slope relative to the vertical line. A zero
-value means a non-italic font, 1.0f means about a 45 degree slope, etc.
-@param thickness Thickness of the text strokes
-@param line_type Type of the strokes, see line description
-
-@sa cvPutText
- */
-CVAPI(void)  cvInitFont( CvFont* font, int font_face,
-                         double hscale, double vscale,
-                         double shear CV_DEFAULT(0),
-                         int thickness CV_DEFAULT(1),
-                         int line_type CV_DEFAULT(8));
-
-CV_INLINE CvFont cvFont( double scale, int thickness CV_DEFAULT(1) )
-{
-    CvFont font;
-    cvInitFont( &font, CV_FONT_HERSHEY_PLAIN, scale, scale, 0, thickness, CV_AA );
-    return font;
-}
-
-/** @brief Renders text stroke with specified font and color at specified location.
-   CvFont should be initialized with cvInitFont
-@see cvInitFont, cvGetTextSize, cvFont, cv::putText
-*/
-CVAPI(void)  cvPutText( CvArr* img, const char* text, CvPoint org,
-                        const CvFont* font, CvScalar color );
-
-/** @brief Calculates bounding box of text stroke (useful for alignment)
-@see cv::getTextSize
-*/
-CVAPI(void)  cvGetTextSize( const char* text_string, const CvFont* font,
-                            CvSize* text_size, int* baseline );
-
-/** @brief Unpacks color value
-
-if arrtype is CV_8UC?, _color_ is treated as packed color value, otherwise the first channels
-(depending on arrtype) of destination scalar are set to the same value = _color_
-*/
-CVAPI(CvScalar)  cvColorToScalar( double packed_color, int arrtype );
-
-/** @brief Returns the polygon points which make up the given ellipse.
-
-The ellipse is define by the box of size 'axes' rotated 'angle' around the 'center'. A partial
-sweep of the ellipse arc can be done by spcifying arc_start and arc_end to be something other than
-0 and 360, respectively. The input array 'pts' must be large enough to hold the result. The total
-number of points stored into 'pts' is returned by this function.
-@see cv::ellipse2Poly
-*/
-CVAPI(int) cvEllipse2Poly( CvPoint center, CvSize axes,
-                 int angle, int arc_start, int arc_end, CvPoint * pts, int delta );
-
-/** @brief Draws contour outlines or filled interiors on the image
-@see cv::drawContours
-*/
-CVAPI(void)  cvDrawContours( CvArr *img, CvSeq* contour,
-                             CvScalar external_color, CvScalar hole_color,
-                             int max_level, int thickness CV_DEFAULT(1),
-                             int line_type CV_DEFAULT(8),
-                             CvPoint offset CV_DEFAULT(cvPoint(0,0)));
-
-/** @} */
-
-#ifdef __cplusplus
-}
-#endif
-
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/imgproc/types_c.h b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/imgproc/types_c.h
deleted file mode 100755
index 13ffe1b..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/imgproc/types_c.h
+++ /dev/null
@@ -1,629 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_IMGPROC_TYPES_C_H
-#define OPENCV_IMGPROC_TYPES_C_H
-
-#include "opencv2/core/core_c.h"
-
-#ifdef __cplusplus
-extern "C" {
-#endif
-
-/** @addtogroup imgproc_c
-  @{
-*/
-
-/** Connected component structure */
-typedef struct CvConnectedComp
-{
-    double area;    /**<area of the connected component  */
-    CvScalar value; /**<average color of the connected component */
-    CvRect rect;    /**<ROI of the component  */
-    CvSeq* contour; /**<optional component boundary
-                      (the contour might have child contours corresponding to the holes)*/
-}
-CvConnectedComp;
-
-/** Image smooth methods */
-enum SmoothMethod_c
-{
-    /** linear convolution with \f$\texttt{size1}\times\texttt{size2}\f$ box kernel (all 1's). If
-    you want to smooth different pixels with different-size box kernels, you can use the integral
-    image that is computed using integral */
-    CV_BLUR_NO_SCALE =0,
-    /** linear convolution with \f$\texttt{size1}\times\texttt{size2}\f$ box kernel (all
-    1's) with subsequent scaling by \f$1/(\texttt{size1}\cdot\texttt{size2})\f$ */
-    CV_BLUR  =1,
-    /** linear convolution with a \f$\texttt{size1}\times\texttt{size2}\f$ Gaussian kernel */
-    CV_GAUSSIAN  =2,
-    /** median filter with a \f$\texttt{size1}\times\texttt{size1}\f$ square aperture */
-    CV_MEDIAN =3,
-    /** bilateral filter with a \f$\texttt{size1}\times\texttt{size1}\f$ square aperture, color
-    sigma= sigma1 and spatial sigma= sigma2. If size1=0, the aperture square side is set to
-    cvRound(sigma2\*1.5)\*2+1. See cv::bilateralFilter */
-    CV_BILATERAL =4
-};
-
-/** Filters used in pyramid decomposition */
-enum
-{
-    CV_GAUSSIAN_5x5 = 7
-};
-
-/** Special filters */
-enum
-{
-    CV_SCHARR =-1,
-    CV_MAX_SOBEL_KSIZE =7
-};
-
-/** Constants for color conversion */
-enum
-{
-    CV_BGR2BGRA    =0,
-    CV_RGB2RGBA    =CV_BGR2BGRA,
-
-    CV_BGRA2BGR    =1,
-    CV_RGBA2RGB    =CV_BGRA2BGR,
-
-    CV_BGR2RGBA    =2,
-    CV_RGB2BGRA    =CV_BGR2RGBA,
-
-    CV_RGBA2BGR    =3,
-    CV_BGRA2RGB    =CV_RGBA2BGR,
-
-    CV_BGR2RGB     =4,
-    CV_RGB2BGR     =CV_BGR2RGB,
-
-    CV_BGRA2RGBA   =5,
-    CV_RGBA2BGRA   =CV_BGRA2RGBA,
-
-    CV_BGR2GRAY    =6,
-    CV_RGB2GRAY    =7,
-    CV_GRAY2BGR    =8,
-    CV_GRAY2RGB    =CV_GRAY2BGR,
-    CV_GRAY2BGRA   =9,
-    CV_GRAY2RGBA   =CV_GRAY2BGRA,
-    CV_BGRA2GRAY   =10,
-    CV_RGBA2GRAY   =11,
-
-    CV_BGR2BGR565  =12,
-    CV_RGB2BGR565  =13,
-    CV_BGR5652BGR  =14,
-    CV_BGR5652RGB  =15,
-    CV_BGRA2BGR565 =16,
-    CV_RGBA2BGR565 =17,
-    CV_BGR5652BGRA =18,
-    CV_BGR5652RGBA =19,
-
-    CV_GRAY2BGR565 =20,
-    CV_BGR5652GRAY =21,
-
-    CV_BGR2BGR555  =22,
-    CV_RGB2BGR555  =23,
-    CV_BGR5552BGR  =24,
-    CV_BGR5552RGB  =25,
-    CV_BGRA2BGR555 =26,
-    CV_RGBA2BGR555 =27,
-    CV_BGR5552BGRA =28,
-    CV_BGR5552RGBA =29,
-
-    CV_GRAY2BGR555 =30,
-    CV_BGR5552GRAY =31,
-
-    CV_BGR2XYZ     =32,
-    CV_RGB2XYZ     =33,
-    CV_XYZ2BGR     =34,
-    CV_XYZ2RGB     =35,
-
-    CV_BGR2YCrCb   =36,
-    CV_RGB2YCrCb   =37,
-    CV_YCrCb2BGR   =38,
-    CV_YCrCb2RGB   =39,
-
-    CV_BGR2HSV     =40,
-    CV_RGB2HSV     =41,
-
-    CV_BGR2Lab     =44,
-    CV_RGB2Lab     =45,
-
-    CV_BayerBG2BGR =46,
-    CV_BayerGB2BGR =47,
-    CV_BayerRG2BGR =48,
-    CV_BayerGR2BGR =49,
-
-    CV_BayerBG2RGB =CV_BayerRG2BGR,
-    CV_BayerGB2RGB =CV_BayerGR2BGR,
-    CV_BayerRG2RGB =CV_BayerBG2BGR,
-    CV_BayerGR2RGB =CV_BayerGB2BGR,
-
-    CV_BGR2Luv     =50,
-    CV_RGB2Luv     =51,
-    CV_BGR2HLS     =52,
-    CV_RGB2HLS     =53,
-
-    CV_HSV2BGR     =54,
-    CV_HSV2RGB     =55,
-
-    CV_Lab2BGR     =56,
-    CV_Lab2RGB     =57,
-    CV_Luv2BGR     =58,
-    CV_Luv2RGB     =59,
-    CV_HLS2BGR     =60,
-    CV_HLS2RGB     =61,
-
-    CV_BayerBG2BGR_VNG =62,
-    CV_BayerGB2BGR_VNG =63,
-    CV_BayerRG2BGR_VNG =64,
-    CV_BayerGR2BGR_VNG =65,
-
-    CV_BayerBG2RGB_VNG =CV_BayerRG2BGR_VNG,
-    CV_BayerGB2RGB_VNG =CV_BayerGR2BGR_VNG,
-    CV_BayerRG2RGB_VNG =CV_BayerBG2BGR_VNG,
-    CV_BayerGR2RGB_VNG =CV_BayerGB2BGR_VNG,
-
-    CV_BGR2HSV_FULL = 66,
-    CV_RGB2HSV_FULL = 67,
-    CV_BGR2HLS_FULL = 68,
-    CV_RGB2HLS_FULL = 69,
-
-    CV_HSV2BGR_FULL = 70,
-    CV_HSV2RGB_FULL = 71,
-    CV_HLS2BGR_FULL = 72,
-    CV_HLS2RGB_FULL = 73,
-
-    CV_LBGR2Lab     = 74,
-    CV_LRGB2Lab     = 75,
-    CV_LBGR2Luv     = 76,
-    CV_LRGB2Luv     = 77,
-
-    CV_Lab2LBGR     = 78,
-    CV_Lab2LRGB     = 79,
-    CV_Luv2LBGR     = 80,
-    CV_Luv2LRGB     = 81,
-
-    CV_BGR2YUV      = 82,
-    CV_RGB2YUV      = 83,
-    CV_YUV2BGR      = 84,
-    CV_YUV2RGB      = 85,
-
-    CV_BayerBG2GRAY = 86,
-    CV_BayerGB2GRAY = 87,
-    CV_BayerRG2GRAY = 88,
-    CV_BayerGR2GRAY = 89,
-
-    //YUV 4:2:0 formats family
-    CV_YUV2RGB_NV12 = 90,
-    CV_YUV2BGR_NV12 = 91,
-    CV_YUV2RGB_NV21 = 92,
-    CV_YUV2BGR_NV21 = 93,
-    CV_YUV420sp2RGB = CV_YUV2RGB_NV21,
-    CV_YUV420sp2BGR = CV_YUV2BGR_NV21,
-
-    CV_YUV2RGBA_NV12 = 94,
-    CV_YUV2BGRA_NV12 = 95,
-    CV_YUV2RGBA_NV21 = 96,
-    CV_YUV2BGRA_NV21 = 97,
-    CV_YUV420sp2RGBA = CV_YUV2RGBA_NV21,
-    CV_YUV420sp2BGRA = CV_YUV2BGRA_NV21,
-
-    CV_YUV2RGB_YV12 = 98,
-    CV_YUV2BGR_YV12 = 99,
-    CV_YUV2RGB_IYUV = 100,
-    CV_YUV2BGR_IYUV = 101,
-    CV_YUV2RGB_I420 = CV_YUV2RGB_IYUV,
-    CV_YUV2BGR_I420 = CV_YUV2BGR_IYUV,
-    CV_YUV420p2RGB = CV_YUV2RGB_YV12,
-    CV_YUV420p2BGR = CV_YUV2BGR_YV12,
-
-    CV_YUV2RGBA_YV12 = 102,
-    CV_YUV2BGRA_YV12 = 103,
-    CV_YUV2RGBA_IYUV = 104,
-    CV_YUV2BGRA_IYUV = 105,
-    CV_YUV2RGBA_I420 = CV_YUV2RGBA_IYUV,
-    CV_YUV2BGRA_I420 = CV_YUV2BGRA_IYUV,
-    CV_YUV420p2RGBA = CV_YUV2RGBA_YV12,
-    CV_YUV420p2BGRA = CV_YUV2BGRA_YV12,
-
-    CV_YUV2GRAY_420 = 106,
-    CV_YUV2GRAY_NV21 = CV_YUV2GRAY_420,
-    CV_YUV2GRAY_NV12 = CV_YUV2GRAY_420,
-    CV_YUV2GRAY_YV12 = CV_YUV2GRAY_420,
-    CV_YUV2GRAY_IYUV = CV_YUV2GRAY_420,
-    CV_YUV2GRAY_I420 = CV_YUV2GRAY_420,
-    CV_YUV420sp2GRAY = CV_YUV2GRAY_420,
-    CV_YUV420p2GRAY = CV_YUV2GRAY_420,
-
-    //YUV 4:2:2 formats family
-    CV_YUV2RGB_UYVY = 107,
-    CV_YUV2BGR_UYVY = 108,
-    //CV_YUV2RGB_VYUY = 109,
-    //CV_YUV2BGR_VYUY = 110,
-    CV_YUV2RGB_Y422 = CV_YUV2RGB_UYVY,
-    CV_YUV2BGR_Y422 = CV_YUV2BGR_UYVY,
-    CV_YUV2RGB_UYNV = CV_YUV2RGB_UYVY,
-    CV_YUV2BGR_UYNV = CV_YUV2BGR_UYVY,
-
-    CV_YUV2RGBA_UYVY = 111,
-    CV_YUV2BGRA_UYVY = 112,
-    //CV_YUV2RGBA_VYUY = 113,
-    //CV_YUV2BGRA_VYUY = 114,
-    CV_YUV2RGBA_Y422 = CV_YUV2RGBA_UYVY,
-    CV_YUV2BGRA_Y422 = CV_YUV2BGRA_UYVY,
-    CV_YUV2RGBA_UYNV = CV_YUV2RGBA_UYVY,
-    CV_YUV2BGRA_UYNV = CV_YUV2BGRA_UYVY,
-
-    CV_YUV2RGB_YUY2 = 115,
-    CV_YUV2BGR_YUY2 = 116,
-    CV_YUV2RGB_YVYU = 117,
-    CV_YUV2BGR_YVYU = 118,
-    CV_YUV2RGB_YUYV = CV_YUV2RGB_YUY2,
-    CV_YUV2BGR_YUYV = CV_YUV2BGR_YUY2,
-    CV_YUV2RGB_YUNV = CV_YUV2RGB_YUY2,
-    CV_YUV2BGR_YUNV = CV_YUV2BGR_YUY2,
-
-    CV_YUV2RGBA_YUY2 = 119,
-    CV_YUV2BGRA_YUY2 = 120,
-    CV_YUV2RGBA_YVYU = 121,
-    CV_YUV2BGRA_YVYU = 122,
-    CV_YUV2RGBA_YUYV = CV_YUV2RGBA_YUY2,
-    CV_YUV2BGRA_YUYV = CV_YUV2BGRA_YUY2,
-    CV_YUV2RGBA_YUNV = CV_YUV2RGBA_YUY2,
-    CV_YUV2BGRA_YUNV = CV_YUV2BGRA_YUY2,
-
-    CV_YUV2GRAY_UYVY = 123,
-    CV_YUV2GRAY_YUY2 = 124,
-    //CV_YUV2GRAY_VYUY = CV_YUV2GRAY_UYVY,
-    CV_YUV2GRAY_Y422 = CV_YUV2GRAY_UYVY,
-    CV_YUV2GRAY_UYNV = CV_YUV2GRAY_UYVY,
-    CV_YUV2GRAY_YVYU = CV_YUV2GRAY_YUY2,
-    CV_YUV2GRAY_YUYV = CV_YUV2GRAY_YUY2,
-    CV_YUV2GRAY_YUNV = CV_YUV2GRAY_YUY2,
-
-    // alpha premultiplication
-    CV_RGBA2mRGBA = 125,
-    CV_mRGBA2RGBA = 126,
-
-    CV_RGB2YUV_I420 = 127,
-    CV_BGR2YUV_I420 = 128,
-    CV_RGB2YUV_IYUV = CV_RGB2YUV_I420,
-    CV_BGR2YUV_IYUV = CV_BGR2YUV_I420,
-
-    CV_RGBA2YUV_I420 = 129,
-    CV_BGRA2YUV_I420 = 130,
-    CV_RGBA2YUV_IYUV = CV_RGBA2YUV_I420,
-    CV_BGRA2YUV_IYUV = CV_BGRA2YUV_I420,
-    CV_RGB2YUV_YV12  = 131,
-    CV_BGR2YUV_YV12  = 132,
-    CV_RGBA2YUV_YV12 = 133,
-    CV_BGRA2YUV_YV12 = 134,
-
-    // Edge-Aware Demosaicing
-    CV_BayerBG2BGR_EA = 135,
-    CV_BayerGB2BGR_EA = 136,
-    CV_BayerRG2BGR_EA = 137,
-    CV_BayerGR2BGR_EA = 138,
-
-    CV_BayerBG2RGB_EA = CV_BayerRG2BGR_EA,
-    CV_BayerGB2RGB_EA = CV_BayerGR2BGR_EA,
-    CV_BayerRG2RGB_EA = CV_BayerBG2BGR_EA,
-    CV_BayerGR2RGB_EA = CV_BayerGB2BGR_EA,
-
-    CV_BayerBG2BGRA =139,
-    CV_BayerGB2BGRA =140,
-    CV_BayerRG2BGRA =141,
-    CV_BayerGR2BGRA =142,
-
-    CV_BayerBG2RGBA =CV_BayerRG2BGRA,
-    CV_BayerGB2RGBA =CV_BayerGR2BGRA,
-    CV_BayerRG2RGBA =CV_BayerBG2BGRA,
-    CV_BayerGR2RGBA =CV_BayerGB2BGRA,
-
-    CV_COLORCVT_MAX  = 143
-};
-
-
-/** Sub-pixel interpolation methods */
-enum
-{
-    CV_INTER_NN        =0,
-    CV_INTER_LINEAR    =1,
-    CV_INTER_CUBIC     =2,
-    CV_INTER_AREA      =3,
-    CV_INTER_LANCZOS4  =4
-};
-
-/** ... and other image warping flags */
-enum
-{
-    CV_WARP_FILL_OUTLIERS =8,
-    CV_WARP_INVERSE_MAP  =16
-};
-
-/** Shapes of a structuring element for morphological operations
-@see cv::MorphShapes, cv::getStructuringElement
-*/
-enum MorphShapes_c
-{
-    CV_SHAPE_RECT      =0,
-    CV_SHAPE_CROSS     =1,
-    CV_SHAPE_ELLIPSE   =2,
-    CV_SHAPE_CUSTOM    =100 //!< custom structuring element
-};
-
-/** Morphological operations */
-enum
-{
-    CV_MOP_ERODE        =0,
-    CV_MOP_DILATE       =1,
-    CV_MOP_OPEN         =2,
-    CV_MOP_CLOSE        =3,
-    CV_MOP_GRADIENT     =4,
-    CV_MOP_TOPHAT       =5,
-    CV_MOP_BLACKHAT     =6
-};
-
-/** Spatial and central moments */
-typedef struct CvMoments
-{
-    double  m00, m10, m01, m20, m11, m02, m30, m21, m12, m03; /**< spatial moments */
-    double  mu20, mu11, mu02, mu30, mu21, mu12, mu03; /**< central moments */
-    double  inv_sqrt_m00; /**< m00 != 0 ? 1/sqrt(m00) : 0 */
-
-#ifdef __cplusplus
-    CvMoments(){}
-    CvMoments(const cv::Moments& m)
-    {
-        m00 = m.m00; m10 = m.m10; m01 = m.m01;
-        m20 = m.m20; m11 = m.m11; m02 = m.m02;
-        m30 = m.m30; m21 = m.m21; m12 = m.m12; m03 = m.m03;
-        mu20 = m.mu20; mu11 = m.mu11; mu02 = m.mu02;
-        mu30 = m.mu30; mu21 = m.mu21; mu12 = m.mu12; mu03 = m.mu03;
-        double am00 = std::abs(m.m00);
-        inv_sqrt_m00 = am00 > DBL_EPSILON ? 1./std::sqrt(am00) : 0;
-    }
-    operator cv::Moments() const
-    {
-        return cv::Moments(m00, m10, m01, m20, m11, m02, m30, m21, m12, m03);
-    }
-#endif
-}
-CvMoments;
-
-/** Hu invariants */
-typedef struct CvHuMoments
-{
-    double hu1, hu2, hu3, hu4, hu5, hu6, hu7; /**< Hu invariants */
-}
-CvHuMoments;
-
-/** Template matching methods */
-enum
-{
-    CV_TM_SQDIFF        =0,
-    CV_TM_SQDIFF_NORMED =1,
-    CV_TM_CCORR         =2,
-    CV_TM_CCORR_NORMED  =3,
-    CV_TM_CCOEFF        =4,
-    CV_TM_CCOEFF_NORMED =5
-};
-
-typedef float (CV_CDECL * CvDistanceFunction)( const float* a, const float* b, void* user_param );
-
-/** Contour retrieval modes */
-enum
-{
-    CV_RETR_EXTERNAL=0,
-    CV_RETR_LIST=1,
-    CV_RETR_CCOMP=2,
-    CV_RETR_TREE=3,
-    CV_RETR_FLOODFILL=4
-};
-
-/** Contour approximation methods */
-enum
-{
-    CV_CHAIN_CODE=0,
-    CV_CHAIN_APPROX_NONE=1,
-    CV_CHAIN_APPROX_SIMPLE=2,
-    CV_CHAIN_APPROX_TC89_L1=3,
-    CV_CHAIN_APPROX_TC89_KCOS=4,
-    CV_LINK_RUNS=5
-};
-
-/*
-Internal structure that is used for sequential retrieving contours from the image.
-It supports both hierarchical and plane variants of Suzuki algorithm.
-*/
-typedef struct _CvContourScanner* CvContourScanner;
-
-/** Freeman chain reader state */
-typedef struct CvChainPtReader
-{
-    CV_SEQ_READER_FIELDS()
-    char      code;
-    CvPoint   pt;
-    schar     deltas[8][2];
-}
-CvChainPtReader;
-
-/** initializes 8-element array for fast access to 3x3 neighborhood of a pixel */
-#define  CV_INIT_3X3_DELTAS( deltas, step, nch )            \
-    ((deltas)[0] =  (nch),  (deltas)[1] = -(step) + (nch),  \
-     (deltas)[2] = -(step), (deltas)[3] = -(step) - (nch),  \
-     (deltas)[4] = -(nch),  (deltas)[5] =  (step) - (nch),  \
-     (deltas)[6] =  (step), (deltas)[7] =  (step) + (nch))
-
-
-/** Contour approximation algorithms */
-enum
-{
-    CV_POLY_APPROX_DP = 0
-};
-
-/** Shape matching methods */
-enum
-{
-    CV_CONTOURS_MATCH_I1  =1, //!< \f[I_1(A,B) =  \sum _{i=1...7}  \left |  \frac{1}{m^A_i} -  \frac{1}{m^B_i} \right |\f]
-    CV_CONTOURS_MATCH_I2  =2, //!< \f[I_2(A,B) =  \sum _{i=1...7}  \left | m^A_i - m^B_i  \right |\f]
-    CV_CONTOURS_MATCH_I3  =3  //!< \f[I_3(A,B) =  \max _{i=1...7}  \frac{ \left| m^A_i - m^B_i \right| }{ \left| m^A_i \right| }\f]
-};
-
-/** Shape orientation */
-enum
-{
-    CV_CLOCKWISE         =1,
-    CV_COUNTER_CLOCKWISE =2
-};
-
-
-/** Convexity defect */
-typedef struct CvConvexityDefect
-{
-    CvPoint* start; /**< point of the contour where the defect begins */
-    CvPoint* end; /**< point of the contour where the defect ends */
-    CvPoint* depth_point; /**< the farthest from the convex hull point within the defect */
-    float depth; /**< distance between the farthest point and the convex hull */
-} CvConvexityDefect;
-
-
-/** Histogram comparison methods */
-enum
-{
-    CV_COMP_CORREL        =0,
-    CV_COMP_CHISQR        =1,
-    CV_COMP_INTERSECT     =2,
-    CV_COMP_BHATTACHARYYA =3,
-    CV_COMP_HELLINGER     =CV_COMP_BHATTACHARYYA,
-    CV_COMP_CHISQR_ALT    =4,
-    CV_COMP_KL_DIV        =5
-};
-
-/** Mask size for distance transform */
-enum
-{
-    CV_DIST_MASK_3   =3,
-    CV_DIST_MASK_5   =5,
-    CV_DIST_MASK_PRECISE =0
-};
-
-/** Content of output label array: connected components or pixels */
-enum
-{
-  CV_DIST_LABEL_CCOMP = 0,
-  CV_DIST_LABEL_PIXEL = 1
-};
-
-/** Distance types for Distance Transform and M-estimators */
-enum
-{
-    CV_DIST_USER    =-1,  /**< User defined distance */
-    CV_DIST_L1      =1,   /**< distance = |x1-x2| + |y1-y2| */
-    CV_DIST_L2      =2,   /**< the simple euclidean distance */
-    CV_DIST_C       =3,   /**< distance = max(|x1-x2|,|y1-y2|) */
-    CV_DIST_L12     =4,   /**< L1-L2 metric: distance = 2(sqrt(1+x*x/2) - 1)) */
-    CV_DIST_FAIR    =5,   /**< distance = c^2(|x|/c-log(1+|x|/c)), c = 1.3998 */
-    CV_DIST_WELSCH  =6,   /**< distance = c^2/2(1-exp(-(x/c)^2)), c = 2.9846 */
-    CV_DIST_HUBER   =7    /**< distance = |x|<c ? x^2/2 : c(|x|-c/2), c=1.345 */
-};
-
-
-/** Threshold types */
-enum
-{
-    CV_THRESH_BINARY      =0,  /**< value = value > threshold ? max_value : 0       */
-    CV_THRESH_BINARY_INV  =1,  /**< value = value > threshold ? 0 : max_value       */
-    CV_THRESH_TRUNC       =2,  /**< value = value > threshold ? threshold : value   */
-    CV_THRESH_TOZERO      =3,  /**< value = value > threshold ? value : 0           */
-    CV_THRESH_TOZERO_INV  =4,  /**< value = value > threshold ? 0 : value           */
-    CV_THRESH_MASK        =7,
-    CV_THRESH_OTSU        =8, /**< use Otsu algorithm to choose the optimal threshold value;
-                                 combine the flag with one of the above CV_THRESH_* values */
-    CV_THRESH_TRIANGLE    =16  /**< use Triangle algorithm to choose the optimal threshold value;
-                                 combine the flag with one of the above CV_THRESH_* values, but not
-                                 with CV_THRESH_OTSU */
-};
-
-/** Adaptive threshold methods */
-enum
-{
-    CV_ADAPTIVE_THRESH_MEAN_C  =0,
-    CV_ADAPTIVE_THRESH_GAUSSIAN_C  =1
-};
-
-/** FloodFill flags */
-enum
-{
-    CV_FLOODFILL_FIXED_RANGE =(1 << 16),
-    CV_FLOODFILL_MASK_ONLY   =(1 << 17)
-};
-
-
-/** Canny edge detector flags */
-enum
-{
-    CV_CANNY_L2_GRADIENT  =(1 << 31)
-};
-
-/** Variants of a Hough transform */
-enum
-{
-    CV_HOUGH_STANDARD =0,
-    CV_HOUGH_PROBABILISTIC =1,
-    CV_HOUGH_MULTI_SCALE =2,
-    CV_HOUGH_GRADIENT =3
-};
-
-
-/* Fast search data structures  */
-struct CvFeatureTree;
-struct CvLSH;
-struct CvLSHOperations;
-
-/** @} */
-
-#ifdef __cplusplus
-}
-#endif
-
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/ml.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/ml.hpp
deleted file mode 100755
index 31e7427..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/ml.hpp
+++ /dev/null
@@ -1,1961 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000, Intel Corporation, all rights reserved.
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
-// Copyright (C) 2014, Itseez Inc, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_ML_HPP
-#define OPENCV_ML_HPP
-
-#ifdef __cplusplus
-#  include "opencv2/core.hpp"
-#endif
-
-#ifdef __cplusplus
-
-#include <float.h>
-#include <map>
-#include <iostream>
-
-/**
-  @defgroup ml Machine Learning
-
-  The Machine Learning Library (MLL) is a set of classes and functions for statistical
-  classification, regression, and clustering of data.
-
-  Most of the classification and regression algorithms are implemented as C++ classes. As the
-  algorithms have different sets of features (like an ability to handle missing measurements or
-  categorical input variables), there is a little common ground between the classes. This common
-  ground is defined by the class cv::ml::StatModel that all the other ML classes are derived from.
-
-  See detailed overview here: @ref ml_intro.
- */
-
-namespace cv
-{
-
-namespace ml
-{
-
-//! @addtogroup ml
-//! @{
-
-/** @brief Variable types */
-enum VariableTypes
-{
-    VAR_NUMERICAL    =0, //!< same as VAR_ORDERED
-    VAR_ORDERED      =0, //!< ordered variables
-    VAR_CATEGORICAL  =1  //!< categorical variables
-};
-
-/** @brief %Error types */
-enum ErrorTypes
-{
-    TEST_ERROR = 0,
-    TRAIN_ERROR = 1
-};
-
-/** @brief Sample types */
-enum SampleTypes
-{
-    ROW_SAMPLE = 0, //!< each training sample is a row of samples
-    COL_SAMPLE = 1  //!< each training sample occupies a column of samples
-};
-
-/** @brief The structure represents the logarithmic grid range of statmodel parameters.
-
-It is used for optimizing statmodel accuracy by varying model parameters, the accuracy estimate
-being computed by cross-validation.
- */
-class CV_EXPORTS_W ParamGrid
-{
-public:
-    /** @brief Default constructor */
-    ParamGrid();
-    /** @brief Constructor with parameters */
-    ParamGrid(double _minVal, double _maxVal, double _logStep);
-
-    CV_PROP_RW double minVal; //!< Minimum value of the statmodel parameter. Default value is 0.
-    CV_PROP_RW double maxVal; //!< Maximum value of the statmodel parameter. Default value is 0.
-    /** @brief Logarithmic step for iterating the statmodel parameter.
-
-    The grid determines the following iteration sequence of the statmodel parameter values:
-    \f[(minVal, minVal*step, minVal*{step}^2, \dots,  minVal*{logStep}^n),\f]
-    where \f$n\f$ is the maximal index satisfying
-    \f[\texttt{minVal} * \texttt{logStep} ^n <  \texttt{maxVal}\f]
-    The grid is logarithmic, so logStep must always be greater then 1. Default value is 1.
-    */
-    CV_PROP_RW double logStep;
-
-    /** @brief Creates a ParamGrid Ptr that can be given to the %SVM::trainAuto method
-
-    @param minVal minimum value of the parameter grid
-    @param maxVal maximum value of the parameter grid
-    @param logstep Logarithmic step for iterating the statmodel parameter
-    */
-    CV_WRAP static Ptr<ParamGrid> create(double minVal=0., double maxVal=0., double logstep=1.);
-};
-
-/** @brief Class encapsulating training data.
-
-Please note that the class only specifies the interface of training data, but not implementation.
-All the statistical model classes in _ml_ module accepts Ptr\<TrainData\> as parameter. In other
-words, you can create your own class derived from TrainData and pass smart pointer to the instance
-of this class into StatModel::train.
-
-@sa @ref ml_intro_data
- */
-class CV_EXPORTS_W TrainData
-{
-public:
-    static inline float missingValue() { return FLT_MAX; }
-    virtual ~TrainData();
-
-    CV_WRAP virtual int getLayout() const = 0;
-    CV_WRAP virtual int getNTrainSamples() const = 0;
-    CV_WRAP virtual int getNTestSamples() const = 0;
-    CV_WRAP virtual int getNSamples() const = 0;
-    CV_WRAP virtual int getNVars() const = 0;
-    CV_WRAP virtual int getNAllVars() const = 0;
-
-    CV_WRAP virtual void getSample(InputArray varIdx, int sidx, float* buf) const = 0;
-    CV_WRAP virtual Mat getSamples() const = 0;
-    CV_WRAP virtual Mat getMissing() const = 0;
-
-    /** @brief Returns matrix of train samples
-
-    @param layout The requested layout. If it's different from the initial one, the matrix is
-        transposed. See ml::SampleTypes.
-    @param compressSamples if true, the function returns only the training samples (specified by
-        sampleIdx)
-    @param compressVars if true, the function returns the shorter training samples, containing only
-        the active variables.
-
-    In current implementation the function tries to avoid physical data copying and returns the
-    matrix stored inside TrainData (unless the transposition or compression is needed).
-     */
-    CV_WRAP virtual Mat getTrainSamples(int layout=ROW_SAMPLE,
-                                bool compressSamples=true,
-                                bool compressVars=true) const = 0;
-
-    /** @brief Returns the vector of responses
-
-    The function returns ordered or the original categorical responses. Usually it's used in
-    regression algorithms.
-     */
-    CV_WRAP virtual Mat getTrainResponses() const = 0;
-
-    /** @brief Returns the vector of normalized categorical responses
-
-    The function returns vector of responses. Each response is integer from `0` to `<number of
-    classes>-1`. The actual label value can be retrieved then from the class label vector, see
-    TrainData::getClassLabels.
-     */
-    CV_WRAP virtual Mat getTrainNormCatResponses() const = 0;
-    CV_WRAP virtual Mat getTestResponses() const = 0;
-    CV_WRAP virtual Mat getTestNormCatResponses() const = 0;
-    CV_WRAP virtual Mat getResponses() const = 0;
-    CV_WRAP virtual Mat getNormCatResponses() const = 0;
-    CV_WRAP virtual Mat getSampleWeights() const = 0;
-    CV_WRAP virtual Mat getTrainSampleWeights() const = 0;
-    CV_WRAP virtual Mat getTestSampleWeights() const = 0;
-    CV_WRAP virtual Mat getVarIdx() const = 0;
-    CV_WRAP virtual Mat getVarType() const = 0;
-    CV_WRAP Mat getVarSymbolFlags() const;
-    CV_WRAP virtual int getResponseType() const = 0;
-    CV_WRAP virtual Mat getTrainSampleIdx() const = 0;
-    CV_WRAP virtual Mat getTestSampleIdx() const = 0;
-    CV_WRAP virtual void getValues(int vi, InputArray sidx, float* values) const = 0;
-    virtual void getNormCatValues(int vi, InputArray sidx, int* values) const = 0;
-    CV_WRAP virtual Mat getDefaultSubstValues() const = 0;
-
-    CV_WRAP virtual int getCatCount(int vi) const = 0;
-
-    /** @brief Returns the vector of class labels
-
-    The function returns vector of unique labels occurred in the responses.
-     */
-    CV_WRAP virtual Mat getClassLabels() const = 0;
-
-    CV_WRAP virtual Mat getCatOfs() const = 0;
-    CV_WRAP virtual Mat getCatMap() const = 0;
-
-    /** @brief Splits the training data into the training and test parts
-    @sa TrainData::setTrainTestSplitRatio
-     */
-    CV_WRAP virtual void setTrainTestSplit(int count, bool shuffle=true) = 0;
-
-    /** @brief Splits the training data into the training and test parts
-
-    The function selects a subset of specified relative size and then returns it as the training
-    set. If the function is not called, all the data is used for training. Please, note that for
-    each of TrainData::getTrain\* there is corresponding TrainData::getTest\*, so that the test
-    subset can be retrieved and processed as well.
-    @sa TrainData::setTrainTestSplit
-     */
-    CV_WRAP virtual void setTrainTestSplitRatio(double ratio, bool shuffle=true) = 0;
-    CV_WRAP virtual void shuffleTrainTest() = 0;
-
-    /** @brief Returns matrix of test samples */
-    CV_WRAP Mat getTestSamples() const;
-
-    /** @brief Returns vector of symbolic names captured in loadFromCSV() */
-    CV_WRAP void getNames(std::vector<String>& names) const;
-
-    CV_WRAP static Mat getSubVector(const Mat& vec, const Mat& idx);
-
-    /** @brief Reads the dataset from a .csv file and returns the ready-to-use training data.
-
-    @param filename The input file name
-    @param headerLineCount The number of lines in the beginning to skip; besides the header, the
-        function also skips empty lines and lines staring with `#`
-    @param responseStartIdx Index of the first output variable. If -1, the function considers the
-        last variable as the response
-    @param responseEndIdx Index of the last output variable + 1. If -1, then there is single
-        response variable at responseStartIdx.
-    @param varTypeSpec The optional text string that specifies the variables' types. It has the
-        format `ord[n1-n2,n3,n4-n5,...]cat[n6,n7-n8,...]`. That is, variables from `n1 to n2`
-        (inclusive range), `n3`, `n4 to n5` ... are considered ordered and `n6`, `n7 to n8` ... are
-        considered as categorical. The range `[n1..n2] + [n3] + [n4..n5] + ... + [n6] + [n7..n8]`
-        should cover all the variables. If varTypeSpec is not specified, then algorithm uses the
-        following rules:
-        - all input variables are considered ordered by default. If some column contains has non-
-          numerical values, e.g. 'apple', 'pear', 'apple', 'apple', 'mango', the corresponding
-          variable is considered categorical.
-        - if there are several output variables, they are all considered as ordered. Error is
-          reported when non-numerical values are used.
-        - if there is a single output variable, then if its values are non-numerical or are all
-          integers, then it's considered categorical. Otherwise, it's considered ordered.
-    @param delimiter The character used to separate values in each line.
-    @param missch The character used to specify missing measurements. It should not be a digit.
-        Although it's a non-numerical value, it surely does not affect the decision of whether the
-        variable ordered or categorical.
-    @note If the dataset only contains input variables and no responses, use responseStartIdx = -2
-        and responseEndIdx = 0. The output variables vector will just contain zeros.
-     */
-    static Ptr<TrainData> loadFromCSV(const String& filename,
-                                      int headerLineCount,
-                                      int responseStartIdx=-1,
-                                      int responseEndIdx=-1,
-                                      const String& varTypeSpec=String(),
-                                      char delimiter=',',
-                                      char missch='?');
-
-    /** @brief Creates training data from in-memory arrays.
-
-    @param samples matrix of samples. It should have CV_32F type.
-    @param layout see ml::SampleTypes.
-    @param responses matrix of responses. If the responses are scalar, they should be stored as a
-        single row or as a single column. The matrix should have type CV_32F or CV_32S (in the
-        former case the responses are considered as ordered by default; in the latter case - as
-        categorical)
-    @param varIdx vector specifying which variables to use for training. It can be an integer vector
-        (CV_32S) containing 0-based variable indices or byte vector (CV_8U) containing a mask of
-        active variables.
-    @param sampleIdx vector specifying which samples to use for training. It can be an integer
-        vector (CV_32S) containing 0-based sample indices or byte vector (CV_8U) containing a mask
-        of training samples.
-    @param sampleWeights optional vector with weights for each sample. It should have CV_32F type.
-    @param varType optional vector of type CV_8U and size `<number_of_variables_in_samples> +
-        <number_of_variables_in_responses>`, containing types of each input and output variable. See
-        ml::VariableTypes.
-     */
-    CV_WRAP static Ptr<TrainData> create(InputArray samples, int layout, InputArray responses,
-                                 InputArray varIdx=noArray(), InputArray sampleIdx=noArray(),
-                                 InputArray sampleWeights=noArray(), InputArray varType=noArray());
-};
-
-/** @brief Base class for statistical models in OpenCV ML.
- */
-class CV_EXPORTS_W StatModel : public Algorithm
-{
-public:
-    /** Predict options */
-    enum Flags {
-        UPDATE_MODEL = 1,
-        RAW_OUTPUT=1, //!< makes the method return the raw results (the sum), not the class label
-        COMPRESSED_INPUT=2,
-        PREPROCESSED_INPUT=4
-    };
-
-    /** @brief Returns the number of variables in training samples */
-    CV_WRAP virtual int getVarCount() const = 0;
-
-    CV_WRAP virtual bool empty() const CV_OVERRIDE;
-
-    /** @brief Returns true if the model is trained */
-    CV_WRAP virtual bool isTrained() const = 0;
-    /** @brief Returns true if the model is classifier */
-    CV_WRAP virtual bool isClassifier() const = 0;
-
-    /** @brief Trains the statistical model
-
-    @param trainData training data that can be loaded from file using TrainData::loadFromCSV or
-        created with TrainData::create.
-    @param flags optional flags, depending on the model. Some of the models can be updated with the
-        new training samples, not completely overwritten (such as NormalBayesClassifier or ANN_MLP).
-     */
-    CV_WRAP virtual bool train( const Ptr<TrainData>& trainData, int flags=0 );
-
-    /** @brief Trains the statistical model
-
-    @param samples training samples
-    @param layout See ml::SampleTypes.
-    @param responses vector of responses associated with the training samples.
-    */
-    CV_WRAP virtual bool train( InputArray samples, int layout, InputArray responses );
-
-    /** @brief Computes error on the training or test dataset
-
-    @param data the training data
-    @param test if true, the error is computed over the test subset of the data, otherwise it's
-        computed over the training subset of the data. Please note that if you loaded a completely
-        different dataset to evaluate already trained classifier, you will probably want not to set
-        the test subset at all with TrainData::setTrainTestSplitRatio and specify test=false, so
-        that the error is computed for the whole new set. Yes, this sounds a bit confusing.
-    @param resp the optional output responses.
-
-    The method uses StatModel::predict to compute the error. For regression models the error is
-    computed as RMS, for classifiers - as a percent of missclassified samples (0%-100%).
-     */
-    CV_WRAP virtual float calcError( const Ptr<TrainData>& data, bool test, OutputArray resp ) const;
-
-    /** @brief Predicts response(s) for the provided sample(s)
-
-    @param samples The input samples, floating-point matrix
-    @param results The optional output matrix of results.
-    @param flags The optional flags, model-dependent. See cv::ml::StatModel::Flags.
-     */
-    CV_WRAP virtual float predict( InputArray samples, OutputArray results=noArray(), int flags=0 ) const = 0;
-
-    /** @brief Create and train model with default parameters
-
-    The class must implement static `create()` method with no parameters or with all default parameter values
-    */
-    template<typename _Tp> static Ptr<_Tp> train(const Ptr<TrainData>& data, int flags=0)
-    {
-        Ptr<_Tp> model = _Tp::create();
-        return !model.empty() && model->train(data, flags) ? model : Ptr<_Tp>();
-    }
-};
-
-/****************************************************************************************\
-*                                 Normal Bayes Classifier                                *
-\****************************************************************************************/
-
-/** @brief Bayes classifier for normally distributed data.
-
-@sa @ref ml_intro_bayes
- */
-class CV_EXPORTS_W NormalBayesClassifier : public StatModel
-{
-public:
-    /** @brief Predicts the response for sample(s).
-
-    The method estimates the most probable classes for input vectors. Input vectors (one or more)
-    are stored as rows of the matrix inputs. In case of multiple input vectors, there should be one
-    output vector outputs. The predicted class for a single input vector is returned by the method.
-    The vector outputProbs contains the output probabilities corresponding to each element of
-    result.
-     */
-    CV_WRAP virtual float predictProb( InputArray inputs, OutputArray outputs,
-                               OutputArray outputProbs, int flags=0 ) const = 0;
-
-    /** Creates empty model
-    Use StatModel::train to train the model after creation. */
-    CV_WRAP static Ptr<NormalBayesClassifier> create();
-
-    /** @brief Loads and creates a serialized NormalBayesClassifier from a file
-     *
-     * Use NormalBayesClassifier::save to serialize and store an NormalBayesClassifier to disk.
-     * Load the NormalBayesClassifier from this file again, by calling this function with the path to the file.
-     * Optionally specify the node for the file containing the classifier
-     *
-     * @param filepath path to serialized NormalBayesClassifier
-     * @param nodeName name of node containing the classifier
-     */
-    CV_WRAP static Ptr<NormalBayesClassifier> load(const String& filepath , const String& nodeName = String());
-};
-
-/****************************************************************************************\
-*                          K-Nearest Neighbour Classifier                                *
-\****************************************************************************************/
-
-/** @brief The class implements K-Nearest Neighbors model
-
-@sa @ref ml_intro_knn
- */
-class CV_EXPORTS_W KNearest : public StatModel
-{
-public:
-
-    /** Default number of neighbors to use in predict method. */
-    /** @see setDefaultK */
-    CV_WRAP virtual int getDefaultK() const = 0;
-    /** @copybrief getDefaultK @see getDefaultK */
-    CV_WRAP virtual void setDefaultK(int val) = 0;
-
-    /** Whether classification or regression model should be trained. */
-    /** @see setIsClassifier */
-    CV_WRAP virtual bool getIsClassifier() const = 0;
-    /** @copybrief getIsClassifier @see getIsClassifier */
-    CV_WRAP virtual void setIsClassifier(bool val) = 0;
-
-    /** Parameter for KDTree implementation. */
-    /** @see setEmax */
-    CV_WRAP virtual int getEmax() const = 0;
-    /** @copybrief getEmax @see getEmax */
-    CV_WRAP virtual void setEmax(int val) = 0;
-
-    /** %Algorithm type, one of KNearest::Types. */
-    /** @see setAlgorithmType */
-    CV_WRAP virtual int getAlgorithmType() const = 0;
-    /** @copybrief getAlgorithmType @see getAlgorithmType */
-    CV_WRAP virtual void setAlgorithmType(int val) = 0;
-
-    /** @brief Finds the neighbors and predicts responses for input vectors.
-
-    @param samples Input samples stored by rows. It is a single-precision floating-point matrix of
-        `<number_of_samples> * k` size.
-    @param k Number of used nearest neighbors. Should be greater than 1.
-    @param results Vector with results of prediction (regression or classification) for each input
-        sample. It is a single-precision floating-point vector with `<number_of_samples>` elements.
-    @param neighborResponses Optional output values for corresponding neighbors. It is a single-
-        precision floating-point matrix of `<number_of_samples> * k` size.
-    @param dist Optional output distances from the input vectors to the corresponding neighbors. It
-        is a single-precision floating-point matrix of `<number_of_samples> * k` size.
-
-    For each input vector (a row of the matrix samples), the method finds the k nearest neighbors.
-    In case of regression, the predicted result is a mean value of the particular vector's neighbor
-    responses. In case of classification, the class is determined by voting.
-
-    For each input vector, the neighbors are sorted by their distances to the vector.
-
-    In case of C++ interface you can use output pointers to empty matrices and the function will
-    allocate memory itself.
-
-    If only a single input vector is passed, all output matrices are optional and the predicted
-    value is returned by the method.
-
-    The function is parallelized with the TBB library.
-     */
-    CV_WRAP virtual float findNearest( InputArray samples, int k,
-                               OutputArray results,
-                               OutputArray neighborResponses=noArray(),
-                               OutputArray dist=noArray() ) const = 0;
-
-    /** @brief Implementations of KNearest algorithm
-       */
-    enum Types
-    {
-        BRUTE_FORCE=1,
-        KDTREE=2
-    };
-
-    /** @brief Creates the empty model
-
-    The static method creates empty %KNearest classifier. It should be then trained using StatModel::train method.
-     */
-    CV_WRAP static Ptr<KNearest> create();
-};
-
-/****************************************************************************************\
-*                                   Support Vector Machines                              *
-\****************************************************************************************/
-
-/** @brief Support Vector Machines.
-
-@sa @ref ml_intro_svm
- */
-class CV_EXPORTS_W SVM : public StatModel
-{
-public:
-
-    class CV_EXPORTS Kernel : public Algorithm
-    {
-    public:
-        virtual int getType() const = 0;
-        virtual void calc( int vcount, int n, const float* vecs, const float* another, float* results ) = 0;
-    };
-
-    /** Type of a %SVM formulation.
-    See SVM::Types. Default value is SVM::C_SVC. */
-    /** @see setType */
-    CV_WRAP virtual int getType() const = 0;
-    /** @copybrief getType @see getType */
-    CV_WRAP virtual void setType(int val) = 0;
-
-    /** Parameter \f$\gamma\f$ of a kernel function.
-    For SVM::POLY, SVM::RBF, SVM::SIGMOID or SVM::CHI2. Default value is 1. */
-    /** @see setGamma */
-    CV_WRAP virtual double getGamma() const = 0;
-    /** @copybrief getGamma @see getGamma */
-    CV_WRAP virtual void setGamma(double val) = 0;
-
-    /** Parameter _coef0_ of a kernel function.
-    For SVM::POLY or SVM::SIGMOID. Default value is 0.*/
-    /** @see setCoef0 */
-    CV_WRAP virtual double getCoef0() const = 0;
-    /** @copybrief getCoef0 @see getCoef0 */
-    CV_WRAP virtual void setCoef0(double val) = 0;
-
-    /** Parameter _degree_ of a kernel function.
-    For SVM::POLY. Default value is 0. */
-    /** @see setDegree */
-    CV_WRAP virtual double getDegree() const = 0;
-    /** @copybrief getDegree @see getDegree */
-    CV_WRAP virtual void setDegree(double val) = 0;
-
-    /** Parameter _C_ of a %SVM optimization problem.
-    For SVM::C_SVC, SVM::EPS_SVR or SVM::NU_SVR. Default value is 0. */
-    /** @see setC */
-    CV_WRAP virtual double getC() const = 0;
-    /** @copybrief getC @see getC */
-    CV_WRAP virtual void setC(double val) = 0;
-
-    /** Parameter \f$\nu\f$ of a %SVM optimization problem.
-    For SVM::NU_SVC, SVM::ONE_CLASS or SVM::NU_SVR. Default value is 0. */
-    /** @see setNu */
-    CV_WRAP virtual double getNu() const = 0;
-    /** @copybrief getNu @see getNu */
-    CV_WRAP virtual void setNu(double val) = 0;
-
-    /** Parameter \f$\epsilon\f$ of a %SVM optimization problem.
-    For SVM::EPS_SVR. Default value is 0. */
-    /** @see setP */
-    CV_WRAP virtual double getP() const = 0;
-    /** @copybrief getP @see getP */
-    CV_WRAP virtual void setP(double val) = 0;
-
-    /** Optional weights in the SVM::C_SVC problem, assigned to particular classes.
-    They are multiplied by _C_ so the parameter _C_ of class _i_ becomes `classWeights(i) * C`. Thus
-    these weights affect the misclassification penalty for different classes. The larger weight,
-    the larger penalty on misclassification of data from the corresponding class. Default value is
-    empty Mat. */
-    /** @see setClassWeights */
-    CV_WRAP virtual cv::Mat getClassWeights() const = 0;
-    /** @copybrief getClassWeights @see getClassWeights */
-    CV_WRAP virtual void setClassWeights(const cv::Mat &val) = 0;
-
-    /** Termination criteria of the iterative %SVM training procedure which solves a partial
-    case of constrained quadratic optimization problem.
-    You can specify tolerance and/or the maximum number of iterations. Default value is
-    `TermCriteria( TermCriteria::MAX_ITER + TermCriteria::EPS, 1000, FLT_EPSILON )`; */
-    /** @see setTermCriteria */
-    CV_WRAP virtual cv::TermCriteria getTermCriteria() const = 0;
-    /** @copybrief getTermCriteria @see getTermCriteria */
-    CV_WRAP virtual void setTermCriteria(const cv::TermCriteria &val) = 0;
-
-    /** Type of a %SVM kernel.
-    See SVM::KernelTypes. Default value is SVM::RBF. */
-    CV_WRAP virtual int getKernelType() const = 0;
-
-    /** Initialize with one of predefined kernels.
-    See SVM::KernelTypes. */
-    CV_WRAP virtual void setKernel(int kernelType) = 0;
-
-    /** Initialize with custom kernel.
-    See SVM::Kernel class for implementation details */
-    virtual void setCustomKernel(const Ptr<Kernel> &_kernel) = 0;
-
-    //! %SVM type
-    enum Types {
-        /** C-Support Vector Classification. n-class classification (n \f$\geq\f$ 2), allows
-        imperfect separation of classes with penalty multiplier C for outliers. */
-        C_SVC=100,
-        /** \f$\nu\f$-Support Vector Classification. n-class classification with possible
-        imperfect separation. Parameter \f$\nu\f$ (in the range 0..1, the larger the value, the smoother
-        the decision boundary) is used instead of C. */
-        NU_SVC=101,
-        /** Distribution Estimation (One-class %SVM). All the training data are from
-        the same class, %SVM builds a boundary that separates the class from the rest of the feature
-        space. */
-        ONE_CLASS=102,
-        /** \f$\epsilon\f$-Support Vector Regression. The distance between feature vectors
-        from the training set and the fitting hyper-plane must be less than p. For outliers the
-        penalty multiplier C is used. */
-        EPS_SVR=103,
-        /** \f$\nu\f$-Support Vector Regression. \f$\nu\f$ is used instead of p.
-        See @cite LibSVM for details. */
-        NU_SVR=104
-    };
-
-    /** @brief %SVM kernel type
-
-    A comparison of different kernels on the following 2D test case with four classes. Four
-    SVM::C_SVC SVMs have been trained (one against rest) with auto_train. Evaluation on three
-    different kernels (SVM::CHI2, SVM::INTER, SVM::RBF). The color depicts the class with max score.
-    Bright means max-score \> 0, dark means max-score \< 0.
-    ![image](pics/SVM_Comparison.png)
-    */
-    enum KernelTypes {
-        /** Returned by SVM::getKernelType in case when custom kernel has been set */
-        CUSTOM=-1,
-        /** Linear kernel. No mapping is done, linear discrimination (or regression) is
-        done in the original feature space. It is the fastest option. \f$K(x_i, x_j) = x_i^T x_j\f$. */
-        LINEAR=0,
-        /** Polynomial kernel:
-        \f$K(x_i, x_j) = (\gamma x_i^T x_j + coef0)^{degree}, \gamma > 0\f$. */
-        POLY=1,
-        /** Radial basis function (RBF), a good choice in most cases.
-        \f$K(x_i, x_j) = e^{-\gamma ||x_i - x_j||^2}, \gamma > 0\f$. */
-        RBF=2,
-        /** Sigmoid kernel: \f$K(x_i, x_j) = \tanh(\gamma x_i^T x_j + coef0)\f$. */
-        SIGMOID=3,
-        /** Exponential Chi2 kernel, similar to the RBF kernel:
-        \f$K(x_i, x_j) = e^{-\gamma \chi^2(x_i,x_j)}, \chi^2(x_i,x_j) = (x_i-x_j)^2/(x_i+x_j), \gamma > 0\f$. */
-        CHI2=4,
-        /** Histogram intersection kernel. A fast kernel. \f$K(x_i, x_j) = min(x_i,x_j)\f$. */
-        INTER=5
-    };
-
-    //! %SVM params type
-    enum ParamTypes {
-        C=0,
-        GAMMA=1,
-        P=2,
-        NU=3,
-        COEF=4,
-        DEGREE=5
-    };
-
-    /** @brief Trains an %SVM with optimal parameters.
-
-    @param data the training data that can be constructed using TrainData::create or
-        TrainData::loadFromCSV.
-    @param kFold Cross-validation parameter. The training set is divided into kFold subsets. One
-        subset is used to test the model, the others form the train set. So, the %SVM algorithm is
-        executed kFold times.
-    @param Cgrid grid for C
-    @param gammaGrid grid for gamma
-    @param pGrid grid for p
-    @param nuGrid grid for nu
-    @param coeffGrid grid for coeff
-    @param degreeGrid grid for degree
-    @param balanced If true and the problem is 2-class classification then the method creates more
-        balanced cross-validation subsets that is proportions between classes in subsets are close
-        to such proportion in the whole train dataset.
-
-    The method trains the %SVM model automatically by choosing the optimal parameters C, gamma, p,
-    nu, coef0, degree. Parameters are considered optimal when the cross-validation
-    estimate of the test set error is minimal.
-
-    If there is no need to optimize a parameter, the corresponding grid step should be set to any
-    value less than or equal to 1. For example, to avoid optimization in gamma, set `gammaGrid.step
-    = 0`, `gammaGrid.minVal`, `gamma_grid.maxVal` as arbitrary numbers. In this case, the value
-    `Gamma` is taken for gamma.
-
-    And, finally, if the optimization in a parameter is required but the corresponding grid is
-    unknown, you may call the function SVM::getDefaultGrid. To generate a grid, for example, for
-    gamma, call `SVM::getDefaultGrid(SVM::GAMMA)`.
-
-    This function works for the classification (SVM::C_SVC or SVM::NU_SVC) as well as for the
-    regression (SVM::EPS_SVR or SVM::NU_SVR). If it is SVM::ONE_CLASS, no optimization is made and
-    the usual %SVM with parameters specified in params is executed.
-     */
-    virtual bool trainAuto( const Ptr<TrainData>& data, int kFold = 10,
-                    ParamGrid Cgrid = getDefaultGrid(C),
-                    ParamGrid gammaGrid  = getDefaultGrid(GAMMA),
-                    ParamGrid pGrid      = getDefaultGrid(P),
-                    ParamGrid nuGrid     = getDefaultGrid(NU),
-                    ParamGrid coeffGrid  = getDefaultGrid(COEF),
-                    ParamGrid degreeGrid = getDefaultGrid(DEGREE),
-                    bool balanced=false) = 0;
-
-    /** @brief Trains an %SVM with optimal parameters
-
-    @param samples training samples
-    @param layout See ml::SampleTypes.
-    @param responses vector of responses associated with the training samples.
-    @param kFold Cross-validation parameter. The training set is divided into kFold subsets. One
-        subset is used to test the model, the others form the train set. So, the %SVM algorithm is
-    @param Cgrid grid for C
-    @param gammaGrid grid for gamma
-    @param pGrid grid for p
-    @param nuGrid grid for nu
-    @param coeffGrid grid for coeff
-    @param degreeGrid grid for degree
-    @param balanced If true and the problem is 2-class classification then the method creates more
-        balanced cross-validation subsets that is proportions between classes in subsets are close
-        to such proportion in the whole train dataset.
-
-    The method trains the %SVM model automatically by choosing the optimal parameters C, gamma, p,
-    nu, coef0, degree. Parameters are considered optimal when the cross-validation
-    estimate of the test set error is minimal.
-
-    This function only makes use of SVM::getDefaultGrid for parameter optimization and thus only
-    offers rudimentary parameter options.
-
-    This function works for the classification (SVM::C_SVC or SVM::NU_SVC) as well as for the
-    regression (SVM::EPS_SVR or SVM::NU_SVR). If it is SVM::ONE_CLASS, no optimization is made and
-    the usual %SVM with parameters specified in params is executed.
-    */
-    CV_WRAP bool trainAuto(InputArray samples,
-            int layout,
-            InputArray responses,
-            int kFold = 10,
-            Ptr<ParamGrid> Cgrid = SVM::getDefaultGridPtr(SVM::C),
-            Ptr<ParamGrid> gammaGrid  = SVM::getDefaultGridPtr(SVM::GAMMA),
-            Ptr<ParamGrid> pGrid      = SVM::getDefaultGridPtr(SVM::P),
-            Ptr<ParamGrid> nuGrid     = SVM::getDefaultGridPtr(SVM::NU),
-            Ptr<ParamGrid> coeffGrid  = SVM::getDefaultGridPtr(SVM::COEF),
-            Ptr<ParamGrid> degreeGrid = SVM::getDefaultGridPtr(SVM::DEGREE),
-            bool balanced=false);
-
-    /** @brief Retrieves all the support vectors
-
-    The method returns all the support vectors as a floating-point matrix, where support vectors are
-    stored as matrix rows.
-     */
-    CV_WRAP virtual Mat getSupportVectors() const = 0;
-
-    /** @brief Retrieves all the uncompressed support vectors of a linear %SVM
-
-    The method returns all the uncompressed support vectors of a linear %SVM that the compressed
-    support vector, used for prediction, was derived from. They are returned in a floating-point
-    matrix, where the support vectors are stored as matrix rows.
-     */
-    CV_WRAP Mat getUncompressedSupportVectors() const;
-
-    /** @brief Retrieves the decision function
-
-    @param i the index of the decision function. If the problem solved is regression, 1-class or
-        2-class classification, then there will be just one decision function and the index should
-        always be 0. Otherwise, in the case of N-class classification, there will be \f$N(N-1)/2\f$
-        decision functions.
-    @param alpha the optional output vector for weights, corresponding to different support vectors.
-        In the case of linear %SVM all the alpha's will be 1's.
-    @param svidx the optional output vector of indices of support vectors within the matrix of
-        support vectors (which can be retrieved by SVM::getSupportVectors). In the case of linear
-        %SVM each decision function consists of a single "compressed" support vector.
-
-    The method returns rho parameter of the decision function, a scalar subtracted from the weighted
-    sum of kernel responses.
-     */
-    CV_WRAP virtual double getDecisionFunction(int i, OutputArray alpha, OutputArray svidx) const = 0;
-
-    /** @brief Generates a grid for %SVM parameters.
-
-    @param param_id %SVM parameters IDs that must be one of the SVM::ParamTypes. The grid is
-    generated for the parameter with this ID.
-
-    The function generates a grid for the specified parameter of the %SVM algorithm. The grid may be
-    passed to the function SVM::trainAuto.
-     */
-    static ParamGrid getDefaultGrid( int param_id );
-
-    /** @brief Generates a grid for %SVM parameters.
-
-    @param param_id %SVM parameters IDs that must be one of the SVM::ParamTypes. The grid is
-    generated for the parameter with this ID.
-
-    The function generates a grid pointer for the specified parameter of the %SVM algorithm.
-    The grid may be passed to the function SVM::trainAuto.
-     */
-    CV_WRAP static Ptr<ParamGrid> getDefaultGridPtr( int param_id );
-
-    /** Creates empty model.
-    Use StatModel::train to train the model. Since %SVM has several parameters, you may want to
-    find the best parameters for your problem, it can be done with SVM::trainAuto. */
-    CV_WRAP static Ptr<SVM> create();
-
-    /** @brief Loads and creates a serialized svm from a file
-     *
-     * Use SVM::save to serialize and store an SVM to disk.
-     * Load the SVM from this file again, by calling this function with the path to the file.
-     *
-     * @param filepath path to serialized svm
-     */
-    CV_WRAP static Ptr<SVM> load(const String& filepath);
-};
-
-/****************************************************************************************\
-*                              Expectation - Maximization                                *
-\****************************************************************************************/
-
-/** @brief The class implements the Expectation Maximization algorithm.
-
-@sa @ref ml_intro_em
- */
-class CV_EXPORTS_W EM : public StatModel
-{
-public:
-    //! Type of covariation matrices
-    enum Types {
-        /** A scaled identity matrix \f$\mu_k * I\f$. There is the only
-        parameter \f$\mu_k\f$ to be estimated for each matrix. The option may be used in special cases,
-        when the constraint is relevant, or as a first step in the optimization (for example in case
-        when the data is preprocessed with PCA). The results of such preliminary estimation may be
-        passed again to the optimization procedure, this time with
-        covMatType=EM::COV_MAT_DIAGONAL. */
-        COV_MAT_SPHERICAL=0,
-        /** A diagonal matrix with positive diagonal elements. The number of
-        free parameters is d for each matrix. This is most commonly used option yielding good
-        estimation results. */
-        COV_MAT_DIAGONAL=1,
-        /** A symmetric positively defined matrix. The number of free
-        parameters in each matrix is about \f$d^2/2\f$. It is not recommended to use this option, unless
-        there is pretty accurate initial estimation of the parameters and/or a huge number of
-        training samples. */
-        COV_MAT_GENERIC=2,
-        COV_MAT_DEFAULT=COV_MAT_DIAGONAL
-    };
-
-    //! Default parameters
-    enum {DEFAULT_NCLUSTERS=5, DEFAULT_MAX_ITERS=100};
-
-    //! The initial step
-    enum {START_E_STEP=1, START_M_STEP=2, START_AUTO_STEP=0};
-
-    /** The number of mixture components in the Gaussian mixture model.
-    Default value of the parameter is EM::DEFAULT_NCLUSTERS=5. Some of %EM implementation could
-    determine the optimal number of mixtures within a specified value range, but that is not the
-    case in ML yet. */
-    /** @see setClustersNumber */
-    CV_WRAP virtual int getClustersNumber() const = 0;
-    /** @copybrief getClustersNumber @see getClustersNumber */
-    CV_WRAP virtual void setClustersNumber(int val) = 0;
-
-    /** Constraint on covariance matrices which defines type of matrices.
-    See EM::Types. */
-    /** @see setCovarianceMatrixType */
-    CV_WRAP virtual int getCovarianceMatrixType() const = 0;
-    /** @copybrief getCovarianceMatrixType @see getCovarianceMatrixType */
-    CV_WRAP virtual void setCovarianceMatrixType(int val) = 0;
-
-    /** The termination criteria of the %EM algorithm.
-    The %EM algorithm can be terminated by the number of iterations termCrit.maxCount (number of
-    M-steps) or when relative change of likelihood logarithm is less than termCrit.epsilon. Default
-    maximum number of iterations is EM::DEFAULT_MAX_ITERS=100. */
-    /** @see setTermCriteria */
-    CV_WRAP virtual TermCriteria getTermCriteria() const = 0;
-    /** @copybrief getTermCriteria @see getTermCriteria */
-    CV_WRAP virtual void setTermCriteria(const TermCriteria &val) = 0;
-
-    /** @brief Returns weights of the mixtures
-
-    Returns vector with the number of elements equal to the number of mixtures.
-     */
-    CV_WRAP virtual Mat getWeights() const = 0;
-    /** @brief Returns the cluster centers (means of the Gaussian mixture)
-
-    Returns matrix with the number of rows equal to the number of mixtures and number of columns
-    equal to the space dimensionality.
-     */
-    CV_WRAP virtual Mat getMeans() const = 0;
-    /** @brief Returns covariation matrices
-
-    Returns vector of covariation matrices. Number of matrices is the number of gaussian mixtures,
-    each matrix is a square floating-point matrix NxN, where N is the space dimensionality.
-     */
-    CV_WRAP virtual void getCovs(CV_OUT std::vector<Mat>& covs) const = 0;
-
-    /** @brief Returns posterior probabilities for the provided samples
-
-    @param samples The input samples, floating-point matrix
-    @param results The optional output \f$ nSamples \times nClusters\f$ matrix of results. It contains
-    posterior probabilities for each sample from the input
-    @param flags This parameter will be ignored
-     */
-    CV_WRAP virtual float predict( InputArray samples, OutputArray results=noArray(), int flags=0 ) const CV_OVERRIDE = 0;
-
-    /** @brief Returns a likelihood logarithm value and an index of the most probable mixture component
-    for the given sample.
-
-    @param sample A sample for classification. It should be a one-channel matrix of
-        \f$1 \times dims\f$ or \f$dims \times 1\f$ size.
-    @param probs Optional output matrix that contains posterior probabilities of each component
-        given the sample. It has \f$1 \times nclusters\f$ size and CV_64FC1 type.
-
-    The method returns a two-element double vector. Zero element is a likelihood logarithm value for
-    the sample. First element is an index of the most probable mixture component for the given
-    sample.
-     */
-    CV_WRAP virtual Vec2d predict2(InputArray sample, OutputArray probs) const = 0;
-
-    /** @brief Estimate the Gaussian mixture parameters from a samples set.
-
-    This variation starts with Expectation step. Initial values of the model parameters will be
-    estimated by the k-means algorithm.
-
-    Unlike many of the ML models, %EM is an unsupervised learning algorithm and it does not take
-    responses (class labels or function values) as input. Instead, it computes the *Maximum
-    Likelihood Estimate* of the Gaussian mixture parameters from an input sample set, stores all the
-    parameters inside the structure: \f$p_{i,k}\f$ in probs, \f$a_k\f$ in means , \f$S_k\f$ in
-    covs[k], \f$\pi_k\f$ in weights , and optionally computes the output "class label" for each
-    sample: \f$\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\f$ (indices of the most
-    probable mixture component for each sample).
-
-    The trained model can be used further for prediction, just like any other classifier. The
-    trained model is similar to the NormalBayesClassifier.
-
-    @param samples Samples from which the Gaussian mixture model will be estimated. It should be a
-        one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type
-        it will be converted to the inner matrix of such type for the further computing.
-    @param logLikelihoods The optional output matrix that contains a likelihood logarithm value for
-        each sample. It has \f$nsamples \times 1\f$ size and CV_64FC1 type.
-    @param labels The optional output "class label" for each sample:
-        \f$\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\f$ (indices of the most probable
-        mixture component for each sample). It has \f$nsamples \times 1\f$ size and CV_32SC1 type.
-    @param probs The optional output matrix that contains posterior probabilities of each Gaussian
-        mixture component given the each sample. It has \f$nsamples \times nclusters\f$ size and
-        CV_64FC1 type.
-     */
-    CV_WRAP virtual bool trainEM(InputArray samples,
-                         OutputArray logLikelihoods=noArray(),
-                         OutputArray labels=noArray(),
-                         OutputArray probs=noArray()) = 0;
-
-    /** @brief Estimate the Gaussian mixture parameters from a samples set.
-
-    This variation starts with Expectation step. You need to provide initial means \f$a_k\f$ of
-    mixture components. Optionally you can pass initial weights \f$\pi_k\f$ and covariance matrices
-    \f$S_k\f$ of mixture components.
-
-    @param samples Samples from which the Gaussian mixture model will be estimated. It should be a
-        one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type
-        it will be converted to the inner matrix of such type for the further computing.
-    @param means0 Initial means \f$a_k\f$ of mixture components. It is a one-channel matrix of
-        \f$nclusters \times dims\f$ size. If the matrix does not have CV_64F type it will be
-        converted to the inner matrix of such type for the further computing.
-    @param covs0 The vector of initial covariance matrices \f$S_k\f$ of mixture components. Each of
-        covariance matrices is a one-channel matrix of \f$dims \times dims\f$ size. If the matrices
-        do not have CV_64F type they will be converted to the inner matrices of such type for the
-        further computing.
-    @param weights0 Initial weights \f$\pi_k\f$ of mixture components. It should be a one-channel
-        floating-point matrix with \f$1 \times nclusters\f$ or \f$nclusters \times 1\f$ size.
-    @param logLikelihoods The optional output matrix that contains a likelihood logarithm value for
-        each sample. It has \f$nsamples \times 1\f$ size and CV_64FC1 type.
-    @param labels The optional output "class label" for each sample:
-        \f$\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\f$ (indices of the most probable
-        mixture component for each sample). It has \f$nsamples \times 1\f$ size and CV_32SC1 type.
-    @param probs The optional output matrix that contains posterior probabilities of each Gaussian
-        mixture component given the each sample. It has \f$nsamples \times nclusters\f$ size and
-        CV_64FC1 type.
-    */
-    CV_WRAP virtual bool trainE(InputArray samples, InputArray means0,
-                        InputArray covs0=noArray(),
-                        InputArray weights0=noArray(),
-                        OutputArray logLikelihoods=noArray(),
-                        OutputArray labels=noArray(),
-                        OutputArray probs=noArray()) = 0;
-
-    /** @brief Estimate the Gaussian mixture parameters from a samples set.
-
-    This variation starts with Maximization step. You need to provide initial probabilities
-    \f$p_{i,k}\f$ to use this option.
-
-    @param samples Samples from which the Gaussian mixture model will be estimated. It should be a
-        one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type
-        it will be converted to the inner matrix of such type for the further computing.
-    @param probs0
-    @param logLikelihoods The optional output matrix that contains a likelihood logarithm value for
-        each sample. It has \f$nsamples \times 1\f$ size and CV_64FC1 type.
-    @param labels The optional output "class label" for each sample:
-        \f$\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\f$ (indices of the most probable
-        mixture component for each sample). It has \f$nsamples \times 1\f$ size and CV_32SC1 type.
-    @param probs The optional output matrix that contains posterior probabilities of each Gaussian
-        mixture component given the each sample. It has \f$nsamples \times nclusters\f$ size and
-        CV_64FC1 type.
-    */
-    CV_WRAP virtual bool trainM(InputArray samples, InputArray probs0,
-                        OutputArray logLikelihoods=noArray(),
-                        OutputArray labels=noArray(),
-                        OutputArray probs=noArray()) = 0;
-
-    /** Creates empty %EM model.
-    The model should be trained then using StatModel::train(traindata, flags) method. Alternatively, you
-    can use one of the EM::train\* methods or load it from file using Algorithm::load\<EM\>(filename).
-     */
-    CV_WRAP static Ptr<EM> create();
-
-    /** @brief Loads and creates a serialized EM from a file
-     *
-     * Use EM::save to serialize and store an EM to disk.
-     * Load the EM from this file again, by calling this function with the path to the file.
-     * Optionally specify the node for the file containing the classifier
-     *
-     * @param filepath path to serialized EM
-     * @param nodeName name of node containing the classifier
-     */
-    CV_WRAP static Ptr<EM> load(const String& filepath , const String& nodeName = String());
-};
-
-/****************************************************************************************\
-*                                      Decision Tree                                     *
-\****************************************************************************************/
-
-/** @brief The class represents a single decision tree or a collection of decision trees.
-
-The current public interface of the class allows user to train only a single decision tree, however
-the class is capable of storing multiple decision trees and using them for prediction (by summing
-responses or using a voting schemes), and the derived from DTrees classes (such as RTrees and Boost)
-use this capability to implement decision tree ensembles.
-
-@sa @ref ml_intro_trees
-*/
-class CV_EXPORTS_W DTrees : public StatModel
-{
-public:
-    /** Predict options */
-    enum Flags { PREDICT_AUTO=0, PREDICT_SUM=(1<<8), PREDICT_MAX_VOTE=(2<<8), PREDICT_MASK=(3<<8) };
-
-    /** Cluster possible values of a categorical variable into K\<=maxCategories clusters to
-    find a suboptimal split.
-    If a discrete variable, on which the training procedure tries to make a split, takes more than
-    maxCategories values, the precise best subset estimation may take a very long time because the
-    algorithm is exponential. Instead, many decision trees engines (including our implementation)
-    try to find sub-optimal split in this case by clustering all the samples into maxCategories
-    clusters that is some categories are merged together. The clustering is applied only in n \>
-    2-class classification problems for categorical variables with N \> max_categories possible
-    values. In case of regression and 2-class classification the optimal split can be found
-    efficiently without employing clustering, thus the parameter is not used in these cases.
-    Default value is 10.*/
-    /** @see setMaxCategories */
-    CV_WRAP virtual int getMaxCategories() const = 0;
-    /** @copybrief getMaxCategories @see getMaxCategories */
-    CV_WRAP virtual void setMaxCategories(int val) = 0;
-
-    /** The maximum possible depth of the tree.
-    That is the training algorithms attempts to split a node while its depth is less than maxDepth.
-    The root node has zero depth. The actual depth may be smaller if the other termination criteria
-    are met (see the outline of the training procedure @ref ml_intro_trees "here"), and/or if the
-    tree is pruned. Default value is INT_MAX.*/
-    /** @see setMaxDepth */
-    CV_WRAP virtual int getMaxDepth() const = 0;
-    /** @copybrief getMaxDepth @see getMaxDepth */
-    CV_WRAP virtual void setMaxDepth(int val) = 0;
-
-    /** If the number of samples in a node is less than this parameter then the node will not be split.
-
-    Default value is 10.*/
-    /** @see setMinSampleCount */
-    CV_WRAP virtual int getMinSampleCount() const = 0;
-    /** @copybrief getMinSampleCount @see getMinSampleCount */
-    CV_WRAP virtual void setMinSampleCount(int val) = 0;
-
-    /** If CVFolds \> 1 then algorithms prunes the built decision tree using K-fold
-    cross-validation procedure where K is equal to CVFolds.
-    Default value is 10.*/
-    /** @see setCVFolds */
-    CV_WRAP virtual int getCVFolds() const = 0;
-    /** @copybrief getCVFolds @see getCVFolds */
-    CV_WRAP virtual void setCVFolds(int val) = 0;
-
-    /** If true then surrogate splits will be built.
-    These splits allow to work with missing data and compute variable importance correctly.
-    Default value is false.
-    @note currently it's not implemented.*/
-    /** @see setUseSurrogates */
-    CV_WRAP virtual bool getUseSurrogates() const = 0;
-    /** @copybrief getUseSurrogates @see getUseSurrogates */
-    CV_WRAP virtual void setUseSurrogates(bool val) = 0;
-
-    /** If true then a pruning will be harsher.
-    This will make a tree more compact and more resistant to the training data noise but a bit less
-    accurate. Default value is true.*/
-    /** @see setUse1SERule */
-    CV_WRAP virtual bool getUse1SERule() const = 0;
-    /** @copybrief getUse1SERule @see getUse1SERule */
-    CV_WRAP virtual void setUse1SERule(bool val) = 0;
-
-    /** If true then pruned branches are physically removed from the tree.
-    Otherwise they are retained and it is possible to get results from the original unpruned (or
-    pruned less aggressively) tree. Default value is true.*/
-    /** @see setTruncatePrunedTree */
-    CV_WRAP virtual bool getTruncatePrunedTree() const = 0;
-    /** @copybrief getTruncatePrunedTree @see getTruncatePrunedTree */
-    CV_WRAP virtual void setTruncatePrunedTree(bool val) = 0;
-
-    /** Termination criteria for regression trees.
-    If all absolute differences between an estimated value in a node and values of train samples
-    in this node are less than this parameter then the node will not be split further. Default
-    value is 0.01f*/
-    /** @see setRegressionAccuracy */
-    CV_WRAP virtual float getRegressionAccuracy() const = 0;
-    /** @copybrief getRegressionAccuracy @see getRegressionAccuracy */
-    CV_WRAP virtual void setRegressionAccuracy(float val) = 0;
-
-    /** @brief The array of a priori class probabilities, sorted by the class label value.
-
-    The parameter can be used to tune the decision tree preferences toward a certain class. For
-    example, if you want to detect some rare anomaly occurrence, the training base will likely
-    contain much more normal cases than anomalies, so a very good classification performance
-    will be achieved just by considering every case as normal. To avoid this, the priors can be
-    specified, where the anomaly probability is artificially increased (up to 0.5 or even
-    greater), so the weight of the misclassified anomalies becomes much bigger, and the tree is
-    adjusted properly.
-
-    You can also think about this parameter as weights of prediction categories which determine
-    relative weights that you give to misclassification. That is, if the weight of the first
-    category is 1 and the weight of the second category is 10, then each mistake in predicting
-    the second category is equivalent to making 10 mistakes in predicting the first category.
-    Default value is empty Mat.*/
-    /** @see setPriors */
-    CV_WRAP virtual cv::Mat getPriors() const = 0;
-    /** @copybrief getPriors @see getPriors */
-    CV_WRAP virtual void setPriors(const cv::Mat &val) = 0;
-
-    /** @brief The class represents a decision tree node.
-     */
-    class CV_EXPORTS Node
-    {
-    public:
-        Node();
-        double value; //!< Value at the node: a class label in case of classification or estimated
-                      //!< function value in case of regression.
-        int classIdx; //!< Class index normalized to 0..class_count-1 range and assigned to the
-                      //!< node. It is used internally in classification trees and tree ensembles.
-        int parent; //!< Index of the parent node
-        int left; //!< Index of the left child node
-        int right; //!< Index of right child node
-        int defaultDir; //!< Default direction where to go (-1: left or +1: right). It helps in the
-                        //!< case of missing values.
-        int split; //!< Index of the first split
-    };
-
-    /** @brief The class represents split in a decision tree.
-     */
-    class CV_EXPORTS Split
-    {
-    public:
-        Split();
-        int varIdx; //!< Index of variable on which the split is created.
-        bool inversed; //!< If true, then the inverse split rule is used (i.e. left and right
-                       //!< branches are exchanged in the rule expressions below).
-        float quality; //!< The split quality, a positive number. It is used to choose the best split.
-        int next; //!< Index of the next split in the list of splits for the node
-        float c; /**< The threshold value in case of split on an ordered variable.
-                      The rule is:
-                      @code{.none}
-                      if var_value < c
-                        then next_node <- left
-                        else next_node <- right
-                      @endcode */
-        int subsetOfs; /**< Offset of the bitset used by the split on a categorical variable.
-                            The rule is:
-                            @code{.none}
-                            if bitset[var_value] == 1
-                                then next_node <- left
-                                else next_node <- right
-                            @endcode */
-    };
-
-    /** @brief Returns indices of root nodes
-    */
-    virtual const std::vector<int>& getRoots() const = 0;
-    /** @brief Returns all the nodes
-
-    all the node indices are indices in the returned vector
-     */
-    virtual const std::vector<Node>& getNodes() const = 0;
-    /** @brief Returns all the splits
-
-    all the split indices are indices in the returned vector
-     */
-    virtual const std::vector<Split>& getSplits() const = 0;
-    /** @brief Returns all the bitsets for categorical splits
-
-    Split::subsetOfs is an offset in the returned vector
-     */
-    virtual const std::vector<int>& getSubsets() const = 0;
-
-    /** @brief Creates the empty model
-
-    The static method creates empty decision tree with the specified parameters. It should be then
-    trained using train method (see StatModel::train). Alternatively, you can load the model from
-    file using Algorithm::load\<DTrees\>(filename).
-     */
-    CV_WRAP static Ptr<DTrees> create();
-
-    /** @brief Loads and creates a serialized DTrees from a file
-     *
-     * Use DTree::save to serialize and store an DTree to disk.
-     * Load the DTree from this file again, by calling this function with the path to the file.
-     * Optionally specify the node for the file containing the classifier
-     *
-     * @param filepath path to serialized DTree
-     * @param nodeName name of node containing the classifier
-     */
-    CV_WRAP static Ptr<DTrees> load(const String& filepath , const String& nodeName = String());
-};
-
-/****************************************************************************************\
-*                                   Random Trees Classifier                              *
-\****************************************************************************************/
-
-/** @brief The class implements the random forest predictor.
-
-@sa @ref ml_intro_rtrees
- */
-class CV_EXPORTS_W RTrees : public DTrees
-{
-public:
-
-    /** If true then variable importance will be calculated and then it can be retrieved by RTrees::getVarImportance.
-    Default value is false.*/
-    /** @see setCalculateVarImportance */
-    CV_WRAP virtual bool getCalculateVarImportance() const = 0;
-    /** @copybrief getCalculateVarImportance @see getCalculateVarImportance */
-    CV_WRAP virtual void setCalculateVarImportance(bool val) = 0;
-
-    /** The size of the randomly selected subset of features at each tree node and that are used
-    to find the best split(s).
-    If you set it to 0 then the size will be set to the square root of the total number of
-    features. Default value is 0.*/
-    /** @see setActiveVarCount */
-    CV_WRAP virtual int getActiveVarCount() const = 0;
-    /** @copybrief getActiveVarCount @see getActiveVarCount */
-    CV_WRAP virtual void setActiveVarCount(int val) = 0;
-
-    /** The termination criteria that specifies when the training algorithm stops.
-    Either when the specified number of trees is trained and added to the ensemble or when
-    sufficient accuracy (measured as OOB error) is achieved. Typically the more trees you have the
-    better the accuracy. However, the improvement in accuracy generally diminishes and asymptotes
-    pass a certain number of trees. Also to keep in mind, the number of tree increases the
-    prediction time linearly. Default value is TermCriteria(TermCriteria::MAX_ITERS +
-    TermCriteria::EPS, 50, 0.1)*/
-    /** @see setTermCriteria */
-    CV_WRAP virtual TermCriteria getTermCriteria() const = 0;
-    /** @copybrief getTermCriteria @see getTermCriteria */
-    CV_WRAP virtual void setTermCriteria(const TermCriteria &val) = 0;
-
-    /** Returns the variable importance array.
-    The method returns the variable importance vector, computed at the training stage when
-    CalculateVarImportance is set to true. If this flag was set to false, the empty matrix is
-    returned.
-     */
-    CV_WRAP virtual Mat getVarImportance() const = 0;
-
-    /** Returns the result of each individual tree in the forest.
-    In case the model is a regression problem, the method will return each of the trees'
-    results for each of the sample cases. If the model is a classifier, it will return
-    a Mat with samples + 1 rows, where the first row gives the class number and the
-    following rows return the votes each class had for each sample.
-        @param samples Array containing the samples for which votes will be calculated.
-        @param results Array where the result of the calculation will be written.
-        @param flags Flags for defining the type of RTrees.
-    */
-    CV_WRAP void getVotes(InputArray samples, OutputArray results, int flags) const;
-
-    /** Creates the empty model.
-    Use StatModel::train to train the model, StatModel::train to create and train the model,
-    Algorithm::load to load the pre-trained model.
-     */
-    CV_WRAP static Ptr<RTrees> create();
-
-    /** @brief Loads and creates a serialized RTree from a file
-     *
-     * Use RTree::save to serialize and store an RTree to disk.
-     * Load the RTree from this file again, by calling this function with the path to the file.
-     * Optionally specify the node for the file containing the classifier
-     *
-     * @param filepath path to serialized RTree
-     * @param nodeName name of node containing the classifier
-     */
-    CV_WRAP static Ptr<RTrees> load(const String& filepath , const String& nodeName = String());
-};
-
-/****************************************************************************************\
-*                                   Boosted tree classifier                              *
-\****************************************************************************************/
-
-/** @brief Boosted tree classifier derived from DTrees
-
-@sa @ref ml_intro_boost
- */
-class CV_EXPORTS_W Boost : public DTrees
-{
-public:
-    /** Type of the boosting algorithm.
-    See Boost::Types. Default value is Boost::REAL. */
-    /** @see setBoostType */
-    CV_WRAP virtual int getBoostType() const = 0;
-    /** @copybrief getBoostType @see getBoostType */
-    CV_WRAP virtual void setBoostType(int val) = 0;
-
-    /** The number of weak classifiers.
-    Default value is 100. */
-    /** @see setWeakCount */
-    CV_WRAP virtual int getWeakCount() const = 0;
-    /** @copybrief getWeakCount @see getWeakCount */
-    CV_WRAP virtual void setWeakCount(int val) = 0;
-
-    /** A threshold between 0 and 1 used to save computational time.
-    Samples with summary weight \f$\leq 1 - weight_trim_rate\f$ do not participate in the *next*
-    iteration of training. Set this parameter to 0 to turn off this functionality. Default value is 0.95.*/
-    /** @see setWeightTrimRate */
-    CV_WRAP virtual double getWeightTrimRate() const = 0;
-    /** @copybrief getWeightTrimRate @see getWeightTrimRate */
-    CV_WRAP virtual void setWeightTrimRate(double val) = 0;
-
-    /** Boosting type.
-    Gentle AdaBoost and Real AdaBoost are often the preferable choices. */
-    enum Types {
-        DISCRETE=0, //!< Discrete AdaBoost.
-        REAL=1, //!< Real AdaBoost. It is a technique that utilizes confidence-rated predictions
-                //!< and works well with categorical data.
-        LOGIT=2, //!< LogitBoost. It can produce good regression fits.
-        GENTLE=3 //!< Gentle AdaBoost. It puts less weight on outlier data points and for that
-                 //!<reason is often good with regression data.
-    };
-
-    /** Creates the empty model.
-    Use StatModel::train to train the model, Algorithm::load\<Boost\>(filename) to load the pre-trained model. */
-    CV_WRAP static Ptr<Boost> create();
-
-    /** @brief Loads and creates a serialized Boost from a file
-     *
-     * Use Boost::save to serialize and store an RTree to disk.
-     * Load the Boost from this file again, by calling this function with the path to the file.
-     * Optionally specify the node for the file containing the classifier
-     *
-     * @param filepath path to serialized Boost
-     * @param nodeName name of node containing the classifier
-     */
-    CV_WRAP static Ptr<Boost> load(const String& filepath , const String& nodeName = String());
-};
-
-/****************************************************************************************\
-*                                   Gradient Boosted Trees                               *
-\****************************************************************************************/
-
-/*class CV_EXPORTS_W GBTrees : public DTrees
-{
-public:
-    struct CV_EXPORTS_W_MAP Params : public DTrees::Params
-    {
-        CV_PROP_RW int weakCount;
-        CV_PROP_RW int lossFunctionType;
-        CV_PROP_RW float subsamplePortion;
-        CV_PROP_RW float shrinkage;
-
-        Params();
-        Params( int lossFunctionType, int weakCount, float shrinkage,
-                float subsamplePortion, int maxDepth, bool useSurrogates );
-    };
-
-    enum {SQUARED_LOSS=0, ABSOLUTE_LOSS, HUBER_LOSS=3, DEVIANCE_LOSS};
-
-    virtual void setK(int k) = 0;
-
-    virtual float predictSerial( InputArray samples,
-                                 OutputArray weakResponses, int flags) const = 0;
-
-    static Ptr<GBTrees> create(const Params& p);
-};*/
-
-/****************************************************************************************\
-*                              Artificial Neural Networks (ANN)                          *
-\****************************************************************************************/
-
-/////////////////////////////////// Multi-Layer Perceptrons //////////////////////////////
-
-/** @brief Artificial Neural Networks - Multi-Layer Perceptrons.
-
-Unlike many other models in ML that are constructed and trained at once, in the MLP model these
-steps are separated. First, a network with the specified topology is created using the non-default
-constructor or the method ANN_MLP::create. All the weights are set to zeros. Then, the network is
-trained using a set of input and output vectors. The training procedure can be repeated more than
-once, that is, the weights can be adjusted based on the new training data.
-
-Additional flags for StatModel::train are available: ANN_MLP::TrainFlags.
-
-@sa @ref ml_intro_ann
- */
-class CV_EXPORTS_W ANN_MLP : public StatModel
-{
-public:
-    /** Available training methods */
-    enum TrainingMethods {
-        BACKPROP=0, //!< The back-propagation algorithm.
-        RPROP = 1, //!< The RPROP algorithm. See @cite RPROP93 for details.
-        ANNEAL = 2 //!< The simulated annealing algorithm. See @cite Kirkpatrick83 for details.
-    };
-
-    /** Sets training method and common parameters.
-    @param method Default value is ANN_MLP::RPROP. See ANN_MLP::TrainingMethods.
-    @param param1 passed to setRpropDW0 for ANN_MLP::RPROP and to setBackpropWeightScale for ANN_MLP::BACKPROP and to initialT for ANN_MLP::ANNEAL.
-    @param param2 passed to setRpropDWMin for ANN_MLP::RPROP and to setBackpropMomentumScale for ANN_MLP::BACKPROP and to finalT for ANN_MLP::ANNEAL.
-    */
-    CV_WRAP virtual void setTrainMethod(int method, double param1 = 0, double param2 = 0) = 0;
-
-    /** Returns current training method */
-    CV_WRAP virtual int getTrainMethod() const = 0;
-
-    /** Initialize the activation function for each neuron.
-    Currently the default and the only fully supported activation function is ANN_MLP::SIGMOID_SYM.
-    @param type The type of activation function. See ANN_MLP::ActivationFunctions.
-    @param param1 The first parameter of the activation function, \f$\alpha\f$. Default value is 0.
-    @param param2 The second parameter of the activation function, \f$\beta\f$. Default value is 0.
-    */
-    CV_WRAP virtual void setActivationFunction(int type, double param1 = 0, double param2 = 0) = 0;
-
-    /**  Integer vector specifying the number of neurons in each layer including the input and output layers.
-    The very first element specifies the number of elements in the input layer.
-    The last element - number of elements in the output layer. Default value is empty Mat.
-    @sa getLayerSizes */
-    CV_WRAP virtual void setLayerSizes(InputArray _layer_sizes) = 0;
-
-    /**  Integer vector specifying the number of neurons in each layer including the input and output layers.
-    The very first element specifies the number of elements in the input layer.
-    The last element - number of elements in the output layer.
-    @sa setLayerSizes */
-    CV_WRAP virtual cv::Mat getLayerSizes() const = 0;
-
-    /** Termination criteria of the training algorithm.
-    You can specify the maximum number of iterations (maxCount) and/or how much the error could
-    change between the iterations to make the algorithm continue (epsilon). Default value is
-    TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 1000, 0.01).*/
-    /** @see setTermCriteria */
-    CV_WRAP virtual TermCriteria getTermCriteria() const = 0;
-    /** @copybrief getTermCriteria @see getTermCriteria */
-    CV_WRAP virtual void setTermCriteria(TermCriteria val) = 0;
-
-    /** BPROP: Strength of the weight gradient term.
-    The recommended value is about 0.1. Default value is 0.1.*/
-    /** @see setBackpropWeightScale */
-    CV_WRAP virtual double getBackpropWeightScale() const = 0;
-    /** @copybrief getBackpropWeightScale @see getBackpropWeightScale */
-    CV_WRAP virtual void setBackpropWeightScale(double val) = 0;
-
-    /** BPROP: Strength of the momentum term (the difference between weights on the 2 previous iterations).
-    This parameter provides some inertia to smooth the random fluctuations of the weights. It can
-    vary from 0 (the feature is disabled) to 1 and beyond. The value 0.1 or so is good enough.
-    Default value is 0.1.*/
-    /** @see setBackpropMomentumScale */
-    CV_WRAP virtual double getBackpropMomentumScale() const = 0;
-    /** @copybrief getBackpropMomentumScale @see getBackpropMomentumScale */
-    CV_WRAP virtual void setBackpropMomentumScale(double val) = 0;
-
-    /** RPROP: Initial value \f$\Delta_0\f$ of update-values \f$\Delta_{ij}\f$.
-    Default value is 0.1.*/
-    /** @see setRpropDW0 */
-    CV_WRAP virtual double getRpropDW0() const = 0;
-    /** @copybrief getRpropDW0 @see getRpropDW0 */
-    CV_WRAP virtual void setRpropDW0(double val) = 0;
-
-    /** RPROP: Increase factor \f$\eta^+\f$.
-    It must be \>1. Default value is 1.2.*/
-    /** @see setRpropDWPlus */
-    CV_WRAP virtual double getRpropDWPlus() const = 0;
-    /** @copybrief getRpropDWPlus @see getRpropDWPlus */
-    CV_WRAP virtual void setRpropDWPlus(double val) = 0;
-
-    /** RPROP: Decrease factor \f$\eta^-\f$.
-    It must be \<1. Default value is 0.5.*/
-    /** @see setRpropDWMinus */
-    CV_WRAP virtual double getRpropDWMinus() const = 0;
-    /** @copybrief getRpropDWMinus @see getRpropDWMinus */
-    CV_WRAP virtual void setRpropDWMinus(double val) = 0;
-
-    /** RPROP: Update-values lower limit \f$\Delta_{min}\f$.
-    It must be positive. Default value is FLT_EPSILON.*/
-    /** @see setRpropDWMin */
-    CV_WRAP virtual double getRpropDWMin() const = 0;
-    /** @copybrief getRpropDWMin @see getRpropDWMin */
-    CV_WRAP virtual void setRpropDWMin(double val) = 0;
-
-    /** RPROP: Update-values upper limit \f$\Delta_{max}\f$.
-    It must be \>1. Default value is 50.*/
-    /** @see setRpropDWMax */
-    CV_WRAP virtual double getRpropDWMax() const = 0;
-    /** @copybrief getRpropDWMax @see getRpropDWMax */
-    CV_WRAP virtual void setRpropDWMax(double val) = 0;
-
-    /** ANNEAL: Update initial temperature.
-    It must be \>=0. Default value is 10.*/
-    /** @see setAnnealInitialT */
-    CV_WRAP double getAnnealInitialT() const;
-    /** @copybrief getAnnealInitialT @see getAnnealInitialT */
-    CV_WRAP void setAnnealInitialT(double val);
-
-    /** ANNEAL: Update final temperature.
-    It must be \>=0 and less than initialT. Default value is 0.1.*/
-    /** @see setAnnealFinalT */
-    CV_WRAP double getAnnealFinalT() const;
-    /** @copybrief getAnnealFinalT @see getAnnealFinalT */
-    CV_WRAP void setAnnealFinalT(double val);
-
-    /** ANNEAL: Update cooling ratio.
-    It must be \>0 and less than 1. Default value is 0.95.*/
-    /** @see setAnnealCoolingRatio */
-    CV_WRAP double getAnnealCoolingRatio() const;
-    /** @copybrief getAnnealCoolingRatio @see getAnnealCoolingRatio */
-    CV_WRAP void setAnnealCoolingRatio(double val);
-
-    /** ANNEAL: Update iteration per step.
-    It must be \>0 . Default value is 10.*/
-    /** @see setAnnealItePerStep */
-    CV_WRAP int getAnnealItePerStep() const;
-    /** @copybrief getAnnealItePerStep @see getAnnealItePerStep */
-    CV_WRAP void setAnnealItePerStep(int val);
-
-    /** @brief Set/initialize anneal RNG */
-    void setAnnealEnergyRNG(const RNG& rng);
-
-    /** possible activation functions */
-    enum ActivationFunctions {
-        /** Identity function: \f$f(x)=x\f$ */
-        IDENTITY = 0,
-        /** Symmetrical sigmoid: \f$f(x)=\beta*(1-e^{-\alpha x})/(1+e^{-\alpha x})\f$
-        @note
-        If you are using the default sigmoid activation function with the default parameter values
-        fparam1=0 and fparam2=0 then the function used is y = 1.7159\*tanh(2/3 \* x), so the output
-        will range from [-1.7159, 1.7159], instead of [0,1].*/
-        SIGMOID_SYM = 1,
-        /** Gaussian function: \f$f(x)=\beta e^{-\alpha x*x}\f$ */
-        GAUSSIAN = 2,
-        /** ReLU function: \f$f(x)=max(0,x)\f$ */
-        RELU = 3,
-        /** Leaky ReLU function: for x>0 \f$f(x)=x \f$ and x<=0 \f$f(x)=\alpha x \f$*/
-        LEAKYRELU= 4
-    };
-
-    /** Train options */
-    enum TrainFlags {
-        /** Update the network weights, rather than compute them from scratch. In the latter case
-        the weights are initialized using the Nguyen-Widrow algorithm. */
-        UPDATE_WEIGHTS = 1,
-        /** Do not normalize the input vectors. If this flag is not set, the training algorithm
-        normalizes each input feature independently, shifting its mean value to 0 and making the
-        standard deviation equal to 1. If the network is assumed to be updated frequently, the new
-        training data could be much different from original one. In this case, you should take care
-        of proper normalization. */
-        NO_INPUT_SCALE = 2,
-        /** Do not normalize the output vectors. If the flag is not set, the training algorithm
-        normalizes each output feature independently, by transforming it to the certain range
-        depending on the used activation function. */
-        NO_OUTPUT_SCALE = 4
-    };
-
-    CV_WRAP virtual Mat getWeights(int layerIdx) const = 0;
-
-    /** @brief Creates empty model
-
-    Use StatModel::train to train the model, Algorithm::load\<ANN_MLP\>(filename) to load the pre-trained model.
-    Note that the train method has optional flags: ANN_MLP::TrainFlags.
-     */
-    CV_WRAP static Ptr<ANN_MLP> create();
-
-    /** @brief Loads and creates a serialized ANN from a file
-     *
-     * Use ANN::save to serialize and store an ANN to disk.
-     * Load the ANN from this file again, by calling this function with the path to the file.
-     *
-     * @param filepath path to serialized ANN
-     */
-    CV_WRAP static Ptr<ANN_MLP> load(const String& filepath);
-
-};
-
-/****************************************************************************************\
-*                           Logistic Regression                                          *
-\****************************************************************************************/
-
-/** @brief Implements Logistic Regression classifier.
-
-@sa @ref ml_intro_lr
- */
-class CV_EXPORTS_W LogisticRegression : public StatModel
-{
-public:
-
-    /** Learning rate. */
-    /** @see setLearningRate */
-    CV_WRAP virtual double getLearningRate() const = 0;
-    /** @copybrief getLearningRate @see getLearningRate */
-    CV_WRAP virtual void setLearningRate(double val) = 0;
-
-    /** Number of iterations. */
-    /** @see setIterations */
-    CV_WRAP virtual int getIterations() const = 0;
-    /** @copybrief getIterations @see getIterations */
-    CV_WRAP virtual void setIterations(int val) = 0;
-
-    /** Kind of regularization to be applied. See LogisticRegression::RegKinds. */
-    /** @see setRegularization */
-    CV_WRAP virtual int getRegularization() const = 0;
-    /** @copybrief getRegularization @see getRegularization */
-    CV_WRAP virtual void setRegularization(int val) = 0;
-
-    /** Kind of training method used. See LogisticRegression::Methods. */
-    /** @see setTrainMethod */
-    CV_WRAP virtual int getTrainMethod() const = 0;
-    /** @copybrief getTrainMethod @see getTrainMethod */
-    CV_WRAP virtual void setTrainMethod(int val) = 0;
-
-    /** Specifies the number of training samples taken in each step of Mini-Batch Gradient
-    Descent. Will only be used if using LogisticRegression::MINI_BATCH training algorithm. It
-    has to take values less than the total number of training samples. */
-    /** @see setMiniBatchSize */
-    CV_WRAP virtual int getMiniBatchSize() const = 0;
-    /** @copybrief getMiniBatchSize @see getMiniBatchSize */
-    CV_WRAP virtual void setMiniBatchSize(int val) = 0;
-
-    /** Termination criteria of the algorithm. */
-    /** @see setTermCriteria */
-    CV_WRAP virtual TermCriteria getTermCriteria() const = 0;
-    /** @copybrief getTermCriteria @see getTermCriteria */
-    CV_WRAP virtual void setTermCriteria(TermCriteria val) = 0;
-
-    //! Regularization kinds
-    enum RegKinds {
-        REG_DISABLE = -1, //!< Regularization disabled
-        REG_L1 = 0, //!< %L1 norm
-        REG_L2 = 1 //!< %L2 norm
-    };
-
-    //! Training methods
-    enum Methods {
-        BATCH = 0,
-        MINI_BATCH = 1 //!< Set MiniBatchSize to a positive integer when using this method.
-    };
-
-    /** @brief Predicts responses for input samples and returns a float type.
-
-    @param samples The input data for the prediction algorithm. Matrix [m x n], where each row
-        contains variables (features) of one object being classified. Should have data type CV_32F.
-    @param results Predicted labels as a column matrix of type CV_32S.
-    @param flags Not used.
-     */
-    CV_WRAP virtual float predict( InputArray samples, OutputArray results=noArray(), int flags=0 ) const CV_OVERRIDE = 0;
-
-    /** @brief This function returns the trained parameters arranged across rows.
-
-    For a two class classifcation problem, it returns a row matrix. It returns learnt parameters of
-    the Logistic Regression as a matrix of type CV_32F.
-     */
-    CV_WRAP virtual Mat get_learnt_thetas() const = 0;
-
-    /** @brief Creates empty model.
-
-    Creates Logistic Regression model with parameters given.
-     */
-    CV_WRAP static Ptr<LogisticRegression> create();
-
-    /** @brief Loads and creates a serialized LogisticRegression from a file
-     *
-     * Use LogisticRegression::save to serialize and store an LogisticRegression to disk.
-     * Load the LogisticRegression from this file again, by calling this function with the path to the file.
-     * Optionally specify the node for the file containing the classifier
-     *
-     * @param filepath path to serialized LogisticRegression
-     * @param nodeName name of node containing the classifier
-     */
-    CV_WRAP static Ptr<LogisticRegression> load(const String& filepath , const String& nodeName = String());
-};
-
-
-/****************************************************************************************\
-*                        Stochastic Gradient Descent SVM Classifier                      *
-\****************************************************************************************/
-
-/*!
-@brief Stochastic Gradient Descent SVM classifier
-
-SVMSGD provides a fast and easy-to-use implementation of the SVM classifier using the Stochastic Gradient Descent approach,
-as presented in @cite bottou2010large.
-
-The classifier has following parameters:
-- model type,
-- margin type,
-- margin regularization (\f$\lambda\f$),
-- initial step size (\f$\gamma_0\f$),
-- step decreasing power (\f$c\f$),
-- and termination criteria.
-
-The model type may have one of the following values: \ref SGD and \ref ASGD.
-
-- \ref SGD is the classic version of SVMSGD classifier: every next step is calculated by the formula
-  \f[w_{t+1} = w_t - \gamma(t) \frac{dQ_i}{dw} |_{w = w_t}\f]
-  where
-  - \f$w_t\f$ is the weights vector for decision function at step \f$t\f$,
-  - \f$\gamma(t)\f$ is the step size of model parameters at the iteration \f$t\f$, it is decreased on each step by the formula
-    \f$\gamma(t) = \gamma_0  (1 + \lambda  \gamma_0 t) ^ {-c}\f$
-  - \f$Q_i\f$ is the target functional from SVM task for sample with number \f$i\f$, this sample is chosen stochastically on each step of the algorithm.
-
-- \ref ASGD is Average Stochastic Gradient Descent SVM Classifier. ASGD classifier averages weights vector on each step of algorithm by the formula
-\f$\widehat{w}_{t+1} = \frac{t}{1+t}\widehat{w}_{t} + \frac{1}{1+t}w_{t+1}\f$
-
-The recommended model type is ASGD (following @cite bottou2010large).
-
-The margin type may have one of the following values: \ref SOFT_MARGIN or \ref HARD_MARGIN.
-
-- You should use \ref HARD_MARGIN type, if you have linearly separable sets.
-- You should use \ref SOFT_MARGIN type, if you have non-linearly separable sets or sets with outliers.
-- In the general case (if you know nothing about linear separability of your sets), use SOFT_MARGIN.
-
-The other parameters may be described as follows:
-- Margin regularization parameter is responsible for weights decreasing at each step and for the strength of restrictions on outliers
-  (the less the parameter, the less probability that an outlier will be ignored).
-  Recommended value for SGD model is 0.0001, for ASGD model is 0.00001.
-
-- Initial step size parameter is the initial value for the step size \f$\gamma(t)\f$.
-  You will have to find the best initial step for your problem.
-
-- Step decreasing power is the power parameter for \f$\gamma(t)\f$ decreasing by the formula, mentioned above.
-  Recommended value for SGD model is 1, for ASGD model is 0.75.
-
-- Termination criteria can be TermCriteria::COUNT, TermCriteria::EPS or TermCriteria::COUNT + TermCriteria::EPS.
-  You will have to find the best termination criteria for your problem.
-
-Note that the parameters margin regularization, initial step size, and step decreasing power should be positive.
-
-To use SVMSGD algorithm do as follows:
-
-- first, create the SVMSGD object. The algoorithm will set optimal parameters by default, but you can set your own parameters via functions setSvmsgdType(),
-  setMarginType(), setMarginRegularization(), setInitialStepSize(), and setStepDecreasingPower().
-
-- then the SVM model can be trained using the train features and the correspondent labels by the method train().
-
-- after that, the label of a new feature vector can be predicted using the method predict().
-
-@code
-// Create empty object
-cv::Ptr<SVMSGD> svmsgd = SVMSGD::create();
-
-// Train the Stochastic Gradient Descent SVM
-svmsgd->train(trainData);
-
-// Predict labels for the new samples
-svmsgd->predict(samples, responses);
-@endcode
-
-*/
-
-class CV_EXPORTS_W SVMSGD : public cv::ml::StatModel
-{
-public:
-
-    /** SVMSGD type.
-    ASGD is often the preferable choice. */
-    enum SvmsgdType
-    {
-        SGD, //!< Stochastic Gradient Descent
-        ASGD //!< Average Stochastic Gradient Descent
-    };
-
-    /** Margin type.*/
-    enum MarginType
-    {
-        SOFT_MARGIN, //!< General case, suits to the case of non-linearly separable sets, allows outliers.
-        HARD_MARGIN  //!< More accurate for the case of linearly separable sets.
-    };
-
-    /**
-     * @return the weights of the trained model (decision function f(x) = weights * x + shift).
-    */
-    CV_WRAP virtual Mat getWeights() = 0;
-
-    /**
-     * @return the shift of the trained model (decision function f(x) = weights * x + shift).
-    */
-    CV_WRAP virtual float getShift() = 0;
-
-    /** @brief Creates empty model.
-     * Use StatModel::train to train the model. Since %SVMSGD has several parameters, you may want to
-     * find the best parameters for your problem or use setOptimalParameters() to set some default parameters.
-    */
-    CV_WRAP static Ptr<SVMSGD> create();
-
-    /** @brief Loads and creates a serialized SVMSGD from a file
-     *
-     * Use SVMSGD::save to serialize and store an SVMSGD to disk.
-     * Load the SVMSGD from this file again, by calling this function with the path to the file.
-     * Optionally specify the node for the file containing the classifier
-     *
-     * @param filepath path to serialized SVMSGD
-     * @param nodeName name of node containing the classifier
-     */
-    CV_WRAP static Ptr<SVMSGD> load(const String& filepath , const String& nodeName = String());
-
-    /** @brief Function sets optimal parameters values for chosen SVM SGD model.
-     * @param svmsgdType is the type of SVMSGD classifier.
-     * @param marginType is the type of margin constraint.
-    */
-    CV_WRAP virtual void setOptimalParameters(int svmsgdType = SVMSGD::ASGD, int marginType = SVMSGD::SOFT_MARGIN) = 0;
-
-    /** @brief %Algorithm type, one of SVMSGD::SvmsgdType. */
-    /** @see setSvmsgdType */
-    CV_WRAP virtual int getSvmsgdType() const = 0;
-    /** @copybrief getSvmsgdType @see getSvmsgdType */
-    CV_WRAP virtual void setSvmsgdType(int svmsgdType) = 0;
-
-    /** @brief %Margin type, one of SVMSGD::MarginType. */
-    /** @see setMarginType */
-    CV_WRAP virtual int getMarginType() const = 0;
-    /** @copybrief getMarginType @see getMarginType */
-    CV_WRAP virtual void setMarginType(int marginType) = 0;
-
-    /** @brief Parameter marginRegularization of a %SVMSGD optimization problem. */
-    /** @see setMarginRegularization */
-    CV_WRAP virtual float getMarginRegularization() const = 0;
-    /** @copybrief getMarginRegularization @see getMarginRegularization */
-    CV_WRAP virtual void setMarginRegularization(float marginRegularization) = 0;
-
-    /** @brief Parameter initialStepSize of a %SVMSGD optimization problem. */
-    /** @see setInitialStepSize */
-    CV_WRAP virtual float getInitialStepSize() const = 0;
-    /** @copybrief getInitialStepSize @see getInitialStepSize */
-    CV_WRAP virtual void setInitialStepSize(float InitialStepSize) = 0;
-
-    /** @brief Parameter stepDecreasingPower of a %SVMSGD optimization problem. */
-    /** @see setStepDecreasingPower */
-    CV_WRAP virtual float getStepDecreasingPower() const = 0;
-    /** @copybrief getStepDecreasingPower @see getStepDecreasingPower */
-    CV_WRAP virtual void setStepDecreasingPower(float stepDecreasingPower) = 0;
-
-    /** @brief Termination criteria of the training algorithm.
-    You can specify the maximum number of iterations (maxCount) and/or how much the error could
-    change between the iterations to make the algorithm continue (epsilon).*/
-    /** @see setTermCriteria */
-    CV_WRAP virtual TermCriteria getTermCriteria() const = 0;
-    /** @copybrief getTermCriteria @see getTermCriteria */
-    CV_WRAP virtual void setTermCriteria(const cv::TermCriteria &val) = 0;
-};
-
-
-/****************************************************************************************\
-*                           Auxiliary functions declarations                              *
-\****************************************************************************************/
-
-/** @brief Generates _sample_ from multivariate normal distribution
-
-@param mean an average row vector
-@param cov symmetric covariation matrix
-@param nsamples returned samples count
-@param samples returned samples array
-*/
-CV_EXPORTS void randMVNormal( InputArray mean, InputArray cov, int nsamples, OutputArray samples);
-
-/** @brief Creates test set */
-CV_EXPORTS void createConcentricSpheresTestSet( int nsamples, int nfeatures, int nclasses,
-                                                OutputArray samples, OutputArray responses);
-
-/** @brief Artificial Neural Networks - Multi-Layer Perceptrons.
-
-@sa @ref ml_intro_ann
-*/
-class CV_EXPORTS_W ANN_MLP_ANNEAL : public ANN_MLP
-{
-public:
-    /** @see setAnnealInitialT */
-    CV_WRAP virtual double getAnnealInitialT() const = 0;
-    /** @copybrief getAnnealInitialT @see getAnnealInitialT */
-    CV_WRAP virtual void setAnnealInitialT(double val) = 0;
-
-    /** ANNEAL: Update final temperature.
-    It must be \>=0 and less than initialT. Default value is 0.1.*/
-    /** @see setAnnealFinalT */
-    CV_WRAP  virtual double getAnnealFinalT() const = 0;
-    /** @copybrief getAnnealFinalT @see getAnnealFinalT */
-    CV_WRAP  virtual void setAnnealFinalT(double val) = 0;
-
-    /** ANNEAL: Update cooling ratio.
-    It must be \>0 and less than 1. Default value is 0.95.*/
-    /** @see setAnnealCoolingRatio */
-    CV_WRAP  virtual double getAnnealCoolingRatio() const = 0;
-    /** @copybrief getAnnealCoolingRatio @see getAnnealCoolingRatio */
-    CV_WRAP  virtual void setAnnealCoolingRatio(double val) = 0;
-
-    /** ANNEAL: Update iteration per step.
-    It must be \>0 . Default value is 10.*/
-    /** @see setAnnealItePerStep */
-    CV_WRAP virtual int getAnnealItePerStep() const = 0;
-    /** @copybrief getAnnealItePerStep @see getAnnealItePerStep */
-    CV_WRAP virtual void setAnnealItePerStep(int val) = 0;
-
-    /** @brief Set/initialize anneal RNG */
-    virtual void setAnnealEnergyRNG(const RNG& rng) = 0;
-};
-
-
-/****************************************************************************************\
-*                                   Simulated annealing solver                             *
-\****************************************************************************************/
-
-#ifdef CV_DOXYGEN
-/** @brief This class declares example interface for system state used in simulated annealing optimization algorithm.
-
-@note This class is not defined in C++ code and can't be use directly - you need your own implementation with the same methods.
-*/
-struct SimulatedAnnealingSolverSystem
-{
-    /** Give energy value for a state of system.*/
-    double energy() const;
-    /** Function which change the state of system (random perturbation).*/
-    void changeState();
-    /** Function to reverse to the previous state. Can be called once only after changeState(). */
-    void reverseState();
-};
-#endif // CV_DOXYGEN
-
-/** @brief The class implements simulated annealing for optimization.
-
-@cite Kirkpatrick83 for details
-
-@param solverSystem optimization system (see SimulatedAnnealingSolverSystem)
-@param initialTemperature initial temperature
-@param finalTemperature final temperature
-@param coolingRatio temperature step multiplies
-@param iterationsPerStep number of iterations per temperature changing step
-@param lastTemperature optional output for last used temperature
-@param rngEnergy specify custom random numbers generator (cv::theRNG() by default)
-*/
-template<class SimulatedAnnealingSolverSystem>
-int simulatedAnnealingSolver(SimulatedAnnealingSolverSystem& solverSystem,
-     double initialTemperature, double finalTemperature, double coolingRatio,
-     size_t iterationsPerStep,
-     CV_OUT double* lastTemperature = NULL,
-     cv::RNG& rngEnergy = cv::theRNG()
-);
-
-//! @} ml
-
-}
-}
-
-#include <opencv2/ml/ml.inl.hpp>
-
-#endif // __cplusplus
-#endif // OPENCV_ML_HPP
-
-/* End of file. */
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/ml/ml.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/ml/ml.hpp
deleted file mode 100755
index f6f9cd8..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/ml/ml.hpp
+++ /dev/null
@@ -1,48 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifdef __OPENCV_BUILD
-#error this is a compatibility header which should not be used inside the OpenCV library
-#endif
-
-#include "opencv2/ml.hpp"
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/ml/ml.inl.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/ml/ml.inl.hpp
deleted file mode 100755
index dc9c783..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/ml/ml.inl.hpp
+++ /dev/null
@@ -1,60 +0,0 @@
-// This file is part of OpenCV project.
-// It is subject to the license terms in the LICENSE file found in the top-level directory
-// of this distribution and at http://opencv.org/license.html.
-
-#ifndef OPENCV_ML_INL_HPP
-#define OPENCV_ML_INL_HPP
-
-namespace cv { namespace ml {
-
-// declared in ml.hpp
-template<class SimulatedAnnealingSolverSystem>
-int simulatedAnnealingSolver(SimulatedAnnealingSolverSystem& solverSystem,
-     double initialTemperature, double finalTemperature, double coolingRatio,
-     size_t iterationsPerStep,
-     CV_OUT double* lastTemperature,
-     cv::RNG& rngEnergy
-)
-{
-    CV_Assert(finalTemperature > 0);
-    CV_Assert(initialTemperature > finalTemperature);
-    CV_Assert(iterationsPerStep > 0);
-    CV_Assert(coolingRatio < 1.0f);
-    double Ti = initialTemperature;
-    double previousEnergy = solverSystem.energy();
-    int exchange = 0;
-    while (Ti > finalTemperature)
-    {
-        for (size_t i = 0; i < iterationsPerStep; i++)
-        {
-            solverSystem.changeState();
-            double newEnergy = solverSystem.energy();
-            if (newEnergy < previousEnergy)
-            {
-                previousEnergy = newEnergy;
-                exchange++;
-            }
-            else
-            {
-                double r = rngEnergy.uniform(0.0, 1.0);
-                if (r < std::exp(-(newEnergy - previousEnergy) / Ti))
-                {
-                    previousEnergy = newEnergy;
-                    exchange++;
-                }
-                else
-                {
-                    solverSystem.reverseState();
-                }
-            }
-        }
-        Ti *= coolingRatio;
-    }
-    if (lastTemperature)
-        *lastTemperature = Ti;
-    return exchange;
-}
-
-}} //namespace
-
-#endif // OPENCV_ML_INL_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/objdetect.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/objdetect.hpp
deleted file mode 100755
index 7c36bac..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/objdetect.hpp
+++ /dev/null
@@ -1,691 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_OBJDETECT_HPP
-#define OPENCV_OBJDETECT_HPP
-
-#include "opencv2/core.hpp"
-
-/**
-@defgroup objdetect Object Detection
-
-Haar Feature-based Cascade Classifier for Object Detection
-----------------------------------------------------------
-
-The object detector described below has been initially proposed by Paul Viola @cite Viola01 and
-improved by Rainer Lienhart @cite Lienhart02 .
-
-First, a classifier (namely a *cascade of boosted classifiers working with haar-like features*) is
-trained with a few hundred sample views of a particular object (i.e., a face or a car), called
-positive examples, that are scaled to the same size (say, 20x20), and negative examples - arbitrary
-images of the same size.
-
-After a classifier is trained, it can be applied to a region of interest (of the same size as used
-during the training) in an input image. The classifier outputs a "1" if the region is likely to show
-the object (i.e., face/car), and "0" otherwise. To search for the object in the whole image one can
-move the search window across the image and check every location using the classifier. The
-classifier is designed so that it can be easily "resized" in order to be able to find the objects of
-interest at different sizes, which is more efficient than resizing the image itself. So, to find an
-object of an unknown size in the image the scan procedure should be done several times at different
-scales.
-
-The word "cascade" in the classifier name means that the resultant classifier consists of several
-simpler classifiers (*stages*) that are applied subsequently to a region of interest until at some
-stage the candidate is rejected or all the stages are passed. The word "boosted" means that the
-classifiers at every stage of the cascade are complex themselves and they are built out of basic
-classifiers using one of four different boosting techniques (weighted voting). Currently Discrete
-Adaboost, Real Adaboost, Gentle Adaboost and Logitboost are supported. The basic classifiers are
-decision-tree classifiers with at least 2 leaves. Haar-like features are the input to the basic
-classifiers, and are calculated as described below. The current algorithm uses the following
-Haar-like features:
-
-![image](pics/haarfeatures.png)
-
-The feature used in a particular classifier is specified by its shape (1a, 2b etc.), position within
-the region of interest and the scale (this scale is not the same as the scale used at the detection
-stage, though these two scales are multiplied). For example, in the case of the third line feature
-(2c) the response is calculated as the difference between the sum of image pixels under the
-rectangle covering the whole feature (including the two white stripes and the black stripe in the
-middle) and the sum of the image pixels under the black stripe multiplied by 3 in order to
-compensate for the differences in the size of areas. The sums of pixel values over a rectangular
-regions are calculated rapidly using integral images (see below and the integral description).
-
-To see the object detector at work, have a look at the facedetect demo:
-<https://github.com/opencv/opencv/tree/3.4/samples/cpp/dbt_face_detection.cpp>
-
-The following reference is for the detection part only. There is a separate application called
-opencv_traincascade that can train a cascade of boosted classifiers from a set of samples.
-
-@note In the new C++ interface it is also possible to use LBP (local binary pattern) features in
-addition to Haar-like features. .. [Viola01] Paul Viola and Michael J. Jones. Rapid Object Detection
-using a Boosted Cascade of Simple Features. IEEE CVPR, 2001. The paper is available online at
-<http://research.microsoft.com/en-us/um/people/viola/Pubs/Detect/violaJones_CVPR2001.pdf>
-
-@{
-    @defgroup objdetect_c C API
-@}
- */
-
-typedef struct CvHaarClassifierCascade CvHaarClassifierCascade;
-
-namespace cv
-{
-
-//! @addtogroup objdetect
-//! @{
-
-///////////////////////////// Object Detection ////////////////////////////
-
-//! class for grouping object candidates, detected by Cascade Classifier, HOG etc.
-//! instance of the class is to be passed to cv::partition (see cxoperations.hpp)
-class CV_EXPORTS SimilarRects
-{
-public:
-    SimilarRects(double _eps) : eps(_eps) {}
-    inline bool operator()(const Rect& r1, const Rect& r2) const
-    {
-        double delta = eps * ((std::min)(r1.width, r2.width) + (std::min)(r1.height, r2.height)) * 0.5;
-        return std::abs(r1.x - r2.x) <= delta &&
-            std::abs(r1.y - r2.y) <= delta &&
-            std::abs(r1.x + r1.width - r2.x - r2.width) <= delta &&
-            std::abs(r1.y + r1.height - r2.y - r2.height) <= delta;
-    }
-    double eps;
-};
-
-/** @brief Groups the object candidate rectangles.
-
-@param rectList Input/output vector of rectangles. Output vector includes retained and grouped
-rectangles. (The Python list is not modified in place.)
-@param groupThreshold Minimum possible number of rectangles minus 1. The threshold is used in a
-group of rectangles to retain it.
-@param eps Relative difference between sides of the rectangles to merge them into a group.
-
-The function is a wrapper for the generic function partition . It clusters all the input rectangles
-using the rectangle equivalence criteria that combines rectangles with similar sizes and similar
-locations. The similarity is defined by eps. When eps=0 , no clustering is done at all. If
-\f$\texttt{eps}\rightarrow +\inf\f$ , all the rectangles are put in one cluster. Then, the small
-clusters containing less than or equal to groupThreshold rectangles are rejected. In each other
-cluster, the average rectangle is computed and put into the output rectangle list.
- */
-CV_EXPORTS   void groupRectangles(std::vector<Rect>& rectList, int groupThreshold, double eps = 0.2);
-/** @overload */
-CV_EXPORTS_W void groupRectangles(CV_IN_OUT std::vector<Rect>& rectList, CV_OUT std::vector<int>& weights,
-                                  int groupThreshold, double eps = 0.2);
-/** @overload */
-CV_EXPORTS   void groupRectangles(std::vector<Rect>& rectList, int groupThreshold,
-                                  double eps, std::vector<int>* weights, std::vector<double>* levelWeights );
-/** @overload */
-CV_EXPORTS   void groupRectangles(std::vector<Rect>& rectList, std::vector<int>& rejectLevels,
-                                  std::vector<double>& levelWeights, int groupThreshold, double eps = 0.2);
-/** @overload */
-CV_EXPORTS   void groupRectangles_meanshift(std::vector<Rect>& rectList, std::vector<double>& foundWeights,
-                                            std::vector<double>& foundScales,
-                                            double detectThreshold = 0.0, Size winDetSize = Size(64, 128));
-
-template<> CV_EXPORTS void DefaultDeleter<CvHaarClassifierCascade>::operator ()(CvHaarClassifierCascade* obj) const;
-
-enum { CASCADE_DO_CANNY_PRUNING    = 1,
-       CASCADE_SCALE_IMAGE         = 2,
-       CASCADE_FIND_BIGGEST_OBJECT = 4,
-       CASCADE_DO_ROUGH_SEARCH     = 8
-     };
-
-class CV_EXPORTS_W BaseCascadeClassifier : public Algorithm
-{
-public:
-    virtual ~BaseCascadeClassifier();
-    virtual bool empty() const CV_OVERRIDE = 0;
-    virtual bool load( const String& filename ) = 0;
-    virtual void detectMultiScale( InputArray image,
-                           CV_OUT std::vector<Rect>& objects,
-                           double scaleFactor,
-                           int minNeighbors, int flags,
-                           Size minSize, Size maxSize ) = 0;
-
-    virtual void detectMultiScale( InputArray image,
-                           CV_OUT std::vector<Rect>& objects,
-                           CV_OUT std::vector<int>& numDetections,
-                           double scaleFactor,
-                           int minNeighbors, int flags,
-                           Size minSize, Size maxSize ) = 0;
-
-    virtual void detectMultiScale( InputArray image,
-                                   CV_OUT std::vector<Rect>& objects,
-                                   CV_OUT std::vector<int>& rejectLevels,
-                                   CV_OUT std::vector<double>& levelWeights,
-                                   double scaleFactor,
-                                   int minNeighbors, int flags,
-                                   Size minSize, Size maxSize,
-                                   bool outputRejectLevels ) = 0;
-
-    virtual bool isOldFormatCascade() const = 0;
-    virtual Size getOriginalWindowSize() const = 0;
-    virtual int getFeatureType() const = 0;
-    virtual void* getOldCascade() = 0;
-
-    class CV_EXPORTS MaskGenerator
-    {
-    public:
-        virtual ~MaskGenerator() {}
-        virtual Mat generateMask(const Mat& src)=0;
-        virtual void initializeMask(const Mat& /*src*/) { }
-    };
-    virtual void setMaskGenerator(const Ptr<MaskGenerator>& maskGenerator) = 0;
-    virtual Ptr<MaskGenerator> getMaskGenerator() = 0;
-};
-
-/** @example facedetect.cpp
-This program demonstrates usage of the Cascade classifier class
-\image html Cascade_Classifier_Tutorial_Result_Haar.jpg "Sample screenshot" width=321 height=254
-*/
-/** @brief Cascade classifier class for object detection.
- */
-class CV_EXPORTS_W CascadeClassifier
-{
-public:
-    CV_WRAP CascadeClassifier();
-    /** @brief Loads a classifier from a file.
-
-    @param filename Name of the file from which the classifier is loaded.
-     */
-    CV_WRAP CascadeClassifier(const String& filename);
-    ~CascadeClassifier();
-    /** @brief Checks whether the classifier has been loaded.
-    */
-    CV_WRAP bool empty() const;
-    /** @brief Loads a classifier from a file.
-
-    @param filename Name of the file from which the classifier is loaded. The file may contain an old
-    HAAR classifier trained by the haartraining application or a new cascade classifier trained by the
-    traincascade application.
-     */
-    CV_WRAP bool load( const String& filename );
-    /** @brief Reads a classifier from a FileStorage node.
-
-    @note The file may contain a new cascade classifier (trained traincascade application) only.
-     */
-    CV_WRAP bool read( const FileNode& node );
-
-    /** @brief Detects objects of different sizes in the input image. The detected objects are returned as a list
-    of rectangles.
-
-    @param image Matrix of the type CV_8U containing an image where objects are detected.
-    @param objects Vector of rectangles where each rectangle contains the detected object, the
-    rectangles may be partially outside the original image.
-    @param scaleFactor Parameter specifying how much the image size is reduced at each image scale.
-    @param minNeighbors Parameter specifying how many neighbors each candidate rectangle should have
-    to retain it.
-    @param flags Parameter with the same meaning for an old cascade as in the function
-    cvHaarDetectObjects. It is not used for a new cascade.
-    @param minSize Minimum possible object size. Objects smaller than that are ignored.
-    @param maxSize Maximum possible object size. Objects larger than that are ignored. If `maxSize == minSize` model is evaluated on single scale.
-
-    The function is parallelized with the TBB library.
-
-    @note
-       -   (Python) A face detection example using cascade classifiers can be found at
-            opencv_source_code/samples/python/facedetect.py
-    */
-    CV_WRAP void detectMultiScale( InputArray image,
-                          CV_OUT std::vector<Rect>& objects,
-                          double scaleFactor = 1.1,
-                          int minNeighbors = 3, int flags = 0,
-                          Size minSize = Size(),
-                          Size maxSize = Size() );
-
-    /** @overload
-    @param image Matrix of the type CV_8U containing an image where objects are detected.
-    @param objects Vector of rectangles where each rectangle contains the detected object, the
-    rectangles may be partially outside the original image.
-    @param numDetections Vector of detection numbers for the corresponding objects. An object's number
-    of detections is the number of neighboring positively classified rectangles that were joined
-    together to form the object.
-    @param scaleFactor Parameter specifying how much the image size is reduced at each image scale.
-    @param minNeighbors Parameter specifying how many neighbors each candidate rectangle should have
-    to retain it.
-    @param flags Parameter with the same meaning for an old cascade as in the function
-    cvHaarDetectObjects. It is not used for a new cascade.
-    @param minSize Minimum possible object size. Objects smaller than that are ignored.
-    @param maxSize Maximum possible object size. Objects larger than that are ignored. If `maxSize == minSize` model is evaluated on single scale.
-    */
-    CV_WRAP_AS(detectMultiScale2) void detectMultiScale( InputArray image,
-                          CV_OUT std::vector<Rect>& objects,
-                          CV_OUT std::vector<int>& numDetections,
-                          double scaleFactor=1.1,
-                          int minNeighbors=3, int flags=0,
-                          Size minSize=Size(),
-                          Size maxSize=Size() );
-
-    /** @overload
-    This function allows you to retrieve the final stage decision certainty of classification.
-    For this, one needs to set `outputRejectLevels` on true and provide the `rejectLevels` and `levelWeights` parameter.
-    For each resulting detection, `levelWeights` will then contain the certainty of classification at the final stage.
-    This value can then be used to separate strong from weaker classifications.
-
-    A code sample on how to use it efficiently can be found below:
-    @code
-    Mat img;
-    vector<double> weights;
-    vector<int> levels;
-    vector<Rect> detections;
-    CascadeClassifier model("/path/to/your/model.xml");
-    model.detectMultiScale(img, detections, levels, weights, 1.1, 3, 0, Size(), Size(), true);
-    cerr << "Detection " << detections[0] << " with weight " << weights[0] << endl;
-    @endcode
-    */
-    CV_WRAP_AS(detectMultiScale3) void detectMultiScale( InputArray image,
-                                  CV_OUT std::vector<Rect>& objects,
-                                  CV_OUT std::vector<int>& rejectLevels,
-                                  CV_OUT std::vector<double>& levelWeights,
-                                  double scaleFactor = 1.1,
-                                  int minNeighbors = 3, int flags = 0,
-                                  Size minSize = Size(),
-                                  Size maxSize = Size(),
-                                  bool outputRejectLevels = false );
-
-    CV_WRAP bool isOldFormatCascade() const;
-    CV_WRAP Size getOriginalWindowSize() const;
-    CV_WRAP int getFeatureType() const;
-    void* getOldCascade();
-
-    CV_WRAP static bool convert(const String& oldcascade, const String& newcascade);
-
-    void setMaskGenerator(const Ptr<BaseCascadeClassifier::MaskGenerator>& maskGenerator);
-    Ptr<BaseCascadeClassifier::MaskGenerator> getMaskGenerator();
-
-    Ptr<BaseCascadeClassifier> cc;
-};
-
-CV_EXPORTS Ptr<BaseCascadeClassifier::MaskGenerator> createFaceDetectionMaskGenerator();
-
-//////////////// HOG (Histogram-of-Oriented-Gradients) Descriptor and Object Detector //////////////
-
-//! struct for detection region of interest (ROI)
-struct DetectionROI
-{
-   //! scale(size) of the bounding box
-   double scale;
-   //! set of requested locations to be evaluated
-   std::vector<cv::Point> locations;
-   //! vector that will contain confidence values for each location
-   std::vector<double> confidences;
-};
-
-/**@brief Implementation of HOG (Histogram of Oriented Gradients) descriptor and object detector.
-
-the HOG descriptor algorithm introduced by Navneet Dalal and Bill Triggs @cite Dalal2005 .
-
-useful links:
-
-https://hal.inria.fr/inria-00548512/document/
-
-https://en.wikipedia.org/wiki/Histogram_of_oriented_gradients
-
-https://software.intel.com/en-us/ipp-dev-reference-histogram-of-oriented-gradients-hog-descriptor
-
-http://www.learnopencv.com/histogram-of-oriented-gradients
-
-http://www.learnopencv.com/handwritten-digits-classification-an-opencv-c-python-tutorial
-
- */
-struct CV_EXPORTS_W HOGDescriptor
-{
-public:
-    enum { L2Hys = 0 //!< Default histogramNormType
-         };
-    enum { DEFAULT_NLEVELS = 64 //!< Default nlevels value.
-         };
-    /**@brief Creates the HOG descriptor and detector with default params.
-
-    aqual to HOGDescriptor(Size(64,128), Size(16,16), Size(8,8), Size(8,8), 9, 1 )
-    */
-    CV_WRAP HOGDescriptor() : winSize(64,128), blockSize(16,16), blockStride(8,8),
-        cellSize(8,8), nbins(9), derivAperture(1), winSigma(-1),
-        histogramNormType(HOGDescriptor::L2Hys), L2HysThreshold(0.2), gammaCorrection(true),
-        free_coef(-1.f), nlevels(HOGDescriptor::DEFAULT_NLEVELS), signedGradient(false)
-    {}
-
-    /** @overload
-    @param _winSize sets winSize with given value.
-    @param _blockSize sets blockSize with given value.
-    @param _blockStride sets blockStride with given value.
-    @param _cellSize sets cellSize with given value.
-    @param _nbins sets nbins with given value.
-    @param _derivAperture sets derivAperture with given value.
-    @param _winSigma sets winSigma with given value.
-    @param _histogramNormType sets histogramNormType with given value.
-    @param _L2HysThreshold sets L2HysThreshold with given value.
-    @param _gammaCorrection sets gammaCorrection with given value.
-    @param _nlevels sets nlevels with given value.
-    @param _signedGradient sets signedGradient with given value.
-    */
-    CV_WRAP HOGDescriptor(Size _winSize, Size _blockSize, Size _blockStride,
-                  Size _cellSize, int _nbins, int _derivAperture=1, double _winSigma=-1,
-                  int _histogramNormType=HOGDescriptor::L2Hys,
-                  double _L2HysThreshold=0.2, bool _gammaCorrection=false,
-                  int _nlevels=HOGDescriptor::DEFAULT_NLEVELS, bool _signedGradient=false)
-    : winSize(_winSize), blockSize(_blockSize), blockStride(_blockStride), cellSize(_cellSize),
-    nbins(_nbins), derivAperture(_derivAperture), winSigma(_winSigma),
-    histogramNormType(_histogramNormType), L2HysThreshold(_L2HysThreshold),
-    gammaCorrection(_gammaCorrection), free_coef(-1.f), nlevels(_nlevels), signedGradient(_signedGradient)
-    {}
-
-    /** @overload
-    @param filename the file name containing  HOGDescriptor properties and coefficients of the trained classifier
-    */
-    CV_WRAP HOGDescriptor(const String& filename)
-    {
-        load(filename);
-    }
-
-    /** @overload
-    @param d the HOGDescriptor which cloned to create a new one.
-    */
-    HOGDescriptor(const HOGDescriptor& d)
-    {
-        d.copyTo(*this);
-    }
-
-    /**@brief Default destructor.
-    */
-    virtual ~HOGDescriptor() {}
-
-    /**@brief Returns the number of coefficients required for the classification.
-    */
-    CV_WRAP size_t getDescriptorSize() const;
-
-    /** @brief Checks if detector size equal to descriptor size.
-    */
-    CV_WRAP bool checkDetectorSize() const;
-
-    /** @brief Returns winSigma value
-    */
-    CV_WRAP double getWinSigma() const;
-
-    /**@example peopledetect.cpp
-    */
-    /**@brief Sets coefficients for the linear SVM classifier.
-    @param _svmdetector coefficients for the linear SVM classifier.
-    */
-    CV_WRAP virtual void setSVMDetector(InputArray _svmdetector);
-
-    /** @brief Reads HOGDescriptor parameters from a file node.
-    @param fn File node
-    */
-    virtual bool read(FileNode& fn);
-
-    /** @brief Stores HOGDescriptor parameters in a file storage.
-    @param fs File storage
-    @param objname Object name
-    */
-    virtual void write(FileStorage& fs, const String& objname) const;
-
-    /** @brief loads coefficients for the linear SVM classifier from a file
-    @param filename Name of the file to read.
-    @param objname The optional name of the node to read (if empty, the first top-level node will be used).
-    */
-    CV_WRAP virtual bool load(const String& filename, const String& objname = String());
-
-    /** @brief saves coefficients for the linear SVM classifier to a file
-    @param filename File name
-    @param objname Object name
-    */
-    CV_WRAP virtual void save(const String& filename, const String& objname = String()) const;
-
-    /** @brief clones the HOGDescriptor
-    @param c cloned HOGDescriptor
-    */
-    virtual void copyTo(HOGDescriptor& c) const;
-
-    /**@example train_HOG.cpp
-    */
-    /** @brief Computes HOG descriptors of given image.
-    @param img Matrix of the type CV_8U containing an image where HOG features will be calculated.
-    @param descriptors Matrix of the type CV_32F
-    @param winStride Window stride. It must be a multiple of block stride.
-    @param padding Padding
-    @param locations Vector of Point
-    */
-    CV_WRAP virtual void compute(InputArray img,
-                         CV_OUT std::vector<float>& descriptors,
-                         Size winStride = Size(), Size padding = Size(),
-                         const std::vector<Point>& locations = std::vector<Point>()) const;
-
-    /** @brief Performs object detection without a multi-scale window.
-    @param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.
-    @param foundLocations Vector of point where each point contains left-top corner point of detected object boundaries.
-    @param weights Vector that will contain confidence values for each detected object.
-    @param hitThreshold Threshold for the distance between features and SVM classifying plane.
-    Usually it is 0 and should be specified in the detector coefficients (as the last free coefficient).
-    But if the free coefficient is omitted (which is allowed), you can specify it manually here.
-    @param winStride Window stride. It must be a multiple of block stride.
-    @param padding Padding
-    @param searchLocations Vector of Point includes set of requested locations to be evaluated.
-    */
-    CV_WRAP virtual void detect(const Mat& img, CV_OUT std::vector<Point>& foundLocations,
-                        CV_OUT std::vector<double>& weights,
-                        double hitThreshold = 0, Size winStride = Size(),
-                        Size padding = Size(),
-                        const std::vector<Point>& searchLocations = std::vector<Point>()) const;
-
-    /** @brief Performs object detection without a multi-scale window.
-    @param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.
-    @param foundLocations Vector of point where each point contains left-top corner point of detected object boundaries.
-    @param hitThreshold Threshold for the distance between features and SVM classifying plane.
-    Usually it is 0 and should be specified in the detector coefficients (as the last free coefficient).
-    But if the free coefficient is omitted (which is allowed), you can specify it manually here.
-    @param winStride Window stride. It must be a multiple of block stride.
-    @param padding Padding
-    @param searchLocations Vector of Point includes locations to search.
-    */
-    virtual void detect(const Mat& img, CV_OUT std::vector<Point>& foundLocations,
-                        double hitThreshold = 0, Size winStride = Size(),
-                        Size padding = Size(),
-                        const std::vector<Point>& searchLocations=std::vector<Point>()) const;
-
-    /** @brief Detects objects of different sizes in the input image. The detected objects are returned as a list
-    of rectangles.
-    @param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.
-    @param foundLocations Vector of rectangles where each rectangle contains the detected object.
-    @param foundWeights Vector that will contain confidence values for each detected object.
-    @param hitThreshold Threshold for the distance between features and SVM classifying plane.
-    Usually it is 0 and should be specified in the detector coefficients (as the last free coefficient).
-    But if the free coefficient is omitted (which is allowed), you can specify it manually here.
-    @param winStride Window stride. It must be a multiple of block stride.
-    @param padding Padding
-    @param scale Coefficient of the detection window increase.
-    @param finalThreshold Final threshold
-    @param useMeanshiftGrouping indicates grouping algorithm
-    */
-    CV_WRAP virtual void detectMultiScale(InputArray img, CV_OUT std::vector<Rect>& foundLocations,
-                                  CV_OUT std::vector<double>& foundWeights, double hitThreshold = 0,
-                                  Size winStride = Size(), Size padding = Size(), double scale = 1.05,
-                                  double finalThreshold = 2.0,bool useMeanshiftGrouping = false) const;
-
-    /** @brief Detects objects of different sizes in the input image. The detected objects are returned as a list
-    of rectangles.
-    @param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.
-    @param foundLocations Vector of rectangles where each rectangle contains the detected object.
-    @param hitThreshold Threshold for the distance between features and SVM classifying plane.
-    Usually it is 0 and should be specified in the detector coefficients (as the last free coefficient).
-    But if the free coefficient is omitted (which is allowed), you can specify it manually here.
-    @param winStride Window stride. It must be a multiple of block stride.
-    @param padding Padding
-    @param scale Coefficient of the detection window increase.
-    @param finalThreshold Final threshold
-    @param useMeanshiftGrouping indicates grouping algorithm
-    */
-    virtual void detectMultiScale(InputArray img, CV_OUT std::vector<Rect>& foundLocations,
-                                  double hitThreshold = 0, Size winStride = Size(),
-                                  Size padding = Size(), double scale = 1.05,
-                                  double finalThreshold = 2.0, bool useMeanshiftGrouping = false) const;
-
-    /** @brief  Computes gradients and quantized gradient orientations.
-    @param img Matrix contains the image to be computed
-    @param grad Matrix of type CV_32FC2 contains computed gradients
-    @param angleOfs Matrix of type CV_8UC2 contains quantized gradient orientations
-    @param paddingTL Padding from top-left
-    @param paddingBR Padding from bottom-right
-    */
-    CV_WRAP virtual void computeGradient(const Mat& img, CV_OUT Mat& grad, CV_OUT Mat& angleOfs,
-                                 Size paddingTL = Size(), Size paddingBR = Size()) const;
-
-    /** @brief Returns coefficients of the classifier trained for people detection (for 64x128 windows).
-    */
-    CV_WRAP static std::vector<float> getDefaultPeopleDetector();
-
-    /**@example hog.cpp
-    */
-    /** @brief Returns coefficients of the classifier trained for people detection (for 48x96 windows).
-    */
-    CV_WRAP static std::vector<float> getDaimlerPeopleDetector();
-
-    //! Detection window size. Align to block size and block stride. Default value is Size(64,128).
-    CV_PROP Size winSize;
-
-    //! Block size in pixels. Align to cell size. Default value is Size(16,16).
-    CV_PROP Size blockSize;
-
-    //! Block stride. It must be a multiple of cell size. Default value is Size(8,8).
-    CV_PROP Size blockStride;
-
-    //! Cell size. Default value is Size(8,8).
-    CV_PROP Size cellSize;
-
-    //! Number of bins used in the calculation of histogram of gradients. Default value is 9.
-    CV_PROP int nbins;
-
-    //! not documented
-    CV_PROP int derivAperture;
-
-    //! Gaussian smoothing window parameter.
-    CV_PROP double winSigma;
-
-    //! histogramNormType
-    CV_PROP int histogramNormType;
-
-    //! L2-Hys normalization method shrinkage.
-    CV_PROP double L2HysThreshold;
-
-    //! Flag to specify whether the gamma correction preprocessing is required or not.
-    CV_PROP bool gammaCorrection;
-
-    //! coefficients for the linear SVM classifier.
-    CV_PROP std::vector<float> svmDetector;
-
-    //! coefficients for the linear SVM classifier used when OpenCL is enabled
-    UMat oclSvmDetector;
-
-    //! not documented
-    float free_coef;
-
-    //! Maximum number of detection window increases. Default value is 64
-    CV_PROP int nlevels;
-
-    //! Indicates signed gradient will be used or not
-    CV_PROP bool signedGradient;
-
-    /** @brief evaluate specified ROI and return confidence value for each location
-    @param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.
-    @param locations Vector of Point
-    @param foundLocations Vector of Point where each Point is detected object's top-left point.
-    @param confidences confidences
-    @param hitThreshold Threshold for the distance between features and SVM classifying plane. Usually
-    it is 0 and should be specified in the detector coefficients (as the last free coefficient). But if
-    the free coefficient is omitted (which is allowed), you can specify it manually here
-    @param winStride winStride
-    @param padding padding
-    */
-    virtual void detectROI(const cv::Mat& img, const std::vector<cv::Point> &locations,
-                                   CV_OUT std::vector<cv::Point>& foundLocations, CV_OUT std::vector<double>& confidences,
-                                   double hitThreshold = 0, cv::Size winStride = Size(),
-                                   cv::Size padding = Size()) const;
-
-    /** @brief evaluate specified ROI and return confidence value for each location in multiple scales
-    @param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected.
-    @param foundLocations Vector of rectangles where each rectangle contains the detected object.
-    @param locations Vector of DetectionROI
-    @param hitThreshold Threshold for the distance between features and SVM classifying plane. Usually it is 0 and should be specified
-    in the detector coefficients (as the last free coefficient). But if the free coefficient is omitted (which is allowed), you can specify it manually here.
-    @param groupThreshold Minimum possible number of rectangles minus 1. The threshold is used in a group of rectangles to retain it.
-    */
-    virtual void detectMultiScaleROI(const cv::Mat& img,
-                                     CV_OUT std::vector<cv::Rect>& foundLocations,
-                                     std::vector<DetectionROI>& locations,
-                                     double hitThreshold = 0,
-                                     int groupThreshold = 0) const;
-
-    /** @brief read/parse Dalal's alt model file
-    @param modelfile Path of Dalal's alt model file.
-    */
-    void readALTModel(String modelfile);
-
-    /** @brief Groups the object candidate rectangles.
-    @param rectList  Input/output vector of rectangles. Output vector includes retained and grouped rectangles. (The Python list is not modified in place.)
-    @param weights Input/output vector of weights of rectangles. Output vector includes weights of retained and grouped rectangles. (The Python list is not modified in place.)
-    @param groupThreshold Minimum possible number of rectangles minus 1. The threshold is used in a group of rectangles to retain it.
-    @param eps Relative difference between sides of the rectangles to merge them into a group.
-    */
-    void groupRectangles(std::vector<cv::Rect>& rectList, std::vector<double>& weights, int groupThreshold, double eps) const;
-};
-
-/** @brief Detect QR code in image and return minimum area of quadrangle that describes QR code.
-    @param in  Matrix of the type CV_8UC1 containing an image where QR code are detected.
-    @param points Output vector of vertices of a quadrangle of minimal area that describes QR code.
-    @param eps_x Epsilon neighborhood, which allows you to determine the horizontal pattern of the scheme 1:1:3:1:1 according to QR code standard.
-    @param eps_y Epsilon neighborhood, which allows you to determine the vertical pattern of the scheme 1:1:3:1:1 according to QR code standard.
-    */
-CV_EXPORTS bool detectQRCode(InputArray in, std::vector<Point> &points, double eps_x = 0.2, double eps_y = 0.1);
-
-//! @} objdetect
-
-}
-
-#include "opencv2/objdetect/detection_based_tracker.hpp"
-
-#ifndef DISABLE_OPENCV_24_COMPATIBILITY
-#include "opencv2/objdetect/objdetect_c.h"
-#endif
-
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/objdetect/detection_based_tracker.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/objdetect/detection_based_tracker.hpp
deleted file mode 100755
index 07dd587..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/objdetect/detection_based_tracker.hpp
+++ /dev/null
@@ -1,227 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_OBJDETECT_DBT_HPP
-#define OPENCV_OBJDETECT_DBT_HPP
-
-#include <opencv2/core.hpp>
-
-// After this condition removal update blacklist for bindings: modules/python/common.cmake
-#if defined(__linux__) || defined(LINUX) || defined(__APPLE__) || defined(__ANDROID__) || \
-  defined(CV_CXX11)
-
-#include <vector>
-
-namespace cv
-{
-
-//! @addtogroup objdetect
-//! @{
-
-class CV_EXPORTS DetectionBasedTracker
-{
-    public:
-        struct CV_EXPORTS Parameters
-        {
-            int maxTrackLifetime;
-            int minDetectionPeriod; //the minimal time between run of the big object detector (on the whole frame) in ms (1000 mean 1 sec), default=0
-
-            Parameters();
-        };
-
-        class IDetector
-        {
-            public:
-                IDetector():
-                    minObjSize(96, 96),
-                    maxObjSize(INT_MAX, INT_MAX),
-                    minNeighbours(2),
-                    scaleFactor(1.1f)
-                {}
-
-                virtual void detect(const cv::Mat& image, std::vector<cv::Rect>& objects) = 0;
-
-                void setMinObjectSize(const cv::Size& min)
-                {
-                    minObjSize = min;
-                }
-                void setMaxObjectSize(const cv::Size& max)
-                {
-                    maxObjSize = max;
-                }
-                cv::Size getMinObjectSize() const
-                {
-                    return minObjSize;
-                }
-                cv::Size getMaxObjectSize() const
-                {
-                    return maxObjSize;
-                }
-                float getScaleFactor()
-                {
-                    return scaleFactor;
-                }
-                void setScaleFactor(float value)
-                {
-                    scaleFactor = value;
-                }
-                int getMinNeighbours()
-                {
-                    return minNeighbours;
-                }
-                void setMinNeighbours(int value)
-                {
-                    minNeighbours = value;
-                }
-                virtual ~IDetector() {}
-
-            protected:
-                cv::Size minObjSize;
-                cv::Size maxObjSize;
-                int minNeighbours;
-                float scaleFactor;
-        };
-
-        DetectionBasedTracker(cv::Ptr<IDetector> mainDetector, cv::Ptr<IDetector> trackingDetector, const Parameters& params);
-        virtual ~DetectionBasedTracker();
-
-        virtual bool run();
-        virtual void stop();
-        virtual void resetTracking();
-
-        virtual void process(const cv::Mat& imageGray);
-
-        bool setParameters(const Parameters& params);
-        const Parameters& getParameters() const;
-
-
-        typedef std::pair<cv::Rect, int> Object;
-        virtual void getObjects(std::vector<cv::Rect>& result) const;
-        virtual void getObjects(std::vector<Object>& result) const;
-
-        enum ObjectStatus
-        {
-            DETECTED_NOT_SHOWN_YET,
-            DETECTED,
-            DETECTED_TEMPORARY_LOST,
-            WRONG_OBJECT
-        };
-        struct ExtObject
-        {
-            int id;
-            cv::Rect location;
-            ObjectStatus status;
-            ExtObject(int _id, cv::Rect _location, ObjectStatus _status)
-                :id(_id), location(_location), status(_status)
-            {
-            }
-        };
-        virtual void getObjects(std::vector<ExtObject>& result) const;
-
-
-        virtual int addObject(const cv::Rect& location); //returns id of the new object
-
-    protected:
-        class SeparateDetectionWork;
-        cv::Ptr<SeparateDetectionWork> separateDetectionWork;
-        friend void* workcycleObjectDetectorFunction(void* p);
-
-        struct InnerParameters
-        {
-            int numLastPositionsToTrack;
-            int numStepsToWaitBeforeFirstShow;
-            int numStepsToTrackWithoutDetectingIfObjectHasNotBeenShown;
-            int numStepsToShowWithoutDetecting;
-
-            float coeffTrackingWindowSize;
-            float coeffObjectSizeToTrack;
-            float coeffObjectSpeedUsingInPrediction;
-
-            InnerParameters();
-        };
-        Parameters parameters;
-        InnerParameters innerParameters;
-
-        struct TrackedObject
-        {
-            typedef std::vector<cv::Rect> PositionsVector;
-
-            PositionsVector lastPositions;
-
-            int numDetectedFrames;
-            int numFramesNotDetected;
-            int id;
-
-            TrackedObject(const cv::Rect& rect):numDetectedFrames(1), numFramesNotDetected(0)
-            {
-                lastPositions.push_back(rect);
-                id=getNextId();
-            };
-
-            static int getNextId()
-            {
-                static int _id=0;
-                return _id++;
-            }
-        };
-
-        int numTrackedSteps;
-        std::vector<TrackedObject> trackedObjects;
-
-        std::vector<float> weightsPositionsSmoothing;
-        std::vector<float> weightsSizesSmoothing;
-
-        cv::Ptr<IDetector> cascadeForTracking;
-
-        void updateTrackedObjects(const std::vector<cv::Rect>& detectedObjects);
-        cv::Rect calcTrackedObjectPositionToShow(int i) const;
-        cv::Rect calcTrackedObjectPositionToShow(int i, ObjectStatus& status) const;
-        void detectInRegion(const cv::Mat& img, const cv::Rect& r, std::vector<cv::Rect>& detectedObjectsInRegions);
-};
-
-//! @} objdetect
-
-} //end of cv namespace
-#endif
-
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/objdetect/objdetect.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/objdetect/objdetect.hpp
deleted file mode 100755
index 3ee284f..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/objdetect/objdetect.hpp
+++ /dev/null
@@ -1,48 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifdef __OPENCV_BUILD
-#error this is a compatibility header which should not be used inside the OpenCV library
-#endif
-
-#include "opencv2/objdetect.hpp"
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/objdetect/objdetect_c.h b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/objdetect/objdetect_c.h
deleted file mode 100755
index 67dc2f4..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/objdetect/objdetect_c.h
+++ /dev/null
@@ -1,166 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_OBJDETECT_C_H
-#define OPENCV_OBJDETECT_C_H
-
-#include "opencv2/core/core_c.h"
-
-#ifdef __cplusplus
-#include <deque>
-#include <vector>
-
-extern "C" {
-#endif
-
-/** @addtogroup objdetect_c
-  @{
-  */
-
-/****************************************************************************************\
-*                         Haar-like Object Detection functions                           *
-\****************************************************************************************/
-
-#define CV_HAAR_MAGIC_VAL    0x42500000
-#define CV_TYPE_NAME_HAAR    "opencv-haar-classifier"
-
-#define CV_IS_HAAR_CLASSIFIER( haar )                                                    \
-    ((haar) != NULL &&                                                                   \
-    (((const CvHaarClassifierCascade*)(haar))->flags & CV_MAGIC_MASK)==CV_HAAR_MAGIC_VAL)
-
-#define CV_HAAR_FEATURE_MAX  3
-#define CV_HAAR_STAGE_MAX 1000
-
-typedef struct CvHaarFeature
-{
-    int tilted;
-    struct
-    {
-        CvRect r;
-        float weight;
-    } rect[CV_HAAR_FEATURE_MAX];
-} CvHaarFeature;
-
-typedef struct CvHaarClassifier
-{
-    int count;
-    CvHaarFeature* haar_feature;
-    float* threshold;
-    int* left;
-    int* right;
-    float* alpha;
-} CvHaarClassifier;
-
-typedef struct CvHaarStageClassifier
-{
-    int  count;
-    float threshold;
-    CvHaarClassifier* classifier;
-
-    int next;
-    int child;
-    int parent;
-} CvHaarStageClassifier;
-
-typedef struct CvHidHaarClassifierCascade CvHidHaarClassifierCascade;
-
-typedef struct CvHaarClassifierCascade
-{
-    int  flags;
-    int  count;
-    CvSize orig_window_size;
-    CvSize real_window_size;
-    double scale;
-    CvHaarStageClassifier* stage_classifier;
-    CvHidHaarClassifierCascade* hid_cascade;
-} CvHaarClassifierCascade;
-
-typedef struct CvAvgComp
-{
-    CvRect rect;
-    int neighbors;
-} CvAvgComp;
-
-/* Loads haar classifier cascade from a directory.
-   It is obsolete: convert your cascade to xml and use cvLoad instead */
-CVAPI(CvHaarClassifierCascade*) cvLoadHaarClassifierCascade(
-                    const char* directory, CvSize orig_window_size);
-
-CVAPI(void) cvReleaseHaarClassifierCascade( CvHaarClassifierCascade** cascade );
-
-#define CV_HAAR_DO_CANNY_PRUNING    1
-#define CV_HAAR_SCALE_IMAGE         2
-#define CV_HAAR_FIND_BIGGEST_OBJECT 4
-#define CV_HAAR_DO_ROUGH_SEARCH     8
-
-CVAPI(CvSeq*) cvHaarDetectObjects( const CvArr* image,
-                     CvHaarClassifierCascade* cascade, CvMemStorage* storage,
-                     double scale_factor CV_DEFAULT(1.1),
-                     int min_neighbors CV_DEFAULT(3), int flags CV_DEFAULT(0),
-                     CvSize min_size CV_DEFAULT(cvSize(0,0)), CvSize max_size CV_DEFAULT(cvSize(0,0)));
-
-/* sets images for haar classifier cascade */
-CVAPI(void) cvSetImagesForHaarClassifierCascade( CvHaarClassifierCascade* cascade,
-                                                const CvArr* sum, const CvArr* sqsum,
-                                                const CvArr* tilted_sum, double scale );
-
-/* runs the cascade on the specified window */
-CVAPI(int) cvRunHaarClassifierCascade( const CvHaarClassifierCascade* cascade,
-                                       CvPoint pt, int start_stage CV_DEFAULT(0));
-
-/** @} objdetect_c */
-
-#ifdef __cplusplus
-}
-
-CV_EXPORTS CvSeq* cvHaarDetectObjectsForROC( const CvArr* image,
-                     CvHaarClassifierCascade* cascade, CvMemStorage* storage,
-                     std::vector<int>& rejectLevels, std::vector<double>& levelWeightds,
-                     double scale_factor = 1.1,
-                     int min_neighbors = 3, int flags = 0,
-                     CvSize min_size = cvSize(0, 0), CvSize max_size = cvSize(0, 0),
-                     bool outputRejectLevels = false );
-
-#endif
-
-#endif /* OPENCV_OBJDETECT_C_H */
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/opencv.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/opencv.hpp
deleted file mode 100755
index 4048158..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/opencv.hpp
+++ /dev/null
@@ -1,139 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009-2010, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_ALL_HPP
-#define OPENCV_ALL_HPP
-
-// File that defines what modules where included during the build of OpenCV
-// These are purely the defines of the correct HAVE_OPENCV_modulename values
-#include "opencv2/opencv_modules.hpp"
-
-// Then the list of defines is checked to include the correct headers
-// Core library is always included --> without no OpenCV functionality available
-#include "opencv2/core.hpp"
-
-// Then the optional modules are checked
-#ifdef HAVE_OPENCV_CALIB3D
-#include "opencv2/calib3d.hpp"
-#endif
-#ifdef HAVE_OPENCV_FEATURES2D
-#include "opencv2/features2d.hpp"
-#endif
-#ifdef HAVE_OPENCV_DNN
-#include "opencv2/dnn.hpp"
-#endif
-#ifdef HAVE_OPENCV_FLANN
-#include "opencv2/flann.hpp"
-#endif
-#ifdef HAVE_OPENCV_HIGHGUI
-#include "opencv2/highgui.hpp"
-#endif
-#ifdef HAVE_OPENCV_IMGCODECS
-#include "opencv2/imgcodecs.hpp"
-#endif
-#ifdef HAVE_OPENCV_IMGPROC
-#include "opencv2/imgproc.hpp"
-#endif
-#ifdef HAVE_OPENCV_ML
-#include "opencv2/ml.hpp"
-#endif
-#ifdef HAVE_OPENCV_OBJDETECT
-#include "opencv2/objdetect.hpp"
-#endif
-#ifdef HAVE_OPENCV_PHOTO
-#include "opencv2/photo.hpp"
-#endif
-#ifdef HAVE_OPENCV_SHAPE
-#include "opencv2/shape.hpp"
-#endif
-#ifdef HAVE_OPENCV_STITCHING
-#include "opencv2/stitching.hpp"
-#endif
-#ifdef HAVE_OPENCV_SUPERRES
-#include "opencv2/superres.hpp"
-#endif
-#ifdef HAVE_OPENCV_VIDEO
-#include "opencv2/video.hpp"
-#endif
-#ifdef HAVE_OPENCV_VIDEOIO
-#include "opencv2/videoio.hpp"
-#endif
-#ifdef HAVE_OPENCV_VIDEOSTAB
-#include "opencv2/videostab.hpp"
-#endif
-#ifdef HAVE_OPENCV_VIZ
-#include "opencv2/viz.hpp"
-#endif
-
-// Finally CUDA specific entries are checked and added
-#ifdef HAVE_OPENCV_CUDAARITHM
-#include "opencv2/cudaarithm.hpp"
-#endif
-#ifdef HAVE_OPENCV_CUDABGSEGM
-#include "opencv2/cudabgsegm.hpp"
-#endif
-#ifdef HAVE_OPENCV_CUDACODEC
-#include "opencv2/cudacodec.hpp"
-#endif
-#ifdef HAVE_OPENCV_CUDAFEATURES2D
-#include "opencv2/cudafeatures2d.hpp"
-#endif
-#ifdef HAVE_OPENCV_CUDAFILTERS
-#include "opencv2/cudafilters.hpp"
-#endif
-#ifdef HAVE_OPENCV_CUDAIMGPROC
-#include "opencv2/cudaimgproc.hpp"
-#endif
-#ifdef HAVE_OPENCV_CUDAOBJDETECT
-#include "opencv2/cudaobjdetect.hpp"
-#endif
-#ifdef HAVE_OPENCV_CUDAOPTFLOW
-#include "opencv2/cudaoptflow.hpp"
-#endif
-#ifdef HAVE_OPENCV_CUDASTEREO
-#include "opencv2/cudastereo.hpp"
-#endif
-#ifdef HAVE_OPENCV_CUDAWARPING
-#include "opencv2/cudawarping.hpp"
-#endif
-
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/opencv_modules.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/opencv_modules.hpp
deleted file mode 100755
index e14f3ec..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/opencv_modules.hpp
+++ /dev/null
@@ -1,31 +0,0 @@
-/*
- *      ** File generated automatically, do not modify **
- *
- * This file defines the list of modules available in current build configuration
- *
- *
-*/
-
-// This definition means that OpenCV is built with enabled non-free code.
-// For example, patented algorithms for non-profit/non-commercial use only.
-/* #undef OPENCV_ENABLE_NONFREE */
-
-#define HAVE_OPENCV_CALIB3D
-#define HAVE_OPENCV_CORE
-#define HAVE_OPENCV_DNN
-#define HAVE_OPENCV_FEATURES2D
-#define HAVE_OPENCV_FLANN
-#define HAVE_OPENCV_HIGHGUI
-#define HAVE_OPENCV_IMGCODECS
-#define HAVE_OPENCV_IMGPROC
-#define HAVE_OPENCV_ML
-#define HAVE_OPENCV_OBJDETECT
-#define HAVE_OPENCV_PHOTO
-#define HAVE_OPENCV_SHAPE
-#define HAVE_OPENCV_STITCHING
-#define HAVE_OPENCV_SUPERRES
-#define HAVE_OPENCV_VIDEO
-#define HAVE_OPENCV_VIDEOIO
-#define HAVE_OPENCV_VIDEOSTAB
-
-
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/photo.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/photo.hpp
deleted file mode 100755
index 8cd1971..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/photo.hpp
+++ /dev/null
@@ -1,876 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2008-2012, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_PHOTO_HPP
-#define OPENCV_PHOTO_HPP
-
-#include "opencv2/core.hpp"
-#include "opencv2/imgproc.hpp"
-
-/**
-@defgroup photo Computational Photography
-@{
-    @defgroup photo_denoise Denoising
-    @defgroup photo_hdr HDR imaging
-
-This section describes high dynamic range imaging algorithms namely tonemapping, exposure alignment,
-camera calibration with multiple exposures and exposure fusion.
-
-    @defgroup photo_clone Seamless Cloning
-    @defgroup photo_render Non-Photorealistic Rendering
-    @defgroup photo_c C API
-@}
-  */
-
-namespace cv
-{
-
-//! @addtogroup photo
-//! @{
-
-//! the inpainting algorithm
-enum
-{
-    INPAINT_NS    = 0, // Navier-Stokes algorithm
-    INPAINT_TELEA = 1 // A. Telea algorithm
-};
-
-enum
-{
-    NORMAL_CLONE = 1,
-    MIXED_CLONE  = 2,
-    MONOCHROME_TRANSFER = 3
-};
-
-enum
-{
-    RECURS_FILTER = 1,
-    NORMCONV_FILTER = 2
-};
-
-/** @brief Restores the selected region in an image using the region neighborhood.
-
-@param src Input 8-bit, 16-bit unsigned or 32-bit float 1-channel or 8-bit 3-channel image.
-@param inpaintMask Inpainting mask, 8-bit 1-channel image. Non-zero pixels indicate the area that
-needs to be inpainted.
-@param dst Output image with the same size and type as src .
-@param inpaintRadius Radius of a circular neighborhood of each point inpainted that is considered
-by the algorithm.
-@param flags Inpainting method that could be one of the following:
--   **INPAINT_NS** Navier-Stokes based method [Navier01]
--   **INPAINT_TELEA** Method by Alexandru Telea @cite Telea04 .
-
-The function reconstructs the selected image area from the pixel near the area boundary. The
-function may be used to remove dust and scratches from a scanned photo, or to remove undesirable
-objects from still images or video. See <http://en.wikipedia.org/wiki/Inpainting> for more details.
-
-@note
-   -   An example using the inpainting technique can be found at
-        opencv_source_code/samples/cpp/inpaint.cpp
-    -   (Python) An example using the inpainting technique can be found at
-        opencv_source_code/samples/python/inpaint.py
- */
-CV_EXPORTS_W void inpaint( InputArray src, InputArray inpaintMask,
-        OutputArray dst, double inpaintRadius, int flags );
-
-//! @addtogroup photo_denoise
-//! @{
-
-/** @brief Perform image denoising using Non-local Means Denoising algorithm
-<http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/> with several computational
-optimizations. Noise expected to be a gaussian white noise
-
-@param src Input 8-bit 1-channel, 2-channel, 3-channel or 4-channel image.
-@param dst Output image with the same size and type as src .
-@param templateWindowSize Size in pixels of the template patch that is used to compute weights.
-Should be odd. Recommended value 7 pixels
-@param searchWindowSize Size in pixels of the window that is used to compute weighted average for
-given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater
-denoising time. Recommended value 21 pixels
-@param h Parameter regulating filter strength. Big h value perfectly removes noise but also
-removes image details, smaller h value preserves details but also preserves some noise
-
-This function expected to be applied to grayscale images. For colored images look at
-fastNlMeansDenoisingColored. Advanced usage of this functions can be manual denoising of colored
-image in different colorspaces. Such approach is used in fastNlMeansDenoisingColored by converting
-image to CIELAB colorspace and then separately denoise L and AB components with different h
-parameter.
- */
-CV_EXPORTS_W void fastNlMeansDenoising( InputArray src, OutputArray dst, float h = 3,
-        int templateWindowSize = 7, int searchWindowSize = 21);
-
-/** @brief Perform image denoising using Non-local Means Denoising algorithm
-<http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/> with several computational
-optimizations. Noise expected to be a gaussian white noise
-
-@param src Input 8-bit or 16-bit (only with NORM_L1) 1-channel,
-2-channel, 3-channel or 4-channel image.
-@param dst Output image with the same size and type as src .
-@param templateWindowSize Size in pixels of the template patch that is used to compute weights.
-Should be odd. Recommended value 7 pixels
-@param searchWindowSize Size in pixels of the window that is used to compute weighted average for
-given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater
-denoising time. Recommended value 21 pixels
-@param h Array of parameters regulating filter strength, either one
-parameter applied to all channels or one per channel in dst. Big h value
-perfectly removes noise but also removes image details, smaller h
-value preserves details but also preserves some noise
-@param normType Type of norm used for weight calculation. Can be either NORM_L2 or NORM_L1
-
-This function expected to be applied to grayscale images. For colored images look at
-fastNlMeansDenoisingColored. Advanced usage of this functions can be manual denoising of colored
-image in different colorspaces. Such approach is used in fastNlMeansDenoisingColored by converting
-image to CIELAB colorspace and then separately denoise L and AB components with different h
-parameter.
- */
-CV_EXPORTS_W void fastNlMeansDenoising( InputArray src, OutputArray dst,
-                                        const std::vector<float>& h,
-                                        int templateWindowSize = 7, int searchWindowSize = 21,
-                                        int normType = NORM_L2);
-
-/** @brief Modification of fastNlMeansDenoising function for colored images
-
-@param src Input 8-bit 3-channel image.
-@param dst Output image with the same size and type as src .
-@param templateWindowSize Size in pixels of the template patch that is used to compute weights.
-Should be odd. Recommended value 7 pixels
-@param searchWindowSize Size in pixels of the window that is used to compute weighted average for
-given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater
-denoising time. Recommended value 21 pixels
-@param h Parameter regulating filter strength for luminance component. Bigger h value perfectly
-removes noise but also removes image details, smaller h value preserves details but also preserves
-some noise
-@param hColor The same as h but for color components. For most images value equals 10
-will be enough to remove colored noise and do not distort colors
-
-The function converts image to CIELAB colorspace and then separately denoise L and AB components
-with given h parameters using fastNlMeansDenoising function.
- */
-CV_EXPORTS_W void fastNlMeansDenoisingColored( InputArray src, OutputArray dst,
-        float h = 3, float hColor = 3,
-        int templateWindowSize = 7, int searchWindowSize = 21);
-
-/** @brief Modification of fastNlMeansDenoising function for images sequence where consecutive images have been
-captured in small period of time. For example video. This version of the function is for grayscale
-images or for manual manipulation with colorspaces. For more details see
-<http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.131.6394>
-
-@param srcImgs Input 8-bit 1-channel, 2-channel, 3-channel or
-4-channel images sequence. All images should have the same type and
-size.
-@param imgToDenoiseIndex Target image to denoise index in srcImgs sequence
-@param temporalWindowSize Number of surrounding images to use for target image denoising. Should
-be odd. Images from imgToDenoiseIndex - temporalWindowSize / 2 to
-imgToDenoiseIndex - temporalWindowSize / 2 from srcImgs will be used to denoise
-srcImgs[imgToDenoiseIndex] image.
-@param dst Output image with the same size and type as srcImgs images.
-@param templateWindowSize Size in pixels of the template patch that is used to compute weights.
-Should be odd. Recommended value 7 pixels
-@param searchWindowSize Size in pixels of the window that is used to compute weighted average for
-given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater
-denoising time. Recommended value 21 pixels
-@param h Parameter regulating filter strength. Bigger h value
-perfectly removes noise but also removes image details, smaller h
-value preserves details but also preserves some noise
- */
-CV_EXPORTS_W void fastNlMeansDenoisingMulti( InputArrayOfArrays srcImgs, OutputArray dst,
-        int imgToDenoiseIndex, int temporalWindowSize,
-        float h = 3, int templateWindowSize = 7, int searchWindowSize = 21);
-
-/** @brief Modification of fastNlMeansDenoising function for images sequence where consecutive images have been
-captured in small period of time. For example video. This version of the function is for grayscale
-images or for manual manipulation with colorspaces. For more details see
-<http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.131.6394>
-
-@param srcImgs Input 8-bit or 16-bit (only with NORM_L1) 1-channel,
-2-channel, 3-channel or 4-channel images sequence. All images should
-have the same type and size.
-@param imgToDenoiseIndex Target image to denoise index in srcImgs sequence
-@param temporalWindowSize Number of surrounding images to use for target image denoising. Should
-be odd. Images from imgToDenoiseIndex - temporalWindowSize / 2 to
-imgToDenoiseIndex - temporalWindowSize / 2 from srcImgs will be used to denoise
-srcImgs[imgToDenoiseIndex] image.
-@param dst Output image with the same size and type as srcImgs images.
-@param templateWindowSize Size in pixels of the template patch that is used to compute weights.
-Should be odd. Recommended value 7 pixels
-@param searchWindowSize Size in pixels of the window that is used to compute weighted average for
-given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater
-denoising time. Recommended value 21 pixels
-@param h Array of parameters regulating filter strength, either one
-parameter applied to all channels or one per channel in dst. Big h value
-perfectly removes noise but also removes image details, smaller h
-value preserves details but also preserves some noise
-@param normType Type of norm used for weight calculation. Can be either NORM_L2 or NORM_L1
- */
-CV_EXPORTS_W void fastNlMeansDenoisingMulti( InputArrayOfArrays srcImgs, OutputArray dst,
-                                             int imgToDenoiseIndex, int temporalWindowSize,
-                                             const std::vector<float>& h,
-                                             int templateWindowSize = 7, int searchWindowSize = 21,
-                                             int normType = NORM_L2);
-
-/** @brief Modification of fastNlMeansDenoisingMulti function for colored images sequences
-
-@param srcImgs Input 8-bit 3-channel images sequence. All images should have the same type and
-size.
-@param imgToDenoiseIndex Target image to denoise index in srcImgs sequence
-@param temporalWindowSize Number of surrounding images to use for target image denoising. Should
-be odd. Images from imgToDenoiseIndex - temporalWindowSize / 2 to
-imgToDenoiseIndex - temporalWindowSize / 2 from srcImgs will be used to denoise
-srcImgs[imgToDenoiseIndex] image.
-@param dst Output image with the same size and type as srcImgs images.
-@param templateWindowSize Size in pixels of the template patch that is used to compute weights.
-Should be odd. Recommended value 7 pixels
-@param searchWindowSize Size in pixels of the window that is used to compute weighted average for
-given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater
-denoising time. Recommended value 21 pixels
-@param h Parameter regulating filter strength for luminance component. Bigger h value perfectly
-removes noise but also removes image details, smaller h value preserves details but also preserves
-some noise.
-@param hColor The same as h but for color components.
-
-The function converts images to CIELAB colorspace and then separately denoise L and AB components
-with given h parameters using fastNlMeansDenoisingMulti function.
- */
-CV_EXPORTS_W void fastNlMeansDenoisingColoredMulti( InputArrayOfArrays srcImgs, OutputArray dst,
-        int imgToDenoiseIndex, int temporalWindowSize,
-        float h = 3, float hColor = 3,
-        int templateWindowSize = 7, int searchWindowSize = 21);
-
-/** @brief Primal-dual algorithm is an algorithm for solving special types of variational problems (that is,
-finding a function to minimize some functional). As the image denoising, in particular, may be seen
-as the variational problem, primal-dual algorithm then can be used to perform denoising and this is
-exactly what is implemented.
-
-It should be noted, that this implementation was taken from the July 2013 blog entry
-@cite MA13 , which also contained (slightly more general) ready-to-use source code on Python.
-Subsequently, that code was rewritten on C++ with the usage of openCV by Vadim Pisarevsky at the end
-of July 2013 and finally it was slightly adapted by later authors.
-
-Although the thorough discussion and justification of the algorithm involved may be found in
-@cite ChambolleEtAl, it might make sense to skim over it here, following @cite MA13 . To begin
-with, we consider the 1-byte gray-level images as the functions from the rectangular domain of
-pixels (it may be seen as set
-\f$\left\{(x,y)\in\mathbb{N}\times\mathbb{N}\mid 1\leq x\leq n,\;1\leq y\leq m\right\}\f$ for some
-\f$m,\;n\in\mathbb{N}\f$) into \f$\{0,1,\dots,255\}\f$. We shall denote the noised images as \f$f_i\f$ and with
-this view, given some image \f$x\f$ of the same size, we may measure how bad it is by the formula
-
-\f[\left\|\left\|\nabla x\right\|\right\| + \lambda\sum_i\left\|\left\|x-f_i\right\|\right\|\f]
-
-\f$\|\|\cdot\|\|\f$ here denotes \f$L_2\f$-norm and as you see, the first addend states that we want our
-image to be smooth (ideally, having zero gradient, thus being constant) and the second states that
-we want our result to be close to the observations we've got. If we treat \f$x\f$ as a function, this is
-exactly the functional what we seek to minimize and here the Primal-Dual algorithm comes into play.
-
-@param observations This array should contain one or more noised versions of the image that is to
-be restored.
-@param result Here the denoised image will be stored. There is no need to do pre-allocation of
-storage space, as it will be automatically allocated, if necessary.
-@param lambda Corresponds to \f$\lambda\f$ in the formulas above. As it is enlarged, the smooth
-(blurred) images are treated more favorably than detailed (but maybe more noised) ones. Roughly
-speaking, as it becomes smaller, the result will be more blur but more sever outliers will be
-removed.
-@param niters Number of iterations that the algorithm will run. Of course, as more iterations as
-better, but it is hard to quantitatively refine this statement, so just use the default and
-increase it if the results are poor.
- */
-CV_EXPORTS_W void denoise_TVL1(const std::vector<Mat>& observations,Mat& result, double lambda=1.0, int niters=30);
-
-//! @} photo_denoise
-
-//! @addtogroup photo_hdr
-//! @{
-
-enum { LDR_SIZE = 256 };
-
-/** @brief Base class for tonemapping algorithms - tools that are used to map HDR image to 8-bit range.
- */
-class CV_EXPORTS_W Tonemap : public Algorithm
-{
-public:
-    /** @brief Tonemaps image
-
-    @param src source image - 32-bit 3-channel Mat
-    @param dst destination image - 32-bit 3-channel Mat with values in [0, 1] range
-     */
-    CV_WRAP virtual void process(InputArray src, OutputArray dst) = 0;
-
-    CV_WRAP virtual float getGamma() const = 0;
-    CV_WRAP virtual void setGamma(float gamma) = 0;
-};
-
-/** @brief Creates simple linear mapper with gamma correction
-
-@param gamma positive value for gamma correction. Gamma value of 1.0 implies no correction, gamma
-equal to 2.2f is suitable for most displays.
-Generally gamma \> 1 brightens the image and gamma \< 1 darkens it.
- */
-CV_EXPORTS_W Ptr<Tonemap> createTonemap(float gamma = 1.0f);
-
-/** @brief Adaptive logarithmic mapping is a fast global tonemapping algorithm that scales the image in
-logarithmic domain.
-
-Since it's a global operator the same function is applied to all the pixels, it is controlled by the
-bias parameter.
-
-Optional saturation enhancement is possible as described in @cite FL02 .
-
-For more information see @cite DM03 .
- */
-class CV_EXPORTS_W TonemapDrago : public Tonemap
-{
-public:
-
-    CV_WRAP virtual float getSaturation() const = 0;
-    CV_WRAP virtual void setSaturation(float saturation) = 0;
-
-    CV_WRAP virtual float getBias() const = 0;
-    CV_WRAP virtual void setBias(float bias) = 0;
-};
-
-/** @brief Creates TonemapDrago object
-
-@param gamma gamma value for gamma correction. See createTonemap
-@param saturation positive saturation enhancement value. 1.0 preserves saturation, values greater
-than 1 increase saturation and values less than 1 decrease it.
-@param bias value for bias function in [0, 1] range. Values from 0.7 to 0.9 usually give best
-results, default value is 0.85.
- */
-CV_EXPORTS_W Ptr<TonemapDrago> createTonemapDrago(float gamma = 1.0f, float saturation = 1.0f, float bias = 0.85f);
-
-/** @brief This algorithm decomposes image into two layers: base layer and detail layer using bilateral filter
-and compresses contrast of the base layer thus preserving all the details.
-
-This implementation uses regular bilateral filter from opencv.
-
-Saturation enhancement is possible as in ocvTonemapDrago.
-
-For more information see @cite DD02 .
- */
-class CV_EXPORTS_W TonemapDurand : public Tonemap
-{
-public:
-
-    CV_WRAP virtual float getSaturation() const = 0;
-    CV_WRAP virtual void setSaturation(float saturation) = 0;
-
-    CV_WRAP virtual float getContrast() const = 0;
-    CV_WRAP virtual void setContrast(float contrast) = 0;
-
-    CV_WRAP virtual float getSigmaSpace() const = 0;
-    CV_WRAP virtual void setSigmaSpace(float sigma_space) = 0;
-
-    CV_WRAP virtual float getSigmaColor() const = 0;
-    CV_WRAP virtual void setSigmaColor(float sigma_color) = 0;
-};
-
-/** @brief Creates TonemapDurand object
-
-@param gamma gamma value for gamma correction. See createTonemap
-@param contrast resulting contrast on logarithmic scale, i. e. log(max / min), where max and min
-are maximum and minimum luminance values of the resulting image.
-@param saturation saturation enhancement value. See createTonemapDrago
-@param sigma_space bilateral filter sigma in color space
-@param sigma_color bilateral filter sigma in coordinate space
- */
-CV_EXPORTS_W Ptr<TonemapDurand>
-createTonemapDurand(float gamma = 1.0f, float contrast = 4.0f, float saturation = 1.0f, float sigma_space = 2.0f, float sigma_color = 2.0f);
-
-/** @brief This is a global tonemapping operator that models human visual system.
-
-Mapping function is controlled by adaptation parameter, that is computed using light adaptation and
-color adaptation.
-
-For more information see @cite RD05 .
- */
-class CV_EXPORTS_W TonemapReinhard : public Tonemap
-{
-public:
-    CV_WRAP virtual float getIntensity() const = 0;
-    CV_WRAP virtual void setIntensity(float intensity) = 0;
-
-    CV_WRAP virtual float getLightAdaptation() const = 0;
-    CV_WRAP virtual void setLightAdaptation(float light_adapt) = 0;
-
-    CV_WRAP virtual float getColorAdaptation() const = 0;
-    CV_WRAP virtual void setColorAdaptation(float color_adapt) = 0;
-};
-
-/** @brief Creates TonemapReinhard object
-
-@param gamma gamma value for gamma correction. See createTonemap
-@param intensity result intensity in [-8, 8] range. Greater intensity produces brighter results.
-@param light_adapt light adaptation in [0, 1] range. If 1 adaptation is based only on pixel
-value, if 0 it's global, otherwise it's a weighted mean of this two cases.
-@param color_adapt chromatic adaptation in [0, 1] range. If 1 channels are treated independently,
-if 0 adaptation level is the same for each channel.
- */
-CV_EXPORTS_W Ptr<TonemapReinhard>
-createTonemapReinhard(float gamma = 1.0f, float intensity = 0.0f, float light_adapt = 1.0f, float color_adapt = 0.0f);
-
-/** @brief This algorithm transforms image to contrast using gradients on all levels of gaussian pyramid,
-transforms contrast values to HVS response and scales the response. After this the image is
-reconstructed from new contrast values.
-
-For more information see @cite MM06 .
- */
-class CV_EXPORTS_W TonemapMantiuk : public Tonemap
-{
-public:
-    CV_WRAP virtual float getScale() const = 0;
-    CV_WRAP virtual void setScale(float scale) = 0;
-
-    CV_WRAP virtual float getSaturation() const = 0;
-    CV_WRAP virtual void setSaturation(float saturation) = 0;
-};
-
-/** @brief Creates TonemapMantiuk object
-
-@param gamma gamma value for gamma correction. See createTonemap
-@param scale contrast scale factor. HVS response is multiplied by this parameter, thus compressing
-dynamic range. Values from 0.6 to 0.9 produce best results.
-@param saturation saturation enhancement value. See createTonemapDrago
- */
-CV_EXPORTS_W Ptr<TonemapMantiuk>
-createTonemapMantiuk(float gamma = 1.0f, float scale = 0.7f, float saturation = 1.0f);
-
-/** @brief The base class for algorithms that align images of the same scene with different exposures
- */
-class CV_EXPORTS_W AlignExposures : public Algorithm
-{
-public:
-    /** @brief Aligns images
-
-    @param src vector of input images
-    @param dst vector of aligned images
-    @param times vector of exposure time values for each image
-    @param response 256x1 matrix with inverse camera response function for each pixel value, it should
-    have the same number of channels as images.
-     */
-    CV_WRAP virtual void process(InputArrayOfArrays src, std::vector<Mat>& dst,
-                                 InputArray times, InputArray response) = 0;
-};
-
-/** @brief This algorithm converts images to median threshold bitmaps (1 for pixels brighter than median
-luminance and 0 otherwise) and than aligns the resulting bitmaps using bit operations.
-
-It is invariant to exposure, so exposure values and camera response are not necessary.
-
-In this implementation new image regions are filled with zeros.
-
-For more information see @cite GW03 .
- */
-class CV_EXPORTS_W AlignMTB : public AlignExposures
-{
-public:
-    CV_WRAP virtual void process(InputArrayOfArrays src, std::vector<Mat>& dst,
-                                 InputArray times, InputArray response) CV_OVERRIDE = 0;
-
-    /** @brief Short version of process, that doesn't take extra arguments.
-
-    @param src vector of input images
-    @param dst vector of aligned images
-     */
-    CV_WRAP virtual void process(InputArrayOfArrays src, std::vector<Mat>& dst) = 0;
-
-    /** @brief Calculates shift between two images, i. e. how to shift the second image to correspond it with the
-    first.
-
-    @param img0 first image
-    @param img1 second image
-     */
-    CV_WRAP virtual Point calculateShift(InputArray img0, InputArray img1) = 0;
-    /** @brief Helper function, that shift Mat filling new regions with zeros.
-
-    @param src input image
-    @param dst result image
-    @param shift shift value
-     */
-    CV_WRAP virtual void shiftMat(InputArray src, OutputArray dst, const Point shift) = 0;
-    /** @brief Computes median threshold and exclude bitmaps of given image.
-
-    @param img input image
-    @param tb median threshold bitmap
-    @param eb exclude bitmap
-     */
-    CV_WRAP virtual void computeBitmaps(InputArray img, OutputArray tb, OutputArray eb) = 0;
-
-    CV_WRAP virtual int getMaxBits() const = 0;
-    CV_WRAP virtual void setMaxBits(int max_bits) = 0;
-
-    CV_WRAP virtual int getExcludeRange() const = 0;
-    CV_WRAP virtual void setExcludeRange(int exclude_range) = 0;
-
-    CV_WRAP virtual bool getCut() const = 0;
-    CV_WRAP virtual void setCut(bool value) = 0;
-};
-
-/** @brief Creates AlignMTB object
-
-@param max_bits logarithm to the base 2 of maximal shift in each dimension. Values of 5 and 6 are
-usually good enough (31 and 63 pixels shift respectively).
-@param exclude_range range for exclusion bitmap that is constructed to suppress noise around the
-median value.
-@param cut if true cuts images, otherwise fills the new regions with zeros.
- */
-CV_EXPORTS_W Ptr<AlignMTB> createAlignMTB(int max_bits = 6, int exclude_range = 4, bool cut = true);
-
-/** @brief The base class for camera response calibration algorithms.
- */
-class CV_EXPORTS_W CalibrateCRF : public Algorithm
-{
-public:
-    /** @brief Recovers inverse camera response.
-
-    @param src vector of input images
-    @param dst 256x1 matrix with inverse camera response function
-    @param times vector of exposure time values for each image
-     */
-    CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst, InputArray times) = 0;
-};
-
-/** @brief Inverse camera response function is extracted for each brightness value by minimizing an objective
-function as linear system. Objective function is constructed using pixel values on the same position
-in all images, extra term is added to make the result smoother.
-
-For more information see @cite DM97 .
- */
-class CV_EXPORTS_W CalibrateDebevec : public CalibrateCRF
-{
-public:
-    CV_WRAP virtual float getLambda() const = 0;
-    CV_WRAP virtual void setLambda(float lambda) = 0;
-
-    CV_WRAP virtual int getSamples() const = 0;
-    CV_WRAP virtual void setSamples(int samples) = 0;
-
-    CV_WRAP virtual bool getRandom() const = 0;
-    CV_WRAP virtual void setRandom(bool random) = 0;
-};
-
-/** @brief Creates CalibrateDebevec object
-
-@param samples number of pixel locations to use
-@param lambda smoothness term weight. Greater values produce smoother results, but can alter the
-response.
-@param random if true sample pixel locations are chosen at random, otherwise they form a
-rectangular grid.
- */
-CV_EXPORTS_W Ptr<CalibrateDebevec> createCalibrateDebevec(int samples = 70, float lambda = 10.0f, bool random = false);
-
-/** @brief Inverse camera response function is extracted for each brightness value by minimizing an objective
-function as linear system. This algorithm uses all image pixels.
-
-For more information see @cite RB99 .
- */
-class CV_EXPORTS_W CalibrateRobertson : public CalibrateCRF
-{
-public:
-    CV_WRAP virtual int getMaxIter() const = 0;
-    CV_WRAP virtual void setMaxIter(int max_iter) = 0;
-
-    CV_WRAP virtual float getThreshold() const = 0;
-    CV_WRAP virtual void setThreshold(float threshold) = 0;
-
-    CV_WRAP virtual Mat getRadiance() const = 0;
-};
-
-/** @brief Creates CalibrateRobertson object
-
-@param max_iter maximal number of Gauss-Seidel solver iterations.
-@param threshold target difference between results of two successive steps of the minimization.
- */
-CV_EXPORTS_W Ptr<CalibrateRobertson> createCalibrateRobertson(int max_iter = 30, float threshold = 0.01f);
-
-/** @brief The base class algorithms that can merge exposure sequence to a single image.
- */
-class CV_EXPORTS_W MergeExposures : public Algorithm
-{
-public:
-    /** @brief Merges images.
-
-    @param src vector of input images
-    @param dst result image
-    @param times vector of exposure time values for each image
-    @param response 256x1 matrix with inverse camera response function for each pixel value, it should
-    have the same number of channels as images.
-     */
-    CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst,
-                                 InputArray times, InputArray response) = 0;
-};
-
-/** @brief The resulting HDR image is calculated as weighted average of the exposures considering exposure
-values and camera response.
-
-For more information see @cite DM97 .
- */
-class CV_EXPORTS_W MergeDebevec : public MergeExposures
-{
-public:
-    CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst,
-                                 InputArray times, InputArray response) CV_OVERRIDE = 0;
-    CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst, InputArray times) = 0;
-};
-
-/** @brief Creates MergeDebevec object
- */
-CV_EXPORTS_W Ptr<MergeDebevec> createMergeDebevec();
-
-/** @brief Pixels are weighted using contrast, saturation and well-exposedness measures, than images are
-combined using laplacian pyramids.
-
-The resulting image weight is constructed as weighted average of contrast, saturation and
-well-exposedness measures.
-
-The resulting image doesn't require tonemapping and can be converted to 8-bit image by multiplying
-by 255, but it's recommended to apply gamma correction and/or linear tonemapping.
-
-For more information see @cite MK07 .
- */
-class CV_EXPORTS_W MergeMertens : public MergeExposures
-{
-public:
-    CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst,
-                                 InputArray times, InputArray response) CV_OVERRIDE = 0;
-    /** @brief Short version of process, that doesn't take extra arguments.
-
-    @param src vector of input images
-    @param dst result image
-     */
-    CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst) = 0;
-
-    CV_WRAP virtual float getContrastWeight() const = 0;
-    CV_WRAP virtual void setContrastWeight(float contrast_weiht) = 0;
-
-    CV_WRAP virtual float getSaturationWeight() const = 0;
-    CV_WRAP virtual void setSaturationWeight(float saturation_weight) = 0;
-
-    CV_WRAP virtual float getExposureWeight() const = 0;
-    CV_WRAP virtual void setExposureWeight(float exposure_weight) = 0;
-};
-
-/** @brief Creates MergeMertens object
-
-@param contrast_weight contrast measure weight. See MergeMertens.
-@param saturation_weight saturation measure weight
-@param exposure_weight well-exposedness measure weight
- */
-CV_EXPORTS_W Ptr<MergeMertens>
-createMergeMertens(float contrast_weight = 1.0f, float saturation_weight = 1.0f, float exposure_weight = 0.0f);
-
-/** @brief The resulting HDR image is calculated as weighted average of the exposures considering exposure
-values and camera response.
-
-For more information see @cite RB99 .
- */
-class CV_EXPORTS_W MergeRobertson : public MergeExposures
-{
-public:
-    CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst,
-                                 InputArray times, InputArray response) CV_OVERRIDE = 0;
-    CV_WRAP virtual void process(InputArrayOfArrays src, OutputArray dst, InputArray times) = 0;
-};
-
-/** @brief Creates MergeRobertson object
- */
-CV_EXPORTS_W Ptr<MergeRobertson> createMergeRobertson();
-
-//! @} photo_hdr
-
-/** @brief Transforms a color image to a grayscale image. It is a basic tool in digital printing, stylized
-black-and-white photograph rendering, and in many single channel image processing applications
-@cite CL12 .
-
-@param src Input 8-bit 3-channel image.
-@param grayscale Output 8-bit 1-channel image.
-@param color_boost Output 8-bit 3-channel image.
-
-This function is to be applied on color images.
- */
-CV_EXPORTS_W void decolor( InputArray src, OutputArray grayscale, OutputArray color_boost);
-
-//! @addtogroup photo_clone
-//! @{
-
-/** @example cloning_demo.cpp
-An example using seamlessClone function
-*/
-/** @brief Image editing tasks concern either global changes (color/intensity corrections, filters,
-deformations) or local changes concerned to a selection. Here we are interested in achieving local
-changes, ones that are restricted to a region manually selected (ROI), in a seamless and effortless
-manner. The extent of the changes ranges from slight distortions to complete replacement by novel
-content @cite PM03 .
-
-@param src Input 8-bit 3-channel image.
-@param dst Input 8-bit 3-channel image.
-@param mask Input 8-bit 1 or 3-channel image.
-@param p Point in dst image where object is placed.
-@param blend Output image with the same size and type as dst.
-@param flags Cloning method that could be one of the following:
--   **NORMAL_CLONE** The power of the method is fully expressed when inserting objects with
-complex outlines into a new background
--   **MIXED_CLONE** The classic method, color-based selection and alpha masking might be time
-consuming and often leaves an undesirable halo. Seamless cloning, even averaged with the
-original image, is not effective. Mixed seamless cloning based on a loose selection proves
-effective.
--   **MONOCHROME_TRANSFER** Monochrome transfer allows the user to easily replace certain features of
-one object by alternative features.
- */
-CV_EXPORTS_W void seamlessClone( InputArray src, InputArray dst, InputArray mask, Point p,
-        OutputArray blend, int flags);
-
-/** @brief Given an original color image, two differently colored versions of this image can be mixed
-seamlessly.
-
-@param src Input 8-bit 3-channel image.
-@param mask Input 8-bit 1 or 3-channel image.
-@param dst Output image with the same size and type as src .
-@param red_mul R-channel multiply factor.
-@param green_mul G-channel multiply factor.
-@param blue_mul B-channel multiply factor.
-
-Multiplication factor is between .5 to 2.5.
- */
-CV_EXPORTS_W void colorChange(InputArray src, InputArray mask, OutputArray dst, float red_mul = 1.0f,
-        float green_mul = 1.0f, float blue_mul = 1.0f);
-
-/** @brief Applying an appropriate non-linear transformation to the gradient field inside the selection and
-then integrating back with a Poisson solver, modifies locally the apparent illumination of an image.
-
-@param src Input 8-bit 3-channel image.
-@param mask Input 8-bit 1 or 3-channel image.
-@param dst Output image with the same size and type as src.
-@param alpha Value ranges between 0-2.
-@param beta Value ranges between 0-2.
-
-This is useful to highlight under-exposed foreground objects or to reduce specular reflections.
- */
-CV_EXPORTS_W void illuminationChange(InputArray src, InputArray mask, OutputArray dst,
-        float alpha = 0.2f, float beta = 0.4f);
-
-/** @brief By retaining only the gradients at edge locations, before integrating with the Poisson solver, one
-washes out the texture of the selected region, giving its contents a flat aspect. Here Canny Edge
-Detector is used.
-
-@param src Input 8-bit 3-channel image.
-@param mask Input 8-bit 1 or 3-channel image.
-@param dst Output image with the same size and type as src.
-@param low_threshold Range from 0 to 100.
-@param high_threshold Value \> 100.
-@param kernel_size The size of the Sobel kernel to be used.
-
-**NOTE:**
-
-The algorithm assumes that the color of the source image is close to that of the destination. This
-assumption means that when the colors don't match, the source image color gets tinted toward the
-color of the destination image.
- */
-CV_EXPORTS_W void textureFlattening(InputArray src, InputArray mask, OutputArray dst,
-        float low_threshold = 30, float high_threshold = 45,
-        int kernel_size = 3);
-
-//! @} photo_clone
-
-//! @addtogroup photo_render
-//! @{
-
-/** @brief Filtering is the fundamental operation in image and video processing. Edge-preserving smoothing
-filters are used in many different applications @cite EM11 .
-
-@param src Input 8-bit 3-channel image.
-@param dst Output 8-bit 3-channel image.
-@param flags Edge preserving filters:
--   **RECURS_FILTER** = 1
--   **NORMCONV_FILTER** = 2
-@param sigma_s Range between 0 to 200.
-@param sigma_r Range between 0 to 1.
- */
-CV_EXPORTS_W void edgePreservingFilter(InputArray src, OutputArray dst, int flags = 1,
-        float sigma_s = 60, float sigma_r = 0.4f);
-
-/** @brief This filter enhances the details of a particular image.
-
-@param src Input 8-bit 3-channel image.
-@param dst Output image with the same size and type as src.
-@param sigma_s Range between 0 to 200.
-@param sigma_r Range between 0 to 1.
- */
-CV_EXPORTS_W void detailEnhance(InputArray src, OutputArray dst, float sigma_s = 10,
-        float sigma_r = 0.15f);
-
-/** @example npr_demo.cpp
-An example using non-photorealistic line drawing functions
-*/
-/** @brief Pencil-like non-photorealistic line drawing
-
-@param src Input 8-bit 3-channel image.
-@param dst1 Output 8-bit 1-channel image.
-@param dst2 Output image with the same size and type as src.
-@param sigma_s Range between 0 to 200.
-@param sigma_r Range between 0 to 1.
-@param shade_factor Range between 0 to 0.1.
- */
-CV_EXPORTS_W void pencilSketch(InputArray src, OutputArray dst1, OutputArray dst2,
-        float sigma_s = 60, float sigma_r = 0.07f, float shade_factor = 0.02f);
-
-/** @brief Stylization aims to produce digital imagery with a wide variety of effects not focused on
-photorealism. Edge-aware filters are ideal for stylization, as they can abstract regions of low
-contrast while preserving, or enhancing, high-contrast features.
-
-@param src Input 8-bit 3-channel image.
-@param dst Output image with the same size and type as src.
-@param sigma_s Range between 0 to 200.
-@param sigma_r Range between 0 to 1.
- */
-CV_EXPORTS_W void stylization(InputArray src, OutputArray dst, float sigma_s = 60,
-        float sigma_r = 0.45f);
-
-//! @} photo_render
-
-//! @} photo
-
-} // cv
-
-#ifndef DISABLE_OPENCV_24_COMPATIBILITY
-#include "opencv2/photo/photo_c.h"
-#endif
-
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/photo/cuda.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/photo/cuda.hpp
deleted file mode 100755
index a2f3816..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/photo/cuda.hpp
+++ /dev/null
@@ -1,132 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2008-2012, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_PHOTO_CUDA_HPP
-#define OPENCV_PHOTO_CUDA_HPP
-
-#include "opencv2/core/cuda.hpp"
-
-namespace cv { namespace cuda {
-
-//! @addtogroup photo_denoise
-//! @{
-
-/** @brief Performs pure non local means denoising without any simplification, and thus it is not fast.
-
-@param src Source image. Supports only CV_8UC1, CV_8UC2 and CV_8UC3.
-@param dst Destination image.
-@param h Filter sigma regulating filter strength for color.
-@param search_window Size of search window.
-@param block_size Size of block used for computing weights.
-@param borderMode Border type. See borderInterpolate for details. BORDER_REFLECT101 ,
-BORDER_REPLICATE , BORDER_CONSTANT , BORDER_REFLECT and BORDER_WRAP are supported for now.
-@param stream Stream for the asynchronous version.
-
-@sa
-   fastNlMeansDenoising
- */
-CV_EXPORTS void nonLocalMeans(InputArray src, OutputArray dst,
-                              float h,
-                              int search_window = 21,
-                              int block_size = 7,
-                              int borderMode = BORDER_DEFAULT,
-                              Stream& stream = Stream::Null());
-
-/** @brief Perform image denoising using Non-local Means Denoising algorithm
-<http://www.ipol.im/pub/algo/bcm_non_local_means_denoising> with several computational
-optimizations. Noise expected to be a gaussian white noise
-
-@param src Input 8-bit 1-channel, 2-channel or 3-channel image.
-@param dst Output image with the same size and type as src .
-@param h Parameter regulating filter strength. Big h value perfectly removes noise but also
-removes image details, smaller h value preserves details but also preserves some noise
-@param search_window Size in pixels of the window that is used to compute weighted average for
-given pixel. Should be odd. Affect performance linearly: greater search_window - greater
-denoising time. Recommended value 21 pixels
-@param block_size Size in pixels of the template patch that is used to compute weights. Should be
-odd. Recommended value 7 pixels
-@param stream Stream for the asynchronous invocations.
-
-This function expected to be applied to grayscale images. For colored images look at
-FastNonLocalMeansDenoising::labMethod.
-
-@sa
-   fastNlMeansDenoising
- */
-CV_EXPORTS void fastNlMeansDenoising(InputArray src, OutputArray dst,
-                                     float h,
-                                     int search_window = 21,
-                                     int block_size = 7,
-                                     Stream& stream = Stream::Null());
-
-/** @brief Modification of fastNlMeansDenoising function for colored images
-
-@param src Input 8-bit 3-channel image.
-@param dst Output image with the same size and type as src .
-@param h_luminance Parameter regulating filter strength. Big h value perfectly removes noise but
-also removes image details, smaller h value preserves details but also preserves some noise
-@param photo_render float The same as h but for color components. For most images value equals 10 will be
-enough to remove colored noise and do not distort colors
-@param search_window Size in pixels of the window that is used to compute weighted average for
-given pixel. Should be odd. Affect performance linearly: greater search_window - greater
-denoising time. Recommended value 21 pixels
-@param block_size Size in pixels of the template patch that is used to compute weights. Should be
-odd. Recommended value 7 pixels
-@param stream Stream for the asynchronous invocations.
-
-The function converts image to CIELAB colorspace and then separately denoise L and AB components
-with given h parameters using FastNonLocalMeansDenoising::simpleMethod function.
-
-@sa
-   fastNlMeansDenoisingColored
- */
-CV_EXPORTS void fastNlMeansDenoisingColored(InputArray src, OutputArray dst,
-                                            float h_luminance, float photo_render,
-                                            int search_window = 21,
-                                            int block_size = 7,
-                                            Stream& stream = Stream::Null());
-
-//! @} photo
-
-}} // namespace cv { namespace cuda {
-
-#endif /* OPENCV_PHOTO_CUDA_HPP */
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/photo/photo.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/photo/photo.hpp
deleted file mode 100755
index 8af5e9f..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/photo/photo.hpp
+++ /dev/null
@@ -1,48 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifdef __OPENCV_BUILD
-#error this is a compatibility header which should not be used inside the OpenCV library
-#endif
-
-#include "opencv2/photo.hpp"
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/photo/photo_c.h b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/photo/photo_c.h
deleted file mode 100755
index cd623c1..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/photo/photo_c.h
+++ /dev/null
@@ -1,74 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2008-2012, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_PHOTO_C_H
-#define OPENCV_PHOTO_C_H
-
-#include "opencv2/core/core_c.h"
-
-#ifdef __cplusplus
-extern "C" {
-#endif
-
-/** @addtogroup photo_c
-  @{
-  */
-
-/* Inpainting algorithms */
-enum InpaintingModes
-{
-    CV_INPAINT_NS      =0,
-    CV_INPAINT_TELEA   =1
-};
-
-
-/* Inpaints the selected region in the image */
-CVAPI(void) cvInpaint( const CvArr* src, const CvArr* inpaint_mask,
-                       CvArr* dst, double inpaintRange, int flags );
-
-/** @} */
-
-#ifdef __cplusplus
-} //extern "C"
-#endif
-
-#endif //OPENCV_PHOTO_C_H
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/shape.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/shape.hpp
deleted file mode 100755
index f302b6b..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/shape.hpp
+++ /dev/null
@@ -1,57 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009-2012, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_SHAPE_HPP
-#define OPENCV_SHAPE_HPP
-
-#include "opencv2/shape/emdL1.hpp"
-#include "opencv2/shape/shape_transformer.hpp"
-#include "opencv2/shape/hist_cost.hpp"
-#include "opencv2/shape/shape_distance.hpp"
-
-/**
-  @defgroup shape Shape Distance and Matching
- */
-
-#endif
-
-/* End of file. */
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/shape/emdL1.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/shape/emdL1.hpp
deleted file mode 100755
index a15d68c..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/shape/emdL1.hpp
+++ /dev/null
@@ -1,72 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009-2012, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_EMD_L1_HPP
-#define OPENCV_EMD_L1_HPP
-
-#include "opencv2/core.hpp"
-
-namespace cv
-{
-/****************************************************************************************\
-*                                   EMDL1 Function                                      *
-\****************************************************************************************/
-
-//! @addtogroup shape
-//! @{
-
-/** @brief Computes the "minimal work" distance between two weighted point configurations base on the papers
-"EMD-L1: An efficient and Robust Algorithm for comparing histogram-based descriptors", by Haibin
-Ling and Kazunori Okuda; and "The Earth Mover's Distance is the Mallows Distance: Some Insights from
-Statistics", by Elizaveta Levina and Peter Bickel.
-
-@param signature1 First signature, a single column floating-point matrix. Each row is the value of
-the histogram in each bin.
-@param signature2 Second signature of the same format and size as signature1.
- */
-CV_EXPORTS float EMDL1(InputArray signature1, InputArray signature2);
-
-//! @}
-
-}//namespace cv
-
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/shape/hist_cost.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/shape/hist_cost.hpp
deleted file mode 100755
index 21d0d68..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/shape/hist_cost.hpp
+++ /dev/null
@@ -1,111 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_HIST_COST_HPP
-#define OPENCV_HIST_COST_HPP
-
-#include "opencv2/imgproc.hpp"
-
-namespace cv
-{
-
-//! @addtogroup shape
-//! @{
-
-/** @brief Abstract base class for histogram cost algorithms.
- */
-class CV_EXPORTS_W HistogramCostExtractor : public Algorithm
-{
-public:
-    CV_WRAP virtual void buildCostMatrix(InputArray descriptors1, InputArray descriptors2, OutputArray costMatrix) = 0;
-
-    CV_WRAP virtual void setNDummies(int nDummies) = 0;
-    CV_WRAP virtual int getNDummies() const = 0;
-
-    CV_WRAP virtual void setDefaultCost(float defaultCost) = 0;
-    CV_WRAP virtual float getDefaultCost() const = 0;
-};
-
-/** @brief A norm based cost extraction. :
- */
-class CV_EXPORTS_W NormHistogramCostExtractor : public HistogramCostExtractor
-{
-public:
-    CV_WRAP virtual void setNormFlag(int flag) = 0;
-    CV_WRAP virtual int getNormFlag() const = 0;
-};
-
-CV_EXPORTS_W Ptr<HistogramCostExtractor>
-    createNormHistogramCostExtractor(int flag=DIST_L2, int nDummies=25, float defaultCost=0.2f);
-
-/** @brief An EMD based cost extraction. :
- */
-class CV_EXPORTS_W EMDHistogramCostExtractor : public HistogramCostExtractor
-{
-public:
-    CV_WRAP virtual void setNormFlag(int flag) = 0;
-    CV_WRAP virtual int getNormFlag() const = 0;
-};
-
-CV_EXPORTS_W Ptr<HistogramCostExtractor>
-    createEMDHistogramCostExtractor(int flag=DIST_L2, int nDummies=25, float defaultCost=0.2f);
-
-/** @brief An Chi based cost extraction. :
- */
-class CV_EXPORTS_W ChiHistogramCostExtractor : public HistogramCostExtractor
-{};
-
-CV_EXPORTS_W Ptr<HistogramCostExtractor> createChiHistogramCostExtractor(int nDummies=25, float defaultCost=0.2f);
-
-/** @brief An EMD-L1 based cost extraction. :
- */
-class CV_EXPORTS_W EMDL1HistogramCostExtractor : public HistogramCostExtractor
-{};
-
-CV_EXPORTS_W Ptr<HistogramCostExtractor>
-    createEMDL1HistogramCostExtractor(int nDummies=25, float defaultCost=0.2f);
-
-//! @}
-
-} // cv
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/shape/shape.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/shape/shape.hpp
deleted file mode 100755
index 5c4da3c..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/shape/shape.hpp
+++ /dev/null
@@ -1,48 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifdef __OPENCV_BUILD
-#error this is a compatibility header which should not be used inside the OpenCV library
-#endif
-
-#include "opencv2/shape.hpp"
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/shape/shape_distance.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/shape/shape_distance.hpp
deleted file mode 100755
index 3a778f0..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/shape/shape_distance.hpp
+++ /dev/null
@@ -1,227 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_SHAPE_SHAPE_DISTANCE_HPP
-#define OPENCV_SHAPE_SHAPE_DISTANCE_HPP
-#include "opencv2/core.hpp"
-#include "opencv2/shape/hist_cost.hpp"
-#include "opencv2/shape/shape_transformer.hpp"
-
-namespace cv
-{
-
-//! @addtogroup shape
-//! @{
-
-/** @example shape_example.cpp
-An example using shape distance algorithm
-*/
-/** @brief Abstract base class for shape distance algorithms.
- */
-class CV_EXPORTS_W ShapeDistanceExtractor : public Algorithm
-{
-public:
-    /** @brief Compute the shape distance between two shapes defined by its contours.
-
-    @param contour1 Contour defining first shape.
-    @param contour2 Contour defining second shape.
-     */
-    CV_WRAP virtual float computeDistance(InputArray contour1, InputArray contour2) = 0;
-};
-
-/***********************************************************************************/
-/***********************************************************************************/
-/***********************************************************************************/
-/** @brief Implementation of the Shape Context descriptor and matching algorithm
-
-proposed by Belongie et al. in "Shape Matching and Object Recognition Using Shape Contexts" (PAMI
-2002). This implementation is packaged in a generic scheme, in order to allow you the
-implementation of the common variations of the original pipeline.
-*/
-class CV_EXPORTS_W ShapeContextDistanceExtractor : public ShapeDistanceExtractor
-{
-public:
-    /** @brief Establish the number of angular bins for the Shape Context Descriptor used in the shape matching
-    pipeline.
-
-    @param nAngularBins The number of angular bins in the shape context descriptor.
-     */
-    CV_WRAP virtual void setAngularBins(int nAngularBins) = 0;
-    CV_WRAP virtual int getAngularBins() const = 0;
-
-    /** @brief Establish the number of radial bins for the Shape Context Descriptor used in the shape matching
-    pipeline.
-
-    @param nRadialBins The number of radial bins in the shape context descriptor.
-     */
-    CV_WRAP virtual void setRadialBins(int nRadialBins) = 0;
-    CV_WRAP virtual int getRadialBins() const = 0;
-
-    /** @brief Set the inner radius of the shape context descriptor.
-
-    @param innerRadius The value of the inner radius.
-     */
-    CV_WRAP virtual void setInnerRadius(float innerRadius) = 0;
-    CV_WRAP virtual float getInnerRadius() const = 0;
-
-    /** @brief Set the outer radius of the shape context descriptor.
-
-    @param outerRadius The value of the outer radius.
-     */
-    CV_WRAP virtual void setOuterRadius(float outerRadius) = 0;
-    CV_WRAP virtual float getOuterRadius() const = 0;
-
-    CV_WRAP virtual void setRotationInvariant(bool rotationInvariant) = 0;
-    CV_WRAP virtual bool getRotationInvariant() const = 0;
-
-    /** @brief Set the weight of the shape context distance in the final value of the shape distance. The shape
-    context distance between two shapes is defined as the symmetric sum of shape context matching costs
-    over best matching points. The final value of the shape distance is a user-defined linear
-    combination of the shape context distance, an image appearance distance, and a bending energy.
-
-    @param shapeContextWeight The weight of the shape context distance in the final distance value.
-     */
-    CV_WRAP virtual void setShapeContextWeight(float shapeContextWeight) = 0;
-    CV_WRAP virtual float getShapeContextWeight() const = 0;
-
-    /** @brief Set the weight of the Image Appearance cost in the final value of the shape distance. The image
-    appearance cost is defined as the sum of squared brightness differences in Gaussian windows around
-    corresponding image points. The final value of the shape distance is a user-defined linear
-    combination of the shape context distance, an image appearance distance, and a bending energy. If
-    this value is set to a number different from 0, is mandatory to set the images that correspond to
-    each shape.
-
-    @param imageAppearanceWeight The weight of the appearance cost in the final distance value.
-     */
-    CV_WRAP virtual void setImageAppearanceWeight(float imageAppearanceWeight) = 0;
-    CV_WRAP virtual float getImageAppearanceWeight() const = 0;
-
-    /** @brief Set the weight of the Bending Energy in the final value of the shape distance. The bending energy
-    definition depends on what transformation is being used to align the shapes. The final value of the
-    shape distance is a user-defined linear combination of the shape context distance, an image
-    appearance distance, and a bending energy.
-
-    @param bendingEnergyWeight The weight of the Bending Energy in the final distance value.
-     */
-    CV_WRAP virtual void setBendingEnergyWeight(float bendingEnergyWeight) = 0;
-    CV_WRAP virtual float getBendingEnergyWeight() const = 0;
-
-    /** @brief Set the images that correspond to each shape. This images are used in the calculation of the Image
-    Appearance cost.
-
-    @param image1 Image corresponding to the shape defined by contours1.
-    @param image2 Image corresponding to the shape defined by contours2.
-     */
-    CV_WRAP virtual void setImages(InputArray image1, InputArray image2) = 0;
-    CV_WRAP virtual void getImages(OutputArray image1, OutputArray image2) const = 0;
-
-    CV_WRAP virtual void setIterations(int iterations) = 0;
-    CV_WRAP virtual int getIterations() const = 0;
-
-    /** @brief Set the algorithm used for building the shape context descriptor cost matrix.
-
-    @param comparer Smart pointer to a HistogramCostExtractor, an algorithm that defines the cost
-    matrix between descriptors.
-     */
-    CV_WRAP virtual void setCostExtractor(Ptr<HistogramCostExtractor> comparer) = 0;
-    CV_WRAP virtual Ptr<HistogramCostExtractor> getCostExtractor() const = 0;
-
-    /** @brief Set the value of the standard deviation for the Gaussian window for the image appearance cost.
-
-    @param sigma Standard Deviation.
-     */
-    CV_WRAP virtual void setStdDev(float sigma) = 0;
-    CV_WRAP virtual float getStdDev() const = 0;
-
-    /** @brief Set the algorithm used for aligning the shapes.
-
-    @param transformer Smart pointer to a ShapeTransformer, an algorithm that defines the aligning
-    transformation.
-     */
-    CV_WRAP virtual void setTransformAlgorithm(Ptr<ShapeTransformer> transformer) = 0;
-    CV_WRAP virtual Ptr<ShapeTransformer> getTransformAlgorithm() const = 0;
-};
-
-/* Complete constructor */
-CV_EXPORTS_W Ptr<ShapeContextDistanceExtractor>
-    createShapeContextDistanceExtractor(int nAngularBins=12, int nRadialBins=4,
-                                        float innerRadius=0.2f, float outerRadius=2, int iterations=3,
-                                        const Ptr<HistogramCostExtractor> &comparer = createChiHistogramCostExtractor(),
-                                        const Ptr<ShapeTransformer> &transformer = createThinPlateSplineShapeTransformer());
-
-/***********************************************************************************/
-/***********************************************************************************/
-/***********************************************************************************/
-/** @brief A simple Hausdorff distance measure between shapes defined by contours
-
-according to the paper "Comparing Images using the Hausdorff distance." by D.P. Huttenlocher, G.A.
-Klanderman, and W.J. Rucklidge. (PAMI 1993). :
- */
-class CV_EXPORTS_W HausdorffDistanceExtractor : public ShapeDistanceExtractor
-{
-public:
-    /** @brief Set the norm used to compute the Hausdorff value between two shapes. It can be L1 or L2 norm.
-
-    @param distanceFlag Flag indicating which norm is used to compute the Hausdorff distance
-    (NORM_L1, NORM_L2).
-     */
-    CV_WRAP virtual void setDistanceFlag(int distanceFlag) = 0;
-    CV_WRAP virtual int getDistanceFlag() const = 0;
-
-    /** @brief This method sets the rank proportion (or fractional value) that establish the Kth ranked value of
-    the partial Hausdorff distance. Experimentally had been shown that 0.6 is a good value to compare
-    shapes.
-
-    @param rankProportion fractional value (between 0 and 1).
-     */
-    CV_WRAP virtual void setRankProportion(float rankProportion) = 0;
-    CV_WRAP virtual float getRankProportion() const = 0;
-};
-
-/* Constructor */
-CV_EXPORTS_W Ptr<HausdorffDistanceExtractor> createHausdorffDistanceExtractor(int distanceFlag=cv::NORM_L2, float rankProp=0.6f);
-
-//! @}
-
-} // cv
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/shape/shape_transformer.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/shape/shape_transformer.hpp
deleted file mode 100755
index 3c3ce20..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/shape/shape_transformer.hpp
+++ /dev/null
@@ -1,132 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_SHAPE_SHAPE_TRANSFORM_HPP
-#define OPENCV_SHAPE_SHAPE_TRANSFORM_HPP
-#include <vector>
-#include "opencv2/core.hpp"
-#include "opencv2/imgproc.hpp"
-
-namespace cv
-{
-
-//! @addtogroup shape
-//! @{
-
-/** @brief Abstract base class for shape transformation algorithms.
- */
-class CV_EXPORTS_W ShapeTransformer : public Algorithm
-{
-public:
-    /** @brief Estimate the transformation parameters of the current transformer algorithm, based on point matches.
-
-    @param transformingShape Contour defining first shape.
-    @param targetShape Contour defining second shape (Target).
-    @param matches Standard vector of Matches between points.
-     */
-    CV_WRAP virtual void estimateTransformation(InputArray transformingShape, InputArray targetShape,
-                                                 std::vector<DMatch>& matches) = 0;
-
-    /** @brief Apply a transformation, given a pre-estimated transformation parameters.
-
-    @param input Contour (set of points) to apply the transformation.
-    @param output Output contour.
-     */
-    CV_WRAP virtual float applyTransformation(InputArray input, OutputArray output=noArray()) = 0;
-
-    /** @brief Apply a transformation, given a pre-estimated transformation parameters, to an Image.
-
-    @param transformingImage Input image.
-    @param output Output image.
-    @param flags Image interpolation method.
-    @param borderMode border style.
-    @param borderValue border value.
-     */
-    CV_WRAP virtual void warpImage(InputArray transformingImage, OutputArray output,
-                                   int flags=INTER_LINEAR, int borderMode=BORDER_CONSTANT,
-                                   const Scalar& borderValue=Scalar()) const = 0;
-};
-
-/***********************************************************************************/
-/***********************************************************************************/
-
-/** @brief Definition of the transformation
-
-occupied in the paper "Principal Warps: Thin-Plate Splines and Decomposition of Deformations", by
-F.L. Bookstein (PAMI 1989). :
- */
-class CV_EXPORTS_W ThinPlateSplineShapeTransformer : public ShapeTransformer
-{
-public:
-    /** @brief Set the regularization parameter for relaxing the exact interpolation requirements of the TPS
-    algorithm.
-
-    @param beta value of the regularization parameter.
-     */
-    CV_WRAP virtual void setRegularizationParameter(double beta) = 0;
-    CV_WRAP virtual double getRegularizationParameter() const = 0;
-};
-
-/** Complete constructor */
-CV_EXPORTS_W Ptr<ThinPlateSplineShapeTransformer>
-    createThinPlateSplineShapeTransformer(double regularizationParameter=0);
-
-/***********************************************************************************/
-/***********************************************************************************/
-
-/** @brief Wrapper class for the OpenCV Affine Transformation algorithm. :
- */
-class CV_EXPORTS_W AffineTransformer : public ShapeTransformer
-{
-public:
-    CV_WRAP virtual void setFullAffine(bool fullAffine) = 0;
-    CV_WRAP virtual bool getFullAffine() const = 0;
-};
-
-/** Complete constructor */
-CV_EXPORTS_W Ptr<AffineTransformer> createAffineTransformer(bool fullAffine);
-
-//! @}
-
-} // cv
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/stitching.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/stitching.hpp
deleted file mode 100755
index 8b1bcc1..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/stitching.hpp
+++ /dev/null
@@ -1,321 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_STITCHING_STITCHER_HPP
-#define OPENCV_STITCHING_STITCHER_HPP
-
-#include "opencv2/core.hpp"
-#include "opencv2/features2d.hpp"
-#include "opencv2/stitching/warpers.hpp"
-#include "opencv2/stitching/detail/matchers.hpp"
-#include "opencv2/stitching/detail/motion_estimators.hpp"
-#include "opencv2/stitching/detail/exposure_compensate.hpp"
-#include "opencv2/stitching/detail/seam_finders.hpp"
-#include "opencv2/stitching/detail/blenders.hpp"
-#include "opencv2/stitching/detail/camera.hpp"
-
-
-#if defined(Status)
-#  warning Detected X11 'Status' macro definition, it can cause build conflicts. Please, include this header before any X11 headers.
-#endif
-
-
-/**
-@defgroup stitching Images stitching
-
-This figure illustrates the stitching module pipeline implemented in the Stitcher class. Using that
-class it's possible to configure/remove some steps, i.e. adjust the stitching pipeline according to
-the particular needs. All building blocks from the pipeline are available in the detail namespace,
-one can combine and use them separately.
-
-The implemented stitching pipeline is very similar to the one proposed in @cite BL07 .
-
-![stitching pipeline](StitchingPipeline.jpg)
-
-Camera models
--------------
-
-There are currently 2 camera models implemented in stitching pipeline.
-
-- _Homography model_ expecting perspective transformations between images
-  implemented in @ref cv::detail::BestOf2NearestMatcher cv::detail::HomographyBasedEstimator
-  cv::detail::BundleAdjusterReproj cv::detail::BundleAdjusterRay
-- _Affine model_ expecting affine transformation with 6 DOF or 4 DOF implemented in
-  @ref cv::detail::AffineBestOf2NearestMatcher cv::detail::AffineBasedEstimator
-  cv::detail::BundleAdjusterAffine cv::detail::BundleAdjusterAffinePartial cv::AffineWarper
-
-Homography model is useful for creating photo panoramas captured by camera,
-while affine-based model can be used to stitch scans and object captured by
-specialized devices. Use @ref cv::Stitcher::create to get preconfigured pipeline for one
-of those models.
-
-@note
-Certain detailed settings of @ref cv::Stitcher might not make sense. Especially
-you should not mix classes implementing affine model and classes implementing
-Homography model, as they work with different transformations.
-
-@{
-    @defgroup stitching_match Features Finding and Images Matching
-    @defgroup stitching_rotation Rotation Estimation
-    @defgroup stitching_autocalib Autocalibration
-    @defgroup stitching_warp Images Warping
-    @defgroup stitching_seam Seam Estimation
-    @defgroup stitching_exposure Exposure Compensation
-    @defgroup stitching_blend Image Blenders
-@}
-  */
-
-namespace cv {
-
-//! @addtogroup stitching
-//! @{
-
-/** @brief High level image stitcher.
-
-It's possible to use this class without being aware of the entire stitching pipeline. However, to
-be able to achieve higher stitching stability and quality of the final images at least being
-familiar with the theory is recommended.
-
-@note
-   -   A basic example on image stitching can be found at
-        opencv_source_code/samples/cpp/stitching.cpp
-    -   A detailed example on image stitching can be found at
-        opencv_source_code/samples/cpp/stitching_detailed.cpp
- */
-class CV_EXPORTS_W Stitcher
-{
-public:
-    enum { ORIG_RESOL = -1 };
-    enum Status
-    {
-        OK = 0,
-        ERR_NEED_MORE_IMGS = 1,
-        ERR_HOMOGRAPHY_EST_FAIL = 2,
-        ERR_CAMERA_PARAMS_ADJUST_FAIL = 3
-    };
-    enum Mode
-    {
-        /** Mode for creating photo panoramas. Expects images under perspective
-        transformation and projects resulting pano to sphere.
-
-        @sa detail::BestOf2NearestMatcher SphericalWarper
-        */
-        PANORAMA = 0,
-        /** Mode for composing scans. Expects images under affine transformation does
-        not compensate exposure by default.
-
-        @sa detail::AffineBestOf2NearestMatcher AffineWarper
-        */
-        SCANS = 1,
-
-    };
-
-   // Stitcher() {}
-    /** @brief Creates a stitcher with the default parameters.
-
-    @param try_use_gpu Flag indicating whether GPU should be used whenever it's possible.
-    @return Stitcher class instance.
-     */
-    static Stitcher createDefault(bool try_use_gpu = false);
-    /** @brief Creates a Stitcher configured in one of the stitching modes.
-
-    @param mode Scenario for stitcher operation. This is usually determined by source of images
-    to stitch and their transformation. Default parameters will be chosen for operation in given
-    scenario.
-    @param try_use_gpu Flag indicating whether GPU should be used whenever it's possible.
-    @return Stitcher class instance.
-     */
-    static Ptr<Stitcher> create(Mode mode = PANORAMA, bool try_use_gpu = false);
-
-    CV_WRAP double registrationResol() const { return registr_resol_; }
-    CV_WRAP void setRegistrationResol(double resol_mpx) { registr_resol_ = resol_mpx; }
-
-    CV_WRAP double seamEstimationResol() const { return seam_est_resol_; }
-    CV_WRAP void setSeamEstimationResol(double resol_mpx) { seam_est_resol_ = resol_mpx; }
-
-    CV_WRAP double compositingResol() const { return compose_resol_; }
-    CV_WRAP void setCompositingResol(double resol_mpx) { compose_resol_ = resol_mpx; }
-
-    CV_WRAP double panoConfidenceThresh() const { return conf_thresh_; }
-    CV_WRAP void setPanoConfidenceThresh(double conf_thresh) { conf_thresh_ = conf_thresh; }
-
-    CV_WRAP bool waveCorrection() const { return do_wave_correct_; }
-    CV_WRAP void setWaveCorrection(bool flag) { do_wave_correct_ = flag; }
-
-    detail::WaveCorrectKind waveCorrectKind() const { return wave_correct_kind_; }
-    void setWaveCorrectKind(detail::WaveCorrectKind kind) { wave_correct_kind_ = kind; }
-
-    Ptr<detail::FeaturesFinder> featuresFinder() { return features_finder_; }
-    const Ptr<detail::FeaturesFinder> featuresFinder() const { return features_finder_; }
-    void setFeaturesFinder(Ptr<detail::FeaturesFinder> features_finder)
-        { features_finder_ = features_finder; }
-
-    Ptr<detail::FeaturesMatcher> featuresMatcher() { return features_matcher_; }
-    const Ptr<detail::FeaturesMatcher> featuresMatcher() const { return features_matcher_; }
-    void setFeaturesMatcher(Ptr<detail::FeaturesMatcher> features_matcher)
-        { features_matcher_ = features_matcher; }
-
-    const cv::UMat& matchingMask() const { return matching_mask_; }
-    void setMatchingMask(const cv::UMat &mask)
-    {
-        CV_Assert(mask.type() == CV_8U && mask.cols == mask.rows);
-        matching_mask_ = mask.clone();
-    }
-
-    Ptr<detail::BundleAdjusterBase> bundleAdjuster() { return bundle_adjuster_; }
-    const Ptr<detail::BundleAdjusterBase> bundleAdjuster() const { return bundle_adjuster_; }
-    void setBundleAdjuster(Ptr<detail::BundleAdjusterBase> bundle_adjuster)
-        { bundle_adjuster_ = bundle_adjuster; }
-
-    /* TODO OpenCV ABI 4.x
-    Ptr<detail::Estimator> estimator() { return estimator_; }
-    const Ptr<detail::Estimator> estimator() const { return estimator_; }
-    void setEstimator(Ptr<detail::Estimator> estimator)
-        { estimator_ = estimator; }
-    */
-
-    Ptr<WarperCreator> warper() { return warper_; }
-    const Ptr<WarperCreator> warper() const { return warper_; }
-    void setWarper(Ptr<WarperCreator> creator) { warper_ = creator; }
-
-    Ptr<detail::ExposureCompensator> exposureCompensator() { return exposure_comp_; }
-    const Ptr<detail::ExposureCompensator> exposureCompensator() const { return exposure_comp_; }
-    void setExposureCompensator(Ptr<detail::ExposureCompensator> exposure_comp)
-        { exposure_comp_ = exposure_comp; }
-
-    Ptr<detail::SeamFinder> seamFinder() { return seam_finder_; }
-    const Ptr<detail::SeamFinder> seamFinder() const { return seam_finder_; }
-    void setSeamFinder(Ptr<detail::SeamFinder> seam_finder) { seam_finder_ = seam_finder; }
-
-    Ptr<detail::Blender> blender() { return blender_; }
-    const Ptr<detail::Blender> blender() const { return blender_; }
-    void setBlender(Ptr<detail::Blender> b) { blender_ = b; }
-
-    /** @overload */
-    CV_WRAP Status estimateTransform(InputArrayOfArrays images);
-    /** @brief These functions try to match the given images and to estimate rotations of each camera.
-
-    @note Use the functions only if you're aware of the stitching pipeline, otherwise use
-    Stitcher::stitch.
-
-    @param images Input images.
-    @param rois Region of interest rectangles.
-    @return Status code.
-     */
-    Status estimateTransform(InputArrayOfArrays images, const std::vector<std::vector<Rect> > &rois);
-
-    /** @overload */
-    CV_WRAP Status composePanorama(OutputArray pano);
-    /** @brief These functions try to compose the given images (or images stored internally from the other function
-    calls) into the final pano under the assumption that the image transformations were estimated
-    before.
-
-    @note Use the functions only if you're aware of the stitching pipeline, otherwise use
-    Stitcher::stitch.
-
-    @param images Input images.
-    @param pano Final pano.
-    @return Status code.
-     */
-    Status composePanorama(InputArrayOfArrays images, OutputArray pano);
-
-    /** @overload */
-    CV_WRAP Status stitch(InputArrayOfArrays images, OutputArray pano);
-    /** @brief These functions try to stitch the given images.
-
-    @param images Input images.
-    @param rois Region of interest rectangles.
-    @param pano Final pano.
-    @return Status code.
-     */
-    Status stitch(InputArrayOfArrays images, const std::vector<std::vector<Rect> > &rois, OutputArray pano);
-
-    std::vector<int> component() const { return indices_; }
-    std::vector<detail::CameraParams> cameras() const { return cameras_; }
-    CV_WRAP double workScale() const { return work_scale_; }
-
-private:
-    //Stitcher() {}
-
-    Status matchImages();
-    Status estimateCameraParams();
-
-    double registr_resol_;
-    double seam_est_resol_;
-    double compose_resol_;
-    double conf_thresh_;
-    Ptr<detail::FeaturesFinder> features_finder_;
-    Ptr<detail::FeaturesMatcher> features_matcher_;
-    cv::UMat matching_mask_;
-    Ptr<detail::BundleAdjusterBase> bundle_adjuster_;
-    /* TODO OpenCV ABI 4.x
-    Ptr<detail::Estimator> estimator_;
-    */
-    bool do_wave_correct_;
-    detail::WaveCorrectKind wave_correct_kind_;
-    Ptr<WarperCreator> warper_;
-    Ptr<detail::ExposureCompensator> exposure_comp_;
-    Ptr<detail::SeamFinder> seam_finder_;
-    Ptr<detail::Blender> blender_;
-
-    std::vector<cv::UMat> imgs_;
-    std::vector<std::vector<cv::Rect> > rois_;
-    std::vector<cv::Size> full_img_sizes_;
-    std::vector<detail::ImageFeatures> features_;
-    std::vector<detail::MatchesInfo> pairwise_matches_;
-    std::vector<cv::UMat> seam_est_imgs_;
-    std::vector<int> indices_;
-    std::vector<detail::CameraParams> cameras_;
-    double work_scale_;
-    double seam_scale_;
-    double seam_work_aspect_;
-    double warped_image_scale_;
-};
-
-CV_EXPORTS_W Ptr<Stitcher> createStitcher(bool try_use_gpu = false);
-CV_EXPORTS_W Ptr<Stitcher> createStitcherScans(bool try_use_gpu = false);
-
-//! @} stitching
-
-} // namespace cv
-
-#endif // OPENCV_STITCHING_STITCHER_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/stitching/detail/autocalib.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/stitching/detail/autocalib.hpp
deleted file mode 100755
index 19705e2..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/stitching/detail/autocalib.hpp
+++ /dev/null
@@ -1,86 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_STITCHING_AUTOCALIB_HPP
-#define OPENCV_STITCHING_AUTOCALIB_HPP
-
-#include "opencv2/core.hpp"
-#include "matchers.hpp"
-
-namespace cv {
-namespace detail {
-
-//! @addtogroup stitching_autocalib
-//! @{
-
-/** @brief Tries to estimate focal lengths from the given homography under the assumption that the camera
-undergoes rotations around its centre only.
-
-@param H Homography.
-@param f0 Estimated focal length along X axis.
-@param f1 Estimated focal length along Y axis.
-@param f0_ok True, if f0 was estimated successfully, false otherwise.
-@param f1_ok True, if f1 was estimated successfully, false otherwise.
-
-See "Construction of Panoramic Image Mosaics with Global and Local Alignment"
-by Heung-Yeung Shum and Richard Szeliski.
- */
-void CV_EXPORTS focalsFromHomography(const Mat &H, double &f0, double &f1, bool &f0_ok, bool &f1_ok);
-
-/** @brief Estimates focal lengths for each given camera.
-
-@param features Features of images.
-@param pairwise_matches Matches between all image pairs.
-@param focals Estimated focal lengths for each camera.
- */
-void CV_EXPORTS estimateFocal(const std::vector<ImageFeatures> &features,
-                              const std::vector<MatchesInfo> &pairwise_matches,
-                              std::vector<double> &focals);
-
-bool CV_EXPORTS calibrateRotatingCamera(const std::vector<Mat> &Hs, Mat &K);
-
-//! @} stitching_autocalib
-
-} // namespace detail
-} // namespace cv
-
-#endif // OPENCV_STITCHING_AUTOCALIB_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/stitching/detail/blenders.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/stitching/detail/blenders.hpp
deleted file mode 100755
index 542f1e4..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/stitching/detail/blenders.hpp
+++ /dev/null
@@ -1,184 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_STITCHING_BLENDERS_HPP
-#define OPENCV_STITCHING_BLENDERS_HPP
-
-#if defined(NO)
-#  warning Detected Apple 'NO' macro definition, it can cause build conflicts. Please, include this header before any Apple headers.
-#endif
-
-#include "opencv2/core.hpp"
-#include "opencv2/core/cuda.hpp"
-
-namespace cv {
-namespace detail {
-
-//! @addtogroup stitching_blend
-//! @{
-
-/** @brief Base class for all blenders.
-
-Simple blender which puts one image over another
-*/
-class CV_EXPORTS Blender
-{
-public:
-    virtual ~Blender() {}
-
-    enum { NO, FEATHER, MULTI_BAND };
-    static Ptr<Blender> createDefault(int type, bool try_gpu = false);
-
-    /** @brief Prepares the blender for blending.
-
-    @param corners Source images top-left corners
-    @param sizes Source image sizes
-     */
-    void prepare(const std::vector<Point> &corners, const std::vector<Size> &sizes);
-    /** @overload */
-    virtual void prepare(Rect dst_roi);
-    /** @brief Processes the image.
-
-    @param img Source image
-    @param mask Source image mask
-    @param tl Source image top-left corners
-     */
-    virtual void feed(InputArray img, InputArray mask, Point tl);
-    /** @brief Blends and returns the final pano.
-
-    @param dst Final pano
-    @param dst_mask Final pano mask
-     */
-    virtual void blend(InputOutputArray dst, InputOutputArray dst_mask);
-
-protected:
-    UMat dst_, dst_mask_;
-    Rect dst_roi_;
-};
-
-/** @brief Simple blender which mixes images at its borders.
- */
-class CV_EXPORTS FeatherBlender : public Blender
-{
-public:
-    FeatherBlender(float sharpness = 0.02f);
-
-    float sharpness() const { return sharpness_; }
-    void setSharpness(float val) { sharpness_ = val; }
-
-    void prepare(Rect dst_roi) CV_OVERRIDE;
-    void feed(InputArray img, InputArray mask, Point tl) CV_OVERRIDE;
-    void blend(InputOutputArray dst, InputOutputArray dst_mask) CV_OVERRIDE;
-
-    //! Creates weight maps for fixed set of source images by their masks and top-left corners.
-    //! Final image can be obtained by simple weighting of the source images.
-    Rect createWeightMaps(const std::vector<UMat> &masks, const std::vector<Point> &corners,
-                          std::vector<UMat> &weight_maps);
-
-private:
-    float sharpness_;
-    UMat weight_map_;
-    UMat dst_weight_map_;
-};
-
-inline FeatherBlender::FeatherBlender(float _sharpness) { setSharpness(_sharpness); }
-
-/** @brief Blender which uses multi-band blending algorithm (see @cite BA83).
- */
-class CV_EXPORTS MultiBandBlender : public Blender
-{
-public:
-    MultiBandBlender(int try_gpu = false, int num_bands = 5, int weight_type = CV_32F);
-
-    int numBands() const { return actual_num_bands_; }
-    void setNumBands(int val) { actual_num_bands_ = val; }
-
-    void prepare(Rect dst_roi) CV_OVERRIDE;
-    void feed(InputArray img, InputArray mask, Point tl) CV_OVERRIDE;
-    void blend(InputOutputArray dst, InputOutputArray dst_mask) CV_OVERRIDE;
-
-private:
-    int actual_num_bands_, num_bands_;
-    std::vector<UMat> dst_pyr_laplace_;
-    std::vector<UMat> dst_band_weights_;
-    Rect dst_roi_final_;
-    bool can_use_gpu_;
-    int weight_type_; //CV_32F or CV_16S
-#if defined(HAVE_OPENCV_CUDAARITHM) && defined(HAVE_OPENCV_CUDAWARPING)
-    std::vector<cuda::GpuMat> gpu_dst_pyr_laplace_;
-    std::vector<cuda::GpuMat> gpu_dst_band_weights_;
-    std::vector<Point> gpu_tl_points_;
-    std::vector<cuda::GpuMat> gpu_imgs_with_border_;
-    std::vector<std::vector<cuda::GpuMat> > gpu_weight_pyr_gauss_vec_;
-    std::vector<std::vector<cuda::GpuMat> > gpu_src_pyr_laplace_vec_;
-    std::vector<std::vector<cuda::GpuMat> > gpu_ups_;
-    cuda::GpuMat gpu_dst_mask_;
-    cuda::GpuMat gpu_mask_;
-    cuda::GpuMat gpu_img_;
-    cuda::GpuMat gpu_weight_map_;
-    cuda::GpuMat gpu_add_mask_;
-    int gpu_feed_idx_;
-    bool gpu_initialized_;
-#endif
-};
-
-
-//////////////////////////////////////////////////////////////////////////////
-// Auxiliary functions
-
-void CV_EXPORTS normalizeUsingWeightMap(InputArray weight, InputOutputArray src);
-
-void CV_EXPORTS createWeightMap(InputArray mask, float sharpness, InputOutputArray weight);
-
-void CV_EXPORTS createLaplacePyr(InputArray img, int num_levels, std::vector<UMat>& pyr);
-void CV_EXPORTS createLaplacePyrGpu(InputArray img, int num_levels, std::vector<UMat>& pyr);
-
-// Restores source image
-void CV_EXPORTS restoreImageFromLaplacePyr(std::vector<UMat>& pyr);
-void CV_EXPORTS restoreImageFromLaplacePyrGpu(std::vector<UMat>& pyr);
-
-//! @}
-
-} // namespace detail
-} // namespace cv
-
-#endif // OPENCV_STITCHING_BLENDERS_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/stitching/detail/camera.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/stitching/detail/camera.hpp
deleted file mode 100755
index 07c6b5b..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/stitching/detail/camera.hpp
+++ /dev/null
@@ -1,78 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_STITCHING_CAMERA_HPP
-#define OPENCV_STITCHING_CAMERA_HPP
-
-#include "opencv2/core.hpp"
-
-namespace cv {
-namespace detail {
-
-//! @addtogroup stitching
-//! @{
-
-/** @brief Describes camera parameters.
-
-@note Translation is assumed to be zero during the whole stitching pipeline. :
- */
-struct CV_EXPORTS CameraParams
-{
-    CameraParams();
-    CameraParams(const CameraParams& other);
-    CameraParams& operator =(const CameraParams& other);
-    Mat K() const;
-
-    double focal; // Focal length
-    double aspect; // Aspect ratio
-    double ppx; // Principal point X
-    double ppy; // Principal point Y
-    Mat R; // Rotation
-    Mat t; // Translation
-};
-
-//! @}
-
-} // namespace detail
-} // namespace cv
-
-#endif // #ifndef OPENCV_STITCHING_CAMERA_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/stitching/detail/exposure_compensate.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/stitching/detail/exposure_compensate.hpp
deleted file mode 100755
index 6c99407..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/stitching/detail/exposure_compensate.hpp
+++ /dev/null
@@ -1,136 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_STITCHING_EXPOSURE_COMPENSATE_HPP
-#define OPENCV_STITCHING_EXPOSURE_COMPENSATE_HPP
-
-#if defined(NO)
-#  warning Detected Apple 'NO' macro definition, it can cause build conflicts. Please, include this header before any Apple headers.
-#endif
-
-#include "opencv2/core.hpp"
-
-namespace cv {
-namespace detail {
-
-//! @addtogroup stitching_exposure
-//! @{
-
-/** @brief Base class for all exposure compensators.
- */
-class CV_EXPORTS ExposureCompensator
-{
-public:
-    virtual ~ExposureCompensator() {}
-
-    enum { NO, GAIN, GAIN_BLOCKS };
-    static Ptr<ExposureCompensator> createDefault(int type);
-
-    /**
-    @param corners Source image top-left corners
-    @param images Source images
-    @param masks Image masks to update (second value in pair specifies the value which should be used
-    to detect where image is)
-     */
-    void feed(const std::vector<Point> &corners, const std::vector<UMat> &images,
-              const std::vector<UMat> &masks);
-    /** @overload */
-    virtual void feed(const std::vector<Point> &corners, const std::vector<UMat> &images,
-                      const std::vector<std::pair<UMat,uchar> > &masks) = 0;
-    /** @brief Compensate exposure in the specified image.
-
-    @param index Image index
-    @param corner Image top-left corner
-    @param image Image to process
-    @param mask Image mask
-     */
-    virtual void apply(int index, Point corner, InputOutputArray image, InputArray mask) = 0;
-};
-
-/** @brief Stub exposure compensator which does nothing.
- */
-class CV_EXPORTS NoExposureCompensator : public ExposureCompensator
-{
-public:
-    void feed(const std::vector<Point> &/*corners*/, const std::vector<UMat> &/*images*/,
-              const std::vector<std::pair<UMat,uchar> > &/*masks*/) CV_OVERRIDE { }
-    void apply(int /*index*/, Point /*corner*/, InputOutputArray /*image*/, InputArray /*mask*/) CV_OVERRIDE { }
-};
-
-/** @brief Exposure compensator which tries to remove exposure related artifacts by adjusting image
-intensities, see @cite BL07 and @cite WJ10 for details.
- */
-class CV_EXPORTS GainCompensator : public ExposureCompensator
-{
-public:
-    void feed(const std::vector<Point> &corners, const std::vector<UMat> &images,
-              const std::vector<std::pair<UMat,uchar> > &masks) CV_OVERRIDE;
-    void apply(int index, Point corner, InputOutputArray image, InputArray mask) CV_OVERRIDE;
-    std::vector<double> gains() const;
-
-private:
-    Mat_<double> gains_;
-};
-
-/** @brief Exposure compensator which tries to remove exposure related artifacts by adjusting image block
-intensities, see @cite UES01 for details.
- */
-class CV_EXPORTS BlocksGainCompensator : public ExposureCompensator
-{
-public:
-    BlocksGainCompensator(int bl_width = 32, int bl_height = 32)
-            : bl_width_(bl_width), bl_height_(bl_height) {}
-    void feed(const std::vector<Point> &corners, const std::vector<UMat> &images,
-              const std::vector<std::pair<UMat,uchar> > &masks) CV_OVERRIDE;
-    void apply(int index, Point corner, InputOutputArray image, InputArray mask) CV_OVERRIDE;
-
-private:
-    int bl_width_, bl_height_;
-    std::vector<UMat> gain_maps_;
-};
-
-//! @}
-
-} // namespace detail
-} // namespace cv
-
-#endif // OPENCV_STITCHING_EXPOSURE_COMPENSATE_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/stitching/detail/matchers.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/stitching/detail/matchers.hpp
deleted file mode 100755
index 4acebea..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/stitching/detail/matchers.hpp
+++ /dev/null
@@ -1,355 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_STITCHING_MATCHERS_HPP
-#define OPENCV_STITCHING_MATCHERS_HPP
-
-#include "opencv2/core.hpp"
-#include "opencv2/features2d.hpp"
-
-#include "opencv2/opencv_modules.hpp"
-
-#ifdef HAVE_OPENCV_XFEATURES2D
-#  include "opencv2/xfeatures2d/cuda.hpp"
-#endif
-
-namespace cv {
-namespace detail {
-
-//! @addtogroup stitching_match
-//! @{
-
-/** @brief Structure containing image keypoints and descriptors. */
-struct CV_EXPORTS ImageFeatures
-{
-    int img_idx;
-    Size img_size;
-    std::vector<KeyPoint> keypoints;
-    UMat descriptors;
-};
-
-/** @brief Feature finders base class */
-class CV_EXPORTS FeaturesFinder
-{
-public:
-    virtual ~FeaturesFinder() {}
-    /** @overload */
-    void operator ()(InputArray image, ImageFeatures &features);
-    /** @brief Finds features in the given image.
-
-    @param image Source image
-    @param features Found features
-    @param rois Regions of interest
-
-    @sa detail::ImageFeatures, Rect_
-    */
-    void operator ()(InputArray image, ImageFeatures &features, const std::vector<cv::Rect> &rois);
-    /** @brief Finds features in the given images in parallel.
-
-    @param images Source images
-    @param features Found features for each image
-    @param rois Regions of interest for each image
-
-    @sa detail::ImageFeatures, Rect_
-    */
-    void operator ()(InputArrayOfArrays images, std::vector<ImageFeatures> &features,
-                     const std::vector<std::vector<cv::Rect> > &rois);
-    /** @overload */
-    void operator ()(InputArrayOfArrays images, std::vector<ImageFeatures> &features);
-    /** @brief Frees unused memory allocated before if there is any. */
-    virtual void collectGarbage() {}
-
-    /* TODO OpenCV ABI 4.x
-    reimplement this as public method similar to FeaturesMatcher and remove private function hack
-    @return True, if it's possible to use the same finder instance in parallel, false otherwise
-    bool isThreadSafe() const { return is_thread_safe_; }
-    */
-
-protected:
-    /** @brief This method must implement features finding logic in order to make the wrappers
-    detail::FeaturesFinder::operator()_ work.
-
-    @param image Source image
-    @param features Found features
-
-    @sa detail::ImageFeatures */
-    virtual void find(InputArray image, ImageFeatures &features) = 0;
-    /** @brief uses dynamic_cast to determine thread-safety
-    @return True, if it's possible to use the same finder instance in parallel, false otherwise
-    */
-    bool isThreadSafe() const;
-};
-
-/** @brief SURF features finder.
-
-@sa detail::FeaturesFinder, SURF
-*/
-class CV_EXPORTS SurfFeaturesFinder : public FeaturesFinder
-{
-public:
-    SurfFeaturesFinder(double hess_thresh = 300., int num_octaves = 3, int num_layers = 4,
-                       int num_octaves_descr = /*4*/3, int num_layers_descr = /*2*/4);
-
-private:
-    void find(InputArray image, ImageFeatures &features) CV_OVERRIDE;
-
-    Ptr<FeatureDetector> detector_;
-    Ptr<DescriptorExtractor> extractor_;
-    Ptr<Feature2D> surf;
-};
-
-/** @brief ORB features finder. :
-
-@sa detail::FeaturesFinder, ORB
-*/
-class CV_EXPORTS OrbFeaturesFinder : public FeaturesFinder
-{
-public:
-    OrbFeaturesFinder(Size _grid_size = Size(3,1), int nfeatures=1500, float scaleFactor=1.3f, int nlevels=5);
-
-private:
-    void find(InputArray image, ImageFeatures &features) CV_OVERRIDE;
-
-    Ptr<ORB> orb;
-    Size grid_size;
-};
-
-/** @brief AKAZE features finder. :
-
-@sa detail::FeaturesFinder, AKAZE
-*/
-class CV_EXPORTS AKAZEFeaturesFinder : public detail::FeaturesFinder
-{
-public:
-    AKAZEFeaturesFinder(int descriptor_type = AKAZE::DESCRIPTOR_MLDB,
-                        int descriptor_size = 0,
-                        int descriptor_channels = 3,
-                        float threshold = 0.001f,
-                        int nOctaves = 4,
-                        int nOctaveLayers = 4,
-                        int diffusivity = KAZE::DIFF_PM_G2);
-
-private:
-    void find(InputArray image, ImageFeatures &features) CV_OVERRIDE;
-
-    Ptr<AKAZE> akaze;
-};
-
-#ifdef HAVE_OPENCV_XFEATURES2D
-class CV_EXPORTS SurfFeaturesFinderGpu : public FeaturesFinder
-{
-public:
-    SurfFeaturesFinderGpu(double hess_thresh = 300., int num_octaves = 3, int num_layers = 4,
-                          int num_octaves_descr = 4, int num_layers_descr = 2);
-
-    void collectGarbage() CV_OVERRIDE;
-
-private:
-    void find(InputArray image, ImageFeatures &features) CV_OVERRIDE;
-
-    cuda::GpuMat image_;
-    cuda::GpuMat gray_image_;
-    cuda::SURF_CUDA surf_;
-    cuda::GpuMat keypoints_;
-    cuda::GpuMat descriptors_;
-    int num_octaves_, num_layers_;
-    int num_octaves_descr_, num_layers_descr_;
-};
-#endif
-
-/** @brief Structure containing information about matches between two images.
-
-It's assumed that there is a transformation between those images. Transformation may be
-homography or affine transformation based on selected matcher.
-
-@sa detail::FeaturesMatcher
-*/
-struct CV_EXPORTS MatchesInfo
-{
-    MatchesInfo();
-    MatchesInfo(const MatchesInfo &other);
-    MatchesInfo& operator =(const MatchesInfo &other);
-
-    int src_img_idx, dst_img_idx;       //!< Images indices (optional)
-    std::vector<DMatch> matches;
-    std::vector<uchar> inliers_mask;    //!< Geometrically consistent matches mask
-    int num_inliers;                    //!< Number of geometrically consistent matches
-    Mat H;                              //!< Estimated transformation
-    double confidence;                  //!< Confidence two images are from the same panorama
-};
-
-/** @brief Feature matchers base class. */
-class CV_EXPORTS FeaturesMatcher
-{
-public:
-    virtual ~FeaturesMatcher() {}
-
-    /** @overload
-    @param features1 First image features
-    @param features2 Second image features
-    @param matches_info Found matches
-    */
-    void operator ()(const ImageFeatures &features1, const ImageFeatures &features2,
-                     MatchesInfo& matches_info) { match(features1, features2, matches_info); }
-
-    /** @brief Performs images matching.
-
-    @param features Features of the source images
-    @param pairwise_matches Found pairwise matches
-    @param mask Mask indicating which image pairs must be matched
-
-    The function is parallelized with the TBB library.
-
-    @sa detail::MatchesInfo
-    */
-    void operator ()(const std::vector<ImageFeatures> &features, std::vector<MatchesInfo> &pairwise_matches,
-                     const cv::UMat &mask = cv::UMat());
-
-    /** @return True, if it's possible to use the same matcher instance in parallel, false otherwise
-    */
-    bool isThreadSafe() const { return is_thread_safe_; }
-
-    /** @brief Frees unused memory allocated before if there is any.
-    */
-    virtual void collectGarbage() {}
-
-protected:
-    FeaturesMatcher(bool is_thread_safe = false) : is_thread_safe_(is_thread_safe) {}
-
-    /** @brief This method must implement matching logic in order to make the wrappers
-    detail::FeaturesMatcher::operator()_ work.
-
-    @param features1 first image features
-    @param features2 second image features
-    @param matches_info found matches
-     */
-    virtual void match(const ImageFeatures &features1, const ImageFeatures &features2,
-                       MatchesInfo& matches_info) = 0;
-
-    bool is_thread_safe_;
-};
-
-/** @brief Features matcher which finds two best matches for each feature and leaves the best one only if the
-ratio between descriptor distances is greater than the threshold match_conf
-
-@sa detail::FeaturesMatcher
- */
-class CV_EXPORTS BestOf2NearestMatcher : public FeaturesMatcher
-{
-public:
-    /** @brief Constructs a "best of 2 nearest" matcher.
-
-    @param try_use_gpu Should try to use GPU or not
-    @param match_conf Match distances ration threshold
-    @param num_matches_thresh1 Minimum number of matches required for the 2D projective transform
-    estimation used in the inliers classification step
-    @param num_matches_thresh2 Minimum number of matches required for the 2D projective transform
-    re-estimation on inliers
-     */
-    BestOf2NearestMatcher(bool try_use_gpu = false, float match_conf = 0.3f, int num_matches_thresh1 = 6,
-                          int num_matches_thresh2 = 6);
-
-    void collectGarbage() CV_OVERRIDE;
-
-protected:
-    void match(const ImageFeatures &features1, const ImageFeatures &features2, MatchesInfo &matches_info) CV_OVERRIDE;
-
-    int num_matches_thresh1_;
-    int num_matches_thresh2_;
-    Ptr<FeaturesMatcher> impl_;
-};
-
-class CV_EXPORTS BestOf2NearestRangeMatcher : public BestOf2NearestMatcher
-{
-public:
-    BestOf2NearestRangeMatcher(int range_width = 5, bool try_use_gpu = false, float match_conf = 0.3f,
-                            int num_matches_thresh1 = 6, int num_matches_thresh2 = 6);
-
-    void operator ()(const std::vector<ImageFeatures> &features, std::vector<MatchesInfo> &pairwise_matches,
-                     const cv::UMat &mask = cv::UMat());
-
-
-protected:
-    int range_width_;
-};
-
-/** @brief Features matcher similar to cv::detail::BestOf2NearestMatcher which
-finds two best matches for each feature and leaves the best one only if the
-ratio between descriptor distances is greater than the threshold match_conf.
-
-Unlike cv::detail::BestOf2NearestMatcher this matcher uses affine
-transformation (affine trasformation estimate will be placed in matches_info).
-
-@sa cv::detail::FeaturesMatcher cv::detail::BestOf2NearestMatcher
- */
-class CV_EXPORTS AffineBestOf2NearestMatcher : public BestOf2NearestMatcher
-{
-public:
-    /** @brief Constructs a "best of 2 nearest" matcher that expects affine trasformation
-    between images
-
-    @param full_affine whether to use full affine transformation with 6 degress of freedom or reduced
-    transformation with 4 degrees of freedom using only rotation, translation and uniform scaling
-    @param try_use_gpu Should try to use GPU or not
-    @param match_conf Match distances ration threshold
-    @param num_matches_thresh1 Minimum number of matches required for the 2D affine transform
-    estimation used in the inliers classification step
-
-    @sa cv::estimateAffine2D cv::estimateAffinePartial2D
-     */
-    AffineBestOf2NearestMatcher(bool full_affine = false, bool try_use_gpu = false,
-                                float match_conf = 0.3f, int num_matches_thresh1 = 6) :
-        BestOf2NearestMatcher(try_use_gpu, match_conf, num_matches_thresh1, num_matches_thresh1),
-        full_affine_(full_affine) {}
-
-protected:
-    void match(const ImageFeatures &features1, const ImageFeatures &features2, MatchesInfo &matches_info) CV_OVERRIDE;
-
-    bool full_affine_;
-};
-
-//! @} stitching_match
-
-} // namespace detail
-} // namespace cv
-
-#endif // OPENCV_STITCHING_MATCHERS_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/stitching/detail/motion_estimators.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/stitching/detail/motion_estimators.hpp
deleted file mode 100755
index 40f12c3..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/stitching/detail/motion_estimators.hpp
+++ /dev/null
@@ -1,359 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_STITCHING_MOTION_ESTIMATORS_HPP
-#define OPENCV_STITCHING_MOTION_ESTIMATORS_HPP
-
-#include "opencv2/core.hpp"
-#include "matchers.hpp"
-#include "util.hpp"
-#include "camera.hpp"
-
-namespace cv {
-namespace detail {
-
-//! @addtogroup stitching_rotation
-//! @{
-
-/** @brief Rotation estimator base class.
-
-It takes features of all images, pairwise matches between all images and estimates rotations of all
-cameras.
-
-@note The coordinate system origin is implementation-dependent, but you can always normalize the
-rotations in respect to the first camera, for instance. :
- */
-class CV_EXPORTS Estimator
-{
-public:
-    virtual ~Estimator() {}
-
-    /** @brief Estimates camera parameters.
-
-    @param features Features of images
-    @param pairwise_matches Pairwise matches of images
-    @param cameras Estimated camera parameters
-    @return True in case of success, false otherwise
-     */
-    bool operator ()(const std::vector<ImageFeatures> &features,
-                     const std::vector<MatchesInfo> &pairwise_matches,
-                     std::vector<CameraParams> &cameras)
-        { return estimate(features, pairwise_matches, cameras); }
-
-protected:
-    /** @brief This method must implement camera parameters estimation logic in order to make the wrapper
-    detail::Estimator::operator()_ work.
-
-    @param features Features of images
-    @param pairwise_matches Pairwise matches of images
-    @param cameras Estimated camera parameters
-    @return True in case of success, false otherwise
-     */
-    virtual bool estimate(const std::vector<ImageFeatures> &features,
-                          const std::vector<MatchesInfo> &pairwise_matches,
-                          std::vector<CameraParams> &cameras) = 0;
-};
-
-/** @brief Homography based rotation estimator.
- */
-class CV_EXPORTS HomographyBasedEstimator : public Estimator
-{
-public:
-    HomographyBasedEstimator(bool is_focals_estimated = false)
-        : is_focals_estimated_(is_focals_estimated) {}
-
-private:
-    virtual bool estimate(const std::vector<ImageFeatures> &features,
-                          const std::vector<MatchesInfo> &pairwise_matches,
-                          std::vector<CameraParams> &cameras) CV_OVERRIDE;
-
-    bool is_focals_estimated_;
-};
-
-/** @brief Affine transformation based estimator.
-
-This estimator uses pairwise transformations estimated by matcher to estimate
-final transformation for each camera.
-
-@sa cv::detail::HomographyBasedEstimator
- */
-class CV_EXPORTS AffineBasedEstimator : public Estimator
-{
-private:
-    virtual bool estimate(const std::vector<ImageFeatures> &features,
-                          const std::vector<MatchesInfo> &pairwise_matches,
-                          std::vector<CameraParams> &cameras) CV_OVERRIDE;
-};
-
-/** @brief Base class for all camera parameters refinement methods.
- */
-class CV_EXPORTS BundleAdjusterBase : public Estimator
-{
-public:
-    const Mat refinementMask() const { return refinement_mask_.clone(); }
-    void setRefinementMask(const Mat &mask)
-    {
-        CV_Assert(mask.type() == CV_8U && mask.size() == Size(3, 3));
-        refinement_mask_ = mask.clone();
-    }
-
-    double confThresh() const { return conf_thresh_; }
-    void setConfThresh(double conf_thresh) { conf_thresh_ = conf_thresh; }
-
-    TermCriteria termCriteria() { return term_criteria_; }
-    void setTermCriteria(const TermCriteria& term_criteria) { term_criteria_ = term_criteria; }
-
-protected:
-    /** @brief Construct a bundle adjuster base instance.
-
-    @param num_params_per_cam Number of parameters per camera
-    @param num_errs_per_measurement Number of error terms (components) per match
-     */
-    BundleAdjusterBase(int num_params_per_cam, int num_errs_per_measurement)
-        : num_images_(0), total_num_matches_(0),
-          num_params_per_cam_(num_params_per_cam),
-          num_errs_per_measurement_(num_errs_per_measurement),
-          features_(0), pairwise_matches_(0), conf_thresh_(0)
-    {
-        setRefinementMask(Mat::ones(3, 3, CV_8U));
-        setConfThresh(1.);
-        setTermCriteria(TermCriteria(TermCriteria::EPS + TermCriteria::COUNT, 1000, DBL_EPSILON));
-    }
-
-    // Runs bundle adjustment
-    virtual bool estimate(const std::vector<ImageFeatures> &features,
-                          const std::vector<MatchesInfo> &pairwise_matches,
-                          std::vector<CameraParams> &cameras) CV_OVERRIDE;
-
-    /** @brief Sets initial camera parameter to refine.
-
-    @param cameras Camera parameters
-     */
-    virtual void setUpInitialCameraParams(const std::vector<CameraParams> &cameras) = 0;
-    /** @brief Gets the refined camera parameters.
-
-    @param cameras Refined camera parameters
-     */
-    virtual void obtainRefinedCameraParams(std::vector<CameraParams> &cameras) const = 0;
-    /** @brief Calculates error vector.
-
-    @param err Error column-vector of length total_num_matches \* num_errs_per_measurement
-     */
-    virtual void calcError(Mat &err) = 0;
-    /** @brief Calculates the cost function jacobian.
-
-    @param jac Jacobian matrix of dimensions
-    (total_num_matches \* num_errs_per_measurement) x (num_images \* num_params_per_cam)
-     */
-    virtual void calcJacobian(Mat &jac) = 0;
-
-    // 3x3 8U mask, where 0 means don't refine respective parameter, != 0 means refine
-    Mat refinement_mask_;
-
-    int num_images_;
-    int total_num_matches_;
-
-    int num_params_per_cam_;
-    int num_errs_per_measurement_;
-
-    const ImageFeatures *features_;
-    const MatchesInfo *pairwise_matches_;
-
-    // Threshold to filter out poorly matched image pairs
-    double conf_thresh_;
-
-    //Levenberg-Marquardt algorithm termination criteria
-    TermCriteria term_criteria_;
-
-    // Camera parameters matrix (CV_64F)
-    Mat cam_params_;
-
-    // Connected images pairs
-    std::vector<std::pair<int,int> > edges_;
-};
-
-
-/** @brief Stub bundle adjuster that does nothing.
- */
-class CV_EXPORTS NoBundleAdjuster : public BundleAdjusterBase
-{
-public:
-    NoBundleAdjuster() : BundleAdjusterBase(0, 0) {}
-
-private:
-    bool estimate(const std::vector<ImageFeatures> &, const std::vector<MatchesInfo> &,
-                  std::vector<CameraParams> &) CV_OVERRIDE
-    {
-        return true;
-    }
-    void setUpInitialCameraParams(const std::vector<CameraParams> &) CV_OVERRIDE {}
-    void obtainRefinedCameraParams(std::vector<CameraParams> &) const CV_OVERRIDE {}
-    void calcError(Mat &) CV_OVERRIDE {}
-    void calcJacobian(Mat &) CV_OVERRIDE {}
-};
-
-
-/** @brief Implementation of the camera parameters refinement algorithm which minimizes sum of the reprojection
-error squares
-
-It can estimate focal length, aspect ratio, principal point.
-You can affect only on them via the refinement mask.
- */
-class CV_EXPORTS BundleAdjusterReproj : public BundleAdjusterBase
-{
-public:
-    BundleAdjusterReproj() : BundleAdjusterBase(7, 2) {}
-
-private:
-    void setUpInitialCameraParams(const std::vector<CameraParams> &cameras) CV_OVERRIDE;
-    void obtainRefinedCameraParams(std::vector<CameraParams> &cameras) const CV_OVERRIDE;
-    void calcError(Mat &err) CV_OVERRIDE;
-    void calcJacobian(Mat &jac) CV_OVERRIDE;
-
-    Mat err1_, err2_;
-};
-
-
-/** @brief Implementation of the camera parameters refinement algorithm which minimizes sum of the distances
-between the rays passing through the camera center and a feature. :
-
-It can estimate focal length. It ignores the refinement mask for now.
- */
-class CV_EXPORTS BundleAdjusterRay : public BundleAdjusterBase
-{
-public:
-    BundleAdjusterRay() : BundleAdjusterBase(4, 3) {}
-
-private:
-    void setUpInitialCameraParams(const std::vector<CameraParams> &cameras) CV_OVERRIDE;
-    void obtainRefinedCameraParams(std::vector<CameraParams> &cameras) const CV_OVERRIDE;
-    void calcError(Mat &err) CV_OVERRIDE;
-    void calcJacobian(Mat &jac) CV_OVERRIDE;
-
-    Mat err1_, err2_;
-};
-
-
-/** @brief Bundle adjuster that expects affine transformation
-represented in homogeneous coordinates in R for each camera param. Implements
-camera parameters refinement algorithm which minimizes sum of the reprojection
-error squares
-
-It estimates all transformation parameters. Refinement mask is ignored.
-
-@sa AffineBasedEstimator AffineBestOf2NearestMatcher BundleAdjusterAffinePartial
- */
-class CV_EXPORTS BundleAdjusterAffine : public BundleAdjusterBase
-{
-public:
-    BundleAdjusterAffine() : BundleAdjusterBase(6, 2) {}
-
-private:
-    void setUpInitialCameraParams(const std::vector<CameraParams> &cameras) CV_OVERRIDE;
-    void obtainRefinedCameraParams(std::vector<CameraParams> &cameras) const CV_OVERRIDE;
-    void calcError(Mat &err) CV_OVERRIDE;
-    void calcJacobian(Mat &jac) CV_OVERRIDE;
-
-    Mat err1_, err2_;
-};
-
-
-/** @brief Bundle adjuster that expects affine transformation with 4 DOF
-represented in homogeneous coordinates in R for each camera param. Implements
-camera parameters refinement algorithm which minimizes sum of the reprojection
-error squares
-
-It estimates all transformation parameters. Refinement mask is ignored.
-
-@sa AffineBasedEstimator AffineBestOf2NearestMatcher BundleAdjusterAffine
- */
-class CV_EXPORTS BundleAdjusterAffinePartial : public BundleAdjusterBase
-{
-public:
-    BundleAdjusterAffinePartial() : BundleAdjusterBase(4, 2) {}
-
-private:
-    void setUpInitialCameraParams(const std::vector<CameraParams> &cameras) CV_OVERRIDE;
-    void obtainRefinedCameraParams(std::vector<CameraParams> &cameras) const CV_OVERRIDE;
-    void calcError(Mat &err) CV_OVERRIDE;
-    void calcJacobian(Mat &jac) CV_OVERRIDE;
-
-    Mat err1_, err2_;
-};
-
-
-enum WaveCorrectKind
-{
-    WAVE_CORRECT_HORIZ,
-    WAVE_CORRECT_VERT
-};
-
-/** @brief Tries to make panorama more horizontal (or vertical).
-
-@param rmats Camera rotation matrices.
-@param kind Correction kind, see detail::WaveCorrectKind.
- */
-void CV_EXPORTS waveCorrect(std::vector<Mat> &rmats, WaveCorrectKind kind);
-
-
-//////////////////////////////////////////////////////////////////////////////
-// Auxiliary functions
-
-// Returns matches graph representation in DOT language
-String CV_EXPORTS matchesGraphAsString(std::vector<String> &pathes, std::vector<MatchesInfo> &pairwise_matches,
-                                            float conf_threshold);
-
-std::vector<int> CV_EXPORTS leaveBiggestComponent(
-        std::vector<ImageFeatures> &features,
-        std::vector<MatchesInfo> &pairwise_matches,
-        float conf_threshold);
-
-void CV_EXPORTS findMaxSpanningTree(
-        int num_images, const std::vector<MatchesInfo> &pairwise_matches,
-        Graph &span_tree, std::vector<int> &centers);
-
-//! @} stitching_rotation
-
-} // namespace detail
-} // namespace cv
-
-#endif // OPENCV_STITCHING_MOTION_ESTIMATORS_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/stitching/detail/seam_finders.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/stitching/detail/seam_finders.hpp
deleted file mode 100755
index 904f0ec..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/stitching/detail/seam_finders.hpp
+++ /dev/null
@@ -1,285 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_STITCHING_SEAM_FINDERS_HPP
-#define OPENCV_STITCHING_SEAM_FINDERS_HPP
-
-#include <set>
-#include "opencv2/core.hpp"
-#include "opencv2/opencv_modules.hpp"
-
-namespace cv {
-namespace detail {
-
-//! @addtogroup stitching_seam
-//! @{
-
-/** @brief Base class for a seam estimator.
- */
-class CV_EXPORTS SeamFinder
-{
-public:
-    virtual ~SeamFinder() {}
-    /** @brief Estimates seams.
-
-    @param src Source images
-    @param corners Source image top-left corners
-    @param masks Source image masks to update
-     */
-    virtual void find(const std::vector<UMat> &src, const std::vector<Point> &corners,
-                      std::vector<UMat> &masks) = 0;
-};
-
-/** @brief Stub seam estimator which does nothing.
- */
-class CV_EXPORTS NoSeamFinder : public SeamFinder
-{
-public:
-    void find(const std::vector<UMat>&, const std::vector<Point>&, std::vector<UMat>&) CV_OVERRIDE {}
-};
-
-/** @brief Base class for all pairwise seam estimators.
- */
-class CV_EXPORTS PairwiseSeamFinder : public SeamFinder
-{
-public:
-    virtual void find(const std::vector<UMat> &src, const std::vector<Point> &corners,
-                      std::vector<UMat> &masks) CV_OVERRIDE;
-
-protected:
-    void run();
-    /** @brief Resolves masks intersection of two specified images in the given ROI.
-
-    @param first First image index
-    @param second Second image index
-    @param roi Region of interest
-     */
-    virtual void findInPair(size_t first, size_t second, Rect roi) = 0;
-
-    std::vector<UMat> images_;
-    std::vector<Size> sizes_;
-    std::vector<Point> corners_;
-    std::vector<UMat> masks_;
-};
-
-/** @brief Voronoi diagram-based seam estimator.
- */
-class CV_EXPORTS VoronoiSeamFinder : public PairwiseSeamFinder
-{
-public:
-    virtual void find(const std::vector<UMat> &src, const std::vector<Point> &corners,
-                      std::vector<UMat> &masks) CV_OVERRIDE;
-    virtual void find(const std::vector<Size> &size, const std::vector<Point> &corners,
-                      std::vector<UMat> &masks);
-private:
-    void findInPair(size_t first, size_t second, Rect roi) CV_OVERRIDE;
-};
-
-
-class CV_EXPORTS DpSeamFinder : public SeamFinder
-{
-public:
-    enum CostFunction { COLOR, COLOR_GRAD };
-
-    DpSeamFinder(CostFunction costFunc = COLOR);
-
-    CostFunction costFunction() const { return costFunc_; }
-    void setCostFunction(CostFunction val) { costFunc_ = val; }
-
-    virtual void find(const std::vector<UMat> &src, const std::vector<Point> &corners,
-                      std::vector<UMat> &masks) CV_OVERRIDE;
-
-private:
-    enum ComponentState
-    {
-        FIRST = 1, SECOND = 2, INTERS = 4,
-        INTERS_FIRST = INTERS | FIRST,
-        INTERS_SECOND = INTERS | SECOND
-    };
-
-    class ImagePairLess
-    {
-    public:
-        ImagePairLess(const std::vector<Mat> &images, const std::vector<Point> &corners)
-            : src_(&images[0]), corners_(&corners[0]) {}
-
-        bool operator() (const std::pair<size_t, size_t> &l, const std::pair<size_t, size_t> &r) const
-        {
-            Point c1 = corners_[l.first] + Point(src_[l.first].cols / 2, src_[l.first].rows / 2);
-            Point c2 = corners_[l.second] + Point(src_[l.second].cols / 2, src_[l.second].rows / 2);
-            int d1 = (c1 - c2).dot(c1 - c2);
-
-            c1 = corners_[r.first] + Point(src_[r.first].cols / 2, src_[r.first].rows / 2);
-            c2 = corners_[r.second] + Point(src_[r.second].cols / 2, src_[r.second].rows / 2);
-            int d2 = (c1 - c2).dot(c1 - c2);
-
-            return d1 < d2;
-        }
-
-    private:
-        const Mat *src_;
-        const Point *corners_;
-    };
-
-    class ClosePoints
-    {
-    public:
-        ClosePoints(int minDist) : minDist_(minDist) {}
-
-        bool operator() (const Point &p1, const Point &p2) const
-        {
-            int dist2 = (p1.x-p2.x) * (p1.x-p2.x) + (p1.y-p2.y) * (p1.y-p2.y);
-            return dist2 < minDist_ * minDist_;
-        }
-
-    private:
-        int minDist_;
-    };
-
-    void process(
-            const Mat &image1, const Mat &image2, Point tl1, Point tl2,  Mat &mask1, Mat &mask2);
-
-    void findComponents();
-
-    void findEdges();
-
-    void resolveConflicts(
-            const Mat &image1, const Mat &image2, Point tl1, Point tl2, Mat &mask1, Mat &mask2);
-
-    void computeGradients(const Mat &image1, const Mat &image2);
-
-    bool hasOnlyOneNeighbor(int comp);
-
-    bool closeToContour(int y, int x, const Mat_<uchar> &contourMask);
-
-    bool getSeamTips(int comp1, int comp2, Point &p1, Point &p2);
-
-    void computeCosts(
-            const Mat &image1, const Mat &image2, Point tl1, Point tl2,
-            int comp, Mat_<float> &costV, Mat_<float> &costH);
-
-    bool estimateSeam(
-            const Mat &image1, const Mat &image2, Point tl1, Point tl2, int comp,
-            Point p1, Point p2, std::vector<Point> &seam, bool &isHorizontal);
-
-    void updateLabelsUsingSeam(
-            int comp1, int comp2, const std::vector<Point> &seam, bool isHorizontalSeam);
-
-    CostFunction costFunc_;
-
-    // processing images pair data
-    Point unionTl_, unionBr_;
-    Size unionSize_;
-    Mat_<uchar> mask1_, mask2_;
-    Mat_<uchar> contour1mask_, contour2mask_;
-    Mat_<float> gradx1_, grady1_;
-    Mat_<float> gradx2_, grady2_;
-
-    // components data
-    int ncomps_;
-    Mat_<int> labels_;
-    std::vector<ComponentState> states_;
-    std::vector<Point> tls_, brs_;
-    std::vector<std::vector<Point> > contours_;
-    std::set<std::pair<int, int> > edges_;
-};
-
-/** @brief Base class for all minimum graph-cut-based seam estimators.
- */
-class CV_EXPORTS GraphCutSeamFinderBase
-{
-public:
-    enum CostType { COST_COLOR, COST_COLOR_GRAD };
-};
-
-/** @brief Minimum graph cut-based seam estimator. See details in @cite V03 .
- */
-class CV_EXPORTS GraphCutSeamFinder : public GraphCutSeamFinderBase, public SeamFinder
-{
-public:
-    GraphCutSeamFinder(int cost_type = COST_COLOR_GRAD, float terminal_cost = 10000.f,
-                       float bad_region_penalty = 1000.f);
-
-    ~GraphCutSeamFinder();
-
-    void find(const std::vector<UMat> &src, const std::vector<Point> &corners,
-              std::vector<UMat> &masks) CV_OVERRIDE;
-
-private:
-    // To avoid GCGraph dependency
-    class Impl;
-    Ptr<PairwiseSeamFinder> impl_;
-};
-
-
-#ifdef HAVE_OPENCV_CUDALEGACY
-class CV_EXPORTS GraphCutSeamFinderGpu : public GraphCutSeamFinderBase, public PairwiseSeamFinder
-{
-public:
-    GraphCutSeamFinderGpu(int cost_type = COST_COLOR_GRAD, float terminal_cost = 10000.f,
-                          float bad_region_penalty = 1000.f)
-                          : cost_type_(cost_type), terminal_cost_(terminal_cost),
-                            bad_region_penalty_(bad_region_penalty) {}
-
-    void find(const std::vector<cv::UMat> &src, const std::vector<cv::Point> &corners,
-              std::vector<cv::UMat> &masks) CV_OVERRIDE;
-    void findInPair(size_t first, size_t second, Rect roi) CV_OVERRIDE;
-
-private:
-    void setGraphWeightsColor(const cv::Mat &img1, const cv::Mat &img2, const cv::Mat &mask1, const cv::Mat &mask2,
-                              cv::Mat &terminals, cv::Mat &leftT, cv::Mat &rightT, cv::Mat &top, cv::Mat &bottom);
-    void setGraphWeightsColorGrad(const cv::Mat &img1, const cv::Mat &img2, const cv::Mat &dx1, const cv::Mat &dx2,
-                                  const cv::Mat &dy1, const cv::Mat &dy2, const cv::Mat &mask1, const cv::Mat &mask2,
-                                  cv::Mat &terminals, cv::Mat &leftT, cv::Mat &rightT, cv::Mat &top, cv::Mat &bottom);
-    std::vector<Mat> dx_, dy_;
-    int cost_type_;
-    float terminal_cost_;
-    float bad_region_penalty_;
-};
-#endif
-
-//! @}
-
-} // namespace detail
-} // namespace cv
-
-#endif // OPENCV_STITCHING_SEAM_FINDERS_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/stitching/detail/timelapsers.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/stitching/detail/timelapsers.hpp
deleted file mode 100755
index 74d797e..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/stitching/detail/timelapsers.hpp
+++ /dev/null
@@ -1,91 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-
-#ifndef OPENCV_STITCHING_TIMELAPSERS_HPP
-#define OPENCV_STITCHING_TIMELAPSERS_HPP
-
-#include "opencv2/core.hpp"
-
-namespace cv {
-namespace detail {
-
-//! @addtogroup stitching
-//! @{
-
-//  Base Timelapser class, takes a sequence of images, applies appropriate shift, stores result in dst_.
-
-class CV_EXPORTS Timelapser
-{
-public:
-
-    enum {AS_IS, CROP};
-
-    virtual ~Timelapser() {}
-
-    static Ptr<Timelapser> createDefault(int type);
-
-    virtual void initialize(const std::vector<Point> &corners, const std::vector<Size> &sizes);
-    virtual void process(InputArray img, InputArray mask, Point tl);
-    virtual const UMat& getDst() {return dst_;}
-
-protected:
-
-    virtual bool test_point(Point pt);
-
-    UMat dst_;
-    Rect dst_roi_;
-};
-
-
-class CV_EXPORTS TimelapserCrop : public Timelapser
-{
-public:
-    virtual void initialize(const std::vector<Point> &corners, const std::vector<Size> &sizes) CV_OVERRIDE;
-};
-
-//! @}
-
-} // namespace detail
-} // namespace cv
-
-#endif // OPENCV_STITCHING_TIMELAPSERS_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/stitching/detail/util.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/stitching/detail/util.hpp
deleted file mode 100755
index 78301b8..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/stitching/detail/util.hpp
+++ /dev/null
@@ -1,121 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_STITCHING_UTIL_HPP
-#define OPENCV_STITCHING_UTIL_HPP
-
-#include <list>
-#include "opencv2/core.hpp"
-
-namespace cv {
-namespace detail {
-
-//! @addtogroup stitching
-//! @{
-
-class CV_EXPORTS DisjointSets
-{
-public:
-    DisjointSets(int elem_count = 0) { createOneElemSets(elem_count); }
-
-    void createOneElemSets(int elem_count);
-    int findSetByElem(int elem);
-    int mergeSets(int set1, int set2);
-
-    std::vector<int> parent;
-    std::vector<int> size;
-
-private:
-    std::vector<int> rank_;
-};
-
-
-struct CV_EXPORTS GraphEdge
-{
-    GraphEdge(int from, int to, float weight);
-    bool operator <(const GraphEdge& other) const { return weight < other.weight; }
-    bool operator >(const GraphEdge& other) const { return weight > other.weight; }
-
-    int from, to;
-    float weight;
-};
-
-inline GraphEdge::GraphEdge(int _from, int _to, float _weight) : from(_from), to(_to), weight(_weight) {}
-
-
-class CV_EXPORTS Graph
-{
-public:
-    Graph(int num_vertices = 0) { create(num_vertices); }
-    void create(int num_vertices) { edges_.assign(num_vertices, std::list<GraphEdge>()); }
-    int numVertices() const { return static_cast<int>(edges_.size()); }
-    void addEdge(int from, int to, float weight);
-    template <typename B> B forEach(B body) const;
-    template <typename B> B walkBreadthFirst(int from, B body) const;
-
-private:
-    std::vector< std::list<GraphEdge> > edges_;
-};
-
-
-//////////////////////////////////////////////////////////////////////////////
-// Auxiliary functions
-
-CV_EXPORTS bool overlapRoi(Point tl1, Point tl2, Size sz1, Size sz2, Rect &roi);
-CV_EXPORTS Rect resultRoi(const std::vector<Point> &corners, const std::vector<UMat> &images);
-CV_EXPORTS Rect resultRoi(const std::vector<Point> &corners, const std::vector<Size> &sizes);
-CV_EXPORTS Rect resultRoiIntersection(const std::vector<Point> &corners, const std::vector<Size> &sizes);
-CV_EXPORTS Point resultTl(const std::vector<Point> &corners);
-
-// Returns random 'count' element subset of the {0,1,...,size-1} set
-CV_EXPORTS void selectRandomSubset(int count, int size, std::vector<int> &subset);
-
-CV_EXPORTS int& stitchingLogLevel();
-
-//! @}
-
-} // namespace detail
-} // namespace cv
-
-#include "util_inl.hpp"
-
-#endif // OPENCV_STITCHING_UTIL_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/stitching/detail/util_inl.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/stitching/detail/util_inl.hpp
deleted file mode 100755
index dafab8b..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/stitching/detail/util_inl.hpp
+++ /dev/null
@@ -1,131 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_STITCHING_UTIL_INL_HPP
-#define OPENCV_STITCHING_UTIL_INL_HPP
-
-#include <queue>
-#include "opencv2/core.hpp"
-#include "util.hpp" // Make your IDE see declarations
-
-//! @cond IGNORED
-
-namespace cv {
-namespace detail {
-
-template <typename B>
-B Graph::forEach(B body) const
-{
-    for (int i = 0; i < numVertices(); ++i)
-    {
-        std::list<GraphEdge>::const_iterator edge = edges_[i].begin();
-        for (; edge != edges_[i].end(); ++edge)
-            body(*edge);
-    }
-    return body;
-}
-
-
-template <typename B>
-B Graph::walkBreadthFirst(int from, B body) const
-{
-    std::vector<bool> was(numVertices(), false);
-    std::queue<int> vertices;
-
-    was[from] = true;
-    vertices.push(from);
-
-    while (!vertices.empty())
-    {
-        int vertex = vertices.front();
-        vertices.pop();
-
-        std::list<GraphEdge>::const_iterator edge = edges_[vertex].begin();
-        for (; edge != edges_[vertex].end(); ++edge)
-        {
-            if (!was[edge->to])
-            {
-                body(*edge);
-                was[edge->to] = true;
-                vertices.push(edge->to);
-            }
-        }
-    }
-
-    return body;
-}
-
-
-//////////////////////////////////////////////////////////////////////////////
-// Some auxiliary math functions
-
-static inline
-float normL2(const Point3f& a)
-{
-    return a.x * a.x + a.y * a.y + a.z * a.z;
-}
-
-
-static inline
-float normL2(const Point3f& a, const Point3f& b)
-{
-    return normL2(a - b);
-}
-
-
-static inline
-double normL2sq(const Mat &r)
-{
-    return r.dot(r);
-}
-
-
-static inline int sqr(int x) { return x * x; }
-static inline float sqr(float x) { return x * x; }
-static inline double sqr(double x) { return x * x; }
-
-} // namespace detail
-} // namespace cv
-
-//! @endcond
-
-#endif // OPENCV_STITCHING_UTIL_INL_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/stitching/detail/warpers.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/stitching/detail/warpers.hpp
deleted file mode 100755
index 1b05651..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/stitching/detail/warpers.hpp
+++ /dev/null
@@ -1,616 +0,0 @@
- /*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_STITCHING_WARPERS_HPP
-#define OPENCV_STITCHING_WARPERS_HPP
-
-#include "opencv2/core.hpp"
-#include "opencv2/core/cuda.hpp"
-#include "opencv2/imgproc.hpp"
-#include "opencv2/opencv_modules.hpp"
-
-namespace cv {
-namespace detail {
-
-//! @addtogroup stitching_warp
-//! @{
-
-/** @brief Rotation-only model image warper interface.
- */
-class CV_EXPORTS RotationWarper
-{
-public:
-    virtual ~RotationWarper() {}
-
-    /** @brief Projects the image point.
-
-    @param pt Source point
-    @param K Camera intrinsic parameters
-    @param R Camera rotation matrix
-    @return Projected point
-     */
-    virtual Point2f warpPoint(const Point2f &pt, InputArray K, InputArray R) = 0;
-
-    /** @brief Builds the projection maps according to the given camera data.
-
-    @param src_size Source image size
-    @param K Camera intrinsic parameters
-    @param R Camera rotation matrix
-    @param xmap Projection map for the x axis
-    @param ymap Projection map for the y axis
-    @return Projected image minimum bounding box
-     */
-    virtual Rect buildMaps(Size src_size, InputArray K, InputArray R, OutputArray xmap, OutputArray ymap) = 0;
-
-    /** @brief Projects the image.
-
-    @param src Source image
-    @param K Camera intrinsic parameters
-    @param R Camera rotation matrix
-    @param interp_mode Interpolation mode
-    @param border_mode Border extrapolation mode
-    @param dst Projected image
-    @return Project image top-left corner
-     */
-    virtual Point warp(InputArray src, InputArray K, InputArray R, int interp_mode, int border_mode,
-                       OutputArray dst) = 0;
-
-    /** @brief Projects the image backward.
-
-    @param src Projected image
-    @param K Camera intrinsic parameters
-    @param R Camera rotation matrix
-    @param interp_mode Interpolation mode
-    @param border_mode Border extrapolation mode
-    @param dst_size Backward-projected image size
-    @param dst Backward-projected image
-     */
-    virtual void warpBackward(InputArray src, InputArray K, InputArray R, int interp_mode, int border_mode,
-                              Size dst_size, OutputArray dst) = 0;
-
-    /**
-    @param src_size Source image bounding box
-    @param K Camera intrinsic parameters
-    @param R Camera rotation matrix
-    @return Projected image minimum bounding box
-     */
-    virtual Rect warpRoi(Size src_size, InputArray K, InputArray R) = 0;
-
-    virtual float getScale() const { return 1.f; }
-    virtual void setScale(float) {}
-};
-
-/** @brief Base class for warping logic implementation.
- */
-struct CV_EXPORTS ProjectorBase
-{
-    void setCameraParams(InputArray K = Mat::eye(3, 3, CV_32F),
-                         InputArray R = Mat::eye(3, 3, CV_32F),
-                         InputArray T = Mat::zeros(3, 1, CV_32F));
-
-    float scale;
-    float k[9];
-    float rinv[9];
-    float r_kinv[9];
-    float k_rinv[9];
-    float t[3];
-};
-
-/** @brief Base class for rotation-based warper using a detail::ProjectorBase_ derived class.
- */
-template <class P>
-class CV_EXPORTS_TEMPLATE RotationWarperBase : public RotationWarper
-{
-public:
-    Point2f warpPoint(const Point2f &pt, InputArray K, InputArray R) CV_OVERRIDE;
-
-    Rect buildMaps(Size src_size, InputArray K, InputArray R, OutputArray xmap, OutputArray ymap) CV_OVERRIDE;
-
-    Point warp(InputArray src, InputArray K, InputArray R, int interp_mode, int border_mode,
-               OutputArray dst) CV_OVERRIDE;
-
-    void warpBackward(InputArray src, InputArray K, InputArray R, int interp_mode, int border_mode,
-                      Size dst_size, OutputArray dst) CV_OVERRIDE;
-
-    Rect warpRoi(Size src_size, InputArray K, InputArray R) CV_OVERRIDE;
-
-    float getScale() const  CV_OVERRIDE{ return projector_.scale; }
-    void setScale(float val) CV_OVERRIDE { projector_.scale = val; }
-
-protected:
-
-    // Detects ROI of the destination image. It's correct for any projection.
-    virtual void detectResultRoi(Size src_size, Point &dst_tl, Point &dst_br);
-
-    // Detects ROI of the destination image by walking over image border.
-    // Correctness for any projection isn't guaranteed.
-    void detectResultRoiByBorder(Size src_size, Point &dst_tl, Point &dst_br);
-
-    P projector_;
-};
-
-
-struct CV_EXPORTS PlaneProjector : ProjectorBase
-{
-    void mapForward(float x, float y, float &u, float &v);
-    void mapBackward(float u, float v, float &x, float &y);
-};
-
-/** @brief Warper that maps an image onto the z = 1 plane.
- */
-class CV_EXPORTS PlaneWarper : public RotationWarperBase<PlaneProjector>
-{
-public:
-    /** @brief Construct an instance of the plane warper class.
-
-    @param scale Projected image scale multiplier
-     */
-    PlaneWarper(float scale = 1.f) { projector_.scale = scale; }
-
-    Point2f warpPoint(const Point2f &pt, InputArray K, InputArray R) CV_OVERRIDE;
-    Point2f warpPoint(const Point2f &pt, InputArray K, InputArray R, InputArray T);
-
-    virtual Rect buildMaps(Size src_size, InputArray K, InputArray R, InputArray T, OutputArray xmap, OutputArray ymap);
-    Rect buildMaps(Size src_size, InputArray K, InputArray R, OutputArray xmap, OutputArray ymap) CV_OVERRIDE;
-
-    Point warp(InputArray src, InputArray K, InputArray R,
-               int interp_mode, int border_mode, OutputArray dst) CV_OVERRIDE;
-    virtual Point warp(InputArray src, InputArray K, InputArray R, InputArray T, int interp_mode, int border_mode,
-               OutputArray dst);
-
-    Rect warpRoi(Size src_size, InputArray K, InputArray R) CV_OVERRIDE;
-    Rect warpRoi(Size src_size, InputArray K, InputArray R, InputArray T);
-
-protected:
-    void detectResultRoi(Size src_size, Point &dst_tl, Point &dst_br) CV_OVERRIDE;
-};
-
-
-/** @brief Affine warper that uses rotations and translations
-
- Uses affine transformation in homogeneous coordinates to represent both rotation and
- translation in camera rotation matrix.
- */
-class CV_EXPORTS AffineWarper : public PlaneWarper
-{
-public:
-    /** @brief Construct an instance of the affine warper class.
-
-    @param scale Projected image scale multiplier
-     */
-    AffineWarper(float scale = 1.f) : PlaneWarper(scale) {}
-
-    Point2f warpPoint(const Point2f &pt, InputArray K, InputArray R) CV_OVERRIDE;
-    Rect buildMaps(Size src_size, InputArray K, InputArray R, OutputArray xmap, OutputArray ymap) CV_OVERRIDE;
-    Point warp(InputArray src, InputArray K, InputArray R,
-               int interp_mode, int border_mode, OutputArray dst) CV_OVERRIDE;
-    Rect warpRoi(Size src_size, InputArray K, InputArray R) CV_OVERRIDE;
-
-protected:
-    /** @brief Extracts rotation and translation matrices from matrix H representing
-        affine transformation in homogeneous coordinates
-     */
-    void getRTfromHomogeneous(InputArray H, Mat &R, Mat &T);
-};
-
-
-struct CV_EXPORTS SphericalProjector : ProjectorBase
-{
-    void mapForward(float x, float y, float &u, float &v);
-    void mapBackward(float u, float v, float &x, float &y);
-};
-
-
-/** @brief Warper that maps an image onto the unit sphere located at the origin.
-
- Projects image onto unit sphere with origin at (0, 0, 0) and radius scale, measured in pixels.
- A 360 panorama would therefore have a resulting width of 2 * scale * PI pixels.
- Poles are located at (0, -1, 0) and (0, 1, 0) points.
-*/
-class CV_EXPORTS SphericalWarper : public RotationWarperBase<SphericalProjector>
-{
-public:
-    /** @brief Construct an instance of the spherical warper class.
-
-    @param scale Radius of the projected sphere, in pixels. An image spanning the
-                 whole sphere will have a width of 2 * scale * PI pixels.
-     */
-    SphericalWarper(float scale) { projector_.scale = scale; }
-
-    Rect buildMaps(Size src_size, InputArray K, InputArray R, OutputArray xmap, OutputArray ymap) CV_OVERRIDE;
-    Point warp(InputArray src, InputArray K, InputArray R, int interp_mode, int border_mode, OutputArray dst) CV_OVERRIDE;
-protected:
-    void detectResultRoi(Size src_size, Point &dst_tl, Point &dst_br) CV_OVERRIDE;
-};
-
-
-struct CV_EXPORTS CylindricalProjector : ProjectorBase
-{
-    void mapForward(float x, float y, float &u, float &v);
-    void mapBackward(float u, float v, float &x, float &y);
-};
-
-
-/** @brief Warper that maps an image onto the x\*x + z\*z = 1 cylinder.
- */
-class CV_EXPORTS CylindricalWarper : public RotationWarperBase<CylindricalProjector>
-{
-public:
-    /** @brief Construct an instance of the cylindrical warper class.
-
-    @param scale Projected image scale multiplier
-     */
-    CylindricalWarper(float scale) { projector_.scale = scale; }
-
-    Rect buildMaps(Size src_size, InputArray K, InputArray R, OutputArray xmap, OutputArray ymap) CV_OVERRIDE;
-    Point warp(InputArray src, InputArray K, InputArray R, int interp_mode, int border_mode, OutputArray dst) CV_OVERRIDE;
-protected:
-    void detectResultRoi(Size src_size, Point &dst_tl, Point &dst_br) CV_OVERRIDE
-    {
-        RotationWarperBase<CylindricalProjector>::detectResultRoiByBorder(src_size, dst_tl, dst_br);
-    }
-};
-
-
-struct CV_EXPORTS FisheyeProjector : ProjectorBase
-{
-    void mapForward(float x, float y, float &u, float &v);
-    void mapBackward(float u, float v, float &x, float &y);
-};
-
-
-class CV_EXPORTS FisheyeWarper : public RotationWarperBase<FisheyeProjector>
-{
-public:
-    FisheyeWarper(float scale) { projector_.scale = scale; }
-};
-
-
-struct CV_EXPORTS StereographicProjector : ProjectorBase
-{
-    void mapForward(float x, float y, float &u, float &v);
-    void mapBackward(float u, float v, float &x, float &y);
-};
-
-
-class CV_EXPORTS StereographicWarper : public RotationWarperBase<StereographicProjector>
-{
-public:
-    StereographicWarper(float scale) { projector_.scale = scale; }
-};
-
-
-struct CV_EXPORTS CompressedRectilinearProjector : ProjectorBase
-{
-    float a, b;
-
-    void mapForward(float x, float y, float &u, float &v);
-    void mapBackward(float u, float v, float &x, float &y);
-};
-
-
-class CV_EXPORTS CompressedRectilinearWarper : public RotationWarperBase<CompressedRectilinearProjector>
-{
-public:
-    CompressedRectilinearWarper(float scale, float A = 1, float B = 1)
-    {
-        projector_.a = A;
-        projector_.b = B;
-        projector_.scale = scale;
-    }
-};
-
-
-struct CV_EXPORTS CompressedRectilinearPortraitProjector : ProjectorBase
-{
-    float a, b;
-
-    void mapForward(float x, float y, float &u, float &v);
-    void mapBackward(float u, float v, float &x, float &y);
-};
-
-
-class CV_EXPORTS CompressedRectilinearPortraitWarper : public RotationWarperBase<CompressedRectilinearPortraitProjector>
-{
-public:
-   CompressedRectilinearPortraitWarper(float scale, float A = 1, float B = 1)
-   {
-       projector_.a = A;
-       projector_.b = B;
-       projector_.scale = scale;
-   }
-};
-
-
-struct CV_EXPORTS PaniniProjector : ProjectorBase
-{
-    float a, b;
-
-    void mapForward(float x, float y, float &u, float &v);
-    void mapBackward(float u, float v, float &x, float &y);
-};
-
-
-class CV_EXPORTS PaniniWarper : public RotationWarperBase<PaniniProjector>
-{
-public:
-   PaniniWarper(float scale, float A = 1, float B = 1)
-   {
-       projector_.a = A;
-       projector_.b = B;
-       projector_.scale = scale;
-   }
-};
-
-
-struct CV_EXPORTS PaniniPortraitProjector : ProjectorBase
-{
-    float a, b;
-
-    void mapForward(float x, float y, float &u, float &v);
-    void mapBackward(float u, float v, float &x, float &y);
-};
-
-
-class CV_EXPORTS PaniniPortraitWarper : public RotationWarperBase<PaniniPortraitProjector>
-{
-public:
-   PaniniPortraitWarper(float scale, float A = 1, float B = 1)
-   {
-       projector_.a = A;
-       projector_.b = B;
-       projector_.scale = scale;
-   }
-
-};
-
-
-struct CV_EXPORTS MercatorProjector : ProjectorBase
-{
-    void mapForward(float x, float y, float &u, float &v);
-    void mapBackward(float u, float v, float &x, float &y);
-};
-
-
-class CV_EXPORTS MercatorWarper : public RotationWarperBase<MercatorProjector>
-{
-public:
-    MercatorWarper(float scale) { projector_.scale = scale; }
-};
-
-
-struct CV_EXPORTS TransverseMercatorProjector : ProjectorBase
-{
-    void mapForward(float x, float y, float &u, float &v);
-    void mapBackward(float u, float v, float &x, float &y);
-};
-
-
-class CV_EXPORTS TransverseMercatorWarper : public RotationWarperBase<TransverseMercatorProjector>
-{
-public:
-    TransverseMercatorWarper(float scale) { projector_.scale = scale; }
-};
-
-
-class CV_EXPORTS PlaneWarperGpu : public PlaneWarper
-{
-public:
-    PlaneWarperGpu(float scale = 1.f) : PlaneWarper(scale) {}
-
-    Rect buildMaps(Size src_size, InputArray K, InputArray R, OutputArray xmap, OutputArray ymap) CV_OVERRIDE
-    {
-        Rect result = buildMaps(src_size, K, R, d_xmap_, d_ymap_);
-        d_xmap_.download(xmap);
-        d_ymap_.download(ymap);
-        return result;
-    }
-
-    Rect buildMaps(Size src_size, InputArray K, InputArray R, InputArray T, OutputArray xmap, OutputArray ymap) CV_OVERRIDE
-    {
-        Rect result = buildMaps(src_size, K, R, T, d_xmap_, d_ymap_);
-        d_xmap_.download(xmap);
-        d_ymap_.download(ymap);
-        return result;
-    }
-
-    Point warp(InputArray src, InputArray K, InputArray R, int interp_mode, int border_mode,
-               OutputArray dst) CV_OVERRIDE
-    {
-        d_src_.upload(src);
-        Point result = warp(d_src_, K, R, interp_mode, border_mode, d_dst_);
-        d_dst_.download(dst);
-        return result;
-    }
-
-    Point warp(InputArray src, InputArray K, InputArray R, InputArray T, int interp_mode, int border_mode,
-               OutputArray dst) CV_OVERRIDE
-    {
-        d_src_.upload(src);
-        Point result = warp(d_src_, K, R, T, interp_mode, border_mode, d_dst_);
-        d_dst_.download(dst);
-        return result;
-    }
-
-    Rect buildMaps(Size src_size, InputArray K, InputArray R, cuda::GpuMat & xmap, cuda::GpuMat & ymap);
-
-    Rect buildMaps(Size src_size, InputArray K, InputArray R, InputArray T, cuda::GpuMat & xmap, cuda::GpuMat & ymap);
-
-    Point warp(const cuda::GpuMat & src, InputArray K, InputArray R, int interp_mode, int border_mode,
-               cuda::GpuMat & dst);
-
-    Point warp(const cuda::GpuMat & src, InputArray K, InputArray R, InputArray T, int interp_mode, int border_mode,
-               cuda::GpuMat & dst);
-
-private:
-    cuda::GpuMat d_xmap_, d_ymap_, d_src_, d_dst_;
-};
-
-
-class CV_EXPORTS SphericalWarperGpu : public SphericalWarper
-{
-public:
-    SphericalWarperGpu(float scale) : SphericalWarper(scale) {}
-
-    Rect buildMaps(Size src_size, InputArray K, InputArray R, OutputArray xmap, OutputArray ymap) CV_OVERRIDE
-    {
-        Rect result = buildMaps(src_size, K, R, d_xmap_, d_ymap_);
-        d_xmap_.download(xmap);
-        d_ymap_.download(ymap);
-        return result;
-    }
-
-    Point warp(InputArray src, InputArray K, InputArray R, int interp_mode, int border_mode,
-               OutputArray dst) CV_OVERRIDE
-    {
-        d_src_.upload(src);
-        Point result = warp(d_src_, K, R, interp_mode, border_mode, d_dst_);
-        d_dst_.download(dst);
-        return result;
-    }
-
-    Rect buildMaps(Size src_size, InputArray K, InputArray R, cuda::GpuMat & xmap, cuda::GpuMat & ymap);
-
-    Point warp(const cuda::GpuMat & src, InputArray K, InputArray R, int interp_mode, int border_mode,
-               cuda::GpuMat & dst);
-
-private:
-    cuda::GpuMat d_xmap_, d_ymap_, d_src_, d_dst_;
-};
-
-
-class CV_EXPORTS CylindricalWarperGpu : public CylindricalWarper
-{
-public:
-    CylindricalWarperGpu(float scale) : CylindricalWarper(scale) {}
-
-    Rect buildMaps(Size src_size, InputArray K, InputArray R, OutputArray xmap, OutputArray ymap) CV_OVERRIDE
-    {
-        Rect result = buildMaps(src_size, K, R, d_xmap_, d_ymap_);
-        d_xmap_.download(xmap);
-        d_ymap_.download(ymap);
-        return result;
-    }
-
-    Point warp(InputArray src, InputArray K, InputArray R, int interp_mode, int border_mode,
-               OutputArray dst) CV_OVERRIDE
-    {
-        d_src_.upload(src);
-        Point result = warp(d_src_, K, R, interp_mode, border_mode, d_dst_);
-        d_dst_.download(dst);
-        return result;
-    }
-
-    Rect buildMaps(Size src_size, InputArray K, InputArray R, cuda::GpuMat & xmap, cuda::GpuMat & ymap);
-
-    Point warp(const cuda::GpuMat & src, InputArray K, InputArray R, int interp_mode, int border_mode,
-               cuda::GpuMat & dst);
-
-private:
-    cuda::GpuMat d_xmap_, d_ymap_, d_src_, d_dst_;
-};
-
-
-struct CV_EXPORTS SphericalPortraitProjector : ProjectorBase
-{
-    void mapForward(float x, float y, float &u, float &v);
-    void mapBackward(float u, float v, float &x, float &y);
-};
-
-
-// Projects image onto unit sphere with origin at (0, 0, 0).
-// Poles are located NOT at (0, -1, 0) and (0, 1, 0) points, BUT at (1, 0, 0) and (-1, 0, 0) points.
-class CV_EXPORTS SphericalPortraitWarper : public RotationWarperBase<SphericalPortraitProjector>
-{
-public:
-    SphericalPortraitWarper(float scale) { projector_.scale = scale; }
-
-protected:
-    void detectResultRoi(Size src_size, Point &dst_tl, Point &dst_br) CV_OVERRIDE;
-};
-
-struct CV_EXPORTS CylindricalPortraitProjector : ProjectorBase
-{
-    void mapForward(float x, float y, float &u, float &v);
-    void mapBackward(float u, float v, float &x, float &y);
-};
-
-
-class CV_EXPORTS CylindricalPortraitWarper : public RotationWarperBase<CylindricalPortraitProjector>
-{
-public:
-    CylindricalPortraitWarper(float scale) { projector_.scale = scale; }
-
-protected:
-    void detectResultRoi(Size src_size, Point &dst_tl, Point &dst_br) CV_OVERRIDE
-    {
-        RotationWarperBase<CylindricalPortraitProjector>::detectResultRoiByBorder(src_size, dst_tl, dst_br);
-    }
-};
-
-struct CV_EXPORTS PlanePortraitProjector : ProjectorBase
-{
-    void mapForward(float x, float y, float &u, float &v);
-    void mapBackward(float u, float v, float &x, float &y);
-};
-
-
-class CV_EXPORTS PlanePortraitWarper : public RotationWarperBase<PlanePortraitProjector>
-{
-public:
-    PlanePortraitWarper(float scale) { projector_.scale = scale; }
-
-protected:
-    void detectResultRoi(Size src_size, Point &dst_tl, Point &dst_br) CV_OVERRIDE
-    {
-        RotationWarperBase<PlanePortraitProjector>::detectResultRoiByBorder(src_size, dst_tl, dst_br);
-    }
-};
-
-//! @} stitching_warp
-
-} // namespace detail
-} // namespace cv
-
-#include "warpers_inl.hpp"
-
-#endif // OPENCV_STITCHING_WARPERS_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/stitching/detail/warpers_inl.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/stitching/detail/warpers_inl.hpp
deleted file mode 100755
index f4a19d9..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/stitching/detail/warpers_inl.hpp
+++ /dev/null
@@ -1,774 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_STITCHING_WARPERS_INL_HPP
-#define OPENCV_STITCHING_WARPERS_INL_HPP
-
-#include "opencv2/core.hpp"
-#include "warpers.hpp" // Make your IDE see declarations
-#include <limits>
-
-//! @cond IGNORED
-
-namespace cv {
-namespace detail {
-
-template <class P>
-Point2f RotationWarperBase<P>::warpPoint(const Point2f &pt, InputArray K, InputArray R)
-{
-    projector_.setCameraParams(K, R);
-    Point2f uv;
-    projector_.mapForward(pt.x, pt.y, uv.x, uv.y);
-    return uv;
-}
-
-
-template <class P>
-Rect RotationWarperBase<P>::buildMaps(Size src_size, InputArray K, InputArray R, OutputArray _xmap, OutputArray _ymap)
-{
-    projector_.setCameraParams(K, R);
-
-    Point dst_tl, dst_br;
-    detectResultRoi(src_size, dst_tl, dst_br);
-
-    _xmap.create(dst_br.y - dst_tl.y + 1, dst_br.x - dst_tl.x + 1, CV_32F);
-    _ymap.create(dst_br.y - dst_tl.y + 1, dst_br.x - dst_tl.x + 1, CV_32F);
-
-    Mat xmap = _xmap.getMat(), ymap = _ymap.getMat();
-
-    float x, y;
-    for (int v = dst_tl.y; v <= dst_br.y; ++v)
-    {
-        for (int u = dst_tl.x; u <= dst_br.x; ++u)
-        {
-            projector_.mapBackward(static_cast<float>(u), static_cast<float>(v), x, y);
-            xmap.at<float>(v - dst_tl.y, u - dst_tl.x) = x;
-            ymap.at<float>(v - dst_tl.y, u - dst_tl.x) = y;
-        }
-    }
-
-    return Rect(dst_tl, dst_br);
-}
-
-
-template <class P>
-Point RotationWarperBase<P>::warp(InputArray src, InputArray K, InputArray R, int interp_mode, int border_mode,
-                                  OutputArray dst)
-{
-    UMat xmap, ymap;
-    Rect dst_roi = buildMaps(src.size(), K, R, xmap, ymap);
-
-    dst.create(dst_roi.height + 1, dst_roi.width + 1, src.type());
-    remap(src, dst, xmap, ymap, interp_mode, border_mode);
-
-    return dst_roi.tl();
-}
-
-
-template <class P>
-void RotationWarperBase<P>::warpBackward(InputArray src, InputArray K, InputArray R, int interp_mode, int border_mode,
-                                         Size dst_size, OutputArray dst)
-{
-    projector_.setCameraParams(K, R);
-
-    Point src_tl, src_br;
-    detectResultRoi(dst_size, src_tl, src_br);
-
-    Size size = src.size();
-    CV_Assert(src_br.x - src_tl.x + 1 == size.width && src_br.y - src_tl.y + 1 == size.height);
-
-    Mat xmap(dst_size, CV_32F);
-    Mat ymap(dst_size, CV_32F);
-
-    float u, v;
-    for (int y = 0; y < dst_size.height; ++y)
-    {
-        for (int x = 0; x < dst_size.width; ++x)
-        {
-            projector_.mapForward(static_cast<float>(x), static_cast<float>(y), u, v);
-            xmap.at<float>(y, x) = u - src_tl.x;
-            ymap.at<float>(y, x) = v - src_tl.y;
-        }
-    }
-
-    dst.create(dst_size, src.type());
-    remap(src, dst, xmap, ymap, interp_mode, border_mode);
-}
-
-
-template <class P>
-Rect RotationWarperBase<P>::warpRoi(Size src_size, InputArray K, InputArray R)
-{
-    projector_.setCameraParams(K, R);
-
-    Point dst_tl, dst_br;
-    detectResultRoi(src_size, dst_tl, dst_br);
-
-    return Rect(dst_tl, Point(dst_br.x + 1, dst_br.y + 1));
-}
-
-
-template <class P>
-void RotationWarperBase<P>::detectResultRoi(Size src_size, Point &dst_tl, Point &dst_br)
-{
-    float tl_uf = (std::numeric_limits<float>::max)();
-    float tl_vf = (std::numeric_limits<float>::max)();
-    float br_uf = -(std::numeric_limits<float>::max)();
-    float br_vf = -(std::numeric_limits<float>::max)();
-
-    float u, v;
-    for (int y = 0; y < src_size.height; ++y)
-    {
-        for (int x = 0; x < src_size.width; ++x)
-        {
-            projector_.mapForward(static_cast<float>(x), static_cast<float>(y), u, v);
-            tl_uf = (std::min)(tl_uf, u); tl_vf = (std::min)(tl_vf, v);
-            br_uf = (std::max)(br_uf, u); br_vf = (std::max)(br_vf, v);
-        }
-    }
-
-    dst_tl.x = static_cast<int>(tl_uf);
-    dst_tl.y = static_cast<int>(tl_vf);
-    dst_br.x = static_cast<int>(br_uf);
-    dst_br.y = static_cast<int>(br_vf);
-}
-
-
-template <class P>
-void RotationWarperBase<P>::detectResultRoiByBorder(Size src_size, Point &dst_tl, Point &dst_br)
-{
-    float tl_uf = (std::numeric_limits<float>::max)();
-    float tl_vf = (std::numeric_limits<float>::max)();
-    float br_uf = -(std::numeric_limits<float>::max)();
-    float br_vf = -(std::numeric_limits<float>::max)();
-
-    float u, v;
-    for (float x = 0; x < src_size.width; ++x)
-    {
-        projector_.mapForward(static_cast<float>(x), 0, u, v);
-        tl_uf = (std::min)(tl_uf, u); tl_vf = (std::min)(tl_vf, v);
-        br_uf = (std::max)(br_uf, u); br_vf = (std::max)(br_vf, v);
-
-        projector_.mapForward(static_cast<float>(x), static_cast<float>(src_size.height - 1), u, v);
-        tl_uf = (std::min)(tl_uf, u); tl_vf = (std::min)(tl_vf, v);
-        br_uf = (std::max)(br_uf, u); br_vf = (std::max)(br_vf, v);
-    }
-    for (int y = 0; y < src_size.height; ++y)
-    {
-        projector_.mapForward(0, static_cast<float>(y), u, v);
-        tl_uf = (std::min)(tl_uf, u); tl_vf = (std::min)(tl_vf, v);
-        br_uf = (std::max)(br_uf, u); br_vf = (std::max)(br_vf, v);
-
-        projector_.mapForward(static_cast<float>(src_size.width - 1), static_cast<float>(y), u, v);
-        tl_uf = (std::min)(tl_uf, u); tl_vf = (std::min)(tl_vf, v);
-        br_uf = (std::max)(br_uf, u); br_vf = (std::max)(br_vf, v);
-    }
-
-    dst_tl.x = static_cast<int>(tl_uf);
-    dst_tl.y = static_cast<int>(tl_vf);
-    dst_br.x = static_cast<int>(br_uf);
-    dst_br.y = static_cast<int>(br_vf);
-}
-
-
-inline
-void PlaneProjector::mapForward(float x, float y, float &u, float &v)
-{
-    float x_ = r_kinv[0] * x + r_kinv[1] * y + r_kinv[2];
-    float y_ = r_kinv[3] * x + r_kinv[4] * y + r_kinv[5];
-    float z_ = r_kinv[6] * x + r_kinv[7] * y + r_kinv[8];
-
-    x_ = t[0] + x_ / z_ * (1 - t[2]);
-    y_ = t[1] + y_ / z_ * (1 - t[2]);
-
-    u = scale * x_;
-    v = scale * y_;
-}
-
-
-inline
-void PlaneProjector::mapBackward(float u, float v, float &x, float &y)
-{
-    u = u / scale - t[0];
-    v = v / scale - t[1];
-
-    float z;
-    x = k_rinv[0] * u + k_rinv[1] * v + k_rinv[2] * (1 - t[2]);
-    y = k_rinv[3] * u + k_rinv[4] * v + k_rinv[5] * (1 - t[2]);
-    z = k_rinv[6] * u + k_rinv[7] * v + k_rinv[8] * (1 - t[2]);
-
-    x /= z;
-    y /= z;
-}
-
-
-inline
-void SphericalProjector::mapForward(float x, float y, float &u, float &v)
-{
-    float x_ = r_kinv[0] * x + r_kinv[1] * y + r_kinv[2];
-    float y_ = r_kinv[3] * x + r_kinv[4] * y + r_kinv[5];
-    float z_ = r_kinv[6] * x + r_kinv[7] * y + r_kinv[8];
-
-    u = scale * atan2f(x_, z_);
-    float w = y_ / sqrtf(x_ * x_ + y_ * y_ + z_ * z_);
-    v = scale * (static_cast<float>(CV_PI) - acosf(w == w ? w : 0));
-}
-
-
-inline
-void SphericalProjector::mapBackward(float u, float v, float &x, float &y)
-{
-    u /= scale;
-    v /= scale;
-
-    float sinv = sinf(static_cast<float>(CV_PI) - v);
-    float x_ = sinv * sinf(u);
-    float y_ = cosf(static_cast<float>(CV_PI) - v);
-    float z_ = sinv * cosf(u);
-
-    float z;
-    x = k_rinv[0] * x_ + k_rinv[1] * y_ + k_rinv[2] * z_;
-    y = k_rinv[3] * x_ + k_rinv[4] * y_ + k_rinv[5] * z_;
-    z = k_rinv[6] * x_ + k_rinv[7] * y_ + k_rinv[8] * z_;
-
-    if (z > 0) { x /= z; y /= z; }
-    else x = y = -1;
-}
-
-
-inline
-void CylindricalProjector::mapForward(float x, float y, float &u, float &v)
-{
-    float x_ = r_kinv[0] * x + r_kinv[1] * y + r_kinv[2];
-    float y_ = r_kinv[3] * x + r_kinv[4] * y + r_kinv[5];
-    float z_ = r_kinv[6] * x + r_kinv[7] * y + r_kinv[8];
-
-    u = scale * atan2f(x_, z_);
-    v = scale * y_ / sqrtf(x_ * x_ + z_ * z_);
-}
-
-
-inline
-void CylindricalProjector::mapBackward(float u, float v, float &x, float &y)
-{
-    u /= scale;
-    v /= scale;
-
-    float x_ = sinf(u);
-    float y_ = v;
-    float z_ = cosf(u);
-
-    float z;
-    x = k_rinv[0] * x_ + k_rinv[1] * y_ + k_rinv[2] * z_;
-    y = k_rinv[3] * x_ + k_rinv[4] * y_ + k_rinv[5] * z_;
-    z = k_rinv[6] * x_ + k_rinv[7] * y_ + k_rinv[8] * z_;
-
-    if (z > 0) { x /= z; y /= z; }
-    else x = y = -1;
-}
-
-inline
-void FisheyeProjector::mapForward(float x, float y, float &u, float &v)
-{
-    float x_ = r_kinv[0] * x + r_kinv[1] * y + r_kinv[2];
-    float y_ = r_kinv[3] * x + r_kinv[4] * y + r_kinv[5];
-    float z_ = r_kinv[6] * x + r_kinv[7] * y + r_kinv[8];
-
-    float u_ = atan2f(x_, z_);
-    float v_ = (float)CV_PI - acosf(y_ / sqrtf(x_ * x_ + y_ * y_ + z_ * z_));
-
-    u = scale * v_ * cosf(u_);
-    v = scale * v_ * sinf(u_);
-}
-
-inline
-void FisheyeProjector::mapBackward(float u, float v, float &x, float &y)
-{
-    u /= scale;
-    v /= scale;
-
-    float u_ = atan2f(v, u);
-    float v_ = sqrtf(u*u + v*v);
-
-    float sinv = sinf((float)CV_PI - v_);
-    float x_ = sinv * sinf(u_);
-    float y_ = cosf((float)CV_PI - v_);
-    float z_ = sinv * cosf(u_);
-
-    float z;
-    x = k_rinv[0] * x_ + k_rinv[1] * y_ + k_rinv[2] * z_;
-    y = k_rinv[3] * x_ + k_rinv[4] * y_ + k_rinv[5] * z_;
-    z = k_rinv[6] * x_ + k_rinv[7] * y_ + k_rinv[8] * z_;
-
-    if (z > 0) { x /= z; y /= z; }
-    else x = y = -1;
-}
-
-inline
-void StereographicProjector::mapForward(float x, float y, float &u, float &v)
-{
-    float x_ = r_kinv[0] * x + r_kinv[1] * y + r_kinv[2];
-    float y_ = r_kinv[3] * x + r_kinv[4] * y + r_kinv[5];
-    float z_ = r_kinv[6] * x + r_kinv[7] * y + r_kinv[8];
-
-    float u_ = atan2f(x_, z_);
-    float v_ = (float)CV_PI - acosf(y_ / sqrtf(x_ * x_ + y_ * y_ + z_ * z_));
-
-    float r = sinf(v_) / (1 - cosf(v_));
-
-    u = scale * r * cos(u_);
-    v = scale * r * sin(u_);
-}
-
-inline
-void StereographicProjector::mapBackward(float u, float v, float &x, float &y)
-{
-    u /= scale;
-    v /= scale;
-
-    float u_ = atan2f(v, u);
-    float r = sqrtf(u*u + v*v);
-    float v_ = 2 * atanf(1.f / r);
-
-    float sinv = sinf((float)CV_PI - v_);
-    float x_ = sinv * sinf(u_);
-    float y_ = cosf((float)CV_PI - v_);
-    float z_ = sinv * cosf(u_);
-
-    float z;
-    x = k_rinv[0] * x_ + k_rinv[1] * y_ + k_rinv[2] * z_;
-    y = k_rinv[3] * x_ + k_rinv[4] * y_ + k_rinv[5] * z_;
-    z = k_rinv[6] * x_ + k_rinv[7] * y_ + k_rinv[8] * z_;
-
-    if (z > 0) { x /= z; y /= z; }
-    else x = y = -1;
-}
-
-inline
-void CompressedRectilinearProjector::mapForward(float x, float y, float &u, float &v)
-{
-    float x_ = r_kinv[0] * x + r_kinv[1] * y + r_kinv[2];
-    float y_ = r_kinv[3] * x + r_kinv[4] * y + r_kinv[5];
-    float z_ = r_kinv[6] * x + r_kinv[7] * y + r_kinv[8];
-
-    float u_ = atan2f(x_, z_);
-    float v_ = asinf(y_ / sqrtf(x_ * x_ + y_ * y_ + z_ * z_));
-
-    u = scale * a * tanf(u_ / a);
-    v = scale * b * tanf(v_) / cosf(u_);
-}
-
-inline
-void CompressedRectilinearProjector::mapBackward(float u, float v, float &x, float &y)
-{
-    u /= scale;
-    v /= scale;
-
-    float aatg = a * atanf(u / a);
-    float u_ = aatg;
-    float v_ = atanf(v * cosf(aatg) / b);
-
-    float cosv = cosf(v_);
-    float x_ = cosv * sinf(u_);
-    float y_ = sinf(v_);
-    float z_ = cosv * cosf(u_);
-
-    float z;
-    x = k_rinv[0] * x_ + k_rinv[1] * y_ + k_rinv[2] * z_;
-    y = k_rinv[3] * x_ + k_rinv[4] * y_ + k_rinv[5] * z_;
-    z = k_rinv[6] * x_ + k_rinv[7] * y_ + k_rinv[8] * z_;
-
-    if (z > 0) { x /= z; y /= z; }
-    else x = y = -1;
-}
-
-inline
-void CompressedRectilinearPortraitProjector::mapForward(float x, float y, float &u, float &v)
-{
-    float y_ = r_kinv[0] * x + r_kinv[1] * y + r_kinv[2];
-    float x_ = r_kinv[3] * x + r_kinv[4] * y + r_kinv[5];
-    float z_ = r_kinv[6] * x + r_kinv[7] * y + r_kinv[8];
-
-    float u_ = atan2f(x_, z_);
-    float v_ = asinf(y_ / sqrtf(x_ * x_ + y_ * y_ + z_ * z_));
-
-    u = - scale * a * tanf(u_ / a);
-    v = scale * b * tanf(v_) / cosf(u_);
-}
-
-inline
-void CompressedRectilinearPortraitProjector::mapBackward(float u, float v, float &x, float &y)
-{
-    u /= - scale;
-    v /= scale;
-
-    float aatg = a * atanf(u / a);
-    float u_ = aatg;
-    float v_ = atanf(v * cosf( aatg ) / b);
-
-    float cosv = cosf(v_);
-    float y_ = cosv * sinf(u_);
-    float x_ = sinf(v_);
-    float z_ = cosv * cosf(u_);
-
-    float z;
-    x = k_rinv[0] * x_ + k_rinv[1] * y_ + k_rinv[2] * z_;
-    y = k_rinv[3] * x_ + k_rinv[4] * y_ + k_rinv[5] * z_;
-    z = k_rinv[6] * x_ + k_rinv[7] * y_ + k_rinv[8] * z_;
-
-    if (z > 0) { x /= z; y /= z; }
-    else x = y = -1;
-}
-
-inline
-void PaniniProjector::mapForward(float x, float y, float &u, float &v)
-{
-    float x_ = r_kinv[0] * x + r_kinv[1] * y + r_kinv[2];
-    float y_ = r_kinv[3] * x + r_kinv[4] * y + r_kinv[5];
-    float z_ = r_kinv[6] * x + r_kinv[7] * y + r_kinv[8];
-
-    float u_ = atan2f(x_, z_);
-    float v_ = asinf(y_ / sqrtf(x_ * x_ + y_ * y_ + z_ * z_));
-
-    float tg = a * tanf(u_ / a);
-    u = scale * tg;
-
-    float sinu = sinf(u_);
-    if ( fabs(sinu) < 1E-7 )
-        v = scale * b * tanf(v_);
-    else
-        v = scale * b * tg * tanf(v_) / sinu;
-}
-
-inline
-void PaniniProjector::mapBackward(float u, float v, float &x, float &y)
-{
-    u /= scale;
-    v /= scale;
-
-    float lamda = a * atanf(u / a);
-    float u_ = lamda;
-
-    float v_;
-    if ( fabs(lamda) > 1E-7)
-        v_ = atanf(v * sinf(lamda) / (b * a * tanf(lamda / a)));
-    else
-        v_ = atanf(v / b);
-
-    float cosv = cosf(v_);
-    float x_ = cosv * sinf(u_);
-    float y_ = sinf(v_);
-    float z_ = cosv * cosf(u_);
-
-    float z;
-    x = k_rinv[0] * x_ + k_rinv[1] * y_ + k_rinv[2] * z_;
-    y = k_rinv[3] * x_ + k_rinv[4] * y_ + k_rinv[5] * z_;
-    z = k_rinv[6] * x_ + k_rinv[7] * y_ + k_rinv[8] * z_;
-
-    if (z > 0) { x /= z; y /= z; }
-    else x = y = -1;
-}
-
-inline
-void PaniniPortraitProjector::mapForward(float x, float y, float &u, float &v)
-{
-    float y_ = r_kinv[0] * x + r_kinv[1] * y + r_kinv[2];
-    float x_ = r_kinv[3] * x + r_kinv[4] * y + r_kinv[5];
-    float z_ = r_kinv[6] * x + r_kinv[7] * y + r_kinv[8];
-
-    float u_ = atan2f(x_, z_);
-    float v_ = asinf(y_ / sqrtf(x_ * x_ + y_ * y_ + z_ * z_));
-
-    float tg = a * tanf(u_ / a);
-    u = - scale * tg;
-
-    float sinu = sinf( u_ );
-    if ( fabs(sinu) < 1E-7 )
-        v = scale * b * tanf(v_);
-    else
-        v = scale * b * tg * tanf(v_) / sinu;
-}
-
-inline
-void PaniniPortraitProjector::mapBackward(float u, float v, float &x, float &y)
-{
-    u /= - scale;
-    v /= scale;
-
-    float lamda = a * atanf(u / a);
-    float u_ = lamda;
-
-    float v_;
-    if ( fabs(lamda) > 1E-7)
-        v_ = atanf(v * sinf(lamda) / (b * a * tanf(lamda/a)));
-    else
-        v_ = atanf(v / b);
-
-    float cosv = cosf(v_);
-    float y_ = cosv * sinf(u_);
-    float x_ = sinf(v_);
-    float z_ = cosv * cosf(u_);
-
-    float z;
-    x = k_rinv[0] * x_ + k_rinv[1] * y_ + k_rinv[2] * z_;
-    y = k_rinv[3] * x_ + k_rinv[4] * y_ + k_rinv[5] * z_;
-    z = k_rinv[6] * x_ + k_rinv[7] * y_ + k_rinv[8] * z_;
-
-    if (z > 0) { x /= z; y /= z; }
-    else x = y = -1;
-}
-
-inline
-void MercatorProjector::mapForward(float x, float y, float &u, float &v)
-{
-    float x_ = r_kinv[0] * x + r_kinv[1] * y + r_kinv[2];
-    float y_ = r_kinv[3] * x + r_kinv[4] * y + r_kinv[5];
-    float z_ = r_kinv[6] * x + r_kinv[7] * y + r_kinv[8];
-
-    float u_ = atan2f(x_, z_);
-    float v_ = asinf(y_ / sqrtf(x_ * x_ + y_ * y_ + z_ * z_));
-
-    u = scale * u_;
-    v = scale * logf( tanf( (float)(CV_PI/4) + v_/2 ) );
-}
-
-inline
-void MercatorProjector::mapBackward(float u, float v, float &x, float &y)
-{
-    u /= scale;
-    v /= scale;
-
-    float v_ = atanf( sinhf(v) );
-    float u_ = u;
-
-    float cosv = cosf(v_);
-    float x_ = cosv * sinf(u_);
-    float y_ = sinf(v_);
-    float z_ = cosv * cosf(u_);
-
-    float z;
-    x = k_rinv[0] * x_ + k_rinv[1] * y_ + k_rinv[2] * z_;
-    y = k_rinv[3] * x_ + k_rinv[4] * y_ + k_rinv[5] * z_;
-    z = k_rinv[6] * x_ + k_rinv[7] * y_ + k_rinv[8] * z_;
-
-    if (z > 0) { x /= z; y /= z; }
-    else x = y = -1;
-}
-
-inline
-void TransverseMercatorProjector::mapForward(float x, float y, float &u, float &v)
-{
-    float x_ = r_kinv[0] * x + r_kinv[1] * y + r_kinv[2];
-    float y_ = r_kinv[3] * x + r_kinv[4] * y + r_kinv[5];
-    float z_ = r_kinv[6] * x + r_kinv[7] * y + r_kinv[8];
-
-    float u_ = atan2f(x_, z_);
-    float v_ = asinf(y_ / sqrtf(x_ * x_ + y_ * y_ + z_ * z_));
-
-    float B = cosf(v_) * sinf(u_);
-
-    u = scale / 2 * logf( (1+B) / (1-B) );
-    v = scale * atan2f(tanf(v_), cosf(u_));
-}
-
-inline
-void TransverseMercatorProjector::mapBackward(float u, float v, float &x, float &y)
-{
-    u /= scale;
-    v /= scale;
-
-    float v_ = asinf( sinf(v) / coshf(u) );
-    float u_ = atan2f( sinhf(u), cos(v) );
-
-    float cosv = cosf(v_);
-    float x_ = cosv * sinf(u_);
-    float y_ = sinf(v_);
-    float z_ = cosv * cosf(u_);
-
-    float z;
-    x = k_rinv[0] * x_ + k_rinv[1] * y_ + k_rinv[2] * z_;
-    y = k_rinv[3] * x_ + k_rinv[4] * y_ + k_rinv[5] * z_;
-    z = k_rinv[6] * x_ + k_rinv[7] * y_ + k_rinv[8] * z_;
-
-    if (z > 0) { x /= z; y /= z; }
-    else x = y = -1;
-}
-
-inline
-void SphericalPortraitProjector::mapForward(float x, float y, float &u0, float &v0)
-{
-    float x0_ = r_kinv[0] * x + r_kinv[1] * y + r_kinv[2];
-    float y0_ = r_kinv[3] * x + r_kinv[4] * y + r_kinv[5];
-    float z_ = r_kinv[6] * x + r_kinv[7] * y + r_kinv[8];
-
-    float x_ = y0_;
-    float y_ = x0_;
-    float u, v;
-
-    u = scale * atan2f(x_, z_);
-    v = scale * (static_cast<float>(CV_PI) - acosf(y_ / sqrtf(x_ * x_ + y_ * y_ + z_ * z_)));
-
-    u0 = -u;//v;
-    v0 = v;//u;
-}
-
-
-inline
-void SphericalPortraitProjector::mapBackward(float u0, float v0, float &x, float &y)
-{
-    float u, v;
-    u = -u0;//v0;
-    v = v0;//u0;
-
-    u /= scale;
-    v /= scale;
-
-    float sinv = sinf(static_cast<float>(CV_PI) - v);
-    float x0_ = sinv * sinf(u);
-    float y0_ = cosf(static_cast<float>(CV_PI) - v);
-    float z_ = sinv * cosf(u);
-
-    float x_ = y0_;
-    float y_ = x0_;
-
-    float z;
-    x = k_rinv[0] * x_ + k_rinv[1] * y_ + k_rinv[2] * z_;
-    y = k_rinv[3] * x_ + k_rinv[4] * y_ + k_rinv[5] * z_;
-    z = k_rinv[6] * x_ + k_rinv[7] * y_ + k_rinv[8] * z_;
-
-    if (z > 0) { x /= z; y /= z; }
-    else x = y = -1;
-}
-
-inline
-void CylindricalPortraitProjector::mapForward(float x, float y, float &u0, float &v0)
-{
-    float x0_ = r_kinv[0] * x + r_kinv[1] * y + r_kinv[2];
-    float y0_ = r_kinv[3] * x + r_kinv[4] * y + r_kinv[5];
-    float z_  = r_kinv[6] * x + r_kinv[7] * y + r_kinv[8];
-
-    float x_ = y0_;
-    float y_ = x0_;
-    float u, v;
-
-    u = scale * atan2f(x_, z_);
-    v = scale * y_ / sqrtf(x_ * x_ + z_ * z_);
-
-    u0 = -u;//v;
-    v0 = v;//u;
-}
-
-
-inline
-void CylindricalPortraitProjector::mapBackward(float u0, float v0, float &x, float &y)
-{
-    float u, v;
-    u = -u0;//v0;
-    v = v0;//u0;
-
-    u /= scale;
-    v /= scale;
-
-    float x0_ = sinf(u);
-    float y0_ = v;
-    float z_  = cosf(u);
-
-    float x_ = y0_;
-    float y_ = x0_;
-
-    float z;
-    x = k_rinv[0] * x_ + k_rinv[1] * y_ + k_rinv[2] * z_;
-    y = k_rinv[3] * x_ + k_rinv[4] * y_ + k_rinv[5] * z_;
-    z = k_rinv[6] * x_ + k_rinv[7] * y_ + k_rinv[8] * z_;
-
-    if (z > 0) { x /= z; y /= z; }
-    else x = y = -1;
-}
-
-inline
-void PlanePortraitProjector::mapForward(float x, float y, float &u0, float &v0)
-{
-    float x0_ = r_kinv[0] * x + r_kinv[1] * y + r_kinv[2];
-    float y0_ = r_kinv[3] * x + r_kinv[4] * y + r_kinv[5];
-    float z_  = r_kinv[6] * x + r_kinv[7] * y + r_kinv[8];
-
-    float x_ = y0_;
-    float y_ = x0_;
-
-    x_ = t[0] + x_ / z_ * (1 - t[2]);
-    y_ = t[1] + y_ / z_ * (1 - t[2]);
-
-    float u,v;
-    u = scale * x_;
-    v = scale * y_;
-
-    u0 = -u;
-    v0 = v;
-}
-
-
-inline
-void PlanePortraitProjector::mapBackward(float u0, float v0, float &x, float &y)
-{
-    float u, v;
-    u = -u0;
-    v = v0;
-
-    u = u / scale - t[0];
-    v = v / scale - t[1];
-
-    float z;
-    x = k_rinv[0] * v + k_rinv[1] * u + k_rinv[2] * (1 - t[2]);
-    y = k_rinv[3] * v + k_rinv[4] * u + k_rinv[5] * (1 - t[2]);
-    z = k_rinv[6] * v + k_rinv[7] * u + k_rinv[8] * (1 - t[2]);
-
-    x /= z;
-    y /= z;
-}
-
-
-} // namespace detail
-} // namespace cv
-
-//! @endcond
-
-#endif // OPENCV_STITCHING_WARPERS_INL_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/stitching/warpers.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/stitching/warpers.hpp
deleted file mode 100755
index cf7699c..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/stitching/warpers.hpp
+++ /dev/null
@@ -1,192 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_STITCHING_WARPER_CREATORS_HPP
-#define OPENCV_STITCHING_WARPER_CREATORS_HPP
-
-#include "opencv2/stitching/detail/warpers.hpp"
-
-namespace cv {
-
-//! @addtogroup stitching_warp
-//! @{
-
-/** @brief Image warper factories base class.
- */
-class WarperCreator
-{
-public:
-    virtual ~WarperCreator() {}
-    virtual Ptr<detail::RotationWarper> create(float scale) const = 0;
-};
-
-/** @brief Plane warper factory class.
-  @sa detail::PlaneWarper
- */
-class PlaneWarper : public WarperCreator
-{
-public:
-    Ptr<detail::RotationWarper> create(float scale) const CV_OVERRIDE { return makePtr<detail::PlaneWarper>(scale); }
-};
-
-/** @brief Affine warper factory class.
-  @sa detail::AffineWarper
- */
-class AffineWarper : public WarperCreator
-{
-public:
-    Ptr<detail::RotationWarper> create(float scale) const CV_OVERRIDE { return makePtr<detail::AffineWarper>(scale); }
-};
-
-/** @brief Cylindrical warper factory class.
-@sa detail::CylindricalWarper
-*/
-class CylindricalWarper: public WarperCreator
-{
-public:
-    Ptr<detail::RotationWarper> create(float scale) const CV_OVERRIDE { return makePtr<detail::CylindricalWarper>(scale); }
-};
-
-/** @brief Spherical warper factory class */
-class SphericalWarper: public WarperCreator
-{
-public:
-    Ptr<detail::RotationWarper> create(float scale) const CV_OVERRIDE { return makePtr<detail::SphericalWarper>(scale); }
-};
-
-class FisheyeWarper : public WarperCreator
-{
-public:
-    Ptr<detail::RotationWarper> create(float scale) const CV_OVERRIDE { return makePtr<detail::FisheyeWarper>(scale); }
-};
-
-class StereographicWarper: public WarperCreator
-{
-public:
-    Ptr<detail::RotationWarper> create(float scale) const CV_OVERRIDE { return makePtr<detail::StereographicWarper>(scale); }
-};
-
-class CompressedRectilinearWarper: public WarperCreator
-{
-    float a, b;
-public:
-    CompressedRectilinearWarper(float A = 1, float B = 1)
-    {
-        a = A; b = B;
-    }
-    Ptr<detail::RotationWarper> create(float scale) const CV_OVERRIDE { return makePtr<detail::CompressedRectilinearWarper>(scale, a, b); }
-};
-
-class CompressedRectilinearPortraitWarper: public WarperCreator
-{
-    float a, b;
-public:
-    CompressedRectilinearPortraitWarper(float A = 1, float B = 1)
-    {
-        a = A; b = B;
-    }
-    Ptr<detail::RotationWarper> create(float scale) const CV_OVERRIDE { return makePtr<detail::CompressedRectilinearPortraitWarper>(scale, a, b); }
-};
-
-class PaniniWarper: public WarperCreator
-{
-    float a, b;
-public:
-    PaniniWarper(float A = 1, float B = 1)
-    {
-        a = A; b = B;
-    }
-    Ptr<detail::RotationWarper> create(float scale) const CV_OVERRIDE { return makePtr<detail::PaniniWarper>(scale, a, b); }
-};
-
-class PaniniPortraitWarper: public WarperCreator
-{
-    float a, b;
-public:
-    PaniniPortraitWarper(float A = 1, float B = 1)
-    {
-        a = A; b = B;
-    }
-    Ptr<detail::RotationWarper> create(float scale) const CV_OVERRIDE { return makePtr<detail::PaniniPortraitWarper>(scale, a, b); }
-};
-
-class MercatorWarper: public WarperCreator
-{
-public:
-    Ptr<detail::RotationWarper> create(float scale) const CV_OVERRIDE { return makePtr<detail::MercatorWarper>(scale); }
-};
-
-class TransverseMercatorWarper: public WarperCreator
-{
-public:
-    Ptr<detail::RotationWarper> create(float scale) const CV_OVERRIDE { return makePtr<detail::TransverseMercatorWarper>(scale); }
-};
-
-
-
-#ifdef HAVE_OPENCV_CUDAWARPING
-class PlaneWarperGpu: public WarperCreator
-{
-public:
-    Ptr<detail::RotationWarper> create(float scale) const CV_OVERRIDE { return makePtr<detail::PlaneWarperGpu>(scale); }
-};
-
-
-class CylindricalWarperGpu: public WarperCreator
-{
-public:
-    Ptr<detail::RotationWarper> create(float scale) const CV_OVERRIDE { return makePtr<detail::CylindricalWarperGpu>(scale); }
-};
-
-
-class SphericalWarperGpu: public WarperCreator
-{
-public:
-    Ptr<detail::RotationWarper> create(float scale) const CV_OVERRIDE { return makePtr<detail::SphericalWarperGpu>(scale); }
-};
-#endif
-
-//! @} stitching_warp
-
-} // namespace cv
-
-#endif // OPENCV_STITCHING_WARPER_CREATORS_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/superres.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/superres.hpp
deleted file mode 100755
index 16c11ac..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/superres.hpp
+++ /dev/null
@@ -1,207 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_SUPERRES_HPP
-#define OPENCV_SUPERRES_HPP
-
-#include "opencv2/core.hpp"
-#include "opencv2/superres/optical_flow.hpp"
-
-/**
-  @defgroup superres Super Resolution
-
-The Super Resolution module contains a set of functions and classes that can be used to solve the
-problem of resolution enhancement. There are a few methods implemented, most of them are described in
-the papers @cite Farsiu03 and @cite Mitzel09 .
-
- */
-
-namespace cv
-{
-    namespace superres
-    {
-
-//! @addtogroup superres
-//! @{
-
-        class CV_EXPORTS FrameSource
-        {
-        public:
-            virtual ~FrameSource();
-
-            virtual void nextFrame(OutputArray frame) = 0;
-            virtual void reset() = 0;
-        };
-
-        CV_EXPORTS Ptr<FrameSource> createFrameSource_Empty();
-
-        CV_EXPORTS Ptr<FrameSource> createFrameSource_Video(const String& fileName);
-        CV_EXPORTS Ptr<FrameSource> createFrameSource_Video_CUDA(const String& fileName);
-
-        CV_EXPORTS Ptr<FrameSource> createFrameSource_Camera(int deviceId = 0);
-
-        /** @brief Base class for Super Resolution algorithms.
-
-        The class is only used to define the common interface for the whole family of Super Resolution
-        algorithms.
-         */
-        class CV_EXPORTS SuperResolution : public cv::Algorithm, public FrameSource
-        {
-        public:
-            /** @brief Set input frame source for Super Resolution algorithm.
-
-            @param frameSource Input frame source
-             */
-            void setInput(const Ptr<FrameSource>& frameSource);
-
-            /** @brief Process next frame from input and return output result.
-
-            @param frame Output result
-             */
-            void nextFrame(OutputArray frame) CV_OVERRIDE;
-            void reset() CV_OVERRIDE;
-
-            /** @brief Clear all inner buffers.
-            */
-            virtual void collectGarbage();
-
-            //! @brief Scale factor
-            /** @see setScale */
-            virtual int getScale() const = 0;
-            /** @copybrief getScale @see getScale */
-            virtual void setScale(int val) = 0;
-
-            //! @brief Iterations count
-            /** @see setIterations */
-            virtual int getIterations() const = 0;
-            /** @copybrief getIterations @see getIterations */
-            virtual void setIterations(int val) = 0;
-
-            //! @brief Asymptotic value of steepest descent method
-            /** @see setTau */
-            virtual double getTau() const = 0;
-            /** @copybrief getTau @see getTau */
-            virtual void setTau(double val) = 0;
-
-            //! @brief Weight parameter to balance data term and smoothness term
-            /** @see setLabmda */
-            virtual double getLabmda() const = 0;
-            /** @copybrief getLabmda @see getLabmda */
-            virtual void setLabmda(double val) = 0;
-
-            //! @brief Parameter of spacial distribution in Bilateral-TV
-            /** @see setAlpha */
-            virtual double getAlpha() const = 0;
-            /** @copybrief getAlpha @see getAlpha */
-            virtual void setAlpha(double val) = 0;
-
-            //! @brief Kernel size of Bilateral-TV filter
-            /** @see setKernelSize */
-            virtual int getKernelSize() const = 0;
-            /** @copybrief getKernelSize @see getKernelSize */
-            virtual void setKernelSize(int val) = 0;
-
-            //! @brief Gaussian blur kernel size
-            /** @see setBlurKernelSize */
-            virtual int getBlurKernelSize() const = 0;
-            /** @copybrief getBlurKernelSize @see getBlurKernelSize */
-            virtual void setBlurKernelSize(int val) = 0;
-
-            //! @brief Gaussian blur sigma
-            /** @see setBlurSigma */
-            virtual double getBlurSigma() const = 0;
-            /** @copybrief getBlurSigma @see getBlurSigma */
-            virtual void setBlurSigma(double val) = 0;
-
-            //! @brief Radius of the temporal search area
-            /** @see setTemporalAreaRadius */
-            virtual int getTemporalAreaRadius() const = 0;
-            /** @copybrief getTemporalAreaRadius @see getTemporalAreaRadius */
-            virtual void setTemporalAreaRadius(int val) = 0;
-
-            //! @brief Dense optical flow algorithm
-            /** @see setOpticalFlow */
-            virtual Ptr<cv::superres::DenseOpticalFlowExt> getOpticalFlow() const = 0;
-            /** @copybrief getOpticalFlow @see getOpticalFlow */
-            virtual void setOpticalFlow(const Ptr<cv::superres::DenseOpticalFlowExt> &val) = 0;
-
-        protected:
-            SuperResolution();
-
-            virtual void initImpl(Ptr<FrameSource>& frameSource) = 0;
-            virtual void processImpl(Ptr<FrameSource>& frameSource, OutputArray output) = 0;
-
-            bool isUmat_;
-
-        private:
-            Ptr<FrameSource> frameSource_;
-            bool firstCall_;
-        };
-
-        /** @brief Create Bilateral TV-L1 Super Resolution.
-
-        This class implements Super Resolution algorithm described in the papers @cite Farsiu03 and
-        @cite Mitzel09 .
-
-        Here are important members of the class that control the algorithm, which you can set after
-        constructing the class instance:
-
-        -   **int scale** Scale factor.
-        -   **int iterations** Iteration count.
-        -   **double tau** Asymptotic value of steepest descent method.
-        -   **double lambda** Weight parameter to balance data term and smoothness term.
-        -   **double alpha** Parameter of spacial distribution in Bilateral-TV.
-        -   **int btvKernelSize** Kernel size of Bilateral-TV filter.
-        -   **int blurKernelSize** Gaussian blur kernel size.
-        -   **double blurSigma** Gaussian blur sigma.
-        -   **int temporalAreaRadius** Radius of the temporal search area.
-        -   **Ptr\<DenseOpticalFlowExt\> opticalFlow** Dense optical flow algorithm.
-         */
-        CV_EXPORTS Ptr<SuperResolution> createSuperResolution_BTVL1();
-        CV_EXPORTS Ptr<SuperResolution> createSuperResolution_BTVL1_CUDA();
-
-//! @} superres
-
-    }
-}
-
-#endif // OPENCV_SUPERRES_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/superres/optical_flow.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/superres/optical_flow.hpp
deleted file mode 100755
index 07e7ca9..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/superres/optical_flow.hpp
+++ /dev/null
@@ -1,203 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_SUPERRES_OPTICAL_FLOW_HPP
-#define OPENCV_SUPERRES_OPTICAL_FLOW_HPP
-
-#include "opencv2/core.hpp"
-
-namespace cv
-{
-    namespace superres
-    {
-
-//! @addtogroup superres
-//! @{
-
-        class CV_EXPORTS DenseOpticalFlowExt : public cv::Algorithm
-        {
-        public:
-            virtual void calc(InputArray frame0, InputArray frame1, OutputArray flow1, OutputArray flow2 = noArray()) = 0;
-            virtual void collectGarbage() = 0;
-        };
-
-
-        class CV_EXPORTS FarnebackOpticalFlow : public virtual DenseOpticalFlowExt
-        {
-        public:
-            /** @see setPyrScale */
-            virtual double getPyrScale() const = 0;
-            /** @copybrief getPyrScale @see getPyrScale */
-            virtual void setPyrScale(double val) = 0;
-            /** @see setLevelsNumber */
-            virtual int getLevelsNumber() const = 0;
-            /** @copybrief getLevelsNumber @see getLevelsNumber */
-            virtual void setLevelsNumber(int val) = 0;
-            /** @see setWindowSize */
-            virtual int getWindowSize() const = 0;
-            /** @copybrief getWindowSize @see getWindowSize */
-            virtual void setWindowSize(int val) = 0;
-            /** @see setIterations */
-            virtual int getIterations() const = 0;
-            /** @copybrief getIterations @see getIterations */
-            virtual void setIterations(int val) = 0;
-            /** @see setPolyN */
-            virtual int getPolyN() const = 0;
-            /** @copybrief getPolyN @see getPolyN */
-            virtual void setPolyN(int val) = 0;
-            /** @see setPolySigma */
-            virtual double getPolySigma() const = 0;
-            /** @copybrief getPolySigma @see getPolySigma */
-            virtual void setPolySigma(double val) = 0;
-            /** @see setFlags */
-            virtual int getFlags() const = 0;
-            /** @copybrief getFlags @see getFlags */
-            virtual void setFlags(int val) = 0;
-        };
-        CV_EXPORTS Ptr<FarnebackOpticalFlow> createOptFlow_Farneback();
-        CV_EXPORTS Ptr<FarnebackOpticalFlow> createOptFlow_Farneback_CUDA();
-
-
-//        CV_EXPORTS Ptr<DenseOpticalFlowExt> createOptFlow_Simple();
-
-
-        class CV_EXPORTS DualTVL1OpticalFlow : public virtual DenseOpticalFlowExt
-        {
-        public:
-            /** @see setTau */
-            virtual double getTau() const = 0;
-            /** @copybrief getTau @see getTau */
-            virtual void setTau(double val) = 0;
-            /** @see setLambda */
-            virtual double getLambda() const = 0;
-            /** @copybrief getLambda @see getLambda */
-            virtual void setLambda(double val) = 0;
-            /** @see setTheta */
-            virtual double getTheta() const = 0;
-            /** @copybrief getTheta @see getTheta */
-            virtual void setTheta(double val) = 0;
-            /** @see setScalesNumber */
-            virtual int getScalesNumber() const = 0;
-            /** @copybrief getScalesNumber @see getScalesNumber */
-            virtual void setScalesNumber(int val) = 0;
-            /** @see setWarpingsNumber */
-            virtual int getWarpingsNumber() const = 0;
-            /** @copybrief getWarpingsNumber @see getWarpingsNumber */
-            virtual void setWarpingsNumber(int val) = 0;
-            /** @see setEpsilon */
-            virtual double getEpsilon() const = 0;
-            /** @copybrief getEpsilon @see getEpsilon */
-            virtual void setEpsilon(double val) = 0;
-            /** @see setIterations */
-            virtual int getIterations() const = 0;
-            /** @copybrief getIterations @see getIterations */
-            virtual void setIterations(int val) = 0;
-            /** @see setUseInitialFlow */
-            virtual bool getUseInitialFlow() const = 0;
-            /** @copybrief getUseInitialFlow @see getUseInitialFlow */
-            virtual void setUseInitialFlow(bool val) = 0;
-        };
-        CV_EXPORTS Ptr<DualTVL1OpticalFlow> createOptFlow_DualTVL1();
-        CV_EXPORTS Ptr<DualTVL1OpticalFlow> createOptFlow_DualTVL1_CUDA();
-
-
-        class CV_EXPORTS BroxOpticalFlow : public virtual DenseOpticalFlowExt
-        {
-        public:
-            //! @brief Flow smoothness
-            /** @see setAlpha */
-            virtual double getAlpha() const = 0;
-            /** @copybrief getAlpha @see getAlpha */
-            virtual void setAlpha(double val) = 0;
-            //! @brief Gradient constancy importance
-            /** @see setGamma */
-            virtual double getGamma() const = 0;
-            /** @copybrief getGamma @see getGamma */
-            virtual void setGamma(double val) = 0;
-            //! @brief Pyramid scale factor
-            /** @see setScaleFactor */
-            virtual double getScaleFactor() const = 0;
-            /** @copybrief getScaleFactor @see getScaleFactor */
-            virtual void setScaleFactor(double val) = 0;
-            //! @brief Number of lagged non-linearity iterations (inner loop)
-            /** @see setInnerIterations */
-            virtual int getInnerIterations() const = 0;
-            /** @copybrief getInnerIterations @see getInnerIterations */
-            virtual void setInnerIterations(int val) = 0;
-            //! @brief Number of warping iterations (number of pyramid levels)
-            /** @see setOuterIterations */
-            virtual int getOuterIterations() const = 0;
-            /** @copybrief getOuterIterations @see getOuterIterations */
-            virtual void setOuterIterations(int val) = 0;
-            //! @brief Number of linear system solver iterations
-            /** @see setSolverIterations */
-            virtual int getSolverIterations() const = 0;
-            /** @copybrief getSolverIterations @see getSolverIterations */
-            virtual void setSolverIterations(int val) = 0;
-        };
-        CV_EXPORTS Ptr<BroxOpticalFlow> createOptFlow_Brox_CUDA();
-
-
-        class PyrLKOpticalFlow : public virtual DenseOpticalFlowExt
-        {
-        public:
-            /** @see setWindowSize */
-            virtual int getWindowSize() const = 0;
-            /** @copybrief getWindowSize @see getWindowSize */
-            virtual void setWindowSize(int val) = 0;
-            /** @see setMaxLevel */
-            virtual int getMaxLevel() const = 0;
-            /** @copybrief getMaxLevel @see getMaxLevel */
-            virtual void setMaxLevel(int val) = 0;
-            /** @see setIterations */
-            virtual int getIterations() const = 0;
-            /** @copybrief getIterations @see getIterations */
-            virtual void setIterations(int val) = 0;
-        };
-        CV_EXPORTS Ptr<PyrLKOpticalFlow> createOptFlow_PyrLK_CUDA();
-
-//! @}
-
-    }
-}
-
-#endif // OPENCV_SUPERRES_OPTICAL_FLOW_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/video.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/video.hpp
deleted file mode 100755
index aa644a9..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/video.hpp
+++ /dev/null
@@ -1,63 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_VIDEO_HPP
-#define OPENCV_VIDEO_HPP
-
-/**
-  @defgroup video Video Analysis
-  @{
-    @defgroup video_motion Motion Analysis
-    @defgroup video_track Object Tracking
-    @defgroup video_c C API
-  @}
-*/
-
-#include "opencv2/video/tracking.hpp"
-#include "opencv2/video/background_segm.hpp"
-
-#ifndef DISABLE_OPENCV_24_COMPATIBILITY
-#include "opencv2/video/tracking_c.h"
-#endif
-
-#endif //OPENCV_VIDEO_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/video/background_segm.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/video/background_segm.hpp
deleted file mode 100755
index e1dfa15..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/video/background_segm.hpp
+++ /dev/null
@@ -1,317 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_BACKGROUND_SEGM_HPP
-#define OPENCV_BACKGROUND_SEGM_HPP
-
-#include "opencv2/core.hpp"
-
-namespace cv
-{
-
-//! @addtogroup video_motion
-//! @{
-
-/** @brief Base class for background/foreground segmentation. :
-
-The class is only used to define the common interface for the whole family of background/foreground
-segmentation algorithms.
- */
-class CV_EXPORTS_W BackgroundSubtractor : public Algorithm
-{
-public:
-    /** @brief Computes a foreground mask.
-
-    @param image Next video frame.
-    @param fgmask The output foreground mask as an 8-bit binary image.
-    @param learningRate The value between 0 and 1 that indicates how fast the background model is
-    learnt. Negative parameter value makes the algorithm to use some automatically chosen learning
-    rate. 0 means that the background model is not updated at all, 1 means that the background model
-    is completely reinitialized from the last frame.
-     */
-    CV_WRAP virtual void apply(InputArray image, OutputArray fgmask, double learningRate=-1) = 0;
-
-    /** @brief Computes a background image.
-
-    @param backgroundImage The output background image.
-
-    @note Sometimes the background image can be very blurry, as it contain the average background
-    statistics.
-     */
-    CV_WRAP virtual void getBackgroundImage(OutputArray backgroundImage) const = 0;
-};
-
-
-/** @brief Gaussian Mixture-based Background/Foreground Segmentation Algorithm.
-
-The class implements the Gaussian mixture model background subtraction described in @cite Zivkovic2004
-and @cite Zivkovic2006 .
- */
-class CV_EXPORTS_W BackgroundSubtractorMOG2 : public BackgroundSubtractor
-{
-public:
-    /** @brief Returns the number of last frames that affect the background model
-    */
-    CV_WRAP virtual int getHistory() const = 0;
-    /** @brief Sets the number of last frames that affect the background model
-    */
-    CV_WRAP virtual void setHistory(int history) = 0;
-
-    /** @brief Returns the number of gaussian components in the background model
-    */
-    CV_WRAP virtual int getNMixtures() const = 0;
-    /** @brief Sets the number of gaussian components in the background model.
-
-    The model needs to be reinitalized to reserve memory.
-    */
-    CV_WRAP virtual void setNMixtures(int nmixtures) = 0;//needs reinitialization!
-
-    /** @brief Returns the "background ratio" parameter of the algorithm
-
-    If a foreground pixel keeps semi-constant value for about backgroundRatio\*history frames, it's
-    considered background and added to the model as a center of a new component. It corresponds to TB
-    parameter in the paper.
-     */
-    CV_WRAP virtual double getBackgroundRatio() const = 0;
-    /** @brief Sets the "background ratio" parameter of the algorithm
-    */
-    CV_WRAP virtual void setBackgroundRatio(double ratio) = 0;
-
-    /** @brief Returns the variance threshold for the pixel-model match
-
-    The main threshold on the squared Mahalanobis distance to decide if the sample is well described by
-    the background model or not. Related to Cthr from the paper.
-     */
-    CV_WRAP virtual double getVarThreshold() const = 0;
-    /** @brief Sets the variance threshold for the pixel-model match
-    */
-    CV_WRAP virtual void setVarThreshold(double varThreshold) = 0;
-
-    /** @brief Returns the variance threshold for the pixel-model match used for new mixture component generation
-
-    Threshold for the squared Mahalanobis distance that helps decide when a sample is close to the
-    existing components (corresponds to Tg in the paper). If a pixel is not close to any component, it
-    is considered foreground or added as a new component. 3 sigma =\> Tg=3\*3=9 is default. A smaller Tg
-    value generates more components. A higher Tg value may result in a small number of components but
-    they can grow too large.
-     */
-    CV_WRAP virtual double getVarThresholdGen() const = 0;
-    /** @brief Sets the variance threshold for the pixel-model match used for new mixture component generation
-    */
-    CV_WRAP virtual void setVarThresholdGen(double varThresholdGen) = 0;
-
-    /** @brief Returns the initial variance of each gaussian component
-    */
-    CV_WRAP virtual double getVarInit() const = 0;
-    /** @brief Sets the initial variance of each gaussian component
-    */
-    CV_WRAP virtual void setVarInit(double varInit) = 0;
-
-    CV_WRAP virtual double getVarMin() const = 0;
-    CV_WRAP virtual void setVarMin(double varMin) = 0;
-
-    CV_WRAP virtual double getVarMax() const = 0;
-    CV_WRAP virtual void setVarMax(double varMax) = 0;
-
-    /** @brief Returns the complexity reduction threshold
-
-    This parameter defines the number of samples needed to accept to prove the component exists. CT=0.05
-    is a default value for all the samples. By setting CT=0 you get an algorithm very similar to the
-    standard Stauffer&Grimson algorithm.
-     */
-    CV_WRAP virtual double getComplexityReductionThreshold() const = 0;
-    /** @brief Sets the complexity reduction threshold
-    */
-    CV_WRAP virtual void setComplexityReductionThreshold(double ct) = 0;
-
-    /** @brief Returns the shadow detection flag
-
-    If true, the algorithm detects shadows and marks them. See createBackgroundSubtractorMOG2 for
-    details.
-     */
-    CV_WRAP virtual bool getDetectShadows() const = 0;
-    /** @brief Enables or disables shadow detection
-    */
-    CV_WRAP virtual void setDetectShadows(bool detectShadows) = 0;
-
-    /** @brief Returns the shadow value
-
-    Shadow value is the value used to mark shadows in the foreground mask. Default value is 127. Value 0
-    in the mask always means background, 255 means foreground.
-     */
-    CV_WRAP virtual int getShadowValue() const = 0;
-    /** @brief Sets the shadow value
-    */
-    CV_WRAP virtual void setShadowValue(int value) = 0;
-
-    /** @brief Returns the shadow threshold
-
-    A shadow is detected if pixel is a darker version of the background. The shadow threshold (Tau in
-    the paper) is a threshold defining how much darker the shadow can be. Tau= 0.5 means that if a pixel
-    is more than twice darker then it is not shadow. See Prati, Mikic, Trivedi and Cucchiara,
-    *Detecting Moving Shadows...*, IEEE PAMI,2003.
-     */
-    CV_WRAP virtual double getShadowThreshold() const = 0;
-    /** @brief Sets the shadow threshold
-    */
-    CV_WRAP virtual void setShadowThreshold(double threshold) = 0;
-
-    /** @brief Computes a foreground mask.
-
-    @param image Next video frame. Floating point frame will be used without scaling and should be in range \f$[0,255]\f$.
-    @param fgmask The output foreground mask as an 8-bit binary image.
-    @param learningRate The value between 0 and 1 that indicates how fast the background model is
-    learnt. Negative parameter value makes the algorithm to use some automatically chosen learning
-    rate. 0 means that the background model is not updated at all, 1 means that the background model
-    is completely reinitialized from the last frame.
-     */
-    CV_WRAP virtual void apply(InputArray image, OutputArray fgmask, double learningRate=-1) CV_OVERRIDE = 0;
-};
-
-/** @brief Creates MOG2 Background Subtractor
-
-@param history Length of the history.
-@param varThreshold Threshold on the squared Mahalanobis distance between the pixel and the model
-to decide whether a pixel is well described by the background model. This parameter does not
-affect the background update.
-@param detectShadows If true, the algorithm will detect shadows and mark them. It decreases the
-speed a bit, so if you do not need this feature, set the parameter to false.
- */
-CV_EXPORTS_W Ptr<BackgroundSubtractorMOG2>
-    createBackgroundSubtractorMOG2(int history=500, double varThreshold=16,
-                                   bool detectShadows=true);
-
-/** @brief K-nearest neighbours - based Background/Foreground Segmentation Algorithm.
-
-The class implements the K-nearest neighbours background subtraction described in @cite Zivkovic2006 .
-Very efficient if number of foreground pixels is low.
- */
-class CV_EXPORTS_W BackgroundSubtractorKNN : public BackgroundSubtractor
-{
-public:
-    /** @brief Returns the number of last frames that affect the background model
-    */
-    CV_WRAP virtual int getHistory() const = 0;
-    /** @brief Sets the number of last frames that affect the background model
-    */
-    CV_WRAP virtual void setHistory(int history) = 0;
-
-    /** @brief Returns the number of data samples in the background model
-    */
-    CV_WRAP virtual int getNSamples() const = 0;
-    /** @brief Sets the number of data samples in the background model.
-
-    The model needs to be reinitalized to reserve memory.
-    */
-    CV_WRAP virtual void setNSamples(int _nN) = 0;//needs reinitialization!
-
-    /** @brief Returns the threshold on the squared distance between the pixel and the sample
-
-    The threshold on the squared distance between the pixel and the sample to decide whether a pixel is
-    close to a data sample.
-     */
-    CV_WRAP virtual double getDist2Threshold() const = 0;
-    /** @brief Sets the threshold on the squared distance
-    */
-    CV_WRAP virtual void setDist2Threshold(double _dist2Threshold) = 0;
-
-    /** @brief Returns the number of neighbours, the k in the kNN.
-
-    K is the number of samples that need to be within dist2Threshold in order to decide that that
-    pixel is matching the kNN background model.
-     */
-    CV_WRAP virtual int getkNNSamples() const = 0;
-    /** @brief Sets the k in the kNN. How many nearest neighbours need to match.
-    */
-    CV_WRAP virtual void setkNNSamples(int _nkNN) = 0;
-
-    /** @brief Returns the shadow detection flag
-
-    If true, the algorithm detects shadows and marks them. See createBackgroundSubtractorKNN for
-    details.
-     */
-    CV_WRAP virtual bool getDetectShadows() const = 0;
-    /** @brief Enables or disables shadow detection
-    */
-    CV_WRAP virtual void setDetectShadows(bool detectShadows) = 0;
-
-    /** @brief Returns the shadow value
-
-    Shadow value is the value used to mark shadows in the foreground mask. Default value is 127. Value 0
-    in the mask always means background, 255 means foreground.
-     */
-    CV_WRAP virtual int getShadowValue() const = 0;
-    /** @brief Sets the shadow value
-    */
-    CV_WRAP virtual void setShadowValue(int value) = 0;
-
-    /** @brief Returns the shadow threshold
-
-    A shadow is detected if pixel is a darker version of the background. The shadow threshold (Tau in
-    the paper) is a threshold defining how much darker the shadow can be. Tau= 0.5 means that if a pixel
-    is more than twice darker then it is not shadow. See Prati, Mikic, Trivedi and Cucchiara,
-    *Detecting Moving Shadows...*, IEEE PAMI,2003.
-     */
-    CV_WRAP virtual double getShadowThreshold() const = 0;
-    /** @brief Sets the shadow threshold
-     */
-    CV_WRAP virtual void setShadowThreshold(double threshold) = 0;
-};
-
-/** @brief Creates KNN Background Subtractor
-
-@param history Length of the history.
-@param dist2Threshold Threshold on the squared distance between the pixel and the sample to decide
-whether a pixel is close to that sample. This parameter does not affect the background update.
-@param detectShadows If true, the algorithm will detect shadows and mark them. It decreases the
-speed a bit, so if you do not need this feature, set the parameter to false.
- */
-CV_EXPORTS_W Ptr<BackgroundSubtractorKNN>
-    createBackgroundSubtractorKNN(int history=500, double dist2Threshold=400.0,
-                                   bool detectShadows=true);
-
-//! @} video_motion
-
-} // cv
-
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/video/tracking.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/video/tracking.hpp
deleted file mode 100755
index 8e01d16..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/video/tracking.hpp
+++ /dev/null
@@ -1,630 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_TRACKING_HPP
-#define OPENCV_TRACKING_HPP
-
-#include "opencv2/core.hpp"
-#include "opencv2/imgproc.hpp"
-
-namespace cv
-{
-
-//! @addtogroup video_track
-//! @{
-
-enum { OPTFLOW_USE_INITIAL_FLOW     = 4,
-       OPTFLOW_LK_GET_MIN_EIGENVALS = 8,
-       OPTFLOW_FARNEBACK_GAUSSIAN   = 256
-     };
-
-/** @brief Finds an object center, size, and orientation.
-
-@param probImage Back projection of the object histogram. See calcBackProject.
-@param window Initial search window.
-@param criteria Stop criteria for the underlying meanShift.
-returns
-(in old interfaces) Number of iterations CAMSHIFT took to converge
-The function implements the CAMSHIFT object tracking algorithm @cite Bradski98 . First, it finds an
-object center using meanShift and then adjusts the window size and finds the optimal rotation. The
-function returns the rotated rectangle structure that includes the object position, size, and
-orientation. The next position of the search window can be obtained with RotatedRect::boundingRect()
-
-See the OpenCV sample camshiftdemo.c that tracks colored objects.
-
-@note
--   (Python) A sample explaining the camshift tracking algorithm can be found at
-    opencv_source_code/samples/python/camshift.py
- */
-CV_EXPORTS_W RotatedRect CamShift( InputArray probImage, CV_IN_OUT Rect& window,
-                                   TermCriteria criteria );
-/** @example camshiftdemo.cpp
-An example using the mean-shift tracking algorithm
-*/
-/** @brief Finds an object on a back projection image.
-
-@param probImage Back projection of the object histogram. See calcBackProject for details.
-@param window Initial search window.
-@param criteria Stop criteria for the iterative search algorithm.
-returns
-:   Number of iterations CAMSHIFT took to converge.
-The function implements the iterative object search algorithm. It takes the input back projection of
-an object and the initial position. The mass center in window of the back projection image is
-computed and the search window center shifts to the mass center. The procedure is repeated until the
-specified number of iterations criteria.maxCount is done or until the window center shifts by less
-than criteria.epsilon. The algorithm is used inside CamShift and, unlike CamShift , the search
-window size or orientation do not change during the search. You can simply pass the output of
-calcBackProject to this function. But better results can be obtained if you pre-filter the back
-projection and remove the noise. For example, you can do this by retrieving connected components
-with findContours , throwing away contours with small area ( contourArea ), and rendering the
-remaining contours with drawContours.
-
- */
-CV_EXPORTS_W int meanShift( InputArray probImage, CV_IN_OUT Rect& window, TermCriteria criteria );
-
-/** @brief Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.
-
-@param img 8-bit input image.
-@param pyramid output pyramid.
-@param winSize window size of optical flow algorithm. Must be not less than winSize argument of
-calcOpticalFlowPyrLK. It is needed to calculate required padding for pyramid levels.
-@param maxLevel 0-based maximal pyramid level number.
-@param withDerivatives set to precompute gradients for the every pyramid level. If pyramid is
-constructed without the gradients then calcOpticalFlowPyrLK will calculate them internally.
-@param pyrBorder the border mode for pyramid layers.
-@param derivBorder the border mode for gradients.
-@param tryReuseInputImage put ROI of input image into the pyramid if possible. You can pass false
-to force data copying.
-@return number of levels in constructed pyramid. Can be less than maxLevel.
- */
-CV_EXPORTS_W int buildOpticalFlowPyramid( InputArray img, OutputArrayOfArrays pyramid,
-                                          Size winSize, int maxLevel, bool withDerivatives = true,
-                                          int pyrBorder = BORDER_REFLECT_101,
-                                          int derivBorder = BORDER_CONSTANT,
-                                          bool tryReuseInputImage = true );
-
-/** @example lkdemo.cpp
-An example using the Lucas-Kanade optical flow algorithm
- */
-/** @brief Calculates an optical flow for a sparse feature set using the iterative Lucas-Kanade method with
-pyramids.
-
-@param prevImg first 8-bit input image or pyramid constructed by buildOpticalFlowPyramid.
-@param nextImg second input image or pyramid of the same size and the same type as prevImg.
-@param prevPts vector of 2D points for which the flow needs to be found; point coordinates must be
-single-precision floating-point numbers.
-@param nextPts output vector of 2D points (with single-precision floating-point coordinates)
-containing the calculated new positions of input features in the second image; when
-OPTFLOW_USE_INITIAL_FLOW flag is passed, the vector must have the same size as in the input.
-@param status output status vector (of unsigned chars); each element of the vector is set to 1 if
-the flow for the corresponding features has been found, otherwise, it is set to 0.
-@param err output vector of errors; each element of the vector is set to an error for the
-corresponding feature, type of the error measure can be set in flags parameter; if the flow wasn't
-found then the error is not defined (use the status parameter to find such cases).
-@param winSize size of the search window at each pyramid level.
-@param maxLevel 0-based maximal pyramid level number; if set to 0, pyramids are not used (single
-level), if set to 1, two levels are used, and so on; if pyramids are passed to input then
-algorithm will use as many levels as pyramids have but no more than maxLevel.
-@param criteria parameter, specifying the termination criteria of the iterative search algorithm
-(after the specified maximum number of iterations criteria.maxCount or when the search window
-moves by less than criteria.epsilon.
-@param flags operation flags:
- -   **OPTFLOW_USE_INITIAL_FLOW** uses initial estimations, stored in nextPts; if the flag is
-     not set, then prevPts is copied to nextPts and is considered the initial estimate.
- -   **OPTFLOW_LK_GET_MIN_EIGENVALS** use minimum eigen values as an error measure (see
-     minEigThreshold description); if the flag is not set, then L1 distance between patches
-     around the original and a moved point, divided by number of pixels in a window, is used as a
-     error measure.
-@param minEigThreshold the algorithm calculates the minimum eigen value of a 2x2 normal matrix of
-optical flow equations (this matrix is called a spatial gradient matrix in @cite Bouguet00), divided
-by number of pixels in a window; if this value is less than minEigThreshold, then a corresponding
-feature is filtered out and its flow is not processed, so it allows to remove bad points and get a
-performance boost.
-
-The function implements a sparse iterative version of the Lucas-Kanade optical flow in pyramids. See
-@cite Bouguet00 . The function is parallelized with the TBB library.
-
-@note
-
--   An example using the Lucas-Kanade optical flow algorithm can be found at
-    opencv_source_code/samples/cpp/lkdemo.cpp
--   (Python) An example using the Lucas-Kanade optical flow algorithm can be found at
-    opencv_source_code/samples/python/lk_track.py
--   (Python) An example using the Lucas-Kanade tracker for homography matching can be found at
-    opencv_source_code/samples/python/lk_homography.py
- */
-CV_EXPORTS_W void calcOpticalFlowPyrLK( InputArray prevImg, InputArray nextImg,
-                                        InputArray prevPts, InputOutputArray nextPts,
-                                        OutputArray status, OutputArray err,
-                                        Size winSize = Size(21,21), int maxLevel = 3,
-                                        TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 0.01),
-                                        int flags = 0, double minEigThreshold = 1e-4 );
-
-/** @brief Computes a dense optical flow using the Gunnar Farneback's algorithm.
-
-@param prev first 8-bit single-channel input image.
-@param next second input image of the same size and the same type as prev.
-@param flow computed flow image that has the same size as prev and type CV_32FC2.
-@param pyr_scale parameter, specifying the image scale (\<1) to build pyramids for each image;
-pyr_scale=0.5 means a classical pyramid, where each next layer is twice smaller than the previous
-one.
-@param levels number of pyramid layers including the initial image; levels=1 means that no extra
-layers are created and only the original images are used.
-@param winsize averaging window size; larger values increase the algorithm robustness to image
-noise and give more chances for fast motion detection, but yield more blurred motion field.
-@param iterations number of iterations the algorithm does at each pyramid level.
-@param poly_n size of the pixel neighborhood used to find polynomial expansion in each pixel;
-larger values mean that the image will be approximated with smoother surfaces, yielding more
-robust algorithm and more blurred motion field, typically poly_n =5 or 7.
-@param poly_sigma standard deviation of the Gaussian that is used to smooth derivatives used as a
-basis for the polynomial expansion; for poly_n=5, you can set poly_sigma=1.1, for poly_n=7, a
-good value would be poly_sigma=1.5.
-@param flags operation flags that can be a combination of the following:
- -   **OPTFLOW_USE_INITIAL_FLOW** uses the input flow as an initial flow approximation.
- -   **OPTFLOW_FARNEBACK_GAUSSIAN** uses the Gaussian \f$\texttt{winsize}\times\texttt{winsize}\f$
-     filter instead of a box filter of the same size for optical flow estimation; usually, this
-     option gives z more accurate flow than with a box filter, at the cost of lower speed;
-     normally, winsize for a Gaussian window should be set to a larger value to achieve the same
-     level of robustness.
-
-The function finds an optical flow for each prev pixel using the @cite Farneback2003 algorithm so that
-
-\f[\texttt{prev} (y,x)  \sim \texttt{next} ( y + \texttt{flow} (y,x)[1],  x + \texttt{flow} (y,x)[0])\f]
-
-@note
-
--   An example using the optical flow algorithm described by Gunnar Farneback can be found at
-    opencv_source_code/samples/cpp/fback.cpp
--   (Python) An example using the optical flow algorithm described by Gunnar Farneback can be
-    found at opencv_source_code/samples/python/opt_flow.py
- */
-CV_EXPORTS_W void calcOpticalFlowFarneback( InputArray prev, InputArray next, InputOutputArray flow,
-                                            double pyr_scale, int levels, int winsize,
-                                            int iterations, int poly_n, double poly_sigma,
-                                            int flags );
-
-/** @brief Computes an optimal affine transformation between two 2D point sets.
-
-@param src First input 2D point set stored in std::vector or Mat, or an image stored in Mat.
-@param dst Second input 2D point set of the same size and the same type as A, or another image.
-@param fullAffine If true, the function finds an optimal affine transformation with no additional
-restrictions (6 degrees of freedom). Otherwise, the class of transformations to choose from is
-limited to combinations of translation, rotation, and uniform scaling (4 degrees of freedom).
-
-The function finds an optimal affine transform *[A|b]* (a 2 x 3 floating-point matrix) that
-approximates best the affine transformation between:
-
-*   Two point sets
-*   Two raster images. In this case, the function first finds some features in the src image and
-    finds the corresponding features in dst image. After that, the problem is reduced to the first
-    case.
-In case of point sets, the problem is formulated as follows: you need to find a 2x2 matrix *A* and
-2x1 vector *b* so that:
-
-\f[[A^*|b^*] = arg  \min _{[A|b]}  \sum _i  \| \texttt{dst}[i] - A { \texttt{src}[i]}^T - b  \| ^2\f]
-where src[i] and dst[i] are the i-th points in src and dst, respectively
-\f$[A|b]\f$ can be either arbitrary (when fullAffine=true ) or have a form of
-\f[\begin{bmatrix} a_{11} & a_{12} & b_1  \\ -a_{12} & a_{11} & b_2  \end{bmatrix}\f]
-when fullAffine=false.
-
-@sa
-estimateAffine2D, estimateAffinePartial2D, getAffineTransform, getPerspectiveTransform, findHomography
- */
-CV_EXPORTS_W Mat estimateRigidTransform( InputArray src, InputArray dst, bool fullAffine);
-CV_EXPORTS_W Mat estimateRigidTransform( InputArray src, InputArray dst, bool fullAffine, int ransacMaxIters, double ransacGoodRatio,
-                                         int ransacSize0);
-
-
-enum
-{
-    MOTION_TRANSLATION = 0,
-    MOTION_EUCLIDEAN   = 1,
-    MOTION_AFFINE      = 2,
-    MOTION_HOMOGRAPHY  = 3
-};
-
-/** @example image_alignment.cpp
-An example using the image alignment ECC algorithm
- */
-
-/** @brief Finds the geometric transform (warp) between two images in terms of the ECC criterion @cite EP08 .
-
-@param templateImage single-channel template image; CV_8U or CV_32F array.
-@param inputImage single-channel input image which should be warped with the final warpMatrix in
-order to provide an image similar to templateImage, same type as temlateImage.
-@param warpMatrix floating-point \f$2\times 3\f$ or \f$3\times 3\f$ mapping matrix (warp).
-@param motionType parameter, specifying the type of motion:
- -   **MOTION_TRANSLATION** sets a translational motion model; warpMatrix is \f$2\times 3\f$ with
-     the first \f$2\times 2\f$ part being the unity matrix and the rest two parameters being
-     estimated.
- -   **MOTION_EUCLIDEAN** sets a Euclidean (rigid) transformation as motion model; three
-     parameters are estimated; warpMatrix is \f$2\times 3\f$.
- -   **MOTION_AFFINE** sets an affine motion model (DEFAULT); six parameters are estimated;
-     warpMatrix is \f$2\times 3\f$.
- -   **MOTION_HOMOGRAPHY** sets a homography as a motion model; eight parameters are
-     estimated;\`warpMatrix\` is \f$3\times 3\f$.
-@param criteria parameter, specifying the termination criteria of the ECC algorithm;
-criteria.epsilon defines the threshold of the increment in the correlation coefficient between two
-iterations (a negative criteria.epsilon makes criteria.maxcount the only termination criterion).
-Default values are shown in the declaration above.
-@param inputMask An optional mask to indicate valid values of inputImage.
-
-The function estimates the optimum transformation (warpMatrix) with respect to ECC criterion
-(@cite EP08), that is
-
-\f[\texttt{warpMatrix} = \texttt{warpMatrix} = \arg\max_{W} \texttt{ECC}(\texttt{templateImage}(x,y),\texttt{inputImage}(x',y'))\f]
-
-where
-
-\f[\begin{bmatrix} x' \\ y' \end{bmatrix} = W \cdot \begin{bmatrix} x \\ y \\ 1 \end{bmatrix}\f]
-
-(the equation holds with homogeneous coordinates for homography). It returns the final enhanced
-correlation coefficient, that is the correlation coefficient between the template image and the
-final warped input image. When a \f$3\times 3\f$ matrix is given with motionType =0, 1 or 2, the third
-row is ignored.
-
-Unlike findHomography and estimateRigidTransform, the function findTransformECC implements an
-area-based alignment that builds on intensity similarities. In essence, the function updates the
-initial transformation that roughly aligns the images. If this information is missing, the identity
-warp (unity matrix) is used as an initialization. Note that if images undergo strong
-displacements/rotations, an initial transformation that roughly aligns the images is necessary
-(e.g., a simple euclidean/similarity transform that allows for the images showing the same image
-content approximately). Use inverse warping in the second image to take an image close to the first
-one, i.e. use the flag WARP_INVERSE_MAP with warpAffine or warpPerspective. See also the OpenCV
-sample image_alignment.cpp that demonstrates the use of the function. Note that the function throws
-an exception if algorithm does not converges.
-
-@sa
-estimateAffine2D, estimateAffinePartial2D, findHomography
- */
-CV_EXPORTS_W double findTransformECC( InputArray templateImage, InputArray inputImage,
-                                      InputOutputArray warpMatrix, int motionType = MOTION_AFFINE,
-                                      TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 50, 0.001),
-                                      InputArray inputMask = noArray());
-
-/** @example kalman.cpp
-An example using the standard Kalman filter
-*/
-/** @brief Kalman filter class.
-
-The class implements a standard Kalman filter <http://en.wikipedia.org/wiki/Kalman_filter>,
-@cite Welch95 . However, you can modify transitionMatrix, controlMatrix, and measurementMatrix to get
-an extended Kalman filter functionality.
-@note In C API when CvKalman\* kalmanFilter structure is not needed anymore, it should be released
-with cvReleaseKalman(&kalmanFilter)
- */
-class CV_EXPORTS_W KalmanFilter
-{
-public:
-    CV_WRAP KalmanFilter();
-    /** @overload
-    @param dynamParams Dimensionality of the state.
-    @param measureParams Dimensionality of the measurement.
-    @param controlParams Dimensionality of the control vector.
-    @param type Type of the created matrices that should be CV_32F or CV_64F.
-    */
-    CV_WRAP KalmanFilter( int dynamParams, int measureParams, int controlParams = 0, int type = CV_32F );
-
-    /** @brief Re-initializes Kalman filter. The previous content is destroyed.
-
-    @param dynamParams Dimensionality of the state.
-    @param measureParams Dimensionality of the measurement.
-    @param controlParams Dimensionality of the control vector.
-    @param type Type of the created matrices that should be CV_32F or CV_64F.
-     */
-    void init( int dynamParams, int measureParams, int controlParams = 0, int type = CV_32F );
-
-    /** @brief Computes a predicted state.
-
-    @param control The optional input control
-     */
-    CV_WRAP const Mat& predict( const Mat& control = Mat() );
-
-    /** @brief Updates the predicted state from the measurement.
-
-    @param measurement The measured system parameters
-     */
-    CV_WRAP const Mat& correct( const Mat& measurement );
-
-    CV_PROP_RW Mat statePre;           //!< predicted state (x'(k)): x(k)=A*x(k-1)+B*u(k)
-    CV_PROP_RW Mat statePost;          //!< corrected state (x(k)): x(k)=x'(k)+K(k)*(z(k)-H*x'(k))
-    CV_PROP_RW Mat transitionMatrix;   //!< state transition matrix (A)
-    CV_PROP_RW Mat controlMatrix;      //!< control matrix (B) (not used if there is no control)
-    CV_PROP_RW Mat measurementMatrix;  //!< measurement matrix (H)
-    CV_PROP_RW Mat processNoiseCov;    //!< process noise covariance matrix (Q)
-    CV_PROP_RW Mat measurementNoiseCov;//!< measurement noise covariance matrix (R)
-    CV_PROP_RW Mat errorCovPre;        //!< priori error estimate covariance matrix (P'(k)): P'(k)=A*P(k-1)*At + Q)*/
-    CV_PROP_RW Mat gain;               //!< Kalman gain matrix (K(k)): K(k)=P'(k)*Ht*inv(H*P'(k)*Ht+R)
-    CV_PROP_RW Mat errorCovPost;       //!< posteriori error estimate covariance matrix (P(k)): P(k)=(I-K(k)*H)*P'(k)
-
-    // temporary matrices
-    Mat temp1;
-    Mat temp2;
-    Mat temp3;
-    Mat temp4;
-    Mat temp5;
-};
-
-
-class CV_EXPORTS_W DenseOpticalFlow : public Algorithm
-{
-public:
-    /** @brief Calculates an optical flow.
-
-    @param I0 first 8-bit single-channel input image.
-    @param I1 second input image of the same size and the same type as prev.
-    @param flow computed flow image that has the same size as prev and type CV_32FC2.
-     */
-    CV_WRAP virtual void calc( InputArray I0, InputArray I1, InputOutputArray flow ) = 0;
-    /** @brief Releases all inner buffers.
-    */
-    CV_WRAP virtual void collectGarbage() = 0;
-};
-
-/** @brief Base interface for sparse optical flow algorithms.
- */
-class CV_EXPORTS_W SparseOpticalFlow : public Algorithm
-{
-public:
-    /** @brief Calculates a sparse optical flow.
-
-    @param prevImg First input image.
-    @param nextImg Second input image of the same size and the same type as prevImg.
-    @param prevPts Vector of 2D points for which the flow needs to be found.
-    @param nextPts Output vector of 2D points containing the calculated new positions of input features in the second image.
-    @param status Output status vector. Each element of the vector is set to 1 if the
-                  flow for the corresponding features has been found. Otherwise, it is set to 0.
-    @param err Optional output vector that contains error response for each point (inverse confidence).
-     */
-    CV_WRAP virtual void calc(InputArray prevImg, InputArray nextImg,
-                      InputArray prevPts, InputOutputArray nextPts,
-                      OutputArray status,
-                      OutputArray err = cv::noArray()) = 0;
-};
-
-/** @brief "Dual TV L1" Optical Flow Algorithm.
-
-The class implements the "Dual TV L1" optical flow algorithm described in @cite Zach2007 and
-@cite Javier2012 .
-Here are important members of the class that control the algorithm, which you can set after
-constructing the class instance:
-
--   member double tau
-    Time step of the numerical scheme.
-
--   member double lambda
-    Weight parameter for the data term, attachment parameter. This is the most relevant
-    parameter, which determines the smoothness of the output. The smaller this parameter is,
-    the smoother the solutions we obtain. It depends on the range of motions of the images, so
-    its value should be adapted to each image sequence.
-
--   member double theta
-    Weight parameter for (u - v)\^2, tightness parameter. It serves as a link between the
-    attachment and the regularization terms. In theory, it should have a small value in order
-    to maintain both parts in correspondence. The method is stable for a large range of values
-    of this parameter.
-
--   member int nscales
-    Number of scales used to create the pyramid of images.
-
--   member int warps
-    Number of warpings per scale. Represents the number of times that I1(x+u0) and grad(
-    I1(x+u0) ) are computed per scale. This is a parameter that assures the stability of the
-    method. It also affects the running time, so it is a compromise between speed and
-    accuracy.
-
--   member double epsilon
-    Stopping criterion threshold used in the numerical scheme, which is a trade-off between
-    precision and running time. A small value will yield more accurate solutions at the
-    expense of a slower convergence.
-
--   member int iterations
-    Stopping criterion iterations number used in the numerical scheme.
-
-C. Zach, T. Pock and H. Bischof, "A Duality Based Approach for Realtime TV-L1 Optical Flow".
-Javier Sanchez, Enric Meinhardt-Llopis and Gabriele Facciolo. "TV-L1 Optical Flow Estimation".
-*/
-class CV_EXPORTS_W DualTVL1OpticalFlow : public DenseOpticalFlow
-{
-public:
-    //! @brief Time step of the numerical scheme
-    /** @see setTau */
-    CV_WRAP virtual double getTau() const = 0;
-    /** @copybrief getTau @see getTau */
-    CV_WRAP virtual void setTau(double val) = 0;
-    //! @brief Weight parameter for the data term, attachment parameter
-    /** @see setLambda */
-    CV_WRAP virtual double getLambda() const = 0;
-    /** @copybrief getLambda @see getLambda */
-    CV_WRAP virtual void setLambda(double val) = 0;
-    //! @brief Weight parameter for (u - v)^2, tightness parameter
-    /** @see setTheta */
-    CV_WRAP virtual double getTheta() const = 0;
-    /** @copybrief getTheta @see getTheta */
-    CV_WRAP virtual void setTheta(double val) = 0;
-    //! @brief coefficient for additional illumination variation term
-    /** @see setGamma */
-    CV_WRAP virtual double getGamma() const = 0;
-    /** @copybrief getGamma @see getGamma */
-    CV_WRAP virtual void setGamma(double val) = 0;
-    //! @brief Number of scales used to create the pyramid of images
-    /** @see setScalesNumber */
-    CV_WRAP virtual int getScalesNumber() const = 0;
-    /** @copybrief getScalesNumber @see getScalesNumber */
-    CV_WRAP virtual void setScalesNumber(int val) = 0;
-    //! @brief Number of warpings per scale
-    /** @see setWarpingsNumber */
-    CV_WRAP virtual int getWarpingsNumber() const = 0;
-    /** @copybrief getWarpingsNumber @see getWarpingsNumber */
-    CV_WRAP virtual void setWarpingsNumber(int val) = 0;
-    //! @brief Stopping criterion threshold used in the numerical scheme, which is a trade-off between precision and running time
-    /** @see setEpsilon */
-    CV_WRAP virtual double getEpsilon() const = 0;
-    /** @copybrief getEpsilon @see getEpsilon */
-    CV_WRAP virtual void setEpsilon(double val) = 0;
-    //! @brief Inner iterations (between outlier filtering) used in the numerical scheme
-    /** @see setInnerIterations */
-    CV_WRAP virtual int getInnerIterations() const = 0;
-    /** @copybrief getInnerIterations @see getInnerIterations */
-    CV_WRAP virtual void setInnerIterations(int val) = 0;
-    //! @brief Outer iterations (number of inner loops) used in the numerical scheme
-    /** @see setOuterIterations */
-    CV_WRAP virtual int getOuterIterations() const = 0;
-    /** @copybrief getOuterIterations @see getOuterIterations */
-    CV_WRAP virtual void setOuterIterations(int val) = 0;
-    //! @brief Use initial flow
-    /** @see setUseInitialFlow */
-    CV_WRAP virtual bool getUseInitialFlow() const = 0;
-    /** @copybrief getUseInitialFlow @see getUseInitialFlow */
-    CV_WRAP virtual void setUseInitialFlow(bool val) = 0;
-    //! @brief Step between scales (<1)
-    /** @see setScaleStep */
-    CV_WRAP virtual double getScaleStep() const = 0;
-    /** @copybrief getScaleStep @see getScaleStep */
-    CV_WRAP virtual void setScaleStep(double val) = 0;
-    //! @brief Median filter kernel size (1 = no filter) (3 or 5)
-    /** @see setMedianFiltering */
-    CV_WRAP virtual int getMedianFiltering() const = 0;
-    /** @copybrief getMedianFiltering @see getMedianFiltering */
-    CV_WRAP virtual void setMedianFiltering(int val) = 0;
-
-    /** @brief Creates instance of cv::DualTVL1OpticalFlow*/
-    CV_WRAP static Ptr<DualTVL1OpticalFlow> create(
-                                            double tau = 0.25,
-                                            double lambda = 0.15,
-                                            double theta = 0.3,
-                                            int nscales = 5,
-                                            int warps = 5,
-                                            double epsilon = 0.01,
-                                            int innnerIterations = 30,
-                                            int outerIterations = 10,
-                                            double scaleStep = 0.8,
-                                            double gamma = 0.0,
-                                            int medianFiltering = 5,
-                                            bool useInitialFlow = false);
-};
-
-/** @brief Creates instance of cv::DenseOpticalFlow
-*/
-CV_EXPORTS_W Ptr<DualTVL1OpticalFlow> createOptFlow_DualTVL1();
-
-/** @brief Class computing a dense optical flow using the Gunnar Farneback's algorithm.
- */
-class CV_EXPORTS_W FarnebackOpticalFlow : public DenseOpticalFlow
-{
-public:
-    CV_WRAP virtual int getNumLevels() const = 0;
-    CV_WRAP virtual void setNumLevels(int numLevels) = 0;
-
-    CV_WRAP virtual double getPyrScale() const = 0;
-    CV_WRAP virtual void setPyrScale(double pyrScale) = 0;
-
-    CV_WRAP virtual bool getFastPyramids() const = 0;
-    CV_WRAP virtual void setFastPyramids(bool fastPyramids) = 0;
-
-    CV_WRAP virtual int getWinSize() const = 0;
-    CV_WRAP virtual void setWinSize(int winSize) = 0;
-
-    CV_WRAP virtual int getNumIters() const = 0;
-    CV_WRAP virtual void setNumIters(int numIters) = 0;
-
-    CV_WRAP virtual int getPolyN() const = 0;
-    CV_WRAP virtual void setPolyN(int polyN) = 0;
-
-    CV_WRAP virtual double getPolySigma() const = 0;
-    CV_WRAP virtual void setPolySigma(double polySigma) = 0;
-
-    CV_WRAP virtual int getFlags() const = 0;
-    CV_WRAP virtual void setFlags(int flags) = 0;
-
-    CV_WRAP static Ptr<FarnebackOpticalFlow> create(
-            int numLevels = 5,
-            double pyrScale = 0.5,
-            bool fastPyramids = false,
-            int winSize = 13,
-            int numIters = 10,
-            int polyN = 5,
-            double polySigma = 1.1,
-            int flags = 0);
-};
-
-
-/** @brief Class used for calculating a sparse optical flow.
-
-The class can calculate an optical flow for a sparse feature set using the
-iterative Lucas-Kanade method with pyramids.
-
-@sa calcOpticalFlowPyrLK
-
-*/
-class CV_EXPORTS_W SparsePyrLKOpticalFlow : public SparseOpticalFlow
-{
-public:
-    CV_WRAP virtual Size getWinSize() const = 0;
-    CV_WRAP virtual void setWinSize(Size winSize) = 0;
-
-    CV_WRAP virtual int getMaxLevel() const = 0;
-    CV_WRAP virtual void setMaxLevel(int maxLevel) = 0;
-
-    CV_WRAP virtual TermCriteria getTermCriteria() const = 0;
-    CV_WRAP virtual void setTermCriteria(TermCriteria& crit) = 0;
-
-    CV_WRAP virtual int getFlags() const = 0;
-    CV_WRAP virtual void setFlags(int flags) = 0;
-
-    CV_WRAP virtual double getMinEigThreshold() const = 0;
-    CV_WRAP virtual void setMinEigThreshold(double minEigThreshold) = 0;
-
-    CV_WRAP static Ptr<SparsePyrLKOpticalFlow> create(
-            Size winSize = Size(21, 21),
-            int maxLevel = 3, TermCriteria crit =
-            TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 0.01),
-            int flags = 0,
-            double minEigThreshold = 1e-4);
-};
-
-//! @} video_track
-
-} // cv
-
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/video/tracking_c.h b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/video/tracking_c.h
deleted file mode 100755
index 3e32fbd..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/video/tracking_c.h
+++ /dev/null
@@ -1,232 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_TRACKING_C_H
-#define OPENCV_TRACKING_C_H
-
-#include "opencv2/imgproc/types_c.h"
-
-#ifdef __cplusplus
-extern "C" {
-#endif
-
-/** @addtogroup video_c
-  @{
-*/
-
-/****************************************************************************************\
-*                                  Motion Analysis                                       *
-\****************************************************************************************/
-
-/************************************ optical flow ***************************************/
-
-#define CV_LKFLOW_PYR_A_READY       1
-#define CV_LKFLOW_PYR_B_READY       2
-#define CV_LKFLOW_INITIAL_GUESSES   4
-#define CV_LKFLOW_GET_MIN_EIGENVALS 8
-
-/* It is Lucas & Kanade method, modified to use pyramids.
-   Also it does several iterations to get optical flow for
-   every point at every pyramid level.
-   Calculates optical flow between two images for certain set of points (i.e.
-   it is a "sparse" optical flow, which is opposite to the previous 3 methods) */
-CVAPI(void)  cvCalcOpticalFlowPyrLK( const CvArr*  prev, const CvArr*  curr,
-                                     CvArr*  prev_pyr, CvArr*  curr_pyr,
-                                     const CvPoint2D32f* prev_features,
-                                     CvPoint2D32f* curr_features,
-                                     int       count,
-                                     CvSize    win_size,
-                                     int       level,
-                                     char*     status,
-                                     float*    track_error,
-                                     CvTermCriteria criteria,
-                                     int       flags );
-
-
-/* Modification of a previous sparse optical flow algorithm to calculate
-   affine flow */
-CVAPI(void)  cvCalcAffineFlowPyrLK( const CvArr*  prev, const CvArr*  curr,
-                                    CvArr*  prev_pyr, CvArr*  curr_pyr,
-                                    const CvPoint2D32f* prev_features,
-                                    CvPoint2D32f* curr_features,
-                                    float* matrices, int  count,
-                                    CvSize win_size, int  level,
-                                    char* status, float* track_error,
-                                    CvTermCriteria criteria, int flags );
-
-/* Estimate rigid transformation between 2 images or 2 point sets */
-CVAPI(int)  cvEstimateRigidTransform( const CvArr* A, const CvArr* B,
-                                      CvMat* M, int full_affine );
-
-/* Estimate optical flow for each pixel using the two-frame G. Farneback algorithm */
-CVAPI(void) cvCalcOpticalFlowFarneback( const CvArr* prev, const CvArr* next,
-                                        CvArr* flow, double pyr_scale, int levels,
-                                        int winsize, int iterations, int poly_n,
-                                        double poly_sigma, int flags );
-
-/********************************* motion templates *************************************/
-
-/****************************************************************************************\
-*        All the motion template functions work only with single channel images.         *
-*        Silhouette image must have depth IPL_DEPTH_8U or IPL_DEPTH_8S                   *
-*        Motion history image must have depth IPL_DEPTH_32F,                             *
-*        Gradient mask - IPL_DEPTH_8U or IPL_DEPTH_8S,                                   *
-*        Motion orientation image - IPL_DEPTH_32F                                        *
-*        Segmentation mask - IPL_DEPTH_32F                                               *
-*        All the angles are in degrees, all the times are in milliseconds                *
-\****************************************************************************************/
-
-/* Updates motion history image given motion silhouette */
-CVAPI(void)    cvUpdateMotionHistory( const CvArr* silhouette, CvArr* mhi,
-                                      double timestamp, double duration );
-
-/* Calculates gradient of the motion history image and fills
-   a mask indicating where the gradient is valid */
-CVAPI(void)    cvCalcMotionGradient( const CvArr* mhi, CvArr* mask, CvArr* orientation,
-                                     double delta1, double delta2,
-                                     int aperture_size CV_DEFAULT(3));
-
-/* Calculates average motion direction within a selected motion region
-   (region can be selected by setting ROIs and/or by composing a valid gradient mask
-   with the region mask) */
-CVAPI(double)  cvCalcGlobalOrientation( const CvArr* orientation, const CvArr* mask,
-                                        const CvArr* mhi, double timestamp,
-                                        double duration );
-
-/* Splits a motion history image into a few parts corresponding to separate independent motions
-   (e.g. left hand, right hand) */
-CVAPI(CvSeq*)  cvSegmentMotion( const CvArr* mhi, CvArr* seg_mask,
-                                CvMemStorage* storage,
-                                double timestamp, double seg_thresh );
-
-/****************************************************************************************\
-*                                       Tracking                                         *
-\****************************************************************************************/
-
-/* Implements CAMSHIFT algorithm - determines object position, size and orientation
-   from the object histogram back project (extension of meanshift) */
-CVAPI(int)  cvCamShift( const CvArr* prob_image, CvRect  window,
-                        CvTermCriteria criteria, CvConnectedComp* comp,
-                        CvBox2D* box CV_DEFAULT(NULL) );
-
-/* Implements MeanShift algorithm - determines object position
-   from the object histogram back project */
-CVAPI(int)  cvMeanShift( const CvArr* prob_image, CvRect  window,
-                         CvTermCriteria criteria, CvConnectedComp* comp );
-
-/*
-standard Kalman filter (in G. Welch' and G. Bishop's notation):
-
-  x(k)=A*x(k-1)+B*u(k)+w(k)  p(w)~N(0,Q)
-  z(k)=H*x(k)+v(k),   p(v)~N(0,R)
-*/
-typedef struct CvKalman
-{
-    int MP;                     /* number of measurement vector dimensions */
-    int DP;                     /* number of state vector dimensions */
-    int CP;                     /* number of control vector dimensions */
-
-    /* backward compatibility fields */
-#if 1
-    float* PosterState;         /* =state_pre->data.fl */
-    float* PriorState;          /* =state_post->data.fl */
-    float* DynamMatr;           /* =transition_matrix->data.fl */
-    float* MeasurementMatr;     /* =measurement_matrix->data.fl */
-    float* MNCovariance;        /* =measurement_noise_cov->data.fl */
-    float* PNCovariance;        /* =process_noise_cov->data.fl */
-    float* KalmGainMatr;        /* =gain->data.fl */
-    float* PriorErrorCovariance;/* =error_cov_pre->data.fl */
-    float* PosterErrorCovariance;/* =error_cov_post->data.fl */
-    float* Temp1;               /* temp1->data.fl */
-    float* Temp2;               /* temp2->data.fl */
-#endif
-
-    CvMat* state_pre;           /* predicted state (x'(k)):
-                                    x(k)=A*x(k-1)+B*u(k) */
-    CvMat* state_post;          /* corrected state (x(k)):
-                                    x(k)=x'(k)+K(k)*(z(k)-H*x'(k)) */
-    CvMat* transition_matrix;   /* state transition matrix (A) */
-    CvMat* control_matrix;      /* control matrix (B)
-                                   (it is not used if there is no control)*/
-    CvMat* measurement_matrix;  /* measurement matrix (H) */
-    CvMat* process_noise_cov;   /* process noise covariance matrix (Q) */
-    CvMat* measurement_noise_cov; /* measurement noise covariance matrix (R) */
-    CvMat* error_cov_pre;       /* priori error estimate covariance matrix (P'(k)):
-                                    P'(k)=A*P(k-1)*At + Q)*/
-    CvMat* gain;                /* Kalman gain matrix (K(k)):
-                                    K(k)=P'(k)*Ht*inv(H*P'(k)*Ht+R)*/
-    CvMat* error_cov_post;      /* posteriori error estimate covariance matrix (P(k)):
-                                    P(k)=(I-K(k)*H)*P'(k) */
-    CvMat* temp1;               /* temporary matrices */
-    CvMat* temp2;
-    CvMat* temp3;
-    CvMat* temp4;
-    CvMat* temp5;
-} CvKalman;
-
-/* Creates Kalman filter and sets A, B, Q, R and state to some initial values */
-CVAPI(CvKalman*) cvCreateKalman( int dynam_params, int measure_params,
-                                 int control_params CV_DEFAULT(0));
-
-/* Releases Kalman filter state */
-CVAPI(void)  cvReleaseKalman( CvKalman** kalman);
-
-/* Updates Kalman filter by time (predicts future state of the system) */
-CVAPI(const CvMat*)  cvKalmanPredict( CvKalman* kalman,
-                                      const CvMat* control CV_DEFAULT(NULL));
-
-/* Updates Kalman filter by measurement
-   (corrects state of the system and internal matrices) */
-CVAPI(const CvMat*)  cvKalmanCorrect( CvKalman* kalman, const CvMat* measurement );
-
-#define cvKalmanUpdateByTime  cvKalmanPredict
-#define cvKalmanUpdateByMeasurement cvKalmanCorrect
-
-/** @} video_c */
-
-#ifdef __cplusplus
-} // extern "C"
-#endif
-
-
-#endif // OPENCV_TRACKING_C_H
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/video/video.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/video/video.hpp
deleted file mode 100755
index 8267b85..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/video/video.hpp
+++ /dev/null
@@ -1,48 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifdef __OPENCV_BUILD
-#error this is a compatibility header which should not be used inside the OpenCV library
-#endif
-
-#include "opencv2/video.hpp"
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/videoio.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/videoio.hpp
deleted file mode 100755
index d43e703..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/videoio.hpp
+++ /dev/null
@@ -1,958 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_VIDEOIO_HPP
-#define OPENCV_VIDEOIO_HPP
-
-#include "opencv2/core.hpp"
-
-/**
-  @defgroup videoio Video I/O
-
-  @brief Read and write video or images sequence with OpenCV
-
-  ### See also:
-  - @ref videoio_overview
-  - Tutorials: @ref tutorial_table_of_content_videoio
-  @{
-    @defgroup videoio_flags_base Flags for video I/O
-    @defgroup videoio_flags_others Additional flags for video I/O API backends
-    @defgroup videoio_c C API for video I/O
-    @defgroup videoio_ios iOS glue for video I/O
-    @defgroup videoio_winrt WinRT glue for video I/O
-  @}
-*/
-
-////////////////////////////////// video io /////////////////////////////////
-
-typedef struct CvCapture CvCapture;
-typedef struct CvVideoWriter CvVideoWriter;
-
-namespace cv
-{
-
-//! @addtogroup videoio
-//! @{
-
-//! @addtogroup videoio_flags_base
-//! @{
-
-
-/** @brief %VideoCapture API backends identifier.
-
-Select preferred API for a capture object.
-To be used in the VideoCapture::VideoCapture() constructor or VideoCapture::open()
-
-@note Backends are available only if they have been built with your OpenCV binaries.
-See @ref videoio_overview for more information.
-*/
-enum VideoCaptureAPIs {
-       CAP_ANY          = 0,            //!< Auto detect == 0
-       CAP_VFW          = 200,          //!< Video For Windows (platform native)
-       CAP_V4L          = 200,          //!< V4L/V4L2 capturing support via libv4l
-       CAP_V4L2         = CAP_V4L,      //!< Same as CAP_V4L
-       CAP_FIREWIRE     = 300,          //!< IEEE 1394 drivers
-       CAP_FIREWARE     = CAP_FIREWIRE, //!< Same as CAP_FIREWIRE
-       CAP_IEEE1394     = CAP_FIREWIRE, //!< Same as CAP_FIREWIRE
-       CAP_DC1394       = CAP_FIREWIRE, //!< Same as CAP_FIREWIRE
-       CAP_CMU1394      = CAP_FIREWIRE, //!< Same as CAP_FIREWIRE
-       CAP_QT           = 500,          //!< QuickTime
-       CAP_UNICAP       = 600,          //!< Unicap drivers
-       CAP_DSHOW        = 700,          //!< DirectShow (via videoInput)
-       CAP_PVAPI        = 800,          //!< PvAPI, Prosilica GigE SDK
-       CAP_OPENNI       = 900,          //!< OpenNI (for Kinect)
-       CAP_OPENNI_ASUS  = 910,          //!< OpenNI (for Asus Xtion)
-       CAP_ANDROID      = 1000,         //!< Android - not used
-       CAP_XIAPI        = 1100,         //!< XIMEA Camera API
-       CAP_AVFOUNDATION = 1200,         //!< AVFoundation framework for iOS (OS X Lion will have the same API)
-       CAP_GIGANETIX    = 1300,         //!< Smartek Giganetix GigEVisionSDK
-       CAP_MSMF         = 1400,         //!< Microsoft Media Foundation (via videoInput)
-       CAP_WINRT        = 1410,         //!< Microsoft Windows Runtime using Media Foundation
-       CAP_INTELPERC    = 1500,         //!< Intel Perceptual Computing SDK
-       CAP_OPENNI2      = 1600,         //!< OpenNI2 (for Kinect)
-       CAP_OPENNI2_ASUS = 1610,         //!< OpenNI2 (for Asus Xtion and Occipital Structure sensors)
-       CAP_GPHOTO2      = 1700,         //!< gPhoto2 connection
-       CAP_GSTREAMER    = 1800,         //!< GStreamer
-       CAP_FFMPEG       = 1900,         //!< Open and record video file or stream using the FFMPEG library
-       CAP_IMAGES       = 2000,         //!< OpenCV Image Sequence (e.g. img_%02d.jpg)
-       CAP_ARAVIS       = 2100,         //!< Aravis SDK
-       CAP_OPENCV_MJPEG = 2200,         //!< Built-in OpenCV MotionJPEG codec
-       CAP_INTEL_MFX    = 2300,         //!< Intel MediaSDK
-       CAP_XINE         = 2400,         //!< XINE engine (Linux)
-     };
-
-/** @brief %VideoCapture generic properties identifier.
-
- Reading / writing properties involves many layers. Some unexpected result might happens along this chain.
- Effective behaviour depends from device hardware, driver and API Backend.
- @sa videoio_flags_others, VideoCapture::get(), VideoCapture::set()
-*/
-enum VideoCaptureProperties {
-       CAP_PROP_POS_MSEC       =0, //!< Current position of the video file in milliseconds.
-       CAP_PROP_POS_FRAMES     =1, //!< 0-based index of the frame to be decoded/captured next.
-       CAP_PROP_POS_AVI_RATIO  =2, //!< Relative position of the video file: 0=start of the film, 1=end of the film.
-       CAP_PROP_FRAME_WIDTH    =3, //!< Width of the frames in the video stream.
-       CAP_PROP_FRAME_HEIGHT   =4, //!< Height of the frames in the video stream.
-       CAP_PROP_FPS            =5, //!< Frame rate.
-       CAP_PROP_FOURCC         =6, //!< 4-character code of codec. see VideoWriter::fourcc .
-       CAP_PROP_FRAME_COUNT    =7, //!< Number of frames in the video file.
-       CAP_PROP_FORMAT         =8, //!< Format of the %Mat objects returned by VideoCapture::retrieve().
-       CAP_PROP_MODE           =9, //!< Backend-specific value indicating the current capture mode.
-       CAP_PROP_BRIGHTNESS    =10, //!< Brightness of the image (only for those cameras that support).
-       CAP_PROP_CONTRAST      =11, //!< Contrast of the image (only for cameras).
-       CAP_PROP_SATURATION    =12, //!< Saturation of the image (only for cameras).
-       CAP_PROP_HUE           =13, //!< Hue of the image (only for cameras).
-       CAP_PROP_GAIN          =14, //!< Gain of the image (only for those cameras that support).
-       CAP_PROP_EXPOSURE      =15, //!< Exposure (only for those cameras that support).
-       CAP_PROP_CONVERT_RGB   =16, //!< Boolean flags indicating whether images should be converted to RGB.
-       CAP_PROP_WHITE_BALANCE_BLUE_U =17, //!< Currently unsupported.
-       CAP_PROP_RECTIFICATION =18, //!< Rectification flag for stereo cameras (note: only supported by DC1394 v 2.x backend currently).
-       CAP_PROP_MONOCHROME    =19,
-       CAP_PROP_SHARPNESS     =20,
-       CAP_PROP_AUTO_EXPOSURE =21, //!< DC1394: exposure control done by camera, user can adjust reference level using this feature.
-       CAP_PROP_GAMMA         =22,
-       CAP_PROP_TEMPERATURE   =23,
-       CAP_PROP_TRIGGER       =24,
-       CAP_PROP_TRIGGER_DELAY =25,
-       CAP_PROP_WHITE_BALANCE_RED_V =26,
-       CAP_PROP_ZOOM          =27,
-       CAP_PROP_FOCUS         =28,
-       CAP_PROP_GUID          =29,
-       CAP_PROP_ISO_SPEED     =30,
-       CAP_PROP_BACKLIGHT     =32,
-       CAP_PROP_PAN           =33,
-       CAP_PROP_TILT          =34,
-       CAP_PROP_ROLL          =35,
-       CAP_PROP_IRIS          =36,
-       CAP_PROP_SETTINGS      =37, //!< Pop up video/camera filter dialog (note: only supported by DSHOW backend currently. The property value is ignored)
-       CAP_PROP_BUFFERSIZE    =38,
-       CAP_PROP_AUTOFOCUS     =39,
-       CAP_PROP_SAR_NUM       =40, //!< Sample aspect ratio: num/den (num)
-       CAP_PROP_SAR_DEN       =41, //!< Sample aspect ratio: num/den (den)
-#ifndef CV_DOXYGEN
-       CV__CAP_PROP_LATEST
-#endif
-     };
-
-
-/** @brief Generic camera output modes identifier.
-@note Currently, these are supported through the libv4l backend only.
-*/
-enum VideoCaptureModes {
-       CAP_MODE_BGR  = 0, //!< BGR24 (default)
-       CAP_MODE_RGB  = 1, //!< RGB24
-       CAP_MODE_GRAY = 2, //!< Y8
-       CAP_MODE_YUYV = 3  //!< YUYV
-     };
-
-/** @brief %VideoWriter generic properties identifier.
- @sa VideoWriter::get(), VideoWriter::set()
-*/
-enum VideoWriterProperties {
-  VIDEOWRITER_PROP_QUALITY = 1,    //!< Current quality (0..100%) of the encoded videostream. Can be adjusted dynamically in some codecs.
-  VIDEOWRITER_PROP_FRAMEBYTES = 2, //!< (Read-only): Size of just encoded video frame. Note that the encoding order may be different from representation order.
-  VIDEOWRITER_PROP_NSTRIPES = 3    //!< Number of stripes for parallel encoding. -1 for auto detection.
-};
-
-//! @} videoio_flags_base
-
-//! @addtogroup videoio_flags_others
-//! @{
-
-/** @name IEEE 1394 drivers
-    @{
-*/
-
-/** @brief Modes of the IEEE 1394 controlling registers
-(can be: auto, manual, auto single push, absolute Latter allowed with any other mode)
-every feature can have only one mode turned on at a time
-*/
-enum { CAP_PROP_DC1394_OFF                = -4, //!< turn the feature off (not controlled manually nor automatically).
-       CAP_PROP_DC1394_MODE_MANUAL        = -3, //!< set automatically when a value of the feature is set by the user.
-       CAP_PROP_DC1394_MODE_AUTO          = -2,
-       CAP_PROP_DC1394_MODE_ONE_PUSH_AUTO = -1,
-       CAP_PROP_DC1394_MAX                = 31
-     };
-
-//! @} IEEE 1394 drivers
-
-/** @name OpenNI (for Kinect)
-    @{
-*/
-
-//! OpenNI map generators
-enum { CAP_OPENNI_DEPTH_GENERATOR = 1 << 31,
-       CAP_OPENNI_IMAGE_GENERATOR = 1 << 30,
-       CAP_OPENNI_IR_GENERATOR    = 1 << 29,
-       CAP_OPENNI_GENERATORS_MASK = CAP_OPENNI_DEPTH_GENERATOR + CAP_OPENNI_IMAGE_GENERATOR + CAP_OPENNI_IR_GENERATOR
-     };
-
-//! Properties of cameras available through OpenNI backend
-enum { CAP_PROP_OPENNI_OUTPUT_MODE       = 100,
-       CAP_PROP_OPENNI_FRAME_MAX_DEPTH   = 101, //!< In mm
-       CAP_PROP_OPENNI_BASELINE          = 102, //!< In mm
-       CAP_PROP_OPENNI_FOCAL_LENGTH      = 103, //!< In pixels
-       CAP_PROP_OPENNI_REGISTRATION      = 104, //!< Flag that synchronizes the remapping depth map to image map
-                                                //!< by changing depth generator's view point (if the flag is "on") or
-                                                //!< sets this view point to its normal one (if the flag is "off").
-       CAP_PROP_OPENNI_REGISTRATION_ON   = CAP_PROP_OPENNI_REGISTRATION,
-       CAP_PROP_OPENNI_APPROX_FRAME_SYNC = 105,
-       CAP_PROP_OPENNI_MAX_BUFFER_SIZE   = 106,
-       CAP_PROP_OPENNI_CIRCLE_BUFFER     = 107,
-       CAP_PROP_OPENNI_MAX_TIME_DURATION = 108,
-       CAP_PROP_OPENNI_GENERATOR_PRESENT = 109,
-       CAP_PROP_OPENNI2_SYNC             = 110,
-       CAP_PROP_OPENNI2_MIRROR           = 111
-     };
-
-//! OpenNI shortcuts
-enum { CAP_OPENNI_IMAGE_GENERATOR_PRESENT         = CAP_OPENNI_IMAGE_GENERATOR + CAP_PROP_OPENNI_GENERATOR_PRESENT,
-       CAP_OPENNI_IMAGE_GENERATOR_OUTPUT_MODE     = CAP_OPENNI_IMAGE_GENERATOR + CAP_PROP_OPENNI_OUTPUT_MODE,
-       CAP_OPENNI_DEPTH_GENERATOR_PRESENT         = CAP_OPENNI_DEPTH_GENERATOR + CAP_PROP_OPENNI_GENERATOR_PRESENT,
-       CAP_OPENNI_DEPTH_GENERATOR_BASELINE        = CAP_OPENNI_DEPTH_GENERATOR + CAP_PROP_OPENNI_BASELINE,
-       CAP_OPENNI_DEPTH_GENERATOR_FOCAL_LENGTH    = CAP_OPENNI_DEPTH_GENERATOR + CAP_PROP_OPENNI_FOCAL_LENGTH,
-       CAP_OPENNI_DEPTH_GENERATOR_REGISTRATION    = CAP_OPENNI_DEPTH_GENERATOR + CAP_PROP_OPENNI_REGISTRATION,
-       CAP_OPENNI_DEPTH_GENERATOR_REGISTRATION_ON = CAP_OPENNI_DEPTH_GENERATOR_REGISTRATION,
-       CAP_OPENNI_IR_GENERATOR_PRESENT            = CAP_OPENNI_IR_GENERATOR + CAP_PROP_OPENNI_GENERATOR_PRESENT,
-     };
-
-//! OpenNI data given from depth generator
-enum { CAP_OPENNI_DEPTH_MAP         = 0, //!< Depth values in mm (CV_16UC1)
-       CAP_OPENNI_POINT_CLOUD_MAP   = 1, //!< XYZ in meters (CV_32FC3)
-       CAP_OPENNI_DISPARITY_MAP     = 2, //!< Disparity in pixels (CV_8UC1)
-       CAP_OPENNI_DISPARITY_MAP_32F = 3, //!< Disparity in pixels (CV_32FC1)
-       CAP_OPENNI_VALID_DEPTH_MASK  = 4, //!< CV_8UC1
-
-       CAP_OPENNI_BGR_IMAGE         = 5, //!< Data given from RGB image generator
-       CAP_OPENNI_GRAY_IMAGE        = 6, //!< Data given from RGB image generator
-
-       CAP_OPENNI_IR_IMAGE          = 7  //!< Data given from IR image generator
-     };
-
-//! Supported output modes of OpenNI image generator
-enum { CAP_OPENNI_VGA_30HZ  = 0,
-       CAP_OPENNI_SXGA_15HZ = 1,
-       CAP_OPENNI_SXGA_30HZ = 2,
-       CAP_OPENNI_QVGA_30HZ = 3,
-       CAP_OPENNI_QVGA_60HZ = 4
-     };
-
-//! @} OpenNI
-
-/** @name GStreamer
-    @{
-*/
-
-enum { CAP_PROP_GSTREAMER_QUEUE_LENGTH = 200 //!< Default is 1
-     };
-
-//! @} GStreamer
-
-/** @name PvAPI, Prosilica GigE SDK
-    @{
-*/
-
-//! PVAPI
-enum { CAP_PROP_PVAPI_MULTICASTIP           = 300, //!< IP for enable multicast master mode. 0 for disable multicast.
-       CAP_PROP_PVAPI_FRAMESTARTTRIGGERMODE = 301, //!< FrameStartTriggerMode: Determines how a frame is initiated.
-       CAP_PROP_PVAPI_DECIMATIONHORIZONTAL  = 302, //!< Horizontal sub-sampling of the image.
-       CAP_PROP_PVAPI_DECIMATIONVERTICAL    = 303, //!< Vertical sub-sampling of the image.
-       CAP_PROP_PVAPI_BINNINGX              = 304, //!< Horizontal binning factor.
-       CAP_PROP_PVAPI_BINNINGY              = 305, //!< Vertical binning factor.
-       CAP_PROP_PVAPI_PIXELFORMAT           = 306  //!< Pixel format.
-     };
-
-//! PVAPI: FrameStartTriggerMode
-enum { CAP_PVAPI_FSTRIGMODE_FREERUN     = 0,    //!< Freerun
-       CAP_PVAPI_FSTRIGMODE_SYNCIN1     = 1,    //!< SyncIn1
-       CAP_PVAPI_FSTRIGMODE_SYNCIN2     = 2,    //!< SyncIn2
-       CAP_PVAPI_FSTRIGMODE_FIXEDRATE   = 3,    //!< FixedRate
-       CAP_PVAPI_FSTRIGMODE_SOFTWARE    = 4     //!< Software
-     };
-
-//! PVAPI: DecimationHorizontal, DecimationVertical
-enum { CAP_PVAPI_DECIMATION_OFF       = 1,    //!< Off
-       CAP_PVAPI_DECIMATION_2OUTOF4   = 2,    //!< 2 out of 4 decimation
-       CAP_PVAPI_DECIMATION_2OUTOF8   = 4,    //!< 2 out of 8 decimation
-       CAP_PVAPI_DECIMATION_2OUTOF16  = 8     //!< 2 out of 16 decimation
-     };
-
-//! PVAPI: PixelFormat
-enum { CAP_PVAPI_PIXELFORMAT_MONO8    = 1,    //!< Mono8
-       CAP_PVAPI_PIXELFORMAT_MONO16   = 2,    //!< Mono16
-       CAP_PVAPI_PIXELFORMAT_BAYER8   = 3,    //!< Bayer8
-       CAP_PVAPI_PIXELFORMAT_BAYER16  = 4,    //!< Bayer16
-       CAP_PVAPI_PIXELFORMAT_RGB24    = 5,    //!< Rgb24
-       CAP_PVAPI_PIXELFORMAT_BGR24    = 6,    //!< Bgr24
-       CAP_PVAPI_PIXELFORMAT_RGBA32   = 7,    //!< Rgba32
-       CAP_PVAPI_PIXELFORMAT_BGRA32   = 8,    //!< Bgra32
-     };
-
-//! @} PvAPI
-
-/** @name XIMEA Camera API
-    @{
-*/
-
-//! Properties of cameras available through XIMEA SDK backend
-enum { CAP_PROP_XI_DOWNSAMPLING                                 = 400, //!< Change image resolution by binning or skipping.
-       CAP_PROP_XI_DATA_FORMAT                                  = 401, //!< Output data format.
-       CAP_PROP_XI_OFFSET_X                                     = 402, //!< Horizontal offset from the origin to the area of interest (in pixels).
-       CAP_PROP_XI_OFFSET_Y                                     = 403, //!< Vertical offset from the origin to the area of interest (in pixels).
-       CAP_PROP_XI_TRG_SOURCE                                   = 404, //!< Defines source of trigger.
-       CAP_PROP_XI_TRG_SOFTWARE                                 = 405, //!< Generates an internal trigger. PRM_TRG_SOURCE must be set to TRG_SOFTWARE.
-       CAP_PROP_XI_GPI_SELECTOR                                 = 406, //!< Selects general purpose input.
-       CAP_PROP_XI_GPI_MODE                                     = 407, //!< Set general purpose input mode.
-       CAP_PROP_XI_GPI_LEVEL                                    = 408, //!< Get general purpose level.
-       CAP_PROP_XI_GPO_SELECTOR                                 = 409, //!< Selects general purpose output.
-       CAP_PROP_XI_GPO_MODE                                     = 410, //!< Set general purpose output mode.
-       CAP_PROP_XI_LED_SELECTOR                                 = 411, //!< Selects camera signalling LED.
-       CAP_PROP_XI_LED_MODE                                     = 412, //!< Define camera signalling LED functionality.
-       CAP_PROP_XI_MANUAL_WB                                    = 413, //!< Calculates White Balance(must be called during acquisition).
-       CAP_PROP_XI_AUTO_WB                                      = 414, //!< Automatic white balance.
-       CAP_PROP_XI_AEAG                                         = 415, //!< Automatic exposure/gain.
-       CAP_PROP_XI_EXP_PRIORITY                                 = 416, //!< Exposure priority (0.5 - exposure 50%, gain 50%).
-       CAP_PROP_XI_AE_MAX_LIMIT                                 = 417, //!< Maximum limit of exposure in AEAG procedure.
-       CAP_PROP_XI_AG_MAX_LIMIT                                 = 418, //!< Maximum limit of gain in AEAG procedure.
-       CAP_PROP_XI_AEAG_LEVEL                                   = 419, //!< Average intensity of output signal AEAG should achieve(in %).
-       CAP_PROP_XI_TIMEOUT                                      = 420, //!< Image capture timeout in milliseconds.
-       CAP_PROP_XI_EXPOSURE                                     = 421, //!< Exposure time in microseconds.
-       CAP_PROP_XI_EXPOSURE_BURST_COUNT                         = 422, //!< Sets the number of times of exposure in one frame.
-       CAP_PROP_XI_GAIN_SELECTOR                                = 423, //!< Gain selector for parameter Gain allows to select different type of gains.
-       CAP_PROP_XI_GAIN                                         = 424, //!< Gain in dB.
-       CAP_PROP_XI_DOWNSAMPLING_TYPE                            = 426, //!< Change image downsampling type.
-       CAP_PROP_XI_BINNING_SELECTOR                             = 427, //!< Binning engine selector.
-       CAP_PROP_XI_BINNING_VERTICAL                             = 428, //!< Vertical Binning - number of vertical photo-sensitive cells to combine together.
-       CAP_PROP_XI_BINNING_HORIZONTAL                           = 429, //!< Horizontal Binning - number of horizontal photo-sensitive cells to combine together.
-       CAP_PROP_XI_BINNING_PATTERN                              = 430, //!< Binning pattern type.
-       CAP_PROP_XI_DECIMATION_SELECTOR                          = 431, //!< Decimation engine selector.
-       CAP_PROP_XI_DECIMATION_VERTICAL                          = 432, //!< Vertical Decimation - vertical sub-sampling of the image - reduces the vertical resolution of the image by the specified vertical decimation factor.
-       CAP_PROP_XI_DECIMATION_HORIZONTAL                        = 433, //!< Horizontal Decimation - horizontal sub-sampling of the image - reduces the horizontal resolution of the image by the specified vertical decimation factor.
-       CAP_PROP_XI_DECIMATION_PATTERN                           = 434, //!< Decimation pattern type.
-       CAP_PROP_XI_TEST_PATTERN_GENERATOR_SELECTOR              = 587, //!< Selects which test pattern generator is controlled by the TestPattern feature.
-       CAP_PROP_XI_TEST_PATTERN                                 = 588, //!< Selects which test pattern type is generated by the selected generator.
-       CAP_PROP_XI_IMAGE_DATA_FORMAT                            = 435, //!< Output data format.
-       CAP_PROP_XI_SHUTTER_TYPE                                 = 436, //!< Change sensor shutter type(CMOS sensor).
-       CAP_PROP_XI_SENSOR_TAPS                                  = 437, //!< Number of taps.
-       CAP_PROP_XI_AEAG_ROI_OFFSET_X                            = 439, //!< Automatic exposure/gain ROI offset X.
-       CAP_PROP_XI_AEAG_ROI_OFFSET_Y                            = 440, //!< Automatic exposure/gain ROI offset Y.
-       CAP_PROP_XI_AEAG_ROI_WIDTH                               = 441, //!< Automatic exposure/gain ROI Width.
-       CAP_PROP_XI_AEAG_ROI_HEIGHT                              = 442, //!< Automatic exposure/gain ROI Height.
-       CAP_PROP_XI_BPC                                          = 445, //!< Correction of bad pixels.
-       CAP_PROP_XI_WB_KR                                        = 448, //!< White balance red coefficient.
-       CAP_PROP_XI_WB_KG                                        = 449, //!< White balance green coefficient.
-       CAP_PROP_XI_WB_KB                                        = 450, //!< White balance blue coefficient.
-       CAP_PROP_XI_WIDTH                                        = 451, //!< Width of the Image provided by the device (in pixels).
-       CAP_PROP_XI_HEIGHT                                       = 452, //!< Height of the Image provided by the device (in pixels).
-       CAP_PROP_XI_REGION_SELECTOR                              = 589, //!< Selects Region in Multiple ROI which parameters are set by width, height, ... ,region mode.
-       CAP_PROP_XI_REGION_MODE                                  = 595, //!< Activates/deactivates Region selected by Region Selector.
-       CAP_PROP_XI_LIMIT_BANDWIDTH                              = 459, //!< Set/get bandwidth(datarate)(in Megabits).
-       CAP_PROP_XI_SENSOR_DATA_BIT_DEPTH                        = 460, //!< Sensor output data bit depth.
-       CAP_PROP_XI_OUTPUT_DATA_BIT_DEPTH                        = 461, //!< Device output data bit depth.
-       CAP_PROP_XI_IMAGE_DATA_BIT_DEPTH                         = 462, //!< bitdepth of data returned by function xiGetImage.
-       CAP_PROP_XI_OUTPUT_DATA_PACKING                          = 463, //!< Device output data packing (or grouping) enabled. Packing could be enabled if output_data_bit_depth > 8 and packing capability is available.
-       CAP_PROP_XI_OUTPUT_DATA_PACKING_TYPE                     = 464, //!< Data packing type. Some cameras supports only specific packing type.
-       CAP_PROP_XI_IS_COOLED                                    = 465, //!< Returns 1 for cameras that support cooling.
-       CAP_PROP_XI_COOLING                                      = 466, //!< Start camera cooling.
-       CAP_PROP_XI_TARGET_TEMP                                  = 467, //!< Set sensor target temperature for cooling.
-       CAP_PROP_XI_CHIP_TEMP                                    = 468, //!< Camera sensor temperature.
-       CAP_PROP_XI_HOUS_TEMP                                    = 469, //!< Camera housing temperature.
-       CAP_PROP_XI_HOUS_BACK_SIDE_TEMP                          = 590, //!< Camera housing back side temperature.
-       CAP_PROP_XI_SENSOR_BOARD_TEMP                            = 596, //!< Camera sensor board temperature.
-       CAP_PROP_XI_CMS                                          = 470, //!< Mode of color management system.
-       CAP_PROP_XI_APPLY_CMS                                    = 471, //!< Enable applying of CMS profiles to xiGetImage (see XI_PRM_INPUT_CMS_PROFILE, XI_PRM_OUTPUT_CMS_PROFILE).
-       CAP_PROP_XI_IMAGE_IS_COLOR                               = 474, //!< Returns 1 for color cameras.
-       CAP_PROP_XI_COLOR_FILTER_ARRAY                           = 475, //!< Returns color filter array type of RAW data.
-       CAP_PROP_XI_GAMMAY                                       = 476, //!< Luminosity gamma.
-       CAP_PROP_XI_GAMMAC                                       = 477, //!< Chromaticity gamma.
-       CAP_PROP_XI_SHARPNESS                                    = 478, //!< Sharpness Strength.
-       CAP_PROP_XI_CC_MATRIX_00                                 = 479, //!< Color Correction Matrix element [0][0].
-       CAP_PROP_XI_CC_MATRIX_01                                 = 480, //!< Color Correction Matrix element [0][1].
-       CAP_PROP_XI_CC_MATRIX_02                                 = 481, //!< Color Correction Matrix element [0][2].
-       CAP_PROP_XI_CC_MATRIX_03                                 = 482, //!< Color Correction Matrix element [0][3].
-       CAP_PROP_XI_CC_MATRIX_10                                 = 483, //!< Color Correction Matrix element [1][0].
-       CAP_PROP_XI_CC_MATRIX_11                                 = 484, //!< Color Correction Matrix element [1][1].
-       CAP_PROP_XI_CC_MATRIX_12                                 = 485, //!< Color Correction Matrix element [1][2].
-       CAP_PROP_XI_CC_MATRIX_13                                 = 486, //!< Color Correction Matrix element [1][3].
-       CAP_PROP_XI_CC_MATRIX_20                                 = 487, //!< Color Correction Matrix element [2][0].
-       CAP_PROP_XI_CC_MATRIX_21                                 = 488, //!< Color Correction Matrix element [2][1].
-       CAP_PROP_XI_CC_MATRIX_22                                 = 489, //!< Color Correction Matrix element [2][2].
-       CAP_PROP_XI_CC_MATRIX_23                                 = 490, //!< Color Correction Matrix element [2][3].
-       CAP_PROP_XI_CC_MATRIX_30                                 = 491, //!< Color Correction Matrix element [3][0].
-       CAP_PROP_XI_CC_MATRIX_31                                 = 492, //!< Color Correction Matrix element [3][1].
-       CAP_PROP_XI_CC_MATRIX_32                                 = 493, //!< Color Correction Matrix element [3][2].
-       CAP_PROP_XI_CC_MATRIX_33                                 = 494, //!< Color Correction Matrix element [3][3].
-       CAP_PROP_XI_DEFAULT_CC_MATRIX                            = 495, //!< Set default Color Correction Matrix.
-       CAP_PROP_XI_TRG_SELECTOR                                 = 498, //!< Selects the type of trigger.
-       CAP_PROP_XI_ACQ_FRAME_BURST_COUNT                        = 499, //!< Sets number of frames acquired by burst. This burst is used only if trigger is set to FrameBurstStart.
-       CAP_PROP_XI_DEBOUNCE_EN                                  = 507, //!< Enable/Disable debounce to selected GPI.
-       CAP_PROP_XI_DEBOUNCE_T0                                  = 508, //!< Debounce time (x * 10us).
-       CAP_PROP_XI_DEBOUNCE_T1                                  = 509, //!< Debounce time (x * 10us).
-       CAP_PROP_XI_DEBOUNCE_POL                                 = 510, //!< Debounce polarity (pol = 1 t0 - falling edge, t1 - rising edge).
-       CAP_PROP_XI_LENS_MODE                                    = 511, //!< Status of lens control interface. This shall be set to XI_ON before any Lens operations.
-       CAP_PROP_XI_LENS_APERTURE_VALUE                          = 512, //!< Current lens aperture value in stops. Examples: 2.8, 4, 5.6, 8, 11.
-       CAP_PROP_XI_LENS_FOCUS_MOVEMENT_VALUE                    = 513, //!< Lens current focus movement value to be used by XI_PRM_LENS_FOCUS_MOVE in motor steps.
-       CAP_PROP_XI_LENS_FOCUS_MOVE                              = 514, //!< Moves lens focus motor by steps set in XI_PRM_LENS_FOCUS_MOVEMENT_VALUE.
-       CAP_PROP_XI_LENS_FOCUS_DISTANCE                          = 515, //!< Lens focus distance in cm.
-       CAP_PROP_XI_LENS_FOCAL_LENGTH                            = 516, //!< Lens focal distance in mm.
-       CAP_PROP_XI_LENS_FEATURE_SELECTOR                        = 517, //!< Selects the current feature which is accessible by XI_PRM_LENS_FEATURE.
-       CAP_PROP_XI_LENS_FEATURE                                 = 518, //!< Allows access to lens feature value currently selected by XI_PRM_LENS_FEATURE_SELECTOR.
-       CAP_PROP_XI_DEVICE_MODEL_ID                              = 521, //!< Returns device model id.
-       CAP_PROP_XI_DEVICE_SN                                    = 522, //!< Returns device serial number.
-       CAP_PROP_XI_IMAGE_DATA_FORMAT_RGB32_ALPHA                = 529, //!< The alpha channel of RGB32 output image format.
-       CAP_PROP_XI_IMAGE_PAYLOAD_SIZE                           = 530, //!< Buffer size in bytes sufficient for output image returned by xiGetImage.
-       CAP_PROP_XI_TRANSPORT_PIXEL_FORMAT                       = 531, //!< Current format of pixels on transport layer.
-       CAP_PROP_XI_SENSOR_CLOCK_FREQ_HZ                         = 532, //!< Sensor clock frequency in Hz.
-       CAP_PROP_XI_SENSOR_CLOCK_FREQ_INDEX                      = 533, //!< Sensor clock frequency index. Sensor with selected frequencies have possibility to set the frequency only by this index.
-       CAP_PROP_XI_SENSOR_OUTPUT_CHANNEL_COUNT                  = 534, //!< Number of output channels from sensor used for data transfer.
-       CAP_PROP_XI_FRAMERATE                                    = 535, //!< Define framerate in Hz.
-       CAP_PROP_XI_COUNTER_SELECTOR                             = 536, //!< Select counter.
-       CAP_PROP_XI_COUNTER_VALUE                                = 537, //!< Counter status.
-       CAP_PROP_XI_ACQ_TIMING_MODE                              = 538, //!< Type of sensor frames timing.
-       CAP_PROP_XI_AVAILABLE_BANDWIDTH                          = 539, //!< Calculate and returns available interface bandwidth(int Megabits).
-       CAP_PROP_XI_BUFFER_POLICY                                = 540, //!< Data move policy.
-       CAP_PROP_XI_LUT_EN                                       = 541, //!< Activates LUT.
-       CAP_PROP_XI_LUT_INDEX                                    = 542, //!< Control the index (offset) of the coefficient to access in the LUT.
-       CAP_PROP_XI_LUT_VALUE                                    = 543, //!< Value at entry LUTIndex of the LUT.
-       CAP_PROP_XI_TRG_DELAY                                    = 544, //!< Specifies the delay in microseconds (us) to apply after the trigger reception before activating it.
-       CAP_PROP_XI_TS_RST_MODE                                  = 545, //!< Defines how time stamp reset engine will be armed.
-       CAP_PROP_XI_TS_RST_SOURCE                                = 546, //!< Defines which source will be used for timestamp reset. Writing this parameter will trigger settings of engine (arming).
-       CAP_PROP_XI_IS_DEVICE_EXIST                              = 547, //!< Returns 1 if camera connected and works properly.
-       CAP_PROP_XI_ACQ_BUFFER_SIZE                              = 548, //!< Acquisition buffer size in buffer_size_unit. Default bytes.
-       CAP_PROP_XI_ACQ_BUFFER_SIZE_UNIT                         = 549, //!< Acquisition buffer size unit in bytes. Default 1. E.g. Value 1024 means that buffer_size is in KiBytes.
-       CAP_PROP_XI_ACQ_TRANSPORT_BUFFER_SIZE                    = 550, //!< Acquisition transport buffer size in bytes.
-       CAP_PROP_XI_BUFFERS_QUEUE_SIZE                           = 551, //!< Queue of field/frame buffers.
-       CAP_PROP_XI_ACQ_TRANSPORT_BUFFER_COMMIT                  = 552, //!< Number of buffers to commit to low level.
-       CAP_PROP_XI_RECENT_FRAME                                 = 553, //!< GetImage returns most recent frame.
-       CAP_PROP_XI_DEVICE_RESET                                 = 554, //!< Resets the camera to default state.
-       CAP_PROP_XI_COLUMN_FPN_CORRECTION                        = 555, //!< Correction of column FPN.
-       CAP_PROP_XI_ROW_FPN_CORRECTION                           = 591, //!< Correction of row FPN.
-       CAP_PROP_XI_SENSOR_MODE                                  = 558, //!< Current sensor mode. Allows to select sensor mode by one integer. Setting of this parameter affects: image dimensions and downsampling.
-       CAP_PROP_XI_HDR                                          = 559, //!< Enable High Dynamic Range feature.
-       CAP_PROP_XI_HDR_KNEEPOINT_COUNT                          = 560, //!< The number of kneepoints in the PWLR.
-       CAP_PROP_XI_HDR_T1                                       = 561, //!< Position of first kneepoint(in % of XI_PRM_EXPOSURE).
-       CAP_PROP_XI_HDR_T2                                       = 562, //!< Position of second kneepoint (in % of XI_PRM_EXPOSURE).
-       CAP_PROP_XI_KNEEPOINT1                                   = 563, //!< Value of first kneepoint (% of sensor saturation).
-       CAP_PROP_XI_KNEEPOINT2                                   = 564, //!< Value of second kneepoint (% of sensor saturation).
-       CAP_PROP_XI_IMAGE_BLACK_LEVEL                            = 565, //!< Last image black level counts. Can be used for Offline processing to recall it.
-       CAP_PROP_XI_HW_REVISION                                  = 571, //!< Returns hardware revision number.
-       CAP_PROP_XI_DEBUG_LEVEL                                  = 572, //!< Set debug level.
-       CAP_PROP_XI_AUTO_BANDWIDTH_CALCULATION                   = 573, //!< Automatic bandwidth calculation.
-       CAP_PROP_XI_FFS_FILE_ID                                  = 594, //!< File number.
-       CAP_PROP_XI_FFS_FILE_SIZE                                = 580, //!< Size of file.
-       CAP_PROP_XI_FREE_FFS_SIZE                                = 581, //!< Size of free camera FFS.
-       CAP_PROP_XI_USED_FFS_SIZE                                = 582, //!< Size of used camera FFS.
-       CAP_PROP_XI_FFS_ACCESS_KEY                               = 583, //!< Setting of key enables file operations on some cameras.
-       CAP_PROP_XI_SENSOR_FEATURE_SELECTOR                      = 585, //!< Selects the current feature which is accessible by XI_PRM_SENSOR_FEATURE_VALUE.
-       CAP_PROP_XI_SENSOR_FEATURE_VALUE                         = 586, //!< Allows access to sensor feature value currently selected by XI_PRM_SENSOR_FEATURE_SELECTOR.
-     };
-
-//! @} XIMEA
-
-/** @name AVFoundation framework for iOS
-    OS X Lion will have the same API
-    @{
-*/
-
-//! Properties of cameras available through AVFOUNDATION backend
-enum { CAP_PROP_IOS_DEVICE_FOCUS        = 9001,
-       CAP_PROP_IOS_DEVICE_EXPOSURE     = 9002,
-       CAP_PROP_IOS_DEVICE_FLASH        = 9003,
-       CAP_PROP_IOS_DEVICE_WHITEBALANCE = 9004,
-       CAP_PROP_IOS_DEVICE_TORCH        = 9005
-     };
-
-/** @name Smartek Giganetix GigEVisionSDK
-    @{
-*/
-
-//! Properties of cameras available through Smartek Giganetix Ethernet Vision backend
-/* --- Vladimir Litvinenko (litvinenko.vladimir@gmail.com) --- */
-enum { CAP_PROP_GIGA_FRAME_OFFSET_X   = 10001,
-       CAP_PROP_GIGA_FRAME_OFFSET_Y   = 10002,
-       CAP_PROP_GIGA_FRAME_WIDTH_MAX  = 10003,
-       CAP_PROP_GIGA_FRAME_HEIGH_MAX  = 10004,
-       CAP_PROP_GIGA_FRAME_SENS_WIDTH = 10005,
-       CAP_PROP_GIGA_FRAME_SENS_HEIGH = 10006
-     };
-
-//! @} Smartek
-
-/** @name Intel Perceptual Computing SDK
-    @{
-*/
-enum { CAP_PROP_INTELPERC_PROFILE_COUNT               = 11001,
-       CAP_PROP_INTELPERC_PROFILE_IDX                 = 11002,
-       CAP_PROP_INTELPERC_DEPTH_LOW_CONFIDENCE_VALUE  = 11003,
-       CAP_PROP_INTELPERC_DEPTH_SATURATION_VALUE      = 11004,
-       CAP_PROP_INTELPERC_DEPTH_CONFIDENCE_THRESHOLD  = 11005,
-       CAP_PROP_INTELPERC_DEPTH_FOCAL_LENGTH_HORZ     = 11006,
-       CAP_PROP_INTELPERC_DEPTH_FOCAL_LENGTH_VERT     = 11007
-     };
-
-//! Intel Perceptual Streams
-enum { CAP_INTELPERC_DEPTH_GENERATOR = 1 << 29,
-       CAP_INTELPERC_IMAGE_GENERATOR = 1 << 28,
-       CAP_INTELPERC_GENERATORS_MASK = CAP_INTELPERC_DEPTH_GENERATOR + CAP_INTELPERC_IMAGE_GENERATOR
-     };
-
-enum { CAP_INTELPERC_DEPTH_MAP              = 0, //!< Each pixel is a 16-bit integer. The value indicates the distance from an object to the camera's XY plane or the Cartesian depth.
-       CAP_INTELPERC_UVDEPTH_MAP            = 1, //!< Each pixel contains two 32-bit floating point values in the range of 0-1, representing the mapping of depth coordinates to the color coordinates.
-       CAP_INTELPERC_IR_MAP                 = 2, //!< Each pixel is a 16-bit integer. The value indicates the intensity of the reflected laser beam.
-       CAP_INTELPERC_IMAGE                  = 3
-     };
-
-//! @} Intel Perceptual
-
-/** @name gPhoto2 connection
-    @{
-*/
-
-/** @brief gPhoto2 properties
-
-If `propertyId` is less than 0 then work on widget with that __additive inversed__ camera setting ID
-Get IDs by using CAP_PROP_GPHOTO2_WIDGET_ENUMERATE.
-@see CvCaptureCAM_GPHOTO2 for more info
-*/
-enum { CAP_PROP_GPHOTO2_PREVIEW           = 17001, //!< Capture only preview from liveview mode.
-       CAP_PROP_GPHOTO2_WIDGET_ENUMERATE  = 17002, //!< Readonly, returns (const char *).
-       CAP_PROP_GPHOTO2_RELOAD_CONFIG     = 17003, //!< Trigger, only by set. Reload camera settings.
-       CAP_PROP_GPHOTO2_RELOAD_ON_CHANGE  = 17004, //!< Reload all settings on set.
-       CAP_PROP_GPHOTO2_COLLECT_MSGS      = 17005, //!< Collect messages with details.
-       CAP_PROP_GPHOTO2_FLUSH_MSGS        = 17006, //!< Readonly, returns (const char *).
-       CAP_PROP_SPEED                     = 17007, //!< Exposure speed. Can be readonly, depends on camera program.
-       CAP_PROP_APERTURE                  = 17008, //!< Aperture. Can be readonly, depends on camera program.
-       CAP_PROP_EXPOSUREPROGRAM           = 17009, //!< Camera exposure program.
-       CAP_PROP_VIEWFINDER                = 17010  //!< Enter liveview mode.
-     };
-
-//! @} gPhoto2
-
-
-/** @name Images backend
-    @{
-*/
-
-/** @brief Images backend properties
-
-*/
-enum { CAP_PROP_IMAGES_BASE = 18000,
-       CAP_PROP_IMAGES_LAST = 19000 // excluding
-     };
-
-//! @} Images
-
-//! @} videoio_flags_others
-
-
-class IVideoCapture;
-
-/** @brief Class for video capturing from video files, image sequences or cameras.
-
-The class provides C++ API for capturing video from cameras or for reading video files and image sequences.
-
-Here is how the class can be used:
-@include samples/cpp/videocapture_basic.cpp
-
-@note In @ref videoio_c "C API" the black-box structure `CvCapture` is used instead of %VideoCapture.
-@note
--   (C++) A basic sample on using the %VideoCapture interface can be found at
-    `OPENCV_SOURCE_CODE/samples/cpp/videocapture_starter.cpp`
--   (Python) A basic sample on using the %VideoCapture interface can be found at
-    `OPENCV_SOURCE_CODE/samples/python/video.py`
--   (Python) A multi threaded video processing sample can be found at
-    `OPENCV_SOURCE_CODE/samples/python/video_threaded.py`
--   (Python) %VideoCapture sample showcasing some features of the Video4Linux2 backend
-    `OPENCV_SOURCE_CODE/samples/python/video_v4l2.py`
- */
-class CV_EXPORTS_W VideoCapture
-{
-public:
-    /** @brief Default constructor
-    @note In @ref videoio_c "C API", when you finished working with video, release CvCapture structure with
-    cvReleaseCapture(), or use Ptr\<CvCapture\> that calls cvReleaseCapture() automatically in the
-    destructor.
-     */
-    CV_WRAP VideoCapture();
-
-    /** @overload
-    @brief  Open video file or a capturing device or a IP video stream for video capturing
-
-    Same as VideoCapture(const String& filename, int apiPreference) but using default Capture API backends
-    */
-    CV_WRAP VideoCapture(const String& filename);
-
-    /** @overload
-    @brief  Open video file or a capturing device or a IP video stream for video capturing with API Preference
-
-    @param filename it can be:
-    - name of video file (eg. `video.avi`)
-    - or image sequence (eg. `img_%02d.jpg`, which will read samples like `img_00.jpg, img_01.jpg, img_02.jpg, ...`)
-    - or URL of video stream (eg. `protocol://host:port/script_name?script_params|auth`).
-      Note that each video stream or IP camera feed has its own URL scheme. Please refer to the
-      documentation of source stream to know the right URL.
-    @param apiPreference preferred Capture API backends to use. Can be used to enforce a specific reader
-    implementation if multiple are available: e.g. cv::CAP_FFMPEG or cv::CAP_IMAGES or cv::CAP_DSHOW.
-    @sa The list of supported API backends cv::VideoCaptureAPIs
-    */
-    CV_WRAP VideoCapture(const String& filename, int apiPreference);
-
-    /** @overload
-    @brief  Open a camera for video capturing
-
-    @param index camera_id + domain_offset (CAP_*) id of the video capturing device to open. To open default camera using default backend just pass 0.
-    Use a `domain_offset` to enforce a specific reader implementation if multiple are available like cv::CAP_FFMPEG or cv::CAP_IMAGES or cv::CAP_DSHOW.
-    e.g. to open Camera 1 using the MS Media Foundation API use `index = 1 + cv::CAP_MSMF`
-
-    @sa The list of supported API backends cv::VideoCaptureAPIs
-    */
-    CV_WRAP VideoCapture(int index);
-
-    /** @brief Default destructor
-
-    The method first calls VideoCapture::release to close the already opened file or camera.
-    */
-    virtual ~VideoCapture();
-
-    /** @brief  Open video file or a capturing device or a IP video stream for video capturing
-
-    @overload
-
-    Parameters are same as the constructor VideoCapture(const String& filename)
-    @return `true` if the file has been successfully opened
-
-    The method first calls VideoCapture::release to close the already opened file or camera.
-     */
-    CV_WRAP virtual bool open(const String& filename);
-
-    /** @brief  Open a camera for video capturing
-
-    @overload
-
-    Parameters are same as the constructor VideoCapture(int index)
-    @return `true` if the camera has been successfully opened.
-
-    The method first calls VideoCapture::release to close the already opened file or camera.
-    */
-    CV_WRAP virtual bool open(int index);
-
-   /** @brief  Open a camera for video capturing
-
-    @overload
-
-    Parameters are similar as the constructor VideoCapture(int index),except it takes an additional argument apiPreference.
-    Definitely, is same as open(int index) where `index=cameraNum + apiPreference`
-    @return `true` if the camera has been successfully opened.
-    */
-    CV_WRAP bool open(int cameraNum, int apiPreference);
-
-    /** @brief Returns true if video capturing has been initialized already.
-
-    If the previous call to VideoCapture constructor or VideoCapture::open() succeeded, the method returns
-    true.
-     */
-    CV_WRAP virtual bool isOpened() const;
-
-    /** @brief Closes video file or capturing device.
-
-    The method is automatically called by subsequent VideoCapture::open and by VideoCapture
-    destructor.
-
-    The C function also deallocates memory and clears \*capture pointer.
-     */
-    CV_WRAP virtual void release();
-
-    /** @brief Grabs the next frame from video file or capturing device.
-
-    @return `true` (non-zero) in the case of success.
-
-    The method/function grabs the next frame from video file or camera and returns true (non-zero) in
-    the case of success.
-
-    The primary use of the function is in multi-camera environments, especially when the cameras do not
-    have hardware synchronization. That is, you call VideoCapture::grab() for each camera and after that
-    call the slower method VideoCapture::retrieve() to decode and get frame from each camera. This way
-    the overhead on demosaicing or motion jpeg decompression etc. is eliminated and the retrieved frames
-    from different cameras will be closer in time.
-
-    Also, when a connected camera is multi-head (for example, a stereo camera or a Kinect device), the
-    correct way of retrieving data from it is to call VideoCapture::grab() first and then call
-    VideoCapture::retrieve() one or more times with different values of the channel parameter.
-
-    @ref tutorial_kinect_openni
-     */
-    CV_WRAP virtual bool grab();
-
-    /** @brief Decodes and returns the grabbed video frame.
-
-    @param [out] image the video frame is returned here. If no frames has been grabbed the image will be empty.
-    @param flag it could be a frame index or a driver specific flag
-    @return `false` if no frames has been grabbed
-
-    The method decodes and returns the just grabbed frame. If no frames has been grabbed
-    (camera has been disconnected, or there are no more frames in video file), the method returns false
-    and the function returns an empty image (with %cv::Mat, test it with Mat::empty()).
-
-    @sa read()
-
-    @note In @ref videoio_c "C API", functions cvRetrieveFrame() and cv.RetrieveFrame() return image stored inside the video
-    capturing structure. It is not allowed to modify or release the image! You can copy the frame using
-    :ocvcvCloneImage and then do whatever you want with the copy.
-     */
-    CV_WRAP virtual bool retrieve(OutputArray image, int flag = 0);
-
-    /** @brief Stream operator to read the next video frame.
-    @sa read()
-    */
-    virtual VideoCapture& operator >> (CV_OUT Mat& image);
-
-    /** @overload
-    @sa read()
-    */
-    virtual VideoCapture& operator >> (CV_OUT UMat& image);
-
-    /** @brief Grabs, decodes and returns the next video frame.
-
-    @param [out] image the video frame is returned here. If no frames has been grabbed the image will be empty.
-    @return `false` if no frames has been grabbed
-
-    The method/function combines VideoCapture::grab() and VideoCapture::retrieve() in one call. This is the
-    most convenient method for reading video files or capturing data from decode and returns the just
-    grabbed frame. If no frames has been grabbed (camera has been disconnected, or there are no more
-    frames in video file), the method returns false and the function returns empty image (with %cv::Mat, test it with Mat::empty()).
-
-    @note In @ref videoio_c "C API", functions cvRetrieveFrame() and cv.RetrieveFrame() return image stored inside the video
-    capturing structure. It is not allowed to modify or release the image! You can copy the frame using
-    :ocvcvCloneImage and then do whatever you want with the copy.
-     */
-    CV_WRAP virtual bool read(OutputArray image);
-
-    /** @brief Sets a property in the VideoCapture.
-
-    @param propId Property identifier from cv::VideoCaptureProperties (eg. cv::CAP_PROP_POS_MSEC, cv::CAP_PROP_POS_FRAMES, ...)
-    or one from @ref videoio_flags_others
-    @param value Value of the property.
-    @return `true` if the property is supported by backend used by the VideoCapture instance.
-    @note Even if it returns `true` this doesn't ensure that the property
-    value has been accepted by the capture device. See note in VideoCapture::get()
-     */
-    CV_WRAP virtual bool set(int propId, double value);
-
-    /** @brief Returns the specified VideoCapture property
-
-    @param propId Property identifier from cv::VideoCaptureProperties (eg. cv::CAP_PROP_POS_MSEC, cv::CAP_PROP_POS_FRAMES, ...)
-    or one from @ref videoio_flags_others
-    @return Value for the specified property. Value 0 is returned when querying a property that is
-    not supported by the backend used by the VideoCapture instance.
-
-    @note Reading / writing properties involves many layers. Some unexpected result might happens
-    along this chain.
-    @code {.txt}
-    `VideoCapture -> API Backend -> Operating System -> Device Driver -> Device Hardware`
-    @endcode
-    The returned value might be different from what really used by the device or it could be encoded
-    using device dependent rules (eg. steps or percentage). Effective behaviour depends from device
-    driver and API Backend
-
-    */
-    CV_WRAP virtual double get(int propId) const;
-
-    /** @brief Open video file or a capturing device or a IP video stream for video capturing with API Preference
-
-    @overload
-
-    Parameters are same as the constructor VideoCapture(const String& filename, int apiPreference)
-    @return `true` if the file has been successfully opened
-
-    The method first calls VideoCapture::release to close the already opened file or camera.
-    */
-    CV_WRAP virtual bool open(const String& filename, int apiPreference);
-
-protected:
-    Ptr<CvCapture> cap;
-    Ptr<IVideoCapture> icap;
-};
-
-class IVideoWriter;
-
-/** @example videowriter_basic.cpp
-An example using VideoCapture and VideoWriter class
- */
-/** @brief Video writer class.
-
-The class provides C++ API for writing video files or image sequences.
- */
-class CV_EXPORTS_W VideoWriter
-{
-public:
-    /** @brief Default constructors
-
-    The constructors/functions initialize video writers.
-    -   On Linux FFMPEG is used to write videos;
-    -   On Windows FFMPEG or VFW is used;
-    -   On MacOSX QTKit is used.
-     */
-    CV_WRAP VideoWriter();
-
-    /** @overload
-    @param filename Name of the output video file.
-    @param fourcc 4-character code of codec used to compress the frames. For example,
-    VideoWriter::fourcc('P','I','M','1') is a MPEG-1 codec, VideoWriter::fourcc('M','J','P','G') is a
-    motion-jpeg codec etc. List of codes can be obtained at [Video Codecs by
-    FOURCC](http://www.fourcc.org/codecs.php) page. FFMPEG backend with MP4 container natively uses
-    other values as fourcc code: see [ObjectType](http://www.mp4ra.org/codecs.html),
-    so you may receive a warning message from OpenCV about fourcc code conversion.
-    @param fps Framerate of the created video stream.
-    @param frameSize Size of the video frames.
-    @param isColor If it is not zero, the encoder will expect and encode color frames, otherwise it
-    will work with grayscale frames (the flag is currently supported on Windows only).
-
-    @b Tips:
-    - With some backends `fourcc=-1` pops up the codec selection dialog from the system.
-    - To save image sequence use a proper filename (eg. `img_%02d.jpg`) and `fourcc=0`
-      OR `fps=0`. Use uncompressed image format (eg. `img_%02d.BMP`) to save raw frames.
-    - Most codecs are lossy. If you want lossless video file you need to use a lossless codecs
-      (eg. FFMPEG FFV1, Huffman HFYU, Lagarith LAGS, etc...)
-    - If FFMPEG is enabled, using `codec=0; fps=0;` you can create an uncompressed (raw) video file.
-    */
-    CV_WRAP VideoWriter(const String& filename, int fourcc, double fps,
-                Size frameSize, bool isColor = true);
-
-    /** @overload
-    The `apiPreference` parameter allows to specify API backends to use. Can be used to enforce a specific reader implementation
-    if multiple are available: e.g. cv::CAP_FFMPEG or cv::CAP_GSTREAMER.
-     */
-    CV_WRAP VideoWriter(const String& filename, int apiPreference, int fourcc, double fps,
-                Size frameSize, bool isColor = true);
-
-    /** @brief Default destructor
-
-    The method first calls VideoWriter::release to close the already opened file.
-    */
-    virtual ~VideoWriter();
-
-    /** @brief Initializes or reinitializes video writer.
-
-    The method opens video writer. Parameters are the same as in the constructor
-    VideoWriter::VideoWriter.
-    @return `true` if video writer has been successfully initialized
-
-    The method first calls VideoWriter::release to close the already opened file.
-     */
-    CV_WRAP virtual bool open(const String& filename, int fourcc, double fps,
-                      Size frameSize, bool isColor = true);
-
-    /** @overload
-     */
-    CV_WRAP bool open(const String& filename, int apiPreference, int fourcc, double fps,
-                      Size frameSize, bool isColor = true);
-
-    /** @brief Returns true if video writer has been successfully initialized.
-    */
-    CV_WRAP virtual bool isOpened() const;
-
-    /** @brief Closes the video writer.
-
-    The method is automatically called by subsequent VideoWriter::open and by the VideoWriter
-    destructor.
-     */
-    CV_WRAP virtual void release();
-
-    /** @brief Stream operator to write the next video frame.
-    @sa write
-    */
-    virtual VideoWriter& operator << (const Mat& image);
-
-    /** @brief Writes the next video frame
-
-    @param image The written frame
-
-    The function/method writes the specified image to video file. It must have the same size as has
-    been specified when opening the video writer.
-     */
-    CV_WRAP virtual void write(const Mat& image);
-
-    /** @brief Sets a property in the VideoWriter.
-
-     @param propId Property identifier from cv::VideoWriterProperties (eg. cv::VIDEOWRITER_PROP_QUALITY)
-     or one of @ref videoio_flags_others
-
-     @param value Value of the property.
-     @return  `true` if the property is supported by the backend used by the VideoWriter instance.
-     */
-    CV_WRAP virtual bool set(int propId, double value);
-
-    /** @brief Returns the specified VideoWriter property
-
-     @param propId Property identifier from cv::VideoWriterProperties (eg. cv::VIDEOWRITER_PROP_QUALITY)
-     or one of @ref videoio_flags_others
-
-     @return Value for the specified property. Value 0 is returned when querying a property that is
-     not supported by the backend used by the VideoWriter instance.
-     */
-    CV_WRAP virtual double get(int propId) const;
-
-    /** @brief Concatenates 4 chars to a fourcc code
-
-    @return a fourcc code
-
-    This static method constructs the fourcc code of the codec to be used in the constructor
-    VideoWriter::VideoWriter or VideoWriter::open.
-     */
-    CV_WRAP static int fourcc(char c1, char c2, char c3, char c4);
-
-protected:
-    Ptr<CvVideoWriter> writer;
-    Ptr<IVideoWriter> iwriter;
-
-    static Ptr<IVideoWriter> create(const String& filename, int fourcc, double fps,
-                                    Size frameSize, bool isColor = true);
-};
-
-template<> CV_EXPORTS void DefaultDeleter<CvCapture>::operator ()(CvCapture* obj) const;
-template<> CV_EXPORTS void DefaultDeleter<CvVideoWriter>::operator ()(CvVideoWriter* obj) const;
-
-//! @} videoio
-
-} // cv
-
-#endif //OPENCV_VIDEOIO_HPP
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/videoio/cap_ios.h b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/videoio/cap_ios.h
deleted file mode 100755
index 0691420..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/videoio/cap_ios.h
+++ /dev/null
@@ -1,150 +0,0 @@
-/*  For iOS video I/O
- *  by Eduard Feicho on 29/07/12
- *  Copyright 2012. All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *
- * 1. Redistributions of source code must retain the above copyright notice,
- *    this list of conditions and the following disclaimer.
- * 2. Redistributions in binary form must reproduce the above copyright notice,
- *    this list of conditions and the following disclaimer in the documentation
- *    and/or other materials provided with the distribution.
- * 3. The name of the author may not be used to endorse or promote products
- *    derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE AUTHOR "AS IS" AND ANY EXPRESS OR IMPLIED
- * WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
- * MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO
- * EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
- * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
- * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
- * OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
- * WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR
- * OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF
- * ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- */
-
-#import <UIKit/UIKit.h>
-#import <Accelerate/Accelerate.h>
-#import <AVFoundation/AVFoundation.h>
-#import <ImageIO/ImageIO.h>
-#include "opencv2/core.hpp"
-
-//! @addtogroup videoio_ios
-//! @{
-
-/////////////////////////////////////// CvAbstractCamera /////////////////////////////////////
-
-@class CvAbstractCamera;
-
-CV_EXPORTS @interface CvAbstractCamera : NSObject
-{
-    UIDeviceOrientation currentDeviceOrientation;
-
-    BOOL cameraAvailable;
-}
-
-@property (nonatomic, strong) AVCaptureSession* captureSession;
-@property (nonatomic, strong) AVCaptureConnection* videoCaptureConnection;
-
-@property (nonatomic, readonly) BOOL running;
-@property (nonatomic, readonly) BOOL captureSessionLoaded;
-
-@property (nonatomic, assign) int defaultFPS;
-@property (nonatomic, readonly) AVCaptureVideoPreviewLayer *captureVideoPreviewLayer;
-@property (nonatomic, assign) AVCaptureDevicePosition defaultAVCaptureDevicePosition;
-@property (nonatomic, assign) AVCaptureVideoOrientation defaultAVCaptureVideoOrientation;
-@property (nonatomic, assign) BOOL useAVCaptureVideoPreviewLayer;
-@property (nonatomic, strong) NSString *const defaultAVCaptureSessionPreset;
-
-@property (nonatomic, assign) int imageWidth;
-@property (nonatomic, assign) int imageHeight;
-
-@property (nonatomic, strong) UIView* parentView;
-
-- (void)start;
-- (void)stop;
-- (void)switchCameras;
-
-- (id)initWithParentView:(UIView*)parent;
-
-- (void)createCaptureOutput;
-- (void)createVideoPreviewLayer;
-- (void)updateOrientation;
-
-- (void)lockFocus;
-- (void)unlockFocus;
-- (void)lockExposure;
-- (void)unlockExposure;
-- (void)lockBalance;
-- (void)unlockBalance;
-
-@end
-
-///////////////////////////////// CvVideoCamera ///////////////////////////////////////////
-
-@class CvVideoCamera;
-
-CV_EXPORTS @protocol CvVideoCameraDelegate <NSObject>
-
-#ifdef __cplusplus
-// delegate method for processing image frames
-- (void)processImage:(cv::Mat&)image;
-#endif
-
-@end
-
-CV_EXPORTS @interface CvVideoCamera : CvAbstractCamera<AVCaptureVideoDataOutputSampleBufferDelegate>
-{
-    AVCaptureVideoDataOutput *videoDataOutput;
-
-    dispatch_queue_t videoDataOutputQueue;
-    CALayer *customPreviewLayer;
-
-    CMTime lastSampleTime;
-
-}
-
-@property (nonatomic, weak) id<CvVideoCameraDelegate> delegate;
-@property (nonatomic, assign) BOOL grayscaleMode;
-
-@property (nonatomic, assign) BOOL recordVideo;
-@property (nonatomic, assign) BOOL rotateVideo;
-@property (nonatomic, strong) AVAssetWriterInput* recordAssetWriterInput;
-@property (nonatomic, strong) AVAssetWriterInputPixelBufferAdaptor* recordPixelBufferAdaptor;
-@property (nonatomic, strong) AVAssetWriter* recordAssetWriter;
-
-- (void)adjustLayoutToInterfaceOrientation:(UIInterfaceOrientation)interfaceOrientation;
-- (void)layoutPreviewLayer;
-- (void)saveVideo;
-- (NSURL *)videoFileURL;
-- (NSString *)videoFileString;
-
-
-@end
-
-///////////////////////////////// CvPhotoCamera ///////////////////////////////////////////
-
-@class CvPhotoCamera;
-
-CV_EXPORTS @protocol CvPhotoCameraDelegate <NSObject>
-
-- (void)photoCamera:(CvPhotoCamera*)photoCamera capturedImage:(UIImage *)image;
-- (void)photoCameraCancel:(CvPhotoCamera*)photoCamera;
-
-@end
-
-CV_EXPORTS @interface CvPhotoCamera : CvAbstractCamera
-{
-    AVCaptureStillImageOutput *stillImageOutput;
-}
-
-@property (nonatomic, weak) id<CvPhotoCameraDelegate> delegate;
-
-- (void)takePicture;
-
-@end
-
-//! @} videoio_ios
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/videoio/videoio.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/videoio/videoio.hpp
deleted file mode 100755
index ec84cf7..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/videoio/videoio.hpp
+++ /dev/null
@@ -1,48 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                          License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifdef __OPENCV_BUILD
-#error this is a compatibility header which should not be used inside the OpenCV library
-#endif
-
-#include "opencv2/videoio.hpp"
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/videoio/videoio_c.h b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/videoio/videoio_c.h
deleted file mode 100755
index 32f6ec7..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/videoio/videoio_c.h
+++ /dev/null
@@ -1,587 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                        Intel License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000, Intel Corporation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of Intel Corporation may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_VIDEOIO_H
-#define OPENCV_VIDEOIO_H
-
-#include "opencv2/core/core_c.h"
-
-#ifdef __cplusplus
-extern "C" {
-#endif /* __cplusplus */
-
-/**
-  @addtogroup videoio_c
-  @{
-*/
-
-/****************************************************************************************\
-*                         Working with Video Files and Cameras                           *
-\****************************************************************************************/
-
-/** @brief "black box" capture structure
-
-In C++ use cv::VideoCapture
-*/
-typedef struct CvCapture CvCapture;
-
-/** @brief start capturing frames from video file
-*/
-CVAPI(CvCapture*) cvCreateFileCapture( const char* filename );
-
-/** @brief start capturing frames from video file. allows specifying a preferred API to use
-*/
-CVAPI(CvCapture*) cvCreateFileCaptureWithPreference( const char* filename , int apiPreference);
-
-enum
-{
-    CV_CAP_ANY      =0,     // autodetect
-
-    CV_CAP_MIL      =100,   // MIL proprietary drivers
-
-    CV_CAP_VFW      =200,   // platform native
-    CV_CAP_V4L      =200,
-    CV_CAP_V4L2     =200,
-
-    CV_CAP_FIREWARE =300,   // IEEE 1394 drivers
-    CV_CAP_FIREWIRE =300,
-    CV_CAP_IEEE1394 =300,
-    CV_CAP_DC1394   =300,
-    CV_CAP_CMU1394  =300,
-
-    CV_CAP_STEREO   =400,   // TYZX proprietary drivers
-    CV_CAP_TYZX     =400,
-    CV_TYZX_LEFT    =400,
-    CV_TYZX_RIGHT   =401,
-    CV_TYZX_COLOR   =402,
-    CV_TYZX_Z       =403,
-
-    CV_CAP_QT       =500,   // QuickTime
-
-    CV_CAP_UNICAP   =600,   // Unicap drivers
-
-    CV_CAP_DSHOW    =700,   // DirectShow (via videoInput)
-    CV_CAP_MSMF     =1400,  // Microsoft Media Foundation (via videoInput)
-
-    CV_CAP_PVAPI    =800,   // PvAPI, Prosilica GigE SDK
-
-    CV_CAP_OPENNI   =900,   // OpenNI (for Kinect)
-    CV_CAP_OPENNI_ASUS =910,   // OpenNI (for Asus Xtion)
-
-    CV_CAP_ANDROID  =1000,  // Android - not used
-    CV_CAP_ANDROID_BACK =CV_CAP_ANDROID+99, // Android back camera - not used
-    CV_CAP_ANDROID_FRONT =CV_CAP_ANDROID+98, // Android front camera - not used
-
-    CV_CAP_XIAPI    =1100,   // XIMEA Camera API
-
-    CV_CAP_AVFOUNDATION = 1200,  // AVFoundation framework for iOS (OS X Lion will have the same API)
-
-    CV_CAP_GIGANETIX = 1300,  // Smartek Giganetix GigEVisionSDK
-
-    CV_CAP_INTELPERC = 1500, // Intel Perceptual Computing
-
-    CV_CAP_OPENNI2 = 1600,   // OpenNI2 (for Kinect)
-    CV_CAP_GPHOTO2 = 1700,
-    CV_CAP_GSTREAMER = 1800, // GStreamer
-    CV_CAP_FFMPEG = 1900,    // FFMPEG
-    CV_CAP_IMAGES = 2000,    // OpenCV Image Sequence (e.g. img_%02d.jpg)
-
-    CV_CAP_ARAVIS = 2100     // Aravis GigE SDK
-};
-
-/** @brief start capturing frames from camera: index = camera_index + domain_offset (CV_CAP_*)
-*/
-CVAPI(CvCapture*) cvCreateCameraCapture( int index );
-
-/** @brief grab a frame, return 1 on success, 0 on fail.
-
-  this function is thought to be fast
-*/
-CVAPI(int) cvGrabFrame( CvCapture* capture );
-
-/** @brief get the frame grabbed with cvGrabFrame(..)
-
-  This function may apply some frame processing like
-  frame decompression, flipping etc.
-  @warning !!!DO NOT RELEASE or MODIFY the retrieved frame!!!
-*/
-CVAPI(IplImage*) cvRetrieveFrame( CvCapture* capture, int streamIdx CV_DEFAULT(0) );
-
-/** @brief Just a combination of cvGrabFrame and cvRetrieveFrame
-
-  @warning !!!DO NOT RELEASE or MODIFY the retrieved frame!!!
-*/
-CVAPI(IplImage*) cvQueryFrame( CvCapture* capture );
-
-/** @brief stop capturing/reading and free resources
-*/
-CVAPI(void) cvReleaseCapture( CvCapture** capture );
-
-enum
-{
-    // modes of the controlling registers (can be: auto, manual, auto single push, absolute Latter allowed with any other mode)
-    // every feature can have only one mode turned on at a time
-    CV_CAP_PROP_DC1394_OFF         = -4,  //turn the feature off (not controlled manually nor automatically)
-    CV_CAP_PROP_DC1394_MODE_MANUAL = -3, //set automatically when a value of the feature is set by the user
-    CV_CAP_PROP_DC1394_MODE_AUTO = -2,
-    CV_CAP_PROP_DC1394_MODE_ONE_PUSH_AUTO = -1,
-    CV_CAP_PROP_POS_MSEC       =0,
-    CV_CAP_PROP_POS_FRAMES     =1,
-    CV_CAP_PROP_POS_AVI_RATIO  =2,
-    CV_CAP_PROP_FRAME_WIDTH    =3,
-    CV_CAP_PROP_FRAME_HEIGHT   =4,
-    CV_CAP_PROP_FPS            =5,
-    CV_CAP_PROP_FOURCC         =6,
-    CV_CAP_PROP_FRAME_COUNT    =7,
-    CV_CAP_PROP_FORMAT         =8,
-    CV_CAP_PROP_MODE           =9,
-    CV_CAP_PROP_BRIGHTNESS    =10,
-    CV_CAP_PROP_CONTRAST      =11,
-    CV_CAP_PROP_SATURATION    =12,
-    CV_CAP_PROP_HUE           =13,
-    CV_CAP_PROP_GAIN          =14,
-    CV_CAP_PROP_EXPOSURE      =15,
-    CV_CAP_PROP_CONVERT_RGB   =16,
-    CV_CAP_PROP_WHITE_BALANCE_BLUE_U =17,
-    CV_CAP_PROP_RECTIFICATION =18,
-    CV_CAP_PROP_MONOCHROME    =19,
-    CV_CAP_PROP_SHARPNESS     =20,
-    CV_CAP_PROP_AUTO_EXPOSURE =21, // exposure control done by camera,
-                                   // user can adjust reference level
-                                   // using this feature
-    CV_CAP_PROP_GAMMA         =22,
-    CV_CAP_PROP_TEMPERATURE   =23,
-    CV_CAP_PROP_TRIGGER       =24,
-    CV_CAP_PROP_TRIGGER_DELAY =25,
-    CV_CAP_PROP_WHITE_BALANCE_RED_V =26,
-    CV_CAP_PROP_ZOOM          =27,
-    CV_CAP_PROP_FOCUS         =28,
-    CV_CAP_PROP_GUID          =29,
-    CV_CAP_PROP_ISO_SPEED     =30,
-    CV_CAP_PROP_MAX_DC1394    =31,
-    CV_CAP_PROP_BACKLIGHT     =32,
-    CV_CAP_PROP_PAN           =33,
-    CV_CAP_PROP_TILT          =34,
-    CV_CAP_PROP_ROLL          =35,
-    CV_CAP_PROP_IRIS          =36,
-    CV_CAP_PROP_SETTINGS      =37,
-    CV_CAP_PROP_BUFFERSIZE    =38,
-    CV_CAP_PROP_AUTOFOCUS     =39,
-    CV_CAP_PROP_SAR_NUM       =40,
-    CV_CAP_PROP_SAR_DEN       =41,
-
-    CV_CAP_PROP_AUTOGRAB      =1024, // property for videoio class CvCapture_Android only
-    CV_CAP_PROP_SUPPORTED_PREVIEW_SIZES_STRING=1025, // readonly, tricky property, returns cpnst char* indeed
-    CV_CAP_PROP_PREVIEW_FORMAT=1026, // readonly, tricky property, returns cpnst char* indeed
-
-    // OpenNI map generators
-    CV_CAP_OPENNI_DEPTH_GENERATOR = 1 << 31,
-    CV_CAP_OPENNI_IMAGE_GENERATOR = 1 << 30,
-    CV_CAP_OPENNI_IR_GENERATOR    = 1 << 29,
-    CV_CAP_OPENNI_GENERATORS_MASK = CV_CAP_OPENNI_DEPTH_GENERATOR + CV_CAP_OPENNI_IMAGE_GENERATOR + CV_CAP_OPENNI_IR_GENERATOR,
-
-    // Properties of cameras available through OpenNI interfaces
-    CV_CAP_PROP_OPENNI_OUTPUT_MODE     = 100,
-    CV_CAP_PROP_OPENNI_FRAME_MAX_DEPTH = 101, // in mm
-    CV_CAP_PROP_OPENNI_BASELINE        = 102, // in mm
-    CV_CAP_PROP_OPENNI_FOCAL_LENGTH    = 103, // in pixels
-    CV_CAP_PROP_OPENNI_REGISTRATION    = 104, // flag
-    CV_CAP_PROP_OPENNI_REGISTRATION_ON = CV_CAP_PROP_OPENNI_REGISTRATION, // flag that synchronizes the remapping depth map to image map
-                                                                          // by changing depth generator's view point (if the flag is "on") or
-                                                                          // sets this view point to its normal one (if the flag is "off").
-    CV_CAP_PROP_OPENNI_APPROX_FRAME_SYNC = 105,
-    CV_CAP_PROP_OPENNI_MAX_BUFFER_SIZE   = 106,
-    CV_CAP_PROP_OPENNI_CIRCLE_BUFFER     = 107,
-    CV_CAP_PROP_OPENNI_MAX_TIME_DURATION = 108,
-
-    CV_CAP_PROP_OPENNI_GENERATOR_PRESENT = 109,
-    CV_CAP_PROP_OPENNI2_SYNC = 110,
-    CV_CAP_PROP_OPENNI2_MIRROR = 111,
-
-    CV_CAP_OPENNI_IMAGE_GENERATOR_PRESENT         = CV_CAP_OPENNI_IMAGE_GENERATOR + CV_CAP_PROP_OPENNI_GENERATOR_PRESENT,
-    CV_CAP_OPENNI_IMAGE_GENERATOR_OUTPUT_MODE     = CV_CAP_OPENNI_IMAGE_GENERATOR + CV_CAP_PROP_OPENNI_OUTPUT_MODE,
-    CV_CAP_OPENNI_DEPTH_GENERATOR_PRESENT         = CV_CAP_OPENNI_DEPTH_GENERATOR + CV_CAP_PROP_OPENNI_GENERATOR_PRESENT,
-    CV_CAP_OPENNI_DEPTH_GENERATOR_BASELINE        = CV_CAP_OPENNI_DEPTH_GENERATOR + CV_CAP_PROP_OPENNI_BASELINE,
-    CV_CAP_OPENNI_DEPTH_GENERATOR_FOCAL_LENGTH    = CV_CAP_OPENNI_DEPTH_GENERATOR + CV_CAP_PROP_OPENNI_FOCAL_LENGTH,
-    CV_CAP_OPENNI_DEPTH_GENERATOR_REGISTRATION    = CV_CAP_OPENNI_DEPTH_GENERATOR + CV_CAP_PROP_OPENNI_REGISTRATION,
-    CV_CAP_OPENNI_DEPTH_GENERATOR_REGISTRATION_ON = CV_CAP_OPENNI_DEPTH_GENERATOR_REGISTRATION,
-    CV_CAP_OPENNI_IR_GENERATOR_PRESENT            = CV_CAP_OPENNI_IR_GENERATOR + CV_CAP_PROP_OPENNI_GENERATOR_PRESENT,
-
-    // Properties of cameras available through GStreamer interface
-    CV_CAP_GSTREAMER_QUEUE_LENGTH           = 200, // default is 1
-
-    // PVAPI
-    CV_CAP_PROP_PVAPI_MULTICASTIP           = 300, // ip for anable multicast master mode. 0 for disable multicast
-    CV_CAP_PROP_PVAPI_FRAMESTARTTRIGGERMODE = 301, // FrameStartTriggerMode: Determines how a frame is initiated
-    CV_CAP_PROP_PVAPI_DECIMATIONHORIZONTAL  = 302, // Horizontal sub-sampling of the image
-    CV_CAP_PROP_PVAPI_DECIMATIONVERTICAL    = 303, // Vertical sub-sampling of the image
-    CV_CAP_PROP_PVAPI_BINNINGX              = 304, // Horizontal binning factor
-    CV_CAP_PROP_PVAPI_BINNINGY              = 305, // Vertical binning factor
-    CV_CAP_PROP_PVAPI_PIXELFORMAT           = 306, // Pixel format
-
-    // Properties of cameras available through XIMEA SDK interface
-    CV_CAP_PROP_XI_DOWNSAMPLING                                 = 400, // Change image resolution by binning or skipping.
-    CV_CAP_PROP_XI_DATA_FORMAT                                  = 401, // Output data format.
-    CV_CAP_PROP_XI_OFFSET_X                                     = 402, // Horizontal offset from the origin to the area of interest (in pixels).
-    CV_CAP_PROP_XI_OFFSET_Y                                     = 403, // Vertical offset from the origin to the area of interest (in pixels).
-    CV_CAP_PROP_XI_TRG_SOURCE                                   = 404, // Defines source of trigger.
-    CV_CAP_PROP_XI_TRG_SOFTWARE                                 = 405, // Generates an internal trigger. PRM_TRG_SOURCE must be set to TRG_SOFTWARE.
-    CV_CAP_PROP_XI_GPI_SELECTOR                                 = 406, // Selects general purpose input
-    CV_CAP_PROP_XI_GPI_MODE                                     = 407, // Set general purpose input mode
-    CV_CAP_PROP_XI_GPI_LEVEL                                    = 408, // Get general purpose level
-    CV_CAP_PROP_XI_GPO_SELECTOR                                 = 409, // Selects general purpose output
-    CV_CAP_PROP_XI_GPO_MODE                                     = 410, // Set general purpose output mode
-    CV_CAP_PROP_XI_LED_SELECTOR                                 = 411, // Selects camera signalling LED
-    CV_CAP_PROP_XI_LED_MODE                                     = 412, // Define camera signalling LED functionality
-    CV_CAP_PROP_XI_MANUAL_WB                                    = 413, // Calculates White Balance(must be called during acquisition)
-    CV_CAP_PROP_XI_AUTO_WB                                      = 414, // Automatic white balance
-    CV_CAP_PROP_XI_AEAG                                         = 415, // Automatic exposure/gain
-    CV_CAP_PROP_XI_EXP_PRIORITY                                 = 416, // Exposure priority (0.5 - exposure 50%, gain 50%).
-    CV_CAP_PROP_XI_AE_MAX_LIMIT                                 = 417, // Maximum limit of exposure in AEAG procedure
-    CV_CAP_PROP_XI_AG_MAX_LIMIT                                 = 418,  // Maximum limit of gain in AEAG procedure
-    CV_CAP_PROP_XI_AEAG_LEVEL                                   = 419, // Average intensity of output signal AEAG should achieve(in %)
-    CV_CAP_PROP_XI_TIMEOUT                                      = 420, // Image capture timeout in milliseconds
-    CV_CAP_PROP_XI_EXPOSURE                                     = 421, // Exposure time in microseconds
-    CV_CAP_PROP_XI_EXPOSURE_BURST_COUNT                         = 422, // Sets the number of times of exposure in one frame.
-    CV_CAP_PROP_XI_GAIN_SELECTOR                                = 423, // Gain selector for parameter Gain allows to select different type of gains.
-    CV_CAP_PROP_XI_GAIN                                         = 424, // Gain in dB
-    CV_CAP_PROP_XI_DOWNSAMPLING_TYPE                            = 426, // Change image downsampling type.
-    CV_CAP_PROP_XI_BINNING_SELECTOR                             = 427, // Binning engine selector.
-    CV_CAP_PROP_XI_BINNING_VERTICAL                             = 428, // Vertical Binning - number of vertical photo-sensitive cells to combine together.
-    CV_CAP_PROP_XI_BINNING_HORIZONTAL                           = 429, // Horizontal Binning - number of horizontal photo-sensitive cells to combine together.
-    CV_CAP_PROP_XI_BINNING_PATTERN                              = 430, // Binning pattern type.
-    CV_CAP_PROP_XI_DECIMATION_SELECTOR                          = 431, // Decimation engine selector.
-    CV_CAP_PROP_XI_DECIMATION_VERTICAL                          = 432, // Vertical Decimation - vertical sub-sampling of the image - reduces the vertical resolution of the image by the specified vertical decimation factor.
-    CV_CAP_PROP_XI_DECIMATION_HORIZONTAL                        = 433, // Horizontal Decimation - horizontal sub-sampling of the image - reduces the horizontal resolution of the image by the specified vertical decimation factor.
-    CV_CAP_PROP_XI_DECIMATION_PATTERN                           = 434, // Decimation pattern type.
-    CV_CAP_PROP_XI_TEST_PATTERN_GENERATOR_SELECTOR              = 587, // Selects which test pattern generator is controlled by the TestPattern feature.
-    CV_CAP_PROP_XI_TEST_PATTERN                                 = 588, // Selects which test pattern type is generated by the selected generator.
-    CV_CAP_PROP_XI_IMAGE_DATA_FORMAT                            = 435, // Output data format.
-    CV_CAP_PROP_XI_SHUTTER_TYPE                                 = 436, // Change sensor shutter type(CMOS sensor).
-    CV_CAP_PROP_XI_SENSOR_TAPS                                  = 437, // Number of taps
-    CV_CAP_PROP_XI_AEAG_ROI_OFFSET_X                            = 439, // Automatic exposure/gain ROI offset X
-    CV_CAP_PROP_XI_AEAG_ROI_OFFSET_Y                            = 440, // Automatic exposure/gain ROI offset Y
-    CV_CAP_PROP_XI_AEAG_ROI_WIDTH                               = 441, // Automatic exposure/gain ROI Width
-    CV_CAP_PROP_XI_AEAG_ROI_HEIGHT                              = 442, // Automatic exposure/gain ROI Height
-    CV_CAP_PROP_XI_BPC                                          = 445, // Correction of bad pixels
-    CV_CAP_PROP_XI_WB_KR                                        = 448, // White balance red coefficient
-    CV_CAP_PROP_XI_WB_KG                                        = 449, // White balance green coefficient
-    CV_CAP_PROP_XI_WB_KB                                        = 450, // White balance blue coefficient
-    CV_CAP_PROP_XI_WIDTH                                        = 451, // Width of the Image provided by the device (in pixels).
-    CV_CAP_PROP_XI_HEIGHT                                       = 452, // Height of the Image provided by the device (in pixels).
-    CV_CAP_PROP_XI_REGION_SELECTOR                              = 589, // Selects Region in Multiple ROI which parameters are set by width, height, ... ,region mode
-    CV_CAP_PROP_XI_REGION_MODE                                  = 595, // Activates/deactivates Region selected by Region Selector
-    CV_CAP_PROP_XI_LIMIT_BANDWIDTH                              = 459, // Set/get bandwidth(datarate)(in Megabits)
-    CV_CAP_PROP_XI_SENSOR_DATA_BIT_DEPTH                        = 460, // Sensor output data bit depth.
-    CV_CAP_PROP_XI_OUTPUT_DATA_BIT_DEPTH                        = 461, // Device output data bit depth.
-    CV_CAP_PROP_XI_IMAGE_DATA_BIT_DEPTH                         = 462, // bitdepth of data returned by function xiGetImage
-    CV_CAP_PROP_XI_OUTPUT_DATA_PACKING                          = 463, // Device output data packing (or grouping) enabled. Packing could be enabled if output_data_bit_depth > 8 and packing capability is available.
-    CV_CAP_PROP_XI_OUTPUT_DATA_PACKING_TYPE                     = 464, // Data packing type. Some cameras supports only specific packing type.
-    CV_CAP_PROP_XI_IS_COOLED                                    = 465, // Returns 1 for cameras that support cooling.
-    CV_CAP_PROP_XI_COOLING                                      = 466, // Start camera cooling.
-    CV_CAP_PROP_XI_TARGET_TEMP                                  = 467, // Set sensor target temperature for cooling.
-    CV_CAP_PROP_XI_CHIP_TEMP                                    = 468, // Camera sensor temperature
-    CV_CAP_PROP_XI_HOUS_TEMP                                    = 469, // Camera housing tepmerature
-    CV_CAP_PROP_XI_HOUS_BACK_SIDE_TEMP                          = 590, // Camera housing back side tepmerature
-    CV_CAP_PROP_XI_SENSOR_BOARD_TEMP                            = 596, // Camera sensor board temperature
-    CV_CAP_PROP_XI_CMS                                          = 470, // Mode of color management system.
-    CV_CAP_PROP_XI_APPLY_CMS                                    = 471, // Enable applying of CMS profiles to xiGetImage (see XI_PRM_INPUT_CMS_PROFILE, XI_PRM_OUTPUT_CMS_PROFILE).
-    CV_CAP_PROP_XI_IMAGE_IS_COLOR                               = 474, // Returns 1 for color cameras.
-    CV_CAP_PROP_XI_COLOR_FILTER_ARRAY                           = 475, // Returns color filter array type of RAW data.
-    CV_CAP_PROP_XI_GAMMAY                                       = 476, // Luminosity gamma
-    CV_CAP_PROP_XI_GAMMAC                                       = 477, // Chromaticity gamma
-    CV_CAP_PROP_XI_SHARPNESS                                    = 478, // Sharpness Strength
-    CV_CAP_PROP_XI_CC_MATRIX_00                                 = 479, // Color Correction Matrix element [0][0]
-    CV_CAP_PROP_XI_CC_MATRIX_01                                 = 480, // Color Correction Matrix element [0][1]
-    CV_CAP_PROP_XI_CC_MATRIX_02                                 = 481, // Color Correction Matrix element [0][2]
-    CV_CAP_PROP_XI_CC_MATRIX_03                                 = 482, // Color Correction Matrix element [0][3]
-    CV_CAP_PROP_XI_CC_MATRIX_10                                 = 483, // Color Correction Matrix element [1][0]
-    CV_CAP_PROP_XI_CC_MATRIX_11                                 = 484, // Color Correction Matrix element [1][1]
-    CV_CAP_PROP_XI_CC_MATRIX_12                                 = 485, // Color Correction Matrix element [1][2]
-    CV_CAP_PROP_XI_CC_MATRIX_13                                 = 486, // Color Correction Matrix element [1][3]
-    CV_CAP_PROP_XI_CC_MATRIX_20                                 = 487, // Color Correction Matrix element [2][0]
-    CV_CAP_PROP_XI_CC_MATRIX_21                                 = 488, // Color Correction Matrix element [2][1]
-    CV_CAP_PROP_XI_CC_MATRIX_22                                 = 489, // Color Correction Matrix element [2][2]
-    CV_CAP_PROP_XI_CC_MATRIX_23                                 = 490, // Color Correction Matrix element [2][3]
-    CV_CAP_PROP_XI_CC_MATRIX_30                                 = 491, // Color Correction Matrix element [3][0]
-    CV_CAP_PROP_XI_CC_MATRIX_31                                 = 492, // Color Correction Matrix element [3][1]
-    CV_CAP_PROP_XI_CC_MATRIX_32                                 = 493, // Color Correction Matrix element [3][2]
-    CV_CAP_PROP_XI_CC_MATRIX_33                                 = 494, // Color Correction Matrix element [3][3]
-    CV_CAP_PROP_XI_DEFAULT_CC_MATRIX                            = 495, // Set default Color Correction Matrix
-    CV_CAP_PROP_XI_TRG_SELECTOR                                 = 498, // Selects the type of trigger.
-    CV_CAP_PROP_XI_ACQ_FRAME_BURST_COUNT                        = 499, // Sets number of frames acquired by burst. This burst is used only if trigger is set to FrameBurstStart
-    CV_CAP_PROP_XI_DEBOUNCE_EN                                  = 507, // Enable/Disable debounce to selected GPI
-    CV_CAP_PROP_XI_DEBOUNCE_T0                                  = 508, // Debounce time (x * 10us)
-    CV_CAP_PROP_XI_DEBOUNCE_T1                                  = 509, // Debounce time (x * 10us)
-    CV_CAP_PROP_XI_DEBOUNCE_POL                                 = 510, // Debounce polarity (pol = 1 t0 - falling edge, t1 - rising edge)
-    CV_CAP_PROP_XI_LENS_MODE                                    = 511, // Status of lens control interface. This shall be set to XI_ON before any Lens operations.
-    CV_CAP_PROP_XI_LENS_APERTURE_VALUE                          = 512, // Current lens aperture value in stops. Examples: 2.8, 4, 5.6, 8, 11
-    CV_CAP_PROP_XI_LENS_FOCUS_MOVEMENT_VALUE                    = 513, // Lens current focus movement value to be used by XI_PRM_LENS_FOCUS_MOVE in motor steps.
-    CV_CAP_PROP_XI_LENS_FOCUS_MOVE                              = 514, // Moves lens focus motor by steps set in XI_PRM_LENS_FOCUS_MOVEMENT_VALUE.
-    CV_CAP_PROP_XI_LENS_FOCUS_DISTANCE                          = 515, // Lens focus distance in cm.
-    CV_CAP_PROP_XI_LENS_FOCAL_LENGTH                            = 516, // Lens focal distance in mm.
-    CV_CAP_PROP_XI_LENS_FEATURE_SELECTOR                        = 517, // Selects the current feature which is accessible by XI_PRM_LENS_FEATURE.
-    CV_CAP_PROP_XI_LENS_FEATURE                                 = 518, // Allows access to lens feature value currently selected by XI_PRM_LENS_FEATURE_SELECTOR.
-    CV_CAP_PROP_XI_DEVICE_MODEL_ID                              = 521, // Return device model id
-    CV_CAP_PROP_XI_DEVICE_SN                                    = 522, // Return device serial number
-    CV_CAP_PROP_XI_IMAGE_DATA_FORMAT_RGB32_ALPHA                = 529, // The alpha channel of RGB32 output image format.
-    CV_CAP_PROP_XI_IMAGE_PAYLOAD_SIZE                           = 530, // Buffer size in bytes sufficient for output image returned by xiGetImage
-    CV_CAP_PROP_XI_TRANSPORT_PIXEL_FORMAT                       = 531, // Current format of pixels on transport layer.
-    CV_CAP_PROP_XI_SENSOR_CLOCK_FREQ_HZ                         = 532, // Sensor clock frequency in Hz.
-    CV_CAP_PROP_XI_SENSOR_CLOCK_FREQ_INDEX                      = 533, // Sensor clock frequency index. Sensor with selected frequencies have possibility to set the frequency only by this index.
-    CV_CAP_PROP_XI_SENSOR_OUTPUT_CHANNEL_COUNT                  = 534, // Number of output channels from sensor used for data transfer.
-    CV_CAP_PROP_XI_FRAMERATE                                    = 535, // Define framerate in Hz
-    CV_CAP_PROP_XI_COUNTER_SELECTOR                             = 536, // Select counter
-    CV_CAP_PROP_XI_COUNTER_VALUE                                = 537, // Counter status
-    CV_CAP_PROP_XI_ACQ_TIMING_MODE                              = 538, // Type of sensor frames timing.
-    CV_CAP_PROP_XI_AVAILABLE_BANDWIDTH                          = 539, // Calculate and return available interface bandwidth(int Megabits)
-    CV_CAP_PROP_XI_BUFFER_POLICY                                = 540, // Data move policy
-    CV_CAP_PROP_XI_LUT_EN                                       = 541, // Activates LUT.
-    CV_CAP_PROP_XI_LUT_INDEX                                    = 542, // Control the index (offset) of the coefficient to access in the LUT.
-    CV_CAP_PROP_XI_LUT_VALUE                                    = 543, // Value at entry LUTIndex of the LUT
-    CV_CAP_PROP_XI_TRG_DELAY                                    = 544, // Specifies the delay in microseconds (us) to apply after the trigger reception before activating it.
-    CV_CAP_PROP_XI_TS_RST_MODE                                  = 545, // Defines how time stamp reset engine will be armed
-    CV_CAP_PROP_XI_TS_RST_SOURCE                                = 546, // Defines which source will be used for timestamp reset. Writing this parameter will trigger settings of engine (arming)
-    CV_CAP_PROP_XI_IS_DEVICE_EXIST                              = 547, // Returns 1 if camera connected and works properly.
-    CV_CAP_PROP_XI_ACQ_BUFFER_SIZE                              = 548, // Acquisition buffer size in buffer_size_unit. Default bytes.
-    CV_CAP_PROP_XI_ACQ_BUFFER_SIZE_UNIT                         = 549, // Acquisition buffer size unit in bytes. Default 1. E.g. Value 1024 means that buffer_size is in KiBytes
-    CV_CAP_PROP_XI_ACQ_TRANSPORT_BUFFER_SIZE                    = 550, // Acquisition transport buffer size in bytes
-    CV_CAP_PROP_XI_BUFFERS_QUEUE_SIZE                           = 551, // Queue of field/frame buffers
-    CV_CAP_PROP_XI_ACQ_TRANSPORT_BUFFER_COMMIT                  = 552, // Number of buffers to commit to low level
-    CV_CAP_PROP_XI_RECENT_FRAME                                 = 553, // GetImage returns most recent frame
-    CV_CAP_PROP_XI_DEVICE_RESET                                 = 554, // Resets the camera to default state.
-    CV_CAP_PROP_XI_COLUMN_FPN_CORRECTION                        = 555, // Correction of column FPN
-    CV_CAP_PROP_XI_ROW_FPN_CORRECTION                           = 591, // Correction of row FPN
-    CV_CAP_PROP_XI_SENSOR_MODE                                  = 558, // Current sensor mode. Allows to select sensor mode by one integer. Setting of this parameter affects: image dimensions and downsampling.
-    CV_CAP_PROP_XI_HDR                                          = 559, // Enable High Dynamic Range feature.
-    CV_CAP_PROP_XI_HDR_KNEEPOINT_COUNT                          = 560, // The number of kneepoints in the PWLR.
-    CV_CAP_PROP_XI_HDR_T1                                       = 561, // position of first kneepoint(in % of XI_PRM_EXPOSURE)
-    CV_CAP_PROP_XI_HDR_T2                                       = 562, // position of second kneepoint (in % of XI_PRM_EXPOSURE)
-    CV_CAP_PROP_XI_KNEEPOINT1                                   = 563, // value of first kneepoint (% of sensor saturation)
-    CV_CAP_PROP_XI_KNEEPOINT2                                   = 564, // value of second kneepoint (% of sensor saturation)
-    CV_CAP_PROP_XI_IMAGE_BLACK_LEVEL                            = 565, // Last image black level counts. Can be used for Offline processing to recall it.
-    CV_CAP_PROP_XI_HW_REVISION                                  = 571, // Returns hardware revision number.
-    CV_CAP_PROP_XI_DEBUG_LEVEL                                  = 572, // Set debug level
-    CV_CAP_PROP_XI_AUTO_BANDWIDTH_CALCULATION                   = 573, // Automatic bandwidth calculation,
-    CV_CAP_PROP_XI_FFS_FILE_ID                                  = 594, // File number.
-    CV_CAP_PROP_XI_FFS_FILE_SIZE                                = 580, // Size of file.
-    CV_CAP_PROP_XI_FREE_FFS_SIZE                                = 581, // Size of free camera FFS.
-    CV_CAP_PROP_XI_USED_FFS_SIZE                                = 582, // Size of used camera FFS.
-    CV_CAP_PROP_XI_FFS_ACCESS_KEY                               = 583, // Setting of key enables file operations on some cameras.
-    CV_CAP_PROP_XI_SENSOR_FEATURE_SELECTOR                      = 585, // Selects the current feature which is accessible by XI_PRM_SENSOR_FEATURE_VALUE.
-    CV_CAP_PROP_XI_SENSOR_FEATURE_VALUE                         = 586, // Allows access to sensor feature value currently selected by XI_PRM_SENSOR_FEATURE_SELECTOR.
-
-
-    // Properties for Android cameras
-    CV_CAP_PROP_ANDROID_FLASH_MODE = 8001,
-    CV_CAP_PROP_ANDROID_FOCUS_MODE = 8002,
-    CV_CAP_PROP_ANDROID_WHITE_BALANCE = 8003,
-    CV_CAP_PROP_ANDROID_ANTIBANDING = 8004,
-    CV_CAP_PROP_ANDROID_FOCAL_LENGTH = 8005,
-    CV_CAP_PROP_ANDROID_FOCUS_DISTANCE_NEAR = 8006,
-    CV_CAP_PROP_ANDROID_FOCUS_DISTANCE_OPTIMAL = 8007,
-    CV_CAP_PROP_ANDROID_FOCUS_DISTANCE_FAR = 8008,
-    CV_CAP_PROP_ANDROID_EXPOSE_LOCK = 8009,
-    CV_CAP_PROP_ANDROID_WHITEBALANCE_LOCK = 8010,
-
-    // Properties of cameras available through AVFOUNDATION interface
-    CV_CAP_PROP_IOS_DEVICE_FOCUS = 9001,
-    CV_CAP_PROP_IOS_DEVICE_EXPOSURE = 9002,
-    CV_CAP_PROP_IOS_DEVICE_FLASH = 9003,
-    CV_CAP_PROP_IOS_DEVICE_WHITEBALANCE = 9004,
-    CV_CAP_PROP_IOS_DEVICE_TORCH = 9005,
-
-    // Properties of cameras available through Smartek Giganetix Ethernet Vision interface
-    /* --- Vladimir Litvinenko (litvinenko.vladimir@gmail.com) --- */
-    CV_CAP_PROP_GIGA_FRAME_OFFSET_X = 10001,
-    CV_CAP_PROP_GIGA_FRAME_OFFSET_Y = 10002,
-    CV_CAP_PROP_GIGA_FRAME_WIDTH_MAX = 10003,
-    CV_CAP_PROP_GIGA_FRAME_HEIGH_MAX = 10004,
-    CV_CAP_PROP_GIGA_FRAME_SENS_WIDTH = 10005,
-    CV_CAP_PROP_GIGA_FRAME_SENS_HEIGH = 10006,
-
-    CV_CAP_PROP_INTELPERC_PROFILE_COUNT               = 11001,
-    CV_CAP_PROP_INTELPERC_PROFILE_IDX                 = 11002,
-    CV_CAP_PROP_INTELPERC_DEPTH_LOW_CONFIDENCE_VALUE  = 11003,
-    CV_CAP_PROP_INTELPERC_DEPTH_SATURATION_VALUE      = 11004,
-    CV_CAP_PROP_INTELPERC_DEPTH_CONFIDENCE_THRESHOLD  = 11005,
-    CV_CAP_PROP_INTELPERC_DEPTH_FOCAL_LENGTH_HORZ     = 11006,
-    CV_CAP_PROP_INTELPERC_DEPTH_FOCAL_LENGTH_VERT     = 11007,
-
-    // Intel PerC streams
-    CV_CAP_INTELPERC_DEPTH_GENERATOR = 1 << 29,
-    CV_CAP_INTELPERC_IMAGE_GENERATOR = 1 << 28,
-    CV_CAP_INTELPERC_GENERATORS_MASK = CV_CAP_INTELPERC_DEPTH_GENERATOR + CV_CAP_INTELPERC_IMAGE_GENERATOR
-};
-
-// Generic camera output modes.
-// Currently, these are supported through the libv4l interface only.
-enum
-{
-    CV_CAP_MODE_BGR  = 0, // BGR24 (default)
-    CV_CAP_MODE_RGB  = 1, // RGB24
-    CV_CAP_MODE_GRAY = 2, // Y8
-    CV_CAP_MODE_YUYV = 3  // YUYV
-};
-
-enum
-{
-    // Data given from depth generator.
-    CV_CAP_OPENNI_DEPTH_MAP                 = 0, // Depth values in mm (CV_16UC1)
-    CV_CAP_OPENNI_POINT_CLOUD_MAP           = 1, // XYZ in meters (CV_32FC3)
-    CV_CAP_OPENNI_DISPARITY_MAP             = 2, // Disparity in pixels (CV_8UC1)
-    CV_CAP_OPENNI_DISPARITY_MAP_32F         = 3, // Disparity in pixels (CV_32FC1)
-    CV_CAP_OPENNI_VALID_DEPTH_MASK          = 4, // CV_8UC1
-
-    // Data given from RGB image generator.
-    CV_CAP_OPENNI_BGR_IMAGE                 = 5,
-    CV_CAP_OPENNI_GRAY_IMAGE                = 6,
-
-    // Data given from IR image generator.
-    CV_CAP_OPENNI_IR_IMAGE                  = 7
-};
-
-// Supported output modes of OpenNI image generator
-enum
-{
-    CV_CAP_OPENNI_VGA_30HZ     = 0,
-    CV_CAP_OPENNI_SXGA_15HZ    = 1,
-    CV_CAP_OPENNI_SXGA_30HZ    = 2,
-    CV_CAP_OPENNI_QVGA_30HZ    = 3,
-    CV_CAP_OPENNI_QVGA_60HZ    = 4
-};
-
-enum
-{
-    CV_CAP_INTELPERC_DEPTH_MAP              = 0, // Each pixel is a 16-bit integer. The value indicates the distance from an object to the camera's XY plane or the Cartesian depth.
-    CV_CAP_INTELPERC_UVDEPTH_MAP            = 1, // Each pixel contains two 32-bit floating point values in the range of 0-1, representing the mapping of depth coordinates to the color coordinates.
-    CV_CAP_INTELPERC_IR_MAP                 = 2, // Each pixel is a 16-bit integer. The value indicates the intensity of the reflected laser beam.
-    CV_CAP_INTELPERC_IMAGE                  = 3
-};
-
-// gPhoto2 properties, if propertyId is less than 0 then work on widget with that __additive inversed__ camera setting ID
-// Get IDs by using CAP_PROP_GPHOTO2_WIDGET_ENUMERATE.
-// @see CvCaptureCAM_GPHOTO2 for more info
-enum
-{
-    CV_CAP_PROP_GPHOTO2_PREVIEW           = 17001, // Capture only preview from liveview mode.
-    CV_CAP_PROP_GPHOTO2_WIDGET_ENUMERATE  = 17002, // Readonly, returns (const char *).
-    CV_CAP_PROP_GPHOTO2_RELOAD_CONFIG     = 17003, // Trigger, only by set. Reload camera settings.
-    CV_CAP_PROP_GPHOTO2_RELOAD_ON_CHANGE  = 17004, // Reload all settings on set.
-    CV_CAP_PROP_GPHOTO2_COLLECT_MSGS      = 17005, // Collect messages with details.
-    CV_CAP_PROP_GPHOTO2_FLUSH_MSGS        = 17006, // Readonly, returns (const char *).
-    CV_CAP_PROP_SPEED                     = 17007, // Exposure speed. Can be readonly, depends on camera program.
-    CV_CAP_PROP_APERTURE                  = 17008, // Aperture. Can be readonly, depends on camera program.
-    CV_CAP_PROP_EXPOSUREPROGRAM           = 17009, // Camera exposure program.
-    CV_CAP_PROP_VIEWFINDER                = 17010  // Enter liveview mode.
-};
-
-/** @brief retrieve capture properties
-*/
-CVAPI(double) cvGetCaptureProperty( CvCapture* capture, int property_id );
-/** @brief set capture properties
-*/
-CVAPI(int)    cvSetCaptureProperty( CvCapture* capture, int property_id, double value );
-
-/** @brief Return the type of the capturer (eg, ::CV_CAP_VFW, ::CV_CAP_UNICAP)
-
-It is unknown if created with ::CV_CAP_ANY
-*/
-CVAPI(int)    cvGetCaptureDomain( CvCapture* capture);
-
-/** @brief "black box" video file writer structure
-
-In C++ use cv::VideoWriter
-*/
-typedef struct CvVideoWriter CvVideoWriter;
-
-//! Macro to construct the fourcc code of the codec. Same as CV_FOURCC()
-#define CV_FOURCC_MACRO(c1, c2, c3, c4) (((c1) & 255) + (((c2) & 255) << 8) + (((c3) & 255) << 16) + (((c4) & 255) << 24))
-
-/** @brief Constructs the fourcc code of the codec function
-
-Simply call it with 4 chars fourcc code like `CV_FOURCC('I', 'Y', 'U', 'V')`
-
-List of codes can be obtained at [Video Codecs by FOURCC](http://www.fourcc.org/codecs.php) page.
-FFMPEG backend with MP4 container natively uses other values as fourcc code:
-see [ObjectType](http://www.mp4ra.org/codecs.html).
-*/
-CV_INLINE int CV_FOURCC(char c1, char c2, char c3, char c4)
-{
-    return CV_FOURCC_MACRO(c1, c2, c3, c4);
-}
-
-//! (Windows only) Open Codec Selection Dialog
-#define CV_FOURCC_PROMPT -1
-//! (Linux only) Use default codec for specified filename
-#define CV_FOURCC_DEFAULT CV_FOURCC('I', 'Y', 'U', 'V')
-
-/** @brief initialize video file writer
-*/
-CVAPI(CvVideoWriter*) cvCreateVideoWriter( const char* filename, int fourcc,
-                                           double fps, CvSize frame_size,
-                                           int is_color CV_DEFAULT(1));
-
-/** @brief write frame to video file
-*/
-CVAPI(int) cvWriteFrame( CvVideoWriter* writer, const IplImage* image );
-
-/** @brief close video file writer
-*/
-CVAPI(void) cvReleaseVideoWriter( CvVideoWriter** writer );
-
-// ***************************************************************************************
-//! @name Obsolete functions/synonyms
-//! @{
-#define cvCaptureFromCAM cvCreateCameraCapture //!< @deprecated use cvCreateCameraCapture() instead
-#define cvCaptureFromFile cvCreateFileCapture  //!< @deprecated use cvCreateFileCapture() instead
-#define cvCaptureFromAVI cvCaptureFromFile     //!< @deprecated use cvCreateFileCapture() instead
-#define cvCreateAVIWriter cvCreateVideoWriter  //!< @deprecated use cvCreateVideoWriter() instead
-#define cvWriteToAVI cvWriteFrame              //!< @deprecated use cvWriteFrame() instead
-//!  @} Obsolete...
-
-//! @} videoio_c
-
-#ifdef __cplusplus
-}
-#endif
-
-#endif //OPENCV_VIDEOIO_H
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/videostab.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/videostab.hpp
deleted file mode 100755
index ca3f5ad..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/videostab.hpp
+++ /dev/null
@@ -1,81 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_VIDEOSTAB_HPP
-#define OPENCV_VIDEOSTAB_HPP
-
-/**
-  @defgroup videostab Video Stabilization
-
-The video stabilization module contains a set of functions and classes that can be used to solve the
-problem of video stabilization. There are a few methods implemented, most of them are described in
-the papers @cite OF06 and @cite G11 . However, there are some extensions and deviations from the original
-paper methods.
-
-### References
-
- 1. "Full-Frame Video Stabilization with Motion Inpainting"
-     Yasuyuki Matsushita, Eyal Ofek, Weina Ge, Xiaoou Tang, Senior Member, and Heung-Yeung Shum
- 2. "Auto-Directed Video Stabilization with Robust L1 Optimal Camera Paths"
-     Matthias Grundmann, Vivek Kwatra, Irfan Essa
-
-     @{
-         @defgroup videostab_motion Global Motion Estimation
-
-The video stabilization module contains a set of functions and classes for global motion estimation
-between point clouds or between images. In the last case features are extracted and matched
-internally. For the sake of convenience the motion estimation functions are wrapped into classes.
-Both the functions and the classes are available.
-
-         @defgroup videostab_marching Fast Marching Method
-
-The Fast Marching Method @cite Telea04 is used in of the video stabilization routines to do motion and
-color inpainting. The method is implemented is a flexible way and it's made public for other users.
-
-     @}
-
-*/
-
-#include "opencv2/videostab/stabilizer.hpp"
-#include "opencv2/videostab/ring_buffer.hpp"
-
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/videostab/deblurring.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/videostab/deblurring.hpp
deleted file mode 100755
index c665640..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/videostab/deblurring.hpp
+++ /dev/null
@@ -1,116 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_VIDEOSTAB_DEBLURRING_HPP
-#define OPENCV_VIDEOSTAB_DEBLURRING_HPP
-
-#include <vector>
-#include "opencv2/core.hpp"
-
-namespace cv
-{
-namespace videostab
-{
-
-//! @addtogroup videostab
-//! @{
-
-CV_EXPORTS float calcBlurriness(const Mat &frame);
-
-class CV_EXPORTS DeblurerBase
-{
-public:
-    DeblurerBase() : radius_(0), frames_(0), motions_(0), blurrinessRates_(0) {}
-
-    virtual ~DeblurerBase() {}
-
-    virtual void setRadius(int val) { radius_ = val; }
-    virtual int radius() const { return radius_; }
-
-    virtual void deblur(int idx, Mat &frame) = 0;
-
-
-    // data from stabilizer
-
-    virtual void setFrames(const std::vector<Mat> &val) { frames_ = &val; }
-    virtual const std::vector<Mat>& frames() const { return *frames_; }
-
-    virtual void setMotions(const std::vector<Mat> &val) { motions_ = &val; }
-    virtual const std::vector<Mat>& motions() const { return *motions_; }
-
-    virtual void setBlurrinessRates(const std::vector<float> &val) { blurrinessRates_ = &val; }
-    virtual const std::vector<float>& blurrinessRates() const { return *blurrinessRates_; }
-
-protected:
-    int radius_;
-    const std::vector<Mat> *frames_;
-    const std::vector<Mat> *motions_;
-    const std::vector<float> *blurrinessRates_;
-};
-
-class CV_EXPORTS NullDeblurer : public DeblurerBase
-{
-public:
-    virtual void deblur(int /*idx*/, Mat &/*frame*/) CV_OVERRIDE {}
-};
-
-class CV_EXPORTS WeightingDeblurer : public DeblurerBase
-{
-public:
-    WeightingDeblurer();
-
-    void setSensitivity(float val) { sensitivity_ = val; }
-    float sensitivity() const { return sensitivity_; }
-
-    virtual void deblur(int idx, Mat &frame) CV_OVERRIDE;
-
-private:
-    float sensitivity_;
-    Mat_<float> bSum_, gSum_, rSum_, wSum_;
-};
-
-//! @}
-
-} // namespace videostab
-} // namespace cv
-
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/videostab/fast_marching.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/videostab/fast_marching.hpp
deleted file mode 100755
index 43f8e4a..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/videostab/fast_marching.hpp
+++ /dev/null
@@ -1,121 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_VIDEOSTAB_FAST_MARCHING_HPP
-#define OPENCV_VIDEOSTAB_FAST_MARCHING_HPP
-
-#include <cmath>
-#include <queue>
-#include <algorithm>
-#include "opencv2/core.hpp"
-
-namespace cv
-{
-namespace videostab
-{
-
-//! @addtogroup videostab_marching
-//! @{
-
-/** @brief Describes the Fast Marching Method implementation.
-
-  See http://iwi.eldoc.ub.rug.nl/FILES/root/2004/JGraphToolsTelea/2004JGraphToolsTelea.pdf
- */
-class CV_EXPORTS FastMarchingMethod
-{
-public:
-    FastMarchingMethod() : inf_(1e6f), size_(0) {}
-
-    /** @brief Template method that runs the Fast Marching Method.
-
-    @param mask Image mask. 0 value indicates that the pixel value must be inpainted, 255 indicates
-    that the pixel value is known, other values aren't acceptable.
-    @param inpaint Inpainting functor that overloads void operator ()(int x, int y).
-    @return Inpainting functor.
-     */
-    template <typename Inpaint>
-    Inpaint run(const Mat &mask, Inpaint inpaint);
-
-    /**
-    @return Distance map that's created during working of the method.
-    */
-    Mat distanceMap() const { return dist_; }
-
-private:
-    enum { INSIDE = 0, BAND = 1, KNOWN = 255 };
-
-    struct DXY
-    {
-        float dist;
-        int x, y;
-
-        DXY() : dist(0), x(0), y(0) {}
-        DXY(float _dist, int _x, int _y) : dist(_dist), x(_x), y(_y) {}
-        bool operator <(const DXY &dxy) const { return dist < dxy.dist; }
-    };
-
-    float solve(int x1, int y1, int x2, int y2) const;
-    int& indexOf(const DXY &dxy) { return index_(dxy.y, dxy.x); }
-
-    void heapUp(int idx);
-    void heapDown(int idx);
-    void heapAdd(const DXY &dxy);
-    void heapRemoveMin();
-
-    float inf_;
-
-    cv::Mat_<uchar> flag_; // flag map
-    cv::Mat_<float> dist_; // distance map
-
-    cv::Mat_<int> index_; // index of point in the narrow band
-    std::vector<DXY> narrowBand_; // narrow band heap
-    int size_; // narrow band size
-};
-
-//! @}
-
-} // namespace videostab
-} // namespace cv
-
-#include "fast_marching_inl.hpp"
-
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/videostab/fast_marching_inl.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/videostab/fast_marching_inl.hpp
deleted file mode 100755
index fdd488a..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/videostab/fast_marching_inl.hpp
+++ /dev/null
@@ -1,165 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_VIDEOSTAB_FAST_MARCHING_INL_HPP
-#define OPENCV_VIDEOSTAB_FAST_MARCHING_INL_HPP
-
-#include "opencv2/videostab/fast_marching.hpp"
-
-namespace cv
-{
-namespace videostab
-{
-
-template <typename Inpaint>
-Inpaint FastMarchingMethod::run(const cv::Mat &mask, Inpaint inpaint)
-{
-    using namespace cv;
-
-    CV_Assert(mask.type() == CV_8U);
-
-    static const int lut[4][2] = {{-1,0}, {0,-1}, {1,0}, {0,1}};
-
-    mask.copyTo(flag_);
-    flag_.create(mask.size());
-    dist_.create(mask.size());
-    index_.create(mask.size());
-    narrowBand_.clear();
-    size_ = 0;
-
-    // init
-    for (int y = 0; y < flag_.rows; ++y)
-    {
-        for (int x = 0; x < flag_.cols; ++x)
-        {
-            if (flag_(y,x) == KNOWN)
-                dist_(y,x) = 0.f;
-            else
-            {
-                int n = 0;
-                int nunknown = 0;
-
-                for (int i = 0; i < 4; ++i)
-                {
-                    int xn = x + lut[i][0];
-                    int yn = y + lut[i][1];
-
-                    if (xn >= 0 && xn < flag_.cols && yn >= 0 && yn < flag_.rows)
-                    {
-                        n++;
-                        if (flag_(yn,xn) != KNOWN)
-                            nunknown++;
-                    }
-                }
-
-                if (n>0 && nunknown == n)
-                {
-                    dist_(y,x) = inf_;
-                    flag_(y,x) = INSIDE;
-                }
-                else
-                {
-                    dist_(y,x) = 0.f;
-                    flag_(y,x) = BAND;
-                    inpaint(x, y);
-
-                    narrowBand_.push_back(DXY(0.f,x,y));
-                    index_(y,x) = size_++;
-                }
-            }
-        }
-    }
-
-    // make heap
-    for (int i = size_/2-1; i >= 0; --i)
-        heapDown(i);
-
-    // main cycle
-    while (size_ > 0)
-    {
-        int x = narrowBand_[0].x;
-        int y = narrowBand_[0].y;
-        heapRemoveMin();
-
-        flag_(y,x) = KNOWN;
-        for (int n = 0; n < 4; ++n)
-        {
-            int xn = x + lut[n][0];
-            int yn = y + lut[n][1];
-
-            if (xn >= 0 && xn < flag_.cols && yn >= 0 && yn < flag_.rows && flag_(yn,xn) != KNOWN)
-            {
-                dist_(yn,xn) = std::min(std::min(solve(xn-1, yn, xn, yn-1), solve(xn+1, yn, xn, yn-1)),
-                                        std::min(solve(xn-1, yn, xn, yn+1), solve(xn+1, yn, xn, yn+1)));
-
-                if (flag_(yn,xn) == INSIDE)
-                {
-                    flag_(yn,xn) = BAND;
-                    inpaint(xn, yn);
-                    heapAdd(DXY(dist_(yn,xn),xn,yn));
-                }
-                else
-                {
-                    int i = index_(yn,xn);
-                    if (dist_(yn,xn) < narrowBand_[i].dist)
-                    {
-                        narrowBand_[i].dist = dist_(yn,xn);
-                        heapUp(i);
-                    }
-                    // works better if it's commented out
-                    /*else if (dist(yn,xn) > narrowBand[i].dist)
-                    {
-                        narrowBand[i].dist = dist(yn,xn);
-                        heapDown(i);
-                    }*/
-                }
-            }
-        }
-    }
-
-    return inpaint;
-}
-
-} // namespace videostab
-} // namespace cv
-
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/videostab/frame_source.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/videostab/frame_source.hpp
deleted file mode 100755
index 171c637..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/videostab/frame_source.hpp
+++ /dev/null
@@ -1,94 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_VIDEOSTAB_FRAME_SOURCE_HPP
-#define OPENCV_VIDEOSTAB_FRAME_SOURCE_HPP
-
-#include <vector>
-#include "opencv2/core.hpp"
-
-namespace cv
-{
-namespace videostab
-{
-
-//! @addtogroup videostab
-//! @{
-
-class CV_EXPORTS IFrameSource
-{
-public:
-    virtual ~IFrameSource() {}
-    virtual void reset() = 0;
-    virtual Mat nextFrame() = 0;
-};
-
-class CV_EXPORTS NullFrameSource : public IFrameSource
-{
-public:
-    virtual void reset() CV_OVERRIDE {}
-    virtual Mat nextFrame() CV_OVERRIDE { return Mat(); }
-};
-
-class CV_EXPORTS VideoFileSource : public IFrameSource
-{
-public:
-    VideoFileSource(const String &path, bool volatileFrame = false);
-
-    virtual void reset() CV_OVERRIDE;
-    virtual Mat nextFrame() CV_OVERRIDE;
-
-    int width();
-    int height();
-    int count();
-    double fps();
-
-private:
-    Ptr<IFrameSource> impl;
-};
-
-//! @}
-
-} // namespace videostab
-} // namespace cv
-
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/videostab/global_motion.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/videostab/global_motion.hpp
deleted file mode 100755
index fedca2c..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/videostab/global_motion.hpp
+++ /dev/null
@@ -1,300 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_VIDEOSTAB_GLOBAL_MOTION_HPP
-#define OPENCV_VIDEOSTAB_GLOBAL_MOTION_HPP
-
-#include <vector>
-#include <fstream>
-#include "opencv2/core.hpp"
-#include "opencv2/features2d.hpp"
-#include "opencv2/opencv_modules.hpp"
-#include "opencv2/videostab/optical_flow.hpp"
-#include "opencv2/videostab/motion_core.hpp"
-#include "opencv2/videostab/outlier_rejection.hpp"
-
-#ifdef HAVE_OPENCV_CUDAIMGPROC
-#  include "opencv2/cudaimgproc.hpp"
-#endif
-
-namespace cv
-{
-namespace videostab
-{
-
-//! @addtogroup videostab_motion
-//! @{
-
-/** @brief Estimates best global motion between two 2D point clouds in the least-squares sense.
-
-@note Works in-place and changes input point arrays.
-
-@param points0 Source set of 2D points (32F).
-@param points1 Destination set of 2D points (32F).
-@param model Motion model (up to MM_AFFINE).
-@param rmse Final root-mean-square error.
-@return 3x3 2D transformation matrix (32F).
- */
-CV_EXPORTS Mat estimateGlobalMotionLeastSquares(
-        InputOutputArray points0, InputOutputArray points1, int model = MM_AFFINE,
-        float *rmse = 0);
-
-/** @brief Estimates best global motion between two 2D point clouds robustly (using RANSAC method).
-
-@param points0 Source set of 2D points (32F).
-@param points1 Destination set of 2D points (32F).
-@param model Motion model. See cv::videostab::MotionModel.
-@param params RANSAC method parameters. See videostab::RansacParams.
-@param rmse Final root-mean-square error.
-@param ninliers Final number of inliers.
- */
-CV_EXPORTS Mat estimateGlobalMotionRansac(
-        InputArray points0, InputArray points1, int model = MM_AFFINE,
-        const RansacParams &params = RansacParams::default2dMotion(MM_AFFINE),
-        float *rmse = 0, int *ninliers = 0);
-
-/** @brief Base class for all global motion estimation methods.
- */
-class CV_EXPORTS MotionEstimatorBase
-{
-public:
-    virtual ~MotionEstimatorBase() {}
-
-    /** @brief Sets motion model.
-
-    @param val Motion model. See cv::videostab::MotionModel.
-     */
-    virtual void setMotionModel(MotionModel val) { motionModel_ = val; }
-
-    /**
-    @return Motion model. See cv::videostab::MotionModel.
-    */
-    virtual MotionModel motionModel() const { return motionModel_; }
-
-    /** @brief Estimates global motion between two 2D point clouds.
-
-    @param points0 Source set of 2D points (32F).
-    @param points1 Destination set of 2D points (32F).
-    @param ok Indicates whether motion was estimated successfully.
-    @return 3x3 2D transformation matrix (32F).
-     */
-    virtual Mat estimate(InputArray points0, InputArray points1, bool *ok = 0) = 0;
-
-protected:
-    MotionEstimatorBase(MotionModel model) { setMotionModel(model); }
-
-private:
-    MotionModel motionModel_;
-};
-
-/** @brief Describes a robust RANSAC-based global 2D motion estimation method which minimizes L2 error.
- */
-class CV_EXPORTS MotionEstimatorRansacL2 : public MotionEstimatorBase
-{
-public:
-    MotionEstimatorRansacL2(MotionModel model = MM_AFFINE);
-
-    void setRansacParams(const RansacParams &val) { ransacParams_ = val; }
-    RansacParams ransacParams() const { return ransacParams_; }
-
-    void setMinInlierRatio(float val) { minInlierRatio_ = val; }
-    float minInlierRatio() const { return minInlierRatio_; }
-
-    virtual Mat estimate(InputArray points0, InputArray points1, bool *ok = 0) CV_OVERRIDE;
-
-private:
-    RansacParams ransacParams_;
-    float minInlierRatio_;
-};
-
-/** @brief Describes a global 2D motion estimation method which minimizes L1 error.
-
-@note To be able to use this method you must build OpenCV with CLP library support. :
- */
-class CV_EXPORTS MotionEstimatorL1 : public MotionEstimatorBase
-{
-public:
-    MotionEstimatorL1(MotionModel model = MM_AFFINE);
-
-    virtual Mat estimate(InputArray points0, InputArray points1, bool *ok = 0) CV_OVERRIDE;
-
-private:
-    std::vector<double> obj_, collb_, colub_;
-    std::vector<double> elems_, rowlb_, rowub_;
-    std::vector<int> rows_, cols_;
-
-    void set(int row, int col, double coef)
-    {
-        rows_.push_back(row);
-        cols_.push_back(col);
-        elems_.push_back(coef);
-    }
-};
-
-/** @brief Base class for global 2D motion estimation methods which take frames as input.
- */
-class CV_EXPORTS ImageMotionEstimatorBase
-{
-public:
-    virtual ~ImageMotionEstimatorBase() {}
-
-    virtual void setMotionModel(MotionModel val) { motionModel_ = val; }
-    virtual MotionModel motionModel() const { return motionModel_; }
-
-    virtual Mat estimate(const Mat &frame0, const Mat &frame1, bool *ok = 0) = 0;
-
-protected:
-    ImageMotionEstimatorBase(MotionModel model) { setMotionModel(model); }
-
-private:
-    MotionModel motionModel_;
-};
-
-class CV_EXPORTS FromFileMotionReader : public ImageMotionEstimatorBase
-{
-public:
-    FromFileMotionReader(const String &path);
-
-    virtual Mat estimate(const Mat &frame0, const Mat &frame1, bool *ok = 0) CV_OVERRIDE;
-
-private:
-    std::ifstream file_;
-};
-
-class CV_EXPORTS ToFileMotionWriter : public ImageMotionEstimatorBase
-{
-public:
-    ToFileMotionWriter(const String &path, Ptr<ImageMotionEstimatorBase> estimator);
-
-    virtual void setMotionModel(MotionModel val) CV_OVERRIDE { motionEstimator_->setMotionModel(val); }
-    virtual MotionModel motionModel() const CV_OVERRIDE { return motionEstimator_->motionModel(); }
-
-    virtual Mat estimate(const Mat &frame0, const Mat &frame1, bool *ok = 0) CV_OVERRIDE;
-
-private:
-    std::ofstream file_;
-    Ptr<ImageMotionEstimatorBase> motionEstimator_;
-};
-
-/** @brief Describes a global 2D motion estimation method which uses keypoints detection and optical flow for
-matching.
- */
-class CV_EXPORTS KeypointBasedMotionEstimator : public ImageMotionEstimatorBase
-{
-public:
-    KeypointBasedMotionEstimator(Ptr<MotionEstimatorBase> estimator);
-
-    virtual void setMotionModel(MotionModel val) CV_OVERRIDE { motionEstimator_->setMotionModel(val); }
-    virtual MotionModel motionModel() const CV_OVERRIDE { return motionEstimator_->motionModel(); }
-
-    void setDetector(Ptr<FeatureDetector> val) { detector_ = val; }
-    Ptr<FeatureDetector> detector() const { return detector_; }
-
-    void setOpticalFlowEstimator(Ptr<ISparseOptFlowEstimator> val) { optFlowEstimator_ = val; }
-    Ptr<ISparseOptFlowEstimator> opticalFlowEstimator() const { return optFlowEstimator_; }
-
-    void setOutlierRejector(Ptr<IOutlierRejector> val) { outlierRejector_ = val; }
-    Ptr<IOutlierRejector> outlierRejector() const { return outlierRejector_; }
-
-    virtual Mat estimate(const Mat &frame0, const Mat &frame1, bool *ok = 0) CV_OVERRIDE;
-    Mat estimate(InputArray frame0, InputArray frame1, bool *ok = 0);
-
-private:
-    Ptr<MotionEstimatorBase> motionEstimator_;
-    Ptr<FeatureDetector> detector_;
-    Ptr<ISparseOptFlowEstimator> optFlowEstimator_;
-    Ptr<IOutlierRejector> outlierRejector_;
-
-    std::vector<uchar> status_;
-    std::vector<KeyPoint> keypointsPrev_;
-    std::vector<Point2f> pointsPrev_, points_;
-    std::vector<Point2f> pointsPrevGood_, pointsGood_;
-};
-
-#if defined(HAVE_OPENCV_CUDAIMGPROC) && defined(HAVE_OPENCV_CUDAOPTFLOW)
-
-class CV_EXPORTS KeypointBasedMotionEstimatorGpu : public ImageMotionEstimatorBase
-{
-public:
-    KeypointBasedMotionEstimatorGpu(Ptr<MotionEstimatorBase> estimator);
-
-    virtual void setMotionModel(MotionModel val) CV_OVERRIDE { motionEstimator_->setMotionModel(val); }
-    virtual MotionModel motionModel() const CV_OVERRIDE { return motionEstimator_->motionModel(); }
-
-    void setOutlierRejector(Ptr<IOutlierRejector> val) { outlierRejector_ = val; }
-    Ptr<IOutlierRejector> outlierRejector() const { return outlierRejector_; }
-
-    virtual Mat estimate(const Mat &frame0, const Mat &frame1, bool *ok = 0) CV_OVERRIDE;
-    Mat estimate(const cuda::GpuMat &frame0, const cuda::GpuMat &frame1, bool *ok = 0);
-
-private:
-    Ptr<MotionEstimatorBase> motionEstimator_;
-    Ptr<cuda::CornersDetector> detector_;
-    SparsePyrLkOptFlowEstimatorGpu optFlowEstimator_;
-    Ptr<IOutlierRejector> outlierRejector_;
-
-    cuda::GpuMat frame0_, grayFrame0_, frame1_;
-    cuda::GpuMat pointsPrev_, points_;
-    cuda::GpuMat status_;
-
-    Mat hostPointsPrev_, hostPoints_;
-    std::vector<Point2f> hostPointsPrevTmp_, hostPointsTmp_;
-    std::vector<uchar> rejectionStatus_;
-};
-
-#endif // defined(HAVE_OPENCV_CUDAIMGPROC) && defined(HAVE_OPENCV_CUDAOPTFLOW)
-
-/** @brief Computes motion between two frames assuming that all the intermediate motions are known.
-
-@param from Source frame index.
-@param to Destination frame index.
-@param motions Pair-wise motions. motions[i] denotes motion from the frame i to the frame i+1
-@return Motion from the Source frame to the Destination frame.
- */
-CV_EXPORTS Mat getMotion(int from, int to, const std::vector<Mat> &motions);
-
-//! @}
-
-} // namespace videostab
-} // namespace cv
-
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/videostab/inpainting.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/videostab/inpainting.hpp
deleted file mode 100755
index 9c123f0..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/videostab/inpainting.hpp
+++ /dev/null
@@ -1,212 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_VIDEOSTAB_INPAINTINT_HPP
-#define OPENCV_VIDEOSTAB_INPAINTINT_HPP
-
-#include <vector>
-#include "opencv2/core.hpp"
-#include "opencv2/videostab/optical_flow.hpp"
-#include "opencv2/videostab/fast_marching.hpp"
-#include "opencv2/videostab/global_motion.hpp"
-#include "opencv2/photo.hpp"
-
-namespace cv
-{
-namespace videostab
-{
-
-//! @addtogroup videostab
-//! @{
-
-class CV_EXPORTS InpainterBase
-{
-public:
-    InpainterBase()
-        : radius_(0), motionModel_(MM_UNKNOWN), frames_(0), motions_(0),
-          stabilizedFrames_(0), stabilizationMotions_(0) {}
-
-    virtual ~InpainterBase() {}
-
-    virtual void setRadius(int val) { radius_ = val; }
-    virtual int radius() const { return radius_; }
-
-    virtual void setMotionModel(MotionModel val) { motionModel_ = val; }
-    virtual MotionModel motionModel() const { return motionModel_; }
-
-    virtual void inpaint(int idx, Mat &frame, Mat &mask) = 0;
-
-
-    // data from stabilizer
-
-    virtual void setFrames(const std::vector<Mat> &val) { frames_ = &val; }
-    virtual const std::vector<Mat>& frames() const { return *frames_; }
-
-    virtual void setMotions(const std::vector<Mat> &val) { motions_ = &val; }
-    virtual const std::vector<Mat>& motions() const { return *motions_; }
-
-    virtual void setStabilizedFrames(const std::vector<Mat> &val) { stabilizedFrames_ = &val; }
-    virtual const std::vector<Mat>& stabilizedFrames() const { return *stabilizedFrames_; }
-
-    virtual void setStabilizationMotions(const std::vector<Mat> &val) { stabilizationMotions_ = &val; }
-    virtual const std::vector<Mat>& stabilizationMotions() const { return *stabilizationMotions_; }
-
-protected:
-    int radius_;
-    MotionModel motionModel_;
-    const std::vector<Mat> *frames_;
-    const std::vector<Mat> *motions_;
-    const std::vector<Mat> *stabilizedFrames_;
-    const std::vector<Mat> *stabilizationMotions_;
-};
-
-class CV_EXPORTS NullInpainter : public InpainterBase
-{
-public:
-    virtual void inpaint(int /*idx*/, Mat &/*frame*/, Mat &/*mask*/) CV_OVERRIDE {}
-};
-
-class CV_EXPORTS InpaintingPipeline : public InpainterBase
-{
-public:
-    void pushBack(Ptr<InpainterBase> inpainter) { inpainters_.push_back(inpainter); }
-    bool empty() const { return inpainters_.empty(); }
-
-    virtual void setRadius(int val) CV_OVERRIDE;
-    virtual void setMotionModel(MotionModel val) CV_OVERRIDE;
-    virtual void setFrames(const std::vector<Mat> &val) CV_OVERRIDE;
-    virtual void setMotions(const std::vector<Mat> &val) CV_OVERRIDE;
-    virtual void setStabilizedFrames(const std::vector<Mat> &val) CV_OVERRIDE;
-    virtual void setStabilizationMotions(const std::vector<Mat> &val) CV_OVERRIDE;
-
-    virtual void inpaint(int idx, Mat &frame, Mat &mask) CV_OVERRIDE;
-
-private:
-    std::vector<Ptr<InpainterBase> > inpainters_;
-};
-
-class CV_EXPORTS ConsistentMosaicInpainter : public InpainterBase
-{
-public:
-    ConsistentMosaicInpainter();
-
-    void setStdevThresh(float val) { stdevThresh_ = val; }
-    float stdevThresh() const { return stdevThresh_; }
-
-    virtual void inpaint(int idx, Mat &frame, Mat &mask) CV_OVERRIDE;
-
-private:
-    float stdevThresh_;
-};
-
-class CV_EXPORTS MotionInpainter : public InpainterBase
-{
-public:
-    MotionInpainter();
-
-    void setOptFlowEstimator(Ptr<IDenseOptFlowEstimator> val) { optFlowEstimator_ = val; }
-    Ptr<IDenseOptFlowEstimator> optFlowEstimator() const { return optFlowEstimator_; }
-
-    void setFlowErrorThreshold(float val) { flowErrorThreshold_ = val; }
-    float flowErrorThreshold() const { return flowErrorThreshold_; }
-
-    void setDistThreshold(float val) { distThresh_ = val; }
-    float distThresh() const { return distThresh_; }
-
-    void setBorderMode(int val) { borderMode_ = val; }
-    int borderMode() const { return borderMode_; }
-
-    virtual void inpaint(int idx, Mat &frame, Mat &mask) CV_OVERRIDE;
-
-private:
-    FastMarchingMethod fmm_;
-    Ptr<IDenseOptFlowEstimator> optFlowEstimator_;
-    float flowErrorThreshold_;
-    float distThresh_;
-    int borderMode_;
-
-    Mat frame1_, transformedFrame1_;
-    Mat_<uchar> grayFrame_, transformedGrayFrame1_;
-    Mat_<uchar> mask1_, transformedMask1_;
-    Mat_<float> flowX_, flowY_, flowErrors_;
-    Mat_<uchar> flowMask_;
-};
-
-class CV_EXPORTS ColorAverageInpainter : public InpainterBase
-{
-public:
-    virtual void inpaint(int idx, Mat &frame, Mat &mask) CV_OVERRIDE;
-
-private:
-    FastMarchingMethod fmm_;
-};
-
-class CV_EXPORTS ColorInpainter : public InpainterBase
-{
-public:
-    ColorInpainter(int method = INPAINT_TELEA, double radius = 2.);
-
-    virtual void inpaint(int idx, Mat &frame, Mat &mask) CV_OVERRIDE;
-
-private:
-    int method_;
-    double radius_;
-    Mat invMask_;
-};
-
-inline ColorInpainter::ColorInpainter(int _method, double _radius)
-        : method_(_method), radius_(_radius) {}
-
-CV_EXPORTS void calcFlowMask(
-        const Mat &flowX, const Mat &flowY, const Mat &errors, float maxError,
-        const Mat &mask0, const Mat &mask1, Mat &flowMask);
-
-CV_EXPORTS void completeFrameAccordingToFlow(
-        const Mat &flowMask, const Mat &flowX, const Mat &flowY, const Mat &frame1, const Mat &mask1,
-        float distThresh, Mat& frame0, Mat &mask0);
-
-//! @}
-
-} // namespace videostab
-} // namespace cv
-
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/videostab/log.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/videostab/log.hpp
deleted file mode 100755
index 73e7049..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/videostab/log.hpp
+++ /dev/null
@@ -1,80 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_VIDEOSTAB_LOG_HPP
-#define OPENCV_VIDEOSTAB_LOG_HPP
-
-#include "opencv2/core.hpp"
-
-namespace cv
-{
-namespace videostab
-{
-
-//! @addtogroup videostab
-//! @{
-
-class CV_EXPORTS ILog
-{
-public:
-    virtual ~ILog() {}
-    virtual void print(const char *format, ...) = 0;
-};
-
-class CV_EXPORTS NullLog : public ILog
-{
-public:
-    virtual void print(const char * /*format*/, ...) CV_OVERRIDE {}
-};
-
-class CV_EXPORTS LogToStdout : public ILog
-{
-public:
-    virtual void print(const char *format, ...) CV_OVERRIDE;
-};
-
-//! @}
-
-} // namespace videostab
-} // namespace cv
-
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/videostab/motion_core.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/videostab/motion_core.hpp
deleted file mode 100755
index 4525cc7..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/videostab/motion_core.hpp
+++ /dev/null
@@ -1,129 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_VIDEOSTAB_MOTION_CORE_HPP
-#define OPENCV_VIDEOSTAB_MOTION_CORE_HPP
-
-#include <cmath>
-#include "opencv2/core.hpp"
-
-namespace cv
-{
-namespace videostab
-{
-
-//! @addtogroup videostab_motion
-//! @{
-
-/** @brief Describes motion model between two point clouds.
- */
-enum MotionModel
-{
-    MM_TRANSLATION = 0,
-    MM_TRANSLATION_AND_SCALE = 1,
-    MM_ROTATION = 2,
-    MM_RIGID = 3,
-    MM_SIMILARITY = 4,
-    MM_AFFINE = 5,
-    MM_HOMOGRAPHY = 6,
-    MM_UNKNOWN = 7
-};
-
-/** @brief Describes RANSAC method parameters.
- */
-struct CV_EXPORTS RansacParams
-{
-    int size; //!< subset size
-    float thresh; //!< max error to classify as inlier
-    float eps; //!< max outliers ratio
-    float prob; //!< probability of success
-
-    RansacParams() : size(0), thresh(0), eps(0), prob(0) {}
-    /** @brief Constructor
-    @param size Subset size.
-    @param thresh Maximum re-projection error value to classify as inlier.
-    @param eps Maximum ratio of incorrect correspondences.
-    @param prob Required success probability.
-     */
-    RansacParams(int size, float thresh, float eps, float prob);
-
-    /**
-    @return Number of iterations that'll be performed by RANSAC method.
-    */
-    int niters() const
-    {
-        return static_cast<int>(
-                std::ceil(std::log(1 - prob) / std::log(1 - std::pow(1 - eps, size))));
-    }
-
-    /**
-    @param model Motion model. See cv::videostab::MotionModel.
-    @return Default RANSAC method parameters for the given motion model.
-    */
-    static RansacParams default2dMotion(MotionModel model)
-    {
-        CV_Assert(model < MM_UNKNOWN);
-        if (model == MM_TRANSLATION)
-            return RansacParams(1, 0.5f, 0.5f, 0.99f);
-        if (model == MM_TRANSLATION_AND_SCALE)
-            return RansacParams(2, 0.5f, 0.5f, 0.99f);
-        if (model == MM_ROTATION)
-            return RansacParams(1, 0.5f, 0.5f, 0.99f);
-        if (model == MM_RIGID)
-            return RansacParams(2, 0.5f, 0.5f, 0.99f);
-        if (model == MM_SIMILARITY)
-            return RansacParams(2, 0.5f, 0.5f, 0.99f);
-        if (model == MM_AFFINE)
-            return RansacParams(3, 0.5f, 0.5f, 0.99f);
-        return RansacParams(4, 0.5f, 0.5f, 0.99f);
-    }
-};
-
-inline RansacParams::RansacParams(int _size, float _thresh, float _eps, float _prob)
-    : size(_size), thresh(_thresh), eps(_eps), prob(_prob) {}
-
-//! @}
-
-} // namespace videostab
-} // namespace cv
-
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/videostab/motion_stabilizing.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/videostab/motion_stabilizing.hpp
deleted file mode 100755
index c50095b..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/videostab/motion_stabilizing.hpp
+++ /dev/null
@@ -1,174 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_VIDEOSTAB_MOTION_STABILIZING_HPP
-#define OPENCV_VIDEOSTAB_MOTION_STABILIZING_HPP
-
-#include <vector>
-#include <utility>
-#include "opencv2/core.hpp"
-#include "opencv2/videostab/global_motion.hpp"
-
-namespace cv
-{
-namespace videostab
-{
-
-//! @addtogroup videostab_motion
-//! @{
-
-class CV_EXPORTS IMotionStabilizer
-{
-public:
-    virtual ~IMotionStabilizer() {}
-
-    //! assumes that [0, size-1) is in or equals to [range.first, range.second)
-    virtual void stabilize(
-            int size, const std::vector<Mat> &motions, std::pair<int,int> range,
-            Mat *stabilizationMotions) = 0;
-};
-
-class CV_EXPORTS MotionStabilizationPipeline : public IMotionStabilizer
-{
-public:
-    void pushBack(Ptr<IMotionStabilizer> stabilizer) { stabilizers_.push_back(stabilizer); }
-    bool empty() const { return stabilizers_.empty(); }
-
-    virtual void stabilize(
-            int size, const std::vector<Mat> &motions, std::pair<int,int> range,
-            Mat *stabilizationMotions) CV_OVERRIDE;
-
-private:
-    std::vector<Ptr<IMotionStabilizer> > stabilizers_;
-};
-
-class CV_EXPORTS MotionFilterBase : public IMotionStabilizer
-{
-public:
-    virtual ~MotionFilterBase() {}
-
-    virtual Mat stabilize(
-            int idx, const std::vector<Mat> &motions, std::pair<int,int> range) = 0;
-
-    virtual void stabilize(
-            int size, const std::vector<Mat> &motions, std::pair<int,int> range,
-            Mat *stabilizationMotions) CV_OVERRIDE;
-};
-
-class CV_EXPORTS GaussianMotionFilter : public MotionFilterBase
-{
-public:
-    GaussianMotionFilter(int radius = 15, float stdev = -1.f);
-
-    void setParams(int radius, float stdev = -1.f);
-    int radius() const { return radius_; }
-    float stdev() const { return stdev_; }
-
-    virtual Mat stabilize(
-            int idx, const std::vector<Mat> &motions, std::pair<int,int> range) CV_OVERRIDE;
-
-private:
-    int radius_;
-    float stdev_;
-    std::vector<float> weight_;
-};
-
-inline GaussianMotionFilter::GaussianMotionFilter(int _radius, float _stdev) { setParams(_radius, _stdev); }
-
-class CV_EXPORTS LpMotionStabilizer : public IMotionStabilizer
-{
-public:
-    LpMotionStabilizer(MotionModel model = MM_SIMILARITY);
-
-    void setMotionModel(MotionModel val) { model_ = val; }
-    MotionModel motionModel() const { return model_; }
-
-    void setFrameSize(Size val) { frameSize_ = val; }
-    Size frameSize() const { return frameSize_; }
-
-    void setTrimRatio(float val) { trimRatio_ = val; }
-    float trimRatio() const { return trimRatio_; }
-
-    void setWeight1(float val) { w1_ = val; }
-    float weight1() const { return w1_; }
-
-    void setWeight2(float val) { w2_ = val; }
-    float weight2() const { return w2_; }
-
-    void setWeight3(float val) { w3_ = val; }
-    float weight3() const { return w3_; }
-
-    void setWeight4(float val) { w4_ = val; }
-    float weight4() const { return w4_; }
-
-    virtual void stabilize(
-            int size, const std::vector<Mat> &motions, std::pair<int,int> range,
-            Mat *stabilizationMotions) CV_OVERRIDE;
-
-private:
-    MotionModel model_;
-    Size frameSize_;
-    float trimRatio_;
-    float w1_, w2_, w3_, w4_;
-
-    std::vector<double> obj_, collb_, colub_;
-    std::vector<int> rows_, cols_;
-    std::vector<double> elems_, rowlb_, rowub_;
-
-    void set(int row, int col, double coef)
-    {
-        rows_.push_back(row);
-        cols_.push_back(col);
-        elems_.push_back(coef);
-    }
-};
-
-CV_EXPORTS Mat ensureInclusionConstraint(const Mat &M, Size size, float trimRatio);
-
-CV_EXPORTS float estimateOptimalTrimRatio(const Mat &M, Size size);
-
-//! @}
-
-} // namespace videostab
-} // namespace
-
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/videostab/optical_flow.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/videostab/optical_flow.hpp
deleted file mode 100755
index 5e06941..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/videostab/optical_flow.hpp
+++ /dev/null
@@ -1,150 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_VIDEOSTAB_OPTICAL_FLOW_HPP
-#define OPENCV_VIDEOSTAB_OPTICAL_FLOW_HPP
-
-#include "opencv2/core.hpp"
-#include "opencv2/opencv_modules.hpp"
-
-#ifdef HAVE_OPENCV_CUDAOPTFLOW
-  #include "opencv2/cudaoptflow.hpp"
-#endif
-
-namespace cv
-{
-namespace videostab
-{
-
-//! @addtogroup videostab
-//! @{
-
-class CV_EXPORTS ISparseOptFlowEstimator
-{
-public:
-    virtual ~ISparseOptFlowEstimator() {}
-    virtual void run(
-            InputArray frame0, InputArray frame1, InputArray points0, InputOutputArray points1,
-            OutputArray status, OutputArray errors) = 0;
-};
-
-class CV_EXPORTS IDenseOptFlowEstimator
-{
-public:
-    virtual ~IDenseOptFlowEstimator() {}
-    virtual void run(
-            InputArray frame0, InputArray frame1, InputOutputArray flowX, InputOutputArray flowY,
-            OutputArray errors) = 0;
-};
-
-class CV_EXPORTS PyrLkOptFlowEstimatorBase
-{
-public:
-    PyrLkOptFlowEstimatorBase() { setWinSize(Size(21, 21)); setMaxLevel(3); }
-
-    virtual void setWinSize(Size val) { winSize_ = val; }
-    virtual Size winSize() const { return winSize_; }
-
-    virtual void setMaxLevel(int val) { maxLevel_ = val; }
-    virtual int maxLevel() const { return maxLevel_; }
-    virtual ~PyrLkOptFlowEstimatorBase() {}
-
-protected:
-    Size winSize_;
-    int maxLevel_;
-};
-
-class CV_EXPORTS SparsePyrLkOptFlowEstimator
-        : public PyrLkOptFlowEstimatorBase, public ISparseOptFlowEstimator
-{
-public:
-    virtual void run(
-            InputArray frame0, InputArray frame1, InputArray points0, InputOutputArray points1,
-            OutputArray status, OutputArray errors) CV_OVERRIDE;
-};
-
-#ifdef HAVE_OPENCV_CUDAOPTFLOW
-
-class CV_EXPORTS SparsePyrLkOptFlowEstimatorGpu
-        : public PyrLkOptFlowEstimatorBase, public ISparseOptFlowEstimator
-{
-public:
-    SparsePyrLkOptFlowEstimatorGpu();
-
-    virtual void run(
-            InputArray frame0, InputArray frame1, InputArray points0, InputOutputArray points1,
-            OutputArray status, OutputArray errors) CV_OVERRIDE;
-
-    void run(const cuda::GpuMat &frame0, const cuda::GpuMat &frame1, const cuda::GpuMat &points0, cuda::GpuMat &points1,
-             cuda::GpuMat &status, cuda::GpuMat &errors);
-
-    void run(const cuda::GpuMat &frame0, const cuda::GpuMat &frame1, const cuda::GpuMat &points0, cuda::GpuMat &points1,
-             cuda::GpuMat &status);
-
-private:
-    Ptr<cuda::SparsePyrLKOpticalFlow> optFlowEstimator_;
-    cuda::GpuMat frame0_, frame1_, points0_, points1_, status_, errors_;
-};
-
-class CV_EXPORTS DensePyrLkOptFlowEstimatorGpu
-        : public PyrLkOptFlowEstimatorBase, public IDenseOptFlowEstimator
-{
-public:
-    DensePyrLkOptFlowEstimatorGpu();
-
-    virtual void run(
-            InputArray frame0, InputArray frame1, InputOutputArray flowX, InputOutputArray flowY,
-            OutputArray errors) CV_OVERRIDE;
-
-private:
-    Ptr<cuda::DensePyrLKOpticalFlow> optFlowEstimator_;
-    cuda::GpuMat frame0_, frame1_, flowX_, flowY_, errors_;
-};
-
-#endif
-
-//! @}
-
-} // namespace videostab
-} // namespace cv
-
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/videostab/outlier_rejection.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/videostab/outlier_rejection.hpp
deleted file mode 100755
index 1d29896..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/videostab/outlier_rejection.hpp
+++ /dev/null
@@ -1,101 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_VIDEOSTAB_OUTLIER_REJECTION_HPP
-#define OPENCV_VIDEOSTAB_OUTLIER_REJECTION_HPP
-
-#include <vector>
-#include "opencv2/core.hpp"
-#include "opencv2/videostab/motion_core.hpp"
-
-namespace cv
-{
-namespace videostab
-{
-
-//! @addtogroup videostab
-//! @{
-
-class CV_EXPORTS IOutlierRejector
-{
-public:
-    virtual ~IOutlierRejector() {}
-
-    virtual void process(
-            Size frameSize, InputArray points0, InputArray points1, OutputArray mask) = 0;
-};
-
-class CV_EXPORTS NullOutlierRejector : public IOutlierRejector
-{
-public:
-    virtual void process(
-            Size frameSize, InputArray points0, InputArray points1, OutputArray mask) CV_OVERRIDE;
-};
-
-class CV_EXPORTS TranslationBasedLocalOutlierRejector : public IOutlierRejector
-{
-public:
-    TranslationBasedLocalOutlierRejector();
-
-    void setCellSize(Size val) { cellSize_ = val; }
-    Size cellSize() const { return cellSize_; }
-
-    void setRansacParams(RansacParams val) { ransacParams_ = val; }
-    RansacParams ransacParams() const { return ransacParams_; }
-
-    virtual void process(
-            Size frameSize, InputArray points0, InputArray points1, OutputArray mask) CV_OVERRIDE;
-
-private:
-    Size cellSize_;
-    RansacParams ransacParams_;
-
-    typedef std::vector<int> Cell;
-    std::vector<Cell> grid_;
-};
-
-//! @}
-
-} // namespace videostab
-} // namespace cv
-
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/videostab/ring_buffer.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/videostab/ring_buffer.hpp
deleted file mode 100755
index 55d5244..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/videostab/ring_buffer.hpp
+++ /dev/null
@@ -1,72 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_VIDEOSTAB_RING_BUFFER_HPP
-#define OPENCV_VIDEOSTAB_RING_BUFFER_HPP
-
-#include <vector>
-#include "opencv2/imgproc.hpp"
-
-namespace cv
-{
-namespace videostab
-{
-
-//! @addtogroup videostab
-//! @{
-
-template <typename T> inline T& at(int idx, std::vector<T> &items)
-{
-    return items[cv::borderInterpolate(idx, static_cast<int>(items.size()), cv::BORDER_WRAP)];
-}
-
-template <typename T> inline const T& at(int idx, const std::vector<T> &items)
-{
-    return items[cv::borderInterpolate(idx, static_cast<int>(items.size()), cv::BORDER_WRAP)];
-}
-
-//! @}
-
-} // namespace videostab
-} // namespace cv
-
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/videostab/stabilizer.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/videostab/stabilizer.hpp
deleted file mode 100755
index 634a0aa..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/videostab/stabilizer.hpp
+++ /dev/null
@@ -1,200 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_VIDEOSTAB_STABILIZER_HPP
-#define OPENCV_VIDEOSTAB_STABILIZER_HPP
-
-#include <vector>
-#include <ctime>
-#include "opencv2/core.hpp"
-#include "opencv2/imgproc.hpp"
-#include "opencv2/videostab/global_motion.hpp"
-#include "opencv2/videostab/motion_stabilizing.hpp"
-#include "opencv2/videostab/frame_source.hpp"
-#include "opencv2/videostab/log.hpp"
-#include "opencv2/videostab/inpainting.hpp"
-#include "opencv2/videostab/deblurring.hpp"
-#include "opencv2/videostab/wobble_suppression.hpp"
-
-namespace cv
-{
-namespace videostab
-{
-
-//! @addtogroup videostab
-//! @{
-
-class CV_EXPORTS StabilizerBase
-{
-public:
-    virtual ~StabilizerBase() {}
-
-    void setLog(Ptr<ILog> ilog) { log_ = ilog; }
-    Ptr<ILog> log() const { return log_; }
-
-    void setRadius(int val) { radius_ = val; }
-    int radius() const { return radius_; }
-
-    void setFrameSource(Ptr<IFrameSource> val) { frameSource_ = val; }
-    Ptr<IFrameSource> frameSource() const { return frameSource_; }
-
-    void setMotionEstimator(Ptr<ImageMotionEstimatorBase> val) { motionEstimator_ = val; }
-    Ptr<ImageMotionEstimatorBase> motionEstimator() const { return motionEstimator_; }
-
-    void setDeblurer(Ptr<DeblurerBase> val) { deblurer_ = val; }
-    Ptr<DeblurerBase> deblurrer() const { return deblurer_; }
-
-    void setTrimRatio(float val) { trimRatio_ = val; }
-    float trimRatio() const { return trimRatio_; }
-
-    void setCorrectionForInclusion(bool val) { doCorrectionForInclusion_ = val; }
-    bool doCorrectionForInclusion() const { return doCorrectionForInclusion_; }
-
-    void setBorderMode(int val) { borderMode_ = val; }
-    int borderMode() const { return borderMode_; }
-
-    void setInpainter(Ptr<InpainterBase> val) { inpainter_ = val; }
-    Ptr<InpainterBase> inpainter() const { return inpainter_; }
-
-protected:
-    StabilizerBase();
-
-    void reset();
-    Mat nextStabilizedFrame();
-    bool doOneIteration();
-    virtual void setUp(const Mat &firstFrame);
-    virtual Mat estimateMotion() = 0;
-    virtual Mat estimateStabilizationMotion() = 0;
-    void stabilizeFrame();
-    virtual Mat postProcessFrame(const Mat &frame);
-    void logProcessingTime();
-
-    Ptr<ILog> log_;
-    Ptr<IFrameSource> frameSource_;
-    Ptr<ImageMotionEstimatorBase> motionEstimator_;
-    Ptr<DeblurerBase> deblurer_;
-    Ptr<InpainterBase> inpainter_;
-    int radius_;
-    float trimRatio_;
-    bool doCorrectionForInclusion_;
-    int borderMode_;
-
-    Size frameSize_;
-    Mat frameMask_;
-    int curPos_;
-    int curStabilizedPos_;
-    bool doDeblurring_;
-    Mat preProcessedFrame_;
-    bool doInpainting_;
-    Mat inpaintingMask_;
-    Mat finalFrame_;
-    std::vector<Mat> frames_;
-    std::vector<Mat> motions_; // motions_[i] is the motion from i-th to i+1-th frame
-    std::vector<float> blurrinessRates_;
-    std::vector<Mat> stabilizedFrames_;
-    std::vector<Mat> stabilizedMasks_;
-    std::vector<Mat> stabilizationMotions_;
-    clock_t processingStartTime_;
-};
-
-class CV_EXPORTS OnePassStabilizer : public StabilizerBase, public IFrameSource
-{
-public:
-    OnePassStabilizer();
-
-    void setMotionFilter(Ptr<MotionFilterBase> val) { motionFilter_ = val; }
-    Ptr<MotionFilterBase> motionFilter() const { return motionFilter_; }
-
-    virtual void reset() CV_OVERRIDE;
-    virtual Mat nextFrame() CV_OVERRIDE { return nextStabilizedFrame(); }
-
-protected:
-    virtual void setUp(const Mat &firstFrame) CV_OVERRIDE;
-    virtual Mat estimateMotion() CV_OVERRIDE;
-    virtual Mat estimateStabilizationMotion() CV_OVERRIDE;
-    virtual Mat postProcessFrame(const Mat &frame) CV_OVERRIDE;
-
-    Ptr<MotionFilterBase> motionFilter_;
-};
-
-class CV_EXPORTS TwoPassStabilizer : public StabilizerBase, public IFrameSource
-{
-public:
-    TwoPassStabilizer();
-
-    void setMotionStabilizer(Ptr<IMotionStabilizer> val) { motionStabilizer_ = val; }
-    Ptr<IMotionStabilizer> motionStabilizer() const { return motionStabilizer_; }
-
-    void setWobbleSuppressor(Ptr<WobbleSuppressorBase> val) { wobbleSuppressor_ = val; }
-    Ptr<WobbleSuppressorBase> wobbleSuppressor() const { return wobbleSuppressor_; }
-
-    void setEstimateTrimRatio(bool val) { mustEstTrimRatio_ = val; }
-    bool mustEstimateTrimaRatio() const { return mustEstTrimRatio_; }
-
-    virtual void reset() CV_OVERRIDE;
-    virtual Mat nextFrame() CV_OVERRIDE;
-
-protected:
-    void runPrePassIfNecessary();
-
-    virtual void setUp(const Mat &firstFrame) CV_OVERRIDE;
-    virtual Mat estimateMotion() CV_OVERRIDE;
-    virtual Mat estimateStabilizationMotion() CV_OVERRIDE;
-    virtual Mat postProcessFrame(const Mat &frame) CV_OVERRIDE;
-
-    Ptr<IMotionStabilizer> motionStabilizer_;
-    Ptr<WobbleSuppressorBase> wobbleSuppressor_;
-    bool mustEstTrimRatio_;
-
-    int frameCount_;
-    bool isPrePassDone_;
-    bool doWobbleSuppression_;
-    std::vector<Mat> motions2_;
-    Mat suppressedFrame_;
-};
-
-//! @}
-
-} // namespace videostab
-} // namespace cv
-
-#endif
diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/videostab/wobble_suppression.hpp b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/videostab/wobble_suppression.hpp
deleted file mode 100755
index d60ae6d..0000000
--- a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/jni/include/opencv2/videostab/wobble_suppression.hpp
+++ /dev/null
@@ -1,140 +0,0 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-//
-//                           License Agreement
-//                For Open Source Computer Vision Library
-//
-// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
-// Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef OPENCV_VIDEOSTAB_WOBBLE_SUPPRESSION_HPP
-#define OPENCV_VIDEOSTAB_WOBBLE_SUPPRESSION_HPP
-
-#include <vector>
-#include "opencv2/core.hpp"
-#include "opencv2/core/cuda.hpp"
-#include "opencv2/videostab/global_motion.hpp"
-#include "opencv2/videostab/log.hpp"
-
-namespace cv
-{
-namespace videostab
-{
-
-//! @addtogroup videostab
-//! @{
-
-class CV_EXPORTS WobbleSuppressorBase
-{
-public:
-    WobbleSuppressorBase();
-
-    virtual ~WobbleSuppressorBase() {}
-
-    void setMotionEstimator(Ptr<ImageMotionEstimatorBase> val) { motionEstimator_ = val; }
-    Ptr<ImageMotionEstimatorBase> motionEstimator() const { return motionEstimator_; }
-
-    virtual void suppress(int idx, const Mat &frame, Mat &result) = 0;
-
-
-    // data from stabilizer
-
-    virtual void setFrameCount(int val) { frameCount_ = val; }
-    virtual int frameCount() const { return frameCount_; }
-
-    virtual void setMotions(const std::vector<Mat> &val) { motions_ = &val; }
-    virtual const std::vector<Mat>& motions() const { return *motions_; }
-
-    virtual void setMotions2(const std::vector<Mat> &val) { motions2_ = &val; }
-    virtual const std::vector<Mat>& motions2() const { return *motions2_; }
-
-    virtual void setStabilizationMotions(const std::vector<Mat> &val) { stabilizationMotions_ = &val; }
-    virtual const std::vector<Mat>& stabilizationMotions() const { return *stabilizationMotions_; }
-
-protected:
-    Ptr<ImageMotionEstimatorBase> motionEstimator_;
-    int frameCount_;
-    const std::vector<Mat> *motions_;
-    const std::vector<Mat> *motions2_;
-    const std::vector<Mat> *stabilizationMotions_;
-};
-
-class CV_EXPORTS NullWobbleSuppressor : public WobbleSuppressorBase
-{
-public:
-    virtual void suppress(int idx, const Mat &frame, Mat &result) CV_OVERRIDE;
-};
-
-class CV_EXPORTS MoreAccurateMotionWobbleSuppressorBase : public WobbleSuppressorBase
-{
-public:
-    virtual void setPeriod(int val) { period_ = val; }
-    virtual int period() const { return period_; }
-
-protected:
-    MoreAccurateMotionWobbleSuppressorBase() { setPeriod(30); }
-
-    int period_;
-};
-
-class CV_EXPORTS MoreAccurateMotionWobbleSuppressor : public MoreAccurateMotionWobbleSuppressorBase
-{
-public:
-    virtual void suppress(int idx, const Mat &frame, Mat &result) CV_OVERRIDE;
-
-private:
-    Mat_<float> mapx_, mapy_;
-};
-
-#if defined(HAVE_OPENCV_CUDAWARPING)
-class CV_EXPORTS MoreAccurateMotionWobbleSuppressorGpu : public MoreAccurateMotionWobbleSuppressorBase
-{
-public:
-    void suppress(int idx, const cuda::GpuMat &frame, cuda::GpuMat &result);
-    virtual void suppress(int idx, const Mat &frame, Mat &result) CV_OVERRIDE;
-
-private:
-    cuda::GpuMat frameDevice_, resultDevice_;
-    cuda::GpuMat mapx_, mapy_;
-};
-#endif
-
-//! @}
-
-} // namespace videostab
-} // namespace cv
-
-#endif
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diff --git a/Prj-Android/app/src/main/cpp/opencv342/sdk/native/staticlibs/armeabi-v7a/libopencv_videostab.a b/Prj-Android/app/src/main/cpp/opencv342/sdk/native/staticlibs/armeabi-v7a/libopencv_videostab.a
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diff --git a/Prj-Android/app/src/main/cpp/src/CNNRecognizer.cpp b/Prj-Android/app/src/main/cpp/src/CNNRecognizer.cpp
deleted file mode 100755
index 909753c..0000000
--- a/Prj-Android/app/src/main/cpp/src/CNNRecognizer.cpp
+++ /dev/null
@@ -1,19 +0,0 @@
-//
-// Created by 庾金科 on 21/10/2017.
-//
-
-#include "../include/CNNRecognizer.h"
-
-namespace pr{
-    CNNRecognizer::CNNRecognizer(std::string prototxt,std::string caffemodel){
-        net = cv::dnn::readNetFromCaffe(prototxt, caffemodel);
-    }
-
-    label CNNRecognizer::recognizeCharacter(cv::Mat charImage){
-        if(charImage.channels()== 3)
-            cv::cvtColor(charImage,charImage,cv::COLOR_BGR2GRAY);
-        cv::Mat inputBlob = cv::dnn::blobFromImage(charImage, 1/255.0, cv::Size(CHAR_INPUT_W,CHAR_INPUT_H), cv::Scalar(0,0,0),false);
-        net.setInput(inputBlob,"data");
-        return net.forward();
-    }
-}
\ No newline at end of file
diff --git a/Prj-Android/app/src/main/cpp/src/FastDeskew.cpp b/Prj-Android/app/src/main/cpp/src/FastDeskew.cpp
deleted file mode 100755
index ce98cba..0000000
--- a/Prj-Android/app/src/main/cpp/src/FastDeskew.cpp
+++ /dev/null
@@ -1,139 +0,0 @@
-//
-// Created by 庾金科 on 02/10/2017.
-//
-
-
-
-#include "../include/FastDeskew.h"
-#include <cmath>
-#include <vector>
-#include <opencv2/core/types.hpp>
-#include <opencv2/core/mat.hpp>
-#include <opencv2/imgproc.hpp>
-#include <opencv2/opencv.hpp>
-
-namespace pr{
-
-
-    const int  ANGLE_MIN = 30 ;
-    const int ANGLE_MAX = 150 ;
-    const int PLATE_H = 36;
-    const int PLATE_W = 136;
-
-    int angle(float x,float y)
-    {
-        return atan2(x,y)*180/3.1415;
-    }
-
-    std::vector<float> avgfilter(std::vector<float> angle_list,int windowsSize) {
-        std::vector<float> angle_list_filtered(angle_list.size() - windowsSize + 1);
-        for (int i = 0; i < angle_list.size() - windowsSize + 1; i++) {
-            float avg = 0.00f;
-            for (int j = 0; j < windowsSize; j++) {
-                avg += angle_list[i + j];
-            }
-            avg = avg / windowsSize;
-            angle_list_filtered[i] = avg;
-        }
-
-        return angle_list_filtered;
-    }
-
-
-    void drawHist(std::vector<float> seq){
-        cv::Mat image(300,seq.size(),CV_8U);
-        image.setTo(0);
-
-        for(int i = 0;i<seq.size();i++)
-        {
-            float l = *std::max_element(seq.begin(),seq.end());
-
-            int p = int(float(seq[i])/l*300);
-
-            cv::line(image,cv::Point(i,300),cv::Point(i,300-p),cv::Scalar(255,255,255));
-        }
-        cv::imshow("vis",image);
-    }
-
-    cv::Mat  correctPlateImage(cv::Mat skewPlate,float angle,float maxAngle)
-    {
-
-        cv::Mat dst;
-
-        cv::Size size_o(skewPlate.cols,skewPlate.rows);
-
-
-        int extend_padding = 0;
-//        if(angle<0)
-            extend_padding = static_cast<int>(skewPlate.rows*tan(cv::abs(angle)/180* 3.14) );
-//        else
-//            extend_padding = static_cast<int>(skewPlate.rows/tan(cv::abs(angle)/180* 3.14) );
-
-//        std::cout<<"extend:"<<extend_padding<<std::endl;
-
-        cv::Size size(skewPlate.cols + extend_padding ,skewPlate.rows);
-
-        float interval = std::abs(sin((angle /180) * 3.14)* skewPlate.rows);
-//        std::cout<<interval<<std::endl;
-
-        cv::Point2f pts1[4] = {cv::Point2f(0,0),cv::Point2f(0,size_o.height),cv::Point2f(size_o.width,0),cv::Point2f(size_o.width,size_o.height)};
-        if(angle>0) {
-            cv::Point2f pts2[4] = {cv::Point2f(interval, 0), cv::Point2f(0, size_o.height),
-                                   cv::Point2f(size_o.width, 0), cv::Point2f(size_o.width - interval, size_o.height)};
-            cv::Mat M  = cv::getPerspectiveTransform(pts1,pts2);
-            cv::warpPerspective(skewPlate,dst,M,size);
-
-
-        }
-        else {
-            cv::Point2f pts2[4] = {cv::Point2f(0, 0), cv::Point2f(interval, size_o.height), cv::Point2f(size_o.width-interval, 0),
-                                   cv::Point2f(size_o.width, size_o.height)};
-            cv::Mat M  = cv::getPerspectiveTransform(pts1,pts2);
-            cv::warpPerspective(skewPlate,dst,M,size,cv::INTER_CUBIC);
-
-        }
-        return  dst;
-    }
-    cv::Mat fastdeskew(cv::Mat skewImage,int blockSize){
-
-
-        const int FILTER_WINDOWS_SIZE = 5;
-        std::vector<float> angle_list(180);
-        memset(angle_list.data(),0,angle_list.size()*sizeof(int));
-
-        cv::Mat bak;
-        skewImage.copyTo(bak);
-        if(skewImage.channels() == 3)
-            cv::cvtColor(skewImage,skewImage,cv::COLOR_RGB2GRAY);
-
-        if(skewImage.channels() == 1)
-        {
-            cv::Mat eigen;
-
-            cv::cornerEigenValsAndVecs(skewImage,eigen,blockSize,5);
-            for( int j = 0; j < skewImage.rows; j+=blockSize )
-            { for( int i = 0; i < skewImage.cols; i+=blockSize )
-                {
-                    float x2 = eigen.at<cv::Vec6f>(j, i)[4];
-                    float y2 = eigen.at<cv::Vec6f>(j, i)[5];
-                    int angle_cell = angle(x2,y2);
-                    angle_list[(angle_cell + 180)%180]+=1.0;
-
-                }
-            }
-        }
-        std::vector<float> filtered = avgfilter(angle_list,5);
-
-        int maxPos = std::max_element(filtered.begin(),filtered.end()) - filtered.begin() + FILTER_WINDOWS_SIZE/2;
-        if(maxPos>ANGLE_MAX)
-            maxPos = (-maxPos+90+180)%180;
-        if(maxPos<ANGLE_MIN)
-            maxPos-=90;
-        maxPos=90-maxPos;
-      cv::Mat deskewed = correctPlateImage(bak, static_cast<float>(maxPos),60.0f);
-        return deskewed;
-    }
-
-
-
-}//namespace pr
diff --git a/Prj-Android/app/src/main/cpp/src/FineMapping.cpp b/Prj-Android/app/src/main/cpp/src/FineMapping.cpp
deleted file mode 100755
index c5a3bef..0000000
--- a/Prj-Android/app/src/main/cpp/src/FineMapping.cpp
+++ /dev/null
@@ -1,205 +0,0 @@
-//
-// Created by 庾金科 on 22/09/2017.
-//
-
-#include "../include/FineMapping.h"
-namespace pr{
-
-    const int FINEMAPPING_H = 60 ;
-    const int FINEMAPPING_W = 140;
-    const int PADDING_UP_DOWN = 30;
-    void drawRect(cv::Mat image,cv::Rect rect)
-    {
-        cv::Point p1(rect.x,rect.y);
-        cv::Point p2(rect.x+rect.width,rect.y+rect.height);
-        cv::rectangle(image,p1,p2,cv::Scalar(0,255,0),1);
-    }
-
-
-    FineMapping::FineMapping(std::string prototxt,std::string caffemodel) {
-         net = cv::dnn::readNetFromCaffe(prototxt, caffemodel);
-
-    }
-
-    cv::Mat FineMapping::FineMappingHorizon(cv::Mat FinedVertical,int leftPadding,int rightPadding)
-    {
-
-//        if(FinedVertical.channels()==1)
-//            cv::cvtColor(FinedVertical,FinedVertical,cv::COLOR_GRAY2BGR);
-        cv::Mat inputBlob = cv::dnn::blobFromImage(FinedVertical, 1/255.0, cv::Size(66,16),
-                                      cv::Scalar(0,0,0),false);
-
-        net.setInput(inputBlob,"data");
-        cv::Mat prob = net.forward();
-        int front = static_cast<int>(prob.at<float>(0,0)*FinedVertical.cols);
-        int back = static_cast<int>(prob.at<float>(0,1)*FinedVertical.cols);
-        front -= leftPadding ;
-        if(front<0) front = 0;
-        back +=rightPadding;
-        if(back>FinedVertical.cols-1) back=FinedVertical.cols - 1;
-        cv::Mat cropped  = FinedVertical.colRange(front,back).clone();
-        return  cropped;
-
-
-    }
-    std::pair<int,int> FitLineRansac(std::vector<cv::Point> pts,int zeroadd = 0 )
-    {
-        std::pair<int,int> res;
-        if(pts.size()>2)
-        {
-            cv::Vec4f line;
-            cv::fitLine(pts,line,cv::DIST_HUBER,0,0.01,0.01);
-            float vx = line[0];
-            float vy = line[1];
-            float x = line[2];
-            float y = line[3];
-            int lefty = static_cast<int>((-x * vy / vx) + y);
-            int righty = static_cast<int>(((136- x) * vy / vx) + y);
-            res.first = lefty+PADDING_UP_DOWN+zeroadd;
-            res.second = righty+PADDING_UP_DOWN+zeroadd;
-            return res;
-        }
-        res.first = zeroadd;
-        res.second = zeroadd;
-        return res;
-    }
-
-    cv::Mat FineMapping::FineMappingVertical(cv::Mat InputProposal,int sliceNum,int upper,int lower,int windows_size){
-
-
-        cv::Mat PreInputProposal;
-        cv::Mat proposal;
-
-        cv::resize(InputProposal,PreInputProposal,cv::Size(FINEMAPPING_W,FINEMAPPING_H));
-//        cv::imwrite("res/cache/finemapping.jpg",PreInputProposal);
-
-        if(InputProposal.channels() == 3)
-            cv::cvtColor(PreInputProposal,proposal,cv::COLOR_BGR2GRAY);
-        else
-            PreInputProposal.copyTo(proposal);
-
-//            proposal = PreInputProposal;
-
-        // this will improve some sen
-        cv::Mat kernal = cv::getStructuringElement(cv::MORPH_ELLIPSE,cv::Size(1,3));
-//        cv::erode(proposal,proposal,kernal);
-
-
-        float diff = static_cast<float>(upper-lower);
-        diff/=static_cast<float>(sliceNum-1);
-        cv::Mat binary_adaptive;
-        std::vector<cv::Point> line_upper;
-        std::vector<cv::Point> line_lower;
-        int contours_nums=0;
-
-        for(int i = 0 ; i < sliceNum ; i++)
-        {
-            std::vector<std::vector<cv::Point> > contours;
-            float k =lower + i*diff;
-            cv::adaptiveThreshold(proposal,binary_adaptive,255,cv::ADAPTIVE_THRESH_MEAN_C,cv::THRESH_BINARY,windows_size,k);
-            cv::Mat draw;
-            binary_adaptive.copyTo(draw);
-            cv::findContours(binary_adaptive,contours,cv::RETR_EXTERNAL,cv::CHAIN_APPROX_SIMPLE);
-            for(auto contour: contours)
-            {
-                cv::Rect bdbox =cv::boundingRect(contour);
-                float lwRatio = bdbox.height/static_cast<float>(bdbox.width);
-                int  bdboxAera = bdbox.width*bdbox.height;
-                if ((   lwRatio>0.7&&bdbox.width*bdbox.height>100 && bdboxAera<300)
-                    || (lwRatio>3.0 && bdboxAera<100 && bdboxAera>10))
-                {
-                    cv::Point p1(bdbox.x, bdbox.y);
-                    cv::Point p2(bdbox.x + bdbox.width, bdbox.y + bdbox.height);
-                    line_upper.push_back(p1);
-                    line_lower.push_back(p2);
-                    contours_nums+=1;
-                }
-            }
-        }
-
-
-        if(contours_nums<41)
-        {
-            cv::bitwise_not(InputProposal,InputProposal);
-            cv::Mat kernal = cv::getStructuringElement(cv::MORPH_ELLIPSE,cv::Size(1,5));
-            cv::Mat bak;
-            cv::resize(InputProposal,bak,cv::Size(FINEMAPPING_W,FINEMAPPING_H));
-            cv::erode(bak,bak,kernal);
-            if(InputProposal.channels() == 3)
-                cv::cvtColor(bak,proposal,cv::COLOR_BGR2GRAY);
-            else
-                proposal = bak;
-            int contours_nums=0;
-
-            for(int i = 0 ; i < sliceNum ; i++)
-            {
-                std::vector<std::vector<cv::Point> > contours;
-                float k =lower + i*diff;
-                cv::adaptiveThreshold(proposal,binary_adaptive,255,cv::ADAPTIVE_THRESH_MEAN_C,cv::THRESH_BINARY,windows_size,k);
-//                cv::imshow("image",binary_adaptive);
-//            cv::waitKey(0);
-                cv::Mat draw;
-                binary_adaptive.copyTo(draw);
-                cv::findContours(binary_adaptive,contours,cv::RETR_EXTERNAL,cv::CHAIN_APPROX_SIMPLE);
-                for(auto contour: contours)
-                {
-                    cv::Rect bdbox =cv::boundingRect(contour);
-                    float lwRatio = bdbox.height/static_cast<float>(bdbox.width);
-                    int  bdboxAera = bdbox.width*bdbox.height;
-                    if ((   lwRatio>0.7&&bdbox.width*bdbox.height>120 && bdboxAera<300)
-                        || (lwRatio>3.0 && bdboxAera<100 && bdboxAera>10))
-                    {
-
-                        cv::Point p1(bdbox.x, bdbox.y);
-                        cv::Point p2(bdbox.x + bdbox.width, bdbox.y + bdbox.height);
-                        line_upper.push_back(p1);
-                        line_lower.push_back(p2);
-                        contours_nums+=1;
-                    }
-                }
-            }
-//            std:: cout<<"contours_nums "<<contours_nums<<std::endl;
-        }
-
-            cv::Mat rgb;
-            cv::copyMakeBorder(PreInputProposal, rgb, PADDING_UP_DOWN, PADDING_UP_DOWN, 0, 0, cv::BORDER_REPLICATE);
-//        cv::imshow("rgb",rgb);
-//        cv::waitKey(0);
-//
-
-
-            std::pair<int, int> A;
-            std::pair<int, int> B;
-            A = FitLineRansac(line_upper, -1);
-            B = FitLineRansac(line_lower, 1);
-            int leftyB = A.first;
-            int rightyB = A.second;
-            int leftyA = B.first;
-            int rightyA = B.second;
-            int cols = rgb.cols;
-            int rows = rgb.rows;
-//        pts_map1  = np.float32([[cols - 1, rightyA], [0, leftyA],[cols - 1, rightyB], [0, leftyB]])
-//        pts_map2 = np.float32([[136,36],[0,36],[136,0],[0,0]])
-//        mat = cv2.getPerspectiveTransform(pts_map1,pts_map2)
-//        image = cv2.warpPerspective(rgb,mat,(136,36),flags=cv2.INTER_CUBIC)
-            std::vector<cv::Point2f> corners(4);
-            corners[0] = cv::Point2f(cols - 1, rightyA);
-            corners[1] = cv::Point2f(0, leftyA);
-            corners[2] = cv::Point2f(cols - 1, rightyB);
-            corners[3] = cv::Point2f(0, leftyB);
-            std::vector<cv::Point2f> corners_trans(4);
-            corners_trans[0] = cv::Point2f(136, 36);
-            corners_trans[1] = cv::Point2f(0, 36);
-            corners_trans[2] = cv::Point2f(136, 0);
-            corners_trans[3] = cv::Point2f(0, 0);
-            cv::Mat transform = cv::getPerspectiveTransform(corners, corners_trans);
-            cv::Mat quad = cv::Mat::zeros(36, 136, CV_8UC3);
-            cv::warpPerspective(rgb, quad, transform, quad.size());
-        return quad;
-
-    }
-
-
-}
-
-
diff --git a/Prj-Android/app/src/main/cpp/src/Pipeline.cpp b/Prj-Android/app/src/main/cpp/src/Pipeline.cpp
deleted file mode 100755
index 6563c86..0000000
--- a/Prj-Android/app/src/main/cpp/src/Pipeline.cpp
+++ /dev/null
@@ -1,95 +0,0 @@
-//
-// Created by 庾金科 on 23/10/2017.
-//
-
-#include "../include/Pipeline.h"
-
-
-namespace pr {
-
-
-
-    const int HorizontalPadding = 4;
-    PipelinePR::PipelinePR(std::string detector_filename,
-                           std::string finemapping_prototxt, std::string finemapping_caffemodel,
-                           std::string segmentation_prototxt, std::string segmentation_caffemodel,
-                           std::string charRecognization_proto, std::string charRecognization_caffemodel,
-                           std::string segmentationfree_proto, std::string segmentationfree_caffemodel) {
-        plateDetection = new PlateDetection(detector_filename);
-        fineMapping = new FineMapping(finemapping_prototxt, finemapping_caffemodel);
-        plateSegmentation = new PlateSegmentation(segmentation_prototxt, segmentation_caffemodel);
-        generalRecognizer = new CNNRecognizer(charRecognization_proto, charRecognization_caffemodel);
-        segmentationFreeRecognizer =  new SegmentationFreeRecognizer(segmentationfree_proto,segmentationfree_caffemodel);
-
-    }
-
-    PipelinePR::~PipelinePR() {
-
-        delete plateDetection;
-        delete fineMapping;
-        delete plateSegmentation;
-        delete generalRecognizer;
-        delete segmentationFreeRecognizer;
-
-
-    }
-
-    std::vector<PlateInfo> PipelinePR:: RunPiplineAsImage(cv::Mat plateImage,int method) {
-        std::vector<PlateInfo> results;
-        std::vector<pr::PlateInfo> plates;
-        plateDetection->plateDetectionRough(plateImage,plates,36,700);
-
-        for (pr::PlateInfo plateinfo:plates) {
-
-            cv::Mat image_finemapping = plateinfo.getPlateImage();
-            image_finemapping = fineMapping->FineMappingVertical(image_finemapping);
-            image_finemapping = pr::fastdeskew(image_finemapping, 5);
-
-
-
-            //Segmentation-based
-
-            if(method==SEGMENTATION_BASED_METHOD)
-            {
-                image_finemapping = fineMapping->FineMappingHorizon(image_finemapping, 2, HorizontalPadding);
-                cv::resize(image_finemapping, image_finemapping, cv::Size(136+HorizontalPadding, 36));
-//            cv::imshow("image_finemapping",image_finemapping);
-//            cv::waitKey(0);
-                plateinfo.setPlateImage(image_finemapping);
-                std::vector<cv::Rect> rects;
-
-                plateSegmentation->segmentPlatePipline(plateinfo, 1, rects);
-                plateSegmentation->ExtractRegions(plateinfo, rects);
-                cv::copyMakeBorder(image_finemapping, image_finemapping, 0, 0, 0, 20, cv::BORDER_REPLICATE);
-                plateinfo.setPlateImage(image_finemapping);
-                generalRecognizer->SegmentBasedSequenceRecognition(plateinfo);
-                plateinfo.decodePlateNormal(pr::CH_PLATE_CODE);
-
-            }
-                //Segmentation-free
-            else if(method==SEGMENTATION_FREE_METHOD)
-            {
-
-                image_finemapping = fineMapping->FineMappingHorizon(image_finemapping, 4, HorizontalPadding+3);
-                cv::resize(image_finemapping, image_finemapping, cv::Size(136+HorizontalPadding, 36));
-                plateinfo.setPlateImage(image_finemapping);
-                std::pair<std::string,float> res = segmentationFreeRecognizer->SegmentationFreeForSinglePlate(plateinfo.getPlateImage(),pr::CH_PLATE_CODE);
-                plateinfo.confidence = res.second;
-                plateinfo.setPlateName(res.first);
-            }
-
-
-
-            results.push_back(plateinfo);
-        }
-
-//        for (auto str:results) {
-//            std::cout << str << std::endl;
-//        }
-        return results;
-
-    }//namespace pr
-
-
-
-}
\ No newline at end of file
diff --git a/Prj-Android/app/src/main/cpp/src/PlateDetection.cpp b/Prj-Android/app/src/main/cpp/src/PlateDetection.cpp
deleted file mode 100755
index 5001e94..0000000
--- a/Prj-Android/app/src/main/cpp/src/PlateDetection.cpp
+++ /dev/null
@@ -1,62 +0,0 @@
-//
-// Created by 庾金科 on 20/09/2017.
-//
-#include "../include/PlateDetection.h"
-
-#include "util.h"
-
-namespace pr{
-
-
-    PlateDetection::PlateDetection(std::string filename_cascade){
-        cascade.load(filename_cascade);
-
-    };
-
-
-    void PlateDetection::plateDetectionRough(cv::Mat InputImage,std::vector<pr::PlateInfo>  &plateInfos,int min_w,int max_w){
-
-        cv::Mat processImage(InputImage);
-//        cv::Mat processImage;
-
-//         cv::cvtColor(InputImage,processImage,cv::COLOR_BGR2GRAY);
-
-
-        std::vector<cv::Rect> platesRegions;
-//        std::vector<PlateInfo> plates;
-        cv::Size minSize(min_w,min_w/4);
-        cv::Size maxSize(max_w,max_w/4);
-//        cv::imshow("input",InputImage);
-//                cv::waitKey(0);
-        cascade.detectMultiScale( processImage, platesRegions,
-                                  1.1, 3, cv::CASCADE_SCALE_IMAGE,minSize,maxSize);
-        for(auto plate:platesRegions)
-        {
-            // extend rects
-//            x -= w * 0.14
-//            w += w * 0.28
-//            y -= h * 0.6
-//            h += h * 1.1;
-            int zeroadd_w  = static_cast<int>(plate.width*0.30);
-            int zeroadd_h = static_cast<int>(plate.height*2);
-            int zeroadd_x = static_cast<int>(plate.width*0.15);
-            int zeroadd_y = static_cast<int>(plate.height*1);
-            plate.x-=zeroadd_x;
-            plate.y-=zeroadd_y;
-            plate.height += zeroadd_h;
-            plate.width += zeroadd_w;
-                cv::Mat plateImage = util::cropFromImage(InputImage,plate);
-            PlateInfo plateInfo(plateImage,plate);
-            plateInfos.push_back(plateInfo);
-
-        }
-    }
-//    std::vector<pr::PlateInfo> PlateDetection::plateDetectionRough(cv::Mat InputImage,cv::Rect roi,int min_w,int max_w){
-//        cv::Mat roi_region = util::cropFromImage(InputImage,roi);
-//        return plateDetectionRough(roi_region,min_w,max_w);
-//    }
-
-
-
-
-}//namespace pr
diff --git a/Prj-Android/app/src/main/cpp/src/PlateSegmentation.cpp b/Prj-Android/app/src/main/cpp/src/PlateSegmentation.cpp
deleted file mode 100755
index 9ac761c..0000000
--- a/Prj-Android/app/src/main/cpp/src/PlateSegmentation.cpp
+++ /dev/null
@@ -1,406 +0,0 @@
-//
-// Created by 庾金科 on 16/10/2017.
-//
-
-#include "../include/PlateSegmentation.h"
-#include "../include/niBlackThreshold.h"
-
-
-//#define DEBUG
-namespace pr{
-
-    PlateSegmentation::PlateSegmentation(std::string prototxt,std::string caffemodel) {
-        net = cv::dnn::readNetFromCaffe(prototxt, caffemodel);
-    }
-    cv::Mat PlateSegmentation::classifyResponse(const cv::Mat &cropped){
-        cv::Mat inputBlob = cv::dnn::blobFromImage(cropped, 1/255.0, cv::Size(22,22), cv::Scalar(0,0,0),false);
-        net.setInput(inputBlob,"data");
-        return net.forward();
-    }
-
-    void drawHist(float* seq,int size,const char* name){
-        cv::Mat image(300,size,CV_8U);
-        image.setTo(0);
-        float* start =seq;
-        float* end = seq+size;
-        float l = *std::max_element(start,end);
-        for(int i = 0;i<size;i++)
-        {
-            int p = int(float(seq[i])/l*300);
-            cv::line(image,cv::Point(i,300),cv::Point(i,300-p),cv::Scalar(255,255,255));
-        }
-        cv::resize(image,image,cv::Size(600,100));
-        cv::imshow(name,image);
-    }
-
-    inline void computeSafeMargin(int &val,const int &rows){
-        val = std::min(val,rows);
-        val = std::max(val,0);
-    }
-
-    cv::Rect boxFromCenter(const cv::Point center,int left,int right,int top,int bottom,cv::Size bdSize)
-    {
-        cv::Point p1(center.x -  left ,center.y - top);
-        cv::Point p2( center.x + right, center.y + bottom);
-        p1.x = std::max(0,p1.x);
-        p1.y = std::max(0,p1.y);
-        p2.x = std::min(p2.x,bdSize.width-1);
-        p2.y = std::min(p2.y,bdSize.height-1);
-        cv::Rect rect(p1,p2);
-        return rect;
-    }
-
-    cv::Rect boxPadding(cv::Rect rect,int left,int right,int top,int bottom,cv::Size bdSize)
-    {
-
-        cv::Point center(rect.x+(rect.width>>1),rect.y + (rect.height>>1));
-        int rebuildLeft  = (rect.width>>1 )+ left;
-        int rebuildRight = (rect.width>>1 )+ right;
-        int rebuildTop = (rect.height>>1 )+ top;
-        int rebuildBottom = (rect.height>>1 )+ bottom;
-        return boxFromCenter(center,rebuildLeft,rebuildRight,rebuildTop,rebuildBottom,bdSize);
-
-    }
-
-
-
-    void PlateSegmentation:: refineRegion(cv::Mat &plateImage,const std::vector<int> &candidatePts,const int padding,std::vector<cv::Rect> &rects){
-        int w = candidatePts[5] - candidatePts[4];
-        int cols = plateImage.cols;
-        int rows = plateImage.rows;
-        for(int i = 0; i < candidatePts.size()  ; i++)
-        {
-            int left = 0;
-            int right = 0 ;
-
-            if(i == 0 ){
-                left= candidatePts[i];
-                right = left+w+padding;
-                }
-            else {
-                left = candidatePts[i] - padding;
-                right = left + w + padding * 2;
-            }
-
-            computeSafeMargin(right,cols);
-            computeSafeMargin(left,cols);
-            cv::Mat roiImage;
-//            plateImage.copyTo(roiImage);
-            cv::Rect roi(left,0,right-left,rows-1);
-            plateImage(roi).copyTo(roiImage);
-
-            if (i>=1)
-            {
-
-                cv::Mat roi_thres;
-//                cv::threshold(roiImage,roi_thres,0,255,cv::THRESH_OTSU|cv::THRESH_BINARY);
-
-                niBlackThreshold(roiImage,roi_thres,255,cv::THRESH_BINARY,15,0.27,BINARIZATION_NIBLACK);
-
-                std::vector<std::vector<cv::Point>> contours;
-                cv::findContours(roi_thres,contours,cv::RETR_LIST,cv::CHAIN_APPROX_SIMPLE);
-                cv::Point boxCenter(roiImage.cols>>1,roiImage.rows>>1);
-
-                cv::Rect final_bdbox;
-                cv::Point final_center;
-                int final_dist = INT_MAX;
-
-
-                for(auto contour:contours)
-                {
-                    cv::Rect bdbox = cv::boundingRect(contour);
-                    cv::Point center(bdbox.x+(bdbox.width>>1),bdbox.y + (bdbox.height>>1));
-                    int dist = (center.x - boxCenter.x)*(center.x - boxCenter.x);
-                    if(dist<final_dist and  bdbox.height > rows>>1)
-                    {   final_dist =dist;
-                        final_center = center;
-                        final_bdbox = bdbox;
-                    }
-                }
-
-                //rebuild box
-                if(final_bdbox.height/ static_cast<float>(final_bdbox.width) > 3.5 && final_bdbox.width*final_bdbox.height<10)
-                    final_bdbox = boxFromCenter(final_center,8,8,(rows>>1)-3 , (rows>>1) - 2,roiImage.size());
-                else {
-                    if(i == candidatePts.size()-1)
-                        final_bdbox = boxPadding(final_bdbox, padding/2, padding, padding/2, padding/2, roiImage.size());
-                    else
-                        final_bdbox = boxPadding(final_bdbox, padding, padding, padding, padding, roiImage.size());
-
-
-//                    std::cout<<final_bdbox<<std::endl;
-//                    std::cout<<roiImage.size()<<std::endl;
-#ifdef DEBUG
-                    cv::imshow("char_thres",roi_thres);
-
-                    cv::imshow("char",roiImage(final_bdbox));
-                    cv::waitKey(0);
-#endif
-
-
-                }
-
-
-                final_bdbox.x += left;
-
-                rects.push_back(final_bdbox);
-//
-
-            }
-            else
-            {
-                rects.push_back(roi);
-            }
-
-//            else
-//            {
-//
-//            }
-
-//            cv::GaussianBlur(roiImage,roiImage,cv::Size(7,7),3);
-//
-//            cv::imshow("image",roiImage);
-//            cv::waitKey(0);
-
-
-        }
-
-
-
-    }
-    void avgfilter(float *angle_list,int size,int windowsSize) {
-        float *filterd = new float[size];
-        for(int i = 0 ; i < size ; i++) filterd [i] = angle_list[i];
-//        memcpy(filterd,angle_list,size);
-
-        cv::Mat kernal_gaussian = cv::getGaussianKernel(windowsSize,3,CV_32F);
-        float *kernal = (float*)kernal_gaussian.data;
-//        kernal+=windowsSize;
-        int r = windowsSize/2;
-
-
-
-
-        for (int i = 0; i < size; i++) {
-            float avg = 0.00f;
-            for (int j = 0; j < windowsSize; j++) {
-                if(i+j-r>0&&i+j+r<size-1)
-                    avg += filterd[i + j-r]*kernal[j];
-            }
-//            avg = avg / windowsSize;
-            angle_list[i] = avg;
-
-        }
-
-        delete[] filterd;
-//        delete filterd;
-    }
-
-    void PlateSegmentation::templateMatchFinding(const cv::Mat &respones,int windowsWidth,std::pair<float,std::vector<int>> &candidatePts){
-        int rows = respones.rows;
-        int cols = respones.cols;
-
-
-
-        float *data = (float*)respones.data;
-        float *engNum_prob = data;
-        float *false_prob = data+cols;
-        float *ch_prob = data+cols*2;
-
-        avgfilter(engNum_prob,cols,5);
-        avgfilter(false_prob,cols,5);
-//        avgfilter(ch_prob,cols,5);
-        std::vector<int> candidate_pts(7);
-#ifdef DEBUG
-        drawHist(engNum_prob,cols,"engNum_prob");
-        drawHist(false_prob,cols,"false_prob");
-        drawHist(ch_prob,cols,"ch_prob");
-                cv::waitKey(0);
-#endif
-
-
-
-
-        int cp_list[7];
-        float loss_selected = -10;
-
-        for(int start = 0 ; start < 20 ; start+=2)
-            for(int  width = windowsWidth-5; width < windowsWidth+5 ; width++ ){
-                for(int interval = windowsWidth/2; interval < windowsWidth; interval++)
-                {
-                    int cp1_ch  = start;
-                    int cp2_p0 = cp1_ch+ width;
-                    int cp3_p1 = cp2_p0+ width + interval;
-                    int cp4_p2 = cp3_p1 + width;
-                    int cp5_p3 = cp4_p2 + width+1;
-                    int cp6_p4 = cp5_p3 + width+2;
-                    int cp7_p5= cp6_p4+ width+2;
-
-                    int md1 = (cp1_ch+cp2_p0)>>1;
-                    int md2 = (cp2_p0+cp3_p1)>>1;
-                    int md3 = (cp3_p1+cp4_p2)>>1;
-                    int md4 = (cp4_p2+cp5_p3)>>1;
-                    int md5 = (cp5_p3+cp6_p4)>>1;
-                    int md6 = (cp6_p4+cp7_p5)>>1;
-
-
-
-
-                    if(cp7_p5>=cols)
-                        continue;
-//                    float loss = ch_prob[cp1_ch]+
-//                       engNum_prob[cp2_p0] +engNum_prob[cp3_p1]+engNum_prob[cp4_p2]+engNum_prob[cp5_p3]+engNum_prob[cp6_p4] +engNum_prob[cp7_p5]
-//                    + (false_prob[md2]+false_prob[md3]+false_prob[md4]+false_prob[md5]+false_prob[md5] + false_prob[md6]);
-                    float loss = ch_prob[cp1_ch]*3 -(false_prob[cp3_p1]+false_prob[cp4_p2]+false_prob[cp5_p3]+false_prob[cp6_p4]+false_prob[cp7_p5]);
-
-                    if(loss>loss_selected)
-                    {
-                        loss_selected = loss;
-                        cp_list[0]= cp1_ch;
-                        cp_list[1]= cp2_p0;
-                        cp_list[2]= cp3_p1;
-                        cp_list[3]= cp4_p2;
-                        cp_list[4]= cp5_p3;
-                        cp_list[5]= cp6_p4;
-                        cp_list[6]= cp7_p5;
-                    }
-                }
-            }
-        candidate_pts[0] = cp_list[0];
-        candidate_pts[1] = cp_list[1];
-        candidate_pts[2] = cp_list[2];
-        candidate_pts[3] = cp_list[3];
-        candidate_pts[4] = cp_list[4];
-        candidate_pts[5] = cp_list[5];
-        candidate_pts[6] = cp_list[6];
-
-        candidatePts.first = loss_selected;
-        candidatePts.second = candidate_pts;
-
-    };
-
-
-    void PlateSegmentation::segmentPlateBySlidingWindows(cv::Mat &plateImage,int windowsWidth,int stride,cv::Mat &respones){
-
-
-//        cv::resize(plateImage,plateImage,cv::Size(136,36));
-
-        cv::Mat plateImageGray;
-        cv::cvtColor(plateImage,plateImageGray,cv::COLOR_BGR2GRAY);
-        int padding  =  plateImage.cols-136 ;
-//        int padding  =  0 ;
-        int height = plateImage.rows - 1;
-        int width = plateImage.cols - 1 - padding;
-        for(int i = 0 ; i < width - windowsWidth +1 ; i +=stride)
-        {
-            cv::Rect roi(i,0,windowsWidth,height);
-            cv::Mat roiImage = plateImageGray(roi);
-            cv::Mat response = classifyResponse(roiImage);
-            respones.push_back(response);
-        }
-
-
-
-
-        respones =  respones.t();
-//        std::pair<float,std::vector<int>> images ;
-//
-//
-//        std::cout<<images.first<<" ";
-//        for(int i = 0 ; i < images.second.size() ; i++)
-//        {
-//            std::cout<<images.second[i]<<" ";
-////            cv::line(plateImageGray,cv::Point(images.second[i],0),cv::Point(images.second[i],36),cv::Scalar(255,255,255),1); //DEBUG
-//        }
-
-//        int w = images.second[5] - images.second[4];
-
-//        cv::line(plateImageGray,cv::Point(images.second[5]+w,0),cv::Point(images.second[5]+w,36),cv::Scalar(255,255,255),1); //DEBUG
-//        cv::line(plateImageGray,cv::Point(images.second[5]+2*w,0),cv::Point(images.second[5]+2*w,36),cv::Scalar(255,255,255),1); //DEBUG
-
-
-//        RefineRegion(plateImageGray,images.second,5);
-
-//        std::cout<<w<<std::endl;
-
-//        std::cout<<<<std::endl;
-
-//        cv::resize(plateImageGray,plateImageGray,cv::Size(600,100));
-
-
-
-    }
-
-//    void filterGaussian(cv::Mat &respones,float sigma){
-//
-//    }
-
-
-    void PlateSegmentation::segmentPlatePipline(PlateInfo &plateInfo,int stride,std::vector<cv::Rect> &Char_rects){
-        cv::Mat plateImage = plateInfo.getPlateImage(); // get src image .
-        cv::Mat plateImageGray;
-        cv::cvtColor(plateImage,plateImageGray,cv::COLOR_BGR2GRAY);
-        //do binarzation
-        //
-        std::pair<float,std::vector<int>> sections ; // segment points variables .
-
-        cv::Mat respones; //three response of every sub region from origin image .
-        segmentPlateBySlidingWindows(plateImage,DEFAULT_WIDTH,1,respones);
-        templateMatchFinding(respones,DEFAULT_WIDTH/stride,sections);
-        for(int i = 0; i < sections.second.size() ; i++)
-        {
-            sections.second[i]*=stride;
-
-        }
-
-//        std::cout<<sections<<std::endl;
-
-        refineRegion(plateImageGray,sections.second,5,Char_rects);
-#ifdef DEBUG
-        for(int i = 0 ; i < sections.second.size() ; i++)
-        {
-            std::cout<<sections.second[i]<<" ";
-            cv::line(plateImageGray,cv::Point(sections.second[i],0),cv::Point(sections.second[i],36),cv::Scalar(255,255,255),1); //DEBUG
-        }
-        cv::imshow("plate",plateImageGray);
-        cv::waitKey(0);
-#endif
-//        cv::waitKey(0);
-
-    }
-
-    void PlateSegmentation::ExtractRegions(PlateInfo &plateInfo,std::vector<cv::Rect> &rects){
-        cv::Mat plateImage = plateInfo.getPlateImage();
-        for(int i = 0 ; i < rects.size() ; i++){
-            cv::Mat charImage;
-            plateImage(rects[i]).copyTo(charImage);
-            if(charImage.channels())
-                cv::cvtColor(charImage,charImage,cv::COLOR_BGR2GRAY);
-//            cv::imshow("image",charImage);
-//            cv::waitKey(0);
-            cv::equalizeHist(charImage,charImage);
-//
-
-//
-
-
-            std::pair<CharType,cv::Mat> char_instance;
-            if(i == 0 ){
-
-                char_instance.first = CHINESE;
-
-
-            } else if(i == 1){
-                char_instance.first = LETTER;
-            }
-            else{
-                char_instance.first = LETTER_NUMS;
-            }
-            char_instance.second = charImage;
-            plateInfo.appendPlateChar(char_instance);
-
-        }
-
-    }
-
-}//namespace pr
diff --git a/Prj-Android/app/src/main/cpp/src/Recognizer.cpp b/Prj-Android/app/src/main/cpp/src/Recognizer.cpp
deleted file mode 100755
index 9a1d520..0000000
--- a/Prj-Android/app/src/main/cpp/src/Recognizer.cpp
+++ /dev/null
@@ -1,29 +0,0 @@
-//
-// Created by 庾金科 on 22/10/2017.
-//
-
-#include "../include/Recognizer.h"
-
-namespace pr{
-    void GeneralRecognizer::SegmentBasedSequenceRecognition(PlateInfo &plateinfo){
-        for(auto char_instance:plateinfo.plateChars)
-        {
-            std::pair<CharType,cv::Mat> res;
-            if(char_instance.second.rows*char_instance.second.cols>40) {
-                label code_table = recognizeCharacter(char_instance.second);
-                res.first = char_instance.first;
-                code_table.copyTo(res.second);
-                plateinfo.appendPlateCoding(res);
-            } else{
-                res.first = INVALID;
-                plateinfo.appendPlateCoding(res);
-
-            }
-
-
-        }
-
-
-
-    }
-}
\ No newline at end of file
diff --git a/Prj-Android/app/src/main/cpp/src/SegmentationFreeRecognizer.cpp b/Prj-Android/app/src/main/cpp/src/SegmentationFreeRecognizer.cpp
deleted file mode 100755
index 1877180..0000000
--- a/Prj-Android/app/src/main/cpp/src/SegmentationFreeRecognizer.cpp
+++ /dev/null
@@ -1,119 +0,0 @@
-//
-// Created by 庾金科 on 28/11/2017.
-//
-#include "../include/SegmentationFreeRecognizer.h"
-
-namespace pr {
-    SegmentationFreeRecognizer::SegmentationFreeRecognizer(std::string prototxt, std::string caffemodel) {
-        net = cv::dnn::readNetFromCaffe(prototxt, caffemodel);
-    }
-
-
-    inline int judgeCharRange(int id)
-    {return id<31 || id>63;
-    }
-
-
-    std::pair<std::string,float> decodeResults(cv::Mat code_table,std::vector<std::string> mapping_table,float thres)
-    {
-//        cv::imshow("imagea",code_table);
-//        cv::waitKey(0);
-
-        cv::MatSize mtsize = code_table.size;
-        int sequencelength = mtsize[2];
-        int labellength = mtsize[1];
-        cv::transpose(code_table.reshape(1,1).reshape(1,labellength),code_table);
-        std::string name = "";
-
-
-        std::vector<int> seq(sequencelength);
-        std::vector<std::pair<int,float>> seq_decode_res;
-
-        for(int i = 0 ; i < sequencelength;  i++) {
-            float *fstart = ((float *) (code_table.data) + i * labellength );
-            int id = std::max_element(fstart,fstart+labellength) - fstart;
-            seq[i] =id;
-        }
-
-        float sum_confidence = 0;
-
-        int plate_lenghth  = 0 ;
-
-
-        for(int i = 0 ; i< sequencelength ; i++)
-        {
-            if(seq[i]!=labellength-1 && (i==0 || seq[i]!=seq[i-1]))
-            {
-                float *fstart = ((float *) (code_table.data) + i * labellength );
-                float confidence = *(fstart+seq[i]);
-                std::pair<int,float> pair_(seq[i],confidence);
-                seq_decode_res.push_back(pair_);
-//
-
-            }
-        }
-
-        int  i = 0;
-
-        if(seq_decode_res.size()>1&&judgeCharRange(seq_decode_res[0].first) && judgeCharRange(seq_decode_res[1].first))
-        {
-            i=2;
-            int c = seq_decode_res[0].second<seq_decode_res[1].second;
-            name+=mapping_table[seq_decode_res[c].first];
-            sum_confidence+=seq_decode_res[c].second;
-            plate_lenghth++;
-        }
-
-        for(; i < seq_decode_res.size();i++)
-        {
-            name+=mapping_table[seq_decode_res[i].first];
-            sum_confidence +=seq_decode_res[i].second;
-            plate_lenghth++;
-        }
-
-
-        std::pair<std::string,float> res;
-
-        res.second = sum_confidence/plate_lenghth;
-        res.first = name;
-        return res;
-
-    }
-
-
-    std::string decodeResults(cv::Mat code_table,std::vector<std::string> mapping_table)
-    {
-        cv::MatSize mtsize = code_table.size;
-        int sequencelength = mtsize[2];
-        int labellength = mtsize[1];
-        cv::transpose(code_table.reshape(1,1).reshape(1,labellength),code_table);
-        std::string name = "";
-        std::vector<int> seq(sequencelength);
-        for(int i = 0 ; i < sequencelength;  i++) {
-            float *fstart = ((float *) (code_table.data) + i * labellength );
-            int id = std::max_element(fstart,fstart+labellength) - fstart;
-            seq[i] =id;
-        }
-        for(int i = 0 ; i< sequencelength ; i++)
-        {
-            if(seq[i]!=labellength-1 && (i==0 || seq[i]!=seq[i-1]))
-                name+=mapping_table[seq[i]];
-        }
-
-        std::cout<<name;
-        return name;
-    }
-
-
-
-    std::pair<std::string,float> SegmentationFreeRecognizer::SegmentationFreeForSinglePlate(cv::Mat Image,std::vector<std::string> mapping_table) {
-        cv::transpose(Image,Image);
-        cv::Mat inputBlob = cv::dnn::blobFromImage(Image, 1 / 255.0, cv::Size(40,160));
-        net.setInput(inputBlob, "data");
-        cv::Mat char_prob_mat = net.forward();
-        return decodeResults(char_prob_mat,mapping_table,0.00);
-
-    }
-
-
-}
diff --git a/Prj-Android/app/src/main/cpp/src/util.h b/Prj-Android/app/src/main/cpp/src/util.h
deleted file mode 100755
index 76a452f..0000000
--- a/Prj-Android/app/src/main/cpp/src/util.h
+++ /dev/null
@@ -1,79 +0,0 @@
-//
-// Created by 庾金科 on 04/04/2017.
-//
-
-#include <opencv2/opencv.hpp>
-
-namespace util{
-
-    template <class T> void swap ( T& a, T& b )
-    {
-        T c(a); a=b; b=c;
-    }
-
-    template <class T> T min(T& a,T& b )
-    {
-        return a>b?b:a;
-
-    }
-
-    cv::Mat cropFromImage(const cv::Mat &image,cv::Rect rect){
-        int w = image.cols-1;
-        int h = image.rows-1;
-        rect.x = std::max(rect.x,0);
-        rect.y = std::max(rect.y,0);
-        rect.height = std::min(rect.height,h-rect.y);
-        rect.width = std::min(rect.width,w-rect.x);
-        cv::Mat temp(rect.size(), image.type());
-        cv::Mat cropped;
-        temp = image(rect);
-        temp.copyTo(cropped);
-        return cropped;
-
-    }
-
-    cv::Mat cropBox2dFromImage(const cv::Mat &image,cv::RotatedRect rect)
-    {
-        cv::Mat M, rotated, cropped;
-        float angle = rect.angle;
-        cv::Size rect_size(rect.size.width,rect.size.height);
-        if (rect.angle < -45.) {
-            angle += 90.0;
-            swap(rect_size.width, rect_size.height);
-        }
-        M = cv::getRotationMatrix2D(rect.center, angle, 1.0);
-        cv::warpAffine(image, rotated, M, image.size(), cv::INTER_CUBIC);
-        cv::getRectSubPix(rotated, rect_size, rect.center, cropped);
-        return cropped;
-    }
-
-    cv::Mat calcHist(const cv::Mat &image)
-    {
-        cv::Mat hsv;
-        std::vector<cv::Mat> hsv_planes;
-        cv::cvtColor(image,hsv,cv::COLOR_BGR2HSV);
-        cv::split(hsv,hsv_planes);
-        cv::Mat hist;
-        int histSize = 256;
-        float range[] = {0,255};
-        const float* histRange = {range};
-
-        cv::calcHist( &hsv_planes[0], 1, 0, cv::Mat(), hist, 1, &histSize, &histRange,true, true);
-        return hist;
-
-    }
-    float computeSimilir(const cv::Mat &A,const cv::Mat &B)
-    {
-
-        cv::Mat histA,histB;
-        histA = calcHist(A);
-        histB = calcHist(B);
-        return cv::compareHist(histA,histB,CV_COMP_CORREL);
-
-    }
-
-
-
-
-
-}//namespace util
diff --git a/Prj-Android/app/src/main/java/pr/platerecognization/CameraActivity.java b/Prj-Android/app/src/main/java/com/hyperai/hyperlpr_sdk_demo/CameraActivity.java
similarity index 58%
rename from Prj-Android/app/src/main/java/pr/platerecognization/CameraActivity.java
rename to Prj-Android/app/src/main/java/com/hyperai/hyperlpr_sdk_demo/CameraActivity.java
index 64986b3..08e8157 100755
--- a/Prj-Android/app/src/main/java/pr/platerecognization/CameraActivity.java
+++ b/Prj-Android/app/src/main/java/com/hyperai/hyperlpr_sdk_demo/CameraActivity.java
@@ -1,30 +1,39 @@
-package pr.platerecognization;
+package com.hyperai.hyperlpr_sdk_demo;
 
+import android.annotation.SuppressLint;
 import android.app.Activity;
-import android.media.Image;
 import android.os.Bundle;
 import android.view.View;
 import android.view.Window;
 import android.view.WindowManager;
+import android.widget.ArrayAdapter;
 import android.widget.FrameLayout;
 import android.widget.ImageView;
+import android.widget.ListView;
 import android.widget.TextView;
 
+
+import com.hyperai.hyperlpr3.HyperLPR3;
+import com.hyperai.hyperlpr3.bean.Plate;
+
 import org.greenrobot.eventbus.EventBus;
 import org.greenrobot.eventbus.Subscribe;
 import org.greenrobot.eventbus.ThreadMode;
 
+import java.util.ArrayList;
+import java.util.List;
+
 
 /**
  * @author by hs-johnny
  * Created on 2019/6/17
  */
-public class CameraActivity extends Activity implements View.OnClickListener {
+public class CameraActivity extends Activity {
 
     FrameLayout previewFl;
     CameraPreviews cameraPreview;
     TextView plateTv;
-    TextView regTv;
+//    TextView regTv;
     ImageView image;
 
     @Override
@@ -36,11 +45,11 @@ public class CameraActivity extends Activity implements View.OnClickListener {
         setContentView(R.layout.activity_camera);
     }
 
-    private void initCamera(){
+    private void initCamera() {
         previewFl = findViewById(R.id.preview_fl);
         plateTv = findViewById(R.id.plate_tv);
-        regTv = findViewById(R.id.reg_tv);
-        regTv.setOnClickListener(this);
+//        regTv = findViewById(R.id.reg_tv);
+//        regTv.setOnClickListener(this);
         image = findViewById(R.id.image);
         cameraPreview = new CameraPreviews(this);
         previewFl.addView(cameraPreview);
@@ -49,7 +58,7 @@ public class CameraActivity extends Activity implements View.OnClickListener {
     @Override
     protected void onResume() {
         super.onResume();
-        if(cameraPreview == null){
+        if (cameraPreview == null) {
             initCamera();
         }
     }
@@ -60,7 +69,7 @@ public class CameraActivity extends Activity implements View.OnClickListener {
         cameraPreview = null;
     }
 
-    private void stopPreview(){
+    private void stopPreview() {
         previewFl.removeAllViews();
     }
 
@@ -76,16 +85,28 @@ public class CameraActivity extends Activity implements View.OnClickListener {
         EventBus.getDefault().unregister(this);
     }
 
+    @SuppressLint("SetTextI18n")
     @Subscribe(threadMode = ThreadMode.MAIN)
-    public void onMessageEvent(PlateInfo plate){
-        plateTv.setText(plate.plateName);
-        image.setImageBitmap(plate.bitmap);
-        stopPreview();
-    }
+    public void onMessageEvent(Plate[] plates) {
 
-    @Override
-    public void onClick(View v) {
-        CameraPreviews cameraPreview = new CameraPreviews(this);
-        previewFl.addView(cameraPreview);
+        String showText = "";
+        for (Plate plate: plates) {
+            String type = "未知车牌";
+            if (plate.getType() != HyperLPR3.PLATE_TYPE_UNKNOWN) {
+                type = HyperLPR3.PLATE_TYPE_MAPS[plate.getType()];
+            }
+            String pStr = "[" + type + "]" + plate.getCode() + "\n";
+            showText += pStr;
+            plateTv.setText(showText);
+
+        }
+
+//        stopPreview();
     }
+
+//    @Override
+//    public void onClick(View v) {
+//        CameraPreviews cameraPreview = new CameraPreviews(this);
+//        previewFl.addView(cameraPreview);
+//    }
 }
diff --git a/Prj-Android/app/src/main/java/pr/platerecognization/CameraPreviews.java b/Prj-Android/app/src/main/java/com/hyperai/hyperlpr_sdk_demo/CameraPreviews.java
similarity index 73%
rename from Prj-Android/app/src/main/java/pr/platerecognization/CameraPreviews.java
rename to Prj-Android/app/src/main/java/com/hyperai/hyperlpr_sdk_demo/CameraPreviews.java
index 310867a..217722c 100755
--- a/Prj-Android/app/src/main/java/pr/platerecognization/CameraPreviews.java
+++ b/Prj-Android/app/src/main/java/com/hyperai/hyperlpr_sdk_demo/CameraPreviews.java
@@ -1,45 +1,31 @@
-package pr.platerecognization;
+package com.hyperai.hyperlpr_sdk_demo;
 
 import android.content.Context;
 import android.graphics.Bitmap;
-import android.graphics.BitmapFactory;
-import android.graphics.Canvas;
-import android.graphics.Color;
-import android.graphics.ImageFormat;
 import android.graphics.Matrix;
 import android.graphics.Paint;
-import android.graphics.PorterDuff;
-import android.graphics.Rect;
-import android.graphics.YuvImage;
 import android.hardware.Camera;
-import android.media.Image;
 import android.os.Handler;
 import android.os.HandlerThread;
-import android.support.v4.content.ContextCompat;
-import android.text.TextUtils;
+//import android.support.v4.content.ContextCompat;
 import android.util.Log;
 import android.view.Display;
 import android.view.MotionEvent;
 import android.view.Surface;
 import android.view.SurfaceHolder;
 import android.view.SurfaceView;
-import android.view.View;
 import android.view.WindowManager;
-import android.widget.ImageView;
+
 
 import org.greenrobot.eventbus.EventBus;
-import org.opencv.core.Mat;
 
-import java.io.ByteArrayOutputStream;
 import java.io.IOException;
-import java.security.Policy;
 import java.util.ArrayList;
-import java.util.HashMap;
-import java.util.HashSet;
-import java.util.Iterator;
 import java.util.List;
-import java.util.Map;
-import java.util.Set;
+
+import com.hyperai.hyperlpr3.HyperLPR3;
+import com.hyperai.hyperlpr3.bean.Parameter;
+import com.hyperai.hyperlpr3.bean.Plate;
 
 /**
  * @author by hs-johnny
@@ -52,21 +38,27 @@ public class CameraPreviews extends SurfaceView implements SurfaceHolder.Callbac
     private SurfaceHolder mHolder;
     public long handle;
     private byte[] lock = new byte[0];
-    private List<String> mResultList = new ArrayList<>();
-    private String currentPlate = "";
     private Paint mPaint;
     private float oldDist = 1f;
     /** 停止识别*/
     private boolean isStopReg;
 
+    private Context mContext;
+
+    HyperLPR3 hyperLPR3;
+
     public CameraPreviews(Context context) {
         super(context);
+        mContext = context;
         mHolder = getHolder();
         mHolder.addCallback(this);
         mPaint = new Paint(Paint.ANTI_ALIAS_FLAG);
         mPaint.setStrokeWidth(2);
         mPaint.setStyle(Paint.Style.STROKE);
-        mPaint.setColor(ContextCompat.getColor(context, R.color.colorAccent));
+//        mPaint.setColor(ContextCompat.getColor(context, R.color.colorAccent));
+
+        Parameter parameter = new Parameter();
+        hyperLPR3 = new HyperLPR3(mContext, parameter);
     }
     public Camera getCameraInstance(){
         if (mCamera == null){
@@ -101,6 +93,7 @@ public class CameraPreviews extends SurfaceView implements SurfaceHolder.Callbac
         mCamera.setDisplayOrientation(rotation);
         Camera.Parameters parameters = mCamera.getParameters();
         parameters.setRotation(rotation);
+//        parameters.setPreviewFormat(ImageFormat.NV21);
         mCamera.setParameters(parameters);
     }
 
@@ -140,43 +133,29 @@ public class CameraPreviews extends SurfaceView implements SurfaceHolder.Callbac
     @Override
     public void onPreviewFrame(final byte[] data, final Camera camera) {
         synchronized (lock){
-            //处理data
+            // 处理data
             Camera.Size previewSize = camera.getParameters().getPreviewSize();
-            BitmapFactory.Options newOpts = new BitmapFactory.Options();
-            newOpts.inJustDecodeBounds = true;
-            YuvImage yuvimage = new YuvImage(
-                    data,
-                    ImageFormat.NV21,
-                    previewSize.width,
-                    previewSize.height,
-                    null);
-            ByteArrayOutputStream baos = new ByteArrayOutputStream();
-            yuvimage.compressToJpeg(new Rect(0, 0, previewSize.width, previewSize.height), 100, baos);
-            byte[] rawImage = baos.toByteArray();
-            //将rawImage转换成bitmap
-            BitmapFactory.Options options = new BitmapFactory.Options();
-            options.inPreferredConfig = Bitmap.Config.RGB_565;
-            Bitmap bitmap = BitmapFactory.decodeByteArray(rawImage, 0, rawImage.length, options);
-            PlateInfo result = MainActivity.simpleRecog(rotateBitmap(bitmap), 8);
-            bitmap.recycle();
-            Log.e(TAG, "onPreviewFrame: "+result.plateName + "----time: "+ System.currentTimeMillis());
-            if(!isStopReg && result != null && !TextUtils.isEmpty(result.plateName)) {
-                isStopReg = true;
-                sendPlate(result);
+
+//            String save_path = mContext.getExternalFilesDir(null).getAbsolutePath() + "/" + "a.jpg";
+//            core.testBuffer(save_path, data, previewSize.height, previewSize.width, 3);
+            Plate[] plates = hyperLPR3.plateRecognition(data, previewSize.height, previewSize.width, HyperLPR3.CAMERA_ROTATION_270, HyperLPR3.STREAM_YUV_NV21);
+            for (Plate plate : plates) {
+                Log.i(TAG,  "" + plate.toString());
             }
+
+            if(!isStopReg && plates.length > 0) {
+
+//                isStopReg = true;
+                sendPlate(plates);
+            }
+
         }
     }
 
-    private void sendPlate(PlateInfo plate){
-        EventBus.getDefault().post(plate);
+    private void sendPlate(Plate[] plates){
+        EventBus.getDefault().post(plates);
     }
 
-    private Bitmap rotateBitmap(Bitmap bmp){
-        Matrix matrix = new Matrix();
-        matrix.postRotate(90);
-        Bitmap rotatedBitMap = Bitmap.createBitmap(bmp, 0, 0, bmp.getWidth(), bmp.getHeight(), matrix, true);
-        return rotatedBitMap;
-    }
 
     private void openCameraOriginal(){
         try {
diff --git a/Prj-Android/app/src/main/java/com/hyperai/hyperlpr_sdk_demo/MainActivity.java b/Prj-Android/app/src/main/java/com/hyperai/hyperlpr_sdk_demo/MainActivity.java
new file mode 100644
index 0000000..e959245
--- /dev/null
+++ b/Prj-Android/app/src/main/java/com/hyperai/hyperlpr_sdk_demo/MainActivity.java
@@ -0,0 +1,68 @@
+package com.hyperai.hyperlpr_sdk_demo;
+
+import androidx.appcompat.app.AppCompatActivity;
+import androidx.core.app.ActivityCompat;
+
+import android.app.Activity;
+import android.content.Intent;
+import android.content.pm.PackageManager;
+import android.os.Bundle;
+import android.view.View;
+import android.widget.Button;
+
+
+public class MainActivity extends AppCompatActivity {
+
+    private Button cameraBtn;
+    private Button albumBtn;
+
+
+
+    private static final int REQUEST_EXTERNAL_STORAGE = 1;
+    private static final String[] PERMISSIONS_STORAGE = {
+            "android.permission.READ_EXTERNAL_STORAGE",
+            "android.permission.WRITE_EXTERNAL_STORAGE",
+            "android.permission.CAMERA"};
+
+    public static void verifyStoragePermissions(Activity activity) {
+
+        try {
+            //检测是否有写的权限
+            int permission = ActivityCompat.checkSelfPermission(activity,
+                    "android.permission.WRITE_EXTERNAL_STORAGE");
+            ActivityCompat.requestPermissions(activity, PERMISSIONS_STORAGE, REQUEST_EXTERNAL_STORAGE);
+
+            if (permission != PackageManager.PERMISSION_GRANTED) {
+                // 没有写的权限，去申请写的权限，会弹出对话框
+                ActivityCompat.requestPermissions(activity, PERMISSIONS_STORAGE, REQUEST_EXTERNAL_STORAGE);
+            }
+        } catch (Exception e) {
+            e.printStackTrace();
+        }
+    }
+
+    @Override
+    protected void onCreate(Bundle savedInstanceState) {
+        super.onCreate(savedInstanceState);
+        setContentView(R.layout.activity_main);
+        cameraBtn = findViewById(R.id.cameraBtn);
+        albumBtn = findViewById(R.id.albumBtn);
+
+        verifyStoragePermissions(this);
+        cameraBtn.setOnClickListener(new View.OnClickListener() {
+            @Override
+            public void onClick(View view) {
+                Intent intent = new Intent(MainActivity.this, CameraActivity.class);
+                MainActivity.this.startActivity(intent);
+            }
+        });
+
+        albumBtn.setOnClickListener(new View.OnClickListener() {
+            @Override
+            public void onClick(View view) {
+
+            }
+        });
+
+    }
+}
\ No newline at end of file
diff --git a/Prj-Android/app/src/main/java/pr/platerecognization/MainActivity.java b/Prj-Android/app/src/main/java/pr/platerecognization/MainActivity.java
deleted file mode 100755
index 36f78cb..0000000
--- a/Prj-Android/app/src/main/java/pr/platerecognization/MainActivity.java
+++ /dev/null
@@ -1,425 +0,0 @@
-package pr.platerecognization;
-
-import android.Manifest;
-import android.app.Activity;
-import android.app.AlertDialog;
-import android.content.ContentValues;
-import android.content.Context;
-import android.content.DialogInterface;
-import android.content.Intent;
-import android.content.pm.PackageManager;
-import android.database.Cursor;
-import android.graphics.Bitmap;
-import android.graphics.BitmapFactory;
-import android.graphics.Matrix;
-import android.media.ExifInterface;
-import android.net.Uri;
-import android.os.Build;
-import android.os.Environment;
-import android.provider.DocumentsContract;
-import android.provider.MediaStore;
-import android.support.v4.app.ActivityCompat;
-import android.os.Bundle;
-import android.util.Log;
-import android.view.View;
-import android.widget.Button;
-import android.widget.ImageView;
-import android.widget.SeekBar;
-import android.widget.TextView;
-
-import org.opencv.android.OpenCVLoader;
-import org.opencv.android.Utils;
-import org.opencv.core.CvType;
-import org.opencv.core.Mat;
-import org.opencv.core.Size;
-import org.opencv.imgproc.Imgproc;
-
-import java.io.File;
-import java.io.FileOutputStream;
-import java.io.InputStream;
-
-public class MainActivity extends Activity implements AlertDialog.OnClickListener,View.OnClickListener{
-
-    // Used to load the 'native-lib' library on application startup.
-    static {
-
-        if(OpenCVLoader.initDebug())
-        {
-            Log.d("Opencv","opencv load_success");
-
-        }
-        else
-        {
-            Log.d("Opencv","opencv can't load opencv .");
-
-        }
-    }
-
-    public Button btn;
-    public Button recogBtn;
-    public TextView resbox;
-    public TextView runtimebox;
-
-    public ImageView imgv;
-    public SeekBar sb;
-
-    private static final int REQUEST_CODE_IMAGE_CAMERA = 1;
-    private static final int REQUEST_CODE_IMAGE_OP = 2;
-    private static final int REQUEST_CODE_OP = 3;
-    private Uri mPath;
-
-    Bitmap latestBitmap;
-
-//    public  PlateRecognition pr;
-
-    public static long handle;
-    private final String TAG = this.getClass().toString();
-
-
-
-    public void copyFilesFromAssets(Context context, String oldPath, String newPath) {
-        try {
-            String[] fileNames = context.getAssets().list(oldPath);
-            if (fileNames.length > 0) {
-                // directory
-                File file = new File(newPath);
-                if (!file.mkdir())
-                {
-                    Log.d("mkdir","can't make folder");
-
-                }
-//                    return false;                // copy recursively
-                for (String fileName : fileNames) {
-                    copyFilesFromAssets(context, oldPath + "/" + fileName,
-                            newPath + "/" + fileName);
-                }
-            } else {
-                // file
-                InputStream is = context.getAssets().open(oldPath);
-                FileOutputStream fos = new FileOutputStream(new File(newPath));
-                byte[] buffer = new byte[1024];
-                int byteCount;
-                while ((byteCount = is.read(buffer)) != -1) {
-                    fos.write(buffer, 0, byteCount);
-                }
-                fos.flush();
-                is.close();
-                fos.close();
-            }
-        } catch (Exception e) {
-            // TODO Auto-generated catch block
-            e.printStackTrace();
-        }
-    }
-
-
-
-
-
-    public void initRecognizer()
-    {
-        String assetPath = "pr";
-        String sdcardPath = Environment.getExternalStorageDirectory()
-                + File.separator + assetPath;
-        copyFilesFromAssets(this, assetPath, sdcardPath);
-        String cascade_filename  =  sdcardPath
-            + File.separator+"cascade.xml";
-        String finemapping_prototxt  =  sdcardPath
-                + File.separator+"HorizonalFinemapping.prototxt";
-        String finemapping_caffemodel  =  sdcardPath
-                + File.separator+"HorizonalFinemapping.caffemodel";
-        String segmentation_prototxt =  sdcardPath
-                + File.separator+"Segmentation.prototxt";
-        String segmentation_caffemodel =  sdcardPath
-                + File.separator+"Segmentation.caffemodel";
-        String character_prototxt =  sdcardPath
-                + File.separator+"CharacterRecognization.prototxt";
-        String character_caffemodel=  sdcardPath
-                + File.separator+"CharacterRecognization.caffemodel";
-        String segmentationfree_prototxt =  sdcardPath
-                + File.separator+"SegmenationFree-Inception.prototxt";
-        String segmentationfree_caffemodel=  sdcardPath
-                + File.separator+"SegmenationFree-Inception.caffemodel";
-        handle  =  PlateRecognition.InitPlateRecognizer(
-                cascade_filename,
-                finemapping_prototxt,finemapping_caffemodel,
-                segmentation_prototxt,segmentation_caffemodel,
-                character_prototxt,character_caffemodel,
-                segmentationfree_prototxt,segmentationfree_caffemodel
-        );
-
-
-    }
-
-    /**
-     * Get the value of the data column for this Uri. This is useful for
-     * MediaStore Uris, and other file-based ContentProviders.
-     *
-     * @param context The context.
-     * @param uri The Uri to query.
-     * @param selection (Optional) Filter used in the query.
-     * @param selectionArgs (Optional) Selection arguments used in the query.
-     * @return The value of the _data column, which is typically a file path.
-     */
-    public static String getDataColumn(Context context, Uri uri, String selection,
-                                       String[] selectionArgs) {
-
-        Cursor cursor = null;
-        final String column = "_data";
-        final String[] projection = {
-                column
-        };
-
-        try {
-            cursor = context.getContentResolver().query(uri, projection, selection, selectionArgs,
-                    null);
-            if (cursor != null && cursor.moveToFirst()) {
-                final int index = cursor.getColumnIndexOrThrow(column);
-                return cursor.getString(index);
-            }
-        } finally {
-            if (cursor != null) {
-                cursor.close();
-            }
-        }
-        return null;
-    }
-
-    /**
-     * @param uri
-     * @return
-     */
-    private String getPath(Uri uri) {
-        if (Build.VERSION.SDK_INT >= Build.VERSION_CODES.KITKAT) {
-            if (DocumentsContract.isDocumentUri(this, uri)) {
-                final String docId = DocumentsContract.getDocumentId(uri);
-                final String[] split = docId.split(":");
-                final String type = split[0];
-
-                Uri contentUri = null;
-                if ("image".equals(type)) {
-                    contentUri = MediaStore.Images.Media.EXTERNAL_CONTENT_URI;
-                } else if ("video".equals(type)) {
-                    contentUri = MediaStore.Video.Media.EXTERNAL_CONTENT_URI;
-                    return null;
-                } else if ("audio".equals(type)) {
-                    contentUri = MediaStore.Audio.Media.EXTERNAL_CONTENT_URI;
-                    return null;
-                }
-
-                final String selection = "_id=?";
-                final String[] selectionArgs = new String[] {
-                        split[1]
-                };
-
-                return getDataColumn(this, contentUri, selection, selectionArgs);
-            }
-        }
-        String[] proj = { MediaStore.Images.Media.DATA };
-        Cursor actualimagecursor = managedQuery(uri, proj,null,null,null);
-        int actual_image_column_index = actualimagecursor.getColumnIndexOrThrow(MediaStore.Images.Media.DATA);
-        actualimagecursor.moveToFirst();
-        String img_path = actualimagecursor.getString(actual_image_column_index);
-        String end = img_path.substring(img_path.length() - 4);
-        if (0 != end.compareToIgnoreCase(".jpg") && 0 != end.compareToIgnoreCase(".png")) {
-            return null;
-        }
-        return img_path;
-    }
-
-    public static Bitmap decodeImage(String path) {
-        Bitmap res;
-        try {
-            ExifInterface exif = new ExifInterface(path);
-            int orientation = exif.getAttributeInt(ExifInterface.TAG_ORIENTATION, ExifInterface.ORIENTATION_NORMAL);
-
-            BitmapFactory.Options op = new BitmapFactory.Options();
-            op.inSampleSize = 1;
-            op.inJustDecodeBounds = false;
-            //op.inMutable = true;
-            res = BitmapFactory.decodeFile(path, op);
-            //rotate and scale.
-            Matrix matrix = new Matrix();
-
-            if (orientation == ExifInterface.ORIENTATION_ROTATE_90) {
-                matrix.postRotate(90);
-            } else if (orientation == ExifInterface.ORIENTATION_ROTATE_180) {
-                matrix.postRotate(180);
-            } else if (orientation == ExifInterface.ORIENTATION_ROTATE_270) {
-                matrix.postRotate(270);
-            }
-
-            Bitmap temp = Bitmap.createBitmap(res, 0, 0, res.getWidth(), res.getHeight(), matrix, true);
-            Log.d("com.arcsoft", "check target Image:" + temp.getWidth() + "X" + temp.getHeight());
-
-            if (!temp.equals(res)) {
-                res.recycle();
-            }
-            return temp;
-        } catch (Exception e) {
-            e.printStackTrace();
-        }
-        return null;
-    }
-
-    public void SimpleRecog(Bitmap bmp,int dp)
-    {
-
-        float dp_asp  = dp/10.f;
-//        imgv.setImageBitmap(bmp);
-        Mat mat_src = new Mat(bmp.getWidth(), bmp.getHeight(), CvType.CV_8UC4);
-
-        float new_w = bmp.getWidth()*dp_asp;
-        float new_h = bmp.getHeight()*dp_asp;
-        Size sz = new Size(new_w,new_h);
-        Utils.bitmapToMat(bmp, mat_src);
-        Imgproc.resize(mat_src,mat_src,sz);
-        long currentTime1 = System.currentTimeMillis();
-//        String res = PlateRecognition.SimpleRecognization(mat_src.getNativeObjAddr(),handle);
-//        resbox.setText("识别结果:"+res);
-
-        PlateInfo plateInfo = PlateRecognition.PlateInfoRecognization(mat_src.getNativeObjAddr(),handle);
-        imgv.setImageBitmap(plateInfo.bitmap);
-        resbox.setText("识别结果:"+plateInfo.plateName);
-        long diff = System.currentTimeMillis() - currentTime1;
-
-        runtimebox.setText(String.valueOf(diff)+"ms");
-
-
-
-    }
-
-    public  static PlateInfo simpleRecog(Bitmap bmp,int dp)
-    {
-
-        float dp_asp  = dp/10.f;
-//        imgv.setImageBitmap(bmp);
-        Mat mat_src = new Mat(bmp.getWidth(), bmp.getHeight(), CvType.CV_8UC4);
-
-        float new_w = bmp.getWidth()*dp_asp;
-        float new_h = bmp.getHeight()*dp_asp;
-        Size sz = new Size(new_w,new_h);
-        Utils.bitmapToMat(bmp, mat_src);
-        Imgproc.resize(mat_src,mat_src,sz);
-        long currentTime1 = System.currentTimeMillis();
-//        String res = PlateRecognition.SimpleRecognization(mat_src.getNativeObjAddr(),handle);
-//        resbox.setText("识别结果:"+res);
-
-        PlateInfo plateInfo = PlateRecognition.PlateInfoRecognization(mat_src.getNativeObjAddr(),handle);
-        return plateInfo;
-
-    }
-
-    @Override
-    protected void onActivityResult(int requestCode, int resultCode, Intent data) {
-        super.onActivityResult(requestCode, resultCode, data);
-
-        if (requestCode == REQUEST_CODE_IMAGE_OP && resultCode == RESULT_OK) {
-            mPath = data.getData();
-            String file = getPath(mPath);
-            Bitmap bmp = decodeImage(file);
-            if (bmp == null || bmp.getWidth() <= 0 || bmp.getHeight() <= 0 ) {
-                Log.e(TAG, "error");
-            } else {
-                Log.i(TAG, "bmp [" + bmp.getWidth() + "," + bmp.getHeight());
-            }
-            latestBitmap = bmp;
-
-            SimpleRecog(bmp,sb.getProgress());
-        } else if (requestCode == REQUEST_CODE_OP) {
-            Log.i(TAG, "RESULT =" + resultCode);
-            if (data == null) {
-                return;
-            }
-            Bundle bundle = data.getExtras();
-            String path = bundle.getString("imagePath");
-            Log.i(TAG, "path="+path);
-        } else if (requestCode == REQUEST_CODE_IMAGE_CAMERA && resultCode == RESULT_OK) {
-            String file = getPath(mPath);
-            Bitmap bmp = decodeImage(file);
-            latestBitmap = bmp;
-            SimpleRecog(bmp,sb.getProgress());
-//            startOilPainting(bmp, file);
-        }
-
-    }
-
-
-
-    @Override
-    protected void onCreate(Bundle savedInstanceState) {
-        super.onCreate(savedInstanceState);
-        setContentView(R.layout.activity_main);
-
-        String permission = Manifest.permission.WRITE_EXTERNAL_STORAGE;
-        if (ActivityCompat.checkSelfPermission(MainActivity.this, permission)
-                != PackageManager.PERMISSION_GRANTED
-                ) {
-            ActivityCompat.requestPermissions(MainActivity.this, new String[]
-                    {permission},123);
-
-        }
-        btn = (Button)findViewById(R.id.button);
-        recogBtn = (Button)findViewById(R.id.button_recog);
-        findViewById(R.id.button_recog_now).setOnClickListener(this);
-        imgv = (ImageView)findViewById(R.id.imageView);
-        resbox = (TextView)findViewById(R.id.textView);
-        sb = (SeekBar)findViewById(R.id.seek);
-        runtimebox = (TextView)findViewById(R.id.textView3);
-
-
-        initRecognizer();
-        btn.setOnClickListener(this);
-        recogBtn.setOnClickListener(this);
-
-    }
-
-    @Override
-    public void onClick(DialogInterface dialogInterface, int which) {
-        switch (which){
-            case 1:
-                Intent getImageByCamera = new Intent(
-                        "android.media.action.IMAGE_CAPTURE");
-                ContentValues values = new ContentValues(1);
-
-                values.put(MediaStore.Images.Media.MIME_TYPE, "image/jpeg");
-                mPath = getContentResolver().insert(MediaStore.Images.Media.EXTERNAL_CONTENT_URI, values);
-                getImageByCamera.putExtra(MediaStore.EXTRA_OUTPUT, mPath);
-
-                startActivityForResult(getImageByCamera, REQUEST_CODE_IMAGE_CAMERA);
-                break;
-            case 0:
-                Intent getImageByalbum = new Intent(Intent.ACTION_GET_CONTENT);
-                getImageByalbum.addCategory(Intent.CATEGORY_OPENABLE);
-                getImageByalbum.setType("image/jpeg");
-                startActivityForResult(getImageByalbum, REQUEST_CODE_IMAGE_OP);
-                break;
-            default:;
-        }
-    }
-
-    @Override
-    public void onClick(View view) {
-        switch (view.getId()) {
-
-            case R.id.button:
-                new AlertDialog.Builder(this)
-                        .setTitle("打开方式")
-                        .setItems(new String[]{"打开图片"}, this)
-                        .show();
-                break;
-            case R.id.button_recog:
-               if(latestBitmap!=null){
-                   SimpleRecog(latestBitmap,sb.getProgress());
-               }
-                break;
-            case R.id.button_recog_now:
-                Intent intent = new Intent(this, CameraActivity.class);
-                startActivity(intent);
-                break;
-            default:
-                break;
-        }
-    }
-}
-
diff --git a/Prj-Android/app/src/main/java/pr/platerecognization/PlateInfo.java b/Prj-Android/app/src/main/java/pr/platerecognization/PlateInfo.java
deleted file mode 100755
index 2abab7f..0000000
--- a/Prj-Android/app/src/main/java/pr/platerecognization/PlateInfo.java
+++ /dev/null
@@ -1,30 +0,0 @@
-package pr.platerecognization;
-
-import android.graphics.Bitmap;
-
-import org.opencv.core.Mat;
-
-/**
- * @author liuxuhui
- * @date 2019/6/20
- */
-public class PlateInfo {
-
-    /**
-     * 车牌号
-     */
-    public String plateName;
-
-    /**
-     * 车牌号图片
-     */
-    public Bitmap bitmap;
-
-    public PlateInfo() {
-    }
-
-    public PlateInfo(String plateName, Bitmap bitmap) {
-        this.plateName = plateName;
-        this.bitmap = bitmap;
-    }
-}
diff --git a/Prj-Android/app/src/main/java/pr/platerecognization/PlateRecognition.java b/Prj-Android/app/src/main/java/pr/platerecognization/PlateRecognition.java
deleted file mode 100755
index e6f9a3c..0000000
--- a/Prj-Android/app/src/main/java/pr/platerecognization/PlateRecognition.java
+++ /dev/null
@@ -1,21 +0,0 @@
-package pr.platerecognization;
-
-/**
- * Created by yujinke on 24/10/2017.
- */
-
-public class PlateRecognition {
-    static {
-        System.loadLibrary("hyperlpr");
-    }
-    static native long InitPlateRecognizer(String casacde_detection,
-                                           String finemapping_prototxt,String finemapping_caffemodel,
-                                           String segmentation_prototxt,String segmentation_caffemodel,
-                                           String charRecognization_proto,String charRecognization_caffemodel,
-                                           String segmentationfree_proto, String segmentationfree_caffemodel);
-
-    static native void ReleasePlateRecognizer(long  object);
-    static native String SimpleRecognization(long  inputMat,long object);
-    static native PlateInfo PlateInfoRecognization(long  inputMat,long object);
-
-}
diff --git a/Prj-Android/app/src/main/res/drawable-v24/ic_launcher_foreground.xml b/Prj-Android/app/src/main/res/drawable-v24/ic_launcher_foreground.xml
old mode 100755
new mode 100644
index c7bd21d..2b068d1
--- a/Prj-Android/app/src/main/res/drawable-v24/ic_launcher_foreground.xml
+++ b/Prj-Android/app/src/main/res/drawable-v24/ic_launcher_foreground.xml
@@ -2,19 +2,15 @@
     xmlns:aapt="http://schemas.android.com/aapt"
     android:width="108dp"
     android:height="108dp"
-    android:viewportHeight="108"
-    android:viewportWidth="108">
-    <path
-        android:fillType="evenOdd"
-        android:pathData="M32,64C32,64 38.39,52.99 44.13,50.95C51.37,48.37 70.14,49.57 70.14,49.57L108.26,87.69L108,109.01L75.97,107.97L32,64Z"
-        android:strokeColor="#00000000"
-        android:strokeWidth="1">
+    android:viewportWidth="108"
+    android:viewportHeight="108">
+    <path android:pathData="M31,63.928c0,0 6.4,-11 12.1,-13.1c7.2,-2.6 26,-1.4 26,-1.4l38.1,38.1L107,108.928l-32,-1L31,63.928z">
         <aapt:attr name="android:fillColor">
             <gradient
-                android:endX="78.5885"
-                android:endY="90.9159"
-                android:startX="48.7653"
-                android:startY="61.0927"
+                android:endX="85.84757"
+                android:endY="92.4963"
+                android:startX="42.9492"
+                android:startY="49.59793"
                 android:type="linear">
                 <item
                     android:color="#44000000"
@@ -28,7 +24,7 @@
     <path
         android:fillColor="#FFFFFF"
         android:fillType="nonZero"
-        android:pathData="M66.94,46.02L66.94,46.02C72.44,50.07 76,56.61 76,64L32,64C32,56.61 35.56,50.11 40.98,46.06L36.18,41.19C35.45,40.45 35.45,39.3 36.18,38.56C36.91,37.81 38.05,37.81 38.78,38.56L44.25,44.05C47.18,42.57 50.48,41.71 54,41.71C57.48,41.71 60.78,42.57 63.68,44.05L69.11,38.56C69.84,37.81 70.98,37.81 71.71,38.56C72.44,39.3 72.44,40.45 71.71,41.19L66.94,46.02ZM62.94,56.92C64.08,56.92 65,56.01 65,54.88C65,53.76 64.08,52.85 62.94,52.85C61.8,52.85 60.88,53.76 60.88,54.88C60.88,56.01 61.8,56.92 62.94,56.92ZM45.06,56.92C46.2,56.92 47.13,56.01 47.13,54.88C47.13,53.76 46.2,52.85 45.06,52.85C43.92,52.85 43,53.76 43,54.88C43,56.01 43.92,56.92 45.06,56.92Z"
-        android:strokeColor="#00000000"
-        android:strokeWidth="1" />
-</vector>
+        android:pathData="M65.3,45.828l3.8,-6.6c0.2,-0.4 0.1,-0.9 -0.3,-1.1c-0.4,-0.2 -0.9,-0.1 -1.1,0.3l-3.9,6.7c-6.3,-2.8 -13.4,-2.8 -19.7,0l-3.9,-6.7c-0.2,-0.4 -0.7,-0.5 -1.1,-0.3C38.8,38.328 38.7,38.828 38.9,39.228l3.8,6.6C36.2,49.428 31.7,56.028 31,63.928h46C76.3,56.028 71.8,49.428 65.3,45.828zM43.4,57.328c-0.8,0 -1.5,-0.5 -1.8,-1.2c-0.3,-0.7 -0.1,-1.5 0.4,-2.1c0.5,-0.5 1.4,-0.7 2.1,-0.4c0.7,0.3 1.2,1 1.2,1.8C45.3,56.528 44.5,57.328 43.4,57.328L43.4,57.328zM64.6,57.328c-0.8,0 -1.5,-0.5 -1.8,-1.2s-0.1,-1.5 0.4,-2.1c0.5,-0.5 1.4,-0.7 2.1,-0.4c0.7,0.3 1.2,1 1.2,1.8C66.5,56.528 65.6,57.328 64.6,57.328L64.6,57.328z"
+        android:strokeWidth="1"
+        android:strokeColor="#00000000" />
+</vector>
\ No newline at end of file
diff --git a/Prj-Android/app/src/main/res/drawable/ic_launcher_background.xml b/Prj-Android/app/src/main/res/drawable/ic_launcher_background.xml
old mode 100755
new mode 100644
index d5fccc5..07d5da9
--- a/Prj-Android/app/src/main/res/drawable/ic_launcher_background.xml
+++ b/Prj-Android/app/src/main/res/drawable/ic_launcher_background.xml
@@ -2,169 +2,169 @@
 <vector xmlns:android="http://schemas.android.com/apk/res/android"
     android:width="108dp"
     android:height="108dp"
-    android:viewportHeight="108"
-    android:viewportWidth="108">
+    android:viewportWidth="108"
+    android:viewportHeight="108">
     <path
-        android:fillColor="#26A69A"
+        android:fillColor="#3DDC84"
         android:pathData="M0,0h108v108h-108z" />
     <path
         android:fillColor="#00000000"
         android:pathData="M9,0L9,108"
-        android:strokeColor="#33FFFFFF"
-        android:strokeWidth="0.8" />
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
     <path
         android:fillColor="#00000000"
         android:pathData="M19,0L19,108"
-        android:strokeColor="#33FFFFFF"
-        android:strokeWidth="0.8" />
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
     <path
         android:fillColor="#00000000"
         android:pathData="M29,0L29,108"
-        android:strokeColor="#33FFFFFF"
-        android:strokeWidth="0.8" />
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
     <path
         android:fillColor="#00000000"
         android:pathData="M39,0L39,108"
-        android:strokeColor="#33FFFFFF"
-        android:strokeWidth="0.8" />
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
     <path
         android:fillColor="#00000000"
         android:pathData="M49,0L49,108"
-        android:strokeColor="#33FFFFFF"
-        android:strokeWidth="0.8" />
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
     <path
         android:fillColor="#00000000"
         android:pathData="M59,0L59,108"
-        android:strokeColor="#33FFFFFF"
-        android:strokeWidth="0.8" />
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
     <path
         android:fillColor="#00000000"
         android:pathData="M69,0L69,108"
-        android:strokeColor="#33FFFFFF"
-        android:strokeWidth="0.8" />
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
     <path
         android:fillColor="#00000000"
         android:pathData="M79,0L79,108"
-        android:strokeColor="#33FFFFFF"
-        android:strokeWidth="0.8" />
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
     <path
         android:fillColor="#00000000"
         android:pathData="M89,0L89,108"
-        android:strokeColor="#33FFFFFF"
-        android:strokeWidth="0.8" />
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
     <path
         android:fillColor="#00000000"
         android:pathData="M99,0L99,108"
-        android:strokeColor="#33FFFFFF"
-        android:strokeWidth="0.8" />
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
     <path
         android:fillColor="#00000000"
         android:pathData="M0,9L108,9"
-        android:strokeColor="#33FFFFFF"
-        android:strokeWidth="0.8" />
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
     <path
         android:fillColor="#00000000"
         android:pathData="M0,19L108,19"
-        android:strokeColor="#33FFFFFF"
-        android:strokeWidth="0.8" />
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
     <path
         android:fillColor="#00000000"
         android:pathData="M0,29L108,29"
-        android:strokeColor="#33FFFFFF"
-        android:strokeWidth="0.8" />
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
     <path
         android:fillColor="#00000000"
         android:pathData="M0,39L108,39"
-        android:strokeColor="#33FFFFFF"
-        android:strokeWidth="0.8" />
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
     <path
         android:fillColor="#00000000"
         android:pathData="M0,49L108,49"
-        android:strokeColor="#33FFFFFF"
-        android:strokeWidth="0.8" />
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
     <path
         android:fillColor="#00000000"
         android:pathData="M0,59L108,59"
-        android:strokeColor="#33FFFFFF"
-        android:strokeWidth="0.8" />
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
     <path
         android:fillColor="#00000000"
         android:pathData="M0,69L108,69"
-        android:strokeColor="#33FFFFFF"
-        android:strokeWidth="0.8" />
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
     <path
         android:fillColor="#00000000"
         android:pathData="M0,79L108,79"
-        android:strokeColor="#33FFFFFF"
-        android:strokeWidth="0.8" />
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
     <path
         android:fillColor="#00000000"
         android:pathData="M0,89L108,89"
-        android:strokeColor="#33FFFFFF"
-        android:strokeWidth="0.8" />
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
     <path
         android:fillColor="#00000000"
         android:pathData="M0,99L108,99"
-        android:strokeColor="#33FFFFFF"
-        android:strokeWidth="0.8" />
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
     <path
         android:fillColor="#00000000"
         android:pathData="M19,29L89,29"
-        android:strokeColor="#33FFFFFF"
-        android:strokeWidth="0.8" />
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
     <path
         android:fillColor="#00000000"
         android:pathData="M19,39L89,39"
-        android:strokeColor="#33FFFFFF"
-        android:strokeWidth="0.8" />
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
     <path
         android:fillColor="#00000000"
         android:pathData="M19,49L89,49"
-        android:strokeColor="#33FFFFFF"
-        android:strokeWidth="0.8" />
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
     <path
         android:fillColor="#00000000"
         android:pathData="M19,59L89,59"
-        android:strokeColor="#33FFFFFF"
-        android:strokeWidth="0.8" />
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
     <path
         android:fillColor="#00000000"
         android:pathData="M19,69L89,69"
-        android:strokeColor="#33FFFFFF"
-        android:strokeWidth="0.8" />
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
     <path
         android:fillColor="#00000000"
         android:pathData="M19,79L89,79"
-        android:strokeColor="#33FFFFFF"
-        android:strokeWidth="0.8" />
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
     <path
         android:fillColor="#00000000"
         android:pathData="M29,19L29,89"
-        android:strokeColor="#33FFFFFF"
-        android:strokeWidth="0.8" />
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
     <path
         android:fillColor="#00000000"
         android:pathData="M39,19L39,89"
-        android:strokeColor="#33FFFFFF"
-        android:strokeWidth="0.8" />
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
     <path
         android:fillColor="#00000000"
         android:pathData="M49,19L49,89"
-        android:strokeColor="#33FFFFFF"
-        android:strokeWidth="0.8" />
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
     <path
         android:fillColor="#00000000"
         android:pathData="M59,19L59,89"
-        android:strokeColor="#33FFFFFF"
-        android:strokeWidth="0.8" />
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
     <path
         android:fillColor="#00000000"
         android:pathData="M69,19L69,89"
-        android:strokeColor="#33FFFFFF"
-        android:strokeWidth="0.8" />
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
     <path
         android:fillColor="#00000000"
         android:pathData="M79,19L79,89"
-        android:strokeColor="#33FFFFFF"
-        android:strokeWidth="0.8" />
+        android:strokeWidth="0.8"
+        android:strokeColor="#33FFFFFF" />
 </vector>
diff --git a/Prj-Android/app/src/main/res/layout/activity_camera.xml b/Prj-Android/app/src/main/res/layout/activity_camera.xml
index 20f07f3..11ed854 100755
--- a/Prj-Android/app/src/main/res/layout/activity_camera.xml
+++ b/Prj-Android/app/src/main/res/layout/activity_camera.xml
@@ -10,7 +10,7 @@
         android:layout_marginTop="30dp"
         android:layout_gravity="center_horizontal"
         android:layout_width="300dp"
-        android:layout_height="300dp"/>
+        android:layout_height="416dp"/>
 
     <TextView
         android:id="@+id/plate_tv"
@@ -18,24 +18,8 @@
         android:layout_height="wrap_content"
         android:layout_marginLeft="30dp"
         android:layout_marginTop="30dp"
-        android:text="车牌号" />
-
-    <ImageView
-        android:id="@+id/image"
-        android:layout_width="150dp"
-        android:layout_height="50dp"
-        android:scaleType="fitCenter"
-        android:layout_marginTop="30dp"
-        android:layout_marginLeft="30dp"/>
-
-    <TextView
-        android:id="@+id/reg_tv"
-        android:layout_width="120dp"
-        android:layout_height="40dp"
-        android:gravity="center"
-        android:layout_marginLeft="30dp"
-        android:layout_marginTop="30dp"
-        android:text="实时识别" />
+        android:textSize="20sp"
+        android:text="请对准车牌" />
 
 
 </LinearLayout>
\ No newline at end of file
diff --git a/Prj-Android/app/src/main/res/layout/activity_main.xml b/Prj-Android/app/src/main/res/layout/activity_main.xml
old mode 100755
new mode 100644
index aa46ce3..63cc736
--- a/Prj-Android/app/src/main/res/layout/activity_main.xml
+++ b/Prj-Android/app/src/main/res/layout/activity_main.xml
@@ -1,113 +1,33 @@
 <?xml version="1.0" encoding="utf-8"?>
-<android.support.constraint.ConstraintLayout xmlns:android="http://schemas.android.com/apk/res/android"
+<androidx.constraintlayout.widget.ConstraintLayout xmlns:android="http://schemas.android.com/apk/res/android"
     xmlns:app="http://schemas.android.com/apk/res-auto"
     xmlns:tools="http://schemas.android.com/tools"
     android:layout_width="match_parent"
     android:layout_height="match_parent"
-    tools:context="pr.platerecognization.MainActivity">
+    tools:context=".MainActivity">
 
     <Button
-        android:id="@+id/button"
+        android:id="@+id/cameraBtn"
         android:layout_width="wrap_content"
         android:layout_height="wrap_content"
-        android:layout_marginBottom="8dp"
-        android:layout_marginLeft="8dp"
-        android:layout_marginRight="8dp"
-        android:layout_marginTop="8dp"
-        android:text="选择图片"
+        android:text="打开相机"
         app:layout_constraintBottom_toBottomOf="parent"
-        app:layout_constraintLeft_toLeftOf="parent"
-        app:layout_constraintRight_toRightOf="parent"
-        app:layout_constraintTop_toBottomOf="@+id/imageView"
-        app:layout_constraintVertical_bias="0.62" />
-    <Button
-        android:id="@+id/button_recog"
-        android:layout_width="wrap_content"
-        android:layout_height="wrap_content"
-        android:text="识别"
-        app:layout_constraintTop_toTopOf="@id/button"
-        app:layout_constraintRight_toLeftOf="@id/button"
-       />
-
-    <Button
-        android:id="@+id/button_recog_now"
-        android:layout_width="wrap_content"
-        android:layout_height="wrap_content"
-        android:text="实时识别"
-        app:layout_constraintTop_toTopOf="@id/button"
-        app:layout_constraintLeft_toRightOf="@id/button"
-        />
-
-    <ImageView
-        android:id="@+id/imageView"
-        android:layout_width="346dp"
-        android:layout_height="306dp"
-        android:layout_marginBottom="8dp"
-        android:layout_marginLeft="8dp"
-        android:layout_marginRight="8dp"
-        android:layout_marginTop="8dp"
-        app:layout_constraintBottom_toBottomOf="@+id/textView"
-        app:layout_constraintLeft_toLeftOf="parent"
-        app:layout_constraintRight_toRightOf="parent"
+        app:layout_constraintEnd_toEndOf="parent"
+        app:layout_constraintHorizontal_bias="0.2"
+        app:layout_constraintStart_toStartOf="parent"
         app:layout_constraintTop_toTopOf="parent"
-        app:layout_constraintVertical_bias="0.813"
-        app:srcCompat="@android:color/holo_blue_dark" />
+        app:layout_constraintVertical_bias="0.863" />
 
-    <SeekBar
-        android:id="@+id/seek"
-        style="@style/Widget.AppCompat.SeekBar.Discrete"
-        android:layout_width="250dp"
-        android:layout_height="25dp"
-        android:max="10"
-        android:progress="3"
-        android:layout_marginRight="8dp"
-        app:layout_constraintRight_toRightOf="parent"
-        android:layout_marginLeft="8dp"
-        app:layout_constraintLeft_toLeftOf="parent"
-        android:layout_marginBottom="8dp"
-        app:layout_constraintBottom_toTopOf="@+id/imageView"
-        app:layout_constraintTop_toTopOf="parent"
-        android:layout_marginTop="8dp"
-        app:layout_constraintVertical_bias="0.814" />
-
-    <TextView
-        android:id="@+id/textView"
-        android:layout_width="wrap_content"
-        android:layout_height="wrap_content"
-        android:layout_marginBottom="9dp"
-        android:layout_marginLeft="8dp"
-        android:layout_marginRight="8dp"
-        android:text=""
-        app:layout_constraintBottom_toTopOf="@+id/button"
-        app:layout_constraintLeft_toLeftOf="parent"
-        app:layout_constraintRight_toRightOf="parent" />
-
-    <TextView
-        android:id="@+id/textView3"
+    <Button
+        android:id="@+id/albumBtn"
         android:layout_width="wrap_content"
         android:layout_height="wrap_content"
-        android:layout_marginBottom="6dp"
-        android:layout_marginLeft="8dp"
-        android:layout_marginRight="8dp"
+        android:text="打开相册"
         app:layout_constraintBottom_toBottomOf="parent"
-        app:layout_constraintHorizontal_bias="0.058"
-        app:layout_constraintLeft_toLeftOf="parent"
-        app:layout_constraintRight_toRightOf="parent" />
-
-    <TextView
-        android:id="@+id/textView4"
-        android:layout_width="wrap_content"
-        android:layout_height="wrap_content"
-        android:text="分辨率调节从原图的0.1倍-1倍"
-        android:layout_marginRight="8dp"
-        app:layout_constraintRight_toRightOf="parent"
-        android:layout_marginLeft="8dp"
-        app:layout_constraintLeft_toLeftOf="parent"
-        android:layout_marginBottom="8dp"
-        app:layout_constraintBottom_toTopOf="@+id/seek"
+        app:layout_constraintEnd_toEndOf="parent"
+        app:layout_constraintHorizontal_bias="0.776"
+        app:layout_constraintStart_toStartOf="parent"
         app:layout_constraintTop_toTopOf="parent"
-        android:layout_marginTop="8dp"
-        app:layout_constraintHorizontal_bias="0.502"
-        app:layout_constraintVertical_bias="0.421" />
+        app:layout_constraintVertical_bias="0.863" />
 
-</android.support.constraint.ConstraintLayout>
+</androidx.constraintlayout.widget.ConstraintLayout>
\ No newline at end of file
diff --git a/Prj-Android/app/src/main/res/mipmap-anydpi-v26/ic_launcher.xml b/Prj-Android/app/src/main/res/mipmap-anydpi-v26/ic_launcher.xml
old mode 100755
new mode 100644
diff --git a/Prj-Android/app/src/main/res/mipmap-anydpi-v26/ic_launcher_round.xml b/Prj-Android/app/src/main/res/mipmap-anydpi-v26/ic_launcher_round.xml
old mode 100755
new mode 100644
diff --git a/Prj-Android/app/src/main/res/mipmap-hdpi/ic_launcher.png b/Prj-Android/app/src/main/res/mipmap-hdpi/ic_launcher.png
deleted file mode 100755
index a2f5908..0000000
Binary files a/Prj-Android/app/src/main/res/mipmap-hdpi/ic_launcher.png and /dev/null differ
diff --git a/Prj-Android/app/src/main/res/mipmap-hdpi/ic_launcher.webp b/Prj-Android/app/src/main/res/mipmap-hdpi/ic_launcher.webp
new file mode 100644
index 0000000..c209e78
Binary files /dev/null and b/Prj-Android/app/src/main/res/mipmap-hdpi/ic_launcher.webp differ
diff --git a/Prj-Android/app/src/main/res/mipmap-hdpi/ic_launcher_round.png b/Prj-Android/app/src/main/res/mipmap-hdpi/ic_launcher_round.png
deleted file mode 100755
index 1b52399..0000000
Binary files a/Prj-Android/app/src/main/res/mipmap-hdpi/ic_launcher_round.png and /dev/null differ
diff --git a/Prj-Android/app/src/main/res/mipmap-hdpi/ic_launcher_round.webp b/Prj-Android/app/src/main/res/mipmap-hdpi/ic_launcher_round.webp
new file mode 100644
index 0000000..b2dfe3d
Binary files /dev/null and b/Prj-Android/app/src/main/res/mipmap-hdpi/ic_launcher_round.webp differ
diff --git a/Prj-Android/app/src/main/res/mipmap-mdpi/ic_launcher.png b/Prj-Android/app/src/main/res/mipmap-mdpi/ic_launcher.png
deleted file mode 100755
index ff10afd..0000000
Binary files a/Prj-Android/app/src/main/res/mipmap-mdpi/ic_launcher.png and /dev/null differ
diff --git a/Prj-Android/app/src/main/res/mipmap-mdpi/ic_launcher.webp b/Prj-Android/app/src/main/res/mipmap-mdpi/ic_launcher.webp
new file mode 100644
index 0000000..4f0f1d6
Binary files /dev/null and b/Prj-Android/app/src/main/res/mipmap-mdpi/ic_launcher.webp differ
diff --git a/Prj-Android/app/src/main/res/mipmap-mdpi/ic_launcher_round.png b/Prj-Android/app/src/main/res/mipmap-mdpi/ic_launcher_round.png
deleted file mode 100755
index 115a4c7..0000000
Binary files a/Prj-Android/app/src/main/res/mipmap-mdpi/ic_launcher_round.png and /dev/null differ
diff --git a/Prj-Android/app/src/main/res/mipmap-mdpi/ic_launcher_round.webp b/Prj-Android/app/src/main/res/mipmap-mdpi/ic_launcher_round.webp
new file mode 100644
index 0000000..62b611d
Binary files /dev/null and b/Prj-Android/app/src/main/res/mipmap-mdpi/ic_launcher_round.webp differ
diff --git a/Prj-Android/app/src/main/res/mipmap-xhdpi/ic_launcher.png b/Prj-Android/app/src/main/res/mipmap-xhdpi/ic_launcher.png
deleted file mode 100755
index dcd3cd8..0000000
Binary files a/Prj-Android/app/src/main/res/mipmap-xhdpi/ic_launcher.png and /dev/null differ
diff --git a/Prj-Android/app/src/main/res/mipmap-xhdpi/ic_launcher.webp b/Prj-Android/app/src/main/res/mipmap-xhdpi/ic_launcher.webp
new file mode 100644
index 0000000..948a307
Binary files /dev/null and b/Prj-Android/app/src/main/res/mipmap-xhdpi/ic_launcher.webp differ
diff --git a/Prj-Android/app/src/main/res/mipmap-xhdpi/ic_launcher_round.png b/Prj-Android/app/src/main/res/mipmap-xhdpi/ic_launcher_round.png
deleted file mode 100755
index 459ca60..0000000
Binary files a/Prj-Android/app/src/main/res/mipmap-xhdpi/ic_launcher_round.png and /dev/null differ
diff --git a/Prj-Android/app/src/main/res/mipmap-xhdpi/ic_launcher_round.webp b/Prj-Android/app/src/main/res/mipmap-xhdpi/ic_launcher_round.webp
new file mode 100644
index 0000000..1b9a695
Binary files /dev/null and b/Prj-Android/app/src/main/res/mipmap-xhdpi/ic_launcher_round.webp differ
diff --git a/Prj-Android/app/src/main/res/mipmap-xxhdpi/ic_launcher.png b/Prj-Android/app/src/main/res/mipmap-xxhdpi/ic_launcher.png
deleted file mode 100755
index 8ca12fe..0000000
Binary files a/Prj-Android/app/src/main/res/mipmap-xxhdpi/ic_launcher.png and /dev/null differ
diff --git a/Prj-Android/app/src/main/res/mipmap-xxhdpi/ic_launcher.webp b/Prj-Android/app/src/main/res/mipmap-xxhdpi/ic_launcher.webp
new file mode 100644
index 0000000..28d4b77
Binary files /dev/null and b/Prj-Android/app/src/main/res/mipmap-xxhdpi/ic_launcher.webp differ
diff --git a/Prj-Android/app/src/main/res/mipmap-xxhdpi/ic_launcher_round.png b/Prj-Android/app/src/main/res/mipmap-xxhdpi/ic_launcher_round.png
deleted file mode 100755
index 8e19b41..0000000
Binary files a/Prj-Android/app/src/main/res/mipmap-xxhdpi/ic_launcher_round.png and /dev/null differ
diff --git a/Prj-Android/app/src/main/res/mipmap-xxhdpi/ic_launcher_round.webp b/Prj-Android/app/src/main/res/mipmap-xxhdpi/ic_launcher_round.webp
new file mode 100644
index 0000000..9287f50
Binary files /dev/null and b/Prj-Android/app/src/main/res/mipmap-xxhdpi/ic_launcher_round.webp differ
diff --git a/Prj-Android/app/src/main/res/mipmap-xxxhdpi/ic_launcher.png b/Prj-Android/app/src/main/res/mipmap-xxxhdpi/ic_launcher.png
deleted file mode 100755
index b824ebd..0000000
Binary files a/Prj-Android/app/src/main/res/mipmap-xxxhdpi/ic_launcher.png and /dev/null differ
diff --git a/Prj-Android/app/src/main/res/mipmap-xxxhdpi/ic_launcher.webp b/Prj-Android/app/src/main/res/mipmap-xxxhdpi/ic_launcher.webp
new file mode 100644
index 0000000..aa7d642
Binary files /dev/null and b/Prj-Android/app/src/main/res/mipmap-xxxhdpi/ic_launcher.webp differ
diff --git a/Prj-Android/app/src/main/res/mipmap-xxxhdpi/ic_launcher_round.png b/Prj-Android/app/src/main/res/mipmap-xxxhdpi/ic_launcher_round.png
deleted file mode 100755
index 4c19a13..0000000
Binary files a/Prj-Android/app/src/main/res/mipmap-xxxhdpi/ic_launcher_round.png and /dev/null differ
diff --git a/Prj-Android/app/src/main/res/mipmap-xxxhdpi/ic_launcher_round.webp b/Prj-Android/app/src/main/res/mipmap-xxxhdpi/ic_launcher_round.webp
new file mode 100644
index 0000000..9126ae3
Binary files /dev/null and b/Prj-Android/app/src/main/res/mipmap-xxxhdpi/ic_launcher_round.webp differ
diff --git a/Prj-Android/app/src/main/res/values/colors.xml b/Prj-Android/app/src/main/res/values/colors.xml
old mode 100755
new mode 100644
index 3ab3e9c..f8c6127
--- a/Prj-Android/app/src/main/res/values/colors.xml
+++ b/Prj-Android/app/src/main/res/values/colors.xml
@@ -1,6 +1,10 @@
 <?xml version="1.0" encoding="utf-8"?>
 <resources>
-    <color name="colorPrimary">#3F51B5</color>
-    <color name="colorPrimaryDark">#303F9F</color>
-    <color name="colorAccent">#FF4081</color>
-</resources>
+    <color name="purple_200">#FFBB86FC</color>
+    <color name="purple_500">#FF6200EE</color>
+    <color name="purple_700">#FF3700B3</color>
+    <color name="teal_200">#FF03DAC5</color>
+    <color name="teal_700">#FF018786</color>
+    <color name="black">#FF000000</color>
+    <color name="white">#FFFFFFFF</color>
+</resources>
\ No newline at end of file
diff --git a/Prj-Android/app/src/main/res/values/strings.xml b/Prj-Android/app/src/main/res/values/strings.xml
old mode 100755
new mode 100644
index 7286e60..3c98151
--- a/Prj-Android/app/src/main/res/values/strings.xml
+++ b/Prj-Android/app/src/main/res/values/strings.xml
@@ -1,3 +1,3 @@
 <resources>
-    <string name="app_name">PrjAndroid</string>
-</resources>
+    <string name="app_name">HyperLPR_SDK_Demo</string>
+</resources>
\ No newline at end of file
diff --git a/Prj-Android/app/src/main/res/values/styles.xml b/Prj-Android/app/src/main/res/values/styles.xml
deleted file mode 100755
index 5885930..0000000
--- a/Prj-Android/app/src/main/res/values/styles.xml
+++ /dev/null
@@ -1,11 +0,0 @@
-<resources>
-
-    <!-- Base application theme. -->
-    <style name="AppTheme" parent="Theme.AppCompat.Light.DarkActionBar">
-        <!-- Customize your theme here. -->
-        <item name="colorPrimary">@color/colorPrimary</item>
-        <item name="colorPrimaryDark">@color/colorPrimaryDark</item>
-        <item name="colorAccent">@color/colorAccent</item>
-    </style>
-
-</resources>
diff --git a/Prj-Android/app/src/test/java/com/hyperai/hyperlpr_sdk_demo/ExampleUnitTest.java b/Prj-Android/app/src/test/java/com/hyperai/hyperlpr_sdk_demo/ExampleUnitTest.java
new file mode 100644
index 0000000..1a67584
--- /dev/null
+++ b/Prj-Android/app/src/test/java/com/hyperai/hyperlpr_sdk_demo/ExampleUnitTest.java
@@ -0,0 +1,17 @@
+package com.hyperai.hyperlpr_sdk_demo;
+
+import org.junit.Test;
+
+import static org.junit.Assert.*;
+
+/**
+ * Example local unit test, which will execute on the development machine (host).
+ *
+ * @see <a href="http://d.android.com/tools/testing">Testing documentation</a>
+ */
+public class ExampleUnitTest {
+    @Test
+    public void addition_isCorrect() {
+        assertEquals(4, 2 + 2);
+    }
+}
\ No newline at end of file
diff --git a/Prj-Android/build.gradle b/Prj-Android/build.gradle
old mode 100755
new mode 100644
index 43c0708..a689e3c
--- a/Prj-Android/build.gradle
+++ b/Prj-Android/build.gradle
@@ -1,27 +1,18 @@
 // Top-level build file where you can add configuration options common to all sub-projects/modules.
-
 buildscript {
-    
     repositories {
         google()
-        jcenter()
+        mavenCentral()
+        maven { url "https://jitpack.io" }
     }
     dependencies {
-        classpath 'com.android.tools.build:gradle:3.1.3'
-        
+        classpath "com.android.tools.build:gradle:7.0.2"
 
         // NOTE: Do not place your application dependencies here; they belong
         // in the individual module build.gradle files
     }
 }
 
-allprojects {
-    repositories {
-        google()
-        jcenter()
-    }
-}
-
 task clean(type: Delete) {
     delete rootProject.buildDir
-}
+}
\ No newline at end of file
diff --git a/Prj-Android/gradle.properties b/Prj-Android/gradle.properties
old mode 100755
new mode 100644
index 743d692..52f5917
--- a/Prj-Android/gradle.properties
+++ b/Prj-Android/gradle.properties
@@ -6,8 +6,14 @@
 # http://www.gradle.org/docs/current/userguide/build_environment.html
 # Specifies the JVM arguments used for the daemon process.
 # The setting is particularly useful for tweaking memory settings.
-org.gradle.jvmargs=-Xmx1536m
+org.gradle.jvmargs=-Xmx2048m -Dfile.encoding=UTF-8
 # When configured, Gradle will run in incubating parallel mode.
 # This option should only be used with decoupled projects. More details, visit
 # http://www.gradle.org/docs/current/userguide/multi_project_builds.html#sec:decoupled_projects
 # org.gradle.parallel=true
+# AndroidX package structure to make it clearer which packages are bundled with the
+# Android operating system, and which are packaged with your app"s APK
+# https://developer.android.com/topic/libraries/support-library/androidx-rn
+android.useAndroidX=true
+# Automatically convert third-party libraries to use AndroidX
+android.enableJetifier=true
\ No newline at end of file
diff --git a/Prj-Android/gradle/wrapper/gradle-wrapper.jar b/Prj-Android/gradle/wrapper/gradle-wrapper.jar
old mode 100755
new mode 100644
index 7a3265e..e708b1c
Binary files a/Prj-Android/gradle/wrapper/gradle-wrapper.jar and b/Prj-Android/gradle/wrapper/gradle-wrapper.jar differ
diff --git a/Prj-Android/gradle/wrapper/gradle-wrapper.properties b/Prj-Android/gradle/wrapper/gradle-wrapper.properties
old mode 100755
new mode 100644
index b78d1e7..785b7bf
--- a/Prj-Android/gradle/wrapper/gradle-wrapper.properties
+++ b/Prj-Android/gradle/wrapper/gradle-wrapper.properties
@@ -1,6 +1,6 @@
-#Sat Jul 21 16:31:56 CST 2018
+#Fri Jan 27 02:05:45 CST 2023
 distributionBase=GRADLE_USER_HOME
+distributionUrl=https\://services.gradle.org/distributions/gradle-7.0.2-bin.zip
 distributionPath=wrapper/dists
-zipStoreBase=GRADLE_USER_HOME
 zipStorePath=wrapper/dists
-distributionUrl=https\://services.gradle.org/distributions/gradle-4.4-all.zip
+zipStoreBase=GRADLE_USER_HOME
diff --git a/Prj-Android/gradlew b/Prj-Android/gradlew
index cccdd3d..4f906e0 100755
--- a/Prj-Android/gradlew
+++ b/Prj-Android/gradlew
@@ -1,5 +1,21 @@
 #!/usr/bin/env sh
 
+#
+# Copyright 2015 the original author or authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
 ##############################################################################
 ##
 ##  Gradle start up script for UN*X
@@ -28,7 +44,7 @@ APP_NAME="Gradle"
 APP_BASE_NAME=`basename "$0"`
 
 # Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to pass JVM options to this script.
-DEFAULT_JVM_OPTS=""
+DEFAULT_JVM_OPTS='"-Xmx64m" "-Xms64m"'
 
 # Use the maximum available, or set MAX_FD != -1 to use that value.
 MAX_FD="maximum"
@@ -66,6 +82,7 @@ esac
 
 CLASSPATH=$APP_HOME/gradle/wrapper/gradle-wrapper.jar
 
+
 # Determine the Java command to use to start the JVM.
 if [ -n "$JAVA_HOME" ] ; then
     if [ -x "$JAVA_HOME/jre/sh/java" ] ; then
@@ -109,10 +126,11 @@ if $darwin; then
     GRADLE_OPTS="$GRADLE_OPTS \"-Xdock:name=$APP_NAME\" \"-Xdock:icon=$APP_HOME/media/gradle.icns\""
 fi
 
-# For Cygwin, switch paths to Windows format before running java
-if $cygwin ; then
+# For Cygwin or MSYS, switch paths to Windows format before running java
+if [ "$cygwin" = "true" -o "$msys" = "true" ] ; then
     APP_HOME=`cygpath --path --mixed "$APP_HOME"`
     CLASSPATH=`cygpath --path --mixed "$CLASSPATH"`
+
     JAVACMD=`cygpath --unix "$JAVACMD"`
 
     # We build the pattern for arguments to be converted via cygpath
@@ -138,19 +156,19 @@ if $cygwin ; then
         else
             eval `echo args$i`="\"$arg\""
         fi
-        i=$((i+1))
+        i=`expr $i + 1`
     done
     case $i in
-        (0) set -- ;;
-        (1) set -- "$args0" ;;
-        (2) set -- "$args0" "$args1" ;;
-        (3) set -- "$args0" "$args1" "$args2" ;;
-        (4) set -- "$args0" "$args1" "$args2" "$args3" ;;
-        (5) set -- "$args0" "$args1" "$args2" "$args3" "$args4" ;;
-        (6) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" ;;
-        (7) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" ;;
-        (8) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" "$args7" ;;
-        (9) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" "$args7" "$args8" ;;
+        0) set -- ;;
+        1) set -- "$args0" ;;
+        2) set -- "$args0" "$args1" ;;
+        3) set -- "$args0" "$args1" "$args2" ;;
+        4) set -- "$args0" "$args1" "$args2" "$args3" ;;
+        5) set -- "$args0" "$args1" "$args2" "$args3" "$args4" ;;
+        6) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" ;;
+        7) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" ;;
+        8) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" "$args7" ;;
+        9) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" "$args7" "$args8" ;;
     esac
 fi
 
@@ -159,14 +177,9 @@ save () {
     for i do printf %s\\n "$i" | sed "s/'/'\\\\''/g;1s/^/'/;\$s/\$/' \\\\/" ; done
     echo " "
 }
-APP_ARGS=$(save "$@")
+APP_ARGS=`save "$@"`
 
 # Collect all arguments for the java command, following the shell quoting and substitution rules
 eval set -- $DEFAULT_JVM_OPTS $JAVA_OPTS $GRADLE_OPTS "\"-Dorg.gradle.appname=$APP_BASE_NAME\"" -classpath "\"$CLASSPATH\"" org.gradle.wrapper.GradleWrapperMain "$APP_ARGS"
 
-# by default we should be in the correct project dir, but when run from Finder on Mac, the cwd is wrong
-if [ "$(uname)" = "Darwin" ] && [ "$HOME" = "$PWD" ]; then
-  cd "$(dirname "$0")"
-fi
-
 exec "$JAVACMD" "$@"
diff --git a/Prj-Android/gradlew.bat b/Prj-Android/gradlew.bat
old mode 100755
new mode 100644
index e95643d..ac1b06f
--- a/Prj-Android/gradlew.bat
+++ b/Prj-Android/gradlew.bat
@@ -1,3 +1,19 @@
+@rem
+@rem Copyright 2015 the original author or authors.
+@rem
+@rem Licensed under the Apache License, Version 2.0 (the "License");
+@rem you may not use this file except in compliance with the License.
+@rem You may obtain a copy of the License at
+@rem
+@rem      https://www.apache.org/licenses/LICENSE-2.0
+@rem
+@rem Unless required by applicable law or agreed to in writing, software
+@rem distributed under the License is distributed on an "AS IS" BASIS,
+@rem WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+@rem See the License for the specific language governing permissions and
+@rem limitations under the License.
+@rem
+
 @if "%DEBUG%" == "" @echo off
 @rem ##########################################################################
 @rem
@@ -13,15 +29,18 @@ if "%DIRNAME%" == "" set DIRNAME=.
 set APP_BASE_NAME=%~n0
 set APP_HOME=%DIRNAME%
 
+@rem Resolve any "." and ".." in APP_HOME to make it shorter.
+for %%i in ("%APP_HOME%") do set APP_HOME=%%~fi
+
 @rem Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to pass JVM options to this script.
-set DEFAULT_JVM_OPTS=
+set DEFAULT_JVM_OPTS="-Xmx64m" "-Xms64m"
 
 @rem Find java.exe
 if defined JAVA_HOME goto findJavaFromJavaHome
 
 set JAVA_EXE=java.exe
 %JAVA_EXE% -version >NUL 2>&1
-if "%ERRORLEVEL%" == "0" goto init
+if "%ERRORLEVEL%" == "0" goto execute
 
 echo.
 echo ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH.
@@ -35,7 +54,7 @@ goto fail
 set JAVA_HOME=%JAVA_HOME:"=%
 set JAVA_EXE=%JAVA_HOME%/bin/java.exe
 
-if exist "%JAVA_EXE%" goto init
+if exist "%JAVA_EXE%" goto execute
 
 echo.
 echo ERROR: JAVA_HOME is set to an invalid directory: %JAVA_HOME%
@@ -45,28 +64,14 @@ echo location of your Java installation.
 
 goto fail
 
-:init
-@rem Get command-line arguments, handling Windows variants
-
-if not "%OS%" == "Windows_NT" goto win9xME_args
-
-:win9xME_args
-@rem Slurp the command line arguments.
-set CMD_LINE_ARGS=
-set _SKIP=2
-
-:win9xME_args_slurp
-if "x%~1" == "x" goto execute
-
-set CMD_LINE_ARGS=%*
-
 :execute
 @rem Setup the command line
 
 set CLASSPATH=%APP_HOME%\gradle\wrapper\gradle-wrapper.jar
 
+
 @rem Execute Gradle
-"%JAVA_EXE%" %DEFAULT_JVM_OPTS% %JAVA_OPTS% %GRADLE_OPTS% "-Dorg.gradle.appname=%APP_BASE_NAME%" -classpath "%CLASSPATH%" org.gradle.wrapper.GradleWrapperMain %CMD_LINE_ARGS%
+"%JAVA_EXE%" %DEFAULT_JVM_OPTS% %JAVA_OPTS% %GRADLE_OPTS% "-Dorg.gradle.appname=%APP_BASE_NAME%" -classpath "%CLASSPATH%" org.gradle.wrapper.GradleWrapperMain %*
 
 :end
 @rem End local scope for the variables with windows NT shell
diff --git a/Prj-Android/hyperlpr3/.gitignore b/Prj-Android/hyperlpr3/.gitignore
new file mode 100644
index 0000000..42afabf
--- /dev/null
+++ b/Prj-Android/hyperlpr3/.gitignore
@@ -0,0 +1 @@
+/build
\ No newline at end of file
diff --git a/Prj-Android/hyperlpr3/build.gradle b/Prj-Android/hyperlpr3/build.gradle
new file mode 100644
index 0000000..3d94d9d
--- /dev/null
+++ b/Prj-Android/hyperlpr3/build.gradle
@@ -0,0 +1,47 @@
+plugins {
+    id 'com.android.library'
+}
+
+android {
+    compileSdk 31
+
+    defaultConfig {
+        minSdk 22
+        targetSdk 31
+        versionCode 1
+        versionName "1.0"
+
+        testInstrumentationRunner "androidx.test.runner.AndroidJUnitRunner"
+        consumerProguardFiles "consumer-rules.pro"
+
+        ndk { abiFilters "armeabi-v7a", "arm64-v8a" }
+    }
+
+    buildTypes {
+        release {
+            minifyEnabled false
+            proguardFiles getDefaultProguardFile('proguard-android-optimize.txt'), 'proguard-rules.pro'
+        }
+    }
+
+    sourceSets {
+        main {
+            jniLibs.srcDirs = ['libs']
+            assets.srcDirs = ['src/main/assets']
+        }
+    }
+
+    compileOptions {
+        sourceCompatibility JavaVersion.VERSION_1_8
+        targetCompatibility JavaVersion.VERSION_1_8
+    }
+}
+
+dependencies {
+
+    implementation 'androidx.appcompat:appcompat:1.2.0'
+    implementation 'com.google.android.material:material:1.3.0'
+    testImplementation 'junit:junit:4.+'
+    androidTestImplementation 'androidx.test.ext:junit:1.1.2'
+    androidTestImplementation 'androidx.test.espresso:espresso-core:3.3.0'
+}
\ No newline at end of file
diff --git a/hyperlpr/__init__.py b/Prj-Android/hyperlpr3/consumer-rules.pro
similarity index 100%
rename from hyperlpr/__init__.py
rename to Prj-Android/hyperlpr3/consumer-rules.pro
diff --git a/Prj-Android/hyperlpr3/proguard-rules.pro b/Prj-Android/hyperlpr3/proguard-rules.pro
new file mode 100644
index 0000000..481bb43
--- /dev/null
+++ b/Prj-Android/hyperlpr3/proguard-rules.pro
@@ -0,0 +1,21 @@
+# Add project specific ProGuard rules here.
+# You can control the set of applied configuration files using the
+# proguardFiles setting in build.gradle.
+#
+# For more details, see
+#   http://developer.android.com/guide/developing/tools/proguard.html
+
+# If your project uses WebView with JS, uncomment the following
+# and specify the fully qualified class name to the JavaScript interface
+# class:
+#-keepclassmembers class fqcn.of.javascript.interface.for.webview {
+#   public *;
+#}
+
+# Uncomment this to preserve the line number information for
+# debugging stack traces.
+#-keepattributes SourceFile,LineNumberTable
+
+# If you keep the line number information, uncomment this to
+# hide the original source file name.
+#-renamesourcefileattribute SourceFile
\ No newline at end of file
diff --git a/Prj-Android/hyperlpr3/src/androidTest/java/com/hyperai/hyperlpr3/ExampleInstrumentedTest.java b/Prj-Android/hyperlpr3/src/androidTest/java/com/hyperai/hyperlpr3/ExampleInstrumentedTest.java
new file mode 100644
index 0000000..6d5f4da
--- /dev/null
+++ b/Prj-Android/hyperlpr3/src/androidTest/java/com/hyperai/hyperlpr3/ExampleInstrumentedTest.java
@@ -0,0 +1,26 @@
+package com.hyperai.hyperlpr3;
+
+import android.content.Context;
+
+import androidx.test.platform.app.InstrumentationRegistry;
+import androidx.test.ext.junit.runners.AndroidJUnit4;
+
+import org.junit.Test;
+import org.junit.runner.RunWith;
+
+import static org.junit.Assert.*;
+
+/**
+ * Instrumented test, which will execute on an Android device.
+ *
+ * @see <a href="http://d.android.com/tools/testing">Testing documentation</a>
+ */
+@RunWith(AndroidJUnit4.class)
+public class ExampleInstrumentedTest {
+    @Test
+    public void useAppContext() {
+        // Context of the app under test.
+        Context appContext = InstrumentationRegistry.getInstrumentation().getTargetContext();
+        assertEquals("com.hyperai.hyperlpr3.test", appContext.getPackageName());
+    }
+}
\ No newline at end of file
diff --git a/Prj-Android/hyperlpr3/src/main/assets/r2_mobile/b320_backbone_h.mnn b/Prj-Android/hyperlpr3/src/main/assets/r2_mobile/b320_backbone_h.mnn
new file mode 100644
index 0000000..b4621a6
Binary files /dev/null and b/Prj-Android/hyperlpr3/src/main/assets/r2_mobile/b320_backbone_h.mnn differ
diff --git a/Prj-Android/hyperlpr3/src/main/assets/r2_mobile/b320_header_h.mnn b/Prj-Android/hyperlpr3/src/main/assets/r2_mobile/b320_header_h.mnn
new file mode 100644
index 0000000..fa94647
Binary files /dev/null and b/Prj-Android/hyperlpr3/src/main/assets/r2_mobile/b320_header_h.mnn differ
diff --git a/Prj-Android/hyperlpr3/src/main/assets/r2_mobile/b640x_backbone_h.mnn b/Prj-Android/hyperlpr3/src/main/assets/r2_mobile/b640x_backbone_h.mnn
new file mode 100644
index 0000000..de7962c
Binary files /dev/null and b/Prj-Android/hyperlpr3/src/main/assets/r2_mobile/b640x_backbone_h.mnn differ
diff --git a/Prj-Android/hyperlpr3/src/main/assets/r2_mobile/b640x_head_h.mnn b/Prj-Android/hyperlpr3/src/main/assets/r2_mobile/b640x_head_h.mnn
new file mode 100644
index 0000000..b464900
Binary files /dev/null and b/Prj-Android/hyperlpr3/src/main/assets/r2_mobile/b640x_head_h.mnn differ
diff --git a/Prj-Android/hyperlpr3/src/main/assets/r2_mobile/litemodel_cls_96xh.mnn b/Prj-Android/hyperlpr3/src/main/assets/r2_mobile/litemodel_cls_96xh.mnn
new file mode 100644
index 0000000..124c7fa
Binary files /dev/null and b/Prj-Android/hyperlpr3/src/main/assets/r2_mobile/litemodel_cls_96xh.mnn differ
diff --git a/Prj-Android/hyperlpr3/src/main/assets/r2_mobile/rpv3_mdict_160h.mnn b/Prj-Android/hyperlpr3/src/main/assets/r2_mobile/rpv3_mdict_160h.mnn
new file mode 100644
index 0000000..58a808b
Binary files /dev/null and b/Prj-Android/hyperlpr3/src/main/assets/r2_mobile/rpv3_mdict_160h.mnn differ
diff --git a/Prj-Android/hyperlpr3/src/main/java/com/hyperai/hyperlpr3/HyperLPR3.java b/Prj-Android/hyperlpr3/src/main/java/com/hyperai/hyperlpr3/HyperLPR3.java
new file mode 100644
index 0000000..993e19b
--- /dev/null
+++ b/Prj-Android/hyperlpr3/src/main/java/com/hyperai/hyperlpr3/HyperLPR3.java
@@ -0,0 +1,64 @@
+package com.hyperai.hyperlpr3;
+
+import android.content.Context;
+import android.util.Log;
+
+import com.hyperai.hyperlpr3.api.APIDefine;
+import com.hyperai.hyperlpr3.bean.Parameter;
+import com.hyperai.hyperlpr3.bean.Plate;
+import com.hyperai.hyperlpr3.core.HyperLPRCore;
+import com.hyperai.hyperlpr3.settings.TypeDefine;
+import com.hyperai.hyperlpr3.settings.SDKConfig;
+import com.hyperai.hyperlpr3.utils.SDKUtils;
+
+public class HyperLPR3 extends TypeDefine implements APIDefine {
+
+    private final String TAG = "HyperLPR3";
+
+    private final HyperLPRCore mCore;
+
+    private final Context mContext;
+
+    public HyperLPR3(Context mContext, Parameter parameter) {
+        this.mContext = mContext;
+        String mResourceFolderPath = mContext.getExternalFilesDir(null).getAbsolutePath() + "/";
+        SDKUtils.copyFilesFromAssets(mContext, SDKConfig.packDirName, mResourceFolderPath);
+        Log.i(TAG, "resource: " + mResourceFolderPath);
+        if (parameter.getModelPath() == null || "".equals(parameter.getModelPath())) {
+            parameter.setModelPath(mResourceFolderPath);
+        }
+        mCore = new HyperLPRCore();
+        mCore.createRecognizerContext(parameter);
+    }
+
+    public Context getContext() {
+        return mContext;
+    }
+
+    public void release() {
+        mCore.release();
+    }
+
+    @Override
+    protected void finalize() throws Throwable {
+        super.finalize();
+        release();
+    }
+
+    /**
+     * License plate recognition interface.
+     *
+     * @param buf      Image data buffer.
+     * @param height   Height of the image
+     * @param width    Width of the image
+     * @param rotation Original data buffer rotation Angle
+     * @param format   Buffer data coded format
+     * @return Resulting object array
+     */
+    @Override
+    public Plate[] plateRecognition(byte[] buf, int height, int width, int rotation, int format) {
+        return mCore.plateRecognitionFromBuffer(buf, height, width, rotation, format);
+    }
+
+
+}
diff --git a/Prj-Android/hyperlpr3/src/main/java/com/hyperai/hyperlpr3/api/APIDefine.java b/Prj-Android/hyperlpr3/src/main/java/com/hyperai/hyperlpr3/api/APIDefine.java
new file mode 100644
index 0000000..250b011
--- /dev/null
+++ b/Prj-Android/hyperlpr3/src/main/java/com/hyperai/hyperlpr3/api/APIDefine.java
@@ -0,0 +1,18 @@
+package com.hyperai.hyperlpr3.api;
+
+import com.hyperai.hyperlpr3.bean.Plate;
+
+public interface APIDefine {
+
+    /**
+     * License plate recognition interface.
+     * @param buf Image data buffer.
+     * @param height Height of the image
+     * @param width Width of the image
+     * @param rotation Original data buffer rotation Angle
+     * @param format Buffer data coded format
+     * @return Resulting object array
+     */
+    Plate[] plateRecognition(byte[] buf, int height, int width, int rotation, int format);
+
+}
diff --git a/Prj-Android/hyperlpr3/src/main/java/com/hyperai/hyperlpr3/bean/Parameter.java b/Prj-Android/hyperlpr3/src/main/java/com/hyperai/hyperlpr3/bean/Parameter.java
new file mode 100644
index 0000000..1067b63
--- /dev/null
+++ b/Prj-Android/hyperlpr3/src/main/java/com/hyperai/hyperlpr3/bean/Parameter.java
@@ -0,0 +1,89 @@
+package com.hyperai.hyperlpr3.bean;
+
+import com.hyperai.hyperlpr3.settings.TypeDefine;
+
+public class Parameter {
+
+    private String modelPath;
+
+    private int threads = 1;
+
+    private boolean useHalf = true;
+
+    private float boxConfThreshold = 0.25f;
+
+    private float nmsThreshold = 0.6f;
+
+    private float recConfidenceThreshold = 0.75f;
+
+    private int detLevel = TypeDefine.DETECT_LEVEL_LOW;
+
+    private int maxNum = 3;
+
+    public Parameter() {
+    }
+
+    public int getMaxNum() {
+        return maxNum;
+    }
+
+    public void setMaxNum(int maxNum) {
+        this.maxNum = maxNum;
+    }
+
+    public String getModelPath() {
+        return modelPath;
+    }
+
+    public void setModelPath(String modelPath) {
+        this.modelPath = modelPath;
+    }
+
+    public int getThreads() {
+        return threads;
+    }
+
+    public void setThreads(int threads) {
+        this.threads = threads;
+    }
+
+    public boolean isUseHalf() {
+        return useHalf;
+    }
+
+    public void setUseHalf(boolean useHalf) {
+        this.useHalf = useHalf;
+    }
+
+    public float getBoxConfThreshold() {
+        return boxConfThreshold;
+    }
+
+    public void setBoxConfThreshold(float boxConfThreshold) {
+        this.boxConfThreshold = boxConfThreshold;
+    }
+
+    public float getNmsThreshold() {
+        return nmsThreshold;
+    }
+
+    public void setNmsThreshold(float nmsThreshold) {
+        this.nmsThreshold = nmsThreshold;
+    }
+
+    public float getRecConfidenceThreshold() {
+        return recConfidenceThreshold;
+    }
+
+    public void setRecConfidenceThreshold(float recConfidenceThreshold) {
+        this.recConfidenceThreshold = recConfidenceThreshold;
+    }
+
+    public int getDetLevel() {
+        return detLevel;
+    }
+
+    public void setDetLevel(int detLevel) {
+        this.detLevel = detLevel;
+    }
+}
diff --git a/Prj-Android/hyperlpr3/src/main/java/com/hyperai/hyperlpr3/bean/Plate.java b/Prj-Android/hyperlpr3/src/main/java/com/hyperai/hyperlpr3/bean/Plate.java
new file mode 100644
index 0000000..5ba9e41
--- /dev/null
+++ b/Prj-Android/hyperlpr3/src/main/java/com/hyperai/hyperlpr3/bean/Plate.java
@@ -0,0 +1,89 @@
+package com.hyperai.hyperlpr3.bean;
+
+public class Plate {
+
+    private float x1;
+
+    private float y1;
+
+    private float x2;
+
+    private float y2;
+
+    private int type;
+
+    private float confidence;
+
+    private String code;
+
+    public Plate() {};
+
+    public float getX1() {
+        return x1;
+    }
+
+    public void setX1(float x1) {
+        this.x1 = x1;
+    }
+
+    public float getY1() {
+        return y1;
+    }
+
+    public void setY1(float y1) {
+        this.y1 = y1;
+    }
+
+    public float getX2() {
+        return x2;
+    }
+
+    public void setX2(float x2) {
+        this.x2 = x2;
+    }
+
+    public float getY2() {
+        return y2;
+    }
+
+    public void setY2(float y2) {
+        this.y2 = y2;
+    }
+
+    public int getType() {
+        return type;
+    }
+
+    public void setType(int type) {
+        this.type = type;
+    }
+
+    public float getConfidence() {
+        return confidence;
+    }
+
+    public void setConfidence(float confidence) {
+        this.confidence = confidence;
+    }
+
+    public String getCode() {
+        return code;
+    }
+
+    public void setCode(String code) {
+        this.code = code;
+    }
+
+    @Override
+    public String toString() {
+        return "Plate{" +
+                "x1=" + x1 +
+                ", y1=" + y1 +
+                ", x2=" + x2 +
+                ", y2=" + y2 +
+                ", type=" + type +
+                ", confidence=" + confidence +
+                ", code='" + code + '\'' +
+                '}';
+    }
+}
diff --git a/Prj-Android/hyperlpr3/src/main/java/com/hyperai/hyperlpr3/core/HyperLPRCore.java b/Prj-Android/hyperlpr3/src/main/java/com/hyperai/hyperlpr3/core/HyperLPRCore.java
new file mode 100644
index 0000000..ac3952c
--- /dev/null
+++ b/Prj-Android/hyperlpr3/src/main/java/com/hyperai/hyperlpr3/core/HyperLPRCore.java
@@ -0,0 +1,53 @@
+package com.hyperai.hyperlpr3.core;
+
+import android.util.Log;
+
+import com.hyperai.hyperlpr3.bean.Parameter;
+import com.hyperai.hyperlpr3.bean.Plate;
+
+public class HyperLPRCore {
+
+    final String TAG = "HyperLPRCore";
+
+    static {
+        System.loadLibrary("hyperlpr3");
+    }
+
+    private long ctxHandle_;
+
+    private boolean isRunning_;
+
+//    public void testBuffer(String savePath, byte[] buf, int height, int width, int rotation) {
+//        TestBuffer(savePath, buf, height, width, rotation);
+//    }
+
+    public void createRecognizerContext(Parameter parameter) {
+        ctxHandle_ = CreateRecognizerContext(parameter);
+        Log.i(TAG, "HANDLE: " + ctxHandle_);
+        isRunning_ = true;
+    }
+
+    public Plate[] plateRecognitionFromBuffer(byte[] buf, int height, int width, int rotation, int format) {
+        return PlateRecognitionFromBuffer(ctxHandle_, buf, height, width, rotation, format);
+    }
+
+    public int release() {
+        int ret = -1;
+        if (isRunning_) {
+            ret = ReleaseRecognizerContext(ctxHandle_);
+            isRunning_ = false;
+            ctxHandle_ = 0;
+        }
+        return ret;
+    }
+
+    // ===================Native==================
+
+//    native void TestBuffer(String savePath, byte[] buf, int height, int width, int rotation);
+
+    native long CreateRecognizerContext(Parameter parameterObj);
+
+    native int ReleaseRecognizerContext(long handle);
+
+    native Plate[] PlateRecognitionFromBuffer(long handle, byte[] buf, int height, int width, int rotation, int format);
+}
diff --git a/Prj-Android/hyperlpr3/src/main/java/com/hyperai/hyperlpr3/settings/SDKConfig.java b/Prj-Android/hyperlpr3/src/main/java/com/hyperai/hyperlpr3/settings/SDKConfig.java
new file mode 100644
index 0000000..e0e816e
--- /dev/null
+++ b/Prj-Android/hyperlpr3/src/main/java/com/hyperai/hyperlpr3/settings/SDKConfig.java
@@ -0,0 +1,7 @@
+package com.hyperai.hyperlpr3.settings;
+
+public class SDKConfig {
+
+    public static final String packDirName = "r2_mobile";
+
+}
diff --git a/Prj-Android/hyperlpr3/src/main/java/com/hyperai/hyperlpr3/settings/TypeDefine.java b/Prj-Android/hyperlpr3/src/main/java/com/hyperai/hyperlpr3/settings/TypeDefine.java
new file mode 100644
index 0000000..aa4ef08
--- /dev/null
+++ b/Prj-Android/hyperlpr3/src/main/java/com/hyperai/hyperlpr3/settings/TypeDefine.java
@@ -0,0 +1,54 @@
+package com.hyperai.hyperlpr3.settings;
+
+public class TypeDefine {
+
+    /** 四种情况的转角 */
+    public static final int CAMERA_ROTATION_0 = 0;
+    public static final int CAMERA_ROTATION_90 = 1;
+    public static final int CAMERA_ROTATION_180 = 2;
+    public static final int CAMERA_ROTATION_270 = 3;
+
+    /** 低开销检测模式 */
+    public static final int DETECT_LEVEL_LOW = 0;
+    /** 高开销检测模式 */
+    public static final int DETECT_LEVEL_HIGH = 1;
+
+    /** Image in RGB format - RGB排列格式的图像 */
+    public static final int STREAM_RGB = 0;
+    /** Image in BGR format (Opencv Mat default) - BGR排列格式的图像(OpenCV的Mat默认) */
+    public static final int STREAM_BGR = 1;
+    /** Image in RGB with alpha channel format -带alpha通道的RGB排列格式的图像 */
+    public static final int STREAM_RGBA = 2;
+    /** Image in BGR with alpha channel format -带alpha通道的BGR排列格式的图像 */
+    public static final int STREAM_BGRA = 3;
+    /** Image in YUV NV12 format - YUV NV12排列的图像格式 */
+    public static final int STREAM_YUV_NV12 = 4;
+    /** Image in YUV NV21 format - YUV NV21排列的图像格式 */
+    public static final int STREAM_YUV_NV21 = 5;
+
+
+    /** 未知车牌 */
+    public static final int PLATE_TYPE_UNKNOWN = -1;
+    /** 蓝牌 */
+    public static final int PLATE_TYPE_BLUE = 0;
+    /** 黄牌单层 */
+    public static final int PLATE_TYPE_YELLOW_SINGLE = 1;
+    /** 白牌单层 */
+    public static final int PLATE_TYPE_WHILE_SINGLE = 2;
+    /** 绿牌新能源 */
+    public static final int PLATE_TYPE_GREEN = 3;
+    /** 黑牌港澳 */
+    public static final int PLATE_TYPE_BLACK_HK_MACAO = 4;
+    /** 香港单层 */
+    public static final int PLATE_TYPE_HK_SINGLE = 5;
+    /** 香港双层 */
+    public static final int PLATE_TYPE_HK_DOUBLE = 6;
+    /** 澳门单层 */
+    public static final int PLATE_TYPE_MACAO_SINGLE = 7;
+    /** 澳门双层 */
+    public static final int PLATE_TYPE_MACAO_DOUBLE = 8;
+    /** 黄牌双层 */
+    public static final int PLATE_TYPE_YELLOW_DOUBLE = 9;
+
+    public static final String[] PLATE_TYPE_MAPS = {"蓝牌", "黄牌单层", "白牌单层", "绿牌新能源", "黑牌港澳", "香港单层", "香港双层", "澳门单层", "澳门双层", "黄牌双层"};
+}
diff --git a/Prj-Android/hyperlpr3/src/main/java/com/hyperai/hyperlpr3/utils/SDKUtils.java b/Prj-Android/hyperlpr3/src/main/java/com/hyperai/hyperlpr3/utils/SDKUtils.java
new file mode 100644
index 0000000..927b530
--- /dev/null
+++ b/Prj-Android/hyperlpr3/src/main/java/com/hyperai/hyperlpr3/utils/SDKUtils.java
@@ -0,0 +1,129 @@
+package com.hyperai.hyperlpr3.utils;
+
+import android.content.Context;
+import android.content.res.AssetManager;
+import android.graphics.Bitmap;
+import android.graphics.BitmapFactory;
+import android.graphics.Canvas;
+import android.graphics.DashPathEffect;
+import android.graphics.Matrix;
+import android.graphics.Paint;
+import android.graphics.Path;
+import android.util.Log;
+
+import java.io.File;
+import java.io.FileOutputStream;
+import java.io.IOException;
+import java.io.InputStream;
+
+public class SDKUtils {
+    static public void copyFilesFromAssets(Context context, String oldPath, String newPath) {
+        try {
+            String[] fileNames = context.getAssets().list(oldPath);
+            if (fileNames.length > 0) {
+                // directory
+                File file = new File(newPath);
+                if (!file.mkdir()) {
+                    Log.d("mkdir", "can't make folder");
+                }
+//                    return false;                // copy recursively
+                for (String fileName : fileNames) {
+                    copyFilesFromAssets(context, oldPath + "/" + fileName,
+                            newPath + "/" + fileName);
+                }
+            } else {
+                // file
+                InputStream is = context.getAssets().open(oldPath);
+                FileOutputStream fos = new FileOutputStream(new File(newPath));
+                byte[] buffer = new byte[1024];
+                int byteCount;
+                while ((byteCount = is.read(buffer)) != -1) {
+                    fos.write(buffer, 0, byteCount);
+                }
+                fos.flush();
+                is.close();
+                fos.close();
+            }
+        } catch (Exception e) {
+            // TODO Auto-generated catch block
+            e.printStackTrace();
+        }
+    }
+
+    static public void mkdirsFeaturesAssets(Context context, String path, String images, String features) {
+        try {
+            File file = new File(path);
+            File imagePath = new File(path + images);
+            File featurePath = new File(path + features);
+            if (!file.mkdir()) {
+                Log.d("mkdir", "can't make folder: " + file);
+            }
+            if (!imagePath.mkdir()) {
+                Log.d("mkdir", "can't make folder: " + imagePath);
+            }
+            if (!featurePath.mkdir()) {
+                Log.d("mkdir", "can't make folder: " + featurePath);
+            }
+        } catch (Exception e) {
+            // TODO Auto-generated catch block
+            e.printStackTrace();
+        }
+    }
+
+    static public String saveBitmap(String savePath, String name, Bitmap bitmap) throws IOException {
+        File f = new File(savePath, name + ".png");
+        if (f.exists()) {
+            f.delete();
+        }
+        FileOutputStream out = new FileOutputStream(f);
+        bitmap.compress(Bitmap.CompressFormat.PNG, 100, out);
+        out.flush();
+        out.close();
+        return f.getAbsolutePath();
+    }
+
+
+    static public boolean cropperBitmap(Bitmap bitmap, int[] rePoints, String sdcardPath, String name) {
+        int newWidth = 160;
+        int newHeight = 192;
+        float scaleWidth = ((float) newWidth) / bitmap.getWidth();
+        float scaleHeight = ((float) newHeight) / bitmap.getHeight();
+        Matrix matrix = new Matrix();
+//                                            matrix.postTranslate()
+        matrix.postScale(scaleWidth, scaleHeight);
+        matrix.setScale(-1, 1);
+        int px = Math.max(0, rePoints[0]);
+        int py = Math.max(0, rePoints[1]);
+        int w = rePoints[2] - px;
+        int h = rePoints[3] - py;
+        int pw = (bitmap.getWidth() - w > 0) ? w - 1 : bitmap.getWidth() - 1;
+        int ph = (bitmap.getHeight() - h > 0) ? h - 1 : bitmap.getHeight() - 1;
+        try {
+            Bitmap crop = Bitmap.createBitmap(bitmap, px, py, pw, ph, matrix, true);
+//            String sdcardPath =  Environment.getExternalStorageDirectory() + File.separator + Const.root + Const.save + Const.images;
+            saveBitmap(sdcardPath, name, crop);
+        } catch (Exception err) {
+            Log.d("crop", "ext");
+            return false;
+        }
+        return true;
+    }
+
+
+    public static Bitmap getImageFromAssetsFile(Context context, String fileName) {
+        Bitmap image = null;
+        AssetManager am = context.getResources().getAssets();
+        try {
+            InputStream is = am.open(fileName);
+            image = BitmapFactory.decodeStream(is);
+            is.close();
+        } catch (IOException e) {
+            e.printStackTrace();
+        }
+
+        return image;
+    }
+
+
+
+}
diff --git a/Prj-Android/hyperlpr3/src/test/java/com/hyperai/hyperlpr3/ExampleUnitTest.java b/Prj-Android/hyperlpr3/src/test/java/com/hyperai/hyperlpr3/ExampleUnitTest.java
new file mode 100644
index 0000000..b68fdab
--- /dev/null
+++ b/Prj-Android/hyperlpr3/src/test/java/com/hyperai/hyperlpr3/ExampleUnitTest.java
@@ -0,0 +1,17 @@
+package com.hyperai.hyperlpr3;
+
+import org.junit.Test;
+
+import static org.junit.Assert.*;
+
+/**
+ * Example local unit test, which will execute on the development machine (host).
+ *
+ * @see <a href="http://d.android.com/tools/testing">Testing documentation</a>
+ */
+public class ExampleUnitTest {
+    @Test
+    public void addition_isCorrect() {
+        assertEquals(4, 2 + 2);
+    }
+}
\ No newline at end of file
diff --git a/Prj-Android/openCVLibrary342/.gitignore b/Prj-Android/openCVLibrary342/.gitignore
deleted file mode 100755
index 9bea547..0000000
--- a/Prj-Android/openCVLibrary342/.gitignore
+++ /dev/null
@@ -1,11 +0,0 @@
-*.iml
-.gradle
-/local.properties
-/.idea/libraries
-/.idea/modules.xml
-/.idea/workspace.xml
-.DS_Store
-/build
-/captures
-.externalNativeBuild
-.src/main/cpp/CMakeFiles
\ No newline at end of file
diff --git a/Prj-Android/openCVLibrary342/build.gradle b/Prj-Android/openCVLibrary342/build.gradle
deleted file mode 100755
index 282da87..0000000
--- a/Prj-Android/openCVLibrary342/build.gradle
+++ /dev/null
@@ -1,18 +0,0 @@
-apply plugin: 'com.android.library'
-
-android {
-    compileSdkVersion 25
-    buildToolsVersion "27.0.3"
-
-    defaultConfig {
-        minSdkVersion 8
-        targetSdkVersion 21
-    }
-
-    buildTypes {
-        release {
-            minifyEnabled false
-            proguardFiles getDefaultProguardFile('proguard-android.txt'), 'proguard-rules.txt'
-        }
-    }
-}
diff --git a/Prj-Android/openCVLibrary342/lint.xml b/Prj-Android/openCVLibrary342/lint.xml
deleted file mode 100755
index 6a5c9c6..0000000
--- a/Prj-Android/openCVLibrary342/lint.xml
+++ /dev/null
@@ -1,9 +0,0 @@
-<?xml version="1.0" encoding="utf-8"?>
-<lint>
-    <issue id="InlinedApi">
-        <ignore path="src\org\opencv\android\JavaCameraView.java" />
-    </issue>
-    <issue id="NewApi">
-        <ignore path="src\org\opencv\android\JavaCameraView.java" />
-    </issue>
-</lint>
diff --git a/Prj-Android/openCVLibrary342/src/main/AndroidManifest.xml b/Prj-Android/openCVLibrary342/src/main/AndroidManifest.xml
deleted file mode 100755
index 9964e20..0000000
--- a/Prj-Android/openCVLibrary342/src/main/AndroidManifest.xml
+++ /dev/null
@@ -1,8 +0,0 @@
-<?xml version="1.0" encoding="utf-8"?>
-<manifest xmlns:android="http://schemas.android.com/apk/res/android"
-      package="org.opencv"
-      android:versionCode="3420"
-      android:versionName="3.4.2">
-
-    <uses-sdk android:minSdkVersion="8" android:targetSdkVersion="21" />
-</manifest>
diff --git a/Prj-Android/openCVLibrary342/src/main/aidl/org/opencv/engine/OpenCVEngineInterface.aidl b/Prj-Android/openCVLibrary342/src/main/aidl/org/opencv/engine/OpenCVEngineInterface.aidl
deleted file mode 100755
index 21fe5f7..0000000
--- a/Prj-Android/openCVLibrary342/src/main/aidl/org/opencv/engine/OpenCVEngineInterface.aidl
+++ /dev/null
@@ -1,33 +0,0 @@
-package org.opencv.engine;
-
-/**
-* Class provides a Java interface for OpenCV Engine Service. It's synchronous with native OpenCVEngine class.
-*/
-interface OpenCVEngineInterface
-{
-    /**
-    * @return Returns service version.
-    */
-    int getEngineVersion();
-
-    /**
-    * Finds an installed OpenCV library.
-    * @param OpenCV version.
-    * @return Returns path to OpenCV native libs or an empty string if OpenCV can not be found.
-    */
-    String getLibPathByVersion(String version);
-
-    /**
-    * Tries to install defined version of OpenCV from Google Play Market.
-    * @param OpenCV version.
-    * @return Returns true if installation was successful or OpenCV package has been already installed.
-    */
-    boolean installVersion(String version);
-
-    /**
-    * Returns list of libraries in loading order, separated by semicolon.
-    * @param OpenCV version.
-    * @return Returns names of OpenCV libraries, separated by semicolon.
-    */
-    String getLibraryList(String version);
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/android/AsyncServiceHelper.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/android/AsyncServiceHelper.java
deleted file mode 100755
index 04a4ac6..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/android/AsyncServiceHelper.java
+++ /dev/null
@@ -1,391 +0,0 @@
-package org.opencv.android;
-
-import java.io.File;
-import java.util.StringTokenizer;
-
-import org.opencv.core.Core;
-import org.opencv.engine.OpenCVEngineInterface;
-
-import android.content.ComponentName;
-import android.content.Context;
-import android.content.Intent;
-import android.content.ServiceConnection;
-import android.net.Uri;
-import android.os.IBinder;
-import android.os.RemoteException;
-import android.util.Log;
-
-class AsyncServiceHelper
-{
-    public static boolean initOpenCV(String Version, final Context AppContext,
-            final LoaderCallbackInterface Callback)
-    {
-        AsyncServiceHelper helper = new AsyncServiceHelper(Version, AppContext, Callback);
-        Intent intent = new Intent("org.opencv.engine.BIND");
-        intent.setPackage("org.opencv.engine");
-        if (AppContext.bindService(intent, helper.mServiceConnection, Context.BIND_AUTO_CREATE))
-        {
-            return true;
-        }
-        else
-        {
-            AppContext.unbindService(helper.mServiceConnection);
-            InstallService(AppContext, Callback);
-            return false;
-        }
-    }
-
-    protected AsyncServiceHelper(String Version, Context AppContext, LoaderCallbackInterface Callback)
-    {
-        mOpenCVersion = Version;
-        mUserAppCallback = Callback;
-        mAppContext = AppContext;
-    }
-
-    protected static final String TAG = "OpenCVManager/Helper";
-    protected static final int MINIMUM_ENGINE_VERSION = 2;
-    protected OpenCVEngineInterface mEngineService;
-    protected LoaderCallbackInterface mUserAppCallback;
-    protected String mOpenCVersion;
-    protected Context mAppContext;
-    protected static boolean mServiceInstallationProgress = false;
-    protected static boolean mLibraryInstallationProgress = false;
-
-    protected static boolean InstallServiceQuiet(Context context)
-    {
-        boolean result = true;
-        try
-        {
-            Intent intent = new Intent(Intent.ACTION_VIEW, Uri.parse(OPEN_CV_SERVICE_URL));
-            intent.addFlags(Intent.FLAG_ACTIVITY_NEW_TASK);
-            context.startActivity(intent);
-        }
-        catch(Exception e)
-        {
-            result = false;
-        }
-
-        return result;
-    }
-
-    protected static void InstallService(final Context AppContext, final LoaderCallbackInterface Callback)
-    {
-        if (!mServiceInstallationProgress)
-        {
-                Log.d(TAG, "Request new service installation");
-                InstallCallbackInterface InstallQuery = new InstallCallbackInterface() {
-                private LoaderCallbackInterface mUserAppCallback = Callback;
-                public String getPackageName()
-                {
-                    return "OpenCV Manager";
-                }
-                public void install() {
-                    Log.d(TAG, "Trying to install OpenCV Manager via Google Play");
-
-                    boolean result = InstallServiceQuiet(AppContext);
-                    if (result)
-                    {
-                        mServiceInstallationProgress = true;
-                        Log.d(TAG, "Package installation started");
-                    }
-                    else
-                    {
-                        Log.d(TAG, "OpenCV package was not installed!");
-                        int Status = LoaderCallbackInterface.MARKET_ERROR;
-                        Log.d(TAG, "Init finished with status " + Status);
-                        Log.d(TAG, "Unbind from service");
-                        Log.d(TAG, "Calling using callback");
-                        mUserAppCallback.onManagerConnected(Status);
-                    }
-                }
-
-                public void cancel()
-                {
-                    Log.d(TAG, "OpenCV library installation was canceled");
-                    int Status = LoaderCallbackInterface.INSTALL_CANCELED;
-                    Log.d(TAG, "Init finished with status " + Status);
-                    Log.d(TAG, "Calling using callback");
-                    mUserAppCallback.onManagerConnected(Status);
-                }
-
-                public void wait_install()
-                {
-                    Log.e(TAG, "Installation was not started! Nothing to wait!");
-                }
-            };
-
-            Callback.onPackageInstall(InstallCallbackInterface.NEW_INSTALLATION, InstallQuery);
-        }
-        else
-        {
-            Log.d(TAG, "Waiting current installation process");
-            InstallCallbackInterface WaitQuery = new InstallCallbackInterface() {
-                private LoaderCallbackInterface mUserAppCallback = Callback;
-                public String getPackageName()
-                {
-                    return "OpenCV Manager";
-                }
-                public void install()
-                {
-                    Log.e(TAG, "Nothing to install we just wait current installation");
-                }
-                public void cancel()
-                {
-                    Log.d(TAG, "Waiting for OpenCV canceled by user");
-                    mServiceInstallationProgress = false;
-                    int Status = LoaderCallbackInterface.INSTALL_CANCELED;
-                    Log.d(TAG, "Init finished with status " + Status);
-                    Log.d(TAG, "Calling using callback");
-                    mUserAppCallback.onManagerConnected(Status);
-                }
-                public void wait_install()
-                {
-                     InstallServiceQuiet(AppContext);
-                }
-            };
-
-            Callback.onPackageInstall(InstallCallbackInterface.INSTALLATION_PROGRESS, WaitQuery);
-        }
-    }
-
-    /**
-     *  URL of OpenCV Manager page on Google Play Market.
-     */
-    protected static final String OPEN_CV_SERVICE_URL = "market://details?id=org.opencv.engine";
-
-    protected ServiceConnection mServiceConnection = new ServiceConnection()
-    {
-        public void onServiceConnected(ComponentName className, IBinder service)
-        {
-            Log.d(TAG, "Service connection created");
-            mEngineService = OpenCVEngineInterface.Stub.asInterface(service);
-            if (null == mEngineService)
-            {
-                Log.d(TAG, "OpenCV Manager Service connection fails. May be service was not installed?");
-                InstallService(mAppContext, mUserAppCallback);
-            }
-            else
-            {
-                mServiceInstallationProgress = false;
-                try
-                {
-                    if (mEngineService.getEngineVersion() < MINIMUM_ENGINE_VERSION)
-                    {
-                        Log.d(TAG, "Init finished with status " + LoaderCallbackInterface.INCOMPATIBLE_MANAGER_VERSION);
-                        Log.d(TAG, "Unbind from service");
-                        mAppContext.unbindService(mServiceConnection);
-                        Log.d(TAG, "Calling using callback");
-                        mUserAppCallback.onManagerConnected(LoaderCallbackInterface.INCOMPATIBLE_MANAGER_VERSION);
-                        return;
-                    }
-
-                    Log.d(TAG, "Trying to get library path");
-                    String path = mEngineService.getLibPathByVersion(mOpenCVersion);
-                    if ((null == path) || (path.length() == 0))
-                    {
-                        if (!mLibraryInstallationProgress)
-                        {
-                            InstallCallbackInterface InstallQuery = new InstallCallbackInterface() {
-                                public String getPackageName()
-                                {
-                                    return "OpenCV library";
-                                }
-                                public void install() {
-                                    Log.d(TAG, "Trying to install OpenCV lib via Google Play");
-                                    try
-                                    {
-                                        if (mEngineService.installVersion(mOpenCVersion))
-                                        {
-                                            mLibraryInstallationProgress = true;
-                                            Log.d(TAG, "Package installation started");
-                                            Log.d(TAG, "Unbind from service");
-                                            mAppContext.unbindService(mServiceConnection);
-                                        }
-                                        else
-                                        {
-                                            Log.d(TAG, "OpenCV package was not installed!");
-                                            Log.d(TAG, "Init finished with status " + LoaderCallbackInterface.MARKET_ERROR);
-                                            Log.d(TAG, "Unbind from service");
-                                            mAppContext.unbindService(mServiceConnection);
-                                            Log.d(TAG, "Calling using callback");
-                                            mUserAppCallback.onManagerConnected(LoaderCallbackInterface.MARKET_ERROR);
-                                        }
-                                    } catch (RemoteException e) {
-                                        e.printStackTrace();;
-                                        Log.d(TAG, "Init finished with status " + LoaderCallbackInterface.INIT_FAILED);
-                                        Log.d(TAG, "Unbind from service");
-                                        mAppContext.unbindService(mServiceConnection);
-                                        Log.d(TAG, "Calling using callback");
-                                        mUserAppCallback.onManagerConnected(LoaderCallbackInterface.INIT_FAILED);
-                                    }
-                                }
-                                public void cancel() {
-                                    Log.d(TAG, "OpenCV library installation was canceled");
-                                    Log.d(TAG, "Init finished with status " + LoaderCallbackInterface.INSTALL_CANCELED);
-                                    Log.d(TAG, "Unbind from service");
-                                    mAppContext.unbindService(mServiceConnection);
-                                    Log.d(TAG, "Calling using callback");
-                                    mUserAppCallback.onManagerConnected(LoaderCallbackInterface.INSTALL_CANCELED);
-                                }
-                                public void wait_install() {
-                                    Log.e(TAG, "Installation was not started! Nothing to wait!");
-                                }
-                            };
-
-                            mUserAppCallback.onPackageInstall(InstallCallbackInterface.NEW_INSTALLATION, InstallQuery);
-                        }
-                        else
-                        {
-                            InstallCallbackInterface WaitQuery = new InstallCallbackInterface() {
-                                public String getPackageName()
-                                {
-                                    return "OpenCV library";
-                                }
-
-                                public void install() {
-                                    Log.e(TAG, "Nothing to install we just wait current installation");
-                                }
-                                public void cancel()
-                                {
-                                    Log.d(TAG, "OpenCV library installation was canceled");
-                                    mLibraryInstallationProgress = false;
-                                    Log.d(TAG, "Init finished with status " + LoaderCallbackInterface.INSTALL_CANCELED);
-                                    Log.d(TAG, "Unbind from service");
-                                    mAppContext.unbindService(mServiceConnection);
-                                    Log.d(TAG, "Calling using callback");
-                                        mUserAppCallback.onManagerConnected(LoaderCallbackInterface.INSTALL_CANCELED);
-                                }
-                                public void wait_install() {
-                                    Log.d(TAG, "Waiting for current installation");
-                                    try
-                                    {
-                                        if (!mEngineService.installVersion(mOpenCVersion))
-                                        {
-                                            Log.d(TAG, "OpenCV package was not installed!");
-                                            Log.d(TAG, "Init finished with status " + LoaderCallbackInterface.MARKET_ERROR);
-                                            Log.d(TAG, "Calling using callback");
-                                            mUserAppCallback.onManagerConnected(LoaderCallbackInterface.MARKET_ERROR);
-                                        }
-                                        else
-                                        {
-                                            Log.d(TAG, "Wating for package installation");
-                                        }
-
-                                        Log.d(TAG, "Unbind from service");
-                                        mAppContext.unbindService(mServiceConnection);
-
-                                    } catch (RemoteException e) {
-                                        e.printStackTrace();
-                                        Log.d(TAG, "Init finished with status " + LoaderCallbackInterface.INIT_FAILED);
-                                        Log.d(TAG, "Unbind from service");
-                                        mAppContext.unbindService(mServiceConnection);
-                                        Log.d(TAG, "Calling using callback");
-                                        mUserAppCallback.onManagerConnected(LoaderCallbackInterface.INIT_FAILED);
-                                    }
-                               }
-                            };
-
-                            mUserAppCallback.onPackageInstall(InstallCallbackInterface.INSTALLATION_PROGRESS, WaitQuery);
-                        }
-                        return;
-                    }
-                    else
-                    {
-                        Log.d(TAG, "Trying to get library list");
-                        mLibraryInstallationProgress = false;
-                        String libs = mEngineService.getLibraryList(mOpenCVersion);
-                        Log.d(TAG, "Library list: \"" + libs + "\"");
-                        Log.d(TAG, "First attempt to load libs");
-                        int status;
-                        if (initOpenCVLibs(path, libs))
-                        {
-                            Log.d(TAG, "First attempt to load libs is OK");
-                            String eol = System.getProperty("line.separator");
-                            for (String str : Core.getBuildInformation().split(eol))
-                                Log.i(TAG, str);
-
-                            status = LoaderCallbackInterface.SUCCESS;
-                        }
-                        else
-                        {
-                            Log.d(TAG, "First attempt to load libs fails");
-                            status = LoaderCallbackInterface.INIT_FAILED;
-                        }
-
-                        Log.d(TAG, "Init finished with status " + status);
-                        Log.d(TAG, "Unbind from service");
-                        mAppContext.unbindService(mServiceConnection);
-                        Log.d(TAG, "Calling using callback");
-                        mUserAppCallback.onManagerConnected(status);
-                    }
-                }
-                catch (RemoteException e)
-                {
-                    e.printStackTrace();
-                    Log.d(TAG, "Init finished with status " + LoaderCallbackInterface.INIT_FAILED);
-                    Log.d(TAG, "Unbind from service");
-                    mAppContext.unbindService(mServiceConnection);
-                    Log.d(TAG, "Calling using callback");
-                    mUserAppCallback.onManagerConnected(LoaderCallbackInterface.INIT_FAILED);
-                }
-            }
-        }
-
-        public void onServiceDisconnected(ComponentName className)
-        {
-            mEngineService = null;
-        }
-    };
-
-    private boolean loadLibrary(String AbsPath)
-    {
-        boolean result = true;
-
-        Log.d(TAG, "Trying to load library " + AbsPath);
-        try
-        {
-            System.load(AbsPath);
-            Log.d(TAG, "OpenCV libs init was ok!");
-        }
-        catch(UnsatisfiedLinkError e)
-        {
-            Log.d(TAG, "Cannot load library \"" + AbsPath + "\"");
-            e.printStackTrace();
-            result = false;
-        }
-
-        return result;
-    }
-
-    private boolean initOpenCVLibs(String Path, String Libs)
-    {
-        Log.d(TAG, "Trying to init OpenCV libs");
-        if ((null != Path) && (Path.length() != 0))
-        {
-            boolean result = true;
-            if ((null != Libs) && (Libs.length() != 0))
-            {
-                Log.d(TAG, "Trying to load libs by dependency list");
-                StringTokenizer splitter = new StringTokenizer(Libs, ";");
-                while(splitter.hasMoreTokens())
-                {
-                    String AbsLibraryPath = Path + File.separator + splitter.nextToken();
-                    result &= loadLibrary(AbsLibraryPath);
-                }
-            }
-            else
-            {
-                // If the dependencies list is not defined or empty.
-                String AbsLibraryPath = Path + File.separator + "libopencv_java3.so";
-                result = loadLibrary(AbsLibraryPath);
-            }
-
-            return result;
-        }
-        else
-        {
-            Log.d(TAG, "Library path \"" + Path + "\" is empty");
-            return false;
-        }
-    }
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/android/BaseLoaderCallback.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/android/BaseLoaderCallback.java
deleted file mode 100755
index 0b8aeed..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/android/BaseLoaderCallback.java
+++ /dev/null
@@ -1,141 +0,0 @@
-package org.opencv.android;
-
-import android.app.Activity;
-import android.app.AlertDialog;
-import android.content.Context;
-import android.content.DialogInterface;
-import android.content.DialogInterface.OnClickListener;
-import android.util.Log;
-
-/**
- * Basic implementation of LoaderCallbackInterface.
- */
-public abstract class BaseLoaderCallback implements LoaderCallbackInterface {
-
-    public BaseLoaderCallback(Context AppContext) {
-        mAppContext = AppContext;
-    }
-
-    public void onManagerConnected(int status)
-    {
-        switch (status)
-        {
-            /** OpenCV initialization was successful. **/
-            case LoaderCallbackInterface.SUCCESS:
-            {
-                /** Application must override this method to handle successful library initialization. **/
-            } break;
-            /** OpenCV loader can not start Google Play Market. **/
-            case LoaderCallbackInterface.MARKET_ERROR:
-            {
-                Log.e(TAG, "Package installation failed!");
-                AlertDialog MarketErrorMessage = new AlertDialog.Builder(mAppContext).create();
-                MarketErrorMessage.setTitle("OpenCV Manager");
-                MarketErrorMessage.setMessage("Package installation failed!");
-                MarketErrorMessage.setCancelable(false); // This blocks the 'BACK' button
-                MarketErrorMessage.setButton(AlertDialog.BUTTON_POSITIVE, "OK", new OnClickListener() {
-                    public void onClick(DialogInterface dialog, int which) {
-                        finish();
-                    }
-                });
-                MarketErrorMessage.show();
-            } break;
-            /** Package installation has been canceled. **/
-            case LoaderCallbackInterface.INSTALL_CANCELED:
-            {
-                Log.d(TAG, "OpenCV library installation was canceled by user");
-                finish();
-            } break;
-            /** Application is incompatible with this version of OpenCV Manager. Possibly, a service update is required. **/
-            case LoaderCallbackInterface.INCOMPATIBLE_MANAGER_VERSION:
-            {
-                Log.d(TAG, "OpenCV Manager Service is uncompatible with this app!");
-                AlertDialog IncomatibilityMessage = new AlertDialog.Builder(mAppContext).create();
-                IncomatibilityMessage.setTitle("OpenCV Manager");
-                IncomatibilityMessage.setMessage("OpenCV Manager service is incompatible with this app. Try to update it via Google Play.");
-                IncomatibilityMessage.setCancelable(false); // This blocks the 'BACK' button
-                IncomatibilityMessage.setButton(AlertDialog.BUTTON_POSITIVE, "OK", new OnClickListener() {
-                    public void onClick(DialogInterface dialog, int which) {
-                        finish();
-                    }
-                });
-                IncomatibilityMessage.show();
-            } break;
-            /** Other status, i.e. INIT_FAILED. **/
-            default:
-            {
-                Log.e(TAG, "OpenCV loading failed!");
-                AlertDialog InitFailedDialog = new AlertDialog.Builder(mAppContext).create();
-                InitFailedDialog.setTitle("OpenCV error");
-                InitFailedDialog.setMessage("OpenCV was not initialised correctly. Application will be shut down");
-                InitFailedDialog.setCancelable(false); // This blocks the 'BACK' button
-                InitFailedDialog.setButton(AlertDialog.BUTTON_POSITIVE, "OK", new OnClickListener() {
-
-                    public void onClick(DialogInterface dialog, int which) {
-                        finish();
-                    }
-                });
-
-                InitFailedDialog.show();
-            } break;
-        }
-    }
-
-    public void onPackageInstall(final int operation, final InstallCallbackInterface callback)
-    {
-        switch (operation)
-        {
-            case InstallCallbackInterface.NEW_INSTALLATION:
-            {
-                AlertDialog InstallMessage = new AlertDialog.Builder(mAppContext).create();
-                InstallMessage.setTitle("Package not found");
-                InstallMessage.setMessage(callback.getPackageName() + " package was not found! Try to install it?");
-                InstallMessage.setCancelable(false); // This blocks the 'BACK' button
-                InstallMessage.setButton(AlertDialog.BUTTON_POSITIVE, "Yes", new OnClickListener()
-                {
-                    public void onClick(DialogInterface dialog, int which)
-                    {
-                        callback.install();
-                    }
-                });
-
-                InstallMessage.setButton(AlertDialog.BUTTON_NEGATIVE, "No", new OnClickListener() {
-
-                    public void onClick(DialogInterface dialog, int which)
-                    {
-                        callback.cancel();
-                    }
-                });
-
-                InstallMessage.show();
-            } break;
-            case InstallCallbackInterface.INSTALLATION_PROGRESS:
-            {
-                AlertDialog WaitMessage = new AlertDialog.Builder(mAppContext).create();
-                WaitMessage.setTitle("OpenCV is not ready");
-                WaitMessage.setMessage("Installation is in progress. Wait or exit?");
-                WaitMessage.setCancelable(false); // This blocks the 'BACK' button
-                WaitMessage.setButton(AlertDialog.BUTTON_POSITIVE, "Wait", new OnClickListener() {
-                    public void onClick(DialogInterface dialog, int which) {
-                        callback.wait_install();
-                    }
-                });
-                WaitMessage.setButton(AlertDialog.BUTTON_NEGATIVE, "Exit", new OnClickListener() {
-                    public void onClick(DialogInterface dialog, int which) {
-                        callback.cancel();
-                    }
-                });
-
-                WaitMessage.show();
-            } break;
-        }
-    }
-
-    void finish()
-    {
-        ((Activity) mAppContext).finish();
-    }
-
-    protected Context mAppContext;
-    private final static String TAG = "OpenCVLoader/BaseLoaderCallback";
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/android/Camera2Renderer.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/android/Camera2Renderer.java
deleted file mode 100755
index 4082140..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/android/Camera2Renderer.java
+++ /dev/null
@@ -1,302 +0,0 @@
-package org.opencv.android;
-
-import java.util.Arrays;
-import java.util.concurrent.Semaphore;
-import java.util.concurrent.TimeUnit;
-import android.annotation.TargetApi;
-import android.content.Context;
-import android.graphics.SurfaceTexture;
-import android.hardware.camera2.CameraAccessException;
-import android.hardware.camera2.CameraCaptureSession;
-import android.hardware.camera2.CameraCharacteristics;
-import android.hardware.camera2.CameraDevice;
-import android.hardware.camera2.CameraManager;
-import android.hardware.camera2.CaptureRequest;
-import android.hardware.camera2.params.StreamConfigurationMap;
-import android.os.Handler;
-import android.os.HandlerThread;
-import android.util.Log;
-import android.util.Size;
-import android.view.Surface;
-
-@TargetApi(21)
-public class Camera2Renderer extends CameraGLRendererBase {
-
-    protected final String LOGTAG = "Camera2Renderer";
-    private CameraDevice mCameraDevice;
-    private CameraCaptureSession mCaptureSession;
-    private CaptureRequest.Builder mPreviewRequestBuilder;
-    private String mCameraID;
-    private Size mPreviewSize = new Size(-1, -1);
-
-    private HandlerThread mBackgroundThread;
-    private Handler mBackgroundHandler;
-    private Semaphore mCameraOpenCloseLock = new Semaphore(1);
-
-    Camera2Renderer(CameraGLSurfaceView view) {
-        super(view);
-    }
-
-    @Override
-    protected void doStart() {
-        Log.d(LOGTAG, "doStart");
-        startBackgroundThread();
-        super.doStart();
-    }
-
-
-    @Override
-    protected void doStop() {
-        Log.d(LOGTAG, "doStop");
-        super.doStop();
-        stopBackgroundThread();
-    }
-
-    boolean cacPreviewSize(final int width, final int height) {
-        Log.i(LOGTAG, "cacPreviewSize: "+width+"x"+height);
-        if(mCameraID == null) {
-            Log.e(LOGTAG, "Camera isn't initialized!");
-            return false;
-        }
-        CameraManager manager = (CameraManager) mView.getContext()
-                .getSystemService(Context.CAMERA_SERVICE);
-        try {
-            CameraCharacteristics characteristics = manager
-                    .getCameraCharacteristics(mCameraID);
-            StreamConfigurationMap map = characteristics
-                    .get(CameraCharacteristics.SCALER_STREAM_CONFIGURATION_MAP);
-            int bestWidth = 0, bestHeight = 0;
-            float aspect = (float)width / height;
-            for (Size psize : map.getOutputSizes(SurfaceTexture.class)) {
-                int w = psize.getWidth(), h = psize.getHeight();
-                Log.d(LOGTAG, "trying size: "+w+"x"+h);
-                if ( width >= w && height >= h &&
-                     bestWidth <= w && bestHeight <= h &&
-                     Math.abs(aspect - (float)w/h) < 0.2 ) {
-                    bestWidth = w;
-                    bestHeight = h;
-                }
-            }
-            Log.i(LOGTAG, "best size: "+bestWidth+"x"+bestHeight);
-            if( bestWidth == 0 || bestHeight == 0 ||
-                mPreviewSize.getWidth() == bestWidth &&
-                mPreviewSize.getHeight() == bestHeight )
-                return false;
-            else {
-                mPreviewSize = new Size(bestWidth, bestHeight);
-                return true;
-            }
-        } catch (CameraAccessException e) {
-            Log.e(LOGTAG, "cacPreviewSize - Camera Access Exception");
-        } catch (IllegalArgumentException e) {
-            Log.e(LOGTAG, "cacPreviewSize - Illegal Argument Exception");
-        } catch (SecurityException e) {
-            Log.e(LOGTAG, "cacPreviewSize - Security Exception");
-        }
-        return false;
-    }
-
-    @Override
-    protected void openCamera(int id) {
-        Log.i(LOGTAG, "openCamera");
-        CameraManager manager = (CameraManager) mView.getContext().getSystemService(Context.CAMERA_SERVICE);
-        try {
-            String camList[] = manager.getCameraIdList();
-            if(camList.length == 0) {
-                Log.e(LOGTAG, "Error: camera isn't detected.");
-                return;
-            }
-            if(id == CameraBridgeViewBase.CAMERA_ID_ANY) {
-                mCameraID = camList[0];
-            } else {
-                for (String cameraID : camList) {
-                    CameraCharacteristics characteristics = manager.getCameraCharacteristics(cameraID);
-                    if( id == CameraBridgeViewBase.CAMERA_ID_BACK &&
-                        characteristics.get(CameraCharacteristics.LENS_FACING) == CameraCharacteristics.LENS_FACING_BACK ||
-                        id == CameraBridgeViewBase.CAMERA_ID_FRONT &&
-                        characteristics.get(CameraCharacteristics.LENS_FACING) == CameraCharacteristics.LENS_FACING_FRONT) {
-                        mCameraID = cameraID;
-                        break;
-                    }
-                }
-            }
-            if(mCameraID != null) {
-                if (!mCameraOpenCloseLock.tryAcquire(2500, TimeUnit.MILLISECONDS)) {
-                    throw new RuntimeException(
-                            "Time out waiting to lock camera opening.");
-                }
-                Log.i(LOGTAG, "Opening camera: " + mCameraID);
-                manager.openCamera(mCameraID, mStateCallback, mBackgroundHandler);
-            }
-        } catch (CameraAccessException e) {
-            Log.e(LOGTAG, "OpenCamera - Camera Access Exception");
-        } catch (IllegalArgumentException e) {
-            Log.e(LOGTAG, "OpenCamera - Illegal Argument Exception");
-        } catch (SecurityException e) {
-            Log.e(LOGTAG, "OpenCamera - Security Exception");
-        } catch (InterruptedException e) {
-            Log.e(LOGTAG, "OpenCamera - Interrupted Exception");
-        }
-    }
-
-    @Override
-    protected void closeCamera() {
-        Log.i(LOGTAG, "closeCamera");
-        try {
-            mCameraOpenCloseLock.acquire();
-            if (null != mCaptureSession) {
-                mCaptureSession.close();
-                mCaptureSession = null;
-            }
-            if (null != mCameraDevice) {
-                mCameraDevice.close();
-                mCameraDevice = null;
-            }
-        } catch (InterruptedException e) {
-            throw new RuntimeException("Interrupted while trying to lock camera closing.", e);
-        } finally {
-            mCameraOpenCloseLock.release();
-        }
-    }
-
-    private final CameraDevice.StateCallback mStateCallback = new CameraDevice.StateCallback() {
-
-        @Override
-        public void onOpened(CameraDevice cameraDevice) {
-            mCameraDevice = cameraDevice;
-            mCameraOpenCloseLock.release();
-            createCameraPreviewSession();
-        }
-
-        @Override
-        public void onDisconnected(CameraDevice cameraDevice) {
-            cameraDevice.close();
-            mCameraDevice = null;
-            mCameraOpenCloseLock.release();
-        }
-
-        @Override
-        public void onError(CameraDevice cameraDevice, int error) {
-            cameraDevice.close();
-            mCameraDevice = null;
-            mCameraOpenCloseLock.release();
-        }
-
-    };
-
-    private void createCameraPreviewSession() {
-        int w=mPreviewSize.getWidth(), h=mPreviewSize.getHeight();
-        Log.i(LOGTAG, "createCameraPreviewSession("+w+"x"+h+")");
-        if(w<0 || h<0)
-            return;
-        try {
-            mCameraOpenCloseLock.acquire();
-            if (null == mCameraDevice) {
-                mCameraOpenCloseLock.release();
-                Log.e(LOGTAG, "createCameraPreviewSession: camera isn't opened");
-                return;
-            }
-            if (null != mCaptureSession) {
-                mCameraOpenCloseLock.release();
-                Log.e(LOGTAG, "createCameraPreviewSession: mCaptureSession is already started");
-                return;
-            }
-            if(null == mSTexture) {
-                mCameraOpenCloseLock.release();
-                Log.e(LOGTAG, "createCameraPreviewSession: preview SurfaceTexture is null");
-                return;
-            }
-            mSTexture.setDefaultBufferSize(w, h);
-
-            Surface surface = new Surface(mSTexture);
-
-            mPreviewRequestBuilder = mCameraDevice
-                    .createCaptureRequest(CameraDevice.TEMPLATE_PREVIEW);
-            mPreviewRequestBuilder.addTarget(surface);
-
-            mCameraDevice.createCaptureSession(Arrays.asList(surface),
-                    new CameraCaptureSession.StateCallback() {
-                        @Override
-                        public void onConfigured( CameraCaptureSession cameraCaptureSession) {
-                            mCaptureSession = cameraCaptureSession;
-                            try {
-                                mPreviewRequestBuilder.set(CaptureRequest.CONTROL_AF_MODE, CaptureRequest.CONTROL_AF_MODE_CONTINUOUS_PICTURE);
-                                mPreviewRequestBuilder.set(CaptureRequest.CONTROL_AE_MODE, CaptureRequest.CONTROL_AE_MODE_ON_AUTO_FLASH);
-
-                                mCaptureSession.setRepeatingRequest(mPreviewRequestBuilder.build(), null, mBackgroundHandler);
-                                Log.i(LOGTAG, "CameraPreviewSession has been started");
-                            } catch (CameraAccessException e) {
-                                Log.e(LOGTAG, "createCaptureSession failed");
-                            }
-                            mCameraOpenCloseLock.release();
-                        }
-
-                        @Override
-                        public void onConfigureFailed(
-                                CameraCaptureSession cameraCaptureSession) {
-                            Log.e(LOGTAG, "createCameraPreviewSession failed");
-                            mCameraOpenCloseLock.release();
-                        }
-                    }, mBackgroundHandler);
-        } catch (CameraAccessException e) {
-            Log.e(LOGTAG, "createCameraPreviewSession");
-        } catch (InterruptedException e) {
-            throw new RuntimeException(
-                    "Interrupted while createCameraPreviewSession", e);
-        }
-        finally {
-            //mCameraOpenCloseLock.release();
-        }
-    }
-
-    private void startBackgroundThread() {
-        Log.i(LOGTAG, "startBackgroundThread");
-        stopBackgroundThread();
-        mBackgroundThread = new HandlerThread("CameraBackground");
-        mBackgroundThread.start();
-        mBackgroundHandler = new Handler(mBackgroundThread.getLooper());
-    }
-
-    private void stopBackgroundThread() {
-        Log.i(LOGTAG, "stopBackgroundThread");
-        if(mBackgroundThread == null)
-            return;
-        mBackgroundThread.quitSafely();
-        try {
-            mBackgroundThread.join();
-            mBackgroundThread = null;
-            mBackgroundHandler = null;
-        } catch (InterruptedException e) {
-            Log.e(LOGTAG, "stopBackgroundThread");
-        }
-    }
-
-    @Override
-    protected void setCameraPreviewSize(int width, int height) {
-        Log.i(LOGTAG, "setCameraPreviewSize("+width+"x"+height+")");
-        if(mMaxCameraWidth  > 0 && mMaxCameraWidth  < width)  width  = mMaxCameraWidth;
-        if(mMaxCameraHeight > 0 && mMaxCameraHeight < height) height = mMaxCameraHeight;
-        try {
-            mCameraOpenCloseLock.acquire();
-
-            boolean needReconfig = cacPreviewSize(width, height);
-            mCameraWidth  = mPreviewSize.getWidth();
-            mCameraHeight = mPreviewSize.getHeight();
-
-            if( !needReconfig ) {
-                mCameraOpenCloseLock.release();
-                return;
-            }
-            if (null != mCaptureSession) {
-                Log.d(LOGTAG, "closing existing previewSession");
-                mCaptureSession.close();
-                mCaptureSession = null;
-            }
-            mCameraOpenCloseLock.release();
-            createCameraPreviewSession();
-        } catch (InterruptedException e) {
-            mCameraOpenCloseLock.release();
-            throw new RuntimeException("Interrupted while setCameraPreviewSize.", e);
-        }
-    }
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/android/CameraBridgeViewBase.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/android/CameraBridgeViewBase.java
deleted file mode 100755
index 5487105..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/android/CameraBridgeViewBase.java
+++ /dev/null
@@ -1,495 +0,0 @@
-package org.opencv.android;
-
-import java.util.List;
-
-import org.opencv.BuildConfig;
-import org.opencv.R;
-import org.opencv.core.Mat;
-import org.opencv.core.Size;
-
-import android.app.Activity;
-import android.app.AlertDialog;
-import android.content.Context;
-import android.content.DialogInterface;
-import android.content.res.TypedArray;
-import android.graphics.Bitmap;
-import android.graphics.Canvas;
-import android.graphics.Rect;
-import android.util.AttributeSet;
-import android.util.Log;
-import android.view.SurfaceHolder;
-import android.view.SurfaceView;
-
-/**
- * This is a basic class, implementing the interaction with Camera and OpenCV library.
- * The main responsibility of it - is to control when camera can be enabled, process the frame,
- * call external listener to make any adjustments to the frame and then draw the resulting
- * frame to the screen.
- * The clients shall implement CvCameraViewListener.
- */
-public abstract class CameraBridgeViewBase extends SurfaceView implements SurfaceHolder.Callback {
-
-    private static final String TAG = "CameraBridge";
-    private static final int MAX_UNSPECIFIED = -1;
-    private static final int STOPPED = 0;
-    private static final int STARTED = 1;
-
-    private int mState = STOPPED;
-    private Bitmap mCacheBitmap;
-    private CvCameraViewListener2 mListener;
-    private boolean mSurfaceExist;
-    private final Object mSyncObject = new Object();
-
-    protected int mFrameWidth;
-    protected int mFrameHeight;
-    protected int mMaxHeight;
-    protected int mMaxWidth;
-    protected float mScale = 0;
-    protected int mPreviewFormat = RGBA;
-    protected int mCameraIndex = CAMERA_ID_ANY;
-    protected boolean mEnabled;
-    protected FpsMeter mFpsMeter = null;
-
-    public static final int CAMERA_ID_ANY   = -1;
-    public static final int CAMERA_ID_BACK  = 99;
-    public static final int CAMERA_ID_FRONT = 98;
-    public static final int RGBA = 1;
-    public static final int GRAY = 2;
-
-    public CameraBridgeViewBase(Context context, int cameraId) {
-        super(context);
-        mCameraIndex = cameraId;
-        getHolder().addCallback(this);
-        mMaxWidth = MAX_UNSPECIFIED;
-        mMaxHeight = MAX_UNSPECIFIED;
-    }
-
-    public CameraBridgeViewBase(Context context, AttributeSet attrs) {
-        super(context, attrs);
-
-        int count = attrs.getAttributeCount();
-        Log.d(TAG, "Attr count: " + Integer.valueOf(count));
-
-        TypedArray styledAttrs = getContext().obtainStyledAttributes(attrs, R.styleable.CameraBridgeViewBase);
-        if (styledAttrs.getBoolean(R.styleable.CameraBridgeViewBase_show_fps, false))
-            enableFpsMeter();
-
-        mCameraIndex = styledAttrs.getInt(R.styleable.CameraBridgeViewBase_camera_id, -1);
-
-        getHolder().addCallback(this);
-        mMaxWidth = MAX_UNSPECIFIED;
-        mMaxHeight = MAX_UNSPECIFIED;
-        styledAttrs.recycle();
-    }
-
-    /**
-     * Sets the camera index
-     * @param cameraIndex new camera index
-     */
-    public void setCameraIndex(int cameraIndex) {
-        this.mCameraIndex = cameraIndex;
-    }
-
-    public interface CvCameraViewListener {
-        /**
-         * This method is invoked when camera preview has started. After this method is invoked
-         * the frames will start to be delivered to client via the onCameraFrame() callback.
-         * @param width -  the width of the frames that will be delivered
-         * @param height - the height of the frames that will be delivered
-         */
-        public void onCameraViewStarted(int width, int height);
-
-        /**
-         * This method is invoked when camera preview has been stopped for some reason.
-         * No frames will be delivered via onCameraFrame() callback after this method is called.
-         */
-        public void onCameraViewStopped();
-
-        /**
-         * This method is invoked when delivery of the frame needs to be done.
-         * The returned values - is a modified frame which needs to be displayed on the screen.
-         * TODO: pass the parameters specifying the format of the frame (BPP, YUV or RGB and etc)
-         */
-        public Mat onCameraFrame(Mat inputFrame);
-    }
-
-    public interface CvCameraViewListener2 {
-        /**
-         * This method is invoked when camera preview has started. After this method is invoked
-         * the frames will start to be delivered to client via the onCameraFrame() callback.
-         * @param width -  the width of the frames that will be delivered
-         * @param height - the height of the frames that will be delivered
-         */
-        public void onCameraViewStarted(int width, int height);
-
-        /**
-         * This method is invoked when camera preview has been stopped for some reason.
-         * No frames will be delivered via onCameraFrame() callback after this method is called.
-         */
-        public void onCameraViewStopped();
-
-        /**
-         * This method is invoked when delivery of the frame needs to be done.
-         * The returned values - is a modified frame which needs to be displayed on the screen.
-         * TODO: pass the parameters specifying the format of the frame (BPP, YUV or RGB and etc)
-         */
-        public Mat onCameraFrame(CvCameraViewFrame inputFrame);
-    };
-
-    protected class CvCameraViewListenerAdapter implements CvCameraViewListener2  {
-        public CvCameraViewListenerAdapter(CvCameraViewListener oldStypeListener) {
-            mOldStyleListener = oldStypeListener;
-        }
-
-        public void onCameraViewStarted(int width, int height) {
-            mOldStyleListener.onCameraViewStarted(width, height);
-        }
-
-        public void onCameraViewStopped() {
-            mOldStyleListener.onCameraViewStopped();
-        }
-
-        public Mat onCameraFrame(CvCameraViewFrame inputFrame) {
-             Mat result = null;
-             switch (mPreviewFormat) {
-                case RGBA:
-                    result = mOldStyleListener.onCameraFrame(inputFrame.rgba());
-                    break;
-                case GRAY:
-                    result = mOldStyleListener.onCameraFrame(inputFrame.gray());
-                    break;
-                default:
-                    Log.e(TAG, "Invalid frame format! Only RGBA and Gray Scale are supported!");
-            };
-
-            return result;
-        }
-
-        public void setFrameFormat(int format) {
-            mPreviewFormat = format;
-        }
-
-        private int mPreviewFormat = RGBA;
-        private CvCameraViewListener mOldStyleListener;
-    };
-
-    /**
-     * This class interface is abstract representation of single frame from camera for onCameraFrame callback
-     * Attention: Do not use objects, that represents this interface out of onCameraFrame callback!
-     */
-    public interface CvCameraViewFrame {
-
-        /**
-         * This method returns RGBA Mat with frame
-         */
-        public Mat rgba();
-
-        /**
-         * This method returns single channel gray scale Mat with frame
-         */
-        public Mat gray();
-    };
-
-    public void surfaceChanged(SurfaceHolder arg0, int arg1, int arg2, int arg3) {
-        Log.d(TAG, "call surfaceChanged event");
-        synchronized(mSyncObject) {
-            if (!mSurfaceExist) {
-                mSurfaceExist = true;
-                checkCurrentState();
-            } else {
-                /** Surface changed. We need to stop camera and restart with new parameters */
-                /* Pretend that old surface has been destroyed */
-                mSurfaceExist = false;
-                checkCurrentState();
-                /* Now use new surface. Say we have it now */
-                mSurfaceExist = true;
-                checkCurrentState();
-            }
-        }
-    }
-
-    public void surfaceCreated(SurfaceHolder holder) {
-        /* Do nothing. Wait until surfaceChanged delivered */
-    }
-
-    public void surfaceDestroyed(SurfaceHolder holder) {
-        synchronized(mSyncObject) {
-            mSurfaceExist = false;
-            checkCurrentState();
-        }
-    }
-
-    /**
-     * This method is provided for clients, so they can enable the camera connection.
-     * The actual onCameraViewStarted callback will be delivered only after both this method is called and surface is available
-     */
-    public void enableView() {
-        synchronized(mSyncObject) {
-            mEnabled = true;
-            checkCurrentState();
-        }
-    }
-
-    /**
-     * This method is provided for clients, so they can disable camera connection and stop
-     * the delivery of frames even though the surface view itself is not destroyed and still stays on the scren
-     */
-    public void disableView() {
-        synchronized(mSyncObject) {
-            mEnabled = false;
-            checkCurrentState();
-        }
-    }
-
-    /**
-     * This method enables label with fps value on the screen
-     */
-    public void enableFpsMeter() {
-        if (mFpsMeter == null) {
-            mFpsMeter = new FpsMeter();
-            mFpsMeter.setResolution(mFrameWidth, mFrameHeight);
-        }
-    }
-
-    public void disableFpsMeter() {
-            mFpsMeter = null;
-    }
-
-    /**
-     *
-     * @param listener
-     */
-
-    public void setCvCameraViewListener(CvCameraViewListener2 listener) {
-        mListener = listener;
-    }
-
-    public void setCvCameraViewListener(CvCameraViewListener listener) {
-        CvCameraViewListenerAdapter adapter = new CvCameraViewListenerAdapter(listener);
-        adapter.setFrameFormat(mPreviewFormat);
-        mListener = adapter;
-    }
-
-    /**
-     * This method sets the maximum size that camera frame is allowed to be. When selecting
-     * size - the biggest size which less or equal the size set will be selected.
-     * As an example - we set setMaxFrameSize(200,200) and we have 176x152 and 320x240 sizes. The
-     * preview frame will be selected with 176x152 size.
-     * This method is useful when need to restrict the size of preview frame for some reason (for example for video recording)
-     * @param maxWidth - the maximum width allowed for camera frame.
-     * @param maxHeight - the maximum height allowed for camera frame
-     */
-    public void setMaxFrameSize(int maxWidth, int maxHeight) {
-        mMaxWidth = maxWidth;
-        mMaxHeight = maxHeight;
-    }
-
-    public void SetCaptureFormat(int format)
-    {
-        mPreviewFormat = format;
-        if (mListener instanceof CvCameraViewListenerAdapter) {
-            CvCameraViewListenerAdapter adapter = (CvCameraViewListenerAdapter) mListener;
-            adapter.setFrameFormat(mPreviewFormat);
-        }
-    }
-
-    /**
-     * Called when mSyncObject lock is held
-     */
-    private void checkCurrentState() {
-        Log.d(TAG, "call checkCurrentState");
-        int targetState;
-
-        if (mEnabled && mSurfaceExist && getVisibility() == VISIBLE) {
-            targetState = STARTED;
-        } else {
-            targetState = STOPPED;
-        }
-
-        if (targetState != mState) {
-            /* The state change detected. Need to exit the current state and enter target state */
-            processExitState(mState);
-            mState = targetState;
-            processEnterState(mState);
-        }
-    }
-
-    private void processEnterState(int state) {
-        Log.d(TAG, "call processEnterState: " + state);
-        switch(state) {
-        case STARTED:
-            onEnterStartedState();
-            if (mListener != null) {
-                mListener.onCameraViewStarted(mFrameWidth, mFrameHeight);
-            }
-            break;
-        case STOPPED:
-            onEnterStoppedState();
-            if (mListener != null) {
-                mListener.onCameraViewStopped();
-            }
-            break;
-        };
-    }
-
-    private void processExitState(int state) {
-        Log.d(TAG, "call processExitState: " + state);
-        switch(state) {
-        case STARTED:
-            onExitStartedState();
-            break;
-        case STOPPED:
-            onExitStoppedState();
-            break;
-        };
-    }
-
-    private void onEnterStoppedState() {
-        /* nothing to do */
-    }
-
-    private void onExitStoppedState() {
-        /* nothing to do */
-    }
-
-    // NOTE: The order of bitmap constructor and camera connection is important for android 4.1.x
-    // Bitmap must be constructed before surface
-    private void onEnterStartedState() {
-        Log.d(TAG, "call onEnterStartedState");
-        /* Connect camera */
-        if (!connectCamera(getWidth(), getHeight())) {
-            AlertDialog ad = new AlertDialog.Builder(getContext()).create();
-            ad.setCancelable(false); // This blocks the 'BACK' button
-            ad.setMessage("It seems that you device does not support camera (or it is locked). Application will be closed.");
-            ad.setButton(DialogInterface.BUTTON_NEUTRAL,  "OK", new DialogInterface.OnClickListener() {
-                public void onClick(DialogInterface dialog, int which) {
-                    dialog.dismiss();
-                    ((Activity) getContext()).finish();
-                }
-            });
-            ad.show();
-
-        }
-    }
-
-    private void onExitStartedState() {
-        disconnectCamera();
-        if (mCacheBitmap != null) {
-            mCacheBitmap.recycle();
-        }
-    }
-
-    /**
-     * This method shall be called by the subclasses when they have valid
-     * object and want it to be delivered to external client (via callback) and
-     * then displayed on the screen.
-     * @param frame - the current frame to be delivered
-     */
-    protected void deliverAndDrawFrame(CvCameraViewFrame frame) {
-        Mat modified;
-
-        if (mListener != null) {
-            modified = mListener.onCameraFrame(frame);
-        } else {
-            modified = frame.rgba();
-        }
-
-        boolean bmpValid = true;
-        if (modified != null) {
-            try {
-                Utils.matToBitmap(modified, mCacheBitmap);
-            } catch(Exception e) {
-                Log.e(TAG, "Mat type: " + modified);
-                Log.e(TAG, "Bitmap type: " + mCacheBitmap.getWidth() + "*" + mCacheBitmap.getHeight());
-                Log.e(TAG, "Utils.matToBitmap() throws an exception: " + e.getMessage());
-                bmpValid = false;
-            }
-        }
-
-        if (bmpValid && mCacheBitmap != null) {
-            Canvas canvas = getHolder().lockCanvas();
-            if (canvas != null) {
-                canvas.drawColor(0, android.graphics.PorterDuff.Mode.CLEAR);
-                if (BuildConfig.DEBUG)
-                    Log.d(TAG, "mStretch value: " + mScale);
-
-                if (mScale != 0) {
-                    canvas.drawBitmap(mCacheBitmap, new Rect(0,0,mCacheBitmap.getWidth(), mCacheBitmap.getHeight()),
-                         new Rect((int)((canvas.getWidth() - mScale*mCacheBitmap.getWidth()) / 2),
-                         (int)((canvas.getHeight() - mScale*mCacheBitmap.getHeight()) / 2),
-                         (int)((canvas.getWidth() - mScale*mCacheBitmap.getWidth()) / 2 + mScale*mCacheBitmap.getWidth()),
-                         (int)((canvas.getHeight() - mScale*mCacheBitmap.getHeight()) / 2 + mScale*mCacheBitmap.getHeight())), null);
-                } else {
-                     canvas.drawBitmap(mCacheBitmap, new Rect(0,0,mCacheBitmap.getWidth(), mCacheBitmap.getHeight()),
-                         new Rect((canvas.getWidth() - mCacheBitmap.getWidth()) / 2,
-                         (canvas.getHeight() - mCacheBitmap.getHeight()) / 2,
-                         (canvas.getWidth() - mCacheBitmap.getWidth()) / 2 + mCacheBitmap.getWidth(),
-                         (canvas.getHeight() - mCacheBitmap.getHeight()) / 2 + mCacheBitmap.getHeight()), null);
-                }
-
-                if (mFpsMeter != null) {
-                    mFpsMeter.measure();
-                    mFpsMeter.draw(canvas, 20, 30);
-                }
-                getHolder().unlockCanvasAndPost(canvas);
-            }
-        }
-    }
-
-    /**
-     * This method is invoked shall perform concrete operation to initialize the camera.
-     * CONTRACT: as a result of this method variables mFrameWidth and mFrameHeight MUST be
-     * initialized with the size of the Camera frames that will be delivered to external processor.
-     * @param width - the width of this SurfaceView
-     * @param height - the height of this SurfaceView
-     */
-    protected abstract boolean connectCamera(int width, int height);
-
-    /**
-     * Disconnects and release the particular camera object being connected to this surface view.
-     * Called when syncObject lock is held
-     */
-    protected abstract void disconnectCamera();
-
-    // NOTE: On Android 4.1.x the function must be called before SurfaceTexture constructor!
-    protected void AllocateCache()
-    {
-        mCacheBitmap = Bitmap.createBitmap(mFrameWidth, mFrameHeight, Bitmap.Config.ARGB_8888);
-    }
-
-    public interface ListItemAccessor {
-        public int getWidth(Object obj);
-        public int getHeight(Object obj);
-    };
-
-    /**
-     * This helper method can be called by subclasses to select camera preview size.
-     * It goes over the list of the supported preview sizes and selects the maximum one which
-     * fits both values set via setMaxFrameSize() and surface frame allocated for this view
-     * @param supportedSizes
-     * @param surfaceWidth
-     * @param surfaceHeight
-     * @return optimal frame size
-     */
-    protected Size calculateCameraFrameSize(List<?> supportedSizes, ListItemAccessor accessor, int surfaceWidth, int surfaceHeight) {
-        int calcWidth = 0;
-        int calcHeight = 0;
-
-        int maxAllowedWidth = (mMaxWidth != MAX_UNSPECIFIED && mMaxWidth < surfaceWidth)? mMaxWidth : surfaceWidth;
-        int maxAllowedHeight = (mMaxHeight != MAX_UNSPECIFIED && mMaxHeight < surfaceHeight)? mMaxHeight : surfaceHeight;
-
-        for (Object size : supportedSizes) {
-            int width = accessor.getWidth(size);
-            int height = accessor.getHeight(size);
-
-            if (width <= maxAllowedWidth && height <= maxAllowedHeight) {
-                if (width >= calcWidth && height >= calcHeight) {
-                    calcWidth = (int) width;
-                    calcHeight = (int) height;
-                }
-            }
-        }
-
-        return new Size(calcWidth, calcHeight);
-    }
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/android/CameraGLRendererBase.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/android/CameraGLRendererBase.java
deleted file mode 100755
index 60c37c3..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/android/CameraGLRendererBase.java
+++ /dev/null
@@ -1,440 +0,0 @@
-package org.opencv.android;
-
-import java.nio.ByteBuffer;
-import java.nio.ByteOrder;
-import java.nio.FloatBuffer;
-
-import javax.microedition.khronos.egl.EGLConfig;
-import javax.microedition.khronos.opengles.GL10;
-
-import org.opencv.android.CameraGLSurfaceView.CameraTextureListener;
-
-import android.annotation.TargetApi;
-import android.graphics.SurfaceTexture;
-import android.opengl.GLES11Ext;
-import android.opengl.GLES20;
-import android.opengl.GLSurfaceView;
-import android.util.Log;
-import android.view.View;
-
-@TargetApi(15)
-public abstract class CameraGLRendererBase implements GLSurfaceView.Renderer, SurfaceTexture.OnFrameAvailableListener {
-
-    protected final String LOGTAG = "CameraGLRendererBase";
-
-    // shaders
-    private final String vss = ""
-            + "attribute vec2 vPosition;\n"
-            + "attribute vec2 vTexCoord;\n" + "varying vec2 texCoord;\n"
-            + "void main() {\n" + "  texCoord = vTexCoord;\n"
-            + "  gl_Position = vec4 ( vPosition.x, vPosition.y, 0.0, 1.0 );\n"
-            + "}";
-
-    private final String fssOES = ""
-            + "#extension GL_OES_EGL_image_external : require\n"
-            + "precision mediump float;\n"
-            + "uniform samplerExternalOES sTexture;\n"
-            + "varying vec2 texCoord;\n"
-            + "void main() {\n"
-            + "  gl_FragColor = texture2D(sTexture,texCoord);\n" + "}";
-
-    private final String fss2D = ""
-            + "precision mediump float;\n"
-            + "uniform sampler2D sTexture;\n"
-            + "varying vec2 texCoord;\n"
-            + "void main() {\n"
-            + "  gl_FragColor = texture2D(sTexture,texCoord);\n" + "}";
-
-    // coord-s
-    private final float vertices[] = {
-           -1, -1,
-           -1,  1,
-            1, -1,
-            1,  1 };
-    private final float texCoordOES[] = {
-            0,  1,
-            0,  0,
-            1,  1,
-            1,  0 };
-    private final float texCoord2D[] = {
-            0,  0,
-            0,  1,
-            1,  0,
-            1,  1 };
-
-    private int[] texCamera = {0}, texFBO = {0}, texDraw = {0};
-    private int[] FBO = {0};
-    private int progOES = -1, prog2D = -1;
-    private int vPosOES, vTCOES, vPos2D, vTC2D;
-
-    private FloatBuffer vert, texOES, tex2D;
-
-    protected int mCameraWidth = -1, mCameraHeight = -1;
-    protected int mFBOWidth = -1, mFBOHeight = -1;
-    protected int mMaxCameraWidth = -1, mMaxCameraHeight = -1;
-    protected int mCameraIndex = CameraBridgeViewBase.CAMERA_ID_ANY;
-
-    protected SurfaceTexture mSTexture;
-
-    protected boolean mHaveSurface = false;
-    protected boolean mHaveFBO = false;
-    protected boolean mUpdateST = false;
-    protected boolean mEnabled = true;
-    protected boolean mIsStarted = false;
-
-    protected CameraGLSurfaceView mView;
-
-    protected abstract void openCamera(int id);
-    protected abstract void closeCamera();
-    protected abstract void setCameraPreviewSize(int width, int height); // updates mCameraWidth & mCameraHeight
-
-    public CameraGLRendererBase(CameraGLSurfaceView view) {
-        mView = view;
-        int bytes = vertices.length * Float.SIZE / Byte.SIZE;
-        vert   = ByteBuffer.allocateDirect(bytes).order(ByteOrder.nativeOrder()).asFloatBuffer();
-        texOES = ByteBuffer.allocateDirect(bytes).order(ByteOrder.nativeOrder()).asFloatBuffer();
-        tex2D  = ByteBuffer.allocateDirect(bytes).order(ByteOrder.nativeOrder()).asFloatBuffer();
-        vert.put(vertices).position(0);
-        texOES.put(texCoordOES).position(0);
-        tex2D.put(texCoord2D).position(0);
-    }
-
-    @Override
-    public synchronized void onFrameAvailable(SurfaceTexture surfaceTexture) {
-        //Log.i(LOGTAG, "onFrameAvailable");
-        mUpdateST = true;
-        mView.requestRender();
-    }
-
-    @Override
-    public void onDrawFrame(GL10 gl) {
-        //Log.i(LOGTAG, "onDrawFrame start");
-
-        if (!mHaveFBO)
-            return;
-
-        synchronized(this) {
-            if (mUpdateST) {
-                mSTexture.updateTexImage();
-                mUpdateST = false;
-            }
-
-            GLES20.glClear(GLES20.GL_COLOR_BUFFER_BIT);
-
-            CameraTextureListener texListener = mView.getCameraTextureListener();
-            if(texListener != null) {
-                //Log.d(LOGTAG, "haveUserCallback");
-                // texCamera(OES) -> texFBO
-                drawTex(texCamera[0], true, FBO[0]);
-
-                // call user code (texFBO -> texDraw)
-                boolean modified = texListener.onCameraTexture(texFBO[0], texDraw[0], mCameraWidth, mCameraHeight);
-
-                if(modified) {
-                    // texDraw -> screen
-                    drawTex(texDraw[0], false, 0);
-                } else {
-                    // texFBO -> screen
-                    drawTex(texFBO[0], false, 0);
-                }
-            } else {
-                Log.d(LOGTAG, "texCamera(OES) -> screen");
-                // texCamera(OES) -> screen
-                drawTex(texCamera[0], true, 0);
-            }
-            //Log.i(LOGTAG, "onDrawFrame end");
-        }
-    }
-
-    @Override
-    public void onSurfaceChanged(GL10 gl, int surfaceWidth, int surfaceHeight) {
-        Log.i(LOGTAG, "onSurfaceChanged("+surfaceWidth+"x"+surfaceHeight+")");
-        mHaveSurface = true;
-        updateState();
-        setPreviewSize(surfaceWidth, surfaceHeight);
-    }
-
-    @Override
-    public void onSurfaceCreated(GL10 gl, EGLConfig config) {
-        Log.i(LOGTAG, "onSurfaceCreated");
-        initShaders();
-    }
-
-    private void initShaders() {
-        String strGLVersion = GLES20.glGetString(GLES20.GL_VERSION);
-        if (strGLVersion != null)
-            Log.i(LOGTAG, "OpenGL ES version: " + strGLVersion);
-
-        GLES20.glClearColor(1.0f, 1.0f, 1.0f, 1.0f);
-
-        progOES = loadShader(vss, fssOES);
-        vPosOES = GLES20.glGetAttribLocation(progOES, "vPosition");
-        vTCOES  = GLES20.glGetAttribLocation(progOES, "vTexCoord");
-        GLES20.glEnableVertexAttribArray(vPosOES);
-        GLES20.glEnableVertexAttribArray(vTCOES);
-
-        prog2D  = loadShader(vss, fss2D);
-        vPos2D = GLES20.glGetAttribLocation(prog2D, "vPosition");
-        vTC2D  = GLES20.glGetAttribLocation(prog2D, "vTexCoord");
-        GLES20.glEnableVertexAttribArray(vPos2D);
-        GLES20.glEnableVertexAttribArray(vTC2D);
-    }
-
-    private void initSurfaceTexture() {
-        Log.d(LOGTAG, "initSurfaceTexture");
-        deleteSurfaceTexture();
-        initTexOES(texCamera);
-        mSTexture = new SurfaceTexture(texCamera[0]);
-        mSTexture.setOnFrameAvailableListener(this);
-    }
-
-    private void deleteSurfaceTexture() {
-        Log.d(LOGTAG, "deleteSurfaceTexture");
-        if(mSTexture != null) {
-            mSTexture.release();
-            mSTexture = null;
-            deleteTex(texCamera);
-        }
-    }
-
-    private void initTexOES(int[] tex) {
-        if(tex.length == 1) {
-            GLES20.glGenTextures(1, tex, 0);
-            GLES20.glBindTexture(GLES11Ext.GL_TEXTURE_EXTERNAL_OES, tex[0]);
-            GLES20.glTexParameteri(GLES11Ext.GL_TEXTURE_EXTERNAL_OES, GLES20.GL_TEXTURE_WRAP_S, GLES20.GL_CLAMP_TO_EDGE);
-            GLES20.glTexParameteri(GLES11Ext.GL_TEXTURE_EXTERNAL_OES, GLES20.GL_TEXTURE_WRAP_T, GLES20.GL_CLAMP_TO_EDGE);
-            GLES20.glTexParameteri(GLES11Ext.GL_TEXTURE_EXTERNAL_OES, GLES20.GL_TEXTURE_MIN_FILTER, GLES20.GL_NEAREST);
-            GLES20.glTexParameteri(GLES11Ext.GL_TEXTURE_EXTERNAL_OES, GLES20.GL_TEXTURE_MAG_FILTER, GLES20.GL_NEAREST);
-        }
-    }
-
-    private static void deleteTex(int[] tex) {
-        if(tex.length == 1) {
-            GLES20.glDeleteTextures(1, tex, 0);
-        }
-    }
-
-    private static int loadShader(String vss, String fss) {
-        Log.d("CameraGLRendererBase", "loadShader");
-        int vshader = GLES20.glCreateShader(GLES20.GL_VERTEX_SHADER);
-        GLES20.glShaderSource(vshader, vss);
-        GLES20.glCompileShader(vshader);
-        int[] status = new int[1];
-        GLES20.glGetShaderiv(vshader, GLES20.GL_COMPILE_STATUS, status, 0);
-        if (status[0] == 0) {
-            Log.e("CameraGLRendererBase", "Could not compile vertex shader: "+GLES20.glGetShaderInfoLog(vshader));
-            GLES20.glDeleteShader(vshader);
-            vshader = 0;
-            return 0;
-        }
-
-        int fshader = GLES20.glCreateShader(GLES20.GL_FRAGMENT_SHADER);
-        GLES20.glShaderSource(fshader, fss);
-        GLES20.glCompileShader(fshader);
-        GLES20.glGetShaderiv(fshader, GLES20.GL_COMPILE_STATUS, status, 0);
-        if (status[0] == 0) {
-            Log.e("CameraGLRendererBase", "Could not compile fragment shader:"+GLES20.glGetShaderInfoLog(fshader));
-            GLES20.glDeleteShader(vshader);
-            GLES20.glDeleteShader(fshader);
-            fshader = 0;
-            return 0;
-        }
-
-        int program = GLES20.glCreateProgram();
-        GLES20.glAttachShader(program, vshader);
-        GLES20.glAttachShader(program, fshader);
-        GLES20.glLinkProgram(program);
-        GLES20.glDeleteShader(vshader);
-        GLES20.glDeleteShader(fshader);
-        GLES20.glGetProgramiv(program, GLES20.GL_LINK_STATUS, status, 0);
-        if (status[0] == 0) {
-            Log.e("CameraGLRendererBase", "Could not link shader program: "+GLES20.glGetProgramInfoLog(program));
-            program = 0;
-            return 0;
-        }
-        GLES20.glValidateProgram(program);
-        GLES20.glGetProgramiv(program, GLES20.GL_VALIDATE_STATUS, status, 0);
-        if (status[0] == 0)
-        {
-            Log.e("CameraGLRendererBase", "Shader program validation error: "+GLES20.glGetProgramInfoLog(program));
-            GLES20.glDeleteProgram(program);
-            program = 0;
-            return 0;
-        }
-
-        Log.d("CameraGLRendererBase", "Shader program is built OK");
-
-        return program;
-    }
-
-    private void deleteFBO()
-    {
-        Log.d(LOGTAG, "deleteFBO("+mFBOWidth+"x"+mFBOHeight+")");
-        GLES20.glBindFramebuffer(GLES20.GL_FRAMEBUFFER, 0);
-        GLES20.glDeleteFramebuffers(1, FBO, 0);
-
-        deleteTex(texFBO);
-        deleteTex(texDraw);
-        mFBOWidth = mFBOHeight = 0;
-    }
-
-    private void initFBO(int width, int height)
-    {
-        Log.d(LOGTAG, "initFBO("+width+"x"+height+")");
-
-        deleteFBO();
-
-        GLES20.glGenTextures(1, texDraw, 0);
-        GLES20.glBindTexture(GLES20.GL_TEXTURE_2D, texDraw[0]);
-        GLES20.glTexImage2D(GLES20.GL_TEXTURE_2D, 0, GLES20.GL_RGBA, width, height, 0, GLES20.GL_RGBA, GLES20.GL_UNSIGNED_BYTE, null);
-        GLES20.glTexParameteri(GLES20.GL_TEXTURE_2D, GLES20.GL_TEXTURE_WRAP_S, GLES20.GL_CLAMP_TO_EDGE);
-        GLES20.glTexParameteri(GLES20.GL_TEXTURE_2D, GLES20.GL_TEXTURE_WRAP_T, GLES20.GL_CLAMP_TO_EDGE);
-        GLES20.glTexParameteri(GLES20.GL_TEXTURE_2D, GLES20.GL_TEXTURE_MIN_FILTER, GLES20.GL_NEAREST);
-        GLES20.glTexParameteri(GLES20.GL_TEXTURE_2D, GLES20.GL_TEXTURE_MAG_FILTER, GLES20.GL_NEAREST);
-
-        GLES20.glGenTextures(1, texFBO, 0);
-        GLES20.glBindTexture(GLES20.GL_TEXTURE_2D, texFBO[0]);
-        GLES20.glTexImage2D(GLES20.GL_TEXTURE_2D, 0, GLES20.GL_RGBA, width, height, 0, GLES20.GL_RGBA, GLES20.GL_UNSIGNED_BYTE, null);
-        GLES20.glTexParameteri(GLES20.GL_TEXTURE_2D, GLES20.GL_TEXTURE_WRAP_S, GLES20.GL_CLAMP_TO_EDGE);
-        GLES20.glTexParameteri(GLES20.GL_TEXTURE_2D, GLES20.GL_TEXTURE_WRAP_T, GLES20.GL_CLAMP_TO_EDGE);
-        GLES20.glTexParameteri(GLES20.GL_TEXTURE_2D, GLES20.GL_TEXTURE_MIN_FILTER, GLES20.GL_NEAREST);
-        GLES20.glTexParameteri(GLES20.GL_TEXTURE_2D, GLES20.GL_TEXTURE_MAG_FILTER, GLES20.GL_NEAREST);
-
-        //int hFBO;
-        GLES20.glGenFramebuffers(1, FBO, 0);
-        GLES20.glBindFramebuffer(GLES20.GL_FRAMEBUFFER, FBO[0]);
-        GLES20.glFramebufferTexture2D(GLES20.GL_FRAMEBUFFER, GLES20.GL_COLOR_ATTACHMENT0, GLES20.GL_TEXTURE_2D, texFBO[0], 0);
-        Log.d(LOGTAG, "initFBO error status: " + GLES20.glGetError());
-
-        int FBOstatus = GLES20.glCheckFramebufferStatus(GLES20.GL_FRAMEBUFFER);
-        if (FBOstatus != GLES20.GL_FRAMEBUFFER_COMPLETE)
-            Log.e(LOGTAG, "initFBO failed, status: " + FBOstatus);
-
-        mFBOWidth  = width;
-        mFBOHeight = height;
-    }
-
-    // draw texture to FBO or to screen if fbo == 0
-    private void drawTex(int tex, boolean isOES, int fbo)
-    {
-        GLES20.glBindFramebuffer(GLES20.GL_FRAMEBUFFER, fbo);
-
-        if(fbo == 0)
-            GLES20.glViewport(0, 0, mView.getWidth(), mView.getHeight());
-        else
-            GLES20.glViewport(0, 0, mFBOWidth, mFBOHeight);
-
-        GLES20.glClear(GLES20.GL_COLOR_BUFFER_BIT);
-
-        if(isOES) {
-            GLES20.glUseProgram(progOES);
-            GLES20.glVertexAttribPointer(vPosOES, 2, GLES20.GL_FLOAT, false, 4*2, vert);
-            GLES20.glVertexAttribPointer(vTCOES,  2, GLES20.GL_FLOAT, false, 4*2, texOES);
-        } else {
-            GLES20.glUseProgram(prog2D);
-            GLES20.glVertexAttribPointer(vPos2D, 2, GLES20.GL_FLOAT, false, 4*2, vert);
-            GLES20.glVertexAttribPointer(vTC2D,  2, GLES20.GL_FLOAT, false, 4*2, tex2D);
-        }
-
-        GLES20.glActiveTexture(GLES20.GL_TEXTURE0);
-
-        if(isOES) {
-            GLES20.glBindTexture(GLES11Ext.GL_TEXTURE_EXTERNAL_OES, tex);
-            GLES20.glUniform1i(GLES20.glGetUniformLocation(progOES, "sTexture"), 0);
-        } else {
-            GLES20.glBindTexture(GLES20.GL_TEXTURE_2D, tex);
-            GLES20.glUniform1i(GLES20.glGetUniformLocation(prog2D, "sTexture"), 0);
-        }
-
-        GLES20.glDrawArrays(GLES20.GL_TRIANGLE_STRIP, 0, 4);
-        GLES20.glFlush();
-    }
-
-    public synchronized void enableView() {
-        Log.d(LOGTAG, "enableView");
-        mEnabled = true;
-        updateState();
-    }
-
-    public synchronized void disableView() {
-        Log.d(LOGTAG, "disableView");
-        mEnabled = false;
-        updateState();
-    }
-
-    protected void updateState() {
-        Log.d(LOGTAG, "updateState");
-        Log.d(LOGTAG, "mEnabled="+mEnabled+", mHaveSurface="+mHaveSurface);
-        boolean willStart = mEnabled && mHaveSurface && mView.getVisibility() == View.VISIBLE;
-        if (willStart != mIsStarted) {
-            if(willStart) doStart();
-            else doStop();
-        } else {
-            Log.d(LOGTAG, "keeping State unchanged");
-        }
-        Log.d(LOGTAG, "updateState end");
-    }
-
-    protected synchronized void doStart() {
-        Log.d(LOGTAG, "doStart");
-        initSurfaceTexture();
-        openCamera(mCameraIndex);
-        mIsStarted = true;
-        if(mCameraWidth>0 && mCameraHeight>0)
-            setPreviewSize(mCameraWidth, mCameraHeight); // start preview and call listener.onCameraViewStarted()
-    }
-
-
-    protected void doStop() {
-        Log.d(LOGTAG, "doStop");
-        synchronized(this) {
-            mUpdateST = false;
-            mIsStarted = false;
-            mHaveFBO = false;
-            closeCamera();
-            deleteSurfaceTexture();
-        }
-        CameraTextureListener listener = mView.getCameraTextureListener();
-        if(listener != null) listener.onCameraViewStopped();
-
-    }
-
-    protected void setPreviewSize(int width, int height) {
-        synchronized(this) {
-            mHaveFBO = false;
-            mCameraWidth  = width;
-            mCameraHeight = height;
-            setCameraPreviewSize(width, height); // can change mCameraWidth & mCameraHeight
-            initFBO(mCameraWidth, mCameraHeight);
-            mHaveFBO = true;
-        }
-
-        CameraTextureListener listener = mView.getCameraTextureListener();
-        if(listener != null) listener.onCameraViewStarted(mCameraWidth, mCameraHeight);
-    }
-
-    public void setCameraIndex(int cameraIndex) {
-        disableView();
-        mCameraIndex = cameraIndex;
-        enableView();
-    }
-
-    public void setMaxCameraPreviewSize(int maxWidth, int maxHeight) {
-        disableView();
-        mMaxCameraWidth  = maxWidth;
-        mMaxCameraHeight = maxHeight;
-        enableView();
-    }
-
-    public void onResume() {
-        Log.i(LOGTAG, "onResume");
-    }
-
-    public void onPause() {
-        Log.i(LOGTAG, "onPause");
-        mHaveSurface = false;
-        updateState();
-        mCameraWidth = mCameraHeight = -1;
-    }
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/android/CameraGLSurfaceView.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/android/CameraGLSurfaceView.java
deleted file mode 100755
index 05f950b..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/android/CameraGLSurfaceView.java
+++ /dev/null
@@ -1,119 +0,0 @@
-package org.opencv.android;
-
-import org.opencv.R;
-
-import android.content.Context;
-import android.content.res.TypedArray;
-import android.opengl.GLSurfaceView;
-import android.util.AttributeSet;
-import android.util.Log;
-import android.view.SurfaceHolder;
-
-public class CameraGLSurfaceView extends GLSurfaceView {
-
-    private static final String LOGTAG = "CameraGLSurfaceView";
-
-    public interface CameraTextureListener {
-        /**
-         * This method is invoked when camera preview has started. After this method is invoked
-         * the frames will start to be delivered to client via the onCameraFrame() callback.
-         * @param width -  the width of the frames that will be delivered
-         * @param height - the height of the frames that will be delivered
-         */
-        public void onCameraViewStarted(int width, int height);
-
-        /**
-         * This method is invoked when camera preview has been stopped for some reason.
-         * No frames will be delivered via onCameraFrame() callback after this method is called.
-         */
-        public void onCameraViewStopped();
-
-        /**
-         * This method is invoked when a new preview frame from Camera is ready.
-         * @param texIn -  the OpenGL texture ID that contains frame in RGBA format
-         * @param texOut - the OpenGL texture ID that can be used to store modified frame image t display
-         * @param width -  the width of the frame
-         * @param height - the height of the frame
-         * @return `true` if `texOut` should be displayed, `false` - to show `texIn`
-         */
-        public boolean onCameraTexture(int texIn, int texOut, int width, int height);
-    };
-
-    private CameraTextureListener mTexListener;
-    private CameraGLRendererBase mRenderer;
-
-    public CameraGLSurfaceView(Context context, AttributeSet attrs) {
-        super(context, attrs);
-
-        TypedArray styledAttrs = getContext().obtainStyledAttributes(attrs, R.styleable.CameraBridgeViewBase);
-        int cameraIndex = styledAttrs.getInt(R.styleable.CameraBridgeViewBase_camera_id, -1);
-        styledAttrs.recycle();
-
-        if(android.os.Build.VERSION.SDK_INT >= 21)
-            mRenderer = new Camera2Renderer(this);
-        else
-            mRenderer = new CameraRenderer(this);
-
-        setCameraIndex(cameraIndex);
-
-        setEGLContextClientVersion(2);
-        setRenderer(mRenderer);
-        setRenderMode(GLSurfaceView.RENDERMODE_WHEN_DIRTY);
-    }
-
-    public void setCameraTextureListener(CameraTextureListener texListener)
-    {
-        mTexListener = texListener;
-    }
-
-    public CameraTextureListener getCameraTextureListener()
-    {
-        return mTexListener;
-    }
-
-    public void setCameraIndex(int cameraIndex) {
-        mRenderer.setCameraIndex(cameraIndex);
-    }
-
-    public void setMaxCameraPreviewSize(int maxWidth, int maxHeight) {
-        mRenderer.setMaxCameraPreviewSize(maxWidth, maxHeight);
-    }
-
-    @Override
-    public void surfaceCreated(SurfaceHolder holder) {
-        super.surfaceCreated(holder);
-    }
-
-    @Override
-    public void surfaceDestroyed(SurfaceHolder holder) {
-        mRenderer.mHaveSurface = false;
-        super.surfaceDestroyed(holder);
-    }
-
-    @Override
-    public void surfaceChanged(SurfaceHolder holder, int format, int w, int h) {
-        super.surfaceChanged(holder, format, w, h);
-    }
-
-    @Override
-    public void onResume() {
-        Log.i(LOGTAG, "onResume");
-        super.onResume();
-        mRenderer.onResume();
-    }
-
-    @Override
-    public void onPause() {
-        Log.i(LOGTAG, "onPause");
-        mRenderer.onPause();
-        super.onPause();
-    }
-
-    public void enableView() {
-        mRenderer.enableView();
-    }
-
-    public void disableView() {
-        mRenderer.disableView();
-    }
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/android/CameraRenderer.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/android/CameraRenderer.java
deleted file mode 100755
index 2d668ff..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/android/CameraRenderer.java
+++ /dev/null
@@ -1,166 +0,0 @@
-package org.opencv.android;
-
-import java.io.IOException;
-import java.util.List;
-
-import android.annotation.TargetApi;
-import android.hardware.Camera;
-import android.hardware.Camera.Size;
-import android.os.Build;
-import android.util.Log;
-
-@TargetApi(15)
-@SuppressWarnings("deprecation")
-public class CameraRenderer extends CameraGLRendererBase {
-
-    public static final String LOGTAG = "CameraRenderer";
-
-    private Camera mCamera;
-    private boolean mPreviewStarted = false;
-
-    CameraRenderer(CameraGLSurfaceView view) {
-        super(view);
-    }
-
-    @Override
-    protected synchronized void closeCamera() {
-        Log.i(LOGTAG, "closeCamera");
-        if(mCamera != null) {
-            mCamera.stopPreview();
-            mPreviewStarted = false;
-            mCamera.release();
-            mCamera = null;
-        }
-    }
-
-    @Override
-    protected synchronized void openCamera(int id) {
-        Log.i(LOGTAG, "openCamera");
-        closeCamera();
-        if (id == CameraBridgeViewBase.CAMERA_ID_ANY) {
-            Log.d(LOGTAG, "Trying to open camera with old open()");
-            try {
-                mCamera = Camera.open();
-            }
-            catch (Exception e){
-                Log.e(LOGTAG, "Camera is not available (in use or does not exist): " + e.getLocalizedMessage());
-            }
-
-            if(mCamera == null && Build.VERSION.SDK_INT >= Build.VERSION_CODES.GINGERBREAD) {
-                boolean connected = false;
-                for (int camIdx = 0; camIdx < Camera.getNumberOfCameras(); ++camIdx) {
-                    Log.d(LOGTAG, "Trying to open camera with new open(" + camIdx + ")");
-                    try {
-                        mCamera = Camera.open(camIdx);
-                        connected = true;
-                    } catch (RuntimeException e) {
-                        Log.e(LOGTAG, "Camera #" + camIdx + "failed to open: " + e.getLocalizedMessage());
-                    }
-                    if (connected) break;
-                }
-            }
-        } else {
-            if (Build.VERSION.SDK_INT >= Build.VERSION_CODES.GINGERBREAD) {
-                int localCameraIndex = mCameraIndex;
-                if (mCameraIndex == CameraBridgeViewBase.CAMERA_ID_BACK) {
-                    Log.i(LOGTAG, "Trying to open BACK camera");
-                    Camera.CameraInfo cameraInfo = new Camera.CameraInfo();
-                    for (int camIdx = 0; camIdx < Camera.getNumberOfCameras(); ++camIdx) {
-                        Camera.getCameraInfo( camIdx, cameraInfo );
-                        if (cameraInfo.facing == Camera.CameraInfo.CAMERA_FACING_BACK) {
-                            localCameraIndex = camIdx;
-                            break;
-                        }
-                    }
-                } else if (mCameraIndex == CameraBridgeViewBase.CAMERA_ID_FRONT) {
-                    Log.i(LOGTAG, "Trying to open FRONT camera");
-                    Camera.CameraInfo cameraInfo = new Camera.CameraInfo();
-                    for (int camIdx = 0; camIdx < Camera.getNumberOfCameras(); ++camIdx) {
-                        Camera.getCameraInfo( camIdx, cameraInfo );
-                        if (cameraInfo.facing == Camera.CameraInfo.CAMERA_FACING_FRONT) {
-                            localCameraIndex = camIdx;
-                            break;
-                        }
-                    }
-                }
-                if (localCameraIndex == CameraBridgeViewBase.CAMERA_ID_BACK) {
-                    Log.e(LOGTAG, "Back camera not found!");
-                } else if (localCameraIndex == CameraBridgeViewBase.CAMERA_ID_FRONT) {
-                    Log.e(LOGTAG, "Front camera not found!");
-                } else {
-                    Log.d(LOGTAG, "Trying to open camera with new open(" + localCameraIndex + ")");
-                    try {
-                        mCamera = Camera.open(localCameraIndex);
-                    } catch (RuntimeException e) {
-                        Log.e(LOGTAG, "Camera #" + localCameraIndex + "failed to open: " + e.getLocalizedMessage());
-                    }
-                }
-            }
-        }
-        if(mCamera == null) {
-            Log.e(LOGTAG, "Error: can't open camera");
-            return;
-        }
-        Camera.Parameters params = mCamera.getParameters();
-        List<String> FocusModes = params.getSupportedFocusModes();
-        if (FocusModes != null && FocusModes.contains(Camera.Parameters.FOCUS_MODE_CONTINUOUS_VIDEO))
-        {
-            params.setFocusMode(Camera.Parameters.FOCUS_MODE_CONTINUOUS_VIDEO);
-        }
-        mCamera.setParameters(params);
-
-        try {
-            mCamera.setPreviewTexture(mSTexture);
-        } catch (IOException ioe) {
-            Log.e(LOGTAG, "setPreviewTexture() failed: " + ioe.getMessage());
-        }
-    }
-
-    @Override
-    public synchronized void setCameraPreviewSize(int width, int height) {
-        Log.i(LOGTAG, "setCameraPreviewSize: "+width+"x"+height);
-        if(mCamera == null) {
-            Log.e(LOGTAG, "Camera isn't initialized!");
-            return;
-        }
-
-        if(mMaxCameraWidth  > 0 && mMaxCameraWidth  < width)  width  = mMaxCameraWidth;
-        if(mMaxCameraHeight > 0 && mMaxCameraHeight < height) height = mMaxCameraHeight;
-
-        Camera.Parameters param = mCamera.getParameters();
-        List<Size> psize = param.getSupportedPreviewSizes();
-        int bestWidth = 0, bestHeight = 0;
-        if (psize.size() > 0) {
-            float aspect = (float)width / height;
-            for (Size size : psize) {
-                int w = size.width, h = size.height;
-                Log.d(LOGTAG, "checking camera preview size: "+w+"x"+h);
-                if ( w <= width && h <= height &&
-                     w >= bestWidth && h >= bestHeight &&
-                     Math.abs(aspect - (float)w/h) < 0.2 ) {
-                    bestWidth = w;
-                    bestHeight = h;
-                }
-            }
-            if(bestWidth <= 0 || bestHeight <= 0) {
-                bestWidth  = psize.get(0).width;
-                bestHeight = psize.get(0).height;
-                Log.e(LOGTAG, "Error: best size was not selected, using "+bestWidth+" x "+bestHeight);
-            } else {
-                Log.i(LOGTAG, "Selected best size: "+bestWidth+" x "+bestHeight);
-            }
-
-            if(mPreviewStarted) {
-                mCamera.stopPreview();
-                mPreviewStarted = false;
-            }
-            mCameraWidth  = bestWidth;
-            mCameraHeight = bestHeight;
-            param.setPreviewSize(bestWidth, bestHeight);
-        }
-        param.set("orientation", "landscape");
-        mCamera.setParameters(param);
-        mCamera.startPreview();
-        mPreviewStarted = true;
-    }
-}
\ No newline at end of file
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/android/FpsMeter.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/android/FpsMeter.java
deleted file mode 100755
index 88e826c..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/android/FpsMeter.java
+++ /dev/null
@@ -1,66 +0,0 @@
-package org.opencv.android;
-
-import java.text.DecimalFormat;
-
-import org.opencv.core.Core;
-
-import android.graphics.Canvas;
-import android.graphics.Color;
-import android.graphics.Paint;
-import android.util.Log;
-
-public class FpsMeter {
-    private static final String TAG               = "FpsMeter";
-    private static final int    STEP              = 20;
-    private static final DecimalFormat FPS_FORMAT = new DecimalFormat("0.00");
-
-    private int                 mFramesCouner;
-    private double              mFrequency;
-    private long                mprevFrameTime;
-    private String              mStrfps;
-    Paint                       mPaint;
-    boolean                     mIsInitialized = false;
-    int                         mWidth = 0;
-    int                         mHeight = 0;
-
-    public void init() {
-        mFramesCouner = 0;
-        mFrequency = Core.getTickFrequency();
-        mprevFrameTime = Core.getTickCount();
-        mStrfps = "";
-
-        mPaint = new Paint();
-        mPaint.setColor(Color.BLUE);
-        mPaint.setTextSize(20);
-    }
-
-    public void measure() {
-        if (!mIsInitialized) {
-            init();
-            mIsInitialized = true;
-        } else {
-            mFramesCouner++;
-            if (mFramesCouner % STEP == 0) {
-                long time = Core.getTickCount();
-                double fps = STEP * mFrequency / (time - mprevFrameTime);
-                mprevFrameTime = time;
-                if (mWidth != 0 && mHeight != 0)
-                    mStrfps = FPS_FORMAT.format(fps) + " FPS@" + Integer.valueOf(mWidth) + "x" + Integer.valueOf(mHeight);
-                else
-                    mStrfps = FPS_FORMAT.format(fps) + " FPS";
-                Log.i(TAG, mStrfps);
-            }
-        }
-    }
-
-    public void setResolution(int width, int height) {
-        mWidth = width;
-        mHeight = height;
-    }
-
-    public void draw(Canvas canvas, float offsetx, float offsety) {
-        Log.d(TAG, mStrfps);
-        canvas.drawText(mStrfps, offsetx, offsety, mPaint);
-    }
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/android/InstallCallbackInterface.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/android/InstallCallbackInterface.java
deleted file mode 100755
index f68027a..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/android/InstallCallbackInterface.java
+++ /dev/null
@@ -1,34 +0,0 @@
-package org.opencv.android;
-
-/**
- * Installation callback interface.
- */
-public interface InstallCallbackInterface
-{
-    /**
-     * New package installation is required.
-     */
-    static final int NEW_INSTALLATION = 0;
-    /**
-     * Current package installation is in progress.
-     */
-    static final int INSTALLATION_PROGRESS = 1;
-
-    /**
-     * Target package name.
-     * @return Return target package name.
-     */
-    public String getPackageName();
-    /**
-     * Installation is approved.
-     */
-    public void install();
-    /**
-     * Installation is canceled.
-     */
-    public void cancel();
-    /**
-     * Wait for package installation.
-     */
-    public void wait_install();
-};
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/android/JavaCamera2View.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/android/JavaCamera2View.java
deleted file mode 100755
index 2cf512f..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/android/JavaCamera2View.java
+++ /dev/null
@@ -1,374 +0,0 @@
-package org.opencv.android;
-
-import java.nio.ByteBuffer;
-import java.util.Arrays;
-
-import android.annotation.TargetApi;
-import android.content.Context;
-import android.graphics.ImageFormat;
-import android.hardware.camera2.CameraAccessException;
-import android.hardware.camera2.CameraCaptureSession;
-import android.hardware.camera2.CameraCharacteristics;
-import android.hardware.camera2.CameraDevice;
-import android.hardware.camera2.CameraManager;
-import android.hardware.camera2.CaptureRequest;
-import android.hardware.camera2.params.StreamConfigurationMap;
-import android.media.Image;
-import android.media.ImageReader;
-import android.os.Handler;
-import android.os.HandlerThread;
-import android.util.AttributeSet;
-import android.util.Log;
-import android.view.Surface;
-import android.view.ViewGroup.LayoutParams;
-
-import org.opencv.core.CvType;
-import org.opencv.core.Mat;
-import org.opencv.imgproc.Imgproc;
-
-/**
- * This class is an implementation of the Bridge View between OpenCV and Java Camera.
- * This class relays on the functionality available in base class and only implements
- * required functions:
- * connectCamera - opens Java camera and sets the PreviewCallback to be delivered.
- * disconnectCamera - closes the camera and stops preview.
- * When frame is delivered via callback from Camera - it processed via OpenCV to be
- * converted to RGBA32 and then passed to the external callback for modifications if required.
- */
-
-@TargetApi(21)
-public class JavaCamera2View extends CameraBridgeViewBase {
-
-    private static final String LOGTAG = "JavaCamera2View";
-
-    private ImageReader mImageReader;
-    private int mPreviewFormat = ImageFormat.YUV_420_888;
-
-    private CameraDevice mCameraDevice;
-    private CameraCaptureSession mCaptureSession;
-    private CaptureRequest.Builder mPreviewRequestBuilder;
-    private String mCameraID;
-    private android.util.Size mPreviewSize = new android.util.Size(-1, -1);
-
-    private HandlerThread mBackgroundThread;
-    private Handler mBackgroundHandler;
-
-    public JavaCamera2View(Context context, int cameraId) {
-        super(context, cameraId);
-    }
-
-    public JavaCamera2View(Context context, AttributeSet attrs) {
-        super(context, attrs);
-    }
-
-    private void startBackgroundThread() {
-        Log.i(LOGTAG, "startBackgroundThread");
-        stopBackgroundThread();
-        mBackgroundThread = new HandlerThread("OpenCVCameraBackground");
-        mBackgroundThread.start();
-        mBackgroundHandler = new Handler(mBackgroundThread.getLooper());
-    }
-
-    private void stopBackgroundThread() {
-        Log.i(LOGTAG, "stopBackgroundThread");
-        if (mBackgroundThread == null)
-            return;
-        mBackgroundThread.quitSafely();
-        try {
-            mBackgroundThread.join();
-            mBackgroundThread = null;
-            mBackgroundHandler = null;
-        } catch (InterruptedException e) {
-            Log.e(LOGTAG, "stopBackgroundThread", e);
-        }
-    }
-
-    protected boolean initializeCamera() {
-        Log.i(LOGTAG, "initializeCamera");
-        CameraManager manager = (CameraManager) getContext().getSystemService(Context.CAMERA_SERVICE);
-        try {
-            String camList[] = manager.getCameraIdList();
-            if (camList.length == 0) {
-                Log.e(LOGTAG, "Error: camera isn't detected.");
-                return false;
-            }
-            if (mCameraIndex == CameraBridgeViewBase.CAMERA_ID_ANY) {
-                mCameraID = camList[0];
-            } else {
-                for (String cameraID : camList) {
-                    CameraCharacteristics characteristics = manager.getCameraCharacteristics(cameraID);
-                    if ((mCameraIndex == CameraBridgeViewBase.CAMERA_ID_BACK &&
-                            characteristics.get(CameraCharacteristics.LENS_FACING) == CameraCharacteristics.LENS_FACING_BACK) ||
-                        (mCameraIndex == CameraBridgeViewBase.CAMERA_ID_FRONT &&
-                            characteristics.get(CameraCharacteristics.LENS_FACING) == CameraCharacteristics.LENS_FACING_FRONT)
-                    ) {
-                        mCameraID = cameraID;
-                        break;
-                    }
-                }
-            }
-            if (mCameraID != null) {
-                Log.i(LOGTAG, "Opening camera: " + mCameraID);
-                manager.openCamera(mCameraID, mStateCallback, mBackgroundHandler);
-            }
-            return true;
-        } catch (CameraAccessException e) {
-            Log.e(LOGTAG, "OpenCamera - Camera Access Exception", e);
-        } catch (IllegalArgumentException e) {
-            Log.e(LOGTAG, "OpenCamera - Illegal Argument Exception", e);
-        } catch (SecurityException e) {
-            Log.e(LOGTAG, "OpenCamera - Security Exception", e);
-        }
-        return false;
-    }
-
-    private final CameraDevice.StateCallback mStateCallback = new CameraDevice.StateCallback() {
-
-        @Override
-        public void onOpened(CameraDevice cameraDevice) {
-            mCameraDevice = cameraDevice;
-            createCameraPreviewSession();
-        }
-
-        @Override
-        public void onDisconnected(CameraDevice cameraDevice) {
-            cameraDevice.close();
-            mCameraDevice = null;
-        }
-
-        @Override
-        public void onError(CameraDevice cameraDevice, int error) {
-            cameraDevice.close();
-            mCameraDevice = null;
-        }
-
-    };
-
-    private void createCameraPreviewSession() {
-        final int w = mPreviewSize.getWidth(), h = mPreviewSize.getHeight();
-        Log.i(LOGTAG, "createCameraPreviewSession(" + w + "x" + h + ")");
-        if (w < 0 || h < 0)
-            return;
-        try {
-            if (null == mCameraDevice) {
-                Log.e(LOGTAG, "createCameraPreviewSession: camera isn't opened");
-                return;
-            }
-            if (null != mCaptureSession) {
-                Log.e(LOGTAG, "createCameraPreviewSession: mCaptureSession is already started");
-                return;
-            }
-
-            mImageReader = ImageReader.newInstance(w, h, mPreviewFormat, 2);
-            mImageReader.setOnImageAvailableListener(new ImageReader.OnImageAvailableListener() {
-                @Override
-                public void onImageAvailable(ImageReader reader) {
-                    Image image = reader.acquireLatestImage();
-                    if (image == null)
-                        return;
-
-                    // sanity checks - 3 planes
-                    Image.Plane[] planes = image.getPlanes();
-                    assert (planes.length == 3);
-                    assert (image.getFormat() == mPreviewFormat);
-
-                    // see also https://developer.android.com/reference/android/graphics/ImageFormat.html#YUV_420_888
-                    // Y plane (0) non-interleaved => stride == 1; U/V plane interleaved => stride == 2
-                    assert (planes[0].getPixelStride() == 1);
-                    assert (planes[1].getPixelStride() == 2);
-                    assert (planes[2].getPixelStride() == 2);
-
-                    ByteBuffer y_plane = planes[0].getBuffer();
-                    ByteBuffer uv_plane = planes[1].getBuffer();
-                    Mat y_mat = new Mat(h, w, CvType.CV_8UC1, y_plane);
-                    Mat uv_mat = new Mat(h / 2, w / 2, CvType.CV_8UC2, uv_plane);
-                    JavaCamera2Frame tempFrame = new JavaCamera2Frame(y_mat, uv_mat, w, h);
-                    deliverAndDrawFrame(tempFrame);
-                    tempFrame.release();
-                    image.close();
-                }
-            }, mBackgroundHandler);
-            Surface surface = mImageReader.getSurface();
-
-            mPreviewRequestBuilder = mCameraDevice.createCaptureRequest(CameraDevice.TEMPLATE_PREVIEW);
-            mPreviewRequestBuilder.addTarget(surface);
-
-            mCameraDevice.createCaptureSession(Arrays.asList(surface),
-                new CameraCaptureSession.StateCallback() {
-                    @Override
-                    public void onConfigured(CameraCaptureSession cameraCaptureSession) {
-                        Log.i(LOGTAG, "createCaptureSession::onConfigured");
-                        if (null == mCameraDevice) {
-                            return; // camera is already closed
-                        }
-                        mCaptureSession = cameraCaptureSession;
-                        try {
-                            mPreviewRequestBuilder.set(CaptureRequest.CONTROL_AF_MODE,
-                                    CaptureRequest.CONTROL_AF_MODE_CONTINUOUS_PICTURE);
-                            mPreviewRequestBuilder.set(CaptureRequest.CONTROL_AE_MODE,
-                                    CaptureRequest.CONTROL_AE_MODE_ON_AUTO_FLASH);
-
-                            mCaptureSession.setRepeatingRequest(mPreviewRequestBuilder.build(), null, mBackgroundHandler);
-                            Log.i(LOGTAG, "CameraPreviewSession has been started");
-                        } catch (Exception e) {
-                            Log.e(LOGTAG, "createCaptureSession failed", e);
-                        }
-                    }
-
-                    @Override
-                    public void onConfigureFailed(CameraCaptureSession cameraCaptureSession) {
-                        Log.e(LOGTAG, "createCameraPreviewSession failed");
-                    }
-                },
-                null
-            );
-        } catch (CameraAccessException e) {
-            Log.e(LOGTAG, "createCameraPreviewSession", e);
-        }
-    }
-
-    @Override
-    protected void disconnectCamera() {
-        Log.i(LOGTAG, "closeCamera");
-        try {
-            CameraDevice c = mCameraDevice;
-            mCameraDevice = null;
-            if (null != mCaptureSession) {
-                mCaptureSession.close();
-                mCaptureSession = null;
-            }
-            if (null != c) {
-                c.close();
-            }
-            if (null != mImageReader) {
-                mImageReader.close();
-                mImageReader = null;
-            }
-        } finally {
-            stopBackgroundThread();
-        }
-    }
-
-    boolean calcPreviewSize(final int width, final int height) {
-        Log.i(LOGTAG, "calcPreviewSize: " + width + "x" + height);
-        if (mCameraID == null) {
-            Log.e(LOGTAG, "Camera isn't initialized!");
-            return false;
-        }
-        CameraManager manager = (CameraManager) getContext().getSystemService(Context.CAMERA_SERVICE);
-        try {
-            CameraCharacteristics characteristics = manager.getCameraCharacteristics(mCameraID);
-            StreamConfigurationMap map = characteristics.get(CameraCharacteristics.SCALER_STREAM_CONFIGURATION_MAP);
-            int bestWidth = 0, bestHeight = 0;
-            float aspect = (float) width / height;
-            android.util.Size[] sizes = map.getOutputSizes(ImageReader.class);
-            bestWidth = sizes[0].getWidth();
-            bestHeight = sizes[0].getHeight();
-            for (android.util.Size sz : sizes) {
-                int w = sz.getWidth(), h = sz.getHeight();
-                Log.d(LOGTAG, "trying size: " + w + "x" + h);
-                if (width >= w && height >= h && bestWidth <= w && bestHeight <= h
-                        && Math.abs(aspect - (float) w / h) < 0.2) {
-                    bestWidth = w;
-                    bestHeight = h;
-                }
-            }
-            Log.i(LOGTAG, "best size: " + bestWidth + "x" + bestHeight);
-            assert(!(bestWidth == 0 || bestHeight == 0));
-            if (mPreviewSize.getWidth() == bestWidth && mPreviewSize.getHeight() == bestHeight)
-                return false;
-            else {
-                mPreviewSize = new android.util.Size(bestWidth, bestHeight);
-                return true;
-            }
-        } catch (CameraAccessException e) {
-            Log.e(LOGTAG, "calcPreviewSize - Camera Access Exception", e);
-        } catch (IllegalArgumentException e) {
-            Log.e(LOGTAG, "calcPreviewSize - Illegal Argument Exception", e);
-        } catch (SecurityException e) {
-            Log.e(LOGTAG, "calcPreviewSize - Security Exception", e);
-        }
-        return false;
-    }
-
-    @Override
-    protected boolean connectCamera(int width, int height) {
-        Log.i(LOGTAG, "setCameraPreviewSize(" + width + "x" + height + ")");
-        startBackgroundThread();
-        initializeCamera();
-        try {
-            boolean needReconfig = calcPreviewSize(width, height);
-            mFrameWidth = mPreviewSize.getWidth();
-            mFrameHeight = mPreviewSize.getHeight();
-
-            if ((getLayoutParams().width == LayoutParams.MATCH_PARENT) && (getLayoutParams().height == LayoutParams.MATCH_PARENT))
-                mScale = Math.min(((float)height)/mFrameHeight, ((float)width)/mFrameWidth);
-            else
-                mScale = 0;
-
-            AllocateCache();
-
-            if (needReconfig) {
-                if (null != mCaptureSession) {
-                    Log.d(LOGTAG, "closing existing previewSession");
-                    mCaptureSession.close();
-                    mCaptureSession = null;
-                }
-                createCameraPreviewSession();
-            }
-        } catch (RuntimeException e) {
-            throw new RuntimeException("Interrupted while setCameraPreviewSize.", e);
-        }
-        return true;
-    }
-
-    private class JavaCamera2Frame implements CvCameraViewFrame {
-        @Override
-        public Mat gray() {
-            return mYuvFrameData.submat(0, mHeight, 0, mWidth);
-        }
-
-        @Override
-        public Mat rgba() {
-            if (mPreviewFormat == ImageFormat.NV21)
-                Imgproc.cvtColor(mYuvFrameData, mRgba, Imgproc.COLOR_YUV2RGBA_NV21, 4);
-            else if (mPreviewFormat == ImageFormat.YV12)
-                Imgproc.cvtColor(mYuvFrameData, mRgba, Imgproc.COLOR_YUV2RGB_I420, 4); // COLOR_YUV2RGBA_YV12 produces inverted colors
-            else if (mPreviewFormat == ImageFormat.YUV_420_888) {
-                assert (mUVFrameData != null);
-                Imgproc.cvtColorTwoPlane(mYuvFrameData, mUVFrameData, mRgba, Imgproc.COLOR_YUV2RGBA_NV21);
-            } else
-                throw new IllegalArgumentException("Preview Format can be NV21 or YV12");
-
-            return mRgba;
-        }
-
-        public JavaCamera2Frame(Mat Yuv420sp, int width, int height) {
-            super();
-            mWidth = width;
-            mHeight = height;
-            mYuvFrameData = Yuv420sp;
-            mUVFrameData = null;
-            mRgba = new Mat();
-        }
-
-        public JavaCamera2Frame(Mat Y, Mat UV, int width, int height) {
-            super();
-            mWidth = width;
-            mHeight = height;
-            mYuvFrameData = Y;
-            mUVFrameData = UV;
-            mRgba = new Mat();
-        }
-
-        public void release() {
-            mRgba.release();
-        }
-
-        private Mat mYuvFrameData;
-        private Mat mUVFrameData;
-        private Mat mRgba;
-        private int mWidth;
-        private int mHeight;
-    };
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/android/JavaCameraView.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/android/JavaCameraView.java
deleted file mode 100755
index a7c72e4..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/android/JavaCameraView.java
+++ /dev/null
@@ -1,379 +0,0 @@
-package org.opencv.android;
-
-import java.util.List;
-
-import android.content.Context;
-import android.graphics.ImageFormat;
-import android.graphics.SurfaceTexture;
-import android.hardware.Camera;
-import android.hardware.Camera.PreviewCallback;
-import android.os.Build;
-import android.util.AttributeSet;
-import android.util.Log;
-import android.view.ViewGroup.LayoutParams;
-
-import org.opencv.BuildConfig;
-import org.opencv.core.CvType;
-import org.opencv.core.Mat;
-import org.opencv.core.Size;
-import org.opencv.imgproc.Imgproc;
-
-/**
- * This class is an implementation of the Bridge View between OpenCV and Java Camera.
- * This class relays on the functionality available in base class and only implements
- * required functions:
- * connectCamera - opens Java camera and sets the PreviewCallback to be delivered.
- * disconnectCamera - closes the camera and stops preview.
- * When frame is delivered via callback from Camera - it processed via OpenCV to be
- * converted to RGBA32 and then passed to the external callback for modifications if required.
- */
-public class JavaCameraView extends CameraBridgeViewBase implements PreviewCallback {
-
-    private static final int MAGIC_TEXTURE_ID = 10;
-    private static final String TAG = "JavaCameraView";
-
-    private byte mBuffer[];
-    private Mat[] mFrameChain;
-    private int mChainIdx = 0;
-    private Thread mThread;
-    private boolean mStopThread;
-
-    protected Camera mCamera;
-    protected JavaCameraFrame[] mCameraFrame;
-    private SurfaceTexture mSurfaceTexture;
-    private int mPreviewFormat = ImageFormat.NV21;
-
-    public static class JavaCameraSizeAccessor implements ListItemAccessor {
-
-        @Override
-        public int getWidth(Object obj) {
-            Camera.Size size = (Camera.Size) obj;
-            return size.width;
-        }
-
-        @Override
-        public int getHeight(Object obj) {
-            Camera.Size size = (Camera.Size) obj;
-            return size.height;
-        }
-    }
-
-    public JavaCameraView(Context context, int cameraId) {
-        super(context, cameraId);
-    }
-
-    public JavaCameraView(Context context, AttributeSet attrs) {
-        super(context, attrs);
-    }
-
-    protected boolean initializeCamera(int width, int height) {
-        Log.d(TAG, "Initialize java camera");
-        boolean result = true;
-        synchronized (this) {
-            mCamera = null;
-
-            if (mCameraIndex == CAMERA_ID_ANY) {
-                Log.d(TAG, "Trying to open camera with old open()");
-                try {
-                    mCamera = Camera.open();
-                }
-                catch (Exception e){
-                    Log.e(TAG, "Camera is not available (in use or does not exist): " + e.getLocalizedMessage());
-                }
-
-                if(mCamera == null && Build.VERSION.SDK_INT >= Build.VERSION_CODES.GINGERBREAD) {
-                    boolean connected = false;
-                    for (int camIdx = 0; camIdx < Camera.getNumberOfCameras(); ++camIdx) {
-                        Log.d(TAG, "Trying to open camera with new open(" + Integer.valueOf(camIdx) + ")");
-                        try {
-                            mCamera = Camera.open(camIdx);
-                            connected = true;
-                        } catch (RuntimeException e) {
-                            Log.e(TAG, "Camera #" + camIdx + "failed to open: " + e.getLocalizedMessage());
-                        }
-                        if (connected) break;
-                    }
-                }
-            } else {
-                if (Build.VERSION.SDK_INT >= Build.VERSION_CODES.GINGERBREAD) {
-                    int localCameraIndex = mCameraIndex;
-                    if (mCameraIndex == CAMERA_ID_BACK) {
-                        Log.i(TAG, "Trying to open back camera");
-                        Camera.CameraInfo cameraInfo = new Camera.CameraInfo();
-                        for (int camIdx = 0; camIdx < Camera.getNumberOfCameras(); ++camIdx) {
-                            Camera.getCameraInfo( camIdx, cameraInfo );
-                            if (cameraInfo.facing == Camera.CameraInfo.CAMERA_FACING_BACK) {
-                                localCameraIndex = camIdx;
-                                break;
-                            }
-                        }
-                    } else if (mCameraIndex == CAMERA_ID_FRONT) {
-                        Log.i(TAG, "Trying to open front camera");
-                        Camera.CameraInfo cameraInfo = new Camera.CameraInfo();
-                        for (int camIdx = 0; camIdx < Camera.getNumberOfCameras(); ++camIdx) {
-                            Camera.getCameraInfo( camIdx, cameraInfo );
-                            if (cameraInfo.facing == Camera.CameraInfo.CAMERA_FACING_FRONT) {
-                                localCameraIndex = camIdx;
-                                break;
-                            }
-                        }
-                    }
-                    if (localCameraIndex == CAMERA_ID_BACK) {
-                        Log.e(TAG, "Back camera not found!");
-                    } else if (localCameraIndex == CAMERA_ID_FRONT) {
-                        Log.e(TAG, "Front camera not found!");
-                    } else {
-                        Log.d(TAG, "Trying to open camera with new open(" + Integer.valueOf(localCameraIndex) + ")");
-                        try {
-                            mCamera = Camera.open(localCameraIndex);
-                        } catch (RuntimeException e) {
-                            Log.e(TAG, "Camera #" + localCameraIndex + "failed to open: " + e.getLocalizedMessage());
-                        }
-                    }
-                }
-            }
-
-            if (mCamera == null)
-                return false;
-
-            /* Now set camera parameters */
-            try {
-                Camera.Parameters params = mCamera.getParameters();
-                Log.d(TAG, "getSupportedPreviewSizes()");
-                List<android.hardware.Camera.Size> sizes = params.getSupportedPreviewSizes();
-
-                if (sizes != null) {
-                    /* Select the size that fits surface considering maximum size allowed */
-                    Size frameSize = calculateCameraFrameSize(sizes, new JavaCameraSizeAccessor(), width, height);
-
-                    /* Image format NV21 causes issues in the Android emulators */
-                    if (Build.FINGERPRINT.startsWith("generic")
-                            || Build.FINGERPRINT.startsWith("unknown")
-                            || Build.MODEL.contains("google_sdk")
-                            || Build.MODEL.contains("Emulator")
-                            || Build.MODEL.contains("Android SDK built for x86")
-                            || Build.MANUFACTURER.contains("Genymotion")
-                            || (Build.BRAND.startsWith("generic") && Build.DEVICE.startsWith("generic"))
-                            || "google_sdk".equals(Build.PRODUCT))
-                        params.setPreviewFormat(ImageFormat.YV12);  // "generic" or "android" = android emulator
-                    else
-                        params.setPreviewFormat(ImageFormat.NV21);
-
-                    mPreviewFormat = params.getPreviewFormat();
-
-                    Log.d(TAG, "Set preview size to " + Integer.valueOf((int)frameSize.width) + "x" + Integer.valueOf((int)frameSize.height));
-                    params.setPreviewSize((int)frameSize.width, (int)frameSize.height);
-
-                    if (Build.VERSION.SDK_INT >= Build.VERSION_CODES.ICE_CREAM_SANDWICH && !android.os.Build.MODEL.equals("GT-I9100"))
-                        params.setRecordingHint(true);
-
-                    List<String> FocusModes = params.getSupportedFocusModes();
-                    if (FocusModes != null && FocusModes.contains(Camera.Parameters.FOCUS_MODE_CONTINUOUS_VIDEO))
-                    {
-                        params.setFocusMode(Camera.Parameters.FOCUS_MODE_CONTINUOUS_VIDEO);
-                    }
-
-                    mCamera.setParameters(params);
-                    params = mCamera.getParameters();
-
-                    mFrameWidth = params.getPreviewSize().width;
-                    mFrameHeight = params.getPreviewSize().height;
-
-                    if ((getLayoutParams().width == LayoutParams.MATCH_PARENT) && (getLayoutParams().height == LayoutParams.MATCH_PARENT))
-                        mScale = Math.min(((float)height)/mFrameHeight, ((float)width)/mFrameWidth);
-                    else
-                        mScale = 0;
-
-                    if (mFpsMeter != null) {
-                        mFpsMeter.setResolution(mFrameWidth, mFrameHeight);
-                    }
-
-                    int size = mFrameWidth * mFrameHeight;
-                    size  = size * ImageFormat.getBitsPerPixel(params.getPreviewFormat()) / 8;
-                    mBuffer = new byte[size];
-
-                    mCamera.addCallbackBuffer(mBuffer);
-                    mCamera.setPreviewCallbackWithBuffer(this);
-
-                    mFrameChain = new Mat[2];
-                    mFrameChain[0] = new Mat(mFrameHeight + (mFrameHeight/2), mFrameWidth, CvType.CV_8UC1);
-                    mFrameChain[1] = new Mat(mFrameHeight + (mFrameHeight/2), mFrameWidth, CvType.CV_8UC1);
-
-                    AllocateCache();
-
-                    mCameraFrame = new JavaCameraFrame[2];
-                    mCameraFrame[0] = new JavaCameraFrame(mFrameChain[0], mFrameWidth, mFrameHeight);
-                    mCameraFrame[1] = new JavaCameraFrame(mFrameChain[1], mFrameWidth, mFrameHeight);
-
-                    if (Build.VERSION.SDK_INT >= Build.VERSION_CODES.HONEYCOMB) {
-                        mSurfaceTexture = new SurfaceTexture(MAGIC_TEXTURE_ID);
-                        mCamera.setPreviewTexture(mSurfaceTexture);
-                    } else
-                       mCamera.setPreviewDisplay(null);
-
-                    /* Finally we are ready to start the preview */
-                    Log.d(TAG, "startPreview");
-                    mCamera.startPreview();
-                }
-                else
-                    result = false;
-            } catch (Exception e) {
-                result = false;
-                e.printStackTrace();
-            }
-        }
-
-        return result;
-    }
-
-    protected void releaseCamera() {
-        synchronized (this) {
-            if (mCamera != null) {
-                mCamera.stopPreview();
-                mCamera.setPreviewCallback(null);
-
-                mCamera.release();
-            }
-            mCamera = null;
-            if (mFrameChain != null) {
-                mFrameChain[0].release();
-                mFrameChain[1].release();
-            }
-            if (mCameraFrame != null) {
-                mCameraFrame[0].release();
-                mCameraFrame[1].release();
-            }
-        }
-    }
-
-    private boolean mCameraFrameReady = false;
-
-    @Override
-    protected boolean connectCamera(int width, int height) {
-
-        /* 1. We need to instantiate camera
-         * 2. We need to start thread which will be getting frames
-         */
-        /* First step - initialize camera connection */
-        Log.d(TAG, "Connecting to camera");
-        if (!initializeCamera(width, height))
-            return false;
-
-        mCameraFrameReady = false;
-
-        /* now we can start update thread */
-        Log.d(TAG, "Starting processing thread");
-        mStopThread = false;
-        mThread = new Thread(new CameraWorker());
-        mThread.start();
-
-        return true;
-    }
-
-    @Override
-    protected void disconnectCamera() {
-        /* 1. We need to stop thread which updating the frames
-         * 2. Stop camera and release it
-         */
-        Log.d(TAG, "Disconnecting from camera");
-        try {
-            mStopThread = true;
-            Log.d(TAG, "Notify thread");
-            synchronized (this) {
-                this.notify();
-            }
-            Log.d(TAG, "Waiting for thread");
-            if (mThread != null)
-                mThread.join();
-        } catch (InterruptedException e) {
-            e.printStackTrace();
-        } finally {
-            mThread =  null;
-        }
-
-        /* Now release camera */
-        releaseCamera();
-
-        mCameraFrameReady = false;
-    }
-
-    @Override
-    public void onPreviewFrame(byte[] frame, Camera arg1) {
-        if (BuildConfig.DEBUG)
-            Log.d(TAG, "Preview Frame received. Frame size: " + frame.length);
-        synchronized (this) {
-            mFrameChain[mChainIdx].put(0, 0, frame);
-            mCameraFrameReady = true;
-            this.notify();
-        }
-        if (mCamera != null)
-            mCamera.addCallbackBuffer(mBuffer);
-    }
-
-    private class JavaCameraFrame implements CvCameraViewFrame {
-        @Override
-        public Mat gray() {
-            return mYuvFrameData.submat(0, mHeight, 0, mWidth);
-        }
-
-        @Override
-        public Mat rgba() {
-            if (mPreviewFormat == ImageFormat.NV21)
-                Imgproc.cvtColor(mYuvFrameData, mRgba, Imgproc.COLOR_YUV2RGBA_NV21, 4);
-            else if (mPreviewFormat == ImageFormat.YV12)
-                Imgproc.cvtColor(mYuvFrameData, mRgba, Imgproc.COLOR_YUV2RGB_I420, 4);  // COLOR_YUV2RGBA_YV12 produces inverted colors
-            else
-                throw new IllegalArgumentException("Preview Format can be NV21 or YV12");
-
-            return mRgba;
-        }
-
-        public JavaCameraFrame(Mat Yuv420sp, int width, int height) {
-            super();
-            mWidth = width;
-            mHeight = height;
-            mYuvFrameData = Yuv420sp;
-            mRgba = new Mat();
-        }
-
-        public void release() {
-            mRgba.release();
-        }
-
-        private Mat mYuvFrameData;
-        private Mat mRgba;
-        private int mWidth;
-        private int mHeight;
-    };
-
-    private class CameraWorker implements Runnable {
-
-        @Override
-        public void run() {
-            do {
-                boolean hasFrame = false;
-                synchronized (JavaCameraView.this) {
-                    try {
-                        while (!mCameraFrameReady && !mStopThread) {
-                            JavaCameraView.this.wait();
-                        }
-                    } catch (InterruptedException e) {
-                        e.printStackTrace();
-                    }
-                    if (mCameraFrameReady)
-                    {
-                        mChainIdx = 1 - mChainIdx;
-                        mCameraFrameReady = false;
-                        hasFrame = true;
-                    }
-                }
-
-                if (!mStopThread && hasFrame) {
-                    if (!mFrameChain[1 - mChainIdx].empty())
-                        deliverAndDrawFrame(mCameraFrame[1 - mChainIdx]);
-                }
-            } while (!mStopThread);
-            Log.d(TAG, "Finish processing thread");
-        }
-    }
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/android/LoaderCallbackInterface.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/android/LoaderCallbackInterface.java
deleted file mode 100755
index a941e83..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/android/LoaderCallbackInterface.java
+++ /dev/null
@@ -1,40 +0,0 @@
-package org.opencv.android;
-
-/**
- * Interface for callback object in case of asynchronous initialization of OpenCV.
- */
-public interface LoaderCallbackInterface
-{
-    /**
-     * OpenCV initialization finished successfully.
-     */
-    static final int SUCCESS = 0;
-    /**
-     * Google Play Market cannot be invoked.
-     */
-    static final int MARKET_ERROR = 2;
-    /**
-     * OpenCV library installation has been canceled by the user.
-     */
-    static final int INSTALL_CANCELED = 3;
-    /**
-     * This version of OpenCV Manager Service is incompatible with the app. Possibly, a service update is required.
-     */
-    static final int INCOMPATIBLE_MANAGER_VERSION = 4;
-    /**
-     * OpenCV library initialization has failed.
-     */
-    static final int INIT_FAILED = 0xff;
-
-    /**
-     * Callback method, called after OpenCV library initialization.
-     * @param status status of initialization (see initialization status constants).
-     */
-    public void onManagerConnected(int status);
-
-    /**
-     * Callback method, called in case the package installation is needed.
-     * @param callback answer object with approve and cancel methods and the package description.
-     */
-    public void onPackageInstall(final int operation, InstallCallbackInterface callback);
-};
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/android/OpenCVLoader.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/android/OpenCVLoader.java
deleted file mode 100755
index 7f0535d..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/android/OpenCVLoader.java
+++ /dev/null
@@ -1,132 +0,0 @@
-package org.opencv.android;
-
-import android.content.Context;
-
-/**
- * Helper class provides common initialization methods for OpenCV library.
- */
-public class OpenCVLoader
-{
-    /**
-     * OpenCV Library version 2.4.2.
-     */
-    public static final String OPENCV_VERSION_2_4_2 = "2.4.2";
-
-    /**
-     * OpenCV Library version 2.4.3.
-     */
-    public static final String OPENCV_VERSION_2_4_3 = "2.4.3";
-
-    /**
-     * OpenCV Library version 2.4.4.
-     */
-    public static final String OPENCV_VERSION_2_4_4 = "2.4.4";
-
-    /**
-     * OpenCV Library version 2.4.5.
-     */
-    public static final String OPENCV_VERSION_2_4_5 = "2.4.5";
-
-    /**
-     * OpenCV Library version 2.4.6.
-     */
-    public static final String OPENCV_VERSION_2_4_6 = "2.4.6";
-
-    /**
-     * OpenCV Library version 2.4.7.
-     */
-    public static final String OPENCV_VERSION_2_4_7 = "2.4.7";
-
-    /**
-     * OpenCV Library version 2.4.8.
-     */
-    public static final String OPENCV_VERSION_2_4_8 = "2.4.8";
-
-    /**
-     * OpenCV Library version 2.4.9.
-     */
-    public static final String OPENCV_VERSION_2_4_9 = "2.4.9";
-
-    /**
-     * OpenCV Library version 2.4.10.
-     */
-    public static final String OPENCV_VERSION_2_4_10 = "2.4.10";
-
-    /**
-     * OpenCV Library version 2.4.11.
-     */
-    public static final String OPENCV_VERSION_2_4_11 = "2.4.11";
-
-    /**
-     * OpenCV Library version 2.4.12.
-     */
-    public static final String OPENCV_VERSION_2_4_12 = "2.4.12";
-
-    /**
-     * OpenCV Library version 2.4.13.
-     */
-    public static final String OPENCV_VERSION_2_4_13 = "2.4.13";
-
-    /**
-     * OpenCV Library version 3.0.0.
-     */
-    public static final String OPENCV_VERSION_3_0_0 = "3.0.0";
-
-    /**
-     * OpenCV Library version 3.1.0.
-     */
-    public static final String OPENCV_VERSION_3_1_0 = "3.1.0";
-
-    /**
-     * OpenCV Library version 3.2.0.
-     */
-    public static final String OPENCV_VERSION_3_2_0 = "3.2.0";
-
-    /**
-     * OpenCV Library version 3.3.0.
-     */
-    public static final String OPENCV_VERSION_3_3_0 = "3.3.0";
-
-    /**
-     * OpenCV Library version 3.4.0.
-     */
-    public static final String OPENCV_VERSION_3_4_0 = "3.4.0";
-
-    /**
-     * Current OpenCV Library version
-     */
-    public static final String OPENCV_VERSION = "3.4.2";
-
-
-    /**
-     * Loads and initializes OpenCV library from current application package. Roughly, it's an analog of system.loadLibrary("opencv_java").
-     * @return Returns true is initialization of OpenCV was successful.
-     */
-    public static boolean initDebug()
-    {
-        return StaticHelper.initOpenCV(false);
-    }
-
-    /**
-     * Loads and initializes OpenCV library from current application package. Roughly, it's an analog of system.loadLibrary("opencv_java").
-     * @param InitCuda load and initialize CUDA runtime libraries.
-     * @return Returns true is initialization of OpenCV was successful.
-     */
-    public static boolean initDebug(boolean InitCuda)
-    {
-        return StaticHelper.initOpenCV(InitCuda);
-    }
-
-    /**
-     * Loads and initializes OpenCV library using OpenCV Engine service.
-     * @param Version OpenCV library version.
-     * @param AppContext application context for connecting to the service.
-     * @param Callback object, that implements LoaderCallbackInterface for handling the connection status.
-     * @return Returns true if initialization of OpenCV is successful.
-     */
-    public static boolean initAsync(String Version, Context AppContext,
-            LoaderCallbackInterface Callback)
-    {
-        return AsyncServiceHelper.initOpenCV(Version, AppContext, Callback);
-    }
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/android/StaticHelper.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/android/StaticHelper.java
deleted file mode 100755
index f670d93..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/android/StaticHelper.java
+++ /dev/null
@@ -1,104 +0,0 @@
-package org.opencv.android;
-
-import org.opencv.core.Core;
-
-import java.util.StringTokenizer;
-import android.util.Log;
-
-class StaticHelper {
-
-    public static boolean initOpenCV(boolean InitCuda)
-    {
-        boolean result;
-        String libs = "";
-
-        if(InitCuda)
-        {
-            loadLibrary("cudart");
-            loadLibrary("nppc");
-            loadLibrary("nppi");
-            loadLibrary("npps");
-            loadLibrary("cufft");
-            loadLibrary("cublas");
-        }
-
-        Log.d(TAG, "Trying to get library list");
-
-        try
-        {
-            System.loadLibrary("opencv_info");
-            libs = getLibraryList();
-        }
-        catch(UnsatisfiedLinkError e)
-        {
-            Log.e(TAG, "OpenCV error: Cannot load info library for OpenCV");
-        }
-
-        Log.d(TAG, "Library list: \"" + libs + "\"");
-        Log.d(TAG, "First attempt to load libs");
-        if (initOpenCVLibs(libs))
-        {
-            Log.d(TAG, "First attempt to load libs is OK");
-            String eol = System.getProperty("line.separator");
-            for (String str : Core.getBuildInformation().split(eol))
-                Log.i(TAG, str);
-
-            result = true;
-        }
-        else
-        {
-            Log.d(TAG, "First attempt to load libs fails");
-            result = false;
-        }
-
-        return result;
-    }
-
-    private static boolean loadLibrary(String Name)
-    {
-        boolean result = true;
-
-        Log.d(TAG, "Trying to load library " + Name);
-        try
-        {
-            System.loadLibrary(Name);
-            Log.d(TAG, "Library " + Name + " loaded");
-        }
-        catch(UnsatisfiedLinkError e)
-        {
-            Log.d(TAG, "Cannot load library \"" + Name + "\"");
-            e.printStackTrace();
-            result = false;
-        }
-
-        return result;
-    }
-
-    private static boolean initOpenCVLibs(String Libs)
-    {
-        Log.d(TAG, "Trying to init OpenCV libs");
-
-        boolean result = true;
-
-        if ((null != Libs) && (Libs.length() != 0))
-        {
-            Log.d(TAG, "Trying to load libs by dependency list");
-            StringTokenizer splitter = new StringTokenizer(Libs, ";");
-            while(splitter.hasMoreTokens())
-            {
-                result &= loadLibrary(splitter.nextToken());
-            }
-        }
-        else
-        {
-            // If dependencies list is not defined or empty.
-            result = loadLibrary("opencv_java3");
-        }
-
-        return result;
-    }
-
-    private static final String TAG = "OpenCV/StaticHelper";
-
-    private static native String getLibraryList();
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/android/Utils.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/android/Utils.java
deleted file mode 100755
index 404c986..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/android/Utils.java
+++ /dev/null
@@ -1,139 +0,0 @@
-package org.opencv.android;
-
-import android.content.Context;
-import android.graphics.Bitmap;
-
-import org.opencv.core.CvException;
-import org.opencv.core.CvType;
-import org.opencv.core.Mat;
-import org.opencv.imgcodecs.Imgcodecs;
-
-import java.io.ByteArrayOutputStream;
-import java.io.File;
-import java.io.FileOutputStream;
-import java.io.IOException;
-import java.io.InputStream;
-
-public class Utils {
-
-    public static String exportResource(Context context, int resourceId) {
-        return exportResource(context, resourceId, "OpenCV_data");
-    }
-
-    public static String exportResource(Context context, int resourceId, String dirname) {
-        String fullname = context.getResources().getString(resourceId);
-        String resName = fullname.substring(fullname.lastIndexOf("/") + 1);
-        try {
-            InputStream is = context.getResources().openRawResource(resourceId);
-            File resDir = context.getDir(dirname, Context.MODE_PRIVATE);
-            File resFile = new File(resDir, resName);
-
-            FileOutputStream os = new FileOutputStream(resFile);
-
-            byte[] buffer = new byte[4096];
-            int bytesRead;
-            while ((bytesRead = is.read(buffer)) != -1) {
-                os.write(buffer, 0, bytesRead);
-            }
-            is.close();
-            os.close();
-
-            return resFile.getAbsolutePath();
-        } catch (IOException e) {
-            e.printStackTrace();
-            throw new CvException("Failed to export resource " + resName
-                    + ". Exception thrown: " + e);
-        }
-    }
-
-    public static Mat loadResource(Context context, int resourceId) throws IOException
-    {
-        return loadResource(context, resourceId, -1);
-    }
-
-    public static Mat loadResource(Context context, int resourceId, int flags) throws IOException
-    {
-        InputStream is = context.getResources().openRawResource(resourceId);
-        ByteArrayOutputStream os = new ByteArrayOutputStream(is.available());
-
-        byte[] buffer = new byte[4096];
-        int bytesRead;
-        while ((bytesRead = is.read(buffer)) != -1) {
-            os.write(buffer, 0, bytesRead);
-        }
-        is.close();
-
-        Mat encoded = new Mat(1, os.size(), CvType.CV_8U);
-        encoded.put(0, 0, os.toByteArray());
-        os.close();
-
-        Mat decoded = Imgcodecs.imdecode(encoded, flags);
-        encoded.release();
-
-        return decoded;
-    }
-
-    /**
-     * Converts Android Bitmap to OpenCV Mat.
-     * <p>
-     * This function converts an Android Bitmap image to the OpenCV Mat.
-     * <br>'ARGB_8888' and 'RGB_565' input Bitmap formats are supported.
-     * <br>The output Mat is always created of the same size as the input Bitmap and of the 'CV_8UC4' type,
-     * it keeps the image in RGBA format.
-     * <br>This function throws an exception if the conversion fails.
-     * @param bmp is a valid input Bitmap object of the type 'ARGB_8888' or 'RGB_565'.
-     * @param mat is a valid output Mat object, it will be reallocated if needed, so it may be empty.
-     * @param unPremultiplyAlpha is a flag, that determines, whether the bitmap needs to be converted from alpha premultiplied format (like Android keeps 'ARGB_8888' ones) to regular one; this flag is ignored for 'RGB_565' bitmaps.
-     */
-    public static void bitmapToMat(Bitmap bmp, Mat mat, boolean unPremultiplyAlpha) {
-        if (bmp == null)
-            throw new java.lang.IllegalArgumentException("bmp == null");
-        if (mat == null)
-            throw new java.lang.IllegalArgumentException("mat == null");
-        nBitmapToMat2(bmp, mat.nativeObj, unPremultiplyAlpha);
-    }
-
-    /**
-     * Short form of the bitmapToMat(bmp, mat, unPremultiplyAlpha=false).
-     * @param bmp is a valid input Bitmap object of the type 'ARGB_8888' or 'RGB_565'.
-     * @param mat is a valid output Mat object, it will be reallocated if needed, so Mat may be empty.
-     */
-    public static void bitmapToMat(Bitmap bmp, Mat mat) {
-        bitmapToMat(bmp, mat, false);
-    }
-
-
-    /**
-     * Converts OpenCV Mat to Android Bitmap.
-     * <p>
-     * <br>This function converts an image in the OpenCV Mat representation to the Android Bitmap.
-     * <br>The input Mat object has to be of the types 'CV_8UC1' (gray-scale), 'CV_8UC3' (RGB) or 'CV_8UC4' (RGBA).
-     * <br>The output Bitmap object has to be of the same size as the input Mat and of the types 'ARGB_8888' or 'RGB_565'.
-     * <br>This function throws an exception if the conversion fails.
-     *
-     * @param mat is a valid input Mat object of types 'CV_8UC1', 'CV_8UC3' or 'CV_8UC4'.
-     * @param bmp is a valid Bitmap object of the same size as the Mat and of type 'ARGB_8888' or 'RGB_565'.
-     * @param premultiplyAlpha is a flag, that determines, whether the Mat needs to be converted to alpha premultiplied format (like Android keeps 'ARGB_8888' bitmaps); the flag is ignored for 'RGB_565' bitmaps.
-     */
-    public static void matToBitmap(Mat mat, Bitmap bmp, boolean premultiplyAlpha) {
-        if (mat == null)
-            throw new java.lang.IllegalArgumentException("mat == null");
-        if (bmp == null)
-            throw new java.lang.IllegalArgumentException("bmp == null");
-        nMatToBitmap2(mat.nativeObj, bmp, premultiplyAlpha);
-    }
-
-    /**
-     * Short form of the <b>matToBitmap(mat, bmp, premultiplyAlpha=false)</b>
-     * @param mat is a valid input Mat object of the types 'CV_8UC1', 'CV_8UC3' or 'CV_8UC4'.
-     * @param bmp is a valid Bitmap object of the same size as the Mat and of type 'ARGB_8888' or 'RGB_565'.
-     */
-    public static void matToBitmap(Mat mat, Bitmap bmp) {
-        matToBitmap(mat, bmp, false);
-    }
-
-
-    private static native void nBitmapToMat2(Bitmap b, long m_addr, boolean unPremultiplyAlpha);
-
-    private static native void nMatToBitmap2(long m_addr, Bitmap b, boolean premultiplyAlpha);
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/calib3d/Calib3d.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/calib3d/Calib3d.java
deleted file mode 100755
index 8c17d68..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/calib3d/Calib3d.java
+++ /dev/null
@@ -1,1646 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.calib3d;
-
-import java.util.ArrayList;
-import java.util.List;
-import org.opencv.core.Mat;
-import org.opencv.core.MatOfDouble;
-import org.opencv.core.MatOfPoint2f;
-import org.opencv.core.MatOfPoint3f;
-import org.opencv.core.Point;
-import org.opencv.core.Rect;
-import org.opencv.core.Size;
-import org.opencv.core.TermCriteria;
-import org.opencv.utils.Converters;
-
-// C++: class Calib3d
-//javadoc: Calib3d
-
-public class Calib3d {
-
-    public static final int
-            CALIB_USE_INTRINSIC_GUESS = 1,
-            CALIB_RECOMPUTE_EXTRINSIC = 2,
-            CALIB_CHECK_COND = 4,
-            CALIB_FIX_SKEW = 8,
-            CALIB_FIX_K1 = 16,
-            CALIB_FIX_K2 = 32,
-            CALIB_FIX_K3 = 64,
-            CALIB_FIX_K4 = 128,
-            CALIB_FIX_INTRINSIC = 256,
-            CV_ITERATIVE = 0,
-            CV_EPNP = 1,
-            CV_P3P = 2,
-            CV_DLS = 3,
-            LMEDS = 4,
-            RANSAC = 8,
-            RHO = 16,
-            SOLVEPNP_ITERATIVE = 0,
-            SOLVEPNP_EPNP = 1,
-            SOLVEPNP_P3P = 2,
-            SOLVEPNP_DLS = 3,
-            SOLVEPNP_UPNP = 4,
-            SOLVEPNP_AP3P = 5,
-            SOLVEPNP_MAX_COUNT = 5+1,
-            CALIB_CB_ADAPTIVE_THRESH = 1,
-            CALIB_CB_NORMALIZE_IMAGE = 2,
-            CALIB_CB_FILTER_QUADS = 4,
-            CALIB_CB_FAST_CHECK = 8,
-            CALIB_CB_SYMMETRIC_GRID = 1,
-            CALIB_CB_ASYMMETRIC_GRID = 2,
-            CALIB_CB_CLUSTERING = 4,
-            CALIB_FIX_ASPECT_RATIO = 0x00002,
-            CALIB_FIX_PRINCIPAL_POINT = 0x00004,
-            CALIB_ZERO_TANGENT_DIST = 0x00008,
-            CALIB_FIX_FOCAL_LENGTH = 0x00010,
-            CALIB_FIX_K5 = 0x01000,
-            CALIB_FIX_K6 = 0x02000,
-            CALIB_RATIONAL_MODEL = 0x04000,
-            CALIB_THIN_PRISM_MODEL = 0x08000,
-            CALIB_FIX_S1_S2_S3_S4 = 0x10000,
-            CALIB_TILTED_MODEL = 0x40000,
-            CALIB_FIX_TAUX_TAUY = 0x80000,
-            CALIB_USE_QR = 0x100000,
-            CALIB_FIX_TANGENT_DIST = 0x200000,
-            CALIB_SAME_FOCAL_LENGTH = 0x00200,
-            CALIB_ZERO_DISPARITY = 0x00400,
-            CALIB_USE_LU = (1 << 17),
-            CALIB_USE_EXTRINSIC_GUESS = (1 << 22),
-            FM_7POINT = 1,
-            FM_8POINT = 2,
-            FM_LMEDS = 4,
-            FM_RANSAC = 8;
-
-
-    //
-    // C++:  Mat estimateAffine2D(Mat from, Mat to, Mat& inliers = Mat(), int method = RANSAC, double ransacReprojThreshold = 3, size_t maxIters = 2000, double confidence = 0.99, size_t refineIters = 10)
-    //
-
-    //javadoc: estimateAffine2D(from, to, inliers, method, ransacReprojThreshold, maxIters, confidence, refineIters)
-    public static Mat estimateAffine2D(Mat from, Mat to, Mat inliers, int method, double ransacReprojThreshold, long maxIters, double confidence, long refineIters)
-    {
-        
-        Mat retVal = new Mat(estimateAffine2D_0(from.nativeObj, to.nativeObj, inliers.nativeObj, method, ransacReprojThreshold, maxIters, confidence, refineIters));
-        
-        return retVal;
-    }
-
-    //javadoc: estimateAffine2D(from, to)
-    public static Mat estimateAffine2D(Mat from, Mat to)
-    {
-        
-        Mat retVal = new Mat(estimateAffine2D_1(from.nativeObj, to.nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Mat estimateAffinePartial2D(Mat from, Mat to, Mat& inliers = Mat(), int method = RANSAC, double ransacReprojThreshold = 3, size_t maxIters = 2000, double confidence = 0.99, size_t refineIters = 10)
-    //
-
-    //javadoc: estimateAffinePartial2D(from, to, inliers, method, ransacReprojThreshold, maxIters, confidence, refineIters)
-    public static Mat estimateAffinePartial2D(Mat from, Mat to, Mat inliers, int method, double ransacReprojThreshold, long maxIters, double confidence, long refineIters)
-    {
-        
-        Mat retVal = new Mat(estimateAffinePartial2D_0(from.nativeObj, to.nativeObj, inliers.nativeObj, method, ransacReprojThreshold, maxIters, confidence, refineIters));
-        
-        return retVal;
-    }
-
-    //javadoc: estimateAffinePartial2D(from, to)
-    public static Mat estimateAffinePartial2D(Mat from, Mat to)
-    {
-        
-        Mat retVal = new Mat(estimateAffinePartial2D_1(from.nativeObj, to.nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Mat findEssentialMat(Mat points1, Mat points2, Mat cameraMatrix, int method = RANSAC, double prob = 0.999, double threshold = 1.0, Mat& mask = Mat())
-    //
-
-    //javadoc: findEssentialMat(points1, points2, cameraMatrix, method, prob, threshold, mask)
-    public static Mat findEssentialMat(Mat points1, Mat points2, Mat cameraMatrix, int method, double prob, double threshold, Mat mask)
-    {
-        
-        Mat retVal = new Mat(findEssentialMat_0(points1.nativeObj, points2.nativeObj, cameraMatrix.nativeObj, method, prob, threshold, mask.nativeObj));
-        
-        return retVal;
-    }
-
-    //javadoc: findEssentialMat(points1, points2, cameraMatrix, method, prob, threshold)
-    public static Mat findEssentialMat(Mat points1, Mat points2, Mat cameraMatrix, int method, double prob, double threshold)
-    {
-        
-        Mat retVal = new Mat(findEssentialMat_1(points1.nativeObj, points2.nativeObj, cameraMatrix.nativeObj, method, prob, threshold));
-        
-        return retVal;
-    }
-
-    //javadoc: findEssentialMat(points1, points2, cameraMatrix)
-    public static Mat findEssentialMat(Mat points1, Mat points2, Mat cameraMatrix)
-    {
-        
-        Mat retVal = new Mat(findEssentialMat_2(points1.nativeObj, points2.nativeObj, cameraMatrix.nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Mat findEssentialMat(Mat points1, Mat points2, double focal = 1.0, Point2d pp = Point2d(0, 0), int method = RANSAC, double prob = 0.999, double threshold = 1.0, Mat& mask = Mat())
-    //
-
-    //javadoc: findEssentialMat(points1, points2, focal, pp, method, prob, threshold, mask)
-    public static Mat findEssentialMat(Mat points1, Mat points2, double focal, Point pp, int method, double prob, double threshold, Mat mask)
-    {
-        
-        Mat retVal = new Mat(findEssentialMat_3(points1.nativeObj, points2.nativeObj, focal, pp.x, pp.y, method, prob, threshold, mask.nativeObj));
-        
-        return retVal;
-    }
-
-    //javadoc: findEssentialMat(points1, points2, focal, pp, method, prob, threshold)
-    public static Mat findEssentialMat(Mat points1, Mat points2, double focal, Point pp, int method, double prob, double threshold)
-    {
-        
-        Mat retVal = new Mat(findEssentialMat_4(points1.nativeObj, points2.nativeObj, focal, pp.x, pp.y, method, prob, threshold));
-        
-        return retVal;
-    }
-
-    //javadoc: findEssentialMat(points1, points2)
-    public static Mat findEssentialMat(Mat points1, Mat points2)
-    {
-        
-        Mat retVal = new Mat(findEssentialMat_5(points1.nativeObj, points2.nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Mat findFundamentalMat(vector_Point2f points1, vector_Point2f points2, int method = FM_RANSAC, double ransacReprojThreshold = 3., double confidence = 0.99, Mat& mask = Mat())
-    //
-
-    //javadoc: findFundamentalMat(points1, points2, method, ransacReprojThreshold, confidence, mask)
-    public static Mat findFundamentalMat(MatOfPoint2f points1, MatOfPoint2f points2, int method, double ransacReprojThreshold, double confidence, Mat mask)
-    {
-        Mat points1_mat = points1;
-        Mat points2_mat = points2;
-        Mat retVal = new Mat(findFundamentalMat_0(points1_mat.nativeObj, points2_mat.nativeObj, method, ransacReprojThreshold, confidence, mask.nativeObj));
-        
-        return retVal;
-    }
-
-    //javadoc: findFundamentalMat(points1, points2, method, ransacReprojThreshold, confidence)
-    public static Mat findFundamentalMat(MatOfPoint2f points1, MatOfPoint2f points2, int method, double ransacReprojThreshold, double confidence)
-    {
-        Mat points1_mat = points1;
-        Mat points2_mat = points2;
-        Mat retVal = new Mat(findFundamentalMat_1(points1_mat.nativeObj, points2_mat.nativeObj, method, ransacReprojThreshold, confidence));
-        
-        return retVal;
-    }
-
-    //javadoc: findFundamentalMat(points1, points2)
-    public static Mat findFundamentalMat(MatOfPoint2f points1, MatOfPoint2f points2)
-    {
-        Mat points1_mat = points1;
-        Mat points2_mat = points2;
-        Mat retVal = new Mat(findFundamentalMat_2(points1_mat.nativeObj, points2_mat.nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Mat findHomography(vector_Point2f srcPoints, vector_Point2f dstPoints, int method = 0, double ransacReprojThreshold = 3, Mat& mask = Mat(), int maxIters = 2000, double confidence = 0.995)
-    //
-
-    //javadoc: findHomography(srcPoints, dstPoints, method, ransacReprojThreshold, mask, maxIters, confidence)
-    public static Mat findHomography(MatOfPoint2f srcPoints, MatOfPoint2f dstPoints, int method, double ransacReprojThreshold, Mat mask, int maxIters, double confidence)
-    {
-        Mat srcPoints_mat = srcPoints;
-        Mat dstPoints_mat = dstPoints;
-        Mat retVal = new Mat(findHomography_0(srcPoints_mat.nativeObj, dstPoints_mat.nativeObj, method, ransacReprojThreshold, mask.nativeObj, maxIters, confidence));
-        
-        return retVal;
-    }
-
-    //javadoc: findHomography(srcPoints, dstPoints, method, ransacReprojThreshold)
-    public static Mat findHomography(MatOfPoint2f srcPoints, MatOfPoint2f dstPoints, int method, double ransacReprojThreshold)
-    {
-        Mat srcPoints_mat = srcPoints;
-        Mat dstPoints_mat = dstPoints;
-        Mat retVal = new Mat(findHomography_1(srcPoints_mat.nativeObj, dstPoints_mat.nativeObj, method, ransacReprojThreshold));
-        
-        return retVal;
-    }
-
-    //javadoc: findHomography(srcPoints, dstPoints)
-    public static Mat findHomography(MatOfPoint2f srcPoints, MatOfPoint2f dstPoints)
-    {
-        Mat srcPoints_mat = srcPoints;
-        Mat dstPoints_mat = dstPoints;
-        Mat retVal = new Mat(findHomography_2(srcPoints_mat.nativeObj, dstPoints_mat.nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Mat getOptimalNewCameraMatrix(Mat cameraMatrix, Mat distCoeffs, Size imageSize, double alpha, Size newImgSize = Size(), Rect* validPixROI = 0, bool centerPrincipalPoint = false)
-    //
-
-    //javadoc: getOptimalNewCameraMatrix(cameraMatrix, distCoeffs, imageSize, alpha, newImgSize, validPixROI, centerPrincipalPoint)
-    public static Mat getOptimalNewCameraMatrix(Mat cameraMatrix, Mat distCoeffs, Size imageSize, double alpha, Size newImgSize, Rect validPixROI, boolean centerPrincipalPoint)
-    {
-        double[] validPixROI_out = new double[4];
-        Mat retVal = new Mat(getOptimalNewCameraMatrix_0(cameraMatrix.nativeObj, distCoeffs.nativeObj, imageSize.width, imageSize.height, alpha, newImgSize.width, newImgSize.height, validPixROI_out, centerPrincipalPoint));
-        if(validPixROI!=null){ validPixROI.x = (int)validPixROI_out[0]; validPixROI.y = (int)validPixROI_out[1]; validPixROI.width = (int)validPixROI_out[2]; validPixROI.height = (int)validPixROI_out[3]; } 
-        return retVal;
-    }
-
-    //javadoc: getOptimalNewCameraMatrix(cameraMatrix, distCoeffs, imageSize, alpha)
-    public static Mat getOptimalNewCameraMatrix(Mat cameraMatrix, Mat distCoeffs, Size imageSize, double alpha)
-    {
-        
-        Mat retVal = new Mat(getOptimalNewCameraMatrix_1(cameraMatrix.nativeObj, distCoeffs.nativeObj, imageSize.width, imageSize.height, alpha));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Mat initCameraMatrix2D(vector_vector_Point3f objectPoints, vector_vector_Point2f imagePoints, Size imageSize, double aspectRatio = 1.0)
-    //
-
-    //javadoc: initCameraMatrix2D(objectPoints, imagePoints, imageSize, aspectRatio)
-    public static Mat initCameraMatrix2D(List<MatOfPoint3f> objectPoints, List<MatOfPoint2f> imagePoints, Size imageSize, double aspectRatio)
-    {
-        List<Mat> objectPoints_tmplm = new ArrayList<Mat>((objectPoints != null) ? objectPoints.size() : 0);
-        Mat objectPoints_mat = Converters.vector_vector_Point3f_to_Mat(objectPoints, objectPoints_tmplm);
-        List<Mat> imagePoints_tmplm = new ArrayList<Mat>((imagePoints != null) ? imagePoints.size() : 0);
-        Mat imagePoints_mat = Converters.vector_vector_Point2f_to_Mat(imagePoints, imagePoints_tmplm);
-        Mat retVal = new Mat(initCameraMatrix2D_0(objectPoints_mat.nativeObj, imagePoints_mat.nativeObj, imageSize.width, imageSize.height, aspectRatio));
-        
-        return retVal;
-    }
-
-    //javadoc: initCameraMatrix2D(objectPoints, imagePoints, imageSize)
-    public static Mat initCameraMatrix2D(List<MatOfPoint3f> objectPoints, List<MatOfPoint2f> imagePoints, Size imageSize)
-    {
-        List<Mat> objectPoints_tmplm = new ArrayList<Mat>((objectPoints != null) ? objectPoints.size() : 0);
-        Mat objectPoints_mat = Converters.vector_vector_Point3f_to_Mat(objectPoints, objectPoints_tmplm);
-        List<Mat> imagePoints_tmplm = new ArrayList<Mat>((imagePoints != null) ? imagePoints.size() : 0);
-        Mat imagePoints_mat = Converters.vector_vector_Point2f_to_Mat(imagePoints, imagePoints_tmplm);
-        Mat retVal = new Mat(initCameraMatrix2D_1(objectPoints_mat.nativeObj, imagePoints_mat.nativeObj, imageSize.width, imageSize.height));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Rect getValidDisparityROI(Rect roi1, Rect roi2, int minDisparity, int numberOfDisparities, int SADWindowSize)
-    //
-
-    //javadoc: getValidDisparityROI(roi1, roi2, minDisparity, numberOfDisparities, SADWindowSize)
-    public static Rect getValidDisparityROI(Rect roi1, Rect roi2, int minDisparity, int numberOfDisparities, int SADWindowSize)
-    {
-        
-        Rect retVal = new Rect(getValidDisparityROI_0(roi1.x, roi1.y, roi1.width, roi1.height, roi2.x, roi2.y, roi2.width, roi2.height, minDisparity, numberOfDisparities, SADWindowSize));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Vec3d RQDecomp3x3(Mat src, Mat& mtxR, Mat& mtxQ, Mat& Qx = Mat(), Mat& Qy = Mat(), Mat& Qz = Mat())
-    //
-
-    //javadoc: RQDecomp3x3(src, mtxR, mtxQ, Qx, Qy, Qz)
-    public static double[] RQDecomp3x3(Mat src, Mat mtxR, Mat mtxQ, Mat Qx, Mat Qy, Mat Qz)
-    {
-        
-        double[] retVal = RQDecomp3x3_0(src.nativeObj, mtxR.nativeObj, mtxQ.nativeObj, Qx.nativeObj, Qy.nativeObj, Qz.nativeObj);
-        
-        return retVal;
-    }
-
-    //javadoc: RQDecomp3x3(src, mtxR, mtxQ)
-    public static double[] RQDecomp3x3(Mat src, Mat mtxR, Mat mtxQ)
-    {
-        
-        double[] retVal = RQDecomp3x3_1(src.nativeObj, mtxR.nativeObj, mtxQ.nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  bool findChessboardCorners(Mat image, Size patternSize, vector_Point2f& corners, int flags = CALIB_CB_ADAPTIVE_THRESH + CALIB_CB_NORMALIZE_IMAGE)
-    //
-
-    //javadoc: findChessboardCorners(image, patternSize, corners, flags)
-    public static boolean findChessboardCorners(Mat image, Size patternSize, MatOfPoint2f corners, int flags)
-    {
-        Mat corners_mat = corners;
-        boolean retVal = findChessboardCorners_0(image.nativeObj, patternSize.width, patternSize.height, corners_mat.nativeObj, flags);
-        
-        return retVal;
-    }
-
-    //javadoc: findChessboardCorners(image, patternSize, corners)
-    public static boolean findChessboardCorners(Mat image, Size patternSize, MatOfPoint2f corners)
-    {
-        Mat corners_mat = corners;
-        boolean retVal = findChessboardCorners_1(image.nativeObj, patternSize.width, patternSize.height, corners_mat.nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  bool findCirclesGrid(Mat image, Size patternSize, Mat& centers, int flags, Ptr_FeatureDetector blobDetector, CirclesGridFinderParameters parameters)
-    //
-
-    // Unknown type 'Ptr_FeatureDetector' (I), skipping the function
-
-
-    //
-    // C++:  bool findCirclesGrid(Mat image, Size patternSize, Mat& centers, int flags = CALIB_CB_SYMMETRIC_GRID, Ptr_FeatureDetector blobDetector = SimpleBlobDetector::create())
-    //
-
-    //javadoc: findCirclesGrid(image, patternSize, centers, flags)
-    public static boolean findCirclesGrid(Mat image, Size patternSize, Mat centers, int flags)
-    {
-        
-        boolean retVal = findCirclesGrid_0(image.nativeObj, patternSize.width, patternSize.height, centers.nativeObj, flags);
-        
-        return retVal;
-    }
-
-    //javadoc: findCirclesGrid(image, patternSize, centers)
-    public static boolean findCirclesGrid(Mat image, Size patternSize, Mat centers)
-    {
-        
-        boolean retVal = findCirclesGrid_1(image.nativeObj, patternSize.width, patternSize.height, centers.nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  bool findCirclesGrid2(Mat image, Size patternSize, Mat& centers, int flags, Ptr_FeatureDetector blobDetector, CirclesGridFinderParameters2 parameters)
-    //
-
-    // Unknown type 'Ptr_FeatureDetector' (I), skipping the function
-
-
-    //
-    // C++:  bool solvePnP(vector_Point3f objectPoints, vector_Point2f imagePoints, Mat cameraMatrix, vector_double distCoeffs, Mat& rvec, Mat& tvec, bool useExtrinsicGuess = false, int flags = SOLVEPNP_ITERATIVE)
-    //
-
-    //javadoc: solvePnP(objectPoints, imagePoints, cameraMatrix, distCoeffs, rvec, tvec, useExtrinsicGuess, flags)
-    public static boolean solvePnP(MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, boolean useExtrinsicGuess, int flags)
-    {
-        Mat objectPoints_mat = objectPoints;
-        Mat imagePoints_mat = imagePoints;
-        Mat distCoeffs_mat = distCoeffs;
-        boolean retVal = solvePnP_0(objectPoints_mat.nativeObj, imagePoints_mat.nativeObj, cameraMatrix.nativeObj, distCoeffs_mat.nativeObj, rvec.nativeObj, tvec.nativeObj, useExtrinsicGuess, flags);
-        
-        return retVal;
-    }
-
-    //javadoc: solvePnP(objectPoints, imagePoints, cameraMatrix, distCoeffs, rvec, tvec)
-    public static boolean solvePnP(MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec)
-    {
-        Mat objectPoints_mat = objectPoints;
-        Mat imagePoints_mat = imagePoints;
-        Mat distCoeffs_mat = distCoeffs;
-        boolean retVal = solvePnP_1(objectPoints_mat.nativeObj, imagePoints_mat.nativeObj, cameraMatrix.nativeObj, distCoeffs_mat.nativeObj, rvec.nativeObj, tvec.nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  bool solvePnPRansac(vector_Point3f objectPoints, vector_Point2f imagePoints, Mat cameraMatrix, vector_double distCoeffs, Mat& rvec, Mat& tvec, bool useExtrinsicGuess = false, int iterationsCount = 100, float reprojectionError = 8.0, double confidence = 0.99, Mat& inliers = Mat(), int flags = SOLVEPNP_ITERATIVE)
-    //
-
-    //javadoc: solvePnPRansac(objectPoints, imagePoints, cameraMatrix, distCoeffs, rvec, tvec, useExtrinsicGuess, iterationsCount, reprojectionError, confidence, inliers, flags)
-    public static boolean solvePnPRansac(MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec, boolean useExtrinsicGuess, int iterationsCount, float reprojectionError, double confidence, Mat inliers, int flags)
-    {
-        Mat objectPoints_mat = objectPoints;
-        Mat imagePoints_mat = imagePoints;
-        Mat distCoeffs_mat = distCoeffs;
-        boolean retVal = solvePnPRansac_0(objectPoints_mat.nativeObj, imagePoints_mat.nativeObj, cameraMatrix.nativeObj, distCoeffs_mat.nativeObj, rvec.nativeObj, tvec.nativeObj, useExtrinsicGuess, iterationsCount, reprojectionError, confidence, inliers.nativeObj, flags);
-        
-        return retVal;
-    }
-
-    //javadoc: solvePnPRansac(objectPoints, imagePoints, cameraMatrix, distCoeffs, rvec, tvec)
-    public static boolean solvePnPRansac(MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat cameraMatrix, MatOfDouble distCoeffs, Mat rvec, Mat tvec)
-    {
-        Mat objectPoints_mat = objectPoints;
-        Mat imagePoints_mat = imagePoints;
-        Mat distCoeffs_mat = distCoeffs;
-        boolean retVal = solvePnPRansac_1(objectPoints_mat.nativeObj, imagePoints_mat.nativeObj, cameraMatrix.nativeObj, distCoeffs_mat.nativeObj, rvec.nativeObj, tvec.nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  bool stereoRectifyUncalibrated(Mat points1, Mat points2, Mat F, Size imgSize, Mat& H1, Mat& H2, double threshold = 5)
-    //
-
-    //javadoc: stereoRectifyUncalibrated(points1, points2, F, imgSize, H1, H2, threshold)
-    public static boolean stereoRectifyUncalibrated(Mat points1, Mat points2, Mat F, Size imgSize, Mat H1, Mat H2, double threshold)
-    {
-        
-        boolean retVal = stereoRectifyUncalibrated_0(points1.nativeObj, points2.nativeObj, F.nativeObj, imgSize.width, imgSize.height, H1.nativeObj, H2.nativeObj, threshold);
-        
-        return retVal;
-    }
-
-    //javadoc: stereoRectifyUncalibrated(points1, points2, F, imgSize, H1, H2)
-    public static boolean stereoRectifyUncalibrated(Mat points1, Mat points2, Mat F, Size imgSize, Mat H1, Mat H2)
-    {
-        
-        boolean retVal = stereoRectifyUncalibrated_1(points1.nativeObj, points2.nativeObj, F.nativeObj, imgSize.width, imgSize.height, H1.nativeObj, H2.nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double calibrateCamera(vector_Mat objectPoints, vector_Mat imagePoints, Size imageSize, Mat& cameraMatrix, Mat& distCoeffs, vector_Mat& rvecs, vector_Mat& tvecs, Mat& stdDeviationsIntrinsics, Mat& stdDeviationsExtrinsics, Mat& perViewErrors, int flags = 0, TermCriteria criteria = TermCriteria( TermCriteria::COUNT + TermCriteria::EPS, 30, DBL_EPSILON))
-    //
-
-    //javadoc: calibrateCamera(objectPoints, imagePoints, imageSize, cameraMatrix, distCoeffs, rvecs, tvecs, stdDeviationsIntrinsics, stdDeviationsExtrinsics, perViewErrors, flags, criteria)
-    public static double calibrateCameraExtended(List<Mat> objectPoints, List<Mat> imagePoints, Size imageSize, Mat cameraMatrix, Mat distCoeffs, List<Mat> rvecs, List<Mat> tvecs, Mat stdDeviationsIntrinsics, Mat stdDeviationsExtrinsics, Mat perViewErrors, int flags, TermCriteria criteria)
-    {
-        Mat objectPoints_mat = Converters.vector_Mat_to_Mat(objectPoints);
-        Mat imagePoints_mat = Converters.vector_Mat_to_Mat(imagePoints);
-        Mat rvecs_mat = new Mat();
-        Mat tvecs_mat = new Mat();
-        double retVal = calibrateCameraExtended_0(objectPoints_mat.nativeObj, imagePoints_mat.nativeObj, imageSize.width, imageSize.height, cameraMatrix.nativeObj, distCoeffs.nativeObj, rvecs_mat.nativeObj, tvecs_mat.nativeObj, stdDeviationsIntrinsics.nativeObj, stdDeviationsExtrinsics.nativeObj, perViewErrors.nativeObj, flags, criteria.type, criteria.maxCount, criteria.epsilon);
-        Converters.Mat_to_vector_Mat(rvecs_mat, rvecs);
-        rvecs_mat.release();
-        Converters.Mat_to_vector_Mat(tvecs_mat, tvecs);
-        tvecs_mat.release();
-        return retVal;
-    }
-
-    //javadoc: calibrateCamera(objectPoints, imagePoints, imageSize, cameraMatrix, distCoeffs, rvecs, tvecs, stdDeviationsIntrinsics, stdDeviationsExtrinsics, perViewErrors, flags)
-    public static double calibrateCameraExtended(List<Mat> objectPoints, List<Mat> imagePoints, Size imageSize, Mat cameraMatrix, Mat distCoeffs, List<Mat> rvecs, List<Mat> tvecs, Mat stdDeviationsIntrinsics, Mat stdDeviationsExtrinsics, Mat perViewErrors, int flags)
-    {
-        Mat objectPoints_mat = Converters.vector_Mat_to_Mat(objectPoints);
-        Mat imagePoints_mat = Converters.vector_Mat_to_Mat(imagePoints);
-        Mat rvecs_mat = new Mat();
-        Mat tvecs_mat = new Mat();
-        double retVal = calibrateCameraExtended_1(objectPoints_mat.nativeObj, imagePoints_mat.nativeObj, imageSize.width, imageSize.height, cameraMatrix.nativeObj, distCoeffs.nativeObj, rvecs_mat.nativeObj, tvecs_mat.nativeObj, stdDeviationsIntrinsics.nativeObj, stdDeviationsExtrinsics.nativeObj, perViewErrors.nativeObj, flags);
-        Converters.Mat_to_vector_Mat(rvecs_mat, rvecs);
-        rvecs_mat.release();
-        Converters.Mat_to_vector_Mat(tvecs_mat, tvecs);
-        tvecs_mat.release();
-        return retVal;
-    }
-
-    //javadoc: calibrateCamera(objectPoints, imagePoints, imageSize, cameraMatrix, distCoeffs, rvecs, tvecs, stdDeviationsIntrinsics, stdDeviationsExtrinsics, perViewErrors)
-    public static double calibrateCameraExtended(List<Mat> objectPoints, List<Mat> imagePoints, Size imageSize, Mat cameraMatrix, Mat distCoeffs, List<Mat> rvecs, List<Mat> tvecs, Mat stdDeviationsIntrinsics, Mat stdDeviationsExtrinsics, Mat perViewErrors)
-    {
-        Mat objectPoints_mat = Converters.vector_Mat_to_Mat(objectPoints);
-        Mat imagePoints_mat = Converters.vector_Mat_to_Mat(imagePoints);
-        Mat rvecs_mat = new Mat();
-        Mat tvecs_mat = new Mat();
-        double retVal = calibrateCameraExtended_2(objectPoints_mat.nativeObj, imagePoints_mat.nativeObj, imageSize.width, imageSize.height, cameraMatrix.nativeObj, distCoeffs.nativeObj, rvecs_mat.nativeObj, tvecs_mat.nativeObj, stdDeviationsIntrinsics.nativeObj, stdDeviationsExtrinsics.nativeObj, perViewErrors.nativeObj);
-        Converters.Mat_to_vector_Mat(rvecs_mat, rvecs);
-        rvecs_mat.release();
-        Converters.Mat_to_vector_Mat(tvecs_mat, tvecs);
-        tvecs_mat.release();
-        return retVal;
-    }
-
-
-    //
-    // C++:  double calibrateCamera(vector_Mat objectPoints, vector_Mat imagePoints, Size imageSize, Mat& cameraMatrix, Mat& distCoeffs, vector_Mat& rvecs, vector_Mat& tvecs, int flags = 0, TermCriteria criteria = TermCriteria( TermCriteria::COUNT + TermCriteria::EPS, 30, DBL_EPSILON))
-    //
-
-    //javadoc: calibrateCamera(objectPoints, imagePoints, imageSize, cameraMatrix, distCoeffs, rvecs, tvecs, flags, criteria)
-    public static double calibrateCamera(List<Mat> objectPoints, List<Mat> imagePoints, Size imageSize, Mat cameraMatrix, Mat distCoeffs, List<Mat> rvecs, List<Mat> tvecs, int flags, TermCriteria criteria)
-    {
-        Mat objectPoints_mat = Converters.vector_Mat_to_Mat(objectPoints);
-        Mat imagePoints_mat = Converters.vector_Mat_to_Mat(imagePoints);
-        Mat rvecs_mat = new Mat();
-        Mat tvecs_mat = new Mat();
-        double retVal = calibrateCamera_0(objectPoints_mat.nativeObj, imagePoints_mat.nativeObj, imageSize.width, imageSize.height, cameraMatrix.nativeObj, distCoeffs.nativeObj, rvecs_mat.nativeObj, tvecs_mat.nativeObj, flags, criteria.type, criteria.maxCount, criteria.epsilon);
-        Converters.Mat_to_vector_Mat(rvecs_mat, rvecs);
-        rvecs_mat.release();
-        Converters.Mat_to_vector_Mat(tvecs_mat, tvecs);
-        tvecs_mat.release();
-        return retVal;
-    }
-
-    //javadoc: calibrateCamera(objectPoints, imagePoints, imageSize, cameraMatrix, distCoeffs, rvecs, tvecs, flags)
-    public static double calibrateCamera(List<Mat> objectPoints, List<Mat> imagePoints, Size imageSize, Mat cameraMatrix, Mat distCoeffs, List<Mat> rvecs, List<Mat> tvecs, int flags)
-    {
-        Mat objectPoints_mat = Converters.vector_Mat_to_Mat(objectPoints);
-        Mat imagePoints_mat = Converters.vector_Mat_to_Mat(imagePoints);
-        Mat rvecs_mat = new Mat();
-        Mat tvecs_mat = new Mat();
-        double retVal = calibrateCamera_1(objectPoints_mat.nativeObj, imagePoints_mat.nativeObj, imageSize.width, imageSize.height, cameraMatrix.nativeObj, distCoeffs.nativeObj, rvecs_mat.nativeObj, tvecs_mat.nativeObj, flags);
-        Converters.Mat_to_vector_Mat(rvecs_mat, rvecs);
-        rvecs_mat.release();
-        Converters.Mat_to_vector_Mat(tvecs_mat, tvecs);
-        tvecs_mat.release();
-        return retVal;
-    }
-
-    //javadoc: calibrateCamera(objectPoints, imagePoints, imageSize, cameraMatrix, distCoeffs, rvecs, tvecs)
-    public static double calibrateCamera(List<Mat> objectPoints, List<Mat> imagePoints, Size imageSize, Mat cameraMatrix, Mat distCoeffs, List<Mat> rvecs, List<Mat> tvecs)
-    {
-        Mat objectPoints_mat = Converters.vector_Mat_to_Mat(objectPoints);
-        Mat imagePoints_mat = Converters.vector_Mat_to_Mat(imagePoints);
-        Mat rvecs_mat = new Mat();
-        Mat tvecs_mat = new Mat();
-        double retVal = calibrateCamera_2(objectPoints_mat.nativeObj, imagePoints_mat.nativeObj, imageSize.width, imageSize.height, cameraMatrix.nativeObj, distCoeffs.nativeObj, rvecs_mat.nativeObj, tvecs_mat.nativeObj);
-        Converters.Mat_to_vector_Mat(rvecs_mat, rvecs);
-        rvecs_mat.release();
-        Converters.Mat_to_vector_Mat(tvecs_mat, tvecs);
-        tvecs_mat.release();
-        return retVal;
-    }
-
-
-    //
-    // C++:  double sampsonDistance(Mat pt1, Mat pt2, Mat F)
-    //
-
-    //javadoc: sampsonDistance(pt1, pt2, F)
-    public static double sampsonDistance(Mat pt1, Mat pt2, Mat F)
-    {
-        
-        double retVal = sampsonDistance_0(pt1.nativeObj, pt2.nativeObj, F.nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double stereoCalibrate(vector_Mat objectPoints, vector_Mat imagePoints1, vector_Mat imagePoints2, Mat& cameraMatrix1, Mat& distCoeffs1, Mat& cameraMatrix2, Mat& distCoeffs2, Size imageSize, Mat& R, Mat& T, Mat& E, Mat& F, Mat& perViewErrors, int flags = CALIB_FIX_INTRINSIC, TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 1e-6))
-    //
-
-    //javadoc: stereoCalibrate(objectPoints, imagePoints1, imagePoints2, cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, imageSize, R, T, E, F, perViewErrors, flags, criteria)
-    public static double stereoCalibrateExtended(List<Mat> objectPoints, List<Mat> imagePoints1, List<Mat> imagePoints2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Size imageSize, Mat R, Mat T, Mat E, Mat F, Mat perViewErrors, int flags, TermCriteria criteria)
-    {
-        Mat objectPoints_mat = Converters.vector_Mat_to_Mat(objectPoints);
-        Mat imagePoints1_mat = Converters.vector_Mat_to_Mat(imagePoints1);
-        Mat imagePoints2_mat = Converters.vector_Mat_to_Mat(imagePoints2);
-        double retVal = stereoCalibrateExtended_0(objectPoints_mat.nativeObj, imagePoints1_mat.nativeObj, imagePoints2_mat.nativeObj, cameraMatrix1.nativeObj, distCoeffs1.nativeObj, cameraMatrix2.nativeObj, distCoeffs2.nativeObj, imageSize.width, imageSize.height, R.nativeObj, T.nativeObj, E.nativeObj, F.nativeObj, perViewErrors.nativeObj, flags, criteria.type, criteria.maxCount, criteria.epsilon);
-        
-        return retVal;
-    }
-
-    //javadoc: stereoCalibrate(objectPoints, imagePoints1, imagePoints2, cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, imageSize, R, T, E, F, perViewErrors, flags)
-    public static double stereoCalibrateExtended(List<Mat> objectPoints, List<Mat> imagePoints1, List<Mat> imagePoints2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Size imageSize, Mat R, Mat T, Mat E, Mat F, Mat perViewErrors, int flags)
-    {
-        Mat objectPoints_mat = Converters.vector_Mat_to_Mat(objectPoints);
-        Mat imagePoints1_mat = Converters.vector_Mat_to_Mat(imagePoints1);
-        Mat imagePoints2_mat = Converters.vector_Mat_to_Mat(imagePoints2);
-        double retVal = stereoCalibrateExtended_1(objectPoints_mat.nativeObj, imagePoints1_mat.nativeObj, imagePoints2_mat.nativeObj, cameraMatrix1.nativeObj, distCoeffs1.nativeObj, cameraMatrix2.nativeObj, distCoeffs2.nativeObj, imageSize.width, imageSize.height, R.nativeObj, T.nativeObj, E.nativeObj, F.nativeObj, perViewErrors.nativeObj, flags);
-        
-        return retVal;
-    }
-
-    //javadoc: stereoCalibrate(objectPoints, imagePoints1, imagePoints2, cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, imageSize, R, T, E, F, perViewErrors)
-    public static double stereoCalibrateExtended(List<Mat> objectPoints, List<Mat> imagePoints1, List<Mat> imagePoints2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Size imageSize, Mat R, Mat T, Mat E, Mat F, Mat perViewErrors)
-    {
-        Mat objectPoints_mat = Converters.vector_Mat_to_Mat(objectPoints);
-        Mat imagePoints1_mat = Converters.vector_Mat_to_Mat(imagePoints1);
-        Mat imagePoints2_mat = Converters.vector_Mat_to_Mat(imagePoints2);
-        double retVal = stereoCalibrateExtended_2(objectPoints_mat.nativeObj, imagePoints1_mat.nativeObj, imagePoints2_mat.nativeObj, cameraMatrix1.nativeObj, distCoeffs1.nativeObj, cameraMatrix2.nativeObj, distCoeffs2.nativeObj, imageSize.width, imageSize.height, R.nativeObj, T.nativeObj, E.nativeObj, F.nativeObj, perViewErrors.nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double stereoCalibrate(vector_Mat objectPoints, vector_Mat imagePoints1, vector_Mat imagePoints2, Mat& cameraMatrix1, Mat& distCoeffs1, Mat& cameraMatrix2, Mat& distCoeffs2, Size imageSize, Mat& R, Mat& T, Mat& E, Mat& F, int flags = CALIB_FIX_INTRINSIC, TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 1e-6))
-    //
-
-    //javadoc: stereoCalibrate(objectPoints, imagePoints1, imagePoints2, cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, imageSize, R, T, E, F, flags, criteria)
-    public static double stereoCalibrate(List<Mat> objectPoints, List<Mat> imagePoints1, List<Mat> imagePoints2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Size imageSize, Mat R, Mat T, Mat E, Mat F, int flags, TermCriteria criteria)
-    {
-        Mat objectPoints_mat = Converters.vector_Mat_to_Mat(objectPoints);
-        Mat imagePoints1_mat = Converters.vector_Mat_to_Mat(imagePoints1);
-        Mat imagePoints2_mat = Converters.vector_Mat_to_Mat(imagePoints2);
-        double retVal = stereoCalibrate_0(objectPoints_mat.nativeObj, imagePoints1_mat.nativeObj, imagePoints2_mat.nativeObj, cameraMatrix1.nativeObj, distCoeffs1.nativeObj, cameraMatrix2.nativeObj, distCoeffs2.nativeObj, imageSize.width, imageSize.height, R.nativeObj, T.nativeObj, E.nativeObj, F.nativeObj, flags, criteria.type, criteria.maxCount, criteria.epsilon);
-        
-        return retVal;
-    }
-
-    //javadoc: stereoCalibrate(objectPoints, imagePoints1, imagePoints2, cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, imageSize, R, T, E, F, flags)
-    public static double stereoCalibrate(List<Mat> objectPoints, List<Mat> imagePoints1, List<Mat> imagePoints2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Size imageSize, Mat R, Mat T, Mat E, Mat F, int flags)
-    {
-        Mat objectPoints_mat = Converters.vector_Mat_to_Mat(objectPoints);
-        Mat imagePoints1_mat = Converters.vector_Mat_to_Mat(imagePoints1);
-        Mat imagePoints2_mat = Converters.vector_Mat_to_Mat(imagePoints2);
-        double retVal = stereoCalibrate_1(objectPoints_mat.nativeObj, imagePoints1_mat.nativeObj, imagePoints2_mat.nativeObj, cameraMatrix1.nativeObj, distCoeffs1.nativeObj, cameraMatrix2.nativeObj, distCoeffs2.nativeObj, imageSize.width, imageSize.height, R.nativeObj, T.nativeObj, E.nativeObj, F.nativeObj, flags);
-        
-        return retVal;
-    }
-
-    //javadoc: stereoCalibrate(objectPoints, imagePoints1, imagePoints2, cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, imageSize, R, T, E, F)
-    public static double stereoCalibrate(List<Mat> objectPoints, List<Mat> imagePoints1, List<Mat> imagePoints2, Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Size imageSize, Mat R, Mat T, Mat E, Mat F)
-    {
-        Mat objectPoints_mat = Converters.vector_Mat_to_Mat(objectPoints);
-        Mat imagePoints1_mat = Converters.vector_Mat_to_Mat(imagePoints1);
-        Mat imagePoints2_mat = Converters.vector_Mat_to_Mat(imagePoints2);
-        double retVal = stereoCalibrate_2(objectPoints_mat.nativeObj, imagePoints1_mat.nativeObj, imagePoints2_mat.nativeObj, cameraMatrix1.nativeObj, distCoeffs1.nativeObj, cameraMatrix2.nativeObj, distCoeffs2.nativeObj, imageSize.width, imageSize.height, R.nativeObj, T.nativeObj, E.nativeObj, F.nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double calibrate(vector_Mat objectPoints, vector_Mat imagePoints, Size image_size, Mat& K, Mat& D, vector_Mat& rvecs, vector_Mat& tvecs, int flags = 0, TermCriteria criteria = TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 100, DBL_EPSILON))
-    //
-
-    //javadoc: calibrate(objectPoints, imagePoints, image_size, K, D, rvecs, tvecs, flags, criteria)
-    public static double calibrate(List<Mat> objectPoints, List<Mat> imagePoints, Size image_size, Mat K, Mat D, List<Mat> rvecs, List<Mat> tvecs, int flags, TermCriteria criteria)
-    {
-        Mat objectPoints_mat = Converters.vector_Mat_to_Mat(objectPoints);
-        Mat imagePoints_mat = Converters.vector_Mat_to_Mat(imagePoints);
-        Mat rvecs_mat = new Mat();
-        Mat tvecs_mat = new Mat();
-        double retVal = calibrate_0(objectPoints_mat.nativeObj, imagePoints_mat.nativeObj, image_size.width, image_size.height, K.nativeObj, D.nativeObj, rvecs_mat.nativeObj, tvecs_mat.nativeObj, flags, criteria.type, criteria.maxCount, criteria.epsilon);
-        Converters.Mat_to_vector_Mat(rvecs_mat, rvecs);
-        rvecs_mat.release();
-        Converters.Mat_to_vector_Mat(tvecs_mat, tvecs);
-        tvecs_mat.release();
-        return retVal;
-    }
-
-    //javadoc: calibrate(objectPoints, imagePoints, image_size, K, D, rvecs, tvecs, flags)
-    public static double calibrate(List<Mat> objectPoints, List<Mat> imagePoints, Size image_size, Mat K, Mat D, List<Mat> rvecs, List<Mat> tvecs, int flags)
-    {
-        Mat objectPoints_mat = Converters.vector_Mat_to_Mat(objectPoints);
-        Mat imagePoints_mat = Converters.vector_Mat_to_Mat(imagePoints);
-        Mat rvecs_mat = new Mat();
-        Mat tvecs_mat = new Mat();
-        double retVal = calibrate_1(objectPoints_mat.nativeObj, imagePoints_mat.nativeObj, image_size.width, image_size.height, K.nativeObj, D.nativeObj, rvecs_mat.nativeObj, tvecs_mat.nativeObj, flags);
-        Converters.Mat_to_vector_Mat(rvecs_mat, rvecs);
-        rvecs_mat.release();
-        Converters.Mat_to_vector_Mat(tvecs_mat, tvecs);
-        tvecs_mat.release();
-        return retVal;
-    }
-
-    //javadoc: calibrate(objectPoints, imagePoints, image_size, K, D, rvecs, tvecs)
-    public static double calibrate(List<Mat> objectPoints, List<Mat> imagePoints, Size image_size, Mat K, Mat D, List<Mat> rvecs, List<Mat> tvecs)
-    {
-        Mat objectPoints_mat = Converters.vector_Mat_to_Mat(objectPoints);
-        Mat imagePoints_mat = Converters.vector_Mat_to_Mat(imagePoints);
-        Mat rvecs_mat = new Mat();
-        Mat tvecs_mat = new Mat();
-        double retVal = calibrate_2(objectPoints_mat.nativeObj, imagePoints_mat.nativeObj, image_size.width, image_size.height, K.nativeObj, D.nativeObj, rvecs_mat.nativeObj, tvecs_mat.nativeObj);
-        Converters.Mat_to_vector_Mat(rvecs_mat, rvecs);
-        rvecs_mat.release();
-        Converters.Mat_to_vector_Mat(tvecs_mat, tvecs);
-        tvecs_mat.release();
-        return retVal;
-    }
-
-
-    //
-    // C++:  double stereoCalibrate(vector_Mat objectPoints, vector_Mat imagePoints1, vector_Mat imagePoints2, Mat& K1, Mat& D1, Mat& K2, Mat& D2, Size imageSize, Mat& R, Mat& T, int flags = fisheye::CALIB_FIX_INTRINSIC, TermCriteria criteria = TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 100, DBL_EPSILON))
-    //
-
-    //javadoc: stereoCalibrate(objectPoints, imagePoints1, imagePoints2, K1, D1, K2, D2, imageSize, R, T, flags, criteria)
-    public static double stereoCalibrate(List<Mat> objectPoints, List<Mat> imagePoints1, List<Mat> imagePoints2, Mat K1, Mat D1, Mat K2, Mat D2, Size imageSize, Mat R, Mat T, int flags, TermCriteria criteria)
-    {
-        Mat objectPoints_mat = Converters.vector_Mat_to_Mat(objectPoints);
-        Mat imagePoints1_mat = Converters.vector_Mat_to_Mat(imagePoints1);
-        Mat imagePoints2_mat = Converters.vector_Mat_to_Mat(imagePoints2);
-        double retVal = stereoCalibrate_3(objectPoints_mat.nativeObj, imagePoints1_mat.nativeObj, imagePoints2_mat.nativeObj, K1.nativeObj, D1.nativeObj, K2.nativeObj, D2.nativeObj, imageSize.width, imageSize.height, R.nativeObj, T.nativeObj, flags, criteria.type, criteria.maxCount, criteria.epsilon);
-        
-        return retVal;
-    }
-
-    //javadoc: stereoCalibrate(objectPoints, imagePoints1, imagePoints2, K1, D1, K2, D2, imageSize, R, T, flags)
-    public static double stereoCalibrate(List<Mat> objectPoints, List<Mat> imagePoints1, List<Mat> imagePoints2, Mat K1, Mat D1, Mat K2, Mat D2, Size imageSize, Mat R, Mat T, int flags)
-    {
-        Mat objectPoints_mat = Converters.vector_Mat_to_Mat(objectPoints);
-        Mat imagePoints1_mat = Converters.vector_Mat_to_Mat(imagePoints1);
-        Mat imagePoints2_mat = Converters.vector_Mat_to_Mat(imagePoints2);
-        double retVal = stereoCalibrate_4(objectPoints_mat.nativeObj, imagePoints1_mat.nativeObj, imagePoints2_mat.nativeObj, K1.nativeObj, D1.nativeObj, K2.nativeObj, D2.nativeObj, imageSize.width, imageSize.height, R.nativeObj, T.nativeObj, flags);
-        
-        return retVal;
-    }
-
-    //javadoc: stereoCalibrate(objectPoints, imagePoints1, imagePoints2, K1, D1, K2, D2, imageSize, R, T)
-    public static double stereoCalibrate(List<Mat> objectPoints, List<Mat> imagePoints1, List<Mat> imagePoints2, Mat K1, Mat D1, Mat K2, Mat D2, Size imageSize, Mat R, Mat T)
-    {
-        Mat objectPoints_mat = Converters.vector_Mat_to_Mat(objectPoints);
-        Mat imagePoints1_mat = Converters.vector_Mat_to_Mat(imagePoints1);
-        Mat imagePoints2_mat = Converters.vector_Mat_to_Mat(imagePoints2);
-        double retVal = stereoCalibrate_5(objectPoints_mat.nativeObj, imagePoints1_mat.nativeObj, imagePoints2_mat.nativeObj, K1.nativeObj, D1.nativeObj, K2.nativeObj, D2.nativeObj, imageSize.width, imageSize.height, R.nativeObj, T.nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  float rectify3Collinear(Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Mat cameraMatrix3, Mat distCoeffs3, vector_Mat imgpt1, vector_Mat imgpt3, Size imageSize, Mat R12, Mat T12, Mat R13, Mat T13, Mat& R1, Mat& R2, Mat& R3, Mat& P1, Mat& P2, Mat& P3, Mat& Q, double alpha, Size newImgSize, Rect* roi1, Rect* roi2, int flags)
-    //
-
-    //javadoc: rectify3Collinear(cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, cameraMatrix3, distCoeffs3, imgpt1, imgpt3, imageSize, R12, T12, R13, T13, R1, R2, R3, P1, P2, P3, Q, alpha, newImgSize, roi1, roi2, flags)
-    public static float rectify3Collinear(Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Mat cameraMatrix3, Mat distCoeffs3, List<Mat> imgpt1, List<Mat> imgpt3, Size imageSize, Mat R12, Mat T12, Mat R13, Mat T13, Mat R1, Mat R2, Mat R3, Mat P1, Mat P2, Mat P3, Mat Q, double alpha, Size newImgSize, Rect roi1, Rect roi2, int flags)
-    {
-        Mat imgpt1_mat = Converters.vector_Mat_to_Mat(imgpt1);
-        Mat imgpt3_mat = Converters.vector_Mat_to_Mat(imgpt3);
-        double[] roi1_out = new double[4];
-        double[] roi2_out = new double[4];
-        float retVal = rectify3Collinear_0(cameraMatrix1.nativeObj, distCoeffs1.nativeObj, cameraMatrix2.nativeObj, distCoeffs2.nativeObj, cameraMatrix3.nativeObj, distCoeffs3.nativeObj, imgpt1_mat.nativeObj, imgpt3_mat.nativeObj, imageSize.width, imageSize.height, R12.nativeObj, T12.nativeObj, R13.nativeObj, T13.nativeObj, R1.nativeObj, R2.nativeObj, R3.nativeObj, P1.nativeObj, P2.nativeObj, P3.nativeObj, Q.nativeObj, alpha, newImgSize.width, newImgSize.height, roi1_out, roi2_out, flags);
-        if(roi1!=null){ roi1.x = (int)roi1_out[0]; roi1.y = (int)roi1_out[1]; roi1.width = (int)roi1_out[2]; roi1.height = (int)roi1_out[3]; } 
-        if(roi2!=null){ roi2.x = (int)roi2_out[0]; roi2.y = (int)roi2_out[1]; roi2.width = (int)roi2_out[2]; roi2.height = (int)roi2_out[3]; } 
-        return retVal;
-    }
-
-
-    //
-    // C++:  int decomposeHomographyMat(Mat H, Mat K, vector_Mat& rotations, vector_Mat& translations, vector_Mat& normals)
-    //
-
-    //javadoc: decomposeHomographyMat(H, K, rotations, translations, normals)
-    public static int decomposeHomographyMat(Mat H, Mat K, List<Mat> rotations, List<Mat> translations, List<Mat> normals)
-    {
-        Mat rotations_mat = new Mat();
-        Mat translations_mat = new Mat();
-        Mat normals_mat = new Mat();
-        int retVal = decomposeHomographyMat_0(H.nativeObj, K.nativeObj, rotations_mat.nativeObj, translations_mat.nativeObj, normals_mat.nativeObj);
-        Converters.Mat_to_vector_Mat(rotations_mat, rotations);
-        rotations_mat.release();
-        Converters.Mat_to_vector_Mat(translations_mat, translations);
-        translations_mat.release();
-        Converters.Mat_to_vector_Mat(normals_mat, normals);
-        normals_mat.release();
-        return retVal;
-    }
-
-
-    //
-    // C++:  int estimateAffine3D(Mat src, Mat dst, Mat& out, Mat& inliers, double ransacThreshold = 3, double confidence = 0.99)
-    //
-
-    //javadoc: estimateAffine3D(src, dst, out, inliers, ransacThreshold, confidence)
-    public static int estimateAffine3D(Mat src, Mat dst, Mat out, Mat inliers, double ransacThreshold, double confidence)
-    {
-        
-        int retVal = estimateAffine3D_0(src.nativeObj, dst.nativeObj, out.nativeObj, inliers.nativeObj, ransacThreshold, confidence);
-        
-        return retVal;
-    }
-
-    //javadoc: estimateAffine3D(src, dst, out, inliers)
-    public static int estimateAffine3D(Mat src, Mat dst, Mat out, Mat inliers)
-    {
-        
-        int retVal = estimateAffine3D_1(src.nativeObj, dst.nativeObj, out.nativeObj, inliers.nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int recoverPose(Mat E, Mat points1, Mat points2, Mat& R, Mat& t, double focal = 1.0, Point2d pp = Point2d(0, 0), Mat& mask = Mat())
-    //
-
-    //javadoc: recoverPose(E, points1, points2, R, t, focal, pp, mask)
-    public static int recoverPose(Mat E, Mat points1, Mat points2, Mat R, Mat t, double focal, Point pp, Mat mask)
-    {
-        
-        int retVal = recoverPose_0(E.nativeObj, points1.nativeObj, points2.nativeObj, R.nativeObj, t.nativeObj, focal, pp.x, pp.y, mask.nativeObj);
-        
-        return retVal;
-    }
-
-    //javadoc: recoverPose(E, points1, points2, R, t, focal, pp)
-    public static int recoverPose(Mat E, Mat points1, Mat points2, Mat R, Mat t, double focal, Point pp)
-    {
-        
-        int retVal = recoverPose_1(E.nativeObj, points1.nativeObj, points2.nativeObj, R.nativeObj, t.nativeObj, focal, pp.x, pp.y);
-        
-        return retVal;
-    }
-
-    //javadoc: recoverPose(E, points1, points2, R, t)
-    public static int recoverPose(Mat E, Mat points1, Mat points2, Mat R, Mat t)
-    {
-        
-        int retVal = recoverPose_2(E.nativeObj, points1.nativeObj, points2.nativeObj, R.nativeObj, t.nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int recoverPose(Mat E, Mat points1, Mat points2, Mat cameraMatrix, Mat& R, Mat& t, Mat& mask = Mat())
-    //
-
-    //javadoc: recoverPose(E, points1, points2, cameraMatrix, R, t, mask)
-    public static int recoverPose(Mat E, Mat points1, Mat points2, Mat cameraMatrix, Mat R, Mat t, Mat mask)
-    {
-        
-        int retVal = recoverPose_3(E.nativeObj, points1.nativeObj, points2.nativeObj, cameraMatrix.nativeObj, R.nativeObj, t.nativeObj, mask.nativeObj);
-        
-        return retVal;
-    }
-
-    //javadoc: recoverPose(E, points1, points2, cameraMatrix, R, t)
-    public static int recoverPose(Mat E, Mat points1, Mat points2, Mat cameraMatrix, Mat R, Mat t)
-    {
-        
-        int retVal = recoverPose_4(E.nativeObj, points1.nativeObj, points2.nativeObj, cameraMatrix.nativeObj, R.nativeObj, t.nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int recoverPose(Mat E, Mat points1, Mat points2, Mat cameraMatrix, Mat& R, Mat& t, double distanceThresh, Mat& mask = Mat(), Mat& triangulatedPoints = Mat())
-    //
-
-    //javadoc: recoverPose(E, points1, points2, cameraMatrix, R, t, distanceThresh, mask, triangulatedPoints)
-    public static int recoverPose(Mat E, Mat points1, Mat points2, Mat cameraMatrix, Mat R, Mat t, double distanceThresh, Mat mask, Mat triangulatedPoints)
-    {
-        
-        int retVal = recoverPose_5(E.nativeObj, points1.nativeObj, points2.nativeObj, cameraMatrix.nativeObj, R.nativeObj, t.nativeObj, distanceThresh, mask.nativeObj, triangulatedPoints.nativeObj);
-        
-        return retVal;
-    }
-
-    //javadoc: recoverPose(E, points1, points2, cameraMatrix, R, t, distanceThresh)
-    public static int recoverPose(Mat E, Mat points1, Mat points2, Mat cameraMatrix, Mat R, Mat t, double distanceThresh)
-    {
-        
-        int retVal = recoverPose_6(E.nativeObj, points1.nativeObj, points2.nativeObj, cameraMatrix.nativeObj, R.nativeObj, t.nativeObj, distanceThresh);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int solveP3P(Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, vector_Mat& rvecs, vector_Mat& tvecs, int flags)
-    //
-
-    //javadoc: solveP3P(objectPoints, imagePoints, cameraMatrix, distCoeffs, rvecs, tvecs, flags)
-    public static int solveP3P(Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, List<Mat> rvecs, List<Mat> tvecs, int flags)
-    {
-        Mat rvecs_mat = new Mat();
-        Mat tvecs_mat = new Mat();
-        int retVal = solveP3P_0(objectPoints.nativeObj, imagePoints.nativeObj, cameraMatrix.nativeObj, distCoeffs.nativeObj, rvecs_mat.nativeObj, tvecs_mat.nativeObj, flags);
-        Converters.Mat_to_vector_Mat(rvecs_mat, rvecs);
-        rvecs_mat.release();
-        Converters.Mat_to_vector_Mat(tvecs_mat, tvecs);
-        tvecs_mat.release();
-        return retVal;
-    }
-
-
-    //
-    // C++:  void Rodrigues(Mat src, Mat& dst, Mat& jacobian = Mat())
-    //
-
-    //javadoc: Rodrigues(src, dst, jacobian)
-    public static void Rodrigues(Mat src, Mat dst, Mat jacobian)
-    {
-        
-        Rodrigues_0(src.nativeObj, dst.nativeObj, jacobian.nativeObj);
-        
-        return;
-    }
-
-    //javadoc: Rodrigues(src, dst)
-    public static void Rodrigues(Mat src, Mat dst)
-    {
-        
-        Rodrigues_1(src.nativeObj, dst.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void calibrationMatrixValues(Mat cameraMatrix, Size imageSize, double apertureWidth, double apertureHeight, double& fovx, double& fovy, double& focalLength, Point2d& principalPoint, double& aspectRatio)
-    //
-
-    //javadoc: calibrationMatrixValues(cameraMatrix, imageSize, apertureWidth, apertureHeight, fovx, fovy, focalLength, principalPoint, aspectRatio)
-    public static void calibrationMatrixValues(Mat cameraMatrix, Size imageSize, double apertureWidth, double apertureHeight, double[] fovx, double[] fovy, double[] focalLength, Point principalPoint, double[] aspectRatio)
-    {
-        double[] fovx_out = new double[1];
-        double[] fovy_out = new double[1];
-        double[] focalLength_out = new double[1];
-        double[] principalPoint_out = new double[2];
-        double[] aspectRatio_out = new double[1];
-        calibrationMatrixValues_0(cameraMatrix.nativeObj, imageSize.width, imageSize.height, apertureWidth, apertureHeight, fovx_out, fovy_out, focalLength_out, principalPoint_out, aspectRatio_out);
-        if(fovx!=null) fovx[0] = (double)fovx_out[0];
-        if(fovy!=null) fovy[0] = (double)fovy_out[0];
-        if(focalLength!=null) focalLength[0] = (double)focalLength_out[0];
-        if(principalPoint!=null){ principalPoint.x = principalPoint_out[0]; principalPoint.y = principalPoint_out[1]; } 
-        if(aspectRatio!=null) aspectRatio[0] = (double)aspectRatio_out[0];
-        return;
-    }
-
-
-    //
-    // C++:  void composeRT(Mat rvec1, Mat tvec1, Mat rvec2, Mat tvec2, Mat& rvec3, Mat& tvec3, Mat& dr3dr1 = Mat(), Mat& dr3dt1 = Mat(), Mat& dr3dr2 = Mat(), Mat& dr3dt2 = Mat(), Mat& dt3dr1 = Mat(), Mat& dt3dt1 = Mat(), Mat& dt3dr2 = Mat(), Mat& dt3dt2 = Mat())
-    //
-
-    //javadoc: composeRT(rvec1, tvec1, rvec2, tvec2, rvec3, tvec3, dr3dr1, dr3dt1, dr3dr2, dr3dt2, dt3dr1, dt3dt1, dt3dr2, dt3dt2)
-    public static void composeRT(Mat rvec1, Mat tvec1, Mat rvec2, Mat tvec2, Mat rvec3, Mat tvec3, Mat dr3dr1, Mat dr3dt1, Mat dr3dr2, Mat dr3dt2, Mat dt3dr1, Mat dt3dt1, Mat dt3dr2, Mat dt3dt2)
-    {
-        
-        composeRT_0(rvec1.nativeObj, tvec1.nativeObj, rvec2.nativeObj, tvec2.nativeObj, rvec3.nativeObj, tvec3.nativeObj, dr3dr1.nativeObj, dr3dt1.nativeObj, dr3dr2.nativeObj, dr3dt2.nativeObj, dt3dr1.nativeObj, dt3dt1.nativeObj, dt3dr2.nativeObj, dt3dt2.nativeObj);
-        
-        return;
-    }
-
-    //javadoc: composeRT(rvec1, tvec1, rvec2, tvec2, rvec3, tvec3)
-    public static void composeRT(Mat rvec1, Mat tvec1, Mat rvec2, Mat tvec2, Mat rvec3, Mat tvec3)
-    {
-        
-        composeRT_1(rvec1.nativeObj, tvec1.nativeObj, rvec2.nativeObj, tvec2.nativeObj, rvec3.nativeObj, tvec3.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void computeCorrespondEpilines(Mat points, int whichImage, Mat F, Mat& lines)
-    //
-
-    //javadoc: computeCorrespondEpilines(points, whichImage, F, lines)
-    public static void computeCorrespondEpilines(Mat points, int whichImage, Mat F, Mat lines)
-    {
-        
-        computeCorrespondEpilines_0(points.nativeObj, whichImage, F.nativeObj, lines.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void convertPointsFromHomogeneous(Mat src, Mat& dst)
-    //
-
-    //javadoc: convertPointsFromHomogeneous(src, dst)
-    public static void convertPointsFromHomogeneous(Mat src, Mat dst)
-    {
-        
-        convertPointsFromHomogeneous_0(src.nativeObj, dst.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void convertPointsToHomogeneous(Mat src, Mat& dst)
-    //
-
-    //javadoc: convertPointsToHomogeneous(src, dst)
-    public static void convertPointsToHomogeneous(Mat src, Mat dst)
-    {
-        
-        convertPointsToHomogeneous_0(src.nativeObj, dst.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void correctMatches(Mat F, Mat points1, Mat points2, Mat& newPoints1, Mat& newPoints2)
-    //
-
-    //javadoc: correctMatches(F, points1, points2, newPoints1, newPoints2)
-    public static void correctMatches(Mat F, Mat points1, Mat points2, Mat newPoints1, Mat newPoints2)
-    {
-        
-        correctMatches_0(F.nativeObj, points1.nativeObj, points2.nativeObj, newPoints1.nativeObj, newPoints2.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void decomposeEssentialMat(Mat E, Mat& R1, Mat& R2, Mat& t)
-    //
-
-    //javadoc: decomposeEssentialMat(E, R1, R2, t)
-    public static void decomposeEssentialMat(Mat E, Mat R1, Mat R2, Mat t)
-    {
-        
-        decomposeEssentialMat_0(E.nativeObj, R1.nativeObj, R2.nativeObj, t.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void decomposeProjectionMatrix(Mat projMatrix, Mat& cameraMatrix, Mat& rotMatrix, Mat& transVect, Mat& rotMatrixX = Mat(), Mat& rotMatrixY = Mat(), Mat& rotMatrixZ = Mat(), Mat& eulerAngles = Mat())
-    //
-
-    //javadoc: decomposeProjectionMatrix(projMatrix, cameraMatrix, rotMatrix, transVect, rotMatrixX, rotMatrixY, rotMatrixZ, eulerAngles)
-    public static void decomposeProjectionMatrix(Mat projMatrix, Mat cameraMatrix, Mat rotMatrix, Mat transVect, Mat rotMatrixX, Mat rotMatrixY, Mat rotMatrixZ, Mat eulerAngles)
-    {
-        
-        decomposeProjectionMatrix_0(projMatrix.nativeObj, cameraMatrix.nativeObj, rotMatrix.nativeObj, transVect.nativeObj, rotMatrixX.nativeObj, rotMatrixY.nativeObj, rotMatrixZ.nativeObj, eulerAngles.nativeObj);
-        
-        return;
-    }
-
-    //javadoc: decomposeProjectionMatrix(projMatrix, cameraMatrix, rotMatrix, transVect)
-    public static void decomposeProjectionMatrix(Mat projMatrix, Mat cameraMatrix, Mat rotMatrix, Mat transVect)
-    {
-        
-        decomposeProjectionMatrix_1(projMatrix.nativeObj, cameraMatrix.nativeObj, rotMatrix.nativeObj, transVect.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void drawChessboardCorners(Mat& image, Size patternSize, vector_Point2f corners, bool patternWasFound)
-    //
-
-    //javadoc: drawChessboardCorners(image, patternSize, corners, patternWasFound)
-    public static void drawChessboardCorners(Mat image, Size patternSize, MatOfPoint2f corners, boolean patternWasFound)
-    {
-        Mat corners_mat = corners;
-        drawChessboardCorners_0(image.nativeObj, patternSize.width, patternSize.height, corners_mat.nativeObj, patternWasFound);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void filterHomographyDecompByVisibleRefpoints(vector_Mat rotations, vector_Mat normals, Mat beforePoints, Mat afterPoints, Mat& possibleSolutions, Mat pointsMask = Mat())
-    //
-
-    //javadoc: filterHomographyDecompByVisibleRefpoints(rotations, normals, beforePoints, afterPoints, possibleSolutions, pointsMask)
-    public static void filterHomographyDecompByVisibleRefpoints(List<Mat> rotations, List<Mat> normals, Mat beforePoints, Mat afterPoints, Mat possibleSolutions, Mat pointsMask)
-    {
-        Mat rotations_mat = Converters.vector_Mat_to_Mat(rotations);
-        Mat normals_mat = Converters.vector_Mat_to_Mat(normals);
-        filterHomographyDecompByVisibleRefpoints_0(rotations_mat.nativeObj, normals_mat.nativeObj, beforePoints.nativeObj, afterPoints.nativeObj, possibleSolutions.nativeObj, pointsMask.nativeObj);
-        
-        return;
-    }
-
-    //javadoc: filterHomographyDecompByVisibleRefpoints(rotations, normals, beforePoints, afterPoints, possibleSolutions)
-    public static void filterHomographyDecompByVisibleRefpoints(List<Mat> rotations, List<Mat> normals, Mat beforePoints, Mat afterPoints, Mat possibleSolutions)
-    {
-        Mat rotations_mat = Converters.vector_Mat_to_Mat(rotations);
-        Mat normals_mat = Converters.vector_Mat_to_Mat(normals);
-        filterHomographyDecompByVisibleRefpoints_1(rotations_mat.nativeObj, normals_mat.nativeObj, beforePoints.nativeObj, afterPoints.nativeObj, possibleSolutions.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void filterSpeckles(Mat& img, double newVal, int maxSpeckleSize, double maxDiff, Mat& buf = Mat())
-    //
-
-    //javadoc: filterSpeckles(img, newVal, maxSpeckleSize, maxDiff, buf)
-    public static void filterSpeckles(Mat img, double newVal, int maxSpeckleSize, double maxDiff, Mat buf)
-    {
-        
-        filterSpeckles_0(img.nativeObj, newVal, maxSpeckleSize, maxDiff, buf.nativeObj);
-        
-        return;
-    }
-
-    //javadoc: filterSpeckles(img, newVal, maxSpeckleSize, maxDiff)
-    public static void filterSpeckles(Mat img, double newVal, int maxSpeckleSize, double maxDiff)
-    {
-        
-        filterSpeckles_1(img.nativeObj, newVal, maxSpeckleSize, maxDiff);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void matMulDeriv(Mat A, Mat B, Mat& dABdA, Mat& dABdB)
-    //
-
-    //javadoc: matMulDeriv(A, B, dABdA, dABdB)
-    public static void matMulDeriv(Mat A, Mat B, Mat dABdA, Mat dABdB)
-    {
-        
-        matMulDeriv_0(A.nativeObj, B.nativeObj, dABdA.nativeObj, dABdB.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void projectPoints(vector_Point3f objectPoints, Mat rvec, Mat tvec, Mat cameraMatrix, vector_double distCoeffs, vector_Point2f& imagePoints, Mat& jacobian = Mat(), double aspectRatio = 0)
-    //
-
-    //javadoc: projectPoints(objectPoints, rvec, tvec, cameraMatrix, distCoeffs, imagePoints, jacobian, aspectRatio)
-    public static void projectPoints(MatOfPoint3f objectPoints, Mat rvec, Mat tvec, Mat cameraMatrix, MatOfDouble distCoeffs, MatOfPoint2f imagePoints, Mat jacobian, double aspectRatio)
-    {
-        Mat objectPoints_mat = objectPoints;
-        Mat distCoeffs_mat = distCoeffs;
-        Mat imagePoints_mat = imagePoints;
-        projectPoints_0(objectPoints_mat.nativeObj, rvec.nativeObj, tvec.nativeObj, cameraMatrix.nativeObj, distCoeffs_mat.nativeObj, imagePoints_mat.nativeObj, jacobian.nativeObj, aspectRatio);
-        
-        return;
-    }
-
-    //javadoc: projectPoints(objectPoints, rvec, tvec, cameraMatrix, distCoeffs, imagePoints)
-    public static void projectPoints(MatOfPoint3f objectPoints, Mat rvec, Mat tvec, Mat cameraMatrix, MatOfDouble distCoeffs, MatOfPoint2f imagePoints)
-    {
-        Mat objectPoints_mat = objectPoints;
-        Mat distCoeffs_mat = distCoeffs;
-        Mat imagePoints_mat = imagePoints;
-        projectPoints_1(objectPoints_mat.nativeObj, rvec.nativeObj, tvec.nativeObj, cameraMatrix.nativeObj, distCoeffs_mat.nativeObj, imagePoints_mat.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void reprojectImageTo3D(Mat disparity, Mat& _3dImage, Mat Q, bool handleMissingValues = false, int ddepth = -1)
-    //
-
-    //javadoc: reprojectImageTo3D(disparity, _3dImage, Q, handleMissingValues, ddepth)
-    public static void reprojectImageTo3D(Mat disparity, Mat _3dImage, Mat Q, boolean handleMissingValues, int ddepth)
-    {
-        
-        reprojectImageTo3D_0(disparity.nativeObj, _3dImage.nativeObj, Q.nativeObj, handleMissingValues, ddepth);
-        
-        return;
-    }
-
-    //javadoc: reprojectImageTo3D(disparity, _3dImage, Q, handleMissingValues)
-    public static void reprojectImageTo3D(Mat disparity, Mat _3dImage, Mat Q, boolean handleMissingValues)
-    {
-        
-        reprojectImageTo3D_1(disparity.nativeObj, _3dImage.nativeObj, Q.nativeObj, handleMissingValues);
-        
-        return;
-    }
-
-    //javadoc: reprojectImageTo3D(disparity, _3dImage, Q)
-    public static void reprojectImageTo3D(Mat disparity, Mat _3dImage, Mat Q)
-    {
-        
-        reprojectImageTo3D_2(disparity.nativeObj, _3dImage.nativeObj, Q.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void stereoRectify(Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Size imageSize, Mat R, Mat T, Mat& R1, Mat& R2, Mat& P1, Mat& P2, Mat& Q, int flags = CALIB_ZERO_DISPARITY, double alpha = -1, Size newImageSize = Size(), Rect* validPixROI1 = 0, Rect* validPixROI2 = 0)
-    //
-
-    //javadoc: stereoRectify(cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, imageSize, R, T, R1, R2, P1, P2, Q, flags, alpha, newImageSize, validPixROI1, validPixROI2)
-    public static void stereoRectify(Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Size imageSize, Mat R, Mat T, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags, double alpha, Size newImageSize, Rect validPixROI1, Rect validPixROI2)
-    {
-        double[] validPixROI1_out = new double[4];
-        double[] validPixROI2_out = new double[4];
-        stereoRectify_0(cameraMatrix1.nativeObj, distCoeffs1.nativeObj, cameraMatrix2.nativeObj, distCoeffs2.nativeObj, imageSize.width, imageSize.height, R.nativeObj, T.nativeObj, R1.nativeObj, R2.nativeObj, P1.nativeObj, P2.nativeObj, Q.nativeObj, flags, alpha, newImageSize.width, newImageSize.height, validPixROI1_out, validPixROI2_out);
-        if(validPixROI1!=null){ validPixROI1.x = (int)validPixROI1_out[0]; validPixROI1.y = (int)validPixROI1_out[1]; validPixROI1.width = (int)validPixROI1_out[2]; validPixROI1.height = (int)validPixROI1_out[3]; } 
-        if(validPixROI2!=null){ validPixROI2.x = (int)validPixROI2_out[0]; validPixROI2.y = (int)validPixROI2_out[1]; validPixROI2.width = (int)validPixROI2_out[2]; validPixROI2.height = (int)validPixROI2_out[3]; } 
-        return;
-    }
-
-    //javadoc: stereoRectify(cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, imageSize, R, T, R1, R2, P1, P2, Q)
-    public static void stereoRectify(Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Size imageSize, Mat R, Mat T, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q)
-    {
-        
-        stereoRectify_1(cameraMatrix1.nativeObj, distCoeffs1.nativeObj, cameraMatrix2.nativeObj, distCoeffs2.nativeObj, imageSize.width, imageSize.height, R.nativeObj, T.nativeObj, R1.nativeObj, R2.nativeObj, P1.nativeObj, P2.nativeObj, Q.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void triangulatePoints(Mat projMatr1, Mat projMatr2, Mat projPoints1, Mat projPoints2, Mat& points4D)
-    //
-
-    //javadoc: triangulatePoints(projMatr1, projMatr2, projPoints1, projPoints2, points4D)
-    public static void triangulatePoints(Mat projMatr1, Mat projMatr2, Mat projPoints1, Mat projPoints2, Mat points4D)
-    {
-        
-        triangulatePoints_0(projMatr1.nativeObj, projMatr2.nativeObj, projPoints1.nativeObj, projPoints2.nativeObj, points4D.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void validateDisparity(Mat& disparity, Mat cost, int minDisparity, int numberOfDisparities, int disp12MaxDisp = 1)
-    //
-
-    //javadoc: validateDisparity(disparity, cost, minDisparity, numberOfDisparities, disp12MaxDisp)
-    public static void validateDisparity(Mat disparity, Mat cost, int minDisparity, int numberOfDisparities, int disp12MaxDisp)
-    {
-        
-        validateDisparity_0(disparity.nativeObj, cost.nativeObj, minDisparity, numberOfDisparities, disp12MaxDisp);
-        
-        return;
-    }
-
-    //javadoc: validateDisparity(disparity, cost, minDisparity, numberOfDisparities)
-    public static void validateDisparity(Mat disparity, Mat cost, int minDisparity, int numberOfDisparities)
-    {
-        
-        validateDisparity_1(disparity.nativeObj, cost.nativeObj, minDisparity, numberOfDisparities);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void distortPoints(Mat undistorted, Mat& distorted, Mat K, Mat D, double alpha = 0)
-    //
-
-    //javadoc: distortPoints(undistorted, distorted, K, D, alpha)
-    public static void distortPoints(Mat undistorted, Mat distorted, Mat K, Mat D, double alpha)
-    {
-        
-        distortPoints_0(undistorted.nativeObj, distorted.nativeObj, K.nativeObj, D.nativeObj, alpha);
-        
-        return;
-    }
-
-    //javadoc: distortPoints(undistorted, distorted, K, D)
-    public static void distortPoints(Mat undistorted, Mat distorted, Mat K, Mat D)
-    {
-        
-        distortPoints_1(undistorted.nativeObj, distorted.nativeObj, K.nativeObj, D.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void estimateNewCameraMatrixForUndistortRectify(Mat K, Mat D, Size image_size, Mat R, Mat& P, double balance = 0.0, Size new_size = Size(), double fov_scale = 1.0)
-    //
-
-    //javadoc: estimateNewCameraMatrixForUndistortRectify(K, D, image_size, R, P, balance, new_size, fov_scale)
-    public static void estimateNewCameraMatrixForUndistortRectify(Mat K, Mat D, Size image_size, Mat R, Mat P, double balance, Size new_size, double fov_scale)
-    {
-        
-        estimateNewCameraMatrixForUndistortRectify_0(K.nativeObj, D.nativeObj, image_size.width, image_size.height, R.nativeObj, P.nativeObj, balance, new_size.width, new_size.height, fov_scale);
-        
-        return;
-    }
-
-    //javadoc: estimateNewCameraMatrixForUndistortRectify(K, D, image_size, R, P)
-    public static void estimateNewCameraMatrixForUndistortRectify(Mat K, Mat D, Size image_size, Mat R, Mat P)
-    {
-        
-        estimateNewCameraMatrixForUndistortRectify_1(K.nativeObj, D.nativeObj, image_size.width, image_size.height, R.nativeObj, P.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void initUndistortRectifyMap(Mat K, Mat D, Mat R, Mat P, Size size, int m1type, Mat& map1, Mat& map2)
-    //
-
-    //javadoc: initUndistortRectifyMap(K, D, R, P, size, m1type, map1, map2)
-    public static void initUndistortRectifyMap(Mat K, Mat D, Mat R, Mat P, Size size, int m1type, Mat map1, Mat map2)
-    {
-        
-        initUndistortRectifyMap_0(K.nativeObj, D.nativeObj, R.nativeObj, P.nativeObj, size.width, size.height, m1type, map1.nativeObj, map2.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void projectPoints(vector_Point3f objectPoints, vector_Point2f& imagePoints, Mat rvec, Mat tvec, Mat K, Mat D, double alpha = 0, Mat& jacobian = Mat())
-    //
-
-    //javadoc: projectPoints(objectPoints, imagePoints, rvec, tvec, K, D, alpha, jacobian)
-    public static void projectPoints(MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat rvec, Mat tvec, Mat K, Mat D, double alpha, Mat jacobian)
-    {
-        Mat objectPoints_mat = objectPoints;
-        Mat imagePoints_mat = imagePoints;
-        projectPoints_2(objectPoints_mat.nativeObj, imagePoints_mat.nativeObj, rvec.nativeObj, tvec.nativeObj, K.nativeObj, D.nativeObj, alpha, jacobian.nativeObj);
-        
-        return;
-    }
-
-    //javadoc: projectPoints(objectPoints, imagePoints, rvec, tvec, K, D)
-    public static void projectPoints(MatOfPoint3f objectPoints, MatOfPoint2f imagePoints, Mat rvec, Mat tvec, Mat K, Mat D)
-    {
-        Mat objectPoints_mat = objectPoints;
-        Mat imagePoints_mat = imagePoints;
-        projectPoints_3(objectPoints_mat.nativeObj, imagePoints_mat.nativeObj, rvec.nativeObj, tvec.nativeObj, K.nativeObj, D.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void stereoRectify(Mat K1, Mat D1, Mat K2, Mat D2, Size imageSize, Mat R, Mat tvec, Mat& R1, Mat& R2, Mat& P1, Mat& P2, Mat& Q, int flags, Size newImageSize = Size(), double balance = 0.0, double fov_scale = 1.0)
-    //
-
-    //javadoc: stereoRectify(K1, D1, K2, D2, imageSize, R, tvec, R1, R2, P1, P2, Q, flags, newImageSize, balance, fov_scale)
-    public static void stereoRectify(Mat K1, Mat D1, Mat K2, Mat D2, Size imageSize, Mat R, Mat tvec, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags, Size newImageSize, double balance, double fov_scale)
-    {
-        
-        stereoRectify_2(K1.nativeObj, D1.nativeObj, K2.nativeObj, D2.nativeObj, imageSize.width, imageSize.height, R.nativeObj, tvec.nativeObj, R1.nativeObj, R2.nativeObj, P1.nativeObj, P2.nativeObj, Q.nativeObj, flags, newImageSize.width, newImageSize.height, balance, fov_scale);
-        
-        return;
-    }
-
-    //javadoc: stereoRectify(K1, D1, K2, D2, imageSize, R, tvec, R1, R2, P1, P2, Q, flags)
-    public static void stereoRectify(Mat K1, Mat D1, Mat K2, Mat D2, Size imageSize, Mat R, Mat tvec, Mat R1, Mat R2, Mat P1, Mat P2, Mat Q, int flags)
-    {
-        
-        stereoRectify_3(K1.nativeObj, D1.nativeObj, K2.nativeObj, D2.nativeObj, imageSize.width, imageSize.height, R.nativeObj, tvec.nativeObj, R1.nativeObj, R2.nativeObj, P1.nativeObj, P2.nativeObj, Q.nativeObj, flags);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void undistortImage(Mat distorted, Mat& undistorted, Mat K, Mat D, Mat Knew = cv::Mat(), Size new_size = Size())
-    //
-
-    //javadoc: undistortImage(distorted, undistorted, K, D, Knew, new_size)
-    public static void undistortImage(Mat distorted, Mat undistorted, Mat K, Mat D, Mat Knew, Size new_size)
-    {
-        
-        undistortImage_0(distorted.nativeObj, undistorted.nativeObj, K.nativeObj, D.nativeObj, Knew.nativeObj, new_size.width, new_size.height);
-        
-        return;
-    }
-
-    //javadoc: undistortImage(distorted, undistorted, K, D)
-    public static void undistortImage(Mat distorted, Mat undistorted, Mat K, Mat D)
-    {
-        
-        undistortImage_1(distorted.nativeObj, undistorted.nativeObj, K.nativeObj, D.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void undistortPoints(Mat distorted, Mat& undistorted, Mat K, Mat D, Mat R = Mat(), Mat P = Mat())
-    //
-
-    //javadoc: undistortPoints(distorted, undistorted, K, D, R, P)
-    public static void undistortPoints(Mat distorted, Mat undistorted, Mat K, Mat D, Mat R, Mat P)
-    {
-        
-        undistortPoints_0(distorted.nativeObj, undistorted.nativeObj, K.nativeObj, D.nativeObj, R.nativeObj, P.nativeObj);
-        
-        return;
-    }
-
-    //javadoc: undistortPoints(distorted, undistorted, K, D)
-    public static void undistortPoints(Mat distorted, Mat undistorted, Mat K, Mat D)
-    {
-        
-        undistortPoints_1(distorted.nativeObj, undistorted.nativeObj, K.nativeObj, D.nativeObj);
-        
-        return;
-    }
-
-
-
-
-    // C++:  Mat estimateAffine2D(Mat from, Mat to, Mat& inliers = Mat(), int method = RANSAC, double ransacReprojThreshold = 3, size_t maxIters = 2000, double confidence = 0.99, size_t refineIters = 10)
-    private static native long estimateAffine2D_0(long from_nativeObj, long to_nativeObj, long inliers_nativeObj, int method, double ransacReprojThreshold, long maxIters, double confidence, long refineIters);
-    private static native long estimateAffine2D_1(long from_nativeObj, long to_nativeObj);
-
-    // C++:  Mat estimateAffinePartial2D(Mat from, Mat to, Mat& inliers = Mat(), int method = RANSAC, double ransacReprojThreshold = 3, size_t maxIters = 2000, double confidence = 0.99, size_t refineIters = 10)
-    private static native long estimateAffinePartial2D_0(long from_nativeObj, long to_nativeObj, long inliers_nativeObj, int method, double ransacReprojThreshold, long maxIters, double confidence, long refineIters);
-    private static native long estimateAffinePartial2D_1(long from_nativeObj, long to_nativeObj);
-
-    // C++:  Mat findEssentialMat(Mat points1, Mat points2, Mat cameraMatrix, int method = RANSAC, double prob = 0.999, double threshold = 1.0, Mat& mask = Mat())
-    private static native long findEssentialMat_0(long points1_nativeObj, long points2_nativeObj, long cameraMatrix_nativeObj, int method, double prob, double threshold, long mask_nativeObj);
-    private static native long findEssentialMat_1(long points1_nativeObj, long points2_nativeObj, long cameraMatrix_nativeObj, int method, double prob, double threshold);
-    private static native long findEssentialMat_2(long points1_nativeObj, long points2_nativeObj, long cameraMatrix_nativeObj);
-
-    // C++:  Mat findEssentialMat(Mat points1, Mat points2, double focal = 1.0, Point2d pp = Point2d(0, 0), int method = RANSAC, double prob = 0.999, double threshold = 1.0, Mat& mask = Mat())
-    private static native long findEssentialMat_3(long points1_nativeObj, long points2_nativeObj, double focal, double pp_x, double pp_y, int method, double prob, double threshold, long mask_nativeObj);
-    private static native long findEssentialMat_4(long points1_nativeObj, long points2_nativeObj, double focal, double pp_x, double pp_y, int method, double prob, double threshold);
-    private static native long findEssentialMat_5(long points1_nativeObj, long points2_nativeObj);
-
-    // C++:  Mat findFundamentalMat(vector_Point2f points1, vector_Point2f points2, int method = FM_RANSAC, double ransacReprojThreshold = 3., double confidence = 0.99, Mat& mask = Mat())
-    private static native long findFundamentalMat_0(long points1_mat_nativeObj, long points2_mat_nativeObj, int method, double ransacReprojThreshold, double confidence, long mask_nativeObj);
-    private static native long findFundamentalMat_1(long points1_mat_nativeObj, long points2_mat_nativeObj, int method, double ransacReprojThreshold, double confidence);
-    private static native long findFundamentalMat_2(long points1_mat_nativeObj, long points2_mat_nativeObj);
-
-    // C++:  Mat findHomography(vector_Point2f srcPoints, vector_Point2f dstPoints, int method = 0, double ransacReprojThreshold = 3, Mat& mask = Mat(), int maxIters = 2000, double confidence = 0.995)
-    private static native long findHomography_0(long srcPoints_mat_nativeObj, long dstPoints_mat_nativeObj, int method, double ransacReprojThreshold, long mask_nativeObj, int maxIters, double confidence);
-    private static native long findHomography_1(long srcPoints_mat_nativeObj, long dstPoints_mat_nativeObj, int method, double ransacReprojThreshold);
-    private static native long findHomography_2(long srcPoints_mat_nativeObj, long dstPoints_mat_nativeObj);
-
-    // C++:  Mat getOptimalNewCameraMatrix(Mat cameraMatrix, Mat distCoeffs, Size imageSize, double alpha, Size newImgSize = Size(), Rect* validPixROI = 0, bool centerPrincipalPoint = false)
-    private static native long getOptimalNewCameraMatrix_0(long cameraMatrix_nativeObj, long distCoeffs_nativeObj, double imageSize_width, double imageSize_height, double alpha, double newImgSize_width, double newImgSize_height, double[] validPixROI_out, boolean centerPrincipalPoint);
-    private static native long getOptimalNewCameraMatrix_1(long cameraMatrix_nativeObj, long distCoeffs_nativeObj, double imageSize_width, double imageSize_height, double alpha);
-
-    // C++:  Mat initCameraMatrix2D(vector_vector_Point3f objectPoints, vector_vector_Point2f imagePoints, Size imageSize, double aspectRatio = 1.0)
-    private static native long initCameraMatrix2D_0(long objectPoints_mat_nativeObj, long imagePoints_mat_nativeObj, double imageSize_width, double imageSize_height, double aspectRatio);
-    private static native long initCameraMatrix2D_1(long objectPoints_mat_nativeObj, long imagePoints_mat_nativeObj, double imageSize_width, double imageSize_height);
-
-    // C++:  Rect getValidDisparityROI(Rect roi1, Rect roi2, int minDisparity, int numberOfDisparities, int SADWindowSize)
-    private static native double[] getValidDisparityROI_0(int roi1_x, int roi1_y, int roi1_width, int roi1_height, int roi2_x, int roi2_y, int roi2_width, int roi2_height, int minDisparity, int numberOfDisparities, int SADWindowSize);
-
-    // C++:  Vec3d RQDecomp3x3(Mat src, Mat& mtxR, Mat& mtxQ, Mat& Qx = Mat(), Mat& Qy = Mat(), Mat& Qz = Mat())
-    private static native double[] RQDecomp3x3_0(long src_nativeObj, long mtxR_nativeObj, long mtxQ_nativeObj, long Qx_nativeObj, long Qy_nativeObj, long Qz_nativeObj);
-    private static native double[] RQDecomp3x3_1(long src_nativeObj, long mtxR_nativeObj, long mtxQ_nativeObj);
-
-    // C++:  bool findChessboardCorners(Mat image, Size patternSize, vector_Point2f& corners, int flags = CALIB_CB_ADAPTIVE_THRESH + CALIB_CB_NORMALIZE_IMAGE)
-    private static native boolean findChessboardCorners_0(long image_nativeObj, double patternSize_width, double patternSize_height, long corners_mat_nativeObj, int flags);
-    private static native boolean findChessboardCorners_1(long image_nativeObj, double patternSize_width, double patternSize_height, long corners_mat_nativeObj);
-
-    // C++:  bool findCirclesGrid(Mat image, Size patternSize, Mat& centers, int flags = CALIB_CB_SYMMETRIC_GRID, Ptr_FeatureDetector blobDetector = SimpleBlobDetector::create())
-    private static native boolean findCirclesGrid_0(long image_nativeObj, double patternSize_width, double patternSize_height, long centers_nativeObj, int flags);
-    private static native boolean findCirclesGrid_1(long image_nativeObj, double patternSize_width, double patternSize_height, long centers_nativeObj);
-
-    // C++:  bool solvePnP(vector_Point3f objectPoints, vector_Point2f imagePoints, Mat cameraMatrix, vector_double distCoeffs, Mat& rvec, Mat& tvec, bool useExtrinsicGuess = false, int flags = SOLVEPNP_ITERATIVE)
-    private static native boolean solvePnP_0(long objectPoints_mat_nativeObj, long imagePoints_mat_nativeObj, long cameraMatrix_nativeObj, long distCoeffs_mat_nativeObj, long rvec_nativeObj, long tvec_nativeObj, boolean useExtrinsicGuess, int flags);
-    private static native boolean solvePnP_1(long objectPoints_mat_nativeObj, long imagePoints_mat_nativeObj, long cameraMatrix_nativeObj, long distCoeffs_mat_nativeObj, long rvec_nativeObj, long tvec_nativeObj);
-
-    // C++:  bool solvePnPRansac(vector_Point3f objectPoints, vector_Point2f imagePoints, Mat cameraMatrix, vector_double distCoeffs, Mat& rvec, Mat& tvec, bool useExtrinsicGuess = false, int iterationsCount = 100, float reprojectionError = 8.0, double confidence = 0.99, Mat& inliers = Mat(), int flags = SOLVEPNP_ITERATIVE)
-    private static native boolean solvePnPRansac_0(long objectPoints_mat_nativeObj, long imagePoints_mat_nativeObj, long cameraMatrix_nativeObj, long distCoeffs_mat_nativeObj, long rvec_nativeObj, long tvec_nativeObj, boolean useExtrinsicGuess, int iterationsCount, float reprojectionError, double confidence, long inliers_nativeObj, int flags);
-    private static native boolean solvePnPRansac_1(long objectPoints_mat_nativeObj, long imagePoints_mat_nativeObj, long cameraMatrix_nativeObj, long distCoeffs_mat_nativeObj, long rvec_nativeObj, long tvec_nativeObj);
-
-    // C++:  bool stereoRectifyUncalibrated(Mat points1, Mat points2, Mat F, Size imgSize, Mat& H1, Mat& H2, double threshold = 5)
-    private static native boolean stereoRectifyUncalibrated_0(long points1_nativeObj, long points2_nativeObj, long F_nativeObj, double imgSize_width, double imgSize_height, long H1_nativeObj, long H2_nativeObj, double threshold);
-    private static native boolean stereoRectifyUncalibrated_1(long points1_nativeObj, long points2_nativeObj, long F_nativeObj, double imgSize_width, double imgSize_height, long H1_nativeObj, long H2_nativeObj);
-
-    // C++:  double calibrateCamera(vector_Mat objectPoints, vector_Mat imagePoints, Size imageSize, Mat& cameraMatrix, Mat& distCoeffs, vector_Mat& rvecs, vector_Mat& tvecs, Mat& stdDeviationsIntrinsics, Mat& stdDeviationsExtrinsics, Mat& perViewErrors, int flags = 0, TermCriteria criteria = TermCriteria( TermCriteria::COUNT + TermCriteria::EPS, 30, DBL_EPSILON))
-    private static native double calibrateCameraExtended_0(long objectPoints_mat_nativeObj, long imagePoints_mat_nativeObj, double imageSize_width, double imageSize_height, long cameraMatrix_nativeObj, long distCoeffs_nativeObj, long rvecs_mat_nativeObj, long tvecs_mat_nativeObj, long stdDeviationsIntrinsics_nativeObj, long stdDeviationsExtrinsics_nativeObj, long perViewErrors_nativeObj, int flags, int criteria_type, int criteria_maxCount, double criteria_epsilon);
-    private static native double calibrateCameraExtended_1(long objectPoints_mat_nativeObj, long imagePoints_mat_nativeObj, double imageSize_width, double imageSize_height, long cameraMatrix_nativeObj, long distCoeffs_nativeObj, long rvecs_mat_nativeObj, long tvecs_mat_nativeObj, long stdDeviationsIntrinsics_nativeObj, long stdDeviationsExtrinsics_nativeObj, long perViewErrors_nativeObj, int flags);
-    private static native double calibrateCameraExtended_2(long objectPoints_mat_nativeObj, long imagePoints_mat_nativeObj, double imageSize_width, double imageSize_height, long cameraMatrix_nativeObj, long distCoeffs_nativeObj, long rvecs_mat_nativeObj, long tvecs_mat_nativeObj, long stdDeviationsIntrinsics_nativeObj, long stdDeviationsExtrinsics_nativeObj, long perViewErrors_nativeObj);
-
-    // C++:  double calibrateCamera(vector_Mat objectPoints, vector_Mat imagePoints, Size imageSize, Mat& cameraMatrix, Mat& distCoeffs, vector_Mat& rvecs, vector_Mat& tvecs, int flags = 0, TermCriteria criteria = TermCriteria( TermCriteria::COUNT + TermCriteria::EPS, 30, DBL_EPSILON))
-    private static native double calibrateCamera_0(long objectPoints_mat_nativeObj, long imagePoints_mat_nativeObj, double imageSize_width, double imageSize_height, long cameraMatrix_nativeObj, long distCoeffs_nativeObj, long rvecs_mat_nativeObj, long tvecs_mat_nativeObj, int flags, int criteria_type, int criteria_maxCount, double criteria_epsilon);
-    private static native double calibrateCamera_1(long objectPoints_mat_nativeObj, long imagePoints_mat_nativeObj, double imageSize_width, double imageSize_height, long cameraMatrix_nativeObj, long distCoeffs_nativeObj, long rvecs_mat_nativeObj, long tvecs_mat_nativeObj, int flags);
-    private static native double calibrateCamera_2(long objectPoints_mat_nativeObj, long imagePoints_mat_nativeObj, double imageSize_width, double imageSize_height, long cameraMatrix_nativeObj, long distCoeffs_nativeObj, long rvecs_mat_nativeObj, long tvecs_mat_nativeObj);
-
-    // C++:  double sampsonDistance(Mat pt1, Mat pt2, Mat F)
-    private static native double sampsonDistance_0(long pt1_nativeObj, long pt2_nativeObj, long F_nativeObj);
-
-    // C++:  double stereoCalibrate(vector_Mat objectPoints, vector_Mat imagePoints1, vector_Mat imagePoints2, Mat& cameraMatrix1, Mat& distCoeffs1, Mat& cameraMatrix2, Mat& distCoeffs2, Size imageSize, Mat& R, Mat& T, Mat& E, Mat& F, Mat& perViewErrors, int flags = CALIB_FIX_INTRINSIC, TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 1e-6))
-    private static native double stereoCalibrateExtended_0(long objectPoints_mat_nativeObj, long imagePoints1_mat_nativeObj, long imagePoints2_mat_nativeObj, long cameraMatrix1_nativeObj, long distCoeffs1_nativeObj, long cameraMatrix2_nativeObj, long distCoeffs2_nativeObj, double imageSize_width, double imageSize_height, long R_nativeObj, long T_nativeObj, long E_nativeObj, long F_nativeObj, long perViewErrors_nativeObj, int flags, int criteria_type, int criteria_maxCount, double criteria_epsilon);
-    private static native double stereoCalibrateExtended_1(long objectPoints_mat_nativeObj, long imagePoints1_mat_nativeObj, long imagePoints2_mat_nativeObj, long cameraMatrix1_nativeObj, long distCoeffs1_nativeObj, long cameraMatrix2_nativeObj, long distCoeffs2_nativeObj, double imageSize_width, double imageSize_height, long R_nativeObj, long T_nativeObj, long E_nativeObj, long F_nativeObj, long perViewErrors_nativeObj, int flags);
-    private static native double stereoCalibrateExtended_2(long objectPoints_mat_nativeObj, long imagePoints1_mat_nativeObj, long imagePoints2_mat_nativeObj, long cameraMatrix1_nativeObj, long distCoeffs1_nativeObj, long cameraMatrix2_nativeObj, long distCoeffs2_nativeObj, double imageSize_width, double imageSize_height, long R_nativeObj, long T_nativeObj, long E_nativeObj, long F_nativeObj, long perViewErrors_nativeObj);
-
-    // C++:  double stereoCalibrate(vector_Mat objectPoints, vector_Mat imagePoints1, vector_Mat imagePoints2, Mat& cameraMatrix1, Mat& distCoeffs1, Mat& cameraMatrix2, Mat& distCoeffs2, Size imageSize, Mat& R, Mat& T, Mat& E, Mat& F, int flags = CALIB_FIX_INTRINSIC, TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 1e-6))
-    private static native double stereoCalibrate_0(long objectPoints_mat_nativeObj, long imagePoints1_mat_nativeObj, long imagePoints2_mat_nativeObj, long cameraMatrix1_nativeObj, long distCoeffs1_nativeObj, long cameraMatrix2_nativeObj, long distCoeffs2_nativeObj, double imageSize_width, double imageSize_height, long R_nativeObj, long T_nativeObj, long E_nativeObj, long F_nativeObj, int flags, int criteria_type, int criteria_maxCount, double criteria_epsilon);
-    private static native double stereoCalibrate_1(long objectPoints_mat_nativeObj, long imagePoints1_mat_nativeObj, long imagePoints2_mat_nativeObj, long cameraMatrix1_nativeObj, long distCoeffs1_nativeObj, long cameraMatrix2_nativeObj, long distCoeffs2_nativeObj, double imageSize_width, double imageSize_height, long R_nativeObj, long T_nativeObj, long E_nativeObj, long F_nativeObj, int flags);
-    private static native double stereoCalibrate_2(long objectPoints_mat_nativeObj, long imagePoints1_mat_nativeObj, long imagePoints2_mat_nativeObj, long cameraMatrix1_nativeObj, long distCoeffs1_nativeObj, long cameraMatrix2_nativeObj, long distCoeffs2_nativeObj, double imageSize_width, double imageSize_height, long R_nativeObj, long T_nativeObj, long E_nativeObj, long F_nativeObj);
-
-    // C++:  double calibrate(vector_Mat objectPoints, vector_Mat imagePoints, Size image_size, Mat& K, Mat& D, vector_Mat& rvecs, vector_Mat& tvecs, int flags = 0, TermCriteria criteria = TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 100, DBL_EPSILON))
-    private static native double calibrate_0(long objectPoints_mat_nativeObj, long imagePoints_mat_nativeObj, double image_size_width, double image_size_height, long K_nativeObj, long D_nativeObj, long rvecs_mat_nativeObj, long tvecs_mat_nativeObj, int flags, int criteria_type, int criteria_maxCount, double criteria_epsilon);
-    private static native double calibrate_1(long objectPoints_mat_nativeObj, long imagePoints_mat_nativeObj, double image_size_width, double image_size_height, long K_nativeObj, long D_nativeObj, long rvecs_mat_nativeObj, long tvecs_mat_nativeObj, int flags);
-    private static native double calibrate_2(long objectPoints_mat_nativeObj, long imagePoints_mat_nativeObj, double image_size_width, double image_size_height, long K_nativeObj, long D_nativeObj, long rvecs_mat_nativeObj, long tvecs_mat_nativeObj);
-
-    // C++:  double stereoCalibrate(vector_Mat objectPoints, vector_Mat imagePoints1, vector_Mat imagePoints2, Mat& K1, Mat& D1, Mat& K2, Mat& D2, Size imageSize, Mat& R, Mat& T, int flags = fisheye::CALIB_FIX_INTRINSIC, TermCriteria criteria = TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 100, DBL_EPSILON))
-    private static native double stereoCalibrate_3(long objectPoints_mat_nativeObj, long imagePoints1_mat_nativeObj, long imagePoints2_mat_nativeObj, long K1_nativeObj, long D1_nativeObj, long K2_nativeObj, long D2_nativeObj, double imageSize_width, double imageSize_height, long R_nativeObj, long T_nativeObj, int flags, int criteria_type, int criteria_maxCount, double criteria_epsilon);
-    private static native double stereoCalibrate_4(long objectPoints_mat_nativeObj, long imagePoints1_mat_nativeObj, long imagePoints2_mat_nativeObj, long K1_nativeObj, long D1_nativeObj, long K2_nativeObj, long D2_nativeObj, double imageSize_width, double imageSize_height, long R_nativeObj, long T_nativeObj, int flags);
-    private static native double stereoCalibrate_5(long objectPoints_mat_nativeObj, long imagePoints1_mat_nativeObj, long imagePoints2_mat_nativeObj, long K1_nativeObj, long D1_nativeObj, long K2_nativeObj, long D2_nativeObj, double imageSize_width, double imageSize_height, long R_nativeObj, long T_nativeObj);
-
-    // C++:  float rectify3Collinear(Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Mat cameraMatrix3, Mat distCoeffs3, vector_Mat imgpt1, vector_Mat imgpt3, Size imageSize, Mat R12, Mat T12, Mat R13, Mat T13, Mat& R1, Mat& R2, Mat& R3, Mat& P1, Mat& P2, Mat& P3, Mat& Q, double alpha, Size newImgSize, Rect* roi1, Rect* roi2, int flags)
-    private static native float rectify3Collinear_0(long cameraMatrix1_nativeObj, long distCoeffs1_nativeObj, long cameraMatrix2_nativeObj, long distCoeffs2_nativeObj, long cameraMatrix3_nativeObj, long distCoeffs3_nativeObj, long imgpt1_mat_nativeObj, long imgpt3_mat_nativeObj, double imageSize_width, double imageSize_height, long R12_nativeObj, long T12_nativeObj, long R13_nativeObj, long T13_nativeObj, long R1_nativeObj, long R2_nativeObj, long R3_nativeObj, long P1_nativeObj, long P2_nativeObj, long P3_nativeObj, long Q_nativeObj, double alpha, double newImgSize_width, double newImgSize_height, double[] roi1_out, double[] roi2_out, int flags);
-
-    // C++:  int decomposeHomographyMat(Mat H, Mat K, vector_Mat& rotations, vector_Mat& translations, vector_Mat& normals)
-    private static native int decomposeHomographyMat_0(long H_nativeObj, long K_nativeObj, long rotations_mat_nativeObj, long translations_mat_nativeObj, long normals_mat_nativeObj);
-
-    // C++:  int estimateAffine3D(Mat src, Mat dst, Mat& out, Mat& inliers, double ransacThreshold = 3, double confidence = 0.99)
-    private static native int estimateAffine3D_0(long src_nativeObj, long dst_nativeObj, long out_nativeObj, long inliers_nativeObj, double ransacThreshold, double confidence);
-    private static native int estimateAffine3D_1(long src_nativeObj, long dst_nativeObj, long out_nativeObj, long inliers_nativeObj);
-
-    // C++:  int recoverPose(Mat E, Mat points1, Mat points2, Mat& R, Mat& t, double focal = 1.0, Point2d pp = Point2d(0, 0), Mat& mask = Mat())
-    private static native int recoverPose_0(long E_nativeObj, long points1_nativeObj, long points2_nativeObj, long R_nativeObj, long t_nativeObj, double focal, double pp_x, double pp_y, long mask_nativeObj);
-    private static native int recoverPose_1(long E_nativeObj, long points1_nativeObj, long points2_nativeObj, long R_nativeObj, long t_nativeObj, double focal, double pp_x, double pp_y);
-    private static native int recoverPose_2(long E_nativeObj, long points1_nativeObj, long points2_nativeObj, long R_nativeObj, long t_nativeObj);
-
-    // C++:  int recoverPose(Mat E, Mat points1, Mat points2, Mat cameraMatrix, Mat& R, Mat& t, Mat& mask = Mat())
-    private static native int recoverPose_3(long E_nativeObj, long points1_nativeObj, long points2_nativeObj, long cameraMatrix_nativeObj, long R_nativeObj, long t_nativeObj, long mask_nativeObj);
-    private static native int recoverPose_4(long E_nativeObj, long points1_nativeObj, long points2_nativeObj, long cameraMatrix_nativeObj, long R_nativeObj, long t_nativeObj);
-
-    // C++:  int recoverPose(Mat E, Mat points1, Mat points2, Mat cameraMatrix, Mat& R, Mat& t, double distanceThresh, Mat& mask = Mat(), Mat& triangulatedPoints = Mat())
-    private static native int recoverPose_5(long E_nativeObj, long points1_nativeObj, long points2_nativeObj, long cameraMatrix_nativeObj, long R_nativeObj, long t_nativeObj, double distanceThresh, long mask_nativeObj, long triangulatedPoints_nativeObj);
-    private static native int recoverPose_6(long E_nativeObj, long points1_nativeObj, long points2_nativeObj, long cameraMatrix_nativeObj, long R_nativeObj, long t_nativeObj, double distanceThresh);
-
-    // C++:  int solveP3P(Mat objectPoints, Mat imagePoints, Mat cameraMatrix, Mat distCoeffs, vector_Mat& rvecs, vector_Mat& tvecs, int flags)
-    private static native int solveP3P_0(long objectPoints_nativeObj, long imagePoints_nativeObj, long cameraMatrix_nativeObj, long distCoeffs_nativeObj, long rvecs_mat_nativeObj, long tvecs_mat_nativeObj, int flags);
-
-    // C++:  void Rodrigues(Mat src, Mat& dst, Mat& jacobian = Mat())
-    private static native void Rodrigues_0(long src_nativeObj, long dst_nativeObj, long jacobian_nativeObj);
-    private static native void Rodrigues_1(long src_nativeObj, long dst_nativeObj);
-
-    // C++:  void calibrationMatrixValues(Mat cameraMatrix, Size imageSize, double apertureWidth, double apertureHeight, double& fovx, double& fovy, double& focalLength, Point2d& principalPoint, double& aspectRatio)
-    private static native void calibrationMatrixValues_0(long cameraMatrix_nativeObj, double imageSize_width, double imageSize_height, double apertureWidth, double apertureHeight, double[] fovx_out, double[] fovy_out, double[] focalLength_out, double[] principalPoint_out, double[] aspectRatio_out);
-
-    // C++:  void composeRT(Mat rvec1, Mat tvec1, Mat rvec2, Mat tvec2, Mat& rvec3, Mat& tvec3, Mat& dr3dr1 = Mat(), Mat& dr3dt1 = Mat(), Mat& dr3dr2 = Mat(), Mat& dr3dt2 = Mat(), Mat& dt3dr1 = Mat(), Mat& dt3dt1 = Mat(), Mat& dt3dr2 = Mat(), Mat& dt3dt2 = Mat())
-    private static native void composeRT_0(long rvec1_nativeObj, long tvec1_nativeObj, long rvec2_nativeObj, long tvec2_nativeObj, long rvec3_nativeObj, long tvec3_nativeObj, long dr3dr1_nativeObj, long dr3dt1_nativeObj, long dr3dr2_nativeObj, long dr3dt2_nativeObj, long dt3dr1_nativeObj, long dt3dt1_nativeObj, long dt3dr2_nativeObj, long dt3dt2_nativeObj);
-    private static native void composeRT_1(long rvec1_nativeObj, long tvec1_nativeObj, long rvec2_nativeObj, long tvec2_nativeObj, long rvec3_nativeObj, long tvec3_nativeObj);
-
-    // C++:  void computeCorrespondEpilines(Mat points, int whichImage, Mat F, Mat& lines)
-    private static native void computeCorrespondEpilines_0(long points_nativeObj, int whichImage, long F_nativeObj, long lines_nativeObj);
-
-    // C++:  void convertPointsFromHomogeneous(Mat src, Mat& dst)
-    private static native void convertPointsFromHomogeneous_0(long src_nativeObj, long dst_nativeObj);
-
-    // C++:  void convertPointsToHomogeneous(Mat src, Mat& dst)
-    private static native void convertPointsToHomogeneous_0(long src_nativeObj, long dst_nativeObj);
-
-    // C++:  void correctMatches(Mat F, Mat points1, Mat points2, Mat& newPoints1, Mat& newPoints2)
-    private static native void correctMatches_0(long F_nativeObj, long points1_nativeObj, long points2_nativeObj, long newPoints1_nativeObj, long newPoints2_nativeObj);
-
-    // C++:  void decomposeEssentialMat(Mat E, Mat& R1, Mat& R2, Mat& t)
-    private static native void decomposeEssentialMat_0(long E_nativeObj, long R1_nativeObj, long R2_nativeObj, long t_nativeObj);
-
-    // C++:  void decomposeProjectionMatrix(Mat projMatrix, Mat& cameraMatrix, Mat& rotMatrix, Mat& transVect, Mat& rotMatrixX = Mat(), Mat& rotMatrixY = Mat(), Mat& rotMatrixZ = Mat(), Mat& eulerAngles = Mat())
-    private static native void decomposeProjectionMatrix_0(long projMatrix_nativeObj, long cameraMatrix_nativeObj, long rotMatrix_nativeObj, long transVect_nativeObj, long rotMatrixX_nativeObj, long rotMatrixY_nativeObj, long rotMatrixZ_nativeObj, long eulerAngles_nativeObj);
-    private static native void decomposeProjectionMatrix_1(long projMatrix_nativeObj, long cameraMatrix_nativeObj, long rotMatrix_nativeObj, long transVect_nativeObj);
-
-    // C++:  void drawChessboardCorners(Mat& image, Size patternSize, vector_Point2f corners, bool patternWasFound)
-    private static native void drawChessboardCorners_0(long image_nativeObj, double patternSize_width, double patternSize_height, long corners_mat_nativeObj, boolean patternWasFound);
-
-    // C++:  void filterHomographyDecompByVisibleRefpoints(vector_Mat rotations, vector_Mat normals, Mat beforePoints, Mat afterPoints, Mat& possibleSolutions, Mat pointsMask = Mat())
-    private static native void filterHomographyDecompByVisibleRefpoints_0(long rotations_mat_nativeObj, long normals_mat_nativeObj, long beforePoints_nativeObj, long afterPoints_nativeObj, long possibleSolutions_nativeObj, long pointsMask_nativeObj);
-    private static native void filterHomographyDecompByVisibleRefpoints_1(long rotations_mat_nativeObj, long normals_mat_nativeObj, long beforePoints_nativeObj, long afterPoints_nativeObj, long possibleSolutions_nativeObj);
-
-    // C++:  void filterSpeckles(Mat& img, double newVal, int maxSpeckleSize, double maxDiff, Mat& buf = Mat())
-    private static native void filterSpeckles_0(long img_nativeObj, double newVal, int maxSpeckleSize, double maxDiff, long buf_nativeObj);
-    private static native void filterSpeckles_1(long img_nativeObj, double newVal, int maxSpeckleSize, double maxDiff);
-
-    // C++:  void matMulDeriv(Mat A, Mat B, Mat& dABdA, Mat& dABdB)
-    private static native void matMulDeriv_0(long A_nativeObj, long B_nativeObj, long dABdA_nativeObj, long dABdB_nativeObj);
-
-    // C++:  void projectPoints(vector_Point3f objectPoints, Mat rvec, Mat tvec, Mat cameraMatrix, vector_double distCoeffs, vector_Point2f& imagePoints, Mat& jacobian = Mat(), double aspectRatio = 0)
-    private static native void projectPoints_0(long objectPoints_mat_nativeObj, long rvec_nativeObj, long tvec_nativeObj, long cameraMatrix_nativeObj, long distCoeffs_mat_nativeObj, long imagePoints_mat_nativeObj, long jacobian_nativeObj, double aspectRatio);
-    private static native void projectPoints_1(long objectPoints_mat_nativeObj, long rvec_nativeObj, long tvec_nativeObj, long cameraMatrix_nativeObj, long distCoeffs_mat_nativeObj, long imagePoints_mat_nativeObj);
-
-    // C++:  void reprojectImageTo3D(Mat disparity, Mat& _3dImage, Mat Q, bool handleMissingValues = false, int ddepth = -1)
-    private static native void reprojectImageTo3D_0(long disparity_nativeObj, long _3dImage_nativeObj, long Q_nativeObj, boolean handleMissingValues, int ddepth);
-    private static native void reprojectImageTo3D_1(long disparity_nativeObj, long _3dImage_nativeObj, long Q_nativeObj, boolean handleMissingValues);
-    private static native void reprojectImageTo3D_2(long disparity_nativeObj, long _3dImage_nativeObj, long Q_nativeObj);
-
-    // C++:  void stereoRectify(Mat cameraMatrix1, Mat distCoeffs1, Mat cameraMatrix2, Mat distCoeffs2, Size imageSize, Mat R, Mat T, Mat& R1, Mat& R2, Mat& P1, Mat& P2, Mat& Q, int flags = CALIB_ZERO_DISPARITY, double alpha = -1, Size newImageSize = Size(), Rect* validPixROI1 = 0, Rect* validPixROI2 = 0)
-    private static native void stereoRectify_0(long cameraMatrix1_nativeObj, long distCoeffs1_nativeObj, long cameraMatrix2_nativeObj, long distCoeffs2_nativeObj, double imageSize_width, double imageSize_height, long R_nativeObj, long T_nativeObj, long R1_nativeObj, long R2_nativeObj, long P1_nativeObj, long P2_nativeObj, long Q_nativeObj, int flags, double alpha, double newImageSize_width, double newImageSize_height, double[] validPixROI1_out, double[] validPixROI2_out);
-    private static native void stereoRectify_1(long cameraMatrix1_nativeObj, long distCoeffs1_nativeObj, long cameraMatrix2_nativeObj, long distCoeffs2_nativeObj, double imageSize_width, double imageSize_height, long R_nativeObj, long T_nativeObj, long R1_nativeObj, long R2_nativeObj, long P1_nativeObj, long P2_nativeObj, long Q_nativeObj);
-
-    // C++:  void triangulatePoints(Mat projMatr1, Mat projMatr2, Mat projPoints1, Mat projPoints2, Mat& points4D)
-    private static native void triangulatePoints_0(long projMatr1_nativeObj, long projMatr2_nativeObj, long projPoints1_nativeObj, long projPoints2_nativeObj, long points4D_nativeObj);
-
-    // C++:  void validateDisparity(Mat& disparity, Mat cost, int minDisparity, int numberOfDisparities, int disp12MaxDisp = 1)
-    private static native void validateDisparity_0(long disparity_nativeObj, long cost_nativeObj, int minDisparity, int numberOfDisparities, int disp12MaxDisp);
-    private static native void validateDisparity_1(long disparity_nativeObj, long cost_nativeObj, int minDisparity, int numberOfDisparities);
-
-    // C++:  void distortPoints(Mat undistorted, Mat& distorted, Mat K, Mat D, double alpha = 0)
-    private static native void distortPoints_0(long undistorted_nativeObj, long distorted_nativeObj, long K_nativeObj, long D_nativeObj, double alpha);
-    private static native void distortPoints_1(long undistorted_nativeObj, long distorted_nativeObj, long K_nativeObj, long D_nativeObj);
-
-    // C++:  void estimateNewCameraMatrixForUndistortRectify(Mat K, Mat D, Size image_size, Mat R, Mat& P, double balance = 0.0, Size new_size = Size(), double fov_scale = 1.0)
-    private static native void estimateNewCameraMatrixForUndistortRectify_0(long K_nativeObj, long D_nativeObj, double image_size_width, double image_size_height, long R_nativeObj, long P_nativeObj, double balance, double new_size_width, double new_size_height, double fov_scale);
-    private static native void estimateNewCameraMatrixForUndistortRectify_1(long K_nativeObj, long D_nativeObj, double image_size_width, double image_size_height, long R_nativeObj, long P_nativeObj);
-
-    // C++:  void initUndistortRectifyMap(Mat K, Mat D, Mat R, Mat P, Size size, int m1type, Mat& map1, Mat& map2)
-    private static native void initUndistortRectifyMap_0(long K_nativeObj, long D_nativeObj, long R_nativeObj, long P_nativeObj, double size_width, double size_height, int m1type, long map1_nativeObj, long map2_nativeObj);
-
-    // C++:  void projectPoints(vector_Point3f objectPoints, vector_Point2f& imagePoints, Mat rvec, Mat tvec, Mat K, Mat D, double alpha = 0, Mat& jacobian = Mat())
-    private static native void projectPoints_2(long objectPoints_mat_nativeObj, long imagePoints_mat_nativeObj, long rvec_nativeObj, long tvec_nativeObj, long K_nativeObj, long D_nativeObj, double alpha, long jacobian_nativeObj);
-    private static native void projectPoints_3(long objectPoints_mat_nativeObj, long imagePoints_mat_nativeObj, long rvec_nativeObj, long tvec_nativeObj, long K_nativeObj, long D_nativeObj);
-
-    // C++:  void stereoRectify(Mat K1, Mat D1, Mat K2, Mat D2, Size imageSize, Mat R, Mat tvec, Mat& R1, Mat& R2, Mat& P1, Mat& P2, Mat& Q, int flags, Size newImageSize = Size(), double balance = 0.0, double fov_scale = 1.0)
-    private static native void stereoRectify_2(long K1_nativeObj, long D1_nativeObj, long K2_nativeObj, long D2_nativeObj, double imageSize_width, double imageSize_height, long R_nativeObj, long tvec_nativeObj, long R1_nativeObj, long R2_nativeObj, long P1_nativeObj, long P2_nativeObj, long Q_nativeObj, int flags, double newImageSize_width, double newImageSize_height, double balance, double fov_scale);
-    private static native void stereoRectify_3(long K1_nativeObj, long D1_nativeObj, long K2_nativeObj, long D2_nativeObj, double imageSize_width, double imageSize_height, long R_nativeObj, long tvec_nativeObj, long R1_nativeObj, long R2_nativeObj, long P1_nativeObj, long P2_nativeObj, long Q_nativeObj, int flags);
-
-    // C++:  void undistortImage(Mat distorted, Mat& undistorted, Mat K, Mat D, Mat Knew = cv::Mat(), Size new_size = Size())
-    private static native void undistortImage_0(long distorted_nativeObj, long undistorted_nativeObj, long K_nativeObj, long D_nativeObj, long Knew_nativeObj, double new_size_width, double new_size_height);
-    private static native void undistortImage_1(long distorted_nativeObj, long undistorted_nativeObj, long K_nativeObj, long D_nativeObj);
-
-    // C++:  void undistortPoints(Mat distorted, Mat& undistorted, Mat K, Mat D, Mat R = Mat(), Mat P = Mat())
-    private static native void undistortPoints_0(long distorted_nativeObj, long undistorted_nativeObj, long K_nativeObj, long D_nativeObj, long R_nativeObj, long P_nativeObj);
-    private static native void undistortPoints_1(long distorted_nativeObj, long undistorted_nativeObj, long K_nativeObj, long D_nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/calib3d/StereoBM.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/calib3d/StereoBM.java
deleted file mode 100755
index 40eca81..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/calib3d/StereoBM.java
+++ /dev/null
@@ -1,334 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.calib3d;
-
-import org.opencv.calib3d.StereoBM;
-import org.opencv.calib3d.StereoMatcher;
-import org.opencv.core.Rect;
-
-// C++: class StereoBM
-//javadoc: StereoBM
-
-public class StereoBM extends StereoMatcher {
-
-    protected StereoBM(long addr) { super(addr); }
-
-    // internal usage only
-    public static StereoBM __fromPtr__(long addr) { return new StereoBM(addr); }
-
-    public static final int
-            PREFILTER_NORMALIZED_RESPONSE = 0,
-            PREFILTER_XSOBEL = 1;
-
-
-    //
-    // C++: static Ptr_StereoBM create(int numDisparities = 0, int blockSize = 21)
-    //
-
-    //javadoc: StereoBM::create(numDisparities, blockSize)
-    public static StereoBM create(int numDisparities, int blockSize)
-    {
-        
-        StereoBM retVal = StereoBM.__fromPtr__(create_0(numDisparities, blockSize));
-        
-        return retVal;
-    }
-
-    //javadoc: StereoBM::create()
-    public static StereoBM create()
-    {
-        
-        StereoBM retVal = StereoBM.__fromPtr__(create_1());
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Rect getROI1()
-    //
-
-    //javadoc: StereoBM::getROI1()
-    public  Rect getROI1()
-    {
-        
-        Rect retVal = new Rect(getROI1_0(nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Rect getROI2()
-    //
-
-    //javadoc: StereoBM::getROI2()
-    public  Rect getROI2()
-    {
-        
-        Rect retVal = new Rect(getROI2_0(nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getPreFilterCap()
-    //
-
-    //javadoc: StereoBM::getPreFilterCap()
-    public  int getPreFilterCap()
-    {
-        
-        int retVal = getPreFilterCap_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getPreFilterSize()
-    //
-
-    //javadoc: StereoBM::getPreFilterSize()
-    public  int getPreFilterSize()
-    {
-        
-        int retVal = getPreFilterSize_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getPreFilterType()
-    //
-
-    //javadoc: StereoBM::getPreFilterType()
-    public  int getPreFilterType()
-    {
-        
-        int retVal = getPreFilterType_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getSmallerBlockSize()
-    //
-
-    //javadoc: StereoBM::getSmallerBlockSize()
-    public  int getSmallerBlockSize()
-    {
-        
-        int retVal = getSmallerBlockSize_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getTextureThreshold()
-    //
-
-    //javadoc: StereoBM::getTextureThreshold()
-    public  int getTextureThreshold()
-    {
-        
-        int retVal = getTextureThreshold_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getUniquenessRatio()
-    //
-
-    //javadoc: StereoBM::getUniquenessRatio()
-    public  int getUniquenessRatio()
-    {
-        
-        int retVal = getUniquenessRatio_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  void setPreFilterCap(int preFilterCap)
-    //
-
-    //javadoc: StereoBM::setPreFilterCap(preFilterCap)
-    public  void setPreFilterCap(int preFilterCap)
-    {
-        
-        setPreFilterCap_0(nativeObj, preFilterCap);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setPreFilterSize(int preFilterSize)
-    //
-
-    //javadoc: StereoBM::setPreFilterSize(preFilterSize)
-    public  void setPreFilterSize(int preFilterSize)
-    {
-        
-        setPreFilterSize_0(nativeObj, preFilterSize);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setPreFilterType(int preFilterType)
-    //
-
-    //javadoc: StereoBM::setPreFilterType(preFilterType)
-    public  void setPreFilterType(int preFilterType)
-    {
-        
-        setPreFilterType_0(nativeObj, preFilterType);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setROI1(Rect roi1)
-    //
-
-    //javadoc: StereoBM::setROI1(roi1)
-    public  void setROI1(Rect roi1)
-    {
-        
-        setROI1_0(nativeObj, roi1.x, roi1.y, roi1.width, roi1.height);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setROI2(Rect roi2)
-    //
-
-    //javadoc: StereoBM::setROI2(roi2)
-    public  void setROI2(Rect roi2)
-    {
-        
-        setROI2_0(nativeObj, roi2.x, roi2.y, roi2.width, roi2.height);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setSmallerBlockSize(int blockSize)
-    //
-
-    //javadoc: StereoBM::setSmallerBlockSize(blockSize)
-    public  void setSmallerBlockSize(int blockSize)
-    {
-        
-        setSmallerBlockSize_0(nativeObj, blockSize);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setTextureThreshold(int textureThreshold)
-    //
-
-    //javadoc: StereoBM::setTextureThreshold(textureThreshold)
-    public  void setTextureThreshold(int textureThreshold)
-    {
-        
-        setTextureThreshold_0(nativeObj, textureThreshold);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setUniquenessRatio(int uniquenessRatio)
-    //
-
-    //javadoc: StereoBM::setUniquenessRatio(uniquenessRatio)
-    public  void setUniquenessRatio(int uniquenessRatio)
-    {
-        
-        setUniquenessRatio_0(nativeObj, uniquenessRatio);
-        
-        return;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++: static Ptr_StereoBM create(int numDisparities = 0, int blockSize = 21)
-    private static native long create_0(int numDisparities, int blockSize);
-    private static native long create_1();
-
-    // C++:  Rect getROI1()
-    private static native double[] getROI1_0(long nativeObj);
-
-    // C++:  Rect getROI2()
-    private static native double[] getROI2_0(long nativeObj);
-
-    // C++:  int getPreFilterCap()
-    private static native int getPreFilterCap_0(long nativeObj);
-
-    // C++:  int getPreFilterSize()
-    private static native int getPreFilterSize_0(long nativeObj);
-
-    // C++:  int getPreFilterType()
-    private static native int getPreFilterType_0(long nativeObj);
-
-    // C++:  int getSmallerBlockSize()
-    private static native int getSmallerBlockSize_0(long nativeObj);
-
-    // C++:  int getTextureThreshold()
-    private static native int getTextureThreshold_0(long nativeObj);
-
-    // C++:  int getUniquenessRatio()
-    private static native int getUniquenessRatio_0(long nativeObj);
-
-    // C++:  void setPreFilterCap(int preFilterCap)
-    private static native void setPreFilterCap_0(long nativeObj, int preFilterCap);
-
-    // C++:  void setPreFilterSize(int preFilterSize)
-    private static native void setPreFilterSize_0(long nativeObj, int preFilterSize);
-
-    // C++:  void setPreFilterType(int preFilterType)
-    private static native void setPreFilterType_0(long nativeObj, int preFilterType);
-
-    // C++:  void setROI1(Rect roi1)
-    private static native void setROI1_0(long nativeObj, int roi1_x, int roi1_y, int roi1_width, int roi1_height);
-
-    // C++:  void setROI2(Rect roi2)
-    private static native void setROI2_0(long nativeObj, int roi2_x, int roi2_y, int roi2_width, int roi2_height);
-
-    // C++:  void setSmallerBlockSize(int blockSize)
-    private static native void setSmallerBlockSize_0(long nativeObj, int blockSize);
-
-    // C++:  void setTextureThreshold(int textureThreshold)
-    private static native void setTextureThreshold_0(long nativeObj, int textureThreshold);
-
-    // C++:  void setUniquenessRatio(int uniquenessRatio)
-    private static native void setUniquenessRatio_0(long nativeObj, int uniquenessRatio);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/calib3d/StereoMatcher.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/calib3d/StereoMatcher.java
deleted file mode 100755
index 7f71f00..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/calib3d/StereoMatcher.java
+++ /dev/null
@@ -1,255 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.calib3d;
-
-import org.opencv.core.Algorithm;
-import org.opencv.core.Mat;
-
-// C++: class StereoMatcher
-//javadoc: StereoMatcher
-
-public class StereoMatcher extends Algorithm {
-
-    protected StereoMatcher(long addr) { super(addr); }
-
-    // internal usage only
-    public static StereoMatcher __fromPtr__(long addr) { return new StereoMatcher(addr); }
-
-    public static final int
-            DISP_SHIFT = 4,
-            DISP_SCALE = (1 << DISP_SHIFT);
-
-
-    //
-    // C++:  int getBlockSize()
-    //
-
-    //javadoc: StereoMatcher::getBlockSize()
-    public  int getBlockSize()
-    {
-        
-        int retVal = getBlockSize_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getDisp12MaxDiff()
-    //
-
-    //javadoc: StereoMatcher::getDisp12MaxDiff()
-    public  int getDisp12MaxDiff()
-    {
-        
-        int retVal = getDisp12MaxDiff_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getMinDisparity()
-    //
-
-    //javadoc: StereoMatcher::getMinDisparity()
-    public  int getMinDisparity()
-    {
-        
-        int retVal = getMinDisparity_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getNumDisparities()
-    //
-
-    //javadoc: StereoMatcher::getNumDisparities()
-    public  int getNumDisparities()
-    {
-        
-        int retVal = getNumDisparities_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getSpeckleRange()
-    //
-
-    //javadoc: StereoMatcher::getSpeckleRange()
-    public  int getSpeckleRange()
-    {
-        
-        int retVal = getSpeckleRange_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getSpeckleWindowSize()
-    //
-
-    //javadoc: StereoMatcher::getSpeckleWindowSize()
-    public  int getSpeckleWindowSize()
-    {
-        
-        int retVal = getSpeckleWindowSize_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  void compute(Mat left, Mat right, Mat& disparity)
-    //
-
-    //javadoc: StereoMatcher::compute(left, right, disparity)
-    public  void compute(Mat left, Mat right, Mat disparity)
-    {
-        
-        compute_0(nativeObj, left.nativeObj, right.nativeObj, disparity.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setBlockSize(int blockSize)
-    //
-
-    //javadoc: StereoMatcher::setBlockSize(blockSize)
-    public  void setBlockSize(int blockSize)
-    {
-        
-        setBlockSize_0(nativeObj, blockSize);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setDisp12MaxDiff(int disp12MaxDiff)
-    //
-
-    //javadoc: StereoMatcher::setDisp12MaxDiff(disp12MaxDiff)
-    public  void setDisp12MaxDiff(int disp12MaxDiff)
-    {
-        
-        setDisp12MaxDiff_0(nativeObj, disp12MaxDiff);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setMinDisparity(int minDisparity)
-    //
-
-    //javadoc: StereoMatcher::setMinDisparity(minDisparity)
-    public  void setMinDisparity(int minDisparity)
-    {
-        
-        setMinDisparity_0(nativeObj, minDisparity);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setNumDisparities(int numDisparities)
-    //
-
-    //javadoc: StereoMatcher::setNumDisparities(numDisparities)
-    public  void setNumDisparities(int numDisparities)
-    {
-        
-        setNumDisparities_0(nativeObj, numDisparities);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setSpeckleRange(int speckleRange)
-    //
-
-    //javadoc: StereoMatcher::setSpeckleRange(speckleRange)
-    public  void setSpeckleRange(int speckleRange)
-    {
-        
-        setSpeckleRange_0(nativeObj, speckleRange);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setSpeckleWindowSize(int speckleWindowSize)
-    //
-
-    //javadoc: StereoMatcher::setSpeckleWindowSize(speckleWindowSize)
-    public  void setSpeckleWindowSize(int speckleWindowSize)
-    {
-        
-        setSpeckleWindowSize_0(nativeObj, speckleWindowSize);
-        
-        return;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++:  int getBlockSize()
-    private static native int getBlockSize_0(long nativeObj);
-
-    // C++:  int getDisp12MaxDiff()
-    private static native int getDisp12MaxDiff_0(long nativeObj);
-
-    // C++:  int getMinDisparity()
-    private static native int getMinDisparity_0(long nativeObj);
-
-    // C++:  int getNumDisparities()
-    private static native int getNumDisparities_0(long nativeObj);
-
-    // C++:  int getSpeckleRange()
-    private static native int getSpeckleRange_0(long nativeObj);
-
-    // C++:  int getSpeckleWindowSize()
-    private static native int getSpeckleWindowSize_0(long nativeObj);
-
-    // C++:  void compute(Mat left, Mat right, Mat& disparity)
-    private static native void compute_0(long nativeObj, long left_nativeObj, long right_nativeObj, long disparity_nativeObj);
-
-    // C++:  void setBlockSize(int blockSize)
-    private static native void setBlockSize_0(long nativeObj, int blockSize);
-
-    // C++:  void setDisp12MaxDiff(int disp12MaxDiff)
-    private static native void setDisp12MaxDiff_0(long nativeObj, int disp12MaxDiff);
-
-    // C++:  void setMinDisparity(int minDisparity)
-    private static native void setMinDisparity_0(long nativeObj, int minDisparity);
-
-    // C++:  void setNumDisparities(int numDisparities)
-    private static native void setNumDisparities_0(long nativeObj, int numDisparities);
-
-    // C++:  void setSpeckleRange(int speckleRange)
-    private static native void setSpeckleRange_0(long nativeObj, int speckleRange);
-
-    // C++:  void setSpeckleWindowSize(int speckleWindowSize)
-    private static native void setSpeckleWindowSize_0(long nativeObj, int speckleWindowSize);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/calib3d/StereoSGBM.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/calib3d/StereoSGBM.java
deleted file mode 100755
index 9a35569..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/calib3d/StereoSGBM.java
+++ /dev/null
@@ -1,233 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.calib3d;
-
-import org.opencv.calib3d.StereoMatcher;
-import org.opencv.calib3d.StereoSGBM;
-
-// C++: class StereoSGBM
-//javadoc: StereoSGBM
-
-public class StereoSGBM extends StereoMatcher {
-
-    protected StereoSGBM(long addr) { super(addr); }
-
-    // internal usage only
-    public static StereoSGBM __fromPtr__(long addr) { return new StereoSGBM(addr); }
-
-    public static final int
-            MODE_SGBM = 0,
-            MODE_HH = 1,
-            MODE_SGBM_3WAY = 2,
-            MODE_HH4 = 3;
-
-
-    //
-    // C++: static Ptr_StereoSGBM create(int minDisparity = 0, int numDisparities = 16, int blockSize = 3, int P1 = 0, int P2 = 0, int disp12MaxDiff = 0, int preFilterCap = 0, int uniquenessRatio = 0, int speckleWindowSize = 0, int speckleRange = 0, int mode = StereoSGBM::MODE_SGBM)
-    //
-
-    //javadoc: StereoSGBM::create(minDisparity, numDisparities, blockSize, P1, P2, disp12MaxDiff, preFilterCap, uniquenessRatio, speckleWindowSize, speckleRange, mode)
-    public static StereoSGBM create(int minDisparity, int numDisparities, int blockSize, int P1, int P2, int disp12MaxDiff, int preFilterCap, int uniquenessRatio, int speckleWindowSize, int speckleRange, int mode)
-    {
-        
-        StereoSGBM retVal = StereoSGBM.__fromPtr__(create_0(minDisparity, numDisparities, blockSize, P1, P2, disp12MaxDiff, preFilterCap, uniquenessRatio, speckleWindowSize, speckleRange, mode));
-        
-        return retVal;
-    }
-
-    //javadoc: StereoSGBM::create()
-    public static StereoSGBM create()
-    {
-        
-        StereoSGBM retVal = StereoSGBM.__fromPtr__(create_1());
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getMode()
-    //
-
-    //javadoc: StereoSGBM::getMode()
-    public  int getMode()
-    {
-        
-        int retVal = getMode_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getP1()
-    //
-
-    //javadoc: StereoSGBM::getP1()
-    public  int getP1()
-    {
-        
-        int retVal = getP1_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getP2()
-    //
-
-    //javadoc: StereoSGBM::getP2()
-    public  int getP2()
-    {
-        
-        int retVal = getP2_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getPreFilterCap()
-    //
-
-    //javadoc: StereoSGBM::getPreFilterCap()
-    public  int getPreFilterCap()
-    {
-        
-        int retVal = getPreFilterCap_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getUniquenessRatio()
-    //
-
-    //javadoc: StereoSGBM::getUniquenessRatio()
-    public  int getUniquenessRatio()
-    {
-        
-        int retVal = getUniquenessRatio_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  void setMode(int mode)
-    //
-
-    //javadoc: StereoSGBM::setMode(mode)
-    public  void setMode(int mode)
-    {
-        
-        setMode_0(nativeObj, mode);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setP1(int P1)
-    //
-
-    //javadoc: StereoSGBM::setP1(P1)
-    public  void setP1(int P1)
-    {
-        
-        setP1_0(nativeObj, P1);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setP2(int P2)
-    //
-
-    //javadoc: StereoSGBM::setP2(P2)
-    public  void setP2(int P2)
-    {
-        
-        setP2_0(nativeObj, P2);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setPreFilterCap(int preFilterCap)
-    //
-
-    //javadoc: StereoSGBM::setPreFilterCap(preFilterCap)
-    public  void setPreFilterCap(int preFilterCap)
-    {
-        
-        setPreFilterCap_0(nativeObj, preFilterCap);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setUniquenessRatio(int uniquenessRatio)
-    //
-
-    //javadoc: StereoSGBM::setUniquenessRatio(uniquenessRatio)
-    public  void setUniquenessRatio(int uniquenessRatio)
-    {
-        
-        setUniquenessRatio_0(nativeObj, uniquenessRatio);
-        
-        return;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++: static Ptr_StereoSGBM create(int minDisparity = 0, int numDisparities = 16, int blockSize = 3, int P1 = 0, int P2 = 0, int disp12MaxDiff = 0, int preFilterCap = 0, int uniquenessRatio = 0, int speckleWindowSize = 0, int speckleRange = 0, int mode = StereoSGBM::MODE_SGBM)
-    private static native long create_0(int minDisparity, int numDisparities, int blockSize, int P1, int P2, int disp12MaxDiff, int preFilterCap, int uniquenessRatio, int speckleWindowSize, int speckleRange, int mode);
-    private static native long create_1();
-
-    // C++:  int getMode()
-    private static native int getMode_0(long nativeObj);
-
-    // C++:  int getP1()
-    private static native int getP1_0(long nativeObj);
-
-    // C++:  int getP2()
-    private static native int getP2_0(long nativeObj);
-
-    // C++:  int getPreFilterCap()
-    private static native int getPreFilterCap_0(long nativeObj);
-
-    // C++:  int getUniquenessRatio()
-    private static native int getUniquenessRatio_0(long nativeObj);
-
-    // C++:  void setMode(int mode)
-    private static native void setMode_0(long nativeObj, int mode);
-
-    // C++:  void setP1(int P1)
-    private static native void setP1_0(long nativeObj, int P1);
-
-    // C++:  void setP2(int P2)
-    private static native void setP2_0(long nativeObj, int P2);
-
-    // C++:  void setPreFilterCap(int preFilterCap)
-    private static native void setPreFilterCap_0(long nativeObj, int preFilterCap);
-
-    // C++:  void setUniquenessRatio(int uniquenessRatio)
-    private static native void setUniquenessRatio_0(long nativeObj, int uniquenessRatio);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/Algorithm.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/Algorithm.java
deleted file mode 100755
index 3e3cdd3..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/Algorithm.java
+++ /dev/null
@@ -1,113 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.core;
-
-import java.lang.String;
-
-// C++: class Algorithm
-//javadoc: Algorithm
-
-public class Algorithm {
-
-    protected final long nativeObj;
-    protected Algorithm(long addr) { nativeObj = addr; }
-
-    public long getNativeObjAddr() { return nativeObj; }
-
-    // internal usage only
-    public static Algorithm __fromPtr__(long addr) { return new Algorithm(addr); }
-
-    //
-    // C++:  String getDefaultName()
-    //
-
-    //javadoc: Algorithm::getDefaultName()
-    public  String getDefaultName()
-    {
-        
-        String retVal = getDefaultName_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  bool empty()
-    //
-
-    //javadoc: Algorithm::empty()
-    public  boolean empty()
-    {
-        
-        boolean retVal = empty_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  void clear()
-    //
-
-    //javadoc: Algorithm::clear()
-    public  void clear()
-    {
-        
-        clear_0(nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void read(FileNode fn)
-    //
-
-    // Unknown type 'FileNode' (I), skipping the function
-
-
-    //
-    // C++:  void save(String filename)
-    //
-
-    //javadoc: Algorithm::save(filename)
-    public  void save(String filename)
-    {
-        
-        save_0(nativeObj, filename);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void write(Ptr_FileStorage fs, String name = String())
-    //
-
-    // Unknown type 'Ptr_FileStorage' (I), skipping the function
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++:  String getDefaultName()
-    private static native String getDefaultName_0(long nativeObj);
-
-    // C++:  bool empty()
-    private static native boolean empty_0(long nativeObj);
-
-    // C++:  void clear()
-    private static native void clear_0(long nativeObj);
-
-    // C++:  void save(String filename)
-    private static native void save_0(long nativeObj, String filename);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/Core.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/Core.java
deleted file mode 100755
index 8f5f4ce..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/Core.java
+++ /dev/null
@@ -1,2829 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.core;
-
-import java.lang.String;
-import java.util.ArrayList;
-import java.util.List;
-import org.opencv.core.Mat;
-import org.opencv.core.MatOfDouble;
-import org.opencv.core.MatOfInt;
-import org.opencv.core.Scalar;
-import org.opencv.core.TermCriteria;
-import org.opencv.utils.Converters;
-
-// C++: class Core
-//javadoc: Core
-
-public class Core {
-    // these constants are wrapped inside functions to prevent inlining
-    private static String getVersion() { return "3.4.2"; }
-    private static String getNativeLibraryName() { return "opencv_java342"; }
-    private static int getVersionMajorJ() { return 3; }
-    private static int getVersionMinorJ() { return 4; }
-    private static int getVersionRevisionJ() { return 2; }
-    private static String getVersionStatusJ() { return ""; }
-
-    public static final String VERSION = getVersion();
-    public static final String NATIVE_LIBRARY_NAME = getNativeLibraryName();
-    public static final int VERSION_MAJOR = getVersionMajorJ();
-    public static final int VERSION_MINOR = getVersionMinorJ();
-    public static final int VERSION_REVISION = getVersionRevisionJ();
-    public static final String VERSION_STATUS = getVersionStatusJ();
-
-    private static final int
-            CV_8U = 0,
-            CV_8S = 1,
-            CV_16U = 2,
-            CV_16S = 3,
-            CV_32S = 4,
-            CV_32F = 5,
-            CV_64F = 6,
-            CV_USRTYPE1 = 7;
-
-
-    public static final int
-            SVD_MODIFY_A = 1,
-            SVD_NO_UV = 2,
-            SVD_FULL_UV = 4,
-            FILLED = -1,
-            REDUCE_SUM = 0,
-            REDUCE_AVG = 1,
-            REDUCE_MAX = 2,
-            REDUCE_MIN = 3,
-            StsOk = 0,
-            StsBackTrace = -1,
-            StsError = -2,
-            StsInternal = -3,
-            StsNoMem = -4,
-            StsBadArg = -5,
-            StsBadFunc = -6,
-            StsNoConv = -7,
-            StsAutoTrace = -8,
-            HeaderIsNull = -9,
-            BadImageSize = -10,
-            BadOffset = -11,
-            BadDataPtr = -12,
-            BadStep = -13,
-            BadModelOrChSeq = -14,
-            BadNumChannels = -15,
-            BadNumChannel1U = -16,
-            BadDepth = -17,
-            BadAlphaChannel = -18,
-            BadOrder = -19,
-            BadOrigin = -20,
-            BadAlign = -21,
-            BadCallBack = -22,
-            BadTileSize = -23,
-            BadCOI = -24,
-            BadROISize = -25,
-            MaskIsTiled = -26,
-            StsNullPtr = -27,
-            StsVecLengthErr = -28,
-            StsFilterStructContentErr = -29,
-            StsKernelStructContentErr = -30,
-            StsFilterOffsetErr = -31,
-            StsBadSize = -201,
-            StsDivByZero = -202,
-            StsInplaceNotSupported = -203,
-            StsObjectNotFound = -204,
-            StsUnmatchedFormats = -205,
-            StsBadFlag = -206,
-            StsBadPoint = -207,
-            StsBadMask = -208,
-            StsUnmatchedSizes = -209,
-            StsUnsupportedFormat = -210,
-            StsOutOfRange = -211,
-            StsParseError = -212,
-            StsNotImplemented = -213,
-            StsBadMemBlock = -214,
-            StsAssert = -215,
-            GpuNotSupported = -216,
-            GpuApiCallError = -217,
-            OpenGlNotSupported = -218,
-            OpenGlApiCallError = -219,
-            OpenCLApiCallError = -220,
-            OpenCLDoubleNotSupported = -221,
-            OpenCLInitError = -222,
-            OpenCLNoAMDBlasFft = -223,
-            DECOMP_LU = 0,
-            DECOMP_SVD = 1,
-            DECOMP_EIG = 2,
-            DECOMP_CHOLESKY = 3,
-            DECOMP_QR = 4,
-            DECOMP_NORMAL = 16,
-            NORM_INF = 1,
-            NORM_L1 = 2,
-            NORM_L2 = 4,
-            NORM_L2SQR = 5,
-            NORM_HAMMING = 6,
-            NORM_HAMMING2 = 7,
-            NORM_TYPE_MASK = 7,
-            NORM_RELATIVE = 8,
-            NORM_MINMAX = 32,
-            CMP_EQ = 0,
-            CMP_GT = 1,
-            CMP_GE = 2,
-            CMP_LT = 3,
-            CMP_LE = 4,
-            CMP_NE = 5,
-            GEMM_1_T = 1,
-            GEMM_2_T = 2,
-            GEMM_3_T = 4,
-            DFT_INVERSE = 1,
-            DFT_SCALE = 2,
-            DFT_ROWS = 4,
-            DFT_COMPLEX_OUTPUT = 16,
-            DFT_REAL_OUTPUT = 32,
-            DFT_COMPLEX_INPUT = 64,
-            DCT_INVERSE = DFT_INVERSE,
-            DCT_ROWS = DFT_ROWS,
-            BORDER_CONSTANT = 0,
-            BORDER_REPLICATE = 1,
-            BORDER_REFLECT = 2,
-            BORDER_WRAP = 3,
-            BORDER_REFLECT_101 = 4,
-            BORDER_TRANSPARENT = 5,
-            BORDER_REFLECT101 = BORDER_REFLECT_101,
-            BORDER_DEFAULT = BORDER_REFLECT_101,
-            BORDER_ISOLATED = 16,
-            SORT_EVERY_ROW = 0,
-            SORT_EVERY_COLUMN = 1,
-            SORT_ASCENDING = 0,
-            SORT_DESCENDING = 16,
-            COVAR_SCRAMBLED = 0,
-            COVAR_NORMAL = 1,
-            COVAR_USE_AVG = 2,
-            COVAR_SCALE = 4,
-            COVAR_ROWS = 8,
-            COVAR_COLS = 16,
-            KMEANS_RANDOM_CENTERS = 0,
-            KMEANS_PP_CENTERS = 2,
-            KMEANS_USE_INITIAL_LABELS = 1,
-            LINE_4 = 4,
-            LINE_8 = 8,
-            LINE_AA = 16,
-            FONT_HERSHEY_SIMPLEX = 0,
-            FONT_HERSHEY_PLAIN = 1,
-            FONT_HERSHEY_DUPLEX = 2,
-            FONT_HERSHEY_COMPLEX = 3,
-            FONT_HERSHEY_TRIPLEX = 4,
-            FONT_HERSHEY_COMPLEX_SMALL = 5,
-            FONT_HERSHEY_SCRIPT_SIMPLEX = 6,
-            FONT_HERSHEY_SCRIPT_COMPLEX = 7,
-            FONT_ITALIC = 16,
-            ROTATE_90_CLOCKWISE = 0,
-            ROTATE_180 = 1,
-            ROTATE_90_COUNTERCLOCKWISE = 2,
-            TYPE_GENERAL = 0,
-            TYPE_MARKER = 0+1,
-            TYPE_WRAPPER = 0+2,
-            TYPE_FUN = 0+3,
-            IMPL_PLAIN = 0,
-            IMPL_IPP = 0+1,
-            IMPL_OPENCL = 0+2,
-            FLAGS_NONE = 0,
-            FLAGS_MAPPING = 0x01,
-            FLAGS_EXPAND_SAME_NAMES = 0x02;
-
-
-    //
-    // C++:  Scalar mean(Mat src, Mat mask = Mat())
-    //
-
-    //javadoc: mean(src, mask)
-    public static Scalar mean(Mat src, Mat mask)
-    {
-        
-        Scalar retVal = new Scalar(mean_0(src.nativeObj, mask.nativeObj));
-        
-        return retVal;
-    }
-
-    //javadoc: mean(src)
-    public static Scalar mean(Mat src)
-    {
-        
-        Scalar retVal = new Scalar(mean_1(src.nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Scalar sum(Mat src)
-    //
-
-    //javadoc: sum(src)
-    public static Scalar sumElems(Mat src)
-    {
-        
-        Scalar retVal = new Scalar(sumElems_0(src.nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Scalar trace(Mat mtx)
-    //
-
-    //javadoc: trace(mtx)
-    public static Scalar trace(Mat mtx)
-    {
-        
-        Scalar retVal = new Scalar(trace_0(mtx.nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  String getBuildInformation()
-    //
-
-    //javadoc: getBuildInformation()
-    public static String getBuildInformation()
-    {
-        
-        String retVal = getBuildInformation_0();
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  String getHardwareFeatureName(int feature)
-    //
-
-    //javadoc: getHardwareFeatureName(feature)
-    public static String getHardwareFeatureName(int feature)
-    {
-        
-        String retVal = getHardwareFeatureName_0(feature);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  String getVersionString()
-    //
-
-    //javadoc: getVersionString()
-    public static String getVersionString()
-    {
-        
-        String retVal = getVersionString_0();
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  String getIppVersion()
-    //
-
-    //javadoc: getIppVersion()
-    public static String getIppVersion()
-    {
-        
-        String retVal = getIppVersion_0();
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  bool checkRange(Mat a, bool quiet = true,  _hidden_ * pos = 0, double minVal = -DBL_MAX, double maxVal = DBL_MAX)
-    //
-
-    //javadoc: checkRange(a, quiet, minVal, maxVal)
-    public static boolean checkRange(Mat a, boolean quiet, double minVal, double maxVal)
-    {
-        
-        boolean retVal = checkRange_0(a.nativeObj, quiet, minVal, maxVal);
-        
-        return retVal;
-    }
-
-    //javadoc: checkRange(a)
-    public static boolean checkRange(Mat a)
-    {
-        
-        boolean retVal = checkRange_1(a.nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  bool eigen(Mat src, Mat& eigenvalues, Mat& eigenvectors = Mat())
-    //
-
-    //javadoc: eigen(src, eigenvalues, eigenvectors)
-    public static boolean eigen(Mat src, Mat eigenvalues, Mat eigenvectors)
-    {
-        
-        boolean retVal = eigen_0(src.nativeObj, eigenvalues.nativeObj, eigenvectors.nativeObj);
-        
-        return retVal;
-    }
-
-    //javadoc: eigen(src, eigenvalues)
-    public static boolean eigen(Mat src, Mat eigenvalues)
-    {
-        
-        boolean retVal = eigen_1(src.nativeObj, eigenvalues.nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  bool solve(Mat src1, Mat src2, Mat& dst, int flags = DECOMP_LU)
-    //
-
-    //javadoc: solve(src1, src2, dst, flags)
-    public static boolean solve(Mat src1, Mat src2, Mat dst, int flags)
-    {
-        
-        boolean retVal = solve_0(src1.nativeObj, src2.nativeObj, dst.nativeObj, flags);
-        
-        return retVal;
-    }
-
-    //javadoc: solve(src1, src2, dst)
-    public static boolean solve(Mat src1, Mat src2, Mat dst)
-    {
-        
-        boolean retVal = solve_1(src1.nativeObj, src2.nativeObj, dst.nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  bool useIPP()
-    //
-
-    //javadoc: useIPP()
-    public static boolean useIPP()
-    {
-        
-        boolean retVal = useIPP_0();
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  bool useIPP_NE()
-    //
-
-    //javadoc: useIPP_NE()
-    public static boolean useIPP_NE()
-    {
-        
-        boolean retVal = useIPP_NE_0();
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double Mahalanobis(Mat v1, Mat v2, Mat icovar)
-    //
-
-    //javadoc: Mahalanobis(v1, v2, icovar)
-    public static double Mahalanobis(Mat v1, Mat v2, Mat icovar)
-    {
-        
-        double retVal = Mahalanobis_0(v1.nativeObj, v2.nativeObj, icovar.nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double PSNR(Mat src1, Mat src2)
-    //
-
-    //javadoc: PSNR(src1, src2)
-    public static double PSNR(Mat src1, Mat src2)
-    {
-        
-        double retVal = PSNR_0(src1.nativeObj, src2.nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double determinant(Mat mtx)
-    //
-
-    //javadoc: determinant(mtx)
-    public static double determinant(Mat mtx)
-    {
-        
-        double retVal = determinant_0(mtx.nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double getTickFrequency()
-    //
-
-    //javadoc: getTickFrequency()
-    public static double getTickFrequency()
-    {
-        
-        double retVal = getTickFrequency_0();
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double invert(Mat src, Mat& dst, int flags = DECOMP_LU)
-    //
-
-    //javadoc: invert(src, dst, flags)
-    public static double invert(Mat src, Mat dst, int flags)
-    {
-        
-        double retVal = invert_0(src.nativeObj, dst.nativeObj, flags);
-        
-        return retVal;
-    }
-
-    //javadoc: invert(src, dst)
-    public static double invert(Mat src, Mat dst)
-    {
-        
-        double retVal = invert_1(src.nativeObj, dst.nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double kmeans(Mat data, int K, Mat& bestLabels, TermCriteria criteria, int attempts, int flags, Mat& centers = Mat())
-    //
-
-    //javadoc: kmeans(data, K, bestLabels, criteria, attempts, flags, centers)
-    public static double kmeans(Mat data, int K, Mat bestLabels, TermCriteria criteria, int attempts, int flags, Mat centers)
-    {
-        
-        double retVal = kmeans_0(data.nativeObj, K, bestLabels.nativeObj, criteria.type, criteria.maxCount, criteria.epsilon, attempts, flags, centers.nativeObj);
-        
-        return retVal;
-    }
-
-    //javadoc: kmeans(data, K, bestLabels, criteria, attempts, flags)
-    public static double kmeans(Mat data, int K, Mat bestLabels, TermCriteria criteria, int attempts, int flags)
-    {
-        
-        double retVal = kmeans_1(data.nativeObj, K, bestLabels.nativeObj, criteria.type, criteria.maxCount, criteria.epsilon, attempts, flags);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double norm(Mat src1, Mat src2, int normType = NORM_L2, Mat mask = Mat())
-    //
-
-    //javadoc: norm(src1, src2, normType, mask)
-    public static double norm(Mat src1, Mat src2, int normType, Mat mask)
-    {
-        
-        double retVal = norm_0(src1.nativeObj, src2.nativeObj, normType, mask.nativeObj);
-        
-        return retVal;
-    }
-
-    //javadoc: norm(src1, src2, normType)
-    public static double norm(Mat src1, Mat src2, int normType)
-    {
-        
-        double retVal = norm_1(src1.nativeObj, src2.nativeObj, normType);
-        
-        return retVal;
-    }
-
-    //javadoc: norm(src1, src2)
-    public static double norm(Mat src1, Mat src2)
-    {
-        
-        double retVal = norm_2(src1.nativeObj, src2.nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double norm(Mat src1, int normType = NORM_L2, Mat mask = Mat())
-    //
-
-    //javadoc: norm(src1, normType, mask)
-    public static double norm(Mat src1, int normType, Mat mask)
-    {
-        
-        double retVal = norm_3(src1.nativeObj, normType, mask.nativeObj);
-        
-        return retVal;
-    }
-
-    //javadoc: norm(src1, normType)
-    public static double norm(Mat src1, int normType)
-    {
-        
-        double retVal = norm_4(src1.nativeObj, normType);
-        
-        return retVal;
-    }
-
-    //javadoc: norm(src1)
-    public static double norm(Mat src1)
-    {
-        
-        double retVal = norm_5(src1.nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double solvePoly(Mat coeffs, Mat& roots, int maxIters = 300)
-    //
-
-    //javadoc: solvePoly(coeffs, roots, maxIters)
-    public static double solvePoly(Mat coeffs, Mat roots, int maxIters)
-    {
-        
-        double retVal = solvePoly_0(coeffs.nativeObj, roots.nativeObj, maxIters);
-        
-        return retVal;
-    }
-
-    //javadoc: solvePoly(coeffs, roots)
-    public static double solvePoly(Mat coeffs, Mat roots)
-    {
-        
-        double retVal = solvePoly_1(coeffs.nativeObj, roots.nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  float cubeRoot(float val)
-    //
-
-    //javadoc: cubeRoot(val)
-    public static float cubeRoot(float val)
-    {
-        
-        float retVal = cubeRoot_0(val);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  float fastAtan2(float y, float x)
-    //
-
-    //javadoc: fastAtan2(y, x)
-    public static float fastAtan2(float y, float x)
-    {
-        
-        float retVal = fastAtan2_0(y, x);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int borderInterpolate(int p, int len, int borderType)
-    //
-
-    //javadoc: borderInterpolate(p, len, borderType)
-    public static int borderInterpolate(int p, int len, int borderType)
-    {
-        
-        int retVal = borderInterpolate_0(p, len, borderType);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int countNonZero(Mat src)
-    //
-
-    //javadoc: countNonZero(src)
-    public static int countNonZero(Mat src)
-    {
-        
-        int retVal = countNonZero_0(src.nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getNumThreads()
-    //
-
-    //javadoc: getNumThreads()
-    public static int getNumThreads()
-    {
-        
-        int retVal = getNumThreads_0();
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getNumberOfCPUs()
-    //
-
-    //javadoc: getNumberOfCPUs()
-    public static int getNumberOfCPUs()
-    {
-        
-        int retVal = getNumberOfCPUs_0();
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getOptimalDFTSize(int vecsize)
-    //
-
-    //javadoc: getOptimalDFTSize(vecsize)
-    public static int getOptimalDFTSize(int vecsize)
-    {
-        
-        int retVal = getOptimalDFTSize_0(vecsize);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getThreadNum()
-    //
-
-    //javadoc: getThreadNum()
-    @Deprecated
-    public static int getThreadNum()
-    {
-        
-        int retVal = getThreadNum_0();
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getVersionMajor()
-    //
-
-    //javadoc: getVersionMajor()
-    public static int getVersionMajor()
-    {
-        
-        int retVal = getVersionMajor_0();
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getVersionMinor()
-    //
-
-    //javadoc: getVersionMinor()
-    public static int getVersionMinor()
-    {
-        
-        int retVal = getVersionMinor_0();
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getVersionRevision()
-    //
-
-    //javadoc: getVersionRevision()
-    public static int getVersionRevision()
-    {
-        
-        int retVal = getVersionRevision_0();
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int solveCubic(Mat coeffs, Mat& roots)
-    //
-
-    //javadoc: solveCubic(coeffs, roots)
-    public static int solveCubic(Mat coeffs, Mat roots)
-    {
-        
-        int retVal = solveCubic_0(coeffs.nativeObj, roots.nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int64 getCPUTickCount()
-    //
-
-    //javadoc: getCPUTickCount()
-    public static long getCPUTickCount()
-    {
-        
-        long retVal = getCPUTickCount_0();
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int64 getTickCount()
-    //
-
-    //javadoc: getTickCount()
-    public static long getTickCount()
-    {
-        
-        long retVal = getTickCount_0();
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  void LUT(Mat src, Mat lut, Mat& dst)
-    //
-
-    //javadoc: LUT(src, lut, dst)
-    public static void LUT(Mat src, Mat lut, Mat dst)
-    {
-        
-        LUT_0(src.nativeObj, lut.nativeObj, dst.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void PCABackProject(Mat data, Mat mean, Mat eigenvectors, Mat& result)
-    //
-
-    //javadoc: PCABackProject(data, mean, eigenvectors, result)
-    public static void PCABackProject(Mat data, Mat mean, Mat eigenvectors, Mat result)
-    {
-        
-        PCABackProject_0(data.nativeObj, mean.nativeObj, eigenvectors.nativeObj, result.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void PCACompute(Mat data, Mat& mean, Mat& eigenvectors, double retainedVariance)
-    //
-
-    //javadoc: PCACompute(data, mean, eigenvectors, retainedVariance)
-    public static void PCACompute(Mat data, Mat mean, Mat eigenvectors, double retainedVariance)
-    {
-        
-        PCACompute_0(data.nativeObj, mean.nativeObj, eigenvectors.nativeObj, retainedVariance);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void PCACompute(Mat data, Mat& mean, Mat& eigenvectors, int maxComponents = 0)
-    //
-
-    //javadoc: PCACompute(data, mean, eigenvectors, maxComponents)
-    public static void PCACompute(Mat data, Mat mean, Mat eigenvectors, int maxComponents)
-    {
-        
-        PCACompute_1(data.nativeObj, mean.nativeObj, eigenvectors.nativeObj, maxComponents);
-        
-        return;
-    }
-
-    //javadoc: PCACompute(data, mean, eigenvectors)
-    public static void PCACompute(Mat data, Mat mean, Mat eigenvectors)
-    {
-        
-        PCACompute_2(data.nativeObj, mean.nativeObj, eigenvectors.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void PCAProject(Mat data, Mat mean, Mat eigenvectors, Mat& result)
-    //
-
-    //javadoc: PCAProject(data, mean, eigenvectors, result)
-    public static void PCAProject(Mat data, Mat mean, Mat eigenvectors, Mat result)
-    {
-        
-        PCAProject_0(data.nativeObj, mean.nativeObj, eigenvectors.nativeObj, result.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void SVBackSubst(Mat w, Mat u, Mat vt, Mat rhs, Mat& dst)
-    //
-
-    //javadoc: SVBackSubst(w, u, vt, rhs, dst)
-    public static void SVBackSubst(Mat w, Mat u, Mat vt, Mat rhs, Mat dst)
-    {
-        
-        SVBackSubst_0(w.nativeObj, u.nativeObj, vt.nativeObj, rhs.nativeObj, dst.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void SVDecomp(Mat src, Mat& w, Mat& u, Mat& vt, int flags = 0)
-    //
-
-    //javadoc: SVDecomp(src, w, u, vt, flags)
-    public static void SVDecomp(Mat src, Mat w, Mat u, Mat vt, int flags)
-    {
-        
-        SVDecomp_0(src.nativeObj, w.nativeObj, u.nativeObj, vt.nativeObj, flags);
-        
-        return;
-    }
-
-    //javadoc: SVDecomp(src, w, u, vt)
-    public static void SVDecomp(Mat src, Mat w, Mat u, Mat vt)
-    {
-        
-        SVDecomp_1(src.nativeObj, w.nativeObj, u.nativeObj, vt.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void absdiff(Mat src1, Mat src2, Mat& dst)
-    //
-
-    //javadoc: absdiff(src1, src2, dst)
-    public static void absdiff(Mat src1, Mat src2, Mat dst)
-    {
-        
-        absdiff_0(src1.nativeObj, src2.nativeObj, dst.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void absdiff(Mat src1, Scalar src2, Mat& dst)
-    //
-
-    //javadoc: absdiff(src1, src2, dst)
-    public static void absdiff(Mat src1, Scalar src2, Mat dst)
-    {
-        
-        absdiff_1(src1.nativeObj, src2.val[0], src2.val[1], src2.val[2], src2.val[3], dst.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void add(Mat src1, Mat src2, Mat& dst, Mat mask = Mat(), int dtype = -1)
-    //
-
-    //javadoc: add(src1, src2, dst, mask, dtype)
-    public static void add(Mat src1, Mat src2, Mat dst, Mat mask, int dtype)
-    {
-        
-        add_0(src1.nativeObj, src2.nativeObj, dst.nativeObj, mask.nativeObj, dtype);
-        
-        return;
-    }
-
-    //javadoc: add(src1, src2, dst, mask)
-    public static void add(Mat src1, Mat src2, Mat dst, Mat mask)
-    {
-        
-        add_1(src1.nativeObj, src2.nativeObj, dst.nativeObj, mask.nativeObj);
-        
-        return;
-    }
-
-    //javadoc: add(src1, src2, dst)
-    public static void add(Mat src1, Mat src2, Mat dst)
-    {
-        
-        add_2(src1.nativeObj, src2.nativeObj, dst.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void add(Mat src1, Scalar src2, Mat& dst, Mat mask = Mat(), int dtype = -1)
-    //
-
-    //javadoc: add(src1, src2, dst, mask, dtype)
-    public static void add(Mat src1, Scalar src2, Mat dst, Mat mask, int dtype)
-    {
-        
-        add_3(src1.nativeObj, src2.val[0], src2.val[1], src2.val[2], src2.val[3], dst.nativeObj, mask.nativeObj, dtype);
-        
-        return;
-    }
-
-    //javadoc: add(src1, src2, dst, mask)
-    public static void add(Mat src1, Scalar src2, Mat dst, Mat mask)
-    {
-        
-        add_4(src1.nativeObj, src2.val[0], src2.val[1], src2.val[2], src2.val[3], dst.nativeObj, mask.nativeObj);
-        
-        return;
-    }
-
-    //javadoc: add(src1, src2, dst)
-    public static void add(Mat src1, Scalar src2, Mat dst)
-    {
-        
-        add_5(src1.nativeObj, src2.val[0], src2.val[1], src2.val[2], src2.val[3], dst.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void addWeighted(Mat src1, double alpha, Mat src2, double beta, double gamma, Mat& dst, int dtype = -1)
-    //
-
-    //javadoc: addWeighted(src1, alpha, src2, beta, gamma, dst, dtype)
-    public static void addWeighted(Mat src1, double alpha, Mat src2, double beta, double gamma, Mat dst, int dtype)
-    {
-        
-        addWeighted_0(src1.nativeObj, alpha, src2.nativeObj, beta, gamma, dst.nativeObj, dtype);
-        
-        return;
-    }
-
-    //javadoc: addWeighted(src1, alpha, src2, beta, gamma, dst)
-    public static void addWeighted(Mat src1, double alpha, Mat src2, double beta, double gamma, Mat dst)
-    {
-        
-        addWeighted_1(src1.nativeObj, alpha, src2.nativeObj, beta, gamma, dst.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void batchDistance(Mat src1, Mat src2, Mat& dist, int dtype, Mat& nidx, int normType = NORM_L2, int K = 0, Mat mask = Mat(), int update = 0, bool crosscheck = false)
-    //
-
-    //javadoc: batchDistance(src1, src2, dist, dtype, nidx, normType, K, mask, update, crosscheck)
-    public static void batchDistance(Mat src1, Mat src2, Mat dist, int dtype, Mat nidx, int normType, int K, Mat mask, int update, boolean crosscheck)
-    {
-        
-        batchDistance_0(src1.nativeObj, src2.nativeObj, dist.nativeObj, dtype, nidx.nativeObj, normType, K, mask.nativeObj, update, crosscheck);
-        
-        return;
-    }
-
-    //javadoc: batchDistance(src1, src2, dist, dtype, nidx, normType, K)
-    public static void batchDistance(Mat src1, Mat src2, Mat dist, int dtype, Mat nidx, int normType, int K)
-    {
-        
-        batchDistance_1(src1.nativeObj, src2.nativeObj, dist.nativeObj, dtype, nidx.nativeObj, normType, K);
-        
-        return;
-    }
-
-    //javadoc: batchDistance(src1, src2, dist, dtype, nidx)
-    public static void batchDistance(Mat src1, Mat src2, Mat dist, int dtype, Mat nidx)
-    {
-        
-        batchDistance_2(src1.nativeObj, src2.nativeObj, dist.nativeObj, dtype, nidx.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void bitwise_and(Mat src1, Mat src2, Mat& dst, Mat mask = Mat())
-    //
-
-    //javadoc: bitwise_and(src1, src2, dst, mask)
-    public static void bitwise_and(Mat src1, Mat src2, Mat dst, Mat mask)
-    {
-        
-        bitwise_and_0(src1.nativeObj, src2.nativeObj, dst.nativeObj, mask.nativeObj);
-        
-        return;
-    }
-
-    //javadoc: bitwise_and(src1, src2, dst)
-    public static void bitwise_and(Mat src1, Mat src2, Mat dst)
-    {
-        
-        bitwise_and_1(src1.nativeObj, src2.nativeObj, dst.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void bitwise_not(Mat src, Mat& dst, Mat mask = Mat())
-    //
-
-    //javadoc: bitwise_not(src, dst, mask)
-    public static void bitwise_not(Mat src, Mat dst, Mat mask)
-    {
-        
-        bitwise_not_0(src.nativeObj, dst.nativeObj, mask.nativeObj);
-        
-        return;
-    }
-
-    //javadoc: bitwise_not(src, dst)
-    public static void bitwise_not(Mat src, Mat dst)
-    {
-        
-        bitwise_not_1(src.nativeObj, dst.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void bitwise_or(Mat src1, Mat src2, Mat& dst, Mat mask = Mat())
-    //
-
-    //javadoc: bitwise_or(src1, src2, dst, mask)
-    public static void bitwise_or(Mat src1, Mat src2, Mat dst, Mat mask)
-    {
-        
-        bitwise_or_0(src1.nativeObj, src2.nativeObj, dst.nativeObj, mask.nativeObj);
-        
-        return;
-    }
-
-    //javadoc: bitwise_or(src1, src2, dst)
-    public static void bitwise_or(Mat src1, Mat src2, Mat dst)
-    {
-        
-        bitwise_or_1(src1.nativeObj, src2.nativeObj, dst.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void bitwise_xor(Mat src1, Mat src2, Mat& dst, Mat mask = Mat())
-    //
-
-    //javadoc: bitwise_xor(src1, src2, dst, mask)
-    public static void bitwise_xor(Mat src1, Mat src2, Mat dst, Mat mask)
-    {
-        
-        bitwise_xor_0(src1.nativeObj, src2.nativeObj, dst.nativeObj, mask.nativeObj);
-        
-        return;
-    }
-
-    //javadoc: bitwise_xor(src1, src2, dst)
-    public static void bitwise_xor(Mat src1, Mat src2, Mat dst)
-    {
-        
-        bitwise_xor_1(src1.nativeObj, src2.nativeObj, dst.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void calcCovarMatrix(Mat samples, Mat& covar, Mat& mean, int flags, int ctype = CV_64F)
-    //
-
-    //javadoc: calcCovarMatrix(samples, covar, mean, flags, ctype)
-    public static void calcCovarMatrix(Mat samples, Mat covar, Mat mean, int flags, int ctype)
-    {
-        
-        calcCovarMatrix_0(samples.nativeObj, covar.nativeObj, mean.nativeObj, flags, ctype);
-        
-        return;
-    }
-
-    //javadoc: calcCovarMatrix(samples, covar, mean, flags)
-    public static void calcCovarMatrix(Mat samples, Mat covar, Mat mean, int flags)
-    {
-        
-        calcCovarMatrix_1(samples.nativeObj, covar.nativeObj, mean.nativeObj, flags);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void cartToPolar(Mat x, Mat y, Mat& magnitude, Mat& angle, bool angleInDegrees = false)
-    //
-
-    //javadoc: cartToPolar(x, y, magnitude, angle, angleInDegrees)
-    public static void cartToPolar(Mat x, Mat y, Mat magnitude, Mat angle, boolean angleInDegrees)
-    {
-        
-        cartToPolar_0(x.nativeObj, y.nativeObj, magnitude.nativeObj, angle.nativeObj, angleInDegrees);
-        
-        return;
-    }
-
-    //javadoc: cartToPolar(x, y, magnitude, angle)
-    public static void cartToPolar(Mat x, Mat y, Mat magnitude, Mat angle)
-    {
-        
-        cartToPolar_1(x.nativeObj, y.nativeObj, magnitude.nativeObj, angle.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void compare(Mat src1, Mat src2, Mat& dst, int cmpop)
-    //
-
-    //javadoc: compare(src1, src2, dst, cmpop)
-    public static void compare(Mat src1, Mat src2, Mat dst, int cmpop)
-    {
-        
-        compare_0(src1.nativeObj, src2.nativeObj, dst.nativeObj, cmpop);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void compare(Mat src1, Scalar src2, Mat& dst, int cmpop)
-    //
-
-    //javadoc: compare(src1, src2, dst, cmpop)
-    public static void compare(Mat src1, Scalar src2, Mat dst, int cmpop)
-    {
-        
-        compare_1(src1.nativeObj, src2.val[0], src2.val[1], src2.val[2], src2.val[3], dst.nativeObj, cmpop);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void completeSymm(Mat& m, bool lowerToUpper = false)
-    //
-
-    //javadoc: completeSymm(m, lowerToUpper)
-    public static void completeSymm(Mat m, boolean lowerToUpper)
-    {
-        
-        completeSymm_0(m.nativeObj, lowerToUpper);
-        
-        return;
-    }
-
-    //javadoc: completeSymm(m)
-    public static void completeSymm(Mat m)
-    {
-        
-        completeSymm_1(m.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void convertFp16(Mat src, Mat& dst)
-    //
-
-    //javadoc: convertFp16(src, dst)
-    public static void convertFp16(Mat src, Mat dst)
-    {
-        
-        convertFp16_0(src.nativeObj, dst.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void convertScaleAbs(Mat src, Mat& dst, double alpha = 1, double beta = 0)
-    //
-
-    //javadoc: convertScaleAbs(src, dst, alpha, beta)
-    public static void convertScaleAbs(Mat src, Mat dst, double alpha, double beta)
-    {
-        
-        convertScaleAbs_0(src.nativeObj, dst.nativeObj, alpha, beta);
-        
-        return;
-    }
-
-    //javadoc: convertScaleAbs(src, dst)
-    public static void convertScaleAbs(Mat src, Mat dst)
-    {
-        
-        convertScaleAbs_1(src.nativeObj, dst.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void copyMakeBorder(Mat src, Mat& dst, int top, int bottom, int left, int right, int borderType, Scalar value = Scalar())
-    //
-
-    //javadoc: copyMakeBorder(src, dst, top, bottom, left, right, borderType, value)
-    public static void copyMakeBorder(Mat src, Mat dst, int top, int bottom, int left, int right, int borderType, Scalar value)
-    {
-        
-        copyMakeBorder_0(src.nativeObj, dst.nativeObj, top, bottom, left, right, borderType, value.val[0], value.val[1], value.val[2], value.val[3]);
-        
-        return;
-    }
-
-    //javadoc: copyMakeBorder(src, dst, top, bottom, left, right, borderType)
-    public static void copyMakeBorder(Mat src, Mat dst, int top, int bottom, int left, int right, int borderType)
-    {
-        
-        copyMakeBorder_1(src.nativeObj, dst.nativeObj, top, bottom, left, right, borderType);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void dct(Mat src, Mat& dst, int flags = 0)
-    //
-
-    //javadoc: dct(src, dst, flags)
-    public static void dct(Mat src, Mat dst, int flags)
-    {
-        
-        dct_0(src.nativeObj, dst.nativeObj, flags);
-        
-        return;
-    }
-
-    //javadoc: dct(src, dst)
-    public static void dct(Mat src, Mat dst)
-    {
-        
-        dct_1(src.nativeObj, dst.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void dft(Mat src, Mat& dst, int flags = 0, int nonzeroRows = 0)
-    //
-
-    //javadoc: dft(src, dst, flags, nonzeroRows)
-    public static void dft(Mat src, Mat dst, int flags, int nonzeroRows)
-    {
-        
-        dft_0(src.nativeObj, dst.nativeObj, flags, nonzeroRows);
-        
-        return;
-    }
-
-    //javadoc: dft(src, dst)
-    public static void dft(Mat src, Mat dst)
-    {
-        
-        dft_1(src.nativeObj, dst.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void divide(Mat src1, Mat src2, Mat& dst, double scale = 1, int dtype = -1)
-    //
-
-    //javadoc: divide(src1, src2, dst, scale, dtype)
-    public static void divide(Mat src1, Mat src2, Mat dst, double scale, int dtype)
-    {
-        
-        divide_0(src1.nativeObj, src2.nativeObj, dst.nativeObj, scale, dtype);
-        
-        return;
-    }
-
-    //javadoc: divide(src1, src2, dst, scale)
-    public static void divide(Mat src1, Mat src2, Mat dst, double scale)
-    {
-        
-        divide_1(src1.nativeObj, src2.nativeObj, dst.nativeObj, scale);
-        
-        return;
-    }
-
-    //javadoc: divide(src1, src2, dst)
-    public static void divide(Mat src1, Mat src2, Mat dst)
-    {
-        
-        divide_2(src1.nativeObj, src2.nativeObj, dst.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void divide(Mat src1, Scalar src2, Mat& dst, double scale = 1, int dtype = -1)
-    //
-
-    //javadoc: divide(src1, src2, dst, scale, dtype)
-    public static void divide(Mat src1, Scalar src2, Mat dst, double scale, int dtype)
-    {
-        
-        divide_3(src1.nativeObj, src2.val[0], src2.val[1], src2.val[2], src2.val[3], dst.nativeObj, scale, dtype);
-        
-        return;
-    }
-
-    //javadoc: divide(src1, src2, dst, scale)
-    public static void divide(Mat src1, Scalar src2, Mat dst, double scale)
-    {
-        
-        divide_4(src1.nativeObj, src2.val[0], src2.val[1], src2.val[2], src2.val[3], dst.nativeObj, scale);
-        
-        return;
-    }
-
-    //javadoc: divide(src1, src2, dst)
-    public static void divide(Mat src1, Scalar src2, Mat dst)
-    {
-        
-        divide_5(src1.nativeObj, src2.val[0], src2.val[1], src2.val[2], src2.val[3], dst.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void divide(double scale, Mat src2, Mat& dst, int dtype = -1)
-    //
-
-    //javadoc: divide(scale, src2, dst, dtype)
-    public static void divide(double scale, Mat src2, Mat dst, int dtype)
-    {
-        
-        divide_6(scale, src2.nativeObj, dst.nativeObj, dtype);
-        
-        return;
-    }
-
-    //javadoc: divide(scale, src2, dst)
-    public static void divide(double scale, Mat src2, Mat dst)
-    {
-        
-        divide_7(scale, src2.nativeObj, dst.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void eigenNonSymmetric(Mat src, Mat& eigenvalues, Mat& eigenvectors)
-    //
-
-    //javadoc: eigenNonSymmetric(src, eigenvalues, eigenvectors)
-    public static void eigenNonSymmetric(Mat src, Mat eigenvalues, Mat eigenvectors)
-    {
-        
-        eigenNonSymmetric_0(src.nativeObj, eigenvalues.nativeObj, eigenvectors.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void exp(Mat src, Mat& dst)
-    //
-
-    //javadoc: exp(src, dst)
-    public static void exp(Mat src, Mat dst)
-    {
-        
-        exp_0(src.nativeObj, dst.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void extractChannel(Mat src, Mat& dst, int coi)
-    //
-
-    //javadoc: extractChannel(src, dst, coi)
-    public static void extractChannel(Mat src, Mat dst, int coi)
-    {
-        
-        extractChannel_0(src.nativeObj, dst.nativeObj, coi);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void findNonZero(Mat src, Mat& idx)
-    //
-
-    //javadoc: findNonZero(src, idx)
-    public static void findNonZero(Mat src, Mat idx)
-    {
-        
-        findNonZero_0(src.nativeObj, idx.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void flip(Mat src, Mat& dst, int flipCode)
-    //
-
-    //javadoc: flip(src, dst, flipCode)
-    public static void flip(Mat src, Mat dst, int flipCode)
-    {
-        
-        flip_0(src.nativeObj, dst.nativeObj, flipCode);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void gemm(Mat src1, Mat src2, double alpha, Mat src3, double beta, Mat& dst, int flags = 0)
-    //
-
-    //javadoc: gemm(src1, src2, alpha, src3, beta, dst, flags)
-    public static void gemm(Mat src1, Mat src2, double alpha, Mat src3, double beta, Mat dst, int flags)
-    {
-        
-        gemm_0(src1.nativeObj, src2.nativeObj, alpha, src3.nativeObj, beta, dst.nativeObj, flags);
-        
-        return;
-    }
-
-    //javadoc: gemm(src1, src2, alpha, src3, beta, dst)
-    public static void gemm(Mat src1, Mat src2, double alpha, Mat src3, double beta, Mat dst)
-    {
-        
-        gemm_1(src1.nativeObj, src2.nativeObj, alpha, src3.nativeObj, beta, dst.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void hconcat(vector_Mat src, Mat& dst)
-    //
-
-    //javadoc: hconcat(src, dst)
-    public static void hconcat(List<Mat> src, Mat dst)
-    {
-        Mat src_mat = Converters.vector_Mat_to_Mat(src);
-        hconcat_0(src_mat.nativeObj, dst.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void idct(Mat src, Mat& dst, int flags = 0)
-    //
-
-    //javadoc: idct(src, dst, flags)
-    public static void idct(Mat src, Mat dst, int flags)
-    {
-        
-        idct_0(src.nativeObj, dst.nativeObj, flags);
-        
-        return;
-    }
-
-    //javadoc: idct(src, dst)
-    public static void idct(Mat src, Mat dst)
-    {
-        
-        idct_1(src.nativeObj, dst.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void idft(Mat src, Mat& dst, int flags = 0, int nonzeroRows = 0)
-    //
-
-    //javadoc: idft(src, dst, flags, nonzeroRows)
-    public static void idft(Mat src, Mat dst, int flags, int nonzeroRows)
-    {
-        
-        idft_0(src.nativeObj, dst.nativeObj, flags, nonzeroRows);
-        
-        return;
-    }
-
-    //javadoc: idft(src, dst)
-    public static void idft(Mat src, Mat dst)
-    {
-        
-        idft_1(src.nativeObj, dst.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void inRange(Mat src, Scalar lowerb, Scalar upperb, Mat& dst)
-    //
-
-    //javadoc: inRange(src, lowerb, upperb, dst)
-    public static void inRange(Mat src, Scalar lowerb, Scalar upperb, Mat dst)
-    {
-        
-        inRange_0(src.nativeObj, lowerb.val[0], lowerb.val[1], lowerb.val[2], lowerb.val[3], upperb.val[0], upperb.val[1], upperb.val[2], upperb.val[3], dst.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void insertChannel(Mat src, Mat& dst, int coi)
-    //
-
-    //javadoc: insertChannel(src, dst, coi)
-    public static void insertChannel(Mat src, Mat dst, int coi)
-    {
-        
-        insertChannel_0(src.nativeObj, dst.nativeObj, coi);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void log(Mat src, Mat& dst)
-    //
-
-    //javadoc: log(src, dst)
-    public static void log(Mat src, Mat dst)
-    {
-        
-        log_0(src.nativeObj, dst.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void magnitude(Mat x, Mat y, Mat& magnitude)
-    //
-
-    //javadoc: magnitude(x, y, magnitude)
-    public static void magnitude(Mat x, Mat y, Mat magnitude)
-    {
-        
-        magnitude_0(x.nativeObj, y.nativeObj, magnitude.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void max(Mat src1, Mat src2, Mat& dst)
-    //
-
-    //javadoc: max(src1, src2, dst)
-    public static void max(Mat src1, Mat src2, Mat dst)
-    {
-        
-        max_0(src1.nativeObj, src2.nativeObj, dst.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void max(Mat src1, Scalar src2, Mat& dst)
-    //
-
-    //javadoc: max(src1, src2, dst)
-    public static void max(Mat src1, Scalar src2, Mat dst)
-    {
-        
-        max_1(src1.nativeObj, src2.val[0], src2.val[1], src2.val[2], src2.val[3], dst.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void meanStdDev(Mat src, vector_double& mean, vector_double& stddev, Mat mask = Mat())
-    //
-
-    //javadoc: meanStdDev(src, mean, stddev, mask)
-    public static void meanStdDev(Mat src, MatOfDouble mean, MatOfDouble stddev, Mat mask)
-    {
-        Mat mean_mat = mean;
-        Mat stddev_mat = stddev;
-        meanStdDev_0(src.nativeObj, mean_mat.nativeObj, stddev_mat.nativeObj, mask.nativeObj);
-        
-        return;
-    }
-
-    //javadoc: meanStdDev(src, mean, stddev)
-    public static void meanStdDev(Mat src, MatOfDouble mean, MatOfDouble stddev)
-    {
-        Mat mean_mat = mean;
-        Mat stddev_mat = stddev;
-        meanStdDev_1(src.nativeObj, mean_mat.nativeObj, stddev_mat.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void merge(vector_Mat mv, Mat& dst)
-    //
-
-    //javadoc: merge(mv, dst)
-    public static void merge(List<Mat> mv, Mat dst)
-    {
-        Mat mv_mat = Converters.vector_Mat_to_Mat(mv);
-        merge_0(mv_mat.nativeObj, dst.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void min(Mat src1, Mat src2, Mat& dst)
-    //
-
-    //javadoc: min(src1, src2, dst)
-    public static void min(Mat src1, Mat src2, Mat dst)
-    {
-        
-        min_0(src1.nativeObj, src2.nativeObj, dst.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void min(Mat src1, Scalar src2, Mat& dst)
-    //
-
-    //javadoc: min(src1, src2, dst)
-    public static void min(Mat src1, Scalar src2, Mat dst)
-    {
-        
-        min_1(src1.nativeObj, src2.val[0], src2.val[1], src2.val[2], src2.val[3], dst.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void mixChannels(vector_Mat src, vector_Mat dst, vector_int fromTo)
-    //
-
-    //javadoc: mixChannels(src, dst, fromTo)
-    public static void mixChannels(List<Mat> src, List<Mat> dst, MatOfInt fromTo)
-    {
-        Mat src_mat = Converters.vector_Mat_to_Mat(src);
-        Mat dst_mat = Converters.vector_Mat_to_Mat(dst);
-        Mat fromTo_mat = fromTo;
-        mixChannels_0(src_mat.nativeObj, dst_mat.nativeObj, fromTo_mat.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void mulSpectrums(Mat a, Mat b, Mat& c, int flags, bool conjB = false)
-    //
-
-    //javadoc: mulSpectrums(a, b, c, flags, conjB)
-    public static void mulSpectrums(Mat a, Mat b, Mat c, int flags, boolean conjB)
-    {
-        
-        mulSpectrums_0(a.nativeObj, b.nativeObj, c.nativeObj, flags, conjB);
-        
-        return;
-    }
-
-    //javadoc: mulSpectrums(a, b, c, flags)
-    public static void mulSpectrums(Mat a, Mat b, Mat c, int flags)
-    {
-        
-        mulSpectrums_1(a.nativeObj, b.nativeObj, c.nativeObj, flags);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void mulTransposed(Mat src, Mat& dst, bool aTa, Mat delta = Mat(), double scale = 1, int dtype = -1)
-    //
-
-    //javadoc: mulTransposed(src, dst, aTa, delta, scale, dtype)
-    public static void mulTransposed(Mat src, Mat dst, boolean aTa, Mat delta, double scale, int dtype)
-    {
-        
-        mulTransposed_0(src.nativeObj, dst.nativeObj, aTa, delta.nativeObj, scale, dtype);
-        
-        return;
-    }
-
-    //javadoc: mulTransposed(src, dst, aTa, delta, scale)
-    public static void mulTransposed(Mat src, Mat dst, boolean aTa, Mat delta, double scale)
-    {
-        
-        mulTransposed_1(src.nativeObj, dst.nativeObj, aTa, delta.nativeObj, scale);
-        
-        return;
-    }
-
-    //javadoc: mulTransposed(src, dst, aTa)
-    public static void mulTransposed(Mat src, Mat dst, boolean aTa)
-    {
-        
-        mulTransposed_2(src.nativeObj, dst.nativeObj, aTa);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void multiply(Mat src1, Mat src2, Mat& dst, double scale = 1, int dtype = -1)
-    //
-
-    //javadoc: multiply(src1, src2, dst, scale, dtype)
-    public static void multiply(Mat src1, Mat src2, Mat dst, double scale, int dtype)
-    {
-        
-        multiply_0(src1.nativeObj, src2.nativeObj, dst.nativeObj, scale, dtype);
-        
-        return;
-    }
-
-    //javadoc: multiply(src1, src2, dst, scale)
-    public static void multiply(Mat src1, Mat src2, Mat dst, double scale)
-    {
-        
-        multiply_1(src1.nativeObj, src2.nativeObj, dst.nativeObj, scale);
-        
-        return;
-    }
-
-    //javadoc: multiply(src1, src2, dst)
-    public static void multiply(Mat src1, Mat src2, Mat dst)
-    {
-        
-        multiply_2(src1.nativeObj, src2.nativeObj, dst.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void multiply(Mat src1, Scalar src2, Mat& dst, double scale = 1, int dtype = -1)
-    //
-
-    //javadoc: multiply(src1, src2, dst, scale, dtype)
-    public static void multiply(Mat src1, Scalar src2, Mat dst, double scale, int dtype)
-    {
-        
-        multiply_3(src1.nativeObj, src2.val[0], src2.val[1], src2.val[2], src2.val[3], dst.nativeObj, scale, dtype);
-        
-        return;
-    }
-
-    //javadoc: multiply(src1, src2, dst, scale)
-    public static void multiply(Mat src1, Scalar src2, Mat dst, double scale)
-    {
-        
-        multiply_4(src1.nativeObj, src2.val[0], src2.val[1], src2.val[2], src2.val[3], dst.nativeObj, scale);
-        
-        return;
-    }
-
-    //javadoc: multiply(src1, src2, dst)
-    public static void multiply(Mat src1, Scalar src2, Mat dst)
-    {
-        
-        multiply_5(src1.nativeObj, src2.val[0], src2.val[1], src2.val[2], src2.val[3], dst.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void normalize(Mat src, Mat& dst, double alpha = 1, double beta = 0, int norm_type = NORM_L2, int dtype = -1, Mat mask = Mat())
-    //
-
-    //javadoc: normalize(src, dst, alpha, beta, norm_type, dtype, mask)
-    public static void normalize(Mat src, Mat dst, double alpha, double beta, int norm_type, int dtype, Mat mask)
-    {
-        
-        normalize_0(src.nativeObj, dst.nativeObj, alpha, beta, norm_type, dtype, mask.nativeObj);
-        
-        return;
-    }
-
-    //javadoc: normalize(src, dst, alpha, beta, norm_type, dtype)
-    public static void normalize(Mat src, Mat dst, double alpha, double beta, int norm_type, int dtype)
-    {
-        
-        normalize_1(src.nativeObj, dst.nativeObj, alpha, beta, norm_type, dtype);
-        
-        return;
-    }
-
-    //javadoc: normalize(src, dst, alpha, beta, norm_type)
-    public static void normalize(Mat src, Mat dst, double alpha, double beta, int norm_type)
-    {
-        
-        normalize_2(src.nativeObj, dst.nativeObj, alpha, beta, norm_type);
-        
-        return;
-    }
-
-    //javadoc: normalize(src, dst)
-    public static void normalize(Mat src, Mat dst)
-    {
-        
-        normalize_3(src.nativeObj, dst.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void patchNaNs(Mat& a, double val = 0)
-    //
-
-    //javadoc: patchNaNs(a, val)
-    public static void patchNaNs(Mat a, double val)
-    {
-        
-        patchNaNs_0(a.nativeObj, val);
-        
-        return;
-    }
-
-    //javadoc: patchNaNs(a)
-    public static void patchNaNs(Mat a)
-    {
-        
-        patchNaNs_1(a.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void perspectiveTransform(Mat src, Mat& dst, Mat m)
-    //
-
-    //javadoc: perspectiveTransform(src, dst, m)
-    public static void perspectiveTransform(Mat src, Mat dst, Mat m)
-    {
-        
-        perspectiveTransform_0(src.nativeObj, dst.nativeObj, m.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void phase(Mat x, Mat y, Mat& angle, bool angleInDegrees = false)
-    //
-
-    //javadoc: phase(x, y, angle, angleInDegrees)
-    public static void phase(Mat x, Mat y, Mat angle, boolean angleInDegrees)
-    {
-        
-        phase_0(x.nativeObj, y.nativeObj, angle.nativeObj, angleInDegrees);
-        
-        return;
-    }
-
-    //javadoc: phase(x, y, angle)
-    public static void phase(Mat x, Mat y, Mat angle)
-    {
-        
-        phase_1(x.nativeObj, y.nativeObj, angle.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void polarToCart(Mat magnitude, Mat angle, Mat& x, Mat& y, bool angleInDegrees = false)
-    //
-
-    //javadoc: polarToCart(magnitude, angle, x, y, angleInDegrees)
-    public static void polarToCart(Mat magnitude, Mat angle, Mat x, Mat y, boolean angleInDegrees)
-    {
-        
-        polarToCart_0(magnitude.nativeObj, angle.nativeObj, x.nativeObj, y.nativeObj, angleInDegrees);
-        
-        return;
-    }
-
-    //javadoc: polarToCart(magnitude, angle, x, y)
-    public static void polarToCart(Mat magnitude, Mat angle, Mat x, Mat y)
-    {
-        
-        polarToCart_1(magnitude.nativeObj, angle.nativeObj, x.nativeObj, y.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void pow(Mat src, double power, Mat& dst)
-    //
-
-    //javadoc: pow(src, power, dst)
-    public static void pow(Mat src, double power, Mat dst)
-    {
-        
-        pow_0(src.nativeObj, power, dst.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void randShuffle(Mat& dst, double iterFactor = 1., RNG* rng = 0)
-    //
-
-    //javadoc: randShuffle(dst, iterFactor)
-    public static void randShuffle(Mat dst, double iterFactor)
-    {
-        
-        randShuffle_0(dst.nativeObj, iterFactor);
-        
-        return;
-    }
-
-    //javadoc: randShuffle(dst)
-    public static void randShuffle(Mat dst)
-    {
-        
-        randShuffle_1(dst.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void randn(Mat& dst, double mean, double stddev)
-    //
-
-    //javadoc: randn(dst, mean, stddev)
-    public static void randn(Mat dst, double mean, double stddev)
-    {
-        
-        randn_0(dst.nativeObj, mean, stddev);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void randu(Mat& dst, double low, double high)
-    //
-
-    //javadoc: randu(dst, low, high)
-    public static void randu(Mat dst, double low, double high)
-    {
-        
-        randu_0(dst.nativeObj, low, high);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void reduce(Mat src, Mat& dst, int dim, int rtype, int dtype = -1)
-    //
-
-    //javadoc: reduce(src, dst, dim, rtype, dtype)
-    public static void reduce(Mat src, Mat dst, int dim, int rtype, int dtype)
-    {
-        
-        reduce_0(src.nativeObj, dst.nativeObj, dim, rtype, dtype);
-        
-        return;
-    }
-
-    //javadoc: reduce(src, dst, dim, rtype)
-    public static void reduce(Mat src, Mat dst, int dim, int rtype)
-    {
-        
-        reduce_1(src.nativeObj, dst.nativeObj, dim, rtype);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void repeat(Mat src, int ny, int nx, Mat& dst)
-    //
-
-    //javadoc: repeat(src, ny, nx, dst)
-    public static void repeat(Mat src, int ny, int nx, Mat dst)
-    {
-        
-        repeat_0(src.nativeObj, ny, nx, dst.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void rotate(Mat src, Mat& dst, int rotateCode)
-    //
-
-    //javadoc: rotate(src, dst, rotateCode)
-    public static void rotate(Mat src, Mat dst, int rotateCode)
-    {
-        
-        rotate_0(src.nativeObj, dst.nativeObj, rotateCode);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void scaleAdd(Mat src1, double alpha, Mat src2, Mat& dst)
-    //
-
-    //javadoc: scaleAdd(src1, alpha, src2, dst)
-    public static void scaleAdd(Mat src1, double alpha, Mat src2, Mat dst)
-    {
-        
-        scaleAdd_0(src1.nativeObj, alpha, src2.nativeObj, dst.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setErrorVerbosity(bool verbose)
-    //
-
-    //javadoc: setErrorVerbosity(verbose)
-    public static void setErrorVerbosity(boolean verbose)
-    {
-        
-        setErrorVerbosity_0(verbose);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setIdentity(Mat& mtx, Scalar s = Scalar(1))
-    //
-
-    //javadoc: setIdentity(mtx, s)
-    public static void setIdentity(Mat mtx, Scalar s)
-    {
-        
-        setIdentity_0(mtx.nativeObj, s.val[0], s.val[1], s.val[2], s.val[3]);
-        
-        return;
-    }
-
-    //javadoc: setIdentity(mtx)
-    public static void setIdentity(Mat mtx)
-    {
-        
-        setIdentity_1(mtx.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setNumThreads(int nthreads)
-    //
-
-    //javadoc: setNumThreads(nthreads)
-    public static void setNumThreads(int nthreads)
-    {
-        
-        setNumThreads_0(nthreads);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setRNGSeed(int seed)
-    //
-
-    //javadoc: setRNGSeed(seed)
-    public static void setRNGSeed(int seed)
-    {
-        
-        setRNGSeed_0(seed);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void sort(Mat src, Mat& dst, int flags)
-    //
-
-    //javadoc: sort(src, dst, flags)
-    public static void sort(Mat src, Mat dst, int flags)
-    {
-        
-        sort_0(src.nativeObj, dst.nativeObj, flags);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void sortIdx(Mat src, Mat& dst, int flags)
-    //
-
-    //javadoc: sortIdx(src, dst, flags)
-    public static void sortIdx(Mat src, Mat dst, int flags)
-    {
-        
-        sortIdx_0(src.nativeObj, dst.nativeObj, flags);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void split(Mat m, vector_Mat& mv)
-    //
-
-    //javadoc: split(m, mv)
-    public static void split(Mat m, List<Mat> mv)
-    {
-        Mat mv_mat = new Mat();
-        split_0(m.nativeObj, mv_mat.nativeObj);
-        Converters.Mat_to_vector_Mat(mv_mat, mv);
-        mv_mat.release();
-        return;
-    }
-
-
-    //
-    // C++:  void sqrt(Mat src, Mat& dst)
-    //
-
-    //javadoc: sqrt(src, dst)
-    public static void sqrt(Mat src, Mat dst)
-    {
-        
-        sqrt_0(src.nativeObj, dst.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void subtract(Mat src1, Mat src2, Mat& dst, Mat mask = Mat(), int dtype = -1)
-    //
-
-    //javadoc: subtract(src1, src2, dst, mask, dtype)
-    public static void subtract(Mat src1, Mat src2, Mat dst, Mat mask, int dtype)
-    {
-        
-        subtract_0(src1.nativeObj, src2.nativeObj, dst.nativeObj, mask.nativeObj, dtype);
-        
-        return;
-    }
-
-    //javadoc: subtract(src1, src2, dst, mask)
-    public static void subtract(Mat src1, Mat src2, Mat dst, Mat mask)
-    {
-        
-        subtract_1(src1.nativeObj, src2.nativeObj, dst.nativeObj, mask.nativeObj);
-        
-        return;
-    }
-
-    //javadoc: subtract(src1, src2, dst)
-    public static void subtract(Mat src1, Mat src2, Mat dst)
-    {
-        
-        subtract_2(src1.nativeObj, src2.nativeObj, dst.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void subtract(Mat src1, Scalar src2, Mat& dst, Mat mask = Mat(), int dtype = -1)
-    //
-
-    //javadoc: subtract(src1, src2, dst, mask, dtype)
-    public static void subtract(Mat src1, Scalar src2, Mat dst, Mat mask, int dtype)
-    {
-        
-        subtract_3(src1.nativeObj, src2.val[0], src2.val[1], src2.val[2], src2.val[3], dst.nativeObj, mask.nativeObj, dtype);
-        
-        return;
-    }
-
-    //javadoc: subtract(src1, src2, dst, mask)
-    public static void subtract(Mat src1, Scalar src2, Mat dst, Mat mask)
-    {
-        
-        subtract_4(src1.nativeObj, src2.val[0], src2.val[1], src2.val[2], src2.val[3], dst.nativeObj, mask.nativeObj);
-        
-        return;
-    }
-
-    //javadoc: subtract(src1, src2, dst)
-    public static void subtract(Mat src1, Scalar src2, Mat dst)
-    {
-        
-        subtract_5(src1.nativeObj, src2.val[0], src2.val[1], src2.val[2], src2.val[3], dst.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void transform(Mat src, Mat& dst, Mat m)
-    //
-
-    //javadoc: transform(src, dst, m)
-    public static void transform(Mat src, Mat dst, Mat m)
-    {
-        
-        transform_0(src.nativeObj, dst.nativeObj, m.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void transpose(Mat src, Mat& dst)
-    //
-
-    //javadoc: transpose(src, dst)
-    public static void transpose(Mat src, Mat dst)
-    {
-        
-        transpose_0(src.nativeObj, dst.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void vconcat(vector_Mat src, Mat& dst)
-    //
-
-    //javadoc: vconcat(src, dst)
-    public static void vconcat(List<Mat> src, Mat dst)
-    {
-        Mat src_mat = Converters.vector_Mat_to_Mat(src);
-        vconcat_0(src_mat.nativeObj, dst.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setUseIPP(bool flag)
-    //
-
-    //javadoc: setUseIPP(flag)
-    public static void setUseIPP(boolean flag)
-    {
-        
-        setUseIPP_0(flag);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setUseIPP_NE(bool flag)
-    //
-
-    //javadoc: setUseIPP_NE(flag)
-    public static void setUseIPP_NE(boolean flag)
-    {
-        
-        setUseIPP_NE_0(flag);
-        
-        return;
-    }
-
-// manual port
-public static class MinMaxLocResult {
-    public double minVal;
-    public double maxVal;
-    public Point minLoc;
-    public Point maxLoc;
-
-
-    public MinMaxLocResult() {
-        minVal=0; maxVal=0;
-        minLoc=new Point();
-        maxLoc=new Point();
-    }
-}
-
-
-// C++: minMaxLoc(Mat src, double* minVal, double* maxVal=0, Point* minLoc=0, Point* maxLoc=0, InputArray mask=noArray())
-
-
-//javadoc: minMaxLoc(src, mask)
-public static MinMaxLocResult minMaxLoc(Mat src, Mat mask) {
-    MinMaxLocResult res = new MinMaxLocResult();
-    long maskNativeObj=0;
-    if (mask != null) {
-        maskNativeObj=mask.nativeObj;
-    }
-    double resarr[] = n_minMaxLocManual(src.nativeObj, maskNativeObj);
-    res.minVal=resarr[0];
-    res.maxVal=resarr[1];
-    res.minLoc.x=resarr[2];
-    res.minLoc.y=resarr[3];
-    res.maxLoc.x=resarr[4];
-    res.maxLoc.y=resarr[5];
-    return res;
-}
-
-
-//javadoc: minMaxLoc(src)
-public static MinMaxLocResult minMaxLoc(Mat src) {
-    return minMaxLoc(src, null);
-}
-
-
-    // C++:  Scalar mean(Mat src, Mat mask = Mat())
-    private static native double[] mean_0(long src_nativeObj, long mask_nativeObj);
-    private static native double[] mean_1(long src_nativeObj);
-
-    // C++:  Scalar sum(Mat src)
-    private static native double[] sumElems_0(long src_nativeObj);
-
-    // C++:  Scalar trace(Mat mtx)
-    private static native double[] trace_0(long mtx_nativeObj);
-
-    // C++:  String getBuildInformation()
-    private static native String getBuildInformation_0();
-
-    // C++:  String getHardwareFeatureName(int feature)
-    private static native String getHardwareFeatureName_0(int feature);
-
-    // C++:  String getVersionString()
-    private static native String getVersionString_0();
-
-    // C++:  String getIppVersion()
-    private static native String getIppVersion_0();
-
-    // C++:  bool checkRange(Mat a, bool quiet = true,  _hidden_ * pos = 0, double minVal = -DBL_MAX, double maxVal = DBL_MAX)
-    private static native boolean checkRange_0(long a_nativeObj, boolean quiet, double minVal, double maxVal);
-    private static native boolean checkRange_1(long a_nativeObj);
-
-    // C++:  bool eigen(Mat src, Mat& eigenvalues, Mat& eigenvectors = Mat())
-    private static native boolean eigen_0(long src_nativeObj, long eigenvalues_nativeObj, long eigenvectors_nativeObj);
-    private static native boolean eigen_1(long src_nativeObj, long eigenvalues_nativeObj);
-
-    // C++:  bool solve(Mat src1, Mat src2, Mat& dst, int flags = DECOMP_LU)
-    private static native boolean solve_0(long src1_nativeObj, long src2_nativeObj, long dst_nativeObj, int flags);
-    private static native boolean solve_1(long src1_nativeObj, long src2_nativeObj, long dst_nativeObj);
-
-    // C++:  bool useIPP()
-    private static native boolean useIPP_0();
-
-    // C++:  bool useIPP_NE()
-    private static native boolean useIPP_NE_0();
-
-    // C++:  double Mahalanobis(Mat v1, Mat v2, Mat icovar)
-    private static native double Mahalanobis_0(long v1_nativeObj, long v2_nativeObj, long icovar_nativeObj);
-
-    // C++:  double PSNR(Mat src1, Mat src2)
-    private static native double PSNR_0(long src1_nativeObj, long src2_nativeObj);
-
-    // C++:  double determinant(Mat mtx)
-    private static native double determinant_0(long mtx_nativeObj);
-
-    // C++:  double getTickFrequency()
-    private static native double getTickFrequency_0();
-
-    // C++:  double invert(Mat src, Mat& dst, int flags = DECOMP_LU)
-    private static native double invert_0(long src_nativeObj, long dst_nativeObj, int flags);
-    private static native double invert_1(long src_nativeObj, long dst_nativeObj);
-
-    // C++:  double kmeans(Mat data, int K, Mat& bestLabels, TermCriteria criteria, int attempts, int flags, Mat& centers = Mat())
-    private static native double kmeans_0(long data_nativeObj, int K, long bestLabels_nativeObj, int criteria_type, int criteria_maxCount, double criteria_epsilon, int attempts, int flags, long centers_nativeObj);
-    private static native double kmeans_1(long data_nativeObj, int K, long bestLabels_nativeObj, int criteria_type, int criteria_maxCount, double criteria_epsilon, int attempts, int flags);
-
-    // C++:  double norm(Mat src1, Mat src2, int normType = NORM_L2, Mat mask = Mat())
-    private static native double norm_0(long src1_nativeObj, long src2_nativeObj, int normType, long mask_nativeObj);
-    private static native double norm_1(long src1_nativeObj, long src2_nativeObj, int normType);
-    private static native double norm_2(long src1_nativeObj, long src2_nativeObj);
-
-    // C++:  double norm(Mat src1, int normType = NORM_L2, Mat mask = Mat())
-    private static native double norm_3(long src1_nativeObj, int normType, long mask_nativeObj);
-    private static native double norm_4(long src1_nativeObj, int normType);
-    private static native double norm_5(long src1_nativeObj);
-
-    // C++:  double solvePoly(Mat coeffs, Mat& roots, int maxIters = 300)
-    private static native double solvePoly_0(long coeffs_nativeObj, long roots_nativeObj, int maxIters);
-    private static native double solvePoly_1(long coeffs_nativeObj, long roots_nativeObj);
-
-    // C++:  float cubeRoot(float val)
-    private static native float cubeRoot_0(float val);
-
-    // C++:  float fastAtan2(float y, float x)
-    private static native float fastAtan2_0(float y, float x);
-
-    // C++:  int borderInterpolate(int p, int len, int borderType)
-    private static native int borderInterpolate_0(int p, int len, int borderType);
-
-    // C++:  int countNonZero(Mat src)
-    private static native int countNonZero_0(long src_nativeObj);
-
-    // C++:  int getNumThreads()
-    private static native int getNumThreads_0();
-
-    // C++:  int getNumberOfCPUs()
-    private static native int getNumberOfCPUs_0();
-
-    // C++:  int getOptimalDFTSize(int vecsize)
-    private static native int getOptimalDFTSize_0(int vecsize);
-
-    // C++:  int getThreadNum()
-    private static native int getThreadNum_0();
-
-    // C++:  int getVersionMajor()
-    private static native int getVersionMajor_0();
-
-    // C++:  int getVersionMinor()
-    private static native int getVersionMinor_0();
-
-    // C++:  int getVersionRevision()
-    private static native int getVersionRevision_0();
-
-    // C++:  int solveCubic(Mat coeffs, Mat& roots)
-    private static native int solveCubic_0(long coeffs_nativeObj, long roots_nativeObj);
-
-    // C++:  int64 getCPUTickCount()
-    private static native long getCPUTickCount_0();
-
-    // C++:  int64 getTickCount()
-    private static native long getTickCount_0();
-
-    // C++:  void LUT(Mat src, Mat lut, Mat& dst)
-    private static native void LUT_0(long src_nativeObj, long lut_nativeObj, long dst_nativeObj);
-
-    // C++:  void PCABackProject(Mat data, Mat mean, Mat eigenvectors, Mat& result)
-    private static native void PCABackProject_0(long data_nativeObj, long mean_nativeObj, long eigenvectors_nativeObj, long result_nativeObj);
-
-    // C++:  void PCACompute(Mat data, Mat& mean, Mat& eigenvectors, double retainedVariance)
-    private static native void PCACompute_0(long data_nativeObj, long mean_nativeObj, long eigenvectors_nativeObj, double retainedVariance);
-
-    // C++:  void PCACompute(Mat data, Mat& mean, Mat& eigenvectors, int maxComponents = 0)
-    private static native void PCACompute_1(long data_nativeObj, long mean_nativeObj, long eigenvectors_nativeObj, int maxComponents);
-    private static native void PCACompute_2(long data_nativeObj, long mean_nativeObj, long eigenvectors_nativeObj);
-
-    // C++:  void PCAProject(Mat data, Mat mean, Mat eigenvectors, Mat& result)
-    private static native void PCAProject_0(long data_nativeObj, long mean_nativeObj, long eigenvectors_nativeObj, long result_nativeObj);
-
-    // C++:  void SVBackSubst(Mat w, Mat u, Mat vt, Mat rhs, Mat& dst)
-    private static native void SVBackSubst_0(long w_nativeObj, long u_nativeObj, long vt_nativeObj, long rhs_nativeObj, long dst_nativeObj);
-
-    // C++:  void SVDecomp(Mat src, Mat& w, Mat& u, Mat& vt, int flags = 0)
-    private static native void SVDecomp_0(long src_nativeObj, long w_nativeObj, long u_nativeObj, long vt_nativeObj, int flags);
-    private static native void SVDecomp_1(long src_nativeObj, long w_nativeObj, long u_nativeObj, long vt_nativeObj);
-
-    // C++:  void absdiff(Mat src1, Mat src2, Mat& dst)
-    private static native void absdiff_0(long src1_nativeObj, long src2_nativeObj, long dst_nativeObj);
-
-    // C++:  void absdiff(Mat src1, Scalar src2, Mat& dst)
-    private static native void absdiff_1(long src1_nativeObj, double src2_val0, double src2_val1, double src2_val2, double src2_val3, long dst_nativeObj);
-
-    // C++:  void add(Mat src1, Mat src2, Mat& dst, Mat mask = Mat(), int dtype = -1)
-    private static native void add_0(long src1_nativeObj, long src2_nativeObj, long dst_nativeObj, long mask_nativeObj, int dtype);
-    private static native void add_1(long src1_nativeObj, long src2_nativeObj, long dst_nativeObj, long mask_nativeObj);
-    private static native void add_2(long src1_nativeObj, long src2_nativeObj, long dst_nativeObj);
-
-    // C++:  void add(Mat src1, Scalar src2, Mat& dst, Mat mask = Mat(), int dtype = -1)
-    private static native void add_3(long src1_nativeObj, double src2_val0, double src2_val1, double src2_val2, double src2_val3, long dst_nativeObj, long mask_nativeObj, int dtype);
-    private static native void add_4(long src1_nativeObj, double src2_val0, double src2_val1, double src2_val2, double src2_val3, long dst_nativeObj, long mask_nativeObj);
-    private static native void add_5(long src1_nativeObj, double src2_val0, double src2_val1, double src2_val2, double src2_val3, long dst_nativeObj);
-
-    // C++:  void addWeighted(Mat src1, double alpha, Mat src2, double beta, double gamma, Mat& dst, int dtype = -1)
-    private static native void addWeighted_0(long src1_nativeObj, double alpha, long src2_nativeObj, double beta, double gamma, long dst_nativeObj, int dtype);
-    private static native void addWeighted_1(long src1_nativeObj, double alpha, long src2_nativeObj, double beta, double gamma, long dst_nativeObj);
-
-    // C++:  void batchDistance(Mat src1, Mat src2, Mat& dist, int dtype, Mat& nidx, int normType = NORM_L2, int K = 0, Mat mask = Mat(), int update = 0, bool crosscheck = false)
-    private static native void batchDistance_0(long src1_nativeObj, long src2_nativeObj, long dist_nativeObj, int dtype, long nidx_nativeObj, int normType, int K, long mask_nativeObj, int update, boolean crosscheck);
-    private static native void batchDistance_1(long src1_nativeObj, long src2_nativeObj, long dist_nativeObj, int dtype, long nidx_nativeObj, int normType, int K);
-    private static native void batchDistance_2(long src1_nativeObj, long src2_nativeObj, long dist_nativeObj, int dtype, long nidx_nativeObj);
-
-    // C++:  void bitwise_and(Mat src1, Mat src2, Mat& dst, Mat mask = Mat())
-    private static native void bitwise_and_0(long src1_nativeObj, long src2_nativeObj, long dst_nativeObj, long mask_nativeObj);
-    private static native void bitwise_and_1(long src1_nativeObj, long src2_nativeObj, long dst_nativeObj);
-
-    // C++:  void bitwise_not(Mat src, Mat& dst, Mat mask = Mat())
-    private static native void bitwise_not_0(long src_nativeObj, long dst_nativeObj, long mask_nativeObj);
-    private static native void bitwise_not_1(long src_nativeObj, long dst_nativeObj);
-
-    // C++:  void bitwise_or(Mat src1, Mat src2, Mat& dst, Mat mask = Mat())
-    private static native void bitwise_or_0(long src1_nativeObj, long src2_nativeObj, long dst_nativeObj, long mask_nativeObj);
-    private static native void bitwise_or_1(long src1_nativeObj, long src2_nativeObj, long dst_nativeObj);
-
-    // C++:  void bitwise_xor(Mat src1, Mat src2, Mat& dst, Mat mask = Mat())
-    private static native void bitwise_xor_0(long src1_nativeObj, long src2_nativeObj, long dst_nativeObj, long mask_nativeObj);
-    private static native void bitwise_xor_1(long src1_nativeObj, long src2_nativeObj, long dst_nativeObj);
-
-    // C++:  void calcCovarMatrix(Mat samples, Mat& covar, Mat& mean, int flags, int ctype = CV_64F)
-    private static native void calcCovarMatrix_0(long samples_nativeObj, long covar_nativeObj, long mean_nativeObj, int flags, int ctype);
-    private static native void calcCovarMatrix_1(long samples_nativeObj, long covar_nativeObj, long mean_nativeObj, int flags);
-
-    // C++:  void cartToPolar(Mat x, Mat y, Mat& magnitude, Mat& angle, bool angleInDegrees = false)
-    private static native void cartToPolar_0(long x_nativeObj, long y_nativeObj, long magnitude_nativeObj, long angle_nativeObj, boolean angleInDegrees);
-    private static native void cartToPolar_1(long x_nativeObj, long y_nativeObj, long magnitude_nativeObj, long angle_nativeObj);
-
-    // C++:  void compare(Mat src1, Mat src2, Mat& dst, int cmpop)
-    private static native void compare_0(long src1_nativeObj, long src2_nativeObj, long dst_nativeObj, int cmpop);
-
-    // C++:  void compare(Mat src1, Scalar src2, Mat& dst, int cmpop)
-    private static native void compare_1(long src1_nativeObj, double src2_val0, double src2_val1, double src2_val2, double src2_val3, long dst_nativeObj, int cmpop);
-
-    // C++:  void completeSymm(Mat& m, bool lowerToUpper = false)
-    private static native void completeSymm_0(long m_nativeObj, boolean lowerToUpper);
-    private static native void completeSymm_1(long m_nativeObj);
-
-    // C++:  void convertFp16(Mat src, Mat& dst)
-    private static native void convertFp16_0(long src_nativeObj, long dst_nativeObj);
-
-    // C++:  void convertScaleAbs(Mat src, Mat& dst, double alpha = 1, double beta = 0)
-    private static native void convertScaleAbs_0(long src_nativeObj, long dst_nativeObj, double alpha, double beta);
-    private static native void convertScaleAbs_1(long src_nativeObj, long dst_nativeObj);
-
-    // C++:  void copyMakeBorder(Mat src, Mat& dst, int top, int bottom, int left, int right, int borderType, Scalar value = Scalar())
-    private static native void copyMakeBorder_0(long src_nativeObj, long dst_nativeObj, int top, int bottom, int left, int right, int borderType, double value_val0, double value_val1, double value_val2, double value_val3);
-    private static native void copyMakeBorder_1(long src_nativeObj, long dst_nativeObj, int top, int bottom, int left, int right, int borderType);
-
-    // C++:  void dct(Mat src, Mat& dst, int flags = 0)
-    private static native void dct_0(long src_nativeObj, long dst_nativeObj, int flags);
-    private static native void dct_1(long src_nativeObj, long dst_nativeObj);
-
-    // C++:  void dft(Mat src, Mat& dst, int flags = 0, int nonzeroRows = 0)
-    private static native void dft_0(long src_nativeObj, long dst_nativeObj, int flags, int nonzeroRows);
-    private static native void dft_1(long src_nativeObj, long dst_nativeObj);
-
-    // C++:  void divide(Mat src1, Mat src2, Mat& dst, double scale = 1, int dtype = -1)
-    private static native void divide_0(long src1_nativeObj, long src2_nativeObj, long dst_nativeObj, double scale, int dtype);
-    private static native void divide_1(long src1_nativeObj, long src2_nativeObj, long dst_nativeObj, double scale);
-    private static native void divide_2(long src1_nativeObj, long src2_nativeObj, long dst_nativeObj);
-
-    // C++:  void divide(Mat src1, Scalar src2, Mat& dst, double scale = 1, int dtype = -1)
-    private static native void divide_3(long src1_nativeObj, double src2_val0, double src2_val1, double src2_val2, double src2_val3, long dst_nativeObj, double scale, int dtype);
-    private static native void divide_4(long src1_nativeObj, double src2_val0, double src2_val1, double src2_val2, double src2_val3, long dst_nativeObj, double scale);
-    private static native void divide_5(long src1_nativeObj, double src2_val0, double src2_val1, double src2_val2, double src2_val3, long dst_nativeObj);
-
-    // C++:  void divide(double scale, Mat src2, Mat& dst, int dtype = -1)
-    private static native void divide_6(double scale, long src2_nativeObj, long dst_nativeObj, int dtype);
-    private static native void divide_7(double scale, long src2_nativeObj, long dst_nativeObj);
-
-    // C++:  void eigenNonSymmetric(Mat src, Mat& eigenvalues, Mat& eigenvectors)
-    private static native void eigenNonSymmetric_0(long src_nativeObj, long eigenvalues_nativeObj, long eigenvectors_nativeObj);
-
-    // C++:  void exp(Mat src, Mat& dst)
-    private static native void exp_0(long src_nativeObj, long dst_nativeObj);
-
-    // C++:  void extractChannel(Mat src, Mat& dst, int coi)
-    private static native void extractChannel_0(long src_nativeObj, long dst_nativeObj, int coi);
-
-    // C++:  void findNonZero(Mat src, Mat& idx)
-    private static native void findNonZero_0(long src_nativeObj, long idx_nativeObj);
-
-    // C++:  void flip(Mat src, Mat& dst, int flipCode)
-    private static native void flip_0(long src_nativeObj, long dst_nativeObj, int flipCode);
-
-    // C++:  void gemm(Mat src1, Mat src2, double alpha, Mat src3, double beta, Mat& dst, int flags = 0)
-    private static native void gemm_0(long src1_nativeObj, long src2_nativeObj, double alpha, long src3_nativeObj, double beta, long dst_nativeObj, int flags);
-    private static native void gemm_1(long src1_nativeObj, long src2_nativeObj, double alpha, long src3_nativeObj, double beta, long dst_nativeObj);
-
-    // C++:  void hconcat(vector_Mat src, Mat& dst)
-    private static native void hconcat_0(long src_mat_nativeObj, long dst_nativeObj);
-
-    // C++:  void idct(Mat src, Mat& dst, int flags = 0)
-    private static native void idct_0(long src_nativeObj, long dst_nativeObj, int flags);
-    private static native void idct_1(long src_nativeObj, long dst_nativeObj);
-
-    // C++:  void idft(Mat src, Mat& dst, int flags = 0, int nonzeroRows = 0)
-    private static native void idft_0(long src_nativeObj, long dst_nativeObj, int flags, int nonzeroRows);
-    private static native void idft_1(long src_nativeObj, long dst_nativeObj);
-
-    // C++:  void inRange(Mat src, Scalar lowerb, Scalar upperb, Mat& dst)
-    private static native void inRange_0(long src_nativeObj, double lowerb_val0, double lowerb_val1, double lowerb_val2, double lowerb_val3, double upperb_val0, double upperb_val1, double upperb_val2, double upperb_val3, long dst_nativeObj);
-
-    // C++:  void insertChannel(Mat src, Mat& dst, int coi)
-    private static native void insertChannel_0(long src_nativeObj, long dst_nativeObj, int coi);
-
-    // C++:  void log(Mat src, Mat& dst)
-    private static native void log_0(long src_nativeObj, long dst_nativeObj);
-
-    // C++:  void magnitude(Mat x, Mat y, Mat& magnitude)
-    private static native void magnitude_0(long x_nativeObj, long y_nativeObj, long magnitude_nativeObj);
-
-    // C++:  void max(Mat src1, Mat src2, Mat& dst)
-    private static native void max_0(long src1_nativeObj, long src2_nativeObj, long dst_nativeObj);
-
-    // C++:  void max(Mat src1, Scalar src2, Mat& dst)
-    private static native void max_1(long src1_nativeObj, double src2_val0, double src2_val1, double src2_val2, double src2_val3, long dst_nativeObj);
-
-    // C++:  void meanStdDev(Mat src, vector_double& mean, vector_double& stddev, Mat mask = Mat())
-    private static native void meanStdDev_0(long src_nativeObj, long mean_mat_nativeObj, long stddev_mat_nativeObj, long mask_nativeObj);
-    private static native void meanStdDev_1(long src_nativeObj, long mean_mat_nativeObj, long stddev_mat_nativeObj);
-
-    // C++:  void merge(vector_Mat mv, Mat& dst)
-    private static native void merge_0(long mv_mat_nativeObj, long dst_nativeObj);
-
-    // C++:  void min(Mat src1, Mat src2, Mat& dst)
-    private static native void min_0(long src1_nativeObj, long src2_nativeObj, long dst_nativeObj);
-
-    // C++:  void min(Mat src1, Scalar src2, Mat& dst)
-    private static native void min_1(long src1_nativeObj, double src2_val0, double src2_val1, double src2_val2, double src2_val3, long dst_nativeObj);
-
-    // C++:  void mixChannels(vector_Mat src, vector_Mat dst, vector_int fromTo)
-    private static native void mixChannels_0(long src_mat_nativeObj, long dst_mat_nativeObj, long fromTo_mat_nativeObj);
-
-    // C++:  void mulSpectrums(Mat a, Mat b, Mat& c, int flags, bool conjB = false)
-    private static native void mulSpectrums_0(long a_nativeObj, long b_nativeObj, long c_nativeObj, int flags, boolean conjB);
-    private static native void mulSpectrums_1(long a_nativeObj, long b_nativeObj, long c_nativeObj, int flags);
-
-    // C++:  void mulTransposed(Mat src, Mat& dst, bool aTa, Mat delta = Mat(), double scale = 1, int dtype = -1)
-    private static native void mulTransposed_0(long src_nativeObj, long dst_nativeObj, boolean aTa, long delta_nativeObj, double scale, int dtype);
-    private static native void mulTransposed_1(long src_nativeObj, long dst_nativeObj, boolean aTa, long delta_nativeObj, double scale);
-    private static native void mulTransposed_2(long src_nativeObj, long dst_nativeObj, boolean aTa);
-
-    // C++:  void multiply(Mat src1, Mat src2, Mat& dst, double scale = 1, int dtype = -1)
-    private static native void multiply_0(long src1_nativeObj, long src2_nativeObj, long dst_nativeObj, double scale, int dtype);
-    private static native void multiply_1(long src1_nativeObj, long src2_nativeObj, long dst_nativeObj, double scale);
-    private static native void multiply_2(long src1_nativeObj, long src2_nativeObj, long dst_nativeObj);
-
-    // C++:  void multiply(Mat src1, Scalar src2, Mat& dst, double scale = 1, int dtype = -1)
-    private static native void multiply_3(long src1_nativeObj, double src2_val0, double src2_val1, double src2_val2, double src2_val3, long dst_nativeObj, double scale, int dtype);
-    private static native void multiply_4(long src1_nativeObj, double src2_val0, double src2_val1, double src2_val2, double src2_val3, long dst_nativeObj, double scale);
-    private static native void multiply_5(long src1_nativeObj, double src2_val0, double src2_val1, double src2_val2, double src2_val3, long dst_nativeObj);
-
-    // C++:  void normalize(Mat src, Mat& dst, double alpha = 1, double beta = 0, int norm_type = NORM_L2, int dtype = -1, Mat mask = Mat())
-    private static native void normalize_0(long src_nativeObj, long dst_nativeObj, double alpha, double beta, int norm_type, int dtype, long mask_nativeObj);
-    private static native void normalize_1(long src_nativeObj, long dst_nativeObj, double alpha, double beta, int norm_type, int dtype);
-    private static native void normalize_2(long src_nativeObj, long dst_nativeObj, double alpha, double beta, int norm_type);
-    private static native void normalize_3(long src_nativeObj, long dst_nativeObj);
-
-    // C++:  void patchNaNs(Mat& a, double val = 0)
-    private static native void patchNaNs_0(long a_nativeObj, double val);
-    private static native void patchNaNs_1(long a_nativeObj);
-
-    // C++:  void perspectiveTransform(Mat src, Mat& dst, Mat m)
-    private static native void perspectiveTransform_0(long src_nativeObj, long dst_nativeObj, long m_nativeObj);
-
-    // C++:  void phase(Mat x, Mat y, Mat& angle, bool angleInDegrees = false)
-    private static native void phase_0(long x_nativeObj, long y_nativeObj, long angle_nativeObj, boolean angleInDegrees);
-    private static native void phase_1(long x_nativeObj, long y_nativeObj, long angle_nativeObj);
-
-    // C++:  void polarToCart(Mat magnitude, Mat angle, Mat& x, Mat& y, bool angleInDegrees = false)
-    private static native void polarToCart_0(long magnitude_nativeObj, long angle_nativeObj, long x_nativeObj, long y_nativeObj, boolean angleInDegrees);
-    private static native void polarToCart_1(long magnitude_nativeObj, long angle_nativeObj, long x_nativeObj, long y_nativeObj);
-
-    // C++:  void pow(Mat src, double power, Mat& dst)
-    private static native void pow_0(long src_nativeObj, double power, long dst_nativeObj);
-
-    // C++:  void randShuffle(Mat& dst, double iterFactor = 1., RNG* rng = 0)
-    private static native void randShuffle_0(long dst_nativeObj, double iterFactor);
-    private static native void randShuffle_1(long dst_nativeObj);
-
-    // C++:  void randn(Mat& dst, double mean, double stddev)
-    private static native void randn_0(long dst_nativeObj, double mean, double stddev);
-
-    // C++:  void randu(Mat& dst, double low, double high)
-    private static native void randu_0(long dst_nativeObj, double low, double high);
-
-    // C++:  void reduce(Mat src, Mat& dst, int dim, int rtype, int dtype = -1)
-    private static native void reduce_0(long src_nativeObj, long dst_nativeObj, int dim, int rtype, int dtype);
-    private static native void reduce_1(long src_nativeObj, long dst_nativeObj, int dim, int rtype);
-
-    // C++:  void repeat(Mat src, int ny, int nx, Mat& dst)
-    private static native void repeat_0(long src_nativeObj, int ny, int nx, long dst_nativeObj);
-
-    // C++:  void rotate(Mat src, Mat& dst, int rotateCode)
-    private static native void rotate_0(long src_nativeObj, long dst_nativeObj, int rotateCode);
-
-    // C++:  void scaleAdd(Mat src1, double alpha, Mat src2, Mat& dst)
-    private static native void scaleAdd_0(long src1_nativeObj, double alpha, long src2_nativeObj, long dst_nativeObj);
-
-    // C++:  void setErrorVerbosity(bool verbose)
-    private static native void setErrorVerbosity_0(boolean verbose);
-
-    // C++:  void setIdentity(Mat& mtx, Scalar s = Scalar(1))
-    private static native void setIdentity_0(long mtx_nativeObj, double s_val0, double s_val1, double s_val2, double s_val3);
-    private static native void setIdentity_1(long mtx_nativeObj);
-
-    // C++:  void setNumThreads(int nthreads)
-    private static native void setNumThreads_0(int nthreads);
-
-    // C++:  void setRNGSeed(int seed)
-    private static native void setRNGSeed_0(int seed);
-
-    // C++:  void sort(Mat src, Mat& dst, int flags)
-    private static native void sort_0(long src_nativeObj, long dst_nativeObj, int flags);
-
-    // C++:  void sortIdx(Mat src, Mat& dst, int flags)
-    private static native void sortIdx_0(long src_nativeObj, long dst_nativeObj, int flags);
-
-    // C++:  void split(Mat m, vector_Mat& mv)
-    private static native void split_0(long m_nativeObj, long mv_mat_nativeObj);
-
-    // C++:  void sqrt(Mat src, Mat& dst)
-    private static native void sqrt_0(long src_nativeObj, long dst_nativeObj);
-
-    // C++:  void subtract(Mat src1, Mat src2, Mat& dst, Mat mask = Mat(), int dtype = -1)
-    private static native void subtract_0(long src1_nativeObj, long src2_nativeObj, long dst_nativeObj, long mask_nativeObj, int dtype);
-    private static native void subtract_1(long src1_nativeObj, long src2_nativeObj, long dst_nativeObj, long mask_nativeObj);
-    private static native void subtract_2(long src1_nativeObj, long src2_nativeObj, long dst_nativeObj);
-
-    // C++:  void subtract(Mat src1, Scalar src2, Mat& dst, Mat mask = Mat(), int dtype = -1)
-    private static native void subtract_3(long src1_nativeObj, double src2_val0, double src2_val1, double src2_val2, double src2_val3, long dst_nativeObj, long mask_nativeObj, int dtype);
-    private static native void subtract_4(long src1_nativeObj, double src2_val0, double src2_val1, double src2_val2, double src2_val3, long dst_nativeObj, long mask_nativeObj);
-    private static native void subtract_5(long src1_nativeObj, double src2_val0, double src2_val1, double src2_val2, double src2_val3, long dst_nativeObj);
-
-    // C++:  void transform(Mat src, Mat& dst, Mat m)
-    private static native void transform_0(long src_nativeObj, long dst_nativeObj, long m_nativeObj);
-
-    // C++:  void transpose(Mat src, Mat& dst)
-    private static native void transpose_0(long src_nativeObj, long dst_nativeObj);
-
-    // C++:  void vconcat(vector_Mat src, Mat& dst)
-    private static native void vconcat_0(long src_mat_nativeObj, long dst_nativeObj);
-
-    // C++:  void setUseIPP(bool flag)
-    private static native void setUseIPP_0(boolean flag);
-
-    // C++:  void setUseIPP_NE(bool flag)
-    private static native void setUseIPP_NE_0(boolean flag);
-private static native double[] n_minMaxLocManual(long src_nativeObj, long mask_nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/CvException.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/CvException.java
deleted file mode 100755
index e9241e6..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/CvException.java
+++ /dev/null
@@ -1,15 +0,0 @@
-package org.opencv.core;
-
-public class CvException extends RuntimeException {
-
-    private static final long serialVersionUID = 1L;
-
-    public CvException(String msg) {
-        super(msg);
-    }
-
-    @Override
-    public String toString() {
-        return "CvException [" + super.toString() + "]";
-    }
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/CvType.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/CvType.java
deleted file mode 100755
index 748c1cd..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/CvType.java
+++ /dev/null
@@ -1,136 +0,0 @@
-package org.opencv.core;
-
-public final class CvType {
-
-    // type depth constants
-    public static final int
-            CV_8U = 0, CV_8S = 1,
-            CV_16U = 2, CV_16S = 3,
-            CV_32S = 4,
-            CV_32F = 5,
-            CV_64F = 6,
-            CV_USRTYPE1 = 7;
-
-    // predefined type constants
-    public static final int
-            CV_8UC1 = CV_8UC(1), CV_8UC2 = CV_8UC(2), CV_8UC3 = CV_8UC(3), CV_8UC4 = CV_8UC(4),
-            CV_8SC1 = CV_8SC(1), CV_8SC2 = CV_8SC(2), CV_8SC3 = CV_8SC(3), CV_8SC4 = CV_8SC(4),
-            CV_16UC1 = CV_16UC(1), CV_16UC2 = CV_16UC(2), CV_16UC3 = CV_16UC(3), CV_16UC4 = CV_16UC(4),
-            CV_16SC1 = CV_16SC(1), CV_16SC2 = CV_16SC(2), CV_16SC3 = CV_16SC(3), CV_16SC4 = CV_16SC(4),
-            CV_32SC1 = CV_32SC(1), CV_32SC2 = CV_32SC(2), CV_32SC3 = CV_32SC(3), CV_32SC4 = CV_32SC(4),
-            CV_32FC1 = CV_32FC(1), CV_32FC2 = CV_32FC(2), CV_32FC3 = CV_32FC(3), CV_32FC4 = CV_32FC(4),
-            CV_64FC1 = CV_64FC(1), CV_64FC2 = CV_64FC(2), CV_64FC3 = CV_64FC(3), CV_64FC4 = CV_64FC(4);
-
-    private static final int CV_CN_MAX = 512, CV_CN_SHIFT = 3, CV_DEPTH_MAX = (1 << CV_CN_SHIFT);
-
-    public static final int makeType(int depth, int channels) {
-        if (channels <= 0 || channels >= CV_CN_MAX) {
-            throw new java.lang.UnsupportedOperationException(
-                    "Channels count should be 1.." + (CV_CN_MAX - 1));
-        }
-        if (depth < 0 || depth >= CV_DEPTH_MAX) {
-            throw new java.lang.UnsupportedOperationException(
-                    "Data type depth should be 0.." + (CV_DEPTH_MAX - 1));
-        }
-        return (depth & (CV_DEPTH_MAX - 1)) + ((channels - 1) << CV_CN_SHIFT);
-    }
-
-    public static final int CV_8UC(int ch) {
-        return makeType(CV_8U, ch);
-    }
-
-    public static final int CV_8SC(int ch) {
-        return makeType(CV_8S, ch);
-    }
-
-    public static final int CV_16UC(int ch) {
-        return makeType(CV_16U, ch);
-    }
-
-    public static final int CV_16SC(int ch) {
-        return makeType(CV_16S, ch);
-    }
-
-    public static final int CV_32SC(int ch) {
-        return makeType(CV_32S, ch);
-    }
-
-    public static final int CV_32FC(int ch) {
-        return makeType(CV_32F, ch);
-    }
-
-    public static final int CV_64FC(int ch) {
-        return makeType(CV_64F, ch);
-    }
-
-    public static final int channels(int type) {
-        return (type >> CV_CN_SHIFT) + 1;
-    }
-
-    public static final int depth(int type) {
-        return type & (CV_DEPTH_MAX - 1);
-    }
-
-    public static final boolean isInteger(int type) {
-        return depth(type) < CV_32F;
-    }
-
-    public static final int ELEM_SIZE(int type) {
-        switch (depth(type)) {
-        case CV_8U:
-        case CV_8S:
-            return channels(type);
-        case CV_16U:
-        case CV_16S:
-            return 2 * channels(type);
-        case CV_32S:
-        case CV_32F:
-            return 4 * channels(type);
-        case CV_64F:
-            return 8 * channels(type);
-        default:
-            throw new java.lang.UnsupportedOperationException(
-                    "Unsupported CvType value: " + type);
-        }
-    }
-
-    public static final String typeToString(int type) {
-        String s;
-        switch (depth(type)) {
-        case CV_8U:
-            s = "CV_8U";
-            break;
-        case CV_8S:
-            s = "CV_8S";
-            break;
-        case CV_16U:
-            s = "CV_16U";
-            break;
-        case CV_16S:
-            s = "CV_16S";
-            break;
-        case CV_32S:
-            s = "CV_32S";
-            break;
-        case CV_32F:
-            s = "CV_32F";
-            break;
-        case CV_64F:
-            s = "CV_64F";
-            break;
-        case CV_USRTYPE1:
-            s = "CV_USRTYPE1";
-            break;
-        default:
-            throw new java.lang.UnsupportedOperationException(
-                    "Unsupported CvType value: " + type);
-        }
-
-        int ch = channels(type);
-        if (ch <= 4)
-            return s + "C" + ch;
-        else
-            return s + "C(" + ch + ")";
-    }
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/DMatch.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/DMatch.java
deleted file mode 100755
index db44d9a..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/DMatch.java
+++ /dev/null
@@ -1,58 +0,0 @@
-package org.opencv.core;
-
-//C++: class DMatch
-
-/**
- * Structure for matching: query descriptor index, train descriptor index, train
- * image index and distance between descriptors.
- */
-public class DMatch {
-
-    /**
-     * Query descriptor index.
-     */
-    public int queryIdx;
-    /**
-     * Train descriptor index.
-     */
-    public int trainIdx;
-    /**
-     * Train image index.
-     */
-    public int imgIdx;
-
-    // javadoc: DMatch::distance
-    public float distance;
-
-    // javadoc: DMatch::DMatch()
-    public DMatch() {
-        this(-1, -1, Float.MAX_VALUE);
-    }
-
-    // javadoc: DMatch::DMatch(_queryIdx, _trainIdx, _distance)
-    public DMatch(int _queryIdx, int _trainIdx, float _distance) {
-        queryIdx = _queryIdx;
-        trainIdx = _trainIdx;
-        imgIdx = -1;
-        distance = _distance;
-    }
-
-    // javadoc: DMatch::DMatch(_queryIdx, _trainIdx, _imgIdx, _distance)
-    public DMatch(int _queryIdx, int _trainIdx, int _imgIdx, float _distance) {
-        queryIdx = _queryIdx;
-        trainIdx = _trainIdx;
-        imgIdx = _imgIdx;
-        distance = _distance;
-    }
-
-    public boolean lessThan(DMatch it) {
-        return distance < it.distance;
-    }
-
-    @Override
-    public String toString() {
-        return "DMatch [queryIdx=" + queryIdx + ", trainIdx=" + trainIdx
-                + ", imgIdx=" + imgIdx + ", distance=" + distance + "]";
-    }
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/KeyPoint.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/KeyPoint.java
deleted file mode 100755
index de5b215..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/KeyPoint.java
+++ /dev/null
@@ -1,83 +0,0 @@
-package org.opencv.core;
-
-import org.opencv.core.Point;
-
-//javadoc: KeyPoint
-public class KeyPoint {
-
-    /**
-     * Coordinates of the keypoint.
-     */
-    public Point pt;
-    /**
-     * Diameter of the useful keypoint adjacent area.
-     */
-    public float size;
-    /**
-     * Computed orientation of the keypoint (-1 if not applicable).
-     */
-    public float angle;
-    /**
-     * The response, by which the strongest keypoints have been selected. Can
-     * be used for further sorting or subsampling.
-     */
-    public float response;
-    /**
-     * Octave (pyramid layer), from which the keypoint has been extracted.
-     */
-    public int octave;
-    /**
-     * Object ID, that can be used to cluster keypoints by an object they
-     * belong to.
-     */
-    public int class_id;
-
-    // javadoc:KeyPoint::KeyPoint(x,y,_size,_angle,_response,_octave,_class_id)
-    public KeyPoint(float x, float y, float _size, float _angle, float _response, int _octave, int _class_id)
-    {
-        pt = new Point(x, y);
-        size = _size;
-        angle = _angle;
-        response = _response;
-        octave = _octave;
-        class_id = _class_id;
-    }
-
-    // javadoc: KeyPoint::KeyPoint()
-    public KeyPoint()
-    {
-        this(0, 0, 0, -1, 0, 0, -1);
-    }
-
-    // javadoc: KeyPoint::KeyPoint(x, y, _size, _angle, _response, _octave)
-    public KeyPoint(float x, float y, float _size, float _angle, float _response, int _octave)
-    {
-        this(x, y, _size, _angle, _response, _octave, -1);
-    }
-
-    // javadoc: KeyPoint::KeyPoint(x, y, _size, _angle, _response)
-    public KeyPoint(float x, float y, float _size, float _angle, float _response)
-    {
-        this(x, y, _size, _angle, _response, 0, -1);
-    }
-
-    // javadoc: KeyPoint::KeyPoint(x, y, _size, _angle)
-    public KeyPoint(float x, float y, float _size, float _angle)
-    {
-        this(x, y, _size, _angle, 0, 0, -1);
-    }
-
-    // javadoc: KeyPoint::KeyPoint(x, y, _size)
-    public KeyPoint(float x, float y, float _size)
-    {
-        this(x, y, _size, -1, 0, 0, -1);
-    }
-
-    @Override
-    public String toString() {
-        return "KeyPoint [pt=" + pt + ", size=" + size + ", angle=" + angle
-                + ", response=" + response + ", octave=" + octave
-                + ", class_id=" + class_id + "]";
-    }
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/Mat.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/Mat.java
deleted file mode 100755
index c975f0c..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/Mat.java
+++ /dev/null
@@ -1,1351 +0,0 @@
-package org.opencv.core;
-
-import java.nio.ByteBuffer;
-
-// C++: class Mat
-//javadoc: Mat
-public class Mat {
-
-    public final long nativeObj;
-
-    public Mat(long addr)
-    {
-        if (addr == 0)
-            throw new java.lang.UnsupportedOperationException("Native object address is NULL");
-        nativeObj = addr;
-    }
-
-    //
-    // C++: Mat::Mat()
-    //
-
-    // javadoc: Mat::Mat()
-    public Mat()
-    {
-
-        nativeObj = n_Mat();
-
-        return;
-    }
-
-    //
-    // C++: Mat::Mat(int rows, int cols, int type)
-    //
-
-    // javadoc: Mat::Mat(rows, cols, type)
-    public Mat(int rows, int cols, int type)
-    {
-
-        nativeObj = n_Mat(rows, cols, type);
-
-        return;
-    }
-
-    //
-    // C++: Mat::Mat(int rows, int cols, int type, void* data)
-    //
-
-    // javadoc: Mat::Mat(rows, cols, type, data)
-    public Mat(int rows, int cols, int type, ByteBuffer data)
-    {
-
-        nativeObj = n_Mat(rows, cols, type, data);
-
-        return;
-    }
-
-    //
-    // C++: Mat::Mat(Size size, int type)
-    //
-
-    // javadoc: Mat::Mat(size, type)
-    public Mat(Size size, int type)
-    {
-
-        nativeObj = n_Mat(size.width, size.height, type);
-
-        return;
-    }
-
-    //
-    // C++: Mat::Mat(int rows, int cols, int type, Scalar s)
-    //
-
-    // javadoc: Mat::Mat(rows, cols, type, s)
-    public Mat(int rows, int cols, int type, Scalar s)
-    {
-
-        nativeObj = n_Mat(rows, cols, type, s.val[0], s.val[1], s.val[2], s.val[3]);
-
-        return;
-    }
-
-    //
-    // C++: Mat::Mat(Size size, int type, Scalar s)
-    //
-
-    // javadoc: Mat::Mat(size, type, s)
-    public Mat(Size size, int type, Scalar s)
-    {
-
-        nativeObj = n_Mat(size.width, size.height, type, s.val[0], s.val[1], s.val[2], s.val[3]);
-
-        return;
-    }
-
-    //
-    // C++: Mat::Mat(Mat m, Range rowRange, Range colRange = Range::all())
-    //
-
-    // javadoc: Mat::Mat(m, rowRange, colRange)
-    public Mat(Mat m, Range rowRange, Range colRange)
-    {
-
-        nativeObj = n_Mat(m.nativeObj, rowRange.start, rowRange.end, colRange.start, colRange.end);
-
-        return;
-    }
-
-    // javadoc: Mat::Mat(m, rowRange)
-    public Mat(Mat m, Range rowRange)
-    {
-
-        nativeObj = n_Mat(m.nativeObj, rowRange.start, rowRange.end);
-
-        return;
-    }
-
-    //
-    // C++: Mat::Mat(Mat m, Rect roi)
-    //
-
-    // javadoc: Mat::Mat(m, roi)
-    public Mat(Mat m, Rect roi)
-    {
-
-        nativeObj = n_Mat(m.nativeObj, roi.y, roi.y + roi.height, roi.x, roi.x + roi.width);
-
-        return;
-    }
-
-    //
-    // C++: Mat Mat::adjustROI(int dtop, int dbottom, int dleft, int dright)
-    //
-
-    // javadoc: Mat::adjustROI(dtop, dbottom, dleft, dright)
-    public Mat adjustROI(int dtop, int dbottom, int dleft, int dright)
-    {
-
-        Mat retVal = new Mat(n_adjustROI(nativeObj, dtop, dbottom, dleft, dright));
-
-        return retVal;
-    }
-
-    //
-    // C++: void Mat::assignTo(Mat m, int type = -1)
-    //
-
-    // javadoc: Mat::assignTo(m, type)
-    public void assignTo(Mat m, int type)
-    {
-
-        n_assignTo(nativeObj, m.nativeObj, type);
-
-        return;
-    }
-
-    // javadoc: Mat::assignTo(m)
-    public void assignTo(Mat m)
-    {
-
-        n_assignTo(nativeObj, m.nativeObj);
-
-        return;
-    }
-
-    //
-    // C++: int Mat::channels()
-    //
-
-    // javadoc: Mat::channels()
-    public int channels()
-    {
-
-        int retVal = n_channels(nativeObj);
-
-        return retVal;
-    }
-
-    //
-    // C++: int Mat::checkVector(int elemChannels, int depth = -1, bool
-    // requireContinuous = true)
-    //
-
-    // javadoc: Mat::checkVector(elemChannels, depth, requireContinuous)
-    public int checkVector(int elemChannels, int depth, boolean requireContinuous)
-    {
-
-        int retVal = n_checkVector(nativeObj, elemChannels, depth, requireContinuous);
-
-        return retVal;
-    }
-
-    // javadoc: Mat::checkVector(elemChannels, depth)
-    public int checkVector(int elemChannels, int depth)
-    {
-
-        int retVal = n_checkVector(nativeObj, elemChannels, depth);
-
-        return retVal;
-    }
-
-    // javadoc: Mat::checkVector(elemChannels)
-    public int checkVector(int elemChannels)
-    {
-
-        int retVal = n_checkVector(nativeObj, elemChannels);
-
-        return retVal;
-    }
-
-    //
-    // C++: Mat Mat::clone()
-    //
-
-    // javadoc: Mat::clone()
-    public Mat clone()
-    {
-
-        Mat retVal = new Mat(n_clone(nativeObj));
-
-        return retVal;
-    }
-
-    //
-    // C++: Mat Mat::col(int x)
-    //
-
-    // javadoc: Mat::col(x)
-    public Mat col(int x)
-    {
-
-        Mat retVal = new Mat(n_col(nativeObj, x));
-
-        return retVal;
-    }
-
-    //
-    // C++: Mat Mat::colRange(int startcol, int endcol)
-    //
-
-    // javadoc: Mat::colRange(startcol, endcol)
-    public Mat colRange(int startcol, int endcol)
-    {
-
-        Mat retVal = new Mat(n_colRange(nativeObj, startcol, endcol));
-
-        return retVal;
-    }
-
-    //
-    // C++: Mat Mat::colRange(Range r)
-    //
-
-    // javadoc: Mat::colRange(r)
-    public Mat colRange(Range r)
-    {
-
-        Mat retVal = new Mat(n_colRange(nativeObj, r.start, r.end));
-
-        return retVal;
-    }
-
-    //
-    // C++: int Mat::dims()
-    //
-
-    // javadoc: Mat::dims()
-    public int dims()
-    {
-
-        int retVal = n_dims(nativeObj);
-
-        return retVal;
-    }
-
-    //
-    // C++: int Mat::cols()
-    //
-
-    // javadoc: Mat::cols()
-    public int cols()
-    {
-
-        int retVal = n_cols(nativeObj);
-
-        return retVal;
-    }
-
-    //
-    // C++: void Mat::convertTo(Mat& m, int rtype, double alpha = 1, double beta
-    // = 0)
-    //
-
-    // javadoc: Mat::convertTo(m, rtype, alpha, beta)
-    public void convertTo(Mat m, int rtype, double alpha, double beta)
-    {
-
-        n_convertTo(nativeObj, m.nativeObj, rtype, alpha, beta);
-
-        return;
-    }
-
-    // javadoc: Mat::convertTo(m, rtype, alpha)
-    public void convertTo(Mat m, int rtype, double alpha)
-    {
-
-        n_convertTo(nativeObj, m.nativeObj, rtype, alpha);
-
-        return;
-    }
-
-    // javadoc: Mat::convertTo(m, rtype)
-    public void convertTo(Mat m, int rtype)
-    {
-
-        n_convertTo(nativeObj, m.nativeObj, rtype);
-
-        return;
-    }
-
-    //
-    // C++: void Mat::copyTo(Mat& m)
-    //
-
-    // javadoc: Mat::copyTo(m)
-    public void copyTo(Mat m)
-    {
-
-        n_copyTo(nativeObj, m.nativeObj);
-
-        return;
-    }
-
-    //
-    // C++: void Mat::copyTo(Mat& m, Mat mask)
-    //
-
-    // javadoc: Mat::copyTo(m, mask)
-    public void copyTo(Mat m, Mat mask)
-    {
-
-        n_copyTo(nativeObj, m.nativeObj, mask.nativeObj);
-
-        return;
-    }
-
-    //
-    // C++: void Mat::create(int rows, int cols, int type)
-    //
-
-    // javadoc: Mat::create(rows, cols, type)
-    public void create(int rows, int cols, int type)
-    {
-
-        n_create(nativeObj, rows, cols, type);
-
-        return;
-    }
-
-    //
-    // C++: void Mat::create(Size size, int type)
-    //
-
-    // javadoc: Mat::create(size, type)
-    public void create(Size size, int type)
-    {
-
-        n_create(nativeObj, size.width, size.height, type);
-
-        return;
-    }
-
-    //
-    // C++: Mat Mat::cross(Mat m)
-    //
-
-    // javadoc: Mat::cross(m)
-    public Mat cross(Mat m)
-    {
-
-        Mat retVal = new Mat(n_cross(nativeObj, m.nativeObj));
-
-        return retVal;
-    }
-
-    //
-    // C++: long Mat::dataAddr()
-    //
-
-    // javadoc: Mat::dataAddr()
-    public long dataAddr()
-    {
-
-        long retVal = n_dataAddr(nativeObj);
-
-        return retVal;
-    }
-
-    //
-    // C++: int Mat::depth()
-    //
-
-    // javadoc: Mat::depth()
-    public int depth()
-    {
-
-        int retVal = n_depth(nativeObj);
-
-        return retVal;
-    }
-
-    //
-    // C++: Mat Mat::diag(int d = 0)
-    //
-
-    // javadoc: Mat::diag(d)
-    public Mat diag(int d)
-    {
-
-        Mat retVal = new Mat(n_diag(nativeObj, d));
-
-        return retVal;
-    }
-
-    // javadoc: Mat::diag()
-    public Mat diag()
-    {
-
-        Mat retVal = new Mat(n_diag(nativeObj, 0));
-
-        return retVal;
-    }
-
-    //
-    // C++: static Mat Mat::diag(Mat d)
-    //
-
-    // javadoc: Mat::diag(d)
-    public static Mat diag(Mat d)
-    {
-
-        Mat retVal = new Mat(n_diag(d.nativeObj));
-
-        return retVal;
-    }
-
-    //
-    // C++: double Mat::dot(Mat m)
-    //
-
-    // javadoc: Mat::dot(m)
-    public double dot(Mat m)
-    {
-
-        double retVal = n_dot(nativeObj, m.nativeObj);
-
-        return retVal;
-    }
-
-    //
-    // C++: size_t Mat::elemSize()
-    //
-
-    // javadoc: Mat::elemSize()
-    public long elemSize()
-    {
-
-        long retVal = n_elemSize(nativeObj);
-
-        return retVal;
-    }
-
-    //
-    // C++: size_t Mat::elemSize1()
-    //
-
-    // javadoc: Mat::elemSize1()
-    public long elemSize1()
-    {
-
-        long retVal = n_elemSize1(nativeObj);
-
-        return retVal;
-    }
-
-    //
-    // C++: bool Mat::empty()
-    //
-
-    // javadoc: Mat::empty()
-    public boolean empty()
-    {
-
-        boolean retVal = n_empty(nativeObj);
-
-        return retVal;
-    }
-
-    //
-    // C++: static Mat Mat::eye(int rows, int cols, int type)
-    //
-
-    // javadoc: Mat::eye(rows, cols, type)
-    public static Mat eye(int rows, int cols, int type)
-    {
-
-        Mat retVal = new Mat(n_eye(rows, cols, type));
-
-        return retVal;
-    }
-
-    //
-    // C++: static Mat Mat::eye(Size size, int type)
-    //
-
-    // javadoc: Mat::eye(size, type)
-    public static Mat eye(Size size, int type)
-    {
-
-        Mat retVal = new Mat(n_eye(size.width, size.height, type));
-
-        return retVal;
-    }
-
-    //
-    // C++: Mat Mat::inv(int method = DECOMP_LU)
-    //
-
-    // javadoc: Mat::inv(method)
-    public Mat inv(int method)
-    {
-
-        Mat retVal = new Mat(n_inv(nativeObj, method));
-
-        return retVal;
-    }
-
-    // javadoc: Mat::inv()
-    public Mat inv()
-    {
-
-        Mat retVal = new Mat(n_inv(nativeObj));
-
-        return retVal;
-    }
-
-    //
-    // C++: bool Mat::isContinuous()
-    //
-
-    // javadoc: Mat::isContinuous()
-    public boolean isContinuous()
-    {
-
-        boolean retVal = n_isContinuous(nativeObj);
-
-        return retVal;
-    }
-
-    //
-    // C++: bool Mat::isSubmatrix()
-    //
-
-    // javadoc: Mat::isSubmatrix()
-    public boolean isSubmatrix()
-    {
-
-        boolean retVal = n_isSubmatrix(nativeObj);
-
-        return retVal;
-    }
-
-    //
-    // C++: void Mat::locateROI(Size wholeSize, Point ofs)
-    //
-
-    // javadoc: Mat::locateROI(wholeSize, ofs)
-    public void locateROI(Size wholeSize, Point ofs)
-    {
-        double[] wholeSize_out = new double[2];
-        double[] ofs_out = new double[2];
-        locateROI_0(nativeObj, wholeSize_out, ofs_out);
-        if(wholeSize!=null){ wholeSize.width = wholeSize_out[0]; wholeSize.height = wholeSize_out[1]; }
-        if(ofs!=null){ ofs.x = ofs_out[0]; ofs.y = ofs_out[1]; }
-        return;
-    }
-
-    //
-    // C++: Mat Mat::mul(Mat m, double scale = 1)
-    //
-
-    // javadoc: Mat::mul(m, scale)
-    public Mat mul(Mat m, double scale)
-    {
-
-        Mat retVal = new Mat(n_mul(nativeObj, m.nativeObj, scale));
-
-        return retVal;
-    }
-
-    // javadoc: Mat::mul(m)
-    public Mat mul(Mat m)
-    {
-
-        Mat retVal = new Mat(n_mul(nativeObj, m.nativeObj));
-
-        return retVal;
-    }
-
-    //
-    // C++: static Mat Mat::ones(int rows, int cols, int type)
-    //
-
-    // javadoc: Mat::ones(rows, cols, type)
-    public static Mat ones(int rows, int cols, int type)
-    {
-
-        Mat retVal = new Mat(n_ones(rows, cols, type));
-
-        return retVal;
-    }
-
-    //
-    // C++: static Mat Mat::ones(Size size, int type)
-    //
-
-    // javadoc: Mat::ones(size, type)
-    public static Mat ones(Size size, int type)
-    {
-
-        Mat retVal = new Mat(n_ones(size.width, size.height, type));
-
-        return retVal;
-    }
-
-    //
-    // C++: void Mat::push_back(Mat m)
-    //
-
-    // javadoc: Mat::push_back(m)
-    public void push_back(Mat m)
-    {
-
-        n_push_back(nativeObj, m.nativeObj);
-
-        return;
-    }
-
-    //
-    // C++: void Mat::release()
-    //
-
-    // javadoc: Mat::release()
-    public void release()
-    {
-
-        n_release(nativeObj);
-
-        return;
-    }
-
-    //
-    // C++: Mat Mat::reshape(int cn, int rows = 0)
-    //
-
-    // javadoc: Mat::reshape(cn, rows)
-    public Mat reshape(int cn, int rows)
-    {
-
-        Mat retVal = new Mat(n_reshape(nativeObj, cn, rows));
-
-        return retVal;
-    }
-
-    // javadoc: Mat::reshape(cn)
-    public Mat reshape(int cn)
-    {
-
-        Mat retVal = new Mat(n_reshape(nativeObj, cn));
-
-        return retVal;
-    }
-
-    //
-    // C++: Mat Mat::row(int y)
-    //
-
-    // javadoc: Mat::row(y)
-    public Mat row(int y)
-    {
-
-        Mat retVal = new Mat(n_row(nativeObj, y));
-
-        return retVal;
-    }
-
-    //
-    // C++: Mat Mat::rowRange(int startrow, int endrow)
-    //
-
-    // javadoc: Mat::rowRange(startrow, endrow)
-    public Mat rowRange(int startrow, int endrow)
-    {
-
-        Mat retVal = new Mat(n_rowRange(nativeObj, startrow, endrow));
-
-        return retVal;
-    }
-
-    //
-    // C++: Mat Mat::rowRange(Range r)
-    //
-
-    // javadoc: Mat::rowRange(r)
-    public Mat rowRange(Range r)
-    {
-
-        Mat retVal = new Mat(n_rowRange(nativeObj, r.start, r.end));
-
-        return retVal;
-    }
-
-    //
-    // C++: int Mat::rows()
-    //
-
-    // javadoc: Mat::rows()
-    public int rows()
-    {
-
-        int retVal = n_rows(nativeObj);
-
-        return retVal;
-    }
-
-    //
-    // C++: Mat Mat::operator =(Scalar s)
-    //
-
-    // javadoc: Mat::operator =(s)
-    public Mat setTo(Scalar s)
-    {
-
-        Mat retVal = new Mat(n_setTo(nativeObj, s.val[0], s.val[1], s.val[2], s.val[3]));
-
-        return retVal;
-    }
-
-    //
-    // C++: Mat Mat::setTo(Scalar value, Mat mask = Mat())
-    //
-
-    // javadoc: Mat::setTo(value, mask)
-    public Mat setTo(Scalar value, Mat mask)
-    {
-
-        Mat retVal = new Mat(n_setTo(nativeObj, value.val[0], value.val[1], value.val[2], value.val[3], mask.nativeObj));
-
-        return retVal;
-    }
-
-    //
-    // C++: Mat Mat::setTo(Mat value, Mat mask = Mat())
-    //
-
-    // javadoc: Mat::setTo(value, mask)
-    public Mat setTo(Mat value, Mat mask)
-    {
-
-        Mat retVal = new Mat(n_setTo(nativeObj, value.nativeObj, mask.nativeObj));
-
-        return retVal;
-    }
-
-    // javadoc: Mat::setTo(value)
-    public Mat setTo(Mat value)
-    {
-
-        Mat retVal = new Mat(n_setTo(nativeObj, value.nativeObj));
-
-        return retVal;
-    }
-
-    //
-    // C++: Size Mat::size()
-    //
-
-    // javadoc: Mat::size()
-    public Size size()
-    {
-
-        Size retVal = new Size(n_size(nativeObj));
-
-        return retVal;
-    }
-
-    //
-    // C++: size_t Mat::step1(int i = 0)
-    //
-
-    // javadoc: Mat::step1(i)
-    public long step1(int i)
-    {
-
-        long retVal = n_step1(nativeObj, i);
-
-        return retVal;
-    }
-
-    // javadoc: Mat::step1()
-    public long step1()
-    {
-
-        long retVal = n_step1(nativeObj);
-
-        return retVal;
-    }
-
-    //
-    // C++: Mat Mat::operator()(int rowStart, int rowEnd, int colStart, int
-    // colEnd)
-    //
-
-    // javadoc: Mat::operator()(rowStart, rowEnd, colStart, colEnd)
-    public Mat submat(int rowStart, int rowEnd, int colStart, int colEnd)
-    {
-
-        Mat retVal = new Mat(n_submat_rr(nativeObj, rowStart, rowEnd, colStart, colEnd));
-
-        return retVal;
-    }
-
-    //
-    // C++: Mat Mat::operator()(Range rowRange, Range colRange)
-    //
-
-    // javadoc: Mat::operator()(rowRange, colRange)
-    public Mat submat(Range rowRange, Range colRange)
-    {
-
-        Mat retVal = new Mat(n_submat_rr(nativeObj, rowRange.start, rowRange.end, colRange.start, colRange.end));
-
-        return retVal;
-    }
-
-    //
-    // C++: Mat Mat::operator()(Rect roi)
-    //
-
-    // javadoc: Mat::operator()(roi)
-    public Mat submat(Rect roi)
-    {
-
-        Mat retVal = new Mat(n_submat(nativeObj, roi.x, roi.y, roi.width, roi.height));
-
-        return retVal;
-    }
-
-    //
-    // C++: Mat Mat::t()
-    //
-
-    // javadoc: Mat::t()
-    public Mat t()
-    {
-
-        Mat retVal = new Mat(n_t(nativeObj));
-
-        return retVal;
-    }
-
-    //
-    // C++: size_t Mat::total()
-    //
-
-    // javadoc: Mat::total()
-    public long total()
-    {
-
-        long retVal = n_total(nativeObj);
-
-        return retVal;
-    }
-
-    //
-    // C++: int Mat::type()
-    //
-
-    // javadoc: Mat::type()
-    public int type()
-    {
-
-        int retVal = n_type(nativeObj);
-
-        return retVal;
-    }
-
-    //
-    // C++: static Mat Mat::zeros(int rows, int cols, int type)
-    //
-
-    // javadoc: Mat::zeros(rows, cols, type)
-    public static Mat zeros(int rows, int cols, int type)
-    {
-
-        Mat retVal = new Mat(n_zeros(rows, cols, type));
-
-        return retVal;
-    }
-
-    //
-    // C++: static Mat Mat::zeros(Size size, int type)
-    //
-
-    // javadoc: Mat::zeros(size, type)
-    public static Mat zeros(Size size, int type)
-    {
-
-        Mat retVal = new Mat(n_zeros(size.width, size.height, type));
-
-        return retVal;
-    }
-
-    @Override
-    protected void finalize() throws Throwable {
-        n_delete(nativeObj);
-        super.finalize();
-    }
-
-    // javadoc:Mat::toString()
-    @Override
-    public String toString() {
-        return "Mat [ " +
-                rows() + "*" + cols() + "*" + CvType.typeToString(type()) +
-                ", isCont=" + isContinuous() + ", isSubmat=" + isSubmatrix() +
-                ", nativeObj=0x" + Long.toHexString(nativeObj) +
-                ", dataAddr=0x" + Long.toHexString(dataAddr()) +
-                " ]";
-    }
-
-    // javadoc:Mat::dump()
-    public String dump() {
-        return nDump(nativeObj);
-    }
-
-    // javadoc:Mat::put(row,col,data)
-    public int put(int row, int col, double... data) {
-        int t = type();
-        if (data == null || data.length % CvType.channels(t) != 0)
-            throw new java.lang.UnsupportedOperationException(
-                    "Provided data element number (" +
-                            (data == null ? 0 : data.length) +
-                            ") should be multiple of the Mat channels count (" +
-                            CvType.channels(t) + ")");
-        return nPutD(nativeObj, row, col, data.length, data);
-    }
-
-    // javadoc:Mat::put(row,col,data)
-    public int put(int row, int col, float[] data) {
-        int t = type();
-        if (data == null || data.length % CvType.channels(t) != 0)
-            throw new java.lang.UnsupportedOperationException(
-                    "Provided data element number (" +
-                            (data == null ? 0 : data.length) +
-                            ") should be multiple of the Mat channels count (" +
-                            CvType.channels(t) + ")");
-        if (CvType.depth(t) == CvType.CV_32F) {
-            return nPutF(nativeObj, row, col, data.length, data);
-        }
-        throw new java.lang.UnsupportedOperationException("Mat data type is not compatible: " + t);
-    }
-
-    // javadoc:Mat::put(row,col,data)
-    public int put(int row, int col, int[] data) {
-        int t = type();
-        if (data == null || data.length % CvType.channels(t) != 0)
-            throw new java.lang.UnsupportedOperationException(
-                    "Provided data element number (" +
-                            (data == null ? 0 : data.length) +
-                            ") should be multiple of the Mat channels count (" +
-                            CvType.channels(t) + ")");
-        if (CvType.depth(t) == CvType.CV_32S) {
-            return nPutI(nativeObj, row, col, data.length, data);
-        }
-        throw new java.lang.UnsupportedOperationException("Mat data type is not compatible: " + t);
-    }
-
-    // javadoc:Mat::put(row,col,data)
-    public int put(int row, int col, short[] data) {
-        int t = type();
-        if (data == null || data.length % CvType.channels(t) != 0)
-            throw new java.lang.UnsupportedOperationException(
-                    "Provided data element number (" +
-                            (data == null ? 0 : data.length) +
-                            ") should be multiple of the Mat channels count (" +
-                            CvType.channels(t) + ")");
-        if (CvType.depth(t) == CvType.CV_16U || CvType.depth(t) == CvType.CV_16S) {
-            return nPutS(nativeObj, row, col, data.length, data);
-        }
-        throw new java.lang.UnsupportedOperationException("Mat data type is not compatible: " + t);
-    }
-
-    // javadoc:Mat::put(row,col,data)
-    public int put(int row, int col, byte[] data) {
-        int t = type();
-        if (data == null || data.length % CvType.channels(t) != 0)
-            throw new java.lang.UnsupportedOperationException(
-                    "Provided data element number (" +
-                            (data == null ? 0 : data.length) +
-                            ") should be multiple of the Mat channels count (" +
-                            CvType.channels(t) + ")");
-        if (CvType.depth(t) == CvType.CV_8U || CvType.depth(t) == CvType.CV_8S) {
-            return nPutB(nativeObj, row, col, data.length, data);
-        }
-        throw new java.lang.UnsupportedOperationException("Mat data type is not compatible: " + t);
-    }
-
-    // javadoc:Mat::put(row,col,data,offset,length)
-    public int put(int row, int col, byte[] data, int offset, int length) {
-        int t = type();
-        if (data == null || length % CvType.channels(t) != 0)
-            throw new java.lang.UnsupportedOperationException(
-                    "Provided data element number (" +
-                            (data == null ? 0 : data.length) +
-                            ") should be multiple of the Mat channels count (" +
-                            CvType.channels(t) + ")");
-        if (CvType.depth(t) == CvType.CV_8U || CvType.depth(t) == CvType.CV_8S) {
-            return nPutBwOffset(nativeObj, row, col, length, offset, data);
-        }
-        throw new java.lang.UnsupportedOperationException("Mat data type is not compatible: " + t);
-    }
-
-    // javadoc:Mat::get(row,col,data)
-    public int get(int row, int col, byte[] data) {
-        int t = type();
-        if (data == null || data.length % CvType.channels(t) != 0)
-            throw new java.lang.UnsupportedOperationException(
-                    "Provided data element number (" +
-                            (data == null ? 0 : data.length) +
-                            ") should be multiple of the Mat channels count (" +
-                            CvType.channels(t) + ")");
-        if (CvType.depth(t) == CvType.CV_8U || CvType.depth(t) == CvType.CV_8S) {
-            return nGetB(nativeObj, row, col, data.length, data);
-        }
-        throw new java.lang.UnsupportedOperationException("Mat data type is not compatible: " + t);
-    }
-
-    // javadoc:Mat::get(row,col,data)
-    public int get(int row, int col, short[] data) {
-        int t = type();
-        if (data == null || data.length % CvType.channels(t) != 0)
-            throw new java.lang.UnsupportedOperationException(
-                    "Provided data element number (" +
-                            (data == null ? 0 : data.length) +
-                            ") should be multiple of the Mat channels count (" +
-                            CvType.channels(t) + ")");
-        if (CvType.depth(t) == CvType.CV_16U || CvType.depth(t) == CvType.CV_16S) {
-            return nGetS(nativeObj, row, col, data.length, data);
-        }
-        throw new java.lang.UnsupportedOperationException("Mat data type is not compatible: " + t);
-    }
-
-    // javadoc:Mat::get(row,col,data)
-    public int get(int row, int col, int[] data) {
-        int t = type();
-        if (data == null || data.length % CvType.channels(t) != 0)
-            throw new java.lang.UnsupportedOperationException(
-                    "Provided data element number (" +
-                            (data == null ? 0 : data.length) +
-                            ") should be multiple of the Mat channels count (" +
-                            CvType.channels(t) + ")");
-        if (CvType.depth(t) == CvType.CV_32S) {
-            return nGetI(nativeObj, row, col, data.length, data);
-        }
-        throw new java.lang.UnsupportedOperationException("Mat data type is not compatible: " + t);
-    }
-
-    // javadoc:Mat::get(row,col,data)
-    public int get(int row, int col, float[] data) {
-        int t = type();
-        if (data == null || data.length % CvType.channels(t) != 0)
-            throw new java.lang.UnsupportedOperationException(
-                    "Provided data element number (" +
-                            (data == null ? 0 : data.length) +
-                            ") should be multiple of the Mat channels count (" +
-                            CvType.channels(t) + ")");
-        if (CvType.depth(t) == CvType.CV_32F) {
-            return nGetF(nativeObj, row, col, data.length, data);
-        }
-        throw new java.lang.UnsupportedOperationException("Mat data type is not compatible: " + t);
-    }
-
-    // javadoc:Mat::get(row,col,data)
-    public int get(int row, int col, double[] data) {
-        int t = type();
-        if (data == null || data.length % CvType.channels(t) != 0)
-            throw new java.lang.UnsupportedOperationException(
-                    "Provided data element number (" +
-                            (data == null ? 0 : data.length) +
-                            ") should be multiple of the Mat channels count (" +
-                            CvType.channels(t) + ")");
-        if (CvType.depth(t) == CvType.CV_64F) {
-            return nGetD(nativeObj, row, col, data.length, data);
-        }
-        throw new java.lang.UnsupportedOperationException("Mat data type is not compatible: " + t);
-    }
-
-    // javadoc:Mat::get(row,col)
-    public double[] get(int row, int col) {
-        return nGet(nativeObj, row, col);
-    }
-
-    // javadoc:Mat::height()
-    public int height() {
-        return rows();
-    }
-
-    // javadoc:Mat::width()
-    public int width() {
-        return cols();
-    }
-
-    // javadoc:Mat::getNativeObjAddr()
-    public long getNativeObjAddr() {
-        return nativeObj;
-    }
-
-    // C++: Mat::Mat()
-    private static native long n_Mat();
-
-    // C++: Mat::Mat(int rows, int cols, int type)
-    private static native long n_Mat(int rows, int cols, int type);
-
-    // C++: Mat::Mat(int rows, int cols, int type, void* data)
-    private static native long n_Mat(int rows, int cols, int type, ByteBuffer data);
-
-    // C++: Mat::Mat(Size size, int type)
-    private static native long n_Mat(double size_width, double size_height, int type);
-
-    // C++: Mat::Mat(int rows, int cols, int type, Scalar s)
-    private static native long n_Mat(int rows, int cols, int type, double s_val0, double s_val1, double s_val2, double s_val3);
-
-    // C++: Mat::Mat(Size size, int type, Scalar s)
-    private static native long n_Mat(double size_width, double size_height, int type, double s_val0, double s_val1, double s_val2, double s_val3);
-
-    // C++: Mat::Mat(Mat m, Range rowRange, Range colRange = Range::all())
-    private static native long n_Mat(long m_nativeObj, int rowRange_start, int rowRange_end, int colRange_start, int colRange_end);
-
-    private static native long n_Mat(long m_nativeObj, int rowRange_start, int rowRange_end);
-
-    // C++: Mat Mat::adjustROI(int dtop, int dbottom, int dleft, int dright)
-    private static native long n_adjustROI(long nativeObj, int dtop, int dbottom, int dleft, int dright);
-
-    // C++: void Mat::assignTo(Mat m, int type = -1)
-    private static native void n_assignTo(long nativeObj, long m_nativeObj, int type);
-
-    private static native void n_assignTo(long nativeObj, long m_nativeObj);
-
-    // C++: int Mat::channels()
-    private static native int n_channels(long nativeObj);
-
-    // C++: int Mat::checkVector(int elemChannels, int depth = -1, bool
-    // requireContinuous = true)
-    private static native int n_checkVector(long nativeObj, int elemChannels, int depth, boolean requireContinuous);
-
-    private static native int n_checkVector(long nativeObj, int elemChannels, int depth);
-
-    private static native int n_checkVector(long nativeObj, int elemChannels);
-
-    // C++: Mat Mat::clone()
-    private static native long n_clone(long nativeObj);
-
-    // C++: Mat Mat::col(int x)
-    private static native long n_col(long nativeObj, int x);
-
-    // C++: Mat Mat::colRange(int startcol, int endcol)
-    private static native long n_colRange(long nativeObj, int startcol, int endcol);
-
-    // C++: int Mat::dims()
-    private static native int n_dims(long nativeObj);
-
-    // C++: int Mat::cols()
-    private static native int n_cols(long nativeObj);
-
-    // C++: void Mat::convertTo(Mat& m, int rtype, double alpha = 1, double beta
-    // = 0)
-    private static native void n_convertTo(long nativeObj, long m_nativeObj, int rtype, double alpha, double beta);
-
-    private static native void n_convertTo(long nativeObj, long m_nativeObj, int rtype, double alpha);
-
-    private static native void n_convertTo(long nativeObj, long m_nativeObj, int rtype);
-
-    // C++: void Mat::copyTo(Mat& m)
-    private static native void n_copyTo(long nativeObj, long m_nativeObj);
-
-    // C++: void Mat::copyTo(Mat& m, Mat mask)
-    private static native void n_copyTo(long nativeObj, long m_nativeObj, long mask_nativeObj);
-
-    // C++: void Mat::create(int rows, int cols, int type)
-    private static native void n_create(long nativeObj, int rows, int cols, int type);
-
-    // C++: void Mat::create(Size size, int type)
-    private static native void n_create(long nativeObj, double size_width, double size_height, int type);
-
-    // C++: Mat Mat::cross(Mat m)
-    private static native long n_cross(long nativeObj, long m_nativeObj);
-
-    // C++: long Mat::dataAddr()
-    private static native long n_dataAddr(long nativeObj);
-
-    // C++: int Mat::depth()
-    private static native int n_depth(long nativeObj);
-
-    // C++: Mat Mat::diag(int d = 0)
-    private static native long n_diag(long nativeObj, int d);
-
-    // C++: static Mat Mat::diag(Mat d)
-    private static native long n_diag(long d_nativeObj);
-
-    // C++: double Mat::dot(Mat m)
-    private static native double n_dot(long nativeObj, long m_nativeObj);
-
-    // C++: size_t Mat::elemSize()
-    private static native long n_elemSize(long nativeObj);
-
-    // C++: size_t Mat::elemSize1()
-    private static native long n_elemSize1(long nativeObj);
-
-    // C++: bool Mat::empty()
-    private static native boolean n_empty(long nativeObj);
-
-    // C++: static Mat Mat::eye(int rows, int cols, int type)
-    private static native long n_eye(int rows, int cols, int type);
-
-    // C++: static Mat Mat::eye(Size size, int type)
-    private static native long n_eye(double size_width, double size_height, int type);
-
-    // C++: Mat Mat::inv(int method = DECOMP_LU)
-    private static native long n_inv(long nativeObj, int method);
-
-    private static native long n_inv(long nativeObj);
-
-    // C++: bool Mat::isContinuous()
-    private static native boolean n_isContinuous(long nativeObj);
-
-    // C++: bool Mat::isSubmatrix()
-    private static native boolean n_isSubmatrix(long nativeObj);
-
-    // C++: void Mat::locateROI(Size wholeSize, Point ofs)
-    private static native void locateROI_0(long nativeObj, double[] wholeSize_out, double[] ofs_out);
-
-    // C++: Mat Mat::mul(Mat m, double scale = 1)
-    private static native long n_mul(long nativeObj, long m_nativeObj, double scale);
-
-    private static native long n_mul(long nativeObj, long m_nativeObj);
-
-    // C++: static Mat Mat::ones(int rows, int cols, int type)
-    private static native long n_ones(int rows, int cols, int type);
-
-    // C++: static Mat Mat::ones(Size size, int type)
-    private static native long n_ones(double size_width, double size_height, int type);
-
-    // C++: void Mat::push_back(Mat m)
-    private static native void n_push_back(long nativeObj, long m_nativeObj);
-
-    // C++: void Mat::release()
-    private static native void n_release(long nativeObj);
-
-    // C++: Mat Mat::reshape(int cn, int rows = 0)
-    private static native long n_reshape(long nativeObj, int cn, int rows);
-
-    private static native long n_reshape(long nativeObj, int cn);
-
-    // C++: Mat Mat::row(int y)
-    private static native long n_row(long nativeObj, int y);
-
-    // C++: Mat Mat::rowRange(int startrow, int endrow)
-    private static native long n_rowRange(long nativeObj, int startrow, int endrow);
-
-    // C++: int Mat::rows()
-    private static native int n_rows(long nativeObj);
-
-    // C++: Mat Mat::operator =(Scalar s)
-    private static native long n_setTo(long nativeObj, double s_val0, double s_val1, double s_val2, double s_val3);
-
-    // C++: Mat Mat::setTo(Scalar value, Mat mask = Mat())
-    private static native long n_setTo(long nativeObj, double s_val0, double s_val1, double s_val2, double s_val3, long mask_nativeObj);
-
-    // C++: Mat Mat::setTo(Mat value, Mat mask = Mat())
-    private static native long n_setTo(long nativeObj, long value_nativeObj, long mask_nativeObj);
-
-    private static native long n_setTo(long nativeObj, long value_nativeObj);
-
-    // C++: Size Mat::size()
-    private static native double[] n_size(long nativeObj);
-
-    // C++: size_t Mat::step1(int i = 0)
-    private static native long n_step1(long nativeObj, int i);
-
-    private static native long n_step1(long nativeObj);
-
-    // C++: Mat Mat::operator()(Range rowRange, Range colRange)
-    private static native long n_submat_rr(long nativeObj, int rowRange_start, int rowRange_end, int colRange_start, int colRange_end);
-
-    // C++: Mat Mat::operator()(Rect roi)
-    private static native long n_submat(long nativeObj, int roi_x, int roi_y, int roi_width, int roi_height);
-
-    // C++: Mat Mat::t()
-    private static native long n_t(long nativeObj);
-
-    // C++: size_t Mat::total()
-    private static native long n_total(long nativeObj);
-
-    // C++: int Mat::type()
-    private static native int n_type(long nativeObj);
-
-    // C++: static Mat Mat::zeros(int rows, int cols, int type)
-    private static native long n_zeros(int rows, int cols, int type);
-
-    // C++: static Mat Mat::zeros(Size size, int type)
-    private static native long n_zeros(double size_width, double size_height, int type);
-
-    // native support for java finalize()
-    private static native void n_delete(long nativeObj);
-
-    private static native int nPutD(long self, int row, int col, int count, double[] data);
-
-    private static native int nPutF(long self, int row, int col, int count, float[] data);
-
-    private static native int nPutI(long self, int row, int col, int count, int[] data);
-
-    private static native int nPutS(long self, int row, int col, int count, short[] data);
-
-    private static native int nPutB(long self, int row, int col, int count, byte[] data);
-
-    private static native int nPutBwOffset(long self, int row, int col, int count, int offset, byte[] data);
-
-    private static native int nGetB(long self, int row, int col, int count, byte[] vals);
-
-    private static native int nGetS(long self, int row, int col, int count, short[] vals);
-
-    private static native int nGetI(long self, int row, int col, int count, int[] vals);
-
-    private static native int nGetF(long self, int row, int col, int count, float[] vals);
-
-    private static native int nGetD(long self, int row, int col, int count, double[] vals);
-
-    private static native double[] nGet(long self, int row, int col);
-
-    private static native String nDump(long self);
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/MatOfByte.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/MatOfByte.java
deleted file mode 100755
index eb928fb..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/MatOfByte.java
+++ /dev/null
@@ -1,98 +0,0 @@
-package org.opencv.core;
-
-import java.util.Arrays;
-import java.util.List;
-
-public class MatOfByte extends Mat {
-    // 8UC(x)
-    private static final int _depth = CvType.CV_8U;
-    private static final int _channels = 1;
-
-    public MatOfByte() {
-        super();
-    }
-
-    protected MatOfByte(long addr) {
-        super(addr);
-        if( !empty() && checkVector(_channels, _depth) < 0 )
-            throw new IllegalArgumentException("Incompatible Mat");
-        //FIXME: do we need release() here?
-    }
-
-    public static MatOfByte fromNativeAddr(long addr) {
-        return new MatOfByte(addr);
-    }
-
-    public MatOfByte(Mat m) {
-        super(m, Range.all());
-        if( !empty() && checkVector(_channels, _depth) < 0 )
-            throw new IllegalArgumentException("Incompatible Mat");
-        //FIXME: do we need release() here?
-    }
-
-    public MatOfByte(byte...a) {
-        super();
-        fromArray(a);
-    }
-
-    public MatOfByte(int offset, int length, byte...a) {
-        super();
-        fromArray(offset, length, a);
-    }
-
-    public void alloc(int elemNumber) {
-        if(elemNumber>0)
-            super.create(elemNumber, 1, CvType.makeType(_depth, _channels));
-    }
-
-    public void fromArray(byte...a) {
-        if(a==null || a.length==0)
-            return;
-        int num = a.length / _channels;
-        alloc(num);
-        put(0, 0, a); //TODO: check ret val!
-    }
-
-    public void fromArray(int offset, int length, byte...a) {
-        if (offset < 0)
-            throw new IllegalArgumentException("offset < 0");
-        if (a == null)
-            throw new NullPointerException();
-        if (length < 0 || length + offset > a.length)
-            throw new IllegalArgumentException("invalid 'length' parameter: " + Integer.toString(length));
-        if (a.length == 0)
-            return;
-        int num = length / _channels;
-        alloc(num);
-        put(0, 0, a, offset, length); //TODO: check ret val!
-    }
-
-    public byte[] toArray() {
-        int num = checkVector(_channels, _depth);
-        if(num < 0)
-            throw new RuntimeException("Native Mat has unexpected type or size: " + toString());
-        byte[] a = new byte[num * _channels];
-        if(num == 0)
-            return a;
-        get(0, 0, a); //TODO: check ret val!
-        return a;
-    }
-
-    public void fromList(List<Byte> lb) {
-        if(lb==null || lb.size()==0)
-            return;
-        Byte ab[] = lb.toArray(new Byte[0]);
-        byte a[] = new byte[ab.length];
-        for(int i=0; i<ab.length; i++)
-            a[i] = ab[i];
-        fromArray(a);
-    }
-
-    public List<Byte> toList() {
-        byte[] a = toArray();
-        Byte ab[] = new Byte[a.length];
-        for(int i=0; i<a.length; i++)
-            ab[i] = a[i];
-        return Arrays.asList(ab);
-    }
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/MatOfDMatch.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/MatOfDMatch.java
deleted file mode 100755
index 2c99e14..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/MatOfDMatch.java
+++ /dev/null
@@ -1,83 +0,0 @@
-package org.opencv.core;
-
-import java.util.Arrays;
-import java.util.List;
-
-import org.opencv.core.DMatch;
-
-public class MatOfDMatch extends Mat {
-    // 32FC4
-    private static final int _depth = CvType.CV_32F;
-    private static final int _channels = 4;
-
-    public MatOfDMatch() {
-        super();
-    }
-
-    protected MatOfDMatch(long addr) {
-        super(addr);
-        if( !empty() && checkVector(_channels, _depth) < 0 )
-            throw new IllegalArgumentException("Incompatible Mat: " + toString());
-        //FIXME: do we need release() here?
-    }
-
-    public static MatOfDMatch fromNativeAddr(long addr) {
-        return new MatOfDMatch(addr);
-    }
-
-    public MatOfDMatch(Mat m) {
-        super(m, Range.all());
-        if( !empty() && checkVector(_channels, _depth) < 0 )
-            throw new IllegalArgumentException("Incompatible Mat: " + toString());
-        //FIXME: do we need release() here?
-    }
-
-    public MatOfDMatch(DMatch...ap) {
-        super();
-        fromArray(ap);
-    }
-
-    public void alloc(int elemNumber) {
-        if(elemNumber>0)
-            super.create(elemNumber, 1, CvType.makeType(_depth, _channels));
-    }
-
-
-    public void fromArray(DMatch...a) {
-        if(a==null || a.length==0)
-            return;
-        int num = a.length;
-        alloc(num);
-        float buff[] = new float[num * _channels];
-        for(int i=0; i<num; i++) {
-            DMatch m = a[i];
-            buff[_channels*i+0] = m.queryIdx;
-            buff[_channels*i+1] = m.trainIdx;
-            buff[_channels*i+2] = m.imgIdx;
-            buff[_channels*i+3] = m.distance;
-        }
-        put(0, 0, buff); //TODO: check ret val!
-    }
-
-    public DMatch[] toArray() {
-        int num = (int) total();
-        DMatch[] a = new DMatch[num];
-        if(num == 0)
-            return a;
-        float buff[] = new float[num * _channels];
-        get(0, 0, buff); //TODO: check ret val!
-        for(int i=0; i<num; i++)
-            a[i] = new DMatch((int) buff[_channels*i+0], (int) buff[_channels*i+1], (int) buff[_channels*i+2], buff[_channels*i+3]);
-        return a;
-    }
-
-    public void fromList(List<DMatch> ldm) {
-        DMatch adm[] = ldm.toArray(new DMatch[0]);
-        fromArray(adm);
-    }
-
-    public List<DMatch> toList() {
-        DMatch[] adm = toArray();
-        return Arrays.asList(adm);
-    }
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/MatOfDouble.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/MatOfDouble.java
deleted file mode 100755
index 1a8e23c..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/MatOfDouble.java
+++ /dev/null
@@ -1,79 +0,0 @@
-package org.opencv.core;
-
-import java.util.Arrays;
-import java.util.List;
-
-public class MatOfDouble extends Mat {
-    // 64FC(x)
-    private static final int _depth = CvType.CV_64F;
-    private static final int _channels = 1;
-
-    public MatOfDouble() {
-        super();
-    }
-
-    protected MatOfDouble(long addr) {
-        super(addr);
-        if( !empty() && checkVector(_channels, _depth) < 0 )
-            throw new IllegalArgumentException("Incompatible Mat");
-        //FIXME: do we need release() here?
-    }
-
-    public static MatOfDouble fromNativeAddr(long addr) {
-        return new MatOfDouble(addr);
-    }
-
-    public MatOfDouble(Mat m) {
-        super(m, Range.all());
-        if( !empty() && checkVector(_channels, _depth) < 0 )
-            throw new IllegalArgumentException("Incompatible Mat");
-        //FIXME: do we need release() here?
-    }
-
-    public MatOfDouble(double...a) {
-        super();
-        fromArray(a);
-    }
-
-    public void alloc(int elemNumber) {
-        if(elemNumber>0)
-            super.create(elemNumber, 1, CvType.makeType(_depth, _channels));
-    }
-
-    public void fromArray(double...a) {
-        if(a==null || a.length==0)
-            return;
-        int num = a.length / _channels;
-        alloc(num);
-        put(0, 0, a); //TODO: check ret val!
-    }
-
-    public double[] toArray() {
-        int num = checkVector(_channels, _depth);
-        if(num < 0)
-            throw new RuntimeException("Native Mat has unexpected type or size: " + toString());
-        double[] a = new double[num * _channels];
-        if(num == 0)
-            return a;
-        get(0, 0, a); //TODO: check ret val!
-        return a;
-    }
-
-    public void fromList(List<Double> lb) {
-        if(lb==null || lb.size()==0)
-            return;
-        Double ab[] = lb.toArray(new Double[0]);
-        double a[] = new double[ab.length];
-        for(int i=0; i<ab.length; i++)
-            a[i] = ab[i];
-        fromArray(a);
-    }
-
-    public List<Double> toList() {
-        double[] a = toArray();
-        Double ab[] = new Double[a.length];
-        for(int i=0; i<a.length; i++)
-            ab[i] = a[i];
-        return Arrays.asList(ab);
-    }
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/MatOfFloat.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/MatOfFloat.java
deleted file mode 100755
index dfd6b84..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/MatOfFloat.java
+++ /dev/null
@@ -1,79 +0,0 @@
-package org.opencv.core;
-
-import java.util.Arrays;
-import java.util.List;
-
-public class MatOfFloat extends Mat {
-    // 32FC1
-    private static final int _depth = CvType.CV_32F;
-    private static final int _channels = 1;
-
-    public MatOfFloat() {
-        super();
-    }
-
-    protected MatOfFloat(long addr) {
-        super(addr);
-        if( !empty() && checkVector(_channels, _depth) < 0 )
-            throw new IllegalArgumentException("Incompatible Mat");
-        //FIXME: do we need release() here?
-    }
-
-    public static MatOfFloat fromNativeAddr(long addr) {
-        return new MatOfFloat(addr);
-    }
-
-    public MatOfFloat(Mat m) {
-        super(m, Range.all());
-        if( !empty() && checkVector(_channels, _depth) < 0 )
-            throw new IllegalArgumentException("Incompatible Mat");
-        //FIXME: do we need release() here?
-    }
-
-    public MatOfFloat(float...a) {
-        super();
-        fromArray(a);
-    }
-
-    public void alloc(int elemNumber) {
-        if(elemNumber>0)
-            super.create(elemNumber, 1, CvType.makeType(_depth, _channels));
-    }
-
-    public void fromArray(float...a) {
-        if(a==null || a.length==0)
-            return;
-        int num = a.length / _channels;
-        alloc(num);
-        put(0, 0, a); //TODO: check ret val!
-    }
-
-    public float[] toArray() {
-        int num = checkVector(_channels, _depth);
-        if(num < 0)
-            throw new RuntimeException("Native Mat has unexpected type or size: " + toString());
-        float[] a = new float[num * _channels];
-        if(num == 0)
-            return a;
-        get(0, 0, a); //TODO: check ret val!
-        return a;
-    }
-
-    public void fromList(List<Float> lb) {
-        if(lb==null || lb.size()==0)
-            return;
-        Float ab[] = lb.toArray(new Float[0]);
-        float a[] = new float[ab.length];
-        for(int i=0; i<ab.length; i++)
-            a[i] = ab[i];
-        fromArray(a);
-    }
-
-    public List<Float> toList() {
-        float[] a = toArray();
-        Float ab[] = new Float[a.length];
-        for(int i=0; i<a.length; i++)
-            ab[i] = a[i];
-        return Arrays.asList(ab);
-    }
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/MatOfFloat4.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/MatOfFloat4.java
deleted file mode 100755
index 96f9e5c..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/MatOfFloat4.java
+++ /dev/null
@@ -1,79 +0,0 @@
-package org.opencv.core;
-
-import java.util.Arrays;
-import java.util.List;
-
-public class MatOfFloat4 extends Mat {
-    // 32FC4
-    private static final int _depth = CvType.CV_32F;
-    private static final int _channels = 4;
-
-    public MatOfFloat4() {
-        super();
-    }
-
-    protected MatOfFloat4(long addr) {
-        super(addr);
-        if( !empty() && checkVector(_channels, _depth) < 0 )
-            throw new IllegalArgumentException("Incompatible Mat");
-        //FIXME: do we need release() here?
-    }
-
-    public static MatOfFloat4 fromNativeAddr(long addr) {
-        return new MatOfFloat4(addr);
-    }
-
-    public MatOfFloat4(Mat m) {
-        super(m, Range.all());
-        if( !empty() && checkVector(_channels, _depth) < 0 )
-            throw new IllegalArgumentException("Incompatible Mat");
-        //FIXME: do we need release() here?
-    }
-
-    public MatOfFloat4(float...a) {
-        super();
-        fromArray(a);
-    }
-
-    public void alloc(int elemNumber) {
-        if(elemNumber>0)
-            super.create(elemNumber, 1, CvType.makeType(_depth, _channels));
-    }
-
-    public void fromArray(float...a) {
-        if(a==null || a.length==0)
-            return;
-        int num = a.length / _channels;
-        alloc(num);
-        put(0, 0, a); //TODO: check ret val!
-    }
-
-    public float[] toArray() {
-        int num = checkVector(_channels, _depth);
-        if(num < 0)
-            throw new RuntimeException("Native Mat has unexpected type or size: " + toString());
-        float[] a = new float[num * _channels];
-        if(num == 0)
-            return a;
-        get(0, 0, a); //TODO: check ret val!
-        return a;
-    }
-
-    public void fromList(List<Float> lb) {
-        if(lb==null || lb.size()==0)
-            return;
-        Float ab[] = lb.toArray(new Float[0]);
-        float a[] = new float[ab.length];
-        for(int i=0; i<ab.length; i++)
-            a[i] = ab[i];
-        fromArray(a);
-    }
-
-    public List<Float> toList() {
-        float[] a = toArray();
-        Float ab[] = new Float[a.length];
-        for(int i=0; i<a.length; i++)
-            ab[i] = a[i];
-        return Arrays.asList(ab);
-    }
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/MatOfFloat6.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/MatOfFloat6.java
deleted file mode 100755
index 140bb57..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/MatOfFloat6.java
+++ /dev/null
@@ -1,79 +0,0 @@
-package org.opencv.core;
-
-import java.util.Arrays;
-import java.util.List;
-
-public class MatOfFloat6 extends Mat {
-    // 32FC6
-    private static final int _depth = CvType.CV_32F;
-    private static final int _channels = 6;
-
-    public MatOfFloat6() {
-        super();
-    }
-
-    protected MatOfFloat6(long addr) {
-        super(addr);
-        if( !empty() && checkVector(_channels, _depth) < 0 )
-            throw new IllegalArgumentException("Incompatible Mat");
-        //FIXME: do we need release() here?
-    }
-
-    public static MatOfFloat6 fromNativeAddr(long addr) {
-        return new MatOfFloat6(addr);
-    }
-
-    public MatOfFloat6(Mat m) {
-        super(m, Range.all());
-        if( !empty() && checkVector(_channels, _depth) < 0 )
-            throw new IllegalArgumentException("Incompatible Mat");
-        //FIXME: do we need release() here?
-    }
-
-    public MatOfFloat6(float...a) {
-        super();
-        fromArray(a);
-    }
-
-    public void alloc(int elemNumber) {
-        if(elemNumber>0)
-            super.create(elemNumber, 1, CvType.makeType(_depth, _channels));
-    }
-
-    public void fromArray(float...a) {
-        if(a==null || a.length==0)
-            return;
-        int num = a.length / _channels;
-        alloc(num);
-        put(0, 0, a); //TODO: check ret val!
-    }
-
-    public float[] toArray() {
-        int num = checkVector(_channels, _depth);
-        if(num < 0)
-            throw new RuntimeException("Native Mat has unexpected type or size: " + toString());
-        float[] a = new float[num * _channels];
-        if(num == 0)
-            return a;
-        get(0, 0, a); //TODO: check ret val!
-        return a;
-    }
-
-    public void fromList(List<Float> lb) {
-        if(lb==null || lb.size()==0)
-            return;
-        Float ab[] = lb.toArray(new Float[0]);
-        float a[] = new float[ab.length];
-        for(int i=0; i<ab.length; i++)
-            a[i] = ab[i];
-        fromArray(a);
-    }
-
-    public List<Float> toList() {
-        float[] a = toArray();
-        Float ab[] = new Float[a.length];
-        for(int i=0; i<a.length; i++)
-            ab[i] = a[i];
-        return Arrays.asList(ab);
-    }
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/MatOfInt.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/MatOfInt.java
deleted file mode 100755
index 7d07687..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/MatOfInt.java
+++ /dev/null
@@ -1,80 +0,0 @@
-package org.opencv.core;
-
-import java.util.Arrays;
-import java.util.List;
-
-
-public class MatOfInt extends Mat {
-    // 32SC1
-    private static final int _depth = CvType.CV_32S;
-    private static final int _channels = 1;
-
-    public MatOfInt() {
-        super();
-    }
-
-    protected MatOfInt(long addr) {
-        super(addr);
-        if( !empty() && checkVector(_channels, _depth) < 0 )
-            throw new IllegalArgumentException("Incompatible Mat");
-        //FIXME: do we need release() here?
-    }
-
-    public static MatOfInt fromNativeAddr(long addr) {
-        return new MatOfInt(addr);
-    }
-
-    public MatOfInt(Mat m) {
-        super(m, Range.all());
-        if( !empty() && checkVector(_channels, _depth) < 0 )
-            throw new IllegalArgumentException("Incompatible Mat");
-        //FIXME: do we need release() here?
-    }
-
-    public MatOfInt(int...a) {
-        super();
-        fromArray(a);
-    }
-
-    public void alloc(int elemNumber) {
-        if(elemNumber>0)
-            super.create(elemNumber, 1, CvType.makeType(_depth, _channels));
-    }
-
-    public void fromArray(int...a) {
-        if(a==null || a.length==0)
-            return;
-        int num = a.length / _channels;
-        alloc(num);
-        put(0, 0, a); //TODO: check ret val!
-    }
-
-    public int[] toArray() {
-        int num = checkVector(_channels, _depth);
-        if(num < 0)
-            throw new RuntimeException("Native Mat has unexpected type or size: " + toString());
-        int[] a = new int[num * _channels];
-        if(num == 0)
-            return a;
-        get(0, 0, a); //TODO: check ret val!
-        return a;
-    }
-
-    public void fromList(List<Integer> lb) {
-        if(lb==null || lb.size()==0)
-            return;
-        Integer ab[] = lb.toArray(new Integer[0]);
-        int a[] = new int[ab.length];
-        for(int i=0; i<ab.length; i++)
-            a[i] = ab[i];
-        fromArray(a);
-    }
-
-    public List<Integer> toList() {
-        int[] a = toArray();
-        Integer ab[] = new Integer[a.length];
-        for(int i=0; i<a.length; i++)
-            ab[i] = a[i];
-        return Arrays.asList(ab);
-    }
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/MatOfInt4.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/MatOfInt4.java
deleted file mode 100755
index 4ca4d9e..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/MatOfInt4.java
+++ /dev/null
@@ -1,80 +0,0 @@
-package org.opencv.core;
-
-import java.util.Arrays;
-import java.util.List;
-
-
-public class MatOfInt4 extends Mat {
-    // 32SC4
-    private static final int _depth = CvType.CV_32S;
-    private static final int _channels = 4;
-
-    public MatOfInt4() {
-        super();
-    }
-
-    protected MatOfInt4(long addr) {
-        super(addr);
-        if( !empty() && checkVector(_channels, _depth) < 0 )
-            throw new IllegalArgumentException("Incompatible Mat");
-        //FIXME: do we need release() here?
-    }
-
-    public static MatOfInt4 fromNativeAddr(long addr) {
-        return new MatOfInt4(addr);
-    }
-
-    public MatOfInt4(Mat m) {
-        super(m, Range.all());
-        if( !empty() && checkVector(_channels, _depth) < 0 )
-            throw new IllegalArgumentException("Incompatible Mat");
-        //FIXME: do we need release() here?
-    }
-
-    public MatOfInt4(int...a) {
-        super();
-        fromArray(a);
-    }
-
-    public void alloc(int elemNumber) {
-        if(elemNumber>0)
-            super.create(elemNumber, 1, CvType.makeType(_depth, _channels));
-    }
-
-    public void fromArray(int...a) {
-        if(a==null || a.length==0)
-            return;
-        int num = a.length / _channels;
-        alloc(num);
-        put(0, 0, a); //TODO: check ret val!
-    }
-
-    public int[] toArray() {
-        int num = checkVector(_channels, _depth);
-        if(num < 0)
-            throw new RuntimeException("Native Mat has unexpected type or size: " + toString());
-        int[] a = new int[num * _channels];
-        if(num == 0)
-            return a;
-        get(0, 0, a); //TODO: check ret val!
-        return a;
-    }
-
-    public void fromList(List<Integer> lb) {
-        if(lb==null || lb.size()==0)
-            return;
-        Integer ab[] = lb.toArray(new Integer[0]);
-        int a[] = new int[ab.length];
-        for(int i=0; i<ab.length; i++)
-            a[i] = ab[i];
-        fromArray(a);
-    }
-
-    public List<Integer> toList() {
-        int[] a = toArray();
-        Integer ab[] = new Integer[a.length];
-        for(int i=0; i<a.length; i++)
-            ab[i] = a[i];
-        return Arrays.asList(ab);
-    }
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/MatOfKeyPoint.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/MatOfKeyPoint.java
deleted file mode 100755
index 24b9a81..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/MatOfKeyPoint.java
+++ /dev/null
@@ -1,86 +0,0 @@
-package org.opencv.core;
-
-import java.util.Arrays;
-import java.util.List;
-
-import org.opencv.core.KeyPoint;
-
-public class MatOfKeyPoint extends Mat {
-    // 32FC7
-    private static final int _depth = CvType.CV_32F;
-    private static final int _channels = 7;
-
-    public MatOfKeyPoint() {
-        super();
-    }
-
-    protected MatOfKeyPoint(long addr) {
-        super(addr);
-        if( !empty() && checkVector(_channels, _depth) < 0 )
-            throw new IllegalArgumentException("Incompatible Mat");
-        //FIXME: do we need release() here?
-    }
-
-    public static MatOfKeyPoint fromNativeAddr(long addr) {
-        return new MatOfKeyPoint(addr);
-    }
-
-    public MatOfKeyPoint(Mat m) {
-        super(m, Range.all());
-        if( !empty() && checkVector(_channels, _depth) < 0 )
-            throw new IllegalArgumentException("Incompatible Mat");
-        //FIXME: do we need release() here?
-    }
-
-    public MatOfKeyPoint(KeyPoint...a) {
-        super();
-        fromArray(a);
-    }
-
-    public void alloc(int elemNumber) {
-        if(elemNumber>0)
-            super.create(elemNumber, 1, CvType.makeType(_depth, _channels));
-    }
-
-    public void fromArray(KeyPoint...a) {
-        if(a==null || a.length==0)
-            return;
-        int num = a.length;
-        alloc(num);
-        float buff[] = new float[num * _channels];
-        for(int i=0; i<num; i++) {
-            KeyPoint kp = a[i];
-            buff[_channels*i+0] = (float) kp.pt.x;
-            buff[_channels*i+1] = (float) kp.pt.y;
-            buff[_channels*i+2] = kp.size;
-            buff[_channels*i+3] = kp.angle;
-            buff[_channels*i+4] = kp.response;
-            buff[_channels*i+5] = kp.octave;
-            buff[_channels*i+6] = kp.class_id;
-        }
-        put(0, 0, buff); //TODO: check ret val!
-    }
-
-    public KeyPoint[] toArray() {
-        int num = (int) total();
-        KeyPoint[] a = new KeyPoint[num];
-        if(num == 0)
-            return a;
-        float buff[] = new float[num * _channels];
-        get(0, 0, buff); //TODO: check ret val!
-        for(int i=0; i<num; i++)
-            a[i] = new KeyPoint( buff[_channels*i+0], buff[_channels*i+1], buff[_channels*i+2], buff[_channels*i+3],
-                                 buff[_channels*i+4], (int) buff[_channels*i+5], (int) buff[_channels*i+6] );
-        return a;
-    }
-
-    public void fromList(List<KeyPoint> lkp) {
-        KeyPoint akp[] = lkp.toArray(new KeyPoint[0]);
-        fromArray(akp);
-    }
-
-    public List<KeyPoint> toList() {
-        KeyPoint[] akp = toArray();
-        return Arrays.asList(akp);
-    }
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/MatOfPoint.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/MatOfPoint.java
deleted file mode 100755
index f4d573b..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/MatOfPoint.java
+++ /dev/null
@@ -1,78 +0,0 @@
-package org.opencv.core;
-
-import java.util.Arrays;
-import java.util.List;
-
-public class MatOfPoint extends Mat {
-    // 32SC2
-    private static final int _depth = CvType.CV_32S;
-    private static final int _channels = 2;
-
-    public MatOfPoint() {
-        super();
-    }
-
-    protected MatOfPoint(long addr) {
-        super(addr);
-        if( !empty() && checkVector(_channels, _depth) < 0 )
-            throw new IllegalArgumentException("Incompatible Mat");
-        //FIXME: do we need release() here?
-    }
-
-    public static MatOfPoint fromNativeAddr(long addr) {
-        return new MatOfPoint(addr);
-    }
-
-    public MatOfPoint(Mat m) {
-        super(m, Range.all());
-        if( !empty() && checkVector(_channels, _depth) < 0 )
-            throw new IllegalArgumentException("Incompatible Mat");
-        //FIXME: do we need release() here?
-    }
-
-    public MatOfPoint(Point...a) {
-        super();
-        fromArray(a);
-    }
-
-    public void alloc(int elemNumber) {
-        if(elemNumber>0)
-            super.create(elemNumber, 1, CvType.makeType(_depth, _channels));
-    }
-
-    public void fromArray(Point...a) {
-        if(a==null || a.length==0)
-            return;
-        int num = a.length;
-        alloc(num);
-        int buff[] = new int[num * _channels];
-        for(int i=0; i<num; i++) {
-            Point p = a[i];
-            buff[_channels*i+0] = (int) p.x;
-            buff[_channels*i+1] = (int) p.y;
-        }
-        put(0, 0, buff); //TODO: check ret val!
-    }
-
-    public Point[] toArray() {
-        int num = (int) total();
-        Point[] ap = new Point[num];
-        if(num == 0)
-            return ap;
-        int buff[] = new int[num * _channels];
-        get(0, 0, buff); //TODO: check ret val!
-        for(int i=0; i<num; i++)
-            ap[i] = new Point(buff[i*_channels], buff[i*_channels+1]);
-        return ap;
-    }
-
-    public void fromList(List<Point> lp) {
-        Point ap[] = lp.toArray(new Point[0]);
-        fromArray(ap);
-    }
-
-    public List<Point> toList() {
-        Point[] ap = toArray();
-        return Arrays.asList(ap);
-    }
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/MatOfPoint2f.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/MatOfPoint2f.java
deleted file mode 100755
index 4b8c926..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/MatOfPoint2f.java
+++ /dev/null
@@ -1,78 +0,0 @@
-package org.opencv.core;
-
-import java.util.Arrays;
-import java.util.List;
-
-public class MatOfPoint2f extends Mat {
-    // 32FC2
-    private static final int _depth = CvType.CV_32F;
-    private static final int _channels = 2;
-
-    public MatOfPoint2f() {
-        super();
-    }
-
-    protected MatOfPoint2f(long addr) {
-        super(addr);
-        if( !empty() && checkVector(_channels, _depth) < 0 )
-            throw new IllegalArgumentException("Incompatible Mat");
-        //FIXME: do we need release() here?
-    }
-
-    public static MatOfPoint2f fromNativeAddr(long addr) {
-        return new MatOfPoint2f(addr);
-    }
-
-    public MatOfPoint2f(Mat m) {
-        super(m, Range.all());
-        if( !empty() && checkVector(_channels, _depth) < 0 )
-            throw new IllegalArgumentException("Incompatible Mat");
-        //FIXME: do we need release() here?
-    }
-
-    public MatOfPoint2f(Point...a) {
-        super();
-        fromArray(a);
-    }
-
-    public void alloc(int elemNumber) {
-        if(elemNumber>0)
-            super.create(elemNumber, 1, CvType.makeType(_depth, _channels));
-    }
-
-    public void fromArray(Point...a) {
-        if(a==null || a.length==0)
-            return;
-        int num = a.length;
-        alloc(num);
-        float buff[] = new float[num * _channels];
-        for(int i=0; i<num; i++) {
-            Point p = a[i];
-            buff[_channels*i+0] = (float) p.x;
-            buff[_channels*i+1] = (float) p.y;
-        }
-        put(0, 0, buff); //TODO: check ret val!
-    }
-
-    public Point[] toArray() {
-        int num = (int) total();
-        Point[] ap = new Point[num];
-        if(num == 0)
-            return ap;
-        float buff[] = new float[num * _channels];
-        get(0, 0, buff); //TODO: check ret val!
-        for(int i=0; i<num; i++)
-            ap[i] = new Point(buff[i*_channels], buff[i*_channels+1]);
-        return ap;
-    }
-
-    public void fromList(List<Point> lp) {
-        Point ap[] = lp.toArray(new Point[0]);
-        fromArray(ap);
-    }
-
-    public List<Point> toList() {
-        Point[] ap = toArray();
-        return Arrays.asList(ap);
-    }
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/MatOfPoint3.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/MatOfPoint3.java
deleted file mode 100755
index 3b50561..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/MatOfPoint3.java
+++ /dev/null
@@ -1,79 +0,0 @@
-package org.opencv.core;
-
-import java.util.Arrays;
-import java.util.List;
-
-public class MatOfPoint3 extends Mat {
-    // 32SC3
-    private static final int _depth = CvType.CV_32S;
-    private static final int _channels = 3;
-
-    public MatOfPoint3() {
-        super();
-    }
-
-    protected MatOfPoint3(long addr) {
-        super(addr);
-        if( !empty() && checkVector(_channels, _depth) < 0 )
-            throw new IllegalArgumentException("Incompatible Mat");
-        //FIXME: do we need release() here?
-    }
-
-    public static MatOfPoint3 fromNativeAddr(long addr) {
-        return new MatOfPoint3(addr);
-    }
-
-    public MatOfPoint3(Mat m) {
-        super(m, Range.all());
-        if( !empty() && checkVector(_channels, _depth) < 0 )
-            throw new IllegalArgumentException("Incompatible Mat");
-        //FIXME: do we need release() here?
-    }
-
-    public MatOfPoint3(Point3...a) {
-        super();
-        fromArray(a);
-    }
-
-    public void alloc(int elemNumber) {
-        if(elemNumber>0)
-            super.create(elemNumber, 1, CvType.makeType(_depth, _channels));
-    }
-
-    public void fromArray(Point3...a) {
-        if(a==null || a.length==0)
-            return;
-        int num = a.length;
-        alloc(num);
-        int buff[] = new int[num * _channels];
-        for(int i=0; i<num; i++) {
-            Point3 p = a[i];
-            buff[_channels*i+0] = (int) p.x;
-            buff[_channels*i+1] = (int) p.y;
-            buff[_channels*i+2] = (int) p.z;
-        }
-        put(0, 0, buff); //TODO: check ret val!
-    }
-
-    public Point3[] toArray() {
-        int num = (int) total();
-        Point3[] ap = new Point3[num];
-        if(num == 0)
-            return ap;
-        int buff[] = new int[num * _channels];
-        get(0, 0, buff); //TODO: check ret val!
-        for(int i=0; i<num; i++)
-            ap[i] = new Point3(buff[i*_channels], buff[i*_channels+1], buff[i*_channels+2]);
-        return ap;
-    }
-
-    public void fromList(List<Point3> lp) {
-        Point3 ap[] = lp.toArray(new Point3[0]);
-        fromArray(ap);
-    }
-
-    public List<Point3> toList() {
-        Point3[] ap = toArray();
-        return Arrays.asList(ap);
-    }
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/MatOfPoint3f.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/MatOfPoint3f.java
deleted file mode 100755
index fc5fee4..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/MatOfPoint3f.java
+++ /dev/null
@@ -1,79 +0,0 @@
-package org.opencv.core;
-
-import java.util.Arrays;
-import java.util.List;
-
-public class MatOfPoint3f extends Mat {
-    // 32FC3
-    private static final int _depth = CvType.CV_32F;
-    private static final int _channels = 3;
-
-    public MatOfPoint3f() {
-        super();
-    }
-
-    protected MatOfPoint3f(long addr) {
-        super(addr);
-        if( !empty() && checkVector(_channels, _depth) < 0 )
-            throw new IllegalArgumentException("Incompatible Mat");
-        //FIXME: do we need release() here?
-    }
-
-    public static MatOfPoint3f fromNativeAddr(long addr) {
-        return new MatOfPoint3f(addr);
-    }
-
-    public MatOfPoint3f(Mat m) {
-        super(m, Range.all());
-        if( !empty() && checkVector(_channels, _depth) < 0 )
-            throw new IllegalArgumentException("Incompatible Mat");
-        //FIXME: do we need release() here?
-    }
-
-    public MatOfPoint3f(Point3...a) {
-        super();
-        fromArray(a);
-    }
-
-    public void alloc(int elemNumber) {
-        if(elemNumber>0)
-            super.create(elemNumber, 1, CvType.makeType(_depth, _channels));
-    }
-
-    public void fromArray(Point3...a) {
-        if(a==null || a.length==0)
-            return;
-        int num = a.length;
-        alloc(num);
-        float buff[] = new float[num * _channels];
-        for(int i=0; i<num; i++) {
-            Point3 p = a[i];
-            buff[_channels*i+0] = (float) p.x;
-            buff[_channels*i+1] = (float) p.y;
-            buff[_channels*i+2] = (float) p.z;
-        }
-        put(0, 0, buff); //TODO: check ret val!
-    }
-
-    public Point3[] toArray() {
-        int num = (int) total();
-        Point3[] ap = new Point3[num];
-        if(num == 0)
-            return ap;
-        float buff[] = new float[num * _channels];
-        get(0, 0, buff); //TODO: check ret val!
-        for(int i=0; i<num; i++)
-            ap[i] = new Point3(buff[i*_channels], buff[i*_channels+1], buff[i*_channels+2]);
-        return ap;
-    }
-
-    public void fromList(List<Point3> lp) {
-        Point3 ap[] = lp.toArray(new Point3[0]);
-        fromArray(ap);
-    }
-
-    public List<Point3> toList() {
-        Point3[] ap = toArray();
-        return Arrays.asList(ap);
-    }
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/MatOfRect.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/MatOfRect.java
deleted file mode 100755
index ec0fb01..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/MatOfRect.java
+++ /dev/null
@@ -1,81 +0,0 @@
-package org.opencv.core;
-
-import java.util.Arrays;
-import java.util.List;
-
-
-public class MatOfRect extends Mat {
-    // 32SC4
-    private static final int _depth = CvType.CV_32S;
-    private static final int _channels = 4;
-
-    public MatOfRect() {
-        super();
-    }
-
-    protected MatOfRect(long addr) {
-        super(addr);
-        if( !empty() && checkVector(_channels, _depth) < 0 )
-            throw new IllegalArgumentException("Incompatible Mat");
-        //FIXME: do we need release() here?
-    }
-
-    public static MatOfRect fromNativeAddr(long addr) {
-        return new MatOfRect(addr);
-    }
-
-    public MatOfRect(Mat m) {
-        super(m, Range.all());
-        if( !empty() && checkVector(_channels, _depth) < 0 )
-            throw new IllegalArgumentException("Incompatible Mat");
-        //FIXME: do we need release() here?
-    }
-
-    public MatOfRect(Rect...a) {
-        super();
-        fromArray(a);
-    }
-
-    public void alloc(int elemNumber) {
-        if(elemNumber>0)
-            super.create(elemNumber, 1, CvType.makeType(_depth, _channels));
-    }
-
-    public void fromArray(Rect...a) {
-        if(a==null || a.length==0)
-            return;
-        int num = a.length;
-        alloc(num);
-        int buff[] = new int[num * _channels];
-        for(int i=0; i<num; i++) {
-            Rect r = a[i];
-            buff[_channels*i+0] = (int) r.x;
-            buff[_channels*i+1] = (int) r.y;
-            buff[_channels*i+2] = (int) r.width;
-            buff[_channels*i+3] = (int) r.height;
-        }
-        put(0, 0, buff); //TODO: check ret val!
-    }
-
-
-    public Rect[] toArray() {
-        int num = (int) total();
-        Rect[] a = new Rect[num];
-        if(num == 0)
-            return a;
-        int buff[] = new int[num * _channels];
-        get(0, 0, buff); //TODO: check ret val!
-        for(int i=0; i<num; i++)
-            a[i] = new Rect(buff[i*_channels], buff[i*_channels+1], buff[i*_channels+2], buff[i*_channels+3]);
-        return a;
-    }
-    public void fromList(List<Rect> lr) {
-        Rect ap[] = lr.toArray(new Rect[0]);
-        fromArray(ap);
-    }
-
-    public List<Rect> toList() {
-        Rect[] ar = toArray();
-        return Arrays.asList(ar);
-    }
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/MatOfRect2d.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/MatOfRect2d.java
deleted file mode 100755
index 71c4b1a..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/MatOfRect2d.java
+++ /dev/null
@@ -1,81 +0,0 @@
-package org.opencv.core;
-
-import java.util.Arrays;
-import java.util.List;
-
-
-public class MatOfRect2d extends Mat {
-    // 64FC4
-    private static final int _depth = CvType.CV_64F;
-    private static final int _channels = 4;
-
-    public MatOfRect2d() {
-        super();
-    }
-
-    protected MatOfRect2d(long addr) {
-        super(addr);
-        if( !empty() && checkVector(_channels, _depth) < 0 )
-            throw new IllegalArgumentException("Incompatible Mat");
-        //FIXME: do we need release() here?
-    }
-
-    public static MatOfRect2d fromNativeAddr(long addr) {
-        return new MatOfRect2d(addr);
-    }
-
-    public MatOfRect2d(Mat m) {
-        super(m, Range.all());
-        if( !empty() && checkVector(_channels, _depth) < 0 )
-            throw new IllegalArgumentException("Incompatible Mat");
-        //FIXME: do we need release() here?
-    }
-
-    public MatOfRect2d(Rect2d...a) {
-        super();
-        fromArray(a);
-    }
-
-    public void alloc(int elemNumber) {
-        if(elemNumber>0)
-            super.create(elemNumber, 1, CvType.makeType(_depth, _channels));
-    }
-
-    public void fromArray(Rect2d...a) {
-        if(a==null || a.length==0)
-            return;
-        int num = a.length;
-        alloc(num);
-        double buff[] = new double[num * _channels];
-        for(int i=0; i<num; i++) {
-            Rect2d r = a[i];
-            buff[_channels*i+0] = (double) r.x;
-            buff[_channels*i+1] = (double) r.y;
-            buff[_channels*i+2] = (double) r.width;
-            buff[_channels*i+3] = (double) r.height;
-        }
-        put(0, 0, buff); //TODO: check ret val!
-    }
-
-
-    public Rect2d[] toArray() {
-        int num = (int) total();
-        Rect2d[] a = new Rect2d[num];
-        if(num == 0)
-            return a;
-        double buff[] = new double[num * _channels];
-        get(0, 0, buff); //TODO: check ret val!
-        for(int i=0; i<num; i++)
-            a[i] = new Rect2d(buff[i*_channels], buff[i*_channels+1], buff[i*_channels+2], buff[i*_channels+3]);
-        return a;
-    }
-    public void fromList(List<Rect2d> lr) {
-        Rect2d ap[] = lr.toArray(new Rect2d[0]);
-        fromArray(ap);
-    }
-
-    public List<Rect2d> toList() {
-        Rect2d[] ar = toArray();
-        return Arrays.asList(ar);
-    }
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/Point.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/Point.java
deleted file mode 100755
index ce493d7..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/Point.java
+++ /dev/null
@@ -1,68 +0,0 @@
-package org.opencv.core;
-
-//javadoc:Point_
-public class Point {
-
-    public double x, y;
-
-    public Point(double x, double y) {
-        this.x = x;
-        this.y = y;
-    }
-
-    public Point() {
-        this(0, 0);
-    }
-
-    public Point(double[] vals) {
-        this();
-        set(vals);
-    }
-
-    public void set(double[] vals) {
-        if (vals != null) {
-            x = vals.length > 0 ? vals[0] : 0;
-            y = vals.length > 1 ? vals[1] : 0;
-        } else {
-            x = 0;
-            y = 0;
-        }
-    }
-
-    public Point clone() {
-        return new Point(x, y);
-    }
-
-    public double dot(Point p) {
-        return x * p.x + y * p.y;
-    }
-
-    @Override
-    public int hashCode() {
-        final int prime = 31;
-        int result = 1;
-        long temp;
-        temp = Double.doubleToLongBits(x);
-        result = prime * result + (int) (temp ^ (temp >>> 32));
-        temp = Double.doubleToLongBits(y);
-        result = prime * result + (int) (temp ^ (temp >>> 32));
-        return result;
-    }
-
-    @Override
-    public boolean equals(Object obj) {
-        if (this == obj) return true;
-        if (!(obj instanceof Point)) return false;
-        Point it = (Point) obj;
-        return x == it.x && y == it.y;
-    }
-
-    public boolean inside(Rect r) {
-        return r.contains(this);
-    }
-
-    @Override
-    public String toString() {
-        return "{" + x + ", " + y + "}";
-    }
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/Point3.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/Point3.java
deleted file mode 100755
index 14b91c6..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/Point3.java
+++ /dev/null
@@ -1,79 +0,0 @@
-package org.opencv.core;
-
-//javadoc:Point3_
-public class Point3 {
-
-    public double x, y, z;
-
-    public Point3(double x, double y, double z) {
-        this.x = x;
-        this.y = y;
-        this.z = z;
-    }
-
-    public Point3() {
-        this(0, 0, 0);
-    }
-
-    public Point3(Point p) {
-        x = p.x;
-        y = p.y;
-        z = 0;
-    }
-
-    public Point3(double[] vals) {
-        this();
-        set(vals);
-    }
-
-    public void set(double[] vals) {
-        if (vals != null) {
-            x = vals.length > 0 ? vals[0] : 0;
-            y = vals.length > 1 ? vals[1] : 0;
-            z = vals.length > 2 ? vals[2] : 0;
-        } else {
-            x = 0;
-            y = 0;
-            z = 0;
-        }
-    }
-
-    public Point3 clone() {
-        return new Point3(x, y, z);
-    }
-
-    public double dot(Point3 p) {
-        return x * p.x + y * p.y + z * p.z;
-    }
-
-    public Point3 cross(Point3 p) {
-        return new Point3(y * p.z - z * p.y, z * p.x - x * p.z, x * p.y - y * p.x);
-    }
-
-    @Override
-    public int hashCode() {
-        final int prime = 31;
-        int result = 1;
-        long temp;
-        temp = Double.doubleToLongBits(x);
-        result = prime * result + (int) (temp ^ (temp >>> 32));
-        temp = Double.doubleToLongBits(y);
-        result = prime * result + (int) (temp ^ (temp >>> 32));
-        temp = Double.doubleToLongBits(z);
-        result = prime * result + (int) (temp ^ (temp >>> 32));
-        return result;
-    }
-
-    @Override
-    public boolean equals(Object obj) {
-        if (this == obj) return true;
-        if (!(obj instanceof Point3)) return false;
-        Point3 it = (Point3) obj;
-        return x == it.x && y == it.y && z == it.z;
-    }
-
-    @Override
-    public String toString() {
-        return "{" + x + ", " + y + ", " + z + "}";
-    }
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/Range.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/Range.java
deleted file mode 100755
index f7eee4d..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/Range.java
+++ /dev/null
@@ -1,82 +0,0 @@
-package org.opencv.core;
-
-//javadoc:Range
-public class Range {
-
-    public int start, end;
-
-    public Range(int s, int e) {
-        this.start = s;
-        this.end = e;
-    }
-
-    public Range() {
-        this(0, 0);
-    }
-
-    public Range(double[] vals) {
-        set(vals);
-    }
-
-    public void set(double[] vals) {
-        if (vals != null) {
-            start = vals.length > 0 ? (int) vals[0] : 0;
-            end = vals.length > 1 ? (int) vals[1] : 0;
-        } else {
-            start = 0;
-            end = 0;
-        }
-
-    }
-
-    public int size() {
-        return empty() ? 0 : end - start;
-    }
-
-    public boolean empty() {
-        return end <= start;
-    }
-
-    public static Range all() {
-        return new Range(Integer.MIN_VALUE, Integer.MAX_VALUE);
-    }
-
-    public Range intersection(Range r1) {
-        Range r = new Range(Math.max(r1.start, this.start), Math.min(r1.end, this.end));
-        r.end = Math.max(r.end, r.start);
-        return r;
-    }
-
-    public Range shift(int delta) {
-        return new Range(start + delta, end + delta);
-    }
-
-    public Range clone() {
-        return new Range(start, end);
-    }
-
-    @Override
-    public int hashCode() {
-        final int prime = 31;
-        int result = 1;
-        long temp;
-        temp = Double.doubleToLongBits(start);
-        result = prime * result + (int) (temp ^ (temp >>> 32));
-        temp = Double.doubleToLongBits(end);
-        result = prime * result + (int) (temp ^ (temp >>> 32));
-        return result;
-    }
-
-    @Override
-    public boolean equals(Object obj) {
-        if (this == obj) return true;
-        if (!(obj instanceof Range)) return false;
-        Range it = (Range) obj;
-        return start == it.start && end == it.end;
-    }
-
-    @Override
-    public String toString() {
-        return "[" + start + ", " + end + ")";
-    }
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/Rect.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/Rect.java
deleted file mode 100755
index c68e818..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/Rect.java
+++ /dev/null
@@ -1,104 +0,0 @@
-package org.opencv.core;
-
-//javadoc:Rect_
-public class Rect {
-
-    public int x, y, width, height;
-
-    public Rect(int x, int y, int width, int height) {
-        this.x = x;
-        this.y = y;
-        this.width = width;
-        this.height = height;
-    }
-
-    public Rect() {
-        this(0, 0, 0, 0);
-    }
-
-    public Rect(Point p1, Point p2) {
-        x = (int) (p1.x < p2.x ? p1.x : p2.x);
-        y = (int) (p1.y < p2.y ? p1.y : p2.y);
-        width = (int) (p1.x > p2.x ? p1.x : p2.x) - x;
-        height = (int) (p1.y > p2.y ? p1.y : p2.y) - y;
-    }
-
-    public Rect(Point p, Size s) {
-        this((int) p.x, (int) p.y, (int) s.width, (int) s.height);
-    }
-
-    public Rect(double[] vals) {
-        set(vals);
-    }
-
-    public void set(double[] vals) {
-        if (vals != null) {
-            x = vals.length > 0 ? (int) vals[0] : 0;
-            y = vals.length > 1 ? (int) vals[1] : 0;
-            width = vals.length > 2 ? (int) vals[2] : 0;
-            height = vals.length > 3 ? (int) vals[3] : 0;
-        } else {
-            x = 0;
-            y = 0;
-            width = 0;
-            height = 0;
-        }
-    }
-
-    public Rect clone() {
-        return new Rect(x, y, width, height);
-    }
-
-    public Point tl() {
-        return new Point(x, y);
-    }
-
-    public Point br() {
-        return new Point(x + width, y + height);
-    }
-
-    public Size size() {
-        return new Size(width, height);
-    }
-
-    public double area() {
-        return width * height;
-    }
-
-    public boolean empty() {
-        return width <= 0 || height <= 0;
-    }
-
-    public boolean contains(Point p) {
-        return x <= p.x && p.x < x + width && y <= p.y && p.y < y + height;
-    }
-
-    @Override
-    public int hashCode() {
-        final int prime = 31;
-        int result = 1;
-        long temp;
-        temp = Double.doubleToLongBits(height);
-        result = prime * result + (int) (temp ^ (temp >>> 32));
-        temp = Double.doubleToLongBits(width);
-        result = prime * result + (int) (temp ^ (temp >>> 32));
-        temp = Double.doubleToLongBits(x);
-        result = prime * result + (int) (temp ^ (temp >>> 32));
-        temp = Double.doubleToLongBits(y);
-        result = prime * result + (int) (temp ^ (temp >>> 32));
-        return result;
-    }
-
-    @Override
-    public boolean equals(Object obj) {
-        if (this == obj) return true;
-        if (!(obj instanceof Rect)) return false;
-        Rect it = (Rect) obj;
-        return x == it.x && y == it.y && width == it.width && height == it.height;
-    }
-
-    @Override
-    public String toString() {
-        return "{" + x + ", " + y + ", " + width + "x" + height + "}";
-    }
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/Rect2d.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/Rect2d.java
deleted file mode 100755
index 4c27869..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/Rect2d.java
+++ /dev/null
@@ -1,104 +0,0 @@
-package org.opencv.core;
-
-//javadoc:Rect2d_
-public class Rect2d {
-
-    public double x, y, width, height;
-
-    public Rect2d(double x, double y, double width, double height) {
-        this.x = x;
-        this.y = y;
-        this.width = width;
-        this.height = height;
-    }
-
-    public Rect2d() {
-        this(0, 0, 0, 0);
-    }
-
-    public Rect2d(Point p1, Point p2) {
-        x = (double) (p1.x < p2.x ? p1.x : p2.x);
-        y = (double) (p1.y < p2.y ? p1.y : p2.y);
-        width = (double) (p1.x > p2.x ? p1.x : p2.x) - x;
-        height = (double) (p1.y > p2.y ? p1.y : p2.y) - y;
-    }
-
-    public Rect2d(Point p, Size s) {
-        this((double) p.x, (double) p.y, (double) s.width, (double) s.height);
-    }
-
-    public Rect2d(double[] vals) {
-        set(vals);
-    }
-
-    public void set(double[] vals) {
-        if (vals != null) {
-            x = vals.length > 0 ? (double) vals[0] : 0;
-            y = vals.length > 1 ? (double) vals[1] : 0;
-            width = vals.length > 2 ? (double) vals[2] : 0;
-            height = vals.length > 3 ? (double) vals[3] : 0;
-        } else {
-            x = 0;
-            y = 0;
-            width = 0;
-            height = 0;
-        }
-    }
-
-    public Rect2d clone() {
-        return new Rect2d(x, y, width, height);
-    }
-
-    public Point tl() {
-        return new Point(x, y);
-    }
-
-    public Point br() {
-        return new Point(x + width, y + height);
-    }
-
-    public Size size() {
-        return new Size(width, height);
-    }
-
-    public double area() {
-        return width * height;
-    }
-
-    public boolean empty() {
-        return width <= 0 || height <= 0;
-    }
-
-    public boolean contains(Point p) {
-        return x <= p.x && p.x < x + width && y <= p.y && p.y < y + height;
-    }
-
-    @Override
-    public int hashCode() {
-        final int prime = 31;
-        int result = 1;
-        long temp;
-        temp = Double.doubleToLongBits(height);
-        result = prime * result + (int) (temp ^ (temp >>> 32));
-        temp = Double.doubleToLongBits(width);
-        result = prime * result + (int) (temp ^ (temp >>> 32));
-        temp = Double.doubleToLongBits(x);
-        result = prime * result + (int) (temp ^ (temp >>> 32));
-        temp = Double.doubleToLongBits(y);
-        result = prime * result + (int) (temp ^ (temp >>> 32));
-        return result;
-    }
-
-    @Override
-    public boolean equals(Object obj) {
-        if (this == obj) return true;
-        if (!(obj instanceof Rect2d)) return false;
-        Rect2d it = (Rect2d) obj;
-        return x == it.x && y == it.y && width == it.width && height == it.height;
-    }
-
-    @Override
-    public String toString() {
-        return "{" + x + ", " + y + ", " + width + "x" + height + "}";
-    }
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/RotatedRect.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/RotatedRect.java
deleted file mode 100755
index f6d1163..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/RotatedRect.java
+++ /dev/null
@@ -1,113 +0,0 @@
-package org.opencv.core;
-
-//javadoc:RotatedRect_
-public class RotatedRect {
-
-    public Point center;
-    public Size size;
-    public double angle;
-
-    public RotatedRect() {
-        this.center = new Point();
-        this.size = new Size();
-        this.angle = 0;
-    }
-
-    public RotatedRect(Point c, Size s, double a) {
-        this.center = c.clone();
-        this.size = s.clone();
-        this.angle = a;
-    }
-
-    public RotatedRect(double[] vals) {
-        this();
-        set(vals);
-    }
-
-    public void set(double[] vals) {
-        if (vals != null) {
-            center.x = vals.length > 0 ? (double) vals[0] : 0;
-            center.y = vals.length > 1 ? (double) vals[1] : 0;
-            size.width = vals.length > 2 ? (double) vals[2] : 0;
-            size.height = vals.length > 3 ? (double) vals[3] : 0;
-            angle = vals.length > 4 ? (double) vals[4] : 0;
-        } else {
-            center.x = 0;
-            center.x = 0;
-            size.width = 0;
-            size.height = 0;
-            angle = 0;
-        }
-    }
-
-    public void points(Point pt[])
-    {
-        double _angle = angle * Math.PI / 180.0;
-        double b = (double) Math.cos(_angle) * 0.5f;
-        double a = (double) Math.sin(_angle) * 0.5f;
-
-        pt[0] = new Point(
-                center.x - a * size.height - b * size.width,
-                center.y + b * size.height - a * size.width);
-
-        pt[1] = new Point(
-                center.x + a * size.height - b * size.width,
-                center.y - b * size.height - a * size.width);
-
-        pt[2] = new Point(
-                2 * center.x - pt[0].x,
-                2 * center.y - pt[0].y);
-
-        pt[3] = new Point(
-                2 * center.x - pt[1].x,
-                2 * center.y - pt[1].y);
-    }
-
-    public Rect boundingRect()
-    {
-        Point pt[] = new Point[4];
-        points(pt);
-        Rect r = new Rect((int) Math.floor(Math.min(Math.min(Math.min(pt[0].x, pt[1].x), pt[2].x), pt[3].x)),
-                (int) Math.floor(Math.min(Math.min(Math.min(pt[0].y, pt[1].y), pt[2].y), pt[3].y)),
-                (int) Math.ceil(Math.max(Math.max(Math.max(pt[0].x, pt[1].x), pt[2].x), pt[3].x)),
-                (int) Math.ceil(Math.max(Math.max(Math.max(pt[0].y, pt[1].y), pt[2].y), pt[3].y)));
-        r.width -= r.x - 1;
-        r.height -= r.y - 1;
-        return r;
-    }
-
-    public RotatedRect clone() {
-        return new RotatedRect(center, size, angle);
-    }
-
-    @Override
-    public int hashCode() {
-        final int prime = 31;
-        int result = 1;
-        long temp;
-        temp = Double.doubleToLongBits(center.x);
-        result = prime * result + (int) (temp ^ (temp >>> 32));
-        temp = Double.doubleToLongBits(center.y);
-        result = prime * result + (int) (temp ^ (temp >>> 32));
-        temp = Double.doubleToLongBits(size.width);
-        result = prime * result + (int) (temp ^ (temp >>> 32));
-        temp = Double.doubleToLongBits(size.height);
-        result = prime * result + (int) (temp ^ (temp >>> 32));
-        temp = Double.doubleToLongBits(angle);
-        result = prime * result + (int) (temp ^ (temp >>> 32));
-        return result;
-    }
-
-    @Override
-    public boolean equals(Object obj) {
-        if (this == obj) return true;
-        if (!(obj instanceof RotatedRect)) return false;
-        RotatedRect it = (RotatedRect) obj;
-        return center.equals(it.center) && size.equals(it.size) && angle == it.angle;
-    }
-
-    @Override
-    public String toString() {
-        return "{ " + center + " " + size + " * " + angle + " }";
-    }
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/Scalar.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/Scalar.java
deleted file mode 100755
index 01676e4..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/Scalar.java
+++ /dev/null
@@ -1,90 +0,0 @@
-package org.opencv.core;
-
-//javadoc:Scalar_
-public class Scalar {
-
-    public double val[];
-
-    public Scalar(double v0, double v1, double v2, double v3) {
-        val = new double[] { v0, v1, v2, v3 };
-    }
-
-    public Scalar(double v0, double v1, double v2) {
-        val = new double[] { v0, v1, v2, 0 };
-    }
-
-    public Scalar(double v0, double v1) {
-        val = new double[] { v0, v1, 0, 0 };
-    }
-
-    public Scalar(double v0) {
-        val = new double[] { v0, 0, 0, 0 };
-    }
-
-    public Scalar(double[] vals) {
-        if (vals != null && vals.length == 4)
-            val = vals.clone();
-        else {
-            val = new double[4];
-            set(vals);
-        }
-    }
-
-    public void set(double[] vals) {
-        if (vals != null) {
-            val[0] = vals.length > 0 ? vals[0] : 0;
-            val[1] = vals.length > 1 ? vals[1] : 0;
-            val[2] = vals.length > 2 ? vals[2] : 0;
-            val[3] = vals.length > 3 ? vals[3] : 0;
-        } else
-            val[0] = val[1] = val[2] = val[3] = 0;
-    }
-
-    public static Scalar all(double v) {
-        return new Scalar(v, v, v, v);
-    }
-
-    public Scalar clone() {
-        return new Scalar(val);
-    }
-
-    public Scalar mul(Scalar it, double scale) {
-        return new Scalar(val[0] * it.val[0] * scale, val[1] * it.val[1] * scale,
-                val[2] * it.val[2] * scale, val[3] * it.val[3] * scale);
-    }
-
-    public Scalar mul(Scalar it) {
-        return mul(it, 1);
-    }
-
-    public Scalar conj() {
-        return new Scalar(val[0], -val[1], -val[2], -val[3]);
-    }
-
-    public boolean isReal() {
-        return val[1] == 0 && val[2] == 0 && val[3] == 0;
-    }
-
-    @Override
-    public int hashCode() {
-        final int prime = 31;
-        int result = 1;
-        result = prime * result + java.util.Arrays.hashCode(val);
-        return result;
-    }
-
-    @Override
-    public boolean equals(Object obj) {
-        if (this == obj) return true;
-        if (!(obj instanceof Scalar)) return false;
-        Scalar it = (Scalar) obj;
-        if (!java.util.Arrays.equals(val, it.val)) return false;
-        return true;
-    }
-
-    @Override
-    public String toString() {
-        return "[" + val[0] + ", " + val[1] + ", " + val[2] + ", " + val[3] + "]";
-    }
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/Size.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/Size.java
deleted file mode 100755
index f7d69f3..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/Size.java
+++ /dev/null
@@ -1,73 +0,0 @@
-package org.opencv.core;
-
-//javadoc:Size_
-public class Size {
-
-    public double width, height;
-
-    public Size(double width, double height) {
-        this.width = width;
-        this.height = height;
-    }
-
-    public Size() {
-        this(0, 0);
-    }
-
-    public Size(Point p) {
-        width = p.x;
-        height = p.y;
-    }
-
-    public Size(double[] vals) {
-        set(vals);
-    }
-
-    public void set(double[] vals) {
-        if (vals != null) {
-            width = vals.length > 0 ? vals[0] : 0;
-            height = vals.length > 1 ? vals[1] : 0;
-        } else {
-            width = 0;
-            height = 0;
-        }
-    }
-
-    public double area() {
-        return width * height;
-    }
-
-    public boolean empty() {
-        return width <= 0 || height <= 0;
-    }
-
-    public Size clone() {
-        return new Size(width, height);
-    }
-
-    @Override
-    public int hashCode() {
-        final int prime = 31;
-        int result = 1;
-        long temp;
-        temp = Double.doubleToLongBits(height);
-        result = prime * result + (int) (temp ^ (temp >>> 32));
-        temp = Double.doubleToLongBits(width);
-        result = prime * result + (int) (temp ^ (temp >>> 32));
-        return result;
-    }
-
-    @Override
-    public boolean equals(Object obj) {
-        if (this == obj) return true;
-        if (!(obj instanceof Size)) return false;
-        Size it = (Size) obj;
-        return width == it.width && height == it.height;
-    }
-
-    @Override
-    public String toString() {
-        return (int)width + "x" + (int)height;
-    }
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/TermCriteria.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/TermCriteria.java
deleted file mode 100755
index c67e51e..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/TermCriteria.java
+++ /dev/null
@@ -1,92 +0,0 @@
-package org.opencv.core;
-
-//javadoc:TermCriteria
-public class TermCriteria {
-
-    /**
-     * The maximum number of iterations or elements to compute
-     */
-    public static final int COUNT = 1;
-    /**
-     * The maximum number of iterations or elements to compute
-     */
-    public static final int MAX_ITER = COUNT;
-    /**
-     * The desired accuracy threshold or change in parameters at which the iterative algorithm is terminated.
-     */
-    public static final int EPS = 2;
-
-    public int type;
-    public int maxCount;
-    public double epsilon;
-
-    /**
-     * Termination criteria for iterative algorithms.
-     *
-     * @param type
-     *            the type of termination criteria: COUNT, EPS or COUNT + EPS.
-     * @param maxCount
-     *            the maximum number of iterations/elements.
-     * @param epsilon
-     *            the desired accuracy.
-     */
-    public TermCriteria(int type, int maxCount, double epsilon) {
-        this.type = type;
-        this.maxCount = maxCount;
-        this.epsilon = epsilon;
-    }
-
-    /**
-     * Termination criteria for iterative algorithms.
-     */
-    public TermCriteria() {
-        this(0, 0, 0.0);
-    }
-
-    public TermCriteria(double[] vals) {
-        set(vals);
-    }
-
-    public void set(double[] vals) {
-        if (vals != null) {
-            type = vals.length > 0 ? (int) vals[0] : 0;
-            maxCount = vals.length > 1 ? (int) vals[1] : 0;
-            epsilon = vals.length > 2 ? (double) vals[2] : 0;
-        } else {
-            type = 0;
-            maxCount = 0;
-            epsilon = 0;
-        }
-    }
-
-    public TermCriteria clone() {
-        return new TermCriteria(type, maxCount, epsilon);
-    }
-
-    @Override
-    public int hashCode() {
-        final int prime = 31;
-        int result = 1;
-        long temp;
-        temp = Double.doubleToLongBits(type);
-        result = prime * result + (int) (temp ^ (temp >>> 32));
-        temp = Double.doubleToLongBits(maxCount);
-        result = prime * result + (int) (temp ^ (temp >>> 32));
-        temp = Double.doubleToLongBits(epsilon);
-        result = prime * result + (int) (temp ^ (temp >>> 32));
-        return result;
-    }
-
-    @Override
-    public boolean equals(Object obj) {
-        if (this == obj) return true;
-        if (!(obj instanceof TermCriteria)) return false;
-        TermCriteria it = (TermCriteria) obj;
-        return type == it.type && maxCount == it.maxCount && epsilon == it.epsilon;
-    }
-
-    @Override
-    public String toString() {
-        return "{ type: " + type + ", maxCount: " + maxCount + ", epsilon: " + epsilon + "}";
-    }
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/TickMeter.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/TickMeter.java
deleted file mode 100755
index 3fd80ca..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/core/TickMeter.java
+++ /dev/null
@@ -1,184 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.core;
-
-
-
-// C++: class TickMeter
-//javadoc: TickMeter
-
-public class TickMeter {
-
-    protected final long nativeObj;
-    protected TickMeter(long addr) { nativeObj = addr; }
-
-    public long getNativeObjAddr() { return nativeObj; }
-
-    // internal usage only
-    public static TickMeter __fromPtr__(long addr) { return new TickMeter(addr); }
-
-    //
-    // C++:   TickMeter()
-    //
-
-    //javadoc: TickMeter::TickMeter()
-    public   TickMeter()
-    {
-        
-        nativeObj = TickMeter_0();
-        
-        return;
-    }
-
-
-    //
-    // C++:  double getTimeMicro()
-    //
-
-    //javadoc: TickMeter::getTimeMicro()
-    public  double getTimeMicro()
-    {
-        
-        double retVal = getTimeMicro_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double getTimeMilli()
-    //
-
-    //javadoc: TickMeter::getTimeMilli()
-    public  double getTimeMilli()
-    {
-        
-        double retVal = getTimeMilli_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double getTimeSec()
-    //
-
-    //javadoc: TickMeter::getTimeSec()
-    public  double getTimeSec()
-    {
-        
-        double retVal = getTimeSec_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int64 getCounter()
-    //
-
-    //javadoc: TickMeter::getCounter()
-    public  long getCounter()
-    {
-        
-        long retVal = getCounter_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int64 getTimeTicks()
-    //
-
-    //javadoc: TickMeter::getTimeTicks()
-    public  long getTimeTicks()
-    {
-        
-        long retVal = getTimeTicks_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  void reset()
-    //
-
-    //javadoc: TickMeter::reset()
-    public  void reset()
-    {
-        
-        reset_0(nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void start()
-    //
-
-    //javadoc: TickMeter::start()
-    public  void start()
-    {
-        
-        start_0(nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void stop()
-    //
-
-    //javadoc: TickMeter::stop()
-    public  void stop()
-    {
-        
-        stop_0(nativeObj);
-        
-        return;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++:   TickMeter()
-    private static native long TickMeter_0();
-
-    // C++:  double getTimeMicro()
-    private static native double getTimeMicro_0(long nativeObj);
-
-    // C++:  double getTimeMilli()
-    private static native double getTimeMilli_0(long nativeObj);
-
-    // C++:  double getTimeSec()
-    private static native double getTimeSec_0(long nativeObj);
-
-    // C++:  int64 getCounter()
-    private static native long getCounter_0(long nativeObj);
-
-    // C++:  int64 getTimeTicks()
-    private static native long getTimeTicks_0(long nativeObj);
-
-    // C++:  void reset()
-    private static native void reset_0(long nativeObj);
-
-    // C++:  void start()
-    private static native void start_0(long nativeObj);
-
-    // C++:  void stop()
-    private static native void stop_0(long nativeObj);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/dnn/DictValue.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/dnn/DictValue.java
deleted file mode 100755
index cb08ef2..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/dnn/DictValue.java
+++ /dev/null
@@ -1,214 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.dnn;
-
-import java.lang.String;
-
-// C++: class DictValue
-//javadoc: DictValue
-
-public class DictValue {
-
-    protected final long nativeObj;
-    protected DictValue(long addr) { nativeObj = addr; }
-
-    public long getNativeObjAddr() { return nativeObj; }
-
-    // internal usage only
-    public static DictValue __fromPtr__(long addr) { return new DictValue(addr); }
-
-    //
-    // C++:   DictValue(String s)
-    //
-
-    //javadoc: DictValue::DictValue(s)
-    public   DictValue(String s)
-    {
-        
-        nativeObj = DictValue_0(s);
-        
-        return;
-    }
-
-
-    //
-    // C++:   DictValue(double p)
-    //
-
-    //javadoc: DictValue::DictValue(p)
-    public   DictValue(double p)
-    {
-        
-        nativeObj = DictValue_1(p);
-        
-        return;
-    }
-
-
-    //
-    // C++:   DictValue(int i)
-    //
-
-    //javadoc: DictValue::DictValue(i)
-    public   DictValue(int i)
-    {
-        
-        nativeObj = DictValue_2(i);
-        
-        return;
-    }
-
-
-    //
-    // C++:  String getStringValue(int idx = -1)
-    //
-
-    //javadoc: DictValue::getStringValue(idx)
-    public  String getStringValue(int idx)
-    {
-        
-        String retVal = getStringValue_0(nativeObj, idx);
-        
-        return retVal;
-    }
-
-    //javadoc: DictValue::getStringValue()
-    public  String getStringValue()
-    {
-        
-        String retVal = getStringValue_1(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  bool isInt()
-    //
-
-    //javadoc: DictValue::isInt()
-    public  boolean isInt()
-    {
-        
-        boolean retVal = isInt_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  bool isReal()
-    //
-
-    //javadoc: DictValue::isReal()
-    public  boolean isReal()
-    {
-        
-        boolean retVal = isReal_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  bool isString()
-    //
-
-    //javadoc: DictValue::isString()
-    public  boolean isString()
-    {
-        
-        boolean retVal = isString_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double getRealValue(int idx = -1)
-    //
-
-    //javadoc: DictValue::getRealValue(idx)
-    public  double getRealValue(int idx)
-    {
-        
-        double retVal = getRealValue_0(nativeObj, idx);
-        
-        return retVal;
-    }
-
-    //javadoc: DictValue::getRealValue()
-    public  double getRealValue()
-    {
-        
-        double retVal = getRealValue_1(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getIntValue(int idx = -1)
-    //
-
-    //javadoc: DictValue::getIntValue(idx)
-    public  int getIntValue(int idx)
-    {
-        
-        int retVal = getIntValue_0(nativeObj, idx);
-        
-        return retVal;
-    }
-
-    //javadoc: DictValue::getIntValue()
-    public  int getIntValue()
-    {
-        
-        int retVal = getIntValue_1(nativeObj);
-        
-        return retVal;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++:   DictValue(String s)
-    private static native long DictValue_0(String s);
-
-    // C++:   DictValue(double p)
-    private static native long DictValue_1(double p);
-
-    // C++:   DictValue(int i)
-    private static native long DictValue_2(int i);
-
-    // C++:  String getStringValue(int idx = -1)
-    private static native String getStringValue_0(long nativeObj, int idx);
-    private static native String getStringValue_1(long nativeObj);
-
-    // C++:  bool isInt()
-    private static native boolean isInt_0(long nativeObj);
-
-    // C++:  bool isReal()
-    private static native boolean isReal_0(long nativeObj);
-
-    // C++:  bool isString()
-    private static native boolean isString_0(long nativeObj);
-
-    // C++:  double getRealValue(int idx = -1)
-    private static native double getRealValue_0(long nativeObj, int idx);
-    private static native double getRealValue_1(long nativeObj);
-
-    // C++:  int getIntValue(int idx = -1)
-    private static native int getIntValue_0(long nativeObj, int idx);
-    private static native int getIntValue_1(long nativeObj);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/dnn/Dnn.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/dnn/Dnn.java
deleted file mode 100755
index c0c71b7..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/dnn/Dnn.java
+++ /dev/null
@@ -1,345 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.dnn;
-
-import java.lang.String;
-import java.util.ArrayList;
-import java.util.List;
-import org.opencv.core.Mat;
-import org.opencv.core.MatOfFloat;
-import org.opencv.core.MatOfInt;
-import org.opencv.core.MatOfRect;
-import org.opencv.core.Scalar;
-import org.opencv.core.Size;
-import org.opencv.dnn.Net;
-import org.opencv.utils.Converters;
-
-// C++: class Dnn
-//javadoc: Dnn
-
-public class Dnn {
-
-    public static final int
-            DNN_BACKEND_DEFAULT = 0,
-            DNN_BACKEND_HALIDE = 1,
-            DNN_BACKEND_INFERENCE_ENGINE = 2,
-            DNN_BACKEND_OPENCV = 3,
-            DNN_TARGET_CPU = 0,
-            DNN_TARGET_OPENCL = 1,
-            DNN_TARGET_OPENCL_FP16 = 2,
-            DNN_TARGET_MYRIAD = 3;
-
-
-    //
-    // C++:  Mat blobFromImage(Mat image, double scalefactor = 1.0, Size size = Size(), Scalar mean = Scalar(), bool swapRB = true, bool crop = true)
-    //
-
-    //javadoc: blobFromImage(image, scalefactor, size, mean, swapRB, crop)
-    public static Mat blobFromImage(Mat image, double scalefactor, Size size, Scalar mean, boolean swapRB, boolean crop)
-    {
-        
-        Mat retVal = new Mat(blobFromImage_0(image.nativeObj, scalefactor, size.width, size.height, mean.val[0], mean.val[1], mean.val[2], mean.val[3], swapRB, crop));
-        
-        return retVal;
-    }
-
-    //javadoc: blobFromImage(image)
-    public static Mat blobFromImage(Mat image)
-    {
-        
-        Mat retVal = new Mat(blobFromImage_1(image.nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Mat blobFromImages(vector_Mat images, double scalefactor = 1.0, Size size = Size(), Scalar mean = Scalar(), bool swapRB = true, bool crop = true)
-    //
-
-    //javadoc: blobFromImages(images, scalefactor, size, mean, swapRB, crop)
-    public static Mat blobFromImages(List<Mat> images, double scalefactor, Size size, Scalar mean, boolean swapRB, boolean crop)
-    {
-        Mat images_mat = Converters.vector_Mat_to_Mat(images);
-        Mat retVal = new Mat(blobFromImages_0(images_mat.nativeObj, scalefactor, size.width, size.height, mean.val[0], mean.val[1], mean.val[2], mean.val[3], swapRB, crop));
-        
-        return retVal;
-    }
-
-    //javadoc: blobFromImages(images)
-    public static Mat blobFromImages(List<Mat> images)
-    {
-        Mat images_mat = Converters.vector_Mat_to_Mat(images);
-        Mat retVal = new Mat(blobFromImages_1(images_mat.nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Mat readTorchBlob(String filename, bool isBinary = true)
-    //
-
-    //javadoc: readTorchBlob(filename, isBinary)
-    public static Mat readTorchBlob(String filename, boolean isBinary)
-    {
-        
-        Mat retVal = new Mat(readTorchBlob_0(filename, isBinary));
-        
-        return retVal;
-    }
-
-    //javadoc: readTorchBlob(filename)
-    public static Mat readTorchBlob(String filename)
-    {
-        
-        Mat retVal = new Mat(readTorchBlob_1(filename));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Net readNet(String model, String config = "", String framework = "")
-    //
-
-    //javadoc: readNet(model, config, framework)
-    public static Net readNet(String model, String config, String framework)
-    {
-        
-        Net retVal = new Net(readNet_0(model, config, framework));
-        
-        return retVal;
-    }
-
-    //javadoc: readNet(model)
-    public static Net readNet(String model)
-    {
-        
-        Net retVal = new Net(readNet_1(model));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Net readNetFromCaffe(String prototxt, String caffeModel = String())
-    //
-
-    //javadoc: readNetFromCaffe(prototxt, caffeModel)
-    public static Net readNetFromCaffe(String prototxt, String caffeModel)
-    {
-        
-        Net retVal = new Net(readNetFromCaffe_0(prototxt, caffeModel));
-        
-        return retVal;
-    }
-
-    //javadoc: readNetFromCaffe(prototxt)
-    public static Net readNetFromCaffe(String prototxt)
-    {
-        
-        Net retVal = new Net(readNetFromCaffe_1(prototxt));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Net readNetFromDarknet(String cfgFile, String darknetModel = String())
-    //
-
-    //javadoc: readNetFromDarknet(cfgFile, darknetModel)
-    public static Net readNetFromDarknet(String cfgFile, String darknetModel)
-    {
-        
-        Net retVal = new Net(readNetFromDarknet_0(cfgFile, darknetModel));
-        
-        return retVal;
-    }
-
-    //javadoc: readNetFromDarknet(cfgFile)
-    public static Net readNetFromDarknet(String cfgFile)
-    {
-        
-        Net retVal = new Net(readNetFromDarknet_1(cfgFile));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Net readNetFromModelOptimizer(String xml, String bin)
-    //
-
-    //javadoc: readNetFromModelOptimizer(xml, bin)
-    public static Net readNetFromModelOptimizer(String xml, String bin)
-    {
-        
-        Net retVal = new Net(readNetFromModelOptimizer_0(xml, bin));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Net readNetFromTensorflow(String model, String config = String())
-    //
-
-    //javadoc: readNetFromTensorflow(model, config)
-    public static Net readNetFromTensorflow(String model, String config)
-    {
-        
-        Net retVal = new Net(readNetFromTensorflow_0(model, config));
-        
-        return retVal;
-    }
-
-    //javadoc: readNetFromTensorflow(model)
-    public static Net readNetFromTensorflow(String model)
-    {
-        
-        Net retVal = new Net(readNetFromTensorflow_1(model));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Net readNetFromTorch(String model, bool isBinary = true)
-    //
-
-    //javadoc: readNetFromTorch(model, isBinary)
-    public static Net readNetFromTorch(String model, boolean isBinary)
-    {
-        
-        Net retVal = new Net(readNetFromTorch_0(model, isBinary));
-        
-        return retVal;
-    }
-
-    //javadoc: readNetFromTorch(model)
-    public static Net readNetFromTorch(String model)
-    {
-        
-        Net retVal = new Net(readNetFromTorch_1(model));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  void NMSBoxes(vector_Rect bboxes, vector_float scores, float score_threshold, float nms_threshold, vector_int& indices, float eta = 1.f, int top_k = 0)
-    //
-
-    //javadoc: NMSBoxes(bboxes, scores, score_threshold, nms_threshold, indices, eta, top_k)
-    public static void NMSBoxes(MatOfRect bboxes, MatOfFloat scores, float score_threshold, float nms_threshold, MatOfInt indices, float eta, int top_k)
-    {
-        Mat bboxes_mat = bboxes;
-        Mat scores_mat = scores;
-        Mat indices_mat = indices;
-        NMSBoxes_0(bboxes_mat.nativeObj, scores_mat.nativeObj, score_threshold, nms_threshold, indices_mat.nativeObj, eta, top_k);
-        
-        return;
-    }
-
-    //javadoc: NMSBoxes(bboxes, scores, score_threshold, nms_threshold, indices)
-    public static void NMSBoxes(MatOfRect bboxes, MatOfFloat scores, float score_threshold, float nms_threshold, MatOfInt indices)
-    {
-        Mat bboxes_mat = bboxes;
-        Mat scores_mat = scores;
-        Mat indices_mat = indices;
-        NMSBoxes_1(bboxes_mat.nativeObj, scores_mat.nativeObj, score_threshold, nms_threshold, indices_mat.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void imagesFromBlob(Mat blob_, vector_Mat& images_)
-    //
-
-    //javadoc: imagesFromBlob(blob_, images_)
-    public static void imagesFromBlob(Mat blob_, List<Mat> images_)
-    {
-        Mat images__mat = new Mat();
-        imagesFromBlob_0(blob_.nativeObj, images__mat.nativeObj);
-        Converters.Mat_to_vector_Mat(images__mat, images_);
-        images__mat.release();
-        return;
-    }
-
-
-    //
-    // C++:  void shrinkCaffeModel(String src, String dst, vector_String layersTypes = std::vector<String>())
-    //
-
-    //javadoc: shrinkCaffeModel(src, dst, layersTypes)
-    public static void shrinkCaffeModel(String src, String dst, List<String> layersTypes)
-    {
-        
-        shrinkCaffeModel_0(src, dst, layersTypes);
-        
-        return;
-    }
-
-    //javadoc: shrinkCaffeModel(src, dst)
-    public static void shrinkCaffeModel(String src, String dst)
-    {
-        
-        shrinkCaffeModel_1(src, dst);
-        
-        return;
-    }
-
-
-
-
-    // C++:  Mat blobFromImage(Mat image, double scalefactor = 1.0, Size size = Size(), Scalar mean = Scalar(), bool swapRB = true, bool crop = true)
-    private static native long blobFromImage_0(long image_nativeObj, double scalefactor, double size_width, double size_height, double mean_val0, double mean_val1, double mean_val2, double mean_val3, boolean swapRB, boolean crop);
-    private static native long blobFromImage_1(long image_nativeObj);
-
-    // C++:  Mat blobFromImages(vector_Mat images, double scalefactor = 1.0, Size size = Size(), Scalar mean = Scalar(), bool swapRB = true, bool crop = true)
-    private static native long blobFromImages_0(long images_mat_nativeObj, double scalefactor, double size_width, double size_height, double mean_val0, double mean_val1, double mean_val2, double mean_val3, boolean swapRB, boolean crop);
-    private static native long blobFromImages_1(long images_mat_nativeObj);
-
-    // C++:  Mat readTorchBlob(String filename, bool isBinary = true)
-    private static native long readTorchBlob_0(String filename, boolean isBinary);
-    private static native long readTorchBlob_1(String filename);
-
-    // C++:  Net readNet(String model, String config = "", String framework = "")
-    private static native long readNet_0(String model, String config, String framework);
-    private static native long readNet_1(String model);
-
-    // C++:  Net readNetFromCaffe(String prototxt, String caffeModel = String())
-    private static native long readNetFromCaffe_0(String prototxt, String caffeModel);
-    private static native long readNetFromCaffe_1(String prototxt);
-
-    // C++:  Net readNetFromDarknet(String cfgFile, String darknetModel = String())
-    private static native long readNetFromDarknet_0(String cfgFile, String darknetModel);
-    private static native long readNetFromDarknet_1(String cfgFile);
-
-    // C++:  Net readNetFromModelOptimizer(String xml, String bin)
-    private static native long readNetFromModelOptimizer_0(String xml, String bin);
-
-    // C++:  Net readNetFromTensorflow(String model, String config = String())
-    private static native long readNetFromTensorflow_0(String model, String config);
-    private static native long readNetFromTensorflow_1(String model);
-
-    // C++:  Net readNetFromTorch(String model, bool isBinary = true)
-    private static native long readNetFromTorch_0(String model, boolean isBinary);
-    private static native long readNetFromTorch_1(String model);
-
-    // C++:  void NMSBoxes(vector_Rect bboxes, vector_float scores, float score_threshold, float nms_threshold, vector_int& indices, float eta = 1.f, int top_k = 0)
-    private static native void NMSBoxes_0(long bboxes_mat_nativeObj, long scores_mat_nativeObj, float score_threshold, float nms_threshold, long indices_mat_nativeObj, float eta, int top_k);
-    private static native void NMSBoxes_1(long bboxes_mat_nativeObj, long scores_mat_nativeObj, float score_threshold, float nms_threshold, long indices_mat_nativeObj);
-
-    // C++:  void imagesFromBlob(Mat blob_, vector_Mat& images_)
-    private static native void imagesFromBlob_0(long blob__nativeObj, long images__mat_nativeObj);
-
-    // C++:  void shrinkCaffeModel(String src, String dst, vector_String layersTypes = std::vector<String>())
-    private static native void shrinkCaffeModel_0(String src, String dst, List<String> layersTypes);
-    private static native void shrinkCaffeModel_1(String src, String dst);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/dnn/Layer.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/dnn/Layer.java
deleted file mode 100755
index ee4e03b..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/dnn/Layer.java
+++ /dev/null
@@ -1,194 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.dnn;
-
-import java.lang.String;
-import java.util.ArrayList;
-import java.util.List;
-import org.opencv.core.Algorithm;
-import org.opencv.core.Mat;
-import org.opencv.utils.Converters;
-
-// C++: class Layer
-//javadoc: Layer
-
-public class Layer extends Algorithm {
-
-    protected Layer(long addr) { super(addr); }
-
-    // internal usage only
-    public static Layer __fromPtr__(long addr) { return new Layer(addr); }
-
-    //
-    // C++:  int outputNameToIndex(String outputName)
-    //
-
-    //javadoc: Layer::outputNameToIndex(outputName)
-    public  int outputNameToIndex(String outputName)
-    {
-        
-        int retVal = outputNameToIndex_0(nativeObj, outputName);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  vector_Mat finalize(vector_Mat inputs)
-    //
-
-    //javadoc: Layer::finalize(inputs)
-    public  List<Mat> finalize(List<Mat> inputs)
-    {
-        Mat inputs_mat = Converters.vector_Mat_to_Mat(inputs);
-        List<Mat> retVal = new ArrayList<Mat>();
-        Mat retValMat = new Mat(finalize_0(nativeObj, inputs_mat.nativeObj));
-        Converters.Mat_to_vector_Mat(retValMat, retVal);
-        return retVal;
-    }
-
-
-    //
-    // C++:  void finalize(vector_Mat inputs, vector_Mat& outputs)
-    //
-
-    //javadoc: Layer::finalize(inputs, outputs)
-    public  void finalize(List<Mat> inputs, List<Mat> outputs)
-    {
-        Mat inputs_mat = Converters.vector_Mat_to_Mat(inputs);
-        Mat outputs_mat = new Mat();
-        finalize_1(nativeObj, inputs_mat.nativeObj, outputs_mat.nativeObj);
-        Converters.Mat_to_vector_Mat(outputs_mat, outputs);
-        outputs_mat.release();
-        return;
-    }
-
-
-    //
-    // C++:  void run(vector_Mat inputs, vector_Mat& outputs, vector_Mat& internals)
-    //
-
-    //javadoc: Layer::run(inputs, outputs, internals)
-    public  void run(List<Mat> inputs, List<Mat> outputs, List<Mat> internals)
-    {
-        Mat inputs_mat = Converters.vector_Mat_to_Mat(inputs);
-        Mat outputs_mat = new Mat();
-        Mat internals_mat = Converters.vector_Mat_to_Mat(internals);
-        run_0(nativeObj, inputs_mat.nativeObj, outputs_mat.nativeObj, internals_mat.nativeObj);
-        Converters.Mat_to_vector_Mat(outputs_mat, outputs);
-        outputs_mat.release();
-        Converters.Mat_to_vector_Mat(internals_mat, internals);
-        internals_mat.release();
-        return;
-    }
-
-
-    //
-    // C++: vector_Mat Layer::blobs
-    //
-
-    //javadoc: Layer::get_blobs()
-    public  List<Mat> get_blobs()
-    {
-        List<Mat> retVal = new ArrayList<Mat>();
-        Mat retValMat = new Mat(get_blobs_0(nativeObj));
-        Converters.Mat_to_vector_Mat(retValMat, retVal);
-        return retVal;
-    }
-
-
-    //
-    // C++: void Layer::blobs
-    //
-
-    //javadoc: Layer::set_blobs(blobs)
-    public  void set_blobs(List<Mat> blobs)
-    {
-        Mat blobs_mat = Converters.vector_Mat_to_Mat(blobs);
-        set_blobs_0(nativeObj, blobs_mat.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++: String Layer::name
-    //
-
-    //javadoc: Layer::get_name()
-    public  String get_name()
-    {
-        
-        String retVal = get_name_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: String Layer::type
-    //
-
-    //javadoc: Layer::get_type()
-    public  String get_type()
-    {
-        
-        String retVal = get_type_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: int Layer::preferableTarget
-    //
-
-    //javadoc: Layer::get_preferableTarget()
-    public  int get_preferableTarget()
-    {
-        
-        int retVal = get_preferableTarget_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++:  int outputNameToIndex(String outputName)
-    private static native int outputNameToIndex_0(long nativeObj, String outputName);
-
-    // C++:  vector_Mat finalize(vector_Mat inputs)
-    private static native long finalize_0(long nativeObj, long inputs_mat_nativeObj);
-
-    // C++:  void finalize(vector_Mat inputs, vector_Mat& outputs)
-    private static native void finalize_1(long nativeObj, long inputs_mat_nativeObj, long outputs_mat_nativeObj);
-
-    // C++:  void run(vector_Mat inputs, vector_Mat& outputs, vector_Mat& internals)
-    private static native void run_0(long nativeObj, long inputs_mat_nativeObj, long outputs_mat_nativeObj, long internals_mat_nativeObj);
-
-    // C++: vector_Mat Layer::blobs
-    private static native long get_blobs_0(long nativeObj);
-
-    // C++: void Layer::blobs
-    private static native void set_blobs_0(long nativeObj, long blobs_mat_nativeObj);
-
-    // C++: String Layer::name
-    private static native String get_name_0(long nativeObj);
-
-    // C++: String Layer::type
-    private static native String get_type_0(long nativeObj);
-
-    // C++: int Layer::preferableTarget
-    private static native int get_preferableTarget_0(long nativeObj);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/dnn/Net.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/dnn/Net.java
deleted file mode 100755
index b74fadf..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/dnn/Net.java
+++ /dev/null
@@ -1,622 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.dnn;
-
-import java.lang.String;
-import java.util.ArrayList;
-import java.util.List;
-import org.opencv.core.Mat;
-import org.opencv.core.MatOfDouble;
-import org.opencv.core.MatOfInt;
-import org.opencv.dnn.DictValue;
-import org.opencv.dnn.Layer;
-import org.opencv.dnn.Net;
-import org.opencv.utils.Converters;
-
-// C++: class Net
-//javadoc: Net
-
-public class Net {
-
-    protected final long nativeObj;
-    protected Net(long addr) { nativeObj = addr; }
-
-    public long getNativeObjAddr() { return nativeObj; }
-
-    // internal usage only
-    public static Net __fromPtr__(long addr) { return new Net(addr); }
-
-    //
-    // C++:   Net()
-    //
-
-    //javadoc: Net::Net()
-    public   Net()
-    {
-        
-        nativeObj = Net_0();
-        
-        return;
-    }
-
-
-    //
-    // C++:  Mat forward(String outputName = String())
-    //
-
-    //javadoc: Net::forward(outputName)
-    public  Mat forward(String outputName)
-    {
-        
-        Mat retVal = new Mat(forward_0(nativeObj, outputName));
-        
-        return retVal;
-    }
-
-    //javadoc: Net::forward()
-    public  Mat forward()
-    {
-        
-        Mat retVal = new Mat(forward_1(nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Mat getParam(LayerId layer, int numParam = 0)
-    //
-
-    //javadoc: Net::getParam(layer, numParam)
-    public  Mat getParam(DictValue layer, int numParam)
-    {
-        
-        Mat retVal = new Mat(getParam_0(nativeObj, layer.getNativeObjAddr(), numParam));
-        
-        return retVal;
-    }
-
-    //javadoc: Net::getParam(layer)
-    public  Mat getParam(DictValue layer)
-    {
-        
-        Mat retVal = new Mat(getParam_1(nativeObj, layer.getNativeObjAddr()));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: static Net readFromModelOptimizer(String xml, String bin)
-    //
-
-    //javadoc: Net::readFromModelOptimizer(xml, bin)
-    public static Net readFromModelOptimizer(String xml, String bin)
-    {
-        
-        Net retVal = new Net(readFromModelOptimizer_0(xml, bin));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Ptr_Layer getLayer(LayerId layerId)
-    //
-
-    //javadoc: Net::getLayer(layerId)
-    public  Layer getLayer(DictValue layerId)
-    {
-        
-        Layer retVal = Layer.__fromPtr__(getLayer_0(nativeObj, layerId.getNativeObjAddr()));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  bool empty()
-    //
-
-    //javadoc: Net::empty()
-    public  boolean empty()
-    {
-        
-        boolean retVal = empty_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getLayerId(String layer)
-    //
-
-    //javadoc: Net::getLayerId(layer)
-    public  int getLayerId(String layer)
-    {
-        
-        int retVal = getLayerId_0(nativeObj, layer);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getLayersCount(String layerType)
-    //
-
-    //javadoc: Net::getLayersCount(layerType)
-    public  int getLayersCount(String layerType)
-    {
-        
-        int retVal = getLayersCount_0(nativeObj, layerType);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int64 getFLOPS(MatShape netInputShape)
-    //
-
-    //javadoc: Net::getFLOPS(netInputShape)
-    public  long getFLOPS(MatOfInt netInputShape)
-    {
-        Mat netInputShape_mat = netInputShape;
-        long retVal = getFLOPS_0(nativeObj, netInputShape_mat.nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int64 getFLOPS(int layerId, MatShape netInputShape)
-    //
-
-    //javadoc: Net::getFLOPS(layerId, netInputShape)
-    public  long getFLOPS(int layerId, MatOfInt netInputShape)
-    {
-        Mat netInputShape_mat = netInputShape;
-        long retVal = getFLOPS_1(nativeObj, layerId, netInputShape_mat.nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int64 getFLOPS(int layerId, vector_MatShape netInputShapes)
-    //
-
-    //javadoc: Net::getFLOPS(layerId, netInputShapes)
-    public  long getFLOPS(int layerId, List<MatOfInt> netInputShapes)
-    {
-        
-        long retVal = getFLOPS_2(nativeObj, layerId, netInputShapes);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int64 getFLOPS(vector_MatShape netInputShapes)
-    //
-
-    //javadoc: Net::getFLOPS(netInputShapes)
-    public  long getFLOPS(List<MatOfInt> netInputShapes)
-    {
-        
-        long retVal = getFLOPS_3(nativeObj, netInputShapes);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int64 getPerfProfile(vector_double& timings)
-    //
-
-    //javadoc: Net::getPerfProfile(timings)
-    public  long getPerfProfile(MatOfDouble timings)
-    {
-        Mat timings_mat = timings;
-        long retVal = getPerfProfile_0(nativeObj, timings_mat.nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  vector_String getLayerNames()
-    //
-
-    //javadoc: Net::getLayerNames()
-    public  List<String> getLayerNames()
-    {
-        
-        List<String> retVal = getLayerNames_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  vector_int getUnconnectedOutLayers()
-    //
-
-    //javadoc: Net::getUnconnectedOutLayers()
-    public  MatOfInt getUnconnectedOutLayers()
-    {
-        
-        MatOfInt retVal = MatOfInt.fromNativeAddr(getUnconnectedOutLayers_0(nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  void connect(String outPin, String inpPin)
-    //
-
-    //javadoc: Net::connect(outPin, inpPin)
-    public  void connect(String outPin, String inpPin)
-    {
-        
-        connect_0(nativeObj, outPin, inpPin);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void deleteLayer(LayerId layer)
-    //
-
-    //javadoc: Net::deleteLayer(layer)
-    public  void deleteLayer(DictValue layer)
-    {
-        
-        deleteLayer_0(nativeObj, layer.getNativeObjAddr());
-        
-        return;
-    }
-
-
-    //
-    // C++:  void enableFusion(bool fusion)
-    //
-
-    //javadoc: Net::enableFusion(fusion)
-    public  void enableFusion(boolean fusion)
-    {
-        
-        enableFusion_0(nativeObj, fusion);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void forward(vector_Mat& outputBlobs, String outputName = String())
-    //
-
-    //javadoc: Net::forward(outputBlobs, outputName)
-    public  void forward(List<Mat> outputBlobs, String outputName)
-    {
-        Mat outputBlobs_mat = new Mat();
-        forward_2(nativeObj, outputBlobs_mat.nativeObj, outputName);
-        Converters.Mat_to_vector_Mat(outputBlobs_mat, outputBlobs);
-        outputBlobs_mat.release();
-        return;
-    }
-
-    //javadoc: Net::forward(outputBlobs)
-    public  void forward(List<Mat> outputBlobs)
-    {
-        Mat outputBlobs_mat = new Mat();
-        forward_3(nativeObj, outputBlobs_mat.nativeObj);
-        Converters.Mat_to_vector_Mat(outputBlobs_mat, outputBlobs);
-        outputBlobs_mat.release();
-        return;
-    }
-
-
-    //
-    // C++:  void forward(vector_Mat& outputBlobs, vector_String outBlobNames)
-    //
-
-    //javadoc: Net::forward(outputBlobs, outBlobNames)
-    public  void forward(List<Mat> outputBlobs, List<String> outBlobNames)
-    {
-        Mat outputBlobs_mat = new Mat();
-        forward_4(nativeObj, outputBlobs_mat.nativeObj, outBlobNames);
-        Converters.Mat_to_vector_Mat(outputBlobs_mat, outputBlobs);
-        outputBlobs_mat.release();
-        return;
-    }
-
-
-    //
-    // C++:  void forward(vector_vector_Mat& outputBlobs, vector_String outBlobNames)
-    //
-
-    // Unknown type 'vector_vector_Mat' (O), skipping the function
-
-
-    //
-    // C++:  void getLayerTypes(vector_String& layersTypes)
-    //
-
-    //javadoc: Net::getLayerTypes(layersTypes)
-    public  void getLayerTypes(List<String> layersTypes)
-    {
-        
-        getLayerTypes_0(nativeObj, layersTypes);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void getLayersShapes(MatShape netInputShape, vector_int& layersIds, vector_vector_MatShape& inLayersShapes, vector_vector_MatShape& outLayersShapes)
-    //
-
-    // Unknown type 'vector_vector_MatShape' (O), skipping the function
-
-
-    //
-    // C++:  void getLayersShapes(vector_MatShape netInputShapes, vector_int& layersIds, vector_vector_MatShape& inLayersShapes, vector_vector_MatShape& outLayersShapes)
-    //
-
-    // Unknown type 'vector_vector_MatShape' (O), skipping the function
-
-
-    //
-    // C++:  void getMemoryConsumption(MatShape netInputShape, size_t& weights, size_t& blobs)
-    //
-
-    //javadoc: Net::getMemoryConsumption(netInputShape, weights, blobs)
-    public  void getMemoryConsumption(MatOfInt netInputShape, long[] weights, long[] blobs)
-    {
-        Mat netInputShape_mat = netInputShape;
-        double[] weights_out = new double[1];
-        double[] blobs_out = new double[1];
-        getMemoryConsumption_0(nativeObj, netInputShape_mat.nativeObj, weights_out, blobs_out);
-        if(weights!=null) weights[0] = (long)weights_out[0];
-        if(blobs!=null) blobs[0] = (long)blobs_out[0];
-        return;
-    }
-
-
-    //
-    // C++:  void getMemoryConsumption(int layerId, MatShape netInputShape, size_t& weights, size_t& blobs)
-    //
-
-    //javadoc: Net::getMemoryConsumption(layerId, netInputShape, weights, blobs)
-    public  void getMemoryConsumption(int layerId, MatOfInt netInputShape, long[] weights, long[] blobs)
-    {
-        Mat netInputShape_mat = netInputShape;
-        double[] weights_out = new double[1];
-        double[] blobs_out = new double[1];
-        getMemoryConsumption_1(nativeObj, layerId, netInputShape_mat.nativeObj, weights_out, blobs_out);
-        if(weights!=null) weights[0] = (long)weights_out[0];
-        if(blobs!=null) blobs[0] = (long)blobs_out[0];
-        return;
-    }
-
-
-    //
-    // C++:  void getMemoryConsumption(int layerId, vector_MatShape netInputShapes, size_t& weights, size_t& blobs)
-    //
-
-    //javadoc: Net::getMemoryConsumption(layerId, netInputShapes, weights, blobs)
-    public  void getMemoryConsumption(int layerId, List<MatOfInt> netInputShapes, long[] weights, long[] blobs)
-    {
-        double[] weights_out = new double[1];
-        double[] blobs_out = new double[1];
-        getMemoryConsumption_2(nativeObj, layerId, netInputShapes, weights_out, blobs_out);
-        if(weights!=null) weights[0] = (long)weights_out[0];
-        if(blobs!=null) blobs[0] = (long)blobs_out[0];
-        return;
-    }
-
-
-    //
-    // C++:  void setHalideScheduler(String scheduler)
-    //
-
-    //javadoc: Net::setHalideScheduler(scheduler)
-    public  void setHalideScheduler(String scheduler)
-    {
-        
-        setHalideScheduler_0(nativeObj, scheduler);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setInput(Mat blob, String name = "")
-    //
-
-    //javadoc: Net::setInput(blob, name)
-    public  void setInput(Mat blob, String name)
-    {
-        
-        setInput_0(nativeObj, blob.nativeObj, name);
-        
-        return;
-    }
-
-    //javadoc: Net::setInput(blob)
-    public  void setInput(Mat blob)
-    {
-        
-        setInput_1(nativeObj, blob.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setInputsNames(vector_String inputBlobNames)
-    //
-
-    //javadoc: Net::setInputsNames(inputBlobNames)
-    public  void setInputsNames(List<String> inputBlobNames)
-    {
-        
-        setInputsNames_0(nativeObj, inputBlobNames);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setParam(LayerId layer, int numParam, Mat blob)
-    //
-
-    //javadoc: Net::setParam(layer, numParam, blob)
-    public  void setParam(DictValue layer, int numParam, Mat blob)
-    {
-        
-        setParam_0(nativeObj, layer.getNativeObjAddr(), numParam, blob.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setPreferableBackend(int backendId)
-    //
-
-    //javadoc: Net::setPreferableBackend(backendId)
-    public  void setPreferableBackend(int backendId)
-    {
-        
-        setPreferableBackend_0(nativeObj, backendId);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setPreferableTarget(int targetId)
-    //
-
-    //javadoc: Net::setPreferableTarget(targetId)
-    public  void setPreferableTarget(int targetId)
-    {
-        
-        setPreferableTarget_0(nativeObj, targetId);
-        
-        return;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++:   Net()
-    private static native long Net_0();
-
-    // C++:  Mat forward(String outputName = String())
-    private static native long forward_0(long nativeObj, String outputName);
-    private static native long forward_1(long nativeObj);
-
-    // C++:  Mat getParam(LayerId layer, int numParam = 0)
-    private static native long getParam_0(long nativeObj, long layer_nativeObj, int numParam);
-    private static native long getParam_1(long nativeObj, long layer_nativeObj);
-
-    // C++: static Net readFromModelOptimizer(String xml, String bin)
-    private static native long readFromModelOptimizer_0(String xml, String bin);
-
-    // C++:  Ptr_Layer getLayer(LayerId layerId)
-    private static native long getLayer_0(long nativeObj, long layerId_nativeObj);
-
-    // C++:  bool empty()
-    private static native boolean empty_0(long nativeObj);
-
-    // C++:  int getLayerId(String layer)
-    private static native int getLayerId_0(long nativeObj, String layer);
-
-    // C++:  int getLayersCount(String layerType)
-    private static native int getLayersCount_0(long nativeObj, String layerType);
-
-    // C++:  int64 getFLOPS(MatShape netInputShape)
-    private static native long getFLOPS_0(long nativeObj, long netInputShape_mat_nativeObj);
-
-    // C++:  int64 getFLOPS(int layerId, MatShape netInputShape)
-    private static native long getFLOPS_1(long nativeObj, int layerId, long netInputShape_mat_nativeObj);
-
-    // C++:  int64 getFLOPS(int layerId, vector_MatShape netInputShapes)
-    private static native long getFLOPS_2(long nativeObj, int layerId, List<MatOfInt> netInputShapes);
-
-    // C++:  int64 getFLOPS(vector_MatShape netInputShapes)
-    private static native long getFLOPS_3(long nativeObj, List<MatOfInt> netInputShapes);
-
-    // C++:  int64 getPerfProfile(vector_double& timings)
-    private static native long getPerfProfile_0(long nativeObj, long timings_mat_nativeObj);
-
-    // C++:  vector_String getLayerNames()
-    private static native List<String> getLayerNames_0(long nativeObj);
-
-    // C++:  vector_int getUnconnectedOutLayers()
-    private static native long getUnconnectedOutLayers_0(long nativeObj);
-
-    // C++:  void connect(String outPin, String inpPin)
-    private static native void connect_0(long nativeObj, String outPin, String inpPin);
-
-    // C++:  void deleteLayer(LayerId layer)
-    private static native void deleteLayer_0(long nativeObj, long layer_nativeObj);
-
-    // C++:  void enableFusion(bool fusion)
-    private static native void enableFusion_0(long nativeObj, boolean fusion);
-
-    // C++:  void forward(vector_Mat& outputBlobs, String outputName = String())
-    private static native void forward_2(long nativeObj, long outputBlobs_mat_nativeObj, String outputName);
-    private static native void forward_3(long nativeObj, long outputBlobs_mat_nativeObj);
-
-    // C++:  void forward(vector_Mat& outputBlobs, vector_String outBlobNames)
-    private static native void forward_4(long nativeObj, long outputBlobs_mat_nativeObj, List<String> outBlobNames);
-
-    // C++:  void getLayerTypes(vector_String& layersTypes)
-    private static native void getLayerTypes_0(long nativeObj, List<String> layersTypes);
-
-    // C++:  void getMemoryConsumption(MatShape netInputShape, size_t& weights, size_t& blobs)
-    private static native void getMemoryConsumption_0(long nativeObj, long netInputShape_mat_nativeObj, double[] weights_out, double[] blobs_out);
-
-    // C++:  void getMemoryConsumption(int layerId, MatShape netInputShape, size_t& weights, size_t& blobs)
-    private static native void getMemoryConsumption_1(long nativeObj, int layerId, long netInputShape_mat_nativeObj, double[] weights_out, double[] blobs_out);
-
-    // C++:  void getMemoryConsumption(int layerId, vector_MatShape netInputShapes, size_t& weights, size_t& blobs)
-    private static native void getMemoryConsumption_2(long nativeObj, int layerId, List<MatOfInt> netInputShapes, double[] weights_out, double[] blobs_out);
-
-    // C++:  void setHalideScheduler(String scheduler)
-    private static native void setHalideScheduler_0(long nativeObj, String scheduler);
-
-    // C++:  void setInput(Mat blob, String name = "")
-    private static native void setInput_0(long nativeObj, long blob_nativeObj, String name);
-    private static native void setInput_1(long nativeObj, long blob_nativeObj);
-
-    // C++:  void setInputsNames(vector_String inputBlobNames)
-    private static native void setInputsNames_0(long nativeObj, List<String> inputBlobNames);
-
-    // C++:  void setParam(LayerId layer, int numParam, Mat blob)
-    private static native void setParam_0(long nativeObj, long layer_nativeObj, int numParam, long blob_nativeObj);
-
-    // C++:  void setPreferableBackend(int backendId)
-    private static native void setPreferableBackend_0(long nativeObj, int backendId);
-
-    // C++:  void setPreferableTarget(int targetId)
-    private static native void setPreferableTarget_0(long nativeObj, int targetId);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/features2d/AKAZE.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/features2d/AKAZE.java
deleted file mode 100755
index 5857032..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/features2d/AKAZE.java
+++ /dev/null
@@ -1,319 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.features2d;
-
-import java.lang.String;
-import org.opencv.features2d.AKAZE;
-import org.opencv.features2d.Feature2D;
-
-// C++: class AKAZE
-//javadoc: AKAZE
-
-public class AKAZE extends Feature2D {
-
-    protected AKAZE(long addr) { super(addr); }
-
-    // internal usage only
-    public static AKAZE __fromPtr__(long addr) { return new AKAZE(addr); }
-
-    public static final int
-            DESCRIPTOR_KAZE_UPRIGHT = 2,
-            DESCRIPTOR_KAZE = 3,
-            DESCRIPTOR_MLDB_UPRIGHT = 4,
-            DESCRIPTOR_MLDB = 5;
-
-
-    //
-    // C++: static Ptr_AKAZE create(int descriptor_type = AKAZE::DESCRIPTOR_MLDB, int descriptor_size = 0, int descriptor_channels = 3, float threshold = 0.001f, int nOctaves = 4, int nOctaveLayers = 4, int diffusivity = KAZE::DIFF_PM_G2)
-    //
-
-    //javadoc: AKAZE::create(descriptor_type, descriptor_size, descriptor_channels, threshold, nOctaves, nOctaveLayers, diffusivity)
-    public static AKAZE create(int descriptor_type, int descriptor_size, int descriptor_channels, float threshold, int nOctaves, int nOctaveLayers, int diffusivity)
-    {
-        
-        AKAZE retVal = AKAZE.__fromPtr__(create_0(descriptor_type, descriptor_size, descriptor_channels, threshold, nOctaves, nOctaveLayers, diffusivity));
-        
-        return retVal;
-    }
-
-    //javadoc: AKAZE::create()
-    public static AKAZE create()
-    {
-        
-        AKAZE retVal = AKAZE.__fromPtr__(create_1());
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  String getDefaultName()
-    //
-
-    //javadoc: AKAZE::getDefaultName()
-    public  String getDefaultName()
-    {
-        
-        String retVal = getDefaultName_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double getThreshold()
-    //
-
-    //javadoc: AKAZE::getThreshold()
-    public  double getThreshold()
-    {
-        
-        double retVal = getThreshold_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getDescriptorChannels()
-    //
-
-    //javadoc: AKAZE::getDescriptorChannels()
-    public  int getDescriptorChannels()
-    {
-        
-        int retVal = getDescriptorChannels_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getDescriptorSize()
-    //
-
-    //javadoc: AKAZE::getDescriptorSize()
-    public  int getDescriptorSize()
-    {
-        
-        int retVal = getDescriptorSize_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getDescriptorType()
-    //
-
-    //javadoc: AKAZE::getDescriptorType()
-    public  int getDescriptorType()
-    {
-        
-        int retVal = getDescriptorType_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getDiffusivity()
-    //
-
-    //javadoc: AKAZE::getDiffusivity()
-    public  int getDiffusivity()
-    {
-        
-        int retVal = getDiffusivity_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getNOctaveLayers()
-    //
-
-    //javadoc: AKAZE::getNOctaveLayers()
-    public  int getNOctaveLayers()
-    {
-        
-        int retVal = getNOctaveLayers_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getNOctaves()
-    //
-
-    //javadoc: AKAZE::getNOctaves()
-    public  int getNOctaves()
-    {
-        
-        int retVal = getNOctaves_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  void setDescriptorChannels(int dch)
-    //
-
-    //javadoc: AKAZE::setDescriptorChannels(dch)
-    public  void setDescriptorChannels(int dch)
-    {
-        
-        setDescriptorChannels_0(nativeObj, dch);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setDescriptorSize(int dsize)
-    //
-
-    //javadoc: AKAZE::setDescriptorSize(dsize)
-    public  void setDescriptorSize(int dsize)
-    {
-        
-        setDescriptorSize_0(nativeObj, dsize);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setDescriptorType(int dtype)
-    //
-
-    //javadoc: AKAZE::setDescriptorType(dtype)
-    public  void setDescriptorType(int dtype)
-    {
-        
-        setDescriptorType_0(nativeObj, dtype);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setDiffusivity(int diff)
-    //
-
-    //javadoc: AKAZE::setDiffusivity(diff)
-    public  void setDiffusivity(int diff)
-    {
-        
-        setDiffusivity_0(nativeObj, diff);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setNOctaveLayers(int octaveLayers)
-    //
-
-    //javadoc: AKAZE::setNOctaveLayers(octaveLayers)
-    public  void setNOctaveLayers(int octaveLayers)
-    {
-        
-        setNOctaveLayers_0(nativeObj, octaveLayers);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setNOctaves(int octaves)
-    //
-
-    //javadoc: AKAZE::setNOctaves(octaves)
-    public  void setNOctaves(int octaves)
-    {
-        
-        setNOctaves_0(nativeObj, octaves);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setThreshold(double threshold)
-    //
-
-    //javadoc: AKAZE::setThreshold(threshold)
-    public  void setThreshold(double threshold)
-    {
-        
-        setThreshold_0(nativeObj, threshold);
-        
-        return;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++: static Ptr_AKAZE create(int descriptor_type = AKAZE::DESCRIPTOR_MLDB, int descriptor_size = 0, int descriptor_channels = 3, float threshold = 0.001f, int nOctaves = 4, int nOctaveLayers = 4, int diffusivity = KAZE::DIFF_PM_G2)
-    private static native long create_0(int descriptor_type, int descriptor_size, int descriptor_channels, float threshold, int nOctaves, int nOctaveLayers, int diffusivity);
-    private static native long create_1();
-
-    // C++:  String getDefaultName()
-    private static native String getDefaultName_0(long nativeObj);
-
-    // C++:  double getThreshold()
-    private static native double getThreshold_0(long nativeObj);
-
-    // C++:  int getDescriptorChannels()
-    private static native int getDescriptorChannels_0(long nativeObj);
-
-    // C++:  int getDescriptorSize()
-    private static native int getDescriptorSize_0(long nativeObj);
-
-    // C++:  int getDescriptorType()
-    private static native int getDescriptorType_0(long nativeObj);
-
-    // C++:  int getDiffusivity()
-    private static native int getDiffusivity_0(long nativeObj);
-
-    // C++:  int getNOctaveLayers()
-    private static native int getNOctaveLayers_0(long nativeObj);
-
-    // C++:  int getNOctaves()
-    private static native int getNOctaves_0(long nativeObj);
-
-    // C++:  void setDescriptorChannels(int dch)
-    private static native void setDescriptorChannels_0(long nativeObj, int dch);
-
-    // C++:  void setDescriptorSize(int dsize)
-    private static native void setDescriptorSize_0(long nativeObj, int dsize);
-
-    // C++:  void setDescriptorType(int dtype)
-    private static native void setDescriptorType_0(long nativeObj, int dtype);
-
-    // C++:  void setDiffusivity(int diff)
-    private static native void setDiffusivity_0(long nativeObj, int diff);
-
-    // C++:  void setNOctaveLayers(int octaveLayers)
-    private static native void setNOctaveLayers_0(long nativeObj, int octaveLayers);
-
-    // C++:  void setNOctaves(int octaves)
-    private static native void setNOctaves_0(long nativeObj, int octaves);
-
-    // C++:  void setThreshold(double threshold)
-    private static native void setThreshold_0(long nativeObj, double threshold);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/features2d/AgastFeatureDetector.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/features2d/AgastFeatureDetector.java
deleted file mode 100755
index e4502bf..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/features2d/AgastFeatureDetector.java
+++ /dev/null
@@ -1,185 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.features2d;
-
-import java.lang.String;
-import org.opencv.features2d.AgastFeatureDetector;
-import org.opencv.features2d.Feature2D;
-
-// C++: class AgastFeatureDetector
-//javadoc: AgastFeatureDetector
-
-public class AgastFeatureDetector extends Feature2D {
-
-    protected AgastFeatureDetector(long addr) { super(addr); }
-
-    // internal usage only
-    public static AgastFeatureDetector __fromPtr__(long addr) { return new AgastFeatureDetector(addr); }
-
-    public static final int
-            AGAST_5_8 = 0,
-            AGAST_7_12d = 1,
-            AGAST_7_12s = 2,
-            OAST_9_16 = 3,
-            THRESHOLD = 10000,
-            NONMAX_SUPPRESSION = 10001;
-
-
-    //
-    // C++: static Ptr_AgastFeatureDetector create(int threshold = 10, bool nonmaxSuppression = true, int type = AgastFeatureDetector::OAST_9_16)
-    //
-
-    //javadoc: AgastFeatureDetector::create(threshold, nonmaxSuppression, type)
-    public static AgastFeatureDetector create(int threshold, boolean nonmaxSuppression, int type)
-    {
-        
-        AgastFeatureDetector retVal = AgastFeatureDetector.__fromPtr__(create_0(threshold, nonmaxSuppression, type));
-        
-        return retVal;
-    }
-
-    //javadoc: AgastFeatureDetector::create()
-    public static AgastFeatureDetector create()
-    {
-        
-        AgastFeatureDetector retVal = AgastFeatureDetector.__fromPtr__(create_1());
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  String getDefaultName()
-    //
-
-    //javadoc: AgastFeatureDetector::getDefaultName()
-    public  String getDefaultName()
-    {
-        
-        String retVal = getDefaultName_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  bool getNonmaxSuppression()
-    //
-
-    //javadoc: AgastFeatureDetector::getNonmaxSuppression()
-    public  boolean getNonmaxSuppression()
-    {
-        
-        boolean retVal = getNonmaxSuppression_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getThreshold()
-    //
-
-    //javadoc: AgastFeatureDetector::getThreshold()
-    public  int getThreshold()
-    {
-        
-        int retVal = getThreshold_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getType()
-    //
-
-    //javadoc: AgastFeatureDetector::getType()
-    public  int getType()
-    {
-        
-        int retVal = getType_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  void setNonmaxSuppression(bool f)
-    //
-
-    //javadoc: AgastFeatureDetector::setNonmaxSuppression(f)
-    public  void setNonmaxSuppression(boolean f)
-    {
-        
-        setNonmaxSuppression_0(nativeObj, f);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setThreshold(int threshold)
-    //
-
-    //javadoc: AgastFeatureDetector::setThreshold(threshold)
-    public  void setThreshold(int threshold)
-    {
-        
-        setThreshold_0(nativeObj, threshold);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setType(int type)
-    //
-
-    //javadoc: AgastFeatureDetector::setType(type)
-    public  void setType(int type)
-    {
-        
-        setType_0(nativeObj, type);
-        
-        return;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++: static Ptr_AgastFeatureDetector create(int threshold = 10, bool nonmaxSuppression = true, int type = AgastFeatureDetector::OAST_9_16)
-    private static native long create_0(int threshold, boolean nonmaxSuppression, int type);
-    private static native long create_1();
-
-    // C++:  String getDefaultName()
-    private static native String getDefaultName_0(long nativeObj);
-
-    // C++:  bool getNonmaxSuppression()
-    private static native boolean getNonmaxSuppression_0(long nativeObj);
-
-    // C++:  int getThreshold()
-    private static native int getThreshold_0(long nativeObj);
-
-    // C++:  int getType()
-    private static native int getType_0(long nativeObj);
-
-    // C++:  void setNonmaxSuppression(bool f)
-    private static native void setNonmaxSuppression_0(long nativeObj, boolean f);
-
-    // C++:  void setThreshold(int threshold)
-    private static native void setThreshold_0(long nativeObj, int threshold);
-
-    // C++:  void setType(int type)
-    private static native void setType_0(long nativeObj, int type);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/features2d/BFMatcher.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/features2d/BFMatcher.java
deleted file mode 100755
index 593cb6f..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/features2d/BFMatcher.java
+++ /dev/null
@@ -1,83 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.features2d;
-
-import org.opencv.features2d.BFMatcher;
-import org.opencv.features2d.DescriptorMatcher;
-
-// C++: class BFMatcher
-//javadoc: BFMatcher
-
-public class BFMatcher extends DescriptorMatcher {
-
-    protected BFMatcher(long addr) { super(addr); }
-
-    // internal usage only
-    public static BFMatcher __fromPtr__(long addr) { return new BFMatcher(addr); }
-
-    //
-    // C++:   BFMatcher(int normType = NORM_L2, bool crossCheck = false)
-    //
-
-    //javadoc: BFMatcher::BFMatcher(normType, crossCheck)
-    public   BFMatcher(int normType, boolean crossCheck)
-    {
-        
-        super( BFMatcher_0(normType, crossCheck) );
-        
-        return;
-    }
-
-    //javadoc: BFMatcher::BFMatcher()
-    public   BFMatcher()
-    {
-        
-        super( BFMatcher_1() );
-        
-        return;
-    }
-
-
-    //
-    // C++: static Ptr_BFMatcher create(int normType = NORM_L2, bool crossCheck = false)
-    //
-
-    //javadoc: BFMatcher::create(normType, crossCheck)
-    public static BFMatcher create(int normType, boolean crossCheck)
-    {
-        
-        BFMatcher retVal = BFMatcher.__fromPtr__(create_0(normType, crossCheck));
-        
-        return retVal;
-    }
-
-    //javadoc: BFMatcher::create()
-    public static BFMatcher create()
-    {
-        
-        BFMatcher retVal = BFMatcher.__fromPtr__(create_1());
-        
-        return retVal;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++:   BFMatcher(int normType = NORM_L2, bool crossCheck = false)
-    private static native long BFMatcher_0(int normType, boolean crossCheck);
-    private static native long BFMatcher_1();
-
-    // C++: static Ptr_BFMatcher create(int normType = NORM_L2, bool crossCheck = false)
-    private static native long create_0(int normType, boolean crossCheck);
-    private static native long create_1();
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/features2d/BOWImgDescriptorExtractor.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/features2d/BOWImgDescriptorExtractor.java
deleted file mode 100755
index 0a69da1..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/features2d/BOWImgDescriptorExtractor.java
+++ /dev/null
@@ -1,127 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.features2d;
-
-import java.util.ArrayList;
-import java.util.List;
-import org.opencv.core.Mat;
-import org.opencv.core.MatOfKeyPoint;
-import org.opencv.utils.Converters;
-
-// C++: class BOWImgDescriptorExtractor
-//javadoc: BOWImgDescriptorExtractor
-
-public class BOWImgDescriptorExtractor {
-
-    protected final long nativeObj;
-    protected BOWImgDescriptorExtractor(long addr) { nativeObj = addr; }
-
-    public long getNativeObjAddr() { return nativeObj; }
-
-    // internal usage only
-    public static BOWImgDescriptorExtractor __fromPtr__(long addr) { return new BOWImgDescriptorExtractor(addr); }
-
-    //
-    // C++:   BOWImgDescriptorExtractor(Ptr_DescriptorExtractor dextractor, Ptr_DescriptorMatcher dmatcher)
-    //
-
-    // Unknown type 'Ptr_DescriptorExtractor' (I), skipping the function
-
-
-    //
-    // C++:  Mat getVocabulary()
-    //
-
-    //javadoc: BOWImgDescriptorExtractor::getVocabulary()
-    public  Mat getVocabulary()
-    {
-        
-        Mat retVal = new Mat(getVocabulary_0(nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int descriptorSize()
-    //
-
-    //javadoc: BOWImgDescriptorExtractor::descriptorSize()
-    public  int descriptorSize()
-    {
-        
-        int retVal = descriptorSize_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int descriptorType()
-    //
-
-    //javadoc: BOWImgDescriptorExtractor::descriptorType()
-    public  int descriptorType()
-    {
-        
-        int retVal = descriptorType_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  void compute2(Mat image, vector_KeyPoint keypoints, Mat& imgDescriptor)
-    //
-
-    //javadoc: BOWImgDescriptorExtractor::compute2(image, keypoints, imgDescriptor)
-    public  void compute(Mat image, MatOfKeyPoint keypoints, Mat imgDescriptor)
-    {
-        Mat keypoints_mat = keypoints;
-        compute_0(nativeObj, image.nativeObj, keypoints_mat.nativeObj, imgDescriptor.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setVocabulary(Mat vocabulary)
-    //
-
-    //javadoc: BOWImgDescriptorExtractor::setVocabulary(vocabulary)
-    public  void setVocabulary(Mat vocabulary)
-    {
-        
-        setVocabulary_0(nativeObj, vocabulary.nativeObj);
-        
-        return;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++:  Mat getVocabulary()
-    private static native long getVocabulary_0(long nativeObj);
-
-    // C++:  int descriptorSize()
-    private static native int descriptorSize_0(long nativeObj);
-
-    // C++:  int descriptorType()
-    private static native int descriptorType_0(long nativeObj);
-
-    // C++:  void compute2(Mat image, vector_KeyPoint keypoints, Mat& imgDescriptor)
-    private static native void compute_0(long nativeObj, long image_nativeObj, long keypoints_mat_nativeObj, long imgDescriptor_nativeObj);
-
-    // C++:  void setVocabulary(Mat vocabulary)
-    private static native void setVocabulary_0(long nativeObj, long vocabulary_nativeObj);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/features2d/BOWKMeansTrainer.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/features2d/BOWKMeansTrainer.java
deleted file mode 100755
index d80eaf4..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/features2d/BOWKMeansTrainer.java
+++ /dev/null
@@ -1,91 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.features2d;
-
-import org.opencv.core.Mat;
-import org.opencv.core.TermCriteria;
-import org.opencv.features2d.BOWTrainer;
-
-// C++: class BOWKMeansTrainer
-//javadoc: BOWKMeansTrainer
-
-public class BOWKMeansTrainer extends BOWTrainer {
-
-    protected BOWKMeansTrainer(long addr) { super(addr); }
-
-    // internal usage only
-    public static BOWKMeansTrainer __fromPtr__(long addr) { return new BOWKMeansTrainer(addr); }
-
-    //
-    // C++:   BOWKMeansTrainer(int clusterCount, TermCriteria termcrit = TermCriteria(), int attempts = 3, int flags = KMEANS_PP_CENTERS)
-    //
-
-    //javadoc: BOWKMeansTrainer::BOWKMeansTrainer(clusterCount, termcrit, attempts, flags)
-    public   BOWKMeansTrainer(int clusterCount, TermCriteria termcrit, int attempts, int flags)
-    {
-        
-        super( BOWKMeansTrainer_0(clusterCount, termcrit.type, termcrit.maxCount, termcrit.epsilon, attempts, flags) );
-        
-        return;
-    }
-
-    //javadoc: BOWKMeansTrainer::BOWKMeansTrainer(clusterCount)
-    public   BOWKMeansTrainer(int clusterCount)
-    {
-        
-        super( BOWKMeansTrainer_1(clusterCount) );
-        
-        return;
-    }
-
-
-    //
-    // C++:  Mat cluster(Mat descriptors)
-    //
-
-    //javadoc: BOWKMeansTrainer::cluster(descriptors)
-    public  Mat cluster(Mat descriptors)
-    {
-        
-        Mat retVal = new Mat(cluster_0(nativeObj, descriptors.nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Mat cluster()
-    //
-
-    //javadoc: BOWKMeansTrainer::cluster()
-    public  Mat cluster()
-    {
-        
-        Mat retVal = new Mat(cluster_1(nativeObj));
-        
-        return retVal;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++:   BOWKMeansTrainer(int clusterCount, TermCriteria termcrit = TermCriteria(), int attempts = 3, int flags = KMEANS_PP_CENTERS)
-    private static native long BOWKMeansTrainer_0(int clusterCount, int termcrit_type, int termcrit_maxCount, double termcrit_epsilon, int attempts, int flags);
-    private static native long BOWKMeansTrainer_1(int clusterCount);
-
-    // C++:  Mat cluster(Mat descriptors)
-    private static native long cluster_0(long nativeObj, long descriptors_nativeObj);
-
-    // C++:  Mat cluster()
-    private static native long cluster_1(long nativeObj);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/features2d/BOWTrainer.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/features2d/BOWTrainer.java
deleted file mode 100755
index da0431d..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/features2d/BOWTrainer.java
+++ /dev/null
@@ -1,136 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.features2d;
-
-import java.util.ArrayList;
-import java.util.List;
-import org.opencv.core.Mat;
-import org.opencv.utils.Converters;
-
-// C++: class BOWTrainer
-//javadoc: BOWTrainer
-
-public class BOWTrainer {
-
-    protected final long nativeObj;
-    protected BOWTrainer(long addr) { nativeObj = addr; }
-
-    public long getNativeObjAddr() { return nativeObj; }
-
-    // internal usage only
-    public static BOWTrainer __fromPtr__(long addr) { return new BOWTrainer(addr); }
-
-    //
-    // C++:  Mat cluster(Mat descriptors)
-    //
-
-    //javadoc: BOWTrainer::cluster(descriptors)
-    public  Mat cluster(Mat descriptors)
-    {
-        
-        Mat retVal = new Mat(cluster_0(nativeObj, descriptors.nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Mat cluster()
-    //
-
-    //javadoc: BOWTrainer::cluster()
-    public  Mat cluster()
-    {
-        
-        Mat retVal = new Mat(cluster_1(nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int descriptorsCount()
-    //
-
-    //javadoc: BOWTrainer::descriptorsCount()
-    public  int descriptorsCount()
-    {
-        
-        int retVal = descriptorsCount_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  vector_Mat getDescriptors()
-    //
-
-    //javadoc: BOWTrainer::getDescriptors()
-    public  List<Mat> getDescriptors()
-    {
-        List<Mat> retVal = new ArrayList<Mat>();
-        Mat retValMat = new Mat(getDescriptors_0(nativeObj));
-        Converters.Mat_to_vector_Mat(retValMat, retVal);
-        return retVal;
-    }
-
-
-    //
-    // C++:  void add(Mat descriptors)
-    //
-
-    //javadoc: BOWTrainer::add(descriptors)
-    public  void add(Mat descriptors)
-    {
-        
-        add_0(nativeObj, descriptors.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void clear()
-    //
-
-    //javadoc: BOWTrainer::clear()
-    public  void clear()
-    {
-        
-        clear_0(nativeObj);
-        
-        return;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++:  Mat cluster(Mat descriptors)
-    private static native long cluster_0(long nativeObj, long descriptors_nativeObj);
-
-    // C++:  Mat cluster()
-    private static native long cluster_1(long nativeObj);
-
-    // C++:  int descriptorsCount()
-    private static native int descriptorsCount_0(long nativeObj);
-
-    // C++:  vector_Mat getDescriptors()
-    private static native long getDescriptors_0(long nativeObj);
-
-    // C++:  void add(Mat descriptors)
-    private static native void add_0(long nativeObj, long descriptors_nativeObj);
-
-    // C++:  void clear()
-    private static native void clear_0(long nativeObj);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/features2d/BRISK.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/features2d/BRISK.java
deleted file mode 100755
index 8f41682..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/features2d/BRISK.java
+++ /dev/null
@@ -1,140 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.features2d;
-
-import java.lang.String;
-import java.util.ArrayList;
-import java.util.List;
-import org.opencv.core.Mat;
-import org.opencv.core.MatOfFloat;
-import org.opencv.core.MatOfInt;
-import org.opencv.features2d.BRISK;
-import org.opencv.features2d.Feature2D;
-import org.opencv.utils.Converters;
-
-// C++: class BRISK
-//javadoc: BRISK
-
-public class BRISK extends Feature2D {
-
-    protected BRISK(long addr) { super(addr); }
-
-    // internal usage only
-    public static BRISK __fromPtr__(long addr) { return new BRISK(addr); }
-
-    //
-    // C++: static Ptr_BRISK create(int thresh, int octaves, vector_float radiusList, vector_int numberList, float dMax = 5.85f, float dMin = 8.2f, vector_int indexChange = std::vector<int>())
-    //
-
-    //javadoc: BRISK::create(thresh, octaves, radiusList, numberList, dMax, dMin, indexChange)
-    public static BRISK create(int thresh, int octaves, MatOfFloat radiusList, MatOfInt numberList, float dMax, float dMin, MatOfInt indexChange)
-    {
-        Mat radiusList_mat = radiusList;
-        Mat numberList_mat = numberList;
-        Mat indexChange_mat = indexChange;
-        BRISK retVal = BRISK.__fromPtr__(create_0(thresh, octaves, radiusList_mat.nativeObj, numberList_mat.nativeObj, dMax, dMin, indexChange_mat.nativeObj));
-        
-        return retVal;
-    }
-
-    //javadoc: BRISK::create(thresh, octaves, radiusList, numberList)
-    public static BRISK create(int thresh, int octaves, MatOfFloat radiusList, MatOfInt numberList)
-    {
-        Mat radiusList_mat = radiusList;
-        Mat numberList_mat = numberList;
-        BRISK retVal = BRISK.__fromPtr__(create_1(thresh, octaves, radiusList_mat.nativeObj, numberList_mat.nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: static Ptr_BRISK create(int thresh = 30, int octaves = 3, float patternScale = 1.0f)
-    //
-
-    //javadoc: BRISK::create(thresh, octaves, patternScale)
-    public static BRISK create(int thresh, int octaves, float patternScale)
-    {
-        
-        BRISK retVal = BRISK.__fromPtr__(create_2(thresh, octaves, patternScale));
-        
-        return retVal;
-    }
-
-    //javadoc: BRISK::create()
-    public static BRISK create()
-    {
-        
-        BRISK retVal = BRISK.__fromPtr__(create_3());
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: static Ptr_BRISK create(vector_float radiusList, vector_int numberList, float dMax = 5.85f, float dMin = 8.2f, vector_int indexChange = std::vector<int>())
-    //
-
-    //javadoc: BRISK::create(radiusList, numberList, dMax, dMin, indexChange)
-    public static BRISK create(MatOfFloat radiusList, MatOfInt numberList, float dMax, float dMin, MatOfInt indexChange)
-    {
-        Mat radiusList_mat = radiusList;
-        Mat numberList_mat = numberList;
-        Mat indexChange_mat = indexChange;
-        BRISK retVal = BRISK.__fromPtr__(create_4(radiusList_mat.nativeObj, numberList_mat.nativeObj, dMax, dMin, indexChange_mat.nativeObj));
-        
-        return retVal;
-    }
-
-    //javadoc: BRISK::create(radiusList, numberList)
-    public static BRISK create(MatOfFloat radiusList, MatOfInt numberList)
-    {
-        Mat radiusList_mat = radiusList;
-        Mat numberList_mat = numberList;
-        BRISK retVal = BRISK.__fromPtr__(create_5(radiusList_mat.nativeObj, numberList_mat.nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  String getDefaultName()
-    //
-
-    //javadoc: BRISK::getDefaultName()
-    public  String getDefaultName()
-    {
-        
-        String retVal = getDefaultName_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++: static Ptr_BRISK create(int thresh, int octaves, vector_float radiusList, vector_int numberList, float dMax = 5.85f, float dMin = 8.2f, vector_int indexChange = std::vector<int>())
-    private static native long create_0(int thresh, int octaves, long radiusList_mat_nativeObj, long numberList_mat_nativeObj, float dMax, float dMin, long indexChange_mat_nativeObj);
-    private static native long create_1(int thresh, int octaves, long radiusList_mat_nativeObj, long numberList_mat_nativeObj);
-
-    // C++: static Ptr_BRISK create(int thresh = 30, int octaves = 3, float patternScale = 1.0f)
-    private static native long create_2(int thresh, int octaves, float patternScale);
-    private static native long create_3();
-
-    // C++: static Ptr_BRISK create(vector_float radiusList, vector_int numberList, float dMax = 5.85f, float dMin = 8.2f, vector_int indexChange = std::vector<int>())
-    private static native long create_4(long radiusList_mat_nativeObj, long numberList_mat_nativeObj, float dMax, float dMin, long indexChange_mat_nativeObj);
-    private static native long create_5(long radiusList_mat_nativeObj, long numberList_mat_nativeObj);
-
-    // C++:  String getDefaultName()
-    private static native String getDefaultName_0(long nativeObj);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/features2d/DescriptorExtractor.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/features2d/DescriptorExtractor.java
deleted file mode 100755
index c7b7cd5..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/features2d/DescriptorExtractor.java
+++ /dev/null
@@ -1,200 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.features2d;
-
-import java.lang.String;
-import java.util.ArrayList;
-import java.util.List;
-import org.opencv.core.Mat;
-import org.opencv.core.MatOfKeyPoint;
-import org.opencv.features2d.DescriptorExtractor;
-import org.opencv.utils.Converters;
-
-// C++: class javaDescriptorExtractor
-//javadoc: javaDescriptorExtractor
-@Deprecated
-public class DescriptorExtractor {
-
-    protected final long nativeObj;
-    protected DescriptorExtractor(long addr) { nativeObj = addr; }
-
-    public long getNativeObjAddr() { return nativeObj; }
-
-    // internal usage only
-    public static DescriptorExtractor __fromPtr__(long addr) { return new DescriptorExtractor(addr); }
-
-    private static final int
-            OPPONENTEXTRACTOR = 1000;
-
-
-    public static final int
-            SIFT = 1,
-            SURF = 2,
-            ORB = 3,
-            BRIEF = 4,
-            BRISK = 5,
-            FREAK = 6,
-            AKAZE = 7,
-            OPPONENT_SIFT = OPPONENTEXTRACTOR + SIFT,
-            OPPONENT_SURF = OPPONENTEXTRACTOR + SURF,
-            OPPONENT_ORB = OPPONENTEXTRACTOR + ORB,
-            OPPONENT_BRIEF = OPPONENTEXTRACTOR + BRIEF,
-            OPPONENT_BRISK = OPPONENTEXTRACTOR + BRISK,
-            OPPONENT_FREAK = OPPONENTEXTRACTOR + FREAK,
-            OPPONENT_AKAZE = OPPONENTEXTRACTOR + AKAZE;
-
-
-    //
-    // C++: static Ptr_javaDescriptorExtractor create(int extractorType)
-    //
-
-    //javadoc: javaDescriptorExtractor::create(extractorType)
-    public static DescriptorExtractor create(int extractorType)
-    {
-        
-        DescriptorExtractor retVal = DescriptorExtractor.__fromPtr__(create_0(extractorType));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  bool empty()
-    //
-
-    //javadoc: javaDescriptorExtractor::empty()
-    public  boolean empty()
-    {
-        
-        boolean retVal = empty_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int descriptorSize()
-    //
-
-    //javadoc: javaDescriptorExtractor::descriptorSize()
-    public  int descriptorSize()
-    {
-        
-        int retVal = descriptorSize_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int descriptorType()
-    //
-
-    //javadoc: javaDescriptorExtractor::descriptorType()
-    public  int descriptorType()
-    {
-        
-        int retVal = descriptorType_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  void compute(Mat image, vector_KeyPoint& keypoints, Mat descriptors)
-    //
-
-    //javadoc: javaDescriptorExtractor::compute(image, keypoints, descriptors)
-    public  void compute(Mat image, MatOfKeyPoint keypoints, Mat descriptors)
-    {
-        Mat keypoints_mat = keypoints;
-        compute_0(nativeObj, image.nativeObj, keypoints_mat.nativeObj, descriptors.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void compute(vector_Mat images, vector_vector_KeyPoint& keypoints, vector_Mat& descriptors)
-    //
-
-    //javadoc: javaDescriptorExtractor::compute(images, keypoints, descriptors)
-    public  void compute(List<Mat> images, List<MatOfKeyPoint> keypoints, List<Mat> descriptors)
-    {
-        Mat images_mat = Converters.vector_Mat_to_Mat(images);
-        List<Mat> keypoints_tmplm = new ArrayList<Mat>((keypoints != null) ? keypoints.size() : 0);
-        Mat keypoints_mat = Converters.vector_vector_KeyPoint_to_Mat(keypoints, keypoints_tmplm);
-        Mat descriptors_mat = new Mat();
-        compute_1(nativeObj, images_mat.nativeObj, keypoints_mat.nativeObj, descriptors_mat.nativeObj);
-        Converters.Mat_to_vector_vector_KeyPoint(keypoints_mat, keypoints);
-        keypoints_mat.release();
-        Converters.Mat_to_vector_Mat(descriptors_mat, descriptors);
-        descriptors_mat.release();
-        return;
-    }
-
-
-    //
-    // C++:  void read(String fileName)
-    //
-
-    //javadoc: javaDescriptorExtractor::read(fileName)
-    public  void read(String fileName)
-    {
-        
-        read_0(nativeObj, fileName);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void write(String fileName)
-    //
-
-    //javadoc: javaDescriptorExtractor::write(fileName)
-    public  void write(String fileName)
-    {
-        
-        write_0(nativeObj, fileName);
-        
-        return;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++: static Ptr_javaDescriptorExtractor create(int extractorType)
-    private static native long create_0(int extractorType);
-
-    // C++:  bool empty()
-    private static native boolean empty_0(long nativeObj);
-
-    // C++:  int descriptorSize()
-    private static native int descriptorSize_0(long nativeObj);
-
-    // C++:  int descriptorType()
-    private static native int descriptorType_0(long nativeObj);
-
-    // C++:  void compute(Mat image, vector_KeyPoint& keypoints, Mat descriptors)
-    private static native void compute_0(long nativeObj, long image_nativeObj, long keypoints_mat_nativeObj, long descriptors_nativeObj);
-
-    // C++:  void compute(vector_Mat images, vector_vector_KeyPoint& keypoints, vector_Mat& descriptors)
-    private static native void compute_1(long nativeObj, long images_mat_nativeObj, long keypoints_mat_nativeObj, long descriptors_mat_nativeObj);
-
-    // C++:  void read(String fileName)
-    private static native void read_0(long nativeObj, String fileName);
-
-    // C++:  void write(String fileName)
-    private static native void write_0(long nativeObj, String fileName);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/features2d/DescriptorMatcher.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/features2d/DescriptorMatcher.java
deleted file mode 100755
index f03a679..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/features2d/DescriptorMatcher.java
+++ /dev/null
@@ -1,428 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.features2d;
-
-import java.lang.String;
-import java.util.ArrayList;
-import java.util.List;
-import org.opencv.core.Algorithm;
-import org.opencv.core.Mat;
-import org.opencv.core.MatOfDMatch;
-import org.opencv.features2d.DescriptorMatcher;
-import org.opencv.utils.Converters;
-
-// C++: class DescriptorMatcher
-//javadoc: DescriptorMatcher
-
-public class DescriptorMatcher extends Algorithm {
-
-    protected DescriptorMatcher(long addr) { super(addr); }
-
-    // internal usage only
-    public static DescriptorMatcher __fromPtr__(long addr) { return new DescriptorMatcher(addr); }
-
-    public static final int
-            FLANNBASED = 1,
-            BRUTEFORCE = 2,
-            BRUTEFORCE_L1 = 3,
-            BRUTEFORCE_HAMMING = 4,
-            BRUTEFORCE_HAMMINGLUT = 5,
-            BRUTEFORCE_SL2 = 6;
-
-
-    //
-    // C++:  Ptr_DescriptorMatcher clone(bool emptyTrainData = false)
-    //
-
-    //javadoc: DescriptorMatcher::clone(emptyTrainData)
-    public  DescriptorMatcher clone(boolean emptyTrainData)
-    {
-        
-        DescriptorMatcher retVal = DescriptorMatcher.__fromPtr__(clone_0(nativeObj, emptyTrainData));
-        
-        return retVal;
-    }
-
-    //javadoc: DescriptorMatcher::clone()
-    public  DescriptorMatcher clone()
-    {
-        
-        DescriptorMatcher retVal = DescriptorMatcher.__fromPtr__(clone_1(nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: static Ptr_DescriptorMatcher create(String descriptorMatcherType)
-    //
-
-    //javadoc: DescriptorMatcher::create(descriptorMatcherType)
-    public static DescriptorMatcher create(String descriptorMatcherType)
-    {
-        
-        DescriptorMatcher retVal = DescriptorMatcher.__fromPtr__(create_0(descriptorMatcherType));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: static Ptr_DescriptorMatcher create(int matcherType)
-    //
-
-    //javadoc: DescriptorMatcher::create(matcherType)
-    public static DescriptorMatcher create(int matcherType)
-    {
-        
-        DescriptorMatcher retVal = DescriptorMatcher.__fromPtr__(create_1(matcherType));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  bool empty()
-    //
-
-    //javadoc: DescriptorMatcher::empty()
-    public  boolean empty()
-    {
-        
-        boolean retVal = empty_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  bool isMaskSupported()
-    //
-
-    //javadoc: DescriptorMatcher::isMaskSupported()
-    public  boolean isMaskSupported()
-    {
-        
-        boolean retVal = isMaskSupported_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  vector_Mat getTrainDescriptors()
-    //
-
-    //javadoc: DescriptorMatcher::getTrainDescriptors()
-    public  List<Mat> getTrainDescriptors()
-    {
-        List<Mat> retVal = new ArrayList<Mat>();
-        Mat retValMat = new Mat(getTrainDescriptors_0(nativeObj));
-        Converters.Mat_to_vector_Mat(retValMat, retVal);
-        return retVal;
-    }
-
-
-    //
-    // C++:  void add(vector_Mat descriptors)
-    //
-
-    //javadoc: DescriptorMatcher::add(descriptors)
-    public  void add(List<Mat> descriptors)
-    {
-        Mat descriptors_mat = Converters.vector_Mat_to_Mat(descriptors);
-        add_0(nativeObj, descriptors_mat.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void clear()
-    //
-
-    //javadoc: DescriptorMatcher::clear()
-    public  void clear()
-    {
-        
-        clear_0(nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void knnMatch(Mat queryDescriptors, Mat trainDescriptors, vector_vector_DMatch& matches, int k, Mat mask = Mat(), bool compactResult = false)
-    //
-
-    //javadoc: DescriptorMatcher::knnMatch(queryDescriptors, trainDescriptors, matches, k, mask, compactResult)
-    public  void knnMatch(Mat queryDescriptors, Mat trainDescriptors, List<MatOfDMatch> matches, int k, Mat mask, boolean compactResult)
-    {
-        Mat matches_mat = new Mat();
-        knnMatch_0(nativeObj, queryDescriptors.nativeObj, trainDescriptors.nativeObj, matches_mat.nativeObj, k, mask.nativeObj, compactResult);
-        Converters.Mat_to_vector_vector_DMatch(matches_mat, matches);
-        matches_mat.release();
-        return;
-    }
-
-    //javadoc: DescriptorMatcher::knnMatch(queryDescriptors, trainDescriptors, matches, k)
-    public  void knnMatch(Mat queryDescriptors, Mat trainDescriptors, List<MatOfDMatch> matches, int k)
-    {
-        Mat matches_mat = new Mat();
-        knnMatch_1(nativeObj, queryDescriptors.nativeObj, trainDescriptors.nativeObj, matches_mat.nativeObj, k);
-        Converters.Mat_to_vector_vector_DMatch(matches_mat, matches);
-        matches_mat.release();
-        return;
-    }
-
-
-    //
-    // C++:  void knnMatch(Mat queryDescriptors, vector_vector_DMatch& matches, int k, vector_Mat masks = vector_Mat(), bool compactResult = false)
-    //
-
-    //javadoc: DescriptorMatcher::knnMatch(queryDescriptors, matches, k, masks, compactResult)
-    public  void knnMatch(Mat queryDescriptors, List<MatOfDMatch> matches, int k, List<Mat> masks, boolean compactResult)
-    {
-        Mat matches_mat = new Mat();
-        Mat masks_mat = Converters.vector_Mat_to_Mat(masks);
-        knnMatch_2(nativeObj, queryDescriptors.nativeObj, matches_mat.nativeObj, k, masks_mat.nativeObj, compactResult);
-        Converters.Mat_to_vector_vector_DMatch(matches_mat, matches);
-        matches_mat.release();
-        return;
-    }
-
-    //javadoc: DescriptorMatcher::knnMatch(queryDescriptors, matches, k)
-    public  void knnMatch(Mat queryDescriptors, List<MatOfDMatch> matches, int k)
-    {
-        Mat matches_mat = new Mat();
-        knnMatch_3(nativeObj, queryDescriptors.nativeObj, matches_mat.nativeObj, k);
-        Converters.Mat_to_vector_vector_DMatch(matches_mat, matches);
-        matches_mat.release();
-        return;
-    }
-
-
-    //
-    // C++:  void match(Mat queryDescriptors, Mat trainDescriptors, vector_DMatch& matches, Mat mask = Mat())
-    //
-
-    //javadoc: DescriptorMatcher::match(queryDescriptors, trainDescriptors, matches, mask)
-    public  void match(Mat queryDescriptors, Mat trainDescriptors, MatOfDMatch matches, Mat mask)
-    {
-        Mat matches_mat = matches;
-        match_0(nativeObj, queryDescriptors.nativeObj, trainDescriptors.nativeObj, matches_mat.nativeObj, mask.nativeObj);
-        
-        return;
-    }
-
-    //javadoc: DescriptorMatcher::match(queryDescriptors, trainDescriptors, matches)
-    public  void match(Mat queryDescriptors, Mat trainDescriptors, MatOfDMatch matches)
-    {
-        Mat matches_mat = matches;
-        match_1(nativeObj, queryDescriptors.nativeObj, trainDescriptors.nativeObj, matches_mat.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void match(Mat queryDescriptors, vector_DMatch& matches, vector_Mat masks = vector_Mat())
-    //
-
-    //javadoc: DescriptorMatcher::match(queryDescriptors, matches, masks)
-    public  void match(Mat queryDescriptors, MatOfDMatch matches, List<Mat> masks)
-    {
-        Mat matches_mat = matches;
-        Mat masks_mat = Converters.vector_Mat_to_Mat(masks);
-        match_2(nativeObj, queryDescriptors.nativeObj, matches_mat.nativeObj, masks_mat.nativeObj);
-        
-        return;
-    }
-
-    //javadoc: DescriptorMatcher::match(queryDescriptors, matches)
-    public  void match(Mat queryDescriptors, MatOfDMatch matches)
-    {
-        Mat matches_mat = matches;
-        match_3(nativeObj, queryDescriptors.nativeObj, matches_mat.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void radiusMatch(Mat queryDescriptors, Mat trainDescriptors, vector_vector_DMatch& matches, float maxDistance, Mat mask = Mat(), bool compactResult = false)
-    //
-
-    //javadoc: DescriptorMatcher::radiusMatch(queryDescriptors, trainDescriptors, matches, maxDistance, mask, compactResult)
-    public  void radiusMatch(Mat queryDescriptors, Mat trainDescriptors, List<MatOfDMatch> matches, float maxDistance, Mat mask, boolean compactResult)
-    {
-        Mat matches_mat = new Mat();
-        radiusMatch_0(nativeObj, queryDescriptors.nativeObj, trainDescriptors.nativeObj, matches_mat.nativeObj, maxDistance, mask.nativeObj, compactResult);
-        Converters.Mat_to_vector_vector_DMatch(matches_mat, matches);
-        matches_mat.release();
-        return;
-    }
-
-    //javadoc: DescriptorMatcher::radiusMatch(queryDescriptors, trainDescriptors, matches, maxDistance)
-    public  void radiusMatch(Mat queryDescriptors, Mat trainDescriptors, List<MatOfDMatch> matches, float maxDistance)
-    {
-        Mat matches_mat = new Mat();
-        radiusMatch_1(nativeObj, queryDescriptors.nativeObj, trainDescriptors.nativeObj, matches_mat.nativeObj, maxDistance);
-        Converters.Mat_to_vector_vector_DMatch(matches_mat, matches);
-        matches_mat.release();
-        return;
-    }
-
-
-    //
-    // C++:  void radiusMatch(Mat queryDescriptors, vector_vector_DMatch& matches, float maxDistance, vector_Mat masks = vector_Mat(), bool compactResult = false)
-    //
-
-    //javadoc: DescriptorMatcher::radiusMatch(queryDescriptors, matches, maxDistance, masks, compactResult)
-    public  void radiusMatch(Mat queryDescriptors, List<MatOfDMatch> matches, float maxDistance, List<Mat> masks, boolean compactResult)
-    {
-        Mat matches_mat = new Mat();
-        Mat masks_mat = Converters.vector_Mat_to_Mat(masks);
-        radiusMatch_2(nativeObj, queryDescriptors.nativeObj, matches_mat.nativeObj, maxDistance, masks_mat.nativeObj, compactResult);
-        Converters.Mat_to_vector_vector_DMatch(matches_mat, matches);
-        matches_mat.release();
-        return;
-    }
-
-    //javadoc: DescriptorMatcher::radiusMatch(queryDescriptors, matches, maxDistance)
-    public  void radiusMatch(Mat queryDescriptors, List<MatOfDMatch> matches, float maxDistance)
-    {
-        Mat matches_mat = new Mat();
-        radiusMatch_3(nativeObj, queryDescriptors.nativeObj, matches_mat.nativeObj, maxDistance);
-        Converters.Mat_to_vector_vector_DMatch(matches_mat, matches);
-        matches_mat.release();
-        return;
-    }
-
-
-    //
-    // C++:  void read(FileNode arg1)
-    //
-
-    // Unknown type 'FileNode' (I), skipping the function
-
-
-    //
-    // C++:  void read(String fileName)
-    //
-
-    //javadoc: DescriptorMatcher::read(fileName)
-    public  void read(String fileName)
-    {
-        
-        read_0(nativeObj, fileName);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void train()
-    //
-
-    //javadoc: DescriptorMatcher::train()
-    public  void train()
-    {
-        
-        train_0(nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void write(Ptr_FileStorage fs, String name = String())
-    //
-
-    // Unknown type 'Ptr_FileStorage' (I), skipping the function
-
-
-    //
-    // C++:  void write(String fileName)
-    //
-
-    //javadoc: DescriptorMatcher::write(fileName)
-    public  void write(String fileName)
-    {
-        
-        write_0(nativeObj, fileName);
-        
-        return;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++:  Ptr_DescriptorMatcher clone(bool emptyTrainData = false)
-    private static native long clone_0(long nativeObj, boolean emptyTrainData);
-    private static native long clone_1(long nativeObj);
-
-    // C++: static Ptr_DescriptorMatcher create(String descriptorMatcherType)
-    private static native long create_0(String descriptorMatcherType);
-
-    // C++: static Ptr_DescriptorMatcher create(int matcherType)
-    private static native long create_1(int matcherType);
-
-    // C++:  bool empty()
-    private static native boolean empty_0(long nativeObj);
-
-    // C++:  bool isMaskSupported()
-    private static native boolean isMaskSupported_0(long nativeObj);
-
-    // C++:  vector_Mat getTrainDescriptors()
-    private static native long getTrainDescriptors_0(long nativeObj);
-
-    // C++:  void add(vector_Mat descriptors)
-    private static native void add_0(long nativeObj, long descriptors_mat_nativeObj);
-
-    // C++:  void clear()
-    private static native void clear_0(long nativeObj);
-
-    // C++:  void knnMatch(Mat queryDescriptors, Mat trainDescriptors, vector_vector_DMatch& matches, int k, Mat mask = Mat(), bool compactResult = false)
-    private static native void knnMatch_0(long nativeObj, long queryDescriptors_nativeObj, long trainDescriptors_nativeObj, long matches_mat_nativeObj, int k, long mask_nativeObj, boolean compactResult);
-    private static native void knnMatch_1(long nativeObj, long queryDescriptors_nativeObj, long trainDescriptors_nativeObj, long matches_mat_nativeObj, int k);
-
-    // C++:  void knnMatch(Mat queryDescriptors, vector_vector_DMatch& matches, int k, vector_Mat masks = vector_Mat(), bool compactResult = false)
-    private static native void knnMatch_2(long nativeObj, long queryDescriptors_nativeObj, long matches_mat_nativeObj, int k, long masks_mat_nativeObj, boolean compactResult);
-    private static native void knnMatch_3(long nativeObj, long queryDescriptors_nativeObj, long matches_mat_nativeObj, int k);
-
-    // C++:  void match(Mat queryDescriptors, Mat trainDescriptors, vector_DMatch& matches, Mat mask = Mat())
-    private static native void match_0(long nativeObj, long queryDescriptors_nativeObj, long trainDescriptors_nativeObj, long matches_mat_nativeObj, long mask_nativeObj);
-    private static native void match_1(long nativeObj, long queryDescriptors_nativeObj, long trainDescriptors_nativeObj, long matches_mat_nativeObj);
-
-    // C++:  void match(Mat queryDescriptors, vector_DMatch& matches, vector_Mat masks = vector_Mat())
-    private static native void match_2(long nativeObj, long queryDescriptors_nativeObj, long matches_mat_nativeObj, long masks_mat_nativeObj);
-    private static native void match_3(long nativeObj, long queryDescriptors_nativeObj, long matches_mat_nativeObj);
-
-    // C++:  void radiusMatch(Mat queryDescriptors, Mat trainDescriptors, vector_vector_DMatch& matches, float maxDistance, Mat mask = Mat(), bool compactResult = false)
-    private static native void radiusMatch_0(long nativeObj, long queryDescriptors_nativeObj, long trainDescriptors_nativeObj, long matches_mat_nativeObj, float maxDistance, long mask_nativeObj, boolean compactResult);
-    private static native void radiusMatch_1(long nativeObj, long queryDescriptors_nativeObj, long trainDescriptors_nativeObj, long matches_mat_nativeObj, float maxDistance);
-
-    // C++:  void radiusMatch(Mat queryDescriptors, vector_vector_DMatch& matches, float maxDistance, vector_Mat masks = vector_Mat(), bool compactResult = false)
-    private static native void radiusMatch_2(long nativeObj, long queryDescriptors_nativeObj, long matches_mat_nativeObj, float maxDistance, long masks_mat_nativeObj, boolean compactResult);
-    private static native void radiusMatch_3(long nativeObj, long queryDescriptors_nativeObj, long matches_mat_nativeObj, float maxDistance);
-
-    // C++:  void read(String fileName)
-    private static native void read_0(long nativeObj, String fileName);
-
-    // C++:  void train()
-    private static native void train_0(long nativeObj);
-
-    // C++:  void write(String fileName)
-    private static native void write_0(long nativeObj, String fileName);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/features2d/FastFeatureDetector.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/features2d/FastFeatureDetector.java
deleted file mode 100755
index 745ac49..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/features2d/FastFeatureDetector.java
+++ /dev/null
@@ -1,185 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.features2d;
-
-import java.lang.String;
-import org.opencv.features2d.FastFeatureDetector;
-import org.opencv.features2d.Feature2D;
-
-// C++: class FastFeatureDetector
-//javadoc: FastFeatureDetector
-
-public class FastFeatureDetector extends Feature2D {
-
-    protected FastFeatureDetector(long addr) { super(addr); }
-
-    // internal usage only
-    public static FastFeatureDetector __fromPtr__(long addr) { return new FastFeatureDetector(addr); }
-
-    public static final int
-            TYPE_5_8 = 0,
-            TYPE_7_12 = 1,
-            TYPE_9_16 = 2,
-            THRESHOLD = 10000,
-            NONMAX_SUPPRESSION = 10001,
-            FAST_N = 10002;
-
-
-    //
-    // C++: static Ptr_FastFeatureDetector create(int threshold = 10, bool nonmaxSuppression = true, int type = FastFeatureDetector::TYPE_9_16)
-    //
-
-    //javadoc: FastFeatureDetector::create(threshold, nonmaxSuppression, type)
-    public static FastFeatureDetector create(int threshold, boolean nonmaxSuppression, int type)
-    {
-        
-        FastFeatureDetector retVal = FastFeatureDetector.__fromPtr__(create_0(threshold, nonmaxSuppression, type));
-        
-        return retVal;
-    }
-
-    //javadoc: FastFeatureDetector::create()
-    public static FastFeatureDetector create()
-    {
-        
-        FastFeatureDetector retVal = FastFeatureDetector.__fromPtr__(create_1());
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  String getDefaultName()
-    //
-
-    //javadoc: FastFeatureDetector::getDefaultName()
-    public  String getDefaultName()
-    {
-        
-        String retVal = getDefaultName_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  bool getNonmaxSuppression()
-    //
-
-    //javadoc: FastFeatureDetector::getNonmaxSuppression()
-    public  boolean getNonmaxSuppression()
-    {
-        
-        boolean retVal = getNonmaxSuppression_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getThreshold()
-    //
-
-    //javadoc: FastFeatureDetector::getThreshold()
-    public  int getThreshold()
-    {
-        
-        int retVal = getThreshold_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getType()
-    //
-
-    //javadoc: FastFeatureDetector::getType()
-    public  int getType()
-    {
-        
-        int retVal = getType_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  void setNonmaxSuppression(bool f)
-    //
-
-    //javadoc: FastFeatureDetector::setNonmaxSuppression(f)
-    public  void setNonmaxSuppression(boolean f)
-    {
-        
-        setNonmaxSuppression_0(nativeObj, f);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setThreshold(int threshold)
-    //
-
-    //javadoc: FastFeatureDetector::setThreshold(threshold)
-    public  void setThreshold(int threshold)
-    {
-        
-        setThreshold_0(nativeObj, threshold);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setType(int type)
-    //
-
-    //javadoc: FastFeatureDetector::setType(type)
-    public  void setType(int type)
-    {
-        
-        setType_0(nativeObj, type);
-        
-        return;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++: static Ptr_FastFeatureDetector create(int threshold = 10, bool nonmaxSuppression = true, int type = FastFeatureDetector::TYPE_9_16)
-    private static native long create_0(int threshold, boolean nonmaxSuppression, int type);
-    private static native long create_1();
-
-    // C++:  String getDefaultName()
-    private static native String getDefaultName_0(long nativeObj);
-
-    // C++:  bool getNonmaxSuppression()
-    private static native boolean getNonmaxSuppression_0(long nativeObj);
-
-    // C++:  int getThreshold()
-    private static native int getThreshold_0(long nativeObj);
-
-    // C++:  int getType()
-    private static native int getType_0(long nativeObj);
-
-    // C++:  void setNonmaxSuppression(bool f)
-    private static native void setNonmaxSuppression_0(long nativeObj, boolean f);
-
-    // C++:  void setThreshold(int threshold)
-    private static native void setThreshold_0(long nativeObj, int threshold);
-
-    // C++:  void setType(int type)
-    private static native void setType_0(long nativeObj, int type);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/features2d/Feature2D.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/features2d/Feature2D.java
deleted file mode 100755
index 011d4a3..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/features2d/Feature2D.java
+++ /dev/null
@@ -1,293 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.features2d;
-
-import java.lang.String;
-import java.util.ArrayList;
-import java.util.List;
-import org.opencv.core.Algorithm;
-import org.opencv.core.Mat;
-import org.opencv.core.MatOfKeyPoint;
-import org.opencv.utils.Converters;
-
-// C++: class Feature2D
-//javadoc: Feature2D
-
-public class Feature2D extends Algorithm {
-
-    protected Feature2D(long addr) { super(addr); }
-
-    // internal usage only
-    public static Feature2D __fromPtr__(long addr) { return new Feature2D(addr); }
-
-    //
-    // C++:  String getDefaultName()
-    //
-
-    //javadoc: Feature2D::getDefaultName()
-    public  String getDefaultName()
-    {
-        
-        String retVal = getDefaultName_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  bool empty()
-    //
-
-    //javadoc: Feature2D::empty()
-    public  boolean empty()
-    {
-        
-        boolean retVal = empty_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int defaultNorm()
-    //
-
-    //javadoc: Feature2D::defaultNorm()
-    public  int defaultNorm()
-    {
-        
-        int retVal = defaultNorm_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int descriptorSize()
-    //
-
-    //javadoc: Feature2D::descriptorSize()
-    public  int descriptorSize()
-    {
-        
-        int retVal = descriptorSize_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int descriptorType()
-    //
-
-    //javadoc: Feature2D::descriptorType()
-    public  int descriptorType()
-    {
-        
-        int retVal = descriptorType_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  void compute(Mat image, vector_KeyPoint& keypoints, Mat& descriptors)
-    //
-
-    //javadoc: Feature2D::compute(image, keypoints, descriptors)
-    public  void compute(Mat image, MatOfKeyPoint keypoints, Mat descriptors)
-    {
-        Mat keypoints_mat = keypoints;
-        compute_0(nativeObj, image.nativeObj, keypoints_mat.nativeObj, descriptors.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void compute(vector_Mat images, vector_vector_KeyPoint& keypoints, vector_Mat& descriptors)
-    //
-
-    //javadoc: Feature2D::compute(images, keypoints, descriptors)
-    public  void compute(List<Mat> images, List<MatOfKeyPoint> keypoints, List<Mat> descriptors)
-    {
-        Mat images_mat = Converters.vector_Mat_to_Mat(images);
-        List<Mat> keypoints_tmplm = new ArrayList<Mat>((keypoints != null) ? keypoints.size() : 0);
-        Mat keypoints_mat = Converters.vector_vector_KeyPoint_to_Mat(keypoints, keypoints_tmplm);
-        Mat descriptors_mat = new Mat();
-        compute_1(nativeObj, images_mat.nativeObj, keypoints_mat.nativeObj, descriptors_mat.nativeObj);
-        Converters.Mat_to_vector_vector_KeyPoint(keypoints_mat, keypoints);
-        keypoints_mat.release();
-        Converters.Mat_to_vector_Mat(descriptors_mat, descriptors);
-        descriptors_mat.release();
-        return;
-    }
-
-
-    //
-    // C++:  void detect(Mat image, vector_KeyPoint& keypoints, Mat mask = Mat())
-    //
-
-    //javadoc: Feature2D::detect(image, keypoints, mask)
-    public  void detect(Mat image, MatOfKeyPoint keypoints, Mat mask)
-    {
-        Mat keypoints_mat = keypoints;
-        detect_0(nativeObj, image.nativeObj, keypoints_mat.nativeObj, mask.nativeObj);
-        
-        return;
-    }
-
-    //javadoc: Feature2D::detect(image, keypoints)
-    public  void detect(Mat image, MatOfKeyPoint keypoints)
-    {
-        Mat keypoints_mat = keypoints;
-        detect_1(nativeObj, image.nativeObj, keypoints_mat.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void detect(vector_Mat images, vector_vector_KeyPoint& keypoints, vector_Mat masks = vector_Mat())
-    //
-
-    //javadoc: Feature2D::detect(images, keypoints, masks)
-    public  void detect(List<Mat> images, List<MatOfKeyPoint> keypoints, List<Mat> masks)
-    {
-        Mat images_mat = Converters.vector_Mat_to_Mat(images);
-        Mat keypoints_mat = new Mat();
-        Mat masks_mat = Converters.vector_Mat_to_Mat(masks);
-        detect_2(nativeObj, images_mat.nativeObj, keypoints_mat.nativeObj, masks_mat.nativeObj);
-        Converters.Mat_to_vector_vector_KeyPoint(keypoints_mat, keypoints);
-        keypoints_mat.release();
-        return;
-    }
-
-    //javadoc: Feature2D::detect(images, keypoints)
-    public  void detect(List<Mat> images, List<MatOfKeyPoint> keypoints)
-    {
-        Mat images_mat = Converters.vector_Mat_to_Mat(images);
-        Mat keypoints_mat = new Mat();
-        detect_3(nativeObj, images_mat.nativeObj, keypoints_mat.nativeObj);
-        Converters.Mat_to_vector_vector_KeyPoint(keypoints_mat, keypoints);
-        keypoints_mat.release();
-        return;
-    }
-
-
-    //
-    // C++:  void detectAndCompute(Mat image, Mat mask, vector_KeyPoint& keypoints, Mat& descriptors, bool useProvidedKeypoints = false)
-    //
-
-    //javadoc: Feature2D::detectAndCompute(image, mask, keypoints, descriptors, useProvidedKeypoints)
-    public  void detectAndCompute(Mat image, Mat mask, MatOfKeyPoint keypoints, Mat descriptors, boolean useProvidedKeypoints)
-    {
-        Mat keypoints_mat = keypoints;
-        detectAndCompute_0(nativeObj, image.nativeObj, mask.nativeObj, keypoints_mat.nativeObj, descriptors.nativeObj, useProvidedKeypoints);
-        
-        return;
-    }
-
-    //javadoc: Feature2D::detectAndCompute(image, mask, keypoints, descriptors)
-    public  void detectAndCompute(Mat image, Mat mask, MatOfKeyPoint keypoints, Mat descriptors)
-    {
-        Mat keypoints_mat = keypoints;
-        detectAndCompute_1(nativeObj, image.nativeObj, mask.nativeObj, keypoints_mat.nativeObj, descriptors.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void read(FileNode arg1)
-    //
-
-    // Unknown type 'FileNode' (I), skipping the function
-
-
-    //
-    // C++:  void read(String fileName)
-    //
-
-    //javadoc: Feature2D::read(fileName)
-    public  void read(String fileName)
-    {
-        
-        read_0(nativeObj, fileName);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void write(Ptr_FileStorage fs, String name = String())
-    //
-
-    // Unknown type 'Ptr_FileStorage' (I), skipping the function
-
-
-    //
-    // C++:  void write(String fileName)
-    //
-
-    //javadoc: Feature2D::write(fileName)
-    public  void write(String fileName)
-    {
-        
-        write_0(nativeObj, fileName);
-        
-        return;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++:  String getDefaultName()
-    private static native String getDefaultName_0(long nativeObj);
-
-    // C++:  bool empty()
-    private static native boolean empty_0(long nativeObj);
-
-    // C++:  int defaultNorm()
-    private static native int defaultNorm_0(long nativeObj);
-
-    // C++:  int descriptorSize()
-    private static native int descriptorSize_0(long nativeObj);
-
-    // C++:  int descriptorType()
-    private static native int descriptorType_0(long nativeObj);
-
-    // C++:  void compute(Mat image, vector_KeyPoint& keypoints, Mat& descriptors)
-    private static native void compute_0(long nativeObj, long image_nativeObj, long keypoints_mat_nativeObj, long descriptors_nativeObj);
-
-    // C++:  void compute(vector_Mat images, vector_vector_KeyPoint& keypoints, vector_Mat& descriptors)
-    private static native void compute_1(long nativeObj, long images_mat_nativeObj, long keypoints_mat_nativeObj, long descriptors_mat_nativeObj);
-
-    // C++:  void detect(Mat image, vector_KeyPoint& keypoints, Mat mask = Mat())
-    private static native void detect_0(long nativeObj, long image_nativeObj, long keypoints_mat_nativeObj, long mask_nativeObj);
-    private static native void detect_1(long nativeObj, long image_nativeObj, long keypoints_mat_nativeObj);
-
-    // C++:  void detect(vector_Mat images, vector_vector_KeyPoint& keypoints, vector_Mat masks = vector_Mat())
-    private static native void detect_2(long nativeObj, long images_mat_nativeObj, long keypoints_mat_nativeObj, long masks_mat_nativeObj);
-    private static native void detect_3(long nativeObj, long images_mat_nativeObj, long keypoints_mat_nativeObj);
-
-    // C++:  void detectAndCompute(Mat image, Mat mask, vector_KeyPoint& keypoints, Mat& descriptors, bool useProvidedKeypoints = false)
-    private static native void detectAndCompute_0(long nativeObj, long image_nativeObj, long mask_nativeObj, long keypoints_mat_nativeObj, long descriptors_nativeObj, boolean useProvidedKeypoints);
-    private static native void detectAndCompute_1(long nativeObj, long image_nativeObj, long mask_nativeObj, long keypoints_mat_nativeObj, long descriptors_nativeObj);
-
-    // C++:  void read(String fileName)
-    private static native void read_0(long nativeObj, String fileName);
-
-    // C++:  void write(String fileName)
-    private static native void write_0(long nativeObj, String fileName);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/features2d/FeatureDetector.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/features2d/FeatureDetector.java
deleted file mode 100755
index 7964615..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/features2d/FeatureDetector.java
+++ /dev/null
@@ -1,222 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.features2d;
-
-import java.lang.String;
-import java.util.ArrayList;
-import java.util.List;
-import org.opencv.core.Mat;
-import org.opencv.core.MatOfKeyPoint;
-import org.opencv.features2d.FeatureDetector;
-import org.opencv.utils.Converters;
-
-// C++: class javaFeatureDetector
-//javadoc: javaFeatureDetector
-@Deprecated
-public class FeatureDetector {
-
-    protected final long nativeObj;
-    protected FeatureDetector(long addr) { nativeObj = addr; }
-
-    public long getNativeObjAddr() { return nativeObj; }
-
-    // internal usage only
-    public static FeatureDetector __fromPtr__(long addr) { return new FeatureDetector(addr); }
-
-    private static final int
-            GRIDDETECTOR = 1000,
-            PYRAMIDDETECTOR = 2000,
-            DYNAMICDETECTOR = 3000;
-
-
-    public static final int
-            FAST = 1,
-            STAR = 2,
-            SIFT = 3,
-            SURF = 4,
-            ORB = 5,
-            MSER = 6,
-            GFTT = 7,
-            HARRIS = 8,
-            SIMPLEBLOB = 9,
-            DENSE = 10,
-            BRISK = 11,
-            AKAZE = 12,
-            GRID_FAST = GRIDDETECTOR + FAST,
-            GRID_STAR = GRIDDETECTOR + STAR,
-            GRID_SIFT = GRIDDETECTOR + SIFT,
-            GRID_SURF = GRIDDETECTOR + SURF,
-            GRID_ORB = GRIDDETECTOR + ORB,
-            GRID_MSER = GRIDDETECTOR + MSER,
-            GRID_GFTT = GRIDDETECTOR + GFTT,
-            GRID_HARRIS = GRIDDETECTOR + HARRIS,
-            GRID_SIMPLEBLOB = GRIDDETECTOR + SIMPLEBLOB,
-            GRID_DENSE = GRIDDETECTOR + DENSE,
-            GRID_BRISK = GRIDDETECTOR + BRISK,
-            GRID_AKAZE = GRIDDETECTOR + AKAZE,
-            PYRAMID_FAST = PYRAMIDDETECTOR + FAST,
-            PYRAMID_STAR = PYRAMIDDETECTOR + STAR,
-            PYRAMID_SIFT = PYRAMIDDETECTOR + SIFT,
-            PYRAMID_SURF = PYRAMIDDETECTOR + SURF,
-            PYRAMID_ORB = PYRAMIDDETECTOR + ORB,
-            PYRAMID_MSER = PYRAMIDDETECTOR + MSER,
-            PYRAMID_GFTT = PYRAMIDDETECTOR + GFTT,
-            PYRAMID_HARRIS = PYRAMIDDETECTOR + HARRIS,
-            PYRAMID_SIMPLEBLOB = PYRAMIDDETECTOR + SIMPLEBLOB,
-            PYRAMID_DENSE = PYRAMIDDETECTOR + DENSE,
-            PYRAMID_BRISK = PYRAMIDDETECTOR + BRISK,
-            PYRAMID_AKAZE = PYRAMIDDETECTOR + AKAZE,
-            DYNAMIC_FAST = DYNAMICDETECTOR + FAST,
-            DYNAMIC_STAR = DYNAMICDETECTOR + STAR,
-            DYNAMIC_SIFT = DYNAMICDETECTOR + SIFT,
-            DYNAMIC_SURF = DYNAMICDETECTOR + SURF,
-            DYNAMIC_ORB = DYNAMICDETECTOR + ORB,
-            DYNAMIC_MSER = DYNAMICDETECTOR + MSER,
-            DYNAMIC_GFTT = DYNAMICDETECTOR + GFTT,
-            DYNAMIC_HARRIS = DYNAMICDETECTOR + HARRIS,
-            DYNAMIC_SIMPLEBLOB = DYNAMICDETECTOR + SIMPLEBLOB,
-            DYNAMIC_DENSE = DYNAMICDETECTOR + DENSE,
-            DYNAMIC_BRISK = DYNAMICDETECTOR + BRISK,
-            DYNAMIC_AKAZE = DYNAMICDETECTOR + AKAZE;
-
-
-    //
-    // C++: static Ptr_javaFeatureDetector create(int detectorType)
-    //
-
-    //javadoc: javaFeatureDetector::create(detectorType)
-    @Deprecated
-    public static FeatureDetector create(int detectorType)
-    {
-        
-        FeatureDetector retVal = FeatureDetector.__fromPtr__(create_0(detectorType));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  bool empty()
-    //
-
-    //javadoc: javaFeatureDetector::empty()
-    public  boolean empty()
-    {
-        
-        boolean retVal = empty_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  void detect(Mat image, vector_KeyPoint& keypoints, Mat mask = Mat())
-    //
-
-    //javadoc: javaFeatureDetector::detect(image, keypoints, mask)
-    public  void detect(Mat image, MatOfKeyPoint keypoints, Mat mask)
-    {
-        Mat keypoints_mat = keypoints;
-        detect_0(nativeObj, image.nativeObj, keypoints_mat.nativeObj, mask.nativeObj);
-        
-        return;
-    }
-
-    //javadoc: javaFeatureDetector::detect(image, keypoints)
-    public  void detect(Mat image, MatOfKeyPoint keypoints)
-    {
-        Mat keypoints_mat = keypoints;
-        detect_1(nativeObj, image.nativeObj, keypoints_mat.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void detect(vector_Mat images, vector_vector_KeyPoint& keypoints, vector_Mat masks = std::vector<Mat>())
-    //
-
-    //javadoc: javaFeatureDetector::detect(images, keypoints, masks)
-    public  void detect(List<Mat> images, List<MatOfKeyPoint> keypoints, List<Mat> masks)
-    {
-        Mat images_mat = Converters.vector_Mat_to_Mat(images);
-        Mat keypoints_mat = new Mat();
-        Mat masks_mat = Converters.vector_Mat_to_Mat(masks);
-        detect_2(nativeObj, images_mat.nativeObj, keypoints_mat.nativeObj, masks_mat.nativeObj);
-        Converters.Mat_to_vector_vector_KeyPoint(keypoints_mat, keypoints);
-        keypoints_mat.release();
-        return;
-    }
-
-    //javadoc: javaFeatureDetector::detect(images, keypoints)
-    public  void detect(List<Mat> images, List<MatOfKeyPoint> keypoints)
-    {
-        Mat images_mat = Converters.vector_Mat_to_Mat(images);
-        Mat keypoints_mat = new Mat();
-        detect_3(nativeObj, images_mat.nativeObj, keypoints_mat.nativeObj);
-        Converters.Mat_to_vector_vector_KeyPoint(keypoints_mat, keypoints);
-        keypoints_mat.release();
-        return;
-    }
-
-
-    //
-    // C++:  void read(String fileName)
-    //
-
-    //javadoc: javaFeatureDetector::read(fileName)
-    public  void read(String fileName)
-    {
-        
-        read_0(nativeObj, fileName);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void write(String fileName)
-    //
-
-    //javadoc: javaFeatureDetector::write(fileName)
-    public  void write(String fileName)
-    {
-        
-        write_0(nativeObj, fileName);
-        
-        return;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++: static Ptr_javaFeatureDetector create(int detectorType)
-    private static native long create_0(int detectorType);
-
-    // C++:  bool empty()
-    private static native boolean empty_0(long nativeObj);
-
-    // C++:  void detect(Mat image, vector_KeyPoint& keypoints, Mat mask = Mat())
-    private static native void detect_0(long nativeObj, long image_nativeObj, long keypoints_mat_nativeObj, long mask_nativeObj);
-    private static native void detect_1(long nativeObj, long image_nativeObj, long keypoints_mat_nativeObj);
-
-    // C++:  void detect(vector_Mat images, vector_vector_KeyPoint& keypoints, vector_Mat masks = std::vector<Mat>())
-    private static native void detect_2(long nativeObj, long images_mat_nativeObj, long keypoints_mat_nativeObj, long masks_mat_nativeObj);
-    private static native void detect_3(long nativeObj, long images_mat_nativeObj, long keypoints_mat_nativeObj);
-
-    // C++:  void read(String fileName)
-    private static native void read_0(long nativeObj, String fileName);
-
-    // C++:  void write(String fileName)
-    private static native void write_0(long nativeObj, String fileName);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/features2d/Features2d.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/features2d/Features2d.java
deleted file mode 100755
index d6771ee..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/features2d/Features2d.java
+++ /dev/null
@@ -1,157 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.features2d;
-
-import java.util.ArrayList;
-import java.util.List;
-import org.opencv.core.Mat;
-import org.opencv.core.MatOfByte;
-import org.opencv.core.MatOfDMatch;
-import org.opencv.core.MatOfKeyPoint;
-import org.opencv.core.Scalar;
-import org.opencv.utils.Converters;
-
-// C++: class Features2d
-//javadoc: Features2d
-
-public class Features2d {
-
-    public static final int
-            DRAW_OVER_OUTIMG = 1,
-            NOT_DRAW_SINGLE_POINTS = 2,
-            DRAW_RICH_KEYPOINTS = 4;
-
-
-    //
-    // C++:  void drawKeypoints(Mat image, vector_KeyPoint keypoints, Mat& outImage, Scalar color = Scalar::all(-1), int flags = DrawMatchesFlags::DEFAULT)
-    //
-
-    //javadoc: drawKeypoints(image, keypoints, outImage, color, flags)
-    public static void drawKeypoints(Mat image, MatOfKeyPoint keypoints, Mat outImage, Scalar color, int flags)
-    {
-        Mat keypoints_mat = keypoints;
-        drawKeypoints_0(image.nativeObj, keypoints_mat.nativeObj, outImage.nativeObj, color.val[0], color.val[1], color.val[2], color.val[3], flags);
-        
-        return;
-    }
-
-    //javadoc: drawKeypoints(image, keypoints, outImage)
-    public static void drawKeypoints(Mat image, MatOfKeyPoint keypoints, Mat outImage)
-    {
-        Mat keypoints_mat = keypoints;
-        drawKeypoints_1(image.nativeObj, keypoints_mat.nativeObj, outImage.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void drawMatches(Mat img1, vector_KeyPoint keypoints1, Mat img2, vector_KeyPoint keypoints2, vector_DMatch matches1to2, Mat& outImg, Scalar matchColor = Scalar::all(-1), Scalar singlePointColor = Scalar::all(-1), vector_char matchesMask = std::vector<char>(), int flags = DrawMatchesFlags::DEFAULT)
-    //
-
-    //javadoc: drawMatches(img1, keypoints1, img2, keypoints2, matches1to2, outImg, matchColor, singlePointColor, matchesMask, flags)
-    public static void drawMatches(Mat img1, MatOfKeyPoint keypoints1, Mat img2, MatOfKeyPoint keypoints2, MatOfDMatch matches1to2, Mat outImg, Scalar matchColor, Scalar singlePointColor, MatOfByte matchesMask, int flags)
-    {
-        Mat keypoints1_mat = keypoints1;
-        Mat keypoints2_mat = keypoints2;
-        Mat matches1to2_mat = matches1to2;
-        Mat matchesMask_mat = matchesMask;
-        drawMatches_0(img1.nativeObj, keypoints1_mat.nativeObj, img2.nativeObj, keypoints2_mat.nativeObj, matches1to2_mat.nativeObj, outImg.nativeObj, matchColor.val[0], matchColor.val[1], matchColor.val[2], matchColor.val[3], singlePointColor.val[0], singlePointColor.val[1], singlePointColor.val[2], singlePointColor.val[3], matchesMask_mat.nativeObj, flags);
-        
-        return;
-    }
-
-    //javadoc: drawMatches(img1, keypoints1, img2, keypoints2, matches1to2, outImg)
-    public static void drawMatches(Mat img1, MatOfKeyPoint keypoints1, Mat img2, MatOfKeyPoint keypoints2, MatOfDMatch matches1to2, Mat outImg)
-    {
-        Mat keypoints1_mat = keypoints1;
-        Mat keypoints2_mat = keypoints2;
-        Mat matches1to2_mat = matches1to2;
-        drawMatches_1(img1.nativeObj, keypoints1_mat.nativeObj, img2.nativeObj, keypoints2_mat.nativeObj, matches1to2_mat.nativeObj, outImg.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void drawMatches(Mat img1, vector_KeyPoint keypoints1, Mat img2, vector_KeyPoint keypoints2, vector_vector_DMatch matches1to2, Mat outImg, Scalar matchColor = Scalar::all(-1), Scalar singlePointColor = Scalar::all(-1), vector_vector_char matchesMask = std::vector<std::vector<char> >(), int flags = 0)
-    //
-
-    //javadoc: drawMatches(img1, keypoints1, img2, keypoints2, matches1to2, outImg, matchColor, singlePointColor, matchesMask, flags)
-    public static void drawMatches2(Mat img1, MatOfKeyPoint keypoints1, Mat img2, MatOfKeyPoint keypoints2, List<MatOfDMatch> matches1to2, Mat outImg, Scalar matchColor, Scalar singlePointColor, List<MatOfByte> matchesMask, int flags)
-    {
-        Mat keypoints1_mat = keypoints1;
-        Mat keypoints2_mat = keypoints2;
-        List<Mat> matches1to2_tmplm = new ArrayList<Mat>((matches1to2 != null) ? matches1to2.size() : 0);
-        Mat matches1to2_mat = Converters.vector_vector_DMatch_to_Mat(matches1to2, matches1to2_tmplm);
-        List<Mat> matchesMask_tmplm = new ArrayList<Mat>((matchesMask != null) ? matchesMask.size() : 0);
-        Mat matchesMask_mat = Converters.vector_vector_char_to_Mat(matchesMask, matchesMask_tmplm);
-        drawMatches2_0(img1.nativeObj, keypoints1_mat.nativeObj, img2.nativeObj, keypoints2_mat.nativeObj, matches1to2_mat.nativeObj, outImg.nativeObj, matchColor.val[0], matchColor.val[1], matchColor.val[2], matchColor.val[3], singlePointColor.val[0], singlePointColor.val[1], singlePointColor.val[2], singlePointColor.val[3], matchesMask_mat.nativeObj, flags);
-        
-        return;
-    }
-
-    //javadoc: drawMatches(img1, keypoints1, img2, keypoints2, matches1to2, outImg)
-    public static void drawMatches2(Mat img1, MatOfKeyPoint keypoints1, Mat img2, MatOfKeyPoint keypoints2, List<MatOfDMatch> matches1to2, Mat outImg)
-    {
-        Mat keypoints1_mat = keypoints1;
-        Mat keypoints2_mat = keypoints2;
-        List<Mat> matches1to2_tmplm = new ArrayList<Mat>((matches1to2 != null) ? matches1to2.size() : 0);
-        Mat matches1to2_mat = Converters.vector_vector_DMatch_to_Mat(matches1to2, matches1to2_tmplm);
-        drawMatches2_1(img1.nativeObj, keypoints1_mat.nativeObj, img2.nativeObj, keypoints2_mat.nativeObj, matches1to2_mat.nativeObj, outImg.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void drawMatches(Mat img1, vector_KeyPoint keypoints1, Mat img2, vector_KeyPoint keypoints2, vector_vector_DMatch matches1to2, Mat& outImg, Scalar matchColor = Scalar::all(-1), Scalar singlePointColor = Scalar::all(-1), vector_vector_char matchesMask = std::vector<std::vector<char> >(), int flags = DrawMatchesFlags::DEFAULT)
-    //
-
-    //javadoc: drawMatches(img1, keypoints1, img2, keypoints2, matches1to2, outImg, matchColor, singlePointColor, matchesMask, flags)
-    public static void drawMatchesKnn(Mat img1, MatOfKeyPoint keypoints1, Mat img2, MatOfKeyPoint keypoints2, List<MatOfDMatch> matches1to2, Mat outImg, Scalar matchColor, Scalar singlePointColor, List<MatOfByte> matchesMask, int flags)
-    {
-        Mat keypoints1_mat = keypoints1;
-        Mat keypoints2_mat = keypoints2;
-        List<Mat> matches1to2_tmplm = new ArrayList<Mat>((matches1to2 != null) ? matches1to2.size() : 0);
-        Mat matches1to2_mat = Converters.vector_vector_DMatch_to_Mat(matches1to2, matches1to2_tmplm);
-        List<Mat> matchesMask_tmplm = new ArrayList<Mat>((matchesMask != null) ? matchesMask.size() : 0);
-        Mat matchesMask_mat = Converters.vector_vector_char_to_Mat(matchesMask, matchesMask_tmplm);
-        drawMatchesKnn_0(img1.nativeObj, keypoints1_mat.nativeObj, img2.nativeObj, keypoints2_mat.nativeObj, matches1to2_mat.nativeObj, outImg.nativeObj, matchColor.val[0], matchColor.val[1], matchColor.val[2], matchColor.val[3], singlePointColor.val[0], singlePointColor.val[1], singlePointColor.val[2], singlePointColor.val[3], matchesMask_mat.nativeObj, flags);
-        
-        return;
-    }
-
-    //javadoc: drawMatches(img1, keypoints1, img2, keypoints2, matches1to2, outImg)
-    public static void drawMatchesKnn(Mat img1, MatOfKeyPoint keypoints1, Mat img2, MatOfKeyPoint keypoints2, List<MatOfDMatch> matches1to2, Mat outImg)
-    {
-        Mat keypoints1_mat = keypoints1;
-        Mat keypoints2_mat = keypoints2;
-        List<Mat> matches1to2_tmplm = new ArrayList<Mat>((matches1to2 != null) ? matches1to2.size() : 0);
-        Mat matches1to2_mat = Converters.vector_vector_DMatch_to_Mat(matches1to2, matches1to2_tmplm);
-        drawMatchesKnn_1(img1.nativeObj, keypoints1_mat.nativeObj, img2.nativeObj, keypoints2_mat.nativeObj, matches1to2_mat.nativeObj, outImg.nativeObj);
-        
-        return;
-    }
-
-
-
-
-    // C++:  void drawKeypoints(Mat image, vector_KeyPoint keypoints, Mat& outImage, Scalar color = Scalar::all(-1), int flags = DrawMatchesFlags::DEFAULT)
-    private static native void drawKeypoints_0(long image_nativeObj, long keypoints_mat_nativeObj, long outImage_nativeObj, double color_val0, double color_val1, double color_val2, double color_val3, int flags);
-    private static native void drawKeypoints_1(long image_nativeObj, long keypoints_mat_nativeObj, long outImage_nativeObj);
-
-    // C++:  void drawMatches(Mat img1, vector_KeyPoint keypoints1, Mat img2, vector_KeyPoint keypoints2, vector_DMatch matches1to2, Mat& outImg, Scalar matchColor = Scalar::all(-1), Scalar singlePointColor = Scalar::all(-1), vector_char matchesMask = std::vector<char>(), int flags = DrawMatchesFlags::DEFAULT)
-    private static native void drawMatches_0(long img1_nativeObj, long keypoints1_mat_nativeObj, long img2_nativeObj, long keypoints2_mat_nativeObj, long matches1to2_mat_nativeObj, long outImg_nativeObj, double matchColor_val0, double matchColor_val1, double matchColor_val2, double matchColor_val3, double singlePointColor_val0, double singlePointColor_val1, double singlePointColor_val2, double singlePointColor_val3, long matchesMask_mat_nativeObj, int flags);
-    private static native void drawMatches_1(long img1_nativeObj, long keypoints1_mat_nativeObj, long img2_nativeObj, long keypoints2_mat_nativeObj, long matches1to2_mat_nativeObj, long outImg_nativeObj);
-
-    // C++:  void drawMatches(Mat img1, vector_KeyPoint keypoints1, Mat img2, vector_KeyPoint keypoints2, vector_vector_DMatch matches1to2, Mat outImg, Scalar matchColor = Scalar::all(-1), Scalar singlePointColor = Scalar::all(-1), vector_vector_char matchesMask = std::vector<std::vector<char> >(), int flags = 0)
-    private static native void drawMatches2_0(long img1_nativeObj, long keypoints1_mat_nativeObj, long img2_nativeObj, long keypoints2_mat_nativeObj, long matches1to2_mat_nativeObj, long outImg_nativeObj, double matchColor_val0, double matchColor_val1, double matchColor_val2, double matchColor_val3, double singlePointColor_val0, double singlePointColor_val1, double singlePointColor_val2, double singlePointColor_val3, long matchesMask_mat_nativeObj, int flags);
-    private static native void drawMatches2_1(long img1_nativeObj, long keypoints1_mat_nativeObj, long img2_nativeObj, long keypoints2_mat_nativeObj, long matches1to2_mat_nativeObj, long outImg_nativeObj);
-
-    // C++:  void drawMatches(Mat img1, vector_KeyPoint keypoints1, Mat img2, vector_KeyPoint keypoints2, vector_vector_DMatch matches1to2, Mat& outImg, Scalar matchColor = Scalar::all(-1), Scalar singlePointColor = Scalar::all(-1), vector_vector_char matchesMask = std::vector<std::vector<char> >(), int flags = DrawMatchesFlags::DEFAULT)
-    private static native void drawMatchesKnn_0(long img1_nativeObj, long keypoints1_mat_nativeObj, long img2_nativeObj, long keypoints2_mat_nativeObj, long matches1to2_mat_nativeObj, long outImg_nativeObj, double matchColor_val0, double matchColor_val1, double matchColor_val2, double matchColor_val3, double singlePointColor_val0, double singlePointColor_val1, double singlePointColor_val2, double singlePointColor_val3, long matchesMask_mat_nativeObj, int flags);
-    private static native void drawMatchesKnn_1(long img1_nativeObj, long keypoints1_mat_nativeObj, long img2_nativeObj, long keypoints2_mat_nativeObj, long matches1to2_mat_nativeObj, long outImg_nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/features2d/FlannBasedMatcher.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/features2d/FlannBasedMatcher.java
deleted file mode 100755
index cf5d9b5..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/features2d/FlannBasedMatcher.java
+++ /dev/null
@@ -1,63 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.features2d;
-
-import org.opencv.features2d.DescriptorMatcher;
-import org.opencv.features2d.FlannBasedMatcher;
-
-// C++: class FlannBasedMatcher
-//javadoc: FlannBasedMatcher
-
-public class FlannBasedMatcher extends DescriptorMatcher {
-
-    protected FlannBasedMatcher(long addr) { super(addr); }
-
-    // internal usage only
-    public static FlannBasedMatcher __fromPtr__(long addr) { return new FlannBasedMatcher(addr); }
-
-    //
-    // C++:   FlannBasedMatcher(Ptr_flann_IndexParams indexParams = makePtr<flann::KDTreeIndexParams>(), Ptr_flann_SearchParams searchParams = makePtr<flann::SearchParams>())
-    //
-
-    //javadoc: FlannBasedMatcher::FlannBasedMatcher()
-    public   FlannBasedMatcher()
-    {
-        
-        super( FlannBasedMatcher_0() );
-        
-        return;
-    }
-
-
-    //
-    // C++: static Ptr_FlannBasedMatcher create()
-    //
-
-    //javadoc: FlannBasedMatcher::create()
-    public static FlannBasedMatcher create()
-    {
-        
-        FlannBasedMatcher retVal = FlannBasedMatcher.__fromPtr__(create_0());
-        
-        return retVal;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++:   FlannBasedMatcher(Ptr_flann_IndexParams indexParams = makePtr<flann::KDTreeIndexParams>(), Ptr_flann_SearchParams searchParams = makePtr<flann::SearchParams>())
-    private static native long FlannBasedMatcher_0();
-
-    // C++: static Ptr_FlannBasedMatcher create()
-    private static native long create_0();
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/features2d/GFTTDetector.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/features2d/GFTTDetector.java
deleted file mode 100755
index ac86693..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/features2d/GFTTDetector.java
+++ /dev/null
@@ -1,305 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.features2d;
-
-import java.lang.String;
-import org.opencv.features2d.Feature2D;
-import org.opencv.features2d.GFTTDetector;
-
-// C++: class GFTTDetector
-//javadoc: GFTTDetector
-
-public class GFTTDetector extends Feature2D {
-
-    protected GFTTDetector(long addr) { super(addr); }
-
-    // internal usage only
-    public static GFTTDetector __fromPtr__(long addr) { return new GFTTDetector(addr); }
-
-    //
-    // C++: static Ptr_GFTTDetector create(int maxCorners, double qualityLevel, double minDistance, int blockSize, int gradiantSize, bool useHarrisDetector = false, double k = 0.04)
-    //
-
-    //javadoc: GFTTDetector::create(maxCorners, qualityLevel, minDistance, blockSize, gradiantSize, useHarrisDetector, k)
-    public static GFTTDetector create(int maxCorners, double qualityLevel, double minDistance, int blockSize, int gradiantSize, boolean useHarrisDetector, double k)
-    {
-        
-        GFTTDetector retVal = GFTTDetector.__fromPtr__(create_0(maxCorners, qualityLevel, minDistance, blockSize, gradiantSize, useHarrisDetector, k));
-        
-        return retVal;
-    }
-
-    //javadoc: GFTTDetector::create(maxCorners, qualityLevel, minDistance, blockSize, gradiantSize)
-    public static GFTTDetector create(int maxCorners, double qualityLevel, double minDistance, int blockSize, int gradiantSize)
-    {
-        
-        GFTTDetector retVal = GFTTDetector.__fromPtr__(create_1(maxCorners, qualityLevel, minDistance, blockSize, gradiantSize));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: static Ptr_GFTTDetector create(int maxCorners = 1000, double qualityLevel = 0.01, double minDistance = 1, int blockSize = 3, bool useHarrisDetector = false, double k = 0.04)
-    //
-
-    //javadoc: GFTTDetector::create(maxCorners, qualityLevel, minDistance, blockSize, useHarrisDetector, k)
-    public static GFTTDetector create(int maxCorners, double qualityLevel, double minDistance, int blockSize, boolean useHarrisDetector, double k)
-    {
-        
-        GFTTDetector retVal = GFTTDetector.__fromPtr__(create_2(maxCorners, qualityLevel, minDistance, blockSize, useHarrisDetector, k));
-        
-        return retVal;
-    }
-
-    //javadoc: GFTTDetector::create()
-    public static GFTTDetector create()
-    {
-        
-        GFTTDetector retVal = GFTTDetector.__fromPtr__(create_3());
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  String getDefaultName()
-    //
-
-    //javadoc: GFTTDetector::getDefaultName()
-    public  String getDefaultName()
-    {
-        
-        String retVal = getDefaultName_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  bool getHarrisDetector()
-    //
-
-    //javadoc: GFTTDetector::getHarrisDetector()
-    public  boolean getHarrisDetector()
-    {
-        
-        boolean retVal = getHarrisDetector_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double getK()
-    //
-
-    //javadoc: GFTTDetector::getK()
-    public  double getK()
-    {
-        
-        double retVal = getK_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double getMinDistance()
-    //
-
-    //javadoc: GFTTDetector::getMinDistance()
-    public  double getMinDistance()
-    {
-        
-        double retVal = getMinDistance_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double getQualityLevel()
-    //
-
-    //javadoc: GFTTDetector::getQualityLevel()
-    public  double getQualityLevel()
-    {
-        
-        double retVal = getQualityLevel_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getBlockSize()
-    //
-
-    //javadoc: GFTTDetector::getBlockSize()
-    public  int getBlockSize()
-    {
-        
-        int retVal = getBlockSize_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getMaxFeatures()
-    //
-
-    //javadoc: GFTTDetector::getMaxFeatures()
-    public  int getMaxFeatures()
-    {
-        
-        int retVal = getMaxFeatures_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  void setBlockSize(int blockSize)
-    //
-
-    //javadoc: GFTTDetector::setBlockSize(blockSize)
-    public  void setBlockSize(int blockSize)
-    {
-        
-        setBlockSize_0(nativeObj, blockSize);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setHarrisDetector(bool val)
-    //
-
-    //javadoc: GFTTDetector::setHarrisDetector(val)
-    public  void setHarrisDetector(boolean val)
-    {
-        
-        setHarrisDetector_0(nativeObj, val);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setK(double k)
-    //
-
-    //javadoc: GFTTDetector::setK(k)
-    public  void setK(double k)
-    {
-        
-        setK_0(nativeObj, k);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setMaxFeatures(int maxFeatures)
-    //
-
-    //javadoc: GFTTDetector::setMaxFeatures(maxFeatures)
-    public  void setMaxFeatures(int maxFeatures)
-    {
-        
-        setMaxFeatures_0(nativeObj, maxFeatures);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setMinDistance(double minDistance)
-    //
-
-    //javadoc: GFTTDetector::setMinDistance(minDistance)
-    public  void setMinDistance(double minDistance)
-    {
-        
-        setMinDistance_0(nativeObj, minDistance);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setQualityLevel(double qlevel)
-    //
-
-    //javadoc: GFTTDetector::setQualityLevel(qlevel)
-    public  void setQualityLevel(double qlevel)
-    {
-        
-        setQualityLevel_0(nativeObj, qlevel);
-        
-        return;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++: static Ptr_GFTTDetector create(int maxCorners, double qualityLevel, double minDistance, int blockSize, int gradiantSize, bool useHarrisDetector = false, double k = 0.04)
-    private static native long create_0(int maxCorners, double qualityLevel, double minDistance, int blockSize, int gradiantSize, boolean useHarrisDetector, double k);
-    private static native long create_1(int maxCorners, double qualityLevel, double minDistance, int blockSize, int gradiantSize);
-
-    // C++: static Ptr_GFTTDetector create(int maxCorners = 1000, double qualityLevel = 0.01, double minDistance = 1, int blockSize = 3, bool useHarrisDetector = false, double k = 0.04)
-    private static native long create_2(int maxCorners, double qualityLevel, double minDistance, int blockSize, boolean useHarrisDetector, double k);
-    private static native long create_3();
-
-    // C++:  String getDefaultName()
-    private static native String getDefaultName_0(long nativeObj);
-
-    // C++:  bool getHarrisDetector()
-    private static native boolean getHarrisDetector_0(long nativeObj);
-
-    // C++:  double getK()
-    private static native double getK_0(long nativeObj);
-
-    // C++:  double getMinDistance()
-    private static native double getMinDistance_0(long nativeObj);
-
-    // C++:  double getQualityLevel()
-    private static native double getQualityLevel_0(long nativeObj);
-
-    // C++:  int getBlockSize()
-    private static native int getBlockSize_0(long nativeObj);
-
-    // C++:  int getMaxFeatures()
-    private static native int getMaxFeatures_0(long nativeObj);
-
-    // C++:  void setBlockSize(int blockSize)
-    private static native void setBlockSize_0(long nativeObj, int blockSize);
-
-    // C++:  void setHarrisDetector(bool val)
-    private static native void setHarrisDetector_0(long nativeObj, boolean val);
-
-    // C++:  void setK(double k)
-    private static native void setK_0(long nativeObj, double k);
-
-    // C++:  void setMaxFeatures(int maxFeatures)
-    private static native void setMaxFeatures_0(long nativeObj, int maxFeatures);
-
-    // C++:  void setMinDistance(double minDistance)
-    private static native void setMinDistance_0(long nativeObj, double minDistance);
-
-    // C++:  void setQualityLevel(double qlevel)
-    private static native void setQualityLevel_0(long nativeObj, double qlevel);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/features2d/KAZE.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/features2d/KAZE.java
deleted file mode 100755
index 3f79298..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/features2d/KAZE.java
+++ /dev/null
@@ -1,285 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.features2d;
-
-import java.lang.String;
-import org.opencv.features2d.Feature2D;
-import org.opencv.features2d.KAZE;
-
-// C++: class KAZE
-//javadoc: KAZE
-
-public class KAZE extends Feature2D {
-
-    protected KAZE(long addr) { super(addr); }
-
-    // internal usage only
-    public static KAZE __fromPtr__(long addr) { return new KAZE(addr); }
-
-    public static final int
-            DIFF_PM_G1 = 0,
-            DIFF_PM_G2 = 1,
-            DIFF_WEICKERT = 2,
-            DIFF_CHARBONNIER = 3;
-
-
-    //
-    // C++: static Ptr_KAZE create(bool extended = false, bool upright = false, float threshold = 0.001f, int nOctaves = 4, int nOctaveLayers = 4, int diffusivity = KAZE::DIFF_PM_G2)
-    //
-
-    //javadoc: KAZE::create(extended, upright, threshold, nOctaves, nOctaveLayers, diffusivity)
-    public static KAZE create(boolean extended, boolean upright, float threshold, int nOctaves, int nOctaveLayers, int diffusivity)
-    {
-        
-        KAZE retVal = KAZE.__fromPtr__(create_0(extended, upright, threshold, nOctaves, nOctaveLayers, diffusivity));
-        
-        return retVal;
-    }
-
-    //javadoc: KAZE::create()
-    public static KAZE create()
-    {
-        
-        KAZE retVal = KAZE.__fromPtr__(create_1());
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  String getDefaultName()
-    //
-
-    //javadoc: KAZE::getDefaultName()
-    public  String getDefaultName()
-    {
-        
-        String retVal = getDefaultName_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  bool getExtended()
-    //
-
-    //javadoc: KAZE::getExtended()
-    public  boolean getExtended()
-    {
-        
-        boolean retVal = getExtended_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  bool getUpright()
-    //
-
-    //javadoc: KAZE::getUpright()
-    public  boolean getUpright()
-    {
-        
-        boolean retVal = getUpright_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double getThreshold()
-    //
-
-    //javadoc: KAZE::getThreshold()
-    public  double getThreshold()
-    {
-        
-        double retVal = getThreshold_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getDiffusivity()
-    //
-
-    //javadoc: KAZE::getDiffusivity()
-    public  int getDiffusivity()
-    {
-        
-        int retVal = getDiffusivity_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getNOctaveLayers()
-    //
-
-    //javadoc: KAZE::getNOctaveLayers()
-    public  int getNOctaveLayers()
-    {
-        
-        int retVal = getNOctaveLayers_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getNOctaves()
-    //
-
-    //javadoc: KAZE::getNOctaves()
-    public  int getNOctaves()
-    {
-        
-        int retVal = getNOctaves_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  void setDiffusivity(int diff)
-    //
-
-    //javadoc: KAZE::setDiffusivity(diff)
-    public  void setDiffusivity(int diff)
-    {
-        
-        setDiffusivity_0(nativeObj, diff);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setExtended(bool extended)
-    //
-
-    //javadoc: KAZE::setExtended(extended)
-    public  void setExtended(boolean extended)
-    {
-        
-        setExtended_0(nativeObj, extended);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setNOctaveLayers(int octaveLayers)
-    //
-
-    //javadoc: KAZE::setNOctaveLayers(octaveLayers)
-    public  void setNOctaveLayers(int octaveLayers)
-    {
-        
-        setNOctaveLayers_0(nativeObj, octaveLayers);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setNOctaves(int octaves)
-    //
-
-    //javadoc: KAZE::setNOctaves(octaves)
-    public  void setNOctaves(int octaves)
-    {
-        
-        setNOctaves_0(nativeObj, octaves);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setThreshold(double threshold)
-    //
-
-    //javadoc: KAZE::setThreshold(threshold)
-    public  void setThreshold(double threshold)
-    {
-        
-        setThreshold_0(nativeObj, threshold);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setUpright(bool upright)
-    //
-
-    //javadoc: KAZE::setUpright(upright)
-    public  void setUpright(boolean upright)
-    {
-        
-        setUpright_0(nativeObj, upright);
-        
-        return;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++: static Ptr_KAZE create(bool extended = false, bool upright = false, float threshold = 0.001f, int nOctaves = 4, int nOctaveLayers = 4, int diffusivity = KAZE::DIFF_PM_G2)
-    private static native long create_0(boolean extended, boolean upright, float threshold, int nOctaves, int nOctaveLayers, int diffusivity);
-    private static native long create_1();
-
-    // C++:  String getDefaultName()
-    private static native String getDefaultName_0(long nativeObj);
-
-    // C++:  bool getExtended()
-    private static native boolean getExtended_0(long nativeObj);
-
-    // C++:  bool getUpright()
-    private static native boolean getUpright_0(long nativeObj);
-
-    // C++:  double getThreshold()
-    private static native double getThreshold_0(long nativeObj);
-
-    // C++:  int getDiffusivity()
-    private static native int getDiffusivity_0(long nativeObj);
-
-    // C++:  int getNOctaveLayers()
-    private static native int getNOctaveLayers_0(long nativeObj);
-
-    // C++:  int getNOctaves()
-    private static native int getNOctaves_0(long nativeObj);
-
-    // C++:  void setDiffusivity(int diff)
-    private static native void setDiffusivity_0(long nativeObj, int diff);
-
-    // C++:  void setExtended(bool extended)
-    private static native void setExtended_0(long nativeObj, boolean extended);
-
-    // C++:  void setNOctaveLayers(int octaveLayers)
-    private static native void setNOctaveLayers_0(long nativeObj, int octaveLayers);
-
-    // C++:  void setNOctaves(int octaves)
-    private static native void setNOctaves_0(long nativeObj, int octaves);
-
-    // C++:  void setThreshold(double threshold)
-    private static native void setThreshold_0(long nativeObj, double threshold);
-
-    // C++:  void setUpright(bool upright)
-    private static native void setUpright_0(long nativeObj, boolean upright);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/features2d/MSER.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/features2d/MSER.java
deleted file mode 100755
index 7a50976..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/features2d/MSER.java
+++ /dev/null
@@ -1,235 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.features2d;
-
-import java.lang.String;
-import java.util.ArrayList;
-import java.util.List;
-import org.opencv.core.Mat;
-import org.opencv.core.MatOfPoint;
-import org.opencv.core.MatOfRect;
-import org.opencv.features2d.Feature2D;
-import org.opencv.features2d.MSER;
-import org.opencv.utils.Converters;
-
-// C++: class MSER
-//javadoc: MSER
-
-public class MSER extends Feature2D {
-
-    protected MSER(long addr) { super(addr); }
-
-    // internal usage only
-    public static MSER __fromPtr__(long addr) { return new MSER(addr); }
-
-    //
-    // C++: static Ptr_MSER create(int _delta = 5, int _min_area = 60, int _max_area = 14400, double _max_variation = 0.25, double _min_diversity = .2, int _max_evolution = 200, double _area_threshold = 1.01, double _min_margin = 0.003, int _edge_blur_size = 5)
-    //
-
-    //javadoc: MSER::create(_delta, _min_area, _max_area, _max_variation, _min_diversity, _max_evolution, _area_threshold, _min_margin, _edge_blur_size)
-    public static MSER create(int _delta, int _min_area, int _max_area, double _max_variation, double _min_diversity, int _max_evolution, double _area_threshold, double _min_margin, int _edge_blur_size)
-    {
-        
-        MSER retVal = MSER.__fromPtr__(create_0(_delta, _min_area, _max_area, _max_variation, _min_diversity, _max_evolution, _area_threshold, _min_margin, _edge_blur_size));
-        
-        return retVal;
-    }
-
-    //javadoc: MSER::create()
-    public static MSER create()
-    {
-        
-        MSER retVal = MSER.__fromPtr__(create_1());
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  String getDefaultName()
-    //
-
-    //javadoc: MSER::getDefaultName()
-    public  String getDefaultName()
-    {
-        
-        String retVal = getDefaultName_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  bool getPass2Only()
-    //
-
-    //javadoc: MSER::getPass2Only()
-    public  boolean getPass2Only()
-    {
-        
-        boolean retVal = getPass2Only_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getDelta()
-    //
-
-    //javadoc: MSER::getDelta()
-    public  int getDelta()
-    {
-        
-        int retVal = getDelta_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getMaxArea()
-    //
-
-    //javadoc: MSER::getMaxArea()
-    public  int getMaxArea()
-    {
-        
-        int retVal = getMaxArea_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getMinArea()
-    //
-
-    //javadoc: MSER::getMinArea()
-    public  int getMinArea()
-    {
-        
-        int retVal = getMinArea_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  void detectRegions(Mat image, vector_vector_Point& msers, vector_Rect& bboxes)
-    //
-
-    //javadoc: MSER::detectRegions(image, msers, bboxes)
-    public  void detectRegions(Mat image, List<MatOfPoint> msers, MatOfRect bboxes)
-    {
-        Mat msers_mat = new Mat();
-        Mat bboxes_mat = bboxes;
-        detectRegions_0(nativeObj, image.nativeObj, msers_mat.nativeObj, bboxes_mat.nativeObj);
-        Converters.Mat_to_vector_vector_Point(msers_mat, msers);
-        msers_mat.release();
-        return;
-    }
-
-
-    //
-    // C++:  void setDelta(int delta)
-    //
-
-    //javadoc: MSER::setDelta(delta)
-    public  void setDelta(int delta)
-    {
-        
-        setDelta_0(nativeObj, delta);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setMaxArea(int maxArea)
-    //
-
-    //javadoc: MSER::setMaxArea(maxArea)
-    public  void setMaxArea(int maxArea)
-    {
-        
-        setMaxArea_0(nativeObj, maxArea);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setMinArea(int minArea)
-    //
-
-    //javadoc: MSER::setMinArea(minArea)
-    public  void setMinArea(int minArea)
-    {
-        
-        setMinArea_0(nativeObj, minArea);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setPass2Only(bool f)
-    //
-
-    //javadoc: MSER::setPass2Only(f)
-    public  void setPass2Only(boolean f)
-    {
-        
-        setPass2Only_0(nativeObj, f);
-        
-        return;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++: static Ptr_MSER create(int _delta = 5, int _min_area = 60, int _max_area = 14400, double _max_variation = 0.25, double _min_diversity = .2, int _max_evolution = 200, double _area_threshold = 1.01, double _min_margin = 0.003, int _edge_blur_size = 5)
-    private static native long create_0(int _delta, int _min_area, int _max_area, double _max_variation, double _min_diversity, int _max_evolution, double _area_threshold, double _min_margin, int _edge_blur_size);
-    private static native long create_1();
-
-    // C++:  String getDefaultName()
-    private static native String getDefaultName_0(long nativeObj);
-
-    // C++:  bool getPass2Only()
-    private static native boolean getPass2Only_0(long nativeObj);
-
-    // C++:  int getDelta()
-    private static native int getDelta_0(long nativeObj);
-
-    // C++:  int getMaxArea()
-    private static native int getMaxArea_0(long nativeObj);
-
-    // C++:  int getMinArea()
-    private static native int getMinArea_0(long nativeObj);
-
-    // C++:  void detectRegions(Mat image, vector_vector_Point& msers, vector_Rect& bboxes)
-    private static native void detectRegions_0(long nativeObj, long image_nativeObj, long msers_mat_nativeObj, long bboxes_mat_nativeObj);
-
-    // C++:  void setDelta(int delta)
-    private static native void setDelta_0(long nativeObj, int delta);
-
-    // C++:  void setMaxArea(int maxArea)
-    private static native void setMaxArea_0(long nativeObj, int maxArea);
-
-    // C++:  void setMinArea(int minArea)
-    private static native void setMinArea_0(long nativeObj, int minArea);
-
-    // C++:  void setPass2Only(bool f)
-    private static native void setPass2Only_0(long nativeObj, boolean f);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/features2d/ORB.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/features2d/ORB.java
deleted file mode 100755
index fc2c0a6..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/features2d/ORB.java
+++ /dev/null
@@ -1,386 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.features2d;
-
-import java.lang.String;
-import org.opencv.features2d.Feature2D;
-import org.opencv.features2d.ORB;
-
-// C++: class ORB
-//javadoc: ORB
-
-public class ORB extends Feature2D {
-
-    protected ORB(long addr) { super(addr); }
-
-    // internal usage only
-    public static ORB __fromPtr__(long addr) { return new ORB(addr); }
-
-    public static final int
-            kBytes = 32,
-            HARRIS_SCORE = 0,
-            FAST_SCORE = 1;
-
-
-    //
-    // C++: static Ptr_ORB create(int nfeatures = 500, float scaleFactor = 1.2f, int nlevels = 8, int edgeThreshold = 31, int firstLevel = 0, int WTA_K = 2, int scoreType = ORB::HARRIS_SCORE, int patchSize = 31, int fastThreshold = 20)
-    //
-
-    //javadoc: ORB::create(nfeatures, scaleFactor, nlevels, edgeThreshold, firstLevel, WTA_K, scoreType, patchSize, fastThreshold)
-    public static ORB create(int nfeatures, float scaleFactor, int nlevels, int edgeThreshold, int firstLevel, int WTA_K, int scoreType, int patchSize, int fastThreshold)
-    {
-        
-        ORB retVal = ORB.__fromPtr__(create_0(nfeatures, scaleFactor, nlevels, edgeThreshold, firstLevel, WTA_K, scoreType, patchSize, fastThreshold));
-        
-        return retVal;
-    }
-
-    //javadoc: ORB::create()
-    public static ORB create()
-    {
-        
-        ORB retVal = ORB.__fromPtr__(create_1());
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  String getDefaultName()
-    //
-
-    //javadoc: ORB::getDefaultName()
-    public  String getDefaultName()
-    {
-        
-        String retVal = getDefaultName_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double getScaleFactor()
-    //
-
-    //javadoc: ORB::getScaleFactor()
-    public  double getScaleFactor()
-    {
-        
-        double retVal = getScaleFactor_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getEdgeThreshold()
-    //
-
-    //javadoc: ORB::getEdgeThreshold()
-    public  int getEdgeThreshold()
-    {
-        
-        int retVal = getEdgeThreshold_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getFastThreshold()
-    //
-
-    //javadoc: ORB::getFastThreshold()
-    public  int getFastThreshold()
-    {
-        
-        int retVal = getFastThreshold_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getFirstLevel()
-    //
-
-    //javadoc: ORB::getFirstLevel()
-    public  int getFirstLevel()
-    {
-        
-        int retVal = getFirstLevel_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getMaxFeatures()
-    //
-
-    //javadoc: ORB::getMaxFeatures()
-    public  int getMaxFeatures()
-    {
-        
-        int retVal = getMaxFeatures_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getNLevels()
-    //
-
-    //javadoc: ORB::getNLevels()
-    public  int getNLevels()
-    {
-        
-        int retVal = getNLevels_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getPatchSize()
-    //
-
-    //javadoc: ORB::getPatchSize()
-    public  int getPatchSize()
-    {
-        
-        int retVal = getPatchSize_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getScoreType()
-    //
-
-    //javadoc: ORB::getScoreType()
-    public  int getScoreType()
-    {
-        
-        int retVal = getScoreType_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getWTA_K()
-    //
-
-    //javadoc: ORB::getWTA_K()
-    public  int getWTA_K()
-    {
-        
-        int retVal = getWTA_K_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  void setEdgeThreshold(int edgeThreshold)
-    //
-
-    //javadoc: ORB::setEdgeThreshold(edgeThreshold)
-    public  void setEdgeThreshold(int edgeThreshold)
-    {
-        
-        setEdgeThreshold_0(nativeObj, edgeThreshold);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setFastThreshold(int fastThreshold)
-    //
-
-    //javadoc: ORB::setFastThreshold(fastThreshold)
-    public  void setFastThreshold(int fastThreshold)
-    {
-        
-        setFastThreshold_0(nativeObj, fastThreshold);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setFirstLevel(int firstLevel)
-    //
-
-    //javadoc: ORB::setFirstLevel(firstLevel)
-    public  void setFirstLevel(int firstLevel)
-    {
-        
-        setFirstLevel_0(nativeObj, firstLevel);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setMaxFeatures(int maxFeatures)
-    //
-
-    //javadoc: ORB::setMaxFeatures(maxFeatures)
-    public  void setMaxFeatures(int maxFeatures)
-    {
-        
-        setMaxFeatures_0(nativeObj, maxFeatures);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setNLevels(int nlevels)
-    //
-
-    //javadoc: ORB::setNLevels(nlevels)
-    public  void setNLevels(int nlevels)
-    {
-        
-        setNLevels_0(nativeObj, nlevels);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setPatchSize(int patchSize)
-    //
-
-    //javadoc: ORB::setPatchSize(patchSize)
-    public  void setPatchSize(int patchSize)
-    {
-        
-        setPatchSize_0(nativeObj, patchSize);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setScaleFactor(double scaleFactor)
-    //
-
-    //javadoc: ORB::setScaleFactor(scaleFactor)
-    public  void setScaleFactor(double scaleFactor)
-    {
-        
-        setScaleFactor_0(nativeObj, scaleFactor);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setScoreType(int scoreType)
-    //
-
-    //javadoc: ORB::setScoreType(scoreType)
-    public  void setScoreType(int scoreType)
-    {
-        
-        setScoreType_0(nativeObj, scoreType);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setWTA_K(int wta_k)
-    //
-
-    //javadoc: ORB::setWTA_K(wta_k)
-    public  void setWTA_K(int wta_k)
-    {
-        
-        setWTA_K_0(nativeObj, wta_k);
-        
-        return;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++: static Ptr_ORB create(int nfeatures = 500, float scaleFactor = 1.2f, int nlevels = 8, int edgeThreshold = 31, int firstLevel = 0, int WTA_K = 2, int scoreType = ORB::HARRIS_SCORE, int patchSize = 31, int fastThreshold = 20)
-    private static native long create_0(int nfeatures, float scaleFactor, int nlevels, int edgeThreshold, int firstLevel, int WTA_K, int scoreType, int patchSize, int fastThreshold);
-    private static native long create_1();
-
-    // C++:  String getDefaultName()
-    private static native String getDefaultName_0(long nativeObj);
-
-    // C++:  double getScaleFactor()
-    private static native double getScaleFactor_0(long nativeObj);
-
-    // C++:  int getEdgeThreshold()
-    private static native int getEdgeThreshold_0(long nativeObj);
-
-    // C++:  int getFastThreshold()
-    private static native int getFastThreshold_0(long nativeObj);
-
-    // C++:  int getFirstLevel()
-    private static native int getFirstLevel_0(long nativeObj);
-
-    // C++:  int getMaxFeatures()
-    private static native int getMaxFeatures_0(long nativeObj);
-
-    // C++:  int getNLevels()
-    private static native int getNLevels_0(long nativeObj);
-
-    // C++:  int getPatchSize()
-    private static native int getPatchSize_0(long nativeObj);
-
-    // C++:  int getScoreType()
-    private static native int getScoreType_0(long nativeObj);
-
-    // C++:  int getWTA_K()
-    private static native int getWTA_K_0(long nativeObj);
-
-    // C++:  void setEdgeThreshold(int edgeThreshold)
-    private static native void setEdgeThreshold_0(long nativeObj, int edgeThreshold);
-
-    // C++:  void setFastThreshold(int fastThreshold)
-    private static native void setFastThreshold_0(long nativeObj, int fastThreshold);
-
-    // C++:  void setFirstLevel(int firstLevel)
-    private static native void setFirstLevel_0(long nativeObj, int firstLevel);
-
-    // C++:  void setMaxFeatures(int maxFeatures)
-    private static native void setMaxFeatures_0(long nativeObj, int maxFeatures);
-
-    // C++:  void setNLevels(int nlevels)
-    private static native void setNLevels_0(long nativeObj, int nlevels);
-
-    // C++:  void setPatchSize(int patchSize)
-    private static native void setPatchSize_0(long nativeObj, int patchSize);
-
-    // C++:  void setScaleFactor(double scaleFactor)
-    private static native void setScaleFactor_0(long nativeObj, double scaleFactor);
-
-    // C++:  void setScoreType(int scoreType)
-    private static native void setScoreType_0(long nativeObj, int scoreType);
-
-    // C++:  void setWTA_K(int wta_k)
-    private static native void setWTA_K_0(long nativeObj, int wta_k);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/features2d/Params.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/features2d/Params.java
deleted file mode 100755
index 921ccc0..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/features2d/Params.java
+++ /dev/null
@@ -1,674 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.features2d;
-
-
-
-// C++: class Params
-//javadoc: Params
-
-public class Params {
-
-    protected final long nativeObj;
-    protected Params(long addr) { nativeObj = addr; }
-
-    public long getNativeObjAddr() { return nativeObj; }
-
-    // internal usage only
-    public static Params __fromPtr__(long addr) { return new Params(addr); }
-
-    //
-    // C++:   Params()
-    //
-
-    //javadoc: Params::Params()
-    public   Params()
-    {
-        
-        nativeObj = Params_0();
-        
-        return;
-    }
-
-
-    //
-    // C++: float Params::thresholdStep
-    //
-
-    //javadoc: Params::get_thresholdStep()
-    public  float get_thresholdStep()
-    {
-        
-        float retVal = get_thresholdStep_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: void Params::thresholdStep
-    //
-
-    //javadoc: Params::set_thresholdStep(thresholdStep)
-    public  void set_thresholdStep(float thresholdStep)
-    {
-        
-        set_thresholdStep_0(nativeObj, thresholdStep);
-        
-        return;
-    }
-
-
-    //
-    // C++: float Params::minThreshold
-    //
-
-    //javadoc: Params::get_minThreshold()
-    public  float get_minThreshold()
-    {
-        
-        float retVal = get_minThreshold_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: void Params::minThreshold
-    //
-
-    //javadoc: Params::set_minThreshold(minThreshold)
-    public  void set_minThreshold(float minThreshold)
-    {
-        
-        set_minThreshold_0(nativeObj, minThreshold);
-        
-        return;
-    }
-
-
-    //
-    // C++: float Params::maxThreshold
-    //
-
-    //javadoc: Params::get_maxThreshold()
-    public  float get_maxThreshold()
-    {
-        
-        float retVal = get_maxThreshold_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: void Params::maxThreshold
-    //
-
-    //javadoc: Params::set_maxThreshold(maxThreshold)
-    public  void set_maxThreshold(float maxThreshold)
-    {
-        
-        set_maxThreshold_0(nativeObj, maxThreshold);
-        
-        return;
-    }
-
-
-    //
-    // C++: size_t Params::minRepeatability
-    //
-
-    //javadoc: Params::get_minRepeatability()
-    public  long get_minRepeatability()
-    {
-        
-        long retVal = get_minRepeatability_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: void Params::minRepeatability
-    //
-
-    //javadoc: Params::set_minRepeatability(minRepeatability)
-    public  void set_minRepeatability(long minRepeatability)
-    {
-        
-        set_minRepeatability_0(nativeObj, minRepeatability);
-        
-        return;
-    }
-
-
-    //
-    // C++: float Params::minDistBetweenBlobs
-    //
-
-    //javadoc: Params::get_minDistBetweenBlobs()
-    public  float get_minDistBetweenBlobs()
-    {
-        
-        float retVal = get_minDistBetweenBlobs_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: void Params::minDistBetweenBlobs
-    //
-
-    //javadoc: Params::set_minDistBetweenBlobs(minDistBetweenBlobs)
-    public  void set_minDistBetweenBlobs(float minDistBetweenBlobs)
-    {
-        
-        set_minDistBetweenBlobs_0(nativeObj, minDistBetweenBlobs);
-        
-        return;
-    }
-
-
-    //
-    // C++: bool Params::filterByColor
-    //
-
-    //javadoc: Params::get_filterByColor()
-    public  boolean get_filterByColor()
-    {
-        
-        boolean retVal = get_filterByColor_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: void Params::filterByColor
-    //
-
-    //javadoc: Params::set_filterByColor(filterByColor)
-    public  void set_filterByColor(boolean filterByColor)
-    {
-        
-        set_filterByColor_0(nativeObj, filterByColor);
-        
-        return;
-    }
-
-
-    //
-    // C++: uchar Params::blobColor
-    //
-
-    // Return type 'uchar' is not supported, skipping the function
-
-
-    //
-    // C++: void Params::blobColor
-    //
-
-    // Unknown type 'uchar' (I), skipping the function
-
-
-    //
-    // C++: bool Params::filterByArea
-    //
-
-    //javadoc: Params::get_filterByArea()
-    public  boolean get_filterByArea()
-    {
-        
-        boolean retVal = get_filterByArea_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: void Params::filterByArea
-    //
-
-    //javadoc: Params::set_filterByArea(filterByArea)
-    public  void set_filterByArea(boolean filterByArea)
-    {
-        
-        set_filterByArea_0(nativeObj, filterByArea);
-        
-        return;
-    }
-
-
-    //
-    // C++: float Params::minArea
-    //
-
-    //javadoc: Params::get_minArea()
-    public  float get_minArea()
-    {
-        
-        float retVal = get_minArea_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: void Params::minArea
-    //
-
-    //javadoc: Params::set_minArea(minArea)
-    public  void set_minArea(float minArea)
-    {
-        
-        set_minArea_0(nativeObj, minArea);
-        
-        return;
-    }
-
-
-    //
-    // C++: float Params::maxArea
-    //
-
-    //javadoc: Params::get_maxArea()
-    public  float get_maxArea()
-    {
-        
-        float retVal = get_maxArea_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: void Params::maxArea
-    //
-
-    //javadoc: Params::set_maxArea(maxArea)
-    public  void set_maxArea(float maxArea)
-    {
-        
-        set_maxArea_0(nativeObj, maxArea);
-        
-        return;
-    }
-
-
-    //
-    // C++: bool Params::filterByCircularity
-    //
-
-    //javadoc: Params::get_filterByCircularity()
-    public  boolean get_filterByCircularity()
-    {
-        
-        boolean retVal = get_filterByCircularity_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: void Params::filterByCircularity
-    //
-
-    //javadoc: Params::set_filterByCircularity(filterByCircularity)
-    public  void set_filterByCircularity(boolean filterByCircularity)
-    {
-        
-        set_filterByCircularity_0(nativeObj, filterByCircularity);
-        
-        return;
-    }
-
-
-    //
-    // C++: float Params::minCircularity
-    //
-
-    //javadoc: Params::get_minCircularity()
-    public  float get_minCircularity()
-    {
-        
-        float retVal = get_minCircularity_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: void Params::minCircularity
-    //
-
-    //javadoc: Params::set_minCircularity(minCircularity)
-    public  void set_minCircularity(float minCircularity)
-    {
-        
-        set_minCircularity_0(nativeObj, minCircularity);
-        
-        return;
-    }
-
-
-    //
-    // C++: float Params::maxCircularity
-    //
-
-    //javadoc: Params::get_maxCircularity()
-    public  float get_maxCircularity()
-    {
-        
-        float retVal = get_maxCircularity_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: void Params::maxCircularity
-    //
-
-    //javadoc: Params::set_maxCircularity(maxCircularity)
-    public  void set_maxCircularity(float maxCircularity)
-    {
-        
-        set_maxCircularity_0(nativeObj, maxCircularity);
-        
-        return;
-    }
-
-
-    //
-    // C++: bool Params::filterByInertia
-    //
-
-    //javadoc: Params::get_filterByInertia()
-    public  boolean get_filterByInertia()
-    {
-        
-        boolean retVal = get_filterByInertia_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: void Params::filterByInertia
-    //
-
-    //javadoc: Params::set_filterByInertia(filterByInertia)
-    public  void set_filterByInertia(boolean filterByInertia)
-    {
-        
-        set_filterByInertia_0(nativeObj, filterByInertia);
-        
-        return;
-    }
-
-
-    //
-    // C++: float Params::minInertiaRatio
-    //
-
-    //javadoc: Params::get_minInertiaRatio()
-    public  float get_minInertiaRatio()
-    {
-        
-        float retVal = get_minInertiaRatio_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: void Params::minInertiaRatio
-    //
-
-    //javadoc: Params::set_minInertiaRatio(minInertiaRatio)
-    public  void set_minInertiaRatio(float minInertiaRatio)
-    {
-        
-        set_minInertiaRatio_0(nativeObj, minInertiaRatio);
-        
-        return;
-    }
-
-
-    //
-    // C++: float Params::maxInertiaRatio
-    //
-
-    //javadoc: Params::get_maxInertiaRatio()
-    public  float get_maxInertiaRatio()
-    {
-        
-        float retVal = get_maxInertiaRatio_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: void Params::maxInertiaRatio
-    //
-
-    //javadoc: Params::set_maxInertiaRatio(maxInertiaRatio)
-    public  void set_maxInertiaRatio(float maxInertiaRatio)
-    {
-        
-        set_maxInertiaRatio_0(nativeObj, maxInertiaRatio);
-        
-        return;
-    }
-
-
-    //
-    // C++: bool Params::filterByConvexity
-    //
-
-    //javadoc: Params::get_filterByConvexity()
-    public  boolean get_filterByConvexity()
-    {
-        
-        boolean retVal = get_filterByConvexity_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: void Params::filterByConvexity
-    //
-
-    //javadoc: Params::set_filterByConvexity(filterByConvexity)
-    public  void set_filterByConvexity(boolean filterByConvexity)
-    {
-        
-        set_filterByConvexity_0(nativeObj, filterByConvexity);
-        
-        return;
-    }
-
-
-    //
-    // C++: float Params::minConvexity
-    //
-
-    //javadoc: Params::get_minConvexity()
-    public  float get_minConvexity()
-    {
-        
-        float retVal = get_minConvexity_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: void Params::minConvexity
-    //
-
-    //javadoc: Params::set_minConvexity(minConvexity)
-    public  void set_minConvexity(float minConvexity)
-    {
-        
-        set_minConvexity_0(nativeObj, minConvexity);
-        
-        return;
-    }
-
-
-    //
-    // C++: float Params::maxConvexity
-    //
-
-    //javadoc: Params::get_maxConvexity()
-    public  float get_maxConvexity()
-    {
-        
-        float retVal = get_maxConvexity_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: void Params::maxConvexity
-    //
-
-    //javadoc: Params::set_maxConvexity(maxConvexity)
-    public  void set_maxConvexity(float maxConvexity)
-    {
-        
-        set_maxConvexity_0(nativeObj, maxConvexity);
-        
-        return;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++:   Params()
-    private static native long Params_0();
-
-    // C++: float Params::thresholdStep
-    private static native float get_thresholdStep_0(long nativeObj);
-
-    // C++: void Params::thresholdStep
-    private static native void set_thresholdStep_0(long nativeObj, float thresholdStep);
-
-    // C++: float Params::minThreshold
-    private static native float get_minThreshold_0(long nativeObj);
-
-    // C++: void Params::minThreshold
-    private static native void set_minThreshold_0(long nativeObj, float minThreshold);
-
-    // C++: float Params::maxThreshold
-    private static native float get_maxThreshold_0(long nativeObj);
-
-    // C++: void Params::maxThreshold
-    private static native void set_maxThreshold_0(long nativeObj, float maxThreshold);
-
-    // C++: size_t Params::minRepeatability
-    private static native long get_minRepeatability_0(long nativeObj);
-
-    // C++: void Params::minRepeatability
-    private static native void set_minRepeatability_0(long nativeObj, long minRepeatability);
-
-    // C++: float Params::minDistBetweenBlobs
-    private static native float get_minDistBetweenBlobs_0(long nativeObj);
-
-    // C++: void Params::minDistBetweenBlobs
-    private static native void set_minDistBetweenBlobs_0(long nativeObj, float minDistBetweenBlobs);
-
-    // C++: bool Params::filterByColor
-    private static native boolean get_filterByColor_0(long nativeObj);
-
-    // C++: void Params::filterByColor
-    private static native void set_filterByColor_0(long nativeObj, boolean filterByColor);
-
-    // C++: bool Params::filterByArea
-    private static native boolean get_filterByArea_0(long nativeObj);
-
-    // C++: void Params::filterByArea
-    private static native void set_filterByArea_0(long nativeObj, boolean filterByArea);
-
-    // C++: float Params::minArea
-    private static native float get_minArea_0(long nativeObj);
-
-    // C++: void Params::minArea
-    private static native void set_minArea_0(long nativeObj, float minArea);
-
-    // C++: float Params::maxArea
-    private static native float get_maxArea_0(long nativeObj);
-
-    // C++: void Params::maxArea
-    private static native void set_maxArea_0(long nativeObj, float maxArea);
-
-    // C++: bool Params::filterByCircularity
-    private static native boolean get_filterByCircularity_0(long nativeObj);
-
-    // C++: void Params::filterByCircularity
-    private static native void set_filterByCircularity_0(long nativeObj, boolean filterByCircularity);
-
-    // C++: float Params::minCircularity
-    private static native float get_minCircularity_0(long nativeObj);
-
-    // C++: void Params::minCircularity
-    private static native void set_minCircularity_0(long nativeObj, float minCircularity);
-
-    // C++: float Params::maxCircularity
-    private static native float get_maxCircularity_0(long nativeObj);
-
-    // C++: void Params::maxCircularity
-    private static native void set_maxCircularity_0(long nativeObj, float maxCircularity);
-
-    // C++: bool Params::filterByInertia
-    private static native boolean get_filterByInertia_0(long nativeObj);
-
-    // C++: void Params::filterByInertia
-    private static native void set_filterByInertia_0(long nativeObj, boolean filterByInertia);
-
-    // C++: float Params::minInertiaRatio
-    private static native float get_minInertiaRatio_0(long nativeObj);
-
-    // C++: void Params::minInertiaRatio
-    private static native void set_minInertiaRatio_0(long nativeObj, float minInertiaRatio);
-
-    // C++: float Params::maxInertiaRatio
-    private static native float get_maxInertiaRatio_0(long nativeObj);
-
-    // C++: void Params::maxInertiaRatio
-    private static native void set_maxInertiaRatio_0(long nativeObj, float maxInertiaRatio);
-
-    // C++: bool Params::filterByConvexity
-    private static native boolean get_filterByConvexity_0(long nativeObj);
-
-    // C++: void Params::filterByConvexity
-    private static native void set_filterByConvexity_0(long nativeObj, boolean filterByConvexity);
-
-    // C++: float Params::minConvexity
-    private static native float get_minConvexity_0(long nativeObj);
-
-    // C++: void Params::minConvexity
-    private static native void set_minConvexity_0(long nativeObj, float minConvexity);
-
-    // C++: float Params::maxConvexity
-    private static native float get_maxConvexity_0(long nativeObj);
-
-    // C++: void Params::maxConvexity
-    private static native void set_maxConvexity_0(long nativeObj, float maxConvexity);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/imgcodecs/Imgcodecs.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/imgcodecs/Imgcodecs.java
deleted file mode 100755
index bdbe7cd..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/imgcodecs/Imgcodecs.java
+++ /dev/null
@@ -1,226 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.imgcodecs;
-
-import java.lang.String;
-import java.util.ArrayList;
-import java.util.List;
-import org.opencv.core.Mat;
-import org.opencv.core.MatOfByte;
-import org.opencv.core.MatOfInt;
-import org.opencv.utils.Converters;
-
-// C++: class Imgcodecs
-//javadoc: Imgcodecs
-
-public class Imgcodecs {
-
-    public static final int
-            CV_LOAD_IMAGE_UNCHANGED = -1,
-            CV_LOAD_IMAGE_GRAYSCALE = 0,
-            CV_LOAD_IMAGE_COLOR = 1,
-            CV_LOAD_IMAGE_ANYDEPTH = 2,
-            CV_LOAD_IMAGE_ANYCOLOR = 4,
-            CV_LOAD_IMAGE_IGNORE_ORIENTATION = 128,
-            CV_IMWRITE_JPEG_QUALITY = 1,
-            CV_IMWRITE_JPEG_PROGRESSIVE = 2,
-            CV_IMWRITE_JPEG_OPTIMIZE = 3,
-            CV_IMWRITE_JPEG_RST_INTERVAL = 4,
-            CV_IMWRITE_JPEG_LUMA_QUALITY = 5,
-            CV_IMWRITE_JPEG_CHROMA_QUALITY = 6,
-            CV_IMWRITE_PNG_COMPRESSION = 16,
-            CV_IMWRITE_PNG_STRATEGY = 17,
-            CV_IMWRITE_PNG_BILEVEL = 18,
-            CV_IMWRITE_PNG_STRATEGY_DEFAULT = 0,
-            CV_IMWRITE_PNG_STRATEGY_FILTERED = 1,
-            CV_IMWRITE_PNG_STRATEGY_HUFFMAN_ONLY = 2,
-            CV_IMWRITE_PNG_STRATEGY_RLE = 3,
-            CV_IMWRITE_PNG_STRATEGY_FIXED = 4,
-            CV_IMWRITE_PXM_BINARY = 32,
-            CV_IMWRITE_EXR_TYPE = 48,
-            CV_IMWRITE_WEBP_QUALITY = 64,
-            CV_IMWRITE_PAM_TUPLETYPE = 128,
-            CV_IMWRITE_PAM_FORMAT_NULL = 0,
-            CV_IMWRITE_PAM_FORMAT_BLACKANDWHITE = 1,
-            CV_IMWRITE_PAM_FORMAT_GRAYSCALE = 2,
-            CV_IMWRITE_PAM_FORMAT_GRAYSCALE_ALPHA = 3,
-            CV_IMWRITE_PAM_FORMAT_RGB = 4,
-            CV_IMWRITE_PAM_FORMAT_RGB_ALPHA = 5,
-            CV_CVTIMG_FLIP = 1,
-            CV_CVTIMG_SWAP_RB = 2,
-            IMREAD_UNCHANGED = -1,
-            IMREAD_GRAYSCALE = 0,
-            IMREAD_COLOR = 1,
-            IMREAD_ANYDEPTH = 2,
-            IMREAD_ANYCOLOR = 4,
-            IMREAD_LOAD_GDAL = 8,
-            IMREAD_REDUCED_GRAYSCALE_2 = 16,
-            IMREAD_REDUCED_COLOR_2 = 17,
-            IMREAD_REDUCED_GRAYSCALE_4 = 32,
-            IMREAD_REDUCED_COLOR_4 = 33,
-            IMREAD_REDUCED_GRAYSCALE_8 = 64,
-            IMREAD_REDUCED_COLOR_8 = 65,
-            IMREAD_IGNORE_ORIENTATION = 128,
-            IMWRITE_JPEG_QUALITY = 1,
-            IMWRITE_JPEG_PROGRESSIVE = 2,
-            IMWRITE_JPEG_OPTIMIZE = 3,
-            IMWRITE_JPEG_RST_INTERVAL = 4,
-            IMWRITE_JPEG_LUMA_QUALITY = 5,
-            IMWRITE_JPEG_CHROMA_QUALITY = 6,
-            IMWRITE_PNG_COMPRESSION = 16,
-            IMWRITE_PNG_STRATEGY = 17,
-            IMWRITE_PNG_BILEVEL = 18,
-            IMWRITE_PXM_BINARY = 32,
-            IMWRITE_EXR_TYPE = (3 << 4) + 0,
-            IMWRITE_WEBP_QUALITY = 64,
-            IMWRITE_PAM_TUPLETYPE = 128,
-            IMWRITE_TIFF_RESUNIT = 256,
-            IMWRITE_TIFF_XDPI = 257,
-            IMWRITE_TIFF_YDPI = 258,
-            IMWRITE_EXR_TYPE_HALF = 1,
-            IMWRITE_EXR_TYPE_FLOAT = 2,
-            IMWRITE_PNG_STRATEGY_DEFAULT = 0,
-            IMWRITE_PNG_STRATEGY_FILTERED = 1,
-            IMWRITE_PNG_STRATEGY_HUFFMAN_ONLY = 2,
-            IMWRITE_PNG_STRATEGY_RLE = 3,
-            IMWRITE_PNG_STRATEGY_FIXED = 4,
-            IMWRITE_PAM_FORMAT_NULL = 0,
-            IMWRITE_PAM_FORMAT_BLACKANDWHITE = 1,
-            IMWRITE_PAM_FORMAT_GRAYSCALE = 2,
-            IMWRITE_PAM_FORMAT_GRAYSCALE_ALPHA = 3,
-            IMWRITE_PAM_FORMAT_RGB = 4,
-            IMWRITE_PAM_FORMAT_RGB_ALPHA = 5;
-
-
-    //
-    // C++:  Mat imdecode(Mat buf, int flags)
-    //
-
-    //javadoc: imdecode(buf, flags)
-    public static Mat imdecode(Mat buf, int flags)
-    {
-        
-        Mat retVal = new Mat(imdecode_0(buf.nativeObj, flags));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Mat imread(String filename, int flags = IMREAD_COLOR)
-    //
-
-    //javadoc: imread(filename, flags)
-    public static Mat imread(String filename, int flags)
-    {
-        
-        Mat retVal = new Mat(imread_0(filename, flags));
-        
-        return retVal;
-    }
-
-    //javadoc: imread(filename)
-    public static Mat imread(String filename)
-    {
-        
-        Mat retVal = new Mat(imread_1(filename));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  bool imencode(String ext, Mat img, vector_uchar& buf, vector_int params = std::vector<int>())
-    //
-
-    //javadoc: imencode(ext, img, buf, params)
-    public static boolean imencode(String ext, Mat img, MatOfByte buf, MatOfInt params)
-    {
-        Mat buf_mat = buf;
-        Mat params_mat = params;
-        boolean retVal = imencode_0(ext, img.nativeObj, buf_mat.nativeObj, params_mat.nativeObj);
-        
-        return retVal;
-    }
-
-    //javadoc: imencode(ext, img, buf)
-    public static boolean imencode(String ext, Mat img, MatOfByte buf)
-    {
-        Mat buf_mat = buf;
-        boolean retVal = imencode_1(ext, img.nativeObj, buf_mat.nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  bool imreadmulti(String filename, vector_Mat& mats, int flags = IMREAD_ANYCOLOR)
-    //
-
-    //javadoc: imreadmulti(filename, mats, flags)
-    public static boolean imreadmulti(String filename, List<Mat> mats, int flags)
-    {
-        Mat mats_mat = new Mat();
-        boolean retVal = imreadmulti_0(filename, mats_mat.nativeObj, flags);
-        Converters.Mat_to_vector_Mat(mats_mat, mats);
-        mats_mat.release();
-        return retVal;
-    }
-
-    //javadoc: imreadmulti(filename, mats)
-    public static boolean imreadmulti(String filename, List<Mat> mats)
-    {
-        Mat mats_mat = new Mat();
-        boolean retVal = imreadmulti_1(filename, mats_mat.nativeObj);
-        Converters.Mat_to_vector_Mat(mats_mat, mats);
-        mats_mat.release();
-        return retVal;
-    }
-
-
-    //
-    // C++:  bool imwrite(String filename, Mat img, vector_int params = std::vector<int>())
-    //
-
-    //javadoc: imwrite(filename, img, params)
-    public static boolean imwrite(String filename, Mat img, MatOfInt params)
-    {
-        Mat params_mat = params;
-        boolean retVal = imwrite_0(filename, img.nativeObj, params_mat.nativeObj);
-        
-        return retVal;
-    }
-
-    //javadoc: imwrite(filename, img)
-    public static boolean imwrite(String filename, Mat img)
-    {
-        
-        boolean retVal = imwrite_1(filename, img.nativeObj);
-        
-        return retVal;
-    }
-
-
-
-
-    // C++:  Mat imdecode(Mat buf, int flags)
-    private static native long imdecode_0(long buf_nativeObj, int flags);
-
-    // C++:  Mat imread(String filename, int flags = IMREAD_COLOR)
-    private static native long imread_0(String filename, int flags);
-    private static native long imread_1(String filename);
-
-    // C++:  bool imencode(String ext, Mat img, vector_uchar& buf, vector_int params = std::vector<int>())
-    private static native boolean imencode_0(String ext, long img_nativeObj, long buf_mat_nativeObj, long params_mat_nativeObj);
-    private static native boolean imencode_1(String ext, long img_nativeObj, long buf_mat_nativeObj);
-
-    // C++:  bool imreadmulti(String filename, vector_Mat& mats, int flags = IMREAD_ANYCOLOR)
-    private static native boolean imreadmulti_0(String filename, long mats_mat_nativeObj, int flags);
-    private static native boolean imreadmulti_1(String filename, long mats_mat_nativeObj);
-
-    // C++:  bool imwrite(String filename, Mat img, vector_int params = std::vector<int>())
-    private static native boolean imwrite_0(String filename, long img_nativeObj, long params_mat_nativeObj);
-    private static native boolean imwrite_1(String filename, long img_nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/imgproc/CLAHE.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/imgproc/CLAHE.java
deleted file mode 100755
index 7cec9c3..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/imgproc/CLAHE.java
+++ /dev/null
@@ -1,132 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.imgproc;
-
-import org.opencv.core.Algorithm;
-import org.opencv.core.Mat;
-import org.opencv.core.Size;
-
-// C++: class CLAHE
-//javadoc: CLAHE
-
-public class CLAHE extends Algorithm {
-
-    protected CLAHE(long addr) { super(addr); }
-
-    // internal usage only
-    public static CLAHE __fromPtr__(long addr) { return new CLAHE(addr); }
-
-    //
-    // C++:  Size getTilesGridSize()
-    //
-
-    //javadoc: CLAHE::getTilesGridSize()
-    public  Size getTilesGridSize()
-    {
-        
-        Size retVal = new Size(getTilesGridSize_0(nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double getClipLimit()
-    //
-
-    //javadoc: CLAHE::getClipLimit()
-    public  double getClipLimit()
-    {
-        
-        double retVal = getClipLimit_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  void apply(Mat src, Mat& dst)
-    //
-
-    //javadoc: CLAHE::apply(src, dst)
-    public  void apply(Mat src, Mat dst)
-    {
-        
-        apply_0(nativeObj, src.nativeObj, dst.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void collectGarbage()
-    //
-
-    //javadoc: CLAHE::collectGarbage()
-    public  void collectGarbage()
-    {
-        
-        collectGarbage_0(nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setClipLimit(double clipLimit)
-    //
-
-    //javadoc: CLAHE::setClipLimit(clipLimit)
-    public  void setClipLimit(double clipLimit)
-    {
-        
-        setClipLimit_0(nativeObj, clipLimit);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setTilesGridSize(Size tileGridSize)
-    //
-
-    //javadoc: CLAHE::setTilesGridSize(tileGridSize)
-    public  void setTilesGridSize(Size tileGridSize)
-    {
-        
-        setTilesGridSize_0(nativeObj, tileGridSize.width, tileGridSize.height);
-        
-        return;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++:  Size getTilesGridSize()
-    private static native double[] getTilesGridSize_0(long nativeObj);
-
-    // C++:  double getClipLimit()
-    private static native double getClipLimit_0(long nativeObj);
-
-    // C++:  void apply(Mat src, Mat& dst)
-    private static native void apply_0(long nativeObj, long src_nativeObj, long dst_nativeObj);
-
-    // C++:  void collectGarbage()
-    private static native void collectGarbage_0(long nativeObj);
-
-    // C++:  void setClipLimit(double clipLimit)
-    private static native void setClipLimit_0(long nativeObj, double clipLimit);
-
-    // C++:  void setTilesGridSize(Size tileGridSize)
-    private static native void setTilesGridSize_0(long nativeObj, double tileGridSize_width, double tileGridSize_height);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/imgproc/Imgproc.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/imgproc/Imgproc.java
deleted file mode 100755
index 7540dbe..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/imgproc/Imgproc.java
+++ /dev/null
@@ -1,3677 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.imgproc;
-
-import java.lang.String;
-import java.util.ArrayList;
-import java.util.List;
-import org.opencv.core.Mat;
-import org.opencv.core.MatOfFloat;
-import org.opencv.core.MatOfInt;
-import org.opencv.core.MatOfInt4;
-import org.opencv.core.MatOfPoint;
-import org.opencv.core.MatOfPoint2f;
-import org.opencv.core.Point;
-import org.opencv.core.Rect;
-import org.opencv.core.RotatedRect;
-import org.opencv.core.Scalar;
-import org.opencv.core.Size;
-import org.opencv.core.TermCriteria;
-import org.opencv.imgproc.CLAHE;
-import org.opencv.imgproc.LineSegmentDetector;
-import org.opencv.utils.Converters;
-
-// C++: class Imgproc
-//javadoc: Imgproc
-
-public class Imgproc {
-
-    private static final int
-            IPL_BORDER_CONSTANT = 0,
-            IPL_BORDER_REPLICATE = 1,
-            IPL_BORDER_REFLECT = 2,
-            IPL_BORDER_WRAP = 3,
-            IPL_BORDER_REFLECT_101 = 4,
-            IPL_BORDER_TRANSPARENT = 5,
-            CV_INTER_NN = 0,
-            CV_INTER_LINEAR = 1,
-            CV_INTER_CUBIC = 2,
-            CV_INTER_AREA = 3,
-            CV_INTER_LANCZOS4 = 4,
-            CV_MOP_ERODE = 0,
-            CV_MOP_DILATE = 1,
-            CV_MOP_OPEN = 2,
-            CV_MOP_CLOSE = 3,
-            CV_MOP_GRADIENT = 4,
-            CV_MOP_TOPHAT = 5,
-            CV_MOP_BLACKHAT = 6,
-            CV_RETR_EXTERNAL = 0,
-            CV_RETR_LIST = 1,
-            CV_RETR_CCOMP = 2,
-            CV_RETR_TREE = 3,
-            CV_RETR_FLOODFILL = 4,
-            CV_CHAIN_APPROX_NONE = 1,
-            CV_CHAIN_APPROX_SIMPLE = 2,
-            CV_CHAIN_APPROX_TC89_L1 = 3,
-            CV_CHAIN_APPROX_TC89_KCOS = 4,
-            CV_THRESH_BINARY = 0,
-            CV_THRESH_BINARY_INV = 1,
-            CV_THRESH_TRUNC = 2,
-            CV_THRESH_TOZERO = 3,
-            CV_THRESH_TOZERO_INV = 4,
-            CV_THRESH_MASK = 7,
-            CV_THRESH_OTSU = 8,
-            CV_THRESH_TRIANGLE = 16;
-
-
-    public static final int
-            LINE_AA = 16,
-            LINE_8 = 8,
-            LINE_4 = 4,
-            CV_BLUR_NO_SCALE = 0,
-            CV_BLUR = 1,
-            CV_GAUSSIAN = 2,
-            CV_MEDIAN = 3,
-            CV_BILATERAL = 4,
-            CV_GAUSSIAN_5x5 = 7,
-            CV_SCHARR = -1,
-            CV_MAX_SOBEL_KSIZE = 7,
-            CV_RGBA2mRGBA = 125,
-            CV_mRGBA2RGBA = 126,
-            CV_WARP_FILL_OUTLIERS = 8,
-            CV_WARP_INVERSE_MAP = 16,
-            CV_SHAPE_RECT = 0,
-            CV_SHAPE_CROSS = 1,
-            CV_SHAPE_ELLIPSE = 2,
-            CV_SHAPE_CUSTOM = 100,
-            CV_CHAIN_CODE = 0,
-            CV_LINK_RUNS = 5,
-            CV_POLY_APPROX_DP = 0,
-            CV_CONTOURS_MATCH_I1 = 1,
-            CV_CONTOURS_MATCH_I2 = 2,
-            CV_CONTOURS_MATCH_I3 = 3,
-            CV_CLOCKWISE = 1,
-            CV_COUNTER_CLOCKWISE = 2,
-            CV_COMP_CORREL = 0,
-            CV_COMP_CHISQR = 1,
-            CV_COMP_INTERSECT = 2,
-            CV_COMP_BHATTACHARYYA = 3,
-            CV_COMP_HELLINGER = CV_COMP_BHATTACHARYYA,
-            CV_COMP_CHISQR_ALT = 4,
-            CV_COMP_KL_DIV = 5,
-            CV_DIST_MASK_3 = 3,
-            CV_DIST_MASK_5 = 5,
-            CV_DIST_MASK_PRECISE = 0,
-            CV_DIST_LABEL_CCOMP = 0,
-            CV_DIST_LABEL_PIXEL = 1,
-            CV_DIST_USER = -1,
-            CV_DIST_L1 = 1,
-            CV_DIST_L2 = 2,
-            CV_DIST_C = 3,
-            CV_DIST_L12 = 4,
-            CV_DIST_FAIR = 5,
-            CV_DIST_WELSCH = 6,
-            CV_DIST_HUBER = 7,
-            CV_CANNY_L2_GRADIENT = (1 << 31),
-            CV_HOUGH_STANDARD = 0,
-            CV_HOUGH_PROBABILISTIC = 1,
-            CV_HOUGH_MULTI_SCALE = 2,
-            CV_HOUGH_GRADIENT = 3,
-            MORPH_ERODE = 0,
-            MORPH_DILATE = 1,
-            MORPH_OPEN = 2,
-            MORPH_CLOSE = 3,
-            MORPH_GRADIENT = 4,
-            MORPH_TOPHAT = 5,
-            MORPH_BLACKHAT = 6,
-            MORPH_HITMISS = 7,
-            MORPH_RECT = 0,
-            MORPH_CROSS = 1,
-            MORPH_ELLIPSE = 2,
-            INTER_NEAREST = 0,
-            INTER_LINEAR = 1,
-            INTER_CUBIC = 2,
-            INTER_AREA = 3,
-            INTER_LANCZOS4 = 4,
-            INTER_LINEAR_EXACT = 5,
-            INTER_MAX = 7,
-            WARP_FILL_OUTLIERS = 8,
-            WARP_INVERSE_MAP = 16,
-            WARP_POLAR_LINEAR = 0,
-            WARP_POLAR_LOG = 256,
-            INTER_BITS = 5,
-            INTER_BITS2 = INTER_BITS * 2,
-            INTER_TAB_SIZE = 1 << INTER_BITS,
-            INTER_TAB_SIZE2 = INTER_TAB_SIZE * INTER_TAB_SIZE,
-            DIST_USER = -1,
-            DIST_L1 = 1,
-            DIST_L2 = 2,
-            DIST_C = 3,
-            DIST_L12 = 4,
-            DIST_FAIR = 5,
-            DIST_WELSCH = 6,
-            DIST_HUBER = 7,
-            DIST_MASK_3 = 3,
-            DIST_MASK_5 = 5,
-            DIST_MASK_PRECISE = 0,
-            THRESH_BINARY = 0,
-            THRESH_BINARY_INV = 1,
-            THRESH_TRUNC = 2,
-            THRESH_TOZERO = 3,
-            THRESH_TOZERO_INV = 4,
-            THRESH_MASK = 7,
-            THRESH_OTSU = 8,
-            THRESH_TRIANGLE = 16,
-            ADAPTIVE_THRESH_MEAN_C = 0,
-            ADAPTIVE_THRESH_GAUSSIAN_C = 1,
-            PROJ_SPHERICAL_ORTHO = 0,
-            PROJ_SPHERICAL_EQRECT = 1,
-            GC_BGD = 0,
-            GC_FGD = 1,
-            GC_PR_BGD = 2,
-            GC_PR_FGD = 3,
-            GC_INIT_WITH_RECT = 0,
-            GC_INIT_WITH_MASK = 1,
-            GC_EVAL = 2,
-            GC_EVAL_FREEZE_MODEL = 3,
-            DIST_LABEL_CCOMP = 0,
-            DIST_LABEL_PIXEL = 1,
-            FLOODFILL_FIXED_RANGE = 1 << 16,
-            FLOODFILL_MASK_ONLY = 1 << 17,
-            CC_STAT_LEFT = 0,
-            CC_STAT_TOP = 1,
-            CC_STAT_WIDTH = 2,
-            CC_STAT_HEIGHT = 3,
-            CC_STAT_AREA = 4,
-            CC_STAT_MAX = 5,
-            CCL_WU = 0,
-            CCL_DEFAULT = -1,
-            CCL_GRANA = 1,
-            RETR_EXTERNAL = 0,
-            RETR_LIST = 1,
-            RETR_CCOMP = 2,
-            RETR_TREE = 3,
-            RETR_FLOODFILL = 4,
-            CHAIN_APPROX_NONE = 1,
-            CHAIN_APPROX_SIMPLE = 2,
-            CHAIN_APPROX_TC89_L1 = 3,
-            CHAIN_APPROX_TC89_KCOS = 4,
-            CONTOURS_MATCH_I1 = 1,
-            CONTOURS_MATCH_I2 = 2,
-            CONTOURS_MATCH_I3 = 3,
-            HOUGH_STANDARD = 0,
-            HOUGH_PROBABILISTIC = 1,
-            HOUGH_MULTI_SCALE = 2,
-            HOUGH_GRADIENT = 3,
-            LSD_REFINE_NONE = 0,
-            LSD_REFINE_STD = 1,
-            LSD_REFINE_ADV = 2,
-            HISTCMP_CORREL = 0,
-            HISTCMP_CHISQR = 1,
-            HISTCMP_INTERSECT = 2,
-            HISTCMP_BHATTACHARYYA = 3,
-            HISTCMP_HELLINGER = HISTCMP_BHATTACHARYYA,
-            HISTCMP_CHISQR_ALT = 4,
-            HISTCMP_KL_DIV = 5,
-            COLOR_BGR2BGRA = 0,
-            COLOR_RGB2RGBA = COLOR_BGR2BGRA,
-            COLOR_BGRA2BGR = 1,
-            COLOR_RGBA2RGB = COLOR_BGRA2BGR,
-            COLOR_BGR2RGBA = 2,
-            COLOR_RGB2BGRA = COLOR_BGR2RGBA,
-            COLOR_RGBA2BGR = 3,
-            COLOR_BGRA2RGB = COLOR_RGBA2BGR,
-            COLOR_BGR2RGB = 4,
-            COLOR_RGB2BGR = COLOR_BGR2RGB,
-            COLOR_BGRA2RGBA = 5,
-            COLOR_RGBA2BGRA = COLOR_BGRA2RGBA,
-            COLOR_BGR2GRAY = 6,
-            COLOR_RGB2GRAY = 7,
-            COLOR_GRAY2BGR = 8,
-            COLOR_GRAY2RGB = COLOR_GRAY2BGR,
-            COLOR_GRAY2BGRA = 9,
-            COLOR_GRAY2RGBA = COLOR_GRAY2BGRA,
-            COLOR_BGRA2GRAY = 10,
-            COLOR_RGBA2GRAY = 11,
-            COLOR_BGR2BGR565 = 12,
-            COLOR_RGB2BGR565 = 13,
-            COLOR_BGR5652BGR = 14,
-            COLOR_BGR5652RGB = 15,
-            COLOR_BGRA2BGR565 = 16,
-            COLOR_RGBA2BGR565 = 17,
-            COLOR_BGR5652BGRA = 18,
-            COLOR_BGR5652RGBA = 19,
-            COLOR_GRAY2BGR565 = 20,
-            COLOR_BGR5652GRAY = 21,
-            COLOR_BGR2BGR555 = 22,
-            COLOR_RGB2BGR555 = 23,
-            COLOR_BGR5552BGR = 24,
-            COLOR_BGR5552RGB = 25,
-            COLOR_BGRA2BGR555 = 26,
-            COLOR_RGBA2BGR555 = 27,
-            COLOR_BGR5552BGRA = 28,
-            COLOR_BGR5552RGBA = 29,
-            COLOR_GRAY2BGR555 = 30,
-            COLOR_BGR5552GRAY = 31,
-            COLOR_BGR2XYZ = 32,
-            COLOR_RGB2XYZ = 33,
-            COLOR_XYZ2BGR = 34,
-            COLOR_XYZ2RGB = 35,
-            COLOR_BGR2YCrCb = 36,
-            COLOR_RGB2YCrCb = 37,
-            COLOR_YCrCb2BGR = 38,
-            COLOR_YCrCb2RGB = 39,
-            COLOR_BGR2HSV = 40,
-            COLOR_RGB2HSV = 41,
-            COLOR_BGR2Lab = 44,
-            COLOR_RGB2Lab = 45,
-            COLOR_BGR2Luv = 50,
-            COLOR_RGB2Luv = 51,
-            COLOR_BGR2HLS = 52,
-            COLOR_RGB2HLS = 53,
-            COLOR_HSV2BGR = 54,
-            COLOR_HSV2RGB = 55,
-            COLOR_Lab2BGR = 56,
-            COLOR_Lab2RGB = 57,
-            COLOR_Luv2BGR = 58,
-            COLOR_Luv2RGB = 59,
-            COLOR_HLS2BGR = 60,
-            COLOR_HLS2RGB = 61,
-            COLOR_BGR2HSV_FULL = 66,
-            COLOR_RGB2HSV_FULL = 67,
-            COLOR_BGR2HLS_FULL = 68,
-            COLOR_RGB2HLS_FULL = 69,
-            COLOR_HSV2BGR_FULL = 70,
-            COLOR_HSV2RGB_FULL = 71,
-            COLOR_HLS2BGR_FULL = 72,
-            COLOR_HLS2RGB_FULL = 73,
-            COLOR_LBGR2Lab = 74,
-            COLOR_LRGB2Lab = 75,
-            COLOR_LBGR2Luv = 76,
-            COLOR_LRGB2Luv = 77,
-            COLOR_Lab2LBGR = 78,
-            COLOR_Lab2LRGB = 79,
-            COLOR_Luv2LBGR = 80,
-            COLOR_Luv2LRGB = 81,
-            COLOR_BGR2YUV = 82,
-            COLOR_RGB2YUV = 83,
-            COLOR_YUV2BGR = 84,
-            COLOR_YUV2RGB = 85,
-            COLOR_YUV2RGB_NV12 = 90,
-            COLOR_YUV2BGR_NV12 = 91,
-            COLOR_YUV2RGB_NV21 = 92,
-            COLOR_YUV2BGR_NV21 = 93,
-            COLOR_YUV420sp2RGB = COLOR_YUV2RGB_NV21,
-            COLOR_YUV420sp2BGR = COLOR_YUV2BGR_NV21,
-            COLOR_YUV2RGBA_NV12 = 94,
-            COLOR_YUV2BGRA_NV12 = 95,
-            COLOR_YUV2RGBA_NV21 = 96,
-            COLOR_YUV2BGRA_NV21 = 97,
-            COLOR_YUV420sp2RGBA = COLOR_YUV2RGBA_NV21,
-            COLOR_YUV420sp2BGRA = COLOR_YUV2BGRA_NV21,
-            COLOR_YUV2RGB_YV12 = 98,
-            COLOR_YUV2BGR_YV12 = 99,
-            COLOR_YUV2RGB_IYUV = 100,
-            COLOR_YUV2BGR_IYUV = 101,
-            COLOR_YUV2RGB_I420 = COLOR_YUV2RGB_IYUV,
-            COLOR_YUV2BGR_I420 = COLOR_YUV2BGR_IYUV,
-            COLOR_YUV420p2RGB = COLOR_YUV2RGB_YV12,
-            COLOR_YUV420p2BGR = COLOR_YUV2BGR_YV12,
-            COLOR_YUV2RGBA_YV12 = 102,
-            COLOR_YUV2BGRA_YV12 = 103,
-            COLOR_YUV2RGBA_IYUV = 104,
-            COLOR_YUV2BGRA_IYUV = 105,
-            COLOR_YUV2RGBA_I420 = COLOR_YUV2RGBA_IYUV,
-            COLOR_YUV2BGRA_I420 = COLOR_YUV2BGRA_IYUV,
-            COLOR_YUV420p2RGBA = COLOR_YUV2RGBA_YV12,
-            COLOR_YUV420p2BGRA = COLOR_YUV2BGRA_YV12,
-            COLOR_YUV2GRAY_420 = 106,
-            COLOR_YUV2GRAY_NV21 = COLOR_YUV2GRAY_420,
-            COLOR_YUV2GRAY_NV12 = COLOR_YUV2GRAY_420,
-            COLOR_YUV2GRAY_YV12 = COLOR_YUV2GRAY_420,
-            COLOR_YUV2GRAY_IYUV = COLOR_YUV2GRAY_420,
-            COLOR_YUV2GRAY_I420 = COLOR_YUV2GRAY_420,
-            COLOR_YUV420sp2GRAY = COLOR_YUV2GRAY_420,
-            COLOR_YUV420p2GRAY = COLOR_YUV2GRAY_420,
-            COLOR_YUV2RGB_UYVY = 107,
-            COLOR_YUV2BGR_UYVY = 108,
-            COLOR_YUV2RGB_Y422 = COLOR_YUV2RGB_UYVY,
-            COLOR_YUV2BGR_Y422 = COLOR_YUV2BGR_UYVY,
-            COLOR_YUV2RGB_UYNV = COLOR_YUV2RGB_UYVY,
-            COLOR_YUV2BGR_UYNV = COLOR_YUV2BGR_UYVY,
-            COLOR_YUV2RGBA_UYVY = 111,
-            COLOR_YUV2BGRA_UYVY = 112,
-            COLOR_YUV2RGBA_Y422 = COLOR_YUV2RGBA_UYVY,
-            COLOR_YUV2BGRA_Y422 = COLOR_YUV2BGRA_UYVY,
-            COLOR_YUV2RGBA_UYNV = COLOR_YUV2RGBA_UYVY,
-            COLOR_YUV2BGRA_UYNV = COLOR_YUV2BGRA_UYVY,
-            COLOR_YUV2RGB_YUY2 = 115,
-            COLOR_YUV2BGR_YUY2 = 116,
-            COLOR_YUV2RGB_YVYU = 117,
-            COLOR_YUV2BGR_YVYU = 118,
-            COLOR_YUV2RGB_YUYV = COLOR_YUV2RGB_YUY2,
-            COLOR_YUV2BGR_YUYV = COLOR_YUV2BGR_YUY2,
-            COLOR_YUV2RGB_YUNV = COLOR_YUV2RGB_YUY2,
-            COLOR_YUV2BGR_YUNV = COLOR_YUV2BGR_YUY2,
-            COLOR_YUV2RGBA_YUY2 = 119,
-            COLOR_YUV2BGRA_YUY2 = 120,
-            COLOR_YUV2RGBA_YVYU = 121,
-            COLOR_YUV2BGRA_YVYU = 122,
-            COLOR_YUV2RGBA_YUYV = COLOR_YUV2RGBA_YUY2,
-            COLOR_YUV2BGRA_YUYV = COLOR_YUV2BGRA_YUY2,
-            COLOR_YUV2RGBA_YUNV = COLOR_YUV2RGBA_YUY2,
-            COLOR_YUV2BGRA_YUNV = COLOR_YUV2BGRA_YUY2,
-            COLOR_YUV2GRAY_UYVY = 123,
-            COLOR_YUV2GRAY_YUY2 = 124,
-            COLOR_YUV2GRAY_Y422 = COLOR_YUV2GRAY_UYVY,
-            COLOR_YUV2GRAY_UYNV = COLOR_YUV2GRAY_UYVY,
-            COLOR_YUV2GRAY_YVYU = COLOR_YUV2GRAY_YUY2,
-            COLOR_YUV2GRAY_YUYV = COLOR_YUV2GRAY_YUY2,
-            COLOR_YUV2GRAY_YUNV = COLOR_YUV2GRAY_YUY2,
-            COLOR_RGBA2mRGBA = 125,
-            COLOR_mRGBA2RGBA = 126,
-            COLOR_RGB2YUV_I420 = 127,
-            COLOR_BGR2YUV_I420 = 128,
-            COLOR_RGB2YUV_IYUV = COLOR_RGB2YUV_I420,
-            COLOR_BGR2YUV_IYUV = COLOR_BGR2YUV_I420,
-            COLOR_RGBA2YUV_I420 = 129,
-            COLOR_BGRA2YUV_I420 = 130,
-            COLOR_RGBA2YUV_IYUV = COLOR_RGBA2YUV_I420,
-            COLOR_BGRA2YUV_IYUV = COLOR_BGRA2YUV_I420,
-            COLOR_RGB2YUV_YV12 = 131,
-            COLOR_BGR2YUV_YV12 = 132,
-            COLOR_RGBA2YUV_YV12 = 133,
-            COLOR_BGRA2YUV_YV12 = 134,
-            COLOR_BayerBG2BGR = 46,
-            COLOR_BayerGB2BGR = 47,
-            COLOR_BayerRG2BGR = 48,
-            COLOR_BayerGR2BGR = 49,
-            COLOR_BayerBG2RGB = COLOR_BayerRG2BGR,
-            COLOR_BayerGB2RGB = COLOR_BayerGR2BGR,
-            COLOR_BayerRG2RGB = COLOR_BayerBG2BGR,
-            COLOR_BayerGR2RGB = COLOR_BayerGB2BGR,
-            COLOR_BayerBG2GRAY = 86,
-            COLOR_BayerGB2GRAY = 87,
-            COLOR_BayerRG2GRAY = 88,
-            COLOR_BayerGR2GRAY = 89,
-            COLOR_BayerBG2BGR_VNG = 62,
-            COLOR_BayerGB2BGR_VNG = 63,
-            COLOR_BayerRG2BGR_VNG = 64,
-            COLOR_BayerGR2BGR_VNG = 65,
-            COLOR_BayerBG2RGB_VNG = COLOR_BayerRG2BGR_VNG,
-            COLOR_BayerGB2RGB_VNG = COLOR_BayerGR2BGR_VNG,
-            COLOR_BayerRG2RGB_VNG = COLOR_BayerBG2BGR_VNG,
-            COLOR_BayerGR2RGB_VNG = COLOR_BayerGB2BGR_VNG,
-            COLOR_BayerBG2BGR_EA = 135,
-            COLOR_BayerGB2BGR_EA = 136,
-            COLOR_BayerRG2BGR_EA = 137,
-            COLOR_BayerGR2BGR_EA = 138,
-            COLOR_BayerBG2RGB_EA = COLOR_BayerRG2BGR_EA,
-            COLOR_BayerGB2RGB_EA = COLOR_BayerGR2BGR_EA,
-            COLOR_BayerRG2RGB_EA = COLOR_BayerBG2BGR_EA,
-            COLOR_BayerGR2RGB_EA = COLOR_BayerGB2BGR_EA,
-            COLOR_BayerBG2BGRA = 139,
-            COLOR_BayerGB2BGRA = 140,
-            COLOR_BayerRG2BGRA = 141,
-            COLOR_BayerGR2BGRA = 142,
-            COLOR_BayerBG2RGBA = COLOR_BayerRG2BGRA,
-            COLOR_BayerGB2RGBA = COLOR_BayerGR2BGRA,
-            COLOR_BayerRG2RGBA = COLOR_BayerBG2BGRA,
-            COLOR_BayerGR2RGBA = COLOR_BayerGB2BGRA,
-            COLOR_COLORCVT_MAX = 143,
-            INTERSECT_NONE = 0,
-            INTERSECT_PARTIAL = 1,
-            INTERSECT_FULL = 2,
-            TM_SQDIFF = 0,
-            TM_SQDIFF_NORMED = 1,
-            TM_CCORR = 2,
-            TM_CCORR_NORMED = 3,
-            TM_CCOEFF = 4,
-            TM_CCOEFF_NORMED = 5,
-            COLORMAP_AUTUMN = 0,
-            COLORMAP_BONE = 1,
-            COLORMAP_JET = 2,
-            COLORMAP_WINTER = 3,
-            COLORMAP_RAINBOW = 4,
-            COLORMAP_OCEAN = 5,
-            COLORMAP_SUMMER = 6,
-            COLORMAP_SPRING = 7,
-            COLORMAP_COOL = 8,
-            COLORMAP_HSV = 9,
-            COLORMAP_PINK = 10,
-            COLORMAP_HOT = 11,
-            COLORMAP_PARULA = 12,
-            MARKER_CROSS = 0,
-            MARKER_TILTED_CROSS = 1,
-            MARKER_STAR = 2,
-            MARKER_DIAMOND = 3,
-            MARKER_SQUARE = 4,
-            MARKER_TRIANGLE_UP = 5,
-            MARKER_TRIANGLE_DOWN = 6;
-
-
-    //
-    // C++:  Mat getAffineTransform(vector_Point2f src, vector_Point2f dst)
-    //
-
-    //javadoc: getAffineTransform(src, dst)
-    public static Mat getAffineTransform(MatOfPoint2f src, MatOfPoint2f dst)
-    {
-        Mat src_mat = src;
-        Mat dst_mat = dst;
-        Mat retVal = new Mat(getAffineTransform_0(src_mat.nativeObj, dst_mat.nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Mat getDefaultNewCameraMatrix(Mat cameraMatrix, Size imgsize = Size(), bool centerPrincipalPoint = false)
-    //
-
-    //javadoc: getDefaultNewCameraMatrix(cameraMatrix, imgsize, centerPrincipalPoint)
-    public static Mat getDefaultNewCameraMatrix(Mat cameraMatrix, Size imgsize, boolean centerPrincipalPoint)
-    {
-        
-        Mat retVal = new Mat(getDefaultNewCameraMatrix_0(cameraMatrix.nativeObj, imgsize.width, imgsize.height, centerPrincipalPoint));
-        
-        return retVal;
-    }
-
-    //javadoc: getDefaultNewCameraMatrix(cameraMatrix)
-    public static Mat getDefaultNewCameraMatrix(Mat cameraMatrix)
-    {
-        
-        Mat retVal = new Mat(getDefaultNewCameraMatrix_1(cameraMatrix.nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Mat getGaborKernel(Size ksize, double sigma, double theta, double lambd, double gamma, double psi = CV_PI*0.5, int ktype = CV_64F)
-    //
-
-    //javadoc: getGaborKernel(ksize, sigma, theta, lambd, gamma, psi, ktype)
-    public static Mat getGaborKernel(Size ksize, double sigma, double theta, double lambd, double gamma, double psi, int ktype)
-    {
-        
-        Mat retVal = new Mat(getGaborKernel_0(ksize.width, ksize.height, sigma, theta, lambd, gamma, psi, ktype));
-        
-        return retVal;
-    }
-
-    //javadoc: getGaborKernel(ksize, sigma, theta, lambd, gamma)
-    public static Mat getGaborKernel(Size ksize, double sigma, double theta, double lambd, double gamma)
-    {
-        
-        Mat retVal = new Mat(getGaborKernel_1(ksize.width, ksize.height, sigma, theta, lambd, gamma));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Mat getGaussianKernel(int ksize, double sigma, int ktype = CV_64F)
-    //
-
-    //javadoc: getGaussianKernel(ksize, sigma, ktype)
-    public static Mat getGaussianKernel(int ksize, double sigma, int ktype)
-    {
-        
-        Mat retVal = new Mat(getGaussianKernel_0(ksize, sigma, ktype));
-        
-        return retVal;
-    }
-
-    //javadoc: getGaussianKernel(ksize, sigma)
-    public static Mat getGaussianKernel(int ksize, double sigma)
-    {
-        
-        Mat retVal = new Mat(getGaussianKernel_1(ksize, sigma));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Mat getPerspectiveTransform(Mat src, Mat dst)
-    //
-
-    //javadoc: getPerspectiveTransform(src, dst)
-    public static Mat getPerspectiveTransform(Mat src, Mat dst)
-    {
-        
-        Mat retVal = new Mat(getPerspectiveTransform_0(src.nativeObj, dst.nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Mat getRotationMatrix2D(Point2f center, double angle, double scale)
-    //
-
-    //javadoc: getRotationMatrix2D(center, angle, scale)
-    public static Mat getRotationMatrix2D(Point center, double angle, double scale)
-    {
-        
-        Mat retVal = new Mat(getRotationMatrix2D_0(center.x, center.y, angle, scale));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Mat getStructuringElement(int shape, Size ksize, Point anchor = Point(-1,-1))
-    //
-
-    //javadoc: getStructuringElement(shape, ksize, anchor)
-    public static Mat getStructuringElement(int shape, Size ksize, Point anchor)
-    {
-        
-        Mat retVal = new Mat(getStructuringElement_0(shape, ksize.width, ksize.height, anchor.x, anchor.y));
-        
-        return retVal;
-    }
-
-    //javadoc: getStructuringElement(shape, ksize)
-    public static Mat getStructuringElement(int shape, Size ksize)
-    {
-        
-        Mat retVal = new Mat(getStructuringElement_1(shape, ksize.width, ksize.height));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Moments moments(Mat array, bool binaryImage = false)
-    //
-
-    //javadoc: moments(array, binaryImage)
-    public static Moments moments(Mat array, boolean binaryImage)
-    {
-        
-        Moments retVal = new Moments(moments_0(array.nativeObj, binaryImage));
-        
-        return retVal;
-    }
-
-    //javadoc: moments(array)
-    public static Moments moments(Mat array)
-    {
-        
-        Moments retVal = new Moments(moments_1(array.nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Point2d phaseCorrelate(Mat src1, Mat src2, Mat window = Mat(), double* response = 0)
-    //
-
-    //javadoc: phaseCorrelate(src1, src2, window, response)
-    public static Point phaseCorrelate(Mat src1, Mat src2, Mat window, double[] response)
-    {
-        double[] response_out = new double[1];
-        Point retVal = new Point(phaseCorrelate_0(src1.nativeObj, src2.nativeObj, window.nativeObj, response_out));
-        if(response!=null) response[0] = (double)response_out[0];
-        return retVal;
-    }
-
-    //javadoc: phaseCorrelate(src1, src2)
-    public static Point phaseCorrelate(Mat src1, Mat src2)
-    {
-        
-        Point retVal = new Point(phaseCorrelate_1(src1.nativeObj, src2.nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Ptr_CLAHE createCLAHE(double clipLimit = 40.0, Size tileGridSize = Size(8, 8))
-    //
-
-    //javadoc: createCLAHE(clipLimit, tileGridSize)
-    public static CLAHE createCLAHE(double clipLimit, Size tileGridSize)
-    {
-        
-        CLAHE retVal = CLAHE.__fromPtr__(createCLAHE_0(clipLimit, tileGridSize.width, tileGridSize.height));
-        
-        return retVal;
-    }
-
-    //javadoc: createCLAHE()
-    public static CLAHE createCLAHE()
-    {
-        
-        CLAHE retVal = CLAHE.__fromPtr__(createCLAHE_1());
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Ptr_LineSegmentDetector createLineSegmentDetector(int _refine = LSD_REFINE_STD, double _scale = 0.8, double _sigma_scale = 0.6, double _quant = 2.0, double _ang_th = 22.5, double _log_eps = 0, double _density_th = 0.7, int _n_bins = 1024)
-    //
-
-    //javadoc: createLineSegmentDetector(_refine, _scale, _sigma_scale, _quant, _ang_th, _log_eps, _density_th, _n_bins)
-    public static LineSegmentDetector createLineSegmentDetector(int _refine, double _scale, double _sigma_scale, double _quant, double _ang_th, double _log_eps, double _density_th, int _n_bins)
-    {
-        
-        LineSegmentDetector retVal = LineSegmentDetector.__fromPtr__(createLineSegmentDetector_0(_refine, _scale, _sigma_scale, _quant, _ang_th, _log_eps, _density_th, _n_bins));
-        
-        return retVal;
-    }
-
-    //javadoc: createLineSegmentDetector()
-    public static LineSegmentDetector createLineSegmentDetector()
-    {
-        
-        LineSegmentDetector retVal = LineSegmentDetector.__fromPtr__(createLineSegmentDetector_1());
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Rect boundingRect(vector_Point points)
-    //
-
-    //javadoc: boundingRect(points)
-    public static Rect boundingRect(MatOfPoint points)
-    {
-        Mat points_mat = points;
-        Rect retVal = new Rect(boundingRect_0(points_mat.nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  RotatedRect fitEllipse(vector_Point2f points)
-    //
-
-    //javadoc: fitEllipse(points)
-    public static RotatedRect fitEllipse(MatOfPoint2f points)
-    {
-        Mat points_mat = points;
-        RotatedRect retVal = new RotatedRect(fitEllipse_0(points_mat.nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  RotatedRect fitEllipseAMS(Mat points)
-    //
-
-    //javadoc: fitEllipseAMS(points)
-    public static RotatedRect fitEllipseAMS(Mat points)
-    {
-        
-        RotatedRect retVal = new RotatedRect(fitEllipseAMS_0(points.nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  RotatedRect fitEllipseDirect(Mat points)
-    //
-
-    //javadoc: fitEllipseDirect(points)
-    public static RotatedRect fitEllipseDirect(Mat points)
-    {
-        
-        RotatedRect retVal = new RotatedRect(fitEllipseDirect_0(points.nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  RotatedRect minAreaRect(vector_Point2f points)
-    //
-
-    //javadoc: minAreaRect(points)
-    public static RotatedRect minAreaRect(MatOfPoint2f points)
-    {
-        Mat points_mat = points;
-        RotatedRect retVal = new RotatedRect(minAreaRect_0(points_mat.nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  bool clipLine(Rect imgRect, Point& pt1, Point& pt2)
-    //
-
-    //javadoc: clipLine(imgRect, pt1, pt2)
-    public static boolean clipLine(Rect imgRect, Point pt1, Point pt2)
-    {
-        double[] pt1_out = new double[2];
-        double[] pt2_out = new double[2];
-        boolean retVal = clipLine_0(imgRect.x, imgRect.y, imgRect.width, imgRect.height, pt1.x, pt1.y, pt1_out, pt2.x, pt2.y, pt2_out);
-        if(pt1!=null){ pt1.x = pt1_out[0]; pt1.y = pt1_out[1]; } 
-        if(pt2!=null){ pt2.x = pt2_out[0]; pt2.y = pt2_out[1]; } 
-        return retVal;
-    }
-
-
-    //
-    // C++:  bool isContourConvex(vector_Point contour)
-    //
-
-    //javadoc: isContourConvex(contour)
-    public static boolean isContourConvex(MatOfPoint contour)
-    {
-        Mat contour_mat = contour;
-        boolean retVal = isContourConvex_0(contour_mat.nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double arcLength(vector_Point2f curve, bool closed)
-    //
-
-    //javadoc: arcLength(curve, closed)
-    public static double arcLength(MatOfPoint2f curve, boolean closed)
-    {
-        Mat curve_mat = curve;
-        double retVal = arcLength_0(curve_mat.nativeObj, closed);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double compareHist(Mat H1, Mat H2, int method)
-    //
-
-    //javadoc: compareHist(H1, H2, method)
-    public static double compareHist(Mat H1, Mat H2, int method)
-    {
-        
-        double retVal = compareHist_0(H1.nativeObj, H2.nativeObj, method);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double contourArea(Mat contour, bool oriented = false)
-    //
-
-    //javadoc: contourArea(contour, oriented)
-    public static double contourArea(Mat contour, boolean oriented)
-    {
-        
-        double retVal = contourArea_0(contour.nativeObj, oriented);
-        
-        return retVal;
-    }
-
-    //javadoc: contourArea(contour)
-    public static double contourArea(Mat contour)
-    {
-        
-        double retVal = contourArea_1(contour.nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double getFontScaleFromHeight(int fontFace, int pixelHeight, int thickness = 1)
-    //
-
-    //javadoc: getFontScaleFromHeight(fontFace, pixelHeight, thickness)
-    public static double getFontScaleFromHeight(int fontFace, int pixelHeight, int thickness)
-    {
-        
-        double retVal = getFontScaleFromHeight_0(fontFace, pixelHeight, thickness);
-        
-        return retVal;
-    }
-
-    //javadoc: getFontScaleFromHeight(fontFace, pixelHeight)
-    public static double getFontScaleFromHeight(int fontFace, int pixelHeight)
-    {
-        
-        double retVal = getFontScaleFromHeight_1(fontFace, pixelHeight);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double matchShapes(Mat contour1, Mat contour2, int method, double parameter)
-    //
-
-    //javadoc: matchShapes(contour1, contour2, method, parameter)
-    public static double matchShapes(Mat contour1, Mat contour2, int method, double parameter)
-    {
-        
-        double retVal = matchShapes_0(contour1.nativeObj, contour2.nativeObj, method, parameter);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double minEnclosingTriangle(Mat points, Mat& triangle)
-    //
-
-    //javadoc: minEnclosingTriangle(points, triangle)
-    public static double minEnclosingTriangle(Mat points, Mat triangle)
-    {
-        
-        double retVal = minEnclosingTriangle_0(points.nativeObj, triangle.nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double pointPolygonTest(vector_Point2f contour, Point2f pt, bool measureDist)
-    //
-
-    //javadoc: pointPolygonTest(contour, pt, measureDist)
-    public static double pointPolygonTest(MatOfPoint2f contour, Point pt, boolean measureDist)
-    {
-        Mat contour_mat = contour;
-        double retVal = pointPolygonTest_0(contour_mat.nativeObj, pt.x, pt.y, measureDist);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double threshold(Mat src, Mat& dst, double thresh, double maxval, int type)
-    //
-
-    //javadoc: threshold(src, dst, thresh, maxval, type)
-    public static double threshold(Mat src, Mat dst, double thresh, double maxval, int type)
-    {
-        
-        double retVal = threshold_0(src.nativeObj, dst.nativeObj, thresh, maxval, type);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  float initWideAngleProjMap(Mat cameraMatrix, Mat distCoeffs, Size imageSize, int destImageWidth, int m1type, Mat& map1, Mat& map2, int projType = PROJ_SPHERICAL_EQRECT, double alpha = 0)
-    //
-
-    //javadoc: initWideAngleProjMap(cameraMatrix, distCoeffs, imageSize, destImageWidth, m1type, map1, map2, projType, alpha)
-    public static float initWideAngleProjMap(Mat cameraMatrix, Mat distCoeffs, Size imageSize, int destImageWidth, int m1type, Mat map1, Mat map2, int projType, double alpha)
-    {
-        
-        float retVal = initWideAngleProjMap_0(cameraMatrix.nativeObj, distCoeffs.nativeObj, imageSize.width, imageSize.height, destImageWidth, m1type, map1.nativeObj, map2.nativeObj, projType, alpha);
-        
-        return retVal;
-    }
-
-    //javadoc: initWideAngleProjMap(cameraMatrix, distCoeffs, imageSize, destImageWidth, m1type, map1, map2)
-    public static float initWideAngleProjMap(Mat cameraMatrix, Mat distCoeffs, Size imageSize, int destImageWidth, int m1type, Mat map1, Mat map2)
-    {
-        
-        float retVal = initWideAngleProjMap_1(cameraMatrix.nativeObj, distCoeffs.nativeObj, imageSize.width, imageSize.height, destImageWidth, m1type, map1.nativeObj, map2.nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  float intersectConvexConvex(Mat _p1, Mat _p2, Mat& _p12, bool handleNested = true)
-    //
-
-    //javadoc: intersectConvexConvex(_p1, _p2, _p12, handleNested)
-    public static float intersectConvexConvex(Mat _p1, Mat _p2, Mat _p12, boolean handleNested)
-    {
-        
-        float retVal = intersectConvexConvex_0(_p1.nativeObj, _p2.nativeObj, _p12.nativeObj, handleNested);
-        
-        return retVal;
-    }
-
-    //javadoc: intersectConvexConvex(_p1, _p2, _p12)
-    public static float intersectConvexConvex(Mat _p1, Mat _p2, Mat _p12)
-    {
-        
-        float retVal = intersectConvexConvex_1(_p1.nativeObj, _p2.nativeObj, _p12.nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  float wrapperEMD(Mat signature1, Mat signature2, int distType, Mat cost = Mat(), Ptr_float& lowerBound = Ptr<float>(), Mat& flow = Mat())
-    //
-
-    //javadoc: wrapperEMD(signature1, signature2, distType, cost, flow)
-    public static float EMD(Mat signature1, Mat signature2, int distType, Mat cost, Mat flow)
-    {
-        
-        float retVal = EMD_0(signature1.nativeObj, signature2.nativeObj, distType, cost.nativeObj, flow.nativeObj);
-        
-        return retVal;
-    }
-
-    //javadoc: wrapperEMD(signature1, signature2, distType)
-    public static float EMD(Mat signature1, Mat signature2, int distType)
-    {
-        
-        float retVal = EMD_1(signature1.nativeObj, signature2.nativeObj, distType);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int connectedComponents(Mat image, Mat& labels, int connectivity, int ltype, int ccltype)
-    //
-
-    //javadoc: connectedComponents(image, labels, connectivity, ltype, ccltype)
-    public static int connectedComponentsWithAlgorithm(Mat image, Mat labels, int connectivity, int ltype, int ccltype)
-    {
-        
-        int retVal = connectedComponentsWithAlgorithm_0(image.nativeObj, labels.nativeObj, connectivity, ltype, ccltype);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int connectedComponents(Mat image, Mat& labels, int connectivity = 8, int ltype = CV_32S)
-    //
-
-    //javadoc: connectedComponents(image, labels, connectivity, ltype)
-    public static int connectedComponents(Mat image, Mat labels, int connectivity, int ltype)
-    {
-        
-        int retVal = connectedComponents_0(image.nativeObj, labels.nativeObj, connectivity, ltype);
-        
-        return retVal;
-    }
-
-    //javadoc: connectedComponents(image, labels)
-    public static int connectedComponents(Mat image, Mat labels)
-    {
-        
-        int retVal = connectedComponents_1(image.nativeObj, labels.nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int connectedComponentsWithStats(Mat image, Mat& labels, Mat& stats, Mat& centroids, int connectivity, int ltype, int ccltype)
-    //
-
-    //javadoc: connectedComponentsWithStats(image, labels, stats, centroids, connectivity, ltype, ccltype)
-    public static int connectedComponentsWithStatsWithAlgorithm(Mat image, Mat labels, Mat stats, Mat centroids, int connectivity, int ltype, int ccltype)
-    {
-        
-        int retVal = connectedComponentsWithStatsWithAlgorithm_0(image.nativeObj, labels.nativeObj, stats.nativeObj, centroids.nativeObj, connectivity, ltype, ccltype);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int connectedComponentsWithStats(Mat image, Mat& labels, Mat& stats, Mat& centroids, int connectivity = 8, int ltype = CV_32S)
-    //
-
-    //javadoc: connectedComponentsWithStats(image, labels, stats, centroids, connectivity, ltype)
-    public static int connectedComponentsWithStats(Mat image, Mat labels, Mat stats, Mat centroids, int connectivity, int ltype)
-    {
-        
-        int retVal = connectedComponentsWithStats_0(image.nativeObj, labels.nativeObj, stats.nativeObj, centroids.nativeObj, connectivity, ltype);
-        
-        return retVal;
-    }
-
-    //javadoc: connectedComponentsWithStats(image, labels, stats, centroids)
-    public static int connectedComponentsWithStats(Mat image, Mat labels, Mat stats, Mat centroids)
-    {
-        
-        int retVal = connectedComponentsWithStats_1(image.nativeObj, labels.nativeObj, stats.nativeObj, centroids.nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int floodFill(Mat& image, Mat& mask, Point seedPoint, Scalar newVal, Rect* rect = 0, Scalar loDiff = Scalar(), Scalar upDiff = Scalar(), int flags = 4)
-    //
-
-    //javadoc: floodFill(image, mask, seedPoint, newVal, rect, loDiff, upDiff, flags)
-    public static int floodFill(Mat image, Mat mask, Point seedPoint, Scalar newVal, Rect rect, Scalar loDiff, Scalar upDiff, int flags)
-    {
-        double[] rect_out = new double[4];
-        int retVal = floodFill_0(image.nativeObj, mask.nativeObj, seedPoint.x, seedPoint.y, newVal.val[0], newVal.val[1], newVal.val[2], newVal.val[3], rect_out, loDiff.val[0], loDiff.val[1], loDiff.val[2], loDiff.val[3], upDiff.val[0], upDiff.val[1], upDiff.val[2], upDiff.val[3], flags);
-        if(rect!=null){ rect.x = (int)rect_out[0]; rect.y = (int)rect_out[1]; rect.width = (int)rect_out[2]; rect.height = (int)rect_out[3]; } 
-        return retVal;
-    }
-
-    //javadoc: floodFill(image, mask, seedPoint, newVal)
-    public static int floodFill(Mat image, Mat mask, Point seedPoint, Scalar newVal)
-    {
-        
-        int retVal = floodFill_1(image.nativeObj, mask.nativeObj, seedPoint.x, seedPoint.y, newVal.val[0], newVal.val[1], newVal.val[2], newVal.val[3]);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int rotatedRectangleIntersection(RotatedRect rect1, RotatedRect rect2, Mat& intersectingRegion)
-    //
-
-    //javadoc: rotatedRectangleIntersection(rect1, rect2, intersectingRegion)
-    public static int rotatedRectangleIntersection(RotatedRect rect1, RotatedRect rect2, Mat intersectingRegion)
-    {
-        
-        int retVal = rotatedRectangleIntersection_0(rect1.center.x, rect1.center.y, rect1.size.width, rect1.size.height, rect1.angle, rect2.center.x, rect2.center.y, rect2.size.width, rect2.size.height, rect2.angle, intersectingRegion.nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  void Canny(Mat dx, Mat dy, Mat& edges, double threshold1, double threshold2, bool L2gradient = false)
-    //
-
-    //javadoc: Canny(dx, dy, edges, threshold1, threshold2, L2gradient)
-    public static void Canny(Mat dx, Mat dy, Mat edges, double threshold1, double threshold2, boolean L2gradient)
-    {
-        
-        Canny_0(dx.nativeObj, dy.nativeObj, edges.nativeObj, threshold1, threshold2, L2gradient);
-        
-        return;
-    }
-
-    //javadoc: Canny(dx, dy, edges, threshold1, threshold2)
-    public static void Canny(Mat dx, Mat dy, Mat edges, double threshold1, double threshold2)
-    {
-        
-        Canny_1(dx.nativeObj, dy.nativeObj, edges.nativeObj, threshold1, threshold2);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void Canny(Mat image, Mat& edges, double threshold1, double threshold2, int apertureSize = 3, bool L2gradient = false)
-    //
-
-    //javadoc: Canny(image, edges, threshold1, threshold2, apertureSize, L2gradient)
-    public static void Canny(Mat image, Mat edges, double threshold1, double threshold2, int apertureSize, boolean L2gradient)
-    {
-        
-        Canny_2(image.nativeObj, edges.nativeObj, threshold1, threshold2, apertureSize, L2gradient);
-        
-        return;
-    }
-
-    //javadoc: Canny(image, edges, threshold1, threshold2)
-    public static void Canny(Mat image, Mat edges, double threshold1, double threshold2)
-    {
-        
-        Canny_3(image.nativeObj, edges.nativeObj, threshold1, threshold2);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void GaussianBlur(Mat src, Mat& dst, Size ksize, double sigmaX, double sigmaY = 0, int borderType = BORDER_DEFAULT)
-    //
-
-    //javadoc: GaussianBlur(src, dst, ksize, sigmaX, sigmaY, borderType)
-    public static void GaussianBlur(Mat src, Mat dst, Size ksize, double sigmaX, double sigmaY, int borderType)
-    {
-        
-        GaussianBlur_0(src.nativeObj, dst.nativeObj, ksize.width, ksize.height, sigmaX, sigmaY, borderType);
-        
-        return;
-    }
-
-    //javadoc: GaussianBlur(src, dst, ksize, sigmaX, sigmaY)
-    public static void GaussianBlur(Mat src, Mat dst, Size ksize, double sigmaX, double sigmaY)
-    {
-        
-        GaussianBlur_1(src.nativeObj, dst.nativeObj, ksize.width, ksize.height, sigmaX, sigmaY);
-        
-        return;
-    }
-
-    //javadoc: GaussianBlur(src, dst, ksize, sigmaX)
-    public static void GaussianBlur(Mat src, Mat dst, Size ksize, double sigmaX)
-    {
-        
-        GaussianBlur_2(src.nativeObj, dst.nativeObj, ksize.width, ksize.height, sigmaX);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void HoughCircles(Mat image, Mat& circles, int method, double dp, double minDist, double param1 = 100, double param2 = 100, int minRadius = 0, int maxRadius = 0)
-    //
-
-    //javadoc: HoughCircles(image, circles, method, dp, minDist, param1, param2, minRadius, maxRadius)
-    public static void HoughCircles(Mat image, Mat circles, int method, double dp, double minDist, double param1, double param2, int minRadius, int maxRadius)
-    {
-        
-        HoughCircles_0(image.nativeObj, circles.nativeObj, method, dp, minDist, param1, param2, minRadius, maxRadius);
-        
-        return;
-    }
-
-    //javadoc: HoughCircles(image, circles, method, dp, minDist)
-    public static void HoughCircles(Mat image, Mat circles, int method, double dp, double minDist)
-    {
-        
-        HoughCircles_1(image.nativeObj, circles.nativeObj, method, dp, minDist);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void HoughLines(Mat image, Mat& lines, double rho, double theta, int threshold, double srn = 0, double stn = 0, double min_theta = 0, double max_theta = CV_PI)
-    //
-
-    //javadoc: HoughLines(image, lines, rho, theta, threshold, srn, stn, min_theta, max_theta)
-    public static void HoughLines(Mat image, Mat lines, double rho, double theta, int threshold, double srn, double stn, double min_theta, double max_theta)
-    {
-        
-        HoughLines_0(image.nativeObj, lines.nativeObj, rho, theta, threshold, srn, stn, min_theta, max_theta);
-        
-        return;
-    }
-
-    //javadoc: HoughLines(image, lines, rho, theta, threshold)
-    public static void HoughLines(Mat image, Mat lines, double rho, double theta, int threshold)
-    {
-        
-        HoughLines_1(image.nativeObj, lines.nativeObj, rho, theta, threshold);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void HoughLinesP(Mat image, Mat& lines, double rho, double theta, int threshold, double minLineLength = 0, double maxLineGap = 0)
-    //
-
-    //javadoc: HoughLinesP(image, lines, rho, theta, threshold, minLineLength, maxLineGap)
-    public static void HoughLinesP(Mat image, Mat lines, double rho, double theta, int threshold, double minLineLength, double maxLineGap)
-    {
-        
-        HoughLinesP_0(image.nativeObj, lines.nativeObj, rho, theta, threshold, minLineLength, maxLineGap);
-        
-        return;
-    }
-
-    //javadoc: HoughLinesP(image, lines, rho, theta, threshold)
-    public static void HoughLinesP(Mat image, Mat lines, double rho, double theta, int threshold)
-    {
-        
-        HoughLinesP_1(image.nativeObj, lines.nativeObj, rho, theta, threshold);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void HoughLinesPointSet(Mat _point, Mat& _lines, int lines_max, int threshold, double min_rho, double max_rho, double rho_step, double min_theta, double max_theta, double theta_step)
-    //
-
-    //javadoc: HoughLinesPointSet(_point, _lines, lines_max, threshold, min_rho, max_rho, rho_step, min_theta, max_theta, theta_step)
-    public static void HoughLinesPointSet(Mat _point, Mat _lines, int lines_max, int threshold, double min_rho, double max_rho, double rho_step, double min_theta, double max_theta, double theta_step)
-    {
-        
-        HoughLinesPointSet_0(_point.nativeObj, _lines.nativeObj, lines_max, threshold, min_rho, max_rho, rho_step, min_theta, max_theta, theta_step);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void HuMoments(Moments m, Mat& hu)
-    //
-
-    //javadoc: HuMoments(m, hu)
-    public static void HuMoments(Moments m, Mat hu)
-    {
-        
-        HuMoments_0(m.m00, m.m10, m.m01, m.m20, m.m11, m.m02, m.m30, m.m21, m.m12, m.m03, hu.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void Laplacian(Mat src, Mat& dst, int ddepth, int ksize = 1, double scale = 1, double delta = 0, int borderType = BORDER_DEFAULT)
-    //
-
-    //javadoc: Laplacian(src, dst, ddepth, ksize, scale, delta, borderType)
-    public static void Laplacian(Mat src, Mat dst, int ddepth, int ksize, double scale, double delta, int borderType)
-    {
-        
-        Laplacian_0(src.nativeObj, dst.nativeObj, ddepth, ksize, scale, delta, borderType);
-        
-        return;
-    }
-
-    //javadoc: Laplacian(src, dst, ddepth, ksize, scale, delta)
-    public static void Laplacian(Mat src, Mat dst, int ddepth, int ksize, double scale, double delta)
-    {
-        
-        Laplacian_1(src.nativeObj, dst.nativeObj, ddepth, ksize, scale, delta);
-        
-        return;
-    }
-
-    //javadoc: Laplacian(src, dst, ddepth)
-    public static void Laplacian(Mat src, Mat dst, int ddepth)
-    {
-        
-        Laplacian_2(src.nativeObj, dst.nativeObj, ddepth);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void Scharr(Mat src, Mat& dst, int ddepth, int dx, int dy, double scale = 1, double delta = 0, int borderType = BORDER_DEFAULT)
-    //
-
-    //javadoc: Scharr(src, dst, ddepth, dx, dy, scale, delta, borderType)
-    public static void Scharr(Mat src, Mat dst, int ddepth, int dx, int dy, double scale, double delta, int borderType)
-    {
-        
-        Scharr_0(src.nativeObj, dst.nativeObj, ddepth, dx, dy, scale, delta, borderType);
-        
-        return;
-    }
-
-    //javadoc: Scharr(src, dst, ddepth, dx, dy, scale, delta)
-    public static void Scharr(Mat src, Mat dst, int ddepth, int dx, int dy, double scale, double delta)
-    {
-        
-        Scharr_1(src.nativeObj, dst.nativeObj, ddepth, dx, dy, scale, delta);
-        
-        return;
-    }
-
-    //javadoc: Scharr(src, dst, ddepth, dx, dy)
-    public static void Scharr(Mat src, Mat dst, int ddepth, int dx, int dy)
-    {
-        
-        Scharr_2(src.nativeObj, dst.nativeObj, ddepth, dx, dy);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void Sobel(Mat src, Mat& dst, int ddepth, int dx, int dy, int ksize = 3, double scale = 1, double delta = 0, int borderType = BORDER_DEFAULT)
-    //
-
-    //javadoc: Sobel(src, dst, ddepth, dx, dy, ksize, scale, delta, borderType)
-    public static void Sobel(Mat src, Mat dst, int ddepth, int dx, int dy, int ksize, double scale, double delta, int borderType)
-    {
-        
-        Sobel_0(src.nativeObj, dst.nativeObj, ddepth, dx, dy, ksize, scale, delta, borderType);
-        
-        return;
-    }
-
-    //javadoc: Sobel(src, dst, ddepth, dx, dy, ksize, scale, delta)
-    public static void Sobel(Mat src, Mat dst, int ddepth, int dx, int dy, int ksize, double scale, double delta)
-    {
-        
-        Sobel_1(src.nativeObj, dst.nativeObj, ddepth, dx, dy, ksize, scale, delta);
-        
-        return;
-    }
-
-    //javadoc: Sobel(src, dst, ddepth, dx, dy)
-    public static void Sobel(Mat src, Mat dst, int ddepth, int dx, int dy)
-    {
-        
-        Sobel_2(src.nativeObj, dst.nativeObj, ddepth, dx, dy);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void accumulate(Mat src, Mat& dst, Mat mask = Mat())
-    //
-
-    //javadoc: accumulate(src, dst, mask)
-    public static void accumulate(Mat src, Mat dst, Mat mask)
-    {
-        
-        accumulate_0(src.nativeObj, dst.nativeObj, mask.nativeObj);
-        
-        return;
-    }
-
-    //javadoc: accumulate(src, dst)
-    public static void accumulate(Mat src, Mat dst)
-    {
-        
-        accumulate_1(src.nativeObj, dst.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void accumulateProduct(Mat src1, Mat src2, Mat& dst, Mat mask = Mat())
-    //
-
-    //javadoc: accumulateProduct(src1, src2, dst, mask)
-    public static void accumulateProduct(Mat src1, Mat src2, Mat dst, Mat mask)
-    {
-        
-        accumulateProduct_0(src1.nativeObj, src2.nativeObj, dst.nativeObj, mask.nativeObj);
-        
-        return;
-    }
-
-    //javadoc: accumulateProduct(src1, src2, dst)
-    public static void accumulateProduct(Mat src1, Mat src2, Mat dst)
-    {
-        
-        accumulateProduct_1(src1.nativeObj, src2.nativeObj, dst.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void accumulateSquare(Mat src, Mat& dst, Mat mask = Mat())
-    //
-
-    //javadoc: accumulateSquare(src, dst, mask)
-    public static void accumulateSquare(Mat src, Mat dst, Mat mask)
-    {
-        
-        accumulateSquare_0(src.nativeObj, dst.nativeObj, mask.nativeObj);
-        
-        return;
-    }
-
-    //javadoc: accumulateSquare(src, dst)
-    public static void accumulateSquare(Mat src, Mat dst)
-    {
-        
-        accumulateSquare_1(src.nativeObj, dst.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void accumulateWeighted(Mat src, Mat& dst, double alpha, Mat mask = Mat())
-    //
-
-    //javadoc: accumulateWeighted(src, dst, alpha, mask)
-    public static void accumulateWeighted(Mat src, Mat dst, double alpha, Mat mask)
-    {
-        
-        accumulateWeighted_0(src.nativeObj, dst.nativeObj, alpha, mask.nativeObj);
-        
-        return;
-    }
-
-    //javadoc: accumulateWeighted(src, dst, alpha)
-    public static void accumulateWeighted(Mat src, Mat dst, double alpha)
-    {
-        
-        accumulateWeighted_1(src.nativeObj, dst.nativeObj, alpha);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void adaptiveThreshold(Mat src, Mat& dst, double maxValue, int adaptiveMethod, int thresholdType, int blockSize, double C)
-    //
-
-    //javadoc: adaptiveThreshold(src, dst, maxValue, adaptiveMethod, thresholdType, blockSize, C)
-    public static void adaptiveThreshold(Mat src, Mat dst, double maxValue, int adaptiveMethod, int thresholdType, int blockSize, double C)
-    {
-        
-        adaptiveThreshold_0(src.nativeObj, dst.nativeObj, maxValue, adaptiveMethod, thresholdType, blockSize, C);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void applyColorMap(Mat src, Mat& dst, Mat userColor)
-    //
-
-    //javadoc: applyColorMap(src, dst, userColor)
-    public static void applyColorMap(Mat src, Mat dst, Mat userColor)
-    {
-        
-        applyColorMap_0(src.nativeObj, dst.nativeObj, userColor.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void applyColorMap(Mat src, Mat& dst, int colormap)
-    //
-
-    //javadoc: applyColorMap(src, dst, colormap)
-    public static void applyColorMap(Mat src, Mat dst, int colormap)
-    {
-        
-        applyColorMap_1(src.nativeObj, dst.nativeObj, colormap);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void approxPolyDP(vector_Point2f curve, vector_Point2f& approxCurve, double epsilon, bool closed)
-    //
-
-    //javadoc: approxPolyDP(curve, approxCurve, epsilon, closed)
-    public static void approxPolyDP(MatOfPoint2f curve, MatOfPoint2f approxCurve, double epsilon, boolean closed)
-    {
-        Mat curve_mat = curve;
-        Mat approxCurve_mat = approxCurve;
-        approxPolyDP_0(curve_mat.nativeObj, approxCurve_mat.nativeObj, epsilon, closed);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void arrowedLine(Mat& img, Point pt1, Point pt2, Scalar color, int thickness = 1, int line_type = 8, int shift = 0, double tipLength = 0.1)
-    //
-
-    //javadoc: arrowedLine(img, pt1, pt2, color, thickness, line_type, shift, tipLength)
-    public static void arrowedLine(Mat img, Point pt1, Point pt2, Scalar color, int thickness, int line_type, int shift, double tipLength)
-    {
-        
-        arrowedLine_0(img.nativeObj, pt1.x, pt1.y, pt2.x, pt2.y, color.val[0], color.val[1], color.val[2], color.val[3], thickness, line_type, shift, tipLength);
-        
-        return;
-    }
-
-    //javadoc: arrowedLine(img, pt1, pt2, color)
-    public static void arrowedLine(Mat img, Point pt1, Point pt2, Scalar color)
-    {
-        
-        arrowedLine_1(img.nativeObj, pt1.x, pt1.y, pt2.x, pt2.y, color.val[0], color.val[1], color.val[2], color.val[3]);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void bilateralFilter(Mat src, Mat& dst, int d, double sigmaColor, double sigmaSpace, int borderType = BORDER_DEFAULT)
-    //
-
-    //javadoc: bilateralFilter(src, dst, d, sigmaColor, sigmaSpace, borderType)
-    public static void bilateralFilter(Mat src, Mat dst, int d, double sigmaColor, double sigmaSpace, int borderType)
-    {
-        
-        bilateralFilter_0(src.nativeObj, dst.nativeObj, d, sigmaColor, sigmaSpace, borderType);
-        
-        return;
-    }
-
-    //javadoc: bilateralFilter(src, dst, d, sigmaColor, sigmaSpace)
-    public static void bilateralFilter(Mat src, Mat dst, int d, double sigmaColor, double sigmaSpace)
-    {
-        
-        bilateralFilter_1(src.nativeObj, dst.nativeObj, d, sigmaColor, sigmaSpace);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void blur(Mat src, Mat& dst, Size ksize, Point anchor = Point(-1,-1), int borderType = BORDER_DEFAULT)
-    //
-
-    //javadoc: blur(src, dst, ksize, anchor, borderType)
-    public static void blur(Mat src, Mat dst, Size ksize, Point anchor, int borderType)
-    {
-        
-        blur_0(src.nativeObj, dst.nativeObj, ksize.width, ksize.height, anchor.x, anchor.y, borderType);
-        
-        return;
-    }
-
-    //javadoc: blur(src, dst, ksize, anchor)
-    public static void blur(Mat src, Mat dst, Size ksize, Point anchor)
-    {
-        
-        blur_1(src.nativeObj, dst.nativeObj, ksize.width, ksize.height, anchor.x, anchor.y);
-        
-        return;
-    }
-
-    //javadoc: blur(src, dst, ksize)
-    public static void blur(Mat src, Mat dst, Size ksize)
-    {
-        
-        blur_2(src.nativeObj, dst.nativeObj, ksize.width, ksize.height);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void boxFilter(Mat src, Mat& dst, int ddepth, Size ksize, Point anchor = Point(-1,-1), bool normalize = true, int borderType = BORDER_DEFAULT)
-    //
-
-    //javadoc: boxFilter(src, dst, ddepth, ksize, anchor, normalize, borderType)
-    public static void boxFilter(Mat src, Mat dst, int ddepth, Size ksize, Point anchor, boolean normalize, int borderType)
-    {
-        
-        boxFilter_0(src.nativeObj, dst.nativeObj, ddepth, ksize.width, ksize.height, anchor.x, anchor.y, normalize, borderType);
-        
-        return;
-    }
-
-    //javadoc: boxFilter(src, dst, ddepth, ksize, anchor, normalize)
-    public static void boxFilter(Mat src, Mat dst, int ddepth, Size ksize, Point anchor, boolean normalize)
-    {
-        
-        boxFilter_1(src.nativeObj, dst.nativeObj, ddepth, ksize.width, ksize.height, anchor.x, anchor.y, normalize);
-        
-        return;
-    }
-
-    //javadoc: boxFilter(src, dst, ddepth, ksize)
-    public static void boxFilter(Mat src, Mat dst, int ddepth, Size ksize)
-    {
-        
-        boxFilter_2(src.nativeObj, dst.nativeObj, ddepth, ksize.width, ksize.height);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void boxPoints(RotatedRect box, Mat& points)
-    //
-
-    //javadoc: boxPoints(box, points)
-    public static void boxPoints(RotatedRect box, Mat points)
-    {
-        
-        boxPoints_0(box.center.x, box.center.y, box.size.width, box.size.height, box.angle, points.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void calcBackProject(vector_Mat images, vector_int channels, Mat hist, Mat& dst, vector_float ranges, double scale)
-    //
-
-    //javadoc: calcBackProject(images, channels, hist, dst, ranges, scale)
-    public static void calcBackProject(List<Mat> images, MatOfInt channels, Mat hist, Mat dst, MatOfFloat ranges, double scale)
-    {
-        Mat images_mat = Converters.vector_Mat_to_Mat(images);
-        Mat channels_mat = channels;
-        Mat ranges_mat = ranges;
-        calcBackProject_0(images_mat.nativeObj, channels_mat.nativeObj, hist.nativeObj, dst.nativeObj, ranges_mat.nativeObj, scale);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void calcHist(vector_Mat images, vector_int channels, Mat mask, Mat& hist, vector_int histSize, vector_float ranges, bool accumulate = false)
-    //
-
-    //javadoc: calcHist(images, channels, mask, hist, histSize, ranges, accumulate)
-    public static void calcHist(List<Mat> images, MatOfInt channels, Mat mask, Mat hist, MatOfInt histSize, MatOfFloat ranges, boolean accumulate)
-    {
-        Mat images_mat = Converters.vector_Mat_to_Mat(images);
-        Mat channels_mat = channels;
-        Mat histSize_mat = histSize;
-        Mat ranges_mat = ranges;
-        calcHist_0(images_mat.nativeObj, channels_mat.nativeObj, mask.nativeObj, hist.nativeObj, histSize_mat.nativeObj, ranges_mat.nativeObj, accumulate);
-        
-        return;
-    }
-
-    //javadoc: calcHist(images, channels, mask, hist, histSize, ranges)
-    public static void calcHist(List<Mat> images, MatOfInt channels, Mat mask, Mat hist, MatOfInt histSize, MatOfFloat ranges)
-    {
-        Mat images_mat = Converters.vector_Mat_to_Mat(images);
-        Mat channels_mat = channels;
-        Mat histSize_mat = histSize;
-        Mat ranges_mat = ranges;
-        calcHist_1(images_mat.nativeObj, channels_mat.nativeObj, mask.nativeObj, hist.nativeObj, histSize_mat.nativeObj, ranges_mat.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void circle(Mat& img, Point center, int radius, Scalar color, int thickness = 1, int lineType = LINE_8, int shift = 0)
-    //
-
-    //javadoc: circle(img, center, radius, color, thickness, lineType, shift)
-    public static void circle(Mat img, Point center, int radius, Scalar color, int thickness, int lineType, int shift)
-    {
-        
-        circle_0(img.nativeObj, center.x, center.y, radius, color.val[0], color.val[1], color.val[2], color.val[3], thickness, lineType, shift);
-        
-        return;
-    }
-
-    //javadoc: circle(img, center, radius, color, thickness)
-    public static void circle(Mat img, Point center, int radius, Scalar color, int thickness)
-    {
-        
-        circle_1(img.nativeObj, center.x, center.y, radius, color.val[0], color.val[1], color.val[2], color.val[3], thickness);
-        
-        return;
-    }
-
-    //javadoc: circle(img, center, radius, color)
-    public static void circle(Mat img, Point center, int radius, Scalar color)
-    {
-        
-        circle_2(img.nativeObj, center.x, center.y, radius, color.val[0], color.val[1], color.val[2], color.val[3]);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void convertMaps(Mat map1, Mat map2, Mat& dstmap1, Mat& dstmap2, int dstmap1type, bool nninterpolation = false)
-    //
-
-    //javadoc: convertMaps(map1, map2, dstmap1, dstmap2, dstmap1type, nninterpolation)
-    public static void convertMaps(Mat map1, Mat map2, Mat dstmap1, Mat dstmap2, int dstmap1type, boolean nninterpolation)
-    {
-        
-        convertMaps_0(map1.nativeObj, map2.nativeObj, dstmap1.nativeObj, dstmap2.nativeObj, dstmap1type, nninterpolation);
-        
-        return;
-    }
-
-    //javadoc: convertMaps(map1, map2, dstmap1, dstmap2, dstmap1type)
-    public static void convertMaps(Mat map1, Mat map2, Mat dstmap1, Mat dstmap2, int dstmap1type)
-    {
-        
-        convertMaps_1(map1.nativeObj, map2.nativeObj, dstmap1.nativeObj, dstmap2.nativeObj, dstmap1type);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void convexHull(vector_Point points, vector_int& hull, bool clockwise = false,  _hidden_  returnPoints = true)
-    //
-
-    //javadoc: convexHull(points, hull, clockwise)
-    public static void convexHull(MatOfPoint points, MatOfInt hull, boolean clockwise)
-    {
-        Mat points_mat = points;
-        Mat hull_mat = hull;
-        convexHull_0(points_mat.nativeObj, hull_mat.nativeObj, clockwise);
-        
-        return;
-    }
-
-    //javadoc: convexHull(points, hull)
-    public static void convexHull(MatOfPoint points, MatOfInt hull)
-    {
-        Mat points_mat = points;
-        Mat hull_mat = hull;
-        convexHull_1(points_mat.nativeObj, hull_mat.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void convexityDefects(vector_Point contour, vector_int convexhull, vector_Vec4i& convexityDefects)
-    //
-
-    //javadoc: convexityDefects(contour, convexhull, convexityDefects)
-    public static void convexityDefects(MatOfPoint contour, MatOfInt convexhull, MatOfInt4 convexityDefects)
-    {
-        Mat contour_mat = contour;
-        Mat convexhull_mat = convexhull;
-        Mat convexityDefects_mat = convexityDefects;
-        convexityDefects_0(contour_mat.nativeObj, convexhull_mat.nativeObj, convexityDefects_mat.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void cornerEigenValsAndVecs(Mat src, Mat& dst, int blockSize, int ksize, int borderType = BORDER_DEFAULT)
-    //
-
-    //javadoc: cornerEigenValsAndVecs(src, dst, blockSize, ksize, borderType)
-    public static void cornerEigenValsAndVecs(Mat src, Mat dst, int blockSize, int ksize, int borderType)
-    {
-        
-        cornerEigenValsAndVecs_0(src.nativeObj, dst.nativeObj, blockSize, ksize, borderType);
-        
-        return;
-    }
-
-    //javadoc: cornerEigenValsAndVecs(src, dst, blockSize, ksize)
-    public static void cornerEigenValsAndVecs(Mat src, Mat dst, int blockSize, int ksize)
-    {
-        
-        cornerEigenValsAndVecs_1(src.nativeObj, dst.nativeObj, blockSize, ksize);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void cornerHarris(Mat src, Mat& dst, int blockSize, int ksize, double k, int borderType = BORDER_DEFAULT)
-    //
-
-    //javadoc: cornerHarris(src, dst, blockSize, ksize, k, borderType)
-    public static void cornerHarris(Mat src, Mat dst, int blockSize, int ksize, double k, int borderType)
-    {
-        
-        cornerHarris_0(src.nativeObj, dst.nativeObj, blockSize, ksize, k, borderType);
-        
-        return;
-    }
-
-    //javadoc: cornerHarris(src, dst, blockSize, ksize, k)
-    public static void cornerHarris(Mat src, Mat dst, int blockSize, int ksize, double k)
-    {
-        
-        cornerHarris_1(src.nativeObj, dst.nativeObj, blockSize, ksize, k);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void cornerMinEigenVal(Mat src, Mat& dst, int blockSize, int ksize = 3, int borderType = BORDER_DEFAULT)
-    //
-
-    //javadoc: cornerMinEigenVal(src, dst, blockSize, ksize, borderType)
-    public static void cornerMinEigenVal(Mat src, Mat dst, int blockSize, int ksize, int borderType)
-    {
-        
-        cornerMinEigenVal_0(src.nativeObj, dst.nativeObj, blockSize, ksize, borderType);
-        
-        return;
-    }
-
-    //javadoc: cornerMinEigenVal(src, dst, blockSize, ksize)
-    public static void cornerMinEigenVal(Mat src, Mat dst, int blockSize, int ksize)
-    {
-        
-        cornerMinEigenVal_1(src.nativeObj, dst.nativeObj, blockSize, ksize);
-        
-        return;
-    }
-
-    //javadoc: cornerMinEigenVal(src, dst, blockSize)
-    public static void cornerMinEigenVal(Mat src, Mat dst, int blockSize)
-    {
-        
-        cornerMinEigenVal_2(src.nativeObj, dst.nativeObj, blockSize);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void cornerSubPix(Mat image, Mat& corners, Size winSize, Size zeroZone, TermCriteria criteria)
-    //
-
-    //javadoc: cornerSubPix(image, corners, winSize, zeroZone, criteria)
-    public static void cornerSubPix(Mat image, Mat corners, Size winSize, Size zeroZone, TermCriteria criteria)
-    {
-        
-        cornerSubPix_0(image.nativeObj, corners.nativeObj, winSize.width, winSize.height, zeroZone.width, zeroZone.height, criteria.type, criteria.maxCount, criteria.epsilon);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void createHanningWindow(Mat& dst, Size winSize, int type)
-    //
-
-    //javadoc: createHanningWindow(dst, winSize, type)
-    public static void createHanningWindow(Mat dst, Size winSize, int type)
-    {
-        
-        createHanningWindow_0(dst.nativeObj, winSize.width, winSize.height, type);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void cvtColor(Mat src, Mat& dst, int code, int dstCn = 0)
-    //
-
-    //javadoc: cvtColor(src, dst, code, dstCn)
-    public static void cvtColor(Mat src, Mat dst, int code, int dstCn)
-    {
-        
-        cvtColor_0(src.nativeObj, dst.nativeObj, code, dstCn);
-        
-        return;
-    }
-
-    //javadoc: cvtColor(src, dst, code)
-    public static void cvtColor(Mat src, Mat dst, int code)
-    {
-        
-        cvtColor_1(src.nativeObj, dst.nativeObj, code);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void cvtColorTwoPlane(Mat src1, Mat src2, Mat& dst, int code)
-    //
-
-    //javadoc: cvtColorTwoPlane(src1, src2, dst, code)
-    public static void cvtColorTwoPlane(Mat src1, Mat src2, Mat dst, int code)
-    {
-        
-        cvtColorTwoPlane_0(src1.nativeObj, src2.nativeObj, dst.nativeObj, code);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void demosaicing(Mat _src, Mat& _dst, int code, int dcn = 0)
-    //
-
-    //javadoc: demosaicing(_src, _dst, code, dcn)
-    public static void demosaicing(Mat _src, Mat _dst, int code, int dcn)
-    {
-        
-        demosaicing_0(_src.nativeObj, _dst.nativeObj, code, dcn);
-        
-        return;
-    }
-
-    //javadoc: demosaicing(_src, _dst, code)
-    public static void demosaicing(Mat _src, Mat _dst, int code)
-    {
-        
-        demosaicing_1(_src.nativeObj, _dst.nativeObj, code);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void dilate(Mat src, Mat& dst, Mat kernel, Point anchor = Point(-1,-1), int iterations = 1, int borderType = BORDER_CONSTANT, Scalar borderValue = morphologyDefaultBorderValue())
-    //
-
-    //javadoc: dilate(src, dst, kernel, anchor, iterations, borderType, borderValue)
-    public static void dilate(Mat src, Mat dst, Mat kernel, Point anchor, int iterations, int borderType, Scalar borderValue)
-    {
-        
-        dilate_0(src.nativeObj, dst.nativeObj, kernel.nativeObj, anchor.x, anchor.y, iterations, borderType, borderValue.val[0], borderValue.val[1], borderValue.val[2], borderValue.val[3]);
-        
-        return;
-    }
-
-    //javadoc: dilate(src, dst, kernel, anchor, iterations)
-    public static void dilate(Mat src, Mat dst, Mat kernel, Point anchor, int iterations)
-    {
-        
-        dilate_1(src.nativeObj, dst.nativeObj, kernel.nativeObj, anchor.x, anchor.y, iterations);
-        
-        return;
-    }
-
-    //javadoc: dilate(src, dst, kernel)
-    public static void dilate(Mat src, Mat dst, Mat kernel)
-    {
-        
-        dilate_2(src.nativeObj, dst.nativeObj, kernel.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void distanceTransform(Mat src, Mat& dst, Mat& labels, int distanceType, int maskSize, int labelType = DIST_LABEL_CCOMP)
-    //
-
-    //javadoc: distanceTransform(src, dst, labels, distanceType, maskSize, labelType)
-    public static void distanceTransformWithLabels(Mat src, Mat dst, Mat labels, int distanceType, int maskSize, int labelType)
-    {
-        
-        distanceTransformWithLabels_0(src.nativeObj, dst.nativeObj, labels.nativeObj, distanceType, maskSize, labelType);
-        
-        return;
-    }
-
-    //javadoc: distanceTransform(src, dst, labels, distanceType, maskSize)
-    public static void distanceTransformWithLabels(Mat src, Mat dst, Mat labels, int distanceType, int maskSize)
-    {
-        
-        distanceTransformWithLabels_1(src.nativeObj, dst.nativeObj, labels.nativeObj, distanceType, maskSize);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void distanceTransform(Mat src, Mat& dst, int distanceType, int maskSize, int dstType = CV_32F)
-    //
-
-    //javadoc: distanceTransform(src, dst, distanceType, maskSize, dstType)
-    public static void distanceTransform(Mat src, Mat dst, int distanceType, int maskSize, int dstType)
-    {
-        
-        distanceTransform_0(src.nativeObj, dst.nativeObj, distanceType, maskSize, dstType);
-        
-        return;
-    }
-
-    //javadoc: distanceTransform(src, dst, distanceType, maskSize)
-    public static void distanceTransform(Mat src, Mat dst, int distanceType, int maskSize)
-    {
-        
-        distanceTransform_1(src.nativeObj, dst.nativeObj, distanceType, maskSize);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void drawContours(Mat& image, vector_vector_Point contours, int contourIdx, Scalar color, int thickness = 1, int lineType = LINE_8, Mat hierarchy = Mat(), int maxLevel = INT_MAX, Point offset = Point())
-    //
-
-    //javadoc: drawContours(image, contours, contourIdx, color, thickness, lineType, hierarchy, maxLevel, offset)
-    public static void drawContours(Mat image, List<MatOfPoint> contours, int contourIdx, Scalar color, int thickness, int lineType, Mat hierarchy, int maxLevel, Point offset)
-    {
-        List<Mat> contours_tmplm = new ArrayList<Mat>((contours != null) ? contours.size() : 0);
-        Mat contours_mat = Converters.vector_vector_Point_to_Mat(contours, contours_tmplm);
-        drawContours_0(image.nativeObj, contours_mat.nativeObj, contourIdx, color.val[0], color.val[1], color.val[2], color.val[3], thickness, lineType, hierarchy.nativeObj, maxLevel, offset.x, offset.y);
-        
-        return;
-    }
-
-    //javadoc: drawContours(image, contours, contourIdx, color, thickness)
-    public static void drawContours(Mat image, List<MatOfPoint> contours, int contourIdx, Scalar color, int thickness)
-    {
-        List<Mat> contours_tmplm = new ArrayList<Mat>((contours != null) ? contours.size() : 0);
-        Mat contours_mat = Converters.vector_vector_Point_to_Mat(contours, contours_tmplm);
-        drawContours_1(image.nativeObj, contours_mat.nativeObj, contourIdx, color.val[0], color.val[1], color.val[2], color.val[3], thickness);
-        
-        return;
-    }
-
-    //javadoc: drawContours(image, contours, contourIdx, color)
-    public static void drawContours(Mat image, List<MatOfPoint> contours, int contourIdx, Scalar color)
-    {
-        List<Mat> contours_tmplm = new ArrayList<Mat>((contours != null) ? contours.size() : 0);
-        Mat contours_mat = Converters.vector_vector_Point_to_Mat(contours, contours_tmplm);
-        drawContours_2(image.nativeObj, contours_mat.nativeObj, contourIdx, color.val[0], color.val[1], color.val[2], color.val[3]);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void drawMarker(Mat& img, Point position, Scalar color, int markerType = MARKER_CROSS, int markerSize = 20, int thickness = 1, int line_type = 8)
-    //
-
-    //javadoc: drawMarker(img, position, color, markerType, markerSize, thickness, line_type)
-    public static void drawMarker(Mat img, Point position, Scalar color, int markerType, int markerSize, int thickness, int line_type)
-    {
-        
-        drawMarker_0(img.nativeObj, position.x, position.y, color.val[0], color.val[1], color.val[2], color.val[3], markerType, markerSize, thickness, line_type);
-        
-        return;
-    }
-
-    //javadoc: drawMarker(img, position, color)
-    public static void drawMarker(Mat img, Point position, Scalar color)
-    {
-        
-        drawMarker_1(img.nativeObj, position.x, position.y, color.val[0], color.val[1], color.val[2], color.val[3]);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void ellipse(Mat& img, Point center, Size axes, double angle, double startAngle, double endAngle, Scalar color, int thickness = 1, int lineType = LINE_8, int shift = 0)
-    //
-
-    //javadoc: ellipse(img, center, axes, angle, startAngle, endAngle, color, thickness, lineType, shift)
-    public static void ellipse(Mat img, Point center, Size axes, double angle, double startAngle, double endAngle, Scalar color, int thickness, int lineType, int shift)
-    {
-        
-        ellipse_0(img.nativeObj, center.x, center.y, axes.width, axes.height, angle, startAngle, endAngle, color.val[0], color.val[1], color.val[2], color.val[3], thickness, lineType, shift);
-        
-        return;
-    }
-
-    //javadoc: ellipse(img, center, axes, angle, startAngle, endAngle, color, thickness)
-    public static void ellipse(Mat img, Point center, Size axes, double angle, double startAngle, double endAngle, Scalar color, int thickness)
-    {
-        
-        ellipse_1(img.nativeObj, center.x, center.y, axes.width, axes.height, angle, startAngle, endAngle, color.val[0], color.val[1], color.val[2], color.val[3], thickness);
-        
-        return;
-    }
-
-    //javadoc: ellipse(img, center, axes, angle, startAngle, endAngle, color)
-    public static void ellipse(Mat img, Point center, Size axes, double angle, double startAngle, double endAngle, Scalar color)
-    {
-        
-        ellipse_2(img.nativeObj, center.x, center.y, axes.width, axes.height, angle, startAngle, endAngle, color.val[0], color.val[1], color.val[2], color.val[3]);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void ellipse(Mat& img, RotatedRect box, Scalar color, int thickness = 1, int lineType = LINE_8)
-    //
-
-    //javadoc: ellipse(img, box, color, thickness, lineType)
-    public static void ellipse(Mat img, RotatedRect box, Scalar color, int thickness, int lineType)
-    {
-        
-        ellipse_3(img.nativeObj, box.center.x, box.center.y, box.size.width, box.size.height, box.angle, color.val[0], color.val[1], color.val[2], color.val[3], thickness, lineType);
-        
-        return;
-    }
-
-    //javadoc: ellipse(img, box, color, thickness)
-    public static void ellipse(Mat img, RotatedRect box, Scalar color, int thickness)
-    {
-        
-        ellipse_4(img.nativeObj, box.center.x, box.center.y, box.size.width, box.size.height, box.angle, color.val[0], color.val[1], color.val[2], color.val[3], thickness);
-        
-        return;
-    }
-
-    //javadoc: ellipse(img, box, color)
-    public static void ellipse(Mat img, RotatedRect box, Scalar color)
-    {
-        
-        ellipse_5(img.nativeObj, box.center.x, box.center.y, box.size.width, box.size.height, box.angle, color.val[0], color.val[1], color.val[2], color.val[3]);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void ellipse2Poly(Point center, Size axes, int angle, int arcStart, int arcEnd, int delta, vector_Point& pts)
-    //
-
-    //javadoc: ellipse2Poly(center, axes, angle, arcStart, arcEnd, delta, pts)
-    public static void ellipse2Poly(Point center, Size axes, int angle, int arcStart, int arcEnd, int delta, MatOfPoint pts)
-    {
-        Mat pts_mat = pts;
-        ellipse2Poly_0(center.x, center.y, axes.width, axes.height, angle, arcStart, arcEnd, delta, pts_mat.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void equalizeHist(Mat src, Mat& dst)
-    //
-
-    //javadoc: equalizeHist(src, dst)
-    public static void equalizeHist(Mat src, Mat dst)
-    {
-        
-        equalizeHist_0(src.nativeObj, dst.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void erode(Mat src, Mat& dst, Mat kernel, Point anchor = Point(-1,-1), int iterations = 1, int borderType = BORDER_CONSTANT, Scalar borderValue = morphologyDefaultBorderValue())
-    //
-
-    //javadoc: erode(src, dst, kernel, anchor, iterations, borderType, borderValue)
-    public static void erode(Mat src, Mat dst, Mat kernel, Point anchor, int iterations, int borderType, Scalar borderValue)
-    {
-        
-        erode_0(src.nativeObj, dst.nativeObj, kernel.nativeObj, anchor.x, anchor.y, iterations, borderType, borderValue.val[0], borderValue.val[1], borderValue.val[2], borderValue.val[3]);
-        
-        return;
-    }
-
-    //javadoc: erode(src, dst, kernel, anchor, iterations)
-    public static void erode(Mat src, Mat dst, Mat kernel, Point anchor, int iterations)
-    {
-        
-        erode_1(src.nativeObj, dst.nativeObj, kernel.nativeObj, anchor.x, anchor.y, iterations);
-        
-        return;
-    }
-
-    //javadoc: erode(src, dst, kernel)
-    public static void erode(Mat src, Mat dst, Mat kernel)
-    {
-        
-        erode_2(src.nativeObj, dst.nativeObj, kernel.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void fillConvexPoly(Mat& img, vector_Point points, Scalar color, int lineType = LINE_8, int shift = 0)
-    //
-
-    //javadoc: fillConvexPoly(img, points, color, lineType, shift)
-    public static void fillConvexPoly(Mat img, MatOfPoint points, Scalar color, int lineType, int shift)
-    {
-        Mat points_mat = points;
-        fillConvexPoly_0(img.nativeObj, points_mat.nativeObj, color.val[0], color.val[1], color.val[2], color.val[3], lineType, shift);
-        
-        return;
-    }
-
-    //javadoc: fillConvexPoly(img, points, color)
-    public static void fillConvexPoly(Mat img, MatOfPoint points, Scalar color)
-    {
-        Mat points_mat = points;
-        fillConvexPoly_1(img.nativeObj, points_mat.nativeObj, color.val[0], color.val[1], color.val[2], color.val[3]);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void fillPoly(Mat& img, vector_vector_Point pts, Scalar color, int lineType = LINE_8, int shift = 0, Point offset = Point())
-    //
-
-    //javadoc: fillPoly(img, pts, color, lineType, shift, offset)
-    public static void fillPoly(Mat img, List<MatOfPoint> pts, Scalar color, int lineType, int shift, Point offset)
-    {
-        List<Mat> pts_tmplm = new ArrayList<Mat>((pts != null) ? pts.size() : 0);
-        Mat pts_mat = Converters.vector_vector_Point_to_Mat(pts, pts_tmplm);
-        fillPoly_0(img.nativeObj, pts_mat.nativeObj, color.val[0], color.val[1], color.val[2], color.val[3], lineType, shift, offset.x, offset.y);
-        
-        return;
-    }
-
-    //javadoc: fillPoly(img, pts, color)
-    public static void fillPoly(Mat img, List<MatOfPoint> pts, Scalar color)
-    {
-        List<Mat> pts_tmplm = new ArrayList<Mat>((pts != null) ? pts.size() : 0);
-        Mat pts_mat = Converters.vector_vector_Point_to_Mat(pts, pts_tmplm);
-        fillPoly_1(img.nativeObj, pts_mat.nativeObj, color.val[0], color.val[1], color.val[2], color.val[3]);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void filter2D(Mat src, Mat& dst, int ddepth, Mat kernel, Point anchor = Point(-1,-1), double delta = 0, int borderType = BORDER_DEFAULT)
-    //
-
-    //javadoc: filter2D(src, dst, ddepth, kernel, anchor, delta, borderType)
-    public static void filter2D(Mat src, Mat dst, int ddepth, Mat kernel, Point anchor, double delta, int borderType)
-    {
-        
-        filter2D_0(src.nativeObj, dst.nativeObj, ddepth, kernel.nativeObj, anchor.x, anchor.y, delta, borderType);
-        
-        return;
-    }
-
-    //javadoc: filter2D(src, dst, ddepth, kernel, anchor, delta)
-    public static void filter2D(Mat src, Mat dst, int ddepth, Mat kernel, Point anchor, double delta)
-    {
-        
-        filter2D_1(src.nativeObj, dst.nativeObj, ddepth, kernel.nativeObj, anchor.x, anchor.y, delta);
-        
-        return;
-    }
-
-    //javadoc: filter2D(src, dst, ddepth, kernel)
-    public static void filter2D(Mat src, Mat dst, int ddepth, Mat kernel)
-    {
-        
-        filter2D_2(src.nativeObj, dst.nativeObj, ddepth, kernel.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void findContours(Mat& image, vector_vector_Point& contours, Mat& hierarchy, int mode, int method, Point offset = Point())
-    //
-
-    //javadoc: findContours(image, contours, hierarchy, mode, method, offset)
-    public static void findContours(Mat image, List<MatOfPoint> contours, Mat hierarchy, int mode, int method, Point offset)
-    {
-        Mat contours_mat = new Mat();
-        findContours_0(image.nativeObj, contours_mat.nativeObj, hierarchy.nativeObj, mode, method, offset.x, offset.y);
-        Converters.Mat_to_vector_vector_Point(contours_mat, contours);
-        contours_mat.release();
-        return;
-    }
-
-    //javadoc: findContours(image, contours, hierarchy, mode, method)
-    public static void findContours(Mat image, List<MatOfPoint> contours, Mat hierarchy, int mode, int method)
-    {
-        Mat contours_mat = new Mat();
-        findContours_1(image.nativeObj, contours_mat.nativeObj, hierarchy.nativeObj, mode, method);
-        Converters.Mat_to_vector_vector_Point(contours_mat, contours);
-        contours_mat.release();
-        return;
-    }
-
-
-    //
-    // C++:  void fitLine(Mat points, Mat& line, int distType, double param, double reps, double aeps)
-    //
-
-    //javadoc: fitLine(points, line, distType, param, reps, aeps)
-    public static void fitLine(Mat points, Mat line, int distType, double param, double reps, double aeps)
-    {
-        
-        fitLine_0(points.nativeObj, line.nativeObj, distType, param, reps, aeps);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void getDerivKernels(Mat& kx, Mat& ky, int dx, int dy, int ksize, bool normalize = false, int ktype = CV_32F)
-    //
-
-    //javadoc: getDerivKernels(kx, ky, dx, dy, ksize, normalize, ktype)
-    public static void getDerivKernels(Mat kx, Mat ky, int dx, int dy, int ksize, boolean normalize, int ktype)
-    {
-        
-        getDerivKernels_0(kx.nativeObj, ky.nativeObj, dx, dy, ksize, normalize, ktype);
-        
-        return;
-    }
-
-    //javadoc: getDerivKernels(kx, ky, dx, dy, ksize)
-    public static void getDerivKernels(Mat kx, Mat ky, int dx, int dy, int ksize)
-    {
-        
-        getDerivKernels_1(kx.nativeObj, ky.nativeObj, dx, dy, ksize);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void getRectSubPix(Mat image, Size patchSize, Point2f center, Mat& patch, int patchType = -1)
-    //
-
-    //javadoc: getRectSubPix(image, patchSize, center, patch, patchType)
-    public static void getRectSubPix(Mat image, Size patchSize, Point center, Mat patch, int patchType)
-    {
-        
-        getRectSubPix_0(image.nativeObj, patchSize.width, patchSize.height, center.x, center.y, patch.nativeObj, patchType);
-        
-        return;
-    }
-
-    //javadoc: getRectSubPix(image, patchSize, center, patch)
-    public static void getRectSubPix(Mat image, Size patchSize, Point center, Mat patch)
-    {
-        
-        getRectSubPix_1(image.nativeObj, patchSize.width, patchSize.height, center.x, center.y, patch.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void goodFeaturesToTrack(Mat image, vector_Point& corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, int blockSize, int gradientSize, bool useHarrisDetector = false, double k = 0.04)
-    //
-
-    //javadoc: goodFeaturesToTrack(image, corners, maxCorners, qualityLevel, minDistance, mask, blockSize, gradientSize, useHarrisDetector, k)
-    public static void goodFeaturesToTrack(Mat image, MatOfPoint corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, int blockSize, int gradientSize, boolean useHarrisDetector, double k)
-    {
-        Mat corners_mat = corners;
-        goodFeaturesToTrack_0(image.nativeObj, corners_mat.nativeObj, maxCorners, qualityLevel, minDistance, mask.nativeObj, blockSize, gradientSize, useHarrisDetector, k);
-        
-        return;
-    }
-
-    //javadoc: goodFeaturesToTrack(image, corners, maxCorners, qualityLevel, minDistance, mask, blockSize, gradientSize)
-    public static void goodFeaturesToTrack(Mat image, MatOfPoint corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, int blockSize, int gradientSize)
-    {
-        Mat corners_mat = corners;
-        goodFeaturesToTrack_1(image.nativeObj, corners_mat.nativeObj, maxCorners, qualityLevel, minDistance, mask.nativeObj, blockSize, gradientSize);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void goodFeaturesToTrack(Mat image, vector_Point& corners, int maxCorners, double qualityLevel, double minDistance, Mat mask = Mat(), int blockSize = 3, bool useHarrisDetector = false, double k = 0.04)
-    //
-
-    //javadoc: goodFeaturesToTrack(image, corners, maxCorners, qualityLevel, minDistance, mask, blockSize, useHarrisDetector, k)
-    public static void goodFeaturesToTrack(Mat image, MatOfPoint corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, int blockSize, boolean useHarrisDetector, double k)
-    {
-        Mat corners_mat = corners;
-        goodFeaturesToTrack_2(image.nativeObj, corners_mat.nativeObj, maxCorners, qualityLevel, minDistance, mask.nativeObj, blockSize, useHarrisDetector, k);
-        
-        return;
-    }
-
-    //javadoc: goodFeaturesToTrack(image, corners, maxCorners, qualityLevel, minDistance)
-    public static void goodFeaturesToTrack(Mat image, MatOfPoint corners, int maxCorners, double qualityLevel, double minDistance)
-    {
-        Mat corners_mat = corners;
-        goodFeaturesToTrack_3(image.nativeObj, corners_mat.nativeObj, maxCorners, qualityLevel, minDistance);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void grabCut(Mat img, Mat& mask, Rect rect, Mat& bgdModel, Mat& fgdModel, int iterCount, int mode = GC_EVAL)
-    //
-
-    //javadoc: grabCut(img, mask, rect, bgdModel, fgdModel, iterCount, mode)
-    public static void grabCut(Mat img, Mat mask, Rect rect, Mat bgdModel, Mat fgdModel, int iterCount, int mode)
-    {
-        
-        grabCut_0(img.nativeObj, mask.nativeObj, rect.x, rect.y, rect.width, rect.height, bgdModel.nativeObj, fgdModel.nativeObj, iterCount, mode);
-        
-        return;
-    }
-
-    //javadoc: grabCut(img, mask, rect, bgdModel, fgdModel, iterCount)
-    public static void grabCut(Mat img, Mat mask, Rect rect, Mat bgdModel, Mat fgdModel, int iterCount)
-    {
-        
-        grabCut_1(img.nativeObj, mask.nativeObj, rect.x, rect.y, rect.width, rect.height, bgdModel.nativeObj, fgdModel.nativeObj, iterCount);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void initUndistortRectifyMap(Mat cameraMatrix, Mat distCoeffs, Mat R, Mat newCameraMatrix, Size size, int m1type, Mat& map1, Mat& map2)
-    //
-
-    //javadoc: initUndistortRectifyMap(cameraMatrix, distCoeffs, R, newCameraMatrix, size, m1type, map1, map2)
-    public static void initUndistortRectifyMap(Mat cameraMatrix, Mat distCoeffs, Mat R, Mat newCameraMatrix, Size size, int m1type, Mat map1, Mat map2)
-    {
-        
-        initUndistortRectifyMap_0(cameraMatrix.nativeObj, distCoeffs.nativeObj, R.nativeObj, newCameraMatrix.nativeObj, size.width, size.height, m1type, map1.nativeObj, map2.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void integral(Mat src, Mat& sum, Mat& sqsum, Mat& tilted, int sdepth = -1, int sqdepth = -1)
-    //
-
-    //javadoc: integral(src, sum, sqsum, tilted, sdepth, sqdepth)
-    public static void integral3(Mat src, Mat sum, Mat sqsum, Mat tilted, int sdepth, int sqdepth)
-    {
-        
-        integral3_0(src.nativeObj, sum.nativeObj, sqsum.nativeObj, tilted.nativeObj, sdepth, sqdepth);
-        
-        return;
-    }
-
-    //javadoc: integral(src, sum, sqsum, tilted)
-    public static void integral3(Mat src, Mat sum, Mat sqsum, Mat tilted)
-    {
-        
-        integral3_1(src.nativeObj, sum.nativeObj, sqsum.nativeObj, tilted.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void integral(Mat src, Mat& sum, Mat& sqsum, int sdepth = -1, int sqdepth = -1)
-    //
-
-    //javadoc: integral(src, sum, sqsum, sdepth, sqdepth)
-    public static void integral2(Mat src, Mat sum, Mat sqsum, int sdepth, int sqdepth)
-    {
-        
-        integral2_0(src.nativeObj, sum.nativeObj, sqsum.nativeObj, sdepth, sqdepth);
-        
-        return;
-    }
-
-    //javadoc: integral(src, sum, sqsum)
-    public static void integral2(Mat src, Mat sum, Mat sqsum)
-    {
-        
-        integral2_1(src.nativeObj, sum.nativeObj, sqsum.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void integral(Mat src, Mat& sum, int sdepth = -1)
-    //
-
-    //javadoc: integral(src, sum, sdepth)
-    public static void integral(Mat src, Mat sum, int sdepth)
-    {
-        
-        integral_0(src.nativeObj, sum.nativeObj, sdepth);
-        
-        return;
-    }
-
-    //javadoc: integral(src, sum)
-    public static void integral(Mat src, Mat sum)
-    {
-        
-        integral_1(src.nativeObj, sum.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void invertAffineTransform(Mat M, Mat& iM)
-    //
-
-    //javadoc: invertAffineTransform(M, iM)
-    public static void invertAffineTransform(Mat M, Mat iM)
-    {
-        
-        invertAffineTransform_0(M.nativeObj, iM.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void line(Mat& img, Point pt1, Point pt2, Scalar color, int thickness = 1, int lineType = LINE_8, int shift = 0)
-    //
-
-    //javadoc: line(img, pt1, pt2, color, thickness, lineType, shift)
-    public static void line(Mat img, Point pt1, Point pt2, Scalar color, int thickness, int lineType, int shift)
-    {
-        
-        line_0(img.nativeObj, pt1.x, pt1.y, pt2.x, pt2.y, color.val[0], color.val[1], color.val[2], color.val[3], thickness, lineType, shift);
-        
-        return;
-    }
-
-    //javadoc: line(img, pt1, pt2, color, thickness)
-    public static void line(Mat img, Point pt1, Point pt2, Scalar color, int thickness)
-    {
-        
-        line_1(img.nativeObj, pt1.x, pt1.y, pt2.x, pt2.y, color.val[0], color.val[1], color.val[2], color.val[3], thickness);
-        
-        return;
-    }
-
-    //javadoc: line(img, pt1, pt2, color)
-    public static void line(Mat img, Point pt1, Point pt2, Scalar color)
-    {
-        
-        line_2(img.nativeObj, pt1.x, pt1.y, pt2.x, pt2.y, color.val[0], color.val[1], color.val[2], color.val[3]);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void linearPolar(Mat src, Mat& dst, Point2f center, double maxRadius, int flags)
-    //
-
-    //javadoc: linearPolar(src, dst, center, maxRadius, flags)
-    @Deprecated
-    public static void linearPolar(Mat src, Mat dst, Point center, double maxRadius, int flags)
-    {
-        
-        linearPolar_0(src.nativeObj, dst.nativeObj, center.x, center.y, maxRadius, flags);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void logPolar(Mat src, Mat& dst, Point2f center, double M, int flags)
-    //
-
-    //javadoc: logPolar(src, dst, center, M, flags)
-    @Deprecated
-    public static void logPolar(Mat src, Mat dst, Point center, double M, int flags)
-    {
-        
-        logPolar_0(src.nativeObj, dst.nativeObj, center.x, center.y, M, flags);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void matchTemplate(Mat image, Mat templ, Mat& result, int method, Mat mask = Mat())
-    //
-
-    //javadoc: matchTemplate(image, templ, result, method, mask)
-    public static void matchTemplate(Mat image, Mat templ, Mat result, int method, Mat mask)
-    {
-        
-        matchTemplate_0(image.nativeObj, templ.nativeObj, result.nativeObj, method, mask.nativeObj);
-        
-        return;
-    }
-
-    //javadoc: matchTemplate(image, templ, result, method)
-    public static void matchTemplate(Mat image, Mat templ, Mat result, int method)
-    {
-        
-        matchTemplate_1(image.nativeObj, templ.nativeObj, result.nativeObj, method);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void medianBlur(Mat src, Mat& dst, int ksize)
-    //
-
-    //javadoc: medianBlur(src, dst, ksize)
-    public static void medianBlur(Mat src, Mat dst, int ksize)
-    {
-        
-        medianBlur_0(src.nativeObj, dst.nativeObj, ksize);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void minEnclosingCircle(vector_Point2f points, Point2f& center, float& radius)
-    //
-
-    //javadoc: minEnclosingCircle(points, center, radius)
-    public static void minEnclosingCircle(MatOfPoint2f points, Point center, float[] radius)
-    {
-        Mat points_mat = points;
-        double[] center_out = new double[2];
-        double[] radius_out = new double[1];
-        minEnclosingCircle_0(points_mat.nativeObj, center_out, radius_out);
-        if(center!=null){ center.x = center_out[0]; center.y = center_out[1]; } 
-        if(radius!=null) radius[0] = (float)radius_out[0];
-        return;
-    }
-
-
-    //
-    // C++:  void morphologyEx(Mat src, Mat& dst, int op, Mat kernel, Point anchor = Point(-1,-1), int iterations = 1, int borderType = BORDER_CONSTANT, Scalar borderValue = morphologyDefaultBorderValue())
-    //
-
-    //javadoc: morphologyEx(src, dst, op, kernel, anchor, iterations, borderType, borderValue)
-    public static void morphologyEx(Mat src, Mat dst, int op, Mat kernel, Point anchor, int iterations, int borderType, Scalar borderValue)
-    {
-        
-        morphologyEx_0(src.nativeObj, dst.nativeObj, op, kernel.nativeObj, anchor.x, anchor.y, iterations, borderType, borderValue.val[0], borderValue.val[1], borderValue.val[2], borderValue.val[3]);
-        
-        return;
-    }
-
-    //javadoc: morphologyEx(src, dst, op, kernel, anchor, iterations)
-    public static void morphologyEx(Mat src, Mat dst, int op, Mat kernel, Point anchor, int iterations)
-    {
-        
-        morphologyEx_1(src.nativeObj, dst.nativeObj, op, kernel.nativeObj, anchor.x, anchor.y, iterations);
-        
-        return;
-    }
-
-    //javadoc: morphologyEx(src, dst, op, kernel)
-    public static void morphologyEx(Mat src, Mat dst, int op, Mat kernel)
-    {
-        
-        morphologyEx_2(src.nativeObj, dst.nativeObj, op, kernel.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void polylines(Mat& img, vector_vector_Point pts, bool isClosed, Scalar color, int thickness = 1, int lineType = LINE_8, int shift = 0)
-    //
-
-    //javadoc: polylines(img, pts, isClosed, color, thickness, lineType, shift)
-    public static void polylines(Mat img, List<MatOfPoint> pts, boolean isClosed, Scalar color, int thickness, int lineType, int shift)
-    {
-        List<Mat> pts_tmplm = new ArrayList<Mat>((pts != null) ? pts.size() : 0);
-        Mat pts_mat = Converters.vector_vector_Point_to_Mat(pts, pts_tmplm);
-        polylines_0(img.nativeObj, pts_mat.nativeObj, isClosed, color.val[0], color.val[1], color.val[2], color.val[3], thickness, lineType, shift);
-        
-        return;
-    }
-
-    //javadoc: polylines(img, pts, isClosed, color, thickness)
-    public static void polylines(Mat img, List<MatOfPoint> pts, boolean isClosed, Scalar color, int thickness)
-    {
-        List<Mat> pts_tmplm = new ArrayList<Mat>((pts != null) ? pts.size() : 0);
-        Mat pts_mat = Converters.vector_vector_Point_to_Mat(pts, pts_tmplm);
-        polylines_1(img.nativeObj, pts_mat.nativeObj, isClosed, color.val[0], color.val[1], color.val[2], color.val[3], thickness);
-        
-        return;
-    }
-
-    //javadoc: polylines(img, pts, isClosed, color)
-    public static void polylines(Mat img, List<MatOfPoint> pts, boolean isClosed, Scalar color)
-    {
-        List<Mat> pts_tmplm = new ArrayList<Mat>((pts != null) ? pts.size() : 0);
-        Mat pts_mat = Converters.vector_vector_Point_to_Mat(pts, pts_tmplm);
-        polylines_2(img.nativeObj, pts_mat.nativeObj, isClosed, color.val[0], color.val[1], color.val[2], color.val[3]);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void preCornerDetect(Mat src, Mat& dst, int ksize, int borderType = BORDER_DEFAULT)
-    //
-
-    //javadoc: preCornerDetect(src, dst, ksize, borderType)
-    public static void preCornerDetect(Mat src, Mat dst, int ksize, int borderType)
-    {
-        
-        preCornerDetect_0(src.nativeObj, dst.nativeObj, ksize, borderType);
-        
-        return;
-    }
-
-    //javadoc: preCornerDetect(src, dst, ksize)
-    public static void preCornerDetect(Mat src, Mat dst, int ksize)
-    {
-        
-        preCornerDetect_1(src.nativeObj, dst.nativeObj, ksize);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void putText(Mat& img, String text, Point org, int fontFace, double fontScale, Scalar color, int thickness = 1, int lineType = LINE_8, bool bottomLeftOrigin = false)
-    //
-
-    //javadoc: putText(img, text, org, fontFace, fontScale, color, thickness, lineType, bottomLeftOrigin)
-    public static void putText(Mat img, String text, Point org, int fontFace, double fontScale, Scalar color, int thickness, int lineType, boolean bottomLeftOrigin)
-    {
-        
-        putText_0(img.nativeObj, text, org.x, org.y, fontFace, fontScale, color.val[0], color.val[1], color.val[2], color.val[3], thickness, lineType, bottomLeftOrigin);
-        
-        return;
-    }
-
-    //javadoc: putText(img, text, org, fontFace, fontScale, color, thickness)
-    public static void putText(Mat img, String text, Point org, int fontFace, double fontScale, Scalar color, int thickness)
-    {
-        
-        putText_1(img.nativeObj, text, org.x, org.y, fontFace, fontScale, color.val[0], color.val[1], color.val[2], color.val[3], thickness);
-        
-        return;
-    }
-
-    //javadoc: putText(img, text, org, fontFace, fontScale, color)
-    public static void putText(Mat img, String text, Point org, int fontFace, double fontScale, Scalar color)
-    {
-        
-        putText_2(img.nativeObj, text, org.x, org.y, fontFace, fontScale, color.val[0], color.val[1], color.val[2], color.val[3]);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void pyrDown(Mat src, Mat& dst, Size dstsize = Size(), int borderType = BORDER_DEFAULT)
-    //
-
-    //javadoc: pyrDown(src, dst, dstsize, borderType)
-    public static void pyrDown(Mat src, Mat dst, Size dstsize, int borderType)
-    {
-        
-        pyrDown_0(src.nativeObj, dst.nativeObj, dstsize.width, dstsize.height, borderType);
-        
-        return;
-    }
-
-    //javadoc: pyrDown(src, dst, dstsize)
-    public static void pyrDown(Mat src, Mat dst, Size dstsize)
-    {
-        
-        pyrDown_1(src.nativeObj, dst.nativeObj, dstsize.width, dstsize.height);
-        
-        return;
-    }
-
-    //javadoc: pyrDown(src, dst)
-    public static void pyrDown(Mat src, Mat dst)
-    {
-        
-        pyrDown_2(src.nativeObj, dst.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void pyrMeanShiftFiltering(Mat src, Mat& dst, double sp, double sr, int maxLevel = 1, TermCriteria termcrit = TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,5,1))
-    //
-
-    //javadoc: pyrMeanShiftFiltering(src, dst, sp, sr, maxLevel, termcrit)
-    public static void pyrMeanShiftFiltering(Mat src, Mat dst, double sp, double sr, int maxLevel, TermCriteria termcrit)
-    {
-        
-        pyrMeanShiftFiltering_0(src.nativeObj, dst.nativeObj, sp, sr, maxLevel, termcrit.type, termcrit.maxCount, termcrit.epsilon);
-        
-        return;
-    }
-
-    //javadoc: pyrMeanShiftFiltering(src, dst, sp, sr)
-    public static void pyrMeanShiftFiltering(Mat src, Mat dst, double sp, double sr)
-    {
-        
-        pyrMeanShiftFiltering_1(src.nativeObj, dst.nativeObj, sp, sr);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void pyrUp(Mat src, Mat& dst, Size dstsize = Size(), int borderType = BORDER_DEFAULT)
-    //
-
-    //javadoc: pyrUp(src, dst, dstsize, borderType)
-    public static void pyrUp(Mat src, Mat dst, Size dstsize, int borderType)
-    {
-        
-        pyrUp_0(src.nativeObj, dst.nativeObj, dstsize.width, dstsize.height, borderType);
-        
-        return;
-    }
-
-    //javadoc: pyrUp(src, dst, dstsize)
-    public static void pyrUp(Mat src, Mat dst, Size dstsize)
-    {
-        
-        pyrUp_1(src.nativeObj, dst.nativeObj, dstsize.width, dstsize.height);
-        
-        return;
-    }
-
-    //javadoc: pyrUp(src, dst)
-    public static void pyrUp(Mat src, Mat dst)
-    {
-        
-        pyrUp_2(src.nativeObj, dst.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void rectangle(Mat& img, Point pt1, Point pt2, Scalar color, int thickness = 1, int lineType = LINE_8, int shift = 0)
-    //
-
-    //javadoc: rectangle(img, pt1, pt2, color, thickness, lineType, shift)
-    public static void rectangle(Mat img, Point pt1, Point pt2, Scalar color, int thickness, int lineType, int shift)
-    {
-        
-        rectangle_0(img.nativeObj, pt1.x, pt1.y, pt2.x, pt2.y, color.val[0], color.val[1], color.val[2], color.val[3], thickness, lineType, shift);
-        
-        return;
-    }
-
-    //javadoc: rectangle(img, pt1, pt2, color, thickness)
-    public static void rectangle(Mat img, Point pt1, Point pt2, Scalar color, int thickness)
-    {
-        
-        rectangle_1(img.nativeObj, pt1.x, pt1.y, pt2.x, pt2.y, color.val[0], color.val[1], color.val[2], color.val[3], thickness);
-        
-        return;
-    }
-
-    //javadoc: rectangle(img, pt1, pt2, color)
-    public static void rectangle(Mat img, Point pt1, Point pt2, Scalar color)
-    {
-        
-        rectangle_2(img.nativeObj, pt1.x, pt1.y, pt2.x, pt2.y, color.val[0], color.val[1], color.val[2], color.val[3]);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void remap(Mat src, Mat& dst, Mat map1, Mat map2, int interpolation, int borderMode = BORDER_CONSTANT, Scalar borderValue = Scalar())
-    //
-
-    //javadoc: remap(src, dst, map1, map2, interpolation, borderMode, borderValue)
-    public static void remap(Mat src, Mat dst, Mat map1, Mat map2, int interpolation, int borderMode, Scalar borderValue)
-    {
-        
-        remap_0(src.nativeObj, dst.nativeObj, map1.nativeObj, map2.nativeObj, interpolation, borderMode, borderValue.val[0], borderValue.val[1], borderValue.val[2], borderValue.val[3]);
-        
-        return;
-    }
-
-    //javadoc: remap(src, dst, map1, map2, interpolation)
-    public static void remap(Mat src, Mat dst, Mat map1, Mat map2, int interpolation)
-    {
-        
-        remap_1(src.nativeObj, dst.nativeObj, map1.nativeObj, map2.nativeObj, interpolation);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void resize(Mat src, Mat& dst, Size dsize, double fx = 0, double fy = 0, int interpolation = INTER_LINEAR)
-    //
-
-    //javadoc: resize(src, dst, dsize, fx, fy, interpolation)
-    public static void resize(Mat src, Mat dst, Size dsize, double fx, double fy, int interpolation)
-    {
-        
-        resize_0(src.nativeObj, dst.nativeObj, dsize.width, dsize.height, fx, fy, interpolation);
-        
-        return;
-    }
-
-    //javadoc: resize(src, dst, dsize)
-    public static void resize(Mat src, Mat dst, Size dsize)
-    {
-        
-        resize_1(src.nativeObj, dst.nativeObj, dsize.width, dsize.height);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void sepFilter2D(Mat src, Mat& dst, int ddepth, Mat kernelX, Mat kernelY, Point anchor = Point(-1,-1), double delta = 0, int borderType = BORDER_DEFAULT)
-    //
-
-    //javadoc: sepFilter2D(src, dst, ddepth, kernelX, kernelY, anchor, delta, borderType)
-    public static void sepFilter2D(Mat src, Mat dst, int ddepth, Mat kernelX, Mat kernelY, Point anchor, double delta, int borderType)
-    {
-        
-        sepFilter2D_0(src.nativeObj, dst.nativeObj, ddepth, kernelX.nativeObj, kernelY.nativeObj, anchor.x, anchor.y, delta, borderType);
-        
-        return;
-    }
-
-    //javadoc: sepFilter2D(src, dst, ddepth, kernelX, kernelY, anchor, delta)
-    public static void sepFilter2D(Mat src, Mat dst, int ddepth, Mat kernelX, Mat kernelY, Point anchor, double delta)
-    {
-        
-        sepFilter2D_1(src.nativeObj, dst.nativeObj, ddepth, kernelX.nativeObj, kernelY.nativeObj, anchor.x, anchor.y, delta);
-        
-        return;
-    }
-
-    //javadoc: sepFilter2D(src, dst, ddepth, kernelX, kernelY)
-    public static void sepFilter2D(Mat src, Mat dst, int ddepth, Mat kernelX, Mat kernelY)
-    {
-        
-        sepFilter2D_2(src.nativeObj, dst.nativeObj, ddepth, kernelX.nativeObj, kernelY.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void spatialGradient(Mat src, Mat& dx, Mat& dy, int ksize = 3, int borderType = BORDER_DEFAULT)
-    //
-
-    //javadoc: spatialGradient(src, dx, dy, ksize, borderType)
-    public static void spatialGradient(Mat src, Mat dx, Mat dy, int ksize, int borderType)
-    {
-        
-        spatialGradient_0(src.nativeObj, dx.nativeObj, dy.nativeObj, ksize, borderType);
-        
-        return;
-    }
-
-    //javadoc: spatialGradient(src, dx, dy, ksize)
-    public static void spatialGradient(Mat src, Mat dx, Mat dy, int ksize)
-    {
-        
-        spatialGradient_1(src.nativeObj, dx.nativeObj, dy.nativeObj, ksize);
-        
-        return;
-    }
-
-    //javadoc: spatialGradient(src, dx, dy)
-    public static void spatialGradient(Mat src, Mat dx, Mat dy)
-    {
-        
-        spatialGradient_2(src.nativeObj, dx.nativeObj, dy.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void sqrBoxFilter(Mat _src, Mat& _dst, int ddepth, Size ksize, Point anchor = Point(-1, -1), bool normalize = true, int borderType = BORDER_DEFAULT)
-    //
-
-    //javadoc: sqrBoxFilter(_src, _dst, ddepth, ksize, anchor, normalize, borderType)
-    public static void sqrBoxFilter(Mat _src, Mat _dst, int ddepth, Size ksize, Point anchor, boolean normalize, int borderType)
-    {
-        
-        sqrBoxFilter_0(_src.nativeObj, _dst.nativeObj, ddepth, ksize.width, ksize.height, anchor.x, anchor.y, normalize, borderType);
-        
-        return;
-    }
-
-    //javadoc: sqrBoxFilter(_src, _dst, ddepth, ksize, anchor, normalize)
-    public static void sqrBoxFilter(Mat _src, Mat _dst, int ddepth, Size ksize, Point anchor, boolean normalize)
-    {
-        
-        sqrBoxFilter_1(_src.nativeObj, _dst.nativeObj, ddepth, ksize.width, ksize.height, anchor.x, anchor.y, normalize);
-        
-        return;
-    }
-
-    //javadoc: sqrBoxFilter(_src, _dst, ddepth, ksize)
-    public static void sqrBoxFilter(Mat _src, Mat _dst, int ddepth, Size ksize)
-    {
-        
-        sqrBoxFilter_2(_src.nativeObj, _dst.nativeObj, ddepth, ksize.width, ksize.height);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void undistort(Mat src, Mat& dst, Mat cameraMatrix, Mat distCoeffs, Mat newCameraMatrix = Mat())
-    //
-
-    //javadoc: undistort(src, dst, cameraMatrix, distCoeffs, newCameraMatrix)
-    public static void undistort(Mat src, Mat dst, Mat cameraMatrix, Mat distCoeffs, Mat newCameraMatrix)
-    {
-        
-        undistort_0(src.nativeObj, dst.nativeObj, cameraMatrix.nativeObj, distCoeffs.nativeObj, newCameraMatrix.nativeObj);
-        
-        return;
-    }
-
-    //javadoc: undistort(src, dst, cameraMatrix, distCoeffs)
-    public static void undistort(Mat src, Mat dst, Mat cameraMatrix, Mat distCoeffs)
-    {
-        
-        undistort_1(src.nativeObj, dst.nativeObj, cameraMatrix.nativeObj, distCoeffs.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void undistortPoints(Mat src, Mat& dst, Mat cameraMatrix, Mat distCoeffs, Mat R, Mat P, TermCriteria criteria)
-    //
-
-    //javadoc: undistortPoints(src, dst, cameraMatrix, distCoeffs, R, P, criteria)
-    public static void undistortPointsIter(Mat src, Mat dst, Mat cameraMatrix, Mat distCoeffs, Mat R, Mat P, TermCriteria criteria)
-    {
-        
-        undistortPointsIter_0(src.nativeObj, dst.nativeObj, cameraMatrix.nativeObj, distCoeffs.nativeObj, R.nativeObj, P.nativeObj, criteria.type, criteria.maxCount, criteria.epsilon);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void undistortPoints(Mat src, Mat& dst, Mat cameraMatrix, Mat distCoeffs, Mat R = Mat(), Mat P = Mat())
-    //
-
-    //javadoc: undistortPoints(src, dst, cameraMatrix, distCoeffs, R, P)
-    public static void undistortPoints(Mat src, Mat dst, Mat cameraMatrix, Mat distCoeffs, Mat R, Mat P)
-    {
-        
-        undistortPoints_0(src.nativeObj, dst.nativeObj, cameraMatrix.nativeObj, distCoeffs.nativeObj, R.nativeObj, P.nativeObj);
-        
-        return;
-    }
-
-    //javadoc: undistortPoints(src, dst, cameraMatrix, distCoeffs)
-    public static void undistortPoints(Mat src, Mat dst, Mat cameraMatrix, Mat distCoeffs)
-    {
-        
-        undistortPoints_1(src.nativeObj, dst.nativeObj, cameraMatrix.nativeObj, distCoeffs.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void warpAffine(Mat src, Mat& dst, Mat M, Size dsize, int flags = INTER_LINEAR, int borderMode = BORDER_CONSTANT, Scalar borderValue = Scalar())
-    //
-
-    //javadoc: warpAffine(src, dst, M, dsize, flags, borderMode, borderValue)
-    public static void warpAffine(Mat src, Mat dst, Mat M, Size dsize, int flags, int borderMode, Scalar borderValue)
-    {
-        
-        warpAffine_0(src.nativeObj, dst.nativeObj, M.nativeObj, dsize.width, dsize.height, flags, borderMode, borderValue.val[0], borderValue.val[1], borderValue.val[2], borderValue.val[3]);
-        
-        return;
-    }
-
-    //javadoc: warpAffine(src, dst, M, dsize, flags)
-    public static void warpAffine(Mat src, Mat dst, Mat M, Size dsize, int flags)
-    {
-        
-        warpAffine_1(src.nativeObj, dst.nativeObj, M.nativeObj, dsize.width, dsize.height, flags);
-        
-        return;
-    }
-
-    //javadoc: warpAffine(src, dst, M, dsize)
-    public static void warpAffine(Mat src, Mat dst, Mat M, Size dsize)
-    {
-        
-        warpAffine_2(src.nativeObj, dst.nativeObj, M.nativeObj, dsize.width, dsize.height);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void warpPerspective(Mat src, Mat& dst, Mat M, Size dsize, int flags = INTER_LINEAR, int borderMode = BORDER_CONSTANT, Scalar borderValue = Scalar())
-    //
-
-    //javadoc: warpPerspective(src, dst, M, dsize, flags, borderMode, borderValue)
-    public static void warpPerspective(Mat src, Mat dst, Mat M, Size dsize, int flags, int borderMode, Scalar borderValue)
-    {
-        
-        warpPerspective_0(src.nativeObj, dst.nativeObj, M.nativeObj, dsize.width, dsize.height, flags, borderMode, borderValue.val[0], borderValue.val[1], borderValue.val[2], borderValue.val[3]);
-        
-        return;
-    }
-
-    //javadoc: warpPerspective(src, dst, M, dsize, flags)
-    public static void warpPerspective(Mat src, Mat dst, Mat M, Size dsize, int flags)
-    {
-        
-        warpPerspective_1(src.nativeObj, dst.nativeObj, M.nativeObj, dsize.width, dsize.height, flags);
-        
-        return;
-    }
-
-    //javadoc: warpPerspective(src, dst, M, dsize)
-    public static void warpPerspective(Mat src, Mat dst, Mat M, Size dsize)
-    {
-        
-        warpPerspective_2(src.nativeObj, dst.nativeObj, M.nativeObj, dsize.width, dsize.height);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void warpPolar(Mat src, Mat& dst, Size dsize, Point2f center, double maxRadius, int flags)
-    //
-
-    //javadoc: warpPolar(src, dst, dsize, center, maxRadius, flags)
-    public static void warpPolar(Mat src, Mat dst, Size dsize, Point center, double maxRadius, int flags)
-    {
-        
-        warpPolar_0(src.nativeObj, dst.nativeObj, dsize.width, dsize.height, center.x, center.y, maxRadius, flags);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void watershed(Mat image, Mat& markers)
-    //
-
-    //javadoc: watershed(image, markers)
-    public static void watershed(Mat image, Mat markers)
-    {
-        
-        watershed_0(image.nativeObj, markers.nativeObj);
-        
-        return;
-    }
-
-
-
-// C++: Size getTextSize(const String& text, int fontFace, double fontScale, int thickness, int* baseLine);
-//javadoc:getTextSize(text, fontFace, fontScale, thickness, baseLine)
-public static Size getTextSize(String text, int fontFace, double fontScale, int thickness, int[] baseLine) {
-    if(baseLine != null && baseLine.length != 1)
-        throw new java.lang.IllegalArgumentException("'baseLine' must be 'int[1]' or 'null'.");
-    Size retVal = new Size(n_getTextSize(text, fontFace, fontScale, thickness, baseLine));
-    return retVal;
-}
-
-
-
-
-    // C++:  Mat getAffineTransform(vector_Point2f src, vector_Point2f dst)
-    private static native long getAffineTransform_0(long src_mat_nativeObj, long dst_mat_nativeObj);
-
-    // C++:  Mat getDefaultNewCameraMatrix(Mat cameraMatrix, Size imgsize = Size(), bool centerPrincipalPoint = false)
-    private static native long getDefaultNewCameraMatrix_0(long cameraMatrix_nativeObj, double imgsize_width, double imgsize_height, boolean centerPrincipalPoint);
-    private static native long getDefaultNewCameraMatrix_1(long cameraMatrix_nativeObj);
-
-    // C++:  Mat getGaborKernel(Size ksize, double sigma, double theta, double lambd, double gamma, double psi = CV_PI*0.5, int ktype = CV_64F)
-    private static native long getGaborKernel_0(double ksize_width, double ksize_height, double sigma, double theta, double lambd, double gamma, double psi, int ktype);
-    private static native long getGaborKernel_1(double ksize_width, double ksize_height, double sigma, double theta, double lambd, double gamma);
-
-    // C++:  Mat getGaussianKernel(int ksize, double sigma, int ktype = CV_64F)
-    private static native long getGaussianKernel_0(int ksize, double sigma, int ktype);
-    private static native long getGaussianKernel_1(int ksize, double sigma);
-
-    // C++:  Mat getPerspectiveTransform(Mat src, Mat dst)
-    private static native long getPerspectiveTransform_0(long src_nativeObj, long dst_nativeObj);
-
-    // C++:  Mat getRotationMatrix2D(Point2f center, double angle, double scale)
-    private static native long getRotationMatrix2D_0(double center_x, double center_y, double angle, double scale);
-
-    // C++:  Mat getStructuringElement(int shape, Size ksize, Point anchor = Point(-1,-1))
-    private static native long getStructuringElement_0(int shape, double ksize_width, double ksize_height, double anchor_x, double anchor_y);
-    private static native long getStructuringElement_1(int shape, double ksize_width, double ksize_height);
-
-    // C++:  Moments moments(Mat array, bool binaryImage = false)
-    private static native double[] moments_0(long array_nativeObj, boolean binaryImage);
-    private static native double[] moments_1(long array_nativeObj);
-
-    // C++:  Point2d phaseCorrelate(Mat src1, Mat src2, Mat window = Mat(), double* response = 0)
-    private static native double[] phaseCorrelate_0(long src1_nativeObj, long src2_nativeObj, long window_nativeObj, double[] response_out);
-    private static native double[] phaseCorrelate_1(long src1_nativeObj, long src2_nativeObj);
-
-    // C++:  Ptr_CLAHE createCLAHE(double clipLimit = 40.0, Size tileGridSize = Size(8, 8))
-    private static native long createCLAHE_0(double clipLimit, double tileGridSize_width, double tileGridSize_height);
-    private static native long createCLAHE_1();
-
-    // C++:  Ptr_LineSegmentDetector createLineSegmentDetector(int _refine = LSD_REFINE_STD, double _scale = 0.8, double _sigma_scale = 0.6, double _quant = 2.0, double _ang_th = 22.5, double _log_eps = 0, double _density_th = 0.7, int _n_bins = 1024)
-    private static native long createLineSegmentDetector_0(int _refine, double _scale, double _sigma_scale, double _quant, double _ang_th, double _log_eps, double _density_th, int _n_bins);
-    private static native long createLineSegmentDetector_1();
-
-    // C++:  Rect boundingRect(vector_Point points)
-    private static native double[] boundingRect_0(long points_mat_nativeObj);
-
-    // C++:  RotatedRect fitEllipse(vector_Point2f points)
-    private static native double[] fitEllipse_0(long points_mat_nativeObj);
-
-    // C++:  RotatedRect fitEllipseAMS(Mat points)
-    private static native double[] fitEllipseAMS_0(long points_nativeObj);
-
-    // C++:  RotatedRect fitEllipseDirect(Mat points)
-    private static native double[] fitEllipseDirect_0(long points_nativeObj);
-
-    // C++:  RotatedRect minAreaRect(vector_Point2f points)
-    private static native double[] minAreaRect_0(long points_mat_nativeObj);
-
-    // C++:  bool clipLine(Rect imgRect, Point& pt1, Point& pt2)
-    private static native boolean clipLine_0(int imgRect_x, int imgRect_y, int imgRect_width, int imgRect_height, double pt1_x, double pt1_y, double[] pt1_out, double pt2_x, double pt2_y, double[] pt2_out);
-
-    // C++:  bool isContourConvex(vector_Point contour)
-    private static native boolean isContourConvex_0(long contour_mat_nativeObj);
-
-    // C++:  double arcLength(vector_Point2f curve, bool closed)
-    private static native double arcLength_0(long curve_mat_nativeObj, boolean closed);
-
-    // C++:  double compareHist(Mat H1, Mat H2, int method)
-    private static native double compareHist_0(long H1_nativeObj, long H2_nativeObj, int method);
-
-    // C++:  double contourArea(Mat contour, bool oriented = false)
-    private static native double contourArea_0(long contour_nativeObj, boolean oriented);
-    private static native double contourArea_1(long contour_nativeObj);
-
-    // C++:  double getFontScaleFromHeight(int fontFace, int pixelHeight, int thickness = 1)
-    private static native double getFontScaleFromHeight_0(int fontFace, int pixelHeight, int thickness);
-    private static native double getFontScaleFromHeight_1(int fontFace, int pixelHeight);
-
-    // C++:  double matchShapes(Mat contour1, Mat contour2, int method, double parameter)
-    private static native double matchShapes_0(long contour1_nativeObj, long contour2_nativeObj, int method, double parameter);
-
-    // C++:  double minEnclosingTriangle(Mat points, Mat& triangle)
-    private static native double minEnclosingTriangle_0(long points_nativeObj, long triangle_nativeObj);
-
-    // C++:  double pointPolygonTest(vector_Point2f contour, Point2f pt, bool measureDist)
-    private static native double pointPolygonTest_0(long contour_mat_nativeObj, double pt_x, double pt_y, boolean measureDist);
-
-    // C++:  double threshold(Mat src, Mat& dst, double thresh, double maxval, int type)
-    private static native double threshold_0(long src_nativeObj, long dst_nativeObj, double thresh, double maxval, int type);
-
-    // C++:  float initWideAngleProjMap(Mat cameraMatrix, Mat distCoeffs, Size imageSize, int destImageWidth, int m1type, Mat& map1, Mat& map2, int projType = PROJ_SPHERICAL_EQRECT, double alpha = 0)
-    private static native float initWideAngleProjMap_0(long cameraMatrix_nativeObj, long distCoeffs_nativeObj, double imageSize_width, double imageSize_height, int destImageWidth, int m1type, long map1_nativeObj, long map2_nativeObj, int projType, double alpha);
-    private static native float initWideAngleProjMap_1(long cameraMatrix_nativeObj, long distCoeffs_nativeObj, double imageSize_width, double imageSize_height, int destImageWidth, int m1type, long map1_nativeObj, long map2_nativeObj);
-
-    // C++:  float intersectConvexConvex(Mat _p1, Mat _p2, Mat& _p12, bool handleNested = true)
-    private static native float intersectConvexConvex_0(long _p1_nativeObj, long _p2_nativeObj, long _p12_nativeObj, boolean handleNested);
-    private static native float intersectConvexConvex_1(long _p1_nativeObj, long _p2_nativeObj, long _p12_nativeObj);
-
-    // C++:  float wrapperEMD(Mat signature1, Mat signature2, int distType, Mat cost = Mat(), Ptr_float& lowerBound = Ptr<float>(), Mat& flow = Mat())
-    private static native float EMD_0(long signature1_nativeObj, long signature2_nativeObj, int distType, long cost_nativeObj, long flow_nativeObj);
-    private static native float EMD_1(long signature1_nativeObj, long signature2_nativeObj, int distType);
-
-    // C++:  int connectedComponents(Mat image, Mat& labels, int connectivity, int ltype, int ccltype)
-    private static native int connectedComponentsWithAlgorithm_0(long image_nativeObj, long labels_nativeObj, int connectivity, int ltype, int ccltype);
-
-    // C++:  int connectedComponents(Mat image, Mat& labels, int connectivity = 8, int ltype = CV_32S)
-    private static native int connectedComponents_0(long image_nativeObj, long labels_nativeObj, int connectivity, int ltype);
-    private static native int connectedComponents_1(long image_nativeObj, long labels_nativeObj);
-
-    // C++:  int connectedComponentsWithStats(Mat image, Mat& labels, Mat& stats, Mat& centroids, int connectivity, int ltype, int ccltype)
-    private static native int connectedComponentsWithStatsWithAlgorithm_0(long image_nativeObj, long labels_nativeObj, long stats_nativeObj, long centroids_nativeObj, int connectivity, int ltype, int ccltype);
-
-    // C++:  int connectedComponentsWithStats(Mat image, Mat& labels, Mat& stats, Mat& centroids, int connectivity = 8, int ltype = CV_32S)
-    private static native int connectedComponentsWithStats_0(long image_nativeObj, long labels_nativeObj, long stats_nativeObj, long centroids_nativeObj, int connectivity, int ltype);
-    private static native int connectedComponentsWithStats_1(long image_nativeObj, long labels_nativeObj, long stats_nativeObj, long centroids_nativeObj);
-
-    // C++:  int floodFill(Mat& image, Mat& mask, Point seedPoint, Scalar newVal, Rect* rect = 0, Scalar loDiff = Scalar(), Scalar upDiff = Scalar(), int flags = 4)
-    private static native int floodFill_0(long image_nativeObj, long mask_nativeObj, double seedPoint_x, double seedPoint_y, double newVal_val0, double newVal_val1, double newVal_val2, double newVal_val3, double[] rect_out, double loDiff_val0, double loDiff_val1, double loDiff_val2, double loDiff_val3, double upDiff_val0, double upDiff_val1, double upDiff_val2, double upDiff_val3, int flags);
-    private static native int floodFill_1(long image_nativeObj, long mask_nativeObj, double seedPoint_x, double seedPoint_y, double newVal_val0, double newVal_val1, double newVal_val2, double newVal_val3);
-
-    // C++:  int rotatedRectangleIntersection(RotatedRect rect1, RotatedRect rect2, Mat& intersectingRegion)
-    private static native int rotatedRectangleIntersection_0(double rect1_center_x, double rect1_center_y, double rect1_size_width, double rect1_size_height, double rect1_angle, double rect2_center_x, double rect2_center_y, double rect2_size_width, double rect2_size_height, double rect2_angle, long intersectingRegion_nativeObj);
-
-    // C++:  void Canny(Mat dx, Mat dy, Mat& edges, double threshold1, double threshold2, bool L2gradient = false)
-    private static native void Canny_0(long dx_nativeObj, long dy_nativeObj, long edges_nativeObj, double threshold1, double threshold2, boolean L2gradient);
-    private static native void Canny_1(long dx_nativeObj, long dy_nativeObj, long edges_nativeObj, double threshold1, double threshold2);
-
-    // C++:  void Canny(Mat image, Mat& edges, double threshold1, double threshold2, int apertureSize = 3, bool L2gradient = false)
-    private static native void Canny_2(long image_nativeObj, long edges_nativeObj, double threshold1, double threshold2, int apertureSize, boolean L2gradient);
-    private static native void Canny_3(long image_nativeObj, long edges_nativeObj, double threshold1, double threshold2);
-
-    // C++:  void GaussianBlur(Mat src, Mat& dst, Size ksize, double sigmaX, double sigmaY = 0, int borderType = BORDER_DEFAULT)
-    private static native void GaussianBlur_0(long src_nativeObj, long dst_nativeObj, double ksize_width, double ksize_height, double sigmaX, double sigmaY, int borderType);
-    private static native void GaussianBlur_1(long src_nativeObj, long dst_nativeObj, double ksize_width, double ksize_height, double sigmaX, double sigmaY);
-    private static native void GaussianBlur_2(long src_nativeObj, long dst_nativeObj, double ksize_width, double ksize_height, double sigmaX);
-
-    // C++:  void HoughCircles(Mat image, Mat& circles, int method, double dp, double minDist, double param1 = 100, double param2 = 100, int minRadius = 0, int maxRadius = 0)
-    private static native void HoughCircles_0(long image_nativeObj, long circles_nativeObj, int method, double dp, double minDist, double param1, double param2, int minRadius, int maxRadius);
-    private static native void HoughCircles_1(long image_nativeObj, long circles_nativeObj, int method, double dp, double minDist);
-
-    // C++:  void HoughLines(Mat image, Mat& lines, double rho, double theta, int threshold, double srn = 0, double stn = 0, double min_theta = 0, double max_theta = CV_PI)
-    private static native void HoughLines_0(long image_nativeObj, long lines_nativeObj, double rho, double theta, int threshold, double srn, double stn, double min_theta, double max_theta);
-    private static native void HoughLines_1(long image_nativeObj, long lines_nativeObj, double rho, double theta, int threshold);
-
-    // C++:  void HoughLinesP(Mat image, Mat& lines, double rho, double theta, int threshold, double minLineLength = 0, double maxLineGap = 0)
-    private static native void HoughLinesP_0(long image_nativeObj, long lines_nativeObj, double rho, double theta, int threshold, double minLineLength, double maxLineGap);
-    private static native void HoughLinesP_1(long image_nativeObj, long lines_nativeObj, double rho, double theta, int threshold);
-
-    // C++:  void HoughLinesPointSet(Mat _point, Mat& _lines, int lines_max, int threshold, double min_rho, double max_rho, double rho_step, double min_theta, double max_theta, double theta_step)
-    private static native void HoughLinesPointSet_0(long _point_nativeObj, long _lines_nativeObj, int lines_max, int threshold, double min_rho, double max_rho, double rho_step, double min_theta, double max_theta, double theta_step);
-
-    // C++:  void HuMoments(Moments m, Mat& hu)
-    private static native void HuMoments_0(double m_m00, double m_m10, double m_m01, double m_m20, double m_m11, double m_m02, double m_m30, double m_m21, double m_m12, double m_m03, long hu_nativeObj);
-
-    // C++:  void Laplacian(Mat src, Mat& dst, int ddepth, int ksize = 1, double scale = 1, double delta = 0, int borderType = BORDER_DEFAULT)
-    private static native void Laplacian_0(long src_nativeObj, long dst_nativeObj, int ddepth, int ksize, double scale, double delta, int borderType);
-    private static native void Laplacian_1(long src_nativeObj, long dst_nativeObj, int ddepth, int ksize, double scale, double delta);
-    private static native void Laplacian_2(long src_nativeObj, long dst_nativeObj, int ddepth);
-
-    // C++:  void Scharr(Mat src, Mat& dst, int ddepth, int dx, int dy, double scale = 1, double delta = 0, int borderType = BORDER_DEFAULT)
-    private static native void Scharr_0(long src_nativeObj, long dst_nativeObj, int ddepth, int dx, int dy, double scale, double delta, int borderType);
-    private static native void Scharr_1(long src_nativeObj, long dst_nativeObj, int ddepth, int dx, int dy, double scale, double delta);
-    private static native void Scharr_2(long src_nativeObj, long dst_nativeObj, int ddepth, int dx, int dy);
-
-    // C++:  void Sobel(Mat src, Mat& dst, int ddepth, int dx, int dy, int ksize = 3, double scale = 1, double delta = 0, int borderType = BORDER_DEFAULT)
-    private static native void Sobel_0(long src_nativeObj, long dst_nativeObj, int ddepth, int dx, int dy, int ksize, double scale, double delta, int borderType);
-    private static native void Sobel_1(long src_nativeObj, long dst_nativeObj, int ddepth, int dx, int dy, int ksize, double scale, double delta);
-    private static native void Sobel_2(long src_nativeObj, long dst_nativeObj, int ddepth, int dx, int dy);
-
-    // C++:  void accumulate(Mat src, Mat& dst, Mat mask = Mat())
-    private static native void accumulate_0(long src_nativeObj, long dst_nativeObj, long mask_nativeObj);
-    private static native void accumulate_1(long src_nativeObj, long dst_nativeObj);
-
-    // C++:  void accumulateProduct(Mat src1, Mat src2, Mat& dst, Mat mask = Mat())
-    private static native void accumulateProduct_0(long src1_nativeObj, long src2_nativeObj, long dst_nativeObj, long mask_nativeObj);
-    private static native void accumulateProduct_1(long src1_nativeObj, long src2_nativeObj, long dst_nativeObj);
-
-    // C++:  void accumulateSquare(Mat src, Mat& dst, Mat mask = Mat())
-    private static native void accumulateSquare_0(long src_nativeObj, long dst_nativeObj, long mask_nativeObj);
-    private static native void accumulateSquare_1(long src_nativeObj, long dst_nativeObj);
-
-    // C++:  void accumulateWeighted(Mat src, Mat& dst, double alpha, Mat mask = Mat())
-    private static native void accumulateWeighted_0(long src_nativeObj, long dst_nativeObj, double alpha, long mask_nativeObj);
-    private static native void accumulateWeighted_1(long src_nativeObj, long dst_nativeObj, double alpha);
-
-    // C++:  void adaptiveThreshold(Mat src, Mat& dst, double maxValue, int adaptiveMethod, int thresholdType, int blockSize, double C)
-    private static native void adaptiveThreshold_0(long src_nativeObj, long dst_nativeObj, double maxValue, int adaptiveMethod, int thresholdType, int blockSize, double C);
-
-    // C++:  void applyColorMap(Mat src, Mat& dst, Mat userColor)
-    private static native void applyColorMap_0(long src_nativeObj, long dst_nativeObj, long userColor_nativeObj);
-
-    // C++:  void applyColorMap(Mat src, Mat& dst, int colormap)
-    private static native void applyColorMap_1(long src_nativeObj, long dst_nativeObj, int colormap);
-
-    // C++:  void approxPolyDP(vector_Point2f curve, vector_Point2f& approxCurve, double epsilon, bool closed)
-    private static native void approxPolyDP_0(long curve_mat_nativeObj, long approxCurve_mat_nativeObj, double epsilon, boolean closed);
-
-    // C++:  void arrowedLine(Mat& img, Point pt1, Point pt2, Scalar color, int thickness = 1, int line_type = 8, int shift = 0, double tipLength = 0.1)
-    private static native void arrowedLine_0(long img_nativeObj, double pt1_x, double pt1_y, double pt2_x, double pt2_y, double color_val0, double color_val1, double color_val2, double color_val3, int thickness, int line_type, int shift, double tipLength);
-    private static native void arrowedLine_1(long img_nativeObj, double pt1_x, double pt1_y, double pt2_x, double pt2_y, double color_val0, double color_val1, double color_val2, double color_val3);
-
-    // C++:  void bilateralFilter(Mat src, Mat& dst, int d, double sigmaColor, double sigmaSpace, int borderType = BORDER_DEFAULT)
-    private static native void bilateralFilter_0(long src_nativeObj, long dst_nativeObj, int d, double sigmaColor, double sigmaSpace, int borderType);
-    private static native void bilateralFilter_1(long src_nativeObj, long dst_nativeObj, int d, double sigmaColor, double sigmaSpace);
-
-    // C++:  void blur(Mat src, Mat& dst, Size ksize, Point anchor = Point(-1,-1), int borderType = BORDER_DEFAULT)
-    private static native void blur_0(long src_nativeObj, long dst_nativeObj, double ksize_width, double ksize_height, double anchor_x, double anchor_y, int borderType);
-    private static native void blur_1(long src_nativeObj, long dst_nativeObj, double ksize_width, double ksize_height, double anchor_x, double anchor_y);
-    private static native void blur_2(long src_nativeObj, long dst_nativeObj, double ksize_width, double ksize_height);
-
-    // C++:  void boxFilter(Mat src, Mat& dst, int ddepth, Size ksize, Point anchor = Point(-1,-1), bool normalize = true, int borderType = BORDER_DEFAULT)
-    private static native void boxFilter_0(long src_nativeObj, long dst_nativeObj, int ddepth, double ksize_width, double ksize_height, double anchor_x, double anchor_y, boolean normalize, int borderType);
-    private static native void boxFilter_1(long src_nativeObj, long dst_nativeObj, int ddepth, double ksize_width, double ksize_height, double anchor_x, double anchor_y, boolean normalize);
-    private static native void boxFilter_2(long src_nativeObj, long dst_nativeObj, int ddepth, double ksize_width, double ksize_height);
-
-    // C++:  void boxPoints(RotatedRect box, Mat& points)
-    private static native void boxPoints_0(double box_center_x, double box_center_y, double box_size_width, double box_size_height, double box_angle, long points_nativeObj);
-
-    // C++:  void calcBackProject(vector_Mat images, vector_int channels, Mat hist, Mat& dst, vector_float ranges, double scale)
-    private static native void calcBackProject_0(long images_mat_nativeObj, long channels_mat_nativeObj, long hist_nativeObj, long dst_nativeObj, long ranges_mat_nativeObj, double scale);
-
-    // C++:  void calcHist(vector_Mat images, vector_int channels, Mat mask, Mat& hist, vector_int histSize, vector_float ranges, bool accumulate = false)
-    private static native void calcHist_0(long images_mat_nativeObj, long channels_mat_nativeObj, long mask_nativeObj, long hist_nativeObj, long histSize_mat_nativeObj, long ranges_mat_nativeObj, boolean accumulate);
-    private static native void calcHist_1(long images_mat_nativeObj, long channels_mat_nativeObj, long mask_nativeObj, long hist_nativeObj, long histSize_mat_nativeObj, long ranges_mat_nativeObj);
-
-    // C++:  void circle(Mat& img, Point center, int radius, Scalar color, int thickness = 1, int lineType = LINE_8, int shift = 0)
-    private static native void circle_0(long img_nativeObj, double center_x, double center_y, int radius, double color_val0, double color_val1, double color_val2, double color_val3, int thickness, int lineType, int shift);
-    private static native void circle_1(long img_nativeObj, double center_x, double center_y, int radius, double color_val0, double color_val1, double color_val2, double color_val3, int thickness);
-    private static native void circle_2(long img_nativeObj, double center_x, double center_y, int radius, double color_val0, double color_val1, double color_val2, double color_val3);
-
-    // C++:  void convertMaps(Mat map1, Mat map2, Mat& dstmap1, Mat& dstmap2, int dstmap1type, bool nninterpolation = false)
-    private static native void convertMaps_0(long map1_nativeObj, long map2_nativeObj, long dstmap1_nativeObj, long dstmap2_nativeObj, int dstmap1type, boolean nninterpolation);
-    private static native void convertMaps_1(long map1_nativeObj, long map2_nativeObj, long dstmap1_nativeObj, long dstmap2_nativeObj, int dstmap1type);
-
-    // C++:  void convexHull(vector_Point points, vector_int& hull, bool clockwise = false,  _hidden_  returnPoints = true)
-    private static native void convexHull_0(long points_mat_nativeObj, long hull_mat_nativeObj, boolean clockwise);
-    private static native void convexHull_1(long points_mat_nativeObj, long hull_mat_nativeObj);
-
-    // C++:  void convexityDefects(vector_Point contour, vector_int convexhull, vector_Vec4i& convexityDefects)
-    private static native void convexityDefects_0(long contour_mat_nativeObj, long convexhull_mat_nativeObj, long convexityDefects_mat_nativeObj);
-
-    // C++:  void cornerEigenValsAndVecs(Mat src, Mat& dst, int blockSize, int ksize, int borderType = BORDER_DEFAULT)
-    private static native void cornerEigenValsAndVecs_0(long src_nativeObj, long dst_nativeObj, int blockSize, int ksize, int borderType);
-    private static native void cornerEigenValsAndVecs_1(long src_nativeObj, long dst_nativeObj, int blockSize, int ksize);
-
-    // C++:  void cornerHarris(Mat src, Mat& dst, int blockSize, int ksize, double k, int borderType = BORDER_DEFAULT)
-    private static native void cornerHarris_0(long src_nativeObj, long dst_nativeObj, int blockSize, int ksize, double k, int borderType);
-    private static native void cornerHarris_1(long src_nativeObj, long dst_nativeObj, int blockSize, int ksize, double k);
-
-    // C++:  void cornerMinEigenVal(Mat src, Mat& dst, int blockSize, int ksize = 3, int borderType = BORDER_DEFAULT)
-    private static native void cornerMinEigenVal_0(long src_nativeObj, long dst_nativeObj, int blockSize, int ksize, int borderType);
-    private static native void cornerMinEigenVal_1(long src_nativeObj, long dst_nativeObj, int blockSize, int ksize);
-    private static native void cornerMinEigenVal_2(long src_nativeObj, long dst_nativeObj, int blockSize);
-
-    // C++:  void cornerSubPix(Mat image, Mat& corners, Size winSize, Size zeroZone, TermCriteria criteria)
-    private static native void cornerSubPix_0(long image_nativeObj, long corners_nativeObj, double winSize_width, double winSize_height, double zeroZone_width, double zeroZone_height, int criteria_type, int criteria_maxCount, double criteria_epsilon);
-
-    // C++:  void createHanningWindow(Mat& dst, Size winSize, int type)
-    private static native void createHanningWindow_0(long dst_nativeObj, double winSize_width, double winSize_height, int type);
-
-    // C++:  void cvtColor(Mat src, Mat& dst, int code, int dstCn = 0)
-    private static native void cvtColor_0(long src_nativeObj, long dst_nativeObj, int code, int dstCn);
-    private static native void cvtColor_1(long src_nativeObj, long dst_nativeObj, int code);
-
-    // C++:  void cvtColorTwoPlane(Mat src1, Mat src2, Mat& dst, int code)
-    private static native void cvtColorTwoPlane_0(long src1_nativeObj, long src2_nativeObj, long dst_nativeObj, int code);
-
-    // C++:  void demosaicing(Mat _src, Mat& _dst, int code, int dcn = 0)
-    private static native void demosaicing_0(long _src_nativeObj, long _dst_nativeObj, int code, int dcn);
-    private static native void demosaicing_1(long _src_nativeObj, long _dst_nativeObj, int code);
-
-    // C++:  void dilate(Mat src, Mat& dst, Mat kernel, Point anchor = Point(-1,-1), int iterations = 1, int borderType = BORDER_CONSTANT, Scalar borderValue = morphologyDefaultBorderValue())
-    private static native void dilate_0(long src_nativeObj, long dst_nativeObj, long kernel_nativeObj, double anchor_x, double anchor_y, int iterations, int borderType, double borderValue_val0, double borderValue_val1, double borderValue_val2, double borderValue_val3);
-    private static native void dilate_1(long src_nativeObj, long dst_nativeObj, long kernel_nativeObj, double anchor_x, double anchor_y, int iterations);
-    private static native void dilate_2(long src_nativeObj, long dst_nativeObj, long kernel_nativeObj);
-
-    // C++:  void distanceTransform(Mat src, Mat& dst, Mat& labels, int distanceType, int maskSize, int labelType = DIST_LABEL_CCOMP)
-    private static native void distanceTransformWithLabels_0(long src_nativeObj, long dst_nativeObj, long labels_nativeObj, int distanceType, int maskSize, int labelType);
-    private static native void distanceTransformWithLabels_1(long src_nativeObj, long dst_nativeObj, long labels_nativeObj, int distanceType, int maskSize);
-
-    // C++:  void distanceTransform(Mat src, Mat& dst, int distanceType, int maskSize, int dstType = CV_32F)
-    private static native void distanceTransform_0(long src_nativeObj, long dst_nativeObj, int distanceType, int maskSize, int dstType);
-    private static native void distanceTransform_1(long src_nativeObj, long dst_nativeObj, int distanceType, int maskSize);
-
-    // C++:  void drawContours(Mat& image, vector_vector_Point contours, int contourIdx, Scalar color, int thickness = 1, int lineType = LINE_8, Mat hierarchy = Mat(), int maxLevel = INT_MAX, Point offset = Point())
-    private static native void drawContours_0(long image_nativeObj, long contours_mat_nativeObj, int contourIdx, double color_val0, double color_val1, double color_val2, double color_val3, int thickness, int lineType, long hierarchy_nativeObj, int maxLevel, double offset_x, double offset_y);
-    private static native void drawContours_1(long image_nativeObj, long contours_mat_nativeObj, int contourIdx, double color_val0, double color_val1, double color_val2, double color_val3, int thickness);
-    private static native void drawContours_2(long image_nativeObj, long contours_mat_nativeObj, int contourIdx, double color_val0, double color_val1, double color_val2, double color_val3);
-
-    // C++:  void drawMarker(Mat& img, Point position, Scalar color, int markerType = MARKER_CROSS, int markerSize = 20, int thickness = 1, int line_type = 8)
-    private static native void drawMarker_0(long img_nativeObj, double position_x, double position_y, double color_val0, double color_val1, double color_val2, double color_val3, int markerType, int markerSize, int thickness, int line_type);
-    private static native void drawMarker_1(long img_nativeObj, double position_x, double position_y, double color_val0, double color_val1, double color_val2, double color_val3);
-
-    // C++:  void ellipse(Mat& img, Point center, Size axes, double angle, double startAngle, double endAngle, Scalar color, int thickness = 1, int lineType = LINE_8, int shift = 0)
-    private static native void ellipse_0(long img_nativeObj, double center_x, double center_y, double axes_width, double axes_height, double angle, double startAngle, double endAngle, double color_val0, double color_val1, double color_val2, double color_val3, int thickness, int lineType, int shift);
-    private static native void ellipse_1(long img_nativeObj, double center_x, double center_y, double axes_width, double axes_height, double angle, double startAngle, double endAngle, double color_val0, double color_val1, double color_val2, double color_val3, int thickness);
-    private static native void ellipse_2(long img_nativeObj, double center_x, double center_y, double axes_width, double axes_height, double angle, double startAngle, double endAngle, double color_val0, double color_val1, double color_val2, double color_val3);
-
-    // C++:  void ellipse(Mat& img, RotatedRect box, Scalar color, int thickness = 1, int lineType = LINE_8)
-    private static native void ellipse_3(long img_nativeObj, double box_center_x, double box_center_y, double box_size_width, double box_size_height, double box_angle, double color_val0, double color_val1, double color_val2, double color_val3, int thickness, int lineType);
-    private static native void ellipse_4(long img_nativeObj, double box_center_x, double box_center_y, double box_size_width, double box_size_height, double box_angle, double color_val0, double color_val1, double color_val2, double color_val3, int thickness);
-    private static native void ellipse_5(long img_nativeObj, double box_center_x, double box_center_y, double box_size_width, double box_size_height, double box_angle, double color_val0, double color_val1, double color_val2, double color_val3);
-
-    // C++:  void ellipse2Poly(Point center, Size axes, int angle, int arcStart, int arcEnd, int delta, vector_Point& pts)
-    private static native void ellipse2Poly_0(double center_x, double center_y, double axes_width, double axes_height, int angle, int arcStart, int arcEnd, int delta, long pts_mat_nativeObj);
-
-    // C++:  void equalizeHist(Mat src, Mat& dst)
-    private static native void equalizeHist_0(long src_nativeObj, long dst_nativeObj);
-
-    // C++:  void erode(Mat src, Mat& dst, Mat kernel, Point anchor = Point(-1,-1), int iterations = 1, int borderType = BORDER_CONSTANT, Scalar borderValue = morphologyDefaultBorderValue())
-    private static native void erode_0(long src_nativeObj, long dst_nativeObj, long kernel_nativeObj, double anchor_x, double anchor_y, int iterations, int borderType, double borderValue_val0, double borderValue_val1, double borderValue_val2, double borderValue_val3);
-    private static native void erode_1(long src_nativeObj, long dst_nativeObj, long kernel_nativeObj, double anchor_x, double anchor_y, int iterations);
-    private static native void erode_2(long src_nativeObj, long dst_nativeObj, long kernel_nativeObj);
-
-    // C++:  void fillConvexPoly(Mat& img, vector_Point points, Scalar color, int lineType = LINE_8, int shift = 0)
-    private static native void fillConvexPoly_0(long img_nativeObj, long points_mat_nativeObj, double color_val0, double color_val1, double color_val2, double color_val3, int lineType, int shift);
-    private static native void fillConvexPoly_1(long img_nativeObj, long points_mat_nativeObj, double color_val0, double color_val1, double color_val2, double color_val3);
-
-    // C++:  void fillPoly(Mat& img, vector_vector_Point pts, Scalar color, int lineType = LINE_8, int shift = 0, Point offset = Point())
-    private static native void fillPoly_0(long img_nativeObj, long pts_mat_nativeObj, double color_val0, double color_val1, double color_val2, double color_val3, int lineType, int shift, double offset_x, double offset_y);
-    private static native void fillPoly_1(long img_nativeObj, long pts_mat_nativeObj, double color_val0, double color_val1, double color_val2, double color_val3);
-
-    // C++:  void filter2D(Mat src, Mat& dst, int ddepth, Mat kernel, Point anchor = Point(-1,-1), double delta = 0, int borderType = BORDER_DEFAULT)
-    private static native void filter2D_0(long src_nativeObj, long dst_nativeObj, int ddepth, long kernel_nativeObj, double anchor_x, double anchor_y, double delta, int borderType);
-    private static native void filter2D_1(long src_nativeObj, long dst_nativeObj, int ddepth, long kernel_nativeObj, double anchor_x, double anchor_y, double delta);
-    private static native void filter2D_2(long src_nativeObj, long dst_nativeObj, int ddepth, long kernel_nativeObj);
-
-    // C++:  void findContours(Mat& image, vector_vector_Point& contours, Mat& hierarchy, int mode, int method, Point offset = Point())
-    private static native void findContours_0(long image_nativeObj, long contours_mat_nativeObj, long hierarchy_nativeObj, int mode, int method, double offset_x, double offset_y);
-    private static native void findContours_1(long image_nativeObj, long contours_mat_nativeObj, long hierarchy_nativeObj, int mode, int method);
-
-    // C++:  void fitLine(Mat points, Mat& line, int distType, double param, double reps, double aeps)
-    private static native void fitLine_0(long points_nativeObj, long line_nativeObj, int distType, double param, double reps, double aeps);
-
-    // C++:  void getDerivKernels(Mat& kx, Mat& ky, int dx, int dy, int ksize, bool normalize = false, int ktype = CV_32F)
-    private static native void getDerivKernels_0(long kx_nativeObj, long ky_nativeObj, int dx, int dy, int ksize, boolean normalize, int ktype);
-    private static native void getDerivKernels_1(long kx_nativeObj, long ky_nativeObj, int dx, int dy, int ksize);
-
-    // C++:  void getRectSubPix(Mat image, Size patchSize, Point2f center, Mat& patch, int patchType = -1)
-    private static native void getRectSubPix_0(long image_nativeObj, double patchSize_width, double patchSize_height, double center_x, double center_y, long patch_nativeObj, int patchType);
-    private static native void getRectSubPix_1(long image_nativeObj, double patchSize_width, double patchSize_height, double center_x, double center_y, long patch_nativeObj);
-
-    // C++:  void goodFeaturesToTrack(Mat image, vector_Point& corners, int maxCorners, double qualityLevel, double minDistance, Mat mask, int blockSize, int gradientSize, bool useHarrisDetector = false, double k = 0.04)
-    private static native void goodFeaturesToTrack_0(long image_nativeObj, long corners_mat_nativeObj, int maxCorners, double qualityLevel, double minDistance, long mask_nativeObj, int blockSize, int gradientSize, boolean useHarrisDetector, double k);
-    private static native void goodFeaturesToTrack_1(long image_nativeObj, long corners_mat_nativeObj, int maxCorners, double qualityLevel, double minDistance, long mask_nativeObj, int blockSize, int gradientSize);
-
-    // C++:  void goodFeaturesToTrack(Mat image, vector_Point& corners, int maxCorners, double qualityLevel, double minDistance, Mat mask = Mat(), int blockSize = 3, bool useHarrisDetector = false, double k = 0.04)
-    private static native void goodFeaturesToTrack_2(long image_nativeObj, long corners_mat_nativeObj, int maxCorners, double qualityLevel, double minDistance, long mask_nativeObj, int blockSize, boolean useHarrisDetector, double k);
-    private static native void goodFeaturesToTrack_3(long image_nativeObj, long corners_mat_nativeObj, int maxCorners, double qualityLevel, double minDistance);
-
-    // C++:  void grabCut(Mat img, Mat& mask, Rect rect, Mat& bgdModel, Mat& fgdModel, int iterCount, int mode = GC_EVAL)
-    private static native void grabCut_0(long img_nativeObj, long mask_nativeObj, int rect_x, int rect_y, int rect_width, int rect_height, long bgdModel_nativeObj, long fgdModel_nativeObj, int iterCount, int mode);
-    private static native void grabCut_1(long img_nativeObj, long mask_nativeObj, int rect_x, int rect_y, int rect_width, int rect_height, long bgdModel_nativeObj, long fgdModel_nativeObj, int iterCount);
-
-    // C++:  void initUndistortRectifyMap(Mat cameraMatrix, Mat distCoeffs, Mat R, Mat newCameraMatrix, Size size, int m1type, Mat& map1, Mat& map2)
-    private static native void initUndistortRectifyMap_0(long cameraMatrix_nativeObj, long distCoeffs_nativeObj, long R_nativeObj, long newCameraMatrix_nativeObj, double size_width, double size_height, int m1type, long map1_nativeObj, long map2_nativeObj);
-
-    // C++:  void integral(Mat src, Mat& sum, Mat& sqsum, Mat& tilted, int sdepth = -1, int sqdepth = -1)
-    private static native void integral3_0(long src_nativeObj, long sum_nativeObj, long sqsum_nativeObj, long tilted_nativeObj, int sdepth, int sqdepth);
-    private static native void integral3_1(long src_nativeObj, long sum_nativeObj, long sqsum_nativeObj, long tilted_nativeObj);
-
-    // C++:  void integral(Mat src, Mat& sum, Mat& sqsum, int sdepth = -1, int sqdepth = -1)
-    private static native void integral2_0(long src_nativeObj, long sum_nativeObj, long sqsum_nativeObj, int sdepth, int sqdepth);
-    private static native void integral2_1(long src_nativeObj, long sum_nativeObj, long sqsum_nativeObj);
-
-    // C++:  void integral(Mat src, Mat& sum, int sdepth = -1)
-    private static native void integral_0(long src_nativeObj, long sum_nativeObj, int sdepth);
-    private static native void integral_1(long src_nativeObj, long sum_nativeObj);
-
-    // C++:  void invertAffineTransform(Mat M, Mat& iM)
-    private static native void invertAffineTransform_0(long M_nativeObj, long iM_nativeObj);
-
-    // C++:  void line(Mat& img, Point pt1, Point pt2, Scalar color, int thickness = 1, int lineType = LINE_8, int shift = 0)
-    private static native void line_0(long img_nativeObj, double pt1_x, double pt1_y, double pt2_x, double pt2_y, double color_val0, double color_val1, double color_val2, double color_val3, int thickness, int lineType, int shift);
-    private static native void line_1(long img_nativeObj, double pt1_x, double pt1_y, double pt2_x, double pt2_y, double color_val0, double color_val1, double color_val2, double color_val3, int thickness);
-    private static native void line_2(long img_nativeObj, double pt1_x, double pt1_y, double pt2_x, double pt2_y, double color_val0, double color_val1, double color_val2, double color_val3);
-
-    // C++:  void linearPolar(Mat src, Mat& dst, Point2f center, double maxRadius, int flags)
-    private static native void linearPolar_0(long src_nativeObj, long dst_nativeObj, double center_x, double center_y, double maxRadius, int flags);
-
-    // C++:  void logPolar(Mat src, Mat& dst, Point2f center, double M, int flags)
-    private static native void logPolar_0(long src_nativeObj, long dst_nativeObj, double center_x, double center_y, double M, int flags);
-
-    // C++:  void matchTemplate(Mat image, Mat templ, Mat& result, int method, Mat mask = Mat())
-    private static native void matchTemplate_0(long image_nativeObj, long templ_nativeObj, long result_nativeObj, int method, long mask_nativeObj);
-    private static native void matchTemplate_1(long image_nativeObj, long templ_nativeObj, long result_nativeObj, int method);
-
-    // C++:  void medianBlur(Mat src, Mat& dst, int ksize)
-    private static native void medianBlur_0(long src_nativeObj, long dst_nativeObj, int ksize);
-
-    // C++:  void minEnclosingCircle(vector_Point2f points, Point2f& center, float& radius)
-    private static native void minEnclosingCircle_0(long points_mat_nativeObj, double[] center_out, double[] radius_out);
-
-    // C++:  void morphologyEx(Mat src, Mat& dst, int op, Mat kernel, Point anchor = Point(-1,-1), int iterations = 1, int borderType = BORDER_CONSTANT, Scalar borderValue = morphologyDefaultBorderValue())
-    private static native void morphologyEx_0(long src_nativeObj, long dst_nativeObj, int op, long kernel_nativeObj, double anchor_x, double anchor_y, int iterations, int borderType, double borderValue_val0, double borderValue_val1, double borderValue_val2, double borderValue_val3);
-    private static native void morphologyEx_1(long src_nativeObj, long dst_nativeObj, int op, long kernel_nativeObj, double anchor_x, double anchor_y, int iterations);
-    private static native void morphologyEx_2(long src_nativeObj, long dst_nativeObj, int op, long kernel_nativeObj);
-
-    // C++:  void polylines(Mat& img, vector_vector_Point pts, bool isClosed, Scalar color, int thickness = 1, int lineType = LINE_8, int shift = 0)
-    private static native void polylines_0(long img_nativeObj, long pts_mat_nativeObj, boolean isClosed, double color_val0, double color_val1, double color_val2, double color_val3, int thickness, int lineType, int shift);
-    private static native void polylines_1(long img_nativeObj, long pts_mat_nativeObj, boolean isClosed, double color_val0, double color_val1, double color_val2, double color_val3, int thickness);
-    private static native void polylines_2(long img_nativeObj, long pts_mat_nativeObj, boolean isClosed, double color_val0, double color_val1, double color_val2, double color_val3);
-
-    // C++:  void preCornerDetect(Mat src, Mat& dst, int ksize, int borderType = BORDER_DEFAULT)
-    private static native void preCornerDetect_0(long src_nativeObj, long dst_nativeObj, int ksize, int borderType);
-    private static native void preCornerDetect_1(long src_nativeObj, long dst_nativeObj, int ksize);
-
-    // C++:  void putText(Mat& img, String text, Point org, int fontFace, double fontScale, Scalar color, int thickness = 1, int lineType = LINE_8, bool bottomLeftOrigin = false)
-    private static native void putText_0(long img_nativeObj, String text, double org_x, double org_y, int fontFace, double fontScale, double color_val0, double color_val1, double color_val2, double color_val3, int thickness, int lineType, boolean bottomLeftOrigin);
-    private static native void putText_1(long img_nativeObj, String text, double org_x, double org_y, int fontFace, double fontScale, double color_val0, double color_val1, double color_val2, double color_val3, int thickness);
-    private static native void putText_2(long img_nativeObj, String text, double org_x, double org_y, int fontFace, double fontScale, double color_val0, double color_val1, double color_val2, double color_val3);
-
-    // C++:  void pyrDown(Mat src, Mat& dst, Size dstsize = Size(), int borderType = BORDER_DEFAULT)
-    private static native void pyrDown_0(long src_nativeObj, long dst_nativeObj, double dstsize_width, double dstsize_height, int borderType);
-    private static native void pyrDown_1(long src_nativeObj, long dst_nativeObj, double dstsize_width, double dstsize_height);
-    private static native void pyrDown_2(long src_nativeObj, long dst_nativeObj);
-
-    // C++:  void pyrMeanShiftFiltering(Mat src, Mat& dst, double sp, double sr, int maxLevel = 1, TermCriteria termcrit = TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,5,1))
-    private static native void pyrMeanShiftFiltering_0(long src_nativeObj, long dst_nativeObj, double sp, double sr, int maxLevel, int termcrit_type, int termcrit_maxCount, double termcrit_epsilon);
-    private static native void pyrMeanShiftFiltering_1(long src_nativeObj, long dst_nativeObj, double sp, double sr);
-
-    // C++:  void pyrUp(Mat src, Mat& dst, Size dstsize = Size(), int borderType = BORDER_DEFAULT)
-    private static native void pyrUp_0(long src_nativeObj, long dst_nativeObj, double dstsize_width, double dstsize_height, int borderType);
-    private static native void pyrUp_1(long src_nativeObj, long dst_nativeObj, double dstsize_width, double dstsize_height);
-    private static native void pyrUp_2(long src_nativeObj, long dst_nativeObj);
-
-    // C++:  void rectangle(Mat& img, Point pt1, Point pt2, Scalar color, int thickness = 1, int lineType = LINE_8, int shift = 0)
-    private static native void rectangle_0(long img_nativeObj, double pt1_x, double pt1_y, double pt2_x, double pt2_y, double color_val0, double color_val1, double color_val2, double color_val3, int thickness, int lineType, int shift);
-    private static native void rectangle_1(long img_nativeObj, double pt1_x, double pt1_y, double pt2_x, double pt2_y, double color_val0, double color_val1, double color_val2, double color_val3, int thickness);
-    private static native void rectangle_2(long img_nativeObj, double pt1_x, double pt1_y, double pt2_x, double pt2_y, double color_val0, double color_val1, double color_val2, double color_val3);
-
-    // C++:  void remap(Mat src, Mat& dst, Mat map1, Mat map2, int interpolation, int borderMode = BORDER_CONSTANT, Scalar borderValue = Scalar())
-    private static native void remap_0(long src_nativeObj, long dst_nativeObj, long map1_nativeObj, long map2_nativeObj, int interpolation, int borderMode, double borderValue_val0, double borderValue_val1, double borderValue_val2, double borderValue_val3);
-    private static native void remap_1(long src_nativeObj, long dst_nativeObj, long map1_nativeObj, long map2_nativeObj, int interpolation);
-
-    // C++:  void resize(Mat src, Mat& dst, Size dsize, double fx = 0, double fy = 0, int interpolation = INTER_LINEAR)
-    private static native void resize_0(long src_nativeObj, long dst_nativeObj, double dsize_width, double dsize_height, double fx, double fy, int interpolation);
-    private static native void resize_1(long src_nativeObj, long dst_nativeObj, double dsize_width, double dsize_height);
-
-    // C++:  void sepFilter2D(Mat src, Mat& dst, int ddepth, Mat kernelX, Mat kernelY, Point anchor = Point(-1,-1), double delta = 0, int borderType = BORDER_DEFAULT)
-    private static native void sepFilter2D_0(long src_nativeObj, long dst_nativeObj, int ddepth, long kernelX_nativeObj, long kernelY_nativeObj, double anchor_x, double anchor_y, double delta, int borderType);
-    private static native void sepFilter2D_1(long src_nativeObj, long dst_nativeObj, int ddepth, long kernelX_nativeObj, long kernelY_nativeObj, double anchor_x, double anchor_y, double delta);
-    private static native void sepFilter2D_2(long src_nativeObj, long dst_nativeObj, int ddepth, long kernelX_nativeObj, long kernelY_nativeObj);
-
-    // C++:  void spatialGradient(Mat src, Mat& dx, Mat& dy, int ksize = 3, int borderType = BORDER_DEFAULT)
-    private static native void spatialGradient_0(long src_nativeObj, long dx_nativeObj, long dy_nativeObj, int ksize, int borderType);
-    private static native void spatialGradient_1(long src_nativeObj, long dx_nativeObj, long dy_nativeObj, int ksize);
-    private static native void spatialGradient_2(long src_nativeObj, long dx_nativeObj, long dy_nativeObj);
-
-    // C++:  void sqrBoxFilter(Mat _src, Mat& _dst, int ddepth, Size ksize, Point anchor = Point(-1, -1), bool normalize = true, int borderType = BORDER_DEFAULT)
-    private static native void sqrBoxFilter_0(long _src_nativeObj, long _dst_nativeObj, int ddepth, double ksize_width, double ksize_height, double anchor_x, double anchor_y, boolean normalize, int borderType);
-    private static native void sqrBoxFilter_1(long _src_nativeObj, long _dst_nativeObj, int ddepth, double ksize_width, double ksize_height, double anchor_x, double anchor_y, boolean normalize);
-    private static native void sqrBoxFilter_2(long _src_nativeObj, long _dst_nativeObj, int ddepth, double ksize_width, double ksize_height);
-
-    // C++:  void undistort(Mat src, Mat& dst, Mat cameraMatrix, Mat distCoeffs, Mat newCameraMatrix = Mat())
-    private static native void undistort_0(long src_nativeObj, long dst_nativeObj, long cameraMatrix_nativeObj, long distCoeffs_nativeObj, long newCameraMatrix_nativeObj);
-    private static native void undistort_1(long src_nativeObj, long dst_nativeObj, long cameraMatrix_nativeObj, long distCoeffs_nativeObj);
-
-    // C++:  void undistortPoints(Mat src, Mat& dst, Mat cameraMatrix, Mat distCoeffs, Mat R, Mat P, TermCriteria criteria)
-    private static native void undistortPointsIter_0(long src_nativeObj, long dst_nativeObj, long cameraMatrix_nativeObj, long distCoeffs_nativeObj, long R_nativeObj, long P_nativeObj, int criteria_type, int criteria_maxCount, double criteria_epsilon);
-
-    // C++:  void undistortPoints(Mat src, Mat& dst, Mat cameraMatrix, Mat distCoeffs, Mat R = Mat(), Mat P = Mat())
-    private static native void undistortPoints_0(long src_nativeObj, long dst_nativeObj, long cameraMatrix_nativeObj, long distCoeffs_nativeObj, long R_nativeObj, long P_nativeObj);
-    private static native void undistortPoints_1(long src_nativeObj, long dst_nativeObj, long cameraMatrix_nativeObj, long distCoeffs_nativeObj);
-
-    // C++:  void warpAffine(Mat src, Mat& dst, Mat M, Size dsize, int flags = INTER_LINEAR, int borderMode = BORDER_CONSTANT, Scalar borderValue = Scalar())
-    private static native void warpAffine_0(long src_nativeObj, long dst_nativeObj, long M_nativeObj, double dsize_width, double dsize_height, int flags, int borderMode, double borderValue_val0, double borderValue_val1, double borderValue_val2, double borderValue_val3);
-    private static native void warpAffine_1(long src_nativeObj, long dst_nativeObj, long M_nativeObj, double dsize_width, double dsize_height, int flags);
-    private static native void warpAffine_2(long src_nativeObj, long dst_nativeObj, long M_nativeObj, double dsize_width, double dsize_height);
-
-    // C++:  void warpPerspective(Mat src, Mat& dst, Mat M, Size dsize, int flags = INTER_LINEAR, int borderMode = BORDER_CONSTANT, Scalar borderValue = Scalar())
-    private static native void warpPerspective_0(long src_nativeObj, long dst_nativeObj, long M_nativeObj, double dsize_width, double dsize_height, int flags, int borderMode, double borderValue_val0, double borderValue_val1, double borderValue_val2, double borderValue_val3);
-    private static native void warpPerspective_1(long src_nativeObj, long dst_nativeObj, long M_nativeObj, double dsize_width, double dsize_height, int flags);
-    private static native void warpPerspective_2(long src_nativeObj, long dst_nativeObj, long M_nativeObj, double dsize_width, double dsize_height);
-
-    // C++:  void warpPolar(Mat src, Mat& dst, Size dsize, Point2f center, double maxRadius, int flags)
-    private static native void warpPolar_0(long src_nativeObj, long dst_nativeObj, double dsize_width, double dsize_height, double center_x, double center_y, double maxRadius, int flags);
-
-    // C++:  void watershed(Mat image, Mat& markers)
-    private static native void watershed_0(long image_nativeObj, long markers_nativeObj);
-private static native double[] n_getTextSize(String text, int fontFace, double fontScale, int thickness, int[] baseLine);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/imgproc/LineSegmentDetector.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/imgproc/LineSegmentDetector.java
deleted file mode 100755
index e2ee17e..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/imgproc/LineSegmentDetector.java
+++ /dev/null
@@ -1,101 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.imgproc;
-
-import org.opencv.core.Algorithm;
-import org.opencv.core.Mat;
-import org.opencv.core.Size;
-
-// C++: class LineSegmentDetector
-//javadoc: LineSegmentDetector
-
-public class LineSegmentDetector extends Algorithm {
-
-    protected LineSegmentDetector(long addr) { super(addr); }
-
-    // internal usage only
-    public static LineSegmentDetector __fromPtr__(long addr) { return new LineSegmentDetector(addr); }
-
-    //
-    // C++:  int compareSegments(Size size, Mat lines1, Mat lines2, Mat& _image = Mat())
-    //
-
-    //javadoc: LineSegmentDetector::compareSegments(size, lines1, lines2, _image)
-    public  int compareSegments(Size size, Mat lines1, Mat lines2, Mat _image)
-    {
-        
-        int retVal = compareSegments_0(nativeObj, size.width, size.height, lines1.nativeObj, lines2.nativeObj, _image.nativeObj);
-        
-        return retVal;
-    }
-
-    //javadoc: LineSegmentDetector::compareSegments(size, lines1, lines2)
-    public  int compareSegments(Size size, Mat lines1, Mat lines2)
-    {
-        
-        int retVal = compareSegments_1(nativeObj, size.width, size.height, lines1.nativeObj, lines2.nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  void detect(Mat _image, Mat& _lines, Mat& width = Mat(), Mat& prec = Mat(), Mat& nfa = Mat())
-    //
-
-    //javadoc: LineSegmentDetector::detect(_image, _lines, width, prec, nfa)
-    public  void detect(Mat _image, Mat _lines, Mat width, Mat prec, Mat nfa)
-    {
-        
-        detect_0(nativeObj, _image.nativeObj, _lines.nativeObj, width.nativeObj, prec.nativeObj, nfa.nativeObj);
-        
-        return;
-    }
-
-    //javadoc: LineSegmentDetector::detect(_image, _lines)
-    public  void detect(Mat _image, Mat _lines)
-    {
-        
-        detect_1(nativeObj, _image.nativeObj, _lines.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void drawSegments(Mat& _image, Mat lines)
-    //
-
-    //javadoc: LineSegmentDetector::drawSegments(_image, lines)
-    public  void drawSegments(Mat _image, Mat lines)
-    {
-        
-        drawSegments_0(nativeObj, _image.nativeObj, lines.nativeObj);
-        
-        return;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++:  int compareSegments(Size size, Mat lines1, Mat lines2, Mat& _image = Mat())
-    private static native int compareSegments_0(long nativeObj, double size_width, double size_height, long lines1_nativeObj, long lines2_nativeObj, long _image_nativeObj);
-    private static native int compareSegments_1(long nativeObj, double size_width, double size_height, long lines1_nativeObj, long lines2_nativeObj);
-
-    // C++:  void detect(Mat _image, Mat& _lines, Mat& width = Mat(), Mat& prec = Mat(), Mat& nfa = Mat())
-    private static native void detect_0(long nativeObj, long _image_nativeObj, long _lines_nativeObj, long width_nativeObj, long prec_nativeObj, long nfa_nativeObj);
-    private static native void detect_1(long nativeObj, long _image_nativeObj, long _lines_nativeObj);
-
-    // C++:  void drawSegments(Mat& _image, Mat lines)
-    private static native void drawSegments_0(long nativeObj, long _image_nativeObj, long lines_nativeObj);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/imgproc/Moments.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/imgproc/Moments.java
deleted file mode 100755
index 2eeebc9..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/imgproc/Moments.java
+++ /dev/null
@@ -1,244 +0,0 @@
-package org.opencv.imgproc;
-
-import java.lang.Math;
-
-//javadoc:Moments
-public class Moments {
-
-    public double m00;
-    public double m10;
-    public double m01;
-    public double m20;
-    public double m11;
-    public double m02;
-    public double m30;
-    public double m21;
-    public double m12;
-    public double m03;
-
-    public double mu20;
-    public double mu11;
-    public double mu02;
-    public double mu30;
-    public double mu21;
-    public double mu12;
-    public double mu03;
-
-    public double nu20;
-    public double nu11;
-    public double nu02;
-    public double nu30;
-    public double nu21;
-    public double nu12;
-    public double nu03;
-
-    public Moments(
-        double m00,
-        double m10,
-        double m01,
-        double m20,
-        double m11,
-        double m02,
-        double m30,
-        double m21,
-        double m12,
-        double m03)
-    {
-        this.m00 = m00;
-        this.m10 = m10;
-        this.m01 = m01;
-        this.m20 = m20;
-        this.m11 = m11;
-        this.m02 = m02;
-        this.m30 = m30;
-        this.m21 = m21;
-        this.m12 = m12;
-        this.m03 = m03;
-        this.completeState();
-    }
-
-    public Moments() {
-        this(0, 0, 0, 0, 0, 0, 0, 0, 0, 0);
-    }
-
-    public Moments(double[] vals) {
-        set(vals);
-    }
-
-    public void set(double[] vals) {
-        if (vals != null) {
-            m00 = vals.length > 0 ? vals[0] : 0;
-            m10 = vals.length > 1 ? vals[1] : 0;
-            m01 = vals.length > 2 ? vals[2] : 0;
-            m20 = vals.length > 3 ? vals[3] : 0;
-            m11 = vals.length > 4 ? vals[4] : 0;
-            m02 = vals.length > 5 ? vals[5] : 0;
-            m30 = vals.length > 6 ? vals[6] : 0;
-            m21 = vals.length > 7 ? vals[7] : 0;
-            m12 = vals.length > 8 ? vals[8] : 0;
-            m03 = vals.length > 9 ? vals[9] : 0;
-            this.completeState();
-        } else {
-            m00 = 0;
-            m10 = 0;
-            m01 = 0;
-            m20 = 0;
-            m11 = 0;
-            m02 = 0;
-            m30 = 0;
-            m21 = 0;
-            m12 = 0;
-            m03 = 0;
-            mu20 = 0;
-            mu11 = 0;
-            mu02 = 0;
-            mu30 = 0;
-            mu21 = 0;
-            mu12 = 0;
-            mu03 = 0;
-            nu20 = 0;
-            nu11 = 0;
-            nu02 = 0;
-            nu30 = 0;
-            nu21 = 0;
-            nu12 = 0;
-            nu03 = 0;
-        }
-    }
-
-    @Override
-    public String toString() {
-        return "Moments [ " +
-            "\n" +
-            "m00=" + m00 + ", " +
-            "\n" +
-            "m10=" + m10 + ", " +
-            "m01=" + m01 + ", " +
-            "\n" +
-            "m20=" + m20 + ", " +
-            "m11=" + m11 + ", " +
-            "m02=" + m02 + ", " +
-            "\n" +
-            "m30=" + m30 + ", " +
-            "m21=" + m21 + ", " +
-            "m12=" + m12 + ", " +
-            "m03=" + m03 + ", " +
-            "\n" +
-            "mu20=" + mu20 + ", " +
-            "mu11=" + mu11 + ", " +
-            "mu02=" + mu02 + ", " +
-            "\n" +
-            "mu30=" + mu30 + ", " +
-            "mu21=" + mu21 + ", " +
-            "mu12=" + mu12 + ", " +
-            "mu03=" + mu03 + ", " +
-            "\n" +
-            "nu20=" + nu20 + ", " +
-            "nu11=" + nu11 + ", " +
-            "nu02=" + nu02 + ", " +
-            "\n" +
-            "nu30=" + nu30 + ", " +
-            "nu21=" + nu21 + ", " +
-            "nu12=" + nu12 + ", " +
-            "nu03=" + nu03 + ", " +
-            "\n]";
-    }
-
-    protected void completeState()
-    {
-        double cx = 0, cy = 0;
-        double mu20, mu11, mu02;
-        double inv_m00 = 0.0;
-
-        if( Math.abs(this.m00) > 0.00000001 )
-        {
-            inv_m00 = 1. / this.m00;
-            cx = this.m10 * inv_m00;
-            cy = this.m01 * inv_m00;
-        }
-
-        // mu20 = m20 - m10*cx
-        mu20 = this.m20 - this.m10 * cx;
-        // mu11 = m11 - m10*cy
-        mu11 = this.m11 - this.m10 * cy;
-        // mu02 = m02 - m01*cy
-        mu02 = this.m02 - this.m01 * cy;
-
-        this.mu20 = mu20;
-        this.mu11 = mu11;
-        this.mu02 = mu02;
-
-        // mu30 = m30 - cx*(3*mu20 + cx*m10)
-        this.mu30 = this.m30 - cx * (3 * mu20 + cx * this.m10);
-        mu11 += mu11;
-        // mu21 = m21 - cx*(2*mu11 + cx*m01) - cy*mu20
-        this.mu21 = this.m21 - cx * (mu11 + cx * this.m01) - cy * mu20;
-        // mu12 = m12 - cy*(2*mu11 + cy*m10) - cx*mu02
-        this.mu12 = this.m12 - cy * (mu11 + cy * this.m10) - cx * mu02;
-        // mu03 = m03 - cy*(3*mu02 + cy*m01)
-        this.mu03 = this.m03 - cy * (3 * mu02 + cy * this.m01);
-
-
-        double inv_sqrt_m00 = Math.sqrt(Math.abs(inv_m00));
-        double s2 = inv_m00*inv_m00, s3 = s2*inv_sqrt_m00;
-
-        this.nu20 = this.mu20*s2;
-        this.nu11 = this.mu11*s2;
-        this.nu02 = this.mu02*s2;
-        this.nu30 = this.mu30*s3;
-        this.nu21 = this.mu21*s3;
-        this.nu12 = this.mu12*s3;
-        this.nu03 = this.mu03*s3;
-
-    }
-
-    public double get_m00() { return this.m00; }
-    public double get_m10() { return this.m10; }
-    public double get_m01() { return this.m01; }
-    public double get_m20() { return this.m20; }
-    public double get_m11() { return this.m11; }
-    public double get_m02() { return this.m02; }
-    public double get_m30() { return this.m30; }
-    public double get_m21() { return this.m21; }
-    public double get_m12() { return this.m12; }
-    public double get_m03() { return this.m03; }
-    public double get_mu20() { return this.mu20; }
-    public double get_mu11() { return this.mu11; }
-    public double get_mu02() { return this.mu02; }
-    public double get_mu30() { return this.mu30; }
-    public double get_mu21() { return this.mu21; }
-    public double get_mu12() { return this.mu12; }
-    public double get_mu03() { return this.mu03; }
-    public double get_nu20() { return this.nu20; }
-    public double get_nu11() { return this.nu11; }
-    public double get_nu02() { return this.nu02; }
-    public double get_nu30() { return this.nu30; }
-    public double get_nu21() { return this.nu21; }
-    public double get_nu12() { return this.nu12; }
-    public double get_nu03() { return this.nu03; }
-
-    public void set_m00(double m00) { this.m00 = m00; }
-    public void set_m10(double m10) { this.m10 = m10; }
-    public void set_m01(double m01) { this.m01 = m01; }
-    public void set_m20(double m20) { this.m20 = m20; }
-    public void set_m11(double m11) { this.m11 = m11; }
-    public void set_m02(double m02) { this.m02 = m02; }
-    public void set_m30(double m30) { this.m30 = m30; }
-    public void set_m21(double m21) { this.m21 = m21; }
-    public void set_m12(double m12) { this.m12 = m12; }
-    public void set_m03(double m03) { this.m03 = m03; }
-    public void set_mu20(double mu20) { this.mu20 = mu20; }
-    public void set_mu11(double mu11) { this.mu11 = mu11; }
-    public void set_mu02(double mu02) { this.mu02 = mu02; }
-    public void set_mu30(double mu30) { this.mu30 = mu30; }
-    public void set_mu21(double mu21) { this.mu21 = mu21; }
-    public void set_mu12(double mu12) { this.mu12 = mu12; }
-    public void set_mu03(double mu03) { this.mu03 = mu03; }
-    public void set_nu20(double nu20) { this.nu20 = nu20; }
-    public void set_nu11(double nu11) { this.nu11 = nu11; }
-    public void set_nu02(double nu02) { this.nu02 = nu02; }
-    public void set_nu30(double nu30) { this.nu30 = nu30; }
-    public void set_nu21(double nu21) { this.nu21 = nu21; }
-    public void set_nu12(double nu12) { this.nu12 = nu12; }
-    public void set_nu03(double nu03) { this.nu03 = nu03; }
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/imgproc/Subdiv2D.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/imgproc/Subdiv2D.java
deleted file mode 100755
index 2b13383..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/imgproc/Subdiv2D.java
+++ /dev/null
@@ -1,407 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.imgproc;
-
-import java.util.ArrayList;
-import java.util.List;
-import org.opencv.core.Mat;
-import org.opencv.core.MatOfFloat4;
-import org.opencv.core.MatOfFloat6;
-import org.opencv.core.MatOfInt;
-import org.opencv.core.MatOfPoint2f;
-import org.opencv.core.Point;
-import org.opencv.core.Rect;
-import org.opencv.utils.Converters;
-
-// C++: class Subdiv2D
-//javadoc: Subdiv2D
-
-public class Subdiv2D {
-
-    protected final long nativeObj;
-    protected Subdiv2D(long addr) { nativeObj = addr; }
-
-    public long getNativeObjAddr() { return nativeObj; }
-
-    // internal usage only
-    public static Subdiv2D __fromPtr__(long addr) { return new Subdiv2D(addr); }
-
-    public static final int
-            PTLOC_ERROR = -2,
-            PTLOC_OUTSIDE_RECT = -1,
-            PTLOC_INSIDE = 0,
-            PTLOC_VERTEX = 1,
-            PTLOC_ON_EDGE = 2,
-            NEXT_AROUND_ORG = 0x00,
-            NEXT_AROUND_DST = 0x22,
-            PREV_AROUND_ORG = 0x11,
-            PREV_AROUND_DST = 0x33,
-            NEXT_AROUND_LEFT = 0x13,
-            NEXT_AROUND_RIGHT = 0x31,
-            PREV_AROUND_LEFT = 0x20,
-            PREV_AROUND_RIGHT = 0x02;
-
-
-    //
-    // C++:   Subdiv2D(Rect rect)
-    //
-
-    //javadoc: Subdiv2D::Subdiv2D(rect)
-    public   Subdiv2D(Rect rect)
-    {
-        
-        nativeObj = Subdiv2D_0(rect.x, rect.y, rect.width, rect.height);
-        
-        return;
-    }
-
-
-    //
-    // C++:   Subdiv2D()
-    //
-
-    //javadoc: Subdiv2D::Subdiv2D()
-    public   Subdiv2D()
-    {
-        
-        nativeObj = Subdiv2D_1();
-        
-        return;
-    }
-
-
-    //
-    // C++:  Point2f getVertex(int vertex, int* firstEdge = 0)
-    //
-
-    //javadoc: Subdiv2D::getVertex(vertex, firstEdge)
-    public  Point getVertex(int vertex, int[] firstEdge)
-    {
-        double[] firstEdge_out = new double[1];
-        Point retVal = new Point(getVertex_0(nativeObj, vertex, firstEdge_out));
-        if(firstEdge!=null) firstEdge[0] = (int)firstEdge_out[0];
-        return retVal;
-    }
-
-    //javadoc: Subdiv2D::getVertex(vertex)
-    public  Point getVertex(int vertex)
-    {
-        
-        Point retVal = new Point(getVertex_1(nativeObj, vertex));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int edgeDst(int edge, Point2f* dstpt = 0)
-    //
-
-    //javadoc: Subdiv2D::edgeDst(edge, dstpt)
-    public  int edgeDst(int edge, Point dstpt)
-    {
-        double[] dstpt_out = new double[2];
-        int retVal = edgeDst_0(nativeObj, edge, dstpt_out);
-        if(dstpt!=null){ dstpt.x = dstpt_out[0]; dstpt.y = dstpt_out[1]; } 
-        return retVal;
-    }
-
-    //javadoc: Subdiv2D::edgeDst(edge)
-    public  int edgeDst(int edge)
-    {
-        
-        int retVal = edgeDst_1(nativeObj, edge);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int edgeOrg(int edge, Point2f* orgpt = 0)
-    //
-
-    //javadoc: Subdiv2D::edgeOrg(edge, orgpt)
-    public  int edgeOrg(int edge, Point orgpt)
-    {
-        double[] orgpt_out = new double[2];
-        int retVal = edgeOrg_0(nativeObj, edge, orgpt_out);
-        if(orgpt!=null){ orgpt.x = orgpt_out[0]; orgpt.y = orgpt_out[1]; } 
-        return retVal;
-    }
-
-    //javadoc: Subdiv2D::edgeOrg(edge)
-    public  int edgeOrg(int edge)
-    {
-        
-        int retVal = edgeOrg_1(nativeObj, edge);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int findNearest(Point2f pt, Point2f* nearestPt = 0)
-    //
-
-    //javadoc: Subdiv2D::findNearest(pt, nearestPt)
-    public  int findNearest(Point pt, Point nearestPt)
-    {
-        double[] nearestPt_out = new double[2];
-        int retVal = findNearest_0(nativeObj, pt.x, pt.y, nearestPt_out);
-        if(nearestPt!=null){ nearestPt.x = nearestPt_out[0]; nearestPt.y = nearestPt_out[1]; } 
-        return retVal;
-    }
-
-    //javadoc: Subdiv2D::findNearest(pt)
-    public  int findNearest(Point pt)
-    {
-        
-        int retVal = findNearest_1(nativeObj, pt.x, pt.y);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getEdge(int edge, int nextEdgeType)
-    //
-
-    //javadoc: Subdiv2D::getEdge(edge, nextEdgeType)
-    public  int getEdge(int edge, int nextEdgeType)
-    {
-        
-        int retVal = getEdge_0(nativeObj, edge, nextEdgeType);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int insert(Point2f pt)
-    //
-
-    //javadoc: Subdiv2D::insert(pt)
-    public  int insert(Point pt)
-    {
-        
-        int retVal = insert_0(nativeObj, pt.x, pt.y);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int locate(Point2f pt, int& edge, int& vertex)
-    //
-
-    //javadoc: Subdiv2D::locate(pt, edge, vertex)
-    public  int locate(Point pt, int[] edge, int[] vertex)
-    {
-        double[] edge_out = new double[1];
-        double[] vertex_out = new double[1];
-        int retVal = locate_0(nativeObj, pt.x, pt.y, edge_out, vertex_out);
-        if(edge!=null) edge[0] = (int)edge_out[0];
-        if(vertex!=null) vertex[0] = (int)vertex_out[0];
-        return retVal;
-    }
-
-
-    //
-    // C++:  int nextEdge(int edge)
-    //
-
-    //javadoc: Subdiv2D::nextEdge(edge)
-    public  int nextEdge(int edge)
-    {
-        
-        int retVal = nextEdge_0(nativeObj, edge);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int rotateEdge(int edge, int rotate)
-    //
-
-    //javadoc: Subdiv2D::rotateEdge(edge, rotate)
-    public  int rotateEdge(int edge, int rotate)
-    {
-        
-        int retVal = rotateEdge_0(nativeObj, edge, rotate);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int symEdge(int edge)
-    //
-
-    //javadoc: Subdiv2D::symEdge(edge)
-    public  int symEdge(int edge)
-    {
-        
-        int retVal = symEdge_0(nativeObj, edge);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  void getEdgeList(vector_Vec4f& edgeList)
-    //
-
-    //javadoc: Subdiv2D::getEdgeList(edgeList)
-    public  void getEdgeList(MatOfFloat4 edgeList)
-    {
-        Mat edgeList_mat = edgeList;
-        getEdgeList_0(nativeObj, edgeList_mat.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void getLeadingEdgeList(vector_int& leadingEdgeList)
-    //
-
-    //javadoc: Subdiv2D::getLeadingEdgeList(leadingEdgeList)
-    public  void getLeadingEdgeList(MatOfInt leadingEdgeList)
-    {
-        Mat leadingEdgeList_mat = leadingEdgeList;
-        getLeadingEdgeList_0(nativeObj, leadingEdgeList_mat.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void getTriangleList(vector_Vec6f& triangleList)
-    //
-
-    //javadoc: Subdiv2D::getTriangleList(triangleList)
-    public  void getTriangleList(MatOfFloat6 triangleList)
-    {
-        Mat triangleList_mat = triangleList;
-        getTriangleList_0(nativeObj, triangleList_mat.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void getVoronoiFacetList(vector_int idx, vector_vector_Point2f& facetList, vector_Point2f& facetCenters)
-    //
-
-    //javadoc: Subdiv2D::getVoronoiFacetList(idx, facetList, facetCenters)
-    public  void getVoronoiFacetList(MatOfInt idx, List<MatOfPoint2f> facetList, MatOfPoint2f facetCenters)
-    {
-        Mat idx_mat = idx;
-        Mat facetList_mat = new Mat();
-        Mat facetCenters_mat = facetCenters;
-        getVoronoiFacetList_0(nativeObj, idx_mat.nativeObj, facetList_mat.nativeObj, facetCenters_mat.nativeObj);
-        Converters.Mat_to_vector_vector_Point2f(facetList_mat, facetList);
-        facetList_mat.release();
-        return;
-    }
-
-
-    //
-    // C++:  void initDelaunay(Rect rect)
-    //
-
-    //javadoc: Subdiv2D::initDelaunay(rect)
-    public  void initDelaunay(Rect rect)
-    {
-        
-        initDelaunay_0(nativeObj, rect.x, rect.y, rect.width, rect.height);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void insert(vector_Point2f ptvec)
-    //
-
-    //javadoc: Subdiv2D::insert(ptvec)
-    public  void insert(MatOfPoint2f ptvec)
-    {
-        Mat ptvec_mat = ptvec;
-        insert_1(nativeObj, ptvec_mat.nativeObj);
-        
-        return;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++:   Subdiv2D(Rect rect)
-    private static native long Subdiv2D_0(int rect_x, int rect_y, int rect_width, int rect_height);
-
-    // C++:   Subdiv2D()
-    private static native long Subdiv2D_1();
-
-    // C++:  Point2f getVertex(int vertex, int* firstEdge = 0)
-    private static native double[] getVertex_0(long nativeObj, int vertex, double[] firstEdge_out);
-    private static native double[] getVertex_1(long nativeObj, int vertex);
-
-    // C++:  int edgeDst(int edge, Point2f* dstpt = 0)
-    private static native int edgeDst_0(long nativeObj, int edge, double[] dstpt_out);
-    private static native int edgeDst_1(long nativeObj, int edge);
-
-    // C++:  int edgeOrg(int edge, Point2f* orgpt = 0)
-    private static native int edgeOrg_0(long nativeObj, int edge, double[] orgpt_out);
-    private static native int edgeOrg_1(long nativeObj, int edge);
-
-    // C++:  int findNearest(Point2f pt, Point2f* nearestPt = 0)
-    private static native int findNearest_0(long nativeObj, double pt_x, double pt_y, double[] nearestPt_out);
-    private static native int findNearest_1(long nativeObj, double pt_x, double pt_y);
-
-    // C++:  int getEdge(int edge, int nextEdgeType)
-    private static native int getEdge_0(long nativeObj, int edge, int nextEdgeType);
-
-    // C++:  int insert(Point2f pt)
-    private static native int insert_0(long nativeObj, double pt_x, double pt_y);
-
-    // C++:  int locate(Point2f pt, int& edge, int& vertex)
-    private static native int locate_0(long nativeObj, double pt_x, double pt_y, double[] edge_out, double[] vertex_out);
-
-    // C++:  int nextEdge(int edge)
-    private static native int nextEdge_0(long nativeObj, int edge);
-
-    // C++:  int rotateEdge(int edge, int rotate)
-    private static native int rotateEdge_0(long nativeObj, int edge, int rotate);
-
-    // C++:  int symEdge(int edge)
-    private static native int symEdge_0(long nativeObj, int edge);
-
-    // C++:  void getEdgeList(vector_Vec4f& edgeList)
-    private static native void getEdgeList_0(long nativeObj, long edgeList_mat_nativeObj);
-
-    // C++:  void getLeadingEdgeList(vector_int& leadingEdgeList)
-    private static native void getLeadingEdgeList_0(long nativeObj, long leadingEdgeList_mat_nativeObj);
-
-    // C++:  void getTriangleList(vector_Vec6f& triangleList)
-    private static native void getTriangleList_0(long nativeObj, long triangleList_mat_nativeObj);
-
-    // C++:  void getVoronoiFacetList(vector_int idx, vector_vector_Point2f& facetList, vector_Point2f& facetCenters)
-    private static native void getVoronoiFacetList_0(long nativeObj, long idx_mat_nativeObj, long facetList_mat_nativeObj, long facetCenters_mat_nativeObj);
-
-    // C++:  void initDelaunay(Rect rect)
-    private static native void initDelaunay_0(long nativeObj, int rect_x, int rect_y, int rect_width, int rect_height);
-
-    // C++:  void insert(vector_Point2f ptvec)
-    private static native void insert_1(long nativeObj, long ptvec_mat_nativeObj);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/ml/ANN_MLP.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/ml/ANN_MLP.java
deleted file mode 100755
index 6f5d9bf..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/ml/ANN_MLP.java
+++ /dev/null
@@ -1,610 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.ml;
-
-import java.lang.String;
-import org.opencv.core.Mat;
-import org.opencv.core.TermCriteria;
-import org.opencv.ml.ANN_MLP;
-import org.opencv.ml.StatModel;
-
-// C++: class ANN_MLP
-//javadoc: ANN_MLP
-
-public class ANN_MLP extends StatModel {
-
-    protected ANN_MLP(long addr) { super(addr); }
-
-    // internal usage only
-    public static ANN_MLP __fromPtr__(long addr) { return new ANN_MLP(addr); }
-
-    public static final int
-            BACKPROP = 0,
-            RPROP = 1,
-            ANNEAL = 2,
-            IDENTITY = 0,
-            SIGMOID_SYM = 1,
-            GAUSSIAN = 2,
-            RELU = 3,
-            LEAKYRELU = 4,
-            UPDATE_WEIGHTS = 1,
-            NO_INPUT_SCALE = 2,
-            NO_OUTPUT_SCALE = 4;
-
-
-    //
-    // C++:  Mat getLayerSizes()
-    //
-
-    //javadoc: ANN_MLP::getLayerSizes()
-    public  Mat getLayerSizes()
-    {
-        
-        Mat retVal = new Mat(getLayerSizes_0(nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Mat getWeights(int layerIdx)
-    //
-
-    //javadoc: ANN_MLP::getWeights(layerIdx)
-    public  Mat getWeights(int layerIdx)
-    {
-        
-        Mat retVal = new Mat(getWeights_0(nativeObj, layerIdx));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: static Ptr_ANN_MLP create()
-    //
-
-    //javadoc: ANN_MLP::create()
-    public static ANN_MLP create()
-    {
-        
-        ANN_MLP retVal = ANN_MLP.__fromPtr__(create_0());
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: static Ptr_ANN_MLP load(String filepath)
-    //
-
-    //javadoc: ANN_MLP::load(filepath)
-    public static ANN_MLP load(String filepath)
-    {
-        
-        ANN_MLP retVal = ANN_MLP.__fromPtr__(load_0(filepath));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  TermCriteria getTermCriteria()
-    //
-
-    //javadoc: ANN_MLP::getTermCriteria()
-    public  TermCriteria getTermCriteria()
-    {
-        
-        TermCriteria retVal = new TermCriteria(getTermCriteria_0(nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double getAnnealCoolingRatio()
-    //
-
-    //javadoc: ANN_MLP::getAnnealCoolingRatio()
-    public  double getAnnealCoolingRatio()
-    {
-        
-        double retVal = getAnnealCoolingRatio_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double getAnnealFinalT()
-    //
-
-    //javadoc: ANN_MLP::getAnnealFinalT()
-    public  double getAnnealFinalT()
-    {
-        
-        double retVal = getAnnealFinalT_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double getAnnealInitialT()
-    //
-
-    //javadoc: ANN_MLP::getAnnealInitialT()
-    public  double getAnnealInitialT()
-    {
-        
-        double retVal = getAnnealInitialT_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double getBackpropMomentumScale()
-    //
-
-    //javadoc: ANN_MLP::getBackpropMomentumScale()
-    public  double getBackpropMomentumScale()
-    {
-        
-        double retVal = getBackpropMomentumScale_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double getBackpropWeightScale()
-    //
-
-    //javadoc: ANN_MLP::getBackpropWeightScale()
-    public  double getBackpropWeightScale()
-    {
-        
-        double retVal = getBackpropWeightScale_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double getRpropDW0()
-    //
-
-    //javadoc: ANN_MLP::getRpropDW0()
-    public  double getRpropDW0()
-    {
-        
-        double retVal = getRpropDW0_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double getRpropDWMax()
-    //
-
-    //javadoc: ANN_MLP::getRpropDWMax()
-    public  double getRpropDWMax()
-    {
-        
-        double retVal = getRpropDWMax_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double getRpropDWMin()
-    //
-
-    //javadoc: ANN_MLP::getRpropDWMin()
-    public  double getRpropDWMin()
-    {
-        
-        double retVal = getRpropDWMin_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double getRpropDWMinus()
-    //
-
-    //javadoc: ANN_MLP::getRpropDWMinus()
-    public  double getRpropDWMinus()
-    {
-        
-        double retVal = getRpropDWMinus_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double getRpropDWPlus()
-    //
-
-    //javadoc: ANN_MLP::getRpropDWPlus()
-    public  double getRpropDWPlus()
-    {
-        
-        double retVal = getRpropDWPlus_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getAnnealItePerStep()
-    //
-
-    //javadoc: ANN_MLP::getAnnealItePerStep()
-    public  int getAnnealItePerStep()
-    {
-        
-        int retVal = getAnnealItePerStep_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getTrainMethod()
-    //
-
-    //javadoc: ANN_MLP::getTrainMethod()
-    public  int getTrainMethod()
-    {
-        
-        int retVal = getTrainMethod_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  void setActivationFunction(int type, double param1 = 0, double param2 = 0)
-    //
-
-    //javadoc: ANN_MLP::setActivationFunction(type, param1, param2)
-    public  void setActivationFunction(int type, double param1, double param2)
-    {
-        
-        setActivationFunction_0(nativeObj, type, param1, param2);
-        
-        return;
-    }
-
-    //javadoc: ANN_MLP::setActivationFunction(type)
-    public  void setActivationFunction(int type)
-    {
-        
-        setActivationFunction_1(nativeObj, type);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setAnnealCoolingRatio(double val)
-    //
-
-    //javadoc: ANN_MLP::setAnnealCoolingRatio(val)
-    public  void setAnnealCoolingRatio(double val)
-    {
-        
-        setAnnealCoolingRatio_0(nativeObj, val);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setAnnealFinalT(double val)
-    //
-
-    //javadoc: ANN_MLP::setAnnealFinalT(val)
-    public  void setAnnealFinalT(double val)
-    {
-        
-        setAnnealFinalT_0(nativeObj, val);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setAnnealInitialT(double val)
-    //
-
-    //javadoc: ANN_MLP::setAnnealInitialT(val)
-    public  void setAnnealInitialT(double val)
-    {
-        
-        setAnnealInitialT_0(nativeObj, val);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setAnnealItePerStep(int val)
-    //
-
-    //javadoc: ANN_MLP::setAnnealItePerStep(val)
-    public  void setAnnealItePerStep(int val)
-    {
-        
-        setAnnealItePerStep_0(nativeObj, val);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setBackpropMomentumScale(double val)
-    //
-
-    //javadoc: ANN_MLP::setBackpropMomentumScale(val)
-    public  void setBackpropMomentumScale(double val)
-    {
-        
-        setBackpropMomentumScale_0(nativeObj, val);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setBackpropWeightScale(double val)
-    //
-
-    //javadoc: ANN_MLP::setBackpropWeightScale(val)
-    public  void setBackpropWeightScale(double val)
-    {
-        
-        setBackpropWeightScale_0(nativeObj, val);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setLayerSizes(Mat _layer_sizes)
-    //
-
-    //javadoc: ANN_MLP::setLayerSizes(_layer_sizes)
-    public  void setLayerSizes(Mat _layer_sizes)
-    {
-        
-        setLayerSizes_0(nativeObj, _layer_sizes.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setRpropDW0(double val)
-    //
-
-    //javadoc: ANN_MLP::setRpropDW0(val)
-    public  void setRpropDW0(double val)
-    {
-        
-        setRpropDW0_0(nativeObj, val);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setRpropDWMax(double val)
-    //
-
-    //javadoc: ANN_MLP::setRpropDWMax(val)
-    public  void setRpropDWMax(double val)
-    {
-        
-        setRpropDWMax_0(nativeObj, val);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setRpropDWMin(double val)
-    //
-
-    //javadoc: ANN_MLP::setRpropDWMin(val)
-    public  void setRpropDWMin(double val)
-    {
-        
-        setRpropDWMin_0(nativeObj, val);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setRpropDWMinus(double val)
-    //
-
-    //javadoc: ANN_MLP::setRpropDWMinus(val)
-    public  void setRpropDWMinus(double val)
-    {
-        
-        setRpropDWMinus_0(nativeObj, val);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setRpropDWPlus(double val)
-    //
-
-    //javadoc: ANN_MLP::setRpropDWPlus(val)
-    public  void setRpropDWPlus(double val)
-    {
-        
-        setRpropDWPlus_0(nativeObj, val);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setTermCriteria(TermCriteria val)
-    //
-
-    //javadoc: ANN_MLP::setTermCriteria(val)
-    public  void setTermCriteria(TermCriteria val)
-    {
-        
-        setTermCriteria_0(nativeObj, val.type, val.maxCount, val.epsilon);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setTrainMethod(int method, double param1 = 0, double param2 = 0)
-    //
-
-    //javadoc: ANN_MLP::setTrainMethod(method, param1, param2)
-    public  void setTrainMethod(int method, double param1, double param2)
-    {
-        
-        setTrainMethod_0(nativeObj, method, param1, param2);
-        
-        return;
-    }
-
-    //javadoc: ANN_MLP::setTrainMethod(method)
-    public  void setTrainMethod(int method)
-    {
-        
-        setTrainMethod_1(nativeObj, method);
-        
-        return;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++:  Mat getLayerSizes()
-    private static native long getLayerSizes_0(long nativeObj);
-
-    // C++:  Mat getWeights(int layerIdx)
-    private static native long getWeights_0(long nativeObj, int layerIdx);
-
-    // C++: static Ptr_ANN_MLP create()
-    private static native long create_0();
-
-    // C++: static Ptr_ANN_MLP load(String filepath)
-    private static native long load_0(String filepath);
-
-    // C++:  TermCriteria getTermCriteria()
-    private static native double[] getTermCriteria_0(long nativeObj);
-
-    // C++:  double getAnnealCoolingRatio()
-    private static native double getAnnealCoolingRatio_0(long nativeObj);
-
-    // C++:  double getAnnealFinalT()
-    private static native double getAnnealFinalT_0(long nativeObj);
-
-    // C++:  double getAnnealInitialT()
-    private static native double getAnnealInitialT_0(long nativeObj);
-
-    // C++:  double getBackpropMomentumScale()
-    private static native double getBackpropMomentumScale_0(long nativeObj);
-
-    // C++:  double getBackpropWeightScale()
-    private static native double getBackpropWeightScale_0(long nativeObj);
-
-    // C++:  double getRpropDW0()
-    private static native double getRpropDW0_0(long nativeObj);
-
-    // C++:  double getRpropDWMax()
-    private static native double getRpropDWMax_0(long nativeObj);
-
-    // C++:  double getRpropDWMin()
-    private static native double getRpropDWMin_0(long nativeObj);
-
-    // C++:  double getRpropDWMinus()
-    private static native double getRpropDWMinus_0(long nativeObj);
-
-    // C++:  double getRpropDWPlus()
-    private static native double getRpropDWPlus_0(long nativeObj);
-
-    // C++:  int getAnnealItePerStep()
-    private static native int getAnnealItePerStep_0(long nativeObj);
-
-    // C++:  int getTrainMethod()
-    private static native int getTrainMethod_0(long nativeObj);
-
-    // C++:  void setActivationFunction(int type, double param1 = 0, double param2 = 0)
-    private static native void setActivationFunction_0(long nativeObj, int type, double param1, double param2);
-    private static native void setActivationFunction_1(long nativeObj, int type);
-
-    // C++:  void setAnnealCoolingRatio(double val)
-    private static native void setAnnealCoolingRatio_0(long nativeObj, double val);
-
-    // C++:  void setAnnealFinalT(double val)
-    private static native void setAnnealFinalT_0(long nativeObj, double val);
-
-    // C++:  void setAnnealInitialT(double val)
-    private static native void setAnnealInitialT_0(long nativeObj, double val);
-
-    // C++:  void setAnnealItePerStep(int val)
-    private static native void setAnnealItePerStep_0(long nativeObj, int val);
-
-    // C++:  void setBackpropMomentumScale(double val)
-    private static native void setBackpropMomentumScale_0(long nativeObj, double val);
-
-    // C++:  void setBackpropWeightScale(double val)
-    private static native void setBackpropWeightScale_0(long nativeObj, double val);
-
-    // C++:  void setLayerSizes(Mat _layer_sizes)
-    private static native void setLayerSizes_0(long nativeObj, long _layer_sizes_nativeObj);
-
-    // C++:  void setRpropDW0(double val)
-    private static native void setRpropDW0_0(long nativeObj, double val);
-
-    // C++:  void setRpropDWMax(double val)
-    private static native void setRpropDWMax_0(long nativeObj, double val);
-
-    // C++:  void setRpropDWMin(double val)
-    private static native void setRpropDWMin_0(long nativeObj, double val);
-
-    // C++:  void setRpropDWMinus(double val)
-    private static native void setRpropDWMinus_0(long nativeObj, double val);
-
-    // C++:  void setRpropDWPlus(double val)
-    private static native void setRpropDWPlus_0(long nativeObj, double val);
-
-    // C++:  void setTermCriteria(TermCriteria val)
-    private static native void setTermCriteria_0(long nativeObj, int val_type, int val_maxCount, double val_epsilon);
-
-    // C++:  void setTrainMethod(int method, double param1 = 0, double param2 = 0)
-    private static native void setTrainMethod_0(long nativeObj, int method, double param1, double param2);
-    private static native void setTrainMethod_1(long nativeObj, int method);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/ml/ANN_MLP_ANNEAL.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/ml/ANN_MLP_ANNEAL.java
deleted file mode 100755
index 5b4ade8..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/ml/ANN_MLP_ANNEAL.java
+++ /dev/null
@@ -1,164 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.ml;
-
-import org.opencv.ml.ANN_MLP;
-
-// C++: class ANN_MLP_ANNEAL
-//javadoc: ANN_MLP_ANNEAL
-
-public class ANN_MLP_ANNEAL extends ANN_MLP {
-
-    protected ANN_MLP_ANNEAL(long addr) { super(addr); }
-
-    // internal usage only
-    public static ANN_MLP_ANNEAL __fromPtr__(long addr) { return new ANN_MLP_ANNEAL(addr); }
-
-    //
-    // C++:  double getAnnealCoolingRatio()
-    //
-
-    //javadoc: ANN_MLP_ANNEAL::getAnnealCoolingRatio()
-    public  double getAnnealCoolingRatio()
-    {
-        
-        double retVal = getAnnealCoolingRatio_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double getAnnealFinalT()
-    //
-
-    //javadoc: ANN_MLP_ANNEAL::getAnnealFinalT()
-    public  double getAnnealFinalT()
-    {
-        
-        double retVal = getAnnealFinalT_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double getAnnealInitialT()
-    //
-
-    //javadoc: ANN_MLP_ANNEAL::getAnnealInitialT()
-    public  double getAnnealInitialT()
-    {
-        
-        double retVal = getAnnealInitialT_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getAnnealItePerStep()
-    //
-
-    //javadoc: ANN_MLP_ANNEAL::getAnnealItePerStep()
-    public  int getAnnealItePerStep()
-    {
-        
-        int retVal = getAnnealItePerStep_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  void setAnnealCoolingRatio(double val)
-    //
-
-    //javadoc: ANN_MLP_ANNEAL::setAnnealCoolingRatio(val)
-    public  void setAnnealCoolingRatio(double val)
-    {
-        
-        setAnnealCoolingRatio_0(nativeObj, val);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setAnnealFinalT(double val)
-    //
-
-    //javadoc: ANN_MLP_ANNEAL::setAnnealFinalT(val)
-    public  void setAnnealFinalT(double val)
-    {
-        
-        setAnnealFinalT_0(nativeObj, val);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setAnnealInitialT(double val)
-    //
-
-    //javadoc: ANN_MLP_ANNEAL::setAnnealInitialT(val)
-    public  void setAnnealInitialT(double val)
-    {
-        
-        setAnnealInitialT_0(nativeObj, val);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setAnnealItePerStep(int val)
-    //
-
-    //javadoc: ANN_MLP_ANNEAL::setAnnealItePerStep(val)
-    public  void setAnnealItePerStep(int val)
-    {
-        
-        setAnnealItePerStep_0(nativeObj, val);
-        
-        return;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++:  double getAnnealCoolingRatio()
-    private static native double getAnnealCoolingRatio_0(long nativeObj);
-
-    // C++:  double getAnnealFinalT()
-    private static native double getAnnealFinalT_0(long nativeObj);
-
-    // C++:  double getAnnealInitialT()
-    private static native double getAnnealInitialT_0(long nativeObj);
-
-    // C++:  int getAnnealItePerStep()
-    private static native int getAnnealItePerStep_0(long nativeObj);
-
-    // C++:  void setAnnealCoolingRatio(double val)
-    private static native void setAnnealCoolingRatio_0(long nativeObj, double val);
-
-    // C++:  void setAnnealFinalT(double val)
-    private static native void setAnnealFinalT_0(long nativeObj, double val);
-
-    // C++:  void setAnnealInitialT(double val)
-    private static native void setAnnealInitialT_0(long nativeObj, double val);
-
-    // C++:  void setAnnealItePerStep(int val)
-    private static native void setAnnealItePerStep_0(long nativeObj, int val);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/ml/Boost.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/ml/Boost.java
deleted file mode 100755
index 2ad5d70..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/ml/Boost.java
+++ /dev/null
@@ -1,183 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.ml;
-
-import java.lang.String;
-import org.opencv.ml.Boost;
-import org.opencv.ml.DTrees;
-
-// C++: class Boost
-//javadoc: Boost
-
-public class Boost extends DTrees {
-
-    protected Boost(long addr) { super(addr); }
-
-    // internal usage only
-    public static Boost __fromPtr__(long addr) { return new Boost(addr); }
-
-    public static final int
-            DISCRETE = 0,
-            REAL = 1,
-            LOGIT = 2,
-            GENTLE = 3;
-
-
-    //
-    // C++: static Ptr_Boost create()
-    //
-
-    //javadoc: Boost::create()
-    public static Boost create()
-    {
-        
-        Boost retVal = Boost.__fromPtr__(create_0());
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: static Ptr_Boost load(String filepath, String nodeName = String())
-    //
-
-    //javadoc: Boost::load(filepath, nodeName)
-    public static Boost load(String filepath, String nodeName)
-    {
-        
-        Boost retVal = Boost.__fromPtr__(load_0(filepath, nodeName));
-        
-        return retVal;
-    }
-
-    //javadoc: Boost::load(filepath)
-    public static Boost load(String filepath)
-    {
-        
-        Boost retVal = Boost.__fromPtr__(load_1(filepath));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double getWeightTrimRate()
-    //
-
-    //javadoc: Boost::getWeightTrimRate()
-    public  double getWeightTrimRate()
-    {
-        
-        double retVal = getWeightTrimRate_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getBoostType()
-    //
-
-    //javadoc: Boost::getBoostType()
-    public  int getBoostType()
-    {
-        
-        int retVal = getBoostType_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getWeakCount()
-    //
-
-    //javadoc: Boost::getWeakCount()
-    public  int getWeakCount()
-    {
-        
-        int retVal = getWeakCount_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  void setBoostType(int val)
-    //
-
-    //javadoc: Boost::setBoostType(val)
-    public  void setBoostType(int val)
-    {
-        
-        setBoostType_0(nativeObj, val);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setWeakCount(int val)
-    //
-
-    //javadoc: Boost::setWeakCount(val)
-    public  void setWeakCount(int val)
-    {
-        
-        setWeakCount_0(nativeObj, val);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setWeightTrimRate(double val)
-    //
-
-    //javadoc: Boost::setWeightTrimRate(val)
-    public  void setWeightTrimRate(double val)
-    {
-        
-        setWeightTrimRate_0(nativeObj, val);
-        
-        return;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++: static Ptr_Boost create()
-    private static native long create_0();
-
-    // C++: static Ptr_Boost load(String filepath, String nodeName = String())
-    private static native long load_0(String filepath, String nodeName);
-    private static native long load_1(String filepath);
-
-    // C++:  double getWeightTrimRate()
-    private static native double getWeightTrimRate_0(long nativeObj);
-
-    // C++:  int getBoostType()
-    private static native int getBoostType_0(long nativeObj);
-
-    // C++:  int getWeakCount()
-    private static native int getWeakCount_0(long nativeObj);
-
-    // C++:  void setBoostType(int val)
-    private static native void setBoostType_0(long nativeObj, int val);
-
-    // C++:  void setWeakCount(int val)
-    private static native void setWeakCount_0(long nativeObj, int val);
-
-    // C++:  void setWeightTrimRate(double val)
-    private static native void setWeightTrimRate_0(long nativeObj, double val);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/ml/DTrees.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/ml/DTrees.java
deleted file mode 100755
index cc5e52f..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/ml/DTrees.java
+++ /dev/null
@@ -1,388 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.ml;
-
-import java.lang.String;
-import org.opencv.core.Mat;
-import org.opencv.ml.DTrees;
-import org.opencv.ml.StatModel;
-
-// C++: class DTrees
-//javadoc: DTrees
-
-public class DTrees extends StatModel {
-
-    protected DTrees(long addr) { super(addr); }
-
-    // internal usage only
-    public static DTrees __fromPtr__(long addr) { return new DTrees(addr); }
-
-    public static final int
-            PREDICT_AUTO = 0,
-            PREDICT_SUM = (1<<8),
-            PREDICT_MAX_VOTE = (2<<8),
-            PREDICT_MASK = (3<<8);
-
-
-    //
-    // C++:  Mat getPriors()
-    //
-
-    //javadoc: DTrees::getPriors()
-    public  Mat getPriors()
-    {
-        
-        Mat retVal = new Mat(getPriors_0(nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: static Ptr_DTrees create()
-    //
-
-    //javadoc: DTrees::create()
-    public static DTrees create()
-    {
-        
-        DTrees retVal = DTrees.__fromPtr__(create_0());
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: static Ptr_DTrees load(String filepath, String nodeName = String())
-    //
-
-    //javadoc: DTrees::load(filepath, nodeName)
-    public static DTrees load(String filepath, String nodeName)
-    {
-        
-        DTrees retVal = DTrees.__fromPtr__(load_0(filepath, nodeName));
-        
-        return retVal;
-    }
-
-    //javadoc: DTrees::load(filepath)
-    public static DTrees load(String filepath)
-    {
-        
-        DTrees retVal = DTrees.__fromPtr__(load_1(filepath));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  bool getTruncatePrunedTree()
-    //
-
-    //javadoc: DTrees::getTruncatePrunedTree()
-    public  boolean getTruncatePrunedTree()
-    {
-        
-        boolean retVal = getTruncatePrunedTree_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  bool getUse1SERule()
-    //
-
-    //javadoc: DTrees::getUse1SERule()
-    public  boolean getUse1SERule()
-    {
-        
-        boolean retVal = getUse1SERule_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  bool getUseSurrogates()
-    //
-
-    //javadoc: DTrees::getUseSurrogates()
-    public  boolean getUseSurrogates()
-    {
-        
-        boolean retVal = getUseSurrogates_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  float getRegressionAccuracy()
-    //
-
-    //javadoc: DTrees::getRegressionAccuracy()
-    public  float getRegressionAccuracy()
-    {
-        
-        float retVal = getRegressionAccuracy_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getCVFolds()
-    //
-
-    //javadoc: DTrees::getCVFolds()
-    public  int getCVFolds()
-    {
-        
-        int retVal = getCVFolds_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getMaxCategories()
-    //
-
-    //javadoc: DTrees::getMaxCategories()
-    public  int getMaxCategories()
-    {
-        
-        int retVal = getMaxCategories_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getMaxDepth()
-    //
-
-    //javadoc: DTrees::getMaxDepth()
-    public  int getMaxDepth()
-    {
-        
-        int retVal = getMaxDepth_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getMinSampleCount()
-    //
-
-    //javadoc: DTrees::getMinSampleCount()
-    public  int getMinSampleCount()
-    {
-        
-        int retVal = getMinSampleCount_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  void setCVFolds(int val)
-    //
-
-    //javadoc: DTrees::setCVFolds(val)
-    public  void setCVFolds(int val)
-    {
-        
-        setCVFolds_0(nativeObj, val);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setMaxCategories(int val)
-    //
-
-    //javadoc: DTrees::setMaxCategories(val)
-    public  void setMaxCategories(int val)
-    {
-        
-        setMaxCategories_0(nativeObj, val);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setMaxDepth(int val)
-    //
-
-    //javadoc: DTrees::setMaxDepth(val)
-    public  void setMaxDepth(int val)
-    {
-        
-        setMaxDepth_0(nativeObj, val);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setMinSampleCount(int val)
-    //
-
-    //javadoc: DTrees::setMinSampleCount(val)
-    public  void setMinSampleCount(int val)
-    {
-        
-        setMinSampleCount_0(nativeObj, val);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setPriors(Mat val)
-    //
-
-    //javadoc: DTrees::setPriors(val)
-    public  void setPriors(Mat val)
-    {
-        
-        setPriors_0(nativeObj, val.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setRegressionAccuracy(float val)
-    //
-
-    //javadoc: DTrees::setRegressionAccuracy(val)
-    public  void setRegressionAccuracy(float val)
-    {
-        
-        setRegressionAccuracy_0(nativeObj, val);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setTruncatePrunedTree(bool val)
-    //
-
-    //javadoc: DTrees::setTruncatePrunedTree(val)
-    public  void setTruncatePrunedTree(boolean val)
-    {
-        
-        setTruncatePrunedTree_0(nativeObj, val);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setUse1SERule(bool val)
-    //
-
-    //javadoc: DTrees::setUse1SERule(val)
-    public  void setUse1SERule(boolean val)
-    {
-        
-        setUse1SERule_0(nativeObj, val);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setUseSurrogates(bool val)
-    //
-
-    //javadoc: DTrees::setUseSurrogates(val)
-    public  void setUseSurrogates(boolean val)
-    {
-        
-        setUseSurrogates_0(nativeObj, val);
-        
-        return;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++:  Mat getPriors()
-    private static native long getPriors_0(long nativeObj);
-
-    // C++: static Ptr_DTrees create()
-    private static native long create_0();
-
-    // C++: static Ptr_DTrees load(String filepath, String nodeName = String())
-    private static native long load_0(String filepath, String nodeName);
-    private static native long load_1(String filepath);
-
-    // C++:  bool getTruncatePrunedTree()
-    private static native boolean getTruncatePrunedTree_0(long nativeObj);
-
-    // C++:  bool getUse1SERule()
-    private static native boolean getUse1SERule_0(long nativeObj);
-
-    // C++:  bool getUseSurrogates()
-    private static native boolean getUseSurrogates_0(long nativeObj);
-
-    // C++:  float getRegressionAccuracy()
-    private static native float getRegressionAccuracy_0(long nativeObj);
-
-    // C++:  int getCVFolds()
-    private static native int getCVFolds_0(long nativeObj);
-
-    // C++:  int getMaxCategories()
-    private static native int getMaxCategories_0(long nativeObj);
-
-    // C++:  int getMaxDepth()
-    private static native int getMaxDepth_0(long nativeObj);
-
-    // C++:  int getMinSampleCount()
-    private static native int getMinSampleCount_0(long nativeObj);
-
-    // C++:  void setCVFolds(int val)
-    private static native void setCVFolds_0(long nativeObj, int val);
-
-    // C++:  void setMaxCategories(int val)
-    private static native void setMaxCategories_0(long nativeObj, int val);
-
-    // C++:  void setMaxDepth(int val)
-    private static native void setMaxDepth_0(long nativeObj, int val);
-
-    // C++:  void setMinSampleCount(int val)
-    private static native void setMinSampleCount_0(long nativeObj, int val);
-
-    // C++:  void setPriors(Mat val)
-    private static native void setPriors_0(long nativeObj, long val_nativeObj);
-
-    // C++:  void setRegressionAccuracy(float val)
-    private static native void setRegressionAccuracy_0(long nativeObj, float val);
-
-    // C++:  void setTruncatePrunedTree(bool val)
-    private static native void setTruncatePrunedTree_0(long nativeObj, boolean val);
-
-    // C++:  void setUse1SERule(bool val)
-    private static native void setUse1SERule_0(long nativeObj, boolean val);
-
-    // C++:  void setUseSurrogates(bool val)
-    private static native void setUseSurrogates_0(long nativeObj, boolean val);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/ml/EM.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/ml/EM.java
deleted file mode 100755
index d9e7409..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/ml/EM.java
+++ /dev/null
@@ -1,370 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.ml;
-
-import java.lang.String;
-import java.util.ArrayList;
-import java.util.List;
-import org.opencv.core.Mat;
-import org.opencv.core.TermCriteria;
-import org.opencv.ml.EM;
-import org.opencv.ml.StatModel;
-import org.opencv.utils.Converters;
-
-// C++: class EM
-//javadoc: EM
-
-public class EM extends StatModel {
-
-    protected EM(long addr) { super(addr); }
-
-    // internal usage only
-    public static EM __fromPtr__(long addr) { return new EM(addr); }
-
-    public static final int
-            COV_MAT_SPHERICAL = 0,
-            COV_MAT_DIAGONAL = 1,
-            COV_MAT_GENERIC = 2,
-            COV_MAT_DEFAULT = COV_MAT_DIAGONAL,
-            DEFAULT_NCLUSTERS = 5,
-            DEFAULT_MAX_ITERS = 100,
-            START_E_STEP = 1,
-            START_M_STEP = 2,
-            START_AUTO_STEP = 0;
-
-
-    //
-    // C++:  Mat getMeans()
-    //
-
-    //javadoc: EM::getMeans()
-    public  Mat getMeans()
-    {
-        
-        Mat retVal = new Mat(getMeans_0(nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Mat getWeights()
-    //
-
-    //javadoc: EM::getWeights()
-    public  Mat getWeights()
-    {
-        
-        Mat retVal = new Mat(getWeights_0(nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: static Ptr_EM create()
-    //
-
-    //javadoc: EM::create()
-    public static EM create()
-    {
-        
-        EM retVal = EM.__fromPtr__(create_0());
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: static Ptr_EM load(String filepath, String nodeName = String())
-    //
-
-    //javadoc: EM::load(filepath, nodeName)
-    public static EM load(String filepath, String nodeName)
-    {
-        
-        EM retVal = EM.__fromPtr__(load_0(filepath, nodeName));
-        
-        return retVal;
-    }
-
-    //javadoc: EM::load(filepath)
-    public static EM load(String filepath)
-    {
-        
-        EM retVal = EM.__fromPtr__(load_1(filepath));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  TermCriteria getTermCriteria()
-    //
-
-    //javadoc: EM::getTermCriteria()
-    public  TermCriteria getTermCriteria()
-    {
-        
-        TermCriteria retVal = new TermCriteria(getTermCriteria_0(nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Vec2d predict2(Mat sample, Mat& probs)
-    //
-
-    //javadoc: EM::predict2(sample, probs)
-    public  double[] predict2(Mat sample, Mat probs)
-    {
-        
-        double[] retVal = predict2_0(nativeObj, sample.nativeObj, probs.nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  bool trainE(Mat samples, Mat means0, Mat covs0 = Mat(), Mat weights0 = Mat(), Mat& logLikelihoods = Mat(), Mat& labels = Mat(), Mat& probs = Mat())
-    //
-
-    //javadoc: EM::trainE(samples, means0, covs0, weights0, logLikelihoods, labels, probs)
-    public  boolean trainE(Mat samples, Mat means0, Mat covs0, Mat weights0, Mat logLikelihoods, Mat labels, Mat probs)
-    {
-        
-        boolean retVal = trainE_0(nativeObj, samples.nativeObj, means0.nativeObj, covs0.nativeObj, weights0.nativeObj, logLikelihoods.nativeObj, labels.nativeObj, probs.nativeObj);
-        
-        return retVal;
-    }
-
-    //javadoc: EM::trainE(samples, means0)
-    public  boolean trainE(Mat samples, Mat means0)
-    {
-        
-        boolean retVal = trainE_1(nativeObj, samples.nativeObj, means0.nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  bool trainEM(Mat samples, Mat& logLikelihoods = Mat(), Mat& labels = Mat(), Mat& probs = Mat())
-    //
-
-    //javadoc: EM::trainEM(samples, logLikelihoods, labels, probs)
-    public  boolean trainEM(Mat samples, Mat logLikelihoods, Mat labels, Mat probs)
-    {
-        
-        boolean retVal = trainEM_0(nativeObj, samples.nativeObj, logLikelihoods.nativeObj, labels.nativeObj, probs.nativeObj);
-        
-        return retVal;
-    }
-
-    //javadoc: EM::trainEM(samples)
-    public  boolean trainEM(Mat samples)
-    {
-        
-        boolean retVal = trainEM_1(nativeObj, samples.nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  bool trainM(Mat samples, Mat probs0, Mat& logLikelihoods = Mat(), Mat& labels = Mat(), Mat& probs = Mat())
-    //
-
-    //javadoc: EM::trainM(samples, probs0, logLikelihoods, labels, probs)
-    public  boolean trainM(Mat samples, Mat probs0, Mat logLikelihoods, Mat labels, Mat probs)
-    {
-        
-        boolean retVal = trainM_0(nativeObj, samples.nativeObj, probs0.nativeObj, logLikelihoods.nativeObj, labels.nativeObj, probs.nativeObj);
-        
-        return retVal;
-    }
-
-    //javadoc: EM::trainM(samples, probs0)
-    public  boolean trainM(Mat samples, Mat probs0)
-    {
-        
-        boolean retVal = trainM_1(nativeObj, samples.nativeObj, probs0.nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  float predict(Mat samples, Mat& results = Mat(), int flags = 0)
-    //
-
-    //javadoc: EM::predict(samples, results, flags)
-    public  float predict(Mat samples, Mat results, int flags)
-    {
-        
-        float retVal = predict_0(nativeObj, samples.nativeObj, results.nativeObj, flags);
-        
-        return retVal;
-    }
-
-    //javadoc: EM::predict(samples)
-    public  float predict(Mat samples)
-    {
-        
-        float retVal = predict_1(nativeObj, samples.nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getClustersNumber()
-    //
-
-    //javadoc: EM::getClustersNumber()
-    public  int getClustersNumber()
-    {
-        
-        int retVal = getClustersNumber_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getCovarianceMatrixType()
-    //
-
-    //javadoc: EM::getCovarianceMatrixType()
-    public  int getCovarianceMatrixType()
-    {
-        
-        int retVal = getCovarianceMatrixType_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  void getCovs(vector_Mat& covs)
-    //
-
-    //javadoc: EM::getCovs(covs)
-    public  void getCovs(List<Mat> covs)
-    {
-        Mat covs_mat = new Mat();
-        getCovs_0(nativeObj, covs_mat.nativeObj);
-        Converters.Mat_to_vector_Mat(covs_mat, covs);
-        covs_mat.release();
-        return;
-    }
-
-
-    //
-    // C++:  void setClustersNumber(int val)
-    //
-
-    //javadoc: EM::setClustersNumber(val)
-    public  void setClustersNumber(int val)
-    {
-        
-        setClustersNumber_0(nativeObj, val);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setCovarianceMatrixType(int val)
-    //
-
-    //javadoc: EM::setCovarianceMatrixType(val)
-    public  void setCovarianceMatrixType(int val)
-    {
-        
-        setCovarianceMatrixType_0(nativeObj, val);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setTermCriteria(TermCriteria val)
-    //
-
-    //javadoc: EM::setTermCriteria(val)
-    public  void setTermCriteria(TermCriteria val)
-    {
-        
-        setTermCriteria_0(nativeObj, val.type, val.maxCount, val.epsilon);
-        
-        return;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++:  Mat getMeans()
-    private static native long getMeans_0(long nativeObj);
-
-    // C++:  Mat getWeights()
-    private static native long getWeights_0(long nativeObj);
-
-    // C++: static Ptr_EM create()
-    private static native long create_0();
-
-    // C++: static Ptr_EM load(String filepath, String nodeName = String())
-    private static native long load_0(String filepath, String nodeName);
-    private static native long load_1(String filepath);
-
-    // C++:  TermCriteria getTermCriteria()
-    private static native double[] getTermCriteria_0(long nativeObj);
-
-    // C++:  Vec2d predict2(Mat sample, Mat& probs)
-    private static native double[] predict2_0(long nativeObj, long sample_nativeObj, long probs_nativeObj);
-
-    // C++:  bool trainE(Mat samples, Mat means0, Mat covs0 = Mat(), Mat weights0 = Mat(), Mat& logLikelihoods = Mat(), Mat& labels = Mat(), Mat& probs = Mat())
-    private static native boolean trainE_0(long nativeObj, long samples_nativeObj, long means0_nativeObj, long covs0_nativeObj, long weights0_nativeObj, long logLikelihoods_nativeObj, long labels_nativeObj, long probs_nativeObj);
-    private static native boolean trainE_1(long nativeObj, long samples_nativeObj, long means0_nativeObj);
-
-    // C++:  bool trainEM(Mat samples, Mat& logLikelihoods = Mat(), Mat& labels = Mat(), Mat& probs = Mat())
-    private static native boolean trainEM_0(long nativeObj, long samples_nativeObj, long logLikelihoods_nativeObj, long labels_nativeObj, long probs_nativeObj);
-    private static native boolean trainEM_1(long nativeObj, long samples_nativeObj);
-
-    // C++:  bool trainM(Mat samples, Mat probs0, Mat& logLikelihoods = Mat(), Mat& labels = Mat(), Mat& probs = Mat())
-    private static native boolean trainM_0(long nativeObj, long samples_nativeObj, long probs0_nativeObj, long logLikelihoods_nativeObj, long labels_nativeObj, long probs_nativeObj);
-    private static native boolean trainM_1(long nativeObj, long samples_nativeObj, long probs0_nativeObj);
-
-    // C++:  float predict(Mat samples, Mat& results = Mat(), int flags = 0)
-    private static native float predict_0(long nativeObj, long samples_nativeObj, long results_nativeObj, int flags);
-    private static native float predict_1(long nativeObj, long samples_nativeObj);
-
-    // C++:  int getClustersNumber()
-    private static native int getClustersNumber_0(long nativeObj);
-
-    // C++:  int getCovarianceMatrixType()
-    private static native int getCovarianceMatrixType_0(long nativeObj);
-
-    // C++:  void getCovs(vector_Mat& covs)
-    private static native void getCovs_0(long nativeObj, long covs_mat_nativeObj);
-
-    // C++:  void setClustersNumber(int val)
-    private static native void setClustersNumber_0(long nativeObj, int val);
-
-    // C++:  void setCovarianceMatrixType(int val)
-    private static native void setCovarianceMatrixType_0(long nativeObj, int val);
-
-    // C++:  void setTermCriteria(TermCriteria val)
-    private static native void setTermCriteria_0(long nativeObj, int val_type, int val_maxCount, double val_epsilon);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/ml/KNearest.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/ml/KNearest.java
deleted file mode 100755
index 31e6461..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/ml/KNearest.java
+++ /dev/null
@@ -1,215 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.ml;
-
-import org.opencv.core.Mat;
-import org.opencv.ml.KNearest;
-import org.opencv.ml.StatModel;
-
-// C++: class KNearest
-//javadoc: KNearest
-
-public class KNearest extends StatModel {
-
-    protected KNearest(long addr) { super(addr); }
-
-    // internal usage only
-    public static KNearest __fromPtr__(long addr) { return new KNearest(addr); }
-
-    public static final int
-            BRUTE_FORCE = 1,
-            KDTREE = 2;
-
-
-    //
-    // C++: static Ptr_KNearest create()
-    //
-
-    //javadoc: KNearest::create()
-    public static KNearest create()
-    {
-        
-        KNearest retVal = KNearest.__fromPtr__(create_0());
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  bool getIsClassifier()
-    //
-
-    //javadoc: KNearest::getIsClassifier()
-    public  boolean getIsClassifier()
-    {
-        
-        boolean retVal = getIsClassifier_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  float findNearest(Mat samples, int k, Mat& results, Mat& neighborResponses = Mat(), Mat& dist = Mat())
-    //
-
-    //javadoc: KNearest::findNearest(samples, k, results, neighborResponses, dist)
-    public  float findNearest(Mat samples, int k, Mat results, Mat neighborResponses, Mat dist)
-    {
-        
-        float retVal = findNearest_0(nativeObj, samples.nativeObj, k, results.nativeObj, neighborResponses.nativeObj, dist.nativeObj);
-        
-        return retVal;
-    }
-
-    //javadoc: KNearest::findNearest(samples, k, results)
-    public  float findNearest(Mat samples, int k, Mat results)
-    {
-        
-        float retVal = findNearest_1(nativeObj, samples.nativeObj, k, results.nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getAlgorithmType()
-    //
-
-    //javadoc: KNearest::getAlgorithmType()
-    public  int getAlgorithmType()
-    {
-        
-        int retVal = getAlgorithmType_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getDefaultK()
-    //
-
-    //javadoc: KNearest::getDefaultK()
-    public  int getDefaultK()
-    {
-        
-        int retVal = getDefaultK_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getEmax()
-    //
-
-    //javadoc: KNearest::getEmax()
-    public  int getEmax()
-    {
-        
-        int retVal = getEmax_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  void setAlgorithmType(int val)
-    //
-
-    //javadoc: KNearest::setAlgorithmType(val)
-    public  void setAlgorithmType(int val)
-    {
-        
-        setAlgorithmType_0(nativeObj, val);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setDefaultK(int val)
-    //
-
-    //javadoc: KNearest::setDefaultK(val)
-    public  void setDefaultK(int val)
-    {
-        
-        setDefaultK_0(nativeObj, val);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setEmax(int val)
-    //
-
-    //javadoc: KNearest::setEmax(val)
-    public  void setEmax(int val)
-    {
-        
-        setEmax_0(nativeObj, val);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setIsClassifier(bool val)
-    //
-
-    //javadoc: KNearest::setIsClassifier(val)
-    public  void setIsClassifier(boolean val)
-    {
-        
-        setIsClassifier_0(nativeObj, val);
-        
-        return;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++: static Ptr_KNearest create()
-    private static native long create_0();
-
-    // C++:  bool getIsClassifier()
-    private static native boolean getIsClassifier_0(long nativeObj);
-
-    // C++:  float findNearest(Mat samples, int k, Mat& results, Mat& neighborResponses = Mat(), Mat& dist = Mat())
-    private static native float findNearest_0(long nativeObj, long samples_nativeObj, int k, long results_nativeObj, long neighborResponses_nativeObj, long dist_nativeObj);
-    private static native float findNearest_1(long nativeObj, long samples_nativeObj, int k, long results_nativeObj);
-
-    // C++:  int getAlgorithmType()
-    private static native int getAlgorithmType_0(long nativeObj);
-
-    // C++:  int getDefaultK()
-    private static native int getDefaultK_0(long nativeObj);
-
-    // C++:  int getEmax()
-    private static native int getEmax_0(long nativeObj);
-
-    // C++:  void setAlgorithmType(int val)
-    private static native void setAlgorithmType_0(long nativeObj, int val);
-
-    // C++:  void setDefaultK(int val)
-    private static native void setDefaultK_0(long nativeObj, int val);
-
-    // C++:  void setEmax(int val)
-    private static native void setEmax_0(long nativeObj, int val);
-
-    // C++:  void setIsClassifier(bool val)
-    private static native void setIsClassifier_0(long nativeObj, boolean val);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/ml/LogisticRegression.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/ml/LogisticRegression.java
deleted file mode 100755
index 65698cb..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/ml/LogisticRegression.java
+++ /dev/null
@@ -1,332 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.ml;
-
-import java.lang.String;
-import org.opencv.core.Mat;
-import org.opencv.core.TermCriteria;
-import org.opencv.ml.LogisticRegression;
-import org.opencv.ml.StatModel;
-
-// C++: class LogisticRegression
-//javadoc: LogisticRegression
-
-public class LogisticRegression extends StatModel {
-
-    protected LogisticRegression(long addr) { super(addr); }
-
-    // internal usage only
-    public static LogisticRegression __fromPtr__(long addr) { return new LogisticRegression(addr); }
-
-    public static final int
-            REG_DISABLE = -1,
-            REG_L1 = 0,
-            REG_L2 = 1,
-            BATCH = 0,
-            MINI_BATCH = 1;
-
-
-    //
-    // C++:  Mat get_learnt_thetas()
-    //
-
-    //javadoc: LogisticRegression::get_learnt_thetas()
-    public  Mat get_learnt_thetas()
-    {
-        
-        Mat retVal = new Mat(get_learnt_thetas_0(nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: static Ptr_LogisticRegression create()
-    //
-
-    //javadoc: LogisticRegression::create()
-    public static LogisticRegression create()
-    {
-        
-        LogisticRegression retVal = LogisticRegression.__fromPtr__(create_0());
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: static Ptr_LogisticRegression load(String filepath, String nodeName = String())
-    //
-
-    //javadoc: LogisticRegression::load(filepath, nodeName)
-    public static LogisticRegression load(String filepath, String nodeName)
-    {
-        
-        LogisticRegression retVal = LogisticRegression.__fromPtr__(load_0(filepath, nodeName));
-        
-        return retVal;
-    }
-
-    //javadoc: LogisticRegression::load(filepath)
-    public static LogisticRegression load(String filepath)
-    {
-        
-        LogisticRegression retVal = LogisticRegression.__fromPtr__(load_1(filepath));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  TermCriteria getTermCriteria()
-    //
-
-    //javadoc: LogisticRegression::getTermCriteria()
-    public  TermCriteria getTermCriteria()
-    {
-        
-        TermCriteria retVal = new TermCriteria(getTermCriteria_0(nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double getLearningRate()
-    //
-
-    //javadoc: LogisticRegression::getLearningRate()
-    public  double getLearningRate()
-    {
-        
-        double retVal = getLearningRate_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  float predict(Mat samples, Mat& results = Mat(), int flags = 0)
-    //
-
-    //javadoc: LogisticRegression::predict(samples, results, flags)
-    public  float predict(Mat samples, Mat results, int flags)
-    {
-        
-        float retVal = predict_0(nativeObj, samples.nativeObj, results.nativeObj, flags);
-        
-        return retVal;
-    }
-
-    //javadoc: LogisticRegression::predict(samples)
-    public  float predict(Mat samples)
-    {
-        
-        float retVal = predict_1(nativeObj, samples.nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getIterations()
-    //
-
-    //javadoc: LogisticRegression::getIterations()
-    public  int getIterations()
-    {
-        
-        int retVal = getIterations_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getMiniBatchSize()
-    //
-
-    //javadoc: LogisticRegression::getMiniBatchSize()
-    public  int getMiniBatchSize()
-    {
-        
-        int retVal = getMiniBatchSize_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getRegularization()
-    //
-
-    //javadoc: LogisticRegression::getRegularization()
-    public  int getRegularization()
-    {
-        
-        int retVal = getRegularization_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getTrainMethod()
-    //
-
-    //javadoc: LogisticRegression::getTrainMethod()
-    public  int getTrainMethod()
-    {
-        
-        int retVal = getTrainMethod_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  void setIterations(int val)
-    //
-
-    //javadoc: LogisticRegression::setIterations(val)
-    public  void setIterations(int val)
-    {
-        
-        setIterations_0(nativeObj, val);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setLearningRate(double val)
-    //
-
-    //javadoc: LogisticRegression::setLearningRate(val)
-    public  void setLearningRate(double val)
-    {
-        
-        setLearningRate_0(nativeObj, val);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setMiniBatchSize(int val)
-    //
-
-    //javadoc: LogisticRegression::setMiniBatchSize(val)
-    public  void setMiniBatchSize(int val)
-    {
-        
-        setMiniBatchSize_0(nativeObj, val);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setRegularization(int val)
-    //
-
-    //javadoc: LogisticRegression::setRegularization(val)
-    public  void setRegularization(int val)
-    {
-        
-        setRegularization_0(nativeObj, val);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setTermCriteria(TermCriteria val)
-    //
-
-    //javadoc: LogisticRegression::setTermCriteria(val)
-    public  void setTermCriteria(TermCriteria val)
-    {
-        
-        setTermCriteria_0(nativeObj, val.type, val.maxCount, val.epsilon);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setTrainMethod(int val)
-    //
-
-    //javadoc: LogisticRegression::setTrainMethod(val)
-    public  void setTrainMethod(int val)
-    {
-        
-        setTrainMethod_0(nativeObj, val);
-        
-        return;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++:  Mat get_learnt_thetas()
-    private static native long get_learnt_thetas_0(long nativeObj);
-
-    // C++: static Ptr_LogisticRegression create()
-    private static native long create_0();
-
-    // C++: static Ptr_LogisticRegression load(String filepath, String nodeName = String())
-    private static native long load_0(String filepath, String nodeName);
-    private static native long load_1(String filepath);
-
-    // C++:  TermCriteria getTermCriteria()
-    private static native double[] getTermCriteria_0(long nativeObj);
-
-    // C++:  double getLearningRate()
-    private static native double getLearningRate_0(long nativeObj);
-
-    // C++:  float predict(Mat samples, Mat& results = Mat(), int flags = 0)
-    private static native float predict_0(long nativeObj, long samples_nativeObj, long results_nativeObj, int flags);
-    private static native float predict_1(long nativeObj, long samples_nativeObj);
-
-    // C++:  int getIterations()
-    private static native int getIterations_0(long nativeObj);
-
-    // C++:  int getMiniBatchSize()
-    private static native int getMiniBatchSize_0(long nativeObj);
-
-    // C++:  int getRegularization()
-    private static native int getRegularization_0(long nativeObj);
-
-    // C++:  int getTrainMethod()
-    private static native int getTrainMethod_0(long nativeObj);
-
-    // C++:  void setIterations(int val)
-    private static native void setIterations_0(long nativeObj, int val);
-
-    // C++:  void setLearningRate(double val)
-    private static native void setLearningRate_0(long nativeObj, double val);
-
-    // C++:  void setMiniBatchSize(int val)
-    private static native void setMiniBatchSize_0(long nativeObj, int val);
-
-    // C++:  void setRegularization(int val)
-    private static native void setRegularization_0(long nativeObj, int val);
-
-    // C++:  void setTermCriteria(TermCriteria val)
-    private static native void setTermCriteria_0(long nativeObj, int val_type, int val_maxCount, double val_epsilon);
-
-    // C++:  void setTrainMethod(int val)
-    private static native void setTrainMethod_0(long nativeObj, int val);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/ml/Ml.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/ml/Ml.java
deleted file mode 100755
index 4f8f379..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/ml/Ml.java
+++ /dev/null
@@ -1,25 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.ml;
-
-
-
-// C++: class Ml
-//javadoc: Ml
-
-public class Ml {
-
-    public static final int
-            VAR_NUMERICAL = 0,
-            VAR_ORDERED = 0,
-            VAR_CATEGORICAL = 1,
-            TEST_ERROR = 0,
-            TRAIN_ERROR = 1,
-            ROW_SAMPLE = 0,
-            COL_SAMPLE = 1;
-
-
-
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/ml/NormalBayesClassifier.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/ml/NormalBayesClassifier.java
deleted file mode 100755
index 10a3012..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/ml/NormalBayesClassifier.java
+++ /dev/null
@@ -1,102 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.ml;
-
-import java.lang.String;
-import org.opencv.core.Mat;
-import org.opencv.ml.NormalBayesClassifier;
-import org.opencv.ml.StatModel;
-
-// C++: class NormalBayesClassifier
-//javadoc: NormalBayesClassifier
-
-public class NormalBayesClassifier extends StatModel {
-
-    protected NormalBayesClassifier(long addr) { super(addr); }
-
-    // internal usage only
-    public static NormalBayesClassifier __fromPtr__(long addr) { return new NormalBayesClassifier(addr); }
-
-    //
-    // C++: static Ptr_NormalBayesClassifier create()
-    //
-
-    //javadoc: NormalBayesClassifier::create()
-    public static NormalBayesClassifier create()
-    {
-        
-        NormalBayesClassifier retVal = NormalBayesClassifier.__fromPtr__(create_0());
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: static Ptr_NormalBayesClassifier load(String filepath, String nodeName = String())
-    //
-
-    //javadoc: NormalBayesClassifier::load(filepath, nodeName)
-    public static NormalBayesClassifier load(String filepath, String nodeName)
-    {
-        
-        NormalBayesClassifier retVal = NormalBayesClassifier.__fromPtr__(load_0(filepath, nodeName));
-        
-        return retVal;
-    }
-
-    //javadoc: NormalBayesClassifier::load(filepath)
-    public static NormalBayesClassifier load(String filepath)
-    {
-        
-        NormalBayesClassifier retVal = NormalBayesClassifier.__fromPtr__(load_1(filepath));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  float predictProb(Mat inputs, Mat& outputs, Mat& outputProbs, int flags = 0)
-    //
-
-    //javadoc: NormalBayesClassifier::predictProb(inputs, outputs, outputProbs, flags)
-    public  float predictProb(Mat inputs, Mat outputs, Mat outputProbs, int flags)
-    {
-        
-        float retVal = predictProb_0(nativeObj, inputs.nativeObj, outputs.nativeObj, outputProbs.nativeObj, flags);
-        
-        return retVal;
-    }
-
-    //javadoc: NormalBayesClassifier::predictProb(inputs, outputs, outputProbs)
-    public  float predictProb(Mat inputs, Mat outputs, Mat outputProbs)
-    {
-        
-        float retVal = predictProb_1(nativeObj, inputs.nativeObj, outputs.nativeObj, outputProbs.nativeObj);
-        
-        return retVal;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++: static Ptr_NormalBayesClassifier create()
-    private static native long create_0();
-
-    // C++: static Ptr_NormalBayesClassifier load(String filepath, String nodeName = String())
-    private static native long load_0(String filepath, String nodeName);
-    private static native long load_1(String filepath);
-
-    // C++:  float predictProb(Mat inputs, Mat& outputs, Mat& outputProbs, int flags = 0)
-    private static native float predictProb_0(long nativeObj, long inputs_nativeObj, long outputs_nativeObj, long outputProbs_nativeObj, int flags);
-    private static native float predictProb_1(long nativeObj, long inputs_nativeObj, long outputs_nativeObj, long outputProbs_nativeObj);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/ml/ParamGrid.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/ml/ParamGrid.java
deleted file mode 100755
index eea685d..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/ml/ParamGrid.java
+++ /dev/null
@@ -1,160 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.ml;
-
-import org.opencv.ml.ParamGrid;
-
-// C++: class ParamGrid
-//javadoc: ParamGrid
-
-public class ParamGrid {
-
-    protected final long nativeObj;
-    protected ParamGrid(long addr) { nativeObj = addr; }
-
-    public long getNativeObjAddr() { return nativeObj; }
-
-    // internal usage only
-    public static ParamGrid __fromPtr__(long addr) { return new ParamGrid(addr); }
-
-    //
-    // C++: static Ptr_ParamGrid create(double minVal = 0., double maxVal = 0., double logstep = 1.)
-    //
-
-    //javadoc: ParamGrid::create(minVal, maxVal, logstep)
-    public static ParamGrid create(double minVal, double maxVal, double logstep)
-    {
-        
-        ParamGrid retVal = ParamGrid.__fromPtr__(create_0(minVal, maxVal, logstep));
-        
-        return retVal;
-    }
-
-    //javadoc: ParamGrid::create()
-    public static ParamGrid create()
-    {
-        
-        ParamGrid retVal = ParamGrid.__fromPtr__(create_1());
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: double ParamGrid::minVal
-    //
-
-    //javadoc: ParamGrid::get_minVal()
-    public  double get_minVal()
-    {
-        
-        double retVal = get_minVal_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: void ParamGrid::minVal
-    //
-
-    //javadoc: ParamGrid::set_minVal(minVal)
-    public  void set_minVal(double minVal)
-    {
-        
-        set_minVal_0(nativeObj, minVal);
-        
-        return;
-    }
-
-
-    //
-    // C++: double ParamGrid::maxVal
-    //
-
-    //javadoc: ParamGrid::get_maxVal()
-    public  double get_maxVal()
-    {
-        
-        double retVal = get_maxVal_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: void ParamGrid::maxVal
-    //
-
-    //javadoc: ParamGrid::set_maxVal(maxVal)
-    public  void set_maxVal(double maxVal)
-    {
-        
-        set_maxVal_0(nativeObj, maxVal);
-        
-        return;
-    }
-
-
-    //
-    // C++: double ParamGrid::logStep
-    //
-
-    //javadoc: ParamGrid::get_logStep()
-    public  double get_logStep()
-    {
-        
-        double retVal = get_logStep_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: void ParamGrid::logStep
-    //
-
-    //javadoc: ParamGrid::set_logStep(logStep)
-    public  void set_logStep(double logStep)
-    {
-        
-        set_logStep_0(nativeObj, logStep);
-        
-        return;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++: static Ptr_ParamGrid create(double minVal = 0., double maxVal = 0., double logstep = 1.)
-    private static native long create_0(double minVal, double maxVal, double logstep);
-    private static native long create_1();
-
-    // C++: double ParamGrid::minVal
-    private static native double get_minVal_0(long nativeObj);
-
-    // C++: void ParamGrid::minVal
-    private static native void set_minVal_0(long nativeObj, double minVal);
-
-    // C++: double ParamGrid::maxVal
-    private static native double get_maxVal_0(long nativeObj);
-
-    // C++: void ParamGrid::maxVal
-    private static native void set_maxVal_0(long nativeObj, double maxVal);
-
-    // C++: double ParamGrid::logStep
-    private static native double get_logStep_0(long nativeObj);
-
-    // C++: void ParamGrid::logStep
-    private static native void set_logStep_0(long nativeObj, double logStep);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/ml/RTrees.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/ml/RTrees.java
deleted file mode 100755
index 47fe9dc..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/ml/RTrees.java
+++ /dev/null
@@ -1,212 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.ml;
-
-import java.lang.String;
-import org.opencv.core.Mat;
-import org.opencv.core.TermCriteria;
-import org.opencv.ml.DTrees;
-import org.opencv.ml.RTrees;
-
-// C++: class RTrees
-//javadoc: RTrees
-
-public class RTrees extends DTrees {
-
-    protected RTrees(long addr) { super(addr); }
-
-    // internal usage only
-    public static RTrees __fromPtr__(long addr) { return new RTrees(addr); }
-
-    //
-    // C++:  Mat getVarImportance()
-    //
-
-    //javadoc: RTrees::getVarImportance()
-    public  Mat getVarImportance()
-    {
-        
-        Mat retVal = new Mat(getVarImportance_0(nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: static Ptr_RTrees create()
-    //
-
-    //javadoc: RTrees::create()
-    public static RTrees create()
-    {
-        
-        RTrees retVal = RTrees.__fromPtr__(create_0());
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: static Ptr_RTrees load(String filepath, String nodeName = String())
-    //
-
-    //javadoc: RTrees::load(filepath, nodeName)
-    public static RTrees load(String filepath, String nodeName)
-    {
-        
-        RTrees retVal = RTrees.__fromPtr__(load_0(filepath, nodeName));
-        
-        return retVal;
-    }
-
-    //javadoc: RTrees::load(filepath)
-    public static RTrees load(String filepath)
-    {
-        
-        RTrees retVal = RTrees.__fromPtr__(load_1(filepath));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  TermCriteria getTermCriteria()
-    //
-
-    //javadoc: RTrees::getTermCriteria()
-    public  TermCriteria getTermCriteria()
-    {
-        
-        TermCriteria retVal = new TermCriteria(getTermCriteria_0(nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  bool getCalculateVarImportance()
-    //
-
-    //javadoc: RTrees::getCalculateVarImportance()
-    public  boolean getCalculateVarImportance()
-    {
-        
-        boolean retVal = getCalculateVarImportance_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getActiveVarCount()
-    //
-
-    //javadoc: RTrees::getActiveVarCount()
-    public  int getActiveVarCount()
-    {
-        
-        int retVal = getActiveVarCount_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  void getVotes(Mat samples, Mat& results, int flags)
-    //
-
-    //javadoc: RTrees::getVotes(samples, results, flags)
-    public  void getVotes(Mat samples, Mat results, int flags)
-    {
-        
-        getVotes_0(nativeObj, samples.nativeObj, results.nativeObj, flags);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setActiveVarCount(int val)
-    //
-
-    //javadoc: RTrees::setActiveVarCount(val)
-    public  void setActiveVarCount(int val)
-    {
-        
-        setActiveVarCount_0(nativeObj, val);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setCalculateVarImportance(bool val)
-    //
-
-    //javadoc: RTrees::setCalculateVarImportance(val)
-    public  void setCalculateVarImportance(boolean val)
-    {
-        
-        setCalculateVarImportance_0(nativeObj, val);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setTermCriteria(TermCriteria val)
-    //
-
-    //javadoc: RTrees::setTermCriteria(val)
-    public  void setTermCriteria(TermCriteria val)
-    {
-        
-        setTermCriteria_0(nativeObj, val.type, val.maxCount, val.epsilon);
-        
-        return;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++:  Mat getVarImportance()
-    private static native long getVarImportance_0(long nativeObj);
-
-    // C++: static Ptr_RTrees create()
-    private static native long create_0();
-
-    // C++: static Ptr_RTrees load(String filepath, String nodeName = String())
-    private static native long load_0(String filepath, String nodeName);
-    private static native long load_1(String filepath);
-
-    // C++:  TermCriteria getTermCriteria()
-    private static native double[] getTermCriteria_0(long nativeObj);
-
-    // C++:  bool getCalculateVarImportance()
-    private static native boolean getCalculateVarImportance_0(long nativeObj);
-
-    // C++:  int getActiveVarCount()
-    private static native int getActiveVarCount_0(long nativeObj);
-
-    // C++:  void getVotes(Mat samples, Mat& results, int flags)
-    private static native void getVotes_0(long nativeObj, long samples_nativeObj, long results_nativeObj, int flags);
-
-    // C++:  void setActiveVarCount(int val)
-    private static native void setActiveVarCount_0(long nativeObj, int val);
-
-    // C++:  void setCalculateVarImportance(bool val)
-    private static native void setCalculateVarImportance_0(long nativeObj, boolean val);
-
-    // C++:  void setTermCriteria(TermCriteria val)
-    private static native void setTermCriteria_0(long nativeObj, int val_type, int val_maxCount, double val_epsilon);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/ml/SVM.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/ml/SVM.java
deleted file mode 100755
index 15ec8aa..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/ml/SVM.java
+++ /dev/null
@@ -1,523 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.ml;
-
-import java.lang.String;
-import org.opencv.core.Mat;
-import org.opencv.core.TermCriteria;
-import org.opencv.ml.ParamGrid;
-import org.opencv.ml.SVM;
-import org.opencv.ml.StatModel;
-
-// C++: class SVM
-//javadoc: SVM
-
-public class SVM extends StatModel {
-
-    protected SVM(long addr) { super(addr); }
-
-    // internal usage only
-    public static SVM __fromPtr__(long addr) { return new SVM(addr); }
-
-    public static final int
-            C_SVC = 100,
-            NU_SVC = 101,
-            ONE_CLASS = 102,
-            EPS_SVR = 103,
-            NU_SVR = 104,
-            CUSTOM = -1,
-            LINEAR = 0,
-            POLY = 1,
-            RBF = 2,
-            SIGMOID = 3,
-            CHI2 = 4,
-            INTER = 5,
-            C = 0,
-            GAMMA = 1,
-            P = 2,
-            NU = 3,
-            COEF = 4,
-            DEGREE = 5;
-
-
-    //
-    // C++:  Mat getClassWeights()
-    //
-
-    //javadoc: SVM::getClassWeights()
-    public  Mat getClassWeights()
-    {
-        
-        Mat retVal = new Mat(getClassWeights_0(nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Mat getSupportVectors()
-    //
-
-    //javadoc: SVM::getSupportVectors()
-    public  Mat getSupportVectors()
-    {
-        
-        Mat retVal = new Mat(getSupportVectors_0(nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Mat getUncompressedSupportVectors()
-    //
-
-    //javadoc: SVM::getUncompressedSupportVectors()
-    public  Mat getUncompressedSupportVectors()
-    {
-        
-        Mat retVal = new Mat(getUncompressedSupportVectors_0(nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: static Ptr_ParamGrid getDefaultGridPtr(int param_id)
-    //
-
-    //javadoc: SVM::getDefaultGridPtr(param_id)
-    public static ParamGrid getDefaultGridPtr(int param_id)
-    {
-        
-        ParamGrid retVal = ParamGrid.__fromPtr__(getDefaultGridPtr_0(param_id));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: static Ptr_SVM create()
-    //
-
-    //javadoc: SVM::create()
-    public static SVM create()
-    {
-        
-        SVM retVal = SVM.__fromPtr__(create_0());
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: static Ptr_SVM load(String filepath)
-    //
-
-    //javadoc: SVM::load(filepath)
-    public static SVM load(String filepath)
-    {
-        
-        SVM retVal = SVM.__fromPtr__(load_0(filepath));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  TermCriteria getTermCriteria()
-    //
-
-    //javadoc: SVM::getTermCriteria()
-    public  TermCriteria getTermCriteria()
-    {
-        
-        TermCriteria retVal = new TermCriteria(getTermCriteria_0(nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  bool trainAuto(Mat samples, int layout, Mat responses, int kFold = 10, Ptr_ParamGrid Cgrid = SVM::getDefaultGridPtr(SVM::C), Ptr_ParamGrid gammaGrid = SVM::getDefaultGridPtr(SVM::GAMMA), Ptr_ParamGrid pGrid = SVM::getDefaultGridPtr(SVM::P), Ptr_ParamGrid nuGrid = SVM::getDefaultGridPtr(SVM::NU), Ptr_ParamGrid coeffGrid = SVM::getDefaultGridPtr(SVM::COEF), Ptr_ParamGrid degreeGrid = SVM::getDefaultGridPtr(SVM::DEGREE), bool balanced = false)
-    //
-
-    //javadoc: SVM::trainAuto(samples, layout, responses, kFold, Cgrid, gammaGrid, pGrid, nuGrid, coeffGrid, degreeGrid, balanced)
-    public  boolean trainAuto(Mat samples, int layout, Mat responses, int kFold, ParamGrid Cgrid, ParamGrid gammaGrid, ParamGrid pGrid, ParamGrid nuGrid, ParamGrid coeffGrid, ParamGrid degreeGrid, boolean balanced)
-    {
-        
-        boolean retVal = trainAuto_0(nativeObj, samples.nativeObj, layout, responses.nativeObj, kFold, Cgrid.getNativeObjAddr(), gammaGrid.getNativeObjAddr(), pGrid.getNativeObjAddr(), nuGrid.getNativeObjAddr(), coeffGrid.getNativeObjAddr(), degreeGrid.getNativeObjAddr(), balanced);
-        
-        return retVal;
-    }
-
-    //javadoc: SVM::trainAuto(samples, layout, responses)
-    public  boolean trainAuto(Mat samples, int layout, Mat responses)
-    {
-        
-        boolean retVal = trainAuto_1(nativeObj, samples.nativeObj, layout, responses.nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double getC()
-    //
-
-    //javadoc: SVM::getC()
-    public  double getC()
-    {
-        
-        double retVal = getC_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double getCoef0()
-    //
-
-    //javadoc: SVM::getCoef0()
-    public  double getCoef0()
-    {
-        
-        double retVal = getCoef0_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double getDecisionFunction(int i, Mat& alpha, Mat& svidx)
-    //
-
-    //javadoc: SVM::getDecisionFunction(i, alpha, svidx)
-    public  double getDecisionFunction(int i, Mat alpha, Mat svidx)
-    {
-        
-        double retVal = getDecisionFunction_0(nativeObj, i, alpha.nativeObj, svidx.nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double getDegree()
-    //
-
-    //javadoc: SVM::getDegree()
-    public  double getDegree()
-    {
-        
-        double retVal = getDegree_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double getGamma()
-    //
-
-    //javadoc: SVM::getGamma()
-    public  double getGamma()
-    {
-        
-        double retVal = getGamma_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double getNu()
-    //
-
-    //javadoc: SVM::getNu()
-    public  double getNu()
-    {
-        
-        double retVal = getNu_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double getP()
-    //
-
-    //javadoc: SVM::getP()
-    public  double getP()
-    {
-        
-        double retVal = getP_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getKernelType()
-    //
-
-    //javadoc: SVM::getKernelType()
-    public  int getKernelType()
-    {
-        
-        int retVal = getKernelType_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getType()
-    //
-
-    //javadoc: SVM::getType()
-    public  int getType()
-    {
-        
-        int retVal = getType_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  void setC(double val)
-    //
-
-    //javadoc: SVM::setC(val)
-    public  void setC(double val)
-    {
-        
-        setC_0(nativeObj, val);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setClassWeights(Mat val)
-    //
-
-    //javadoc: SVM::setClassWeights(val)
-    public  void setClassWeights(Mat val)
-    {
-        
-        setClassWeights_0(nativeObj, val.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setCoef0(double val)
-    //
-
-    //javadoc: SVM::setCoef0(val)
-    public  void setCoef0(double val)
-    {
-        
-        setCoef0_0(nativeObj, val);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setDegree(double val)
-    //
-
-    //javadoc: SVM::setDegree(val)
-    public  void setDegree(double val)
-    {
-        
-        setDegree_0(nativeObj, val);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setGamma(double val)
-    //
-
-    //javadoc: SVM::setGamma(val)
-    public  void setGamma(double val)
-    {
-        
-        setGamma_0(nativeObj, val);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setKernel(int kernelType)
-    //
-
-    //javadoc: SVM::setKernel(kernelType)
-    public  void setKernel(int kernelType)
-    {
-        
-        setKernel_0(nativeObj, kernelType);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setNu(double val)
-    //
-
-    //javadoc: SVM::setNu(val)
-    public  void setNu(double val)
-    {
-        
-        setNu_0(nativeObj, val);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setP(double val)
-    //
-
-    //javadoc: SVM::setP(val)
-    public  void setP(double val)
-    {
-        
-        setP_0(nativeObj, val);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setTermCriteria(TermCriteria val)
-    //
-
-    //javadoc: SVM::setTermCriteria(val)
-    public  void setTermCriteria(TermCriteria val)
-    {
-        
-        setTermCriteria_0(nativeObj, val.type, val.maxCount, val.epsilon);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setType(int val)
-    //
-
-    //javadoc: SVM::setType(val)
-    public  void setType(int val)
-    {
-        
-        setType_0(nativeObj, val);
-        
-        return;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++:  Mat getClassWeights()
-    private static native long getClassWeights_0(long nativeObj);
-
-    // C++:  Mat getSupportVectors()
-    private static native long getSupportVectors_0(long nativeObj);
-
-    // C++:  Mat getUncompressedSupportVectors()
-    private static native long getUncompressedSupportVectors_0(long nativeObj);
-
-    // C++: static Ptr_ParamGrid getDefaultGridPtr(int param_id)
-    private static native long getDefaultGridPtr_0(int param_id);
-
-    // C++: static Ptr_SVM create()
-    private static native long create_0();
-
-    // C++: static Ptr_SVM load(String filepath)
-    private static native long load_0(String filepath);
-
-    // C++:  TermCriteria getTermCriteria()
-    private static native double[] getTermCriteria_0(long nativeObj);
-
-    // C++:  bool trainAuto(Mat samples, int layout, Mat responses, int kFold = 10, Ptr_ParamGrid Cgrid = SVM::getDefaultGridPtr(SVM::C), Ptr_ParamGrid gammaGrid = SVM::getDefaultGridPtr(SVM::GAMMA), Ptr_ParamGrid pGrid = SVM::getDefaultGridPtr(SVM::P), Ptr_ParamGrid nuGrid = SVM::getDefaultGridPtr(SVM::NU), Ptr_ParamGrid coeffGrid = SVM::getDefaultGridPtr(SVM::COEF), Ptr_ParamGrid degreeGrid = SVM::getDefaultGridPtr(SVM::DEGREE), bool balanced = false)
-    private static native boolean trainAuto_0(long nativeObj, long samples_nativeObj, int layout, long responses_nativeObj, int kFold, long Cgrid_nativeObj, long gammaGrid_nativeObj, long pGrid_nativeObj, long nuGrid_nativeObj, long coeffGrid_nativeObj, long degreeGrid_nativeObj, boolean balanced);
-    private static native boolean trainAuto_1(long nativeObj, long samples_nativeObj, int layout, long responses_nativeObj);
-
-    // C++:  double getC()
-    private static native double getC_0(long nativeObj);
-
-    // C++:  double getCoef0()
-    private static native double getCoef0_0(long nativeObj);
-
-    // C++:  double getDecisionFunction(int i, Mat& alpha, Mat& svidx)
-    private static native double getDecisionFunction_0(long nativeObj, int i, long alpha_nativeObj, long svidx_nativeObj);
-
-    // C++:  double getDegree()
-    private static native double getDegree_0(long nativeObj);
-
-    // C++:  double getGamma()
-    private static native double getGamma_0(long nativeObj);
-
-    // C++:  double getNu()
-    private static native double getNu_0(long nativeObj);
-
-    // C++:  double getP()
-    private static native double getP_0(long nativeObj);
-
-    // C++:  int getKernelType()
-    private static native int getKernelType_0(long nativeObj);
-
-    // C++:  int getType()
-    private static native int getType_0(long nativeObj);
-
-    // C++:  void setC(double val)
-    private static native void setC_0(long nativeObj, double val);
-
-    // C++:  void setClassWeights(Mat val)
-    private static native void setClassWeights_0(long nativeObj, long val_nativeObj);
-
-    // C++:  void setCoef0(double val)
-    private static native void setCoef0_0(long nativeObj, double val);
-
-    // C++:  void setDegree(double val)
-    private static native void setDegree_0(long nativeObj, double val);
-
-    // C++:  void setGamma(double val)
-    private static native void setGamma_0(long nativeObj, double val);
-
-    // C++:  void setKernel(int kernelType)
-    private static native void setKernel_0(long nativeObj, int kernelType);
-
-    // C++:  void setNu(double val)
-    private static native void setNu_0(long nativeObj, double val);
-
-    // C++:  void setP(double val)
-    private static native void setP_0(long nativeObj, double val);
-
-    // C++:  void setTermCriteria(TermCriteria val)
-    private static native void setTermCriteria_0(long nativeObj, int val_type, int val_maxCount, double val_epsilon);
-
-    // C++:  void setType(int val)
-    private static native void setType_0(long nativeObj, int val);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/ml/SVMSGD.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/ml/SVMSGD.java
deleted file mode 100755
index a9f70dc..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/ml/SVMSGD.java
+++ /dev/null
@@ -1,348 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.ml;
-
-import java.lang.String;
-import org.opencv.core.Mat;
-import org.opencv.core.TermCriteria;
-import org.opencv.ml.SVMSGD;
-import org.opencv.ml.StatModel;
-
-// C++: class SVMSGD
-//javadoc: SVMSGD
-
-public class SVMSGD extends StatModel {
-
-    protected SVMSGD(long addr) { super(addr); }
-
-    // internal usage only
-    public static SVMSGD __fromPtr__(long addr) { return new SVMSGD(addr); }
-
-    public static final int
-            SGD = 0,
-            ASGD = 1,
-            SOFT_MARGIN = 0,
-            HARD_MARGIN = 1;
-
-
-    //
-    // C++:  Mat getWeights()
-    //
-
-    //javadoc: SVMSGD::getWeights()
-    public  Mat getWeights()
-    {
-        
-        Mat retVal = new Mat(getWeights_0(nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: static Ptr_SVMSGD create()
-    //
-
-    //javadoc: SVMSGD::create()
-    public static SVMSGD create()
-    {
-        
-        SVMSGD retVal = SVMSGD.__fromPtr__(create_0());
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: static Ptr_SVMSGD load(String filepath, String nodeName = String())
-    //
-
-    //javadoc: SVMSGD::load(filepath, nodeName)
-    public static SVMSGD load(String filepath, String nodeName)
-    {
-        
-        SVMSGD retVal = SVMSGD.__fromPtr__(load_0(filepath, nodeName));
-        
-        return retVal;
-    }
-
-    //javadoc: SVMSGD::load(filepath)
-    public static SVMSGD load(String filepath)
-    {
-        
-        SVMSGD retVal = SVMSGD.__fromPtr__(load_1(filepath));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  TermCriteria getTermCriteria()
-    //
-
-    //javadoc: SVMSGD::getTermCriteria()
-    public  TermCriteria getTermCriteria()
-    {
-        
-        TermCriteria retVal = new TermCriteria(getTermCriteria_0(nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  float getInitialStepSize()
-    //
-
-    //javadoc: SVMSGD::getInitialStepSize()
-    public  float getInitialStepSize()
-    {
-        
-        float retVal = getInitialStepSize_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  float getMarginRegularization()
-    //
-
-    //javadoc: SVMSGD::getMarginRegularization()
-    public  float getMarginRegularization()
-    {
-        
-        float retVal = getMarginRegularization_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  float getShift()
-    //
-
-    //javadoc: SVMSGD::getShift()
-    public  float getShift()
-    {
-        
-        float retVal = getShift_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  float getStepDecreasingPower()
-    //
-
-    //javadoc: SVMSGD::getStepDecreasingPower()
-    public  float getStepDecreasingPower()
-    {
-        
-        float retVal = getStepDecreasingPower_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getMarginType()
-    //
-
-    //javadoc: SVMSGD::getMarginType()
-    public  int getMarginType()
-    {
-        
-        int retVal = getMarginType_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getSvmsgdType()
-    //
-
-    //javadoc: SVMSGD::getSvmsgdType()
-    public  int getSvmsgdType()
-    {
-        
-        int retVal = getSvmsgdType_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  void setInitialStepSize(float InitialStepSize)
-    //
-
-    //javadoc: SVMSGD::setInitialStepSize(InitialStepSize)
-    public  void setInitialStepSize(float InitialStepSize)
-    {
-        
-        setInitialStepSize_0(nativeObj, InitialStepSize);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setMarginRegularization(float marginRegularization)
-    //
-
-    //javadoc: SVMSGD::setMarginRegularization(marginRegularization)
-    public  void setMarginRegularization(float marginRegularization)
-    {
-        
-        setMarginRegularization_0(nativeObj, marginRegularization);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setMarginType(int marginType)
-    //
-
-    //javadoc: SVMSGD::setMarginType(marginType)
-    public  void setMarginType(int marginType)
-    {
-        
-        setMarginType_0(nativeObj, marginType);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setOptimalParameters(int svmsgdType = SVMSGD::ASGD, int marginType = SVMSGD::SOFT_MARGIN)
-    //
-
-    //javadoc: SVMSGD::setOptimalParameters(svmsgdType, marginType)
-    public  void setOptimalParameters(int svmsgdType, int marginType)
-    {
-        
-        setOptimalParameters_0(nativeObj, svmsgdType, marginType);
-        
-        return;
-    }
-
-    //javadoc: SVMSGD::setOptimalParameters()
-    public  void setOptimalParameters()
-    {
-        
-        setOptimalParameters_1(nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setStepDecreasingPower(float stepDecreasingPower)
-    //
-
-    //javadoc: SVMSGD::setStepDecreasingPower(stepDecreasingPower)
-    public  void setStepDecreasingPower(float stepDecreasingPower)
-    {
-        
-        setStepDecreasingPower_0(nativeObj, stepDecreasingPower);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setSvmsgdType(int svmsgdType)
-    //
-
-    //javadoc: SVMSGD::setSvmsgdType(svmsgdType)
-    public  void setSvmsgdType(int svmsgdType)
-    {
-        
-        setSvmsgdType_0(nativeObj, svmsgdType);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setTermCriteria(TermCriteria val)
-    //
-
-    //javadoc: SVMSGD::setTermCriteria(val)
-    public  void setTermCriteria(TermCriteria val)
-    {
-        
-        setTermCriteria_0(nativeObj, val.type, val.maxCount, val.epsilon);
-        
-        return;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++:  Mat getWeights()
-    private static native long getWeights_0(long nativeObj);
-
-    // C++: static Ptr_SVMSGD create()
-    private static native long create_0();
-
-    // C++: static Ptr_SVMSGD load(String filepath, String nodeName = String())
-    private static native long load_0(String filepath, String nodeName);
-    private static native long load_1(String filepath);
-
-    // C++:  TermCriteria getTermCriteria()
-    private static native double[] getTermCriteria_0(long nativeObj);
-
-    // C++:  float getInitialStepSize()
-    private static native float getInitialStepSize_0(long nativeObj);
-
-    // C++:  float getMarginRegularization()
-    private static native float getMarginRegularization_0(long nativeObj);
-
-    // C++:  float getShift()
-    private static native float getShift_0(long nativeObj);
-
-    // C++:  float getStepDecreasingPower()
-    private static native float getStepDecreasingPower_0(long nativeObj);
-
-    // C++:  int getMarginType()
-    private static native int getMarginType_0(long nativeObj);
-
-    // C++:  int getSvmsgdType()
-    private static native int getSvmsgdType_0(long nativeObj);
-
-    // C++:  void setInitialStepSize(float InitialStepSize)
-    private static native void setInitialStepSize_0(long nativeObj, float InitialStepSize);
-
-    // C++:  void setMarginRegularization(float marginRegularization)
-    private static native void setMarginRegularization_0(long nativeObj, float marginRegularization);
-
-    // C++:  void setMarginType(int marginType)
-    private static native void setMarginType_0(long nativeObj, int marginType);
-
-    // C++:  void setOptimalParameters(int svmsgdType = SVMSGD::ASGD, int marginType = SVMSGD::SOFT_MARGIN)
-    private static native void setOptimalParameters_0(long nativeObj, int svmsgdType, int marginType);
-    private static native void setOptimalParameters_1(long nativeObj);
-
-    // C++:  void setStepDecreasingPower(float stepDecreasingPower)
-    private static native void setStepDecreasingPower_0(long nativeObj, float stepDecreasingPower);
-
-    // C++:  void setSvmsgdType(int svmsgdType)
-    private static native void setSvmsgdType_0(long nativeObj, int svmsgdType);
-
-    // C++:  void setTermCriteria(TermCriteria val)
-    private static native void setTermCriteria_0(long nativeObj, int val_type, int val_maxCount, double val_epsilon);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/ml/StatModel.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/ml/StatModel.java
deleted file mode 100755
index 4f8b21d..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/ml/StatModel.java
+++ /dev/null
@@ -1,193 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.ml;
-
-import org.opencv.core.Algorithm;
-import org.opencv.core.Mat;
-import org.opencv.ml.TrainData;
-
-// C++: class StatModel
-//javadoc: StatModel
-
-public class StatModel extends Algorithm {
-
-    protected StatModel(long addr) { super(addr); }
-
-    // internal usage only
-    public static StatModel __fromPtr__(long addr) { return new StatModel(addr); }
-
-    public static final int
-            UPDATE_MODEL = 1,
-            RAW_OUTPUT = 1,
-            COMPRESSED_INPUT = 2,
-            PREPROCESSED_INPUT = 4;
-
-
-    //
-    // C++:  bool empty()
-    //
-
-    //javadoc: StatModel::empty()
-    public  boolean empty()
-    {
-        
-        boolean retVal = empty_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  bool isClassifier()
-    //
-
-    //javadoc: StatModel::isClassifier()
-    public  boolean isClassifier()
-    {
-        
-        boolean retVal = isClassifier_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  bool isTrained()
-    //
-
-    //javadoc: StatModel::isTrained()
-    public  boolean isTrained()
-    {
-        
-        boolean retVal = isTrained_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  bool train(Mat samples, int layout, Mat responses)
-    //
-
-    //javadoc: StatModel::train(samples, layout, responses)
-    public  boolean train(Mat samples, int layout, Mat responses)
-    {
-        
-        boolean retVal = train_0(nativeObj, samples.nativeObj, layout, responses.nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  bool train(Ptr_TrainData trainData, int flags = 0)
-    //
-
-    //javadoc: StatModel::train(trainData, flags)
-    public  boolean train(TrainData trainData, int flags)
-    {
-        
-        boolean retVal = train_1(nativeObj, trainData.getNativeObjAddr(), flags);
-        
-        return retVal;
-    }
-
-    //javadoc: StatModel::train(trainData)
-    public  boolean train(TrainData trainData)
-    {
-        
-        boolean retVal = train_2(nativeObj, trainData.getNativeObjAddr());
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  float calcError(Ptr_TrainData data, bool test, Mat& resp)
-    //
-
-    //javadoc: StatModel::calcError(data, test, resp)
-    public  float calcError(TrainData data, boolean test, Mat resp)
-    {
-        
-        float retVal = calcError_0(nativeObj, data.getNativeObjAddr(), test, resp.nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  float predict(Mat samples, Mat& results = Mat(), int flags = 0)
-    //
-
-    //javadoc: StatModel::predict(samples, results, flags)
-    public  float predict(Mat samples, Mat results, int flags)
-    {
-        
-        float retVal = predict_0(nativeObj, samples.nativeObj, results.nativeObj, flags);
-        
-        return retVal;
-    }
-
-    //javadoc: StatModel::predict(samples)
-    public  float predict(Mat samples)
-    {
-        
-        float retVal = predict_1(nativeObj, samples.nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getVarCount()
-    //
-
-    //javadoc: StatModel::getVarCount()
-    public  int getVarCount()
-    {
-        
-        int retVal = getVarCount_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++:  bool empty()
-    private static native boolean empty_0(long nativeObj);
-
-    // C++:  bool isClassifier()
-    private static native boolean isClassifier_0(long nativeObj);
-
-    // C++:  bool isTrained()
-    private static native boolean isTrained_0(long nativeObj);
-
-    // C++:  bool train(Mat samples, int layout, Mat responses)
-    private static native boolean train_0(long nativeObj, long samples_nativeObj, int layout, long responses_nativeObj);
-
-    // C++:  bool train(Ptr_TrainData trainData, int flags = 0)
-    private static native boolean train_1(long nativeObj, long trainData_nativeObj, int flags);
-    private static native boolean train_2(long nativeObj, long trainData_nativeObj);
-
-    // C++:  float calcError(Ptr_TrainData data, bool test, Mat& resp)
-    private static native float calcError_0(long nativeObj, long data_nativeObj, boolean test, long resp_nativeObj);
-
-    // C++:  float predict(Mat samples, Mat& results = Mat(), int flags = 0)
-    private static native float predict_0(long nativeObj, long samples_nativeObj, long results_nativeObj, int flags);
-    private static native float predict_1(long nativeObj, long samples_nativeObj);
-
-    // C++:  int getVarCount()
-    private static native int getVarCount_0(long nativeObj);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/ml/TrainData.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/ml/TrainData.java
deleted file mode 100755
index 58a3c81..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/ml/TrainData.java
+++ /dev/null
@@ -1,722 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.ml;
-
-import java.lang.String;
-import java.util.ArrayList;
-import java.util.List;
-import org.opencv.core.Mat;
-import org.opencv.ml.TrainData;
-import org.opencv.utils.Converters;
-
-// C++: class TrainData
-//javadoc: TrainData
-
-public class TrainData {
-
-    protected final long nativeObj;
-    protected TrainData(long addr) { nativeObj = addr; }
-
-    public long getNativeObjAddr() { return nativeObj; }
-
-    // internal usage only
-    public static TrainData __fromPtr__(long addr) { return new TrainData(addr); }
-
-    //
-    // C++:  Mat getCatMap()
-    //
-
-    //javadoc: TrainData::getCatMap()
-    public  Mat getCatMap()
-    {
-        
-        Mat retVal = new Mat(getCatMap_0(nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Mat getCatOfs()
-    //
-
-    //javadoc: TrainData::getCatOfs()
-    public  Mat getCatOfs()
-    {
-        
-        Mat retVal = new Mat(getCatOfs_0(nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Mat getClassLabels()
-    //
-
-    //javadoc: TrainData::getClassLabels()
-    public  Mat getClassLabels()
-    {
-        
-        Mat retVal = new Mat(getClassLabels_0(nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Mat getDefaultSubstValues()
-    //
-
-    //javadoc: TrainData::getDefaultSubstValues()
-    public  Mat getDefaultSubstValues()
-    {
-        
-        Mat retVal = new Mat(getDefaultSubstValues_0(nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Mat getMissing()
-    //
-
-    //javadoc: TrainData::getMissing()
-    public  Mat getMissing()
-    {
-        
-        Mat retVal = new Mat(getMissing_0(nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Mat getNormCatResponses()
-    //
-
-    //javadoc: TrainData::getNormCatResponses()
-    public  Mat getNormCatResponses()
-    {
-        
-        Mat retVal = new Mat(getNormCatResponses_0(nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Mat getResponses()
-    //
-
-    //javadoc: TrainData::getResponses()
-    public  Mat getResponses()
-    {
-        
-        Mat retVal = new Mat(getResponses_0(nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Mat getSampleWeights()
-    //
-
-    //javadoc: TrainData::getSampleWeights()
-    public  Mat getSampleWeights()
-    {
-        
-        Mat retVal = new Mat(getSampleWeights_0(nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Mat getSamples()
-    //
-
-    //javadoc: TrainData::getSamples()
-    public  Mat getSamples()
-    {
-        
-        Mat retVal = new Mat(getSamples_0(nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: static Mat getSubVector(Mat vec, Mat idx)
-    //
-
-    //javadoc: TrainData::getSubVector(vec, idx)
-    public static Mat getSubVector(Mat vec, Mat idx)
-    {
-        
-        Mat retVal = new Mat(getSubVector_0(vec.nativeObj, idx.nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Mat getTestNormCatResponses()
-    //
-
-    //javadoc: TrainData::getTestNormCatResponses()
-    public  Mat getTestNormCatResponses()
-    {
-        
-        Mat retVal = new Mat(getTestNormCatResponses_0(nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Mat getTestResponses()
-    //
-
-    //javadoc: TrainData::getTestResponses()
-    public  Mat getTestResponses()
-    {
-        
-        Mat retVal = new Mat(getTestResponses_0(nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Mat getTestSampleIdx()
-    //
-
-    //javadoc: TrainData::getTestSampleIdx()
-    public  Mat getTestSampleIdx()
-    {
-        
-        Mat retVal = new Mat(getTestSampleIdx_0(nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Mat getTestSampleWeights()
-    //
-
-    //javadoc: TrainData::getTestSampleWeights()
-    public  Mat getTestSampleWeights()
-    {
-        
-        Mat retVal = new Mat(getTestSampleWeights_0(nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Mat getTestSamples()
-    //
-
-    //javadoc: TrainData::getTestSamples()
-    public  Mat getTestSamples()
-    {
-        
-        Mat retVal = new Mat(getTestSamples_0(nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Mat getTrainNormCatResponses()
-    //
-
-    //javadoc: TrainData::getTrainNormCatResponses()
-    public  Mat getTrainNormCatResponses()
-    {
-        
-        Mat retVal = new Mat(getTrainNormCatResponses_0(nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Mat getTrainResponses()
-    //
-
-    //javadoc: TrainData::getTrainResponses()
-    public  Mat getTrainResponses()
-    {
-        
-        Mat retVal = new Mat(getTrainResponses_0(nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Mat getTrainSampleIdx()
-    //
-
-    //javadoc: TrainData::getTrainSampleIdx()
-    public  Mat getTrainSampleIdx()
-    {
-        
-        Mat retVal = new Mat(getTrainSampleIdx_0(nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Mat getTrainSampleWeights()
-    //
-
-    //javadoc: TrainData::getTrainSampleWeights()
-    public  Mat getTrainSampleWeights()
-    {
-        
-        Mat retVal = new Mat(getTrainSampleWeights_0(nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Mat getTrainSamples(int layout = ROW_SAMPLE, bool compressSamples = true, bool compressVars = true)
-    //
-
-    //javadoc: TrainData::getTrainSamples(layout, compressSamples, compressVars)
-    public  Mat getTrainSamples(int layout, boolean compressSamples, boolean compressVars)
-    {
-        
-        Mat retVal = new Mat(getTrainSamples_0(nativeObj, layout, compressSamples, compressVars));
-        
-        return retVal;
-    }
-
-    //javadoc: TrainData::getTrainSamples()
-    public  Mat getTrainSamples()
-    {
-        
-        Mat retVal = new Mat(getTrainSamples_1(nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Mat getVarIdx()
-    //
-
-    //javadoc: TrainData::getVarIdx()
-    public  Mat getVarIdx()
-    {
-        
-        Mat retVal = new Mat(getVarIdx_0(nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Mat getVarSymbolFlags()
-    //
-
-    //javadoc: TrainData::getVarSymbolFlags()
-    public  Mat getVarSymbolFlags()
-    {
-        
-        Mat retVal = new Mat(getVarSymbolFlags_0(nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Mat getVarType()
-    //
-
-    //javadoc: TrainData::getVarType()
-    public  Mat getVarType()
-    {
-        
-        Mat retVal = new Mat(getVarType_0(nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: static Ptr_TrainData create(Mat samples, int layout, Mat responses, Mat varIdx = Mat(), Mat sampleIdx = Mat(), Mat sampleWeights = Mat(), Mat varType = Mat())
-    //
-
-    //javadoc: TrainData::create(samples, layout, responses, varIdx, sampleIdx, sampleWeights, varType)
-    public static TrainData create(Mat samples, int layout, Mat responses, Mat varIdx, Mat sampleIdx, Mat sampleWeights, Mat varType)
-    {
-        
-        TrainData retVal = TrainData.__fromPtr__(create_0(samples.nativeObj, layout, responses.nativeObj, varIdx.nativeObj, sampleIdx.nativeObj, sampleWeights.nativeObj, varType.nativeObj));
-        
-        return retVal;
-    }
-
-    //javadoc: TrainData::create(samples, layout, responses)
-    public static TrainData create(Mat samples, int layout, Mat responses)
-    {
-        
-        TrainData retVal = TrainData.__fromPtr__(create_1(samples.nativeObj, layout, responses.nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getCatCount(int vi)
-    //
-
-    //javadoc: TrainData::getCatCount(vi)
-    public  int getCatCount(int vi)
-    {
-        
-        int retVal = getCatCount_0(nativeObj, vi);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getLayout()
-    //
-
-    //javadoc: TrainData::getLayout()
-    public  int getLayout()
-    {
-        
-        int retVal = getLayout_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getNAllVars()
-    //
-
-    //javadoc: TrainData::getNAllVars()
-    public  int getNAllVars()
-    {
-        
-        int retVal = getNAllVars_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getNSamples()
-    //
-
-    //javadoc: TrainData::getNSamples()
-    public  int getNSamples()
-    {
-        
-        int retVal = getNSamples_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getNTestSamples()
-    //
-
-    //javadoc: TrainData::getNTestSamples()
-    public  int getNTestSamples()
-    {
-        
-        int retVal = getNTestSamples_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getNTrainSamples()
-    //
-
-    //javadoc: TrainData::getNTrainSamples()
-    public  int getNTrainSamples()
-    {
-        
-        int retVal = getNTrainSamples_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getNVars()
-    //
-
-    //javadoc: TrainData::getNVars()
-    public  int getNVars()
-    {
-        
-        int retVal = getNVars_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getResponseType()
-    //
-
-    //javadoc: TrainData::getResponseType()
-    public  int getResponseType()
-    {
-        
-        int retVal = getResponseType_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  void getNames(vector_String names)
-    //
-
-    //javadoc: TrainData::getNames(names)
-    public  void getNames(List<String> names)
-    {
-        
-        getNames_0(nativeObj, names);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void getSample(Mat varIdx, int sidx, float* buf)
-    //
-
-    //javadoc: TrainData::getSample(varIdx, sidx, buf)
-    public  void getSample(Mat varIdx, int sidx, float buf)
-    {
-        
-        getSample_0(nativeObj, varIdx.nativeObj, sidx, buf);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void getValues(int vi, Mat sidx, float* values)
-    //
-
-    //javadoc: TrainData::getValues(vi, sidx, values)
-    public  void getValues(int vi, Mat sidx, float values)
-    {
-        
-        getValues_0(nativeObj, vi, sidx.nativeObj, values);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setTrainTestSplit(int count, bool shuffle = true)
-    //
-
-    //javadoc: TrainData::setTrainTestSplit(count, shuffle)
-    public  void setTrainTestSplit(int count, boolean shuffle)
-    {
-        
-        setTrainTestSplit_0(nativeObj, count, shuffle);
-        
-        return;
-    }
-
-    //javadoc: TrainData::setTrainTestSplit(count)
-    public  void setTrainTestSplit(int count)
-    {
-        
-        setTrainTestSplit_1(nativeObj, count);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setTrainTestSplitRatio(double ratio, bool shuffle = true)
-    //
-
-    //javadoc: TrainData::setTrainTestSplitRatio(ratio, shuffle)
-    public  void setTrainTestSplitRatio(double ratio, boolean shuffle)
-    {
-        
-        setTrainTestSplitRatio_0(nativeObj, ratio, shuffle);
-        
-        return;
-    }
-
-    //javadoc: TrainData::setTrainTestSplitRatio(ratio)
-    public  void setTrainTestSplitRatio(double ratio)
-    {
-        
-        setTrainTestSplitRatio_1(nativeObj, ratio);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void shuffleTrainTest()
-    //
-
-    //javadoc: TrainData::shuffleTrainTest()
-    public  void shuffleTrainTest()
-    {
-        
-        shuffleTrainTest_0(nativeObj);
-        
-        return;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++:  Mat getCatMap()
-    private static native long getCatMap_0(long nativeObj);
-
-    // C++:  Mat getCatOfs()
-    private static native long getCatOfs_0(long nativeObj);
-
-    // C++:  Mat getClassLabels()
-    private static native long getClassLabels_0(long nativeObj);
-
-    // C++:  Mat getDefaultSubstValues()
-    private static native long getDefaultSubstValues_0(long nativeObj);
-
-    // C++:  Mat getMissing()
-    private static native long getMissing_0(long nativeObj);
-
-    // C++:  Mat getNormCatResponses()
-    private static native long getNormCatResponses_0(long nativeObj);
-
-    // C++:  Mat getResponses()
-    private static native long getResponses_0(long nativeObj);
-
-    // C++:  Mat getSampleWeights()
-    private static native long getSampleWeights_0(long nativeObj);
-
-    // C++:  Mat getSamples()
-    private static native long getSamples_0(long nativeObj);
-
-    // C++: static Mat getSubVector(Mat vec, Mat idx)
-    private static native long getSubVector_0(long vec_nativeObj, long idx_nativeObj);
-
-    // C++:  Mat getTestNormCatResponses()
-    private static native long getTestNormCatResponses_0(long nativeObj);
-
-    // C++:  Mat getTestResponses()
-    private static native long getTestResponses_0(long nativeObj);
-
-    // C++:  Mat getTestSampleIdx()
-    private static native long getTestSampleIdx_0(long nativeObj);
-
-    // C++:  Mat getTestSampleWeights()
-    private static native long getTestSampleWeights_0(long nativeObj);
-
-    // C++:  Mat getTestSamples()
-    private static native long getTestSamples_0(long nativeObj);
-
-    // C++:  Mat getTrainNormCatResponses()
-    private static native long getTrainNormCatResponses_0(long nativeObj);
-
-    // C++:  Mat getTrainResponses()
-    private static native long getTrainResponses_0(long nativeObj);
-
-    // C++:  Mat getTrainSampleIdx()
-    private static native long getTrainSampleIdx_0(long nativeObj);
-
-    // C++:  Mat getTrainSampleWeights()
-    private static native long getTrainSampleWeights_0(long nativeObj);
-
-    // C++:  Mat getTrainSamples(int layout = ROW_SAMPLE, bool compressSamples = true, bool compressVars = true)
-    private static native long getTrainSamples_0(long nativeObj, int layout, boolean compressSamples, boolean compressVars);
-    private static native long getTrainSamples_1(long nativeObj);
-
-    // C++:  Mat getVarIdx()
-    private static native long getVarIdx_0(long nativeObj);
-
-    // C++:  Mat getVarSymbolFlags()
-    private static native long getVarSymbolFlags_0(long nativeObj);
-
-    // C++:  Mat getVarType()
-    private static native long getVarType_0(long nativeObj);
-
-    // C++: static Ptr_TrainData create(Mat samples, int layout, Mat responses, Mat varIdx = Mat(), Mat sampleIdx = Mat(), Mat sampleWeights = Mat(), Mat varType = Mat())
-    private static native long create_0(long samples_nativeObj, int layout, long responses_nativeObj, long varIdx_nativeObj, long sampleIdx_nativeObj, long sampleWeights_nativeObj, long varType_nativeObj);
-    private static native long create_1(long samples_nativeObj, int layout, long responses_nativeObj);
-
-    // C++:  int getCatCount(int vi)
-    private static native int getCatCount_0(long nativeObj, int vi);
-
-    // C++:  int getLayout()
-    private static native int getLayout_0(long nativeObj);
-
-    // C++:  int getNAllVars()
-    private static native int getNAllVars_0(long nativeObj);
-
-    // C++:  int getNSamples()
-    private static native int getNSamples_0(long nativeObj);
-
-    // C++:  int getNTestSamples()
-    private static native int getNTestSamples_0(long nativeObj);
-
-    // C++:  int getNTrainSamples()
-    private static native int getNTrainSamples_0(long nativeObj);
-
-    // C++:  int getNVars()
-    private static native int getNVars_0(long nativeObj);
-
-    // C++:  int getResponseType()
-    private static native int getResponseType_0(long nativeObj);
-
-    // C++:  void getNames(vector_String names)
-    private static native void getNames_0(long nativeObj, List<String> names);
-
-    // C++:  void getSample(Mat varIdx, int sidx, float* buf)
-    private static native void getSample_0(long nativeObj, long varIdx_nativeObj, int sidx, float buf);
-
-    // C++:  void getValues(int vi, Mat sidx, float* values)
-    private static native void getValues_0(long nativeObj, int vi, long sidx_nativeObj, float values);
-
-    // C++:  void setTrainTestSplit(int count, bool shuffle = true)
-    private static native void setTrainTestSplit_0(long nativeObj, int count, boolean shuffle);
-    private static native void setTrainTestSplit_1(long nativeObj, int count);
-
-    // C++:  void setTrainTestSplitRatio(double ratio, bool shuffle = true)
-    private static native void setTrainTestSplitRatio_0(long nativeObj, double ratio, boolean shuffle);
-    private static native void setTrainTestSplitRatio_1(long nativeObj, double ratio);
-
-    // C++:  void shuffleTrainTest()
-    private static native void shuffleTrainTest_0(long nativeObj);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/objdetect/BaseCascadeClassifier.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/objdetect/BaseCascadeClassifier.java
deleted file mode 100755
index 8e9afa0..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/objdetect/BaseCascadeClassifier.java
+++ /dev/null
@@ -1,28 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.objdetect;
-
-import org.opencv.core.Algorithm;
-
-// C++: class BaseCascadeClassifier
-//javadoc: BaseCascadeClassifier
-
-public class BaseCascadeClassifier extends Algorithm {
-
-    protected BaseCascadeClassifier(long addr) { super(addr); }
-
-    // internal usage only
-    public static BaseCascadeClassifier __fromPtr__(long addr) { return new BaseCascadeClassifier(addr); }
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/objdetect/CascadeClassifier.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/objdetect/CascadeClassifier.java
deleted file mode 100755
index 242fc7d..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/objdetect/CascadeClassifier.java
+++ /dev/null
@@ -1,269 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.objdetect;
-
-import java.lang.String;
-import java.util.ArrayList;
-import java.util.List;
-import org.opencv.core.Mat;
-import org.opencv.core.MatOfDouble;
-import org.opencv.core.MatOfInt;
-import org.opencv.core.MatOfRect;
-import org.opencv.core.Size;
-import org.opencv.utils.Converters;
-
-// C++: class CascadeClassifier
-//javadoc: CascadeClassifier
-
-public class CascadeClassifier {
-
-    protected final long nativeObj;
-    protected CascadeClassifier(long addr) { nativeObj = addr; }
-
-    public long getNativeObjAddr() { return nativeObj; }
-
-    // internal usage only
-    public static CascadeClassifier __fromPtr__(long addr) { return new CascadeClassifier(addr); }
-
-    //
-    // C++:   CascadeClassifier(String filename)
-    //
-
-    //javadoc: CascadeClassifier::CascadeClassifier(filename)
-    public   CascadeClassifier(String filename)
-    {
-        
-        nativeObj = CascadeClassifier_0(filename);
-        
-        return;
-    }
-
-
-    //
-    // C++:   CascadeClassifier()
-    //
-
-    //javadoc: CascadeClassifier::CascadeClassifier()
-    public   CascadeClassifier()
-    {
-        
-        nativeObj = CascadeClassifier_1();
-        
-        return;
-    }
-
-
-    //
-    // C++:  Size getOriginalWindowSize()
-    //
-
-    //javadoc: CascadeClassifier::getOriginalWindowSize()
-    public  Size getOriginalWindowSize()
-    {
-        
-        Size retVal = new Size(getOriginalWindowSize_0(nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: static bool convert(String oldcascade, String newcascade)
-    //
-
-    //javadoc: CascadeClassifier::convert(oldcascade, newcascade)
-    public static boolean convert(String oldcascade, String newcascade)
-    {
-        
-        boolean retVal = convert_0(oldcascade, newcascade);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  bool empty()
-    //
-
-    //javadoc: CascadeClassifier::empty()
-    public  boolean empty()
-    {
-        
-        boolean retVal = empty_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  bool isOldFormatCascade()
-    //
-
-    //javadoc: CascadeClassifier::isOldFormatCascade()
-    public  boolean isOldFormatCascade()
-    {
-        
-        boolean retVal = isOldFormatCascade_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  bool load(String filename)
-    //
-
-    //javadoc: CascadeClassifier::load(filename)
-    public  boolean load(String filename)
-    {
-        
-        boolean retVal = load_0(nativeObj, filename);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  bool read(FileNode node)
-    //
-
-    // Unknown type 'FileNode' (I), skipping the function
-
-
-    //
-    // C++:  int getFeatureType()
-    //
-
-    //javadoc: CascadeClassifier::getFeatureType()
-    public  int getFeatureType()
-    {
-        
-        int retVal = getFeatureType_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  void detectMultiScale(Mat image, vector_Rect& objects, double scaleFactor = 1.1, int minNeighbors = 3, int flags = 0, Size minSize = Size(), Size maxSize = Size())
-    //
-
-    //javadoc: CascadeClassifier::detectMultiScale(image, objects, scaleFactor, minNeighbors, flags, minSize, maxSize)
-    public  void detectMultiScale(Mat image, MatOfRect objects, double scaleFactor, int minNeighbors, int flags, Size minSize, Size maxSize)
-    {
-        Mat objects_mat = objects;
-        detectMultiScale_0(nativeObj, image.nativeObj, objects_mat.nativeObj, scaleFactor, minNeighbors, flags, minSize.width, minSize.height, maxSize.width, maxSize.height);
-        
-        return;
-    }
-
-    //javadoc: CascadeClassifier::detectMultiScale(image, objects)
-    public  void detectMultiScale(Mat image, MatOfRect objects)
-    {
-        Mat objects_mat = objects;
-        detectMultiScale_1(nativeObj, image.nativeObj, objects_mat.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void detectMultiScale(Mat image, vector_Rect& objects, vector_int& numDetections, double scaleFactor = 1.1, int minNeighbors = 3, int flags = 0, Size minSize = Size(), Size maxSize = Size())
-    //
-
-    //javadoc: CascadeClassifier::detectMultiScale(image, objects, numDetections, scaleFactor, minNeighbors, flags, minSize, maxSize)
-    public  void detectMultiScale2(Mat image, MatOfRect objects, MatOfInt numDetections, double scaleFactor, int minNeighbors, int flags, Size minSize, Size maxSize)
-    {
-        Mat objects_mat = objects;
-        Mat numDetections_mat = numDetections;
-        detectMultiScale2_0(nativeObj, image.nativeObj, objects_mat.nativeObj, numDetections_mat.nativeObj, scaleFactor, minNeighbors, flags, minSize.width, minSize.height, maxSize.width, maxSize.height);
-        
-        return;
-    }
-
-    //javadoc: CascadeClassifier::detectMultiScale(image, objects, numDetections)
-    public  void detectMultiScale2(Mat image, MatOfRect objects, MatOfInt numDetections)
-    {
-        Mat objects_mat = objects;
-        Mat numDetections_mat = numDetections;
-        detectMultiScale2_1(nativeObj, image.nativeObj, objects_mat.nativeObj, numDetections_mat.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void detectMultiScale(Mat image, vector_Rect& objects, vector_int& rejectLevels, vector_double& levelWeights, double scaleFactor = 1.1, int minNeighbors = 3, int flags = 0, Size minSize = Size(), Size maxSize = Size(), bool outputRejectLevels = false)
-    //
-
-    //javadoc: CascadeClassifier::detectMultiScale(image, objects, rejectLevels, levelWeights, scaleFactor, minNeighbors, flags, minSize, maxSize, outputRejectLevels)
-    public  void detectMultiScale3(Mat image, MatOfRect objects, MatOfInt rejectLevels, MatOfDouble levelWeights, double scaleFactor, int minNeighbors, int flags, Size minSize, Size maxSize, boolean outputRejectLevels)
-    {
-        Mat objects_mat = objects;
-        Mat rejectLevels_mat = rejectLevels;
-        Mat levelWeights_mat = levelWeights;
-        detectMultiScale3_0(nativeObj, image.nativeObj, objects_mat.nativeObj, rejectLevels_mat.nativeObj, levelWeights_mat.nativeObj, scaleFactor, minNeighbors, flags, minSize.width, minSize.height, maxSize.width, maxSize.height, outputRejectLevels);
-        
-        return;
-    }
-
-    //javadoc: CascadeClassifier::detectMultiScale(image, objects, rejectLevels, levelWeights)
-    public  void detectMultiScale3(Mat image, MatOfRect objects, MatOfInt rejectLevels, MatOfDouble levelWeights)
-    {
-        Mat objects_mat = objects;
-        Mat rejectLevels_mat = rejectLevels;
-        Mat levelWeights_mat = levelWeights;
-        detectMultiScale3_1(nativeObj, image.nativeObj, objects_mat.nativeObj, rejectLevels_mat.nativeObj, levelWeights_mat.nativeObj);
-        
-        return;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++:   CascadeClassifier(String filename)
-    private static native long CascadeClassifier_0(String filename);
-
-    // C++:   CascadeClassifier()
-    private static native long CascadeClassifier_1();
-
-    // C++:  Size getOriginalWindowSize()
-    private static native double[] getOriginalWindowSize_0(long nativeObj);
-
-    // C++: static bool convert(String oldcascade, String newcascade)
-    private static native boolean convert_0(String oldcascade, String newcascade);
-
-    // C++:  bool empty()
-    private static native boolean empty_0(long nativeObj);
-
-    // C++:  bool isOldFormatCascade()
-    private static native boolean isOldFormatCascade_0(long nativeObj);
-
-    // C++:  bool load(String filename)
-    private static native boolean load_0(long nativeObj, String filename);
-
-    // C++:  int getFeatureType()
-    private static native int getFeatureType_0(long nativeObj);
-
-    // C++:  void detectMultiScale(Mat image, vector_Rect& objects, double scaleFactor = 1.1, int minNeighbors = 3, int flags = 0, Size minSize = Size(), Size maxSize = Size())
-    private static native void detectMultiScale_0(long nativeObj, long image_nativeObj, long objects_mat_nativeObj, double scaleFactor, int minNeighbors, int flags, double minSize_width, double minSize_height, double maxSize_width, double maxSize_height);
-    private static native void detectMultiScale_1(long nativeObj, long image_nativeObj, long objects_mat_nativeObj);
-
-    // C++:  void detectMultiScale(Mat image, vector_Rect& objects, vector_int& numDetections, double scaleFactor = 1.1, int minNeighbors = 3, int flags = 0, Size minSize = Size(), Size maxSize = Size())
-    private static native void detectMultiScale2_0(long nativeObj, long image_nativeObj, long objects_mat_nativeObj, long numDetections_mat_nativeObj, double scaleFactor, int minNeighbors, int flags, double minSize_width, double minSize_height, double maxSize_width, double maxSize_height);
-    private static native void detectMultiScale2_1(long nativeObj, long image_nativeObj, long objects_mat_nativeObj, long numDetections_mat_nativeObj);
-
-    // C++:  void detectMultiScale(Mat image, vector_Rect& objects, vector_int& rejectLevels, vector_double& levelWeights, double scaleFactor = 1.1, int minNeighbors = 3, int flags = 0, Size minSize = Size(), Size maxSize = Size(), bool outputRejectLevels = false)
-    private static native void detectMultiScale3_0(long nativeObj, long image_nativeObj, long objects_mat_nativeObj, long rejectLevels_mat_nativeObj, long levelWeights_mat_nativeObj, double scaleFactor, int minNeighbors, int flags, double minSize_width, double minSize_height, double maxSize_width, double maxSize_height, boolean outputRejectLevels);
-    private static native void detectMultiScale3_1(long nativeObj, long image_nativeObj, long objects_mat_nativeObj, long rejectLevels_mat_nativeObj, long levelWeights_mat_nativeObj);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/objdetect/HOGDescriptor.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/objdetect/HOGDescriptor.java
deleted file mode 100755
index 4f279d8..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/objdetect/HOGDescriptor.java
+++ /dev/null
@@ -1,597 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.objdetect;
-
-import java.lang.String;
-import java.util.ArrayList;
-import java.util.List;
-import org.opencv.core.Mat;
-import org.opencv.core.MatOfDouble;
-import org.opencv.core.MatOfFloat;
-import org.opencv.core.MatOfPoint;
-import org.opencv.core.MatOfRect;
-import org.opencv.core.Size;
-import org.opencv.utils.Converters;
-
-// C++: class HOGDescriptor
-//javadoc: HOGDescriptor
-
-public class HOGDescriptor {
-
-    protected final long nativeObj;
-    protected HOGDescriptor(long addr) { nativeObj = addr; }
-
-    public long getNativeObjAddr() { return nativeObj; }
-
-    // internal usage only
-    public static HOGDescriptor __fromPtr__(long addr) { return new HOGDescriptor(addr); }
-
-    public static final int
-            L2Hys = 0,
-            DEFAULT_NLEVELS = 64;
-
-
-    //
-    // C++:   HOGDescriptor(Size _winSize, Size _blockSize, Size _blockStride, Size _cellSize, int _nbins, int _derivAperture = 1, double _winSigma = -1, int _histogramNormType = HOGDescriptor::L2Hys, double _L2HysThreshold = 0.2, bool _gammaCorrection = false, int _nlevels = HOGDescriptor::DEFAULT_NLEVELS, bool _signedGradient = false)
-    //
-
-    //javadoc: HOGDescriptor::HOGDescriptor(_winSize, _blockSize, _blockStride, _cellSize, _nbins, _derivAperture, _winSigma, _histogramNormType, _L2HysThreshold, _gammaCorrection, _nlevels, _signedGradient)
-    public   HOGDescriptor(Size _winSize, Size _blockSize, Size _blockStride, Size _cellSize, int _nbins, int _derivAperture, double _winSigma, int _histogramNormType, double _L2HysThreshold, boolean _gammaCorrection, int _nlevels, boolean _signedGradient)
-    {
-        
-        nativeObj = HOGDescriptor_0(_winSize.width, _winSize.height, _blockSize.width, _blockSize.height, _blockStride.width, _blockStride.height, _cellSize.width, _cellSize.height, _nbins, _derivAperture, _winSigma, _histogramNormType, _L2HysThreshold, _gammaCorrection, _nlevels, _signedGradient);
-        
-        return;
-    }
-
-    //javadoc: HOGDescriptor::HOGDescriptor(_winSize, _blockSize, _blockStride, _cellSize, _nbins)
-    public   HOGDescriptor(Size _winSize, Size _blockSize, Size _blockStride, Size _cellSize, int _nbins)
-    {
-        
-        nativeObj = HOGDescriptor_1(_winSize.width, _winSize.height, _blockSize.width, _blockSize.height, _blockStride.width, _blockStride.height, _cellSize.width, _cellSize.height, _nbins);
-        
-        return;
-    }
-
-
-    //
-    // C++:   HOGDescriptor(String filename)
-    //
-
-    //javadoc: HOGDescriptor::HOGDescriptor(filename)
-    public   HOGDescriptor(String filename)
-    {
-        
-        nativeObj = HOGDescriptor_2(filename);
-        
-        return;
-    }
-
-
-    //
-    // C++:   HOGDescriptor()
-    //
-
-    //javadoc: HOGDescriptor::HOGDescriptor()
-    public   HOGDescriptor()
-    {
-        
-        nativeObj = HOGDescriptor_3();
-        
-        return;
-    }
-
-
-    //
-    // C++:  bool checkDetectorSize()
-    //
-
-    //javadoc: HOGDescriptor::checkDetectorSize()
-    public  boolean checkDetectorSize()
-    {
-        
-        boolean retVal = checkDetectorSize_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  bool load(String filename, String objname = String())
-    //
-
-    //javadoc: HOGDescriptor::load(filename, objname)
-    public  boolean load(String filename, String objname)
-    {
-        
-        boolean retVal = load_0(nativeObj, filename, objname);
-        
-        return retVal;
-    }
-
-    //javadoc: HOGDescriptor::load(filename)
-    public  boolean load(String filename)
-    {
-        
-        boolean retVal = load_1(nativeObj, filename);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double getWinSigma()
-    //
-
-    //javadoc: HOGDescriptor::getWinSigma()
-    public  double getWinSigma()
-    {
-        
-        double retVal = getWinSigma_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  size_t getDescriptorSize()
-    //
-
-    //javadoc: HOGDescriptor::getDescriptorSize()
-    public  long getDescriptorSize()
-    {
-        
-        long retVal = getDescriptorSize_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: static vector_float getDaimlerPeopleDetector()
-    //
-
-    //javadoc: HOGDescriptor::getDaimlerPeopleDetector()
-    public static MatOfFloat getDaimlerPeopleDetector()
-    {
-        
-        MatOfFloat retVal = MatOfFloat.fromNativeAddr(getDaimlerPeopleDetector_0());
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: static vector_float getDefaultPeopleDetector()
-    //
-
-    //javadoc: HOGDescriptor::getDefaultPeopleDetector()
-    public static MatOfFloat getDefaultPeopleDetector()
-    {
-        
-        MatOfFloat retVal = MatOfFloat.fromNativeAddr(getDefaultPeopleDetector_0());
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  void compute(Mat img, vector_float& descriptors, Size winStride = Size(), Size padding = Size(), vector_Point locations = std::vector<Point>())
-    //
-
-    //javadoc: HOGDescriptor::compute(img, descriptors, winStride, padding, locations)
-    public  void compute(Mat img, MatOfFloat descriptors, Size winStride, Size padding, MatOfPoint locations)
-    {
-        Mat descriptors_mat = descriptors;
-        Mat locations_mat = locations;
-        compute_0(nativeObj, img.nativeObj, descriptors_mat.nativeObj, winStride.width, winStride.height, padding.width, padding.height, locations_mat.nativeObj);
-        
-        return;
-    }
-
-    //javadoc: HOGDescriptor::compute(img, descriptors)
-    public  void compute(Mat img, MatOfFloat descriptors)
-    {
-        Mat descriptors_mat = descriptors;
-        compute_1(nativeObj, img.nativeObj, descriptors_mat.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void computeGradient(Mat img, Mat& grad, Mat& angleOfs, Size paddingTL = Size(), Size paddingBR = Size())
-    //
-
-    //javadoc: HOGDescriptor::computeGradient(img, grad, angleOfs, paddingTL, paddingBR)
-    public  void computeGradient(Mat img, Mat grad, Mat angleOfs, Size paddingTL, Size paddingBR)
-    {
-        
-        computeGradient_0(nativeObj, img.nativeObj, grad.nativeObj, angleOfs.nativeObj, paddingTL.width, paddingTL.height, paddingBR.width, paddingBR.height);
-        
-        return;
-    }
-
-    //javadoc: HOGDescriptor::computeGradient(img, grad, angleOfs)
-    public  void computeGradient(Mat img, Mat grad, Mat angleOfs)
-    {
-        
-        computeGradient_1(nativeObj, img.nativeObj, grad.nativeObj, angleOfs.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void detect(Mat img, vector_Point& foundLocations, vector_double& weights, double hitThreshold = 0, Size winStride = Size(), Size padding = Size(), vector_Point searchLocations = std::vector<Point>())
-    //
-
-    //javadoc: HOGDescriptor::detect(img, foundLocations, weights, hitThreshold, winStride, padding, searchLocations)
-    public  void detect(Mat img, MatOfPoint foundLocations, MatOfDouble weights, double hitThreshold, Size winStride, Size padding, MatOfPoint searchLocations)
-    {
-        Mat foundLocations_mat = foundLocations;
-        Mat weights_mat = weights;
-        Mat searchLocations_mat = searchLocations;
-        detect_0(nativeObj, img.nativeObj, foundLocations_mat.nativeObj, weights_mat.nativeObj, hitThreshold, winStride.width, winStride.height, padding.width, padding.height, searchLocations_mat.nativeObj);
-        
-        return;
-    }
-
-    //javadoc: HOGDescriptor::detect(img, foundLocations, weights)
-    public  void detect(Mat img, MatOfPoint foundLocations, MatOfDouble weights)
-    {
-        Mat foundLocations_mat = foundLocations;
-        Mat weights_mat = weights;
-        detect_1(nativeObj, img.nativeObj, foundLocations_mat.nativeObj, weights_mat.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void detectMultiScale(Mat img, vector_Rect& foundLocations, vector_double& foundWeights, double hitThreshold = 0, Size winStride = Size(), Size padding = Size(), double scale = 1.05, double finalThreshold = 2.0, bool useMeanshiftGrouping = false)
-    //
-
-    //javadoc: HOGDescriptor::detectMultiScale(img, foundLocations, foundWeights, hitThreshold, winStride, padding, scale, finalThreshold, useMeanshiftGrouping)
-    public  void detectMultiScale(Mat img, MatOfRect foundLocations, MatOfDouble foundWeights, double hitThreshold, Size winStride, Size padding, double scale, double finalThreshold, boolean useMeanshiftGrouping)
-    {
-        Mat foundLocations_mat = foundLocations;
-        Mat foundWeights_mat = foundWeights;
-        detectMultiScale_0(nativeObj, img.nativeObj, foundLocations_mat.nativeObj, foundWeights_mat.nativeObj, hitThreshold, winStride.width, winStride.height, padding.width, padding.height, scale, finalThreshold, useMeanshiftGrouping);
-        
-        return;
-    }
-
-    //javadoc: HOGDescriptor::detectMultiScale(img, foundLocations, foundWeights)
-    public  void detectMultiScale(Mat img, MatOfRect foundLocations, MatOfDouble foundWeights)
-    {
-        Mat foundLocations_mat = foundLocations;
-        Mat foundWeights_mat = foundWeights;
-        detectMultiScale_1(nativeObj, img.nativeObj, foundLocations_mat.nativeObj, foundWeights_mat.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void save(String filename, String objname = String())
-    //
-
-    //javadoc: HOGDescriptor::save(filename, objname)
-    public  void save(String filename, String objname)
-    {
-        
-        save_0(nativeObj, filename, objname);
-        
-        return;
-    }
-
-    //javadoc: HOGDescriptor::save(filename)
-    public  void save(String filename)
-    {
-        
-        save_1(nativeObj, filename);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setSVMDetector(Mat _svmdetector)
-    //
-
-    //javadoc: HOGDescriptor::setSVMDetector(_svmdetector)
-    public  void setSVMDetector(Mat _svmdetector)
-    {
-        
-        setSVMDetector_0(nativeObj, _svmdetector.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++: Size HOGDescriptor::winSize
-    //
-
-    //javadoc: HOGDescriptor::get_winSize()
-    public  Size get_winSize()
-    {
-        
-        Size retVal = new Size(get_winSize_0(nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: Size HOGDescriptor::blockSize
-    //
-
-    //javadoc: HOGDescriptor::get_blockSize()
-    public  Size get_blockSize()
-    {
-        
-        Size retVal = new Size(get_blockSize_0(nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: Size HOGDescriptor::blockStride
-    //
-
-    //javadoc: HOGDescriptor::get_blockStride()
-    public  Size get_blockStride()
-    {
-        
-        Size retVal = new Size(get_blockStride_0(nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: Size HOGDescriptor::cellSize
-    //
-
-    //javadoc: HOGDescriptor::get_cellSize()
-    public  Size get_cellSize()
-    {
-        
-        Size retVal = new Size(get_cellSize_0(nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: int HOGDescriptor::nbins
-    //
-
-    //javadoc: HOGDescriptor::get_nbins()
-    public  int get_nbins()
-    {
-        
-        int retVal = get_nbins_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: int HOGDescriptor::derivAperture
-    //
-
-    //javadoc: HOGDescriptor::get_derivAperture()
-    public  int get_derivAperture()
-    {
-        
-        int retVal = get_derivAperture_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: double HOGDescriptor::winSigma
-    //
-
-    //javadoc: HOGDescriptor::get_winSigma()
-    public  double get_winSigma()
-    {
-        
-        double retVal = get_winSigma_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: int HOGDescriptor::histogramNormType
-    //
-
-    //javadoc: HOGDescriptor::get_histogramNormType()
-    public  int get_histogramNormType()
-    {
-        
-        int retVal = get_histogramNormType_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: double HOGDescriptor::L2HysThreshold
-    //
-
-    //javadoc: HOGDescriptor::get_L2HysThreshold()
-    public  double get_L2HysThreshold()
-    {
-        
-        double retVal = get_L2HysThreshold_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: bool HOGDescriptor::gammaCorrection
-    //
-
-    //javadoc: HOGDescriptor::get_gammaCorrection()
-    public  boolean get_gammaCorrection()
-    {
-        
-        boolean retVal = get_gammaCorrection_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: vector_float HOGDescriptor::svmDetector
-    //
-
-    //javadoc: HOGDescriptor::get_svmDetector()
-    public  MatOfFloat get_svmDetector()
-    {
-        
-        MatOfFloat retVal = MatOfFloat.fromNativeAddr(get_svmDetector_0(nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: int HOGDescriptor::nlevels
-    //
-
-    //javadoc: HOGDescriptor::get_nlevels()
-    public  int get_nlevels()
-    {
-        
-        int retVal = get_nlevels_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: bool HOGDescriptor::signedGradient
-    //
-
-    //javadoc: HOGDescriptor::get_signedGradient()
-    public  boolean get_signedGradient()
-    {
-        
-        boolean retVal = get_signedGradient_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++:   HOGDescriptor(Size _winSize, Size _blockSize, Size _blockStride, Size _cellSize, int _nbins, int _derivAperture = 1, double _winSigma = -1, int _histogramNormType = HOGDescriptor::L2Hys, double _L2HysThreshold = 0.2, bool _gammaCorrection = false, int _nlevels = HOGDescriptor::DEFAULT_NLEVELS, bool _signedGradient = false)
-    private static native long HOGDescriptor_0(double _winSize_width, double _winSize_height, double _blockSize_width, double _blockSize_height, double _blockStride_width, double _blockStride_height, double _cellSize_width, double _cellSize_height, int _nbins, int _derivAperture, double _winSigma, int _histogramNormType, double _L2HysThreshold, boolean _gammaCorrection, int _nlevels, boolean _signedGradient);
-    private static native long HOGDescriptor_1(double _winSize_width, double _winSize_height, double _blockSize_width, double _blockSize_height, double _blockStride_width, double _blockStride_height, double _cellSize_width, double _cellSize_height, int _nbins);
-
-    // C++:   HOGDescriptor(String filename)
-    private static native long HOGDescriptor_2(String filename);
-
-    // C++:   HOGDescriptor()
-    private static native long HOGDescriptor_3();
-
-    // C++:  bool checkDetectorSize()
-    private static native boolean checkDetectorSize_0(long nativeObj);
-
-    // C++:  bool load(String filename, String objname = String())
-    private static native boolean load_0(long nativeObj, String filename, String objname);
-    private static native boolean load_1(long nativeObj, String filename);
-
-    // C++:  double getWinSigma()
-    private static native double getWinSigma_0(long nativeObj);
-
-    // C++:  size_t getDescriptorSize()
-    private static native long getDescriptorSize_0(long nativeObj);
-
-    // C++: static vector_float getDaimlerPeopleDetector()
-    private static native long getDaimlerPeopleDetector_0();
-
-    // C++: static vector_float getDefaultPeopleDetector()
-    private static native long getDefaultPeopleDetector_0();
-
-    // C++:  void compute(Mat img, vector_float& descriptors, Size winStride = Size(), Size padding = Size(), vector_Point locations = std::vector<Point>())
-    private static native void compute_0(long nativeObj, long img_nativeObj, long descriptors_mat_nativeObj, double winStride_width, double winStride_height, double padding_width, double padding_height, long locations_mat_nativeObj);
-    private static native void compute_1(long nativeObj, long img_nativeObj, long descriptors_mat_nativeObj);
-
-    // C++:  void computeGradient(Mat img, Mat& grad, Mat& angleOfs, Size paddingTL = Size(), Size paddingBR = Size())
-    private static native void computeGradient_0(long nativeObj, long img_nativeObj, long grad_nativeObj, long angleOfs_nativeObj, double paddingTL_width, double paddingTL_height, double paddingBR_width, double paddingBR_height);
-    private static native void computeGradient_1(long nativeObj, long img_nativeObj, long grad_nativeObj, long angleOfs_nativeObj);
-
-    // C++:  void detect(Mat img, vector_Point& foundLocations, vector_double& weights, double hitThreshold = 0, Size winStride = Size(), Size padding = Size(), vector_Point searchLocations = std::vector<Point>())
-    private static native void detect_0(long nativeObj, long img_nativeObj, long foundLocations_mat_nativeObj, long weights_mat_nativeObj, double hitThreshold, double winStride_width, double winStride_height, double padding_width, double padding_height, long searchLocations_mat_nativeObj);
-    private static native void detect_1(long nativeObj, long img_nativeObj, long foundLocations_mat_nativeObj, long weights_mat_nativeObj);
-
-    // C++:  void detectMultiScale(Mat img, vector_Rect& foundLocations, vector_double& foundWeights, double hitThreshold = 0, Size winStride = Size(), Size padding = Size(), double scale = 1.05, double finalThreshold = 2.0, bool useMeanshiftGrouping = false)
-    private static native void detectMultiScale_0(long nativeObj, long img_nativeObj, long foundLocations_mat_nativeObj, long foundWeights_mat_nativeObj, double hitThreshold, double winStride_width, double winStride_height, double padding_width, double padding_height, double scale, double finalThreshold, boolean useMeanshiftGrouping);
-    private static native void detectMultiScale_1(long nativeObj, long img_nativeObj, long foundLocations_mat_nativeObj, long foundWeights_mat_nativeObj);
-
-    // C++:  void save(String filename, String objname = String())
-    private static native void save_0(long nativeObj, String filename, String objname);
-    private static native void save_1(long nativeObj, String filename);
-
-    // C++:  void setSVMDetector(Mat _svmdetector)
-    private static native void setSVMDetector_0(long nativeObj, long _svmdetector_nativeObj);
-
-    // C++: Size HOGDescriptor::winSize
-    private static native double[] get_winSize_0(long nativeObj);
-
-    // C++: Size HOGDescriptor::blockSize
-    private static native double[] get_blockSize_0(long nativeObj);
-
-    // C++: Size HOGDescriptor::blockStride
-    private static native double[] get_blockStride_0(long nativeObj);
-
-    // C++: Size HOGDescriptor::cellSize
-    private static native double[] get_cellSize_0(long nativeObj);
-
-    // C++: int HOGDescriptor::nbins
-    private static native int get_nbins_0(long nativeObj);
-
-    // C++: int HOGDescriptor::derivAperture
-    private static native int get_derivAperture_0(long nativeObj);
-
-    // C++: double HOGDescriptor::winSigma
-    private static native double get_winSigma_0(long nativeObj);
-
-    // C++: int HOGDescriptor::histogramNormType
-    private static native int get_histogramNormType_0(long nativeObj);
-
-    // C++: double HOGDescriptor::L2HysThreshold
-    private static native double get_L2HysThreshold_0(long nativeObj);
-
-    // C++: bool HOGDescriptor::gammaCorrection
-    private static native boolean get_gammaCorrection_0(long nativeObj);
-
-    // C++: vector_float HOGDescriptor::svmDetector
-    private static native long get_svmDetector_0(long nativeObj);
-
-    // C++: int HOGDescriptor::nlevels
-    private static native int get_nlevels_0(long nativeObj);
-
-    // C++: bool HOGDescriptor::signedGradient
-    private static native boolean get_signedGradient_0(long nativeObj);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/objdetect/Objdetect.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/objdetect/Objdetect.java
deleted file mode 100755
index 4ad2f23..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/objdetect/Objdetect.java
+++ /dev/null
@@ -1,56 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.objdetect;
-
-import java.util.ArrayList;
-import java.util.List;
-import org.opencv.core.Mat;
-import org.opencv.core.MatOfInt;
-import org.opencv.core.MatOfRect;
-import org.opencv.utils.Converters;
-
-// C++: class Objdetect
-//javadoc: Objdetect
-
-public class Objdetect {
-
-    public static final int
-            CASCADE_DO_CANNY_PRUNING = 1,
-            CASCADE_SCALE_IMAGE = 2,
-            CASCADE_FIND_BIGGEST_OBJECT = 4,
-            CASCADE_DO_ROUGH_SEARCH = 8;
-
-
-    //
-    // C++:  void groupRectangles(vector_Rect& rectList, vector_int& weights, int groupThreshold, double eps = 0.2)
-    //
-
-    //javadoc: groupRectangles(rectList, weights, groupThreshold, eps)
-    public static void groupRectangles(MatOfRect rectList, MatOfInt weights, int groupThreshold, double eps)
-    {
-        Mat rectList_mat = rectList;
-        Mat weights_mat = weights;
-        groupRectangles_0(rectList_mat.nativeObj, weights_mat.nativeObj, groupThreshold, eps);
-        
-        return;
-    }
-
-    //javadoc: groupRectangles(rectList, weights, groupThreshold)
-    public static void groupRectangles(MatOfRect rectList, MatOfInt weights, int groupThreshold)
-    {
-        Mat rectList_mat = rectList;
-        Mat weights_mat = weights;
-        groupRectangles_1(rectList_mat.nativeObj, weights_mat.nativeObj, groupThreshold);
-        
-        return;
-    }
-
-
-
-
-    // C++:  void groupRectangles(vector_Rect& rectList, vector_int& weights, int groupThreshold, double eps = 0.2)
-    private static native void groupRectangles_0(long rectList_mat_nativeObj, long weights_mat_nativeObj, int groupThreshold, double eps);
-    private static native void groupRectangles_1(long rectList_mat_nativeObj, long weights_mat_nativeObj, int groupThreshold);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/osgi/OpenCVInterface.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/osgi/OpenCVInterface.java
deleted file mode 100755
index 83ffee1..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/osgi/OpenCVInterface.java
+++ /dev/null
@@ -1,8 +0,0 @@
-package org.opencv.osgi;
-
-/**
- * Dummy interface to allow some integration testing within OSGi implementation.
- */
-public interface OpenCVInterface
-{
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/osgi/OpenCVNativeLoader.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/osgi/OpenCVNativeLoader.java
deleted file mode 100755
index b2ccc67..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/osgi/OpenCVNativeLoader.java
+++ /dev/null
@@ -1,18 +0,0 @@
-package org.opencv.osgi;
-
-import java.util.logging.Level;
-import java.util.logging.Logger;
-
-/**
- * This class is intended to provide a convenient way to load OpenCV's native
- * library from the Java bundle. If Blueprint is enabled in the OSGi container
- * this class will be instantiated automatically and the init() method called
- * loading the native library.
- */
-public class OpenCVNativeLoader implements OpenCVInterface {
-
-    public void init() {
-        System.loadLibrary("opencv_java3");
-        Logger.getLogger("org.opencv.osgi").log(Level.INFO, "Successfully loaded OpenCV native library.");
-    }
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/photo/AlignExposures.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/photo/AlignExposures.java
deleted file mode 100755
index a0d6a94..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/photo/AlignExposures.java
+++ /dev/null
@@ -1,50 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.photo;
-
-import java.util.ArrayList;
-import java.util.List;
-import org.opencv.core.Algorithm;
-import org.opencv.core.Mat;
-import org.opencv.utils.Converters;
-
-// C++: class AlignExposures
-//javadoc: AlignExposures
-
-public class AlignExposures extends Algorithm {
-
-    protected AlignExposures(long addr) { super(addr); }
-
-    // internal usage only
-    public static AlignExposures __fromPtr__(long addr) { return new AlignExposures(addr); }
-
-    //
-    // C++:  void process(vector_Mat src, vector_Mat dst, Mat times, Mat response)
-    //
-
-    //javadoc: AlignExposures::process(src, dst, times, response)
-    public  void process(List<Mat> src, List<Mat> dst, Mat times, Mat response)
-    {
-        Mat src_mat = Converters.vector_Mat_to_Mat(src);
-        Mat dst_mat = Converters.vector_Mat_to_Mat(dst);
-        process_0(nativeObj, src_mat.nativeObj, dst_mat.nativeObj, times.nativeObj, response.nativeObj);
-        
-        return;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++:  void process(vector_Mat src, vector_Mat dst, Mat times, Mat response)
-    private static native void process_0(long nativeObj, long src_mat_nativeObj, long dst_mat_nativeObj, long times_nativeObj, long response_nativeObj);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/photo/AlignMTB.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/photo/AlignMTB.java
deleted file mode 100755
index 3474e1d..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/photo/AlignMTB.java
+++ /dev/null
@@ -1,222 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.photo;
-
-import java.util.ArrayList;
-import java.util.List;
-import org.opencv.core.Mat;
-import org.opencv.core.Point;
-import org.opencv.photo.AlignExposures;
-import org.opencv.utils.Converters;
-
-// C++: class AlignMTB
-//javadoc: AlignMTB
-
-public class AlignMTB extends AlignExposures {
-
-    protected AlignMTB(long addr) { super(addr); }
-
-    // internal usage only
-    public static AlignMTB __fromPtr__(long addr) { return new AlignMTB(addr); }
-
-    //
-    // C++:  Point calculateShift(Mat img0, Mat img1)
-    //
-
-    //javadoc: AlignMTB::calculateShift(img0, img1)
-    public  Point calculateShift(Mat img0, Mat img1)
-    {
-        
-        Point retVal = new Point(calculateShift_0(nativeObj, img0.nativeObj, img1.nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  bool getCut()
-    //
-
-    //javadoc: AlignMTB::getCut()
-    public  boolean getCut()
-    {
-        
-        boolean retVal = getCut_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getExcludeRange()
-    //
-
-    //javadoc: AlignMTB::getExcludeRange()
-    public  int getExcludeRange()
-    {
-        
-        int retVal = getExcludeRange_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getMaxBits()
-    //
-
-    //javadoc: AlignMTB::getMaxBits()
-    public  int getMaxBits()
-    {
-        
-        int retVal = getMaxBits_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  void computeBitmaps(Mat img, Mat& tb, Mat& eb)
-    //
-
-    //javadoc: AlignMTB::computeBitmaps(img, tb, eb)
-    public  void computeBitmaps(Mat img, Mat tb, Mat eb)
-    {
-        
-        computeBitmaps_0(nativeObj, img.nativeObj, tb.nativeObj, eb.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void process(vector_Mat src, vector_Mat dst, Mat times, Mat response)
-    //
-
-    //javadoc: AlignMTB::process(src, dst, times, response)
-    public  void process(List<Mat> src, List<Mat> dst, Mat times, Mat response)
-    {
-        Mat src_mat = Converters.vector_Mat_to_Mat(src);
-        Mat dst_mat = Converters.vector_Mat_to_Mat(dst);
-        process_0(nativeObj, src_mat.nativeObj, dst_mat.nativeObj, times.nativeObj, response.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void process(vector_Mat src, vector_Mat dst)
-    //
-
-    //javadoc: AlignMTB::process(src, dst)
-    public  void process(List<Mat> src, List<Mat> dst)
-    {
-        Mat src_mat = Converters.vector_Mat_to_Mat(src);
-        Mat dst_mat = Converters.vector_Mat_to_Mat(dst);
-        process_1(nativeObj, src_mat.nativeObj, dst_mat.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setCut(bool value)
-    //
-
-    //javadoc: AlignMTB::setCut(value)
-    public  void setCut(boolean value)
-    {
-        
-        setCut_0(nativeObj, value);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setExcludeRange(int exclude_range)
-    //
-
-    //javadoc: AlignMTB::setExcludeRange(exclude_range)
-    public  void setExcludeRange(int exclude_range)
-    {
-        
-        setExcludeRange_0(nativeObj, exclude_range);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setMaxBits(int max_bits)
-    //
-
-    //javadoc: AlignMTB::setMaxBits(max_bits)
-    public  void setMaxBits(int max_bits)
-    {
-        
-        setMaxBits_0(nativeObj, max_bits);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void shiftMat(Mat src, Mat& dst, Point shift)
-    //
-
-    //javadoc: AlignMTB::shiftMat(src, dst, shift)
-    public  void shiftMat(Mat src, Mat dst, Point shift)
-    {
-        
-        shiftMat_0(nativeObj, src.nativeObj, dst.nativeObj, shift.x, shift.y);
-        
-        return;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++:  Point calculateShift(Mat img0, Mat img1)
-    private static native double[] calculateShift_0(long nativeObj, long img0_nativeObj, long img1_nativeObj);
-
-    // C++:  bool getCut()
-    private static native boolean getCut_0(long nativeObj);
-
-    // C++:  int getExcludeRange()
-    private static native int getExcludeRange_0(long nativeObj);
-
-    // C++:  int getMaxBits()
-    private static native int getMaxBits_0(long nativeObj);
-
-    // C++:  void computeBitmaps(Mat img, Mat& tb, Mat& eb)
-    private static native void computeBitmaps_0(long nativeObj, long img_nativeObj, long tb_nativeObj, long eb_nativeObj);
-
-    // C++:  void process(vector_Mat src, vector_Mat dst, Mat times, Mat response)
-    private static native void process_0(long nativeObj, long src_mat_nativeObj, long dst_mat_nativeObj, long times_nativeObj, long response_nativeObj);
-
-    // C++:  void process(vector_Mat src, vector_Mat dst)
-    private static native void process_1(long nativeObj, long src_mat_nativeObj, long dst_mat_nativeObj);
-
-    // C++:  void setCut(bool value)
-    private static native void setCut_0(long nativeObj, boolean value);
-
-    // C++:  void setExcludeRange(int exclude_range)
-    private static native void setExcludeRange_0(long nativeObj, int exclude_range);
-
-    // C++:  void setMaxBits(int max_bits)
-    private static native void setMaxBits_0(long nativeObj, int max_bits);
-
-    // C++:  void shiftMat(Mat src, Mat& dst, Point shift)
-    private static native void shiftMat_0(long nativeObj, long src_nativeObj, long dst_nativeObj, double shift_x, double shift_y);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/photo/CalibrateCRF.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/photo/CalibrateCRF.java
deleted file mode 100755
index 700e8fb..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/photo/CalibrateCRF.java
+++ /dev/null
@@ -1,49 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.photo;
-
-import java.util.ArrayList;
-import java.util.List;
-import org.opencv.core.Algorithm;
-import org.opencv.core.Mat;
-import org.opencv.utils.Converters;
-
-// C++: class CalibrateCRF
-//javadoc: CalibrateCRF
-
-public class CalibrateCRF extends Algorithm {
-
-    protected CalibrateCRF(long addr) { super(addr); }
-
-    // internal usage only
-    public static CalibrateCRF __fromPtr__(long addr) { return new CalibrateCRF(addr); }
-
-    //
-    // C++:  void process(vector_Mat src, Mat& dst, Mat times)
-    //
-
-    //javadoc: CalibrateCRF::process(src, dst, times)
-    public  void process(List<Mat> src, Mat dst, Mat times)
-    {
-        Mat src_mat = Converters.vector_Mat_to_Mat(src);
-        process_0(nativeObj, src_mat.nativeObj, dst.nativeObj, times.nativeObj);
-        
-        return;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++:  void process(vector_Mat src, Mat& dst, Mat times)
-    private static native void process_0(long nativeObj, long src_mat_nativeObj, long dst_nativeObj, long times_nativeObj);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/photo/CalibrateDebevec.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/photo/CalibrateDebevec.java
deleted file mode 100755
index ed23fce..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/photo/CalibrateDebevec.java
+++ /dev/null
@@ -1,130 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.photo;
-
-import org.opencv.photo.CalibrateCRF;
-
-// C++: class CalibrateDebevec
-//javadoc: CalibrateDebevec
-
-public class CalibrateDebevec extends CalibrateCRF {
-
-    protected CalibrateDebevec(long addr) { super(addr); }
-
-    // internal usage only
-    public static CalibrateDebevec __fromPtr__(long addr) { return new CalibrateDebevec(addr); }
-
-    //
-    // C++:  bool getRandom()
-    //
-
-    //javadoc: CalibrateDebevec::getRandom()
-    public  boolean getRandom()
-    {
-        
-        boolean retVal = getRandom_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  float getLambda()
-    //
-
-    //javadoc: CalibrateDebevec::getLambda()
-    public  float getLambda()
-    {
-        
-        float retVal = getLambda_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getSamples()
-    //
-
-    //javadoc: CalibrateDebevec::getSamples()
-    public  int getSamples()
-    {
-        
-        int retVal = getSamples_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  void setLambda(float lambda)
-    //
-
-    //javadoc: CalibrateDebevec::setLambda(lambda)
-    public  void setLambda(float lambda)
-    {
-        
-        setLambda_0(nativeObj, lambda);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setRandom(bool random)
-    //
-
-    //javadoc: CalibrateDebevec::setRandom(random)
-    public  void setRandom(boolean random)
-    {
-        
-        setRandom_0(nativeObj, random);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setSamples(int samples)
-    //
-
-    //javadoc: CalibrateDebevec::setSamples(samples)
-    public  void setSamples(int samples)
-    {
-        
-        setSamples_0(nativeObj, samples);
-        
-        return;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++:  bool getRandom()
-    private static native boolean getRandom_0(long nativeObj);
-
-    // C++:  float getLambda()
-    private static native float getLambda_0(long nativeObj);
-
-    // C++:  int getSamples()
-    private static native int getSamples_0(long nativeObj);
-
-    // C++:  void setLambda(float lambda)
-    private static native void setLambda_0(long nativeObj, float lambda);
-
-    // C++:  void setRandom(bool random)
-    private static native void setRandom_0(long nativeObj, boolean random);
-
-    // C++:  void setSamples(int samples)
-    private static native void setSamples_0(long nativeObj, int samples);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/photo/CalibrateRobertson.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/photo/CalibrateRobertson.java
deleted file mode 100755
index 2fe63f6..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/photo/CalibrateRobertson.java
+++ /dev/null
@@ -1,114 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.photo;
-
-import org.opencv.core.Mat;
-import org.opencv.photo.CalibrateCRF;
-
-// C++: class CalibrateRobertson
-//javadoc: CalibrateRobertson
-
-public class CalibrateRobertson extends CalibrateCRF {
-
-    protected CalibrateRobertson(long addr) { super(addr); }
-
-    // internal usage only
-    public static CalibrateRobertson __fromPtr__(long addr) { return new CalibrateRobertson(addr); }
-
-    //
-    // C++:  Mat getRadiance()
-    //
-
-    //javadoc: CalibrateRobertson::getRadiance()
-    public  Mat getRadiance()
-    {
-        
-        Mat retVal = new Mat(getRadiance_0(nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  float getThreshold()
-    //
-
-    //javadoc: CalibrateRobertson::getThreshold()
-    public  float getThreshold()
-    {
-        
-        float retVal = getThreshold_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getMaxIter()
-    //
-
-    //javadoc: CalibrateRobertson::getMaxIter()
-    public  int getMaxIter()
-    {
-        
-        int retVal = getMaxIter_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  void setMaxIter(int max_iter)
-    //
-
-    //javadoc: CalibrateRobertson::setMaxIter(max_iter)
-    public  void setMaxIter(int max_iter)
-    {
-        
-        setMaxIter_0(nativeObj, max_iter);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setThreshold(float threshold)
-    //
-
-    //javadoc: CalibrateRobertson::setThreshold(threshold)
-    public  void setThreshold(float threshold)
-    {
-        
-        setThreshold_0(nativeObj, threshold);
-        
-        return;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++:  Mat getRadiance()
-    private static native long getRadiance_0(long nativeObj);
-
-    // C++:  float getThreshold()
-    private static native float getThreshold_0(long nativeObj);
-
-    // C++:  int getMaxIter()
-    private static native int getMaxIter_0(long nativeObj);
-
-    // C++:  void setMaxIter(int max_iter)
-    private static native void setMaxIter_0(long nativeObj, int max_iter);
-
-    // C++:  void setThreshold(float threshold)
-    private static native void setThreshold_0(long nativeObj, float threshold);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/photo/MergeDebevec.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/photo/MergeDebevec.java
deleted file mode 100755
index c1436ef..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/photo/MergeDebevec.java
+++ /dev/null
@@ -1,66 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.photo;
-
-import java.util.ArrayList;
-import java.util.List;
-import org.opencv.core.Mat;
-import org.opencv.photo.MergeExposures;
-import org.opencv.utils.Converters;
-
-// C++: class MergeDebevec
-//javadoc: MergeDebevec
-
-public class MergeDebevec extends MergeExposures {
-
-    protected MergeDebevec(long addr) { super(addr); }
-
-    // internal usage only
-    public static MergeDebevec __fromPtr__(long addr) { return new MergeDebevec(addr); }
-
-    //
-    // C++:  void process(vector_Mat src, Mat& dst, Mat times, Mat response)
-    //
-
-    //javadoc: MergeDebevec::process(src, dst, times, response)
-    public  void process(List<Mat> src, Mat dst, Mat times, Mat response)
-    {
-        Mat src_mat = Converters.vector_Mat_to_Mat(src);
-        process_0(nativeObj, src_mat.nativeObj, dst.nativeObj, times.nativeObj, response.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void process(vector_Mat src, Mat& dst, Mat times)
-    //
-
-    //javadoc: MergeDebevec::process(src, dst, times)
-    public  void process(List<Mat> src, Mat dst, Mat times)
-    {
-        Mat src_mat = Converters.vector_Mat_to_Mat(src);
-        process_1(nativeObj, src_mat.nativeObj, dst.nativeObj, times.nativeObj);
-        
-        return;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++:  void process(vector_Mat src, Mat& dst, Mat times, Mat response)
-    private static native void process_0(long nativeObj, long src_mat_nativeObj, long dst_nativeObj, long times_nativeObj, long response_nativeObj);
-
-    // C++:  void process(vector_Mat src, Mat& dst, Mat times)
-    private static native void process_1(long nativeObj, long src_mat_nativeObj, long dst_nativeObj, long times_nativeObj);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/photo/MergeExposures.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/photo/MergeExposures.java
deleted file mode 100755
index 7425020..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/photo/MergeExposures.java
+++ /dev/null
@@ -1,49 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.photo;
-
-import java.util.ArrayList;
-import java.util.List;
-import org.opencv.core.Algorithm;
-import org.opencv.core.Mat;
-import org.opencv.utils.Converters;
-
-// C++: class MergeExposures
-//javadoc: MergeExposures
-
-public class MergeExposures extends Algorithm {
-
-    protected MergeExposures(long addr) { super(addr); }
-
-    // internal usage only
-    public static MergeExposures __fromPtr__(long addr) { return new MergeExposures(addr); }
-
-    //
-    // C++:  void process(vector_Mat src, Mat& dst, Mat times, Mat response)
-    //
-
-    //javadoc: MergeExposures::process(src, dst, times, response)
-    public  void process(List<Mat> src, Mat dst, Mat times, Mat response)
-    {
-        Mat src_mat = Converters.vector_Mat_to_Mat(src);
-        process_0(nativeObj, src_mat.nativeObj, dst.nativeObj, times.nativeObj, response.nativeObj);
-        
-        return;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++:  void process(vector_Mat src, Mat& dst, Mat times, Mat response)
-    private static native void process_0(long nativeObj, long src_mat_nativeObj, long dst_nativeObj, long times_nativeObj, long response_nativeObj);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/photo/MergeMertens.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/photo/MergeMertens.java
deleted file mode 100755
index 6ef1e46..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/photo/MergeMertens.java
+++ /dev/null
@@ -1,168 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.photo;
-
-import java.util.ArrayList;
-import java.util.List;
-import org.opencv.core.Mat;
-import org.opencv.photo.MergeExposures;
-import org.opencv.utils.Converters;
-
-// C++: class MergeMertens
-//javadoc: MergeMertens
-
-public class MergeMertens extends MergeExposures {
-
-    protected MergeMertens(long addr) { super(addr); }
-
-    // internal usage only
-    public static MergeMertens __fromPtr__(long addr) { return new MergeMertens(addr); }
-
-    //
-    // C++:  float getContrastWeight()
-    //
-
-    //javadoc: MergeMertens::getContrastWeight()
-    public  float getContrastWeight()
-    {
-        
-        float retVal = getContrastWeight_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  float getExposureWeight()
-    //
-
-    //javadoc: MergeMertens::getExposureWeight()
-    public  float getExposureWeight()
-    {
-        
-        float retVal = getExposureWeight_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  float getSaturationWeight()
-    //
-
-    //javadoc: MergeMertens::getSaturationWeight()
-    public  float getSaturationWeight()
-    {
-        
-        float retVal = getSaturationWeight_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  void process(vector_Mat src, Mat& dst, Mat times, Mat response)
-    //
-
-    //javadoc: MergeMertens::process(src, dst, times, response)
-    public  void process(List<Mat> src, Mat dst, Mat times, Mat response)
-    {
-        Mat src_mat = Converters.vector_Mat_to_Mat(src);
-        process_0(nativeObj, src_mat.nativeObj, dst.nativeObj, times.nativeObj, response.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void process(vector_Mat src, Mat& dst)
-    //
-
-    //javadoc: MergeMertens::process(src, dst)
-    public  void process(List<Mat> src, Mat dst)
-    {
-        Mat src_mat = Converters.vector_Mat_to_Mat(src);
-        process_1(nativeObj, src_mat.nativeObj, dst.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setContrastWeight(float contrast_weiht)
-    //
-
-    //javadoc: MergeMertens::setContrastWeight(contrast_weiht)
-    public  void setContrastWeight(float contrast_weiht)
-    {
-        
-        setContrastWeight_0(nativeObj, contrast_weiht);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setExposureWeight(float exposure_weight)
-    //
-
-    //javadoc: MergeMertens::setExposureWeight(exposure_weight)
-    public  void setExposureWeight(float exposure_weight)
-    {
-        
-        setExposureWeight_0(nativeObj, exposure_weight);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setSaturationWeight(float saturation_weight)
-    //
-
-    //javadoc: MergeMertens::setSaturationWeight(saturation_weight)
-    public  void setSaturationWeight(float saturation_weight)
-    {
-        
-        setSaturationWeight_0(nativeObj, saturation_weight);
-        
-        return;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++:  float getContrastWeight()
-    private static native float getContrastWeight_0(long nativeObj);
-
-    // C++:  float getExposureWeight()
-    private static native float getExposureWeight_0(long nativeObj);
-
-    // C++:  float getSaturationWeight()
-    private static native float getSaturationWeight_0(long nativeObj);
-
-    // C++:  void process(vector_Mat src, Mat& dst, Mat times, Mat response)
-    private static native void process_0(long nativeObj, long src_mat_nativeObj, long dst_nativeObj, long times_nativeObj, long response_nativeObj);
-
-    // C++:  void process(vector_Mat src, Mat& dst)
-    private static native void process_1(long nativeObj, long src_mat_nativeObj, long dst_nativeObj);
-
-    // C++:  void setContrastWeight(float contrast_weiht)
-    private static native void setContrastWeight_0(long nativeObj, float contrast_weiht);
-
-    // C++:  void setExposureWeight(float exposure_weight)
-    private static native void setExposureWeight_0(long nativeObj, float exposure_weight);
-
-    // C++:  void setSaturationWeight(float saturation_weight)
-    private static native void setSaturationWeight_0(long nativeObj, float saturation_weight);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/photo/MergeRobertson.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/photo/MergeRobertson.java
deleted file mode 100755
index f1d7284..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/photo/MergeRobertson.java
+++ /dev/null
@@ -1,66 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.photo;
-
-import java.util.ArrayList;
-import java.util.List;
-import org.opencv.core.Mat;
-import org.opencv.photo.MergeExposures;
-import org.opencv.utils.Converters;
-
-// C++: class MergeRobertson
-//javadoc: MergeRobertson
-
-public class MergeRobertson extends MergeExposures {
-
-    protected MergeRobertson(long addr) { super(addr); }
-
-    // internal usage only
-    public static MergeRobertson __fromPtr__(long addr) { return new MergeRobertson(addr); }
-
-    //
-    // C++:  void process(vector_Mat src, Mat& dst, Mat times, Mat response)
-    //
-
-    //javadoc: MergeRobertson::process(src, dst, times, response)
-    public  void process(List<Mat> src, Mat dst, Mat times, Mat response)
-    {
-        Mat src_mat = Converters.vector_Mat_to_Mat(src);
-        process_0(nativeObj, src_mat.nativeObj, dst.nativeObj, times.nativeObj, response.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void process(vector_Mat src, Mat& dst, Mat times)
-    //
-
-    //javadoc: MergeRobertson::process(src, dst, times)
-    public  void process(List<Mat> src, Mat dst, Mat times)
-    {
-        Mat src_mat = Converters.vector_Mat_to_Mat(src);
-        process_1(nativeObj, src_mat.nativeObj, dst.nativeObj, times.nativeObj);
-        
-        return;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++:  void process(vector_Mat src, Mat& dst, Mat times, Mat response)
-    private static native void process_0(long nativeObj, long src_mat_nativeObj, long dst_nativeObj, long times_nativeObj, long response_nativeObj);
-
-    // C++:  void process(vector_Mat src, Mat& dst, Mat times)
-    private static native void process_1(long nativeObj, long src_mat_nativeObj, long dst_nativeObj, long times_nativeObj);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/photo/Photo.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/photo/Photo.java
deleted file mode 100755
index 80996bd..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/photo/Photo.java
+++ /dev/null
@@ -1,755 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.photo;
-
-import java.util.ArrayList;
-import java.util.List;
-import org.opencv.core.Mat;
-import org.opencv.core.MatOfFloat;
-import org.opencv.core.Point;
-import org.opencv.photo.AlignMTB;
-import org.opencv.photo.CalibrateDebevec;
-import org.opencv.photo.CalibrateRobertson;
-import org.opencv.photo.MergeDebevec;
-import org.opencv.photo.MergeMertens;
-import org.opencv.photo.MergeRobertson;
-import org.opencv.photo.Tonemap;
-import org.opencv.photo.TonemapDrago;
-import org.opencv.photo.TonemapDurand;
-import org.opencv.photo.TonemapMantiuk;
-import org.opencv.photo.TonemapReinhard;
-import org.opencv.utils.Converters;
-
-// C++: class Photo
-//javadoc: Photo
-
-public class Photo {
-
-    private static final int
-            CV_INPAINT_NS = 0,
-            CV_INPAINT_TELEA = 1;
-
-
-    public static final int
-            INPAINT_NS = 0,
-            INPAINT_TELEA = 1,
-            NORMAL_CLONE = 1,
-            MIXED_CLONE = 2,
-            MONOCHROME_TRANSFER = 3,
-            RECURS_FILTER = 1,
-            NORMCONV_FILTER = 2,
-            LDR_SIZE = 256;
-
-
-    //
-    // C++:  Ptr_AlignMTB createAlignMTB(int max_bits = 6, int exclude_range = 4, bool cut = true)
-    //
-
-    //javadoc: createAlignMTB(max_bits, exclude_range, cut)
-    public static AlignMTB createAlignMTB(int max_bits, int exclude_range, boolean cut)
-    {
-        
-        AlignMTB retVal = AlignMTB.__fromPtr__(createAlignMTB_0(max_bits, exclude_range, cut));
-        
-        return retVal;
-    }
-
-    //javadoc: createAlignMTB()
-    public static AlignMTB createAlignMTB()
-    {
-        
-        AlignMTB retVal = AlignMTB.__fromPtr__(createAlignMTB_1());
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Ptr_CalibrateDebevec createCalibrateDebevec(int samples = 70, float lambda = 10.0f, bool random = false)
-    //
-
-    //javadoc: createCalibrateDebevec(samples, lambda, random)
-    public static CalibrateDebevec createCalibrateDebevec(int samples, float lambda, boolean random)
-    {
-        
-        CalibrateDebevec retVal = CalibrateDebevec.__fromPtr__(createCalibrateDebevec_0(samples, lambda, random));
-        
-        return retVal;
-    }
-
-    //javadoc: createCalibrateDebevec()
-    public static CalibrateDebevec createCalibrateDebevec()
-    {
-        
-        CalibrateDebevec retVal = CalibrateDebevec.__fromPtr__(createCalibrateDebevec_1());
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Ptr_CalibrateRobertson createCalibrateRobertson(int max_iter = 30, float threshold = 0.01f)
-    //
-
-    //javadoc: createCalibrateRobertson(max_iter, threshold)
-    public static CalibrateRobertson createCalibrateRobertson(int max_iter, float threshold)
-    {
-        
-        CalibrateRobertson retVal = CalibrateRobertson.__fromPtr__(createCalibrateRobertson_0(max_iter, threshold));
-        
-        return retVal;
-    }
-
-    //javadoc: createCalibrateRobertson()
-    public static CalibrateRobertson createCalibrateRobertson()
-    {
-        
-        CalibrateRobertson retVal = CalibrateRobertson.__fromPtr__(createCalibrateRobertson_1());
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Ptr_MergeDebevec createMergeDebevec()
-    //
-
-    //javadoc: createMergeDebevec()
-    public static MergeDebevec createMergeDebevec()
-    {
-        
-        MergeDebevec retVal = MergeDebevec.__fromPtr__(createMergeDebevec_0());
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Ptr_MergeMertens createMergeMertens(float contrast_weight = 1.0f, float saturation_weight = 1.0f, float exposure_weight = 0.0f)
-    //
-
-    //javadoc: createMergeMertens(contrast_weight, saturation_weight, exposure_weight)
-    public static MergeMertens createMergeMertens(float contrast_weight, float saturation_weight, float exposure_weight)
-    {
-        
-        MergeMertens retVal = MergeMertens.__fromPtr__(createMergeMertens_0(contrast_weight, saturation_weight, exposure_weight));
-        
-        return retVal;
-    }
-
-    //javadoc: createMergeMertens()
-    public static MergeMertens createMergeMertens()
-    {
-        
-        MergeMertens retVal = MergeMertens.__fromPtr__(createMergeMertens_1());
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Ptr_MergeRobertson createMergeRobertson()
-    //
-
-    //javadoc: createMergeRobertson()
-    public static MergeRobertson createMergeRobertson()
-    {
-        
-        MergeRobertson retVal = MergeRobertson.__fromPtr__(createMergeRobertson_0());
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Ptr_Tonemap createTonemap(float gamma = 1.0f)
-    //
-
-    //javadoc: createTonemap(gamma)
-    public static Tonemap createTonemap(float gamma)
-    {
-        
-        Tonemap retVal = Tonemap.__fromPtr__(createTonemap_0(gamma));
-        
-        return retVal;
-    }
-
-    //javadoc: createTonemap()
-    public static Tonemap createTonemap()
-    {
-        
-        Tonemap retVal = Tonemap.__fromPtr__(createTonemap_1());
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Ptr_TonemapDrago createTonemapDrago(float gamma = 1.0f, float saturation = 1.0f, float bias = 0.85f)
-    //
-
-    //javadoc: createTonemapDrago(gamma, saturation, bias)
-    public static TonemapDrago createTonemapDrago(float gamma, float saturation, float bias)
-    {
-        
-        TonemapDrago retVal = TonemapDrago.__fromPtr__(createTonemapDrago_0(gamma, saturation, bias));
-        
-        return retVal;
-    }
-
-    //javadoc: createTonemapDrago()
-    public static TonemapDrago createTonemapDrago()
-    {
-        
-        TonemapDrago retVal = TonemapDrago.__fromPtr__(createTonemapDrago_1());
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Ptr_TonemapDurand createTonemapDurand(float gamma = 1.0f, float contrast = 4.0f, float saturation = 1.0f, float sigma_space = 2.0f, float sigma_color = 2.0f)
-    //
-
-    //javadoc: createTonemapDurand(gamma, contrast, saturation, sigma_space, sigma_color)
-    public static TonemapDurand createTonemapDurand(float gamma, float contrast, float saturation, float sigma_space, float sigma_color)
-    {
-        
-        TonemapDurand retVal = TonemapDurand.__fromPtr__(createTonemapDurand_0(gamma, contrast, saturation, sigma_space, sigma_color));
-        
-        return retVal;
-    }
-
-    //javadoc: createTonemapDurand()
-    public static TonemapDurand createTonemapDurand()
-    {
-        
-        TonemapDurand retVal = TonemapDurand.__fromPtr__(createTonemapDurand_1());
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Ptr_TonemapMantiuk createTonemapMantiuk(float gamma = 1.0f, float scale = 0.7f, float saturation = 1.0f)
-    //
-
-    //javadoc: createTonemapMantiuk(gamma, scale, saturation)
-    public static TonemapMantiuk createTonemapMantiuk(float gamma, float scale, float saturation)
-    {
-        
-        TonemapMantiuk retVal = TonemapMantiuk.__fromPtr__(createTonemapMantiuk_0(gamma, scale, saturation));
-        
-        return retVal;
-    }
-
-    //javadoc: createTonemapMantiuk()
-    public static TonemapMantiuk createTonemapMantiuk()
-    {
-        
-        TonemapMantiuk retVal = TonemapMantiuk.__fromPtr__(createTonemapMantiuk_1());
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Ptr_TonemapReinhard createTonemapReinhard(float gamma = 1.0f, float intensity = 0.0f, float light_adapt = 1.0f, float color_adapt = 0.0f)
-    //
-
-    //javadoc: createTonemapReinhard(gamma, intensity, light_adapt, color_adapt)
-    public static TonemapReinhard createTonemapReinhard(float gamma, float intensity, float light_adapt, float color_adapt)
-    {
-        
-        TonemapReinhard retVal = TonemapReinhard.__fromPtr__(createTonemapReinhard_0(gamma, intensity, light_adapt, color_adapt));
-        
-        return retVal;
-    }
-
-    //javadoc: createTonemapReinhard()
-    public static TonemapReinhard createTonemapReinhard()
-    {
-        
-        TonemapReinhard retVal = TonemapReinhard.__fromPtr__(createTonemapReinhard_1());
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  void colorChange(Mat src, Mat mask, Mat& dst, float red_mul = 1.0f, float green_mul = 1.0f, float blue_mul = 1.0f)
-    //
-
-    //javadoc: colorChange(src, mask, dst, red_mul, green_mul, blue_mul)
-    public static void colorChange(Mat src, Mat mask, Mat dst, float red_mul, float green_mul, float blue_mul)
-    {
-        
-        colorChange_0(src.nativeObj, mask.nativeObj, dst.nativeObj, red_mul, green_mul, blue_mul);
-        
-        return;
-    }
-
-    //javadoc: colorChange(src, mask, dst)
-    public static void colorChange(Mat src, Mat mask, Mat dst)
-    {
-        
-        colorChange_1(src.nativeObj, mask.nativeObj, dst.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void decolor(Mat src, Mat& grayscale, Mat& color_boost)
-    //
-
-    //javadoc: decolor(src, grayscale, color_boost)
-    public static void decolor(Mat src, Mat grayscale, Mat color_boost)
-    {
-        
-        decolor_0(src.nativeObj, grayscale.nativeObj, color_boost.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void denoise_TVL1(vector_Mat observations, Mat result, double lambda = 1.0, int niters = 30)
-    //
-
-    //javadoc: denoise_TVL1(observations, result, lambda, niters)
-    public static void denoise_TVL1(List<Mat> observations, Mat result, double lambda, int niters)
-    {
-        Mat observations_mat = Converters.vector_Mat_to_Mat(observations);
-        denoise_TVL1_0(observations_mat.nativeObj, result.nativeObj, lambda, niters);
-        
-        return;
-    }
-
-    //javadoc: denoise_TVL1(observations, result)
-    public static void denoise_TVL1(List<Mat> observations, Mat result)
-    {
-        Mat observations_mat = Converters.vector_Mat_to_Mat(observations);
-        denoise_TVL1_1(observations_mat.nativeObj, result.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void detailEnhance(Mat src, Mat& dst, float sigma_s = 10, float sigma_r = 0.15f)
-    //
-
-    //javadoc: detailEnhance(src, dst, sigma_s, sigma_r)
-    public static void detailEnhance(Mat src, Mat dst, float sigma_s, float sigma_r)
-    {
-        
-        detailEnhance_0(src.nativeObj, dst.nativeObj, sigma_s, sigma_r);
-        
-        return;
-    }
-
-    //javadoc: detailEnhance(src, dst)
-    public static void detailEnhance(Mat src, Mat dst)
-    {
-        
-        detailEnhance_1(src.nativeObj, dst.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void edgePreservingFilter(Mat src, Mat& dst, int flags = 1, float sigma_s = 60, float sigma_r = 0.4f)
-    //
-
-    //javadoc: edgePreservingFilter(src, dst, flags, sigma_s, sigma_r)
-    public static void edgePreservingFilter(Mat src, Mat dst, int flags, float sigma_s, float sigma_r)
-    {
-        
-        edgePreservingFilter_0(src.nativeObj, dst.nativeObj, flags, sigma_s, sigma_r);
-        
-        return;
-    }
-
-    //javadoc: edgePreservingFilter(src, dst)
-    public static void edgePreservingFilter(Mat src, Mat dst)
-    {
-        
-        edgePreservingFilter_1(src.nativeObj, dst.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void fastNlMeansDenoising(Mat src, Mat& dst, float h = 3, int templateWindowSize = 7, int searchWindowSize = 21)
-    //
-
-    //javadoc: fastNlMeansDenoising(src, dst, h, templateWindowSize, searchWindowSize)
-    public static void fastNlMeansDenoising(Mat src, Mat dst, float h, int templateWindowSize, int searchWindowSize)
-    {
-        
-        fastNlMeansDenoising_0(src.nativeObj, dst.nativeObj, h, templateWindowSize, searchWindowSize);
-        
-        return;
-    }
-
-    //javadoc: fastNlMeansDenoising(src, dst)
-    public static void fastNlMeansDenoising(Mat src, Mat dst)
-    {
-        
-        fastNlMeansDenoising_1(src.nativeObj, dst.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void fastNlMeansDenoising(Mat src, Mat& dst, vector_float h, int templateWindowSize = 7, int searchWindowSize = 21, int normType = NORM_L2)
-    //
-
-    //javadoc: fastNlMeansDenoising(src, dst, h, templateWindowSize, searchWindowSize, normType)
-    public static void fastNlMeansDenoising(Mat src, Mat dst, MatOfFloat h, int templateWindowSize, int searchWindowSize, int normType)
-    {
-        Mat h_mat = h;
-        fastNlMeansDenoising_2(src.nativeObj, dst.nativeObj, h_mat.nativeObj, templateWindowSize, searchWindowSize, normType);
-        
-        return;
-    }
-
-    //javadoc: fastNlMeansDenoising(src, dst, h)
-    public static void fastNlMeansDenoising(Mat src, Mat dst, MatOfFloat h)
-    {
-        Mat h_mat = h;
-        fastNlMeansDenoising_3(src.nativeObj, dst.nativeObj, h_mat.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void fastNlMeansDenoisingColored(Mat src, Mat& dst, float h = 3, float hColor = 3, int templateWindowSize = 7, int searchWindowSize = 21)
-    //
-
-    //javadoc: fastNlMeansDenoisingColored(src, dst, h, hColor, templateWindowSize, searchWindowSize)
-    public static void fastNlMeansDenoisingColored(Mat src, Mat dst, float h, float hColor, int templateWindowSize, int searchWindowSize)
-    {
-        
-        fastNlMeansDenoisingColored_0(src.nativeObj, dst.nativeObj, h, hColor, templateWindowSize, searchWindowSize);
-        
-        return;
-    }
-
-    //javadoc: fastNlMeansDenoisingColored(src, dst)
-    public static void fastNlMeansDenoisingColored(Mat src, Mat dst)
-    {
-        
-        fastNlMeansDenoisingColored_1(src.nativeObj, dst.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void fastNlMeansDenoisingColoredMulti(vector_Mat srcImgs, Mat& dst, int imgToDenoiseIndex, int temporalWindowSize, float h = 3, float hColor = 3, int templateWindowSize = 7, int searchWindowSize = 21)
-    //
-
-    //javadoc: fastNlMeansDenoisingColoredMulti(srcImgs, dst, imgToDenoiseIndex, temporalWindowSize, h, hColor, templateWindowSize, searchWindowSize)
-    public static void fastNlMeansDenoisingColoredMulti(List<Mat> srcImgs, Mat dst, int imgToDenoiseIndex, int temporalWindowSize, float h, float hColor, int templateWindowSize, int searchWindowSize)
-    {
-        Mat srcImgs_mat = Converters.vector_Mat_to_Mat(srcImgs);
-        fastNlMeansDenoisingColoredMulti_0(srcImgs_mat.nativeObj, dst.nativeObj, imgToDenoiseIndex, temporalWindowSize, h, hColor, templateWindowSize, searchWindowSize);
-        
-        return;
-    }
-
-    //javadoc: fastNlMeansDenoisingColoredMulti(srcImgs, dst, imgToDenoiseIndex, temporalWindowSize)
-    public static void fastNlMeansDenoisingColoredMulti(List<Mat> srcImgs, Mat dst, int imgToDenoiseIndex, int temporalWindowSize)
-    {
-        Mat srcImgs_mat = Converters.vector_Mat_to_Mat(srcImgs);
-        fastNlMeansDenoisingColoredMulti_1(srcImgs_mat.nativeObj, dst.nativeObj, imgToDenoiseIndex, temporalWindowSize);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void fastNlMeansDenoisingMulti(vector_Mat srcImgs, Mat& dst, int imgToDenoiseIndex, int temporalWindowSize, float h = 3, int templateWindowSize = 7, int searchWindowSize = 21)
-    //
-
-    //javadoc: fastNlMeansDenoisingMulti(srcImgs, dst, imgToDenoiseIndex, temporalWindowSize, h, templateWindowSize, searchWindowSize)
-    public static void fastNlMeansDenoisingMulti(List<Mat> srcImgs, Mat dst, int imgToDenoiseIndex, int temporalWindowSize, float h, int templateWindowSize, int searchWindowSize)
-    {
-        Mat srcImgs_mat = Converters.vector_Mat_to_Mat(srcImgs);
-        fastNlMeansDenoisingMulti_0(srcImgs_mat.nativeObj, dst.nativeObj, imgToDenoiseIndex, temporalWindowSize, h, templateWindowSize, searchWindowSize);
-        
-        return;
-    }
-
-    //javadoc: fastNlMeansDenoisingMulti(srcImgs, dst, imgToDenoiseIndex, temporalWindowSize)
-    public static void fastNlMeansDenoisingMulti(List<Mat> srcImgs, Mat dst, int imgToDenoiseIndex, int temporalWindowSize)
-    {
-        Mat srcImgs_mat = Converters.vector_Mat_to_Mat(srcImgs);
-        fastNlMeansDenoisingMulti_1(srcImgs_mat.nativeObj, dst.nativeObj, imgToDenoiseIndex, temporalWindowSize);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void fastNlMeansDenoisingMulti(vector_Mat srcImgs, Mat& dst, int imgToDenoiseIndex, int temporalWindowSize, vector_float h, int templateWindowSize = 7, int searchWindowSize = 21, int normType = NORM_L2)
-    //
-
-    //javadoc: fastNlMeansDenoisingMulti(srcImgs, dst, imgToDenoiseIndex, temporalWindowSize, h, templateWindowSize, searchWindowSize, normType)
-    public static void fastNlMeansDenoisingMulti(List<Mat> srcImgs, Mat dst, int imgToDenoiseIndex, int temporalWindowSize, MatOfFloat h, int templateWindowSize, int searchWindowSize, int normType)
-    {
-        Mat srcImgs_mat = Converters.vector_Mat_to_Mat(srcImgs);
-        Mat h_mat = h;
-        fastNlMeansDenoisingMulti_2(srcImgs_mat.nativeObj, dst.nativeObj, imgToDenoiseIndex, temporalWindowSize, h_mat.nativeObj, templateWindowSize, searchWindowSize, normType);
-        
-        return;
-    }
-
-    //javadoc: fastNlMeansDenoisingMulti(srcImgs, dst, imgToDenoiseIndex, temporalWindowSize, h)
-    public static void fastNlMeansDenoisingMulti(List<Mat> srcImgs, Mat dst, int imgToDenoiseIndex, int temporalWindowSize, MatOfFloat h)
-    {
-        Mat srcImgs_mat = Converters.vector_Mat_to_Mat(srcImgs);
-        Mat h_mat = h;
-        fastNlMeansDenoisingMulti_3(srcImgs_mat.nativeObj, dst.nativeObj, imgToDenoiseIndex, temporalWindowSize, h_mat.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void illuminationChange(Mat src, Mat mask, Mat& dst, float alpha = 0.2f, float beta = 0.4f)
-    //
-
-    //javadoc: illuminationChange(src, mask, dst, alpha, beta)
-    public static void illuminationChange(Mat src, Mat mask, Mat dst, float alpha, float beta)
-    {
-        
-        illuminationChange_0(src.nativeObj, mask.nativeObj, dst.nativeObj, alpha, beta);
-        
-        return;
-    }
-
-    //javadoc: illuminationChange(src, mask, dst)
-    public static void illuminationChange(Mat src, Mat mask, Mat dst)
-    {
-        
-        illuminationChange_1(src.nativeObj, mask.nativeObj, dst.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void inpaint(Mat src, Mat inpaintMask, Mat& dst, double inpaintRadius, int flags)
-    //
-
-    //javadoc: inpaint(src, inpaintMask, dst, inpaintRadius, flags)
-    public static void inpaint(Mat src, Mat inpaintMask, Mat dst, double inpaintRadius, int flags)
-    {
-        
-        inpaint_0(src.nativeObj, inpaintMask.nativeObj, dst.nativeObj, inpaintRadius, flags);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void pencilSketch(Mat src, Mat& dst1, Mat& dst2, float sigma_s = 60, float sigma_r = 0.07f, float shade_factor = 0.02f)
-    //
-
-    //javadoc: pencilSketch(src, dst1, dst2, sigma_s, sigma_r, shade_factor)
-    public static void pencilSketch(Mat src, Mat dst1, Mat dst2, float sigma_s, float sigma_r, float shade_factor)
-    {
-        
-        pencilSketch_0(src.nativeObj, dst1.nativeObj, dst2.nativeObj, sigma_s, sigma_r, shade_factor);
-        
-        return;
-    }
-
-    //javadoc: pencilSketch(src, dst1, dst2)
-    public static void pencilSketch(Mat src, Mat dst1, Mat dst2)
-    {
-        
-        pencilSketch_1(src.nativeObj, dst1.nativeObj, dst2.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void seamlessClone(Mat src, Mat dst, Mat mask, Point p, Mat& blend, int flags)
-    //
-
-    //javadoc: seamlessClone(src, dst, mask, p, blend, flags)
-    public static void seamlessClone(Mat src, Mat dst, Mat mask, Point p, Mat blend, int flags)
-    {
-        
-        seamlessClone_0(src.nativeObj, dst.nativeObj, mask.nativeObj, p.x, p.y, blend.nativeObj, flags);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void stylization(Mat src, Mat& dst, float sigma_s = 60, float sigma_r = 0.45f)
-    //
-
-    //javadoc: stylization(src, dst, sigma_s, sigma_r)
-    public static void stylization(Mat src, Mat dst, float sigma_s, float sigma_r)
-    {
-        
-        stylization_0(src.nativeObj, dst.nativeObj, sigma_s, sigma_r);
-        
-        return;
-    }
-
-    //javadoc: stylization(src, dst)
-    public static void stylization(Mat src, Mat dst)
-    {
-        
-        stylization_1(src.nativeObj, dst.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void textureFlattening(Mat src, Mat mask, Mat& dst, float low_threshold = 30, float high_threshold = 45, int kernel_size = 3)
-    //
-
-    //javadoc: textureFlattening(src, mask, dst, low_threshold, high_threshold, kernel_size)
-    public static void textureFlattening(Mat src, Mat mask, Mat dst, float low_threshold, float high_threshold, int kernel_size)
-    {
-        
-        textureFlattening_0(src.nativeObj, mask.nativeObj, dst.nativeObj, low_threshold, high_threshold, kernel_size);
-        
-        return;
-    }
-
-    //javadoc: textureFlattening(src, mask, dst)
-    public static void textureFlattening(Mat src, Mat mask, Mat dst)
-    {
-        
-        textureFlattening_1(src.nativeObj, mask.nativeObj, dst.nativeObj);
-        
-        return;
-    }
-
-
-
-
-    // C++:  Ptr_AlignMTB createAlignMTB(int max_bits = 6, int exclude_range = 4, bool cut = true)
-    private static native long createAlignMTB_0(int max_bits, int exclude_range, boolean cut);
-    private static native long createAlignMTB_1();
-
-    // C++:  Ptr_CalibrateDebevec createCalibrateDebevec(int samples = 70, float lambda = 10.0f, bool random = false)
-    private static native long createCalibrateDebevec_0(int samples, float lambda, boolean random);
-    private static native long createCalibrateDebevec_1();
-
-    // C++:  Ptr_CalibrateRobertson createCalibrateRobertson(int max_iter = 30, float threshold = 0.01f)
-    private static native long createCalibrateRobertson_0(int max_iter, float threshold);
-    private static native long createCalibrateRobertson_1();
-
-    // C++:  Ptr_MergeDebevec createMergeDebevec()
-    private static native long createMergeDebevec_0();
-
-    // C++:  Ptr_MergeMertens createMergeMertens(float contrast_weight = 1.0f, float saturation_weight = 1.0f, float exposure_weight = 0.0f)
-    private static native long createMergeMertens_0(float contrast_weight, float saturation_weight, float exposure_weight);
-    private static native long createMergeMertens_1();
-
-    // C++:  Ptr_MergeRobertson createMergeRobertson()
-    private static native long createMergeRobertson_0();
-
-    // C++:  Ptr_Tonemap createTonemap(float gamma = 1.0f)
-    private static native long createTonemap_0(float gamma);
-    private static native long createTonemap_1();
-
-    // C++:  Ptr_TonemapDrago createTonemapDrago(float gamma = 1.0f, float saturation = 1.0f, float bias = 0.85f)
-    private static native long createTonemapDrago_0(float gamma, float saturation, float bias);
-    private static native long createTonemapDrago_1();
-
-    // C++:  Ptr_TonemapDurand createTonemapDurand(float gamma = 1.0f, float contrast = 4.0f, float saturation = 1.0f, float sigma_space = 2.0f, float sigma_color = 2.0f)
-    private static native long createTonemapDurand_0(float gamma, float contrast, float saturation, float sigma_space, float sigma_color);
-    private static native long createTonemapDurand_1();
-
-    // C++:  Ptr_TonemapMantiuk createTonemapMantiuk(float gamma = 1.0f, float scale = 0.7f, float saturation = 1.0f)
-    private static native long createTonemapMantiuk_0(float gamma, float scale, float saturation);
-    private static native long createTonemapMantiuk_1();
-
-    // C++:  Ptr_TonemapReinhard createTonemapReinhard(float gamma = 1.0f, float intensity = 0.0f, float light_adapt = 1.0f, float color_adapt = 0.0f)
-    private static native long createTonemapReinhard_0(float gamma, float intensity, float light_adapt, float color_adapt);
-    private static native long createTonemapReinhard_1();
-
-    // C++:  void colorChange(Mat src, Mat mask, Mat& dst, float red_mul = 1.0f, float green_mul = 1.0f, float blue_mul = 1.0f)
-    private static native void colorChange_0(long src_nativeObj, long mask_nativeObj, long dst_nativeObj, float red_mul, float green_mul, float blue_mul);
-    private static native void colorChange_1(long src_nativeObj, long mask_nativeObj, long dst_nativeObj);
-
-    // C++:  void decolor(Mat src, Mat& grayscale, Mat& color_boost)
-    private static native void decolor_0(long src_nativeObj, long grayscale_nativeObj, long color_boost_nativeObj);
-
-    // C++:  void denoise_TVL1(vector_Mat observations, Mat result, double lambda = 1.0, int niters = 30)
-    private static native void denoise_TVL1_0(long observations_mat_nativeObj, long result_nativeObj, double lambda, int niters);
-    private static native void denoise_TVL1_1(long observations_mat_nativeObj, long result_nativeObj);
-
-    // C++:  void detailEnhance(Mat src, Mat& dst, float sigma_s = 10, float sigma_r = 0.15f)
-    private static native void detailEnhance_0(long src_nativeObj, long dst_nativeObj, float sigma_s, float sigma_r);
-    private static native void detailEnhance_1(long src_nativeObj, long dst_nativeObj);
-
-    // C++:  void edgePreservingFilter(Mat src, Mat& dst, int flags = 1, float sigma_s = 60, float sigma_r = 0.4f)
-    private static native void edgePreservingFilter_0(long src_nativeObj, long dst_nativeObj, int flags, float sigma_s, float sigma_r);
-    private static native void edgePreservingFilter_1(long src_nativeObj, long dst_nativeObj);
-
-    // C++:  void fastNlMeansDenoising(Mat src, Mat& dst, float h = 3, int templateWindowSize = 7, int searchWindowSize = 21)
-    private static native void fastNlMeansDenoising_0(long src_nativeObj, long dst_nativeObj, float h, int templateWindowSize, int searchWindowSize);
-    private static native void fastNlMeansDenoising_1(long src_nativeObj, long dst_nativeObj);
-
-    // C++:  void fastNlMeansDenoising(Mat src, Mat& dst, vector_float h, int templateWindowSize = 7, int searchWindowSize = 21, int normType = NORM_L2)
-    private static native void fastNlMeansDenoising_2(long src_nativeObj, long dst_nativeObj, long h_mat_nativeObj, int templateWindowSize, int searchWindowSize, int normType);
-    private static native void fastNlMeansDenoising_3(long src_nativeObj, long dst_nativeObj, long h_mat_nativeObj);
-
-    // C++:  void fastNlMeansDenoisingColored(Mat src, Mat& dst, float h = 3, float hColor = 3, int templateWindowSize = 7, int searchWindowSize = 21)
-    private static native void fastNlMeansDenoisingColored_0(long src_nativeObj, long dst_nativeObj, float h, float hColor, int templateWindowSize, int searchWindowSize);
-    private static native void fastNlMeansDenoisingColored_1(long src_nativeObj, long dst_nativeObj);
-
-    // C++:  void fastNlMeansDenoisingColoredMulti(vector_Mat srcImgs, Mat& dst, int imgToDenoiseIndex, int temporalWindowSize, float h = 3, float hColor = 3, int templateWindowSize = 7, int searchWindowSize = 21)
-    private static native void fastNlMeansDenoisingColoredMulti_0(long srcImgs_mat_nativeObj, long dst_nativeObj, int imgToDenoiseIndex, int temporalWindowSize, float h, float hColor, int templateWindowSize, int searchWindowSize);
-    private static native void fastNlMeansDenoisingColoredMulti_1(long srcImgs_mat_nativeObj, long dst_nativeObj, int imgToDenoiseIndex, int temporalWindowSize);
-
-    // C++:  void fastNlMeansDenoisingMulti(vector_Mat srcImgs, Mat& dst, int imgToDenoiseIndex, int temporalWindowSize, float h = 3, int templateWindowSize = 7, int searchWindowSize = 21)
-    private static native void fastNlMeansDenoisingMulti_0(long srcImgs_mat_nativeObj, long dst_nativeObj, int imgToDenoiseIndex, int temporalWindowSize, float h, int templateWindowSize, int searchWindowSize);
-    private static native void fastNlMeansDenoisingMulti_1(long srcImgs_mat_nativeObj, long dst_nativeObj, int imgToDenoiseIndex, int temporalWindowSize);
-
-    // C++:  void fastNlMeansDenoisingMulti(vector_Mat srcImgs, Mat& dst, int imgToDenoiseIndex, int temporalWindowSize, vector_float h, int templateWindowSize = 7, int searchWindowSize = 21, int normType = NORM_L2)
-    private static native void fastNlMeansDenoisingMulti_2(long srcImgs_mat_nativeObj, long dst_nativeObj, int imgToDenoiseIndex, int temporalWindowSize, long h_mat_nativeObj, int templateWindowSize, int searchWindowSize, int normType);
-    private static native void fastNlMeansDenoisingMulti_3(long srcImgs_mat_nativeObj, long dst_nativeObj, int imgToDenoiseIndex, int temporalWindowSize, long h_mat_nativeObj);
-
-    // C++:  void illuminationChange(Mat src, Mat mask, Mat& dst, float alpha = 0.2f, float beta = 0.4f)
-    private static native void illuminationChange_0(long src_nativeObj, long mask_nativeObj, long dst_nativeObj, float alpha, float beta);
-    private static native void illuminationChange_1(long src_nativeObj, long mask_nativeObj, long dst_nativeObj);
-
-    // C++:  void inpaint(Mat src, Mat inpaintMask, Mat& dst, double inpaintRadius, int flags)
-    private static native void inpaint_0(long src_nativeObj, long inpaintMask_nativeObj, long dst_nativeObj, double inpaintRadius, int flags);
-
-    // C++:  void pencilSketch(Mat src, Mat& dst1, Mat& dst2, float sigma_s = 60, float sigma_r = 0.07f, float shade_factor = 0.02f)
-    private static native void pencilSketch_0(long src_nativeObj, long dst1_nativeObj, long dst2_nativeObj, float sigma_s, float sigma_r, float shade_factor);
-    private static native void pencilSketch_1(long src_nativeObj, long dst1_nativeObj, long dst2_nativeObj);
-
-    // C++:  void seamlessClone(Mat src, Mat dst, Mat mask, Point p, Mat& blend, int flags)
-    private static native void seamlessClone_0(long src_nativeObj, long dst_nativeObj, long mask_nativeObj, double p_x, double p_y, long blend_nativeObj, int flags);
-
-    // C++:  void stylization(Mat src, Mat& dst, float sigma_s = 60, float sigma_r = 0.45f)
-    private static native void stylization_0(long src_nativeObj, long dst_nativeObj, float sigma_s, float sigma_r);
-    private static native void stylization_1(long src_nativeObj, long dst_nativeObj);
-
-    // C++:  void textureFlattening(Mat src, Mat mask, Mat& dst, float low_threshold = 30, float high_threshold = 45, int kernel_size = 3)
-    private static native void textureFlattening_0(long src_nativeObj, long mask_nativeObj, long dst_nativeObj, float low_threshold, float high_threshold, int kernel_size);
-    private static native void textureFlattening_1(long src_nativeObj, long mask_nativeObj, long dst_nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/photo/Tonemap.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/photo/Tonemap.java
deleted file mode 100755
index ba07ab4..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/photo/Tonemap.java
+++ /dev/null
@@ -1,80 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.photo;
-
-import org.opencv.core.Algorithm;
-import org.opencv.core.Mat;
-
-// C++: class Tonemap
-//javadoc: Tonemap
-
-public class Tonemap extends Algorithm {
-
-    protected Tonemap(long addr) { super(addr); }
-
-    // internal usage only
-    public static Tonemap __fromPtr__(long addr) { return new Tonemap(addr); }
-
-    //
-    // C++:  float getGamma()
-    //
-
-    //javadoc: Tonemap::getGamma()
-    public  float getGamma()
-    {
-        
-        float retVal = getGamma_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  void process(Mat src, Mat& dst)
-    //
-
-    //javadoc: Tonemap::process(src, dst)
-    public  void process(Mat src, Mat dst)
-    {
-        
-        process_0(nativeObj, src.nativeObj, dst.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setGamma(float gamma)
-    //
-
-    //javadoc: Tonemap::setGamma(gamma)
-    public  void setGamma(float gamma)
-    {
-        
-        setGamma_0(nativeObj, gamma);
-        
-        return;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++:  float getGamma()
-    private static native float getGamma_0(long nativeObj);
-
-    // C++:  void process(Mat src, Mat& dst)
-    private static native void process_0(long nativeObj, long src_nativeObj, long dst_nativeObj);
-
-    // C++:  void setGamma(float gamma)
-    private static native void setGamma_0(long nativeObj, float gamma);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/photo/TonemapDrago.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/photo/TonemapDrago.java
deleted file mode 100755
index 97a0db3..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/photo/TonemapDrago.java
+++ /dev/null
@@ -1,96 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.photo;
-
-import org.opencv.photo.Tonemap;
-
-// C++: class TonemapDrago
-//javadoc: TonemapDrago
-
-public class TonemapDrago extends Tonemap {
-
-    protected TonemapDrago(long addr) { super(addr); }
-
-    // internal usage only
-    public static TonemapDrago __fromPtr__(long addr) { return new TonemapDrago(addr); }
-
-    //
-    // C++:  float getBias()
-    //
-
-    //javadoc: TonemapDrago::getBias()
-    public  float getBias()
-    {
-        
-        float retVal = getBias_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  float getSaturation()
-    //
-
-    //javadoc: TonemapDrago::getSaturation()
-    public  float getSaturation()
-    {
-        
-        float retVal = getSaturation_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  void setBias(float bias)
-    //
-
-    //javadoc: TonemapDrago::setBias(bias)
-    public  void setBias(float bias)
-    {
-        
-        setBias_0(nativeObj, bias);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setSaturation(float saturation)
-    //
-
-    //javadoc: TonemapDrago::setSaturation(saturation)
-    public  void setSaturation(float saturation)
-    {
-        
-        setSaturation_0(nativeObj, saturation);
-        
-        return;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++:  float getBias()
-    private static native float getBias_0(long nativeObj);
-
-    // C++:  float getSaturation()
-    private static native float getSaturation_0(long nativeObj);
-
-    // C++:  void setBias(float bias)
-    private static native void setBias_0(long nativeObj, float bias);
-
-    // C++:  void setSaturation(float saturation)
-    private static native void setSaturation_0(long nativeObj, float saturation);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/photo/TonemapDurand.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/photo/TonemapDurand.java
deleted file mode 100755
index 3069969..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/photo/TonemapDurand.java
+++ /dev/null
@@ -1,164 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.photo;
-
-import org.opencv.photo.Tonemap;
-
-// C++: class TonemapDurand
-//javadoc: TonemapDurand
-
-public class TonemapDurand extends Tonemap {
-
-    protected TonemapDurand(long addr) { super(addr); }
-
-    // internal usage only
-    public static TonemapDurand __fromPtr__(long addr) { return new TonemapDurand(addr); }
-
-    //
-    // C++:  float getContrast()
-    //
-
-    //javadoc: TonemapDurand::getContrast()
-    public  float getContrast()
-    {
-        
-        float retVal = getContrast_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  float getSaturation()
-    //
-
-    //javadoc: TonemapDurand::getSaturation()
-    public  float getSaturation()
-    {
-        
-        float retVal = getSaturation_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  float getSigmaColor()
-    //
-
-    //javadoc: TonemapDurand::getSigmaColor()
-    public  float getSigmaColor()
-    {
-        
-        float retVal = getSigmaColor_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  float getSigmaSpace()
-    //
-
-    //javadoc: TonemapDurand::getSigmaSpace()
-    public  float getSigmaSpace()
-    {
-        
-        float retVal = getSigmaSpace_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  void setContrast(float contrast)
-    //
-
-    //javadoc: TonemapDurand::setContrast(contrast)
-    public  void setContrast(float contrast)
-    {
-        
-        setContrast_0(nativeObj, contrast);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setSaturation(float saturation)
-    //
-
-    //javadoc: TonemapDurand::setSaturation(saturation)
-    public  void setSaturation(float saturation)
-    {
-        
-        setSaturation_0(nativeObj, saturation);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setSigmaColor(float sigma_color)
-    //
-
-    //javadoc: TonemapDurand::setSigmaColor(sigma_color)
-    public  void setSigmaColor(float sigma_color)
-    {
-        
-        setSigmaColor_0(nativeObj, sigma_color);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setSigmaSpace(float sigma_space)
-    //
-
-    //javadoc: TonemapDurand::setSigmaSpace(sigma_space)
-    public  void setSigmaSpace(float sigma_space)
-    {
-        
-        setSigmaSpace_0(nativeObj, sigma_space);
-        
-        return;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++:  float getContrast()
-    private static native float getContrast_0(long nativeObj);
-
-    // C++:  float getSaturation()
-    private static native float getSaturation_0(long nativeObj);
-
-    // C++:  float getSigmaColor()
-    private static native float getSigmaColor_0(long nativeObj);
-
-    // C++:  float getSigmaSpace()
-    private static native float getSigmaSpace_0(long nativeObj);
-
-    // C++:  void setContrast(float contrast)
-    private static native void setContrast_0(long nativeObj, float contrast);
-
-    // C++:  void setSaturation(float saturation)
-    private static native void setSaturation_0(long nativeObj, float saturation);
-
-    // C++:  void setSigmaColor(float sigma_color)
-    private static native void setSigmaColor_0(long nativeObj, float sigma_color);
-
-    // C++:  void setSigmaSpace(float sigma_space)
-    private static native void setSigmaSpace_0(long nativeObj, float sigma_space);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/photo/TonemapMantiuk.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/photo/TonemapMantiuk.java
deleted file mode 100755
index 220b071..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/photo/TonemapMantiuk.java
+++ /dev/null
@@ -1,96 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.photo;
-
-import org.opencv.photo.Tonemap;
-
-// C++: class TonemapMantiuk
-//javadoc: TonemapMantiuk
-
-public class TonemapMantiuk extends Tonemap {
-
-    protected TonemapMantiuk(long addr) { super(addr); }
-
-    // internal usage only
-    public static TonemapMantiuk __fromPtr__(long addr) { return new TonemapMantiuk(addr); }
-
-    //
-    // C++:  float getSaturation()
-    //
-
-    //javadoc: TonemapMantiuk::getSaturation()
-    public  float getSaturation()
-    {
-        
-        float retVal = getSaturation_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  float getScale()
-    //
-
-    //javadoc: TonemapMantiuk::getScale()
-    public  float getScale()
-    {
-        
-        float retVal = getScale_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  void setSaturation(float saturation)
-    //
-
-    //javadoc: TonemapMantiuk::setSaturation(saturation)
-    public  void setSaturation(float saturation)
-    {
-        
-        setSaturation_0(nativeObj, saturation);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setScale(float scale)
-    //
-
-    //javadoc: TonemapMantiuk::setScale(scale)
-    public  void setScale(float scale)
-    {
-        
-        setScale_0(nativeObj, scale);
-        
-        return;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++:  float getSaturation()
-    private static native float getSaturation_0(long nativeObj);
-
-    // C++:  float getScale()
-    private static native float getScale_0(long nativeObj);
-
-    // C++:  void setSaturation(float saturation)
-    private static native void setSaturation_0(long nativeObj, float saturation);
-
-    // C++:  void setScale(float scale)
-    private static native void setScale_0(long nativeObj, float scale);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/photo/TonemapReinhard.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/photo/TonemapReinhard.java
deleted file mode 100755
index 928a272..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/photo/TonemapReinhard.java
+++ /dev/null
@@ -1,130 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.photo;
-
-import org.opencv.photo.Tonemap;
-
-// C++: class TonemapReinhard
-//javadoc: TonemapReinhard
-
-public class TonemapReinhard extends Tonemap {
-
-    protected TonemapReinhard(long addr) { super(addr); }
-
-    // internal usage only
-    public static TonemapReinhard __fromPtr__(long addr) { return new TonemapReinhard(addr); }
-
-    //
-    // C++:  float getColorAdaptation()
-    //
-
-    //javadoc: TonemapReinhard::getColorAdaptation()
-    public  float getColorAdaptation()
-    {
-        
-        float retVal = getColorAdaptation_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  float getIntensity()
-    //
-
-    //javadoc: TonemapReinhard::getIntensity()
-    public  float getIntensity()
-    {
-        
-        float retVal = getIntensity_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  float getLightAdaptation()
-    //
-
-    //javadoc: TonemapReinhard::getLightAdaptation()
-    public  float getLightAdaptation()
-    {
-        
-        float retVal = getLightAdaptation_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  void setColorAdaptation(float color_adapt)
-    //
-
-    //javadoc: TonemapReinhard::setColorAdaptation(color_adapt)
-    public  void setColorAdaptation(float color_adapt)
-    {
-        
-        setColorAdaptation_0(nativeObj, color_adapt);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setIntensity(float intensity)
-    //
-
-    //javadoc: TonemapReinhard::setIntensity(intensity)
-    public  void setIntensity(float intensity)
-    {
-        
-        setIntensity_0(nativeObj, intensity);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setLightAdaptation(float light_adapt)
-    //
-
-    //javadoc: TonemapReinhard::setLightAdaptation(light_adapt)
-    public  void setLightAdaptation(float light_adapt)
-    {
-        
-        setLightAdaptation_0(nativeObj, light_adapt);
-        
-        return;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++:  float getColorAdaptation()
-    private static native float getColorAdaptation_0(long nativeObj);
-
-    // C++:  float getIntensity()
-    private static native float getIntensity_0(long nativeObj);
-
-    // C++:  float getLightAdaptation()
-    private static native float getLightAdaptation_0(long nativeObj);
-
-    // C++:  void setColorAdaptation(float color_adapt)
-    private static native void setColorAdaptation_0(long nativeObj, float color_adapt);
-
-    // C++:  void setIntensity(float intensity)
-    private static native void setIntensity_0(long nativeObj, float intensity);
-
-    // C++:  void setLightAdaptation(float light_adapt)
-    private static native void setLightAdaptation_0(long nativeObj, float light_adapt);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/utils/Converters.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/utils/Converters.java
deleted file mode 100755
index c0575a6..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/utils/Converters.java
+++ /dev/null
@@ -1,773 +0,0 @@
-package org.opencv.utils;
-
-import java.util.ArrayList;
-import java.util.List;
-
-import org.opencv.core.CvType;
-import org.opencv.core.Mat;
-import org.opencv.core.MatOfByte;
-import org.opencv.core.MatOfDMatch;
-import org.opencv.core.MatOfKeyPoint;
-import org.opencv.core.MatOfPoint;
-import org.opencv.core.MatOfPoint2f;
-import org.opencv.core.MatOfPoint3f;
-import org.opencv.core.Point;
-import org.opencv.core.Point3;
-import org.opencv.core.Rect;
-import org.opencv.core.Rect2d;
-import org.opencv.core.DMatch;
-import org.opencv.core.KeyPoint;
-
-public class Converters {
-
-    public static Mat vector_Point_to_Mat(List<Point> pts) {
-        return vector_Point_to_Mat(pts, CvType.CV_32S);
-    }
-
-    public static Mat vector_Point2f_to_Mat(List<Point> pts) {
-        return vector_Point_to_Mat(pts, CvType.CV_32F);
-    }
-
-    public static Mat vector_Point2d_to_Mat(List<Point> pts) {
-        return vector_Point_to_Mat(pts, CvType.CV_64F);
-    }
-
-    public static Mat vector_Point_to_Mat(List<Point> pts, int typeDepth) {
-        Mat res;
-        int count = (pts != null) ? pts.size() : 0;
-        if (count > 0) {
-            switch (typeDepth) {
-            case CvType.CV_32S: {
-                res = new Mat(count, 1, CvType.CV_32SC2);
-                int[] buff = new int[count * 2];
-                for (int i = 0; i < count; i++) {
-                    Point p = pts.get(i);
-                    buff[i * 2] = (int) p.x;
-                    buff[i * 2 + 1] = (int) p.y;
-                }
-                res.put(0, 0, buff);
-            }
-                break;
-
-            case CvType.CV_32F: {
-                res = new Mat(count, 1, CvType.CV_32FC2);
-                float[] buff = new float[count * 2];
-                for (int i = 0; i < count; i++) {
-                    Point p = pts.get(i);
-                    buff[i * 2] = (float) p.x;
-                    buff[i * 2 + 1] = (float) p.y;
-                }
-                res.put(0, 0, buff);
-            }
-                break;
-
-            case CvType.CV_64F: {
-                res = new Mat(count, 1, CvType.CV_64FC2);
-                double[] buff = new double[count * 2];
-                for (int i = 0; i < count; i++) {
-                    Point p = pts.get(i);
-                    buff[i * 2] = p.x;
-                    buff[i * 2 + 1] = p.y;
-                }
-                res.put(0, 0, buff);
-            }
-                break;
-
-            default:
-                throw new IllegalArgumentException("'typeDepth' can be CV_32S, CV_32F or CV_64F");
-            }
-        } else {
-            res = new Mat();
-        }
-        return res;
-    }
-
-    public static Mat vector_Point3i_to_Mat(List<Point3> pts) {
-        return vector_Point3_to_Mat(pts, CvType.CV_32S);
-    }
-
-    public static Mat vector_Point3f_to_Mat(List<Point3> pts) {
-        return vector_Point3_to_Mat(pts, CvType.CV_32F);
-    }
-
-    public static Mat vector_Point3d_to_Mat(List<Point3> pts) {
-        return vector_Point3_to_Mat(pts, CvType.CV_64F);
-    }
-
-    public static Mat vector_Point3_to_Mat(List<Point3> pts, int typeDepth) {
-        Mat res;
-        int count = (pts != null) ? pts.size() : 0;
-        if (count > 0) {
-            switch (typeDepth) {
-            case CvType.CV_32S: {
-                res = new Mat(count, 1, CvType.CV_32SC3);
-                int[] buff = new int[count * 3];
-                for (int i = 0; i < count; i++) {
-                    Point3 p = pts.get(i);
-                    buff[i * 3] = (int) p.x;
-                    buff[i * 3 + 1] = (int) p.y;
-                    buff[i * 3 + 2] = (int) p.z;
-                }
-                res.put(0, 0, buff);
-            }
-                break;
-
-            case CvType.CV_32F: {
-                res = new Mat(count, 1, CvType.CV_32FC3);
-                float[] buff = new float[count * 3];
-                for (int i = 0; i < count; i++) {
-                    Point3 p = pts.get(i);
-                    buff[i * 3] = (float) p.x;
-                    buff[i * 3 + 1] = (float) p.y;
-                    buff[i * 3 + 2] = (float) p.z;
-                }
-                res.put(0, 0, buff);
-            }
-                break;
-
-            case CvType.CV_64F: {
-                res = new Mat(count, 1, CvType.CV_64FC3);
-                double[] buff = new double[count * 3];
-                for (int i = 0; i < count; i++) {
-                    Point3 p = pts.get(i);
-                    buff[i * 3] = p.x;
-                    buff[i * 3 + 1] = p.y;
-                    buff[i * 3 + 2] = p.z;
-                }
-                res.put(0, 0, buff);
-            }
-                break;
-
-            default:
-                throw new IllegalArgumentException("'typeDepth' can be CV_32S, CV_32F or CV_64F");
-            }
-        } else {
-            res = new Mat();
-        }
-        return res;
-    }
-
-    public static void Mat_to_vector_Point2f(Mat m, List<Point> pts) {
-        Mat_to_vector_Point(m, pts);
-    }
-
-    public static void Mat_to_vector_Point2d(Mat m, List<Point> pts) {
-        Mat_to_vector_Point(m, pts);
-    }
-
-    public static void Mat_to_vector_Point(Mat m, List<Point> pts) {
-        if (pts == null)
-            throw new java.lang.IllegalArgumentException("Output List can't be null");
-        int count = m.rows();
-        int type = m.type();
-        if (m.cols() != 1)
-            throw new java.lang.IllegalArgumentException("Input Mat should have one column\n" + m);
-
-        pts.clear();
-        if (type == CvType.CV_32SC2) {
-            int[] buff = new int[2 * count];
-            m.get(0, 0, buff);
-            for (int i = 0; i < count; i++) {
-                pts.add(new Point(buff[i * 2], buff[i * 2 + 1]));
-            }
-        } else if (type == CvType.CV_32FC2) {
-            float[] buff = new float[2 * count];
-            m.get(0, 0, buff);
-            for (int i = 0; i < count; i++) {
-                pts.add(new Point(buff[i * 2], buff[i * 2 + 1]));
-            }
-        } else if (type == CvType.CV_64FC2) {
-            double[] buff = new double[2 * count];
-            m.get(0, 0, buff);
-            for (int i = 0; i < count; i++) {
-                pts.add(new Point(buff[i * 2], buff[i * 2 + 1]));
-            }
-        } else {
-            throw new java.lang.IllegalArgumentException(
-                    "Input Mat should be of CV_32SC2, CV_32FC2 or CV_64FC2 type\n" + m);
-        }
-    }
-
-    public static void Mat_to_vector_Point3i(Mat m, List<Point3> pts) {
-        Mat_to_vector_Point3(m, pts);
-    }
-
-    public static void Mat_to_vector_Point3f(Mat m, List<Point3> pts) {
-        Mat_to_vector_Point3(m, pts);
-    }
-
-    public static void Mat_to_vector_Point3d(Mat m, List<Point3> pts) {
-        Mat_to_vector_Point3(m, pts);
-    }
-
-    public static void Mat_to_vector_Point3(Mat m, List<Point3> pts) {
-        if (pts == null)
-            throw new java.lang.IllegalArgumentException("Output List can't be null");
-        int count = m.rows();
-        int type = m.type();
-        if (m.cols() != 1)
-            throw new java.lang.IllegalArgumentException("Input Mat should have one column\n" + m);
-
-        pts.clear();
-        if (type == CvType.CV_32SC3) {
-            int[] buff = new int[3 * count];
-            m.get(0, 0, buff);
-            for (int i = 0; i < count; i++) {
-                pts.add(new Point3(buff[i * 3], buff[i * 3 + 1], buff[i * 3 + 2]));
-            }
-        } else if (type == CvType.CV_32FC3) {
-            float[] buff = new float[3 * count];
-            m.get(0, 0, buff);
-            for (int i = 0; i < count; i++) {
-                pts.add(new Point3(buff[i * 3], buff[i * 3 + 1], buff[i * 3 + 2]));
-            }
-        } else if (type == CvType.CV_64FC3) {
-            double[] buff = new double[3 * count];
-            m.get(0, 0, buff);
-            for (int i = 0; i < count; i++) {
-                pts.add(new Point3(buff[i * 3], buff[i * 3 + 1], buff[i * 3 + 2]));
-            }
-        } else {
-            throw new java.lang.IllegalArgumentException(
-                    "Input Mat should be of CV_32SC3, CV_32FC3 or CV_64FC3 type\n" + m);
-        }
-    }
-
-    public static Mat vector_Mat_to_Mat(List<Mat> mats) {
-        Mat res;
-        int count = (mats != null) ? mats.size() : 0;
-        if (count > 0) {
-            res = new Mat(count, 1, CvType.CV_32SC2);
-            int[] buff = new int[count * 2];
-            for (int i = 0; i < count; i++) {
-                long addr = mats.get(i).nativeObj;
-                buff[i * 2] = (int) (addr >> 32);
-                buff[i * 2 + 1] = (int) (addr & 0xffffffff);
-            }
-            res.put(0, 0, buff);
-        } else {
-            res = new Mat();
-        }
-        return res;
-    }
-
-    public static void Mat_to_vector_Mat(Mat m, List<Mat> mats) {
-        if (mats == null)
-            throw new java.lang.IllegalArgumentException("mats == null");
-        int count = m.rows();
-        if (CvType.CV_32SC2 != m.type() || m.cols() != 1)
-            throw new java.lang.IllegalArgumentException(
-                    "CvType.CV_32SC2 != m.type() ||  m.cols()!=1\n" + m);
-
-        mats.clear();
-        int[] buff = new int[count * 2];
-        m.get(0, 0, buff);
-        for (int i = 0; i < count; i++) {
-            long addr = (((long) buff[i * 2]) << 32) | (((long) buff[i * 2 + 1]) & 0xffffffffL);
-            mats.add(new Mat(addr));
-        }
-    }
-
-    public static Mat vector_float_to_Mat(List<Float> fs) {
-        Mat res;
-        int count = (fs != null) ? fs.size() : 0;
-        if (count > 0) {
-            res = new Mat(count, 1, CvType.CV_32FC1);
-            float[] buff = new float[count];
-            for (int i = 0; i < count; i++) {
-                float f = fs.get(i);
-                buff[i] = f;
-            }
-            res.put(0, 0, buff);
-        } else {
-            res = new Mat();
-        }
-        return res;
-    }
-
-    public static void Mat_to_vector_float(Mat m, List<Float> fs) {
-        if (fs == null)
-            throw new java.lang.IllegalArgumentException("fs == null");
-        int count = m.rows();
-        if (CvType.CV_32FC1 != m.type() || m.cols() != 1)
-            throw new java.lang.IllegalArgumentException(
-                    "CvType.CV_32FC1 != m.type() ||  m.cols()!=1\n" + m);
-
-        fs.clear();
-        float[] buff = new float[count];
-        m.get(0, 0, buff);
-        for (int i = 0; i < count; i++) {
-            fs.add(buff[i]);
-        }
-    }
-
-    public static Mat vector_uchar_to_Mat(List<Byte> bs) {
-        Mat res;
-        int count = (bs != null) ? bs.size() : 0;
-        if (count > 0) {
-            res = new Mat(count, 1, CvType.CV_8UC1);
-            byte[] buff = new byte[count];
-            for (int i = 0; i < count; i++) {
-                byte b = bs.get(i);
-                buff[i] = b;
-            }
-            res.put(0, 0, buff);
-        } else {
-            res = new Mat();
-        }
-        return res;
-    }
-
-    public static void Mat_to_vector_uchar(Mat m, List<Byte> us) {
-        if (us == null)
-            throw new java.lang.IllegalArgumentException("Output List can't be null");
-        int count = m.rows();
-        if (CvType.CV_8UC1 != m.type() || m.cols() != 1)
-            throw new java.lang.IllegalArgumentException(
-                    "CvType.CV_8UC1 != m.type() ||  m.cols()!=1\n" + m);
-
-        us.clear();
-        byte[] buff = new byte[count];
-        m.get(0, 0, buff);
-        for (int i = 0; i < count; i++) {
-            us.add(buff[i]);
-        }
-    }
-
-    public static Mat vector_char_to_Mat(List<Byte> bs) {
-        Mat res;
-        int count = (bs != null) ? bs.size() : 0;
-        if (count > 0) {
-            res = new Mat(count, 1, CvType.CV_8SC1);
-            byte[] buff = new byte[count];
-            for (int i = 0; i < count; i++) {
-                byte b = bs.get(i);
-                buff[i] = b;
-            }
-            res.put(0, 0, buff);
-        } else {
-            res = new Mat();
-        }
-        return res;
-    }
-
-    public static Mat vector_int_to_Mat(List<Integer> is) {
-        Mat res;
-        int count = (is != null) ? is.size() : 0;
-        if (count > 0) {
-            res = new Mat(count, 1, CvType.CV_32SC1);
-            int[] buff = new int[count];
-            for (int i = 0; i < count; i++) {
-                int v = is.get(i);
-                buff[i] = v;
-            }
-            res.put(0, 0, buff);
-        } else {
-            res = new Mat();
-        }
-        return res;
-    }
-
-    public static void Mat_to_vector_int(Mat m, List<Integer> is) {
-        if (is == null)
-            throw new java.lang.IllegalArgumentException("is == null");
-        int count = m.rows();
-        if (CvType.CV_32SC1 != m.type() || m.cols() != 1)
-            throw new java.lang.IllegalArgumentException(
-                    "CvType.CV_32SC1 != m.type() ||  m.cols()!=1\n" + m);
-
-        is.clear();
-        int[] buff = new int[count];
-        m.get(0, 0, buff);
-        for (int i = 0; i < count; i++) {
-            is.add(buff[i]);
-        }
-    }
-
-    public static void Mat_to_vector_char(Mat m, List<Byte> bs) {
-        if (bs == null)
-            throw new java.lang.IllegalArgumentException("Output List can't be null");
-        int count = m.rows();
-        if (CvType.CV_8SC1 != m.type() || m.cols() != 1)
-            throw new java.lang.IllegalArgumentException(
-                    "CvType.CV_8SC1 != m.type() ||  m.cols()!=1\n" + m);
-
-        bs.clear();
-        byte[] buff = new byte[count];
-        m.get(0, 0, buff);
-        for (int i = 0; i < count; i++) {
-            bs.add(buff[i]);
-        }
-    }
-
-    public static Mat vector_Rect_to_Mat(List<Rect> rs) {
-        Mat res;
-        int count = (rs != null) ? rs.size() : 0;
-        if (count > 0) {
-            res = new Mat(count, 1, CvType.CV_32SC4);
-            int[] buff = new int[4 * count];
-            for (int i = 0; i < count; i++) {
-                Rect r = rs.get(i);
-                buff[4 * i] = r.x;
-                buff[4 * i + 1] = r.y;
-                buff[4 * i + 2] = r.width;
-                buff[4 * i + 3] = r.height;
-            }
-            res.put(0, 0, buff);
-        } else {
-            res = new Mat();
-        }
-        return res;
-    }
-
-    public static void Mat_to_vector_Rect(Mat m, List<Rect> rs) {
-        if (rs == null)
-            throw new java.lang.IllegalArgumentException("rs == null");
-        int count = m.rows();
-        if (CvType.CV_32SC4 != m.type() || m.cols() != 1)
-            throw new java.lang.IllegalArgumentException(
-                    "CvType.CV_32SC4 != m.type() ||  m.rows()!=1\n" + m);
-
-        rs.clear();
-        int[] buff = new int[4 * count];
-        m.get(0, 0, buff);
-        for (int i = 0; i < count; i++) {
-            rs.add(new Rect(buff[4 * i], buff[4 * i + 1], buff[4 * i + 2], buff[4 * i + 3]));
-        }
-    }
-
-    public static Mat vector_Rect2d_to_Mat(List<Rect2d> rs) {
-        Mat res;
-        int count = (rs != null) ? rs.size() : 0;
-        if (count > 0) {
-            res = new Mat(count, 1, CvType.CV_64FC4);
-            double[] buff = new double[4 * count];
-            for (int i = 0; i < count; i++) {
-                Rect2d r = rs.get(i);
-                buff[4 * i] = r.x;
-                buff[4 * i + 1] = r.y;
-                buff[4 * i + 2] = r.width;
-                buff[4 * i + 3] = r.height;
-            }
-            res.put(0, 0, buff);
-        } else {
-            res = new Mat();
-        }
-        return res;
-    }
-
-    public static void Mat_to_vector_Rect2d(Mat m, List<Rect2d> rs) {
-        if (rs == null)
-            throw new java.lang.IllegalArgumentException("rs == null");
-        int count = m.rows();
-        if (CvType.CV_64FC4 != m.type() || m.cols() != 1)
-            throw new java.lang.IllegalArgumentException(
-                                                         "CvType.CV_64FC4 != m.type() ||  m.rows()!=1\n" + m);
-
-        rs.clear();
-        double[] buff = new double[4 * count];
-        m.get(0, 0, buff);
-        for (int i = 0; i < count; i++) {
-            rs.add(new Rect2d(buff[4 * i], buff[4 * i + 1], buff[4 * i + 2], buff[4 * i + 3]));
-        }
-    }
-
-    public static Mat vector_KeyPoint_to_Mat(List<KeyPoint> kps) {
-        Mat res;
-        int count = (kps != null) ? kps.size() : 0;
-        if (count > 0) {
-            res = new Mat(count, 1, CvType.CV_64FC(7));
-            double[] buff = new double[count * 7];
-            for (int i = 0; i < count; i++) {
-                KeyPoint kp = kps.get(i);
-                buff[7 * i] = kp.pt.x;
-                buff[7 * i + 1] = kp.pt.y;
-                buff[7 * i + 2] = kp.size;
-                buff[7 * i + 3] = kp.angle;
-                buff[7 * i + 4] = kp.response;
-                buff[7 * i + 5] = kp.octave;
-                buff[7 * i + 6] = kp.class_id;
-            }
-            res.put(0, 0, buff);
-        } else {
-            res = new Mat();
-        }
-        return res;
-    }
-
-    public static void Mat_to_vector_KeyPoint(Mat m, List<KeyPoint> kps) {
-        if (kps == null)
-            throw new java.lang.IllegalArgumentException("Output List can't be null");
-        int count = m.rows();
-        if (CvType.CV_64FC(7) != m.type() || m.cols() != 1)
-            throw new java.lang.IllegalArgumentException(
-                    "CvType.CV_64FC(7) != m.type() ||  m.cols()!=1\n" + m);
-
-        kps.clear();
-        double[] buff = new double[7 * count];
-        m.get(0, 0, buff);
-        for (int i = 0; i < count; i++) {
-            kps.add(new KeyPoint((float) buff[7 * i], (float) buff[7 * i + 1], (float) buff[7 * i + 2], (float) buff[7 * i + 3],
-                    (float) buff[7 * i + 4], (int) buff[7 * i + 5], (int) buff[7 * i + 6]));
-        }
-    }
-
-    // vector_vector_Point
-    public static Mat vector_vector_Point_to_Mat(List<MatOfPoint> pts, List<Mat> mats) {
-        Mat res;
-        int lCount = (pts != null) ? pts.size() : 0;
-        if (lCount > 0) {
-            for (MatOfPoint vpt : pts)
-                mats.add(vpt);
-            res = vector_Mat_to_Mat(mats);
-        } else {
-            res = new Mat();
-        }
-        return res;
-    }
-
-    public static void Mat_to_vector_vector_Point(Mat m, List<MatOfPoint> pts) {
-        if (pts == null)
-            throw new java.lang.IllegalArgumentException("Output List can't be null");
-
-        if (m == null)
-            throw new java.lang.IllegalArgumentException("Input Mat can't be null");
-
-        List<Mat> mats = new ArrayList<Mat>(m.rows());
-        Mat_to_vector_Mat(m, mats);
-        for (Mat mi : mats) {
-            MatOfPoint pt = new MatOfPoint(mi);
-            pts.add(pt);
-            mi.release();
-        }
-        mats.clear();
-    }
-
-    // vector_vector_Point2f
-    public static void Mat_to_vector_vector_Point2f(Mat m, List<MatOfPoint2f> pts) {
-        if (pts == null)
-            throw new java.lang.IllegalArgumentException("Output List can't be null");
-
-        if (m == null)
-            throw new java.lang.IllegalArgumentException("Input Mat can't be null");
-
-        List<Mat> mats = new ArrayList<Mat>(m.rows());
-        Mat_to_vector_Mat(m, mats);
-        for (Mat mi : mats) {
-            MatOfPoint2f pt = new MatOfPoint2f(mi);
-            pts.add(pt);
-            mi.release();
-        }
-        mats.clear();
-    }
-
-    // vector_vector_Point2f
-    public static Mat vector_vector_Point2f_to_Mat(List<MatOfPoint2f> pts, List<Mat> mats) {
-        Mat res;
-        int lCount = (pts != null) ? pts.size() : 0;
-        if (lCount > 0) {
-            for (MatOfPoint2f vpt : pts)
-                mats.add(vpt);
-            res = vector_Mat_to_Mat(mats);
-        } else {
-            res = new Mat();
-        }
-        return res;
-    }
-
-    // vector_vector_Point3f
-    public static void Mat_to_vector_vector_Point3f(Mat m, List<MatOfPoint3f> pts) {
-        if (pts == null)
-            throw new java.lang.IllegalArgumentException("Output List can't be null");
-
-        if (m == null)
-            throw new java.lang.IllegalArgumentException("Input Mat can't be null");
-
-        List<Mat> mats = new ArrayList<Mat>(m.rows());
-        Mat_to_vector_Mat(m, mats);
-        for (Mat mi : mats) {
-            MatOfPoint3f pt = new MatOfPoint3f(mi);
-            pts.add(pt);
-            mi.release();
-        }
-        mats.clear();
-    }
-
-    // vector_vector_Point3f
-    public static Mat vector_vector_Point3f_to_Mat(List<MatOfPoint3f> pts, List<Mat> mats) {
-        Mat res;
-        int lCount = (pts != null) ? pts.size() : 0;
-        if (lCount > 0) {
-            for (MatOfPoint3f vpt : pts)
-                mats.add(vpt);
-            res = vector_Mat_to_Mat(mats);
-        } else {
-            res = new Mat();
-        }
-        return res;
-    }
-
-    // vector_vector_KeyPoint
-    public static Mat vector_vector_KeyPoint_to_Mat(List<MatOfKeyPoint> kps, List<Mat> mats) {
-        Mat res;
-        int lCount = (kps != null) ? kps.size() : 0;
-        if (lCount > 0) {
-            for (MatOfKeyPoint vkp : kps)
-                mats.add(vkp);
-            res = vector_Mat_to_Mat(mats);
-        } else {
-            res = new Mat();
-        }
-        return res;
-    }
-
-    public static void Mat_to_vector_vector_KeyPoint(Mat m, List<MatOfKeyPoint> kps) {
-        if (kps == null)
-            throw new java.lang.IllegalArgumentException("Output List can't be null");
-
-        if (m == null)
-            throw new java.lang.IllegalArgumentException("Input Mat can't be null");
-
-        List<Mat> mats = new ArrayList<Mat>(m.rows());
-        Mat_to_vector_Mat(m, mats);
-        for (Mat mi : mats) {
-            MatOfKeyPoint vkp = new MatOfKeyPoint(mi);
-            kps.add(vkp);
-            mi.release();
-        }
-        mats.clear();
-    }
-
-    public static Mat vector_double_to_Mat(List<Double> ds) {
-        Mat res;
-        int count = (ds != null) ? ds.size() : 0;
-        if (count > 0) {
-            res = new Mat(count, 1, CvType.CV_64FC1);
-            double[] buff = new double[count];
-            for (int i = 0; i < count; i++) {
-                double v = ds.get(i);
-                buff[i] = v;
-            }
-            res.put(0, 0, buff);
-        } else {
-            res = new Mat();
-        }
-        return res;
-    }
-
-    public static void Mat_to_vector_double(Mat m, List<Double> ds) {
-        if (ds == null)
-            throw new java.lang.IllegalArgumentException("ds == null");
-        int count = m.rows();
-        if (CvType.CV_64FC1 != m.type() || m.cols() != 1)
-            throw new java.lang.IllegalArgumentException(
-                    "CvType.CV_64FC1 != m.type() ||  m.cols()!=1\n" + m);
-
-        ds.clear();
-        double[] buff = new double[count];
-        m.get(0, 0, buff);
-        for (int i = 0; i < count; i++) {
-            ds.add(buff[i]);
-        }
-    }
-
-    public static Mat vector_DMatch_to_Mat(List<DMatch> matches) {
-        Mat res;
-        int count = (matches != null) ? matches.size() : 0;
-        if (count > 0) {
-            res = new Mat(count, 1, CvType.CV_64FC4);
-            double[] buff = new double[count * 4];
-            for (int i = 0; i < count; i++) {
-                DMatch m = matches.get(i);
-                buff[4 * i] = m.queryIdx;
-                buff[4 * i + 1] = m.trainIdx;
-                buff[4 * i + 2] = m.imgIdx;
-                buff[4 * i + 3] = m.distance;
-            }
-            res.put(0, 0, buff);
-        } else {
-            res = new Mat();
-        }
-        return res;
-    }
-
-    public static void Mat_to_vector_DMatch(Mat m, List<DMatch> matches) {
-        if (matches == null)
-            throw new java.lang.IllegalArgumentException("Output List can't be null");
-        int count = m.rows();
-        if (CvType.CV_64FC4 != m.type() || m.cols() != 1)
-            throw new java.lang.IllegalArgumentException(
-                    "CvType.CV_64FC4 != m.type() ||  m.cols()!=1\n" + m);
-
-        matches.clear();
-        double[] buff = new double[4 * count];
-        m.get(0, 0, buff);
-        for (int i = 0; i < count; i++) {
-            matches.add(new DMatch((int) buff[4 * i], (int) buff[4 * i + 1], (int) buff[4 * i + 2], (float) buff[4 * i + 3]));
-        }
-    }
-
-    // vector_vector_DMatch
-    public static Mat vector_vector_DMatch_to_Mat(List<MatOfDMatch> lvdm, List<Mat> mats) {
-        Mat res;
-        int lCount = (lvdm != null) ? lvdm.size() : 0;
-        if (lCount > 0) {
-            for (MatOfDMatch vdm : lvdm)
-                mats.add(vdm);
-            res = vector_Mat_to_Mat(mats);
-        } else {
-            res = new Mat();
-        }
-        return res;
-    }
-
-    public static void Mat_to_vector_vector_DMatch(Mat m, List<MatOfDMatch> lvdm) {
-        if (lvdm == null)
-            throw new java.lang.IllegalArgumentException("Output List can't be null");
-
-        if (m == null)
-            throw new java.lang.IllegalArgumentException("Input Mat can't be null");
-
-        List<Mat> mats = new ArrayList<Mat>(m.rows());
-        Mat_to_vector_Mat(m, mats);
-        lvdm.clear();
-        for (Mat mi : mats) {
-            MatOfDMatch vdm = new MatOfDMatch(mi);
-            lvdm.add(vdm);
-            mi.release();
-        }
-        mats.clear();
-    }
-
-    // vector_vector_char
-    public static Mat vector_vector_char_to_Mat(List<MatOfByte> lvb, List<Mat> mats) {
-        Mat res;
-        int lCount = (lvb != null) ? lvb.size() : 0;
-        if (lCount > 0) {
-            for (MatOfByte vb : lvb)
-                mats.add(vb);
-            res = vector_Mat_to_Mat(mats);
-        } else {
-            res = new Mat();
-        }
-        return res;
-    }
-
-    public static void Mat_to_vector_vector_char(Mat m, List<List<Byte>> llb) {
-        if (llb == null)
-            throw new java.lang.IllegalArgumentException("Output List can't be null");
-
-        if (m == null)
-            throw new java.lang.IllegalArgumentException("Input Mat can't be null");
-
-        List<Mat> mats = new ArrayList<Mat>(m.rows());
-        Mat_to_vector_Mat(m, mats);
-        for (Mat mi : mats) {
-            List<Byte> lb = new ArrayList<Byte>();
-            Mat_to_vector_char(mi, lb);
-            llb.add(lb);
-            mi.release();
-        }
-        mats.clear();
-    }
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/video/BackgroundSubtractor.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/video/BackgroundSubtractor.java
deleted file mode 100755
index a2f75e0..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/video/BackgroundSubtractor.java
+++ /dev/null
@@ -1,73 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.video;
-
-import org.opencv.core.Algorithm;
-import org.opencv.core.Mat;
-
-// C++: class BackgroundSubtractor
-//javadoc: BackgroundSubtractor
-
-public class BackgroundSubtractor extends Algorithm {
-
-    protected BackgroundSubtractor(long addr) { super(addr); }
-
-    // internal usage only
-    public static BackgroundSubtractor __fromPtr__(long addr) { return new BackgroundSubtractor(addr); }
-
-    //
-    // C++:  void apply(Mat image, Mat& fgmask, double learningRate = -1)
-    //
-
-    //javadoc: BackgroundSubtractor::apply(image, fgmask, learningRate)
-    public  void apply(Mat image, Mat fgmask, double learningRate)
-    {
-        
-        apply_0(nativeObj, image.nativeObj, fgmask.nativeObj, learningRate);
-        
-        return;
-    }
-
-    //javadoc: BackgroundSubtractor::apply(image, fgmask)
-    public  void apply(Mat image, Mat fgmask)
-    {
-        
-        apply_1(nativeObj, image.nativeObj, fgmask.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void getBackgroundImage(Mat& backgroundImage)
-    //
-
-    //javadoc: BackgroundSubtractor::getBackgroundImage(backgroundImage)
-    public  void getBackgroundImage(Mat backgroundImage)
-    {
-        
-        getBackgroundImage_0(nativeObj, backgroundImage.nativeObj);
-        
-        return;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++:  void apply(Mat image, Mat& fgmask, double learningRate = -1)
-    private static native void apply_0(long nativeObj, long image_nativeObj, long fgmask_nativeObj, double learningRate);
-    private static native void apply_1(long nativeObj, long image_nativeObj, long fgmask_nativeObj);
-
-    // C++:  void getBackgroundImage(Mat& backgroundImage)
-    private static native void getBackgroundImage_0(long nativeObj, long backgroundImage_nativeObj);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/video/BackgroundSubtractorKNN.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/video/BackgroundSubtractorKNN.java
deleted file mode 100755
index 264c550..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/video/BackgroundSubtractorKNN.java
+++ /dev/null
@@ -1,266 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.video;
-
-import org.opencv.video.BackgroundSubtractor;
-
-// C++: class BackgroundSubtractorKNN
-//javadoc: BackgroundSubtractorKNN
-
-public class BackgroundSubtractorKNN extends BackgroundSubtractor {
-
-    protected BackgroundSubtractorKNN(long addr) { super(addr); }
-
-    // internal usage only
-    public static BackgroundSubtractorKNN __fromPtr__(long addr) { return new BackgroundSubtractorKNN(addr); }
-
-    //
-    // C++:  bool getDetectShadows()
-    //
-
-    //javadoc: BackgroundSubtractorKNN::getDetectShadows()
-    public  boolean getDetectShadows()
-    {
-        
-        boolean retVal = getDetectShadows_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double getDist2Threshold()
-    //
-
-    //javadoc: BackgroundSubtractorKNN::getDist2Threshold()
-    public  double getDist2Threshold()
-    {
-        
-        double retVal = getDist2Threshold_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double getShadowThreshold()
-    //
-
-    //javadoc: BackgroundSubtractorKNN::getShadowThreshold()
-    public  double getShadowThreshold()
-    {
-        
-        double retVal = getShadowThreshold_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getHistory()
-    //
-
-    //javadoc: BackgroundSubtractorKNN::getHistory()
-    public  int getHistory()
-    {
-        
-        int retVal = getHistory_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getNSamples()
-    //
-
-    //javadoc: BackgroundSubtractorKNN::getNSamples()
-    public  int getNSamples()
-    {
-        
-        int retVal = getNSamples_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getShadowValue()
-    //
-
-    //javadoc: BackgroundSubtractorKNN::getShadowValue()
-    public  int getShadowValue()
-    {
-        
-        int retVal = getShadowValue_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getkNNSamples()
-    //
-
-    //javadoc: BackgroundSubtractorKNN::getkNNSamples()
-    public  int getkNNSamples()
-    {
-        
-        int retVal = getkNNSamples_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  void setDetectShadows(bool detectShadows)
-    //
-
-    //javadoc: BackgroundSubtractorKNN::setDetectShadows(detectShadows)
-    public  void setDetectShadows(boolean detectShadows)
-    {
-        
-        setDetectShadows_0(nativeObj, detectShadows);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setDist2Threshold(double _dist2Threshold)
-    //
-
-    //javadoc: BackgroundSubtractorKNN::setDist2Threshold(_dist2Threshold)
-    public  void setDist2Threshold(double _dist2Threshold)
-    {
-        
-        setDist2Threshold_0(nativeObj, _dist2Threshold);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setHistory(int history)
-    //
-
-    //javadoc: BackgroundSubtractorKNN::setHistory(history)
-    public  void setHistory(int history)
-    {
-        
-        setHistory_0(nativeObj, history);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setNSamples(int _nN)
-    //
-
-    //javadoc: BackgroundSubtractorKNN::setNSamples(_nN)
-    public  void setNSamples(int _nN)
-    {
-        
-        setNSamples_0(nativeObj, _nN);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setShadowThreshold(double threshold)
-    //
-
-    //javadoc: BackgroundSubtractorKNN::setShadowThreshold(threshold)
-    public  void setShadowThreshold(double threshold)
-    {
-        
-        setShadowThreshold_0(nativeObj, threshold);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setShadowValue(int value)
-    //
-
-    //javadoc: BackgroundSubtractorKNN::setShadowValue(value)
-    public  void setShadowValue(int value)
-    {
-        
-        setShadowValue_0(nativeObj, value);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setkNNSamples(int _nkNN)
-    //
-
-    //javadoc: BackgroundSubtractorKNN::setkNNSamples(_nkNN)
-    public  void setkNNSamples(int _nkNN)
-    {
-        
-        setkNNSamples_0(nativeObj, _nkNN);
-        
-        return;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++:  bool getDetectShadows()
-    private static native boolean getDetectShadows_0(long nativeObj);
-
-    // C++:  double getDist2Threshold()
-    private static native double getDist2Threshold_0(long nativeObj);
-
-    // C++:  double getShadowThreshold()
-    private static native double getShadowThreshold_0(long nativeObj);
-
-    // C++:  int getHistory()
-    private static native int getHistory_0(long nativeObj);
-
-    // C++:  int getNSamples()
-    private static native int getNSamples_0(long nativeObj);
-
-    // C++:  int getShadowValue()
-    private static native int getShadowValue_0(long nativeObj);
-
-    // C++:  int getkNNSamples()
-    private static native int getkNNSamples_0(long nativeObj);
-
-    // C++:  void setDetectShadows(bool detectShadows)
-    private static native void setDetectShadows_0(long nativeObj, boolean detectShadows);
-
-    // C++:  void setDist2Threshold(double _dist2Threshold)
-    private static native void setDist2Threshold_0(long nativeObj, double _dist2Threshold);
-
-    // C++:  void setHistory(int history)
-    private static native void setHistory_0(long nativeObj, int history);
-
-    // C++:  void setNSamples(int _nN)
-    private static native void setNSamples_0(long nativeObj, int _nN);
-
-    // C++:  void setShadowThreshold(double threshold)
-    private static native void setShadowThreshold_0(long nativeObj, double threshold);
-
-    // C++:  void setShadowValue(int value)
-    private static native void setShadowValue_0(long nativeObj, int value);
-
-    // C++:  void setkNNSamples(int _nkNN)
-    private static native void setkNNSamples_0(long nativeObj, int _nkNN);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/video/BackgroundSubtractorMOG2.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/video/BackgroundSubtractorMOG2.java
deleted file mode 100755
index cae7072..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/video/BackgroundSubtractorMOG2.java
+++ /dev/null
@@ -1,464 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.video;
-
-import org.opencv.core.Mat;
-import org.opencv.video.BackgroundSubtractor;
-
-// C++: class BackgroundSubtractorMOG2
-//javadoc: BackgroundSubtractorMOG2
-
-public class BackgroundSubtractorMOG2 extends BackgroundSubtractor {
-
-    protected BackgroundSubtractorMOG2(long addr) { super(addr); }
-
-    // internal usage only
-    public static BackgroundSubtractorMOG2 __fromPtr__(long addr) { return new BackgroundSubtractorMOG2(addr); }
-
-    //
-    // C++:  bool getDetectShadows()
-    //
-
-    //javadoc: BackgroundSubtractorMOG2::getDetectShadows()
-    public  boolean getDetectShadows()
-    {
-        
-        boolean retVal = getDetectShadows_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double getBackgroundRatio()
-    //
-
-    //javadoc: BackgroundSubtractorMOG2::getBackgroundRatio()
-    public  double getBackgroundRatio()
-    {
-        
-        double retVal = getBackgroundRatio_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double getComplexityReductionThreshold()
-    //
-
-    //javadoc: BackgroundSubtractorMOG2::getComplexityReductionThreshold()
-    public  double getComplexityReductionThreshold()
-    {
-        
-        double retVal = getComplexityReductionThreshold_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double getShadowThreshold()
-    //
-
-    //javadoc: BackgroundSubtractorMOG2::getShadowThreshold()
-    public  double getShadowThreshold()
-    {
-        
-        double retVal = getShadowThreshold_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double getVarInit()
-    //
-
-    //javadoc: BackgroundSubtractorMOG2::getVarInit()
-    public  double getVarInit()
-    {
-        
-        double retVal = getVarInit_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double getVarMax()
-    //
-
-    //javadoc: BackgroundSubtractorMOG2::getVarMax()
-    public  double getVarMax()
-    {
-        
-        double retVal = getVarMax_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double getVarMin()
-    //
-
-    //javadoc: BackgroundSubtractorMOG2::getVarMin()
-    public  double getVarMin()
-    {
-        
-        double retVal = getVarMin_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double getVarThreshold()
-    //
-
-    //javadoc: BackgroundSubtractorMOG2::getVarThreshold()
-    public  double getVarThreshold()
-    {
-        
-        double retVal = getVarThreshold_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double getVarThresholdGen()
-    //
-
-    //javadoc: BackgroundSubtractorMOG2::getVarThresholdGen()
-    public  double getVarThresholdGen()
-    {
-        
-        double retVal = getVarThresholdGen_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getHistory()
-    //
-
-    //javadoc: BackgroundSubtractorMOG2::getHistory()
-    public  int getHistory()
-    {
-        
-        int retVal = getHistory_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getNMixtures()
-    //
-
-    //javadoc: BackgroundSubtractorMOG2::getNMixtures()
-    public  int getNMixtures()
-    {
-        
-        int retVal = getNMixtures_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getShadowValue()
-    //
-
-    //javadoc: BackgroundSubtractorMOG2::getShadowValue()
-    public  int getShadowValue()
-    {
-        
-        int retVal = getShadowValue_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  void apply(Mat image, Mat& fgmask, double learningRate = -1)
-    //
-
-    //javadoc: BackgroundSubtractorMOG2::apply(image, fgmask, learningRate)
-    public  void apply(Mat image, Mat fgmask, double learningRate)
-    {
-        
-        apply_0(nativeObj, image.nativeObj, fgmask.nativeObj, learningRate);
-        
-        return;
-    }
-
-    //javadoc: BackgroundSubtractorMOG2::apply(image, fgmask)
-    public  void apply(Mat image, Mat fgmask)
-    {
-        
-        apply_1(nativeObj, image.nativeObj, fgmask.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setBackgroundRatio(double ratio)
-    //
-
-    //javadoc: BackgroundSubtractorMOG2::setBackgroundRatio(ratio)
-    public  void setBackgroundRatio(double ratio)
-    {
-        
-        setBackgroundRatio_0(nativeObj, ratio);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setComplexityReductionThreshold(double ct)
-    //
-
-    //javadoc: BackgroundSubtractorMOG2::setComplexityReductionThreshold(ct)
-    public  void setComplexityReductionThreshold(double ct)
-    {
-        
-        setComplexityReductionThreshold_0(nativeObj, ct);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setDetectShadows(bool detectShadows)
-    //
-
-    //javadoc: BackgroundSubtractorMOG2::setDetectShadows(detectShadows)
-    public  void setDetectShadows(boolean detectShadows)
-    {
-        
-        setDetectShadows_0(nativeObj, detectShadows);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setHistory(int history)
-    //
-
-    //javadoc: BackgroundSubtractorMOG2::setHistory(history)
-    public  void setHistory(int history)
-    {
-        
-        setHistory_0(nativeObj, history);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setNMixtures(int nmixtures)
-    //
-
-    //javadoc: BackgroundSubtractorMOG2::setNMixtures(nmixtures)
-    public  void setNMixtures(int nmixtures)
-    {
-        
-        setNMixtures_0(nativeObj, nmixtures);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setShadowThreshold(double threshold)
-    //
-
-    //javadoc: BackgroundSubtractorMOG2::setShadowThreshold(threshold)
-    public  void setShadowThreshold(double threshold)
-    {
-        
-        setShadowThreshold_0(nativeObj, threshold);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setShadowValue(int value)
-    //
-
-    //javadoc: BackgroundSubtractorMOG2::setShadowValue(value)
-    public  void setShadowValue(int value)
-    {
-        
-        setShadowValue_0(nativeObj, value);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setVarInit(double varInit)
-    //
-
-    //javadoc: BackgroundSubtractorMOG2::setVarInit(varInit)
-    public  void setVarInit(double varInit)
-    {
-        
-        setVarInit_0(nativeObj, varInit);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setVarMax(double varMax)
-    //
-
-    //javadoc: BackgroundSubtractorMOG2::setVarMax(varMax)
-    public  void setVarMax(double varMax)
-    {
-        
-        setVarMax_0(nativeObj, varMax);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setVarMin(double varMin)
-    //
-
-    //javadoc: BackgroundSubtractorMOG2::setVarMin(varMin)
-    public  void setVarMin(double varMin)
-    {
-        
-        setVarMin_0(nativeObj, varMin);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setVarThreshold(double varThreshold)
-    //
-
-    //javadoc: BackgroundSubtractorMOG2::setVarThreshold(varThreshold)
-    public  void setVarThreshold(double varThreshold)
-    {
-        
-        setVarThreshold_0(nativeObj, varThreshold);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setVarThresholdGen(double varThresholdGen)
-    //
-
-    //javadoc: BackgroundSubtractorMOG2::setVarThresholdGen(varThresholdGen)
-    public  void setVarThresholdGen(double varThresholdGen)
-    {
-        
-        setVarThresholdGen_0(nativeObj, varThresholdGen);
-        
-        return;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++:  bool getDetectShadows()
-    private static native boolean getDetectShadows_0(long nativeObj);
-
-    // C++:  double getBackgroundRatio()
-    private static native double getBackgroundRatio_0(long nativeObj);
-
-    // C++:  double getComplexityReductionThreshold()
-    private static native double getComplexityReductionThreshold_0(long nativeObj);
-
-    // C++:  double getShadowThreshold()
-    private static native double getShadowThreshold_0(long nativeObj);
-
-    // C++:  double getVarInit()
-    private static native double getVarInit_0(long nativeObj);
-
-    // C++:  double getVarMax()
-    private static native double getVarMax_0(long nativeObj);
-
-    // C++:  double getVarMin()
-    private static native double getVarMin_0(long nativeObj);
-
-    // C++:  double getVarThreshold()
-    private static native double getVarThreshold_0(long nativeObj);
-
-    // C++:  double getVarThresholdGen()
-    private static native double getVarThresholdGen_0(long nativeObj);
-
-    // C++:  int getHistory()
-    private static native int getHistory_0(long nativeObj);
-
-    // C++:  int getNMixtures()
-    private static native int getNMixtures_0(long nativeObj);
-
-    // C++:  int getShadowValue()
-    private static native int getShadowValue_0(long nativeObj);
-
-    // C++:  void apply(Mat image, Mat& fgmask, double learningRate = -1)
-    private static native void apply_0(long nativeObj, long image_nativeObj, long fgmask_nativeObj, double learningRate);
-    private static native void apply_1(long nativeObj, long image_nativeObj, long fgmask_nativeObj);
-
-    // C++:  void setBackgroundRatio(double ratio)
-    private static native void setBackgroundRatio_0(long nativeObj, double ratio);
-
-    // C++:  void setComplexityReductionThreshold(double ct)
-    private static native void setComplexityReductionThreshold_0(long nativeObj, double ct);
-
-    // C++:  void setDetectShadows(bool detectShadows)
-    private static native void setDetectShadows_0(long nativeObj, boolean detectShadows);
-
-    // C++:  void setHistory(int history)
-    private static native void setHistory_0(long nativeObj, int history);
-
-    // C++:  void setNMixtures(int nmixtures)
-    private static native void setNMixtures_0(long nativeObj, int nmixtures);
-
-    // C++:  void setShadowThreshold(double threshold)
-    private static native void setShadowThreshold_0(long nativeObj, double threshold);
-
-    // C++:  void setShadowValue(int value)
-    private static native void setShadowValue_0(long nativeObj, int value);
-
-    // C++:  void setVarInit(double varInit)
-    private static native void setVarInit_0(long nativeObj, double varInit);
-
-    // C++:  void setVarMax(double varMax)
-    private static native void setVarMax_0(long nativeObj, double varMax);
-
-    // C++:  void setVarMin(double varMin)
-    private static native void setVarMin_0(long nativeObj, double varMin);
-
-    // C++:  void setVarThreshold(double varThreshold)
-    private static native void setVarThreshold_0(long nativeObj, double varThreshold);
-
-    // C++:  void setVarThresholdGen(double varThresholdGen)
-    private static native void setVarThresholdGen_0(long nativeObj, double varThresholdGen);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/video/DenseOpticalFlow.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/video/DenseOpticalFlow.java
deleted file mode 100755
index c3e559a..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/video/DenseOpticalFlow.java
+++ /dev/null
@@ -1,63 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.video;
-
-import org.opencv.core.Algorithm;
-import org.opencv.core.Mat;
-
-// C++: class DenseOpticalFlow
-//javadoc: DenseOpticalFlow
-
-public class DenseOpticalFlow extends Algorithm {
-
-    protected DenseOpticalFlow(long addr) { super(addr); }
-
-    // internal usage only
-    public static DenseOpticalFlow __fromPtr__(long addr) { return new DenseOpticalFlow(addr); }
-
-    //
-    // C++:  void calc(Mat I0, Mat I1, Mat& flow)
-    //
-
-    //javadoc: DenseOpticalFlow::calc(I0, I1, flow)
-    public  void calc(Mat I0, Mat I1, Mat flow)
-    {
-        
-        calc_0(nativeObj, I0.nativeObj, I1.nativeObj, flow.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void collectGarbage()
-    //
-
-    //javadoc: DenseOpticalFlow::collectGarbage()
-    public  void collectGarbage()
-    {
-        
-        collectGarbage_0(nativeObj);
-        
-        return;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++:  void calc(Mat I0, Mat I1, Mat& flow)
-    private static native void calc_0(long nativeObj, long I0_nativeObj, long I1_nativeObj, long flow_nativeObj);
-
-    // C++:  void collectGarbage()
-    private static native void collectGarbage_0(long nativeObj);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/video/DualTVL1OpticalFlow.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/video/DualTVL1OpticalFlow.java
deleted file mode 100755
index e4aedc2..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/video/DualTVL1OpticalFlow.java
+++ /dev/null
@@ -1,464 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.video;
-
-import org.opencv.video.DenseOpticalFlow;
-import org.opencv.video.DualTVL1OpticalFlow;
-
-// C++: class DualTVL1OpticalFlow
-//javadoc: DualTVL1OpticalFlow
-
-public class DualTVL1OpticalFlow extends DenseOpticalFlow {
-
-    protected DualTVL1OpticalFlow(long addr) { super(addr); }
-
-    // internal usage only
-    public static DualTVL1OpticalFlow __fromPtr__(long addr) { return new DualTVL1OpticalFlow(addr); }
-
-    //
-    // C++: static Ptr_DualTVL1OpticalFlow create(double tau = 0.25, double lambda = 0.15, double theta = 0.3, int nscales = 5, int warps = 5, double epsilon = 0.01, int innnerIterations = 30, int outerIterations = 10, double scaleStep = 0.8, double gamma = 0.0, int medianFiltering = 5, bool useInitialFlow = false)
-    //
-
-    //javadoc: DualTVL1OpticalFlow::create(tau, lambda, theta, nscales, warps, epsilon, innnerIterations, outerIterations, scaleStep, gamma, medianFiltering, useInitialFlow)
-    public static DualTVL1OpticalFlow create(double tau, double lambda, double theta, int nscales, int warps, double epsilon, int innnerIterations, int outerIterations, double scaleStep, double gamma, int medianFiltering, boolean useInitialFlow)
-    {
-        
-        DualTVL1OpticalFlow retVal = DualTVL1OpticalFlow.__fromPtr__(create_0(tau, lambda, theta, nscales, warps, epsilon, innnerIterations, outerIterations, scaleStep, gamma, medianFiltering, useInitialFlow));
-        
-        return retVal;
-    }
-
-    //javadoc: DualTVL1OpticalFlow::create()
-    public static DualTVL1OpticalFlow create()
-    {
-        
-        DualTVL1OpticalFlow retVal = DualTVL1OpticalFlow.__fromPtr__(create_1());
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  bool getUseInitialFlow()
-    //
-
-    //javadoc: DualTVL1OpticalFlow::getUseInitialFlow()
-    public  boolean getUseInitialFlow()
-    {
-        
-        boolean retVal = getUseInitialFlow_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double getEpsilon()
-    //
-
-    //javadoc: DualTVL1OpticalFlow::getEpsilon()
-    public  double getEpsilon()
-    {
-        
-        double retVal = getEpsilon_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double getGamma()
-    //
-
-    //javadoc: DualTVL1OpticalFlow::getGamma()
-    public  double getGamma()
-    {
-        
-        double retVal = getGamma_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double getLambda()
-    //
-
-    //javadoc: DualTVL1OpticalFlow::getLambda()
-    public  double getLambda()
-    {
-        
-        double retVal = getLambda_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double getScaleStep()
-    //
-
-    //javadoc: DualTVL1OpticalFlow::getScaleStep()
-    public  double getScaleStep()
-    {
-        
-        double retVal = getScaleStep_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double getTau()
-    //
-
-    //javadoc: DualTVL1OpticalFlow::getTau()
-    public  double getTau()
-    {
-        
-        double retVal = getTau_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double getTheta()
-    //
-
-    //javadoc: DualTVL1OpticalFlow::getTheta()
-    public  double getTheta()
-    {
-        
-        double retVal = getTheta_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getInnerIterations()
-    //
-
-    //javadoc: DualTVL1OpticalFlow::getInnerIterations()
-    public  int getInnerIterations()
-    {
-        
-        int retVal = getInnerIterations_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getMedianFiltering()
-    //
-
-    //javadoc: DualTVL1OpticalFlow::getMedianFiltering()
-    public  int getMedianFiltering()
-    {
-        
-        int retVal = getMedianFiltering_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getOuterIterations()
-    //
-
-    //javadoc: DualTVL1OpticalFlow::getOuterIterations()
-    public  int getOuterIterations()
-    {
-        
-        int retVal = getOuterIterations_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getScalesNumber()
-    //
-
-    //javadoc: DualTVL1OpticalFlow::getScalesNumber()
-    public  int getScalesNumber()
-    {
-        
-        int retVal = getScalesNumber_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getWarpingsNumber()
-    //
-
-    //javadoc: DualTVL1OpticalFlow::getWarpingsNumber()
-    public  int getWarpingsNumber()
-    {
-        
-        int retVal = getWarpingsNumber_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  void setEpsilon(double val)
-    //
-
-    //javadoc: DualTVL1OpticalFlow::setEpsilon(val)
-    public  void setEpsilon(double val)
-    {
-        
-        setEpsilon_0(nativeObj, val);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setGamma(double val)
-    //
-
-    //javadoc: DualTVL1OpticalFlow::setGamma(val)
-    public  void setGamma(double val)
-    {
-        
-        setGamma_0(nativeObj, val);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setInnerIterations(int val)
-    //
-
-    //javadoc: DualTVL1OpticalFlow::setInnerIterations(val)
-    public  void setInnerIterations(int val)
-    {
-        
-        setInnerIterations_0(nativeObj, val);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setLambda(double val)
-    //
-
-    //javadoc: DualTVL1OpticalFlow::setLambda(val)
-    public  void setLambda(double val)
-    {
-        
-        setLambda_0(nativeObj, val);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setMedianFiltering(int val)
-    //
-
-    //javadoc: DualTVL1OpticalFlow::setMedianFiltering(val)
-    public  void setMedianFiltering(int val)
-    {
-        
-        setMedianFiltering_0(nativeObj, val);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setOuterIterations(int val)
-    //
-
-    //javadoc: DualTVL1OpticalFlow::setOuterIterations(val)
-    public  void setOuterIterations(int val)
-    {
-        
-        setOuterIterations_0(nativeObj, val);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setScaleStep(double val)
-    //
-
-    //javadoc: DualTVL1OpticalFlow::setScaleStep(val)
-    public  void setScaleStep(double val)
-    {
-        
-        setScaleStep_0(nativeObj, val);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setScalesNumber(int val)
-    //
-
-    //javadoc: DualTVL1OpticalFlow::setScalesNumber(val)
-    public  void setScalesNumber(int val)
-    {
-        
-        setScalesNumber_0(nativeObj, val);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setTau(double val)
-    //
-
-    //javadoc: DualTVL1OpticalFlow::setTau(val)
-    public  void setTau(double val)
-    {
-        
-        setTau_0(nativeObj, val);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setTheta(double val)
-    //
-
-    //javadoc: DualTVL1OpticalFlow::setTheta(val)
-    public  void setTheta(double val)
-    {
-        
-        setTheta_0(nativeObj, val);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setUseInitialFlow(bool val)
-    //
-
-    //javadoc: DualTVL1OpticalFlow::setUseInitialFlow(val)
-    public  void setUseInitialFlow(boolean val)
-    {
-        
-        setUseInitialFlow_0(nativeObj, val);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setWarpingsNumber(int val)
-    //
-
-    //javadoc: DualTVL1OpticalFlow::setWarpingsNumber(val)
-    public  void setWarpingsNumber(int val)
-    {
-        
-        setWarpingsNumber_0(nativeObj, val);
-        
-        return;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++: static Ptr_DualTVL1OpticalFlow create(double tau = 0.25, double lambda = 0.15, double theta = 0.3, int nscales = 5, int warps = 5, double epsilon = 0.01, int innnerIterations = 30, int outerIterations = 10, double scaleStep = 0.8, double gamma = 0.0, int medianFiltering = 5, bool useInitialFlow = false)
-    private static native long create_0(double tau, double lambda, double theta, int nscales, int warps, double epsilon, int innnerIterations, int outerIterations, double scaleStep, double gamma, int medianFiltering, boolean useInitialFlow);
-    private static native long create_1();
-
-    // C++:  bool getUseInitialFlow()
-    private static native boolean getUseInitialFlow_0(long nativeObj);
-
-    // C++:  double getEpsilon()
-    private static native double getEpsilon_0(long nativeObj);
-
-    // C++:  double getGamma()
-    private static native double getGamma_0(long nativeObj);
-
-    // C++:  double getLambda()
-    private static native double getLambda_0(long nativeObj);
-
-    // C++:  double getScaleStep()
-    private static native double getScaleStep_0(long nativeObj);
-
-    // C++:  double getTau()
-    private static native double getTau_0(long nativeObj);
-
-    // C++:  double getTheta()
-    private static native double getTheta_0(long nativeObj);
-
-    // C++:  int getInnerIterations()
-    private static native int getInnerIterations_0(long nativeObj);
-
-    // C++:  int getMedianFiltering()
-    private static native int getMedianFiltering_0(long nativeObj);
-
-    // C++:  int getOuterIterations()
-    private static native int getOuterIterations_0(long nativeObj);
-
-    // C++:  int getScalesNumber()
-    private static native int getScalesNumber_0(long nativeObj);
-
-    // C++:  int getWarpingsNumber()
-    private static native int getWarpingsNumber_0(long nativeObj);
-
-    // C++:  void setEpsilon(double val)
-    private static native void setEpsilon_0(long nativeObj, double val);
-
-    // C++:  void setGamma(double val)
-    private static native void setGamma_0(long nativeObj, double val);
-
-    // C++:  void setInnerIterations(int val)
-    private static native void setInnerIterations_0(long nativeObj, int val);
-
-    // C++:  void setLambda(double val)
-    private static native void setLambda_0(long nativeObj, double val);
-
-    // C++:  void setMedianFiltering(int val)
-    private static native void setMedianFiltering_0(long nativeObj, int val);
-
-    // C++:  void setOuterIterations(int val)
-    private static native void setOuterIterations_0(long nativeObj, int val);
-
-    // C++:  void setScaleStep(double val)
-    private static native void setScaleStep_0(long nativeObj, double val);
-
-    // C++:  void setScalesNumber(int val)
-    private static native void setScalesNumber_0(long nativeObj, int val);
-
-    // C++:  void setTau(double val)
-    private static native void setTau_0(long nativeObj, double val);
-
-    // C++:  void setTheta(double val)
-    private static native void setTheta_0(long nativeObj, double val);
-
-    // C++:  void setUseInitialFlow(bool val)
-    private static native void setUseInitialFlow_0(long nativeObj, boolean val);
-
-    // C++:  void setWarpingsNumber(int val)
-    private static native void setWarpingsNumber_0(long nativeObj, int val);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/video/FarnebackOpticalFlow.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/video/FarnebackOpticalFlow.java
deleted file mode 100755
index d9b8083..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/video/FarnebackOpticalFlow.java
+++ /dev/null
@@ -1,328 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.video;
-
-import org.opencv.video.DenseOpticalFlow;
-import org.opencv.video.FarnebackOpticalFlow;
-
-// C++: class FarnebackOpticalFlow
-//javadoc: FarnebackOpticalFlow
-
-public class FarnebackOpticalFlow extends DenseOpticalFlow {
-
-    protected FarnebackOpticalFlow(long addr) { super(addr); }
-
-    // internal usage only
-    public static FarnebackOpticalFlow __fromPtr__(long addr) { return new FarnebackOpticalFlow(addr); }
-
-    //
-    // C++: static Ptr_FarnebackOpticalFlow create(int numLevels = 5, double pyrScale = 0.5, bool fastPyramids = false, int winSize = 13, int numIters = 10, int polyN = 5, double polySigma = 1.1, int flags = 0)
-    //
-
-    //javadoc: FarnebackOpticalFlow::create(numLevels, pyrScale, fastPyramids, winSize, numIters, polyN, polySigma, flags)
-    public static FarnebackOpticalFlow create(int numLevels, double pyrScale, boolean fastPyramids, int winSize, int numIters, int polyN, double polySigma, int flags)
-    {
-        
-        FarnebackOpticalFlow retVal = FarnebackOpticalFlow.__fromPtr__(create_0(numLevels, pyrScale, fastPyramids, winSize, numIters, polyN, polySigma, flags));
-        
-        return retVal;
-    }
-
-    //javadoc: FarnebackOpticalFlow::create()
-    public static FarnebackOpticalFlow create()
-    {
-        
-        FarnebackOpticalFlow retVal = FarnebackOpticalFlow.__fromPtr__(create_1());
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  bool getFastPyramids()
-    //
-
-    //javadoc: FarnebackOpticalFlow::getFastPyramids()
-    public  boolean getFastPyramids()
-    {
-        
-        boolean retVal = getFastPyramids_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double getPolySigma()
-    //
-
-    //javadoc: FarnebackOpticalFlow::getPolySigma()
-    public  double getPolySigma()
-    {
-        
-        double retVal = getPolySigma_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double getPyrScale()
-    //
-
-    //javadoc: FarnebackOpticalFlow::getPyrScale()
-    public  double getPyrScale()
-    {
-        
-        double retVal = getPyrScale_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getFlags()
-    //
-
-    //javadoc: FarnebackOpticalFlow::getFlags()
-    public  int getFlags()
-    {
-        
-        int retVal = getFlags_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getNumIters()
-    //
-
-    //javadoc: FarnebackOpticalFlow::getNumIters()
-    public  int getNumIters()
-    {
-        
-        int retVal = getNumIters_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getNumLevels()
-    //
-
-    //javadoc: FarnebackOpticalFlow::getNumLevels()
-    public  int getNumLevels()
-    {
-        
-        int retVal = getNumLevels_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getPolyN()
-    //
-
-    //javadoc: FarnebackOpticalFlow::getPolyN()
-    public  int getPolyN()
-    {
-        
-        int retVal = getPolyN_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getWinSize()
-    //
-
-    //javadoc: FarnebackOpticalFlow::getWinSize()
-    public  int getWinSize()
-    {
-        
-        int retVal = getWinSize_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  void setFastPyramids(bool fastPyramids)
-    //
-
-    //javadoc: FarnebackOpticalFlow::setFastPyramids(fastPyramids)
-    public  void setFastPyramids(boolean fastPyramids)
-    {
-        
-        setFastPyramids_0(nativeObj, fastPyramids);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setFlags(int flags)
-    //
-
-    //javadoc: FarnebackOpticalFlow::setFlags(flags)
-    public  void setFlags(int flags)
-    {
-        
-        setFlags_0(nativeObj, flags);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setNumIters(int numIters)
-    //
-
-    //javadoc: FarnebackOpticalFlow::setNumIters(numIters)
-    public  void setNumIters(int numIters)
-    {
-        
-        setNumIters_0(nativeObj, numIters);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setNumLevels(int numLevels)
-    //
-
-    //javadoc: FarnebackOpticalFlow::setNumLevels(numLevels)
-    public  void setNumLevels(int numLevels)
-    {
-        
-        setNumLevels_0(nativeObj, numLevels);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setPolyN(int polyN)
-    //
-
-    //javadoc: FarnebackOpticalFlow::setPolyN(polyN)
-    public  void setPolyN(int polyN)
-    {
-        
-        setPolyN_0(nativeObj, polyN);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setPolySigma(double polySigma)
-    //
-
-    //javadoc: FarnebackOpticalFlow::setPolySigma(polySigma)
-    public  void setPolySigma(double polySigma)
-    {
-        
-        setPolySigma_0(nativeObj, polySigma);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setPyrScale(double pyrScale)
-    //
-
-    //javadoc: FarnebackOpticalFlow::setPyrScale(pyrScale)
-    public  void setPyrScale(double pyrScale)
-    {
-        
-        setPyrScale_0(nativeObj, pyrScale);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setWinSize(int winSize)
-    //
-
-    //javadoc: FarnebackOpticalFlow::setWinSize(winSize)
-    public  void setWinSize(int winSize)
-    {
-        
-        setWinSize_0(nativeObj, winSize);
-        
-        return;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++: static Ptr_FarnebackOpticalFlow create(int numLevels = 5, double pyrScale = 0.5, bool fastPyramids = false, int winSize = 13, int numIters = 10, int polyN = 5, double polySigma = 1.1, int flags = 0)
-    private static native long create_0(int numLevels, double pyrScale, boolean fastPyramids, int winSize, int numIters, int polyN, double polySigma, int flags);
-    private static native long create_1();
-
-    // C++:  bool getFastPyramids()
-    private static native boolean getFastPyramids_0(long nativeObj);
-
-    // C++:  double getPolySigma()
-    private static native double getPolySigma_0(long nativeObj);
-
-    // C++:  double getPyrScale()
-    private static native double getPyrScale_0(long nativeObj);
-
-    // C++:  int getFlags()
-    private static native int getFlags_0(long nativeObj);
-
-    // C++:  int getNumIters()
-    private static native int getNumIters_0(long nativeObj);
-
-    // C++:  int getNumLevels()
-    private static native int getNumLevels_0(long nativeObj);
-
-    // C++:  int getPolyN()
-    private static native int getPolyN_0(long nativeObj);
-
-    // C++:  int getWinSize()
-    private static native int getWinSize_0(long nativeObj);
-
-    // C++:  void setFastPyramids(bool fastPyramids)
-    private static native void setFastPyramids_0(long nativeObj, boolean fastPyramids);
-
-    // C++:  void setFlags(int flags)
-    private static native void setFlags_0(long nativeObj, int flags);
-
-    // C++:  void setNumIters(int numIters)
-    private static native void setNumIters_0(long nativeObj, int numIters);
-
-    // C++:  void setNumLevels(int numLevels)
-    private static native void setNumLevels_0(long nativeObj, int numLevels);
-
-    // C++:  void setPolyN(int polyN)
-    private static native void setPolyN_0(long nativeObj, int polyN);
-
-    // C++:  void setPolySigma(double polySigma)
-    private static native void setPolySigma_0(long nativeObj, double polySigma);
-
-    // C++:  void setPyrScale(double pyrScale)
-    private static native void setPyrScale_0(long nativeObj, double pyrScale);
-
-    // C++:  void setWinSize(int winSize)
-    private static native void setWinSize_0(long nativeObj, int winSize);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/video/KalmanFilter.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/video/KalmanFilter.java
deleted file mode 100755
index 7c5da9d..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/video/KalmanFilter.java
+++ /dev/null
@@ -1,459 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.video;
-
-import org.opencv.core.Mat;
-
-// C++: class KalmanFilter
-//javadoc: KalmanFilter
-
-public class KalmanFilter {
-
-    protected final long nativeObj;
-    protected KalmanFilter(long addr) { nativeObj = addr; }
-
-    public long getNativeObjAddr() { return nativeObj; }
-
-    // internal usage only
-    public static KalmanFilter __fromPtr__(long addr) { return new KalmanFilter(addr); }
-
-    //
-    // C++:   KalmanFilter(int dynamParams, int measureParams, int controlParams = 0, int type = CV_32F)
-    //
-
-    //javadoc: KalmanFilter::KalmanFilter(dynamParams, measureParams, controlParams, type)
-    public   KalmanFilter(int dynamParams, int measureParams, int controlParams, int type)
-    {
-        
-        nativeObj = KalmanFilter_0(dynamParams, measureParams, controlParams, type);
-        
-        return;
-    }
-
-    //javadoc: KalmanFilter::KalmanFilter(dynamParams, measureParams)
-    public   KalmanFilter(int dynamParams, int measureParams)
-    {
-        
-        nativeObj = KalmanFilter_1(dynamParams, measureParams);
-        
-        return;
-    }
-
-
-    //
-    // C++:   KalmanFilter()
-    //
-
-    //javadoc: KalmanFilter::KalmanFilter()
-    public   KalmanFilter()
-    {
-        
-        nativeObj = KalmanFilter_2();
-        
-        return;
-    }
-
-
-    //
-    // C++:  Mat correct(Mat measurement)
-    //
-
-    //javadoc: KalmanFilter::correct(measurement)
-    public  Mat correct(Mat measurement)
-    {
-        
-        Mat retVal = new Mat(correct_0(nativeObj, measurement.nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Mat predict(Mat control = Mat())
-    //
-
-    //javadoc: KalmanFilter::predict(control)
-    public  Mat predict(Mat control)
-    {
-        
-        Mat retVal = new Mat(predict_0(nativeObj, control.nativeObj));
-        
-        return retVal;
-    }
-
-    //javadoc: KalmanFilter::predict()
-    public  Mat predict()
-    {
-        
-        Mat retVal = new Mat(predict_1(nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: Mat KalmanFilter::statePre
-    //
-
-    //javadoc: KalmanFilter::get_statePre()
-    public  Mat get_statePre()
-    {
-        
-        Mat retVal = new Mat(get_statePre_0(nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: void KalmanFilter::statePre
-    //
-
-    //javadoc: KalmanFilter::set_statePre(statePre)
-    public  void set_statePre(Mat statePre)
-    {
-        
-        set_statePre_0(nativeObj, statePre.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++: Mat KalmanFilter::statePost
-    //
-
-    //javadoc: KalmanFilter::get_statePost()
-    public  Mat get_statePost()
-    {
-        
-        Mat retVal = new Mat(get_statePost_0(nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: void KalmanFilter::statePost
-    //
-
-    //javadoc: KalmanFilter::set_statePost(statePost)
-    public  void set_statePost(Mat statePost)
-    {
-        
-        set_statePost_0(nativeObj, statePost.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++: Mat KalmanFilter::transitionMatrix
-    //
-
-    //javadoc: KalmanFilter::get_transitionMatrix()
-    public  Mat get_transitionMatrix()
-    {
-        
-        Mat retVal = new Mat(get_transitionMatrix_0(nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: void KalmanFilter::transitionMatrix
-    //
-
-    //javadoc: KalmanFilter::set_transitionMatrix(transitionMatrix)
-    public  void set_transitionMatrix(Mat transitionMatrix)
-    {
-        
-        set_transitionMatrix_0(nativeObj, transitionMatrix.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++: Mat KalmanFilter::controlMatrix
-    //
-
-    //javadoc: KalmanFilter::get_controlMatrix()
-    public  Mat get_controlMatrix()
-    {
-        
-        Mat retVal = new Mat(get_controlMatrix_0(nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: void KalmanFilter::controlMatrix
-    //
-
-    //javadoc: KalmanFilter::set_controlMatrix(controlMatrix)
-    public  void set_controlMatrix(Mat controlMatrix)
-    {
-        
-        set_controlMatrix_0(nativeObj, controlMatrix.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++: Mat KalmanFilter::measurementMatrix
-    //
-
-    //javadoc: KalmanFilter::get_measurementMatrix()
-    public  Mat get_measurementMatrix()
-    {
-        
-        Mat retVal = new Mat(get_measurementMatrix_0(nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: void KalmanFilter::measurementMatrix
-    //
-
-    //javadoc: KalmanFilter::set_measurementMatrix(measurementMatrix)
-    public  void set_measurementMatrix(Mat measurementMatrix)
-    {
-        
-        set_measurementMatrix_0(nativeObj, measurementMatrix.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++: Mat KalmanFilter::processNoiseCov
-    //
-
-    //javadoc: KalmanFilter::get_processNoiseCov()
-    public  Mat get_processNoiseCov()
-    {
-        
-        Mat retVal = new Mat(get_processNoiseCov_0(nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: void KalmanFilter::processNoiseCov
-    //
-
-    //javadoc: KalmanFilter::set_processNoiseCov(processNoiseCov)
-    public  void set_processNoiseCov(Mat processNoiseCov)
-    {
-        
-        set_processNoiseCov_0(nativeObj, processNoiseCov.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++: Mat KalmanFilter::measurementNoiseCov
-    //
-
-    //javadoc: KalmanFilter::get_measurementNoiseCov()
-    public  Mat get_measurementNoiseCov()
-    {
-        
-        Mat retVal = new Mat(get_measurementNoiseCov_0(nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: void KalmanFilter::measurementNoiseCov
-    //
-
-    //javadoc: KalmanFilter::set_measurementNoiseCov(measurementNoiseCov)
-    public  void set_measurementNoiseCov(Mat measurementNoiseCov)
-    {
-        
-        set_measurementNoiseCov_0(nativeObj, measurementNoiseCov.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++: Mat KalmanFilter::errorCovPre
-    //
-
-    //javadoc: KalmanFilter::get_errorCovPre()
-    public  Mat get_errorCovPre()
-    {
-        
-        Mat retVal = new Mat(get_errorCovPre_0(nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: void KalmanFilter::errorCovPre
-    //
-
-    //javadoc: KalmanFilter::set_errorCovPre(errorCovPre)
-    public  void set_errorCovPre(Mat errorCovPre)
-    {
-        
-        set_errorCovPre_0(nativeObj, errorCovPre.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++: Mat KalmanFilter::gain
-    //
-
-    //javadoc: KalmanFilter::get_gain()
-    public  Mat get_gain()
-    {
-        
-        Mat retVal = new Mat(get_gain_0(nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: void KalmanFilter::gain
-    //
-
-    //javadoc: KalmanFilter::set_gain(gain)
-    public  void set_gain(Mat gain)
-    {
-        
-        set_gain_0(nativeObj, gain.nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++: Mat KalmanFilter::errorCovPost
-    //
-
-    //javadoc: KalmanFilter::get_errorCovPost()
-    public  Mat get_errorCovPost()
-    {
-        
-        Mat retVal = new Mat(get_errorCovPost_0(nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: void KalmanFilter::errorCovPost
-    //
-
-    //javadoc: KalmanFilter::set_errorCovPost(errorCovPost)
-    public  void set_errorCovPost(Mat errorCovPost)
-    {
-        
-        set_errorCovPost_0(nativeObj, errorCovPost.nativeObj);
-        
-        return;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++:   KalmanFilter(int dynamParams, int measureParams, int controlParams = 0, int type = CV_32F)
-    private static native long KalmanFilter_0(int dynamParams, int measureParams, int controlParams, int type);
-    private static native long KalmanFilter_1(int dynamParams, int measureParams);
-
-    // C++:   KalmanFilter()
-    private static native long KalmanFilter_2();
-
-    // C++:  Mat correct(Mat measurement)
-    private static native long correct_0(long nativeObj, long measurement_nativeObj);
-
-    // C++:  Mat predict(Mat control = Mat())
-    private static native long predict_0(long nativeObj, long control_nativeObj);
-    private static native long predict_1(long nativeObj);
-
-    // C++: Mat KalmanFilter::statePre
-    private static native long get_statePre_0(long nativeObj);
-
-    // C++: void KalmanFilter::statePre
-    private static native void set_statePre_0(long nativeObj, long statePre_nativeObj);
-
-    // C++: Mat KalmanFilter::statePost
-    private static native long get_statePost_0(long nativeObj);
-
-    // C++: void KalmanFilter::statePost
-    private static native void set_statePost_0(long nativeObj, long statePost_nativeObj);
-
-    // C++: Mat KalmanFilter::transitionMatrix
-    private static native long get_transitionMatrix_0(long nativeObj);
-
-    // C++: void KalmanFilter::transitionMatrix
-    private static native void set_transitionMatrix_0(long nativeObj, long transitionMatrix_nativeObj);
-
-    // C++: Mat KalmanFilter::controlMatrix
-    private static native long get_controlMatrix_0(long nativeObj);
-
-    // C++: void KalmanFilter::controlMatrix
-    private static native void set_controlMatrix_0(long nativeObj, long controlMatrix_nativeObj);
-
-    // C++: Mat KalmanFilter::measurementMatrix
-    private static native long get_measurementMatrix_0(long nativeObj);
-
-    // C++: void KalmanFilter::measurementMatrix
-    private static native void set_measurementMatrix_0(long nativeObj, long measurementMatrix_nativeObj);
-
-    // C++: Mat KalmanFilter::processNoiseCov
-    private static native long get_processNoiseCov_0(long nativeObj);
-
-    // C++: void KalmanFilter::processNoiseCov
-    private static native void set_processNoiseCov_0(long nativeObj, long processNoiseCov_nativeObj);
-
-    // C++: Mat KalmanFilter::measurementNoiseCov
-    private static native long get_measurementNoiseCov_0(long nativeObj);
-
-    // C++: void KalmanFilter::measurementNoiseCov
-    private static native void set_measurementNoiseCov_0(long nativeObj, long measurementNoiseCov_nativeObj);
-
-    // C++: Mat KalmanFilter::errorCovPre
-    private static native long get_errorCovPre_0(long nativeObj);
-
-    // C++: void KalmanFilter::errorCovPre
-    private static native void set_errorCovPre_0(long nativeObj, long errorCovPre_nativeObj);
-
-    // C++: Mat KalmanFilter::gain
-    private static native long get_gain_0(long nativeObj);
-
-    // C++: void KalmanFilter::gain
-    private static native void set_gain_0(long nativeObj, long gain_nativeObj);
-
-    // C++: Mat KalmanFilter::errorCovPost
-    private static native long get_errorCovPost_0(long nativeObj);
-
-    // C++: void KalmanFilter::errorCovPost
-    private static native void set_errorCovPost_0(long nativeObj, long errorCovPost_nativeObj);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/video/SparseOpticalFlow.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/video/SparseOpticalFlow.java
deleted file mode 100755
index e267864..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/video/SparseOpticalFlow.java
+++ /dev/null
@@ -1,56 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.video;
-
-import org.opencv.core.Algorithm;
-import org.opencv.core.Mat;
-
-// C++: class SparseOpticalFlow
-//javadoc: SparseOpticalFlow
-
-public class SparseOpticalFlow extends Algorithm {
-
-    protected SparseOpticalFlow(long addr) { super(addr); }
-
-    // internal usage only
-    public static SparseOpticalFlow __fromPtr__(long addr) { return new SparseOpticalFlow(addr); }
-
-    //
-    // C++:  void calc(Mat prevImg, Mat nextImg, Mat prevPts, Mat& nextPts, Mat& status, Mat& err = cv::Mat())
-    //
-
-    //javadoc: SparseOpticalFlow::calc(prevImg, nextImg, prevPts, nextPts, status, err)
-    public  void calc(Mat prevImg, Mat nextImg, Mat prevPts, Mat nextPts, Mat status, Mat err)
-    {
-        
-        calc_0(nativeObj, prevImg.nativeObj, nextImg.nativeObj, prevPts.nativeObj, nextPts.nativeObj, status.nativeObj, err.nativeObj);
-        
-        return;
-    }
-
-    //javadoc: SparseOpticalFlow::calc(prevImg, nextImg, prevPts, nextPts, status)
-    public  void calc(Mat prevImg, Mat nextImg, Mat prevPts, Mat nextPts, Mat status)
-    {
-        
-        calc_1(nativeObj, prevImg.nativeObj, nextImg.nativeObj, prevPts.nativeObj, nextPts.nativeObj, status.nativeObj);
-        
-        return;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++:  void calc(Mat prevImg, Mat nextImg, Mat prevPts, Mat& nextPts, Mat& status, Mat& err = cv::Mat())
-    private static native void calc_0(long nativeObj, long prevImg_nativeObj, long nextImg_nativeObj, long prevPts_nativeObj, long nextPts_nativeObj, long status_nativeObj, long err_nativeObj);
-    private static native void calc_1(long nativeObj, long prevImg_nativeObj, long nextImg_nativeObj, long prevPts_nativeObj, long nextPts_nativeObj, long status_nativeObj);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/video/SparsePyrLKOpticalFlow.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/video/SparsePyrLKOpticalFlow.java
deleted file mode 100755
index b915f3e..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/video/SparsePyrLKOpticalFlow.java
+++ /dev/null
@@ -1,228 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.video;
-
-import org.opencv.core.Size;
-import org.opencv.core.TermCriteria;
-import org.opencv.video.SparseOpticalFlow;
-import org.opencv.video.SparsePyrLKOpticalFlow;
-
-// C++: class SparsePyrLKOpticalFlow
-//javadoc: SparsePyrLKOpticalFlow
-
-public class SparsePyrLKOpticalFlow extends SparseOpticalFlow {
-
-    protected SparsePyrLKOpticalFlow(long addr) { super(addr); }
-
-    // internal usage only
-    public static SparsePyrLKOpticalFlow __fromPtr__(long addr) { return new SparsePyrLKOpticalFlow(addr); }
-
-    //
-    // C++: static Ptr_SparsePyrLKOpticalFlow create(Size winSize = Size(21, 21), int maxLevel = 3, TermCriteria crit = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 0.01), int flags = 0, double minEigThreshold = 1e-4)
-    //
-
-    //javadoc: SparsePyrLKOpticalFlow::create(winSize, maxLevel, crit, flags, minEigThreshold)
-    public static SparsePyrLKOpticalFlow create(Size winSize, int maxLevel, TermCriteria crit, int flags, double minEigThreshold)
-    {
-        
-        SparsePyrLKOpticalFlow retVal = SparsePyrLKOpticalFlow.__fromPtr__(create_0(winSize.width, winSize.height, maxLevel, crit.type, crit.maxCount, crit.epsilon, flags, minEigThreshold));
-        
-        return retVal;
-    }
-
-    //javadoc: SparsePyrLKOpticalFlow::create()
-    public static SparsePyrLKOpticalFlow create()
-    {
-        
-        SparsePyrLKOpticalFlow retVal = SparsePyrLKOpticalFlow.__fromPtr__(create_1());
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Size getWinSize()
-    //
-
-    //javadoc: SparsePyrLKOpticalFlow::getWinSize()
-    public  Size getWinSize()
-    {
-        
-        Size retVal = new Size(getWinSize_0(nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  TermCriteria getTermCriteria()
-    //
-
-    //javadoc: SparsePyrLKOpticalFlow::getTermCriteria()
-    public  TermCriteria getTermCriteria()
-    {
-        
-        TermCriteria retVal = new TermCriteria(getTermCriteria_0(nativeObj));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double getMinEigThreshold()
-    //
-
-    //javadoc: SparsePyrLKOpticalFlow::getMinEigThreshold()
-    public  double getMinEigThreshold()
-    {
-        
-        double retVal = getMinEigThreshold_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getFlags()
-    //
-
-    //javadoc: SparsePyrLKOpticalFlow::getFlags()
-    public  int getFlags()
-    {
-        
-        int retVal = getFlags_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int getMaxLevel()
-    //
-
-    //javadoc: SparsePyrLKOpticalFlow::getMaxLevel()
-    public  int getMaxLevel()
-    {
-        
-        int retVal = getMaxLevel_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  void setFlags(int flags)
-    //
-
-    //javadoc: SparsePyrLKOpticalFlow::setFlags(flags)
-    public  void setFlags(int flags)
-    {
-        
-        setFlags_0(nativeObj, flags);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setMaxLevel(int maxLevel)
-    //
-
-    //javadoc: SparsePyrLKOpticalFlow::setMaxLevel(maxLevel)
-    public  void setMaxLevel(int maxLevel)
-    {
-        
-        setMaxLevel_0(nativeObj, maxLevel);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setMinEigThreshold(double minEigThreshold)
-    //
-
-    //javadoc: SparsePyrLKOpticalFlow::setMinEigThreshold(minEigThreshold)
-    public  void setMinEigThreshold(double minEigThreshold)
-    {
-        
-        setMinEigThreshold_0(nativeObj, minEigThreshold);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setTermCriteria(TermCriteria crit)
-    //
-
-    //javadoc: SparsePyrLKOpticalFlow::setTermCriteria(crit)
-    public  void setTermCriteria(TermCriteria crit)
-    {
-        
-        setTermCriteria_0(nativeObj, crit.type, crit.maxCount, crit.epsilon);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void setWinSize(Size winSize)
-    //
-
-    //javadoc: SparsePyrLKOpticalFlow::setWinSize(winSize)
-    public  void setWinSize(Size winSize)
-    {
-        
-        setWinSize_0(nativeObj, winSize.width, winSize.height);
-        
-        return;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++: static Ptr_SparsePyrLKOpticalFlow create(Size winSize = Size(21, 21), int maxLevel = 3, TermCriteria crit = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 0.01), int flags = 0, double minEigThreshold = 1e-4)
-    private static native long create_0(double winSize_width, double winSize_height, int maxLevel, int crit_type, int crit_maxCount, double crit_epsilon, int flags, double minEigThreshold);
-    private static native long create_1();
-
-    // C++:  Size getWinSize()
-    private static native double[] getWinSize_0(long nativeObj);
-
-    // C++:  TermCriteria getTermCriteria()
-    private static native double[] getTermCriteria_0(long nativeObj);
-
-    // C++:  double getMinEigThreshold()
-    private static native double getMinEigThreshold_0(long nativeObj);
-
-    // C++:  int getFlags()
-    private static native int getFlags_0(long nativeObj);
-
-    // C++:  int getMaxLevel()
-    private static native int getMaxLevel_0(long nativeObj);
-
-    // C++:  void setFlags(int flags)
-    private static native void setFlags_0(long nativeObj, int flags);
-
-    // C++:  void setMaxLevel(int maxLevel)
-    private static native void setMaxLevel_0(long nativeObj, int maxLevel);
-
-    // C++:  void setMinEigThreshold(double minEigThreshold)
-    private static native void setMinEigThreshold_0(long nativeObj, double minEigThreshold);
-
-    // C++:  void setTermCriteria(TermCriteria crit)
-    private static native void setTermCriteria_0(long nativeObj, int crit_type, int crit_maxCount, double crit_epsilon);
-
-    // C++:  void setWinSize(Size winSize)
-    private static native void setWinSize_0(long nativeObj, double winSize_width, double winSize_height);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/video/Video.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/video/Video.java
deleted file mode 100755
index 48fbef0..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/video/Video.java
+++ /dev/null
@@ -1,311 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.video;
-
-import java.util.ArrayList;
-import java.util.List;
-import org.opencv.core.Mat;
-import org.opencv.core.MatOfByte;
-import org.opencv.core.MatOfFloat;
-import org.opencv.core.MatOfPoint2f;
-import org.opencv.core.Rect;
-import org.opencv.core.RotatedRect;
-import org.opencv.core.Size;
-import org.opencv.core.TermCriteria;
-import org.opencv.utils.Converters;
-import org.opencv.video.BackgroundSubtractorKNN;
-import org.opencv.video.BackgroundSubtractorMOG2;
-import org.opencv.video.DualTVL1OpticalFlow;
-
-// C++: class Video
-//javadoc: Video
-
-public class Video {
-
-    private static final int
-            CV_LKFLOW_INITIAL_GUESSES = 4,
-            CV_LKFLOW_GET_MIN_EIGENVALS = 8;
-
-
-    public static final int
-            OPTFLOW_USE_INITIAL_FLOW = 4,
-            OPTFLOW_LK_GET_MIN_EIGENVALS = 8,
-            OPTFLOW_FARNEBACK_GAUSSIAN = 256,
-            MOTION_TRANSLATION = 0,
-            MOTION_EUCLIDEAN = 1,
-            MOTION_AFFINE = 2,
-            MOTION_HOMOGRAPHY = 3;
-
-
-    //
-    // C++:  Mat estimateRigidTransform(Mat src, Mat dst, bool fullAffine, int ransacMaxIters, double ransacGoodRatio, int ransacSize0)
-    //
-
-    //javadoc: estimateRigidTransform(src, dst, fullAffine, ransacMaxIters, ransacGoodRatio, ransacSize0)
-    public static Mat estimateRigidTransform(Mat src, Mat dst, boolean fullAffine, int ransacMaxIters, double ransacGoodRatio, int ransacSize0)
-    {
-        
-        Mat retVal = new Mat(estimateRigidTransform_0(src.nativeObj, dst.nativeObj, fullAffine, ransacMaxIters, ransacGoodRatio, ransacSize0));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Mat estimateRigidTransform(Mat src, Mat dst, bool fullAffine)
-    //
-
-    //javadoc: estimateRigidTransform(src, dst, fullAffine)
-    public static Mat estimateRigidTransform(Mat src, Mat dst, boolean fullAffine)
-    {
-        
-        Mat retVal = new Mat(estimateRigidTransform_1(src.nativeObj, dst.nativeObj, fullAffine));
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Ptr_BackgroundSubtractorKNN createBackgroundSubtractorKNN(int history = 500, double dist2Threshold = 400.0, bool detectShadows = true)
-    //
-
-    //javadoc: createBackgroundSubtractorKNN(history, dist2Threshold, detectShadows)
-    public static BackgroundSubtractorKNN createBackgroundSubtractorKNN(int history, double dist2Threshold, boolean detectShadows)
-    {
-        
-        BackgroundSubtractorKNN retVal = BackgroundSubtractorKNN.__fromPtr__(createBackgroundSubtractorKNN_0(history, dist2Threshold, detectShadows));
-        
-        return retVal;
-    }
-
-    //javadoc: createBackgroundSubtractorKNN()
-    public static BackgroundSubtractorKNN createBackgroundSubtractorKNN()
-    {
-        
-        BackgroundSubtractorKNN retVal = BackgroundSubtractorKNN.__fromPtr__(createBackgroundSubtractorKNN_1());
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Ptr_BackgroundSubtractorMOG2 createBackgroundSubtractorMOG2(int history = 500, double varThreshold = 16, bool detectShadows = true)
-    //
-
-    //javadoc: createBackgroundSubtractorMOG2(history, varThreshold, detectShadows)
-    public static BackgroundSubtractorMOG2 createBackgroundSubtractorMOG2(int history, double varThreshold, boolean detectShadows)
-    {
-        
-        BackgroundSubtractorMOG2 retVal = BackgroundSubtractorMOG2.__fromPtr__(createBackgroundSubtractorMOG2_0(history, varThreshold, detectShadows));
-        
-        return retVal;
-    }
-
-    //javadoc: createBackgroundSubtractorMOG2()
-    public static BackgroundSubtractorMOG2 createBackgroundSubtractorMOG2()
-    {
-        
-        BackgroundSubtractorMOG2 retVal = BackgroundSubtractorMOG2.__fromPtr__(createBackgroundSubtractorMOG2_1());
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  Ptr_DualTVL1OpticalFlow createOptFlow_DualTVL1()
-    //
-
-    //javadoc: createOptFlow_DualTVL1()
-    public static DualTVL1OpticalFlow createOptFlow_DualTVL1()
-    {
-        
-        DualTVL1OpticalFlow retVal = DualTVL1OpticalFlow.__fromPtr__(createOptFlow_DualTVL1_0());
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  RotatedRect CamShift(Mat probImage, Rect& window, TermCriteria criteria)
-    //
-
-    //javadoc: CamShift(probImage, window, criteria)
-    public static RotatedRect CamShift(Mat probImage, Rect window, TermCriteria criteria)
-    {
-        double[] window_out = new double[4];
-        RotatedRect retVal = new RotatedRect(CamShift_0(probImage.nativeObj, window.x, window.y, window.width, window.height, window_out, criteria.type, criteria.maxCount, criteria.epsilon));
-        if(window!=null){ window.x = (int)window_out[0]; window.y = (int)window_out[1]; window.width = (int)window_out[2]; window.height = (int)window_out[3]; } 
-        return retVal;
-    }
-
-
-    //
-    // C++:  double findTransformECC(Mat templateImage, Mat inputImage, Mat& warpMatrix, int motionType = MOTION_AFFINE, TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 50, 0.001), Mat inputMask = Mat())
-    //
-
-    //javadoc: findTransformECC(templateImage, inputImage, warpMatrix, motionType, criteria, inputMask)
-    public static double findTransformECC(Mat templateImage, Mat inputImage, Mat warpMatrix, int motionType, TermCriteria criteria, Mat inputMask)
-    {
-        
-        double retVal = findTransformECC_0(templateImage.nativeObj, inputImage.nativeObj, warpMatrix.nativeObj, motionType, criteria.type, criteria.maxCount, criteria.epsilon, inputMask.nativeObj);
-        
-        return retVal;
-    }
-
-    //javadoc: findTransformECC(templateImage, inputImage, warpMatrix, motionType)
-    public static double findTransformECC(Mat templateImage, Mat inputImage, Mat warpMatrix, int motionType)
-    {
-        
-        double retVal = findTransformECC_1(templateImage.nativeObj, inputImage.nativeObj, warpMatrix.nativeObj, motionType);
-        
-        return retVal;
-    }
-
-    //javadoc: findTransformECC(templateImage, inputImage, warpMatrix)
-    public static double findTransformECC(Mat templateImage, Mat inputImage, Mat warpMatrix)
-    {
-        
-        double retVal = findTransformECC_2(templateImage.nativeObj, inputImage.nativeObj, warpMatrix.nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  int buildOpticalFlowPyramid(Mat img, vector_Mat& pyramid, Size winSize, int maxLevel, bool withDerivatives = true, int pyrBorder = BORDER_REFLECT_101, int derivBorder = BORDER_CONSTANT, bool tryReuseInputImage = true)
-    //
-
-    //javadoc: buildOpticalFlowPyramid(img, pyramid, winSize, maxLevel, withDerivatives, pyrBorder, derivBorder, tryReuseInputImage)
-    public static int buildOpticalFlowPyramid(Mat img, List<Mat> pyramid, Size winSize, int maxLevel, boolean withDerivatives, int pyrBorder, int derivBorder, boolean tryReuseInputImage)
-    {
-        Mat pyramid_mat = new Mat();
-        int retVal = buildOpticalFlowPyramid_0(img.nativeObj, pyramid_mat.nativeObj, winSize.width, winSize.height, maxLevel, withDerivatives, pyrBorder, derivBorder, tryReuseInputImage);
-        Converters.Mat_to_vector_Mat(pyramid_mat, pyramid);
-        pyramid_mat.release();
-        return retVal;
-    }
-
-    //javadoc: buildOpticalFlowPyramid(img, pyramid, winSize, maxLevel)
-    public static int buildOpticalFlowPyramid(Mat img, List<Mat> pyramid, Size winSize, int maxLevel)
-    {
-        Mat pyramid_mat = new Mat();
-        int retVal = buildOpticalFlowPyramid_1(img.nativeObj, pyramid_mat.nativeObj, winSize.width, winSize.height, maxLevel);
-        Converters.Mat_to_vector_Mat(pyramid_mat, pyramid);
-        pyramid_mat.release();
-        return retVal;
-    }
-
-
-    //
-    // C++:  int meanShift(Mat probImage, Rect& window, TermCriteria criteria)
-    //
-
-    //javadoc: meanShift(probImage, window, criteria)
-    public static int meanShift(Mat probImage, Rect window, TermCriteria criteria)
-    {
-        double[] window_out = new double[4];
-        int retVal = meanShift_0(probImage.nativeObj, window.x, window.y, window.width, window.height, window_out, criteria.type, criteria.maxCount, criteria.epsilon);
-        if(window!=null){ window.x = (int)window_out[0]; window.y = (int)window_out[1]; window.width = (int)window_out[2]; window.height = (int)window_out[3]; } 
-        return retVal;
-    }
-
-
-    //
-    // C++:  void calcOpticalFlowFarneback(Mat prev, Mat next, Mat& flow, double pyr_scale, int levels, int winsize, int iterations, int poly_n, double poly_sigma, int flags)
-    //
-
-    //javadoc: calcOpticalFlowFarneback(prev, next, flow, pyr_scale, levels, winsize, iterations, poly_n, poly_sigma, flags)
-    public static void calcOpticalFlowFarneback(Mat prev, Mat next, Mat flow, double pyr_scale, int levels, int winsize, int iterations, int poly_n, double poly_sigma, int flags)
-    {
-        
-        calcOpticalFlowFarneback_0(prev.nativeObj, next.nativeObj, flow.nativeObj, pyr_scale, levels, winsize, iterations, poly_n, poly_sigma, flags);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void calcOpticalFlowPyrLK(Mat prevImg, Mat nextImg, vector_Point2f prevPts, vector_Point2f& nextPts, vector_uchar& status, vector_float& err, Size winSize = Size(21,21), int maxLevel = 3, TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 0.01), int flags = 0, double minEigThreshold = 1e-4)
-    //
-
-    //javadoc: calcOpticalFlowPyrLK(prevImg, nextImg, prevPts, nextPts, status, err, winSize, maxLevel, criteria, flags, minEigThreshold)
-    public static void calcOpticalFlowPyrLK(Mat prevImg, Mat nextImg, MatOfPoint2f prevPts, MatOfPoint2f nextPts, MatOfByte status, MatOfFloat err, Size winSize, int maxLevel, TermCriteria criteria, int flags, double minEigThreshold)
-    {
-        Mat prevPts_mat = prevPts;
-        Mat nextPts_mat = nextPts;
-        Mat status_mat = status;
-        Mat err_mat = err;
-        calcOpticalFlowPyrLK_0(prevImg.nativeObj, nextImg.nativeObj, prevPts_mat.nativeObj, nextPts_mat.nativeObj, status_mat.nativeObj, err_mat.nativeObj, winSize.width, winSize.height, maxLevel, criteria.type, criteria.maxCount, criteria.epsilon, flags, minEigThreshold);
-        
-        return;
-    }
-
-    //javadoc: calcOpticalFlowPyrLK(prevImg, nextImg, prevPts, nextPts, status, err, winSize, maxLevel)
-    public static void calcOpticalFlowPyrLK(Mat prevImg, Mat nextImg, MatOfPoint2f prevPts, MatOfPoint2f nextPts, MatOfByte status, MatOfFloat err, Size winSize, int maxLevel)
-    {
-        Mat prevPts_mat = prevPts;
-        Mat nextPts_mat = nextPts;
-        Mat status_mat = status;
-        Mat err_mat = err;
-        calcOpticalFlowPyrLK_1(prevImg.nativeObj, nextImg.nativeObj, prevPts_mat.nativeObj, nextPts_mat.nativeObj, status_mat.nativeObj, err_mat.nativeObj, winSize.width, winSize.height, maxLevel);
-        
-        return;
-    }
-
-    //javadoc: calcOpticalFlowPyrLK(prevImg, nextImg, prevPts, nextPts, status, err)
-    public static void calcOpticalFlowPyrLK(Mat prevImg, Mat nextImg, MatOfPoint2f prevPts, MatOfPoint2f nextPts, MatOfByte status, MatOfFloat err)
-    {
-        Mat prevPts_mat = prevPts;
-        Mat nextPts_mat = nextPts;
-        Mat status_mat = status;
-        Mat err_mat = err;
-        calcOpticalFlowPyrLK_2(prevImg.nativeObj, nextImg.nativeObj, prevPts_mat.nativeObj, nextPts_mat.nativeObj, status_mat.nativeObj, err_mat.nativeObj);
-        
-        return;
-    }
-
-
-
-
-    // C++:  Mat estimateRigidTransform(Mat src, Mat dst, bool fullAffine, int ransacMaxIters, double ransacGoodRatio, int ransacSize0)
-    private static native long estimateRigidTransform_0(long src_nativeObj, long dst_nativeObj, boolean fullAffine, int ransacMaxIters, double ransacGoodRatio, int ransacSize0);
-
-    // C++:  Mat estimateRigidTransform(Mat src, Mat dst, bool fullAffine)
-    private static native long estimateRigidTransform_1(long src_nativeObj, long dst_nativeObj, boolean fullAffine);
-
-    // C++:  Ptr_BackgroundSubtractorKNN createBackgroundSubtractorKNN(int history = 500, double dist2Threshold = 400.0, bool detectShadows = true)
-    private static native long createBackgroundSubtractorKNN_0(int history, double dist2Threshold, boolean detectShadows);
-    private static native long createBackgroundSubtractorKNN_1();
-
-    // C++:  Ptr_BackgroundSubtractorMOG2 createBackgroundSubtractorMOG2(int history = 500, double varThreshold = 16, bool detectShadows = true)
-    private static native long createBackgroundSubtractorMOG2_0(int history, double varThreshold, boolean detectShadows);
-    private static native long createBackgroundSubtractorMOG2_1();
-
-    // C++:  Ptr_DualTVL1OpticalFlow createOptFlow_DualTVL1()
-    private static native long createOptFlow_DualTVL1_0();
-
-    // C++:  RotatedRect CamShift(Mat probImage, Rect& window, TermCriteria criteria)
-    private static native double[] CamShift_0(long probImage_nativeObj, int window_x, int window_y, int window_width, int window_height, double[] window_out, int criteria_type, int criteria_maxCount, double criteria_epsilon);
-
-    // C++:  double findTransformECC(Mat templateImage, Mat inputImage, Mat& warpMatrix, int motionType = MOTION_AFFINE, TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 50, 0.001), Mat inputMask = Mat())
-    private static native double findTransformECC_0(long templateImage_nativeObj, long inputImage_nativeObj, long warpMatrix_nativeObj, int motionType, int criteria_type, int criteria_maxCount, double criteria_epsilon, long inputMask_nativeObj);
-    private static native double findTransformECC_1(long templateImage_nativeObj, long inputImage_nativeObj, long warpMatrix_nativeObj, int motionType);
-    private static native double findTransformECC_2(long templateImage_nativeObj, long inputImage_nativeObj, long warpMatrix_nativeObj);
-
-    // C++:  int buildOpticalFlowPyramid(Mat img, vector_Mat& pyramid, Size winSize, int maxLevel, bool withDerivatives = true, int pyrBorder = BORDER_REFLECT_101, int derivBorder = BORDER_CONSTANT, bool tryReuseInputImage = true)
-    private static native int buildOpticalFlowPyramid_0(long img_nativeObj, long pyramid_mat_nativeObj, double winSize_width, double winSize_height, int maxLevel, boolean withDerivatives, int pyrBorder, int derivBorder, boolean tryReuseInputImage);
-    private static native int buildOpticalFlowPyramid_1(long img_nativeObj, long pyramid_mat_nativeObj, double winSize_width, double winSize_height, int maxLevel);
-
-    // C++:  int meanShift(Mat probImage, Rect& window, TermCriteria criteria)
-    private static native int meanShift_0(long probImage_nativeObj, int window_x, int window_y, int window_width, int window_height, double[] window_out, int criteria_type, int criteria_maxCount, double criteria_epsilon);
-
-    // C++:  void calcOpticalFlowFarneback(Mat prev, Mat next, Mat& flow, double pyr_scale, int levels, int winsize, int iterations, int poly_n, double poly_sigma, int flags)
-    private static native void calcOpticalFlowFarneback_0(long prev_nativeObj, long next_nativeObj, long flow_nativeObj, double pyr_scale, int levels, int winsize, int iterations, int poly_n, double poly_sigma, int flags);
-
-    // C++:  void calcOpticalFlowPyrLK(Mat prevImg, Mat nextImg, vector_Point2f prevPts, vector_Point2f& nextPts, vector_uchar& status, vector_float& err, Size winSize = Size(21,21), int maxLevel = 3, TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 0.01), int flags = 0, double minEigThreshold = 1e-4)
-    private static native void calcOpticalFlowPyrLK_0(long prevImg_nativeObj, long nextImg_nativeObj, long prevPts_mat_nativeObj, long nextPts_mat_nativeObj, long status_mat_nativeObj, long err_mat_nativeObj, double winSize_width, double winSize_height, int maxLevel, int criteria_type, int criteria_maxCount, double criteria_epsilon, int flags, double minEigThreshold);
-    private static native void calcOpticalFlowPyrLK_1(long prevImg_nativeObj, long nextImg_nativeObj, long prevPts_mat_nativeObj, long nextPts_mat_nativeObj, long status_mat_nativeObj, long err_mat_nativeObj, double winSize_width, double winSize_height, int maxLevel);
-    private static native void calcOpticalFlowPyrLK_2(long prevImg_nativeObj, long nextImg_nativeObj, long prevPts_mat_nativeObj, long nextPts_mat_nativeObj, long status_mat_nativeObj, long err_mat_nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/videoio/VideoCapture.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/videoio/VideoCapture.java
deleted file mode 100755
index 5196436..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/videoio/VideoCapture.java
+++ /dev/null
@@ -1,297 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.videoio;
-
-import java.lang.String;
-import org.opencv.core.Mat;
-
-// C++: class VideoCapture
-//javadoc: VideoCapture
-
-public class VideoCapture {
-
-    protected final long nativeObj;
-    protected VideoCapture(long addr) { nativeObj = addr; }
-
-    public long getNativeObjAddr() { return nativeObj; }
-
-    // internal usage only
-    public static VideoCapture __fromPtr__(long addr) { return new VideoCapture(addr); }
-
-    //
-    // C++:   VideoCapture(String filename, int apiPreference)
-    //
-
-    //javadoc: VideoCapture::VideoCapture(filename, apiPreference)
-    public   VideoCapture(String filename, int apiPreference)
-    {
-        
-        nativeObj = VideoCapture_0(filename, apiPreference);
-        
-        return;
-    }
-
-
-    //
-    // C++:   VideoCapture(String filename)
-    //
-
-    //javadoc: VideoCapture::VideoCapture(filename)
-    public   VideoCapture(String filename)
-    {
-        
-        nativeObj = VideoCapture_1(filename);
-        
-        return;
-    }
-
-
-    //
-    // C++:   VideoCapture(int index)
-    //
-
-    //javadoc: VideoCapture::VideoCapture(index)
-    public   VideoCapture(int index)
-    {
-        
-        nativeObj = VideoCapture_2(index);
-        
-        return;
-    }
-
-
-    //
-    // C++:   VideoCapture()
-    //
-
-    //javadoc: VideoCapture::VideoCapture()
-    public   VideoCapture()
-    {
-        
-        nativeObj = VideoCapture_3();
-        
-        return;
-    }
-
-
-    //
-    // C++:  bool grab()
-    //
-
-    //javadoc: VideoCapture::grab()
-    public  boolean grab()
-    {
-        
-        boolean retVal = grab_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  bool isOpened()
-    //
-
-    //javadoc: VideoCapture::isOpened()
-    public  boolean isOpened()
-    {
-        
-        boolean retVal = isOpened_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  bool open(String filename, int apiPreference)
-    //
-
-    //javadoc: VideoCapture::open(filename, apiPreference)
-    public  boolean open(String filename, int apiPreference)
-    {
-        
-        boolean retVal = open_0(nativeObj, filename, apiPreference);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  bool open(String filename)
-    //
-
-    //javadoc: VideoCapture::open(filename)
-    public  boolean open(String filename)
-    {
-        
-        boolean retVal = open_1(nativeObj, filename);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  bool open(int cameraNum, int apiPreference)
-    //
-
-    //javadoc: VideoCapture::open(cameraNum, apiPreference)
-    public  boolean open(int cameraNum, int apiPreference)
-    {
-        
-        boolean retVal = open_2(nativeObj, cameraNum, apiPreference);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  bool open(int index)
-    //
-
-    //javadoc: VideoCapture::open(index)
-    public  boolean open(int index)
-    {
-        
-        boolean retVal = open_3(nativeObj, index);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  bool read(Mat& image)
-    //
-
-    //javadoc: VideoCapture::read(image)
-    public  boolean read(Mat image)
-    {
-        
-        boolean retVal = read_0(nativeObj, image.nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  bool retrieve(Mat& image, int flag = 0)
-    //
-
-    //javadoc: VideoCapture::retrieve(image, flag)
-    public  boolean retrieve(Mat image, int flag)
-    {
-        
-        boolean retVal = retrieve_0(nativeObj, image.nativeObj, flag);
-        
-        return retVal;
-    }
-
-    //javadoc: VideoCapture::retrieve(image)
-    public  boolean retrieve(Mat image)
-    {
-        
-        boolean retVal = retrieve_1(nativeObj, image.nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  bool set(int propId, double value)
-    //
-
-    //javadoc: VideoCapture::set(propId, value)
-    public  boolean set(int propId, double value)
-    {
-        
-        boolean retVal = set_0(nativeObj, propId, value);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double get(int propId)
-    //
-
-    //javadoc: VideoCapture::get(propId)
-    public  double get(int propId)
-    {
-        
-        double retVal = get_0(nativeObj, propId);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  void release()
-    //
-
-    //javadoc: VideoCapture::release()
-    public  void release()
-    {
-        
-        release_0(nativeObj);
-        
-        return;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++:   VideoCapture(String filename, int apiPreference)
-    private static native long VideoCapture_0(String filename, int apiPreference);
-
-    // C++:   VideoCapture(String filename)
-    private static native long VideoCapture_1(String filename);
-
-    // C++:   VideoCapture(int index)
-    private static native long VideoCapture_2(int index);
-
-    // C++:   VideoCapture()
-    private static native long VideoCapture_3();
-
-    // C++:  bool grab()
-    private static native boolean grab_0(long nativeObj);
-
-    // C++:  bool isOpened()
-    private static native boolean isOpened_0(long nativeObj);
-
-    // C++:  bool open(String filename, int apiPreference)
-    private static native boolean open_0(long nativeObj, String filename, int apiPreference);
-
-    // C++:  bool open(String filename)
-    private static native boolean open_1(long nativeObj, String filename);
-
-    // C++:  bool open(int cameraNum, int apiPreference)
-    private static native boolean open_2(long nativeObj, int cameraNum, int apiPreference);
-
-    // C++:  bool open(int index)
-    private static native boolean open_3(long nativeObj, int index);
-
-    // C++:  bool read(Mat& image)
-    private static native boolean read_0(long nativeObj, long image_nativeObj);
-
-    // C++:  bool retrieve(Mat& image, int flag = 0)
-    private static native boolean retrieve_0(long nativeObj, long image_nativeObj, int flag);
-    private static native boolean retrieve_1(long nativeObj, long image_nativeObj);
-
-    // C++:  bool set(int propId, double value)
-    private static native boolean set_0(long nativeObj, int propId, double value);
-
-    // C++:  double get(int propId)
-    private static native double get_0(long nativeObj, int propId);
-
-    // C++:  void release()
-    private static native void release_0(long nativeObj);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/videoio/VideoWriter.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/videoio/VideoWriter.java
deleted file mode 100755
index faa9401..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/videoio/VideoWriter.java
+++ /dev/null
@@ -1,260 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.videoio;
-
-import java.lang.String;
-import org.opencv.core.Mat;
-import org.opencv.core.Size;
-
-// C++: class VideoWriter
-//javadoc: VideoWriter
-
-public class VideoWriter {
-
-    protected final long nativeObj;
-    protected VideoWriter(long addr) { nativeObj = addr; }
-
-    public long getNativeObjAddr() { return nativeObj; }
-
-    // internal usage only
-    public static VideoWriter __fromPtr__(long addr) { return new VideoWriter(addr); }
-
-    //
-    // C++:   VideoWriter(String filename, int apiPreference, int fourcc, double fps, Size frameSize, bool isColor = true)
-    //
-
-    //javadoc: VideoWriter::VideoWriter(filename, apiPreference, fourcc, fps, frameSize, isColor)
-    public   VideoWriter(String filename, int apiPreference, int fourcc, double fps, Size frameSize, boolean isColor)
-    {
-        
-        nativeObj = VideoWriter_0(filename, apiPreference, fourcc, fps, frameSize.width, frameSize.height, isColor);
-        
-        return;
-    }
-
-    //javadoc: VideoWriter::VideoWriter(filename, apiPreference, fourcc, fps, frameSize)
-    public   VideoWriter(String filename, int apiPreference, int fourcc, double fps, Size frameSize)
-    {
-        
-        nativeObj = VideoWriter_1(filename, apiPreference, fourcc, fps, frameSize.width, frameSize.height);
-        
-        return;
-    }
-
-
-    //
-    // C++:   VideoWriter(String filename, int fourcc, double fps, Size frameSize, bool isColor = true)
-    //
-
-    //javadoc: VideoWriter::VideoWriter(filename, fourcc, fps, frameSize, isColor)
-    public   VideoWriter(String filename, int fourcc, double fps, Size frameSize, boolean isColor)
-    {
-        
-        nativeObj = VideoWriter_2(filename, fourcc, fps, frameSize.width, frameSize.height, isColor);
-        
-        return;
-    }
-
-    //javadoc: VideoWriter::VideoWriter(filename, fourcc, fps, frameSize)
-    public   VideoWriter(String filename, int fourcc, double fps, Size frameSize)
-    {
-        
-        nativeObj = VideoWriter_3(filename, fourcc, fps, frameSize.width, frameSize.height);
-        
-        return;
-    }
-
-
-    //
-    // C++:   VideoWriter()
-    //
-
-    //javadoc: VideoWriter::VideoWriter()
-    public   VideoWriter()
-    {
-        
-        nativeObj = VideoWriter_4();
-        
-        return;
-    }
-
-
-    //
-    // C++:  bool isOpened()
-    //
-
-    //javadoc: VideoWriter::isOpened()
-    public  boolean isOpened()
-    {
-        
-        boolean retVal = isOpened_0(nativeObj);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  bool open(String filename, int apiPreference, int fourcc, double fps, Size frameSize, bool isColor = true)
-    //
-
-    //javadoc: VideoWriter::open(filename, apiPreference, fourcc, fps, frameSize, isColor)
-    public  boolean open(String filename, int apiPreference, int fourcc, double fps, Size frameSize, boolean isColor)
-    {
-        
-        boolean retVal = open_0(nativeObj, filename, apiPreference, fourcc, fps, frameSize.width, frameSize.height, isColor);
-        
-        return retVal;
-    }
-
-    //javadoc: VideoWriter::open(filename, apiPreference, fourcc, fps, frameSize)
-    public  boolean open(String filename, int apiPreference, int fourcc, double fps, Size frameSize)
-    {
-        
-        boolean retVal = open_1(nativeObj, filename, apiPreference, fourcc, fps, frameSize.width, frameSize.height);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  bool open(String filename, int fourcc, double fps, Size frameSize, bool isColor = true)
-    //
-
-    //javadoc: VideoWriter::open(filename, fourcc, fps, frameSize, isColor)
-    public  boolean open(String filename, int fourcc, double fps, Size frameSize, boolean isColor)
-    {
-        
-        boolean retVal = open_2(nativeObj, filename, fourcc, fps, frameSize.width, frameSize.height, isColor);
-        
-        return retVal;
-    }
-
-    //javadoc: VideoWriter::open(filename, fourcc, fps, frameSize)
-    public  boolean open(String filename, int fourcc, double fps, Size frameSize)
-    {
-        
-        boolean retVal = open_3(nativeObj, filename, fourcc, fps, frameSize.width, frameSize.height);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  bool set(int propId, double value)
-    //
-
-    //javadoc: VideoWriter::set(propId, value)
-    public  boolean set(int propId, double value)
-    {
-        
-        boolean retVal = set_0(nativeObj, propId, value);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  double get(int propId)
-    //
-
-    //javadoc: VideoWriter::get(propId)
-    public  double get(int propId)
-    {
-        
-        double retVal = get_0(nativeObj, propId);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++: static int fourcc(char c1, char c2, char c3, char c4)
-    //
-
-    //javadoc: VideoWriter::fourcc(c1, c2, c3, c4)
-    public static int fourcc(char c1, char c2, char c3, char c4)
-    {
-        
-        int retVal = fourcc_0(c1, c2, c3, c4);
-        
-        return retVal;
-    }
-
-
-    //
-    // C++:  void release()
-    //
-
-    //javadoc: VideoWriter::release()
-    public  void release()
-    {
-        
-        release_0(nativeObj);
-        
-        return;
-    }
-
-
-    //
-    // C++:  void write(Mat image)
-    //
-
-    //javadoc: VideoWriter::write(image)
-    public  void write(Mat image)
-    {
-        
-        write_0(nativeObj, image.nativeObj);
-        
-        return;
-    }
-
-
-    @Override
-    protected void finalize() throws Throwable {
-        delete(nativeObj);
-    }
-
-
-
-    // C++:   VideoWriter(String filename, int apiPreference, int fourcc, double fps, Size frameSize, bool isColor = true)
-    private static native long VideoWriter_0(String filename, int apiPreference, int fourcc, double fps, double frameSize_width, double frameSize_height, boolean isColor);
-    private static native long VideoWriter_1(String filename, int apiPreference, int fourcc, double fps, double frameSize_width, double frameSize_height);
-
-    // C++:   VideoWriter(String filename, int fourcc, double fps, Size frameSize, bool isColor = true)
-    private static native long VideoWriter_2(String filename, int fourcc, double fps, double frameSize_width, double frameSize_height, boolean isColor);
-    private static native long VideoWriter_3(String filename, int fourcc, double fps, double frameSize_width, double frameSize_height);
-
-    // C++:   VideoWriter()
-    private static native long VideoWriter_4();
-
-    // C++:  bool isOpened()
-    private static native boolean isOpened_0(long nativeObj);
-
-    // C++:  bool open(String filename, int apiPreference, int fourcc, double fps, Size frameSize, bool isColor = true)
-    private static native boolean open_0(long nativeObj, String filename, int apiPreference, int fourcc, double fps, double frameSize_width, double frameSize_height, boolean isColor);
-    private static native boolean open_1(long nativeObj, String filename, int apiPreference, int fourcc, double fps, double frameSize_width, double frameSize_height);
-
-    // C++:  bool open(String filename, int fourcc, double fps, Size frameSize, bool isColor = true)
-    private static native boolean open_2(long nativeObj, String filename, int fourcc, double fps, double frameSize_width, double frameSize_height, boolean isColor);
-    private static native boolean open_3(long nativeObj, String filename, int fourcc, double fps, double frameSize_width, double frameSize_height);
-
-    // C++:  bool set(int propId, double value)
-    private static native boolean set_0(long nativeObj, int propId, double value);
-
-    // C++:  double get(int propId)
-    private static native double get_0(long nativeObj, int propId);
-
-    // C++: static int fourcc(char c1, char c2, char c3, char c4)
-    private static native int fourcc_0(char c1, char c2, char c3, char c4);
-
-    // C++:  void release()
-    private static native void release_0(long nativeObj);
-
-    // C++:  void write(Mat image)
-    private static native void write_0(long nativeObj, long image_nativeObj);
-
-    // native support for java finalize()
-    private static native void delete(long nativeObj);
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/videoio/Videoio.java b/Prj-Android/openCVLibrary342/src/main/java/org/opencv/videoio/Videoio.java
deleted file mode 100755
index 6d01995..0000000
--- a/Prj-Android/openCVLibrary342/src/main/java/org/opencv/videoio/Videoio.java
+++ /dev/null
@@ -1,683 +0,0 @@
-//
-// This file is auto-generated. Please don't modify it!
-//
-package org.opencv.videoio;
-
-
-
-// C++: class Videoio
-//javadoc: Videoio
-
-public class Videoio {
-
-    public static final int
-            CV_CAP_ANY = 0,
-            CV_CAP_MIL = 100,
-            CV_CAP_VFW = 200,
-            CV_CAP_V4L = 200,
-            CV_CAP_V4L2 = 200,
-            CV_CAP_FIREWARE = 300,
-            CV_CAP_FIREWIRE = 300,
-            CV_CAP_IEEE1394 = 300,
-            CV_CAP_DC1394 = 300,
-            CV_CAP_CMU1394 = 300,
-            CV_CAP_STEREO = 400,
-            CV_CAP_TYZX = 400,
-            CV_TYZX_LEFT = 400,
-            CV_TYZX_RIGHT = 401,
-            CV_TYZX_COLOR = 402,
-            CV_TYZX_Z = 403,
-            CV_CAP_QT = 500,
-            CV_CAP_UNICAP = 600,
-            CV_CAP_DSHOW = 700,
-            CV_CAP_MSMF = 1400,
-            CV_CAP_PVAPI = 800,
-            CV_CAP_OPENNI = 900,
-            CV_CAP_OPENNI_ASUS = 910,
-            CV_CAP_ANDROID = 1000,
-            CV_CAP_ANDROID_BACK = CV_CAP_ANDROID+99,
-            CV_CAP_ANDROID_FRONT = CV_CAP_ANDROID+98,
-            CV_CAP_XIAPI = 1100,
-            CV_CAP_AVFOUNDATION = 1200,
-            CV_CAP_GIGANETIX = 1300,
-            CV_CAP_INTELPERC = 1500,
-            CV_CAP_OPENNI2 = 1600,
-            CV_CAP_GPHOTO2 = 1700,
-            CV_CAP_GSTREAMER = 1800,
-            CV_CAP_FFMPEG = 1900,
-            CV_CAP_IMAGES = 2000,
-            CV_CAP_ARAVIS = 2100,
-            CV_CAP_PROP_DC1394_OFF = -4,
-            CV_CAP_PROP_DC1394_MODE_MANUAL = -3,
-            CV_CAP_PROP_DC1394_MODE_AUTO = -2,
-            CV_CAP_PROP_DC1394_MODE_ONE_PUSH_AUTO = -1,
-            CV_CAP_PROP_POS_MSEC = 0,
-            CV_CAP_PROP_POS_FRAMES = 1,
-            CV_CAP_PROP_POS_AVI_RATIO = 2,
-            CV_CAP_PROP_FRAME_WIDTH = 3,
-            CV_CAP_PROP_FRAME_HEIGHT = 4,
-            CV_CAP_PROP_FPS = 5,
-            CV_CAP_PROP_FOURCC = 6,
-            CV_CAP_PROP_FRAME_COUNT = 7,
-            CV_CAP_PROP_FORMAT = 8,
-            CV_CAP_PROP_MODE = 9,
-            CV_CAP_PROP_BRIGHTNESS = 10,
-            CV_CAP_PROP_CONTRAST = 11,
-            CV_CAP_PROP_SATURATION = 12,
-            CV_CAP_PROP_HUE = 13,
-            CV_CAP_PROP_GAIN = 14,
-            CV_CAP_PROP_EXPOSURE = 15,
-            CV_CAP_PROP_CONVERT_RGB = 16,
-            CV_CAP_PROP_WHITE_BALANCE_BLUE_U = 17,
-            CV_CAP_PROP_RECTIFICATION = 18,
-            CV_CAP_PROP_MONOCHROME = 19,
-            CV_CAP_PROP_SHARPNESS = 20,
-            CV_CAP_PROP_AUTO_EXPOSURE = 21,
-            CV_CAP_PROP_GAMMA = 22,
-            CV_CAP_PROP_TEMPERATURE = 23,
-            CV_CAP_PROP_TRIGGER = 24,
-            CV_CAP_PROP_TRIGGER_DELAY = 25,
-            CV_CAP_PROP_WHITE_BALANCE_RED_V = 26,
-            CV_CAP_PROP_ZOOM = 27,
-            CV_CAP_PROP_FOCUS = 28,
-            CV_CAP_PROP_GUID = 29,
-            CV_CAP_PROP_ISO_SPEED = 30,
-            CV_CAP_PROP_MAX_DC1394 = 31,
-            CV_CAP_PROP_BACKLIGHT = 32,
-            CV_CAP_PROP_PAN = 33,
-            CV_CAP_PROP_TILT = 34,
-            CV_CAP_PROP_ROLL = 35,
-            CV_CAP_PROP_IRIS = 36,
-            CV_CAP_PROP_SETTINGS = 37,
-            CV_CAP_PROP_BUFFERSIZE = 38,
-            CV_CAP_PROP_AUTOFOCUS = 39,
-            CV_CAP_PROP_SAR_NUM = 40,
-            CV_CAP_PROP_SAR_DEN = 41,
-            CV_CAP_PROP_AUTOGRAB = 1024,
-            CV_CAP_PROP_SUPPORTED_PREVIEW_SIZES_STRING = 1025,
-            CV_CAP_PROP_PREVIEW_FORMAT = 1026,
-            CV_CAP_OPENNI_DEPTH_GENERATOR = 1 << 31,
-            CV_CAP_OPENNI_IMAGE_GENERATOR = 1 << 30,
-            CV_CAP_OPENNI_IR_GENERATOR = 1 << 29,
-            CV_CAP_OPENNI_GENERATORS_MASK = CV_CAP_OPENNI_DEPTH_GENERATOR + CV_CAP_OPENNI_IMAGE_GENERATOR + CV_CAP_OPENNI_IR_GENERATOR,
-            CV_CAP_PROP_OPENNI_OUTPUT_MODE = 100,
-            CV_CAP_PROP_OPENNI_FRAME_MAX_DEPTH = 101,
-            CV_CAP_PROP_OPENNI_BASELINE = 102,
-            CV_CAP_PROP_OPENNI_FOCAL_LENGTH = 103,
-            CV_CAP_PROP_OPENNI_REGISTRATION = 104,
-            CV_CAP_PROP_OPENNI_REGISTRATION_ON = CV_CAP_PROP_OPENNI_REGISTRATION,
-            CV_CAP_PROP_OPENNI_APPROX_FRAME_SYNC = 105,
-            CV_CAP_PROP_OPENNI_MAX_BUFFER_SIZE = 106,
-            CV_CAP_PROP_OPENNI_CIRCLE_BUFFER = 107,
-            CV_CAP_PROP_OPENNI_MAX_TIME_DURATION = 108,
-            CV_CAP_PROP_OPENNI_GENERATOR_PRESENT = 109,
-            CV_CAP_PROP_OPENNI2_SYNC = 110,
-            CV_CAP_PROP_OPENNI2_MIRROR = 111,
-            CV_CAP_OPENNI_IMAGE_GENERATOR_PRESENT = CV_CAP_OPENNI_IMAGE_GENERATOR + CV_CAP_PROP_OPENNI_GENERATOR_PRESENT,
-            CV_CAP_OPENNI_IMAGE_GENERATOR_OUTPUT_MODE = CV_CAP_OPENNI_IMAGE_GENERATOR + CV_CAP_PROP_OPENNI_OUTPUT_MODE,
-            CV_CAP_OPENNI_DEPTH_GENERATOR_PRESENT = CV_CAP_OPENNI_DEPTH_GENERATOR + CV_CAP_PROP_OPENNI_GENERATOR_PRESENT,
-            CV_CAP_OPENNI_DEPTH_GENERATOR_BASELINE = CV_CAP_OPENNI_DEPTH_GENERATOR + CV_CAP_PROP_OPENNI_BASELINE,
-            CV_CAP_OPENNI_DEPTH_GENERATOR_FOCAL_LENGTH = CV_CAP_OPENNI_DEPTH_GENERATOR + CV_CAP_PROP_OPENNI_FOCAL_LENGTH,
-            CV_CAP_OPENNI_DEPTH_GENERATOR_REGISTRATION = CV_CAP_OPENNI_DEPTH_GENERATOR + CV_CAP_PROP_OPENNI_REGISTRATION,
-            CV_CAP_OPENNI_DEPTH_GENERATOR_REGISTRATION_ON = CV_CAP_OPENNI_DEPTH_GENERATOR_REGISTRATION,
-            CV_CAP_OPENNI_IR_GENERATOR_PRESENT = CV_CAP_OPENNI_IR_GENERATOR + CV_CAP_PROP_OPENNI_GENERATOR_PRESENT,
-            CV_CAP_GSTREAMER_QUEUE_LENGTH = 200,
-            CV_CAP_PROP_PVAPI_MULTICASTIP = 300,
-            CV_CAP_PROP_PVAPI_FRAMESTARTTRIGGERMODE = 301,
-            CV_CAP_PROP_PVAPI_DECIMATIONHORIZONTAL = 302,
-            CV_CAP_PROP_PVAPI_DECIMATIONVERTICAL = 303,
-            CV_CAP_PROP_PVAPI_BINNINGX = 304,
-            CV_CAP_PROP_PVAPI_BINNINGY = 305,
-            CV_CAP_PROP_PVAPI_PIXELFORMAT = 306,
-            CV_CAP_PROP_XI_DOWNSAMPLING = 400,
-            CV_CAP_PROP_XI_DATA_FORMAT = 401,
-            CV_CAP_PROP_XI_OFFSET_X = 402,
-            CV_CAP_PROP_XI_OFFSET_Y = 403,
-            CV_CAP_PROP_XI_TRG_SOURCE = 404,
-            CV_CAP_PROP_XI_TRG_SOFTWARE = 405,
-            CV_CAP_PROP_XI_GPI_SELECTOR = 406,
-            CV_CAP_PROP_XI_GPI_MODE = 407,
-            CV_CAP_PROP_XI_GPI_LEVEL = 408,
-            CV_CAP_PROP_XI_GPO_SELECTOR = 409,
-            CV_CAP_PROP_XI_GPO_MODE = 410,
-            CV_CAP_PROP_XI_LED_SELECTOR = 411,
-            CV_CAP_PROP_XI_LED_MODE = 412,
-            CV_CAP_PROP_XI_MANUAL_WB = 413,
-            CV_CAP_PROP_XI_AUTO_WB = 414,
-            CV_CAP_PROP_XI_AEAG = 415,
-            CV_CAP_PROP_XI_EXP_PRIORITY = 416,
-            CV_CAP_PROP_XI_AE_MAX_LIMIT = 417,
-            CV_CAP_PROP_XI_AG_MAX_LIMIT = 418,
-            CV_CAP_PROP_XI_AEAG_LEVEL = 419,
-            CV_CAP_PROP_XI_TIMEOUT = 420,
-            CV_CAP_PROP_XI_EXPOSURE = 421,
-            CV_CAP_PROP_XI_EXPOSURE_BURST_COUNT = 422,
-            CV_CAP_PROP_XI_GAIN_SELECTOR = 423,
-            CV_CAP_PROP_XI_GAIN = 424,
-            CV_CAP_PROP_XI_DOWNSAMPLING_TYPE = 426,
-            CV_CAP_PROP_XI_BINNING_SELECTOR = 427,
-            CV_CAP_PROP_XI_BINNING_VERTICAL = 428,
-            CV_CAP_PROP_XI_BINNING_HORIZONTAL = 429,
-            CV_CAP_PROP_XI_BINNING_PATTERN = 430,
-            CV_CAP_PROP_XI_DECIMATION_SELECTOR = 431,
-            CV_CAP_PROP_XI_DECIMATION_VERTICAL = 432,
-            CV_CAP_PROP_XI_DECIMATION_HORIZONTAL = 433,
-            CV_CAP_PROP_XI_DECIMATION_PATTERN = 434,
-            CV_CAP_PROP_XI_TEST_PATTERN_GENERATOR_SELECTOR = 587,
-            CV_CAP_PROP_XI_TEST_PATTERN = 588,
-            CV_CAP_PROP_XI_IMAGE_DATA_FORMAT = 435,
-            CV_CAP_PROP_XI_SHUTTER_TYPE = 436,
-            CV_CAP_PROP_XI_SENSOR_TAPS = 437,
-            CV_CAP_PROP_XI_AEAG_ROI_OFFSET_X = 439,
-            CV_CAP_PROP_XI_AEAG_ROI_OFFSET_Y = 440,
-            CV_CAP_PROP_XI_AEAG_ROI_WIDTH = 441,
-            CV_CAP_PROP_XI_AEAG_ROI_HEIGHT = 442,
-            CV_CAP_PROP_XI_BPC = 445,
-            CV_CAP_PROP_XI_WB_KR = 448,
-            CV_CAP_PROP_XI_WB_KG = 449,
-            CV_CAP_PROP_XI_WB_KB = 450,
-            CV_CAP_PROP_XI_WIDTH = 451,
-            CV_CAP_PROP_XI_HEIGHT = 452,
-            CV_CAP_PROP_XI_REGION_SELECTOR = 589,
-            CV_CAP_PROP_XI_REGION_MODE = 595,
-            CV_CAP_PROP_XI_LIMIT_BANDWIDTH = 459,
-            CV_CAP_PROP_XI_SENSOR_DATA_BIT_DEPTH = 460,
-            CV_CAP_PROP_XI_OUTPUT_DATA_BIT_DEPTH = 461,
-            CV_CAP_PROP_XI_IMAGE_DATA_BIT_DEPTH = 462,
-            CV_CAP_PROP_XI_OUTPUT_DATA_PACKING = 463,
-            CV_CAP_PROP_XI_OUTPUT_DATA_PACKING_TYPE = 464,
-            CV_CAP_PROP_XI_IS_COOLED = 465,
-            CV_CAP_PROP_XI_COOLING = 466,
-            CV_CAP_PROP_XI_TARGET_TEMP = 467,
-            CV_CAP_PROP_XI_CHIP_TEMP = 468,
-            CV_CAP_PROP_XI_HOUS_TEMP = 469,
-            CV_CAP_PROP_XI_HOUS_BACK_SIDE_TEMP = 590,
-            CV_CAP_PROP_XI_SENSOR_BOARD_TEMP = 596,
-            CV_CAP_PROP_XI_CMS = 470,
-            CV_CAP_PROP_XI_APPLY_CMS = 471,
-            CV_CAP_PROP_XI_IMAGE_IS_COLOR = 474,
-            CV_CAP_PROP_XI_COLOR_FILTER_ARRAY = 475,
-            CV_CAP_PROP_XI_GAMMAY = 476,
-            CV_CAP_PROP_XI_GAMMAC = 477,
-            CV_CAP_PROP_XI_SHARPNESS = 478,
-            CV_CAP_PROP_XI_CC_MATRIX_00 = 479,
-            CV_CAP_PROP_XI_CC_MATRIX_01 = 480,
-            CV_CAP_PROP_XI_CC_MATRIX_02 = 481,
-            CV_CAP_PROP_XI_CC_MATRIX_03 = 482,
-            CV_CAP_PROP_XI_CC_MATRIX_10 = 483,
-            CV_CAP_PROP_XI_CC_MATRIX_11 = 484,
-            CV_CAP_PROP_XI_CC_MATRIX_12 = 485,
-            CV_CAP_PROP_XI_CC_MATRIX_13 = 486,
-            CV_CAP_PROP_XI_CC_MATRIX_20 = 487,
-            CV_CAP_PROP_XI_CC_MATRIX_21 = 488,
-            CV_CAP_PROP_XI_CC_MATRIX_22 = 489,
-            CV_CAP_PROP_XI_CC_MATRIX_23 = 490,
-            CV_CAP_PROP_XI_CC_MATRIX_30 = 491,
-            CV_CAP_PROP_XI_CC_MATRIX_31 = 492,
-            CV_CAP_PROP_XI_CC_MATRIX_32 = 493,
-            CV_CAP_PROP_XI_CC_MATRIX_33 = 494,
-            CV_CAP_PROP_XI_DEFAULT_CC_MATRIX = 495,
-            CV_CAP_PROP_XI_TRG_SELECTOR = 498,
-            CV_CAP_PROP_XI_ACQ_FRAME_BURST_COUNT = 499,
-            CV_CAP_PROP_XI_DEBOUNCE_EN = 507,
-            CV_CAP_PROP_XI_DEBOUNCE_T0 = 508,
-            CV_CAP_PROP_XI_DEBOUNCE_T1 = 509,
-            CV_CAP_PROP_XI_DEBOUNCE_POL = 510,
-            CV_CAP_PROP_XI_LENS_MODE = 511,
-            CV_CAP_PROP_XI_LENS_APERTURE_VALUE = 512,
-            CV_CAP_PROP_XI_LENS_FOCUS_MOVEMENT_VALUE = 513,
-            CV_CAP_PROP_XI_LENS_FOCUS_MOVE = 514,
-            CV_CAP_PROP_XI_LENS_FOCUS_DISTANCE = 515,
-            CV_CAP_PROP_XI_LENS_FOCAL_LENGTH = 516,
-            CV_CAP_PROP_XI_LENS_FEATURE_SELECTOR = 517,
-            CV_CAP_PROP_XI_LENS_FEATURE = 518,
-            CV_CAP_PROP_XI_DEVICE_MODEL_ID = 521,
-            CV_CAP_PROP_XI_DEVICE_SN = 522,
-            CV_CAP_PROP_XI_IMAGE_DATA_FORMAT_RGB32_ALPHA = 529,
-            CV_CAP_PROP_XI_IMAGE_PAYLOAD_SIZE = 530,
-            CV_CAP_PROP_XI_TRANSPORT_PIXEL_FORMAT = 531,
-            CV_CAP_PROP_XI_SENSOR_CLOCK_FREQ_HZ = 532,
-            CV_CAP_PROP_XI_SENSOR_CLOCK_FREQ_INDEX = 533,
-            CV_CAP_PROP_XI_SENSOR_OUTPUT_CHANNEL_COUNT = 534,
-            CV_CAP_PROP_XI_FRAMERATE = 535,
-            CV_CAP_PROP_XI_COUNTER_SELECTOR = 536,
-            CV_CAP_PROP_XI_COUNTER_VALUE = 537,
-            CV_CAP_PROP_XI_ACQ_TIMING_MODE = 538,
-            CV_CAP_PROP_XI_AVAILABLE_BANDWIDTH = 539,
-            CV_CAP_PROP_XI_BUFFER_POLICY = 540,
-            CV_CAP_PROP_XI_LUT_EN = 541,
-            CV_CAP_PROP_XI_LUT_INDEX = 542,
-            CV_CAP_PROP_XI_LUT_VALUE = 543,
-            CV_CAP_PROP_XI_TRG_DELAY = 544,
-            CV_CAP_PROP_XI_TS_RST_MODE = 545,
-            CV_CAP_PROP_XI_TS_RST_SOURCE = 546,
-            CV_CAP_PROP_XI_IS_DEVICE_EXIST = 547,
-            CV_CAP_PROP_XI_ACQ_BUFFER_SIZE = 548,
-            CV_CAP_PROP_XI_ACQ_BUFFER_SIZE_UNIT = 549,
-            CV_CAP_PROP_XI_ACQ_TRANSPORT_BUFFER_SIZE = 550,
-            CV_CAP_PROP_XI_BUFFERS_QUEUE_SIZE = 551,
-            CV_CAP_PROP_XI_ACQ_TRANSPORT_BUFFER_COMMIT = 552,
-            CV_CAP_PROP_XI_RECENT_FRAME = 553,
-            CV_CAP_PROP_XI_DEVICE_RESET = 554,
-            CV_CAP_PROP_XI_COLUMN_FPN_CORRECTION = 555,
-            CV_CAP_PROP_XI_ROW_FPN_CORRECTION = 591,
-            CV_CAP_PROP_XI_SENSOR_MODE = 558,
-            CV_CAP_PROP_XI_HDR = 559,
-            CV_CAP_PROP_XI_HDR_KNEEPOINT_COUNT = 560,
-            CV_CAP_PROP_XI_HDR_T1 = 561,
-            CV_CAP_PROP_XI_HDR_T2 = 562,
-            CV_CAP_PROP_XI_KNEEPOINT1 = 563,
-            CV_CAP_PROP_XI_KNEEPOINT2 = 564,
-            CV_CAP_PROP_XI_IMAGE_BLACK_LEVEL = 565,
-            CV_CAP_PROP_XI_HW_REVISION = 571,
-            CV_CAP_PROP_XI_DEBUG_LEVEL = 572,
-            CV_CAP_PROP_XI_AUTO_BANDWIDTH_CALCULATION = 573,
-            CV_CAP_PROP_XI_FFS_FILE_ID = 594,
-            CV_CAP_PROP_XI_FFS_FILE_SIZE = 580,
-            CV_CAP_PROP_XI_FREE_FFS_SIZE = 581,
-            CV_CAP_PROP_XI_USED_FFS_SIZE = 582,
-            CV_CAP_PROP_XI_FFS_ACCESS_KEY = 583,
-            CV_CAP_PROP_XI_SENSOR_FEATURE_SELECTOR = 585,
-            CV_CAP_PROP_XI_SENSOR_FEATURE_VALUE = 586,
-            CV_CAP_PROP_ANDROID_FLASH_MODE = 8001,
-            CV_CAP_PROP_ANDROID_FOCUS_MODE = 8002,
-            CV_CAP_PROP_ANDROID_WHITE_BALANCE = 8003,
-            CV_CAP_PROP_ANDROID_ANTIBANDING = 8004,
-            CV_CAP_PROP_ANDROID_FOCAL_LENGTH = 8005,
-            CV_CAP_PROP_ANDROID_FOCUS_DISTANCE_NEAR = 8006,
-            CV_CAP_PROP_ANDROID_FOCUS_DISTANCE_OPTIMAL = 8007,
-            CV_CAP_PROP_ANDROID_FOCUS_DISTANCE_FAR = 8008,
-            CV_CAP_PROP_ANDROID_EXPOSE_LOCK = 8009,
-            CV_CAP_PROP_ANDROID_WHITEBALANCE_LOCK = 8010,
-            CV_CAP_PROP_IOS_DEVICE_FOCUS = 9001,
-            CV_CAP_PROP_IOS_DEVICE_EXPOSURE = 9002,
-            CV_CAP_PROP_IOS_DEVICE_FLASH = 9003,
-            CV_CAP_PROP_IOS_DEVICE_WHITEBALANCE = 9004,
-            CV_CAP_PROP_IOS_DEVICE_TORCH = 9005,
-            CV_CAP_PROP_GIGA_FRAME_OFFSET_X = 10001,
-            CV_CAP_PROP_GIGA_FRAME_OFFSET_Y = 10002,
-            CV_CAP_PROP_GIGA_FRAME_WIDTH_MAX = 10003,
-            CV_CAP_PROP_GIGA_FRAME_HEIGH_MAX = 10004,
-            CV_CAP_PROP_GIGA_FRAME_SENS_WIDTH = 10005,
-            CV_CAP_PROP_GIGA_FRAME_SENS_HEIGH = 10006,
-            CV_CAP_PROP_INTELPERC_PROFILE_COUNT = 11001,
-            CV_CAP_PROP_INTELPERC_PROFILE_IDX = 11002,
-            CV_CAP_PROP_INTELPERC_DEPTH_LOW_CONFIDENCE_VALUE = 11003,
-            CV_CAP_PROP_INTELPERC_DEPTH_SATURATION_VALUE = 11004,
-            CV_CAP_PROP_INTELPERC_DEPTH_CONFIDENCE_THRESHOLD = 11005,
-            CV_CAP_PROP_INTELPERC_DEPTH_FOCAL_LENGTH_HORZ = 11006,
-            CV_CAP_PROP_INTELPERC_DEPTH_FOCAL_LENGTH_VERT = 11007,
-            CV_CAP_INTELPERC_DEPTH_GENERATOR = 1 << 29,
-            CV_CAP_INTELPERC_IMAGE_GENERATOR = 1 << 28,
-            CV_CAP_INTELPERC_GENERATORS_MASK = CV_CAP_INTELPERC_DEPTH_GENERATOR + CV_CAP_INTELPERC_IMAGE_GENERATOR,
-            CV_CAP_MODE_BGR = 0,
-            CV_CAP_MODE_RGB = 1,
-            CV_CAP_MODE_GRAY = 2,
-            CV_CAP_MODE_YUYV = 3,
-            CV_CAP_OPENNI_DEPTH_MAP = 0,
-            CV_CAP_OPENNI_POINT_CLOUD_MAP = 1,
-            CV_CAP_OPENNI_DISPARITY_MAP = 2,
-            CV_CAP_OPENNI_DISPARITY_MAP_32F = 3,
-            CV_CAP_OPENNI_VALID_DEPTH_MASK = 4,
-            CV_CAP_OPENNI_BGR_IMAGE = 5,
-            CV_CAP_OPENNI_GRAY_IMAGE = 6,
-            CV_CAP_OPENNI_IR_IMAGE = 7,
-            CV_CAP_OPENNI_VGA_30HZ = 0,
-            CV_CAP_OPENNI_SXGA_15HZ = 1,
-            CV_CAP_OPENNI_SXGA_30HZ = 2,
-            CV_CAP_OPENNI_QVGA_30HZ = 3,
-            CV_CAP_OPENNI_QVGA_60HZ = 4,
-            CV_CAP_INTELPERC_DEPTH_MAP = 0,
-            CV_CAP_INTELPERC_UVDEPTH_MAP = 1,
-            CV_CAP_INTELPERC_IR_MAP = 2,
-            CV_CAP_INTELPERC_IMAGE = 3,
-            CV_CAP_PROP_GPHOTO2_PREVIEW = 17001,
-            CV_CAP_PROP_GPHOTO2_WIDGET_ENUMERATE = 17002,
-            CV_CAP_PROP_GPHOTO2_RELOAD_CONFIG = 17003,
-            CV_CAP_PROP_GPHOTO2_RELOAD_ON_CHANGE = 17004,
-            CV_CAP_PROP_GPHOTO2_COLLECT_MSGS = 17005,
-            CV_CAP_PROP_GPHOTO2_FLUSH_MSGS = 17006,
-            CV_CAP_PROP_SPEED = 17007,
-            CV_CAP_PROP_APERTURE = 17008,
-            CV_CAP_PROP_EXPOSUREPROGRAM = 17009,
-            CV_CAP_PROP_VIEWFINDER = 17010,
-            CAP_ANY = 0,
-            CAP_VFW = 200,
-            CAP_V4L = 200,
-            CAP_V4L2 = CAP_V4L,
-            CAP_FIREWIRE = 300,
-            CAP_FIREWARE = CAP_FIREWIRE,
-            CAP_IEEE1394 = CAP_FIREWIRE,
-            CAP_DC1394 = CAP_FIREWIRE,
-            CAP_CMU1394 = CAP_FIREWIRE,
-            CAP_QT = 500,
-            CAP_UNICAP = 600,
-            CAP_DSHOW = 700,
-            CAP_PVAPI = 800,
-            CAP_OPENNI = 900,
-            CAP_OPENNI_ASUS = 910,
-            CAP_ANDROID = 1000,
-            CAP_XIAPI = 1100,
-            CAP_AVFOUNDATION = 1200,
-            CAP_GIGANETIX = 1300,
-            CAP_MSMF = 1400,
-            CAP_WINRT = 1410,
-            CAP_INTELPERC = 1500,
-            CAP_OPENNI2 = 1600,
-            CAP_OPENNI2_ASUS = 1610,
-            CAP_GPHOTO2 = 1700,
-            CAP_GSTREAMER = 1800,
-            CAP_FFMPEG = 1900,
-            CAP_IMAGES = 2000,
-            CAP_ARAVIS = 2100,
-            CAP_OPENCV_MJPEG = 2200,
-            CAP_INTEL_MFX = 2300,
-            CAP_XINE = 2400,
-            CAP_PROP_POS_MSEC = 0,
-            CAP_PROP_POS_FRAMES = 1,
-            CAP_PROP_POS_AVI_RATIO = 2,
-            CAP_PROP_FRAME_WIDTH = 3,
-            CAP_PROP_FRAME_HEIGHT = 4,
-            CAP_PROP_FPS = 5,
-            CAP_PROP_FOURCC = 6,
-            CAP_PROP_FRAME_COUNT = 7,
-            CAP_PROP_FORMAT = 8,
-            CAP_PROP_MODE = 9,
-            CAP_PROP_BRIGHTNESS = 10,
-            CAP_PROP_CONTRAST = 11,
-            CAP_PROP_SATURATION = 12,
-            CAP_PROP_HUE = 13,
-            CAP_PROP_GAIN = 14,
-            CAP_PROP_EXPOSURE = 15,
-            CAP_PROP_CONVERT_RGB = 16,
-            CAP_PROP_WHITE_BALANCE_BLUE_U = 17,
-            CAP_PROP_RECTIFICATION = 18,
-            CAP_PROP_MONOCHROME = 19,
-            CAP_PROP_SHARPNESS = 20,
-            CAP_PROP_AUTO_EXPOSURE = 21,
-            CAP_PROP_GAMMA = 22,
-            CAP_PROP_TEMPERATURE = 23,
-            CAP_PROP_TRIGGER = 24,
-            CAP_PROP_TRIGGER_DELAY = 25,
-            CAP_PROP_WHITE_BALANCE_RED_V = 26,
-            CAP_PROP_ZOOM = 27,
-            CAP_PROP_FOCUS = 28,
-            CAP_PROP_GUID = 29,
-            CAP_PROP_ISO_SPEED = 30,
-            CAP_PROP_BACKLIGHT = 32,
-            CAP_PROP_PAN = 33,
-            CAP_PROP_TILT = 34,
-            CAP_PROP_ROLL = 35,
-            CAP_PROP_IRIS = 36,
-            CAP_PROP_SETTINGS = 37,
-            CAP_PROP_BUFFERSIZE = 38,
-            CAP_PROP_AUTOFOCUS = 39,
-            CAP_PROP_SAR_NUM = 40,
-            CAP_PROP_SAR_DEN = 41,
-            CAP_MODE_BGR = 0,
-            CAP_MODE_RGB = 1,
-            CAP_MODE_GRAY = 2,
-            CAP_MODE_YUYV = 3,
-            VIDEOWRITER_PROP_QUALITY = 1,
-            VIDEOWRITER_PROP_FRAMEBYTES = 2,
-            VIDEOWRITER_PROP_NSTRIPES = 3,
-            CAP_PROP_DC1394_OFF = -4,
-            CAP_PROP_DC1394_MODE_MANUAL = -3,
-            CAP_PROP_DC1394_MODE_AUTO = -2,
-            CAP_PROP_DC1394_MODE_ONE_PUSH_AUTO = -1,
-            CAP_PROP_DC1394_MAX = 31,
-            CAP_OPENNI_DEPTH_GENERATOR = 1 << 31,
-            CAP_OPENNI_IMAGE_GENERATOR = 1 << 30,
-            CAP_OPENNI_IR_GENERATOR = 1 << 29,
-            CAP_OPENNI_GENERATORS_MASK = CAP_OPENNI_DEPTH_GENERATOR + CAP_OPENNI_IMAGE_GENERATOR + CAP_OPENNI_IR_GENERATOR,
-            CAP_PROP_OPENNI_OUTPUT_MODE = 100,
-            CAP_PROP_OPENNI_FRAME_MAX_DEPTH = 101,
-            CAP_PROP_OPENNI_BASELINE = 102,
-            CAP_PROP_OPENNI_FOCAL_LENGTH = 103,
-            CAP_PROP_OPENNI_REGISTRATION = 104,
-            CAP_PROP_OPENNI_REGISTRATION_ON = CAP_PROP_OPENNI_REGISTRATION,
-            CAP_PROP_OPENNI_APPROX_FRAME_SYNC = 105,
-            CAP_PROP_OPENNI_MAX_BUFFER_SIZE = 106,
-            CAP_PROP_OPENNI_CIRCLE_BUFFER = 107,
-            CAP_PROP_OPENNI_MAX_TIME_DURATION = 108,
-            CAP_PROP_OPENNI_GENERATOR_PRESENT = 109,
-            CAP_PROP_OPENNI2_SYNC = 110,
-            CAP_PROP_OPENNI2_MIRROR = 111,
-            CAP_OPENNI_IMAGE_GENERATOR_PRESENT = CAP_OPENNI_IMAGE_GENERATOR + CAP_PROP_OPENNI_GENERATOR_PRESENT,
-            CAP_OPENNI_IMAGE_GENERATOR_OUTPUT_MODE = CAP_OPENNI_IMAGE_GENERATOR + CAP_PROP_OPENNI_OUTPUT_MODE,
-            CAP_OPENNI_DEPTH_GENERATOR_PRESENT = CAP_OPENNI_DEPTH_GENERATOR + CAP_PROP_OPENNI_GENERATOR_PRESENT,
-            CAP_OPENNI_DEPTH_GENERATOR_BASELINE = CAP_OPENNI_DEPTH_GENERATOR + CAP_PROP_OPENNI_BASELINE,
-            CAP_OPENNI_DEPTH_GENERATOR_FOCAL_LENGTH = CAP_OPENNI_DEPTH_GENERATOR + CAP_PROP_OPENNI_FOCAL_LENGTH,
-            CAP_OPENNI_DEPTH_GENERATOR_REGISTRATION = CAP_OPENNI_DEPTH_GENERATOR + CAP_PROP_OPENNI_REGISTRATION,
-            CAP_OPENNI_DEPTH_GENERATOR_REGISTRATION_ON = CAP_OPENNI_DEPTH_GENERATOR_REGISTRATION,
-            CAP_OPENNI_IR_GENERATOR_PRESENT = CAP_OPENNI_IR_GENERATOR + CAP_PROP_OPENNI_GENERATOR_PRESENT,
-            CAP_OPENNI_DEPTH_MAP = 0,
-            CAP_OPENNI_POINT_CLOUD_MAP = 1,
-            CAP_OPENNI_DISPARITY_MAP = 2,
-            CAP_OPENNI_DISPARITY_MAP_32F = 3,
-            CAP_OPENNI_VALID_DEPTH_MASK = 4,
-            CAP_OPENNI_BGR_IMAGE = 5,
-            CAP_OPENNI_GRAY_IMAGE = 6,
-            CAP_OPENNI_IR_IMAGE = 7,
-            CAP_OPENNI_VGA_30HZ = 0,
-            CAP_OPENNI_SXGA_15HZ = 1,
-            CAP_OPENNI_SXGA_30HZ = 2,
-            CAP_OPENNI_QVGA_30HZ = 3,
-            CAP_OPENNI_QVGA_60HZ = 4,
-            CAP_PROP_GSTREAMER_QUEUE_LENGTH = 200,
-            CAP_PROP_PVAPI_MULTICASTIP = 300,
-            CAP_PROP_PVAPI_FRAMESTARTTRIGGERMODE = 301,
-            CAP_PROP_PVAPI_DECIMATIONHORIZONTAL = 302,
-            CAP_PROP_PVAPI_DECIMATIONVERTICAL = 303,
-            CAP_PROP_PVAPI_BINNINGX = 304,
-            CAP_PROP_PVAPI_BINNINGY = 305,
-            CAP_PROP_PVAPI_PIXELFORMAT = 306,
-            CAP_PVAPI_FSTRIGMODE_FREERUN = 0,
-            CAP_PVAPI_FSTRIGMODE_SYNCIN1 = 1,
-            CAP_PVAPI_FSTRIGMODE_SYNCIN2 = 2,
-            CAP_PVAPI_FSTRIGMODE_FIXEDRATE = 3,
-            CAP_PVAPI_FSTRIGMODE_SOFTWARE = 4,
-            CAP_PVAPI_DECIMATION_OFF = 1,
-            CAP_PVAPI_DECIMATION_2OUTOF4 = 2,
-            CAP_PVAPI_DECIMATION_2OUTOF8 = 4,
-            CAP_PVAPI_DECIMATION_2OUTOF16 = 8,
-            CAP_PVAPI_PIXELFORMAT_MONO8 = 1,
-            CAP_PVAPI_PIXELFORMAT_MONO16 = 2,
-            CAP_PVAPI_PIXELFORMAT_BAYER8 = 3,
-            CAP_PVAPI_PIXELFORMAT_BAYER16 = 4,
-            CAP_PVAPI_PIXELFORMAT_RGB24 = 5,
-            CAP_PVAPI_PIXELFORMAT_BGR24 = 6,
-            CAP_PVAPI_PIXELFORMAT_RGBA32 = 7,
-            CAP_PVAPI_PIXELFORMAT_BGRA32 = 8,
-            CAP_PROP_XI_DOWNSAMPLING = 400,
-            CAP_PROP_XI_DATA_FORMAT = 401,
-            CAP_PROP_XI_OFFSET_X = 402,
-            CAP_PROP_XI_OFFSET_Y = 403,
-            CAP_PROP_XI_TRG_SOURCE = 404,
-            CAP_PROP_XI_TRG_SOFTWARE = 405,
-            CAP_PROP_XI_GPI_SELECTOR = 406,
-            CAP_PROP_XI_GPI_MODE = 407,
-            CAP_PROP_XI_GPI_LEVEL = 408,
-            CAP_PROP_XI_GPO_SELECTOR = 409,
-            CAP_PROP_XI_GPO_MODE = 410,
-            CAP_PROP_XI_LED_SELECTOR = 411,
-            CAP_PROP_XI_LED_MODE = 412,
-            CAP_PROP_XI_MANUAL_WB = 413,
-            CAP_PROP_XI_AUTO_WB = 414,
-            CAP_PROP_XI_AEAG = 415,
-            CAP_PROP_XI_EXP_PRIORITY = 416,
-            CAP_PROP_XI_AE_MAX_LIMIT = 417,
-            CAP_PROP_XI_AG_MAX_LIMIT = 418,
-            CAP_PROP_XI_AEAG_LEVEL = 419,
-            CAP_PROP_XI_TIMEOUT = 420,
-            CAP_PROP_XI_EXPOSURE = 421,
-            CAP_PROP_XI_EXPOSURE_BURST_COUNT = 422,
-            CAP_PROP_XI_GAIN_SELECTOR = 423,
-            CAP_PROP_XI_GAIN = 424,
-            CAP_PROP_XI_DOWNSAMPLING_TYPE = 426,
-            CAP_PROP_XI_BINNING_SELECTOR = 427,
-            CAP_PROP_XI_BINNING_VERTICAL = 428,
-            CAP_PROP_XI_BINNING_HORIZONTAL = 429,
-            CAP_PROP_XI_BINNING_PATTERN = 430,
-            CAP_PROP_XI_DECIMATION_SELECTOR = 431,
-            CAP_PROP_XI_DECIMATION_VERTICAL = 432,
-            CAP_PROP_XI_DECIMATION_HORIZONTAL = 433,
-            CAP_PROP_XI_DECIMATION_PATTERN = 434,
-            CAP_PROP_XI_TEST_PATTERN_GENERATOR_SELECTOR = 587,
-            CAP_PROP_XI_TEST_PATTERN = 588,
-            CAP_PROP_XI_IMAGE_DATA_FORMAT = 435,
-            CAP_PROP_XI_SHUTTER_TYPE = 436,
-            CAP_PROP_XI_SENSOR_TAPS = 437,
-            CAP_PROP_XI_AEAG_ROI_OFFSET_X = 439,
-            CAP_PROP_XI_AEAG_ROI_OFFSET_Y = 440,
-            CAP_PROP_XI_AEAG_ROI_WIDTH = 441,
-            CAP_PROP_XI_AEAG_ROI_HEIGHT = 442,
-            CAP_PROP_XI_BPC = 445,
-            CAP_PROP_XI_WB_KR = 448,
-            CAP_PROP_XI_WB_KG = 449,
-            CAP_PROP_XI_WB_KB = 450,
-            CAP_PROP_XI_WIDTH = 451,
-            CAP_PROP_XI_HEIGHT = 452,
-            CAP_PROP_XI_REGION_SELECTOR = 589,
-            CAP_PROP_XI_REGION_MODE = 595,
-            CAP_PROP_XI_LIMIT_BANDWIDTH = 459,
-            CAP_PROP_XI_SENSOR_DATA_BIT_DEPTH = 460,
-            CAP_PROP_XI_OUTPUT_DATA_BIT_DEPTH = 461,
-            CAP_PROP_XI_IMAGE_DATA_BIT_DEPTH = 462,
-            CAP_PROP_XI_OUTPUT_DATA_PACKING = 463,
-            CAP_PROP_XI_OUTPUT_DATA_PACKING_TYPE = 464,
-            CAP_PROP_XI_IS_COOLED = 465,
-            CAP_PROP_XI_COOLING = 466,
-            CAP_PROP_XI_TARGET_TEMP = 467,
-            CAP_PROP_XI_CHIP_TEMP = 468,
-            CAP_PROP_XI_HOUS_TEMP = 469,
-            CAP_PROP_XI_HOUS_BACK_SIDE_TEMP = 590,
-            CAP_PROP_XI_SENSOR_BOARD_TEMP = 596,
-            CAP_PROP_XI_CMS = 470,
-            CAP_PROP_XI_APPLY_CMS = 471,
-            CAP_PROP_XI_IMAGE_IS_COLOR = 474,
-            CAP_PROP_XI_COLOR_FILTER_ARRAY = 475,
-            CAP_PROP_XI_GAMMAY = 476,
-            CAP_PROP_XI_GAMMAC = 477,
-            CAP_PROP_XI_SHARPNESS = 478,
-            CAP_PROP_XI_CC_MATRIX_00 = 479,
-            CAP_PROP_XI_CC_MATRIX_01 = 480,
-            CAP_PROP_XI_CC_MATRIX_02 = 481,
-            CAP_PROP_XI_CC_MATRIX_03 = 482,
-            CAP_PROP_XI_CC_MATRIX_10 = 483,
-            CAP_PROP_XI_CC_MATRIX_11 = 484,
-            CAP_PROP_XI_CC_MATRIX_12 = 485,
-            CAP_PROP_XI_CC_MATRIX_13 = 486,
-            CAP_PROP_XI_CC_MATRIX_20 = 487,
-            CAP_PROP_XI_CC_MATRIX_21 = 488,
-            CAP_PROP_XI_CC_MATRIX_22 = 489,
-            CAP_PROP_XI_CC_MATRIX_23 = 490,
-            CAP_PROP_XI_CC_MATRIX_30 = 491,
-            CAP_PROP_XI_CC_MATRIX_31 = 492,
-            CAP_PROP_XI_CC_MATRIX_32 = 493,
-            CAP_PROP_XI_CC_MATRIX_33 = 494,
-            CAP_PROP_XI_DEFAULT_CC_MATRIX = 495,
-            CAP_PROP_XI_TRG_SELECTOR = 498,
-            CAP_PROP_XI_ACQ_FRAME_BURST_COUNT = 499,
-            CAP_PROP_XI_DEBOUNCE_EN = 507,
-            CAP_PROP_XI_DEBOUNCE_T0 = 508,
-            CAP_PROP_XI_DEBOUNCE_T1 = 509,
-            CAP_PROP_XI_DEBOUNCE_POL = 510,
-            CAP_PROP_XI_LENS_MODE = 511,
-            CAP_PROP_XI_LENS_APERTURE_VALUE = 512,
-            CAP_PROP_XI_LENS_FOCUS_MOVEMENT_VALUE = 513,
-            CAP_PROP_XI_LENS_FOCUS_MOVE = 514,
-            CAP_PROP_XI_LENS_FOCUS_DISTANCE = 515,
-            CAP_PROP_XI_LENS_FOCAL_LENGTH = 516,
-            CAP_PROP_XI_LENS_FEATURE_SELECTOR = 517,
-            CAP_PROP_XI_LENS_FEATURE = 518,
-            CAP_PROP_XI_DEVICE_MODEL_ID = 521,
-            CAP_PROP_XI_DEVICE_SN = 522,
-            CAP_PROP_XI_IMAGE_DATA_FORMAT_RGB32_ALPHA = 529,
-            CAP_PROP_XI_IMAGE_PAYLOAD_SIZE = 530,
-            CAP_PROP_XI_TRANSPORT_PIXEL_FORMAT = 531,
-            CAP_PROP_XI_SENSOR_CLOCK_FREQ_HZ = 532,
-            CAP_PROP_XI_SENSOR_CLOCK_FREQ_INDEX = 533,
-            CAP_PROP_XI_SENSOR_OUTPUT_CHANNEL_COUNT = 534,
-            CAP_PROP_XI_FRAMERATE = 535,
-            CAP_PROP_XI_COUNTER_SELECTOR = 536,
-            CAP_PROP_XI_COUNTER_VALUE = 537,
-            CAP_PROP_XI_ACQ_TIMING_MODE = 538,
-            CAP_PROP_XI_AVAILABLE_BANDWIDTH = 539,
-            CAP_PROP_XI_BUFFER_POLICY = 540,
-            CAP_PROP_XI_LUT_EN = 541,
-            CAP_PROP_XI_LUT_INDEX = 542,
-            CAP_PROP_XI_LUT_VALUE = 543,
-            CAP_PROP_XI_TRG_DELAY = 544,
-            CAP_PROP_XI_TS_RST_MODE = 545,
-            CAP_PROP_XI_TS_RST_SOURCE = 546,
-            CAP_PROP_XI_IS_DEVICE_EXIST = 547,
-            CAP_PROP_XI_ACQ_BUFFER_SIZE = 548,
-            CAP_PROP_XI_ACQ_BUFFER_SIZE_UNIT = 549,
-            CAP_PROP_XI_ACQ_TRANSPORT_BUFFER_SIZE = 550,
-            CAP_PROP_XI_BUFFERS_QUEUE_SIZE = 551,
-            CAP_PROP_XI_ACQ_TRANSPORT_BUFFER_COMMIT = 552,
-            CAP_PROP_XI_RECENT_FRAME = 553,
-            CAP_PROP_XI_DEVICE_RESET = 554,
-            CAP_PROP_XI_COLUMN_FPN_CORRECTION = 555,
-            CAP_PROP_XI_ROW_FPN_CORRECTION = 591,
-            CAP_PROP_XI_SENSOR_MODE = 558,
-            CAP_PROP_XI_HDR = 559,
-            CAP_PROP_XI_HDR_KNEEPOINT_COUNT = 560,
-            CAP_PROP_XI_HDR_T1 = 561,
-            CAP_PROP_XI_HDR_T2 = 562,
-            CAP_PROP_XI_KNEEPOINT1 = 563,
-            CAP_PROP_XI_KNEEPOINT2 = 564,
-            CAP_PROP_XI_IMAGE_BLACK_LEVEL = 565,
-            CAP_PROP_XI_HW_REVISION = 571,
-            CAP_PROP_XI_DEBUG_LEVEL = 572,
-            CAP_PROP_XI_AUTO_BANDWIDTH_CALCULATION = 573,
-            CAP_PROP_XI_FFS_FILE_ID = 594,
-            CAP_PROP_XI_FFS_FILE_SIZE = 580,
-            CAP_PROP_XI_FREE_FFS_SIZE = 581,
-            CAP_PROP_XI_USED_FFS_SIZE = 582,
-            CAP_PROP_XI_FFS_ACCESS_KEY = 583,
-            CAP_PROP_XI_SENSOR_FEATURE_SELECTOR = 585,
-            CAP_PROP_XI_SENSOR_FEATURE_VALUE = 586,
-            CAP_PROP_IOS_DEVICE_FOCUS = 9001,
-            CAP_PROP_IOS_DEVICE_EXPOSURE = 9002,
-            CAP_PROP_IOS_DEVICE_FLASH = 9003,
-            CAP_PROP_IOS_DEVICE_WHITEBALANCE = 9004,
-            CAP_PROP_IOS_DEVICE_TORCH = 9005,
-            CAP_PROP_GIGA_FRAME_OFFSET_X = 10001,
-            CAP_PROP_GIGA_FRAME_OFFSET_Y = 10002,
-            CAP_PROP_GIGA_FRAME_WIDTH_MAX = 10003,
-            CAP_PROP_GIGA_FRAME_HEIGH_MAX = 10004,
-            CAP_PROP_GIGA_FRAME_SENS_WIDTH = 10005,
-            CAP_PROP_GIGA_FRAME_SENS_HEIGH = 10006,
-            CAP_PROP_INTELPERC_PROFILE_COUNT = 11001,
-            CAP_PROP_INTELPERC_PROFILE_IDX = 11002,
-            CAP_PROP_INTELPERC_DEPTH_LOW_CONFIDENCE_VALUE = 11003,
-            CAP_PROP_INTELPERC_DEPTH_SATURATION_VALUE = 11004,
-            CAP_PROP_INTELPERC_DEPTH_CONFIDENCE_THRESHOLD = 11005,
-            CAP_PROP_INTELPERC_DEPTH_FOCAL_LENGTH_HORZ = 11006,
-            CAP_PROP_INTELPERC_DEPTH_FOCAL_LENGTH_VERT = 11007,
-            CAP_INTELPERC_DEPTH_GENERATOR = 1 << 29,
-            CAP_INTELPERC_IMAGE_GENERATOR = 1 << 28,
-            CAP_INTELPERC_GENERATORS_MASK = CAP_INTELPERC_DEPTH_GENERATOR + CAP_INTELPERC_IMAGE_GENERATOR,
-            CAP_INTELPERC_DEPTH_MAP = 0,
-            CAP_INTELPERC_UVDEPTH_MAP = 1,
-            CAP_INTELPERC_IR_MAP = 2,
-            CAP_INTELPERC_IMAGE = 3,
-            CAP_PROP_GPHOTO2_PREVIEW = 17001,
-            CAP_PROP_GPHOTO2_WIDGET_ENUMERATE = 17002,
-            CAP_PROP_GPHOTO2_RELOAD_CONFIG = 17003,
-            CAP_PROP_GPHOTO2_RELOAD_ON_CHANGE = 17004,
-            CAP_PROP_GPHOTO2_COLLECT_MSGS = 17005,
-            CAP_PROP_GPHOTO2_FLUSH_MSGS = 17006,
-            CAP_PROP_SPEED = 17007,
-            CAP_PROP_APERTURE = 17008,
-            CAP_PROP_EXPOSUREPROGRAM = 17009,
-            CAP_PROP_VIEWFINDER = 17010,
-            CAP_PROP_IMAGES_BASE = 18000,
-            CAP_PROP_IMAGES_LAST = 19000;
-
-
-
-
-}
diff --git a/Prj-Android/openCVLibrary342/src/main/res/values/attrs.xml b/Prj-Android/openCVLibrary342/src/main/res/values/attrs.xml
deleted file mode 100755
index 6902621..0000000
--- a/Prj-Android/openCVLibrary342/src/main/res/values/attrs.xml
+++ /dev/null
@@ -1,11 +0,0 @@
-<?xml version="1.0" encoding="utf-8"?>
-<resources>
-    <declare-styleable name = "CameraBridgeViewBase" >
-       <attr name="show_fps" format="boolean"/>
-       <attr name="camera_id" format="integer" >
-          <enum name="any" value="-1" />
-          <enum name="back" value="99" />
-          <enum name="front" value="98" />
-       </attr>
-    </declare-styleable>
-</resources>
diff --git a/Prj-Android/settings.gradle b/Prj-Android/settings.gradle
old mode 100755
new mode 100644
index 833b4d7..cd62d19
--- a/Prj-Android/settings.gradle
+++ b/Prj-Android/settings.gradle
@@ -1,2 +1,11 @@
+dependencyResolutionManagement {
+    repositoriesMode.set(RepositoriesMode.FAIL_ON_PROJECT_REPOS)
+    repositories {
+        google()
+        mavenCentral()
+        jcenter() // Warning: this repository is going to shut down soon
+    }
+}
+rootProject.name = "HyperLPR_SDK_Demo"
 include ':app'
-include ':openCVLibrary342'
+include ':hyperlpr3'
diff --git a/Prj-Linux/CMakeLists.txt b/Prj-Linux/CMakeLists.txt
new file mode 100644
index 0000000..71d1296
--- /dev/null
+++ b/Prj-Linux/CMakeLists.txt
@@ -0,0 +1,15 @@
+cmake_minimum_required(VERSION 3.10.2)
+project(HyperLPR3-LinuxDemo)
+
+set(CMAKE_CXX_STANDARD 11)
+
+# use opencv
+find_package(OpenCV REQUIRED)
+include_directories(${OpenCV_INCLUDE_DIRS})
+
+# use hyperlpr3 sdk
+include_directories(${CMAKE_CURRENT_SOURCE_DIR}/hyperlpr3/include)
+link_directories(${CMAKE_CURRENT_SOURCE_DIR}/hyperlpr3/lib)
+
+add_executable(PlateRecDemo ${CMAKE_CURRENT_SOURCE_DIR}/plate_rec_demo.cpp)
+target_link_libraries(PlateRecDemo hyperlpr3 ${OpenCV_LIBS})
\ No newline at end of file
diff --git a/Prj-Linux/hyperlpr/CMakeLists.txt b/Prj-Linux/hyperlpr/CMakeLists.txt
deleted file mode 100644
index e215fbe..0000000
--- a/Prj-Linux/hyperlpr/CMakeLists.txt
+++ /dev/null
@@ -1,55 +0,0 @@
-cmake_minimum_required(VERSION 3.6)
-project(SwiftPR)
-
-set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11")
-set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR})
-find_package(OpenCV REQUIRED)
-
-include_directories( ${OpenCV_INCLUDE_DIRS})
-include_directories(include)
-
-set(SRC_DETECTION src/PlateDetection.cpp  src/util.h include/PlateDetection.h)
-
-set(SRC_FINEMAPPING src/FineMapping.cpp )
-
-set(SRC_FASTDESKEW src/FastDeskew.cpp )
-
-set(SRC_SEGMENTATION src/PlateSegmentation.cpp )
-
-set(SRC_RECOGNIZE src/Recognizer.cpp src/CNNRecognizer.cpp)
-
-set(SRC_PIPLINE src/Pipeline.cpp)
-
-set(SRC_SEGMENTATIONFREE src/SegmentationFreeRecognizer.cpp )
-
-#TEST_DETECTION
-add_executable(TEST_Detection ${SRC_DETECTION} demos/test_detection.cpp)
-target_link_libraries(TEST_Detection ${OpenCV_LIBS})
-
-#TEST_FINEMAPPING
-add_executable(TEST_FINEMAPPING ${SRC_FINEMAPPING} demos/test_finemapping.cpp)
-target_link_libraries(TEST_FINEMAPPING ${OpenCV_LIBS})
-
-#TEST_DESKEW
-
-add_executable(TEST_FASTDESKEW ${SRC_FASTDESKEW} demos/test_fastdeskew.cpp)
-target_link_libraries(TEST_FASTDESKEW ${OpenCV_LIBS})
-
-#TEST_SEGMENTATION
-
-add_executable(TEST_SEGMENTATION ${SRC_SEGMENTATION} ${SRC_RECOGNIZE} demos/test_segmentation.cpp)
-target_link_libraries(TEST_SEGMENTATION ${OpenCV_LIBS})
-
-#TEST_RECOGNIZATION
-
-add_executable(TEST_RECOGNIZATION  ${SRC_RECOGNIZE} demos/test_recognization.cpp)
-target_link_libraries(TEST_RECOGNIZATION ${OpenCV_LIBS})
-
-#TEST_SEGMENTATIONFREE
-add_executable(TEST_SEGMENTATIONFREE ${SRC_SEGMENTATIONFREE} demos/test_segmentationFree.cpp)
-target_link_libraries(TEST_SEGMENTATIONFREE ${OpenCV_LIBS})
-
-#TEST_PIPELINE
-
-add_executable(TEST_PIPLINE ${SRC_DETECTION} ${SRC_FINEMAPPING} ${SRC_FASTDESKEW} ${SRC_SEGMENTATION} ${SRC_RECOGNIZE} ${SRC_PIPLINE} ${SRC_SEGMENTATIONFREE} demos/test_pipeline.cpp)
-target_link_libraries(TEST_PIPLINE ${OpenCV_LIBS})
diff --git a/Prj-Linux/hyperlpr/demos/test_detection.cpp b/Prj-Linux/hyperlpr/demos/test_detection.cpp
deleted file mode 100644
index db73366..0000000
--- a/Prj-Linux/hyperlpr/demos/test_detection.cpp
+++ /dev/null
@@ -1,34 +0,0 @@
-//
-// Created by 庾金科 on 20/09/2017.
-//
-
-#include <../include/PlateDetection.h>
-
-
-void drawRect(cv::Mat image,cv::Rect rect)
-{
-    cv::Point p1(rect.x,rect.y);
-    cv::Point p2(rect.x+rect.width,rect.y+rect.height);
-    cv::rectangle(image,p1,p2,cv::Scalar(0,255,0),1);
-}
-
-
-int main()
-{
-    cv::Mat image = cv::imread("res/test1.jpg");
-    pr::PlateDetection plateDetection("model/cascade.xml");
-    std::vector<pr::PlateInfo> plates;
-    plateDetection.plateDetectionRough(image,plates);
-    for(pr::PlateInfo platex:plates)
-    {
-        drawRect(image,platex.getPlateRect());
-        cv::imwrite("res/cache/test.png",platex.getPlateImage());
-        cv::imshow("image",platex.getPlateImage());
-        cv::waitKey(0);
-    }
-    cv::imshow("image",image);
-    cv::waitKey(0);
-    return 0 ;
-
-
-}
diff --git a/Prj-Linux/hyperlpr/demos/test_fastdeskew.cpp b/Prj-Linux/hyperlpr/demos/test_fastdeskew.cpp
deleted file mode 100644
index 3740147..0000000
--- a/Prj-Linux/hyperlpr/demos/test_fastdeskew.cpp
+++ /dev/null
@@ -1,34 +0,0 @@
-//
-// Created by Jack Yu on 02/10/2017.
-//
-
-
-#include <../include/FastDeskew.h>
-
-
-void drawRect(cv::Mat image,cv::Rect rect)
-{
-    cv::Point p1(rect.x,rect.y);
-    cv::Point p2(rect.x+rect.width,rect.y+rect.height);
-    cv::rectangle(image,p1,p2,cv::Scalar(0,255,0),1);
-}
-void TEST_DESKEW(){
-
-    cv::Mat image = cv::imread("res/3.png",cv::IMREAD_GRAYSCALE);
-//    cv::resize(image,image,cv::Size(136*2,36*2));
-    cv::Mat deskewed = pr::fastdeskew(image,12);
-//    cv::imwrite("./res/4.png",deskewed);
-//     cv::Mat deskewed2 = pr::fastdeskew(deskewed,12);
-//
-    cv::imshow("image",deskewed);
-    cv::waitKey(0);
-
-}
-int main()
-{
-
-    TEST_DESKEW();
-    return 0 ;
-
-
-}
diff --git a/Prj-Linux/hyperlpr/demos/test_finemapping.cpp b/Prj-Linux/hyperlpr/demos/test_finemapping.cpp
deleted file mode 100644
index dd7e26f..0000000
--- a/Prj-Linux/hyperlpr/demos/test_finemapping.cpp
+++ /dev/null
@@ -1,25 +0,0 @@
-//
-// Created by Jack Yu on 24/09/2017.
-//
-
-#include "FineMapping.h"
-
-
-
-
-
-
-int main()
-{
-    cv::Mat image = cv::imread("res/cache/test.png");
-    cv::Mat image_finemapping = pr::FineMapping::FineMappingVertical(image);
-    pr::FineMapping finemapper =  pr::FineMapping("model/HorizonalFinemapping.prototxt","model/HorizonalFinemapping.caffemodel");
-     image_finemapping = finemapper.FineMappingHorizon(image_finemapping,0,-3);
-    cv::imwrite("res/cache/finemappingres.png",image_finemapping);
-    cv::imshow("image",image_finemapping);
-    cv::waitKey(0);
-
-
-    return 0 ;
-
-}
diff --git a/Prj-Linux/hyperlpr/demos/test_pipeline.cpp b/Prj-Linux/hyperlpr/demos/test_pipeline.cpp
deleted file mode 100644
index 4e8b345..0000000
--- a/Prj-Linux/hyperlpr/demos/test_pipeline.cpp
+++ /dev/null
@@ -1,199 +0,0 @@
-//
-// Created by Jack Yu on 23/10/2017.
-//
-
-#include "../include/Pipeline.h"
-
-
-
-
-using namespace std;
-
-template<class T>
-static unsigned int levenshtein_distance(const T &s1, const T &s2) {
-    const size_t len1 = s1.size(), len2 = s2.size();
-    std::vector<unsigned int> col(len2 + 1), prevCol(len2 + 1);
-
-    for (unsigned int i = 0; i < prevCol.size(); i++) prevCol[i] = i;
-    for (unsigned int i = 0; i < len1; i++) {
-        col[0] = i + 1;
-        for (unsigned int j = 0; j < len2; j++)
-            col[j + 1] = min(
-                    min(prevCol[1 + j] + 1, col[j] + 1),
-                    prevCol[j] + (s1[i] == s2[j] ? 0 : 1));
-        col.swap(prevCol);
-    }
-    return prevCol[len2];
-}
-
-
-
-
-void TEST_ACC(){
-
-    pr::PipelinePR prc("model/cascade.xml",
-                       "model/HorizonalFinemapping.prototxt","model/HorizonalFinemapping.caffemodel",
-                       "model/Segmentation.prototxt","model/Segmentation.caffemodel",
-                       "model/CharacterRecognization.prototxt","model/CharacterRecognization.caffemodel",
-                       "model/SegmenationFree-Inception.prototxt","model/SegmenationFree-Inception.caffemodel"
-    );
-
-    ifstream file;
-    string imagename;
-    int n = 0,correct = 0,j = 0,sum = 0;
-    char filename[] = "/Users/yujinke/Downloads/general_test/1.txt";
-    string pathh = "/Users/yujinke/Downloads/general_test/";
-    file.open(filename, ios::in);
-    while (!file.eof())
-    {
-        file >> imagename;
-        string imgpath = pathh + imagename;
-        std::cout << "------------------------------------------------" << endl;
-        cout << "图片名：" << imagename << endl;
-        cv::Mat image = cv::imread(imgpath);
-//		cv::imshow("image", image);
-//		cv::waitKey(0);
-
-        std::vector<pr::PlateInfo> res = prc.RunPiplineAsImage(image,pr::SEGMENTATION_FREE_METHOD);
-
-        float conf = 0;
-        vector<float> con ;
-        vector<string> name;
-        for (auto st : res) {
-            if (st.confidence > 0.1) {
-                //std::cout << st.getPlateName() << " " << st.confidence << std::endl;
-                con.push_back(st.confidence);
-                name.push_back(st.getPlateName());
-                //conf += st.confidence;
-            }
-            else
-                cout << "no string" << endl;
-        }
-        //	std::cout << conf << std::endl;
-        int num = con.size();
-        float max = 0;
-        string platestr, chpr, ch;
-        int diff = 0,dif = 0;
-        for (int i = 0; i < num; i++) {
-
-            if (con.at(i) > max)
-            {
-                max = con.at(i);
-                platestr = name.at(i);
-            }
-
-        }
-        //	cout << "max:"<<max << endl;
-        cout << "string:" << platestr << endl;
-        chpr = platestr.substr(0, 2);
-        ch = imagename.substr(0, 2);
-        diff = levenshtein_distance(imagename, platestr);
-        dif = diff - 4;
-        cout << "差距:" <<dif << endl;
-        sum += dif;
-        if (ch != chpr) n++;
-        if (diff == 0)	correct++;
-        j++;
-    }
-    float cha = 1 - float(n) / float(j);
-    std::cout << "------------------------------------------------" << endl;
-    cout << "车牌总数：" << j << endl;
-    cout << "汉字识别准确率："<<cha << endl;
-    float chaccuracy = 1 - float(sum - n * 2) /float(j * 8);
-    cout << "字符识别准确率：" << chaccuracy << endl;
-
-}
-
-
-void TEST_PIPELINE(){
-
-    pr::PipelinePR prc("model/cascade.xml",
-                      "model/HorizonalFinemapping.prototxt","model/HorizonalFinemapping.caffemodel",
-                      "model/Segmentation.prototxt","model/Segmentation.caffemodel",
-                      "model/CharacterRecognization.prototxt","model/CharacterRecognization.caffemodel",
-                       "model/SegmenationFree-Inception.prototxt","model/SegmenationFree-Inception.caffemodel"
-                    );
-
-    cv::Mat image = cv::imread("/Users/yujinke/ClionProjects/cpp_ocr_demo/test.png");
-
-
-    std::vector<pr::PlateInfo> res = prc.RunPiplineAsImage(image,pr::SEGMENTATION_FREE_METHOD);
-
-    for(auto st:res) {
-        if(st.confidence>0.75) {
-            std::cout << st.getPlateName() << " " << st.confidence << std::endl;
-            cv::Rect region = st.getPlateRect();
-
-            cv::rectangle(image,cv::Point(region.x,region.y),cv::Point(region.x+region.width,region.y+region.height),cv::Scalar(255,255,0),2);
-        }
-    }
-
-    cv::imshow("image",image);
-    cv::waitKey(0);
-
-}
-
-
-void TEST_CAM()
-{
-
-    cv::VideoCapture capture("test1.mp4");
-    cv::Mat frame;
-
-    pr::PipelinePR prc("model/cascade.xml",
-                       "model/HorizonalFinemapping.prototxt","model/HorizonalFinemapping.caffemodel",
-                       "model/Segmentation.prototxt","model/Segmentation.caffemodel",
-                       "model/CharacterRecognization.prototxt","model/CharacterRecognization.caffemodel",
-                       "model/SegmentationFree.prototxt","model/SegmentationFree.caffemodel"
-    );
-
-
-
-
-
-    while(1) {
-        //读取下一帧
-        if (!capture.read(frame)) {
-            std::cout << "读取视频失败" << std::endl;
-            exit(1);
-        }
-//
-//        cv::transpose(frame,frame);
-//        cv::flip(frame,frame,2);
-
-//        cv::resize(frame,frame,cv::Size(frame.cols/2,frame.rows/2));
-
-
-
-        std::vector<pr::PlateInfo> res = prc.RunPiplineAsImage(frame,pr::SEGMENTATION_FREE_METHOD);
-
-        for(auto st:res) {
-            if(st.confidence>0.75) {
-                std::cout << st.getPlateName() << " " << st.confidence << std::endl;
-                cv::Rect region = st.getPlateRect();
-
-                cv::rectangle(frame,cv::Point(region.x,region.y),cv::Point(region.x+region.width,region.y+region.height),cv::Scalar(255,255,0),2);
-            }
-        }
-
-        cv::imshow("image",frame);
-        cv::waitKey(1);
-
-
-
-    }
-
-}
-
-
-int main()
-{
-    TEST_ACC();
-
-//    TEST_CAM();
-//    TEST_PIPELINE();
-
-    return 0 ;
-
-
-}
diff --git a/Prj-Linux/hyperlpr/demos/test_recognization.cpp b/Prj-Linux/hyperlpr/demos/test_recognization.cpp
deleted file mode 100644
index 9244f95..0000000
--- a/Prj-Linux/hyperlpr/demos/test_recognization.cpp
+++ /dev/null
@@ -1,54 +0,0 @@
-//
-// Created by Jack Yu on 23/10/2017.
-//
-
-#include "../include/CNNRecognizer.h"
-
-std::vector<std::string> chars{"京","沪","津","渝","冀","晋","蒙","辽","吉","黑","苏","浙","皖","闽","赣","鲁","豫","鄂","湘","粤","桂","琼","川","贵","云","藏","陕","甘","青","宁","新","0","1","2","3","4","5","6","7","8","9","A","B","C","D","E","F","G","H","J","K","L","M","N","P","Q","R","S","T","U","V","W","X","Y","Z"};
-
-#include <opencv2/dnn.hpp>
-using namespace cv::dnn;
-
-
-void getMaxClass(cv::Mat &probBlob, int *classId, double *classProb)
-{
-//    cv::Mat probMat = probBlob.matRefConst().reshape(1, 1); //reshape the blob to 1x1000 matrix
-    cv::Point classNumber;
-
-    cv::minMaxLoc(probBlob, NULL, classProb, NULL, &classNumber);
-
-    *classId = classNumber.x;
-}
-
-void TEST_RECOGNIZATION(){
-//        pr::CNNRecognizer instance("model/CharacterRecognization.prototxt","model/CharacterRecognization.caffemodel");
-         Net net  = cv::dnn::readNetFromCaffe("model/CharacterRecognization.prototxt","model/CharacterRecognization.caffemodel");
-    cv::Mat image = cv::imread("res/char1.png",cv::IMREAD_GRAYSCALE);
-    cv::resize(image,image,cv::Size(14,30));
-    cv::equalizeHist(image,image);
-    cv::Mat inputBlob = cv::dnn::blobFromImage(image, 1/255.0, cv::Size(14,30), false);
-
-    net.setInput(inputBlob,"data");
-
-    cv::Mat res = net.forward();
-    std::cout<<res<<std::endl;
-    float *p = (float*)res.data;
-    int maxid= 0;
-    double prob = 0;
-
-    getMaxClass(res,&maxid,&prob);
-
-
-
-        std::cout<<chars[maxid]<<std::endl;
-
-
-
-
-
-};
-int main()
-{TEST_RECOGNIZATION();
-
-
-}
diff --git a/Prj-Linux/hyperlpr/demos/test_segmentation.cpp b/Prj-Linux/hyperlpr/demos/test_segmentation.cpp
deleted file mode 100644
index b379406..0000000
--- a/Prj-Linux/hyperlpr/demos/test_segmentation.cpp
+++ /dev/null
@@ -1,43 +0,0 @@
-//
-// Created by Jack Yu on 16/10/2017.
-//
-
-
-#include "../include/PlateSegmentation.h"
-#include "../include/CNNRecognizer.h"
-#include "../include/Recognizer.h"
-
-
-std::vector<std::string> chars{"京","沪","津","渝","冀","晋","蒙","辽","吉","黑","苏","浙","皖","闽","赣","鲁","豫","鄂","湘","粤","桂","琼","川","贵","云","藏","陕","甘","青","宁","新","0","1","2","3","4","5","6","7","8","9","A","B","C","D","E","F","G","H","J","K","L","M","N","P","Q","R","S","T","U","V","W","X","Y","Z"};
-
-
-void TEST_SLIDINGWINDOWS_EVAL(){
-    cv::Mat demo = cv::imread("res/cache/finemappingres.png");
-    cv::resize(demo,demo,cv::Size(136,36));
-
-    cv::Mat respones;
-    pr::PlateSegmentation plateSegmentation("model/Segmentation.prototxt","model/Segmentation.caffemodel");
-    pr::PlateInfo plate;
-    plate.setPlateImage(demo);
-    std::vector<cv::Rect> rects;
-    plateSegmentation.segmentPlatePipline(plate,1,rects);
-    plateSegmentation.ExtractRegions(plate,rects);
-
-    pr::GeneralRecognizer *recognizer = new pr::CNNRecognizer("model/CharacterRecognization.prototxt","model/CharacterRecognization.caffemodel");
-    recognizer->SegmentBasedSequenceRecognition(plate);
-    std::cout<<plate.decodePlateNormal(chars)<<std::endl;
-
-
-    delete(recognizer);
-
-
-
-
-
-}
-int main(){
-
-    TEST_SLIDINGWINDOWS_EVAL();
-
-    return  0;
-}
diff --git a/Prj-Linux/hyperlpr/demos/test_segmentationFree.cpp b/Prj-Linux/hyperlpr/demos/test_segmentationFree.cpp
deleted file mode 100644
index 4f948d2..0000000
--- a/Prj-Linux/hyperlpr/demos/test_segmentationFree.cpp
+++ /dev/null
@@ -1,54 +0,0 @@
-//
-// Created by Jack Yu on 29/11/2017.
-//
-#include "../include/SegmentationFreeRecognizer.h"
-#include "../include/Pipeline.h"
-
-#include "../include/PlateInfo.h"
-
-
-
-std::string decodeResults(cv::Mat code_table,std::vector<std::string> mapping_table)
-{
-    cv::MatSize mtsize = code_table.size;
-    int sequencelength = mtsize[2];
-    int labellength = mtsize[1];
-    cv::transpose(code_table.reshape(1,1).reshape(1,labellength),code_table);
-    std::string name = "";
-    std::vector<int> seq(sequencelength);
-    for(int i = 0 ; i < sequencelength;  i++) {
-        float *fstart = ((float *) (code_table.data) + i * labellength );
-        int id = std::max_element(fstart,fstart+labellength) - fstart;
-        seq[i] =id;
-    }
-    for(int i = 0 ; i< sequencelength ; i++)
-    {
-        if(seq[i]!=labellength-1 && (i==0 || seq[i]!=seq[i-1]))
-            name+=mapping_table[seq[i]];
-    }
-    std::cout<<name;
-    return name;
-}
-
-
-int main()
-{
-    cv::Mat image = cv::imread("res/cache/chars_segment.jpg");
-//    cv::transpose(image,image);
-
-//    cv::resize(image,image,cv::Size(160,40));
-    cv::imshow("xxx",image);
-    cv::waitKey(0);
-    pr::SegmentationFreeRecognizer recognizr("model/SegmenationFree-Inception.prototxt","model/ISegmenationFree-Inception.caffemodel");
-    std::pair<std::string,float> res = recognizr.SegmentationFreeForSinglePlate(image,pr::CH_PLATE_CODE);
-    std::cout<<res.first<<" "
-                  <<res.second<<std::endl;
-
-
-//    decodeResults(plate,pr::CH_PLATE_CODE);
-    cv::imshow("image",image);
-    cv::waitKey(0);
-
-    return  0;
-
-}
diff --git a/Prj-Linux/hyperlpr/include/CNNRecognizer.h b/Prj-Linux/hyperlpr/include/CNNRecognizer.h
deleted file mode 100644
index 9885f1b..0000000
--- a/Prj-Linux/hyperlpr/include/CNNRecognizer.h
+++ /dev/null
@@ -1,24 +0,0 @@
-//
-// Created by Jack Yu on 21/10/2017.
-//
-
-#ifndef HYPERPR_CNNRECOGNIZER_H
-#define HYPERPR_CNNRECOGNIZER_H
-
-#include "Recognizer.h"
-namespace pr {
-class CNNRecognizer : public GeneralRecognizer {
-public:
-  const int CHAR_INPUT_W = 14;
-  const int CHAR_INPUT_H = 30;
-
-  CNNRecognizer(std::string prototxt, std::string caffemodel);
-  label recognizeCharacter(cv::Mat character);
-
-private:
-  cv::dnn::Net net;
-};
-
-} // namespace pr
-
-#endif // HYPERPR_CNNRECOGNIZER_H
diff --git a/Prj-Linux/hyperlpr/include/FastDeskew.h b/Prj-Linux/hyperlpr/include/FastDeskew.h
deleted file mode 100644
index dabf234..0000000
--- a/Prj-Linux/hyperlpr/include/FastDeskew.h
+++ /dev/null
@@ -1,17 +0,0 @@
-//
-// Created by Jack Yu on 22/09/2017.
-//
-
-#ifndef HYPERPR_FASTDESKEW_H
-#define HYPERPR_FASTDESKEW_H
-
-#include <math.h>
-#include <opencv2/opencv.hpp>
-namespace pr {
-
-cv::Mat fastdeskew(cv::Mat skewImage, int blockSize);
-//    cv::Mat spatialTransformer(cv::Mat skewImage);
-
-} // namespace pr
-
-#endif // HYPERPR_FASTDESKEW_H
diff --git a/Prj-Linux/hyperlpr/include/FineMapping.h b/Prj-Linux/hyperlpr/include/FineMapping.h
deleted file mode 100644
index 56dbedf..0000000
--- a/Prj-Linux/hyperlpr/include/FineMapping.h
+++ /dev/null
@@ -1,29 +0,0 @@
-//
-// Created by Jack Yu on 22/09/2017.
-//
-
-#ifndef HYPERPR_FINEMAPPING_H
-#define HYPERPR_FINEMAPPING_H
-
-#include <opencv2/dnn.hpp>
-#include <opencv2/opencv.hpp>
-
-#include <string>
-namespace pr {
-class FineMapping {
-public:
-  FineMapping();
-
-  FineMapping(std::string prototxt, std::string caffemodel);
-  static cv::Mat FineMappingVertical(cv::Mat InputProposal, int sliceNum = 15,
-                                     int upper = 0, int lower = -50,
-                                     int windows_size = 17);
-  cv::Mat FineMappingHorizon(cv::Mat FinedVertical, int leftPadding,
-                             int rightPadding);
-
-private:
-  cv::dnn::Net net;
-};
-
-} // namespace pr
-#endif // HYPERPR_FINEMAPPING_H
diff --git a/Prj-Linux/hyperlpr/include/Pipeline.h b/Prj-Linux/hyperlpr/include/Pipeline.h
deleted file mode 100644
index 9cc01a0..0000000
--- a/Prj-Linux/hyperlpr/include/Pipeline.h
+++ /dev/null
@@ -1,54 +0,0 @@
-//
-// Created by Jack Yu on 22/10/2017.
-//
-
-#ifndef HYPERPR_PIPLINE_H
-#define HYPERPR_PIPLINE_H
-
-#include "CNNRecognizer.h"
-#include "FastDeskew.h"
-#include "FineMapping.h"
-#include "PlateDetection.h"
-#include "PlateInfo.h"
-#include "PlateSegmentation.h"
-#include "Recognizer.h"
-#include "SegmentationFreeRecognizer.h"
-
-namespace pr {
-
-const std::vector<std::string> CH_PLATE_CODE{
-    "京", "沪", "津", "渝", "冀", "晋", "蒙", "辽", "吉", "黑", "苏", "浙",
-    "皖", "闽", "赣", "鲁", "豫", "鄂", "湘", "粤", "桂", "琼", "川", "贵",
-    "云", "藏", "陕", "甘", "青", "宁", "新", "0",  "1",  "2",  "3",  "4",
-    "5",  "6",  "7",  "8",  "9",  "A",  "B",  "C",  "D",  "E",  "F",  "G",
-    "H",  "J",  "K",  "L",  "M",  "N",  "P",  "Q",  "R",  "S",  "T",  "U",
-    "V",  "W",  "X",  "Y",  "Z",  "港", "学", "使", "警", "澳", "挂", "军",
-    "北", "南", "广", "沈", "兰", "成", "济", "海", "民", "航", "空"};
-
-const int SEGMENTATION_FREE_METHOD = 0;
-const int SEGMENTATION_BASED_METHOD = 1;
-
-class PipelinePR {
-public:
-  GeneralRecognizer *generalRecognizer;
-  PlateDetection *plateDetection;
-  PlateSegmentation *plateSegmentation;
-  FineMapping *fineMapping;
-  SegmentationFreeRecognizer *segmentationFreeRecognizer;
-
-  PipelinePR(std::string detector_filename, std::string finemapping_prototxt,
-             std::string finemapping_caffemodel,
-             std::string segmentation_prototxt,
-             std::string segmentation_caffemodel,
-             std::string charRecognization_proto,
-             std::string charRecognization_caffemodel,
-             std::string segmentationfree_proto,
-             std::string segmentationfree_caffemodel);
-  ~PipelinePR();
-
-  std::vector<std::string> plateRes;
-  std::vector<PlateInfo> RunPiplineAsImage(cv::Mat plateImage, int method);
-};
-
-} // namespace pr
-#endif // HYPERPR_PIPLINE_H
diff --git a/Prj-Linux/hyperlpr/include/PlateDetection.h b/Prj-Linux/hyperlpr/include/PlateDetection.h
deleted file mode 100644
index aa4cece..0000000
--- a/Prj-Linux/hyperlpr/include/PlateDetection.h
+++ /dev/null
@@ -1,32 +0,0 @@
-//
-// Created by Jack Yu on 20/09/2017.
-//
-
-#ifndef HYPERPR_PLATEDETECTION_H
-#define HYPERPR_PLATEDETECTION_H
-
-#include <PlateInfo.h>
-#include <opencv2/opencv.hpp>
-#include <vector>
-namespace pr {
-class PlateDetection {
-public:
-  PlateDetection(std::string filename_cascade);
-  PlateDetection();
-  void LoadModel(std::string filename_cascade);
-  void plateDetectionRough(cv::Mat InputImage,
-                           std::vector<pr::PlateInfo> &plateInfos,
-                           int min_w = 36, int max_w = 800);
-  //        std::vector<pr::PlateInfo> plateDetectionRough(cv::Mat
-  //        InputImage,int min_w= 60,int max_h = 400);
-
-  //        std::vector<pr::PlateInfo>
-  //        plateDetectionRoughByMultiScaleEdge(cv::Mat InputImage);
-
-private:
-  cv::CascadeClassifier cascade;
-};
-
-} // namespace pr
-
-#endif // HYPERPR_PLATEDETECTION_H
diff --git a/Prj-Linux/hyperlpr/include/PlateInfo.h b/Prj-Linux/hyperlpr/include/PlateInfo.h
deleted file mode 100644
index d3cd02b..0000000
--- a/Prj-Linux/hyperlpr/include/PlateInfo.h
+++ /dev/null
@@ -1,94 +0,0 @@
-//
-// Created by Jack Yu on 20/09/2017.
-//
-
-#ifndef HYPERPR_PLATEINFO_H
-#define HYPERPR_PLATEINFO_H
-#include <opencv2/opencv.hpp>
-namespace pr {
-
-typedef std::vector<cv::Mat> Character;
-
-enum PlateColor { BLUE, YELLOW, WHITE, GREEN, BLACK, UNKNOWN };
-enum CharType { CHINESE, LETTER, LETTER_NUMS, INVALID };
-
-class PlateInfo {
-public:
-  std::vector<std::pair<CharType, cv::Mat>> plateChars;
-  std::vector<std::pair<CharType, cv::Mat>> plateCoding;
-  float confidence = 0;
-  PlateInfo(const cv::Mat &plateData, std::string plateName, cv::Rect plateRect,
-            PlateColor plateType) {
-    licensePlate = plateData;
-    name = plateName;
-    ROI = plateRect;
-    Type = plateType;
-  }
-  PlateInfo(const cv::Mat &plateData, cv::Rect plateRect,
-            PlateColor plateType) {
-    licensePlate = plateData;
-    ROI = plateRect;
-    Type = plateType;
-  }
-  PlateInfo(const cv::Mat &plateData, cv::Rect plateRect) {
-    licensePlate = plateData;
-    ROI = plateRect;
-  }
-  PlateInfo() {}
-
-  cv::Mat getPlateImage() { return licensePlate; }
-
-  void setPlateImage(cv::Mat plateImage) { licensePlate = plateImage; }
-
-  cv::Rect getPlateRect() { return ROI; }
-
-  void setPlateRect(cv::Rect plateRect) { ROI = plateRect; }
-  cv::String getPlateName() { return name; }
-  void setPlateName(cv::String plateName) { name = plateName; }
-  int getPlateType() { return Type; }
-
-  void appendPlateChar(const std::pair<CharType, cv::Mat> &plateChar) {
-    plateChars.push_back(plateChar);
-  }
-
-  void appendPlateCoding(const std::pair<CharType, cv::Mat> &charProb) {
-    plateCoding.push_back(charProb);
-  }
-  std::string decodePlateNormal(std::vector<std::string> mappingTable) {
-    std::string decode;
-    for (auto plate : plateCoding) {
-      float *prob = (float *)plate.second.data;
-      if (plate.first == CHINESE) {
-        decode += mappingTable[std::max_element(prob, prob + 31) - prob];
-        confidence += *std::max_element(prob, prob + 31);
-      }
-
-      else if (plate.first == LETTER) {
-        decode += mappingTable[std::max_element(prob + 41, prob + 65) - prob];
-        confidence += *std::max_element(prob + 41, prob + 65);
-      }
-
-      else if (plate.first == LETTER_NUMS) {
-        decode += mappingTable[std::max_element(prob + 31, prob + 65) - prob];
-        confidence += *std::max_element(prob + 31, prob + 65);
-
-      } else if (plate.first == INVALID) {
-        decode += '*';
-      }
-    }
-    name = decode;
-
-    confidence /= 7;
-
-    return decode;
-  }
-
-private:
-  cv::Mat licensePlate;
-  cv::Rect ROI;
-  std::string name;
-  PlateColor Type;
-};
-} // namespace pr
-
-#endif // HYPERPR_PLATEINFO_H
diff --git a/Prj-Linux/hyperlpr/include/PlateSegmentation.h b/Prj-Linux/hyperlpr/include/PlateSegmentation.h
deleted file mode 100644
index 623d2f5..0000000
--- a/Prj-Linux/hyperlpr/include/PlateSegmentation.h
+++ /dev/null
@@ -1,35 +0,0 @@
-#ifndef HYPERPR_PLATESEGMENTATION_H
-#define HYPERPR_PLATESEGMENTATION_H
-
-#include "PlateInfo.h"
-#include "opencv2/opencv.hpp"
-#include <opencv2/dnn.hpp>
-
-namespace pr {
-
-class PlateSegmentation {
-public:
-  const int PLATE_NORMAL = 6;
-  const int PLATE_NORMAL_GREEN = 7;
-  const int DEFAULT_WIDTH = 20;
-  PlateSegmentation(std::string phototxt, std::string caffemodel);
-  PlateSegmentation() {}
-  void segmentPlatePipline(PlateInfo &plateInfo, int stride,
-                           std::vector<cv::Rect> &Char_rects);
-
-  void segmentPlateBySlidingWindows(cv::Mat &plateImage, int windowsWidth,
-                                    int stride, cv::Mat &respones);
-  void templateMatchFinding(const cv::Mat &respones, int windowsWidth,
-                            std::pair<float, std::vector<int>> &candidatePts);
-  void refineRegion(cv::Mat &plateImage, const std::vector<int> &candidatePts,
-                    const int padding, std::vector<cv::Rect> &rects);
-  void ExtractRegions(PlateInfo &plateInfo, std::vector<cv::Rect> &rects);
-  cv::Mat classifyResponse(const cv::Mat &cropped);
-
-private:
-  cv::dnn::Net net;
-};
-
-} // namespace pr
-
-#endif // HYPERPR_PLATESEGMENTATION_H
diff --git a/Prj-Linux/hyperlpr/include/Recognizer.h b/Prj-Linux/hyperlpr/include/Recognizer.h
deleted file mode 100644
index 3fc9765..0000000
--- a/Prj-Linux/hyperlpr/include/Recognizer.h
+++ /dev/null
@@ -1,22 +0,0 @@
-//
-// Created by Jack Yu on 20/10/2017.
-//
-
-#ifndef HYPERPR_RECOGNIZER_H
-#define HYPERPR_RECOGNIZER_H
-
-#include "PlateInfo.h"
-#include "opencv2/dnn.hpp"
-namespace pr {
-typedef cv::Mat label;
-class GeneralRecognizer {
-public:
-  virtual label recognizeCharacter(cv::Mat character) = 0;
-  //            virtual cv::Mat SegmentationFreeForSinglePlate(cv::Mat plate) =
-  //            0;
-  void SegmentBasedSequenceRecognition(PlateInfo &plateinfo);
-  void SegmentationFreeSequenceRecognition(PlateInfo &plateInfo);
-};
-
-} // namespace pr
-#endif // HYPERPR_RECOGNIZER_H
diff --git a/Prj-Linux/hyperlpr/include/SegmentationFreeRecognizer.h b/Prj-Linux/hyperlpr/include/SegmentationFreeRecognizer.h
deleted file mode 100644
index 458192b..0000000
--- a/Prj-Linux/hyperlpr/include/SegmentationFreeRecognizer.h
+++ /dev/null
@@ -1,27 +0,0 @@
-//
-// Created by Jack Yu on 28/11/2017.
-//
-
-#ifndef HYPERPR_SEGMENTATIONFREERECOGNIZER_H
-#define HYPERPR_SEGMENTATIONFREERECOGNIZER_H
-
-#include "Recognizer.h"
-namespace pr {
-
-class SegmentationFreeRecognizer {
-public:
-  const int CHAR_INPUT_W = 14;
-  const int CHAR_INPUT_H = 30;
-  const int CHAR_LEN = 84;
-
-  SegmentationFreeRecognizer(std::string prototxt, std::string caffemodel);
-  std::pair<std::string, float>
-  SegmentationFreeForSinglePlate(cv::Mat plate,
-                                 std::vector<std::string> mapping_table);
-
-private:
-  cv::dnn::Net net;
-};
-
-} // namespace pr
-#endif // HYPERPR_SEGMENTATIONFREERECOGNIZER_H
diff --git a/Prj-Linux/hyperlpr/include/niBlackThreshold.h b/Prj-Linux/hyperlpr/include/niBlackThreshold.h
deleted file mode 100644
index c1a4d0b..0000000
--- a/Prj-Linux/hyperlpr/include/niBlackThreshold.h
+++ /dev/null
@@ -1,105 +0,0 @@
-//
-// Created by Jack Yu on 26/10/2017.
-//
-
-#ifndef HYPERPR_NIBLACKTHRESHOLD_H
-#define HYPERPR_NIBLACKTHRESHOLD_H
-
-#include <opencv2/opencv.hpp>
-using namespace cv;
-
-enum LocalBinarizationMethods {
-  BINARIZATION_NIBLACK =
-      0, //!< Classic Niblack binarization. See @cite Niblack1985 .
-  BINARIZATION_SAUVOLA = 1, //!< Sauvola's technique. See @cite Sauvola1997 .
-  BINARIZATION_WOLF = 2,    //!< Wolf's technique. See @cite Wolf2004 .
-  BINARIZATION_NICK = 3     //!< NICK technique. See @cite Khurshid2009 .
-};
-
-void niBlackThreshold(InputArray _src, OutputArray _dst, double maxValue,
-                      int type, int blockSize, double k,
-                      int binarizationMethod) {
-  // Input grayscale image
-  Mat src = _src.getMat();
-  CV_Assert(src.channels() == 1);
-  CV_Assert(blockSize % 2 == 1 && blockSize > 1);
-  if (binarizationMethod == BINARIZATION_SAUVOLA) {
-    CV_Assert(src.depth() == CV_8U);
-  }
-  type &= THRESH_MASK;
-  // Compute local threshold (T = mean + k * stddev)
-  // using mean and standard deviation in the neighborhood of each pixel
-  // (intermediate calculations are done with floating-point precision)
-  Mat test;
-  Mat thresh;
-  {
-    // note that: Var[X] = E[X^2] - E[X]^2
-    Mat mean, sqmean, variance, stddev, sqrtVarianceMeanSum;
-    double srcMin, stddevMax;
-    boxFilter(src, mean, CV_32F, Size(blockSize, blockSize), Point(-1, -1),
-              true, BORDER_REPLICATE);
-    sqrBoxFilter(src, sqmean, CV_32F, Size(blockSize, blockSize), Point(-1, -1),
-                 true, BORDER_REPLICATE);
-    variance = sqmean - mean.mul(mean);
-    sqrt(variance, stddev);
-    switch (binarizationMethod) {
-    case BINARIZATION_NIBLACK:
-      thresh = mean + stddev * static_cast<float>(k);
-
-      break;
-    case BINARIZATION_SAUVOLA:
-      thresh = mean.mul(1. + static_cast<float>(k) * (stddev / 128.0 - 1.));
-      break;
-    case BINARIZATION_WOLF:
-      minMaxIdx(src, &srcMin, NULL);
-      minMaxIdx(stddev, NULL, &stddevMax);
-      thresh =
-          mean - static_cast<float>(k) *
-                     (mean - srcMin - stddev.mul(mean - srcMin) / stddevMax);
-      break;
-    case BINARIZATION_NICK:
-      sqrt(variance + sqmean, sqrtVarianceMeanSum);
-      thresh = mean + static_cast<float>(k) * sqrtVarianceMeanSum;
-      break;
-    default:
-      CV_Error(CV_StsBadArg, "Unknown binarization method");
-      break;
-    }
-    thresh.convertTo(thresh, src.depth());
-
-    thresh.convertTo(test, src.depth());
-    //
-    //        cv::imshow("imagex",test);
-    //        cv::waitKey(0);
-  }
-  // Prepare output image
-  _dst.create(src.size(), src.type());
-  Mat dst = _dst.getMat();
-  CV_Assert(src.data != dst.data); // no inplace processing
-  // Apply thresholding: ( pixel > threshold ) ? foreground : background
-  Mat mask;
-  switch (type) {
-  case THRESH_BINARY:     // dst = (src > thresh) ? maxval : 0
-  case THRESH_BINARY_INV: // dst = (src > thresh) ? 0 : maxval
-    compare(src, thresh, mask, (type == THRESH_BINARY ? CMP_GT : CMP_LE));
-    dst.setTo(0);
-    dst.setTo(maxValue, mask);
-    break;
-  case THRESH_TRUNC: // dst = (src > thresh) ? thresh : src
-    compare(src, thresh, mask, CMP_GT);
-    src.copyTo(dst);
-    thresh.copyTo(dst, mask);
-    break;
-  case THRESH_TOZERO:     // dst = (src > thresh) ? src : 0
-  case THRESH_TOZERO_INV: // dst = (src > thresh) ? 0 : src
-    compare(src, thresh, mask, (type == THRESH_TOZERO ? CMP_GT : CMP_LE));
-    dst.setTo(0);
-    src.copyTo(dst, mask);
-    break;
-  default:
-    CV_Error(CV_StsBadArg, "Unknown threshold type");
-    break;
-  }
-}
-
-#endif // HYPERPR_NIBLACKTHRESHOLD_H
diff --git a/Prj-Linux/hyperlpr/model/CharacterRecognization.caffemodel b/Prj-Linux/hyperlpr/model/CharacterRecognization.caffemodel
deleted file mode 100644
index d424408..0000000
Binary files a/Prj-Linux/hyperlpr/model/CharacterRecognization.caffemodel and /dev/null differ
diff --git a/Prj-Linux/hyperlpr/model/CharacterRecognization.prototxt b/Prj-Linux/hyperlpr/model/CharacterRecognization.prototxt
deleted file mode 100644
index 91aa29e..0000000
--- a/Prj-Linux/hyperlpr/model/CharacterRecognization.prototxt
+++ /dev/null
@@ -1,123 +0,0 @@
-input: "data"
-input_dim: 1
-input_dim: 1
-input_dim: 30
-input_dim: 14
-layer {
-  name: "conv2d_1"
-  type: "Convolution"
-  bottom: "data"
-  top: "conv2d_1"
-  convolution_param {
-    num_output: 32
-    bias_term: true
-    pad: 0
-    kernel_size: 3
-    stride: 1
-  }
-}
-layer {
-  name: "activation_1"
-  type: "ReLU"
-  bottom: "conv2d_1"
-  top: "activation_1"
-}
-layer {
-  name: "max_pooling2d_1"
-  type: "Pooling"
-  bottom: "activation_1"
-  top: "max_pooling2d_1"
-  pooling_param {
-    pool: MAX
-    kernel_size: 2
-    stride: 2
-    pad: 0
-  }
-}
-layer {
-  name: "conv2d_2"
-  type: "Convolution"
-  bottom: "max_pooling2d_1"
-  top: "conv2d_2"
-  convolution_param {
-    num_output: 64
-    bias_term: true
-    pad: 0
-    kernel_size: 3
-    stride: 1
-  }
-}
-layer {
-  name: "activation_2"
-  type: "ReLU"
-  bottom: "conv2d_2"
-  top: "activation_2"
-}
-layer {
-  name: "max_pooling2d_2"
-  type: "Pooling"
-  bottom: "activation_2"
-  top: "max_pooling2d_2"
-  pooling_param {
-    pool: MAX
-    kernel_size: 2
-    stride: 2
-    pad: 0
-  }
-}
-layer {
-  name: "conv2d_3"
-  type: "Convolution"
-  bottom: "max_pooling2d_2"
-  top: "conv2d_3"
-  convolution_param {
-    num_output: 128
-    bias_term: true
-    pad: 0
-    kernel_size: 2
-    stride: 1
-  }
-}
-layer {
-  name: "activation_3"
-  type: "ReLU"
-  bottom: "conv2d_3"
-  top: "activation_3"
-}
-layer {
-  name: "flatten_1"
-  type: "Flatten"
-  bottom: "activation_3"
-  top: "flatten_1"
-}
-layer {
-  name: "dense_1"
-  type: "InnerProduct"
-  bottom: "flatten_1"
-  top: "dense_1"
-  inner_product_param {
-    num_output: 256
-  }
-}
-layer {
-  name: "relu2"
-  type: "ReLU"
-  bottom: "dense_1"
-  top: "relu2"
-}
-layer {
-  name: "dense2"
-  type: "InnerProduct"
-  bottom: "relu2"
-  top: "dense2"
-  inner_product_param {
-    num_output: 65
-  }
-}
-
-layer {
-  name: "prob"
-  type: "Softmax"
-  bottom: "dense2"
-  top: "prob"
-}
\ No newline at end of file
diff --git a/Prj-Linux/hyperlpr/model/HorizonalFinemapping.caffemodel b/Prj-Linux/hyperlpr/model/HorizonalFinemapping.caffemodel
deleted file mode 100644
index 1af5863..0000000
Binary files a/Prj-Linux/hyperlpr/model/HorizonalFinemapping.caffemodel and /dev/null differ
diff --git a/Prj-Linux/hyperlpr/model/HorizonalFinemapping.prototxt b/Prj-Linux/hyperlpr/model/HorizonalFinemapping.prototxt
deleted file mode 100644
index 21726dd..0000000
--- a/Prj-Linux/hyperlpr/model/HorizonalFinemapping.prototxt
+++ /dev/null
@@ -1,95 +0,0 @@
-input: "data"
-input_dim: 1
-input_dim: 3
-input_dim: 16
-input_dim: 66
-layer {
-  name: "conv1"
-  type: "Convolution"
-  bottom: "data"
-  top: "conv1"
-  convolution_param {
-    num_output: 10
-    bias_term: true
-    pad: 0
-    kernel_size: 3
-    stride: 1
-  }
-}
-layer {
-  name: "relu1"
-  type: "ReLU"
-  bottom: "conv1"
-  top: "conv1"
-}
-layer {
-  name: "max_pooling2d_3"
-  type: "Pooling"
-  bottom: "conv1"
-  top: "max_pooling2d_3"
-  pooling_param {
-    pool: MAX
-    kernel_size: 2
-    stride: 2
-    pad: 0
-  }
-}
-layer {
-  name: "conv2"
-  type: "Convolution"
-  bottom: "max_pooling2d_3"
-  top: "conv2"
-  convolution_param {
-    num_output: 16
-    bias_term: true
-    pad: 0
-    kernel_size: 3
-    stride: 1
-  }
-}
-layer {
-  name: "relu2"
-  type: "ReLU"
-  bottom: "conv2"
-  top: "conv2"
-}
-layer {
-  name: "conv3"
-  type: "Convolution"
-  bottom: "conv2"
-  top: "conv3"
-  convolution_param {
-    num_output: 32
-    bias_term: true
-    pad: 0
-    kernel_size: 3
-    stride: 1
-  }
-}
-layer {
-  name: "relu3"
-  type: "ReLU"
-  bottom: "conv3"
-  top: "conv3"
-}
-layer {
-  name: "flatten_2"
-  type: "Flatten"
-  bottom: "conv3"
-  top: "flatten_2"
-}
-layer {
-  name: "dense"
-  type: "InnerProduct"
-  bottom: "flatten_2"
-  top: "dense"
-  inner_product_param {
-    num_output: 2
-  }
-}
-layer {
-  name: "relu4"
-  type: "ReLU"
-  bottom: "dense"
-  top: "dense"
-}
diff --git a/Prj-Linux/hyperlpr/model/SegmenationFree-Inception.caffemodel b/Prj-Linux/hyperlpr/model/SegmenationFree-Inception.caffemodel
deleted file mode 100644
index 311ee17..0000000
Binary files a/Prj-Linux/hyperlpr/model/SegmenationFree-Inception.caffemodel and /dev/null differ
diff --git a/Prj-Linux/hyperlpr/model/SegmenationFree-Inception.prototxt b/Prj-Linux/hyperlpr/model/SegmenationFree-Inception.prototxt
deleted file mode 100644
index 8419763..0000000
--- a/Prj-Linux/hyperlpr/model/SegmenationFree-Inception.prototxt
+++ /dev/null
@@ -1,454 +0,0 @@
-input: "data"
-input_dim: 1
-input_dim: 3
-input_dim: 160
-input_dim: 40
-layer {
-  name: "conv0"
-  type: "Convolution"
-  bottom: "data"
-  top: "conv0"
-  convolution_param {
-    num_output: 32
-    bias_term: true
-    pad_h: 1
-    pad_w: 1
-    kernel_h: 3
-    kernel_w: 3
-    stride_h: 1
-    stride_w: 1
-  }
-}
-layer {
-  name: "bn0"
-  type: "BatchNorm"
-  bottom: "conv0"
-  top: "bn0"
-  batch_norm_param {
-    moving_average_fraction: 0.99
-    eps: 0.001
-  }
-}
-layer {
-  name: "bn0_scale"
-  type: "Scale"
-  bottom: "bn0"
-  top: "bn0"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "relu0"
-  type: "ReLU"
-  bottom: "bn0"
-  top: "bn0"
-}
-layer {
-  name: "pool0"
-  type: "Pooling"
-  bottom: "bn0"
-  top: "pool0"
-  pooling_param {
-    pool: MAX
-    kernel_h: 2
-    kernel_w: 2
-    stride_h: 2
-    stride_w: 2
-    pad_h: 0
-    pad_w: 0
-  }
-}
-layer {
-  name: "conv1"
-  type: "Convolution"
-  bottom: "pool0"
-  top: "conv1"
-  convolution_param {
-    num_output: 64
-    bias_term: true
-    pad_h: 1
-    pad_w: 1
-    kernel_h: 3
-    kernel_w: 3
-    stride_h: 1
-    stride_w: 1
-  }
-}
-layer {
-  name: "bn1"
-  type: "BatchNorm"
-  bottom: "conv1"
-  top: "bn1"
-  batch_norm_param {
-    moving_average_fraction: 0.99
-    eps: 0.001
-  }
-}
-layer {
-  name: "bn1_scale"
-  type: "Scale"
-  bottom: "bn1"
-  top: "bn1"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "relu1"
-  type: "ReLU"
-  bottom: "bn1"
-  top: "bn1"
-}
-layer {
-  name: "pool1"
-  type: "Pooling"
-  bottom: "bn1"
-  top: "pool1"
-  pooling_param {
-    pool: MAX
-    kernel_h: 2
-    kernel_w: 2
-    stride_h: 2
-    stride_w: 2
-    pad_h: 0
-    pad_w: 0
-  }
-}
-layer {
-  name: "conv2"
-  type: "Convolution"
-  bottom: "pool1"
-  top: "conv2"
-  convolution_param {
-    num_output: 128
-    bias_term: true
-    pad_h: 1
-    pad_w: 1
-    kernel_h: 3
-    kernel_w: 3
-    stride_h: 1
-    stride_w: 1
-  }
-}
-layer {
-  name: "bn2"
-  type: "BatchNorm"
-  bottom: "conv2"
-  top: "bn2"
-  batch_norm_param {
-    moving_average_fraction: 0.99
-    eps: 0.001
-  }
-}
-layer {
-  name: "bn2_scale"
-  type: "Scale"
-  bottom: "bn2"
-  top: "bn2"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "relu2"
-  type: "ReLU"
-  bottom: "bn2"
-  top: "bn2"
-}
-layer {
-  name: "pool2"
-  type: "Pooling"
-  bottom: "bn2"
-  top: "pool2"
-  pooling_param {
-    pool: MAX
-    kernel_h: 2
-    kernel_w: 2
-    stride_h: 2
-    stride_w: 2
-    pad_h: 0
-    pad_w: 0
-  }
-}
-layer {
-  name: "conv2d_1"
-  type: "Convolution"
-  bottom: "pool2"
-  top: "conv2d_1"
-  convolution_param {
-    num_output: 256
-    bias_term: true
-    pad_h: 0
-    pad_w: 0
-    kernel_h: 1
-    kernel_w: 5
-    stride_h: 1
-    stride_w: 1
-  }
-}
-layer {
-  name: "batch_normalization_1"
-  type: "BatchNorm"
-  bottom: "conv2d_1"
-  top: "batch_normalization_1"
-  batch_norm_param {
-    moving_average_fraction: 0.99
-    eps: 0.001
-  }
-}
-layer {
-  name: "batch_normalization_1_scale"
-  type: "Scale"
-  bottom: "batch_normalization_1"
-  top: "batch_normalization_1"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "activation_1"
-  type: "ReLU"
-  bottom: "batch_normalization_1"
-  top: "batch_normalization_1"
-}
-layer {
-  name: "conv2d_2"
-  type: "Convolution"
-  bottom: "batch_normalization_1"
-  top: "conv2d_2"
-  convolution_param {
-    num_output: 256
-    bias_term: true
-    pad_h: 3
-    pad_w: 0
-    kernel_h: 7
-    kernel_w: 1
-    stride_h: 1
-    stride_w: 1
-  }
-}
-layer {
-  name: "conv2d_3"
-  type: "Convolution"
-  bottom: "batch_normalization_1"
-  top: "conv2d_3"
-  convolution_param {
-    num_output: 256
-    bias_term: true
-    pad_h: 2
-    pad_w: 0
-    kernel_h: 5
-    kernel_w: 1
-    stride_h: 1
-    stride_w: 1
-  }
-}
-layer {
-  name: "conv2d_4"
-  type: "Convolution"
-  bottom: "batch_normalization_1"
-  top: "conv2d_4"
-  convolution_param {
-    num_output: 256
-    bias_term: true
-    pad_h: 1
-    pad_w: 0
-    kernel_h: 3
-    kernel_w: 1
-    stride_h: 1
-    stride_w: 1
-  }
-}
-layer {
-  name: "conv2d_5"
-  type: "Convolution"
-  bottom: "batch_normalization_1"
-  top: "conv2d_5"
-  convolution_param {
-    num_output: 256
-    bias_term: true
-    pad_h: 0
-    pad_w: 0
-    kernel_h: 1
-    kernel_w: 1
-    stride_h: 1
-    stride_w: 1
-  }
-}
-layer {
-  name: "batch_normalization_2"
-  type: "BatchNorm"
-  bottom: "conv2d_2"
-  top: "batch_normalization_2"
-  batch_norm_param {
-    moving_average_fraction: 0.99
-    eps: 0.001
-  }
-}
-layer {
-  name: "batch_normalization_2_scale"
-  type: "Scale"
-  bottom: "batch_normalization_2"
-  top: "batch_normalization_2"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "batch_normalization_3"
-  type: "BatchNorm"
-  bottom: "conv2d_3"
-  top: "batch_normalization_3"
-  batch_norm_param {
-    moving_average_fraction: 0.99
-    eps: 0.001
-  }
-}
-layer {
-  name: "batch_normalization_3_scale"
-  type: "Scale"
-  bottom: "batch_normalization_3"
-  top: "batch_normalization_3"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "batch_normalization_4"
-  type: "BatchNorm"
-  bottom: "conv2d_4"
-  top: "batch_normalization_4"
-  batch_norm_param {
-    moving_average_fraction: 0.99
-    eps: 0.001
-  }
-}
-layer {
-  name: "batch_normalization_4_scale"
-  type: "Scale"
-  bottom: "batch_normalization_4"
-  top: "batch_normalization_4"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "batch_normalization_5"
-  type: "BatchNorm"
-  bottom: "conv2d_5"
-  top: "batch_normalization_5"
-  batch_norm_param {
-    moving_average_fraction: 0.99
-    eps: 0.001
-  }
-}
-layer {
-  name: "batch_normalization_5_scale"
-  type: "Scale"
-  bottom: "batch_normalization_5"
-  top: "batch_normalization_5"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "activation_2"
-  type: "ReLU"
-  bottom: "batch_normalization_2"
-  top: "batch_normalization_2"
-}
-layer {
-  name: "activation_3"
-  type: "ReLU"
-  bottom: "batch_normalization_3"
-  top: "batch_normalization_3"
-}
-layer {
-  name: "activation_4"
-  type: "ReLU"
-  bottom: "batch_normalization_4"
-  top: "batch_normalization_4"
-}
-layer {
-  name: "activation_5"
-  type: "ReLU"
-  bottom: "batch_normalization_5"
-  top: "batch_normalization_5"
-}
-layer {
-  name: "concatenate_1"
-  type: "Concat"
-  bottom: "batch_normalization_2"
-  bottom: "batch_normalization_3"
-  bottom: "batch_normalization_4"
-  bottom: "batch_normalization_5"
-  top: "concatenate_1"
-  concat_param {
-    axis: 1
-  }
-}
-layer {
-  name: "conv_1024_11"
-  type: "Convolution"
-  bottom: "concatenate_1"
-  top: "conv_1024_11"
-  convolution_param {
-    num_output: 1024
-    bias_term: true
-    pad_h: 0
-    pad_w: 0
-    kernel_h: 1
-    kernel_w: 1
-    stride_h: 1
-    stride_w: 1
-  }
-}
-layer {
-  name: "batch_normalization_6"
-  type: "BatchNorm"
-  bottom: "conv_1024_11"
-  top: "batch_normalization_6"
-  batch_norm_param {
-    moving_average_fraction: 0.99
-    eps: 0.001
-  }
-}
-layer {
-  name: "batch_normalization_6_scale"
-  type: "Scale"
-  bottom: "batch_normalization_6"
-  top: "batch_normalization_6"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "activation_6"
-  type: "ReLU"
-  bottom: "batch_normalization_6"
-  top: "batch_normalization_6"
-}
-layer {
-  name: "conv_class_11"
-  type: "Convolution"
-  bottom: "batch_normalization_6"
-  top: "conv_class_11"
-  convolution_param {
-    num_output: 84
-    bias_term: true
-    pad_h: 0
-    pad_w: 0
-    kernel_h: 1
-    kernel_w: 1
-    stride_h: 1
-    stride_w: 1
-  }
-}
-layer {
-  name: "prob"
-  type: "Softmax"
-  bottom: "conv_class_11"
-  top: "prob"
-}
-
diff --git a/Prj-Linux/hyperlpr/model/Segmentation.caffemodel b/Prj-Linux/hyperlpr/model/Segmentation.caffemodel
deleted file mode 100644
index 208269d..0000000
Binary files a/Prj-Linux/hyperlpr/model/Segmentation.caffemodel and /dev/null differ
diff --git a/Prj-Linux/hyperlpr/model/Segmentation.prototxt b/Prj-Linux/hyperlpr/model/Segmentation.prototxt
deleted file mode 100644
index 45c6829..0000000
--- a/Prj-Linux/hyperlpr/model/Segmentation.prototxt
+++ /dev/null
@@ -1,114 +0,0 @@
-input: "data"
-input_dim: 1
-input_dim: 1
-input_dim: 22
-input_dim: 22
-layer {
-  name: "conv2d_12"
-  type: "Convolution"
-  bottom: "data"
-  top: "conv2d_12"
-  convolution_param {
-    num_output: 16
-    bias_term: true
-    pad: 0
-    kernel_size: 3
-    stride: 1
-  }
-}
-layer {
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-  type: "ReLU"
-  bottom: "conv2d_12"
-  top: "activation_18"
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-layer {
-  name: "max_pooling2d_10"
-  type: "Pooling"
-  bottom: "activation_18"
-  top: "max_pooling2d_10"
-  pooling_param {
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-    kernel_size: 2
-    stride: 2
-    pad: 0
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-}
-layer {
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-  type: "Convolution"
-  bottom: "max_pooling2d_10"
-  top: "conv2d_13"
-  convolution_param {
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-  top: "activation_19"
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-layer {
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-  top: "max_pooling2d_11"
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-  top: "flatten_6"
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-  inner_product_param {
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-layer {
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-  bottom: "dense_9"
-  top: "dropout_9"
-  dropout_param {
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-layer {
-  name: "activation_20"
-  type: "ReLU"
-  bottom: "dropout_9"
-  top: "activation_20"
-}
-layer {
-  name: "dense_10"
-  type: "InnerProduct"
-  bottom: "activation_20"
-  top: "dense_10"
-  inner_product_param {
-    num_output: 3
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-
-
-  layer {
-  name: "prob"
-  type: "Softmax"
-  bottom: "dense_10"
-  top: "prob"
-}
\ No newline at end of file
diff --git a/Prj-Linux/hyperlpr/model/cascade.xml b/Prj-Linux/hyperlpr/model/cascade.xml
deleted file mode 100644
index 277775b..0000000
--- a/Prj-Linux/hyperlpr/model/cascade.xml
+++ /dev/null
@@ -1,12117 +0,0 @@
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diff --git a/Prj-Linux/hyperlpr/src/CNNRecognizer.cpp b/Prj-Linux/hyperlpr/src/CNNRecognizer.cpp
deleted file mode 100644
index e3aa713..0000000
--- a/Prj-Linux/hyperlpr/src/CNNRecognizer.cpp
+++ /dev/null
@@ -1,21 +0,0 @@
-//
-// Created by Jack Yu on 21/10/2017.
-//
-
-#include "../include/CNNRecognizer.h"
-
-namespace pr {
-CNNRecognizer::CNNRecognizer(std::string prototxt, std::string caffemodel) {
-  net = cv::dnn::readNetFromCaffe(prototxt, caffemodel);
-}
-
-label CNNRecognizer::recognizeCharacter(cv::Mat charImage) {
-  if (charImage.channels() == 3)
-    cv::cvtColor(charImage, charImage, cv::COLOR_BGR2GRAY);
-  cv::Mat inputBlob = cv::dnn::blobFromImage(
-      charImage, 1 / 255.0, cv::Size(CHAR_INPUT_W, CHAR_INPUT_H),
-      cv::Scalar(0, 0, 0), false);
-  net.setInput(inputBlob, "data");
-  return net.forward();
-}
-} // namespace pr
diff --git a/Prj-Linux/hyperlpr/src/FastDeskew.cpp b/Prj-Linux/hyperlpr/src/FastDeskew.cpp
deleted file mode 100644
index 7fd14b6..0000000
--- a/Prj-Linux/hyperlpr/src/FastDeskew.cpp
+++ /dev/null
@@ -1,104 +0,0 @@
-//
-// Created by Jack Yu on 02/10/2017.
-//
-
-#include <../include/FastDeskew.h>
-
-namespace pr {
-const int ANGLE_MIN = 30;
-const int ANGLE_MAX = 150;
-const int PLATE_H = 36;
-const int PLATE_W = 136;
-
-int angle(float x, float y) { return atan2(x, y) * 180 / 3.1415; }
-
-std::vector<float> avgfilter(std::vector<float> angle_list, int windowsSize) {
-  std::vector<float> angle_list_filtered(angle_list.size() - windowsSize + 1);
-  for (int i = 0; i < angle_list.size() - windowsSize + 1; i++) {
-    float avg = 0.00f;
-    for (int j = 0; j < windowsSize; j++) {
-      avg += angle_list[i + j];
-    }
-    avg = avg / windowsSize;
-    angle_list_filtered[i] = avg;
-  }
-
-  return angle_list_filtered;
-}
-
-void drawHist(std::vector<float> seq) {
-  cv::Mat image(300, seq.size(), CV_8U);
-  image.setTo(0);
-
-  for (int i = 0; i < seq.size(); i++) {
-    float l = *std::max_element(seq.begin(), seq.end());
-
-    int p = int(float(seq[i]) / l * 300);
-
-    cv::line(image, cv::Point(i, 300), cv::Point(i, 300 - p),
-             cv::Scalar(255, 255, 255));
-  }
-  cv::imshow("vis", image);
-}
-
-cv::Mat correctPlateImage(cv::Mat skewPlate, float angle, float maxAngle) {
-  cv::Mat dst;
-  cv::Size size_o(skewPlate.cols, skewPlate.rows);
-  int extend_padding = 0;
-  extend_padding =
-      static_cast<int>(skewPlate.rows * tan(cv::abs(angle) / 180 * 3.14));
-  cv::Size size(skewPlate.cols + extend_padding, skewPlate.rows);
-  float interval = abs(sin((angle / 180) * 3.14) * skewPlate.rows);
-  cv::Point2f pts1[4] = {cv::Point2f(0, 0), cv::Point2f(0, size_o.height),
-                         cv::Point2f(size_o.width, 0),
-                         cv::Point2f(size_o.width, size_o.height)};
-  if (angle > 0) {
-    cv::Point2f pts2[4] = {cv::Point2f(interval, 0),
-                           cv::Point2f(0, size_o.height),
-                           cv::Point2f(size_o.width, 0),
-                           cv::Point2f(size_o.width - interval, size_o.height)};
-    cv::Mat M = cv::getPerspectiveTransform(pts1, pts2);
-    cv::warpPerspective(skewPlate, dst, M, size);
-  } else {
-    cv::Point2f pts2[4] = {cv::Point2f(0, 0),
-                           cv::Point2f(interval, size_o.height),
-                           cv::Point2f(size_o.width - interval, 0),
-                           cv::Point2f(size_o.width, size_o.height)};
-    cv::Mat M = cv::getPerspectiveTransform(pts1, pts2);
-    cv::warpPerspective(skewPlate, dst, M, size, cv::INTER_CUBIC);
-  }
-  return dst;
-}
-cv::Mat fastdeskew(cv::Mat skewImage, int blockSize) {
-  const int FILTER_WINDOWS_SIZE = 5;
-  std::vector<float> angle_list(180);
-  memset(angle_list.data(), 0, angle_list.size() * sizeof(int));
-  cv::Mat bak;
-  skewImage.copyTo(bak);
-  if (skewImage.channels() == 3)
-    cv::cvtColor(skewImage, skewImage, cv::COLOR_RGB2GRAY);
-  if (skewImage.channels() == 1) {
-    cv::Mat eigen;
-    cv::cornerEigenValsAndVecs(skewImage, eigen, blockSize, 5);
-    for (int j = 0; j < skewImage.rows; j += blockSize) {
-      for (int i = 0; i < skewImage.cols; i += blockSize) {
-        float x2 = eigen.at<cv::Vec6f>(j, i)[4];
-        float y2 = eigen.at<cv::Vec6f>(j, i)[5];
-        int angle_cell = angle(x2, y2);
-        angle_list[(angle_cell + 180) % 180] += 1.0;
-      }
-    }
-  }
-  std::vector<float> filtered = avgfilter(angle_list, 5);
-  int maxPos = std::max_element(filtered.begin(), filtered.end()) -
-               filtered.begin() + FILTER_WINDOWS_SIZE / 2;
-  if (maxPos > ANGLE_MAX)
-    maxPos = (-maxPos + 90 + 180) % 180;
-  if (maxPos < ANGLE_MIN)
-    maxPos -= 90;
-  maxPos = 90 - maxPos;
-  cv::Mat deskewed = correctPlateImage(bak, static_cast<float>(maxPos), 60.0f);
-  return deskewed;
-}
-
-} // namespace pr
diff --git a/Prj-Linux/hyperlpr/src/FineMapping.cpp b/Prj-Linux/hyperlpr/src/FineMapping.cpp
deleted file mode 100644
index 8117ffe..0000000
--- a/Prj-Linux/hyperlpr/src/FineMapping.cpp
+++ /dev/null
@@ -1,165 +0,0 @@
-#include "FineMapping.h"
-namespace pr {
-
-const int FINEMAPPING_H = 60;
-const int FINEMAPPING_W = 140;
-const int PADDING_UP_DOWN = 30;
-void drawRect(cv::Mat image, cv::Rect rect) {
-  cv::Point p1(rect.x, rect.y);
-  cv::Point p2(rect.x + rect.width, rect.y + rect.height);
-  cv::rectangle(image, p1, p2, cv::Scalar(0, 255, 0), 1);
-}
-
-FineMapping::FineMapping(std::string prototxt, std::string caffemodel) {
-  net = cv::dnn::readNetFromCaffe(prototxt, caffemodel);
-}
-
-cv::Mat FineMapping::FineMappingHorizon(cv::Mat FinedVertical, int leftPadding,
-                                        int rightPadding) {
-  cv::Mat inputBlob = cv::dnn::blobFromImage(
-      FinedVertical, 1 / 255.0, cv::Size(66, 16), cv::Scalar(0, 0, 0), false);
-  net.setInput(inputBlob, "data");
-  cv::Mat prob = net.forward();
-  int front = static_cast<int>(prob.at<float>(0, 0) * FinedVertical.cols);
-  int back = static_cast<int>(prob.at<float>(0, 1) * FinedVertical.cols);
-  front -= leftPadding;
-  if (front < 0)
-    front = 0;
-  back += rightPadding;
-  if (back > FinedVertical.cols - 1)
-    back = FinedVertical.cols - 1;
-  cv::Mat cropped = FinedVertical.colRange(front, back).clone();
-  return cropped;
-}
-std::pair<int, int> FitLineRansac(std::vector<cv::Point> pts, int zeroadd = 0) {
-  std::pair<int, int> res;
-  if (pts.size() > 2) {
-    cv::Vec4f line;
-    cv::fitLine(pts, line, cv::DIST_HUBER, 0, 0.01, 0.01);
-    float vx = line[0];
-    float vy = line[1];
-    float x = line[2];
-    float y = line[3];
-    int lefty = static_cast<int>((-x * vy / vx) + y);
-    int righty = static_cast<int>(((136 - x) * vy / vx) + y);
-    res.first = lefty + PADDING_UP_DOWN + zeroadd;
-    res.second = righty + PADDING_UP_DOWN + zeroadd;
-    return res;
-  }
-  res.first = zeroadd;
-  res.second = zeroadd;
-  return res;
-}
-
-cv::Mat FineMapping::FineMappingVertical(cv::Mat InputProposal, int sliceNum,
-                                         int upper, int lower,
-                                         int windows_size) {
-  cv::Mat PreInputProposal;
-  cv::Mat proposal;
-  cv::resize(InputProposal, PreInputProposal,
-             cv::Size(FINEMAPPING_W, FINEMAPPING_H));
-  if (InputProposal.channels() == 3)
-    cv::cvtColor(PreInputProposal, proposal, cv::COLOR_BGR2GRAY);
-  else
-    PreInputProposal.copyTo(proposal);
-  // this will improve some sen
-  cv::Mat kernal = cv::getStructuringElement(cv::MORPH_ELLIPSE, cv::Size(1, 3));
-  float diff = static_cast<float>(upper - lower);
-  diff /= static_cast<float>(sliceNum - 1);
-  cv::Mat binary_adaptive;
-  std::vector<cv::Point> line_upper;
-  std::vector<cv::Point> line_lower;
-  int contours_nums = 0;
-  for (int i = 0; i < sliceNum; i++) {
-    std::vector<std::vector<cv::Point>> contours;
-    float k = lower + i * diff;
-    cv::adaptiveThreshold(proposal, binary_adaptive, 255,
-                          cv::ADAPTIVE_THRESH_MEAN_C, cv::THRESH_BINARY,
-                          windows_size, k);
-    cv::Mat draw;
-    binary_adaptive.copyTo(draw);
-    cv::findContours(binary_adaptive, contours, cv::RETR_EXTERNAL,
-                     cv::CHAIN_APPROX_SIMPLE);
-    for (auto contour : contours) {
-      cv::Rect bdbox = cv::boundingRect(contour);
-      float lwRatio = bdbox.height / static_cast<float>(bdbox.width);
-      int bdboxAera = bdbox.width * bdbox.height;
-      if ((lwRatio > 0.7 && bdbox.width * bdbox.height > 100 &&
-           bdboxAera < 300) ||
-          (lwRatio > 3.0 && bdboxAera < 100 && bdboxAera > 10)) {
-        cv::Point p1(bdbox.x, bdbox.y);
-        cv::Point p2(bdbox.x + bdbox.width, bdbox.y + bdbox.height);
-        line_upper.push_back(p1);
-        line_lower.push_back(p2);
-        contours_nums += 1;
-      }
-    }
-  }
-  if (contours_nums < 41) {
-    cv::bitwise_not(InputProposal, InputProposal);
-    cv::Mat kernal =
-        cv::getStructuringElement(cv::MORPH_ELLIPSE, cv::Size(1, 5));
-    cv::Mat bak;
-    cv::resize(InputProposal, bak, cv::Size(FINEMAPPING_W, FINEMAPPING_H));
-    cv::erode(bak, bak, kernal);
-    if (InputProposal.channels() == 3)
-      cv::cvtColor(bak, proposal, cv::COLOR_BGR2GRAY);
-    else
-      proposal = bak;
-    int contours_nums = 0;
-    for (int i = 0; i < sliceNum; i++) {
-      std::vector<std::vector<cv::Point>> contours;
-      float k = lower + i * diff;
-      cv::adaptiveThreshold(proposal, binary_adaptive, 255,
-                            cv::ADAPTIVE_THRESH_MEAN_C, cv::THRESH_BINARY,
-                            windows_size, k);
-      cv::Mat draw;
-      binary_adaptive.copyTo(draw);
-      cv::findContours(binary_adaptive, contours, cv::RETR_EXTERNAL,
-                       cv::CHAIN_APPROX_SIMPLE);
-      for (auto contour : contours) {
-        cv::Rect bdbox = cv::boundingRect(contour);
-        float lwRatio = bdbox.height / static_cast<float>(bdbox.width);
-        int bdboxAera = bdbox.width * bdbox.height;
-        if ((lwRatio > 0.7 && bdbox.width * bdbox.height > 120 &&
-             bdboxAera < 300) ||
-            (lwRatio > 3.0 && bdboxAera < 100 && bdboxAera > 10)) {
-
-          cv::Point p1(bdbox.x, bdbox.y);
-          cv::Point p2(bdbox.x + bdbox.width, bdbox.y + bdbox.height);
-          line_upper.push_back(p1);
-          line_lower.push_back(p2);
-          contours_nums += 1;
-        }
-      }
-    }
-  }
-  cv::Mat rgb;
-  cv::copyMakeBorder(PreInputProposal, rgb, PADDING_UP_DOWN, PADDING_UP_DOWN, 0,
-                     0, cv::BORDER_REPLICATE);
-  std::pair<int, int> A;
-  std::pair<int, int> B;
-  A = FitLineRansac(line_upper, -1);
-  B = FitLineRansac(line_lower, 1);
-  int leftyB = A.first;
-  int rightyB = A.second;
-  int leftyA = B.first;
-  int rightyA = B.second;
-  int cols = rgb.cols;
-  int rows = rgb.rows;
-  std::vector<cv::Point2f> corners(4);
-  corners[0] = cv::Point2f(cols - 1, rightyA);
-  corners[1] = cv::Point2f(0, leftyA);
-  corners[2] = cv::Point2f(cols - 1, rightyB);
-  corners[3] = cv::Point2f(0, leftyB);
-  std::vector<cv::Point2f> corners_trans(4);
-  corners_trans[0] = cv::Point2f(136, 36);
-  corners_trans[1] = cv::Point2f(0, 36);
-  corners_trans[2] = cv::Point2f(136, 0);
-  corners_trans[3] = cv::Point2f(0, 0);
-  cv::Mat transform = cv::getPerspectiveTransform(corners, corners_trans);
-  cv::Mat quad = cv::Mat::zeros(36, 136, CV_8UC3);
-  cv::warpPerspective(rgb, quad, transform, quad.size());
-  return quad;
-}
-} // namespace pr
diff --git a/Prj-Linux/hyperlpr/src/Pipeline.cpp b/Prj-Linux/hyperlpr/src/Pipeline.cpp
deleted file mode 100644
index 36ba5e3..0000000
--- a/Prj-Linux/hyperlpr/src/Pipeline.cpp
+++ /dev/null
@@ -1,82 +0,0 @@
-//
-// Created by Jack Yu on 23/10/2017.
-//
-
-#include "../include/Pipeline.h"
-namespace pr {
-
-const int HorizontalPadding = 4;
-PipelinePR::PipelinePR(std::string detector_filename,
-                       std::string finemapping_prototxt,
-                       std::string finemapping_caffemodel,
-                       std::string segmentation_prototxt,
-                       std::string segmentation_caffemodel,
-                       std::string charRecognization_proto,
-                       std::string charRecognization_caffemodel,
-                       std::string segmentationfree_proto,
-                       std::string segmentationfree_caffemodel) {
-  plateDetection = new PlateDetection(detector_filename);
-  fineMapping = new FineMapping(finemapping_prototxt, finemapping_caffemodel);
-  plateSegmentation =
-      new PlateSegmentation(segmentation_prototxt, segmentation_caffemodel);
-  generalRecognizer =
-      new CNNRecognizer(charRecognization_proto, charRecognization_caffemodel);
-  segmentationFreeRecognizer = new SegmentationFreeRecognizer(
-      segmentationfree_proto, segmentationfree_caffemodel);
-}
-
-PipelinePR::~PipelinePR() {
-
-  delete plateDetection;
-  delete fineMapping;
-  delete plateSegmentation;
-  delete generalRecognizer;
-  delete segmentationFreeRecognizer;
-}
-
-std::vector<PlateInfo> PipelinePR::RunPiplineAsImage(cv::Mat plateImage,
-                                                     int method) {
-  std::vector<PlateInfo> results;
-  std::vector<pr::PlateInfo> plates;
-  plateDetection->plateDetectionRough(plateImage, plates, 36, 700);
-
-  for (pr::PlateInfo plateinfo : plates) {
-
-    cv::Mat image_finemapping = plateinfo.getPlateImage();
-    image_finemapping = fineMapping->FineMappingVertical(image_finemapping);
-    image_finemapping = pr::fastdeskew(image_finemapping, 5);
-    // Segmentation-based
-    if (method == SEGMENTATION_BASED_METHOD) {
-      image_finemapping = fineMapping->FineMappingHorizon(image_finemapping, 2,
-                                                          HorizontalPadding);
-      cv::resize(image_finemapping, image_finemapping,
-                 cv::Size(136 + HorizontalPadding, 36));
-      plateinfo.setPlateImage(image_finemapping);
-      std::vector<cv::Rect> rects;
-      plateSegmentation->segmentPlatePipline(plateinfo, 1, rects);
-      plateSegmentation->ExtractRegions(plateinfo, rects);
-      cv::copyMakeBorder(image_finemapping, image_finemapping, 0, 0, 0, 20,
-                         cv::BORDER_REPLICATE);
-      plateinfo.setPlateImage(image_finemapping);
-      generalRecognizer->SegmentBasedSequenceRecognition(plateinfo);
-      plateinfo.decodePlateNormal(pr::CH_PLATE_CODE);
-
-    }
-    // Segmentation-free
-    else if (method == SEGMENTATION_FREE_METHOD) {
-      image_finemapping = fineMapping->FineMappingHorizon(
-          image_finemapping, 4, HorizontalPadding + 3);
-      cv::resize(image_finemapping, image_finemapping,
-                 cv::Size(136 + HorizontalPadding, 36));
-      plateinfo.setPlateImage(image_finemapping);
-      std::pair<std::string, float> res =
-          segmentationFreeRecognizer->SegmentationFreeForSinglePlate(
-              plateinfo.getPlateImage(), pr::CH_PLATE_CODE);
-      plateinfo.confidence = res.second;
-      plateinfo.setPlateName(res.first);
-    }
-    results.push_back(plateinfo);
-  }
-  return results;
-}
-} // namespace pr
diff --git a/Prj-Linux/hyperlpr/src/PlateDetection.cpp b/Prj-Linux/hyperlpr/src/PlateDetection.cpp
deleted file mode 100644
index d3f3137..0000000
--- a/Prj-Linux/hyperlpr/src/PlateDetection.cpp
+++ /dev/null
@@ -1,31 +0,0 @@
-#include "../include/PlateDetection.h"
-#include "util.h"
-namespace pr {
-PlateDetection::PlateDetection(std::string filename_cascade) {
-  cascade.load(filename_cascade);
-};
-void PlateDetection::plateDetectionRough(cv::Mat InputImage,
-                                         std::vector<pr::PlateInfo> &plateInfos,
-                                         int min_w, int max_w) {
-  cv::Mat processImage;
-  cv::cvtColor(InputImage, processImage, cv::COLOR_BGR2GRAY);
-  std::vector<cv::Rect> platesRegions;
-  cv::Size minSize(min_w, min_w / 4);
-  cv::Size maxSize(max_w, max_w / 4);
-  cascade.detectMultiScale(processImage, platesRegions, 1.1, 3,
-                           cv::CASCADE_SCALE_IMAGE, minSize, maxSize);
-  for (auto plate : platesRegions) {
-    int zeroadd_w = static_cast<int>(plate.width * 0.30);
-    int zeroadd_h = static_cast<int>(plate.height * 2);
-    int zeroadd_x = static_cast<int>(plate.width * 0.15);
-    int zeroadd_y = static_cast<int>(plate.height * 1);
-    plate.x -= zeroadd_x;
-    plate.y -= zeroadd_y;
-    plate.height += zeroadd_h;
-    plate.width += zeroadd_w;
-    cv::Mat plateImage = util::cropFromImage(InputImage, plate);
-    PlateInfo plateInfo(plateImage, plate);
-    plateInfos.push_back(plateInfo);
-  }
-}
-} // namespace pr
diff --git a/Prj-Linux/hyperlpr/src/PlateSegmentation.cpp b/Prj-Linux/hyperlpr/src/PlateSegmentation.cpp
deleted file mode 100644
index 9365781..0000000
--- a/Prj-Linux/hyperlpr/src/PlateSegmentation.cpp
+++ /dev/null
@@ -1,305 +0,0 @@
-//
-// Created by Jack  Yu on 16/10/2017.
-//
-
-#include "../include/PlateSegmentation.h"
-#include "../include/niBlackThreshold.h"
-
-namespace pr {
-PlateSegmentation::PlateSegmentation(std::string prototxt,
-                                     std::string caffemodel) {
-  net = cv::dnn::readNetFromCaffe(prototxt, caffemodel);
-}
-cv::Mat PlateSegmentation::classifyResponse(const cv::Mat &cropped) {
-  cv::Mat inputBlob = cv::dnn::blobFromImage(
-      cropped, 1 / 255.0, cv::Size(22, 22), cv::Scalar(0, 0, 0), false);
-  net.setInput(inputBlob, "data");
-  return net.forward();
-}
-
-void drawHist(float *seq, int size, const char *name) {
-  cv::Mat image(300, size, CV_8U);
-  image.setTo(0);
-  float *start = seq;
-  float *end = seq + size;
-  float l = *std::max_element(start, end);
-  for (int i = 0; i < size; i++) {
-    int p = int(float(seq[i]) / l * 300);
-    cv::line(image, cv::Point(i, 300), cv::Point(i, 300 - p),
-             cv::Scalar(255, 255, 255));
-  }
-  cv::resize(image, image, cv::Size(600, 100));
-  cv::imshow(name, image);
-}
-
-inline void computeSafeMargin(int &val, const int &rows) {
-  val = std::min(val, rows);
-  val = std::max(val, 0);
-}
-
-cv::Rect boxFromCenter(const cv::Point center, int left, int right, int top,
-                       int bottom, cv::Size bdSize) {
-  cv::Point p1(center.x - left, center.y - top);
-  cv::Point p2(center.x + right, center.y + bottom);
-  p1.x = std::max(0, p1.x);
-  p1.y = std::max(0, p1.y);
-  p2.x = std::min(p2.x, bdSize.width - 1);
-  p2.y = std::min(p2.y, bdSize.height - 1);
-  cv::Rect rect(p1, p2);
-  return rect;
-}
-
-cv::Rect boxPadding(cv::Rect rect, int left, int right, int top, int bottom,
-                    cv::Size bdSize) {
-
-  cv::Point center(rect.x + (rect.width >> 1), rect.y + (rect.height >> 1));
-  int rebuildLeft = (rect.width >> 1) + left;
-  int rebuildRight = (rect.width >> 1) + right;
-  int rebuildTop = (rect.height >> 1) + top;
-  int rebuildBottom = (rect.height >> 1) + bottom;
-  return boxFromCenter(center, rebuildLeft, rebuildRight, rebuildTop,
-                       rebuildBottom, bdSize);
-}
-
-void PlateSegmentation::refineRegion(cv::Mat &plateImage,
-                                     const std::vector<int> &candidatePts,
-                                     const int padding,
-                                     std::vector<cv::Rect> &rects) {
-  int w = candidatePts[5] - candidatePts[4];
-  int cols = plateImage.cols;
-  int rows = plateImage.rows;
-  for (int i = 0; i < candidatePts.size(); i++) {
-    int left = 0;
-    int right = 0;
-
-    if (i == 0) {
-      left = candidatePts[i];
-      right = left + w + padding;
-    } else {
-      left = candidatePts[i] - padding;
-      right = left + w + padding * 2;
-    }
-
-    computeSafeMargin(right, cols);
-    computeSafeMargin(left, cols);
-    cv::Rect roi(left, 0, right - left, rows - 1);
-    cv::Mat roiImage;
-    plateImage(roi).copyTo(roiImage);
-
-    if (i >= 1) {
-
-      cv::Mat roi_thres;
-      //                cv::threshold(roiImage,roi_thres,0,255,cv::THRESH_OTSU|cv::THRESH_BINARY);
-
-      niBlackThreshold(roiImage, roi_thres, 255, cv::THRESH_BINARY, 15, 0.27,
-                       BINARIZATION_NIBLACK);
-
-      std::vector<std::vector<cv::Point>> contours;
-      cv::findContours(roi_thres, contours, cv::RETR_LIST,
-                       cv::CHAIN_APPROX_SIMPLE);
-      cv::Point boxCenter(roiImage.cols >> 1, roiImage.rows >> 1);
-
-      cv::Rect final_bdbox;
-      cv::Point final_center;
-      int final_dist = INT_MAX;
-
-      for (auto contour : contours) {
-        cv::Rect bdbox = cv::boundingRect(contour);
-        cv::Point center(bdbox.x + (bdbox.width >> 1),
-                         bdbox.y + (bdbox.height >> 1));
-        int dist = (center.x - boxCenter.x) * (center.x - boxCenter.x);
-        if (dist < final_dist and bdbox.height > rows >> 1) {
-          final_dist = dist;
-          final_center = center;
-          final_bdbox = bdbox;
-        }
-      }
-
-      // rebuild box
-      if (final_bdbox.height / static_cast<float>(final_bdbox.width) > 3.5 &&
-          final_bdbox.width * final_bdbox.height < 10)
-        final_bdbox = boxFromCenter(final_center, 8, 8, (rows >> 1) - 3,
-                                    (rows >> 1) - 2, roiImage.size());
-      else {
-        if (i == candidatePts.size() - 1)
-          final_bdbox = boxPadding(final_bdbox, padding / 2, padding,
-                                   padding / 2, padding / 2, roiImage.size());
-        else
-          final_bdbox = boxPadding(final_bdbox, padding, padding, padding,
-                                   padding, roiImage.size());
-
-//                    std::cout<<final_bdbox<<std::endl;
-//                    std::cout<<roiImage.size()<<std::endl;
-#ifdef DEBUG
-        cv::imshow("char_thres", roi_thres);
-
-        cv::imshow("char", roiImage(final_bdbox));
-        cv::waitKey(0);
-#endif
-      }
-
-      final_bdbox.x += left;
-
-      rects.push_back(final_bdbox);
-      //
-
-    } else {
-      rects.push_back(roi);
-    }
-
-    //            else
-    //            {
-    //
-    //            }
-
-    //            cv::GaussianBlur(roiImage,roiImage,cv::Size(7,7),3);
-    //
-    //            cv::imshow("image",roiImage);
-    //            cv::waitKey(0);
-  }
-}
-void avgfilter(float *angle_list, int size, int windowsSize) {
-  float *filterd = new float[size];
-  for (int i = 0; i < size; i++)
-    filterd[i] = angle_list[i];
-  //        memcpy(filterd,angle_list,size);
-
-  cv::Mat kernal_gaussian = cv::getGaussianKernel(windowsSize, 3, CV_32F);
-  float *kernal = (float *)kernal_gaussian.data;
-  //        kernal+=windowsSize;
-  int r = windowsSize / 2;
-
-  for (int i = 0; i < size; i++) {
-    float avg = 0.00f;
-    for (int j = 0; j < windowsSize; j++) {
-      if (i + j - r > 0 && i + j + r < size - 1)
-        avg += filterd[i + j - r] * kernal[j];
-    }
-    //            avg = avg / windowsSize;
-    angle_list[i] = avg;
-  }
-
-  delete filterd;
-}
-
-void PlateSegmentation::templateMatchFinding(
-    const cv::Mat &respones, int windowsWidth,
-    std::pair<float, std::vector<int>> &candidatePts) {
-  int rows = respones.rows;
-  int cols = respones.cols;
-  float *data = (float *)respones.data;
-  float *engNum_prob = data;
-  float *false_prob = data + cols;
-  float *ch_prob = data + cols * 2;
-  avgfilter(engNum_prob, cols, 5);
-  avgfilter(false_prob, cols, 5);
-  std::vector<int> candidate_pts(7);
-  int cp_list[7];
-  float loss_selected = -10;
-
-  for (int start = 0; start < 20; start += 2)
-    for (int width = windowsWidth - 5; width < windowsWidth + 5; width++) {
-      for (int interval = windowsWidth / 2; interval < windowsWidth;
-           interval++) {
-        int cp1_ch = start;
-        int cp2_p0 = cp1_ch + width;
-        int cp3_p1 = cp2_p0 + width + interval;
-        int cp4_p2 = cp3_p1 + width;
-        int cp5_p3 = cp4_p2 + width + 1;
-        int cp6_p4 = cp5_p3 + width + 2;
-        int cp7_p5 = cp6_p4 + width + 2;
-        int md1 = (cp1_ch + cp2_p0) >> 1;
-        int md2 = (cp2_p0 + cp3_p1) >> 1;
-        int md3 = (cp3_p1 + cp4_p2) >> 1;
-        int md4 = (cp4_p2 + cp5_p3) >> 1;
-        int md5 = (cp5_p3 + cp6_p4) >> 1;
-        int md6 = (cp6_p4 + cp7_p5) >> 1;
-
-        if (cp7_p5 >= cols)
-          continue;
-        float loss =
-            ch_prob[cp1_ch] * 3 -
-            (false_prob[cp3_p1] + false_prob[cp4_p2] + false_prob[cp5_p3] +
-             false_prob[cp6_p4] + false_prob[cp7_p5]);
-
-        if (loss > loss_selected) {
-          loss_selected = loss;
-          cp_list[0] = cp1_ch;
-          cp_list[1] = cp2_p0;
-          cp_list[2] = cp3_p1;
-          cp_list[3] = cp4_p2;
-          cp_list[4] = cp5_p3;
-          cp_list[5] = cp6_p4;
-          cp_list[6] = cp7_p5;
-        }
-      }
-    }
-  candidate_pts[0] = cp_list[0];
-  candidate_pts[1] = cp_list[1];
-  candidate_pts[2] = cp_list[2];
-  candidate_pts[3] = cp_list[3];
-  candidate_pts[4] = cp_list[4];
-  candidate_pts[5] = cp_list[5];
-  candidate_pts[6] = cp_list[6];
-
-  candidatePts.first = loss_selected;
-  candidatePts.second = candidate_pts;
-};
-
-void PlateSegmentation::segmentPlateBySlidingWindows(cv::Mat &plateImage,
-                                                     int windowsWidth,
-                                                     int stride,
-                                                     cv::Mat &respones) {
-  cv::Mat plateImageGray;
-  cv::cvtColor(plateImage, plateImageGray, cv::COLOR_BGR2GRAY);
-  int padding = plateImage.cols - 136;
-  int height = plateImage.rows - 1;
-  int width = plateImage.cols - 1 - padding;
-  for (int i = 0; i < width - windowsWidth + 1; i += stride) {
-    cv::Rect roi(i, 0, windowsWidth, height);
-    cv::Mat roiImage = plateImageGray(roi);
-    cv::Mat response = classifyResponse(roiImage);
-    respones.push_back(response);
-  }
-  respones = respones.t();
-}
-
-void PlateSegmentation::segmentPlatePipline(PlateInfo &plateInfo, int stride,
-                                            std::vector<cv::Rect> &Char_rects) {
-  cv::Mat plateImage = plateInfo.getPlateImage(); // get src image .
-  cv::Mat plateImageGray;
-  cv::cvtColor(plateImage, plateImageGray, cv::COLOR_BGR2GRAY);
-  // do binarzation
-  std::pair<float, std::vector<int>> sections; // segment points variables .
-  cv::Mat respones; // three response of every sub region from origin image .
-  segmentPlateBySlidingWindows(plateImage, DEFAULT_WIDTH, 1, respones);
-  templateMatchFinding(respones, DEFAULT_WIDTH / stride, sections);
-  for (int i = 0; i < sections.second.size(); i++) {
-    sections.second[i] *= stride;
-  }
-  refineRegion(plateImageGray, sections.second, 5, Char_rects);
-}
-
-void PlateSegmentation::ExtractRegions(PlateInfo &plateInfo,
-                                       std::vector<cv::Rect> &rects) {
-  cv::Mat plateImage = plateInfo.getPlateImage();
-  for (int i = 0; i < rects.size(); i++) {
-    cv::Mat charImage;
-    plateImage(rects[i]).copyTo(charImage);
-    if (charImage.channels())
-      cv::cvtColor(charImage, charImage, cv::COLOR_BGR2GRAY);
-    cv::equalizeHist(charImage, charImage);
-    std::pair<CharType, cv::Mat> char_instance;
-    if (i == 0) {
-      char_instance.first = CHINESE;
-    } else if (i == 1) {
-      char_instance.first = LETTER;
-    } else {
-      char_instance.first = LETTER_NUMS;
-    }
-    char_instance.second = charImage;
-    plateInfo.appendPlateChar(char_instance);
-  }
-}
-
-} // namespace pr
diff --git a/Prj-Linux/hyperlpr/src/Recognizer.cpp b/Prj-Linux/hyperlpr/src/Recognizer.cpp
deleted file mode 100644
index 7b486da..0000000
--- a/Prj-Linux/hyperlpr/src/Recognizer.cpp
+++ /dev/null
@@ -1,22 +0,0 @@
-//
-// Created by Jack Yu on 22/10/2017.
-//
-
-#include "../include/Recognizer.h"
-
-namespace pr {
-void GeneralRecognizer::SegmentBasedSequenceRecognition(PlateInfo &plateinfo) {
-  for (auto char_instance : plateinfo.plateChars) {
-    std::pair<CharType, cv::Mat> res;
-    if (char_instance.second.rows * char_instance.second.cols > 40) {
-      label code_table = recognizeCharacter(char_instance.second);
-      res.first = char_instance.first;
-      code_table.copyTo(res.second);
-      plateinfo.appendPlateCoding(res);
-    } else {
-      res.first = INVALID;
-      plateinfo.appendPlateCoding(res);
-    }
-  }
-}
-} // namespace pr
diff --git a/Prj-Linux/hyperlpr/src/SegmentationFreeRecognizer.cpp b/Prj-Linux/hyperlpr/src/SegmentationFreeRecognizer.cpp
deleted file mode 100644
index 6b13903..0000000
--- a/Prj-Linux/hyperlpr/src/SegmentationFreeRecognizer.cpp
+++ /dev/null
@@ -1,87 +0,0 @@
-//
-// Created by Jack Yu on 28/11/2017.
-//
-#include "../include/SegmentationFreeRecognizer.h"
-
-namespace pr {
-SegmentationFreeRecognizer::SegmentationFreeRecognizer(std::string prototxt,
-                                                       std::string caffemodel) {
-  net = cv::dnn::readNetFromCaffe(prototxt, caffemodel);
-}
-inline int judgeCharRange(int id) { return id < 31 || id > 63; }
-std::pair<std::string, float>
-decodeResults(cv::Mat code_table, std::vector<std::string> mapping_table,
-              float thres) {
-  cv::MatSize mtsize = code_table.size;
-  int sequencelength = mtsize[2];
-  int labellength = mtsize[1];
-  cv::transpose(code_table.reshape(1, 1).reshape(1, labellength), code_table);
-  std::string name = "";
-  std::vector<int> seq(sequencelength);
-  std::vector<std::pair<int, float>> seq_decode_res;
-  for (int i = 0; i < sequencelength; i++) {
-    float *fstart = ((float *)(code_table.data) + i * labellength);
-    int id = std::max_element(fstart, fstart + labellength) - fstart;
-    seq[i] = id;
-  }
-
-  float sum_confidence = 0;
-  int plate_lenghth = 0;
-  for (int i = 0; i < sequencelength; i++) {
-    if (seq[i] != labellength - 1 && (i == 0 || seq[i] != seq[i - 1])) {
-      float *fstart = ((float *)(code_table.data) + i * labellength);
-      float confidence = *(fstart + seq[i]);
-      std::pair<int, float> pair_(seq[i], confidence);
-      seq_decode_res.push_back(pair_);
-    }
-  }
-  int i = 0;
-  if (seq_decode_res.size() > 1 && judgeCharRange(seq_decode_res[0].first) &&
-      judgeCharRange(seq_decode_res[1].first)) {
-    i = 2;
-    int c = seq_decode_res[0].second < seq_decode_res[1].second;
-    name += mapping_table[seq_decode_res[c].first];
-    sum_confidence += seq_decode_res[c].second;
-    plate_lenghth++;
-  }
-
-  for (; i < seq_decode_res.size(); i++) {
-    name += mapping_table[seq_decode_res[i].first];
-    sum_confidence += seq_decode_res[i].second;
-    plate_lenghth++;
-  }
-  std::pair<std::string, float> res;
-  res.second = sum_confidence / plate_lenghth;
-  res.first = name;
-  return res;
-}
-std::string decodeResults(cv::Mat code_table,
-                          std::vector<std::string> mapping_table) {
-  cv::MatSize mtsize = code_table.size;
-  int sequencelength = mtsize[2];
-  int labellength = mtsize[1];
-  cv::transpose(code_table.reshape(1, 1).reshape(1, labellength), code_table);
-  std::string name = "";
-  std::vector<int> seq(sequencelength);
-  for (int i = 0; i < sequencelength; i++) {
-    float *fstart = ((float *)(code_table.data) + i * labellength);
-    int id = std::max_element(fstart, fstart + labellength) - fstart;
-    seq[i] = id;
-  }
-  for (int i = 0; i < sequencelength; i++) {
-    if (seq[i] != labellength - 1 && (i == 0 || seq[i] != seq[i - 1]))
-      name += mapping_table[seq[i]];
-  }
-  return name;
-}
-std::pair<std::string, float>
-SegmentationFreeRecognizer::SegmentationFreeForSinglePlate(
-    cv::Mat Image, std::vector<std::string> mapping_table) {
-  cv::transpose(Image, Image);
-  cv::Mat inputBlob =
-      cv::dnn::blobFromImage(Image, 1 / 255.0, cv::Size(40, 160));
-  net.setInput(inputBlob, "data");
-  cv::Mat char_prob_mat = net.forward();
-  return decodeResults(char_prob_mat, mapping_table, 0.00);
-}
-} // namespace pr
diff --git a/Prj-Linux/hyperlpr/src/util.h b/Prj-Linux/hyperlpr/src/util.h
deleted file mode 100644
index 7649d5d..0000000
--- a/Prj-Linux/hyperlpr/src/util.h
+++ /dev/null
@@ -1,62 +0,0 @@
-//
-// Created by Jack Yu on 04/04/2017.
-//
-
-#include <opencv2/opencv.hpp>
-namespace util {
-template <class T> void swap(T &a, T &b) {
-  T c(a);
-  a = b;
-  b = c;
-}
-template <class T> T min(T &a, T &b) { return a > b ? b : a; }
-
-cv::Mat cropFromImage(const cv::Mat &image, cv::Rect rect) {
-  int w = image.cols - 1;
-  int h = image.rows - 1;
-  rect.x = std::max(rect.x, 0);
-  rect.y = std::max(rect.y, 0);
-  rect.height = std::min(rect.height, h - rect.y);
-  rect.width = std::min(rect.width, w - rect.x);
-  cv::Mat temp(rect.size(), image.type());
-  cv::Mat cropped;
-  temp = image(rect);
-  temp.copyTo(cropped);
-  return cropped;
-}
-
-cv::Mat cropBox2dFromImage(const cv::Mat &image, cv::RotatedRect rect) {
-  cv::Mat M, rotated, cropped;
-  float angle = rect.angle;
-  cv::Size rect_size(rect.size.width, rect.size.height);
-  if (rect.angle < -45.) {
-    angle += 90.0;
-    swap(rect_size.width, rect_size.height);
-  }
-  M = cv::getRotationMatrix2D(rect.center, angle, 1.0);
-  cv::warpAffine(image, rotated, M, image.size(), cv::INTER_CUBIC);
-  cv::getRectSubPix(rotated, rect_size, rect.center, cropped);
-  return cropped;
-}
-
-cv::Mat calcHist(const cv::Mat &image) {
-  cv::Mat hsv;
-  std::vector<cv::Mat> hsv_planes;
-  cv::cvtColor(image, hsv, cv::COLOR_BGR2HSV);
-  cv::split(hsv, hsv_planes);
-  cv::Mat hist;
-  int histSize = 256;
-  float range[] = {0, 255};
-  const float *histRange = {range};
-  cv::calcHist(&hsv_planes[0], 1, 0, cv::Mat(), hist, 1, &histSize, &histRange,
-               true, true);
-  return hist;
-}
-
-float computeSimilir(const cv::Mat &A, const cv::Mat &B) {
-  cv::Mat histA, histB;
-  histA = calcHist(A);
-  histB = calcHist(B);
-  return cv::compareHist(histA, histB, CV_COMP_CORREL);
-}
-} // namespace util
diff --git a/Prj-Linux/hyperlpr3 b/Prj-Linux/hyperlpr3
new file mode 120000
index 0000000..198a8e1
--- /dev/null
+++ b/Prj-Linux/hyperlpr3
@@ -0,0 +1 @@
+../build/linux/install/hyperlpr3
\ No newline at end of file
diff --git a/Prj-Linux/plate_rec_demo.cpp b/Prj-Linux/plate_rec_demo.cpp
new file mode 100644
index 0000000..3aa67d6
--- /dev/null
+++ b/Prj-Linux/plate_rec_demo.cpp
@@ -0,0 +1,67 @@
+//
+// Created by Tunm-Air13 on 2023/2/23.
+//
+#include <iostream>
+#include "hyper_lpr_sdk.h"
+#include "opencv2/opencv.hpp"
+
+static const std::vector<std::string> TYPES = {"蓝牌", "黄牌单层", "白牌单层", "绿牌新能源", "黑牌港澳", "香港单层", "香港双层", "澳门单层", "澳门双层", "黄牌双层"};
+
+
+int main(int argc, char **argv) {
+    char *model_path = argv[1];
+    char *image_path = argv[2];
+    // 读取图像
+    cv::Mat image = cv::imread(image_path);
+    // 创建ImageData
+    HLPR_ImageData data = {0};
+    data.data = image.ptr<uint8_t>(0);      // 设置图像数据流
+    data.width = image.cols;                   // 设置图像宽
+    data.height = image.rows;                  // 设置图像高
+    data.format = STREAM_BGR;                  // 设置当前图像编码格式
+    data.rotation = CAMERA_ROTATION_0;         // 设置当前图像转角
+    // 创建数据Buffer
+    P_HLPR_DataBuffer buffer = HLPR_CreateDataBuffer(&data);
+
+    // 配置车牌识别参数
+    HLPR_ContextConfiguration configuration = {0};
+    configuration.models_path = model_path;         // 模型文件夹路径
+    configuration.max_num = 5;                      // 最大识别车牌数量
+    configuration.det_level = DETECT_LEVEL_LOW;     // 检测器等级
+    configuration.use_half = false;
+    configuration.nms_threshold = 0.5f;             // 非极大值抑制置信度阈值
+    configuration.rec_confidence_threshold = 0.5f;  // 车牌号文本阈值
+    configuration.box_conf_threshold = 0.30f;       // 检测器阈值
+    configuration.threads = 1;
+    // 实例化车牌识别算法Context
+    P_HLPR_Context ctx = HLPR_CreateContext(&configuration);
+    // 查询实例化状态
+    HREESULT ret = HLPR_ContextQueryStatus(ctx);
+    if (ret != HResultCode::Ok) {
+        printf("create error.\n");
+        return -1;
+    }
+    HLPR_PlateResultList results = {0};
+    // 执行车牌识别算法
+    HLPR_ContextUpdateStream(ctx, buffer, &results);
+
+    for (int i = 0; i < results.plate_size; ++i) {
+        // 解析识别后的数据
+        std::string type;
+        if (results.plates[i].type == HLPR_PlateType::PLATE_TYPE_UNKNOWN) {
+            type = "未知";
+        } else {
+            type = TYPES[results.plates[i].type];
+        }
+
+        printf("<%d> %s, %s, %f\n", i + 1, type.c_str(),
+               results.plates[i].code, results.plates[i].text_confidence);
+    }
+
+    // 销毁Buffer
+    HLPR_ReleaseDataBuffer(buffer);
+    // 销毁Context
+    HLPR_ReleaseContext(ctx);
+
+    return 0;
+}
\ No newline at end of file
diff --git a/Prj-PHP/CMakeLists.txt b/Prj-PHP/CMakeLists.txt
deleted file mode 100644
index ee599ae..0000000
--- a/Prj-PHP/CMakeLists.txt
+++ /dev/null
@@ -1,46 +0,0 @@
-cmake_minimum_required(VERSION 3.6)
-project(platescan)
-
-set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11")
-set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR})
-find_package(OpenCV 3.3.0 REQUIRED)
-include_directories( ${OpenCV_INCLUDE_DIRS})
-include_directories(include)
-
-add_library( # Sets the name of the library.
-    platescan
-
-    # Sets the library as a shared library.
-    SHARED
-
-    # Provides a relative path to your source file(s).
-    main.cpp 
-
-    lpr/CNNRecognizer.cpp
-    lpr/FastDeskew.cpp
-    lpr/FineMapping.cpp
-    lpr/Pipeline.cpp
-    lpr/PlateDetection.cpp
-    lpr/PlateSegmentation.cpp
-    lpr/Recognizer.cpp
-    lpr/SegmentationFreeRecognizer.cpp
-)
-
-
-target_link_libraries(
-  platescan
-  phpcpp
-  ${OpenCV_LIBS}
-)
-
-execute_process(COMMAND php-config --extension-dir
-        OUTPUT_VARIABLE LIBRARY_DIR)
-
-message("PHP_EXTENSIONS_DIR ${LIBRARY_DIR}")
-
-add_custom_command(TARGET platescan
-        POST_BUILD
-        COMMAND mv ./libplatescan.so ./platescan.so
-        COMMAND cp -f ./platescan.so ${LIBRARY_DIR}
-        COMMAND ${POST_COMPILE_COMMAND}
-        )        
diff --git a/Prj-PHP/README.MD b/Prj-PHP/README.MD
deleted file mode 100644
index db19c82..0000000
--- a/Prj-PHP/README.MD
+++ /dev/null
@@ -1,50 +0,0 @@
-
-# Prj-PHP
-
-HyperLPR 在PHP扩展程序中的实现，核心代码拷贝了 Prj-Linux 中的代码。
-
-## CPP 依赖
-
-+ Opencv 3.3
-+ PHPCPP
-
-## Linux/Mac 编译
-
-```bash
-mkdir build
-cd build
-cmake ../
-make -j
-```
-
-## DEMO
-
-```php
-// 也可编译完后直接在tests目录直接运行 php platescan.php
-$path = realpath("demo.png");   // 图片文件所在路径,realpath转为绝对路径
-$model = realpath("model");     // 模型资源所在文件夹
-$res = platescan($path, $model, 0.8);
-var_dump($res); // string(9) "苏ED0N19"
-
-```
-
-最终实现上边这样的PHP调用
-
-![image](./tests/demo.png)
-
-## 测试备忘
-
-经多次测试发现部分图片难以识别或者容易识别错误，经过筛选反推出如下规律：
-
-### 识别精度高的图片
-
-+ 图片中包含有整个车身
-+ 图片清晰度较高，不模糊
-+ 图片拍摄角度为正面拍摄，车牌在图片中没有较大幅度的倾斜
-+ 图片中的车牌在整个图片中所占位置较小
-
-### 识别精度低的图片
-
-+ 图片模糊，程序未能裁切出正确的车牌，或者裁切后因图片太模糊而无法识别（此处应该可以通过训练更多样本解决）
-+ 图片中的车牌在整个图片中占了1/3的位置甚至更高
-+ 图片拍摄角度为俯角拍摄，车牌在图片中是倾斜着的
diff --git a/Prj-PHP/include/CNNRecognizer.h b/Prj-PHP/include/CNNRecognizer.h
deleted file mode 100644
index ad491a0..0000000
--- a/Prj-PHP/include/CNNRecognizer.h
+++ /dev/null
@@ -1,24 +0,0 @@
-//
-// Created by 庾金科 on 21/10/2017.
-//
-
-#ifndef SWIFTPR_CNNRECOGNIZER_H
-#define SWIFTPR_CNNRECOGNIZER_H
-
-#include "Recognizer.h"
-namespace pr{
-    class CNNRecognizer: public GeneralRecognizer{
-    public:
-        const int CHAR_INPUT_W = 14;
-        const int CHAR_INPUT_H = 30;
-
-        CNNRecognizer(std::string prototxt,std::string caffemodel);
-        label recognizeCharacter(cv::Mat character);
-    private:
-        cv::dnn::Net net;
-
-    };
-
-}
-
-#endif //SWIFTPR_CNNRECOGNIZER_H
diff --git a/Prj-PHP/include/FastDeskew.h b/Prj-PHP/include/FastDeskew.h
deleted file mode 100644
index 08359e5..0000000
--- a/Prj-PHP/include/FastDeskew.h
+++ /dev/null
@@ -1,18 +0,0 @@
-//
-// Created by 庾金科 on 22/09/2017.
-//
-
-#ifndef SWIFTPR_FASTDESKEW_H
-#define SWIFTPR_FASTDESKEW_H
-
-#include <math.h>
-#include <opencv2/opencv.hpp>
-namespace pr{
-
-    cv::Mat fastdeskew(cv::Mat skewImage,int blockSize);
-//    cv::Mat spatialTransformer(cv::Mat skewImage);
-
-}//namepace pr
-
-
-#endif //SWIFTPR_FASTDESKEW_H
diff --git a/Prj-PHP/include/FineMapping.h b/Prj-PHP/include/FineMapping.h
deleted file mode 100644
index 352202e..0000000
--- a/Prj-PHP/include/FineMapping.h
+++ /dev/null
@@ -1,32 +0,0 @@
-//
-// Created by 庾金科 on 22/09/2017.
-//
-
-#ifndef SWIFTPR_FINEMAPPING_H
-#define SWIFTPR_FINEMAPPING_H
-
-#include <opencv2/opencv.hpp>
-#include <opencv2/dnn.hpp>
-
-#include <string>
-namespace pr{
-    class FineMapping{
-    public:
-        FineMapping();
-
-
-        FineMapping(std::string prototxt,std::string caffemodel);
-        static cv::Mat FineMappingVertical(cv::Mat InputProposal,int sliceNum=15,int upper=0,int lower=-50,int windows_size=17);
-        cv::Mat FineMappingHorizon(cv::Mat FinedVertical,int leftPadding,int rightPadding);
-
-
-    private:
-        cv::dnn::Net net;
-
-    };
-
-
-
-
-}
-#endif //SWIFTPR_FINEMAPPING_H
diff --git a/Prj-PHP/include/Pipeline.h b/Prj-PHP/include/Pipeline.h
deleted file mode 100644
index 4e82955..0000000
--- a/Prj-PHP/include/Pipeline.h
+++ /dev/null
@@ -1,60 +0,0 @@
-//
-// Created by 庾金科 on 22/10/2017.
-//
-
-#ifndef SWIFTPR_PIPLINE_H
-#define SWIFTPR_PIPLINE_H
-
-#include "PlateDetection.h"
-#include "PlateSegmentation.h"
-#include "CNNRecognizer.h"
-#include "PlateInfo.h"
-#include "FastDeskew.h"
-#include "FineMapping.h"
-#include "Recognizer.h"
-#include "SegmentationFreeRecognizer.h"
-
-namespace pr{
-
-    const std::vector<std::string> CH_PLATE_CODE{"京", "沪", "津", "渝", "冀", "晋", "蒙", "辽", "吉", "黑", "苏", "浙", "皖", "闽", "赣", "鲁", "豫", "鄂", "湘", "粤", "桂",
-                                        "琼", "川", "贵", "云", "藏", "陕", "甘", "青", "宁", "新", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "A",
-                                        "B", "C", "D", "E", "F", "G", "H", "J", "K", "L", "M", "N", "P", "Q", "R", "S", "T", "U", "V", "W", "X",
-                                        "Y", "Z","港","学","使","警","澳","挂","军","北","南","广","沈","兰","成","济","海","民","航","空"};
-
-
-
-    const int SEGMENTATION_FREE_METHOD = 0;
-    const int SEGMENTATION_BASED_METHOD = 1;
-
-    class PipelinePR{
-        public:
-            GeneralRecognizer *generalRecognizer;
-            PlateDetection *plateDetection;
-            PlateSegmentation *plateSegmentation;
-            FineMapping *fineMapping;
-            SegmentationFreeRecognizer *segmentationFreeRecognizer;
-
-            PipelinePR(std::string detector_filename,
-                       std::string finemapping_prototxt,std::string finemapping_caffemodel,
-                       std::string segmentation_prototxt,std::string segmentation_caffemodel,
-                       std::string charRecognization_proto,std::string charRecognization_caffemodel,
-                       std::string segmentationfree_proto,std::string segmentationfree_caffemodel
-                       );
-            ~PipelinePR();
-
-
-
-            std::vector<std::string> plateRes;
-            std::vector<PlateInfo> RunPiplineAsImage(cv::Mat plateImage,int method);
-
-
-
-
-
-
-
-    };
-
-
-}
-#endif //SWIFTPR_PIPLINE_H
diff --git a/Prj-PHP/include/PlateDetection.h b/Prj-PHP/include/PlateDetection.h
deleted file mode 100644
index 71ad9af..0000000
--- a/Prj-PHP/include/PlateDetection.h
+++ /dev/null
@@ -1,33 +0,0 @@
-//
-// Created by 庾金科 on 20/09/2017.
-//
-
-#ifndef SWIFTPR_PLATEDETECTION_H
-#define SWIFTPR_PLATEDETECTION_H
-
-#include <opencv2/opencv.hpp>
-#include <PlateInfo.h>
-#include <vector>
-namespace pr{
-    class PlateDetection{
-    public:
-        PlateDetection(std::string filename_cascade);
-        PlateDetection();
-        void LoadModel(std::string filename_cascade);
-        void plateDetectionRough(cv::Mat InputImage,std::vector<pr::PlateInfo>  &plateInfos,int min_w=36,int max_w=800);
-//        std::vector<pr::PlateInfo> plateDetectionRough(cv::Mat InputImage,int min_w= 60,int max_h = 400);
-
-
-//        std::vector<pr::PlateInfo> plateDetectionRoughByMultiScaleEdge(cv::Mat InputImage);
-
-
-
-    private:
-        cv::CascadeClassifier cascade;
-
-
-    };
-
-}// namespace pr
-
-#endif //SWIFTPR_PLATEDETECTION_H
diff --git a/Prj-PHP/include/PlateInfo.h b/Prj-PHP/include/PlateInfo.h
deleted file mode 100644
index f500bb5..0000000
--- a/Prj-PHP/include/PlateInfo.h
+++ /dev/null
@@ -1,126 +0,0 @@
-//
-// Created by 庾金科 on 20/09/2017.
-//
-
-#ifndef SWIFTPR_PLATEINFO_H
-#define SWIFTPR_PLATEINFO_H
-#include <opencv2/opencv.hpp>
-namespace pr {
-
-    typedef std::vector<cv::Mat> Character;
-
-    enum PlateColor { BLUE, YELLOW, WHITE, GREEN, BLACK,UNKNOWN};
-    enum CharType {CHINESE,LETTER,LETTER_NUMS,INVALID};
-
-
-    class PlateInfo {
-        public:
-            std::vector<std::pair<CharType,cv::Mat>> plateChars;
-            std::vector<std::pair<CharType,cv::Mat>> plateCoding;
-            float confidence = 0;
-            PlateInfo(const cv::Mat &plateData, std::string plateName, cv::Rect plateRect, PlateColor plateType) {
-                    licensePlate = plateData;
-                    name = plateName;
-                    ROI = plateRect;
-                    Type = plateType;
-                }
-            PlateInfo(const cv::Mat &plateData, cv::Rect plateRect, PlateColor plateType) {
-                licensePlate = plateData;
-                ROI = plateRect;
-                Type = plateType;
-            }
-            PlateInfo(const cv::Mat &plateData, cv::Rect plateRect) {
-                licensePlate = plateData;
-                ROI = plateRect;
-            }
-            PlateInfo() {
-
-            }
-
-            cv::Mat getPlateImage() {
-                return licensePlate;
-            }
-
-            void setPlateImage(cv::Mat plateImage){
-                licensePlate = plateImage;
-            }
-
-            cv::Rect getPlateRect() {
-                return ROI;
-            }
-
-            void setPlateRect(cv::Rect plateRect)   {
-                ROI = plateRect;
-            }
-            cv::String getPlateName() {
-                return name;
-
-            }
-            void setPlateName(cv::String plateName) {
-                name = plateName;
-            }
-            int getPlateType() {
-                return Type;
-            }
-
-            void appendPlateChar(const std::pair<CharType,cv::Mat> &plateChar)
-            {
-                plateChars.push_back(plateChar);
-            }
-
-            void appendPlateCoding(const std::pair<CharType,cv::Mat> &charProb){
-                plateCoding.push_back(charProb);
-            }
-
-    //        cv::Mat getPlateChars(int id) {
-    //            if(id<PlateChars.size())
-    //                return PlateChars[id];
-    //        }
-            std::string decodePlateNormal(std::vector<std::string> mappingTable) {
-                std::string decode;
-                for(auto plate:plateCoding) {
-                    float *prob = (float *)plate.second.data;
-                    if(plate.first == CHINESE) {
-
-                        decode += mappingTable[std::max_element(prob,prob+31) - prob];
-                        confidence+=*std::max_element(prob,prob+31);
-
-
-//                        std::cout<<*std::max_element(prob,prob+31)<<std::endl;
-
-                    }
-
-                    else if(plate.first == LETTER) {
-                        decode += mappingTable[std::max_element(prob+41,prob+65)- prob];
-                        confidence+=*std::max_element(prob+41,prob+65);
-                    }
-
-                    else if(plate.first == LETTER_NUMS) {
-                        decode += mappingTable[std::max_element(prob+31,prob+65)- prob];
-                        confidence+=*std::max_element(prob+31,prob+65);
-//                        std::cout<<*std::max_element(prob+31,prob+65)<<std::endl;
-
-                    }
-                    else if(plate.first == INVALID)
-                    {
-                        decode+='*';
-                    }
-
-                }
-                name = decode;
-
-                confidence/=7;
-
-                return decode;
-            }
-
-    private:
-        cv::Mat licensePlate;
-        cv::Rect ROI;
-        std::string name ;
-        PlateColor Type;
-    };
-}
-
-
-#endif //SWIFTPR_PLATEINFO_H
diff --git a/Prj-PHP/include/PlateSegmentation.h b/Prj-PHP/include/PlateSegmentation.h
deleted file mode 100644
index 8974818..0000000
--- a/Prj-PHP/include/PlateSegmentation.h
+++ /dev/null
@@ -1,39 +0,0 @@
-//
-// Created by 庾金科 on 16/10/2017.
-//
-
-#ifndef SWIFTPR_PLATESEGMENTATION_H
-#define SWIFTPR_PLATESEGMENTATION_H
-
-#include "opencv2/opencv.hpp"
-#include <opencv2/dnn.hpp>
-#include "PlateInfo.h"
-
-namespace pr{
-
-
-    class PlateSegmentation{
-    public:
-        const int PLATE_NORMAL = 6;
-        const int PLATE_NORMAL_GREEN = 7;
-        const int DEFAULT_WIDTH = 20;
-        PlateSegmentation(std::string phototxt,std::string caffemodel);
-        PlateSegmentation(){}
-        void segmentPlatePipline(PlateInfo &plateInfo,int stride,std::vector<cv::Rect> &Char_rects);
-
-        void segmentPlateBySlidingWindows(cv::Mat &plateImage,int windowsWidth,int stride,cv::Mat &respones);
-        void templateMatchFinding(const cv::Mat &respones,int windowsWidth,std::pair<float,std::vector<int>> &candidatePts);
-        void refineRegion(cv::Mat &plateImage,const std::vector<int> &candidatePts,const int padding,std::vector<cv::Rect> &rects);
-        void ExtractRegions(PlateInfo &plateInfo,std::vector<cv::Rect> &rects);
-        cv::Mat classifyResponse(const cv::Mat &cropped);
-    private:
-        cv::dnn::Net net;
-
-
-//        RefineRegion()
-
-    };
-
-}//namespace pr
-
-#endif //SWIFTPR_PLATESEGMENTATION_H
diff --git a/Prj-PHP/include/Recognizer.h b/Prj-PHP/include/Recognizer.h
deleted file mode 100644
index 556e42e..0000000
--- a/Prj-PHP/include/Recognizer.h
+++ /dev/null
@@ -1,23 +0,0 @@
-//
-// Created by 庾金科 on 20/10/2017.
-//
-
-
-#ifndef SWIFTPR_RECOGNIZER_H
-#define SWIFTPR_RECOGNIZER_H
-
-#include "PlateInfo.h"
-#include "opencv2/dnn.hpp"
-namespace pr{
-    typedef cv::Mat label;
-    class GeneralRecognizer{
-        public:
-            virtual label recognizeCharacter(cv::Mat character) = 0;
-//            virtual cv::Mat SegmentationFreeForSinglePlate(cv::Mat plate) = 0;
-            void SegmentBasedSequenceRecognition(PlateInfo &plateinfo);
-            void SegmentationFreeSequenceRecognition(PlateInfo &plateInfo);
-
-    };
-
-}
-#endif //SWIFTPR_RECOGNIZER_H
diff --git a/Prj-PHP/include/SegmentationFreeRecognizer.h b/Prj-PHP/include/SegmentationFreeRecognizer.h
deleted file mode 100644
index 583899e..0000000
--- a/Prj-PHP/include/SegmentationFreeRecognizer.h
+++ /dev/null
@@ -1,28 +0,0 @@
-//
-// Created by 庾金科 on 28/11/2017.
-//
-
-#ifndef SWIFTPR_SEGMENTATIONFREERECOGNIZER_H
-#define SWIFTPR_SEGMENTATIONFREERECOGNIZER_H
-
-#include "Recognizer.h"
-namespace pr{
-
-
-    class SegmentationFreeRecognizer{
-    public:
-        const int CHAR_INPUT_W = 14;
-        const int CHAR_INPUT_H = 30;
-        const int CHAR_LEN = 84;
-
-        SegmentationFreeRecognizer(std::string prototxt,std::string caffemodel);
-        std::pair<std::string,float> SegmentationFreeForSinglePlate(cv::Mat plate,std::vector<std::string> mapping_table);
-
-
-    private:
-        cv::dnn::Net net;
-
-    };
-
-}
-#endif //SWIFTPR_SEGMENTATIONFREERECOGNIZER_H
diff --git a/Prj-PHP/include/niBlackThreshold.h b/Prj-PHP/include/niBlackThreshold.h
deleted file mode 100644
index 5ad7e14..0000000
--- a/Prj-PHP/include/niBlackThreshold.h
+++ /dev/null
@@ -1,107 +0,0 @@
-//
-// Created by 庾金科 on 26/10/2017.
-//
-
-#ifndef SWIFTPR_NIBLACKTHRESHOLD_H
-#define SWIFTPR_NIBLACKTHRESHOLD_H
-
-
-#include <opencv2/opencv.hpp>
-using namespace cv;
-
-enum LocalBinarizationMethods{
-    BINARIZATION_NIBLACK = 0, //!< Classic Niblack binarization. See @cite Niblack1985 .
-    BINARIZATION_SAUVOLA = 1, //!< Sauvola's technique. See @cite Sauvola1997 .
-    BINARIZATION_WOLF = 2,    //!< Wolf's technique. See @cite Wolf2004 .
-    BINARIZATION_NICK = 3     //!< NICK technique. See @cite Khurshid2009 .
-};
-
-
-void niBlackThreshold( InputArray _src, OutputArray _dst, double maxValue,
-                       int type, int blockSize, double k, int binarizationMethod )
-{
-    // Input grayscale image
-    Mat src = _src.getMat();
-    CV_Assert(src.channels() == 1);
-    CV_Assert(blockSize % 2 == 1 && blockSize > 1);
-    if (binarizationMethod == BINARIZATION_SAUVOLA) {
-        CV_Assert(src.depth() == CV_8U);
-    }
-    type &= THRESH_MASK;
-    // Compute local threshold (T = mean + k * stddev)
-    // using mean and standard deviation in the neighborhood of each pixel
-    // (intermediate calculations are done with floating-point precision)
-    Mat test;
-    Mat thresh;
-    {
-        // note that: Var[X] = E[X^2] - E[X]^2
-        Mat mean, sqmean, variance, stddev, sqrtVarianceMeanSum;
-        double srcMin, stddevMax;
-        boxFilter(src, mean, CV_32F, Size(blockSize, blockSize),
-                  Point(-1,-1), true, BORDER_REPLICATE);
-        sqrBoxFilter(src, sqmean, CV_32F, Size(blockSize, blockSize),
-                     Point(-1,-1), true, BORDER_REPLICATE);
-        variance = sqmean - mean.mul(mean);
-        sqrt(variance, stddev);
-        switch (binarizationMethod)
-        {
-            case BINARIZATION_NIBLACK:
-                thresh = mean + stddev * static_cast<float>(k);
-
-                break;
-            case BINARIZATION_SAUVOLA:
-                thresh = mean.mul(1. + static_cast<float>(k) * (stddev / 128.0 - 1.));
-                break;
-            case BINARIZATION_WOLF:
-                minMaxIdx(src, &srcMin,NULL);
-                minMaxIdx(stddev, NULL, &stddevMax);
-                thresh = mean - static_cast<float>(k) * (mean - srcMin - stddev.mul(mean - srcMin) / stddevMax);
-                break;
-            case BINARIZATION_NICK:
-                sqrt(variance + sqmean, sqrtVarianceMeanSum);
-                thresh = mean + static_cast<float>(k) * sqrtVarianceMeanSum;
-                break;
-            default:
-                CV_Error( CV_StsBadArg, "Unknown binarization method" );
-                break;
-        }
-        thresh.convertTo(thresh, src.depth());
-
-        thresh.convertTo(test, src.depth());
-//
-//        cv::imshow("imagex",test);
-//        cv::waitKey(0);
-
-    }
-    // Prepare output image
-    _dst.create(src.size(), src.type());
-    Mat dst = _dst.getMat();
-    CV_Assert(src.data != dst.data);  // no inplace processing
-    // Apply thresholding: ( pixel > threshold ) ? foreground : background
-    Mat mask;
-    switch (type)
-    {
-        case THRESH_BINARY:      // dst = (src > thresh) ? maxval : 0
-        case THRESH_BINARY_INV:  // dst = (src > thresh) ? 0 : maxval
-            compare(src, thresh, mask, (type == THRESH_BINARY ? CMP_GT : CMP_LE));
-            dst.setTo(0);
-            dst.setTo(maxValue, mask);
-            break;
-        case THRESH_TRUNC:       // dst = (src > thresh) ? thresh : src
-            compare(src, thresh, mask, CMP_GT);
-            src.copyTo(dst);
-            thresh.copyTo(dst, mask);
-            break;
-        case THRESH_TOZERO:      // dst = (src > thresh) ? src : 0
-        case THRESH_TOZERO_INV:  // dst = (src > thresh) ? 0 : src
-            compare(src, thresh, mask, (type == THRESH_TOZERO ? CMP_GT : CMP_LE));
-            dst.setTo(0);
-            src.copyTo(dst, mask);
-            break;
-        default:
-            CV_Error( CV_StsBadArg, "Unknown threshold type" );
-            break;
-    }
-}
-
-#endif //SWIFTPR_NIBLACKTHRESHOLD_H
diff --git a/Prj-PHP/lpr/CNNRecognizer.cpp b/Prj-PHP/lpr/CNNRecognizer.cpp
deleted file mode 100644
index ff0991e..0000000
--- a/Prj-PHP/lpr/CNNRecognizer.cpp
+++ /dev/null
@@ -1,19 +0,0 @@
-//
-// Created by 庾金科 on 21/10/2017.
-//
-
-#include "CNNRecognizer.h"
-
-namespace pr{
-    CNNRecognizer::CNNRecognizer(std::string prototxt,std::string caffemodel){
-        net = cv::dnn::readNetFromCaffe(prototxt, caffemodel);
-    }
-
-    label CNNRecognizer::recognizeCharacter(cv::Mat charImage){
-        if(charImage.channels()== 3)
-            cv::cvtColor(charImage,charImage,cv::COLOR_BGR2GRAY);
-        cv::Mat inputBlob = cv::dnn::blobFromImage(charImage, 1/255.0, cv::Size(CHAR_INPUT_W,CHAR_INPUT_H), cv::Scalar(0,0,0),false);
-        net.setInput(inputBlob,"data");
-        return net.forward();
-    }
-}
\ No newline at end of file
diff --git a/Prj-PHP/lpr/FastDeskew.cpp b/Prj-PHP/lpr/FastDeskew.cpp
deleted file mode 100644
index 5da3644..0000000
--- a/Prj-PHP/lpr/FastDeskew.cpp
+++ /dev/null
@@ -1,108 +0,0 @@
-//
-// Created by Jack Yu on 02/10/2017.
-//
-
-
-
-#include "FastDeskew.h"
-
-namespace pr{
-    const int ANGLE_MIN = 30 ;
-    const int ANGLE_MAX = 150 ;
-    const int PLATE_H = 36;
-    const int PLATE_W = 136;
-    
-    int angle(float x,float y)
-    {
-        return atan2(x,y)*180/3.1415;
-    }
-
-    std::vector<float> avgfilter(std::vector<float> angle_list,int windowsSize) {
-        std::vector<float> angle_list_filtered(angle_list.size() - windowsSize + 1);
-        for (int i = 0; i < angle_list.size() - windowsSize + 1; i++) {
-            float avg = 0.00f;
-            for (int j = 0; j < windowsSize; j++) {
-                avg += angle_list[i + j];
-            }
-            avg = avg / windowsSize;
-            angle_list_filtered[i] = avg;
-        }
-
-        return angle_list_filtered;
-    }
-
-
-    void drawHist(std::vector<float> seq){
-        cv::Mat image(300,seq.size(),CV_8U);
-        image.setTo(0);
-
-        for(int i = 0;i<seq.size();i++)
-        {
-            float l = *std::max_element(seq.begin(),seq.end());
-
-            int p = int(float(seq[i])/l*300);
-
-            cv::line(image,cv::Point(i,300),cv::Point(i,300-p),cv::Scalar(255,255,255));
-        }
-        cv::imshow("vis",image);
-    }
-
-    cv::Mat  correctPlateImage(cv::Mat skewPlate,float angle,float maxAngle)
-    {
-        cv::Mat dst;
-        cv::Size size_o(skewPlate.cols,skewPlate.rows);
-        int extend_padding = 0;
-        extend_padding = static_cast<int>(skewPlate.rows*tan(cv::abs(angle)/180* 3.14) );
-        cv::Size size(skewPlate.cols + extend_padding ,skewPlate.rows);
-        float interval = abs(sin((angle /180) * 3.14)* skewPlate.rows);
-        cv::Point2f pts1[4] = {cv::Point2f(0,0),cv::Point2f(0,size_o.height),cv::Point2f(size_o.width,0),cv::Point2f(size_o.width,size_o.height)};
-        if(angle>0) {
-            cv::Point2f pts2[4] = {cv::Point2f(interval, 0), cv::Point2f(0, size_o.height),
-                                   cv::Point2f(size_o.width, 0), cv::Point2f(size_o.width - interval, size_o.height)};
-            cv::Mat M  = cv::getPerspectiveTransform(pts1,pts2);
-            cv::warpPerspective(skewPlate,dst,M,size);
-        }
-        else {
-            cv::Point2f pts2[4] = {cv::Point2f(0, 0), cv::Point2f(interval, size_o.height), cv::Point2f(size_o.width-interval, 0),
-                                   cv::Point2f(size_o.width, size_o.height)};
-            cv::Mat M  = cv::getPerspectiveTransform(pts1,pts2);
-            cv::warpPerspective(skewPlate,dst,M,size,cv::INTER_CUBIC);
-        }
-        return  dst;
-    }
-    cv::Mat fastdeskew(cv::Mat skewImage,int blockSize){
-        const int FILTER_WINDOWS_SIZE = 5;
-        std::vector<float> angle_list(180);
-        memset(angle_list.data(),0,angle_list.size()*sizeof(int));
-        cv::Mat bak;
-        skewImage.copyTo(bak);
-        if(skewImage.channels() == 3)
-            cv::cvtColor(skewImage,skewImage,cv::COLOR_RGB2GRAY);
-        if(skewImage.channels() == 1)
-        {
-            cv::Mat eigen;
-            cv::cornerEigenValsAndVecs(skewImage,eigen,blockSize,5);
-            for( int j = 0; j < skewImage.rows; j+=blockSize )
-            { for( int i = 0; i < skewImage.cols; i+=blockSize )
-                {
-                    float x2 = eigen.at<cv::Vec6f>(j, i)[4];
-                    float y2 = eigen.at<cv::Vec6f>(j, i)[5];
-                    int angle_cell = angle(x2,y2);
-                    angle_list[(angle_cell + 180)%180]+=1.0;
-                }
-            }
-        }
-        std::vector<float> filtered = avgfilter(angle_list,5);
-        int maxPos = std::max_element(filtered.begin(),filtered.end()) - filtered.begin() + FILTER_WINDOWS_SIZE/2;
-        if(maxPos>ANGLE_MAX)
-            maxPos = (-maxPos+90+180)%180;
-        if(maxPos<ANGLE_MIN)
-            maxPos-=90;
-        maxPos=90-maxPos;
-      cv::Mat deskewed = correctPlateImage(bak, static_cast<float>(maxPos),60.0f);
-        return deskewed;
-    }
-
-
-
-}//namespace pr
diff --git a/Prj-PHP/lpr/FineMapping.cpp b/Prj-PHP/lpr/FineMapping.cpp
deleted file mode 100644
index 2032b15..0000000
--- a/Prj-PHP/lpr/FineMapping.cpp
+++ /dev/null
@@ -1,170 +0,0 @@
-#include "FineMapping.h"
-namespace pr{
-
-    const int FINEMAPPING_H = 60 ;
-    const int FINEMAPPING_W = 140;
-    const int PADDING_UP_DOWN = 30;
-    void drawRect(cv::Mat image,cv::Rect rect)
-    {
-        cv::Point p1(rect.x,rect.y);
-        cv::Point p2(rect.x+rect.width,rect.y+rect.height);
-        cv::rectangle(image,p1,p2,cv::Scalar(0,255,0),1);
-    }
-
-
-    FineMapping::FineMapping(std::string prototxt,std::string caffemodel) {
-         net = cv::dnn::readNetFromCaffe(prototxt, caffemodel);
-
-    }
-
-    cv::Mat FineMapping::FineMappingHorizon(cv::Mat FinedVertical,int leftPadding,int rightPadding)
-    {
-
-//        if(FinedVertical.channels()==1)
-//            cv::cvtColor(FinedVertical,FinedVertical,cv::COLOR_GRAY2BGR);
-        cv::Mat inputBlob = cv::dnn::blobFromImage(FinedVertical, 1/255.0, cv::Size(66,16),
-                                      cv::Scalar(0,0,0),false);
-
-        net.setInput(inputBlob,"data");
-        cv::Mat prob = net.forward();
-        int front = static_cast<int>(prob.at<float>(0,0)*FinedVertical.cols);
-        int back = static_cast<int>(prob.at<float>(0,1)*FinedVertical.cols);
-        front -= leftPadding ;
-        if(front<0) front = 0;
-        back +=rightPadding;
-        if(back>FinedVertical.cols-1) back=FinedVertical.cols - 1;
-        cv::Mat cropped  = FinedVertical.colRange(front,back).clone();
-        return  cropped;
-
-
-    }
-    std::pair<int,int> FitLineRansac(std::vector<cv::Point> pts,int zeroadd = 0 )
-    {
-        std::pair<int,int> res;
-        if(pts.size()>2)
-        {
-            cv::Vec4f line;
-            cv::fitLine(pts,line,cv::DIST_HUBER,0,0.01,0.01);
-            float vx = line[0];
-            float vy = line[1];
-            float x = line[2];
-            float y = line[3];
-            int lefty = static_cast<int>((-x * vy / vx) + y);
-            int righty = static_cast<int>(((136- x) * vy / vx) + y);
-            res.first = lefty+PADDING_UP_DOWN+zeroadd;
-            res.second = righty+PADDING_UP_DOWN+zeroadd;
-            return res;
-        }
-        res.first = zeroadd;
-        res.second = zeroadd;
-        return res;
-    }
-
-    cv::Mat FineMapping::FineMappingVertical(cv::Mat InputProposal,int sliceNum,int upper,int lower,int windows_size){
-        cv::Mat PreInputProposal;
-        cv::Mat proposal;
-        cv::resize(InputProposal,PreInputProposal,cv::Size(FINEMAPPING_W,FINEMAPPING_H));
-        if(InputProposal.channels() == 3)
-            cv::cvtColor(PreInputProposal,proposal,cv::COLOR_BGR2GRAY);
-        else
-            PreInputProposal.copyTo(proposal);
-        // this will improve some sen
-        cv::Mat kernal = cv::getStructuringElement(cv::MORPH_ELLIPSE,cv::Size(1,3));
-        float diff = static_cast<float>(upper-lower);
-        diff/=static_cast<float>(sliceNum-1);
-        cv::Mat binary_adaptive;
-        std::vector<cv::Point> line_upper;
-        std::vector<cv::Point> line_lower;
-        int contours_nums=0;
-        for(int i = 0 ; i < sliceNum ; i++)
-        {
-            std::vector<std::vector<cv::Point> > contours;
-            float k =lower + i*diff;
-            cv::adaptiveThreshold(proposal,binary_adaptive,255,cv::ADAPTIVE_THRESH_MEAN_C,cv::THRESH_BINARY,windows_size,k);
-            cv::Mat draw;
-            binary_adaptive.copyTo(draw);
-            cv::findContours(binary_adaptive,contours,cv::RETR_EXTERNAL,cv::CHAIN_APPROX_SIMPLE);
-            for(auto contour: contours)
-            {
-                cv::Rect bdbox =cv::boundingRect(contour);
-                float lwRatio = bdbox.height/static_cast<float>(bdbox.width);
-                int  bdboxAera = bdbox.width*bdbox.height;
-                if ((   lwRatio>0.7&&bdbox.width*bdbox.height>100 && bdboxAera<300)
-                    || (lwRatio>3.0 && bdboxAera<100 && bdboxAera>10))
-                {
-                    cv::Point p1(bdbox.x, bdbox.y);
-                    cv::Point p2(bdbox.x + bdbox.width, bdbox.y + bdbox.height);
-                    line_upper.push_back(p1);
-                    line_lower.push_back(p2);
-                    contours_nums+=1;
-                }
-            }
-        }
-        if(contours_nums<41)
-        {
-            cv::bitwise_not(InputProposal,InputProposal);
-            cv::Mat kernal = cv::getStructuringElement(cv::MORPH_ELLIPSE,cv::Size(1,5));
-            cv::Mat bak;
-            cv::resize(InputProposal,bak,cv::Size(FINEMAPPING_W,FINEMAPPING_H));
-            cv::erode(bak,bak,kernal);
-            if(InputProposal.channels() == 3)
-                cv::cvtColor(bak,proposal,cv::COLOR_BGR2GRAY);
-            else
-                proposal = bak;
-            int contours_nums=0;
-            for(int i = 0 ; i < sliceNum ; i++)
-            {
-                std::vector<std::vector<cv::Point> > contours;
-                float k =lower + i*diff;
-                cv::adaptiveThreshold(proposal,binary_adaptive,255,cv::ADAPTIVE_THRESH_MEAN_C,cv::THRESH_BINARY,windows_size,k);
-                cv::Mat draw;
-                binary_adaptive.copyTo(draw);
-                cv::findContours(binary_adaptive,contours,cv::RETR_EXTERNAL,cv::CHAIN_APPROX_SIMPLE);
-                for(auto contour: contours)
-                {
-                    cv::Rect bdbox =cv::boundingRect(contour);
-                    float lwRatio = bdbox.height/static_cast<float>(bdbox.width);
-                    int  bdboxAera = bdbox.width*bdbox.height;
-                    if ((   lwRatio>0.7&&bdbox.width*bdbox.height>120 && bdboxAera<300)
-                        || (lwRatio>3.0 && bdboxAera<100 && bdboxAera>10))
-                    {
-
-                        cv::Point p1(bdbox.x, bdbox.y);
-                        cv::Point p2(bdbox.x + bdbox.width, bdbox.y + bdbox.height);
-                        line_upper.push_back(p1);
-                        line_lower.push_back(p2);
-                        contours_nums+=1;
-                    }
-                }
-            }
-        }
-            cv::Mat rgb;
-            cv::copyMakeBorder(PreInputProposal, rgb, PADDING_UP_DOWN, PADDING_UP_DOWN, 0, 0, cv::BORDER_REPLICATE);
-            std::pair<int, int> A;
-            std::pair<int, int> B;
-            A = FitLineRansac(line_upper, -1);
-            B = FitLineRansac(line_lower, 1);
-            int leftyB = A.first;
-            int rightyB = A.second;
-            int leftyA = B.first;
-            int rightyA = B.second;
-            int cols = rgb.cols;
-            int rows = rgb.rows;
-            std::vector<cv::Point2f> corners(4);
-            corners[0] = cv::Point2f(cols - 1, rightyA);
-            corners[1] = cv::Point2f(0, leftyA);
-            corners[2] = cv::Point2f(cols - 1, rightyB);
-            corners[3] = cv::Point2f(0, leftyB);
-            std::vector<cv::Point2f> corners_trans(4);
-            corners_trans[0] = cv::Point2f(136, 36);
-            corners_trans[1] = cv::Point2f(0, 36);
-            corners_trans[2] = cv::Point2f(136, 0);
-            corners_trans[3] = cv::Point2f(0, 0);
-            cv::Mat transform = cv::getPerspectiveTransform(corners, corners_trans);
-            cv::Mat quad = cv::Mat::zeros(36, 136, CV_8UC3);
-            cv::warpPerspective(rgb, quad, transform, quad.size());
-        return quad;
-    }
-}
-
-
diff --git a/Prj-PHP/lpr/Pipeline.cpp b/Prj-PHP/lpr/Pipeline.cpp
deleted file mode 100644
index f4eb8b2..0000000
--- a/Prj-PHP/lpr/Pipeline.cpp
+++ /dev/null
@@ -1,101 +0,0 @@
-//
-// Created by 庾金科 on 23/10/2017.
-//
-
-#include "Pipeline.h"
-
-
-namespace pr {
-
-
-
-    const int HorizontalPadding = 4;
-    PipelinePR::PipelinePR(std::string detector_filename,
-                           std::string finemapping_prototxt, std::string finemapping_caffemodel,
-                           std::string segmentation_prototxt, std::string segmentation_caffemodel,
-                           std::string charRecognization_proto, std::string charRecognization_caffemodel,
-                           std::string segmentationfree_proto,std::string segmentationfree_caffemodel) {
-        plateDetection = new PlateDetection(detector_filename);
-        fineMapping = new FineMapping(finemapping_prototxt, finemapping_caffemodel);
-        plateSegmentation = new PlateSegmentation(segmentation_prototxt, segmentation_caffemodel);
-        generalRecognizer = new CNNRecognizer(charRecognization_proto, charRecognization_caffemodel);
-        segmentationFreeRecognizer =  new SegmentationFreeRecognizer(segmentationfree_proto,segmentationfree_caffemodel);
-
-    }
-
-    PipelinePR::~PipelinePR() {
-
-        delete plateDetection;
-        delete fineMapping;
-        delete plateSegmentation;
-        delete generalRecognizer;
-        delete segmentationFreeRecognizer;
-
-
-    }
-
-    std::vector<PlateInfo> PipelinePR:: RunPiplineAsImage(cv::Mat plateImage,int method) {
-        std::vector<PlateInfo> results;
-        std::vector<pr::PlateInfo> plates;
-        plateDetection->plateDetectionRough(plateImage,plates,36,700);
-
-        for (pr::PlateInfo plateinfo:plates) {
-
-            cv::Mat image_finemapping = plateinfo.getPlateImage();
-            image_finemapping = fineMapping->FineMappingVertical(image_finemapping);
-            image_finemapping = pr::fastdeskew(image_finemapping, 5);
-
-
-
-            //Segmentation-based
-
-            if(method==SEGMENTATION_BASED_METHOD)
-            {
-                image_finemapping = fineMapping->FineMappingHorizon(image_finemapping, 2, HorizontalPadding);
-                cv::resize(image_finemapping, image_finemapping, cv::Size(136+HorizontalPadding, 36));
-//            cv::imshow("image_finemapping",image_finemapping);
-//            cv::waitKey(0);
-                plateinfo.setPlateImage(image_finemapping);
-                std::vector<cv::Rect> rects;
-
-                plateSegmentation->segmentPlatePipline(plateinfo, 1, rects);
-                plateSegmentation->ExtractRegions(plateinfo, rects);
-                cv::copyMakeBorder(image_finemapping, image_finemapping, 0, 0, 0, 20, cv::BORDER_REPLICATE);
-                plateinfo.setPlateImage(image_finemapping);
-                generalRecognizer->SegmentBasedSequenceRecognition(plateinfo);
-                plateinfo.decodePlateNormal(pr::CH_PLATE_CODE);
-
-            }
-                //Segmentation-free
-            else if(method==SEGMENTATION_FREE_METHOD)
-            {
-
-                image_finemapping = fineMapping->FineMappingHorizon(image_finemapping, 4, HorizontalPadding+3);
-
-                cv::resize(image_finemapping, image_finemapping, cv::Size(136+HorizontalPadding, 36));
-//                cv::imwrite("./test.png",image_finemapping);
-//                cv::imshow("image_finemapping",image_finemapping);
-//                cv::waitKey(0);
-                plateinfo.setPlateImage(image_finemapping);
-//                std::vector<cv::Rect> rects;
-
-                std::pair<std::string,float> res = segmentationFreeRecognizer->SegmentationFreeForSinglePlate(plateinfo.getPlateImage(),pr::CH_PLATE_CODE);
-                plateinfo.confidence = res.second;
-                plateinfo.setPlateName(res.first);
-            }
-
-
-
-            results.push_back(plateinfo);
-        }
-
-//        for (auto str:results) {
-//            std::cout << str << std::endl;
-//        }
-        return results;
-
-    }//namespace pr
-
-
-
-}
\ No newline at end of file
diff --git a/Prj-PHP/lpr/PlateDetection.cpp b/Prj-PHP/lpr/PlateDetection.cpp
deleted file mode 100644
index cb3eeef..0000000
--- a/Prj-PHP/lpr/PlateDetection.cpp
+++ /dev/null
@@ -1,32 +0,0 @@
-#include "PlateDetection.h"
-#include "util.h"
-namespace pr{
-    PlateDetection::PlateDetection(std::string filename_cascade){
-        cascade.load(filename_cascade);
-
-    };
-    void PlateDetection::plateDetectionRough(cv::Mat InputImage,std::vector<pr::PlateInfo>  &plateInfos,int min_w,int max_w){
-        cv::Mat processImage;
-        cv::cvtColor(InputImage,processImage,cv::COLOR_BGR2GRAY);
-        std::vector<cv::Rect> platesRegions;
-        cv::Size minSize(min_w,min_w/4);
-        cv::Size maxSize(max_w,max_w/4);
-        cascade.detectMultiScale( processImage, platesRegions,
-                                  1.1, 3, cv::CASCADE_SCALE_IMAGE,minSize,maxSize);
-        for(auto plate:platesRegions)
-        {
-            int zeroadd_w  = static_cast<int>(plate.width*0.30);
-            int zeroadd_h = static_cast<int>(plate.height*2);
-            int zeroadd_x = static_cast<int>(plate.width*0.15);
-            int zeroadd_y = static_cast<int>(plate.height*1);
-            plate.x-=zeroadd_x;
-            plate.y-=zeroadd_y;
-            plate.height += zeroadd_h;
-            plate.width += zeroadd_w;
-                cv::Mat plateImage = util::cropFromImage(InputImage,plate);
-            PlateInfo plateInfo(plateImage,plate);
-            plateInfos.push_back(plateInfo);
-
-        }
-    }
-}//namespace pr
diff --git a/Prj-PHP/lpr/PlateSegmentation.cpp b/Prj-PHP/lpr/PlateSegmentation.cpp
deleted file mode 100644
index 69e1aeb..0000000
--- a/Prj-PHP/lpr/PlateSegmentation.cpp
+++ /dev/null
@@ -1,404 +0,0 @@
-//
-// Created by 庾金科 on 16/10/2017.
-//
-
-#include "PlateSegmentation.h"
-#include "niBlackThreshold.h"
-
-
-//#define DEBUG
-namespace pr{
-
-    PlateSegmentation::PlateSegmentation(std::string prototxt,std::string caffemodel) {
-        net = cv::dnn::readNetFromCaffe(prototxt, caffemodel);
-    }
-    cv::Mat PlateSegmentation::classifyResponse(const cv::Mat &cropped){
-        cv::Mat inputBlob = cv::dnn::blobFromImage(cropped, 1/255.0, cv::Size(22,22), cv::Scalar(0,0,0),false);
-        net.setInput(inputBlob,"data");
-        return net.forward();
-    }
-
-    void drawHist(float* seq,int size,const char* name){
-        cv::Mat image(300,size,CV_8U);
-        image.setTo(0);
-        float* start =seq;
-        float* end = seq+size;
-        float l = *std::max_element(start,end);
-        for(int i = 0;i<size;i++)
-        {
-            int p = int(float(seq[i])/l*300);
-            cv::line(image,cv::Point(i,300),cv::Point(i,300-p),cv::Scalar(255,255,255));
-        }
-        cv::resize(image,image,cv::Size(600,100));
-        cv::imshow(name,image);
-    }
-
-    inline void computeSafeMargin(int &val,const int &rows){
-        val = std::min(val,rows);
-        val = std::max(val,0);
-    }
-
-    cv::Rect boxFromCenter(const cv::Point center,int left,int right,int top,int bottom,cv::Size bdSize)
-    {
-        cv::Point p1(center.x -  left ,center.y - top);
-        cv::Point p2( center.x + right, center.y + bottom);
-        p1.x = std::max(0,p1.x);
-        p1.y = std::max(0,p1.y);
-        p2.x = std::min(p2.x,bdSize.width-1);
-        p2.y = std::min(p2.y,bdSize.height-1);
-        cv::Rect rect(p1,p2);
-        return rect;
-    }
-
-    cv::Rect boxPadding(cv::Rect rect,int left,int right,int top,int bottom,cv::Size bdSize)
-    {
-
-        cv::Point center(rect.x+(rect.width>>1),rect.y + (rect.height>>1));
-        int rebuildLeft  = (rect.width>>1 )+ left;
-        int rebuildRight = (rect.width>>1 )+ right;
-        int rebuildTop = (rect.height>>1 )+ top;
-        int rebuildBottom = (rect.height>>1 )+ bottom;
-        return boxFromCenter(center,rebuildLeft,rebuildRight,rebuildTop,rebuildBottom,bdSize);
-
-    }
-
-
-
-    void PlateSegmentation:: refineRegion(cv::Mat &plateImage,const std::vector<int> &candidatePts,const int padding,std::vector<cv::Rect> &rects){
-        int w = candidatePts[5] - candidatePts[4];
-        int cols = plateImage.cols;
-        int rows = plateImage.rows;
-        for(int i = 0 ; i < candidatePts.size()  ; i++)
-        {
-            int left = 0;
-            int right = 0 ;
-
-            if(i == 0 ){
-                left= candidatePts[i];
-                right = left+w+padding;
-                }
-            else {
-                left = candidatePts[i] - padding;
-                right = left + w + padding * 2;
-            }
-
-            computeSafeMargin(right,cols);
-            computeSafeMargin(left,cols);
-            cv::Rect roi(left,0,right - left,rows-1);
-            cv::Mat roiImage;
-            plateImage(roi).copyTo(roiImage);
-
-            if (i>=1)
-            {
-
-                cv::Mat roi_thres;
-//                cv::threshold(roiImage,roi_thres,0,255,cv::THRESH_OTSU|cv::THRESH_BINARY);
-
-                niBlackThreshold(roiImage,roi_thres,255,cv::THRESH_BINARY,15,0.27,BINARIZATION_NIBLACK);
-
-                std::vector<std::vector<cv::Point>> contours;
-                cv::findContours(roi_thres,contours,cv::RETR_LIST,cv::CHAIN_APPROX_SIMPLE);
-                cv::Point boxCenter(roiImage.cols>>1,roiImage.rows>>1);
-
-                cv::Rect final_bdbox;
-                cv::Point final_center;
-                int final_dist = INT_MAX;
-
-
-                for(auto contour:contours)
-                {
-                    cv::Rect bdbox = cv::boundingRect(contour);
-                    cv::Point center(bdbox.x+(bdbox.width>>1),bdbox.y + (bdbox.height>>1));
-                    int dist = (center.x - boxCenter.x)*(center.x - boxCenter.x);
-                    if(dist<final_dist and  bdbox.height > rows>>1)
-                    {   final_dist =dist;
-                        final_center = center;
-                        final_bdbox = bdbox;
-                    }
-                }
-
-                //rebuild box
-                if(final_bdbox.height/ static_cast<float>(final_bdbox.width) > 3.5 && final_bdbox.width*final_bdbox.height<10)
-                    final_bdbox = boxFromCenter(final_center,8,8,(rows>>1)-3 , (rows>>1) - 2,roiImage.size());
-                else {
-                    if(i == candidatePts.size()-1)
-                        final_bdbox = boxPadding(final_bdbox, padding/2, padding, padding/2, padding/2, roiImage.size());
-                    else
-                        final_bdbox = boxPadding(final_bdbox, padding, padding, padding, padding, roiImage.size());
-
-
-//                    std::cout<<final_bdbox<<std::endl;
-//                    std::cout<<roiImage.size()<<std::endl;
-#ifdef DEBUG
-                    cv::imshow("char_thres",roi_thres);
-
-                    cv::imshow("char",roiImage(final_bdbox));
-                    cv::waitKey(0);
-#endif
-
-
-                }
-
-
-                final_bdbox.x += left;
-
-                rects.push_back(final_bdbox);
-//
-
-            }
-            else
-            {
-                rects.push_back(roi);
-            }
-
-//            else
-//            {
-//
-//            }
-
-//            cv::GaussianBlur(roiImage,roiImage,cv::Size(7,7),3);
-//
-//            cv::imshow("image",roiImage);
-//            cv::waitKey(0);
-
-
-        }
-
-
-
-    }
-    void avgfilter(float *angle_list,int size,int windowsSize) {
-        float *filterd = new float[size];
-        for(int i = 0 ; i < size ; i++) filterd [i] = angle_list[i];
-//        memcpy(filterd,angle_list,size);
-
-        cv::Mat kernal_gaussian = cv::getGaussianKernel(windowsSize,3,CV_32F);
-        float *kernal = (float*)kernal_gaussian.data;
-//        kernal+=windowsSize;
-        int r = windowsSize/2;
-
-
-
-
-        for (int i = 0; i < size; i++) {
-            float avg = 0.00f;
-            for (int j = 0; j < windowsSize; j++) {
-                if(i+j-r>0&&i+j+r<size-1)
-                    avg += filterd[i + j-r]*kernal[j];
-            }
-//            avg = avg / windowsSize;
-            angle_list[i] = avg;
-
-        }
-
-        delete filterd;
-    }
-
-    void PlateSegmentation::templateMatchFinding(const cv::Mat &respones,int windowsWidth,std::pair<float,std::vector<int>> &candidatePts){
-        int rows = respones.rows;
-        int cols = respones.cols;
-
-
-
-        float *data = (float*)respones.data;
-        float *engNum_prob = data;
-        float *false_prob = data+cols;
-        float *ch_prob = data+cols*2;
-
-        avgfilter(engNum_prob,cols,5);
-        avgfilter(false_prob,cols,5);
-//        avgfilter(ch_prob,cols,5);
-        std::vector<int> candidate_pts(7);
-#ifdef DEBUG
-        drawHist(engNum_prob,cols,"engNum_prob");
-        drawHist(false_prob,cols,"false_prob");
-        drawHist(ch_prob,cols,"ch_prob");
-                cv::waitKey(0);
-#endif
-
-
-
-
-        int cp_list[7];
-        float loss_selected = -10;
-
-        for(int start = 0 ; start < 20 ; start+=2)
-            for(int  width = windowsWidth-5; width < windowsWidth+5 ; width++ ){
-                for(int interval = windowsWidth/2; interval < windowsWidth; interval++)
-                {
-                    int cp1_ch  = start;
-                    int cp2_p0 = cp1_ch+ width;
-                    int cp3_p1 = cp2_p0+ width + interval;
-                    int cp4_p2 = cp3_p1 + width;
-                    int cp5_p3 = cp4_p2 + width+1;
-                    int cp6_p4 = cp5_p3 + width+2;
-                    int cp7_p5= cp6_p4+ width+2;
-
-                    int md1 = (cp1_ch+cp2_p0)>>1;
-                    int md2 = (cp2_p0+cp3_p1)>>1;
-                    int md3 = (cp3_p1+cp4_p2)>>1;
-                    int md4 = (cp4_p2+cp5_p3)>>1;
-                    int md5 = (cp5_p3+cp6_p4)>>1;
-                    int md6 = (cp6_p4+cp7_p5)>>1;
-
-
-
-
-                    if(cp7_p5>=cols)
-                        continue;
-//                    float loss = ch_prob[cp1_ch]+
-//                       engNum_prob[cp2_p0] +engNum_prob[cp3_p1]+engNum_prob[cp4_p2]+engNum_prob[cp5_p3]+engNum_prob[cp6_p4] +engNum_prob[cp7_p5]
-//                    + (false_prob[md2]+false_prob[md3]+false_prob[md4]+false_prob[md5]+false_prob[md5] + false_prob[md6]);
-                    float loss = ch_prob[cp1_ch]*3 -(false_prob[cp3_p1]+false_prob[cp4_p2]+false_prob[cp5_p3]+false_prob[cp6_p4]+false_prob[cp7_p5]);
-
-                    if(loss>loss_selected)
-                    {
-                        loss_selected = loss;
-                        cp_list[0]= cp1_ch;
-                        cp_list[1]= cp2_p0;
-                        cp_list[2]= cp3_p1;
-                        cp_list[3]= cp4_p2;
-                        cp_list[4]= cp5_p3;
-                        cp_list[5]= cp6_p4;
-                        cp_list[6]= cp7_p5;
-                    }
-                }
-            }
-        candidate_pts[0] = cp_list[0];
-        candidate_pts[1] = cp_list[1];
-        candidate_pts[2] = cp_list[2];
-        candidate_pts[3] = cp_list[3];
-        candidate_pts[4] = cp_list[4];
-        candidate_pts[5] = cp_list[5];
-        candidate_pts[6] = cp_list[6];
-
-        candidatePts.first = loss_selected;
-        candidatePts.second = candidate_pts;
-
-    };
-
-
-    void PlateSegmentation::segmentPlateBySlidingWindows(cv::Mat &plateImage,int windowsWidth,int stride,cv::Mat &respones){
-
-
-//        cv::resize(plateImage,plateImage,cv::Size(136,36));
-
-        cv::Mat plateImageGray;
-        cv::cvtColor(plateImage,plateImageGray,cv::COLOR_BGR2GRAY);
-        int padding  =  plateImage.cols-136 ;
-//        int padding  =  0 ;
-        int height = plateImage.rows - 1;
-        int width = plateImage.cols - 1 - padding;
-        for(int i = 0 ; i < width - windowsWidth +1 ; i +=stride)
-        {
-            cv::Rect roi(i,0,windowsWidth,height);
-            cv::Mat roiImage = plateImageGray(roi);
-            cv::Mat response = classifyResponse(roiImage);
-            respones.push_back(response);
-        }
-
-
-
-
-        respones =  respones.t();
-//        std::pair<float,std::vector<int>> images ;
-//
-//
-//        std::cout<<images.first<<" ";
-//        for(int i = 0 ; i < images.second.size() ; i++)
-//        {
-//            std::cout<<images.second[i]<<" ";
-////            cv::line(plateImageGray,cv::Point(images.second[i],0),cv::Point(images.second[i],36),cv::Scalar(255,255,255),1); //DEBUG
-//        }
-
-//        int w = images.second[5] - images.second[4];
-
-//        cv::line(plateImageGray,cv::Point(images.second[5]+w,0),cv::Point(images.second[5]+w,36),cv::Scalar(255,255,255),1); //DEBUG
-//        cv::line(plateImageGray,cv::Point(images.second[5]+2*w,0),cv::Point(images.second[5]+2*w,36),cv::Scalar(255,255,255),1); //DEBUG
-
-
-//        RefineRegion(plateImageGray,images.second,5);
-
-//        std::cout<<w<<std::endl;
-
-//        std::cout<<<<std::endl;
-
-//        cv::resize(plateImageGray,plateImageGray,cv::Size(600,100));
-
-
-
-    }
-
-//    void filterGaussian(cv::Mat &respones,float sigma){
-//
-//    }
-
-
-    void PlateSegmentation::segmentPlatePipline(PlateInfo &plateInfo,int stride,std::vector<cv::Rect> &Char_rects){
-        cv::Mat plateImage = plateInfo.getPlateImage(); // get src image .
-        cv::Mat plateImageGray;
-        cv::cvtColor(plateImage,plateImageGray,cv::COLOR_BGR2GRAY);
-        //do binarzation
-        //
-        std::pair<float,std::vector<int>> sections ; // segment points variables .
-
-        cv::Mat respones; //three response of every sub region from origin image .
-        segmentPlateBySlidingWindows(plateImage,DEFAULT_WIDTH,1,respones);
-        templateMatchFinding(respones,DEFAULT_WIDTH/stride,sections);
-        for(int i = 0; i < sections.second.size() ; i++)
-        {
-            sections.second[i]*=stride;
-
-        }
-
-//        std::cout<<sections<<std::endl;
-
-        refineRegion(plateImageGray,sections.second,5,Char_rects);
-#ifdef DEBUG
-        for(int i = 0 ; i < sections.second.size() ; i++)
-        {
-            std::cout<<sections.second[i]<<" ";
-            cv::line(plateImageGray,cv::Point(sections.second[i],0),cv::Point(sections.second[i],36),cv::Scalar(255,255,255),1); //DEBUG
-        }
-        cv::imshow("plate",plateImageGray);
-        cv::waitKey(0);
-#endif
-//        cv::waitKey(0);
-
-    }
-
-    void PlateSegmentation::ExtractRegions(PlateInfo &plateInfo,std::vector<cv::Rect> &rects){
-        cv::Mat plateImage = plateInfo.getPlateImage();
-        for(int i = 0 ; i < rects.size() ; i++){
-            cv::Mat charImage;
-            plateImage(rects[i]).copyTo(charImage);
-            if(charImage.channels())
-                cv::cvtColor(charImage,charImage,cv::COLOR_BGR2GRAY);
-//            cv::imshow("image",charImage);
-//            cv::waitKey(0);
-            cv::equalizeHist(charImage,charImage);
-//
-
-//
-
-
-            std::pair<CharType,cv::Mat> char_instance;
-            if(i == 0 ){
-
-                char_instance.first = CHINESE;
-
-
-            } else if(i == 1){
-                char_instance.first = LETTER;
-            }
-            else{
-                char_instance.first = LETTER_NUMS;
-            }
-            char_instance.second = charImage;
-            plateInfo.appendPlateChar(char_instance);
-
-        }
-
-    }
-
-}//namespace pr
diff --git a/Prj-PHP/lpr/Recognizer.cpp b/Prj-PHP/lpr/Recognizer.cpp
deleted file mode 100644
index 3bfafef..0000000
--- a/Prj-PHP/lpr/Recognizer.cpp
+++ /dev/null
@@ -1,23 +0,0 @@
-//
-// Created by Jack Yu on 22/10/2017.
-//
-
-#include "Recognizer.h"
-
-namespace pr{
-    void GeneralRecognizer::SegmentBasedSequenceRecognition(PlateInfo &plateinfo){
-        for(auto char_instance:plateinfo.plateChars)
-        {
-            std::pair<CharType,cv::Mat> res;
-            if(char_instance.second.rows*char_instance.second.cols>40) {
-                label code_table = recognizeCharacter(char_instance.second);
-                res.first = char_instance.first;
-                code_table.copyTo(res.second);
-                plateinfo.appendPlateCoding(res);
-            } else{
-                res.first = INVALID;
-                plateinfo.appendPlateCoding(res);
-            }
-        }
-    }
-}
diff --git a/Prj-PHP/lpr/SegmentationFreeRecognizer.cpp b/Prj-PHP/lpr/SegmentationFreeRecognizer.cpp
deleted file mode 100644
index ec0f7e9..0000000
--- a/Prj-PHP/lpr/SegmentationFreeRecognizer.cpp
+++ /dev/null
@@ -1,89 +0,0 @@
-//
-// Created by Jack Yu on 28/11/2017.
-//
-#include "SegmentationFreeRecognizer.h"
-
-namespace pr {
-    SegmentationFreeRecognizer::SegmentationFreeRecognizer(std::string prototxt, std::string caffemodel) {
-        net = cv::dnn::readNetFromCaffe(prototxt, caffemodel);
-    }
-    inline int judgeCharRange(int id)
-    {return id<31 || id>63;
-    }
-    std::pair<std::string,float> decodeResults(cv::Mat code_table,std::vector<std::string> mapping_table,float thres)
-    {
-        cv::MatSize mtsize = code_table.size;
-        int sequencelength = mtsize[2];
-        int labellength = mtsize[1];
-        cv::transpose(code_table.reshape(1,1).reshape(1,labellength),code_table);
-        std::string name = "";
-        std::vector<int> seq(sequencelength);
-        std::vector<std::pair<int,float>> seq_decode_res;
-        for(int i = 0 ; i < sequencelength;  i++) {
-            float *fstart = ((float *) (code_table.data) + i * labellength );
-            int id = std::max_element(fstart,fstart+labellength) - fstart;
-            seq[i] =id;
-        }
-
-        float sum_confidence = 0;
-        int plate_lenghth  = 0 ;
-        for(int i = 0 ; i< sequencelength ; i++)
-        {
-            if(seq[i]!=labellength-1 && (i==0 || seq[i]!=seq[i-1]))
-            {
-                float *fstart = ((float *) (code_table.data) + i * labellength );
-                float confidence = *(fstart+seq[i]);
-                std::pair<int,float> pair_(seq[i],confidence);
-                seq_decode_res.push_back(pair_);
-            }
-        }
-        int  i = 0;
-        if (seq_decode_res.size()>1 && judgeCharRange(seq_decode_res[0].first) && judgeCharRange(seq_decode_res[1].first))
-        {
-            i=2;
-            int c = seq_decode_res[0].second<seq_decode_res[1].second;
-            name+=mapping_table[seq_decode_res[c].first];
-            sum_confidence+=seq_decode_res[c].second;
-            plate_lenghth++;
-        }
-
-        for(; i < seq_decode_res.size();i++)
-        {
-            name+=mapping_table[seq_decode_res[i].first];
-            sum_confidence +=seq_decode_res[i].second;
-            plate_lenghth++;
-        }
-        std::pair<std::string,float> res;
-        res.second = sum_confidence/plate_lenghth;
-        res.first = name;
-        return res;
-
-    }
-    std::string decodeResults(cv::Mat code_table,std::vector<std::string> mapping_table)
-    {
-        cv::MatSize mtsize = code_table.size;
-        int sequencelength = mtsize[2];
-        int labellength = mtsize[1];
-        cv::transpose(code_table.reshape(1,1).reshape(1,labellength),code_table);
-        std::string name = "";
-        std::vector<int> seq(sequencelength);
-        for(int i = 0 ; i < sequencelength;  i++) {
-            float *fstart = ((float *) (code_table.data) + i * labellength );
-            int id = std::max_element(fstart,fstart+labellength) - fstart;
-            seq[i] =id;
-        }
-        for(int i = 0 ; i< sequencelength ; i++)
-        {
-            if(seq[i]!=labellength-1 && (i==0 || seq[i]!=seq[i-1]))
-                name+=mapping_table[seq[i]];
-        }
-        return name;
-    }
-    std::pair<std::string,float> SegmentationFreeRecognizer::SegmentationFreeForSinglePlate(cv::Mat Image,std::vector<std::string> mapping_table) {
-        cv::transpose(Image,Image);
-        cv::Mat inputBlob = cv::dnn::blobFromImage(Image, 1 / 255.0, cv::Size(40,160));
-        net.setInput(inputBlob, "data");
-        cv::Mat char_prob_mat = net.forward();
-        return decodeResults(char_prob_mat,mapping_table,0.00);
-    }
-}
diff --git a/Prj-PHP/lpr/util.h b/Prj-PHP/lpr/util.h
deleted file mode 100644
index 3f9f52c..0000000
--- a/Prj-PHP/lpr/util.h
+++ /dev/null
@@ -1,67 +0,0 @@
-//
-// Created by Jack Yu on 04/04/2017.
-//
-
-#include <opencv2/opencv.hpp>
-namespace util{
-    template <class T> void swap ( T& a, T& b )
-    {
-        T c(a); a=b; b=c;
-    }
-    template <class T> T min(T& a,T& b )
-    {
-        return a>b?b:a;
-    }
-
-    cv::Mat cropFromImage(const cv::Mat &image,cv::Rect rect){
-        int w = image.cols-1;
-        int h = image.rows-1;
-        rect.x = std::max(rect.x,0);
-        rect.y = std::max(rect.y,0);
-        rect.height = std::min(rect.height,h-rect.y);
-        rect.width = std::min(rect.width,w-rect.x);
-        cv::Mat temp(rect.size(), image.type());
-        cv::Mat cropped;
-        temp = image(rect);
-        temp.copyTo(cropped);
-        return cropped;
-
-    }
-
-    cv::Mat cropBox2dFromImage(const cv::Mat &image,cv::RotatedRect rect)
-    {
-        cv::Mat M, rotated, cropped;
-        float angle = rect.angle;
-        cv::Size rect_size(rect.size.width,rect.size.height);
-        if (rect.angle < -45.) {
-            angle += 90.0;
-            swap(rect_size.width, rect_size.height);
-        }
-        M = cv::getRotationMatrix2D(rect.center, angle, 1.0);
-        cv::warpAffine(image, rotated, M, image.size(), cv::INTER_CUBIC);
-        cv::getRectSubPix(rotated, rect_size, rect.center, cropped);
-        return cropped;
-    }
-
-    cv::Mat calcHist(const cv::Mat &image)
-    {
-        cv::Mat hsv;
-        std::vector<cv::Mat> hsv_planes;
-        cv::cvtColor(image,hsv,cv::COLOR_BGR2HSV);
-        cv::split(hsv,hsv_planes);
-        cv::Mat hist;
-        int histSize = 256;
-        float range[] = {0,255};
-        const float* histRange = {range};
-        cv::calcHist( &hsv_planes[0], 1, 0, cv::Mat(), hist, 1, &histSize, &histRange,true, true);
-        return hist;
-    }
-    
-    float computeSimilir(const cv::Mat &A,const cv::Mat &B)
-    {
-        cv::Mat histA,histB;
-        histA = calcHist(A);
-        histB = calcHist(B);
-        return cv::compareHist(histA,histB,CV_COMP_CORREL);
-    }
-}//namespace util
diff --git a/Prj-PHP/main.cpp b/Prj-PHP/main.cpp
deleted file mode 100644
index 43e3378..0000000
--- a/Prj-PHP/main.cpp
+++ /dev/null
@@ -1,107 +0,0 @@
-//
-// Created by Coleflowers on 20/06/2018.
-// Updated by Justid on 02/07/2018.
-// 
-#include <phpcpp.h>
-#include <iostream>
-
-#include "PlateSegmentation.h"
-#include "CNNRecognizer.h"
-#include "Recognizer.h"
-#include "PlateDetection.h"
-#include "FastDeskew.h"
-#include "FineMapping.h"
-#include "Pipeline.h"
-
-/* 关闭原opencv报错输出 */
-int handleError( int status, const char* func_name,
-            const char* err_msg, const char* file_name,
-            int line, void* userdata )
-{
-    //Do nothing -- will suppress console output
-    return 0;   //Return value is not used
-}
-
-/**
- * 车牌图片识别
- * @params  imgpath 图片的绝对路径
- * @params  modelpath 识别模型所在文件夹路径
- * @params  confidence 可信度要求，可选参数，默认值0.75
- * @return  车牌号 图片路径的图片不存在的话 返回空值
- */
-cv::String scan(std::string imgpath, std::string modelpath, double confidence){
-    cv::redirectError(handleError);
-    try {
-        pr::PipelinePR prc(modelpath+"/cascade.xml",
-                        modelpath+"/HorizonalFinemapping.prototxt",modelpath+"/HorizonalFinemapping.caffemodel",
-                        modelpath+"/Segmentation.prototxt",modelpath+"/Segmentation.caffemodel",
-                        modelpath+"/CharacterRecognization.prototxt",modelpath+"/CharacterRecognization.caffemodel",
-                        modelpath+"/SegmenationFree-Inception.prototxt",modelpath+"/SegmenationFree-Inception.caffemodel"
-                    );
-        cv::Mat image = cv::imread(imgpath);
-        std::vector<pr::PlateInfo> res = prc.RunPiplineAsImage(image,pr::SEGMENTATION_FREE_METHOD);
-
-        cv::String platenum = "";
-        for(auto st:res) {
-            if(st.confidence>confidence) {
-                platenum = st.getPlateName();
-                break;
-            }
-        }
-
-        return platenum;
-    }
-    catch( cv::Exception& e )
-    {
-        const char* err_msg = e.what();
-        throw Php::Exception(err_msg);
-    }
-
-}
-
-
-Php::Value funcWrapper(Php::Parameters &params) {
-    // 图片路径
-    std::string img = params[0]; 
-    // 模型路径（文件夹）
-    std::string model = params[1]; 
-    // 可信度要求
-    double confidence = 0.75;
-
-    if (params.size() == 3){
-        confidence = (double)params[2];
-    }
-
-    cv::String res = scan(img, model, confidence);
-    return res.c_str();
-}
-
-
-/**
- *  tell the compiler that the get_module is a pure C function
- */
-extern "C" {
-    
-    /**
-     *  Function that is called by PHP right after the PHP process
-     *  has started, and that returns an address of an internal PHP
-     *  strucure with all the details and features of your extension
-     *
-     *  @return void*   a pointer to an address that is understood by PHP
-     */
-    PHPCPP_EXPORT void *get_module() {
-        // static(!) Php::Extension object that should stay in memory
-        // for the entire duration of the process (that's why it's static)
-        static Php::Extension extension("platescan", "1.0");
-        
-        // @todo    add your own functions, classes, namespaces to the extension
-        extension.add<funcWrapper>("platescan", {
-            Php::ByVal("imgpath", Php::Type::String, true),
-            Php::ByVal("modelpath", Php::Type::String, true),
-            Php::ByVal("confidence", Php::Type::Float, false)
-        });       
- 
-        // return the extension
-        return extension;
-    }
-}
diff --git a/Prj-PHP/platescan.ini b/Prj-PHP/platescan.ini
deleted file mode 100644
index aca617e..0000000
--- a/Prj-PHP/platescan.ini
+++ /dev/null
@@ -1,2 +0,0 @@
-extension=platescan.so
-
diff --git a/Prj-PHP/tests/demo.png b/Prj-PHP/tests/demo.png
deleted file mode 100644
index e1dd608..0000000
Binary files a/Prj-PHP/tests/demo.png and /dev/null differ
diff --git a/Prj-PHP/tests/model/README.md b/Prj-PHP/tests/model/README.md
deleted file mode 100644
index 326c563..0000000
--- a/Prj-PHP/tests/model/README.md
+++ /dev/null
@@ -1,21 +0,0 @@
-将/Prj-Linux/lpr/model目录下的
-
-cascade.xml
-
-CharacterRecognization.caffemodel
-
-CharacterRecognization.prototxt
-
-HorizonalFinemapping.caffemodel
-
-HorizonalFinemapping.prototxt
-
-SegmenationFree-Inception.caffemodel
-
-SegmenationFree-Inception.prototxt
-
-Segmentation.caffemodel
-
-Segmentation.prototxt
-
-放置在该目录
\ No newline at end of file
diff --git a/Prj-PHP/tests/platescan.php b/Prj-PHP/tests/platescan.php
deleted file mode 100644
index 7206faa..0000000
--- a/Prj-PHP/tests/platescan.php
+++ /dev/null
@@ -1,13 +0,0 @@
-<?php
-//
-// Created by Coleflowers on 20/06/2018.
-// Updated by JustID on 03/07/2018.
-// 
-
-/**
- * 车牌识别PHP扩展程序测试
- */
-$res = platescan(realpath("demo.png"),realpath("model"),0.8);
-var_dump($res);
-
-?>
diff --git a/Prj-Python/.gitignore b/Prj-Python/.gitignore
new file mode 100644
index 0000000..c01e35a
--- /dev/null
+++ b/Prj-Python/.gitignore
@@ -0,0 +1,7 @@
+.idea/
+__pycache__
+resource
+build/
+dist/
+hyperlpr3.egg-info/
+venv/
diff --git a/Prj-Python/fast_test.py b/Prj-Python/fast_test.py
new file mode 100644
index 0000000..f691099
--- /dev/null
+++ b/Prj-Python/fast_test.py
@@ -0,0 +1,11 @@
+# 导入opencv库
+import cv2
+# 导入依赖包
+import hyperlpr3 as lpr3
+
+# 实例化识别对象
+catcher = lpr3.LicensePlateCatcher()
+# 读取图片
+image = cv2.imread("../resource/images/test_img.jpg")
+# 识别结果
+print(catcher(image))
\ No newline at end of file
diff --git a/Prj-Python/hyperlpr3/__init__.py b/Prj-Python/hyperlpr3/__init__.py
new file mode 100644
index 0000000..e212d29
--- /dev/null
+++ b/Prj-Python/hyperlpr3/__init__.py
@@ -0,0 +1,9 @@
+from .hyperlpr3 import LicensePlateCatcher
+from .common.typedef import *
+from .config.configuration import initialization
+
+initialization()
+
+
+
+
diff --git a/hyperlpr_py3/__init__.py b/Prj-Python/hyperlpr3/command/__init__.py
similarity index 100%
rename from hyperlpr_py3/__init__.py
rename to Prj-Python/hyperlpr3/command/__init__.py
diff --git a/Prj-Python/hyperlpr3/command/aliased_group.py b/Prj-Python/hyperlpr3/command/aliased_group.py
new file mode 100644
index 0000000..f349998
--- /dev/null
+++ b/Prj-Python/hyperlpr3/command/aliased_group.py
@@ -0,0 +1,16 @@
+import click
+
+
+class AliasedGroup(click.Group):
+    def get_command(self, ctx, cmd_name):
+        rv = click.Group.get_command(self, ctx, cmd_name)
+        if rv is not None:
+            return rv
+        matches = [
+            x for x in self.list_commands(ctx) if x.startswith(cmd_name)
+        ]
+        if not matches:
+            return None
+        elif len(matches) == 1:
+            return click.Group.get_command(self, ctx, matches[0])
+        ctx.fail('Too many matches: %s' % ', '.join(sorted(matches)))
\ No newline at end of file
diff --git a/Prj-Python/hyperlpr3/command/cli.py b/Prj-Python/hyperlpr3/command/cli.py
new file mode 100644
index 0000000..b3e7537
--- /dev/null
+++ b/Prj-Python/hyperlpr3/command/cli.py
@@ -0,0 +1,20 @@
+import click
+from hyperlpr3.command.aliased_group import AliasedGroup
+from hyperlpr3.command.sample import sample
+from hyperlpr3.command.serve import rest
+
+__all__ = ['cli']
+
+CONTEXT_SETTINGS = dict(help_option_names=['-h', '--help'])
+
+
+@click.command(cls=AliasedGroup, context_settings=CONTEXT_SETTINGS)
+def cli():
+    pass
+
+
+cli.add_command(sample)
+cli.add_command(rest)
+
+if __name__ == '__main__':
+    cli()
\ No newline at end of file
diff --git a/Prj-Python/hyperlpr3/command/sample.py b/Prj-Python/hyperlpr3/command/sample.py
new file mode 100644
index 0000000..00ca94d
--- /dev/null
+++ b/Prj-Python/hyperlpr3/command/sample.py
@@ -0,0 +1,77 @@
+# -*- coding: utf-8 -*-
+import hyperlpr3 as lpr3
+import cv2
+import urllib
+import numpy as np
+import re
+import click
+from loguru import logger
+
+type_list = ["蓝牌", "黄牌单层", "白牌单层", "绿牌新能源", "黑牌港澳", "香港单层", "香港双层", "澳门单层", "澳门双层", "黄牌双层"]
+
+
+def is_http_url(s):
+    regex = re.compile(
+        r'^(?:http|ftp)s?://'  # http:// or https://
+        r'(?:(?:[A-Z0-9](?:[A-Z0-9-]{0,61}[A-Z0-9])?\.)+(?:[A-Z]{2,6}\.?|[A-Z0-9-]{2,}\.?)|'
+        r'localhost|'  # localhost...
+        r'\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3})'  # ...or ip
+        r'(?::\d+)?'  # optional port
+        r'(?:/?|[/?]\S+)$', re.IGNORECASE)
+
+    if regex.match(s):
+        return True
+    else:
+        return False
+
+
+def url_to_image(url):
+    try:
+        resp = urllib.request.urlopen(url)
+        image = np.asarray(bytearray(resp.read()), dtype="uint8")
+        image = cv2.imdecode(image, cv2.IMREAD_COLOR)
+    except Exception as err:
+        return None
+
+    return image
+
+
+def get_image(path: str):
+    image = None
+    if is_http_url(path):
+        # url
+        image = url_to_image(path)
+    else:
+        # local path
+        if path.split('.')[-1].lower() in ('jpg', 'png', 'jpeg', 'bmp',):
+            image = cv2.imread(path)
+    try:
+        h, w, c = image.shape
+    except Exception as err:
+        logger.error("Failed to read image from path or url.")
+        return False, None
+
+    return True, image
+
+
+@click.command(help="Exec HyperLPR3 Test Sample.")
+@click.option("-src", "--src", type=str, )
+@click.option("-det", "--det", default='low', type=click.Choice(['low', 'high']), )
+def sample(src, det):
+    ret, image = get_image(src)
+    if ret:
+        if det == 'low':
+            level = lpr3.DETECT_LEVEL_LOW
+        else:
+            level = lpr3.DETECT_LEVEL_HIGH
+        catcher = lpr3.LicensePlateCatcher(detect_level=level)
+        print("--" * 20)
+        result = catcher(image)
+        logger.info(f"共检测到车牌: {len(result)}")
+        for res in result:
+            code, conf, plate_type, box = res
+            logger.success(f'[{type_list[plate_type]}]{code} {conf} {box}')
+
+
+if __name__ == "__main__":
+    sample()
diff --git a/Prj-Python/hyperlpr3/command/serve.py b/Prj-Python/hyperlpr3/command/serve.py
new file mode 100644
index 0000000..5dd9243
--- /dev/null
+++ b/Prj-Python/hyperlpr3/command/serve.py
@@ -0,0 +1,129 @@
+# -*- coding: utf-8 -*-
+from fastapi import FastAPI, APIRouter, UploadFile, File
+from fastapi.middleware.cors import CORSMiddleware
+from typing import List
+from fastapi.responses import JSONResponse
+import numpy as np
+import cv2
+import hyperlpr3 as lpr3
+import uvicorn
+import click
+
+type_list = ["蓝牌", "黄牌单层", "白牌单层", "绿牌新能源", "黑牌港澳", "香港单层", "香港双层", "澳门单层", "澳门双层", "黄牌双层"]
+
+catcher = lpr3.LicensePlateCatcher(detect_level=lpr3.DETECT_LEVEL_HIGH)
+
+class BaseResponse():
+    def __init__(self, *args, **kwags) -> None:
+        self.response = {
+            'result': None,
+        }
+        # self.response.update(response)
+
+    def http_ok_response(self, response_data):
+        self.response['result'] = response_data
+        self.response['code'] = 5000
+        self.response['msg'] = '请求成功'
+        return JSONResponse(self.response)
+
+    def http_prermission_denied_response(self, response_data=None, error_msg=None):
+        """
+        权限错误
+        """
+        self.response['result'] = response_data
+        self.response['code'] = 5005
+        if error_msg:
+            self.response['msg'] = error_msg
+        else:
+            self.response['msg'] = '权限校验失败，请重新检查权限'
+
+        return JSONResponse(self.response)
+
+    def http_request_parameter_error(self, response_data=None, error_msg=None):
+        """
+        请求参数错误
+        """
+        self.response['result'] = response_data
+        self.response['code'] = 5007
+        if error_msg:
+            self.response['msg'] = error_msg
+        else:
+            self.response['msg'] = '提交参数异常，请重新检查接口参数'
+
+        return JSONResponse(self.response)
+
+    def http_server_error(self, response_data=None, error_msg=None):
+        self.response['result'] = response_data
+        self.response['code'] = 5009
+        if error_msg:
+            self.response['msg'] = error_msg
+        else:
+            self.response['msg'] = '服务异常'
+
+        return JSONResponse(self.response)
+
+
+app = FastAPI(
+    title="HyperLPR3-Api",
+    version='0.0.9',
+    docs_url='/api/v1/docs',
+    description='HyperLPR3 Api Serving'
+)
+
+origins = [
+    'http://localhost.slsmart.com',
+    'https://localhost.slsmart.com',
+    'http://localhost',
+    'http://localhost:8715'
+]
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=origins,
+    allow_credentials=True,
+    allow_methods=['*'],
+    allow_headers=['*'],
+)
+
+
+@app.get("/")
+async def running():
+    '''当前api服务程序有在正常运行'''
+    return """HyperLpr3 WebApi Server Running..."""
+
+
+@app.post("/api/v1/rec", tags=['车牌识别'])
+async def vehicle_license_plate_recognition(file: List[UploadFile] = File(...)):
+    """上传图片进行车牌识别，上传必须为图片类型png/jpg/jpge/wabp"""
+    if len(file[0].filename) == 0:
+        return BaseResponse().http_request_parameter_error(error_msg='单次上传图片不能为空')
+    if len(file) > 1:
+        return BaseResponse().http_request_parameter_error(error_msg='该接口仅支持单张图片上传')
+    if len(file) == 1:
+        if file[0].filename.rsplit('.', 1)[1].lower() not in ['png', 'jpeg', 'jpg', 'wabp']:
+            return BaseResponse().http_request_parameter_error(error_msg='上传必须为图片类型png/jpg/jpge')
+        content = await file[0].read()
+        nparr = np.fromstring(content, np.uint8)
+        img = cv2.imdecode(nparr, cv2.IMREAD_COLOR).astype(np.uint8)
+        plates = catcher(img)
+        results = list()
+        for code, conf, plate_type, box in plates:
+            plate = dict(code=code, conf=float(conf), plate_type=type_list[plate_type], box=box)
+            results.append(plate)
+        return BaseResponse().http_ok_response({'plate_list': results})
+
+
+def get_application():
+    return app
+
+
+@click.command(help="Exec HyperLPR3 WebApi Server.")
+@click.option("-host", "--host", default="0.0.0.0", type=str, )
+@click.option("-port", "--port", default=8715, type=int, )
+@click.option("-workers", "--workers", default=1, type=int, )
+def rest(host, port, workers):
+    uvicorn.run(app="hyperlpr3.command.serve:app", host=host, port=port, workers=workers)
+
+
+if __name__ == "__main__":
+    rest()
diff --git a/hyperlpr/plateStructure.py b/Prj-Python/hyperlpr3/common/__init__.py
similarity index 100%
rename from hyperlpr/plateStructure.py
rename to Prj-Python/hyperlpr3/common/__init__.py
diff --git a/Prj-Python/hyperlpr3/common/mnn_adapt.py b/Prj-Python/hyperlpr3/common/mnn_adapt.py
new file mode 100644
index 0000000..02cba4f
--- /dev/null
+++ b/Prj-Python/hyperlpr3/common/mnn_adapt.py
@@ -0,0 +1,37 @@
+import numpy as np
+import MNN
+from loguru import logger
+
+
+class MNNAdapter(object):
+
+    def __init__(self, model_path: str, input_shape: tuple,
+                 dim_type: int = MNN.Tensor_DimensionType_Caffe, outputs_name=None, outputs_shape=None):
+        self.interpreter = MNN.Interpreter(model_path)
+        self.session = self.interpreter.createSession()
+        self.input_tensor = self.interpreter.getSessionInput(self.session)
+        self.input_shape = input_shape
+        self.dim_type = dim_type
+        self.outputs_name = outputs_name
+        self.outputs_shape = outputs_shape
+
+    def inference(self, tensor: np.ndarray) -> np.ndarray:
+        tensor = tensor.astype(np.float32)
+        tmp_input = MNN.Tensor(self.input_shape, MNN.Halide_Type_Float, tensor, self.dim_type)
+        self.input_tensor.copyFrom(tmp_input)
+        self.interpreter.runSession(self.session)
+        output_tensor = list()
+        if self.outputs_name:
+            if self.outputs_shape:
+                for idx, shape in enumerate(self.outputs_shape):
+                    tmp_output = MNN.Tensor(shape, MNN.Halide_Type_Float, np.ones(shape).astype(np.float32), self.dim_type)
+                    tmp_tensor = self.interpreter.getSessionOutput(self.session, self.outputs_name[idx])
+                    tmp_tensor.copyToHostTensor(tmp_output)
+                    output_tensor.append(np.asarray(tmp_output.getData()))
+            else:
+                output_tensor = [np.asarray(self.interpreter.getSessionOutput(self.session, name).getData()) for name in self.outputs_name]
+        else:
+            output_tensor.append(self.interpreter.getSessionOutput(self.session).getData())
+        res = np.asarray(output_tensor)
+
+        return res
diff --git a/Prj-Python/hyperlpr3/common/tokenize.py b/Prj-Python/hyperlpr3/common/tokenize.py
new file mode 100644
index 0000000..69981cb
--- /dev/null
+++ b/Prj-Python/hyperlpr3/common/tokenize.py
@@ -0,0 +1,4 @@
+token = ["blank", "'", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "A", "B", "C", "D", "E", "F", "G", "H", "J",
+         "K", "L", "M", "N", "O", "P", "Q", "R", "S", "T", "U", "V", "W", "X", "Y", "Z", "云", "京", "冀", "吉", "学", "宁",
+         "川", "挂", "新", "晋", "桂", "民", "沪", "津", "浙", "渝", "港", "湘", "琼", "甘", "皖", "粤", "航", "苏", "蒙", "藏", "警", "豫",
+         "贵", "赣", "辽", "鄂", "闽", "陕", "青", "鲁", "黑", '领', '使', '澳', ]
\ No newline at end of file
diff --git a/Prj-Python/hyperlpr3/common/tools_process.py b/Prj-Python/hyperlpr3/common/tools_process.py
new file mode 100644
index 0000000..3882327
--- /dev/null
+++ b/Prj-Python/hyperlpr3/common/tools_process.py
@@ -0,0 +1,300 @@
+import numpy as np
+import cv2
+import time
+from functools import wraps
+
+
+def find_the_adjacent_boxes(boxes: list):
+    list_index = list()
+    for i, a in enumerate(boxes):
+        for j, b in enumerate(boxes):
+            if i == j:
+                continue
+            if j in list_index:
+                continue
+            ax, ay, aw, ah = single_xyxy2cxcywh(a)
+            bx, by, bw, bh = single_xyxy2cxcywh(b)
+            dis = l2((ax, ay), (bx, by))
+            if dis < 2 * aw or dis < 2 * bw:
+                list_index.append(i)
+                list_index.append(j)
+
+    list_index = set(list_index)
+
+    return list(list_index)
+
+
+
+def l2(p1, p2):
+    x0, y0 = p1
+    x1, y1 = p2
+    return np.sqrt((x0 - x1) ** 2 + (y0 - y1) ** 2)
+
+
+def single_xyxy2cxcywh(box):
+    x1, y1, x2, y2 = box
+    w = x2 - x1
+    h = y2 - y1
+    x = x1 + w / 2
+    y = y1 + h / 2
+    return x, y, w, h
+
+
+def sigmoid(x):
+    return 1 / (1 + np.exp(-x))
+
+
+def xywh2xyxy(x):
+    # Convert [x, y, w, h] to [x1, y1, x2, y2]
+    y = np.copy(x)
+    y[:, 0] = x[:, 0] - x[:, 2] / 2  # top left x
+    y[:, 1] = x[:, 1] - x[:, 3] / 2  # top left y
+    y[:, 2] = x[:, 0] + x[:, 2] / 2  # bottom right x
+    y[:, 3] = x[:, 1] + x[:, 3] / 2  # bottom right y
+    return y
+
+
+def process(input, mask, anchors, size):
+    anchors = [anchors[i] for i in mask]
+    grid_h, grid_w = map(int, input.shape[0:2])
+
+    box_confidence = sigmoid(input[..., 4])
+    box_confidence = np.expand_dims(box_confidence, axis=-1)
+
+    box_class_probs = sigmoid(input[..., 5:])
+
+    box_xy = sigmoid(input[..., :2]) * 2 - 0.5
+
+    col = np.tile(np.arange(0, grid_w), grid_h).reshape(-1, grid_w)
+    row = np.tile(np.arange(0, grid_h).reshape(-1, 1), grid_w)
+    col = col.reshape(grid_h, grid_w, 1, 1).repeat(3, axis=-2)
+    row = row.reshape(grid_h, grid_w, 1, 1).repeat(3, axis=-2)
+    grid = np.concatenate((col, row), axis=-1)
+    box_xy += grid
+    box_xy *= (int(size[1] / grid_h), int(size[0] / grid_w))
+
+    box_wh = pow(sigmoid(input[..., 2:4]) * 2, 2)
+    box_wh = box_wh * anchors
+
+    box = np.concatenate((box_xy, box_wh), axis=-1)
+
+    return box, box_confidence, box_class_probs
+
+
+def filter_boxes(boxes, box_confidences, box_class_probs, box_threshold, nms_threshold):
+    """Filter boxes with box threshold. It's a bit different with origin yolov5 post process!
+    # Arguments
+        boxes: ndarray, boxes of objects.
+        box_confidences: ndarray, confidences of objects.
+        box_class_probs: ndarray, class_probs of objects.
+    # Returns
+        boxes: ndarray, filtered boxes.
+        classes: ndarray, classes for boxes.
+        scores: ndarray, scores for boxes.
+    """
+    boxes = boxes.reshape(-1, 4)
+    box_confidences = box_confidences.reshape(-1)
+    box_class_probs = box_class_probs.reshape(-1, box_class_probs.shape[-1])
+
+    _box_pos = np.where(box_confidences >= box_threshold)
+    boxes = boxes[_box_pos]
+    box_confidences = box_confidences[_box_pos]
+    box_class_probs = box_class_probs[_box_pos]
+
+    class_max_score = np.max(box_class_probs, axis=-1)
+    classes = np.argmax(box_class_probs, axis=-1)
+    _class_pos = np.where(class_max_score * box_confidences >= box_threshold)
+
+    boxes = boxes[_class_pos]
+    classes = classes[_class_pos]
+    scores = (class_max_score * box_confidences)[_class_pos]
+
+    return boxes, classes, scores
+
+
+def nms_boxes(boxes, scores, nms_threshold):
+    """Suppress non-maximal boxes.
+    # Arguments
+        boxes: ndarray, boxes of objects.
+        scores: ndarray, scores of objects.
+    # Returns
+        keep: ndarray, index of effective boxes.
+    """
+    x = boxes[:, 0]
+    y = boxes[:, 1]
+    w = boxes[:, 2] - boxes[:, 0]
+    h = boxes[:, 3] - boxes[:, 1]
+
+    areas = w * h
+    order = scores.argsort()[::-1]
+
+    keep = []
+    while order.size > 0:
+        i = order[0]
+        keep.append(i)
+
+        xx1 = np.maximum(x[i], x[order[1:]])
+        yy1 = np.maximum(y[i], y[order[1:]])
+        xx2 = np.minimum(x[i] + w[i], x[order[1:]] + w[order[1:]])
+        yy2 = np.minimum(y[i] + h[i], y[order[1:]] + h[order[1:]])
+
+        w1 = np.maximum(0.0, xx2 - xx1 + 0.00001)
+        h1 = np.maximum(0.0, yy2 - yy1 + 0.00001)
+        inter = w1 * h1
+
+        ovr = inter / (areas[i] + areas[order[1:]] - inter)
+        inds = np.where(ovr <= nms_threshold)[0]
+        order = order[inds + 1]
+    keep = np.array(keep)
+    return keep
+
+
+def restore_bound_box(boxes: list, ratio: tuple, pad_size: tuple):
+    if len(boxes) > 0:
+        pad_width, pad_height = pad_size
+        boxes_array = np.asarray(boxes)
+        # print(boxes_array)
+        boxes_array[:, 0] = (boxes_array[:, 0] - pad_width) / ratio[0]
+        boxes_array[:, 1] = (boxes_array[:, 1] - pad_height) / ratio[1]
+        boxes_array[:, 2] = (boxes_array[:, 2] - pad_width) / ratio[0]
+        boxes_array[:, 3] = (boxes_array[:, 3] - pad_height) / ratio[1]
+        # print(boxes_array)
+        boxes = boxes_array.tolist()
+
+    return boxes
+
+
+def letterbox(im, new_shape=(640, 640), color=(0, 0, 0)):
+    # Resize and pad image while meeting stride-multiple constraints
+    shape = im.shape[:2]  # current shape [height, width]
+    if isinstance(new_shape, int):
+        new_shape = (new_shape, new_shape)
+
+    # Scale ratio (new / old)
+    r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
+
+    # Compute padding
+    ratio = r, r  # width, height ratios
+    new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
+    dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1]  # wh padding
+
+    dw /= 2  # divide padding into 2 sides
+    dh /= 2
+
+    if shape[::-1] != new_unpad:  # resize
+        im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)
+    top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
+    left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
+    im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)  # add border
+    return im, ratio, (dw, dh)
+
+
+def cost(tag=''):
+    try:
+        '''
+        :param tag: 装饰器的参数
+        '''
+        from loguru import logger
+        def wrapper(fn):
+            @wraps(fn)
+            def wrapper_use_time(*args, **kw):
+                '''
+                函数传参方式 fn(arg1, arg2, param1=a, param2=b)
+                :param args: 位置参数(arg1, arg2, ....)  传参方式 fn(arg1, arg2, ...)
+                :param kw: 字典参数{param1=a, param2=b, .....} 传参方式 fn(param1=a, param2=b, ....)
+                :return:
+                '''
+                t1 = time.time()
+                try:
+                    res = fn(*args, **kw)
+                except Exception as e:
+                    logger.error(f"@use_time %s(%s) execute error" % (fn.__name__, tag))
+                    return None
+                else:
+                    t2 = time.time()
+                    logger.info(f"{tag}@UseTime: {t2 - t1}")
+                    return res
+
+            return wrapper_use_time
+
+        return wrapper
+    except Exception as err:
+        print(err)
+
+
+
+def align_box(imgs, bbox, size=96, scale_factor=1.0, center_bias=0, borderValue=(0, 0, 0)):
+    bias_x = (-1 + 2 * np.random.sample()) * center_bias
+    bias_y = (-1 + 2 * np.random.sample()) * center_bias
+    b_x1, b_y1, b_x2, b_y2 = bbox
+    cx, cy = (b_x1 + b_x2) // 2, (b_y1 + b_y2) // 2
+    w = b_x2 - b_x1
+    h = b_y2 - b_y1
+    cx += w * bias_x
+    cy += h * bias_y
+
+    base_r = max(w, h)
+    j_x = 0
+    j_y = 0
+    j_r = 0
+    base_r += j_r
+    r = int(base_r / 2 * scale_factor)
+    cy -= int(base_r * 0)
+    cx += j_x
+    cy += j_y
+    x1, y1, x2, y2 = cx - r, cy - r, cx + r, cy + r
+    x3, y3 = cx - r, cy + r
+    _x1, _y1, _x2, _y2, _x3, _y3 = [0, 0, size, size, 0, size]
+    src = np.array([x1, y1, x2, y2, x3, y3], dtype=np.float32).reshape(3, 2)
+    sv = np.asarray([[b_x1, b_y1, 1], [b_x2, b_y2, 1]])
+
+    dst = np.array([_x1, _y1, _x2, _y2, _x3, _y3], dtype=np.float32).reshape(3, 2)
+    assert src.dtype == np.float32
+    assert dst.dtype == np.float32
+    assert src.shape == (3, 2)
+    assert dst.shape == (3, 2)
+    mat = cv2.getAffineTransform(src, dst)
+    p = sv.dot(mat.T).reshape(-1)
+    if type(imgs) == list:
+        imgs = [cv2.warpAffine(img, mat, (size, size), borderValue=borderValue) for img in imgs]
+    else:
+        imgs = cv2.warpAffine(imgs, mat, (size, size), borderValue=borderValue)
+    return imgs, p, mat
+
+
+def get_rotate_crop_image(img, points):
+    '''
+    img_height, img_width = img.shape[0:2]
+    left = int(np.min(points[:, 0]))
+    right = int(np.max(points[:, 0]))
+    top = int(np.min(points[:, 1]))
+    bottom = int(np.max(points[:, 1]))
+    img_crop = img[top:bottom, left:right, :].copy()
+    points[:, 0] = points[:, 0] - left
+    points[:, 1] = points[:, 1] - top
+    '''
+    assert len(points) == 4, "shape of points must be 4*2"
+    img_crop_width = int(
+        max(
+            np.linalg.norm(points[0] - points[1]),
+            np.linalg.norm(points[2] - points[3])))
+    img_crop_height = int(
+        max(
+            np.linalg.norm(points[0] - points[3]),
+            np.linalg.norm(points[1] - points[2])))
+    pts_std = np.float32([[0, 0], [img_crop_width, 0],
+                          [img_crop_width, img_crop_height],
+                          [0, img_crop_height]])
+    points = points.astype(np.float32)
+    # print(points.shape, pts_std.shape)
+    M = cv2.getPerspectiveTransform(points, pts_std)
+    dst_img = cv2.warpPerspective(
+        img,
+        M, (img_crop_width, img_crop_height),
+        borderMode=cv2.BORDER_REPLICATE,
+        flags=cv2.INTER_CUBIC)
+    dst_img_height, dst_img_width = dst_img.shape[0:2]
+    if dst_img_height * 1.0 / dst_img_width >= 1.5:
+        dst_img = np.rot90(dst_img)
+
+    return dst_img
diff --git a/Prj-Python/hyperlpr3/common/typedef.py b/Prj-Python/hyperlpr3/common/typedef.py
new file mode 100644
index 0000000..cabfcf5
--- /dev/null
+++ b/Prj-Python/hyperlpr3/common/typedef.py
@@ -0,0 +1,79 @@
+import numpy as np
+
+PLATE_TYPE_BLUE = 0
+PLATE_TYPE_GREEN = 1
+PLATE_TYPE_YELLOW = 2
+
+INFER_ONNX_RUNTIME = 0
+INFER_MNN = 1
+
+DETECT_LEVEL_LOW = 0
+DETECT_LEVEL_HIGH = 1
+
+MONO = 0    # 单层车牌
+DOUBLE = 1  # 双层车牌
+
+UNKNOWN = -1                         # 未知车牌
+BLUE = 0                             # 蓝牌
+YELLOW_SINGLE = 1                    # 黄牌单层
+WHILE_SINGLE = 2                     # 白牌单层
+GREEN = 3                            # 绿牌新能源
+BLACK_HK_MACAO = 4                   # 黑牌港澳
+HK_SINGLE = 5                        # 香港单层
+HK_DOUBLE = 6                        # 香港双层
+MACAO_SINGLE = 7                     # 澳门单层
+MACAO_DOUBLE = 8                     # 澳门双层
+YELLOW_DOUBLE = 9                    # 黄牌双层
+
+
+def code_filter(code: str) -> int:
+    plate_type = UNKNOWN
+    if code[0] == 'W' and code[1] == 'J':
+        plate_type = WHILE_SINGLE
+    elif len(code) == 8:
+        plate_type = GREEN
+    elif '学' in code:
+        plate_type = BLUE
+    elif '港' in code:
+        plate_type = BLACK_HK_MACAO
+    elif '澳' in code:
+        plate_type = BLACK_HK_MACAO
+    elif '警' in code:
+        plate_type = WHILE_SINGLE
+    elif '粤Z' in code:
+        plate_type = BLACK_HK_MACAO
+
+    return plate_type
+
+
+class Plate(object):
+
+    def __init__(self,
+                 vertex: np.ndarray,
+                 plate_code: str,
+                 rec_confidence: float,
+                 det_bound_box,
+                 dex_bound_confidence: float,
+                 plate_type: int):
+        assert vertex.shape == (4, 2)
+        self.vertex = vertex
+        self.det_bound_box = det_bound_box
+        self.plate_code = plate_code
+        self.rec_confidence = rec_confidence
+        self.dex_bound_confidence = dex_bound_confidence
+
+        self.left_top, self.right_top, self.right_bottom, self.left_bottom = vertex
+        self.plate_type = plate_type
+
+    def to_dict(self):
+        return dict(plate_code=self.plate_code, rec_confidence=self.rec_confidence,
+                    det_bound_box=self.det_bound_box, plate_type=self.plate_type)
+
+    def to_result(self):
+        return [self.plate_code, self.rec_confidence, self.plate_type, self.det_bound_box.tolist(),]
+
+    def __dict__(self):
+        return self.to_dict()
+
+    def __str__(self):
+        return str(self.to_dict())
diff --git a/hyperlpr/precise.py b/Prj-Python/hyperlpr3/config/__init__.py
similarity index 100%
rename from hyperlpr/precise.py
rename to Prj-Python/hyperlpr3/config/__init__.py
diff --git a/Prj-Python/hyperlpr3/config/configuration.py b/Prj-Python/hyperlpr3/config/configuration.py
new file mode 100644
index 0000000..298c945
--- /dev/null
+++ b/Prj-Python/hyperlpr3/config/configuration.py
@@ -0,0 +1,36 @@
+import requests
+from tqdm import tqdm
+import zipfile
+import os
+from .settings import _DEFAULT_FOLDER_, _MODEL_VERSION_, _ONLINE_URL_
+
+
+def down_model_zip(url, save_path, is_unzip=False):
+    resp = requests.get(url, stream=True)
+    total = int(resp.headers.get('content-length', 0))
+    name = os.path.join(save_path, os.path.basename(url))
+    with open(name, 'wb') as file, tqdm(
+            desc="pull",
+            total=total,
+            unit='iB',
+            unit_scale=True,
+            unit_divisor=1024,
+    ) as bar:
+        for data in resp.iter_content(chunk_size=1024):
+            size = file.write(data)
+            bar.update(size)
+
+    if is_unzip:
+        f = zipfile.ZipFile(name, "r")
+        for file in f.namelist():
+            f.extract(file, save_path)
+        os.remove(name)
+
+
+def initialization(re_download=False):
+    os.makedirs(_DEFAULT_FOLDER_, exist_ok=True)
+    models_dir = os.path.join(_DEFAULT_FOLDER_, _MODEL_VERSION_)
+    # print(models_dir)
+    if not os.path.exists(models_dir) or re_download:
+        target_url = os.path.join(_ONLINE_URL_, _MODEL_VERSION_) + '.zip'
+        down_model_zip(target_url, _DEFAULT_FOLDER_, True)
diff --git a/Prj-Python/hyperlpr3/config/settings.py b/Prj-Python/hyperlpr3/config/settings.py
new file mode 100644
index 0000000..eaf64e2
--- /dev/null
+++ b/Prj-Python/hyperlpr3/config/settings.py
@@ -0,0 +1,19 @@
+import os
+import sys
+
+_MODEL_VERSION_ = "20230228"
+
+if 'win32' in sys.platform:
+    _DEFAULT_FOLDER_ = os.path.join(os.environ['HOMEPATH'], ".hyperlpr3")
+else:
+    _DEFAULT_FOLDER_ = os.path.join(os.environ['HOME'], ".hyperlpr3")
+
+_ONLINE_URL_ = "https://tunm.oss-cn-hangzhou.aliyuncs.com/hyperlpr3/"
+
+onnx_runtime_config = dict(
+    det_model_path_320x=os.path.join(_MODEL_VERSION_, "onnx", "y5fu_320x_sim.onnx"),
+    det_model_path_640x=os.path.join(_MODEL_VERSION_, "onnx", "y5fu_640x_sim.onnx"),
+    rec_model_path=os.path.join(_MODEL_VERSION_, "onnx", "rpv3_mdict_160_r3.onnx"),
+    cls_model_path=os.path.join(_MODEL_VERSION_, "onnx", "litemodel_cls_96x_r1.onnx"),
+)
+
diff --git a/Prj-Python/hyperlpr3/hyperlpr3.py b/Prj-Python/hyperlpr3/hyperlpr3.py
new file mode 100644
index 0000000..b9c725c
--- /dev/null
+++ b/Prj-Python/hyperlpr3/hyperlpr3.py
@@ -0,0 +1,36 @@
+from .config.settings import onnx_runtime_config as ort_cfg
+from .inference.pipeline import LPRMultiTaskPipeline
+from .common.typedef import *
+from os.path import join
+from .config.settings import _DEFAULT_FOLDER_
+
+
+class LicensePlateCatcher(object):
+
+    def __init__(self,
+                 inference: int = INFER_ONNX_RUNTIME,
+                 folder: str = _DEFAULT_FOLDER_,
+                 detect_level: int = DETECT_LEVEL_LOW,
+                 logger_level: int = 3):
+        if inference == INFER_ONNX_RUNTIME:
+            from hyperlpr3.inference.multitask_detect import MultiTaskDetectorORT
+            from hyperlpr3.inference.recognition import PPRCNNRecognitionORT
+            from hyperlpr3.inference.classification import ClassificationORT
+            import onnxruntime as ort
+            ort.set_default_logger_severity(logger_level)
+
+            if detect_level == DETECT_LEVEL_LOW:
+                # print(join(folder, ort_cfg['det_model_path_320x']))
+                det = MultiTaskDetectorORT(join(folder, ort_cfg['det_model_path_320x']), input_size=(320, 320))
+            elif detect_level == DETECT_LEVEL_HIGH:
+                det = MultiTaskDetectorORT(join(folder, ort_cfg['det_model_path_640x']), input_size=(640, 640))
+            else:
+                raise NotImplemented
+            rec = PPRCNNRecognitionORT(join(folder, ort_cfg['rec_model_path']), input_size=(48, 160))
+            cls = ClassificationORT(join(folder, ort_cfg['cls_model_path']), input_size=(96, 96))
+            self.pipeline = LPRMultiTaskPipeline(detector=det, recognizer=rec, classifier=cls)
+        else:
+            raise NotImplemented
+
+    def __call__(self, image: np.ndarray, *args, **kwargs):
+        return self.pipeline(image)
diff --git a/hyperlpr_py3/plateStructure.py b/Prj-Python/hyperlpr3/inference/__init__.py
similarity index 100%
rename from hyperlpr_py3/plateStructure.py
rename to Prj-Python/hyperlpr3/inference/__init__.py
diff --git a/hyperlpr_py3/precise.py b/Prj-Python/hyperlpr3/inference/base/__init__.py
similarity index 100%
rename from hyperlpr_py3/precise.py
rename to Prj-Python/hyperlpr3/inference/base/__init__.py
diff --git a/Prj-Python/hyperlpr3/inference/base/base.py b/Prj-Python/hyperlpr3/inference/base/base.py
new file mode 100644
index 0000000..cefe991
--- /dev/null
+++ b/Prj-Python/hyperlpr3/inference/base/base.py
@@ -0,0 +1,27 @@
+from abc import ABCMeta, abstractmethod
+
+
+class HamburgerABC(metaclass=ABCMeta):
+
+    def __init__(self, input_size: tuple = None, *args,
+                 **kwargs):
+        self.input_size = input_size
+
+    @abstractmethod
+    def _run_session(self, data):
+        pass
+
+    @abstractmethod
+    def _postprocess(self, data):
+        pass
+
+    @abstractmethod
+    def _preprocess(self, image):
+        pass
+
+    def __call__(self, image):
+        flow = self._preprocess(image)
+        flow = self._run_session(flow)
+        result = self._postprocess(flow)
+
+        return result
diff --git a/Prj-Python/hyperlpr3/inference/classification.py b/Prj-Python/hyperlpr3/inference/classification.py
new file mode 100644
index 0000000..522c8dd
--- /dev/null
+++ b/Prj-Python/hyperlpr3/inference/classification.py
@@ -0,0 +1,49 @@
+import cv2
+import numpy as np
+from .base.base import HamburgerABC
+from hyperlpr3.common.tools_process import cost
+
+
+def encode_images(image: np.ndarray):
+    image_encode = image / 255.0
+    if len(image_encode.shape) == 4:
+        image_encode = image_encode.transpose(0, 3, 1, 2)
+    else:
+        image_encode = image_encode.transpose(2, 0, 1)
+    image_encode = image_encode.astype(np.float32)
+
+    return image_encode
+
+
+class ClassificationORT(HamburgerABC):
+
+    def __init__(self, onnx_path, *args, **kwargs):
+        import onnxruntime as ort
+        super().__init__(*args, **kwargs)
+        self.session = ort.InferenceSession(onnx_path, None)
+        self.input_config = self.session.get_inputs()[0]
+        self.output_config = self.session.get_outputs()[0]
+        self.input_size = tuple(self.input_config.shape[2:])
+
+    # @cost('Cls')
+    def _run_session(self, data) -> np.ndarray:
+        result = self.session.run([self.output_config.name], {self.input_config.name: data})
+
+        return result[0]
+
+    def _postprocess(self, data) -> np.ndarray:
+        return data
+
+    def _preprocess(self, image) -> np.ndarray:
+        assert len(
+            image.shape) == 3, "The dimensions of the input image object do not match. The input supports a single " \
+                               "image. "
+        # print(self.input_size)
+        image_resize = cv2.resize(image, self.input_size)
+        encode = encode_images(image_resize)
+        encode = encode.astype(np.float32)
+        input_tensor = np.expand_dims(encode, 0)
+
+        return input_tensor
+
+
diff --git a/Prj-Python/hyperlpr3/inference/detect.py b/Prj-Python/hyperlpr3/inference/detect.py
new file mode 100644
index 0000000..a29cba9
--- /dev/null
+++ b/Prj-Python/hyperlpr3/inference/detect.py
@@ -0,0 +1,200 @@
+from hyperlpr3.common.tools_process import *
+from .base.base import HamburgerABC
+
+ANCHORS_MAP = {
+    320: [[9.38281, 3.08398], [15.53125, 4.93750], [19.98438, 7.78906],
+          [31.10938, 10.35156], [45.21875, 14.14844], [32.34375, 21.04688],
+          [65.62500, 19.57812], [76.12500, 46.12500], [253.25000, 137.50000]],
+    640: [[10, 13], [16, 30], [33, 23], [30, 61], [62, 45],
+          [59, 119], [116, 90], [156, 198], [373, 326]]
+}
+
+
+def image_to_input_tensor(image):
+    data = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    data = data.transpose(2, 0, 1) / 255.0
+    data = np.expand_dims(data, 0)
+    data = data.astype(np.float32)
+
+    return data
+
+
+class Y5rkDetectorMNN(HamburgerABC):
+    def __init__(self, mnn_path, box_threshold: float = 0.5, nms_threshold: float = 0.6, *args, **kwargs):
+        from .common.mnn_adapt import MNNAdapter
+        super().__init__(*args, **kwargs)
+        self.box_threshold = box_threshold
+        self.nms_threshold = nms_threshold
+        self.input_shape = (1, 3, self.input_size[0], self.input_size[1])
+        self.tensor_shape = ((1, 18, 40, 40), (1, 18, 20, 20), (1, 18, 10, 10))
+        self.session = MNNAdapter(mnn_path, self.input_shape, outputs_name=['output', '335', '336'],
+                                  outputs_shape=self.tensor_shape)
+        assert self.input_size[0] == self.input_size[1]
+        self.anchors = ANCHORS_MAP[self.input_size[0]]
+
+    def _run_session(self, data):
+        outputs = self.session.inference(data)
+        result = list()
+        for idx, output in enumerate(outputs):
+            result.append(output.reshape(self.tensor_shape[idx]))
+
+        return result
+
+    def _postprocess(self, data):
+        ratio, (dw, dh) = self.temp_pack
+        input0_data = data[0]
+        input1_data = data[1]
+        input2_data = data[2]
+        input0_data = input0_data.reshape([3, -1] + list(input0_data.shape[-2:]))
+        input1_data = input1_data.reshape([3, -1] + list(input1_data.shape[-2:]))
+        input2_data = input2_data.reshape([3, -1] + list(input2_data.shape[-2:]))
+        input_data = list()
+        input_data.append(np.transpose(input0_data, (2, 3, 0, 1)))
+        input_data.append(np.transpose(input1_data, (2, 3, 0, 1)))
+        input_data.append(np.transpose(input2_data, (2, 3, 0, 1)))
+
+        boxes, classes, scores = self.decode_outputs(input_data, self.input_size)
+
+        boxes = restore_bound_box(boxes, ratio, (dw, dh))
+
+        return boxes, classes, scores
+
+    def _preprocess(self, image):
+        h, w, _ = image.shape
+        resize_img, ratio, (dw, dh) = letterbox(image, new_shape=(self.input_size[1], self.input_size[0]))
+        data = image_to_input_tensor(resize_img)
+        self.temp_pack = ratio, (dw, dh)
+
+        return data
+
+    def decode_outputs(self, input_data, size):
+        masks = [[0, 1, 2], [3, 4, 5], [6, 7, 8]]
+        anchors = self.anchors
+
+        boxes, classes, scores = [], [], []
+        for input, mask in zip(input_data, masks):
+            b, c, s = process(input, mask, anchors, size)
+            b, c, s = filter_boxes(b, c, s, self.box_threshold, self.nms_threshold)
+            boxes.append(b)
+            classes.append(c)
+            scores.append(s)
+
+        boxes = np.concatenate(boxes)
+        boxes = xywh2xyxy(boxes)
+        classes = np.concatenate(classes)
+        scores = np.concatenate(scores)
+
+        nboxes, nclasses, nscores = [], [], []
+        for c in set(classes):
+            inds = np.where(classes == c)
+            b = boxes[inds]
+            c = classes[inds]
+            s = scores[inds]
+
+            keep = nms_boxes(b, s, self.nms_threshold)
+
+            nboxes.append(b[keep])
+            nclasses.append(c[keep])
+            nscores.append(s[keep])
+
+        if not nclasses and not nscores:
+            return None, None, None
+
+        boxes = np.concatenate(nboxes)
+        classes = np.concatenate(nclasses)
+        scores = np.concatenate(nscores)
+
+        return boxes, classes, scores
+
+
+class Y5rkDetectorORT(HamburgerABC):
+
+    def __init__(self, onnx_path, box_threshold: float = 0.5, nms_threshold: float = 0.6, *args, **kwargs):
+        import onnxruntime as ort
+        super().__init__(*args, **kwargs)
+        self.box_threshold = box_threshold
+        self.nms_threshold = nms_threshold
+        self.session = ort.InferenceSession(onnx_path, None)
+        self.inputs_option = self.session.get_inputs()
+        self.outputs_option = self.session.get_outputs()
+        input_option = self.inputs_option[0]
+        input_size_ = tuple(input_option.shape[2:])
+        self.input_size = tuple(self.input_size)
+        if not self.input_size:
+            self.input_size = input_size_
+        assert self.input_size == input_size_, 'The dimensions of the input do not match the model expectations.'
+        assert self.input_size[0] == self.input_size[1]
+        self.input_name = input_option.name
+        self.anchors = ANCHORS_MAP[self.input_size[0]]
+
+    def decode_outputs(self, input_data, size):
+        masks = [[0, 1, 2], [3, 4, 5], [6, 7, 8]]
+        anchors = self.anchors
+
+        boxes, classes, scores = [], [], []
+        for input, mask in zip(input_data, masks):
+            b, c, s = process(input, mask, anchors, size)
+            b, c, s = filter_boxes(b, c, s, self.box_threshold, self.nms_threshold)
+            boxes.append(b)
+            classes.append(c)
+            scores.append(s)
+
+        boxes = np.concatenate(boxes)
+        boxes = xywh2xyxy(boxes)
+        classes = np.concatenate(classes)
+        scores = np.concatenate(scores)
+
+        nboxes, nclasses, nscores = [], [], []
+        for c in set(classes):
+            inds = np.where(classes == c)
+            b = boxes[inds]
+            c = classes[inds]
+            s = scores[inds]
+
+            keep = nms_boxes(b, s, self.nms_threshold)
+
+            nboxes.append(b[keep])
+            nclasses.append(c[keep])
+            nscores.append(s[keep])
+
+        if not nclasses and not nscores:
+            return None, None, None
+
+        boxes = np.concatenate(nboxes)
+        classes = np.concatenate(nclasses)
+        scores = np.concatenate(nscores)
+
+        return boxes, classes, scores
+
+    @cost("Detect")
+    def _run_session(self, data):
+        outputs = self.session.run([], {"images": data})
+
+        return outputs
+
+    def _postprocess(self, data):
+        ratio, (dw, dh) = self.temp_pack
+        input0_data = data[0]
+        input1_data = data[1]
+        input2_data = data[2]
+        input0_data = input0_data.reshape([3, -1] + list(input0_data.shape[-2:]))
+        input1_data = input1_data.reshape([3, -1] + list(input1_data.shape[-2:]))
+        input2_data = input2_data.reshape([3, -1] + list(input2_data.shape[-2:]))
+        input_data = list()
+        input_data.append(np.transpose(input0_data, (2, 3, 0, 1)))
+        input_data.append(np.transpose(input1_data, (2, 3, 0, 1)))
+        input_data.append(np.transpose(input2_data, (2, 3, 0, 1)))
+
+        boxes, classes, scores = self.decode_outputs(input_data, self.input_size)
+
+        boxes = restore_bound_box(boxes, ratio, (dw, dh))
+
+        return boxes, classes, scores
+
+    def _preprocess(self, image):
+        h, w, _ = image.shape
+        resize_img, ratio, (dw, dh) = letterbox(image, new_shape=(self.input_size[1], self.input_size[0]))
+        data = image_to_input_tensor(resize_img)
+        self.temp_pack = ratio, (dw, dh)
+
+        return data
diff --git a/Prj-Python/hyperlpr3/inference/multitask_detect.py b/Prj-Python/hyperlpr3/inference/multitask_detect.py
new file mode 100644
index 0000000..c6e9004
--- /dev/null
+++ b/Prj-Python/hyperlpr3/inference/multitask_detect.py
@@ -0,0 +1,184 @@
+import numpy as np
+import cv2
+import copy
+from .base.base import HamburgerABC
+
+
+def xywh2xyxy(boxes):
+    xywh = copy.deepcopy(boxes)
+    xywh[:, 0] = boxes[:, 0] - boxes[:, 2] / 2
+    xywh[:, 1] = boxes[:, 1] - boxes[:, 3] / 2
+    xywh[:, 2] = boxes[:, 0] + boxes[:, 2] / 2
+    xywh[:, 3] = boxes[:, 1] + boxes[:, 3] / 2
+    return xywh
+
+
+def nms(boxes, iou_thresh):  # nms
+    index = np.argsort(boxes[:, 4])[::-1]
+    keep = []
+    while index.size > 0:
+        i = index[0]
+        keep.append(i)
+        x1 = np.maximum(boxes[i, 0], boxes[index[1:], 0])
+        y1 = np.maximum(boxes[i, 1], boxes[index[1:], 1])
+        x2 = np.minimum(boxes[i, 2], boxes[index[1:], 2])
+        y2 = np.minimum(boxes[i, 3], boxes[index[1:], 3])
+
+        w = np.maximum(0, x2 - x1)
+        h = np.maximum(0, y2 - y1)
+
+        inter_area = w * h
+        union_area = (boxes[i, 2] - boxes[i, 0]) * (boxes[i, 3] - boxes[i, 1]) + (
+                boxes[index[1:], 2] - boxes[index[1:], 0]) * (boxes[index[1:], 3] - boxes[index[1:], 1])
+        iou = inter_area / (union_area - inter_area)
+        idx = np.where(iou <= iou_thresh)[0]
+        index = index[idx + 1]
+    return keep
+
+
+def restore_box(boxes, r, left, top):
+    boxes[:, [0, 2, 5, 7, 9, 11]] -= left
+    boxes[:, [1, 3, 6, 8, 10, 12]] -= top
+
+    boxes[:, [0, 2, 5, 7, 9, 11]] /= r
+    boxes[:, [1, 3, 6, 8, 10, 12]] /= r
+    return boxes
+
+
+def detect_pre_precessing(img, img_size):
+    img, r, left, top = letter_box(img, img_size)
+    img = img[:, :, ::-1].transpose(2, 0, 1).copy().astype(np.float32)
+    img = img / 255
+    img = img.reshape(1, *img.shape)
+    return img, r, left, top
+
+
+def post_precessing(dets, r, left, top, conf_thresh=0.25, iou_thresh=0.5):
+    choice = dets[:, :, 4] > conf_thresh
+    dets = dets[choice]
+    dets[:, 13:15] *= dets[:, 4:5]
+    box = dets[:, :4]
+    boxes = xywh2xyxy(box)
+    score = np.max(dets[:, 13:15], axis=-1, keepdims=True)
+    index = np.argmax(dets[:, 13:15], axis=-1).reshape(-1, 1)
+    output = np.concatenate((boxes, score, dets[:, 5:13], index), axis=1)
+    reserve_ = nms(output, iou_thresh)
+    output = output[reserve_]
+    output = restore_box(output, r, left, top)
+    return output
+
+
+def letter_box(img, size=(640, 640)):
+    h, w, c = img.shape
+    r = min(size[0] / h, size[1] / w)
+    new_h, new_w = int(h * r), int(w * r)
+    top = int((size[0] - new_h) / 2)
+    left = int((size[1] - new_w) / 2)
+
+    bottom = size[0] - new_h - top
+    right = size[1] - new_w - left
+    img_resize = cv2.resize(img, (new_w, new_h))
+    img = cv2.copyMakeBorder(img_resize, top, bottom, left, right, borderType=cv2.BORDER_CONSTANT,
+                             value=(0, 0, 0))
+    return img, r, left, top
+
+
+class MultiTaskDetectorMNN(HamburgerABC):
+
+    def __init__(self, mnn_path, box_threshold: float = 0.5, nms_threshold: float = 0.6, *args, **kwargs):
+        from hyperlpr3.common.mnn_adapt import MNNAdapter
+        super().__init__(*args, **kwargs)
+        assert self.input_size[0] == self.input_size[1]
+        self.box_threshold = box_threshold
+        self.nms_threshold = nms_threshold
+        self.input_shape = (1, 3, self.input_size[0], self.input_size[1])
+        if self.input_size[0] == 320:
+            self.tensor_shape = [(1, 6300, 15)]
+        elif self.input_size[0] == 640:
+            self.tensor_shape = [(1, 25200, 15)]
+        self.session = MNNAdapter(mnn_path, self.input_shape, outputs_name=['output', ],
+                                  outputs_shape=self.tensor_shape)
+
+    def _run_session(self, data):
+        outputs = self.session.inference(data)
+        result = list()
+        for idx, output in enumerate(outputs):
+            result.append(output.reshape(self.tensor_shape[idx]))
+        result = np.asarray(result)
+
+        return result[0]
+
+    def _postprocess(self, data):
+        r, left, top = self.tmp_pack
+
+        return post_precessing(data, r, left, top)
+
+    def _preprocess(self, image):
+        img, r, left, top = detect_pre_precessing(image, self.input_size)
+        self.tmp_pack = r, left, top
+
+        return img
+
+
+class MultiTaskDetectorDNN(HamburgerABC):
+
+    def __init__(self, onnx_path, box_threshold: float = 0.5, nms_threshold: float = 0.6, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.box_threshold = box_threshold
+        self.nms_threshold = nms_threshold
+        self.session = cv2.dnn.readNetFromONNX(onnx_path)
+        self.input_shape = (1, 3, self.input_size[0], self.input_size[1])
+        self.tensor_shape = [(1, 6300, 15)]
+
+    def _run_session(self, data):
+        self.session.setInput(data)
+        outputs = self.session.forward()
+
+        return outputs
+
+    def _postprocess(self, data):
+
+        r, left, top = self.tmp_pack
+
+        return post_precessing(data, r, left, top)
+
+    def _preprocess(self, image):
+        img, r, left, top = detect_pre_precessing(image, self.input_size)
+        self.tmp_pack = r, left, top
+
+        return img
+
+
+class MultiTaskDetectorORT(HamburgerABC):
+
+    def __init__(self, onnx_path, box_threshold: float = 0.5, nms_threshold: float = 0.6, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        import onnxruntime as ort
+        self.box_threshold = box_threshold
+        self.nms_threshold = nms_threshold
+        self.session = ort.InferenceSession(onnx_path, providers=['CPUExecutionProvider'])
+        self.inputs_option = self.session.get_inputs()
+        self.outputs_option = self.session.get_outputs()
+        input_option = self.inputs_option[0]
+        input_size_ = tuple(input_option.shape[2:])
+        self.input_size = tuple(self.input_size)
+        if not self.input_size:
+            self.input_size = input_size_
+        assert self.input_size == input_size_, 'The dimensions of the input do not match the model expectations.'
+        assert self.input_size[0] == self.input_size[1]
+        self.input_name = input_option.name
+
+    def _run_session(self, data):
+        result = self.session.run([self.outputs_option[0].name], {self.input_name: data})[0]
+
+        return result
+
+    def _postprocess(self, data):
+        r, left, top = self.tmp_pack
+        return post_precessing(data, r, left, top)
+
+    def _preprocess(self, image):
+        img, r, left, top = detect_pre_precessing(image, self.input_size)
+        self.tmp_pack = r, left, top
+
+        return img
diff --git a/Prj-Python/hyperlpr3/inference/pipeline.py b/Prj-Python/hyperlpr3/inference/pipeline.py
new file mode 100644
index 0000000..75f3af5
--- /dev/null
+++ b/Prj-Python/hyperlpr3/inference/pipeline.py
@@ -0,0 +1,117 @@
+import numpy as np
+
+from hyperlpr3.common.typedef import *
+from hyperlpr3.common.tools_process import *
+
+
+class LPRMultiTaskPipeline(object):
+
+    def __init__(self, detector, recognizer, classifier):
+        self.detector = detector
+        self.recognizer = recognizer
+        self.classifier = classifier
+
+    def run(self, image: np.ndarray) -> list:
+        result = list()
+        assert len(image.shape) == 3, "Input image must be 3 channels."
+        assert image is not None, "Input image cannot be empty."
+        outputs = self.detector(image)
+        for out in outputs:
+            rect = out[:4].astype(int)
+            score = out[4]
+            land_marks = out[5:13].reshape(4, 2).astype(int)
+            layer_num = int(out[13])
+            print(layer_num)
+            pad = get_rotate_crop_image(image, land_marks)
+            if layer_num == DOUBLE:
+                # double
+                h, w, _ = pad.shape
+                line = int(h * 0.4)
+                top = pad[:line, :, ]
+                bottom = pad[line:, :]
+                top_code, top_confidence = self.recognizer(top)
+                bottom_code, bottom_confidence = self.recognizer(bottom)
+                plate_code = top_code + bottom_code
+                rec_confidence = (top_confidence + bottom_confidence) / 2
+                # cv2.imshow("top", top)
+                # cv2.imshow("bottom", bottom)
+                # cv2.waitKey(0)
+            else:
+                plate_code, rec_confidence = self.recognizer(pad)
+            if plate_code == '':
+                continue
+            if len(plate_code) >= 7:
+                plate_type = code_filter(plate_code)
+                if plate_type == UNKNOWN:
+                    cls = self.classifier(pad)
+                    idx = int(np.argmax(cls))
+                    if idx == PLATE_TYPE_YELLOW:
+                        if layer_num == DOUBLE:
+                            plate_type = YELLOW_DOUBLE
+                        else:
+                            plate_type = YELLOW_SINGLE
+                    elif idx == PLATE_TYPE_BLUE:
+                        plate_type = BLUE
+                    elif idx == PLATE_TYPE_GREEN:
+                        plate_type = GREEN
+                plate = Plate(vertex=land_marks, plate_code=plate_code, det_bound_box=np.asarray(rect),
+                              rec_confidence=rec_confidence, dex_bound_confidence=score, plate_type=plate_type)
+                result.append(plate.to_result())
+
+        return result
+
+    def __call__(self, image: np.ndarray, *args, **kwargs):
+        return self.run(image)
+
+
+class LPRPipeline(object):
+
+    def __init__(self, detector, vertex_predictor, recognizer, ):
+        self.detector = detector
+        self.vertex_predictor = vertex_predictor
+        self.recognizer = recognizer
+
+    # @cost("PipelineTotalCost")
+    def run(self, image: np.ndarray) -> list:
+        result = list()
+        boxes, classes, scores = self.detector(image)
+        fp_boxes_index = find_the_adjacent_boxes(boxes)
+        image_blacks = list()
+        if len(fp_boxes_index) > 0:
+            for idx in fp_boxes_index:
+                image_black = np.zeros_like(image)
+                box = boxes[idx]
+                x1, y1, x2, y2 = np.asarray(box).astype(int)
+                image_black[y1:y2, x1:x2] = image[y1:y2, x1:x2]
+                image_blacks.append(image_black)
+        if boxes:
+            fp = 0
+            for idx, box in enumerate(boxes):
+                det_confidence = scores[idx]
+                if idx in fp_boxes_index:
+                    warped, p, mat = align_box(image_blacks[fp], box, scale_factor=1.2, size=96)
+                    fp += 1
+                else:
+                    warped, p, mat = align_box(image, box, scale_factor=1.2, size=96)
+                kps = self.vertex_predictor(warped)
+                polyline = list()
+                for point in kps:
+                    polyline.append([point[0], point[1], 1])
+                polyline = np.asarray(polyline)
+                inv = cv2.invertAffineTransform(mat)
+                trans_points = np.dot(inv, polyline.T).T
+                pad = get_rotate_crop_image(image, trans_points)
+                # print(pad.shape)
+                # cv2.imshow("pad", pad)
+                # cv2.waitKey(0)
+                plate_code, rec_confidence = self.recognizer(pad)
+                if plate_code == '':
+                    continue
+                plate = Plate(vertex=trans_points, plate_code=plate_code, det_bound_box=np.asarray(box),
+                              rec_confidence=rec_confidence, dex_bound_confidence=det_confidence)
+                result.append(plate.to_dict())
+
+        return result
+
+    def __call__(self, image: np.ndarray, *args, **kwargs):
+        return self.run(image)
diff --git a/Prj-Python/hyperlpr3/inference/recognition.py b/Prj-Python/hyperlpr3/inference/recognition.py
new file mode 100644
index 0000000..1cc7008
--- /dev/null
+++ b/Prj-Python/hyperlpr3/inference/recognition.py
@@ -0,0 +1,233 @@
+import cv2
+import numpy as np
+from .base.base import HamburgerABC
+from hyperlpr3.common.tools_process import cost
+import math
+from hyperlpr3.common.tokenize import token
+
+
+def encode_images(image: np.ndarray, max_wh_ratio, target_shape, limited_max_width=160, limited_min_width=48):
+    imgC = 3
+    imgH, imgW = target_shape
+    # cv2.imshow("image", image)
+    # cv2.waitKey(0)
+    assert imgC == image.shape[2]
+    max_wh_ratio = max(max_wh_ratio, imgW / imgH)
+    imgW = int((imgH * max_wh_ratio))
+    imgW = max(min(imgW, limited_max_width), limited_min_width)
+    h, w = image.shape[:2]
+    ratio = w / float(h)
+    ratio_imgH = math.ceil(imgH * ratio)
+    ratio_imgH = max(ratio_imgH, limited_min_width)
+    if ratio_imgH > imgW:
+        resized_w = imgW
+    else:
+        resized_w = int(ratio_imgH)
+    resized_image = cv2.resize(image, (resized_w, imgH))
+    # print((resized_w, imgH))
+    # padding_im1 = np.ones((imgH, imgW, imgC), dtype=np.uint8) * 128
+    # padding_im1[:, 0:resized_w, :] = resized_image
+    # cv2.imwrite("pad.jpg", padding_im1)
+
+    resized_image = resized_image.astype('float32')
+    resized_image = (resized_image.transpose((2, 0, 1)) - 127.5) / 127.5
+    # resized_image -= 0.5
+    # resized_image *= 2
+    padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32)
+    padding_im[:, :, 0:resized_w] = resized_image
+
+    # np.save('fk.npy', padding_im)
+
+    return padding_im
+
+
+def get_ignored_tokens():
+    return [0]  # for ctc blank
+
+
+class PPRCNNRecognitionMNN(HamburgerABC):
+
+    def __init__(self, mnn_path, character_file, *args, **kwargs):
+        from hyperlpr3.common.mnn_adapt import MNNAdapter
+        super().__init__(*args, **kwargs)
+        self.input_shape = (1, 3, self.input_size[0], self.input_size[1])
+        self.session = MNNAdapter(mnn_path, input_shape=self.input_shape, outputs_name=['output'])
+        self.character_list = token
+
+    def decode(self, text_index, text_prob=None, is_remove_duplicate=False):
+        """ convert text-index into text-label. """
+        result_list = []
+        ignored_tokens = get_ignored_tokens()
+        batch_size = len(text_index)
+        for batch_idx in range(batch_size):
+            char_list = []
+            conf_list = []
+            for idx in range(len(text_index[batch_idx])):
+                if text_index[batch_idx][idx] in ignored_tokens:
+                    continue
+                if is_remove_duplicate:
+                    # only for predict
+                    if idx > 0 and text_index[batch_idx][idx - 1] == text_index[batch_idx][idx]:
+                        continue
+                char_list.append(self.character_list[int(text_index[batch_idx][idx])])
+                if text_prob is not None:
+                    conf_list.append(text_prob[batch_idx][idx])
+                else:
+                    conf_list.append(1)
+            text = ''.join(char_list)
+            result_list.append((text, np.mean(conf_list)))
+        return result_list
+
+    def _run_session(self, data):
+        output = self.session.inference(data)
+        output = output.reshape(40, 6625)
+        # print(output[:, 0])
+        output = np.expand_dims([output], 0)
+        return output
+
+    def _postprocess(self, data):
+        prod = data[0]
+        argmax = np.argmax(prod, axis=2)
+        # print(argmax)
+        rmax = np.max(prod, axis=2)
+        # print(rmax)
+        result = self.decode(argmax, rmax, is_remove_duplicate=True)
+
+        return result[0]
+
+    def _preprocess(self, image):
+        assert len(
+            image.shape) == 3, "The dimensions of the input image object do not match. The input supports a single " \
+                               "image. "
+        h, w, _ = image.shape
+        wh_ratio = w * 1.0 / h
+        data = encode_images(image, wh_ratio, self.input_size, )
+        data = np.expand_dims(data, 0)
+
+        return data
+
+
+class PPRCNNRecognitionORT(HamburgerABC):
+
+    def __init__(self, onnx_path, token_dict=token, *args, **kwargs):
+        import onnxruntime as ort
+        super().__init__(*args, **kwargs)
+        self.session = ort.InferenceSession(onnx_path, None)
+        self.input_config = self.session.get_inputs()[0]
+        self.output_config = self.session.get_outputs()[0]
+        self.input_size = self.input_config.shape[2:]
+        # print(self.input_size)
+        self.character_list = token_dict
+
+    def decode(self, text_index, text_prob=None, is_remove_duplicate=False):
+        """ convert text-index into text-label. """
+        result_list = []
+        ignored_tokens = get_ignored_tokens()
+        batch_size = len(text_index)
+        for batch_idx in range(batch_size):
+            char_list = []
+            conf_list = []
+            for idx in range(len(text_index[batch_idx])):
+                if text_index[batch_idx][idx] in ignored_tokens:
+                    continue
+                if is_remove_duplicate:
+                    # only for predict
+                    if idx > 0 and text_index[batch_idx][idx - 1] == text_index[batch_idx][idx]:
+                        continue
+                # print(int(text_index[batch_idx][idx]))
+                char_list.append(self.character_list[int(text_index[batch_idx][idx])])
+                if text_prob is not None:
+                    conf_list.append(text_prob[batch_idx][idx])
+                else:
+                    conf_list.append(1)
+            text = ''.join(char_list)
+            result_list.append((text, np.mean(conf_list)))
+        return result_list
+
+    # @cost("Recognition")
+    def _run_session(self, data) -> np.ndarray:
+        result = self.session.run([self.output_config.name], {self.input_config.name: data})
+
+        return result
+
+    def _postprocess(self, data) -> tuple:
+        if data:
+            prod = data[0]
+            argmax = np.argmax(prod, axis=2)
+            rmax = np.max(prod, axis=2)
+            result = self.decode(argmax, rmax, is_remove_duplicate=True)
+
+            return result[0]
+        else:
+            return '', 0.0
+
+    def _preprocess(self, image) -> np.ndarray:
+        assert len(
+            image.shape) == 3, "The dimensions of the input image object do not match. The input supports a single " \
+                               "image. "
+        h, w, _ = image.shape
+        wh_ratio = w * 1.0 / h
+        data = encode_images(image, wh_ratio, self.input_size, )
+        data = np.expand_dims(data, 0)
+        # print(data.shape)
+
+        return data
+
+
+class PPRCNNRecognitionDNN(HamburgerABC):
+
+    def __init__(self, onnx_path, character_file, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.session = cv2.dnn.readNetFromONNX(onnx_path)
+        self.input_shape = (1, 3, self.input_size[0], self.input_size[1])
+        self.character_list = token
+
+    def decode(self, text_index, text_prob=None, is_remove_duplicate=False):
+        result_list = []
+        ignored_tokens = get_ignored_tokens()
+        batch_size = len(text_index)
+        for batch_idx in range(batch_size):
+            char_list = []
+            conf_list = []
+            for idx in range(len(text_index[batch_idx])):
+                if text_index[batch_idx][idx] in ignored_tokens:
+                    continue
+                if is_remove_duplicate:
+                    # only for predict
+                    if idx > 0 and text_index[batch_idx][idx - 1] == text_index[batch_idx][idx]:
+                        continue
+                char_list.append(self.character_list[int(text_index[batch_idx][idx])])
+                if text_prob is not None:
+                    conf_list.append(text_prob[batch_idx][idx])
+                else:
+                    conf_list.append(1)
+            text = ''.join(char_list)
+            result_list.append((text, np.mean(conf_list)))
+        return result_list
+
+    def _run_session(self, data):
+        self.session.setInput(data)
+        outputs = self.session.forward()
+        outputs = np.expand_dims(outputs, 0)
+        # print(outputs.shape)
+
+        return outputs
+
+    def _postprocess(self, data):
+        prod = data[0]
+        argmax = np.argmax(prod, axis=2)
+        rmax = np.max(prod, axis=2)
+        result = self.decode(argmax, rmax, is_remove_duplicate=True)
+
+        return result[0]
+
+    def _preprocess(self, image):
+        assert len(
+            image.shape) == 3, "The dimensions of the input image object do not match. The input supports a single " \
+                               "image. "
+        h, w, _ = image.shape
+        wh_ratio = w * 1.0 / h
+        data = encode_images(image, wh_ratio, self.input_size, )
+        data = np.expand_dims(data, 0)
+
+        return data
diff --git a/Prj-Python/hyperlpr3/inference/vertex.py b/Prj-Python/hyperlpr3/inference/vertex.py
new file mode 100644
index 0000000..61830d8
--- /dev/null
+++ b/Prj-Python/hyperlpr3/inference/vertex.py
@@ -0,0 +1,83 @@
+import cv2
+import numpy as np
+from .base.base import HamburgerABC
+from hyperlpr3.common.tools_process import cost
+
+
+def encode_images(image: np.ndarray):
+    image_encode = image / 255.0
+    if len(image_encode.shape) == 4:
+        image_encode = image_encode.transpose(0, 3, 1, 2)
+    else:
+        image_encode = image_encode.transpose(2, 0, 1)
+    image_encode = image_encode.astype(np.float32)
+
+    return image_encode
+
+
+class BVTVertexMNN(HamburgerABC):
+
+    def __init__(self, mnn_path, *args, **kwargs):
+        from .common.mnn_adapt import MNNAdapter
+        super().__init__(*args, **kwargs)
+        self.input_shape = (1, 3, self.input_size[0], self.input_size[1])
+        self.session = MNNAdapter(mnn_path, self.input_shape)
+
+    def _run_session(self, data):
+        outputs = self.session.inference(data)
+        return outputs
+
+    def _postprocess(self, data):
+        assert data.shape[0] == 1
+        data = np.asarray(data).reshape(-1, 4, 2)
+        data[:, :, 0] *= self.input_size[1]
+        data[:, :, 1] *= self.input_size[0]
+
+        return data[0]
+
+    def _preprocess(self, image):
+        assert len(
+            image.shape) == 3, "The dimensions of the input image object do not match. The input supports a single " \
+                               "image. "
+        image_resize = cv2.resize(image, self.input_size)
+        encode = encode_images(image_resize)
+        encode = encode.astype(np.float32)
+        input_tensor = np.expand_dims(encode, 0)
+
+        return input_tensor
+
+
+class BVTVertexORT(HamburgerABC):
+
+    def __init__(self, onnx_path, *args, **kwargs):
+        import onnxruntime as ort
+        super().__init__(*args, **kwargs)
+        self.session = ort.InferenceSession(onnx_path, None)
+        self.input_config = self.session.get_inputs()[0]
+        self.output_config = self.session.get_outputs()[0]
+        self.input_size = self.input_config.shape[2:]
+
+    # @cost('Vertex')
+    def _run_session(self, data) -> np.ndarray:
+        result = self.session.run([self.output_config.name], {self.input_config.name: data})
+
+        return result[0]
+
+    def _postprocess(self, data) -> np.ndarray:
+        assert data.shape[0] == 1
+        data = np.asarray(data).reshape(-1, 4, 2)
+        data[:, :, 0] *= self.input_size[1]
+        data[:, :, 1] *= self.input_size[0]
+
+        return data[0]
+
+    def _preprocess(self, image) -> np.ndarray:
+        assert len(
+            image.shape) == 3, "The dimensions of the input image object do not match. The input supports a single " \
+                               "image. "
+        image_resize = cv2.resize(image, self.input_size)
+        encode = encode_images(image_resize)
+        encode = encode.astype(np.float32)
+        input_tensor = np.expand_dims(encode, 0)
+
+        return input_tensor
diff --git a/Prj-Python/requirements.txt b/Prj-Python/requirements.txt
new file mode 100644
index 0000000..1311b04
--- /dev/null
+++ b/Prj-Python/requirements.txt
@@ -0,0 +1,30 @@
+anyio==3.6.2
+certifi==2022.12.7
+charset-normalizer==3.0.1
+click==8.1.3
+coloredlogs==15.0.1
+fastapi==0.92.0
+flatbuffers==23.1.21
+h11==0.14.0
+humanfriendly==10.0
+idna==3.4
+importlib-metadata==6.0.0
+loguru==0.6.0
+mpmath==1.2.1
+numpy==1.21.6
+onnxruntime==1.14.0
+opencv-python==4.7.0.68
+packaging==23.0
+protobuf==4.22.0
+pydantic==1.10.5
+python-multipart==0.0.5
+requests==2.28.2
+six==1.16.0
+sniffio==1.3.0
+starlette==0.25.0
+sympy==1.10.1
+tqdm==4.64.1
+typing-extensions==4.5.0
+urllib3==1.26.14
+uvicorn==0.20.0
+zipp==3.14.0
diff --git a/Prj-Python/setup.py b/Prj-Python/setup.py
new file mode 100644
index 0000000..2cc3522
--- /dev/null
+++ b/Prj-Python/setup.py
@@ -0,0 +1,37 @@
+# !/usr/bin/env python
+from setuptools import find_packages, setup
+
+__version__ = "0.1.1"
+
+if __name__ == "__main__":
+    setup(
+        name="hyperlpr3",
+        version=__version__,
+        description="vehicle license plate recognition.",
+        url="https://github.com/szad670401/HyperLPR",
+        author="HyperInspire",
+        author_email="tunmxy@163.com",
+        keywords="vehicle license plate recognition",
+        packages=find_packages(),
+        classifiers=[
+            "License :: OSI Approved :: Apache Software License",
+            "Operating System :: OS Independent",
+            "Programming Language :: Python :: 3",
+        ],
+        install_requires=[
+            "opencv-python",
+            "onnxruntime",
+            "tqdm",
+            "requests",
+            "fastapi",
+            "uvicorn",
+            "python-multipart",
+            "loguru"
+        ],
+        license="Apache License 2.0",
+        zip_safe=False,
+        entry_points="""
+                [console_scripts]
+                lpr3=hyperlpr3.command.cli:cli
+            """
+    )
\ No newline at end of file
diff --git a/Prj-Python/utils/visual_tools.py b/Prj-Python/utils/visual_tools.py
new file mode 100644
index 0000000..a16c3bf
--- /dev/null
+++ b/Prj-Python/utils/visual_tools.py
@@ -0,0 +1,35 @@
+import cv2
+import numpy as np
+from PIL import Image, ImageDraw, ImageFont
+
+
+def cv2ImgAddText(img, text, left, top, textColor=(255, 0, 0), textSize=20):
+    if (isinstance(img, np.ndarray)):  # 判断是否OpenCV图片类型
+        img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
+    draw = ImageDraw.Draw(img)
+    fontText = ImageFont.truetype("resource/font/platech.ttf", textSize, encoding="utf-8")
+    draw.text((left, top), text, textColor, font=fontText)
+
+    return cv2.cvtColor(np.asarray(img), cv2.COLOR_RGB2BGR)
+
+
+def draw_full(image: np.ndarray, plate_list: dict, ) -> np.ndarray:
+    canvas = image.copy()
+    for plate_info in plate_list:
+        kps = plate_info['vertex']
+        bdbox = plate_info['det_bound_box']
+        text = plate_info['plate_code']
+        x1, y1, x2, y2 = bdbox.astype(int)
+        bd_y = y2 - y1
+        bd_w = x2 - x1
+        point_size = bd_w // 20
+        line_size = bd_w // 40
+        cv2.polylines(canvas, [kps.astype(np.int32)], True, (0, 0, 200), line_size, )
+        for x, y in kps.astype(np.int32):
+            cv2.line(canvas, (x, y), (x, y), (80, 240, 100), point_size)
+        text_x = kps[0][0]
+        text_y = kps[0][1] - bd_y // 1.4
+        canvas = cv2ImgAddText(canvas, text, text_x, text_y, textSize=bd_w // 5)
+
+
+    return canvas
diff --git a/Prj-ROS/README.md b/Prj-ROS/README.md
deleted file mode 100644
index 5f05eee..0000000
--- a/Prj-ROS/README.md
+++ /dev/null
@@ -1,48 +0,0 @@
-# Prj-ROS
-
-HyperLPR的ROS包，封装了Prj-Linux的代码并为适配ROS进行了少量修改，在Ubuntu20.04中ros-noetic上进行了测试。
-
-## 安装依赖
-
-``` bash
-sudo apt install ros-noetic-desktop-full
-```
-ros-noetic使用的opencv版本为opencv4，可能会与opencv3冲突，请注意停用opencv3以免无法编译运行
-
-## 编译
-
-本文件所在路径为catkin_ws，rospackage路径为 `/HyperLPR/Prj-ROS/src/hyperlpr`
-
-``` bash
-cd Prj-ROS
-catkin build
-echo "source ${PATH_TO_HYPERLPR}/Prj-ROS/devel/setup.bash" >> ~/.bashrc
-```
-
-进入`/Prj-ROS/src/hyperlpr/launch/bringup.launch`，修改`CameraTopic`为自己相机图像的topic
-
-## 运行
-
-``` bash
-roslaunch hyperlpr bringup.launch
-```
-
-## 说明
-
-与ros node有关的代码位于`/Prj-ROS/src/hyperlpr/src`中的`hyperlpr_node.h`和`hyperlpr_node.cpp`中，可以按需修改
-
-运行时的rqt_graph如下
-
-![image](./docs/rosgraph.png)
-
-测试时使用的相机为Intel-RealSense D455，`bringup.launch`中默认CameraTopic为RealSense彩色图像topic
-
-发布的三个topic分别为
-
-+ /recognition_confidence 识别的置信度
-
-+ /recognition_result 识别的结果，不过ROS不支持中文字符，所以在使用时需要对订阅的数据另作处理，想立即确定中文结果也可以查看terminal中ROS_INFO输出的结果
-
-+ /recognized_image 框选出一帧所有可识别车牌的图像
-
-以上三个topic均只对置信度大于75%的识别作出反应，可以调整代码中的置信度阈值以获得期待的识别效果
diff --git a/Prj-ROS/docs/rosgraph.png b/Prj-ROS/docs/rosgraph.png
deleted file mode 100644
index 7dbee9c..0000000
Binary files a/Prj-ROS/docs/rosgraph.png and /dev/null differ
diff --git a/Prj-ROS/src/hyperlpr/CMakeLists.txt b/Prj-ROS/src/hyperlpr/CMakeLists.txt
deleted file mode 100644
index c90144c..0000000
--- a/Prj-ROS/src/hyperlpr/CMakeLists.txt
+++ /dev/null
@@ -1,50 +0,0 @@
-cmake_minimum_required(VERSION 3.0.2)
-project(hyperlpr)
-
-set(SRC_DETECTION src/PlateDetection.cpp  src/util.h include/PlateDetection.h)
-
-set(SRC_FINEMAPPING src/FineMapping.cpp )
-
-set(SRC_FASTDESKEW src/FastDeskew.cpp )
-
-set(SRC_SEGMENTATION src/PlateSegmentation.cpp )
-
-set(SRC_RECOGNIZE src/Recognizer.cpp src/CNNRecognizer.cpp)
-
-set(SRC_PIPLINE src/Pipeline.cpp)
-
-set(SRC_SEGMENTATIONFREE src/SegmentationFreeRecognizer.cpp )
-
-
-## Find catkin macros and libraries
-## if COMPONENTS list like find_package(catkin REQUIRED COMPONENTS xyz)
-## is used, also find other catkin packages
-find_package(OpenCV REQUIRED)
-
-find_package(catkin REQUIRED COMPONENTS
-  cmake_modules
-  roscpp
-  rospy
-  std_msgs
-  sensor_msgs
-  geometry_msgs
-  cv_bridge
-  image_transport
-)
-
-catkin_package(
-)
-
-include_directories(
-  include
-  ${catkin_INCLUDE_DIRS}
-  ${OpenCV_INCLUDE_DIRS}
-)
-
-
-add_executable(${PROJECT_NAME}_node ${SRC_DETECTION} ${SRC_FINEMAPPING} ${SRC_FASTDESKEW} ${SRC_SEGMENTATION} ${SRC_RECOGNIZE} ${SRC_PIPLINE} ${SRC_SEGMENTATIONFREE} src/hyperlpr_node.cpp src/main.cpp)
-
-target_link_libraries(${PROJECT_NAME}_node
-  ${catkin_LIBRARIES}
-  ${OpenCV_LIBS}
-)
diff --git a/Prj-ROS/src/hyperlpr/include/CNNRecognizer.h b/Prj-ROS/src/hyperlpr/include/CNNRecognizer.h
deleted file mode 100644
index 9885f1b..0000000
--- a/Prj-ROS/src/hyperlpr/include/CNNRecognizer.h
+++ /dev/null
@@ -1,24 +0,0 @@
-//
-// Created by Jack Yu on 21/10/2017.
-//
-
-#ifndef HYPERPR_CNNRECOGNIZER_H
-#define HYPERPR_CNNRECOGNIZER_H
-
-#include "Recognizer.h"
-namespace pr {
-class CNNRecognizer : public GeneralRecognizer {
-public:
-  const int CHAR_INPUT_W = 14;
-  const int CHAR_INPUT_H = 30;
-
-  CNNRecognizer(std::string prototxt, std::string caffemodel);
-  label recognizeCharacter(cv::Mat character);
-
-private:
-  cv::dnn::Net net;
-};
-
-} // namespace pr
-
-#endif // HYPERPR_CNNRECOGNIZER_H
diff --git a/Prj-ROS/src/hyperlpr/include/FastDeskew.h b/Prj-ROS/src/hyperlpr/include/FastDeskew.h
deleted file mode 100644
index dabf234..0000000
--- a/Prj-ROS/src/hyperlpr/include/FastDeskew.h
+++ /dev/null
@@ -1,17 +0,0 @@
-//
-// Created by Jack Yu on 22/09/2017.
-//
-
-#ifndef HYPERPR_FASTDESKEW_H
-#define HYPERPR_FASTDESKEW_H
-
-#include <math.h>
-#include <opencv2/opencv.hpp>
-namespace pr {
-
-cv::Mat fastdeskew(cv::Mat skewImage, int blockSize);
-//    cv::Mat spatialTransformer(cv::Mat skewImage);
-
-} // namespace pr
-
-#endif // HYPERPR_FASTDESKEW_H
diff --git a/Prj-ROS/src/hyperlpr/include/FineMapping.h b/Prj-ROS/src/hyperlpr/include/FineMapping.h
deleted file mode 100644
index 56dbedf..0000000
--- a/Prj-ROS/src/hyperlpr/include/FineMapping.h
+++ /dev/null
@@ -1,29 +0,0 @@
-//
-// Created by Jack Yu on 22/09/2017.
-//
-
-#ifndef HYPERPR_FINEMAPPING_H
-#define HYPERPR_FINEMAPPING_H
-
-#include <opencv2/dnn.hpp>
-#include <opencv2/opencv.hpp>
-
-#include <string>
-namespace pr {
-class FineMapping {
-public:
-  FineMapping();
-
-  FineMapping(std::string prototxt, std::string caffemodel);
-  static cv::Mat FineMappingVertical(cv::Mat InputProposal, int sliceNum = 15,
-                                     int upper = 0, int lower = -50,
-                                     int windows_size = 17);
-  cv::Mat FineMappingHorizon(cv::Mat FinedVertical, int leftPadding,
-                             int rightPadding);
-
-private:
-  cv::dnn::Net net;
-};
-
-} // namespace pr
-#endif // HYPERPR_FINEMAPPING_H
diff --git a/Prj-ROS/src/hyperlpr/include/Pipeline.h b/Prj-ROS/src/hyperlpr/include/Pipeline.h
deleted file mode 100644
index 11d61ab..0000000
--- a/Prj-ROS/src/hyperlpr/include/Pipeline.h
+++ /dev/null
@@ -1,57 +0,0 @@
-//
-// Created by Jack Yu on 22/10/2017.
-//
-
-#ifndef HYPERPR_PIPLINE_H
-#define HYPERPR_PIPLINE_H
-
-#include "CNNRecognizer.h"
-#include "FastDeskew.h"
-#include "FineMapping.h"
-#include "PlateDetection.h"
-#include "PlateInfo.h"
-#include "PlateSegmentation.h"
-#include "Recognizer.h"
-#include "SegmentationFreeRecognizer.h"
-
-namespace pr {
-
-const std::vector<std::string> CH_PLATE_CODE{
-    "京", "沪", "津", "渝", "冀", "晋", "蒙", "辽", "吉", "黑", "苏", "浙",
-    "皖", "闽", "赣", "鲁", "豫", "鄂", "湘", "粤", "桂", "琼", "川", "贵",
-    "云", "藏", "陕", "甘", "青", "宁", "新", "0",  "1",  "2",  "3",  "4",
-    "5",  "6",  "7",  "8",  "9",  "A",  "B",  "C",  "D",  "E",  "F",  "G",
-    "H",  "J",  "K",  "L",  "M",  "N",  "P",  "Q",  "R",  "S",  "T",  "U",
-    "V",  "W",  "X",  "Y",  "Z",  "港", "学", "使", "警", "澳", "挂", "军",
-    "北", "南", "广", "沈", "兰", "成", "济", "海", "民", "航", "空"};
-
-const int SEGMENTATION_FREE_METHOD = 0;
-const int SEGMENTATION_BASED_METHOD = 1;
-
-class PipelinePR {
-public:
-  GeneralRecognizer *generalRecognizer;
-  PlateDetection *plateDetection;
-  PlateSegmentation *plateSegmentation;
-  FineMapping *fineMapping;
-  SegmentationFreeRecognizer *segmentationFreeRecognizer;
-
-  PipelinePR();
-  ~PipelinePR();
-
-  void initialize(std::string detector_filename,
-                  std::string finemapping_prototxt,
-                  std::string finemapping_caffemodel,
-                  std::string segmentation_prototxt,
-                  std::string segmentation_caffemodel,
-                  std::string charRecognization_proto,
-                  std::string charRecognization_caffemodel,
-                  std::string segmentationfree_proto,
-                  std::string segmentationfree_caffemodel);
-
-  std::vector<std::string> plateRes;
-  std::vector<PlateInfo> RunPiplineAsImage(cv::Mat plateImage, int method);
-};
-
-} // namespace pr
-#endif // HYPERPR_PIPLINE_H
diff --git a/Prj-ROS/src/hyperlpr/include/PlateDetection.h b/Prj-ROS/src/hyperlpr/include/PlateDetection.h
deleted file mode 100644
index aa4cece..0000000
--- a/Prj-ROS/src/hyperlpr/include/PlateDetection.h
+++ /dev/null
@@ -1,32 +0,0 @@
-//
-// Created by Jack Yu on 20/09/2017.
-//
-
-#ifndef HYPERPR_PLATEDETECTION_H
-#define HYPERPR_PLATEDETECTION_H
-
-#include <PlateInfo.h>
-#include <opencv2/opencv.hpp>
-#include <vector>
-namespace pr {
-class PlateDetection {
-public:
-  PlateDetection(std::string filename_cascade);
-  PlateDetection();
-  void LoadModel(std::string filename_cascade);
-  void plateDetectionRough(cv::Mat InputImage,
-                           std::vector<pr::PlateInfo> &plateInfos,
-                           int min_w = 36, int max_w = 800);
-  //        std::vector<pr::PlateInfo> plateDetectionRough(cv::Mat
-  //        InputImage,int min_w= 60,int max_h = 400);
-
-  //        std::vector<pr::PlateInfo>
-  //        plateDetectionRoughByMultiScaleEdge(cv::Mat InputImage);
-
-private:
-  cv::CascadeClassifier cascade;
-};
-
-} // namespace pr
-
-#endif // HYPERPR_PLATEDETECTION_H
diff --git a/Prj-ROS/src/hyperlpr/include/PlateInfo.h b/Prj-ROS/src/hyperlpr/include/PlateInfo.h
deleted file mode 100644
index d3cd02b..0000000
--- a/Prj-ROS/src/hyperlpr/include/PlateInfo.h
+++ /dev/null
@@ -1,94 +0,0 @@
-//
-// Created by Jack Yu on 20/09/2017.
-//
-
-#ifndef HYPERPR_PLATEINFO_H
-#define HYPERPR_PLATEINFO_H
-#include <opencv2/opencv.hpp>
-namespace pr {
-
-typedef std::vector<cv::Mat> Character;
-
-enum PlateColor { BLUE, YELLOW, WHITE, GREEN, BLACK, UNKNOWN };
-enum CharType { CHINESE, LETTER, LETTER_NUMS, INVALID };
-
-class PlateInfo {
-public:
-  std::vector<std::pair<CharType, cv::Mat>> plateChars;
-  std::vector<std::pair<CharType, cv::Mat>> plateCoding;
-  float confidence = 0;
-  PlateInfo(const cv::Mat &plateData, std::string plateName, cv::Rect plateRect,
-            PlateColor plateType) {
-    licensePlate = plateData;
-    name = plateName;
-    ROI = plateRect;
-    Type = plateType;
-  }
-  PlateInfo(const cv::Mat &plateData, cv::Rect plateRect,
-            PlateColor plateType) {
-    licensePlate = plateData;
-    ROI = plateRect;
-    Type = plateType;
-  }
-  PlateInfo(const cv::Mat &plateData, cv::Rect plateRect) {
-    licensePlate = plateData;
-    ROI = plateRect;
-  }
-  PlateInfo() {}
-
-  cv::Mat getPlateImage() { return licensePlate; }
-
-  void setPlateImage(cv::Mat plateImage) { licensePlate = plateImage; }
-
-  cv::Rect getPlateRect() { return ROI; }
-
-  void setPlateRect(cv::Rect plateRect) { ROI = plateRect; }
-  cv::String getPlateName() { return name; }
-  void setPlateName(cv::String plateName) { name = plateName; }
-  int getPlateType() { return Type; }
-
-  void appendPlateChar(const std::pair<CharType, cv::Mat> &plateChar) {
-    plateChars.push_back(plateChar);
-  }
-
-  void appendPlateCoding(const std::pair<CharType, cv::Mat> &charProb) {
-    plateCoding.push_back(charProb);
-  }
-  std::string decodePlateNormal(std::vector<std::string> mappingTable) {
-    std::string decode;
-    for (auto plate : plateCoding) {
-      float *prob = (float *)plate.second.data;
-      if (plate.first == CHINESE) {
-        decode += mappingTable[std::max_element(prob, prob + 31) - prob];
-        confidence += *std::max_element(prob, prob + 31);
-      }
-
-      else if (plate.first == LETTER) {
-        decode += mappingTable[std::max_element(prob + 41, prob + 65) - prob];
-        confidence += *std::max_element(prob + 41, prob + 65);
-      }
-
-      else if (plate.first == LETTER_NUMS) {
-        decode += mappingTable[std::max_element(prob + 31, prob + 65) - prob];
-        confidence += *std::max_element(prob + 31, prob + 65);
-
-      } else if (plate.first == INVALID) {
-        decode += '*';
-      }
-    }
-    name = decode;
-
-    confidence /= 7;
-
-    return decode;
-  }
-
-private:
-  cv::Mat licensePlate;
-  cv::Rect ROI;
-  std::string name;
-  PlateColor Type;
-};
-} // namespace pr
-
-#endif // HYPERPR_PLATEINFO_H
diff --git a/Prj-ROS/src/hyperlpr/include/PlateSegmentation.h b/Prj-ROS/src/hyperlpr/include/PlateSegmentation.h
deleted file mode 100644
index 623d2f5..0000000
--- a/Prj-ROS/src/hyperlpr/include/PlateSegmentation.h
+++ /dev/null
@@ -1,35 +0,0 @@
-#ifndef HYPERPR_PLATESEGMENTATION_H
-#define HYPERPR_PLATESEGMENTATION_H
-
-#include "PlateInfo.h"
-#include "opencv2/opencv.hpp"
-#include <opencv2/dnn.hpp>
-
-namespace pr {
-
-class PlateSegmentation {
-public:
-  const int PLATE_NORMAL = 6;
-  const int PLATE_NORMAL_GREEN = 7;
-  const int DEFAULT_WIDTH = 20;
-  PlateSegmentation(std::string phototxt, std::string caffemodel);
-  PlateSegmentation() {}
-  void segmentPlatePipline(PlateInfo &plateInfo, int stride,
-                           std::vector<cv::Rect> &Char_rects);
-
-  void segmentPlateBySlidingWindows(cv::Mat &plateImage, int windowsWidth,
-                                    int stride, cv::Mat &respones);
-  void templateMatchFinding(const cv::Mat &respones, int windowsWidth,
-                            std::pair<float, std::vector<int>> &candidatePts);
-  void refineRegion(cv::Mat &plateImage, const std::vector<int> &candidatePts,
-                    const int padding, std::vector<cv::Rect> &rects);
-  void ExtractRegions(PlateInfo &plateInfo, std::vector<cv::Rect> &rects);
-  cv::Mat classifyResponse(const cv::Mat &cropped);
-
-private:
-  cv::dnn::Net net;
-};
-
-} // namespace pr
-
-#endif // HYPERPR_PLATESEGMENTATION_H
diff --git a/Prj-ROS/src/hyperlpr/include/Recognizer.h b/Prj-ROS/src/hyperlpr/include/Recognizer.h
deleted file mode 100644
index 3fc9765..0000000
--- a/Prj-ROS/src/hyperlpr/include/Recognizer.h
+++ /dev/null
@@ -1,22 +0,0 @@
-//
-// Created by Jack Yu on 20/10/2017.
-//
-
-#ifndef HYPERPR_RECOGNIZER_H
-#define HYPERPR_RECOGNIZER_H
-
-#include "PlateInfo.h"
-#include "opencv2/dnn.hpp"
-namespace pr {
-typedef cv::Mat label;
-class GeneralRecognizer {
-public:
-  virtual label recognizeCharacter(cv::Mat character) = 0;
-  //            virtual cv::Mat SegmentationFreeForSinglePlate(cv::Mat plate) =
-  //            0;
-  void SegmentBasedSequenceRecognition(PlateInfo &plateinfo);
-  void SegmentationFreeSequenceRecognition(PlateInfo &plateInfo);
-};
-
-} // namespace pr
-#endif // HYPERPR_RECOGNIZER_H
diff --git a/Prj-ROS/src/hyperlpr/include/SegmentationFreeRecognizer.h b/Prj-ROS/src/hyperlpr/include/SegmentationFreeRecognizer.h
deleted file mode 100644
index 458192b..0000000
--- a/Prj-ROS/src/hyperlpr/include/SegmentationFreeRecognizer.h
+++ /dev/null
@@ -1,27 +0,0 @@
-//
-// Created by Jack Yu on 28/11/2017.
-//
-
-#ifndef HYPERPR_SEGMENTATIONFREERECOGNIZER_H
-#define HYPERPR_SEGMENTATIONFREERECOGNIZER_H
-
-#include "Recognizer.h"
-namespace pr {
-
-class SegmentationFreeRecognizer {
-public:
-  const int CHAR_INPUT_W = 14;
-  const int CHAR_INPUT_H = 30;
-  const int CHAR_LEN = 84;
-
-  SegmentationFreeRecognizer(std::string prototxt, std::string caffemodel);
-  std::pair<std::string, float>
-  SegmentationFreeForSinglePlate(cv::Mat plate,
-                                 std::vector<std::string> mapping_table);
-
-private:
-  cv::dnn::Net net;
-};
-
-} // namespace pr
-#endif // HYPERPR_SEGMENTATIONFREERECOGNIZER_H
diff --git a/Prj-ROS/src/hyperlpr/include/hyperlpr_node.h b/Prj-ROS/src/hyperlpr/include/hyperlpr_node.h
deleted file mode 100644
index 533824d..0000000
--- a/Prj-ROS/src/hyperlpr/include/hyperlpr_node.h
+++ /dev/null
@@ -1,49 +0,0 @@
-#ifndef HYPERLPR_NODE_H
-#define HYPERLPR_NODE_H
-
-#include "Pipeline.h"
-#include "ros/ros.h"
-#include "sensor_msgs/CompressedImage.h"
-#include "sensor_msgs/image_encodings.h"
-#include "std_msgs/String.h"
-#include "std_msgs/Float32.h"
-#include <cv_bridge/cv_bridge.h>
-#include <opencv2/opencv.hpp>
-#include <iostream>
-
-class SubPuber
-{
-private:
-
-  ros::NodeHandle nodeHandle;
-  ros::Subscriber cameraImageSub;
-  ros::Publisher recognitionResultPub;
-  ros::Publisher recognitionConfidencePub;
-  ros::Publisher recognizedImagePub;
-
-  pr::PipelinePR prc;
-
-public:
-
-  // 模型路径，从launch文件中获得，用于寻找模型
-  std::string modelPath;
-  void SendPath();
-
-  SubPuber(){
-    // 获取相机话题
-    std::string cameraTopic;
-    ros::param::get("CameraTopic", cameraTopic);
-    // 订阅相机话题，此处默认为realsense彩色图像的topic
-    cameraImageSub = nodeHandle.subscribe(cameraTopic, 1, &SubPuber::PlateRecognitionCallback, this);
-    // 发布识别结果
-    recognitionResultPub = nodeHandle.advertise<std_msgs::String>("/recognition_result", 1);
-    // 发布识别置信度
-    recognitionConfidencePub = nodeHandle.advertise<std_msgs::Float32>("/recognition_confidence", 1);
-    // 发布框选出车牌后的图像
-    recognizedImagePub = nodeHandle.advertise<sensor_msgs::Image>("/recognized_image", 1);
-  }
-
-  void PlateRecognitionCallback(const sensor_msgs::ImageConstPtr &cameraImage);
-};
-
-#endif
\ No newline at end of file
diff --git a/Prj-ROS/src/hyperlpr/include/niBlackThreshold.h b/Prj-ROS/src/hyperlpr/include/niBlackThreshold.h
deleted file mode 100644
index 52ec582..0000000
--- a/Prj-ROS/src/hyperlpr/include/niBlackThreshold.h
+++ /dev/null
@@ -1,107 +0,0 @@
-//
-// Created by Jack Yu on 26/10/2017.
-//
-
-#ifndef HYPERPR_NIBLACKTHRESHOLD_H
-#define HYPERPR_NIBLACKTHRESHOLD_H
-
-#include <opencv2/opencv.hpp>
-#include <opencv2/imgproc/types_c.h>
-
-using namespace cv;
-
-enum LocalBinarizationMethods {
-  BINARIZATION_NIBLACK =
-      0, //!< Classic Niblack binarization. See @cite Niblack1985 .
-  BINARIZATION_SAUVOLA = 1, //!< Sauvola's technique. See @cite Sauvola1997 .
-  BINARIZATION_WOLF = 2,    //!< Wolf's technique. See @cite Wolf2004 .
-  BINARIZATION_NICK = 3     //!< NICK technique. See @cite Khurshid2009 .
-};
-
-void niBlackThreshold(InputArray _src, OutputArray _dst, double maxValue,
-                      int type, int blockSize, double k,
-                      int binarizationMethod) {
-  // Input grayscale image
-  Mat src = _src.getMat();
-  CV_Assert(src.channels() == 1);
-  CV_Assert(blockSize % 2 == 1 && blockSize > 1);
-  if (binarizationMethod == BINARIZATION_SAUVOLA) {
-    CV_Assert(src.depth() == CV_8U);
-  }
-  type &= THRESH_MASK;
-  // Compute local threshold (T = mean + k * stddev)
-  // using mean and standard deviation in the neighborhood of each pixel
-  // (intermediate calculations are done with floating-point precision)
-  Mat test;
-  Mat thresh;
-  {
-    // note that: Var[X] = E[X^2] - E[X]^2
-    Mat mean, sqmean, variance, stddev, sqrtVarianceMeanSum;
-    double srcMin, stddevMax;
-    boxFilter(src, mean, CV_32F, Size(blockSize, blockSize), Point(-1, -1),
-              true, BORDER_REPLICATE);
-    sqrBoxFilter(src, sqmean, CV_32F, Size(blockSize, blockSize), Point(-1, -1),
-                 true, BORDER_REPLICATE);
-    variance = sqmean - mean.mul(mean);
-    sqrt(variance, stddev);
-    switch (binarizationMethod) {
-    case BINARIZATION_NIBLACK:
-      thresh = mean + stddev * static_cast<float>(k);
-
-      break;
-    case BINARIZATION_SAUVOLA:
-      thresh = mean.mul(1. + static_cast<float>(k) * (stddev / 128.0 - 1.));
-      break;
-    case BINARIZATION_WOLF:
-      minMaxIdx(src, &srcMin, NULL);
-      minMaxIdx(stddev, NULL, &stddevMax);
-      thresh =
-          mean - static_cast<float>(k) *
-                     (mean - srcMin - stddev.mul(mean - srcMin) / stddevMax);
-      break;
-    case BINARIZATION_NICK:
-      sqrt(variance + sqmean, sqrtVarianceMeanSum);
-      thresh = mean + static_cast<float>(k) * sqrtVarianceMeanSum;
-      break;
-    default:
-      CV_Error(CV_StsBadArg, "Unknown binarization method");
-      break;
-    }
-    thresh.convertTo(thresh, src.depth());
-
-    thresh.convertTo(test, src.depth());
-    //
-    //        cv::imshow("imagex",test);
-    //        cv::waitKey(0);
-  }
-  // Prepare output image
-  _dst.create(src.size(), src.type());
-  Mat dst = _dst.getMat();
-  CV_Assert(src.data != dst.data); // no inplace processing
-  // Apply thresholding: ( pixel > threshold ) ? foreground : background
-  Mat mask;
-  switch (type) {
-  case THRESH_BINARY:     // dst = (src > thresh) ? maxval : 0
-  case THRESH_BINARY_INV: // dst = (src > thresh) ? 0 : maxval
-    compare(src, thresh, mask, (type == THRESH_BINARY ? CMP_GT : CMP_LE));
-    dst.setTo(0);
-    dst.setTo(maxValue, mask);
-    break;
-  case THRESH_TRUNC: // dst = (src > thresh) ? thresh : src
-    compare(src, thresh, mask, CMP_GT);
-    src.copyTo(dst);
-    thresh.copyTo(dst, mask);
-    break;
-  case THRESH_TOZERO:     // dst = (src > thresh) ? src : 0
-  case THRESH_TOZERO_INV: // dst = (src > thresh) ? 0 : src
-    compare(src, thresh, mask, (type == THRESH_TOZERO ? CMP_GT : CMP_LE));
-    dst.setTo(0);
-    src.copyTo(dst, mask);
-    break;
-  default:
-    CV_Error(CV_StsBadArg, "Unknown threshold type");
-    break;
-  }
-}
-
-#endif // HYPERPR_NIBLACKTHRESHOLD_H
diff --git a/Prj-ROS/src/hyperlpr/launch/bringup.launch b/Prj-ROS/src/hyperlpr/launch/bringup.launch
deleted file mode 100644
index 1ca4898..0000000
--- a/Prj-ROS/src/hyperlpr/launch/bringup.launch
+++ /dev/null
@@ -1,7 +0,0 @@
-<launch>
-	<param name="ModelPath" value="$(find hyperlpr)/model" />
-	<param name="CameraTopic" value="/camera/color/image_raw" />
-	<node pkg="hyperlpr" type="hyperlpr_node" name="hyperlpr_node" output="screen"/>
-
-  <node name="rviz" pkg="rviz" type="rviz" args="-d $(find hyperlpr)/launch/hyperlpr.rviz" />
-</launch>
\ No newline at end of file
diff --git a/Prj-ROS/src/hyperlpr/launch/hyperlpr.rviz b/Prj-ROS/src/hyperlpr/launch/hyperlpr.rviz
deleted file mode 100644
index c93300c..0000000
--- a/Prj-ROS/src/hyperlpr/launch/hyperlpr.rviz
+++ /dev/null
@@ -1,133 +0,0 @@
-Panels:
-  - Class: rviz/Displays
-    Help Height: 138
-    Name: Displays
-    Property Tree Widget:
-      Expanded:
-        - /Global Options1
-        - /Status1
-      Splitter Ratio: 0.5
-    Tree Height: 1090
-  - Class: rviz/Selection
-    Name: Selection
-  - Class: rviz/Tool Properties
-    Expanded:
-      - /2D Pose Estimate1
-      - /2D Nav Goal1
-      - /Publish Point1
-    Name: Tool Properties
-    Splitter Ratio: 0.5886790156364441
-  - Class: rviz/Views
-    Expanded:
-      - /Current View1
-    Name: Views
-    Splitter Ratio: 0.5
-  - Class: rviz/Time
-    Name: Time
-    SyncMode: 0
-    SyncSource: Image
-Preferences:
-  PromptSaveOnExit: true
-Toolbars:
-  toolButtonStyle: 2
-Visualization Manager:
-  Class: ""
-  Displays:
-    - Alpha: 0.5
-      Cell Size: 1
-      Class: rviz/Grid
-      Color: 160; 160; 164
-      Enabled: true
-      Line Style:
-        Line Width: 0.029999999329447746
-        Value: Lines
-      Name: Grid
-      Normal Cell Count: 0
-      Offset:
-        X: 0
-        Y: 0
-        Z: 0
-      Plane: XY
-      Plane Cell Count: 10
-      Reference Frame: <Fixed Frame>
-      Value: true
-    - Class: rviz/Image
-      Enabled: true
-      Image Topic: /recognized_image
-      Max Value: 1
-      Median window: 5
-      Min Value: 0
-      Name: Image
-      Normalize Range: true
-      Queue Size: 2
-      Transport Hint: raw
-      Unreliable: false
-      Value: true
-  Enabled: true
-  Global Options:
-    Background Color: 48; 48; 48
-    Default Light: true
-    Fixed Frame: map
-    Frame Rate: 30
-  Name: root
-  Tools:
-    - Class: rviz/Interact
-      Hide Inactive Objects: true
-    - Class: rviz/MoveCamera
-    - Class: rviz/Select
-    - Class: rviz/FocusCamera
-    - Class: rviz/Measure
-    - Class: rviz/SetInitialPose
-      Theta std deviation: 0.2617993950843811
-      Topic: /initialpose
-      X std deviation: 0.5
-      Y std deviation: 0.5
-    - Class: rviz/SetGoal
-      Topic: /move_base_simple/goal
-    - Class: rviz/PublishPoint
-      Single click: true
-      Topic: /clicked_point
-  Value: true
-  Views:
-    Current:
-      Class: rviz/Orbit
-      Distance: 10
-      Enable Stereo Rendering:
-        Stereo Eye Separation: 0.05999999865889549
-        Stereo Focal Distance: 1
-        Swap Stereo Eyes: false
-        Value: false
-      Field of View: 0.7853981852531433
-      Focal Point:
-        X: 0
-        Y: 0
-        Z: 0
-      Focal Shape Fixed Size: true
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diff --git a/Prj-ROS/src/hyperlpr/model/CharacterRecognization.caffemodel b/Prj-ROS/src/hyperlpr/model/CharacterRecognization.caffemodel
deleted file mode 100644
index d424408..0000000
Binary files a/Prj-ROS/src/hyperlpr/model/CharacterRecognization.caffemodel and /dev/null differ
diff --git a/Prj-ROS/src/hyperlpr/model/CharacterRecognization.prototxt b/Prj-ROS/src/hyperlpr/model/CharacterRecognization.prototxt
deleted file mode 100644
index 91aa29e..0000000
--- a/Prj-ROS/src/hyperlpr/model/CharacterRecognization.prototxt
+++ /dev/null
@@ -1,123 +0,0 @@
-input: "data"
-input_dim: 1
-input_dim: 1
-input_dim: 30
-input_dim: 14
-layer {
-  name: "conv2d_1"
-  type: "Convolution"
-  bottom: "data"
-  top: "conv2d_1"
-  convolution_param {
-    num_output: 32
-    bias_term: true
-    pad: 0
-    kernel_size: 3
-    stride: 1
-  }
-}
-layer {
-  name: "activation_1"
-  type: "ReLU"
-  bottom: "conv2d_1"
-  top: "activation_1"
-}
-layer {
-  name: "max_pooling2d_1"
-  type: "Pooling"
-  bottom: "activation_1"
-  top: "max_pooling2d_1"
-  pooling_param {
-    pool: MAX
-    kernel_size: 2
-    stride: 2
-    pad: 0
-  }
-}
-layer {
-  name: "conv2d_2"
-  type: "Convolution"
-  bottom: "max_pooling2d_1"
-  top: "conv2d_2"
-  convolution_param {
-    num_output: 64
-    bias_term: true
-    pad: 0
-    kernel_size: 3
-    stride: 1
-  }
-}
-layer {
-  name: "activation_2"
-  type: "ReLU"
-  bottom: "conv2d_2"
-  top: "activation_2"
-}
-layer {
-  name: "max_pooling2d_2"
-  type: "Pooling"
-  bottom: "activation_2"
-  top: "max_pooling2d_2"
-  pooling_param {
-    pool: MAX
-    kernel_size: 2
-    stride: 2
-    pad: 0
-  }
-}
-layer {
-  name: "conv2d_3"
-  type: "Convolution"
-  bottom: "max_pooling2d_2"
-  top: "conv2d_3"
-  convolution_param {
-    num_output: 128
-    bias_term: true
-    pad: 0
-    kernel_size: 2
-    stride: 1
-  }
-}
-layer {
-  name: "activation_3"
-  type: "ReLU"
-  bottom: "conv2d_3"
-  top: "activation_3"
-}
-layer {
-  name: "flatten_1"
-  type: "Flatten"
-  bottom: "activation_3"
-  top: "flatten_1"
-}
-layer {
-  name: "dense_1"
-  type: "InnerProduct"
-  bottom: "flatten_1"
-  top: "dense_1"
-  inner_product_param {
-    num_output: 256
-  }
-}
-layer {
-  name: "relu2"
-  type: "ReLU"
-  bottom: "dense_1"
-  top: "relu2"
-}
-layer {
-  name: "dense2"
-  type: "InnerProduct"
-  bottom: "relu2"
-  top: "dense2"
-  inner_product_param {
-    num_output: 65
-  }
-}
-
-layer {
-  name: "prob"
-  type: "Softmax"
-  bottom: "dense2"
-  top: "prob"
-}
\ No newline at end of file
diff --git a/Prj-ROS/src/hyperlpr/model/HorizonalFinemapping.caffemodel b/Prj-ROS/src/hyperlpr/model/HorizonalFinemapping.caffemodel
deleted file mode 100644
index 1af5863..0000000
Binary files a/Prj-ROS/src/hyperlpr/model/HorizonalFinemapping.caffemodel and /dev/null differ
diff --git a/Prj-ROS/src/hyperlpr/model/HorizonalFinemapping.prototxt b/Prj-ROS/src/hyperlpr/model/HorizonalFinemapping.prototxt
deleted file mode 100644
index 21726dd..0000000
--- a/Prj-ROS/src/hyperlpr/model/HorizonalFinemapping.prototxt
+++ /dev/null
@@ -1,95 +0,0 @@
-input: "data"
-input_dim: 1
-input_dim: 3
-input_dim: 16
-input_dim: 66
-layer {
-  name: "conv1"
-  type: "Convolution"
-  bottom: "data"
-  top: "conv1"
-  convolution_param {
-    num_output: 10
-    bias_term: true
-    pad: 0
-    kernel_size: 3
-    stride: 1
-  }
-}
-layer {
-  name: "relu1"
-  type: "ReLU"
-  bottom: "conv1"
-  top: "conv1"
-}
-layer {
-  name: "max_pooling2d_3"
-  type: "Pooling"
-  bottom: "conv1"
-  top: "max_pooling2d_3"
-  pooling_param {
-    pool: MAX
-    kernel_size: 2
-    stride: 2
-    pad: 0
-  }
-}
-layer {
-  name: "conv2"
-  type: "Convolution"
-  bottom: "max_pooling2d_3"
-  top: "conv2"
-  convolution_param {
-    num_output: 16
-    bias_term: true
-    pad: 0
-    kernel_size: 3
-    stride: 1
-  }
-}
-layer {
-  name: "relu2"
-  type: "ReLU"
-  bottom: "conv2"
-  top: "conv2"
-}
-layer {
-  name: "conv3"
-  type: "Convolution"
-  bottom: "conv2"
-  top: "conv3"
-  convolution_param {
-    num_output: 32
-    bias_term: true
-    pad: 0
-    kernel_size: 3
-    stride: 1
-  }
-}
-layer {
-  name: "relu3"
-  type: "ReLU"
-  bottom: "conv3"
-  top: "conv3"
-}
-layer {
-  name: "flatten_2"
-  type: "Flatten"
-  bottom: "conv3"
-  top: "flatten_2"
-}
-layer {
-  name: "dense"
-  type: "InnerProduct"
-  bottom: "flatten_2"
-  top: "dense"
-  inner_product_param {
-    num_output: 2
-  }
-}
-layer {
-  name: "relu4"
-  type: "ReLU"
-  bottom: "dense"
-  top: "dense"
-}
diff --git a/Prj-ROS/src/hyperlpr/model/SegmenationFree-Inception.caffemodel b/Prj-ROS/src/hyperlpr/model/SegmenationFree-Inception.caffemodel
deleted file mode 100644
index 311ee17..0000000
Binary files a/Prj-ROS/src/hyperlpr/model/SegmenationFree-Inception.caffemodel and /dev/null differ
diff --git a/Prj-ROS/src/hyperlpr/model/SegmenationFree-Inception.prototxt b/Prj-ROS/src/hyperlpr/model/SegmenationFree-Inception.prototxt
deleted file mode 100644
index 8419763..0000000
--- a/Prj-ROS/src/hyperlpr/model/SegmenationFree-Inception.prototxt
+++ /dev/null
@@ -1,454 +0,0 @@
-input: "data"
-input_dim: 1
-input_dim: 3
-input_dim: 160
-input_dim: 40
-layer {
-  name: "conv0"
-  type: "Convolution"
-  bottom: "data"
-  top: "conv0"
-  convolution_param {
-    num_output: 32
-    bias_term: true
-    pad_h: 1
-    pad_w: 1
-    kernel_h: 3
-    kernel_w: 3
-    stride_h: 1
-    stride_w: 1
-  }
-}
-layer {
-  name: "bn0"
-  type: "BatchNorm"
-  bottom: "conv0"
-  top: "bn0"
-  batch_norm_param {
-    moving_average_fraction: 0.99
-    eps: 0.001
-  }
-}
-layer {
-  name: "bn0_scale"
-  type: "Scale"
-  bottom: "bn0"
-  top: "bn0"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "relu0"
-  type: "ReLU"
-  bottom: "bn0"
-  top: "bn0"
-}
-layer {
-  name: "pool0"
-  type: "Pooling"
-  bottom: "bn0"
-  top: "pool0"
-  pooling_param {
-    pool: MAX
-    kernel_h: 2
-    kernel_w: 2
-    stride_h: 2
-    stride_w: 2
-    pad_h: 0
-    pad_w: 0
-  }
-}
-layer {
-  name: "conv1"
-  type: "Convolution"
-  bottom: "pool0"
-  top: "conv1"
-  convolution_param {
-    num_output: 64
-    bias_term: true
-    pad_h: 1
-    pad_w: 1
-    kernel_h: 3
-    kernel_w: 3
-    stride_h: 1
-    stride_w: 1
-  }
-}
-layer {
-  name: "bn1"
-  type: "BatchNorm"
-  bottom: "conv1"
-  top: "bn1"
-  batch_norm_param {
-    moving_average_fraction: 0.99
-    eps: 0.001
-  }
-}
-layer {
-  name: "bn1_scale"
-  type: "Scale"
-  bottom: "bn1"
-  top: "bn1"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "relu1"
-  type: "ReLU"
-  bottom: "bn1"
-  top: "bn1"
-}
-layer {
-  name: "pool1"
-  type: "Pooling"
-  bottom: "bn1"
-  top: "pool1"
-  pooling_param {
-    pool: MAX
-    kernel_h: 2
-    kernel_w: 2
-    stride_h: 2
-    stride_w: 2
-    pad_h: 0
-    pad_w: 0
-  }
-}
-layer {
-  name: "conv2"
-  type: "Convolution"
-  bottom: "pool1"
-  top: "conv2"
-  convolution_param {
-    num_output: 128
-    bias_term: true
-    pad_h: 1
-    pad_w: 1
-    kernel_h: 3
-    kernel_w: 3
-    stride_h: 1
-    stride_w: 1
-  }
-}
-layer {
-  name: "bn2"
-  type: "BatchNorm"
-  bottom: "conv2"
-  top: "bn2"
-  batch_norm_param {
-    moving_average_fraction: 0.99
-    eps: 0.001
-  }
-}
-layer {
-  name: "bn2_scale"
-  type: "Scale"
-  bottom: "bn2"
-  top: "bn2"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "relu2"
-  type: "ReLU"
-  bottom: "bn2"
-  top: "bn2"
-}
-layer {
-  name: "pool2"
-  type: "Pooling"
-  bottom: "bn2"
-  top: "pool2"
-  pooling_param {
-    pool: MAX
-    kernel_h: 2
-    kernel_w: 2
-    stride_h: 2
-    stride_w: 2
-    pad_h: 0
-    pad_w: 0
-  }
-}
-layer {
-  name: "conv2d_1"
-  type: "Convolution"
-  bottom: "pool2"
-  top: "conv2d_1"
-  convolution_param {
-    num_output: 256
-    bias_term: true
-    pad_h: 0
-    pad_w: 0
-    kernel_h: 1
-    kernel_w: 5
-    stride_h: 1
-    stride_w: 1
-  }
-}
-layer {
-  name: "batch_normalization_1"
-  type: "BatchNorm"
-  bottom: "conv2d_1"
-  top: "batch_normalization_1"
-  batch_norm_param {
-    moving_average_fraction: 0.99
-    eps: 0.001
-  }
-}
-layer {
-  name: "batch_normalization_1_scale"
-  type: "Scale"
-  bottom: "batch_normalization_1"
-  top: "batch_normalization_1"
-  scale_param {
-    bias_term: true
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-}
-layer {
-  name: "activation_1"
-  type: "ReLU"
-  bottom: "batch_normalization_1"
-  top: "batch_normalization_1"
-}
-layer {
-  name: "conv2d_2"
-  type: "Convolution"
-  bottom: "batch_normalization_1"
-  top: "conv2d_2"
-  convolution_param {
-    num_output: 256
-    bias_term: true
-    pad_h: 3
-    pad_w: 0
-    kernel_h: 7
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-layer {
-  name: "conv2d_3"
-  type: "Convolution"
-  bottom: "batch_normalization_1"
-  top: "conv2d_3"
-  convolution_param {
-    num_output: 256
-    bias_term: true
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-    kernel_h: 5
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-  name: "conv2d_4"
-  type: "Convolution"
-  bottom: "batch_normalization_1"
-  top: "conv2d_4"
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-    stride_h: 1
-    stride_w: 1
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-layer {
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-  type: "BatchNorm"
-  bottom: "conv2d_2"
-  top: "batch_normalization_2"
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-    moving_average_fraction: 0.99
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-layer {
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-  type: "Scale"
-  bottom: "batch_normalization_2"
-  top: "batch_normalization_2"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "batch_normalization_3"
-  type: "BatchNorm"
-  bottom: "conv2d_3"
-  top: "batch_normalization_3"
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-    moving_average_fraction: 0.99
-    eps: 0.001
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-  top: "batch_normalization_3"
-  scale_param {
-    bias_term: true
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-layer {
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-  type: "BatchNorm"
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-}
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-    moving_average_fraction: 0.99
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-  top: "batch_normalization_5"
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-    bias_term: true
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-  type: "ReLU"
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-  top: "batch_normalization_2"
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-  top: "batch_normalization_4"
-}
-layer {
-  name: "activation_5"
-  type: "ReLU"
-  bottom: "batch_normalization_5"
-  top: "batch_normalization_5"
-}
-layer {
-  name: "concatenate_1"
-  type: "Concat"
-  bottom: "batch_normalization_2"
-  bottom: "batch_normalization_3"
-  bottom: "batch_normalization_4"
-  bottom: "batch_normalization_5"
-  top: "concatenate_1"
-  concat_param {
-    axis: 1
-  }
-}
-layer {
-  name: "conv_1024_11"
-  type: "Convolution"
-  bottom: "concatenate_1"
-  top: "conv_1024_11"
-  convolution_param {
-    num_output: 1024
-    bias_term: true
-    pad_h: 0
-    pad_w: 0
-    kernel_h: 1
-    kernel_w: 1
-    stride_h: 1
-    stride_w: 1
-  }
-}
-layer {
-  name: "batch_normalization_6"
-  type: "BatchNorm"
-  bottom: "conv_1024_11"
-  top: "batch_normalization_6"
-  batch_norm_param {
-    moving_average_fraction: 0.99
-    eps: 0.001
-  }
-}
-layer {
-  name: "batch_normalization_6_scale"
-  type: "Scale"
-  bottom: "batch_normalization_6"
-  top: "batch_normalization_6"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "activation_6"
-  type: "ReLU"
-  bottom: "batch_normalization_6"
-  top: "batch_normalization_6"
-}
-layer {
-  name: "conv_class_11"
-  type: "Convolution"
-  bottom: "batch_normalization_6"
-  top: "conv_class_11"
-  convolution_param {
-    num_output: 84
-    bias_term: true
-    pad_h: 0
-    pad_w: 0
-    kernel_h: 1
-    kernel_w: 1
-    stride_h: 1
-    stride_w: 1
-  }
-}
-layer {
-  name: "prob"
-  type: "Softmax"
-  bottom: "conv_class_11"
-  top: "prob"
-}
-
diff --git a/Prj-ROS/src/hyperlpr/model/Segmentation.caffemodel b/Prj-ROS/src/hyperlpr/model/Segmentation.caffemodel
deleted file mode 100644
index 208269d..0000000
Binary files a/Prj-ROS/src/hyperlpr/model/Segmentation.caffemodel and /dev/null differ
diff --git a/Prj-ROS/src/hyperlpr/model/Segmentation.prototxt b/Prj-ROS/src/hyperlpr/model/Segmentation.prototxt
deleted file mode 100644
index 45c6829..0000000
--- a/Prj-ROS/src/hyperlpr/model/Segmentation.prototxt
+++ /dev/null
@@ -1,114 +0,0 @@
-input: "data"
-input_dim: 1
-input_dim: 1
-input_dim: 22
-input_dim: 22
-layer {
-  name: "conv2d_12"
-  type: "Convolution"
-  bottom: "data"
-  top: "conv2d_12"
-  convolution_param {
-    num_output: 16
-    bias_term: true
-    pad: 0
-    kernel_size: 3
-    stride: 1
-  }
-}
-layer {
-  name: "activation_18"
-  type: "ReLU"
-  bottom: "conv2d_12"
-  top: "activation_18"
-}
-layer {
-  name: "max_pooling2d_10"
-  type: "Pooling"
-  bottom: "activation_18"
-  top: "max_pooling2d_10"
-  pooling_param {
-    pool: MAX
-    kernel_size: 2
-    stride: 2
-    pad: 0
-  }
-}
-layer {
-  name: "conv2d_13"
-  type: "Convolution"
-  bottom: "max_pooling2d_10"
-  top: "conv2d_13"
-  convolution_param {
-    num_output: 16
-    bias_term: true
-    pad: 0
-    kernel_size: 3
-    stride: 1
-  }
-}
-layer {
-  name: "activation_19"
-  type: "ReLU"
-  bottom: "conv2d_13"
-  top: "activation_19"
-}
-layer {
-  name: "max_pooling2d_11"
-  type: "Pooling"
-  bottom: "activation_19"
-  top: "max_pooling2d_11"
-  pooling_param {
-    pool: MAX
-    kernel_size: 2
-    stride: 2
-    pad: 0
-  }
-}
-layer {
-  name: "flatten_6"
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-layer {
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-  layer {
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-  top: "prob"
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\ No newline at end of file
diff --git a/Prj-ROS/src/hyperlpr/model/cascade.xml b/Prj-ROS/src/hyperlpr/model/cascade.xml
deleted file mode 100644
index 277775b..0000000
--- a/Prj-ROS/src/hyperlpr/model/cascade.xml
+++ /dev/null
@@ -1,12117 +0,0 @@
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diff --git a/Prj-ROS/src/hyperlpr/package.xml b/Prj-ROS/src/hyperlpr/package.xml
deleted file mode 100644
index 97393cc..0000000
--- a/Prj-ROS/src/hyperlpr/package.xml
+++ /dev/null
@@ -1,58 +0,0 @@
-<?xml version="1.0"?>
-<package format="2">
-  <name>hyperlpr</name>
-  <version>0.0.0</version>
-  <description>The hyperlpr package</description>
-
-  <!-- One maintainer tag required, multiple allowed, one person per tag -->
-  <!-- Example:  -->
-  <!-- <maintainer email="jane.doe@example.com">Jane Doe</maintainer> -->
-  <maintainer email="bssn@todo.todo">bssn</maintainer>
-
-
-  <!-- One license tag required, multiple allowed, one license per tag -->
-  <!-- Commonly used license strings: -->
-  <!--   BSD, MIT, Boost Software License, GPLv2, GPLv3, LGPLv2.1, LGPLv3 -->
-  <license>TODO</license>
-
-
-  <!-- Url tags are optional, but multiple are allowed, one per tag -->
-  <!-- Optional attribute type can be: website, bugtracker, or repository -->
-  <!-- Example: -->
-  <!-- <url type="website">http://wiki.ros.org/hyperlpr</url> -->
-
-
-  <!-- Author tags are optional, multiple are allowed, one per tag -->
-  <!-- Authors do not have to be maintainers, but could be -->
-  <!-- Example: -->
-  <!-- <author email="jane.doe@example.com">Jane Doe</author> -->
-
-
-  <!-- The *depend tags are used to specify dependencies -->
-  <!-- Dependencies can be catkin packages or system dependencies -->
-  <!-- Examples: -->
-  <!-- Use depend as a shortcut for packages that are both build and exec dependencies -->
-  <!--   <depend>roscpp</depend> -->
-  <!--   Note that this is equivalent to the following: -->
-  <!--   <build_depend>roscpp</build_depend> -->
-  <!--   <exec_depend>roscpp</exec_depend> -->
-  <!-- Use build_depend for packages you need at compile time: -->
-  <!--   <build_depend>message_generation</build_depend> -->
-  <!-- Use build_export_depend for packages you need in order to build against this package: -->
-  <!--   <build_export_depend>message_generation</build_export_depend> -->
-  <!-- Use buildtool_depend for build tool packages: -->
-  <!--   <buildtool_depend>catkin</buildtool_depend> -->
-  <!-- Use exec_depend for packages you need at runtime: -->
-  <!--   <exec_depend>message_runtime</exec_depend> -->
-  <!-- Use test_depend for packages you need only for testing: -->
-  <!--   <test_depend>gtest</test_depend> -->
-  <!-- Use doc_depend for packages you need only for building documentation: -->
-  <!--   <doc_depend>doxygen</doc_depend> -->
-  <buildtool_depend>catkin</buildtool_depend>
-
-  <!-- The export tag contains other, unspecified, tags -->
-  <export>
-    <!-- Other tools can request additional information be placed here -->
-
-  </export>
-</package>
diff --git a/Prj-ROS/src/hyperlpr/src/CNNRecognizer.cpp b/Prj-ROS/src/hyperlpr/src/CNNRecognizer.cpp
deleted file mode 100644
index e3aa713..0000000
--- a/Prj-ROS/src/hyperlpr/src/CNNRecognizer.cpp
+++ /dev/null
@@ -1,21 +0,0 @@
-//
-// Created by Jack Yu on 21/10/2017.
-//
-
-#include "../include/CNNRecognizer.h"
-
-namespace pr {
-CNNRecognizer::CNNRecognizer(std::string prototxt, std::string caffemodel) {
-  net = cv::dnn::readNetFromCaffe(prototxt, caffemodel);
-}
-
-label CNNRecognizer::recognizeCharacter(cv::Mat charImage) {
-  if (charImage.channels() == 3)
-    cv::cvtColor(charImage, charImage, cv::COLOR_BGR2GRAY);
-  cv::Mat inputBlob = cv::dnn::blobFromImage(
-      charImage, 1 / 255.0, cv::Size(CHAR_INPUT_W, CHAR_INPUT_H),
-      cv::Scalar(0, 0, 0), false);
-  net.setInput(inputBlob, "data");
-  return net.forward();
-}
-} // namespace pr
diff --git a/Prj-ROS/src/hyperlpr/src/FastDeskew.cpp b/Prj-ROS/src/hyperlpr/src/FastDeskew.cpp
deleted file mode 100644
index 7fd14b6..0000000
--- a/Prj-ROS/src/hyperlpr/src/FastDeskew.cpp
+++ /dev/null
@@ -1,104 +0,0 @@
-//
-// Created by Jack Yu on 02/10/2017.
-//
-
-#include <../include/FastDeskew.h>
-
-namespace pr {
-const int ANGLE_MIN = 30;
-const int ANGLE_MAX = 150;
-const int PLATE_H = 36;
-const int PLATE_W = 136;
-
-int angle(float x, float y) { return atan2(x, y) * 180 / 3.1415; }
-
-std::vector<float> avgfilter(std::vector<float> angle_list, int windowsSize) {
-  std::vector<float> angle_list_filtered(angle_list.size() - windowsSize + 1);
-  for (int i = 0; i < angle_list.size() - windowsSize + 1; i++) {
-    float avg = 0.00f;
-    for (int j = 0; j < windowsSize; j++) {
-      avg += angle_list[i + j];
-    }
-    avg = avg / windowsSize;
-    angle_list_filtered[i] = avg;
-  }
-
-  return angle_list_filtered;
-}
-
-void drawHist(std::vector<float> seq) {
-  cv::Mat image(300, seq.size(), CV_8U);
-  image.setTo(0);
-
-  for (int i = 0; i < seq.size(); i++) {
-    float l = *std::max_element(seq.begin(), seq.end());
-
-    int p = int(float(seq[i]) / l * 300);
-
-    cv::line(image, cv::Point(i, 300), cv::Point(i, 300 - p),
-             cv::Scalar(255, 255, 255));
-  }
-  cv::imshow("vis", image);
-}
-
-cv::Mat correctPlateImage(cv::Mat skewPlate, float angle, float maxAngle) {
-  cv::Mat dst;
-  cv::Size size_o(skewPlate.cols, skewPlate.rows);
-  int extend_padding = 0;
-  extend_padding =
-      static_cast<int>(skewPlate.rows * tan(cv::abs(angle) / 180 * 3.14));
-  cv::Size size(skewPlate.cols + extend_padding, skewPlate.rows);
-  float interval = abs(sin((angle / 180) * 3.14) * skewPlate.rows);
-  cv::Point2f pts1[4] = {cv::Point2f(0, 0), cv::Point2f(0, size_o.height),
-                         cv::Point2f(size_o.width, 0),
-                         cv::Point2f(size_o.width, size_o.height)};
-  if (angle > 0) {
-    cv::Point2f pts2[4] = {cv::Point2f(interval, 0),
-                           cv::Point2f(0, size_o.height),
-                           cv::Point2f(size_o.width, 0),
-                           cv::Point2f(size_o.width - interval, size_o.height)};
-    cv::Mat M = cv::getPerspectiveTransform(pts1, pts2);
-    cv::warpPerspective(skewPlate, dst, M, size);
-  } else {
-    cv::Point2f pts2[4] = {cv::Point2f(0, 0),
-                           cv::Point2f(interval, size_o.height),
-                           cv::Point2f(size_o.width - interval, 0),
-                           cv::Point2f(size_o.width, size_o.height)};
-    cv::Mat M = cv::getPerspectiveTransform(pts1, pts2);
-    cv::warpPerspective(skewPlate, dst, M, size, cv::INTER_CUBIC);
-  }
-  return dst;
-}
-cv::Mat fastdeskew(cv::Mat skewImage, int blockSize) {
-  const int FILTER_WINDOWS_SIZE = 5;
-  std::vector<float> angle_list(180);
-  memset(angle_list.data(), 0, angle_list.size() * sizeof(int));
-  cv::Mat bak;
-  skewImage.copyTo(bak);
-  if (skewImage.channels() == 3)
-    cv::cvtColor(skewImage, skewImage, cv::COLOR_RGB2GRAY);
-  if (skewImage.channels() == 1) {
-    cv::Mat eigen;
-    cv::cornerEigenValsAndVecs(skewImage, eigen, blockSize, 5);
-    for (int j = 0; j < skewImage.rows; j += blockSize) {
-      for (int i = 0; i < skewImage.cols; i += blockSize) {
-        float x2 = eigen.at<cv::Vec6f>(j, i)[4];
-        float y2 = eigen.at<cv::Vec6f>(j, i)[5];
-        int angle_cell = angle(x2, y2);
-        angle_list[(angle_cell + 180) % 180] += 1.0;
-      }
-    }
-  }
-  std::vector<float> filtered = avgfilter(angle_list, 5);
-  int maxPos = std::max_element(filtered.begin(), filtered.end()) -
-               filtered.begin() + FILTER_WINDOWS_SIZE / 2;
-  if (maxPos > ANGLE_MAX)
-    maxPos = (-maxPos + 90 + 180) % 180;
-  if (maxPos < ANGLE_MIN)
-    maxPos -= 90;
-  maxPos = 90 - maxPos;
-  cv::Mat deskewed = correctPlateImage(bak, static_cast<float>(maxPos), 60.0f);
-  return deskewed;
-}
-
-} // namespace pr
diff --git a/Prj-ROS/src/hyperlpr/src/FineMapping.cpp b/Prj-ROS/src/hyperlpr/src/FineMapping.cpp
deleted file mode 100644
index 8117ffe..0000000
--- a/Prj-ROS/src/hyperlpr/src/FineMapping.cpp
+++ /dev/null
@@ -1,165 +0,0 @@
-#include "FineMapping.h"
-namespace pr {
-
-const int FINEMAPPING_H = 60;
-const int FINEMAPPING_W = 140;
-const int PADDING_UP_DOWN = 30;
-void drawRect(cv::Mat image, cv::Rect rect) {
-  cv::Point p1(rect.x, rect.y);
-  cv::Point p2(rect.x + rect.width, rect.y + rect.height);
-  cv::rectangle(image, p1, p2, cv::Scalar(0, 255, 0), 1);
-}
-
-FineMapping::FineMapping(std::string prototxt, std::string caffemodel) {
-  net = cv::dnn::readNetFromCaffe(prototxt, caffemodel);
-}
-
-cv::Mat FineMapping::FineMappingHorizon(cv::Mat FinedVertical, int leftPadding,
-                                        int rightPadding) {
-  cv::Mat inputBlob = cv::dnn::blobFromImage(
-      FinedVertical, 1 / 255.0, cv::Size(66, 16), cv::Scalar(0, 0, 0), false);
-  net.setInput(inputBlob, "data");
-  cv::Mat prob = net.forward();
-  int front = static_cast<int>(prob.at<float>(0, 0) * FinedVertical.cols);
-  int back = static_cast<int>(prob.at<float>(0, 1) * FinedVertical.cols);
-  front -= leftPadding;
-  if (front < 0)
-    front = 0;
-  back += rightPadding;
-  if (back > FinedVertical.cols - 1)
-    back = FinedVertical.cols - 1;
-  cv::Mat cropped = FinedVertical.colRange(front, back).clone();
-  return cropped;
-}
-std::pair<int, int> FitLineRansac(std::vector<cv::Point> pts, int zeroadd = 0) {
-  std::pair<int, int> res;
-  if (pts.size() > 2) {
-    cv::Vec4f line;
-    cv::fitLine(pts, line, cv::DIST_HUBER, 0, 0.01, 0.01);
-    float vx = line[0];
-    float vy = line[1];
-    float x = line[2];
-    float y = line[3];
-    int lefty = static_cast<int>((-x * vy / vx) + y);
-    int righty = static_cast<int>(((136 - x) * vy / vx) + y);
-    res.first = lefty + PADDING_UP_DOWN + zeroadd;
-    res.second = righty + PADDING_UP_DOWN + zeroadd;
-    return res;
-  }
-  res.first = zeroadd;
-  res.second = zeroadd;
-  return res;
-}
-
-cv::Mat FineMapping::FineMappingVertical(cv::Mat InputProposal, int sliceNum,
-                                         int upper, int lower,
-                                         int windows_size) {
-  cv::Mat PreInputProposal;
-  cv::Mat proposal;
-  cv::resize(InputProposal, PreInputProposal,
-             cv::Size(FINEMAPPING_W, FINEMAPPING_H));
-  if (InputProposal.channels() == 3)
-    cv::cvtColor(PreInputProposal, proposal, cv::COLOR_BGR2GRAY);
-  else
-    PreInputProposal.copyTo(proposal);
-  // this will improve some sen
-  cv::Mat kernal = cv::getStructuringElement(cv::MORPH_ELLIPSE, cv::Size(1, 3));
-  float diff = static_cast<float>(upper - lower);
-  diff /= static_cast<float>(sliceNum - 1);
-  cv::Mat binary_adaptive;
-  std::vector<cv::Point> line_upper;
-  std::vector<cv::Point> line_lower;
-  int contours_nums = 0;
-  for (int i = 0; i < sliceNum; i++) {
-    std::vector<std::vector<cv::Point>> contours;
-    float k = lower + i * diff;
-    cv::adaptiveThreshold(proposal, binary_adaptive, 255,
-                          cv::ADAPTIVE_THRESH_MEAN_C, cv::THRESH_BINARY,
-                          windows_size, k);
-    cv::Mat draw;
-    binary_adaptive.copyTo(draw);
-    cv::findContours(binary_adaptive, contours, cv::RETR_EXTERNAL,
-                     cv::CHAIN_APPROX_SIMPLE);
-    for (auto contour : contours) {
-      cv::Rect bdbox = cv::boundingRect(contour);
-      float lwRatio = bdbox.height / static_cast<float>(bdbox.width);
-      int bdboxAera = bdbox.width * bdbox.height;
-      if ((lwRatio > 0.7 && bdbox.width * bdbox.height > 100 &&
-           bdboxAera < 300) ||
-          (lwRatio > 3.0 && bdboxAera < 100 && bdboxAera > 10)) {
-        cv::Point p1(bdbox.x, bdbox.y);
-        cv::Point p2(bdbox.x + bdbox.width, bdbox.y + bdbox.height);
-        line_upper.push_back(p1);
-        line_lower.push_back(p2);
-        contours_nums += 1;
-      }
-    }
-  }
-  if (contours_nums < 41) {
-    cv::bitwise_not(InputProposal, InputProposal);
-    cv::Mat kernal =
-        cv::getStructuringElement(cv::MORPH_ELLIPSE, cv::Size(1, 5));
-    cv::Mat bak;
-    cv::resize(InputProposal, bak, cv::Size(FINEMAPPING_W, FINEMAPPING_H));
-    cv::erode(bak, bak, kernal);
-    if (InputProposal.channels() == 3)
-      cv::cvtColor(bak, proposal, cv::COLOR_BGR2GRAY);
-    else
-      proposal = bak;
-    int contours_nums = 0;
-    for (int i = 0; i < sliceNum; i++) {
-      std::vector<std::vector<cv::Point>> contours;
-      float k = lower + i * diff;
-      cv::adaptiveThreshold(proposal, binary_adaptive, 255,
-                            cv::ADAPTIVE_THRESH_MEAN_C, cv::THRESH_BINARY,
-                            windows_size, k);
-      cv::Mat draw;
-      binary_adaptive.copyTo(draw);
-      cv::findContours(binary_adaptive, contours, cv::RETR_EXTERNAL,
-                       cv::CHAIN_APPROX_SIMPLE);
-      for (auto contour : contours) {
-        cv::Rect bdbox = cv::boundingRect(contour);
-        float lwRatio = bdbox.height / static_cast<float>(bdbox.width);
-        int bdboxAera = bdbox.width * bdbox.height;
-        if ((lwRatio > 0.7 && bdbox.width * bdbox.height > 120 &&
-             bdboxAera < 300) ||
-            (lwRatio > 3.0 && bdboxAera < 100 && bdboxAera > 10)) {
-
-          cv::Point p1(bdbox.x, bdbox.y);
-          cv::Point p2(bdbox.x + bdbox.width, bdbox.y + bdbox.height);
-          line_upper.push_back(p1);
-          line_lower.push_back(p2);
-          contours_nums += 1;
-        }
-      }
-    }
-  }
-  cv::Mat rgb;
-  cv::copyMakeBorder(PreInputProposal, rgb, PADDING_UP_DOWN, PADDING_UP_DOWN, 0,
-                     0, cv::BORDER_REPLICATE);
-  std::pair<int, int> A;
-  std::pair<int, int> B;
-  A = FitLineRansac(line_upper, -1);
-  B = FitLineRansac(line_lower, 1);
-  int leftyB = A.first;
-  int rightyB = A.second;
-  int leftyA = B.first;
-  int rightyA = B.second;
-  int cols = rgb.cols;
-  int rows = rgb.rows;
-  std::vector<cv::Point2f> corners(4);
-  corners[0] = cv::Point2f(cols - 1, rightyA);
-  corners[1] = cv::Point2f(0, leftyA);
-  corners[2] = cv::Point2f(cols - 1, rightyB);
-  corners[3] = cv::Point2f(0, leftyB);
-  std::vector<cv::Point2f> corners_trans(4);
-  corners_trans[0] = cv::Point2f(136, 36);
-  corners_trans[1] = cv::Point2f(0, 36);
-  corners_trans[2] = cv::Point2f(136, 0);
-  corners_trans[3] = cv::Point2f(0, 0);
-  cv::Mat transform = cv::getPerspectiveTransform(corners, corners_trans);
-  cv::Mat quad = cv::Mat::zeros(36, 136, CV_8UC3);
-  cv::warpPerspective(rgb, quad, transform, quad.size());
-  return quad;
-}
-} // namespace pr
diff --git a/Prj-ROS/src/hyperlpr/src/Pipeline.cpp b/Prj-ROS/src/hyperlpr/src/Pipeline.cpp
deleted file mode 100644
index c9da0ff..0000000
--- a/Prj-ROS/src/hyperlpr/src/Pipeline.cpp
+++ /dev/null
@@ -1,82 +0,0 @@
-//
-// Created by Jack Yu on 23/10/2017.
-//
-
-#include "Pipeline.h"
-namespace pr {
-
-const int HorizontalPadding = 4;
-PipelinePR::PipelinePR(){};
-PipelinePR::~PipelinePR() {
-  delete plateDetection;
-  delete fineMapping;
-  delete plateSegmentation;
-  delete generalRecognizer;
-  delete segmentationFreeRecognizer;
-}
-
-void PipelinePR::initialize(std::string detector_filename,
-                            std::string finemapping_prototxt,
-                            std::string finemapping_caffemodel,
-                            std::string segmentation_prototxt,
-                            std::string segmentation_caffemodel,
-                            std::string charRecognization_proto,
-                            std::string charRecognization_caffemodel,
-                            std::string segmentationfree_proto,
-                            std::string segmentationfree_caffemodel) {
-  plateDetection = new PlateDetection(detector_filename);
-  fineMapping = new FineMapping(finemapping_prototxt, finemapping_caffemodel);
-  plateSegmentation =
-      new PlateSegmentation(segmentation_prototxt, segmentation_caffemodel);
-  generalRecognizer =
-      new CNNRecognizer(charRecognization_proto, charRecognization_caffemodel);
-  segmentationFreeRecognizer = new SegmentationFreeRecognizer(
-      segmentationfree_proto, segmentationfree_caffemodel);
-}
-
-std::vector<PlateInfo> PipelinePR::RunPiplineAsImage(cv::Mat plateImage,
-                                                     int method) {
-  std::vector<PlateInfo> results;
-  std::vector<pr::PlateInfo> plates;
-  plateDetection->plateDetectionRough(plateImage, plates, 36, 700);
-
-  for (pr::PlateInfo plateinfo : plates) {
-
-    cv::Mat image_finemapping = plateinfo.getPlateImage();
-    image_finemapping = fineMapping->FineMappingVertical(image_finemapping);
-    image_finemapping = pr::fastdeskew(image_finemapping, 5);
-    // Segmentation-based
-    if (method == SEGMENTATION_BASED_METHOD) {
-      image_finemapping = fineMapping->FineMappingHorizon(image_finemapping, 2,
-                                                          HorizontalPadding);
-      cv::resize(image_finemapping, image_finemapping,
-                 cv::Size(136 + HorizontalPadding, 36));
-      plateinfo.setPlateImage(image_finemapping);
-      std::vector<cv::Rect> rects;
-      plateSegmentation->segmentPlatePipline(plateinfo, 1, rects);
-      plateSegmentation->ExtractRegions(plateinfo, rects);
-      cv::copyMakeBorder(image_finemapping, image_finemapping, 0, 0, 0, 20,
-                         cv::BORDER_REPLICATE);
-      plateinfo.setPlateImage(image_finemapping);
-      generalRecognizer->SegmentBasedSequenceRecognition(plateinfo);
-      plateinfo.decodePlateNormal(pr::CH_PLATE_CODE);
-
-    }
-    // Segmentation-free
-    else if (method == SEGMENTATION_FREE_METHOD) {
-      image_finemapping = fineMapping->FineMappingHorizon(
-          image_finemapping, 4, HorizontalPadding + 3);
-      cv::resize(image_finemapping, image_finemapping,
-                 cv::Size(136 + HorizontalPadding, 36));
-      plateinfo.setPlateImage(image_finemapping);
-      std::pair<std::string, float> res =
-          segmentationFreeRecognizer->SegmentationFreeForSinglePlate(
-              plateinfo.getPlateImage(), pr::CH_PLATE_CODE);
-      plateinfo.confidence = res.second;
-      plateinfo.setPlateName(res.first);
-    }
-    results.push_back(plateinfo);
-  }
-  return results;
-}
-} // namespace pr
diff --git a/Prj-ROS/src/hyperlpr/src/PlateDetection.cpp b/Prj-ROS/src/hyperlpr/src/PlateDetection.cpp
deleted file mode 100644
index d3f3137..0000000
--- a/Prj-ROS/src/hyperlpr/src/PlateDetection.cpp
+++ /dev/null
@@ -1,31 +0,0 @@
-#include "../include/PlateDetection.h"
-#include "util.h"
-namespace pr {
-PlateDetection::PlateDetection(std::string filename_cascade) {
-  cascade.load(filename_cascade);
-};
-void PlateDetection::plateDetectionRough(cv::Mat InputImage,
-                                         std::vector<pr::PlateInfo> &plateInfos,
-                                         int min_w, int max_w) {
-  cv::Mat processImage;
-  cv::cvtColor(InputImage, processImage, cv::COLOR_BGR2GRAY);
-  std::vector<cv::Rect> platesRegions;
-  cv::Size minSize(min_w, min_w / 4);
-  cv::Size maxSize(max_w, max_w / 4);
-  cascade.detectMultiScale(processImage, platesRegions, 1.1, 3,
-                           cv::CASCADE_SCALE_IMAGE, minSize, maxSize);
-  for (auto plate : platesRegions) {
-    int zeroadd_w = static_cast<int>(plate.width * 0.30);
-    int zeroadd_h = static_cast<int>(plate.height * 2);
-    int zeroadd_x = static_cast<int>(plate.width * 0.15);
-    int zeroadd_y = static_cast<int>(plate.height * 1);
-    plate.x -= zeroadd_x;
-    plate.y -= zeroadd_y;
-    plate.height += zeroadd_h;
-    plate.width += zeroadd_w;
-    cv::Mat plateImage = util::cropFromImage(InputImage, plate);
-    PlateInfo plateInfo(plateImage, plate);
-    plateInfos.push_back(plateInfo);
-  }
-}
-} // namespace pr
diff --git a/Prj-ROS/src/hyperlpr/src/PlateSegmentation.cpp b/Prj-ROS/src/hyperlpr/src/PlateSegmentation.cpp
deleted file mode 100644
index 9365781..0000000
--- a/Prj-ROS/src/hyperlpr/src/PlateSegmentation.cpp
+++ /dev/null
@@ -1,305 +0,0 @@
-//
-// Created by Jack  Yu on 16/10/2017.
-//
-
-#include "../include/PlateSegmentation.h"
-#include "../include/niBlackThreshold.h"
-
-namespace pr {
-PlateSegmentation::PlateSegmentation(std::string prototxt,
-                                     std::string caffemodel) {
-  net = cv::dnn::readNetFromCaffe(prototxt, caffemodel);
-}
-cv::Mat PlateSegmentation::classifyResponse(const cv::Mat &cropped) {
-  cv::Mat inputBlob = cv::dnn::blobFromImage(
-      cropped, 1 / 255.0, cv::Size(22, 22), cv::Scalar(0, 0, 0), false);
-  net.setInput(inputBlob, "data");
-  return net.forward();
-}
-
-void drawHist(float *seq, int size, const char *name) {
-  cv::Mat image(300, size, CV_8U);
-  image.setTo(0);
-  float *start = seq;
-  float *end = seq + size;
-  float l = *std::max_element(start, end);
-  for (int i = 0; i < size; i++) {
-    int p = int(float(seq[i]) / l * 300);
-    cv::line(image, cv::Point(i, 300), cv::Point(i, 300 - p),
-             cv::Scalar(255, 255, 255));
-  }
-  cv::resize(image, image, cv::Size(600, 100));
-  cv::imshow(name, image);
-}
-
-inline void computeSafeMargin(int &val, const int &rows) {
-  val = std::min(val, rows);
-  val = std::max(val, 0);
-}
-
-cv::Rect boxFromCenter(const cv::Point center, int left, int right, int top,
-                       int bottom, cv::Size bdSize) {
-  cv::Point p1(center.x - left, center.y - top);
-  cv::Point p2(center.x + right, center.y + bottom);
-  p1.x = std::max(0, p1.x);
-  p1.y = std::max(0, p1.y);
-  p2.x = std::min(p2.x, bdSize.width - 1);
-  p2.y = std::min(p2.y, bdSize.height - 1);
-  cv::Rect rect(p1, p2);
-  return rect;
-}
-
-cv::Rect boxPadding(cv::Rect rect, int left, int right, int top, int bottom,
-                    cv::Size bdSize) {
-
-  cv::Point center(rect.x + (rect.width >> 1), rect.y + (rect.height >> 1));
-  int rebuildLeft = (rect.width >> 1) + left;
-  int rebuildRight = (rect.width >> 1) + right;
-  int rebuildTop = (rect.height >> 1) + top;
-  int rebuildBottom = (rect.height >> 1) + bottom;
-  return boxFromCenter(center, rebuildLeft, rebuildRight, rebuildTop,
-                       rebuildBottom, bdSize);
-}
-
-void PlateSegmentation::refineRegion(cv::Mat &plateImage,
-                                     const std::vector<int> &candidatePts,
-                                     const int padding,
-                                     std::vector<cv::Rect> &rects) {
-  int w = candidatePts[5] - candidatePts[4];
-  int cols = plateImage.cols;
-  int rows = plateImage.rows;
-  for (int i = 0; i < candidatePts.size(); i++) {
-    int left = 0;
-    int right = 0;
-
-    if (i == 0) {
-      left = candidatePts[i];
-      right = left + w + padding;
-    } else {
-      left = candidatePts[i] - padding;
-      right = left + w + padding * 2;
-    }
-
-    computeSafeMargin(right, cols);
-    computeSafeMargin(left, cols);
-    cv::Rect roi(left, 0, right - left, rows - 1);
-    cv::Mat roiImage;
-    plateImage(roi).copyTo(roiImage);
-
-    if (i >= 1) {
-
-      cv::Mat roi_thres;
-      //                cv::threshold(roiImage,roi_thres,0,255,cv::THRESH_OTSU|cv::THRESH_BINARY);
-
-      niBlackThreshold(roiImage, roi_thres, 255, cv::THRESH_BINARY, 15, 0.27,
-                       BINARIZATION_NIBLACK);
-
-      std::vector<std::vector<cv::Point>> contours;
-      cv::findContours(roi_thres, contours, cv::RETR_LIST,
-                       cv::CHAIN_APPROX_SIMPLE);
-      cv::Point boxCenter(roiImage.cols >> 1, roiImage.rows >> 1);
-
-      cv::Rect final_bdbox;
-      cv::Point final_center;
-      int final_dist = INT_MAX;
-
-      for (auto contour : contours) {
-        cv::Rect bdbox = cv::boundingRect(contour);
-        cv::Point center(bdbox.x + (bdbox.width >> 1),
-                         bdbox.y + (bdbox.height >> 1));
-        int dist = (center.x - boxCenter.x) * (center.x - boxCenter.x);
-        if (dist < final_dist and bdbox.height > rows >> 1) {
-          final_dist = dist;
-          final_center = center;
-          final_bdbox = bdbox;
-        }
-      }
-
-      // rebuild box
-      if (final_bdbox.height / static_cast<float>(final_bdbox.width) > 3.5 &&
-          final_bdbox.width * final_bdbox.height < 10)
-        final_bdbox = boxFromCenter(final_center, 8, 8, (rows >> 1) - 3,
-                                    (rows >> 1) - 2, roiImage.size());
-      else {
-        if (i == candidatePts.size() - 1)
-          final_bdbox = boxPadding(final_bdbox, padding / 2, padding,
-                                   padding / 2, padding / 2, roiImage.size());
-        else
-          final_bdbox = boxPadding(final_bdbox, padding, padding, padding,
-                                   padding, roiImage.size());
-
-//                    std::cout<<final_bdbox<<std::endl;
-//                    std::cout<<roiImage.size()<<std::endl;
-#ifdef DEBUG
-        cv::imshow("char_thres", roi_thres);
-
-        cv::imshow("char", roiImage(final_bdbox));
-        cv::waitKey(0);
-#endif
-      }
-
-      final_bdbox.x += left;
-
-      rects.push_back(final_bdbox);
-      //
-
-    } else {
-      rects.push_back(roi);
-    }
-
-    //            else
-    //            {
-    //
-    //            }
-
-    //            cv::GaussianBlur(roiImage,roiImage,cv::Size(7,7),3);
-    //
-    //            cv::imshow("image",roiImage);
-    //            cv::waitKey(0);
-  }
-}
-void avgfilter(float *angle_list, int size, int windowsSize) {
-  float *filterd = new float[size];
-  for (int i = 0; i < size; i++)
-    filterd[i] = angle_list[i];
-  //        memcpy(filterd,angle_list,size);
-
-  cv::Mat kernal_gaussian = cv::getGaussianKernel(windowsSize, 3, CV_32F);
-  float *kernal = (float *)kernal_gaussian.data;
-  //        kernal+=windowsSize;
-  int r = windowsSize / 2;
-
-  for (int i = 0; i < size; i++) {
-    float avg = 0.00f;
-    for (int j = 0; j < windowsSize; j++) {
-      if (i + j - r > 0 && i + j + r < size - 1)
-        avg += filterd[i + j - r] * kernal[j];
-    }
-    //            avg = avg / windowsSize;
-    angle_list[i] = avg;
-  }
-
-  delete filterd;
-}
-
-void PlateSegmentation::templateMatchFinding(
-    const cv::Mat &respones, int windowsWidth,
-    std::pair<float, std::vector<int>> &candidatePts) {
-  int rows = respones.rows;
-  int cols = respones.cols;
-  float *data = (float *)respones.data;
-  float *engNum_prob = data;
-  float *false_prob = data + cols;
-  float *ch_prob = data + cols * 2;
-  avgfilter(engNum_prob, cols, 5);
-  avgfilter(false_prob, cols, 5);
-  std::vector<int> candidate_pts(7);
-  int cp_list[7];
-  float loss_selected = -10;
-
-  for (int start = 0; start < 20; start += 2)
-    for (int width = windowsWidth - 5; width < windowsWidth + 5; width++) {
-      for (int interval = windowsWidth / 2; interval < windowsWidth;
-           interval++) {
-        int cp1_ch = start;
-        int cp2_p0 = cp1_ch + width;
-        int cp3_p1 = cp2_p0 + width + interval;
-        int cp4_p2 = cp3_p1 + width;
-        int cp5_p3 = cp4_p2 + width + 1;
-        int cp6_p4 = cp5_p3 + width + 2;
-        int cp7_p5 = cp6_p4 + width + 2;
-        int md1 = (cp1_ch + cp2_p0) >> 1;
-        int md2 = (cp2_p0 + cp3_p1) >> 1;
-        int md3 = (cp3_p1 + cp4_p2) >> 1;
-        int md4 = (cp4_p2 + cp5_p3) >> 1;
-        int md5 = (cp5_p3 + cp6_p4) >> 1;
-        int md6 = (cp6_p4 + cp7_p5) >> 1;
-
-        if (cp7_p5 >= cols)
-          continue;
-        float loss =
-            ch_prob[cp1_ch] * 3 -
-            (false_prob[cp3_p1] + false_prob[cp4_p2] + false_prob[cp5_p3] +
-             false_prob[cp6_p4] + false_prob[cp7_p5]);
-
-        if (loss > loss_selected) {
-          loss_selected = loss;
-          cp_list[0] = cp1_ch;
-          cp_list[1] = cp2_p0;
-          cp_list[2] = cp3_p1;
-          cp_list[3] = cp4_p2;
-          cp_list[4] = cp5_p3;
-          cp_list[5] = cp6_p4;
-          cp_list[6] = cp7_p5;
-        }
-      }
-    }
-  candidate_pts[0] = cp_list[0];
-  candidate_pts[1] = cp_list[1];
-  candidate_pts[2] = cp_list[2];
-  candidate_pts[3] = cp_list[3];
-  candidate_pts[4] = cp_list[4];
-  candidate_pts[5] = cp_list[5];
-  candidate_pts[6] = cp_list[6];
-
-  candidatePts.first = loss_selected;
-  candidatePts.second = candidate_pts;
-};
-
-void PlateSegmentation::segmentPlateBySlidingWindows(cv::Mat &plateImage,
-                                                     int windowsWidth,
-                                                     int stride,
-                                                     cv::Mat &respones) {
-  cv::Mat plateImageGray;
-  cv::cvtColor(plateImage, plateImageGray, cv::COLOR_BGR2GRAY);
-  int padding = plateImage.cols - 136;
-  int height = plateImage.rows - 1;
-  int width = plateImage.cols - 1 - padding;
-  for (int i = 0; i < width - windowsWidth + 1; i += stride) {
-    cv::Rect roi(i, 0, windowsWidth, height);
-    cv::Mat roiImage = plateImageGray(roi);
-    cv::Mat response = classifyResponse(roiImage);
-    respones.push_back(response);
-  }
-  respones = respones.t();
-}
-
-void PlateSegmentation::segmentPlatePipline(PlateInfo &plateInfo, int stride,
-                                            std::vector<cv::Rect> &Char_rects) {
-  cv::Mat plateImage = plateInfo.getPlateImage(); // get src image .
-  cv::Mat plateImageGray;
-  cv::cvtColor(plateImage, plateImageGray, cv::COLOR_BGR2GRAY);
-  // do binarzation
-  std::pair<float, std::vector<int>> sections; // segment points variables .
-  cv::Mat respones; // three response of every sub region from origin image .
-  segmentPlateBySlidingWindows(plateImage, DEFAULT_WIDTH, 1, respones);
-  templateMatchFinding(respones, DEFAULT_WIDTH / stride, sections);
-  for (int i = 0; i < sections.second.size(); i++) {
-    sections.second[i] *= stride;
-  }
-  refineRegion(plateImageGray, sections.second, 5, Char_rects);
-}
-
-void PlateSegmentation::ExtractRegions(PlateInfo &plateInfo,
-                                       std::vector<cv::Rect> &rects) {
-  cv::Mat plateImage = plateInfo.getPlateImage();
-  for (int i = 0; i < rects.size(); i++) {
-    cv::Mat charImage;
-    plateImage(rects[i]).copyTo(charImage);
-    if (charImage.channels())
-      cv::cvtColor(charImage, charImage, cv::COLOR_BGR2GRAY);
-    cv::equalizeHist(charImage, charImage);
-    std::pair<CharType, cv::Mat> char_instance;
-    if (i == 0) {
-      char_instance.first = CHINESE;
-    } else if (i == 1) {
-      char_instance.first = LETTER;
-    } else {
-      char_instance.first = LETTER_NUMS;
-    }
-    char_instance.second = charImage;
-    plateInfo.appendPlateChar(char_instance);
-  }
-}
-
-} // namespace pr
diff --git a/Prj-ROS/src/hyperlpr/src/Recognizer.cpp b/Prj-ROS/src/hyperlpr/src/Recognizer.cpp
deleted file mode 100644
index 7b486da..0000000
--- a/Prj-ROS/src/hyperlpr/src/Recognizer.cpp
+++ /dev/null
@@ -1,22 +0,0 @@
-//
-// Created by Jack Yu on 22/10/2017.
-//
-
-#include "../include/Recognizer.h"
-
-namespace pr {
-void GeneralRecognizer::SegmentBasedSequenceRecognition(PlateInfo &plateinfo) {
-  for (auto char_instance : plateinfo.plateChars) {
-    std::pair<CharType, cv::Mat> res;
-    if (char_instance.second.rows * char_instance.second.cols > 40) {
-      label code_table = recognizeCharacter(char_instance.second);
-      res.first = char_instance.first;
-      code_table.copyTo(res.second);
-      plateinfo.appendPlateCoding(res);
-    } else {
-      res.first = INVALID;
-      plateinfo.appendPlateCoding(res);
-    }
-  }
-}
-} // namespace pr
diff --git a/Prj-ROS/src/hyperlpr/src/SegmentationFreeRecognizer.cpp b/Prj-ROS/src/hyperlpr/src/SegmentationFreeRecognizer.cpp
deleted file mode 100644
index 6b13903..0000000
--- a/Prj-ROS/src/hyperlpr/src/SegmentationFreeRecognizer.cpp
+++ /dev/null
@@ -1,87 +0,0 @@
-//
-// Created by Jack Yu on 28/11/2017.
-//
-#include "../include/SegmentationFreeRecognizer.h"
-
-namespace pr {
-SegmentationFreeRecognizer::SegmentationFreeRecognizer(std::string prototxt,
-                                                       std::string caffemodel) {
-  net = cv::dnn::readNetFromCaffe(prototxt, caffemodel);
-}
-inline int judgeCharRange(int id) { return id < 31 || id > 63; }
-std::pair<std::string, float>
-decodeResults(cv::Mat code_table, std::vector<std::string> mapping_table,
-              float thres) {
-  cv::MatSize mtsize = code_table.size;
-  int sequencelength = mtsize[2];
-  int labellength = mtsize[1];
-  cv::transpose(code_table.reshape(1, 1).reshape(1, labellength), code_table);
-  std::string name = "";
-  std::vector<int> seq(sequencelength);
-  std::vector<std::pair<int, float>> seq_decode_res;
-  for (int i = 0; i < sequencelength; i++) {
-    float *fstart = ((float *)(code_table.data) + i * labellength);
-    int id = std::max_element(fstart, fstart + labellength) - fstart;
-    seq[i] = id;
-  }
-
-  float sum_confidence = 0;
-  int plate_lenghth = 0;
-  for (int i = 0; i < sequencelength; i++) {
-    if (seq[i] != labellength - 1 && (i == 0 || seq[i] != seq[i - 1])) {
-      float *fstart = ((float *)(code_table.data) + i * labellength);
-      float confidence = *(fstart + seq[i]);
-      std::pair<int, float> pair_(seq[i], confidence);
-      seq_decode_res.push_back(pair_);
-    }
-  }
-  int i = 0;
-  if (seq_decode_res.size() > 1 && judgeCharRange(seq_decode_res[0].first) &&
-      judgeCharRange(seq_decode_res[1].first)) {
-    i = 2;
-    int c = seq_decode_res[0].second < seq_decode_res[1].second;
-    name += mapping_table[seq_decode_res[c].first];
-    sum_confidence += seq_decode_res[c].second;
-    plate_lenghth++;
-  }
-
-  for (; i < seq_decode_res.size(); i++) {
-    name += mapping_table[seq_decode_res[i].first];
-    sum_confidence += seq_decode_res[i].second;
-    plate_lenghth++;
-  }
-  std::pair<std::string, float> res;
-  res.second = sum_confidence / plate_lenghth;
-  res.first = name;
-  return res;
-}
-std::string decodeResults(cv::Mat code_table,
-                          std::vector<std::string> mapping_table) {
-  cv::MatSize mtsize = code_table.size;
-  int sequencelength = mtsize[2];
-  int labellength = mtsize[1];
-  cv::transpose(code_table.reshape(1, 1).reshape(1, labellength), code_table);
-  std::string name = "";
-  std::vector<int> seq(sequencelength);
-  for (int i = 0; i < sequencelength; i++) {
-    float *fstart = ((float *)(code_table.data) + i * labellength);
-    int id = std::max_element(fstart, fstart + labellength) - fstart;
-    seq[i] = id;
-  }
-  for (int i = 0; i < sequencelength; i++) {
-    if (seq[i] != labellength - 1 && (i == 0 || seq[i] != seq[i - 1]))
-      name += mapping_table[seq[i]];
-  }
-  return name;
-}
-std::pair<std::string, float>
-SegmentationFreeRecognizer::SegmentationFreeForSinglePlate(
-    cv::Mat Image, std::vector<std::string> mapping_table) {
-  cv::transpose(Image, Image);
-  cv::Mat inputBlob =
-      cv::dnn::blobFromImage(Image, 1 / 255.0, cv::Size(40, 160));
-  net.setInput(inputBlob, "data");
-  cv::Mat char_prob_mat = net.forward();
-  return decodeResults(char_prob_mat, mapping_table, 0.00);
-}
-} // namespace pr
diff --git a/Prj-ROS/src/hyperlpr/src/hyperlpr_node.cpp b/Prj-ROS/src/hyperlpr/src/hyperlpr_node.cpp
deleted file mode 100644
index 4031ff0..0000000
--- a/Prj-ROS/src/hyperlpr/src/hyperlpr_node.cpp
+++ /dev/null
@@ -1,48 +0,0 @@
-#include "hyperlpr_node.h"
-
-void SubPuber::SendPath(){
-  prc.initialize(modelPath+"/cascade.xml",
-                 modelPath+"/HorizonalFinemapping.prototxt",
-                 modelPath+"/HorizonalFinemapping.caffemodel",
-                 modelPath+"/Segmentation.prototxt",
-                 modelPath+"/Segmentation.caffemodel",
-                 modelPath+"/CharacterRecognization.prototxt",
-                 modelPath+"/CharacterRecognization.caffemodel",
-                 modelPath+"/SegmenationFree-Inception.prototxt",
-                 modelPath+"/SegmenationFree-Inception.caffemodel");
-}
-
-void SubPuber::PlateRecognitionCallback(const sensor_msgs::ImageConstPtr &cameraImage)
-{
-  cv::Mat img = cv_bridge::toCvShare(cameraImage, "bgr8")->image;
-
-  //使用端到端模型模型进行识别 识别结果将会保存在res里面
-  std::vector<pr::PlateInfo> res = prc.RunPiplineAsImage(img, pr::SEGMENTATION_FREE_METHOD);
-
-  for(auto st:res) {
-    if(st.confidence>0.75) {
-      std_msgs::String recognitionResult;
-      recognitionResult.data = st.getPlateName();
-
-      std_msgs::Float32 recognitionConfidence;
-      recognitionConfidence.data = st.confidence;
-
-      // 使用ROS_INFO输出中文车牌与置信度
-      setlocale(LC_CTYPE, "zh_CN.utf8");
-      ROS_INFO("Plate found: %s, confidence is: %f", recognitionResult.data.c_str(), recognitionConfidence.data);
-
-      // 获取车牌位置
-      cv::Rect region = st.getPlateRect();
-      // 框选出车牌位置
-      cv::rectangle(img, cv::Point(region.x,region.y),cv::Point(region.x+region.width,region.y+region.height),cv::Scalar(255,255,0),2);
-
-      // 将识别结果作为一个topic发布
-      recognitionResultPub.publish(recognitionResult);
-      // 将识别置信度作为一个topic发布
-      recognitionConfidencePub.publish(recognitionConfidence);
-    }
-  }
-  // 将框选出当前帧所有车牌位置的图像作为一个topic发布
-  sensor_msgs::ImagePtr msg = cv_bridge::CvImage(std_msgs::Header(), "bgr8", img).toImageMsg();
-  recognizedImagePub.publish(*msg);
-}
\ No newline at end of file
diff --git a/Prj-ROS/src/hyperlpr/src/main.cpp b/Prj-ROS/src/hyperlpr/src/main.cpp
deleted file mode 100644
index fff2232..0000000
--- a/Prj-ROS/src/hyperlpr/src/main.cpp
+++ /dev/null
@@ -1,17 +0,0 @@
-#include "hyperlpr_node.h"
-
-int main(int argc, char **argv)
-{
-  ros::init(argc, argv, "hyperlpr_node");
-
-  // 使用一个类完成订阅 识别 发布
-  SubPuber plateRecognizer;
-
-  // 获取模型路径
-  ros::param::get("ModelPath", plateRecognizer.modelPath);
-  // 发送路径，初始化图像处理管线
-  plateRecognizer.SendPath();
-
-  ros::spin();
-  return 0;
-}
diff --git a/Prj-ROS/src/hyperlpr/src/util.h b/Prj-ROS/src/hyperlpr/src/util.h
deleted file mode 100644
index af0f79d..0000000
--- a/Prj-ROS/src/hyperlpr/src/util.h
+++ /dev/null
@@ -1,64 +0,0 @@
-//
-// Created by Jack Yu on 04/04/2017.
-//
-
-#include <opencv2/opencv.hpp>
-#include <opencv2/imgproc/types_c.h>
-
-namespace util {
-template <class T> void swap(T &a, T &b) {
-  T c(a);
-  a = b;
-  b = c;
-}
-template <class T> T min(T &a, T &b) { return a > b ? b : a; }
-
-cv::Mat cropFromImage(const cv::Mat &image, cv::Rect rect) {
-  int w = image.cols - 1;
-  int h = image.rows - 1;
-  rect.x = std::max(rect.x, 0);
-  rect.y = std::max(rect.y, 0);
-  rect.height = std::min(rect.height, h - rect.y);
-  rect.width = std::min(rect.width, w - rect.x);
-  cv::Mat temp(rect.size(), image.type());
-  cv::Mat cropped;
-  temp = image(rect);
-  temp.copyTo(cropped);
-  return cropped;
-}
-
-cv::Mat cropBox2dFromImage(const cv::Mat &image, cv::RotatedRect rect) {
-  cv::Mat M, rotated, cropped;
-  float angle = rect.angle;
-  cv::Size rect_size(rect.size.width, rect.size.height);
-  if (rect.angle < -45.) {
-    angle += 90.0;
-    swap(rect_size.width, rect_size.height);
-  }
-  M = cv::getRotationMatrix2D(rect.center, angle, 1.0);
-  cv::warpAffine(image, rotated, M, image.size(), cv::INTER_CUBIC);
-  cv::getRectSubPix(rotated, rect_size, rect.center, cropped);
-  return cropped;
-}
-
-cv::Mat calcHist(const cv::Mat &image) {
-  cv::Mat hsv;
-  std::vector<cv::Mat> hsv_planes;
-  cv::cvtColor(image, hsv, cv::COLOR_BGR2HSV);
-  cv::split(hsv, hsv_planes);
-  cv::Mat hist;
-  int histSize = 256;
-  float range[] = {0, 255};
-  const float *histRange = {range};
-  cv::calcHist(&hsv_planes[0], 1, 0, cv::Mat(), hist, 1, &histSize, &histRange,
-               true, true);
-  return hist;
-}
-
-float computeSimilir(const cv::Mat &A, const cv::Mat &B) {
-  cv::Mat histA, histB;
-  histA = calcHist(A);
-  histB = calcHist(B);
-  return cv::compareHist(histA, histB, CV_COMP_CORREL);
-}
-} // namespace util
diff --git a/Prj-iOS/DemoImage/Screen Shot 2018-10-26 at 3.43.44 PM.png b/Prj-iOS/DemoImage/Screen Shot 2018-10-26 at 3.43.44 PM.png
deleted file mode 100755
index 4f5befc..0000000
Binary files a/Prj-iOS/DemoImage/Screen Shot 2018-10-26 at 3.43.44 PM.png and /dev/null differ
diff --git a/Prj-iOS/DemoImage/Screen Shot 2018-10-26 at 3.43.53 PM.png b/Prj-iOS/DemoImage/Screen Shot 2018-10-26 at 3.43.53 PM.png
deleted file mode 100755
index c6028ea..0000000
Binary files a/Prj-iOS/DemoImage/Screen Shot 2018-10-26 at 3.43.53 PM.png and /dev/null differ
diff --git a/Prj-iOS/DemoImage/Screen Shot 2018-10-26 at 3.44.04 PM.png b/Prj-iOS/DemoImage/Screen Shot 2018-10-26 at 3.44.04 PM.png
deleted file mode 100755
index b3fa038..0000000
Binary files a/Prj-iOS/DemoImage/Screen Shot 2018-10-26 at 3.44.04 PM.png and /dev/null differ
diff --git a/Prj-iOS/Podfile b/Prj-iOS/Podfile
deleted file mode 100755
index 00459e4..0000000
--- a/Prj-iOS/Podfile
+++ /dev/null
@@ -1,8 +0,0 @@
-platform :ios, '10.1'
-
-target "lpr" do
-
-pod 'OpenCV', '~> 3.4.2'
-pod 'Masonry'
-
-end
diff --git a/Prj-iOS/Podfile.lock b/Prj-iOS/Podfile.lock
deleted file mode 100755
index e4511f2..0000000
--- a/Prj-iOS/Podfile.lock
+++ /dev/null
@@ -1,20 +0,0 @@
-PODS:
-  - Masonry (1.1.0)
-  - OpenCV (3.4.2)
-
-DEPENDENCIES:
-  - Masonry
-  - OpenCV (~> 3.4.2)
-
-SPEC REPOS:
-  https://github.com/cocoapods/specs.git:
-    - Masonry
-    - OpenCV
-
-SPEC CHECKSUMS:
-  Masonry: 678fab65091a9290e40e2832a55e7ab731aad201
-  OpenCV: 452909747854c92e0f59670961ed5131f5286cb5
-
-PODFILE CHECKSUM: 94db02cda76a5ac6371a03e6e2ca4e9035fc0da6
-
-COCOAPODS: 1.5.3
diff --git a/Prj-iOS/README.md b/Prj-iOS/README.md
deleted file mode 100755
index 404c617..0000000
--- a/Prj-iOS/README.md
+++ /dev/null
@@ -1,4 +0,0 @@
-
-Depency Library:
-- Masonry, 1.1.0
-- OpenCV, 3.4.2
diff --git a/Prj-iOS/Resource/README.md b/Prj-iOS/Resource/README.md
deleted file mode 100755
index 2c524eb..0000000
--- a/Prj-iOS/Resource/README.md
+++ /dev/null
@@ -1,17 +0,0 @@
-将/Prj-Linux/lpr/model目录下的
-
-cascade.xml
-
-CharacterRecognization.caffemodel
-
-CharacterRecognization.prototxt
-
-HorizonalFinemapping.caffemodel
-
-HorizonalFinemapping.prototxt
-
-Segmentation.caffemodel
-
-Segmentation.prototxt
-
-放置在该目录
\ No newline at end of file
diff --git a/Prj-iOS/lpr.xcodeproj/project.pbxproj b/Prj-iOS/lpr.xcodeproj/project.pbxproj
deleted file mode 100755
index e9f6167..0000000
--- a/Prj-iOS/lpr.xcodeproj/project.pbxproj
+++ /dev/null
@@ -1,530 +0,0 @@
-// !$*UTF8*$!
-{
-	archiveVersion = 1;
-	classes = {
-	};
-	objectVersion = 50;
-	objects = {
-
-/* Begin PBXBuildFile section */
-		34ECFDD12182DD9300B162D4 /* AppDelegate.m in Sources */ = {isa = PBXBuildFile; fileRef = 34ECFDD02182DD9300B162D4 /* AppDelegate.m */; };
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-/* End PBXBuildFile section */
-
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-				34ECFDE42182DD9400B162D4 /* Release */,
-			);
-			defaultConfigurationIsVisible = 0;
-			defaultConfigurationName = Release;
-		};
-/* End XCConfigurationList section */
-	};
-	rootObject = 34ECFDC42182DD9300B162D4 /* Project object */;
-}
diff --git a/Prj-iOS/lpr.xcodeproj/project.xcworkspace/contents.xcworkspacedata b/Prj-iOS/lpr.xcodeproj/project.xcworkspace/contents.xcworkspacedata
deleted file mode 100755
index 39e1b8b..0000000
--- a/Prj-iOS/lpr.xcodeproj/project.xcworkspace/contents.xcworkspacedata
+++ /dev/null
@@ -1,7 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<Workspace
-   version = "1.0">
-   <FileRef
-      location = "self:lpr.xcodeproj">
-   </FileRef>
-</Workspace>
diff --git a/Prj-iOS/lpr.xcodeproj/project.xcworkspace/xcshareddata/IDEWorkspaceChecks.plist b/Prj-iOS/lpr.xcodeproj/project.xcworkspace/xcshareddata/IDEWorkspaceChecks.plist
deleted file mode 100755
index 18d9810..0000000
--- a/Prj-iOS/lpr.xcodeproj/project.xcworkspace/xcshareddata/IDEWorkspaceChecks.plist
+++ /dev/null
@@ -1,8 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
-<plist version="1.0">
-<dict>
-	<key>IDEDidComputeMac32BitWarning</key>
-	<true/>
-</dict>
-</plist>
diff --git a/Prj-iOS/lpr.xcodeproj/project.xcworkspace/xcuserdata/tbao.xcuserdatad/UserInterfaceState.xcuserstate b/Prj-iOS/lpr.xcodeproj/project.xcworkspace/xcuserdata/tbao.xcuserdatad/UserInterfaceState.xcuserstate
deleted file mode 100755
index 7314580..0000000
Binary files a/Prj-iOS/lpr.xcodeproj/project.xcworkspace/xcuserdata/tbao.xcuserdatad/UserInterfaceState.xcuserstate and /dev/null differ
diff --git a/Prj-iOS/lpr.xcodeproj/xcuserdata/tbao.xcuserdatad/xcschemes/xcschememanagement.plist b/Prj-iOS/lpr.xcodeproj/xcuserdata/tbao.xcuserdatad/xcschemes/xcschememanagement.plist
deleted file mode 100755
index eb32267..0000000
--- a/Prj-iOS/lpr.xcodeproj/xcuserdata/tbao.xcuserdatad/xcschemes/xcschememanagement.plist
+++ /dev/null
@@ -1,14 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
-<plist version="1.0">
-<dict>
-	<key>SchemeUserState</key>
-	<dict>
-		<key>lpr.xcscheme</key>
-		<dict>
-			<key>orderHint</key>
-			<integer>2</integer>
-		</dict>
-	</dict>
-</dict>
-</plist>
diff --git a/Prj-iOS/lpr.xcworkspace/contents.xcworkspacedata b/Prj-iOS/lpr.xcworkspace/contents.xcworkspacedata
deleted file mode 100755
index d677399..0000000
--- a/Prj-iOS/lpr.xcworkspace/contents.xcworkspacedata
+++ /dev/null
@@ -1,10 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<Workspace
-   version = "1.0">
-   <FileRef
-      location = "group:lpr.xcodeproj">
-   </FileRef>
-   <FileRef
-      location = "group:Pods/Pods.xcodeproj">
-   </FileRef>
-</Workspace>
diff --git a/Prj-iOS/lpr.xcworkspace/xcshareddata/IDEWorkspaceChecks.plist b/Prj-iOS/lpr.xcworkspace/xcshareddata/IDEWorkspaceChecks.plist
deleted file mode 100755
index 18d9810..0000000
--- a/Prj-iOS/lpr.xcworkspace/xcshareddata/IDEWorkspaceChecks.plist
+++ /dev/null
@@ -1,8 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
-<plist version="1.0">
-<dict>
-	<key>IDEDidComputeMac32BitWarning</key>
-	<true/>
-</dict>
-</plist>
diff --git a/Prj-iOS/lpr.xcworkspace/xcuserdata/tbao.xcuserdatad/UserInterfaceState.xcuserstate b/Prj-iOS/lpr.xcworkspace/xcuserdata/tbao.xcuserdatad/UserInterfaceState.xcuserstate
deleted file mode 100755
index 71ec865..0000000
Binary files a/Prj-iOS/lpr.xcworkspace/xcuserdata/tbao.xcuserdatad/UserInterfaceState.xcuserstate and /dev/null differ
diff --git a/Prj-iOS/lpr.xcworkspace/xcuserdata/tbao.xcuserdatad/xcdebugger/Breakpoints_v2.xcbkptlist b/Prj-iOS/lpr.xcworkspace/xcuserdata/tbao.xcuserdatad/xcdebugger/Breakpoints_v2.xcbkptlist
deleted file mode 100755
index 37996a6..0000000
--- a/Prj-iOS/lpr.xcworkspace/xcuserdata/tbao.xcuserdatad/xcdebugger/Breakpoints_v2.xcbkptlist
+++ /dev/null
@@ -1,37 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<Bucket
-   type = "0"
-   version = "2.0">
-   <Breakpoints>
-      <BreakpointProxy
-         BreakpointExtensionID = "Xcode.Breakpoint.ExceptionBreakpoint">
-         <BreakpointContent
-            shouldBeEnabled = "Yes"
-            ignoreCount = "0"
-            continueAfterRunningActions = "No"
-            scope = "0"
-            stopOnStyle = "0">
-         </BreakpointContent>
-      </BreakpointProxy>
-      <BreakpointProxy
-         BreakpointExtensionID = "Xcode.Breakpoint.OpenGLErrorBreakpoint">
-         <BreakpointContent
-            shouldBeEnabled = "Yes"
-            ignoreCount = "0"
-            continueAfterRunningActions = "No"
-            breakpointStackSelectionBehavior = "1"
-            symbolName = "__GPUTOOLS_HAS_DETECTED_AN_OPENGL_ERROR__"
-            moduleName = "libglInterpose.dylib">
-            <Actions>
-               <BreakpointActionProxy
-                  ActionExtensionID = "Xcode.BreakpointAction.OpenGLError">
-                  <ActionContent>
-                  </ActionContent>
-               </BreakpointActionProxy>
-            </Actions>
-            <Locations>
-            </Locations>
-         </BreakpointContent>
-      </BreakpointProxy>
-   </Breakpoints>
-</Bucket>
diff --git a/Prj-iOS/lpr/.gitignore b/Prj-iOS/lpr/.gitignore
deleted file mode 100755
index ec9e31b..0000000
--- a/Prj-iOS/lpr/.gitignore
+++ /dev/null
@@ -1,3 +0,0 @@
-.DS_Store
-Pods/
-.clang-format
diff --git a/Prj-iOS/lpr/AppDelegate.h b/Prj-iOS/lpr/AppDelegate.h
deleted file mode 100755
index 8ef26f5..0000000
--- a/Prj-iOS/lpr/AppDelegate.h
+++ /dev/null
@@ -1,17 +0,0 @@
-//
-//  AppDelegate.h
-//  lpr
-//
-//  Created by baotim on 2018/10/26.
-//  Copyright © 2018 lprSample. All rights reserved.
-//
-
-#import <UIKit/UIKit.h>
-
-@interface AppDelegate : UIResponder <UIApplicationDelegate>
-
-@property (strong, nonatomic) UIWindow *window;
-
-
-@end
-
diff --git a/Prj-iOS/lpr/AppDelegate.m b/Prj-iOS/lpr/AppDelegate.m
deleted file mode 100755
index e022066..0000000
--- a/Prj-iOS/lpr/AppDelegate.m
+++ /dev/null
@@ -1,55 +0,0 @@
-//
-//  AppDelegate.m
-//  lpr
-//
-//  Created by baotim on 2018/10/26.
-//  Copyright © 2018 lprSample. All rights reserved.
-//
-
-#import "AppDelegate.h"
-#import "RootViewController.h"
-
-@interface AppDelegate ()
-
-@end
-
-@implementation AppDelegate
-
-
-- (BOOL)application:(UIApplication *)application didFinishLaunchingWithOptions:(NSDictionary *)launchOptions {
-    // Override point for customization after application launch.
-    self.window = [[UIWindow alloc] initWithFrame:[[UIScreen mainScreen] bounds]];
-    self.window.rootViewController = [[UINavigationController alloc] initWithRootViewController:[RootViewController new]];
-    [self.window makeKeyAndVisible];
-    return YES;
-}
-
-
-- (void)applicationWillResignActive:(UIApplication *)application {
-    // Sent when the application is about to move from active to inactive state. This can occur for certain types of temporary interruptions (such as an incoming phone call or SMS message) or when the user quits the application and it begins the transition to the background state.
-    // Use this method to pause ongoing tasks, disable timers, and invalidate graphics rendering callbacks. Games should use this method to pause the game.
-}
-
-
-- (void)applicationDidEnterBackground:(UIApplication *)application {
-    // Use this method to release shared resources, save user data, invalidate timers, and store enough application state information to restore your application to its current state in case it is terminated later.
-    // If your application supports background execution, this method is called instead of applicationWillTerminate: when the user quits.
-}
-
-
-- (void)applicationWillEnterForeground:(UIApplication *)application {
-    // Called as part of the transition from the background to the active state; here you can undo many of the changes made on entering the background.
-}
-
-
-- (void)applicationDidBecomeActive:(UIApplication *)application {
-    // Restart any tasks that were paused (or not yet started) while the application was inactive. If the application was previously in the background, optionally refresh the user interface.
-}
-
-
-- (void)applicationWillTerminate:(UIApplication *)application {
-    // Called when the application is about to terminate. Save data if appropriate. See also applicationDidEnterBackground:.
-}
-
-
-@end
diff --git a/Prj-iOS/lpr/Assets.xcassets/AppIcon.appiconset/Contents.json b/Prj-iOS/lpr/Assets.xcassets/AppIcon.appiconset/Contents.json
deleted file mode 100755
index d8db8d6..0000000
--- a/Prj-iOS/lpr/Assets.xcassets/AppIcon.appiconset/Contents.json
+++ /dev/null
@@ -1,98 +0,0 @@
-{
-  "images" : [
-    {
-      "idiom" : "iphone",
-      "size" : "20x20",
-      "scale" : "2x"
-    },
-    {
-      "idiom" : "iphone",
-      "size" : "20x20",
-      "scale" : "3x"
-    },
-    {
-      "idiom" : "iphone",
-      "size" : "29x29",
-      "scale" : "2x"
-    },
-    {
-      "idiom" : "iphone",
-      "size" : "29x29",
-      "scale" : "3x"
-    },
-    {
-      "idiom" : "iphone",
-      "size" : "40x40",
-      "scale" : "2x"
-    },
-    {
-      "idiom" : "iphone",
-      "size" : "40x40",
-      "scale" : "3x"
-    },
-    {
-      "idiom" : "iphone",
-      "size" : "60x60",
-      "scale" : "2x"
-    },
-    {
-      "idiom" : "iphone",
-      "size" : "60x60",
-      "scale" : "3x"
-    },
-    {
-      "idiom" : "ipad",
-      "size" : "20x20",
-      "scale" : "1x"
-    },
-    {
-      "idiom" : "ipad",
-      "size" : "20x20",
-      "scale" : "2x"
-    },
-    {
-      "idiom" : "ipad",
-      "size" : "29x29",
-      "scale" : "1x"
-    },
-    {
-      "idiom" : "ipad",
-      "size" : "29x29",
-      "scale" : "2x"
-    },
-    {
-      "idiom" : "ipad",
-      "size" : "40x40",
-      "scale" : "1x"
-    },
-    {
-      "idiom" : "ipad",
-      "size" : "40x40",
-      "scale" : "2x"
-    },
-    {
-      "idiom" : "ipad",
-      "size" : "76x76",
-      "scale" : "1x"
-    },
-    {
-      "idiom" : "ipad",
-      "size" : "76x76",
-      "scale" : "2x"
-    },
-    {
-      "idiom" : "ipad",
-      "size" : "83.5x83.5",
-      "scale" : "2x"
-    },
-    {
-      "idiom" : "ios-marketing",
-      "size" : "1024x1024",
-      "scale" : "1x"
-    }
-  ],
-  "info" : {
-    "version" : 1,
-    "author" : "xcode"
-  }
-}
\ No newline at end of file
diff --git a/Prj-iOS/lpr/Assets.xcassets/Contents.json b/Prj-iOS/lpr/Assets.xcassets/Contents.json
deleted file mode 100755
index da4a164..0000000
--- a/Prj-iOS/lpr/Assets.xcassets/Contents.json
+++ /dev/null
@@ -1,6 +0,0 @@
-{
-  "info" : {
-    "version" : 1,
-    "author" : "xcode"
-  }
-}
\ No newline at end of file
diff --git a/Prj-iOS/lpr/Assets.xcassets/back_camera_btn.imageset/Contents.json b/Prj-iOS/lpr/Assets.xcassets/back_camera_btn.imageset/Contents.json
deleted file mode 100755
index 8ca20de..0000000
--- a/Prj-iOS/lpr/Assets.xcassets/back_camera_btn.imageset/Contents.json
+++ /dev/null
@@ -1,22 +0,0 @@
-{
-  "images" : [
-    {
-      "idiom" : "universal",
-      "scale" : "1x"
-    },
-    {
-      "idiom" : "universal",
-      "filename" : "back_camera_btn@2x.png",
-      "scale" : "2x"
-    },
-    {
-      "idiom" : "universal",
-      "filename" : "back_camera_btn@3x.png",
-      "scale" : "3x"
-    }
-  ],
-  "info" : {
-    "version" : 1,
-    "author" : "xcode"
-  }
-}
\ No newline at end of file
diff --git a/Prj-iOS/lpr/Assets.xcassets/back_camera_btn.imageset/back_camera_btn@2x.png b/Prj-iOS/lpr/Assets.xcassets/back_camera_btn.imageset/back_camera_btn@2x.png
deleted file mode 100755
index 41e7387..0000000
Binary files a/Prj-iOS/lpr/Assets.xcassets/back_camera_btn.imageset/back_camera_btn@2x.png and /dev/null differ
diff --git a/Prj-iOS/lpr/Assets.xcassets/back_camera_btn.imageset/back_camera_btn@3x.png b/Prj-iOS/lpr/Assets.xcassets/back_camera_btn.imageset/back_camera_btn@3x.png
deleted file mode 100755
index be4379d..0000000
Binary files a/Prj-iOS/lpr/Assets.xcassets/back_camera_btn.imageset/back_camera_btn@3x.png and /dev/null differ
diff --git a/Prj-iOS/lpr/Assets.xcassets/flash_camera_btn.imageset/Contents.json b/Prj-iOS/lpr/Assets.xcassets/flash_camera_btn.imageset/Contents.json
deleted file mode 100755
index fc0efcf..0000000
--- a/Prj-iOS/lpr/Assets.xcassets/flash_camera_btn.imageset/Contents.json
+++ /dev/null
@@ -1,22 +0,0 @@
-{
-  "images" : [
-    {
-      "idiom" : "universal",
-      "scale" : "1x"
-    },
-    {
-      "idiom" : "universal",
-      "filename" : "flash_camera_btn@2x.png",
-      "scale" : "2x"
-    },
-    {
-      "idiom" : "universal",
-      "filename" : "flash_camera_btn@3x.png",
-      "scale" : "3x"
-    }
-  ],
-  "info" : {
-    "version" : 1,
-    "author" : "xcode"
-  }
-}
\ No newline at end of file
diff --git a/Prj-iOS/lpr/Assets.xcassets/flash_camera_btn.imageset/flash_camera_btn@2x.png b/Prj-iOS/lpr/Assets.xcassets/flash_camera_btn.imageset/flash_camera_btn@2x.png
deleted file mode 100755
index 91027cd..0000000
Binary files a/Prj-iOS/lpr/Assets.xcassets/flash_camera_btn.imageset/flash_camera_btn@2x.png and /dev/null differ
diff --git a/Prj-iOS/lpr/Assets.xcassets/flash_camera_btn.imageset/flash_camera_btn@3x.png b/Prj-iOS/lpr/Assets.xcassets/flash_camera_btn.imageset/flash_camera_btn@3x.png
deleted file mode 100755
index e751b60..0000000
Binary files a/Prj-iOS/lpr/Assets.xcassets/flash_camera_btn.imageset/flash_camera_btn@3x.png and /dev/null differ
diff --git a/Prj-iOS/lpr/Assets.xcassets/locker_btn_def.imageset/Contents.json b/Prj-iOS/lpr/Assets.xcassets/locker_btn_def.imageset/Contents.json
deleted file mode 100755
index 7f06e8f..0000000
--- a/Prj-iOS/lpr/Assets.xcassets/locker_btn_def.imageset/Contents.json
+++ /dev/null
@@ -1,22 +0,0 @@
-{
-  "images" : [
-    {
-      "idiom" : "universal",
-      "scale" : "1x"
-    },
-    {
-      "idiom" : "universal",
-      "filename" : "locker_btn_def@2x.png",
-      "scale" : "2x"
-    },
-    {
-      "idiom" : "universal",
-      "filename" : "locker_btn_def@3x.png",
-      "scale" : "3x"
-    }
-  ],
-  "info" : {
-    "version" : 1,
-    "author" : "xcode"
-  }
-}
\ No newline at end of file
diff --git a/Prj-iOS/lpr/Assets.xcassets/locker_btn_def.imageset/locker_btn_def@2x.png b/Prj-iOS/lpr/Assets.xcassets/locker_btn_def.imageset/locker_btn_def@2x.png
deleted file mode 100755
index daeadf0..0000000
Binary files a/Prj-iOS/lpr/Assets.xcassets/locker_btn_def.imageset/locker_btn_def@2x.png and /dev/null differ
diff --git a/Prj-iOS/lpr/Assets.xcassets/locker_btn_def.imageset/locker_btn_def@3x.png b/Prj-iOS/lpr/Assets.xcassets/locker_btn_def.imageset/locker_btn_def@3x.png
deleted file mode 100755
index 2e05dae..0000000
Binary files a/Prj-iOS/lpr/Assets.xcassets/locker_btn_def.imageset/locker_btn_def@3x.png and /dev/null differ
diff --git a/Prj-iOS/lpr/Assets.xcassets/take_pic_btn.imageset/Contents.json b/Prj-iOS/lpr/Assets.xcassets/take_pic_btn.imageset/Contents.json
deleted file mode 100755
index 5fccab2..0000000
--- a/Prj-iOS/lpr/Assets.xcassets/take_pic_btn.imageset/Contents.json
+++ /dev/null
@@ -1,22 +0,0 @@
-{
-  "images" : [
-    {
-      "idiom" : "universal",
-      "scale" : "1x"
-    },
-    {
-      "idiom" : "universal",
-      "filename" : "take_pic_btn@2x.png",
-      "scale" : "2x"
-    },
-    {
-      "idiom" : "universal",
-      "filename" : "take_pic_btn@3x.png",
-      "scale" : "3x"
-    }
-  ],
-  "info" : {
-    "version" : 1,
-    "author" : "xcode"
-  }
-}
\ No newline at end of file
diff --git a/Prj-iOS/lpr/Assets.xcassets/take_pic_btn.imageset/take_pic_btn@2x.png b/Prj-iOS/lpr/Assets.xcassets/take_pic_btn.imageset/take_pic_btn@2x.png
deleted file mode 100755
index fb47d20..0000000
Binary files a/Prj-iOS/lpr/Assets.xcassets/take_pic_btn.imageset/take_pic_btn@2x.png and /dev/null differ
diff --git a/Prj-iOS/lpr/Assets.xcassets/take_pic_btn.imageset/take_pic_btn@3x.png b/Prj-iOS/lpr/Assets.xcassets/take_pic_btn.imageset/take_pic_btn@3x.png
deleted file mode 100755
index db0393b..0000000
Binary files a/Prj-iOS/lpr/Assets.xcassets/take_pic_btn.imageset/take_pic_btn@3x.png and /dev/null differ
diff --git a/Prj-iOS/lpr/Base.lproj/LaunchScreen.storyboard b/Prj-iOS/lpr/Base.lproj/LaunchScreen.storyboard
deleted file mode 100755
index bfa3612..0000000
--- a/Prj-iOS/lpr/Base.lproj/LaunchScreen.storyboard
+++ /dev/null
@@ -1,25 +0,0 @@
-<?xml version="1.0" encoding="UTF-8" standalone="no"?>
-<document type="com.apple.InterfaceBuilder3.CocoaTouch.Storyboard.XIB" version="3.0" toolsVersion="13122.16" targetRuntime="iOS.CocoaTouch" propertyAccessControl="none" useAutolayout="YES" launchScreen="YES" useTraitCollections="YES" useSafeAreas="YES" colorMatched="YES" initialViewController="01J-lp-oVM">
-    <dependencies>
-        <plugIn identifier="com.apple.InterfaceBuilder.IBCocoaTouchPlugin" version="13104.12"/>
-        <capability name="Safe area layout guides" minToolsVersion="9.0"/>
-        <capability name="documents saved in the Xcode 8 format" minToolsVersion="8.0"/>
-    </dependencies>
-    <scenes>
-        <!--View Controller-->
-        <scene sceneID="EHf-IW-A2E">
-            <objects>
-                <viewController id="01J-lp-oVM" sceneMemberID="viewController">
-                    <view key="view" contentMode="scaleToFill" id="Ze5-6b-2t3">
-                        <rect key="frame" x="0.0" y="0.0" width="375" height="667"/>
-                        <autoresizingMask key="autoresizingMask" widthSizable="YES" heightSizable="YES"/>
-                        <color key="backgroundColor" red="1" green="1" blue="1" alpha="1" colorSpace="custom" customColorSpace="sRGB"/>
-                        <viewLayoutGuide key="safeArea" id="6Tk-OE-BBY"/>
-                    </view>
-                </viewController>
-                <placeholder placeholderIdentifier="IBFirstResponder" id="iYj-Kq-Ea1" userLabel="First Responder" sceneMemberID="firstResponder"/>
-            </objects>
-            <point key="canvasLocation" x="53" y="375"/>
-        </scene>
-    </scenes>
-</document>
diff --git a/Prj-iOS/lpr/CameraViewController.h b/Prj-iOS/lpr/CameraViewController.h
deleted file mode 100755
index 8331b68..0000000
--- a/Prj-iOS/lpr/CameraViewController.h
+++ /dev/null
@@ -1,32 +0,0 @@
-//
-//  CameraViewController.h
-//  lpr
-//
-//  Created by baotim on 2018/10/26.
-//  Copyright © 2018 lprSample. All rights reserved.
-//
-
-#ifdef __cplusplus
-#import <opencv2/opencv.hpp>
-#import <opencv2/imgcodecs/ios.h>
-#endif
-
-#ifdef __OBJC__
-#import <UIKit/UIKit.h>
-#import <Foundation/Foundation.h>
-#import <AVFoundation/AVFoundation.h>
-#import <AudioToolbox/AudioToolbox.h>
-#import <CoreMedia/CoreMedia.h>
-#endif
-
-NS_ASSUME_NONNULL_BEGIN
-
-typedef void(^ResultCallBack)(NSString *reuslt, UIImage *image);
-
-@interface CameraViewController : UIViewController
-
-@property(nonatomic, copy) ResultCallBack resultCB;
-
-@end
-
-NS_ASSUME_NONNULL_END
diff --git a/Prj-iOS/lpr/CameraViewController.mm b/Prj-iOS/lpr/CameraViewController.mm
deleted file mode 100755
index 43f75e8..0000000
--- a/Prj-iOS/lpr/CameraViewController.mm
+++ /dev/null
@@ -1,499 +0,0 @@
-//
-//  CameraViewController.m
-//  lpr
-//
-//  Created by baotim on 2018/10/26.
-//  Copyright © 2018 lprSample. All rights reserved.
-//
-
-#import "CameraViewController.h"
-#import "Utility.h"
-#import "Pipeline.h"
-
-//屏幕的宽、高
-#define kScreenWidth  [UIScreen mainScreen].bounds.size.width
-#define kScreenHeight [UIScreen mainScreen].bounds.size.height
-
-@interface CameraViewController () <UIAlertViewDelegate, AVCaptureVideoDataOutputSampleBufferDelegate>
-{
-    AVCaptureSession *_session;
-    AVCaptureDeviceInput *_captureInput;
-    AVCaptureStillImageOutput *_captureOutput;
-    AVCaptureVideoPreviewLayer *_preview;
-    AVCaptureDevice *_device;
-
-    NSTimer *_timer; //定时器
-    BOOL _on; //闪光灯状态
-    BOOL _capture;//导航栏动画是否完成
-    BOOL _isFoucePixel;//是否相位对焦
-    CGRect _imgRect;//拍照裁剪
-    int _count;//每几帧识别
-    CGFloat _isLensChanged;//镜头位置
-    
-    /*相位聚焦下镜头位置 镜头晃动 值不停的改变 */
-    CGFloat _isIOS8AndFoucePixelLensPosition;
-    
-    /*
-     控制识别速度，最小值为1！数值越大识别越慢。
-     相机初始化时，设置默认值为1（不要改动），判断设备若为相位对焦时，设置此值为2（可以修改，最小为1，越大越慢）
-     此值的功能是为了减小相位对焦下，因识别速度过快
-     此值在相机初始化中设置，在相机代理中使用，用户若无特殊需求不用修改。
-     */
-    int _MaxFR;
-    
-    cv::Mat source_image;
-}
-
-@property (assign, nonatomic) BOOL adjustingFocus;
-@property (nonatomic, retain) CALayer *customLayer;
-@property (nonatomic,assign) BOOL isProcessingImage;
-
-@property (nonatomic, strong) UIImage* image;
-
-@end
-
-@implementation CameraViewController
-
-- (void)viewDidLoad {
-    [super viewDidLoad];
-    // Do any additional setup after loading the view.
-    self.view.backgroundColor = [UIColor clearColor];
-    
-    //初始化相机
-    [self initialize];
-    
-    //创建相机界面控件
-    [self createCameraView];
-}
-
-- (void)viewWillAppear:(BOOL)animated {
-    [super viewWillAppear:animated];
-    
-    _capture = NO;
-    [self performSelector:@selector(changeCapture) withObject:nil afterDelay:0.4];
-    //不支持相位对焦情况下(iPhone6以后的手机支持相位对焦) 设置定时器 开启连续对焦
-    if (!_isFoucePixel) {
-        _timer = [NSTimer scheduledTimerWithTimeInterval:1.3 target:self selector:@selector(fouceMode) userInfo:nil repeats:YES];
-    }
-    
-    AVCaptureDevice*camDevice = [AVCaptureDevice defaultDeviceWithMediaType:AVMediaTypeVideo];
-    int flags = NSKeyValueObservingOptionNew;
-    //注册通知
-    [camDevice addObserver:self forKeyPath:@"adjustingFocus" options:flags context:nil];
-    if (_isFoucePixel) {
-        [camDevice addObserver:self forKeyPath:@"lensPosition" options:flags context:nil];
-    }
-    [_session startRunning];
-}
-
-- (void)viewWillDisappear:(BOOL)animated {
-    [super viewWillDisappear:animated];
-    self.navigationController.navigationBarHidden = NO;
-}
-
-- (void)viewDidDisappear:(BOOL)animated {
-    [super viewDidDisappear:animated];
-    
-    if (!_isFoucePixel) {
-        [_timer invalidate];
-        _timer = nil;
-    }
-    AVCaptureDevice*camDevice =[AVCaptureDevice defaultDeviceWithMediaType:AVMediaTypeVideo];
-    [camDevice removeObserver:self forKeyPath:@"adjustingFocus"];
-    if (_isFoucePixel) {
-        [camDevice removeObserver:self forKeyPath:@"lensPosition"];
-    }
-    [_session stopRunning];
-    
-    _capture = NO;
-}
-
-- (void)changeCapture {
-    _capture = YES;
-}
-
-#pragma mark - Private Methods
-//初始化相机
-- (void)initialize {
-    //判断摄像头授权
-    AVAuthorizationStatus authStatus = [AVCaptureDevice authorizationStatusForMediaType:AVMediaTypeVideo];
-    if(authStatus == AVAuthorizationStatusRestricted || authStatus == AVAuthorizationStatusDenied){
-        self.view.backgroundColor = [UIColor blackColor];
-        UIAlertView * alt = [[UIAlertView alloc] initWithTitle:@"未获得授权使用摄像头" message:@"请在'设置-隐私-相机'打开" delegate:self cancelButtonTitle:nil otherButtonTitles:@"OK", nil];
-        [alt show];
-        return;
-    }
-    
-    _MaxFR = 1;
-    //1.创建会话层
-    _session = [[AVCaptureSession alloc] init];
-    [_session setSessionPreset:AVCaptureSessionPreset1920x1080];
-    
-    //2.创建、配置输入设备
-    NSArray *devices = [AVCaptureDevice devicesWithMediaType:AVMediaTypeVideo];
-    for (AVCaptureDevice *device in devices){
-        if (device.position == AVCaptureDevicePositionBack){
-            _device = device;
-            _captureInput = [AVCaptureDeviceInput deviceInputWithDevice:device error:nil];
-        }
-    }
-    [_session addInput:_captureInput];
-    
-    //2.创建视频流输出
-    AVCaptureVideoDataOutput *captureOutput = [[AVCaptureVideoDataOutput alloc] init];
-    captureOutput.alwaysDiscardsLateVideoFrames = YES;
-    dispatch_queue_t queue;
-    queue = dispatch_queue_create("cameraQueue", NULL);
-    [captureOutput setSampleBufferDelegate:self queue:queue];
-    NSString* key = (NSString*)kCVPixelBufferPixelFormatTypeKey;
-    NSNumber* value = [NSNumber numberWithUnsignedInt:kCVPixelFormatType_32BGRA];
-    NSDictionary* videoSettings = [NSDictionary dictionaryWithObject:value forKey:key];
-    [captureOutput setVideoSettings:videoSettings];
-    [_session addOutput:captureOutput];
-    
-    //3.创建、配置静态拍照输出
-    _captureOutput = [[AVCaptureStillImageOutput alloc] init];
-    NSDictionary *outputSettings = [[NSDictionary alloc] initWithObjectsAndKeys:AVVideoCodecJPEG,AVVideoCodecKey,nil];
-    [_captureOutput setOutputSettings:outputSettings];
-    [_session addOutput:_captureOutput];
-    
-    //4.预览图层
-    _preview = [AVCaptureVideoPreviewLayer layerWithSession: _session];
-    _preview.frame = CGRectMake(0, 0, self.view.frame.size.width, self.view.frame.size.height);
-    _preview.videoGravity = AVLayerVideoGravityResizeAspectFill;
-    [self.view.layer addSublayer:_preview];
-    
-    //判断是否相位对焦
-    if ([[[UIDevice currentDevice] systemVersion] floatValue] >= 8.0) {
-        AVCaptureDeviceFormat *deviceFormat = _device.activeFormat;
-        if (deviceFormat.autoFocusSystem == AVCaptureAutoFocusSystemPhaseDetection){
-            _isFoucePixel = YES;
-            _MaxFR = 2;
-        }
-    }
-}
-
-- (void)createCameraView
-{
-    //设置覆盖层
-    CAShapeLayer *maskWithHole = [CAShapeLayer layer];
-    // Both frames are defined in the same coordinate system
-    CGRect biggerRect = self.view.bounds;
-    CGFloat offset = 1.0f;
-    if ([[UIScreen mainScreen] scale] >= 2) {
-        offset = 0.5;
-    }
-    CGRect smallFrame = CGRectMake(45, 100, 300, 500);
-    CGRect smallerRect = CGRectInset(smallFrame, -offset, -offset) ;
-    UIBezierPath *maskPath = [UIBezierPath bezierPath];
-    [maskPath moveToPoint:CGPointMake(CGRectGetMinX(biggerRect), CGRectGetMinY(biggerRect))];
-    [maskPath addLineToPoint:CGPointMake(CGRectGetMinX(biggerRect), CGRectGetMaxY(biggerRect))];
-    [maskPath addLineToPoint:CGPointMake(CGRectGetMaxX(biggerRect), CGRectGetMaxY(biggerRect))];
-    [maskPath addLineToPoint:CGPointMake(CGRectGetMaxX(biggerRect), CGRectGetMinY(biggerRect))];
-    [maskPath addLineToPoint:CGPointMake(CGRectGetMinX(biggerRect), CGRectGetMinY(biggerRect))];
-    [maskPath moveToPoint:CGPointMake(CGRectGetMinX(smallerRect), CGRectGetMinY(smallerRect))];
-    [maskPath addLineToPoint:CGPointMake(CGRectGetMinX(smallerRect), CGRectGetMaxY(smallerRect))];
-    [maskPath addLineToPoint:CGPointMake(CGRectGetMaxX(smallerRect), CGRectGetMaxY(smallerRect))];
-    [maskPath addLineToPoint:CGPointMake(CGRectGetMaxX(smallerRect), CGRectGetMinY(smallerRect))];
-    [maskPath addLineToPoint:CGPointMake(CGRectGetMinX(smallerRect), CGRectGetMinY(smallerRect))];
-    [maskWithHole setPath:[maskPath CGPath]];
-    [maskWithHole setFillRule:kCAFillRuleEvenOdd];
-    [maskWithHole setFillColor:[[UIColor colorWithWhite:0 alpha:0.35] CGColor]];
-    [self.view.layer addSublayer:maskWithHole];
-    [self.view.layer setMasksToBounds:YES];
-    
-    /* 相机按钮 适配了iPhone和ipad 不同需求自行修改界面*/
-    //返回、闪光灯按钮
-    CGFloat backWidth = 35;
-    if (kScreenHeight>=1024) {
-        backWidth = 50;
-    }
-    CGFloat s = 80;
-    CGFloat s1 = 0;
-    if (kScreenHeight==480) {
-        s = 60;
-        s1 = 10;
-    }
-    UIButton *backBtn = [[UIButton alloc]initWithFrame:CGRectMake(kScreenWidth/16,kScreenWidth/16-s1, backWidth, backWidth)];
-    [backBtn addTarget:self action:@selector(backAction) forControlEvents:UIControlEventTouchUpInside];
-    [backBtn setImage:[UIImage imageNamed:@"back_camera_btn"] forState:UIControlStateNormal];
-    backBtn.titleLabel.textAlignment = NSTextAlignmentLeft;
-    [self.view addSubview:backBtn];
-    
-    UIButton *flashBtn = [[UIButton alloc]initWithFrame:CGRectMake(kScreenWidth-kScreenWidth/16-backWidth,kScreenWidth/16-s1, backWidth, backWidth)];
-    [flashBtn setImage:[UIImage imageNamed:@"flash_camera_btn"] forState:UIControlStateNormal];
-    [flashBtn addTarget:self action:@selector(modeBtn) forControlEvents:UIControlEventTouchUpInside];
-    [self.view addSubview:flashBtn];
-    
-    //拍照视图 上拉按钮 拍照按钮
-    UIButton *upBtn = [[UIButton alloc]initWithFrame:CGRectMake(kScreenWidth/2-60, kScreenHeight-20, 120, 20)];
-    upBtn.tag = 1001;
-    [upBtn addTarget:self action:@selector(upBtn:) forControlEvents:UIControlEventTouchUpInside];
-    [upBtn setImage:[UIImage imageNamed:@"locker_btn_def"] forState:UIControlStateNormal];
-    [self.view addSubview:upBtn];
-    
-    UIButton *photoBtn = [[UIButton alloc]initWithFrame:CGRectMake(kScreenWidth/2-30,kScreenHeight-s,60, 60)];
-    photoBtn.tag = 1000;
-    photoBtn.hidden = YES;
-    [photoBtn setImage:[UIImage imageNamed:@"take_pic_btn"] forState:UIControlStateNormal];
-    [photoBtn addTarget:self action:@selector(photoBtn) forControlEvents:UIControlEventTouchUpInside];
-    [photoBtn setTitleColor:[UIColor grayColor] forState:UIControlStateHighlighted];
-    [self.view addSubview:photoBtn];
-    [self.view bringSubviewToFront:photoBtn];
-}
-
-#pragma mark - UIAlertViewDelegate
-- (void)alertView:(UIAlertView *)alertView clickedButtonAtIndex:(NSInteger)buttonIndex{
-    [self.navigationController popViewControllerAnimated:YES];
-}
-
-//隐藏状态栏
-- (UIStatusBarStyle)preferredStatusBarStyle{
-    return UIStatusBarStyleDefault;
-}
-- (BOOL)prefersStatusBarHidden{
-    return YES;
-}
-
-//对焦
-- (void)fouceMode
-{
-    NSError *error;
-    AVCaptureDevice *device = [self cameraWithPosition:AVCaptureDevicePositionBack];
-    if ([device isFocusModeSupported:AVCaptureFocusModeAutoFocus])
-    {
-        if ([device lockForConfiguration:&error]) {
-            CGPoint cameraPoint = [_preview captureDevicePointOfInterestForPoint:self.view.center];
-            [device setFocusPointOfInterest:cameraPoint];
-            [device setFocusMode:AVCaptureFocusModeAutoFocus];
-            [device unlockForConfiguration];
-        } else {
-            //NSLog(@"Error: %@", error);
-        }
-    }
-}
-
-- (AVCaptureDevice *)cameraWithPosition:(AVCaptureDevicePosition)position
-{
-    NSArray *devices = [AVCaptureDevice devicesWithMediaType:AVMediaTypeVideo];
-    for (AVCaptureDevice *device in devices){
-        if (device.position == position){
-            return device;
-        }
-    }
-    return nil;
-}
-
-- (NSString *)getPath:(NSString*)fileName
-{
-    NSString *bundlePath = [NSBundle mainBundle].bundlePath;
-    NSString *path = [bundlePath stringByAppendingPathComponent:fileName];
-    return path;
-}
-
-- (NSString *)simpleRecognition:(cv::Mat&)src
-{
-    NSString *path_1 = [self getPath:@"cascade.xml"];
-    NSString *path_2 = [self getPath:@"HorizonalFinemapping.prototxt"];
-    NSString *path_3 = [self getPath:@"HorizonalFinemapping.caffemodel"];
-    NSString *path_4 = [self getPath:@"Segmentation.prototxt"];
-    NSString *path_5 = [self getPath:@"Segmentation.caffemodel"];
-    NSString *path_6 = [self getPath:@"CharacterRecognization.prototxt"];
-    NSString *path_7 = [self getPath:@"CharacterRecognization.caffemodel"];
-    
-    std::string *cpath_1 = new std::string([path_1 UTF8String]);
-    std::string *cpath_2 = new std::string([path_2 UTF8String]);
-    std::string *cpath_3 = new std::string([path_3 UTF8String]);
-    std::string *cpath_4 = new std::string([path_4 UTF8String]);
-    std::string *cpath_5 = new std::string([path_5 UTF8String]);
-    std::string *cpath_6 = new std::string([path_6 UTF8String]);
-    std::string *cpath_7 = new std::string([path_7 UTF8String]);
-    
-    
-    pr::PipelinePR pr2 = pr::PipelinePR(*cpath_1, *cpath_2, *cpath_3, *cpath_4, *cpath_5, *cpath_6, *cpath_7);
-    
-    std::vector<pr::PlateInfo> list_res = pr2.RunPiplineAsImage(src);
-    std::string concat_results = "";
-    for(auto one:list_res) {
-        if(one.confidence>0.7) {
-            concat_results += one.getPlateName()+",";
-        }
-    }
-    
-    NSString *str = [NSString stringWithCString:concat_results.c_str() encoding:NSUTF8StringEncoding];
-    if (str.length > 0) {
-        str = [str substringToIndex:str.length-1];
-        str = [NSString stringWithFormat:@"%@",str];
-    } else {
-        str = [NSString stringWithFormat:@"未识别成功"];
-    }
-    NSLog(@"===> 识别结果 = %@", str);
-    
-    return str;
-}
-
-#pragma mark - Actions
-//返回按钮按钮点击事件
-- (void)backAction
-{
-    [self dismissViewControllerAnimated:YES completion:nil];
-}
-
-//闪光灯按钮点击事件
-- (void)modeBtn
-{
-    if (![_device hasTorch]) {
-        //NSLog(@"no torch");
-    } else {
-        [_device lockForConfiguration:nil];
-        if (!_on) {
-            [_device setTorchMode: AVCaptureTorchModeOn];
-            _on = YES;
-        }else{
-            [_device setTorchMode: AVCaptureTorchModeOff];
-            _on = NO;
-        }
-        [_device unlockForConfiguration];
-    }
-}
-
-//上拉按钮点击事件
-- (void)upBtn:(UIButton *)upBtn
-{
-    UIButton *photoBtn = (UIButton *)[self.view viewWithTag:1000];
-    photoBtn.hidden = NO;
-    upBtn.hidden = YES;
-}
-
-//拍照按钮点击事件
-- (void)photoBtn
-{
-    self.isProcessingImage = YES;
-    //get connection
-    AVCaptureConnection *videoConnection = nil;
-    for (AVCaptureConnection *connection in _captureOutput.connections) {
-        for (AVCaptureInputPort *port in [connection inputPorts]) {
-            if ([[port mediaType] isEqual:AVMediaTypeVideo] ) {
-                videoConnection = connection;
-                break;
-            }
-        }
-        if (videoConnection) { break; }
-    }
-    
-    //get UIImage
-    [_captureOutput captureStillImageAsynchronouslyFromConnection:videoConnection completionHandler:
-     ^(CMSampleBufferRef imageSampleBuffer, NSError *error) {
-         if (imageSampleBuffer != NULL) {
-             //停止取景
-             [_session stopRunning];
-             
-             NSData *imageData = [AVCaptureStillImageOutput jpegStillImageNSDataRepresentation:imageSampleBuffer];
-             UIImage *tempImage = [[UIImage alloc] initWithData:imageData];
-             
-             UIImage *temp_image = [Utility scaleAndRotateImageBackCamera:tempImage];
-             source_image = [Utility cvMatFromUIImage:temp_image];
-             NSString* text = [self simpleRecognition:source_image];
-             
-             NSMutableDictionary* resultDict = [NSMutableDictionary new];
-             resultDict[@"image"] = temp_image;
-             resultDict[@"text"] = text;
-             [self performSelectorOnMainThread:@selector(readyToGetImage:) withObject:resultDict waitUntilDone:NO];
-             self.isProcessingImage = NO;
-         }
-     }];
-}
-
-//从摄像头缓冲区获取图像
-#pragma mark - AVCaptureSession delegate
-- (void)captureOutput:(AVCaptureOutput *)captureOutput
-didOutputSampleBuffer:(CMSampleBufferRef)sampleBuffer
-       fromConnection:(AVCaptureConnection *)connection
-{
-    CVImageBufferRef imageBuffer = CMSampleBufferGetImageBuffer(sampleBuffer);
-    CVPixelBufferLockBaseAddress(imageBuffer,0);
-    uint8_t *baseAddress = (uint8_t *)CVPixelBufferGetBaseAddress(imageBuffer);
-    size_t bytesPerRow = CVPixelBufferGetBytesPerRow(imageBuffer);
-    size_t width = CVPixelBufferGetWidth(imageBuffer);
-    size_t height = CVPixelBufferGetHeight(imageBuffer);
-    
-    /*We unlock the  image buffer*/
-    CVPixelBufferUnlockBaseAddress(imageBuffer,0);
-    
-    /*Create a CGImageRef from the CVImageBufferRef*/
-    CGColorSpaceRef colorSpace = CGColorSpaceCreateDeviceRGB();
-    CGContextRef newContext = CGBitmapContextCreate(baseAddress, width, height, 8, bytesPerRow, colorSpace, kCGBitmapByteOrder32Little | kCGImageAlphaPremultipliedFirst);
-    CGImageRef newImage = CGBitmapContextCreateImage(newContext);
-    
-    /*We release some components*/
-    CGContextRelease(newContext);
-    CGColorSpaceRelease(colorSpace);
-    
-    /*We display the result on the image view (We need to change the orientation of the image so that the video is displayed correctly)*/
-    self.image = [UIImage imageWithCGImage:newImage scale:1.0 orientation:UIImageOrientationUp];
-    /*We relase the CGImageRef*/
-    CGImageRelease(newImage);
-    
-    //检边识别
-    if (_capture == YES) { //导航栏动画完成
-        if (self.isProcessingImage==NO) {  //点击拍照后 不去识别
-            if (!self.adjustingFocus) {  //反差对焦下 非正在对焦状态（相位对焦下self.adjustingFocus此值不会改变）
-                if (_isLensChanged == _isIOS8AndFoucePixelLensPosition) {
-                    _count++;
-                    if (_count >= _MaxFR) {
-                        
-                        //识别
-                        UIImage *temp_image = [Utility scaleAndRotateImageBackCamera:self.image];
-                        source_image = [Utility cvMatFromUIImage:temp_image];
-                        NSString* text = [self simpleRecognition:source_image];
-                        
-                        if (text.length == 7) { //识别成功
-                            _count = 0;
-                            // 停止取景
-                            [_session stopRunning];
-                            //设置震动
-                            AudioServicesPlaySystemSound(kSystemSoundID_Vibrate);
-                            
-                            NSMutableDictionary* resultDict = [NSMutableDictionary new];
-                            resultDict[@"image"] = temp_image;
-                            resultDict[@"text"] = text;
-
-                            [self performSelectorOnMainThread:@selector(readyToGetImage:) withObject:resultDict waitUntilDone:NO];
-                        }
-                    }
-                } else {
-                    _isLensChanged = _isIOS8AndFoucePixelLensPosition;
-                    _count = 0;
-                }
-            }
-        }
-    }
-    CVPixelBufferUnlockBaseAddress(imageBuffer,0);
-}
-
-//找边成功开始拍照
-- (void)readyToGetImage:(NSDictionary *)resultDict
-{
-    [self dismissViewControllerAnimated:NO completion:^{
-    }];
-    
-    if (self.resultCB) {
-        self.resultCB(resultDict[@"text"], resultDict[@"image"]);
-    }
-}
-
-- (void)observeValueForKeyPath:(NSString*)keyPath ofObject:(id)object change:(NSDictionary*)change context:(void*)context {
-    
-    /*反差对焦 监听反差对焦此*/
-    if([keyPath isEqualToString:@"adjustingFocus"]){
-        self.adjustingFocus =[[change objectForKey:NSKeyValueChangeNewKey] isEqualToNumber:[NSNumber numberWithInt:1]];
-    }
-    /*监听相位对焦此*/
-    if([keyPath isEqualToString:@"lensPosition"]){
-        _isIOS8AndFoucePixelLensPosition =[[change objectForKey:NSKeyValueChangeNewKey] floatValue];
-        //NSLog(@"监听_isIOS8AndFoucePixelLensPosition == %f",_isIOS8AndFoucePixelLensPosition);
-    }
-}
-
-
-@end
diff --git a/Prj-iOS/lpr/DemoImage/Screen Shot 2018-10-26 at 3.43.44 PM.png b/Prj-iOS/lpr/DemoImage/Screen Shot 2018-10-26 at 3.43.44 PM.png
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diff --git a/Prj-iOS/lpr/DemoImage/Screen Shot 2018-10-26 at 3.43.53 PM.png b/Prj-iOS/lpr/DemoImage/Screen Shot 2018-10-26 at 3.43.53 PM.png
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diff --git a/Prj-iOS/lpr/DemoImage/Screen Shot 2018-10-26 at 3.44.04 PM.png b/Prj-iOS/lpr/DemoImage/Screen Shot 2018-10-26 at 3.44.04 PM.png
deleted file mode 100755
index b3fa038..0000000
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diff --git a/Prj-iOS/lpr/Info.plist b/Prj-iOS/lpr/Info.plist
deleted file mode 100755
index 19d69a9..0000000
--- a/Prj-iOS/lpr/Info.plist
+++ /dev/null
@@ -1,45 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
-<plist version="1.0">
-<dict>
-	<key>CFBundleDevelopmentRegion</key>
-	<string>$(DEVELOPMENT_LANGUAGE)</string>
-	<key>CFBundleExecutable</key>
-	<string>$(EXECUTABLE_NAME)</string>
-	<key>CFBundleIdentifier</key>
-	<string>$(PRODUCT_BUNDLE_IDENTIFIER)</string>
-	<key>CFBundleInfoDictionaryVersion</key>
-	<string>6.0</string>
-	<key>CFBundleName</key>
-	<string>$(PRODUCT_NAME)</string>
-	<key>CFBundlePackageType</key>
-	<string>APPL</string>
-	<key>CFBundleShortVersionString</key>
-	<string>1.0</string>
-	<key>CFBundleVersion</key>
-	<string>1</string>
-	<key>LSRequiresIPhoneOS</key>
-	<true/>
-	<key>NSCameraUsageDescription</key>
-	<string>欢迎使用相机</string>
-	<key>NSPhotoLibraryUsageDescription</key>
-	<string>欢迎使用相册</string>
-	<key>UILaunchStoryboardName</key>
-	<string>LaunchScreen</string>
-	<key>UIRequiredDeviceCapabilities</key>
-	<array>
-		<string>armv7</string>
-	</array>
-	<key>UISupportedInterfaceOrientations</key>
-	<array>
-		<string>UIInterfaceOrientationPortrait</string>
-	</array>
-	<key>UISupportedInterfaceOrientations~ipad</key>
-	<array>
-		<string>UIInterfaceOrientationPortrait</string>
-		<string>UIInterfaceOrientationPortraitUpsideDown</string>
-		<string>UIInterfaceOrientationLandscapeLeft</string>
-		<string>UIInterfaceOrientationLandscapeRight</string>
-	</array>
-</dict>
-</plist>
diff --git a/Prj-iOS/lpr/Podfile b/Prj-iOS/lpr/Podfile
deleted file mode 100755
index 00459e4..0000000
--- a/Prj-iOS/lpr/Podfile
+++ /dev/null
@@ -1,8 +0,0 @@
-platform :ios, '10.1'
-
-target "lpr" do
-
-pod 'OpenCV', '~> 3.4.2'
-pod 'Masonry'
-
-end
diff --git a/Prj-iOS/lpr/Podfile.lock b/Prj-iOS/lpr/Podfile.lock
deleted file mode 100755
index e4511f2..0000000
--- a/Prj-iOS/lpr/Podfile.lock
+++ /dev/null
@@ -1,20 +0,0 @@
-PODS:
-  - Masonry (1.1.0)
-  - OpenCV (3.4.2)
-
-DEPENDENCIES:
-  - Masonry
-  - OpenCV (~> 3.4.2)
-
-SPEC REPOS:
-  https://github.com/cocoapods/specs.git:
-    - Masonry
-    - OpenCV
-
-SPEC CHECKSUMS:
-  Masonry: 678fab65091a9290e40e2832a55e7ab731aad201
-  OpenCV: 452909747854c92e0f59670961ed5131f5286cb5
-
-PODFILE CHECKSUM: 94db02cda76a5ac6371a03e6e2ca4e9035fc0da6
-
-COCOAPODS: 1.5.3
diff --git a/Prj-iOS/lpr/README.md b/Prj-iOS/lpr/README.md
deleted file mode 100755
index 404c617..0000000
--- a/Prj-iOS/lpr/README.md
+++ /dev/null
@@ -1,4 +0,0 @@
-
-Depency Library:
-- Masonry, 1.1.0
-- OpenCV, 3.4.2
diff --git a/Prj-iOS/lpr/Resource/CharacterRecognization.caffemodel b/Prj-iOS/lpr/Resource/CharacterRecognization.caffemodel
deleted file mode 100755
index d424408..0000000
Binary files a/Prj-iOS/lpr/Resource/CharacterRecognization.caffemodel and /dev/null differ
diff --git a/Prj-iOS/lpr/Resource/CharacterRecognization.prototxt b/Prj-iOS/lpr/Resource/CharacterRecognization.prototxt
deleted file mode 100755
index 91aa29e..0000000
--- a/Prj-iOS/lpr/Resource/CharacterRecognization.prototxt
+++ /dev/null
@@ -1,123 +0,0 @@
-input: "data"
-input_dim: 1
-input_dim: 1
-input_dim: 30
-input_dim: 14
-layer {
-  name: "conv2d_1"
-  type: "Convolution"
-  bottom: "data"
-  top: "conv2d_1"
-  convolution_param {
-    num_output: 32
-    bias_term: true
-    pad: 0
-    kernel_size: 3
-    stride: 1
-  }
-}
-layer {
-  name: "activation_1"
-  type: "ReLU"
-  bottom: "conv2d_1"
-  top: "activation_1"
-}
-layer {
-  name: "max_pooling2d_1"
-  type: "Pooling"
-  bottom: "activation_1"
-  top: "max_pooling2d_1"
-  pooling_param {
-    pool: MAX
-    kernel_size: 2
-    stride: 2
-    pad: 0
-  }
-}
-layer {
-  name: "conv2d_2"
-  type: "Convolution"
-  bottom: "max_pooling2d_1"
-  top: "conv2d_2"
-  convolution_param {
-    num_output: 64
-    bias_term: true
-    pad: 0
-    kernel_size: 3
-    stride: 1
-  }
-}
-layer {
-  name: "activation_2"
-  type: "ReLU"
-  bottom: "conv2d_2"
-  top: "activation_2"
-}
-layer {
-  name: "max_pooling2d_2"
-  type: "Pooling"
-  bottom: "activation_2"
-  top: "max_pooling2d_2"
-  pooling_param {
-    pool: MAX
-    kernel_size: 2
-    stride: 2
-    pad: 0
-  }
-}
-layer {
-  name: "conv2d_3"
-  type: "Convolution"
-  bottom: "max_pooling2d_2"
-  top: "conv2d_3"
-  convolution_param {
-    num_output: 128
-    bias_term: true
-    pad: 0
-    kernel_size: 2
-    stride: 1
-  }
-}
-layer {
-  name: "activation_3"
-  type: "ReLU"
-  bottom: "conv2d_3"
-  top: "activation_3"
-}
-layer {
-  name: "flatten_1"
-  type: "Flatten"
-  bottom: "activation_3"
-  top: "flatten_1"
-}
-layer {
-  name: "dense_1"
-  type: "InnerProduct"
-  bottom: "flatten_1"
-  top: "dense_1"
-  inner_product_param {
-    num_output: 256
-  }
-}
-layer {
-  name: "relu2"
-  type: "ReLU"
-  bottom: "dense_1"
-  top: "relu2"
-}
-layer {
-  name: "dense2"
-  type: "InnerProduct"
-  bottom: "relu2"
-  top: "dense2"
-  inner_product_param {
-    num_output: 65
-  }
-}
-
-layer {
-  name: "prob"
-  type: "Softmax"
-  bottom: "dense2"
-  top: "prob"
-}
\ No newline at end of file
diff --git a/Prj-iOS/lpr/Resource/HorizonalFinemapping.caffemodel b/Prj-iOS/lpr/Resource/HorizonalFinemapping.caffemodel
deleted file mode 100755
index 1af5863..0000000
Binary files a/Prj-iOS/lpr/Resource/HorizonalFinemapping.caffemodel and /dev/null differ
diff --git a/Prj-iOS/lpr/Resource/HorizonalFinemapping.prototxt b/Prj-iOS/lpr/Resource/HorizonalFinemapping.prototxt
deleted file mode 100755
index 21726dd..0000000
--- a/Prj-iOS/lpr/Resource/HorizonalFinemapping.prototxt
+++ /dev/null
@@ -1,95 +0,0 @@
-input: "data"
-input_dim: 1
-input_dim: 3
-input_dim: 16
-input_dim: 66
-layer {
-  name: "conv1"
-  type: "Convolution"
-  bottom: "data"
-  top: "conv1"
-  convolution_param {
-    num_output: 10
-    bias_term: true
-    pad: 0
-    kernel_size: 3
-    stride: 1
-  }
-}
-layer {
-  name: "relu1"
-  type: "ReLU"
-  bottom: "conv1"
-  top: "conv1"
-}
-layer {
-  name: "max_pooling2d_3"
-  type: "Pooling"
-  bottom: "conv1"
-  top: "max_pooling2d_3"
-  pooling_param {
-    pool: MAX
-    kernel_size: 2
-    stride: 2
-    pad: 0
-  }
-}
-layer {
-  name: "conv2"
-  type: "Convolution"
-  bottom: "max_pooling2d_3"
-  top: "conv2"
-  convolution_param {
-    num_output: 16
-    bias_term: true
-    pad: 0
-    kernel_size: 3
-    stride: 1
-  }
-}
-layer {
-  name: "relu2"
-  type: "ReLU"
-  bottom: "conv2"
-  top: "conv2"
-}
-layer {
-  name: "conv3"
-  type: "Convolution"
-  bottom: "conv2"
-  top: "conv3"
-  convolution_param {
-    num_output: 32
-    bias_term: true
-    pad: 0
-    kernel_size: 3
-    stride: 1
-  }
-}
-layer {
-  name: "relu3"
-  type: "ReLU"
-  bottom: "conv3"
-  top: "conv3"
-}
-layer {
-  name: "flatten_2"
-  type: "Flatten"
-  bottom: "conv3"
-  top: "flatten_2"
-}
-layer {
-  name: "dense"
-  type: "InnerProduct"
-  bottom: "flatten_2"
-  top: "dense"
-  inner_product_param {
-    num_output: 2
-  }
-}
-layer {
-  name: "relu4"
-  type: "ReLU"
-  bottom: "dense"
-  top: "dense"
-}
diff --git a/Prj-iOS/lpr/Resource/SegmenationFree-Inception.caffemodel b/Prj-iOS/lpr/Resource/SegmenationFree-Inception.caffemodel
deleted file mode 100755
index 311ee17..0000000
Binary files a/Prj-iOS/lpr/Resource/SegmenationFree-Inception.caffemodel and /dev/null differ
diff --git a/Prj-iOS/lpr/Resource/SegmenationFree-Inception.prototxt b/Prj-iOS/lpr/Resource/SegmenationFree-Inception.prototxt
deleted file mode 100755
index 8419763..0000000
--- a/Prj-iOS/lpr/Resource/SegmenationFree-Inception.prototxt
+++ /dev/null
@@ -1,454 +0,0 @@
-input: "data"
-input_dim: 1
-input_dim: 3
-input_dim: 160
-input_dim: 40
-layer {
-  name: "conv0"
-  type: "Convolution"
-  bottom: "data"
-  top: "conv0"
-  convolution_param {
-    num_output: 32
-    bias_term: true
-    pad_h: 1
-    pad_w: 1
-    kernel_h: 3
-    kernel_w: 3
-    stride_h: 1
-    stride_w: 1
-  }
-}
-layer {
-  name: "bn0"
-  type: "BatchNorm"
-  bottom: "conv0"
-  top: "bn0"
-  batch_norm_param {
-    moving_average_fraction: 0.99
-    eps: 0.001
-  }
-}
-layer {
-  name: "bn0_scale"
-  type: "Scale"
-  bottom: "bn0"
-  top: "bn0"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "relu0"
-  type: "ReLU"
-  bottom: "bn0"
-  top: "bn0"
-}
-layer {
-  name: "pool0"
-  type: "Pooling"
-  bottom: "bn0"
-  top: "pool0"
-  pooling_param {
-    pool: MAX
-    kernel_h: 2
-    kernel_w: 2
-    stride_h: 2
-    stride_w: 2
-    pad_h: 0
-    pad_w: 0
-  }
-}
-layer {
-  name: "conv1"
-  type: "Convolution"
-  bottom: "pool0"
-  top: "conv1"
-  convolution_param {
-    num_output: 64
-    bias_term: true
-    pad_h: 1
-    pad_w: 1
-    kernel_h: 3
-    kernel_w: 3
-    stride_h: 1
-    stride_w: 1
-  }
-}
-layer {
-  name: "bn1"
-  type: "BatchNorm"
-  bottom: "conv1"
-  top: "bn1"
-  batch_norm_param {
-    moving_average_fraction: 0.99
-    eps: 0.001
-  }
-}
-layer {
-  name: "bn1_scale"
-  type: "Scale"
-  bottom: "bn1"
-  top: "bn1"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "relu1"
-  type: "ReLU"
-  bottom: "bn1"
-  top: "bn1"
-}
-layer {
-  name: "pool1"
-  type: "Pooling"
-  bottom: "bn1"
-  top: "pool1"
-  pooling_param {
-    pool: MAX
-    kernel_h: 2
-    kernel_w: 2
-    stride_h: 2
-    stride_w: 2
-    pad_h: 0
-    pad_w: 0
-  }
-}
-layer {
-  name: "conv2"
-  type: "Convolution"
-  bottom: "pool1"
-  top: "conv2"
-  convolution_param {
-    num_output: 128
-    bias_term: true
-    pad_h: 1
-    pad_w: 1
-    kernel_h: 3
-    kernel_w: 3
-    stride_h: 1
-    stride_w: 1
-  }
-}
-layer {
-  name: "bn2"
-  type: "BatchNorm"
-  bottom: "conv2"
-  top: "bn2"
-  batch_norm_param {
-    moving_average_fraction: 0.99
-    eps: 0.001
-  }
-}
-layer {
-  name: "bn2_scale"
-  type: "Scale"
-  bottom: "bn2"
-  top: "bn2"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "relu2"
-  type: "ReLU"
-  bottom: "bn2"
-  top: "bn2"
-}
-layer {
-  name: "pool2"
-  type: "Pooling"
-  bottom: "bn2"
-  top: "pool2"
-  pooling_param {
-    pool: MAX
-    kernel_h: 2
-    kernel_w: 2
-    stride_h: 2
-    stride_w: 2
-    pad_h: 0
-    pad_w: 0
-  }
-}
-layer {
-  name: "conv2d_1"
-  type: "Convolution"
-  bottom: "pool2"
-  top: "conv2d_1"
-  convolution_param {
-    num_output: 256
-    bias_term: true
-    pad_h: 0
-    pad_w: 0
-    kernel_h: 1
-    kernel_w: 5
-    stride_h: 1
-    stride_w: 1
-  }
-}
-layer {
-  name: "batch_normalization_1"
-  type: "BatchNorm"
-  bottom: "conv2d_1"
-  top: "batch_normalization_1"
-  batch_norm_param {
-    moving_average_fraction: 0.99
-    eps: 0.001
-  }
-}
-layer {
-  name: "batch_normalization_1_scale"
-  type: "Scale"
-  bottom: "batch_normalization_1"
-  top: "batch_normalization_1"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "activation_1"
-  type: "ReLU"
-  bottom: "batch_normalization_1"
-  top: "batch_normalization_1"
-}
-layer {
-  name: "conv2d_2"
-  type: "Convolution"
-  bottom: "batch_normalization_1"
-  top: "conv2d_2"
-  convolution_param {
-    num_output: 256
-    bias_term: true
-    pad_h: 3
-    pad_w: 0
-    kernel_h: 7
-    kernel_w: 1
-    stride_h: 1
-    stride_w: 1
-  }
-}
-layer {
-  name: "conv2d_3"
-  type: "Convolution"
-  bottom: "batch_normalization_1"
-  top: "conv2d_3"
-  convolution_param {
-    num_output: 256
-    bias_term: true
-    pad_h: 2
-    pad_w: 0
-    kernel_h: 5
-    kernel_w: 1
-    stride_h: 1
-    stride_w: 1
-  }
-}
-layer {
-  name: "conv2d_4"
-  type: "Convolution"
-  bottom: "batch_normalization_1"
-  top: "conv2d_4"
-  convolution_param {
-    num_output: 256
-    bias_term: true
-    pad_h: 1
-    pad_w: 0
-    kernel_h: 3
-    kernel_w: 1
-    stride_h: 1
-    stride_w: 1
-  }
-}
-layer {
-  name: "conv2d_5"
-  type: "Convolution"
-  bottom: "batch_normalization_1"
-  top: "conv2d_5"
-  convolution_param {
-    num_output: 256
-    bias_term: true
-    pad_h: 0
-    pad_w: 0
-    kernel_h: 1
-    kernel_w: 1
-    stride_h: 1
-    stride_w: 1
-  }
-}
-layer {
-  name: "batch_normalization_2"
-  type: "BatchNorm"
-  bottom: "conv2d_2"
-  top: "batch_normalization_2"
-  batch_norm_param {
-    moving_average_fraction: 0.99
-    eps: 0.001
-  }
-}
-layer {
-  name: "batch_normalization_2_scale"
-  type: "Scale"
-  bottom: "batch_normalization_2"
-  top: "batch_normalization_2"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "batch_normalization_3"
-  type: "BatchNorm"
-  bottom: "conv2d_3"
-  top: "batch_normalization_3"
-  batch_norm_param {
-    moving_average_fraction: 0.99
-    eps: 0.001
-  }
-}
-layer {
-  name: "batch_normalization_3_scale"
-  type: "Scale"
-  bottom: "batch_normalization_3"
-  top: "batch_normalization_3"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "batch_normalization_4"
-  type: "BatchNorm"
-  bottom: "conv2d_4"
-  top: "batch_normalization_4"
-  batch_norm_param {
-    moving_average_fraction: 0.99
-    eps: 0.001
-  }
-}
-layer {
-  name: "batch_normalization_4_scale"
-  type: "Scale"
-  bottom: "batch_normalization_4"
-  top: "batch_normalization_4"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "batch_normalization_5"
-  type: "BatchNorm"
-  bottom: "conv2d_5"
-  top: "batch_normalization_5"
-  batch_norm_param {
-    moving_average_fraction: 0.99
-    eps: 0.001
-  }
-}
-layer {
-  name: "batch_normalization_5_scale"
-  type: "Scale"
-  bottom: "batch_normalization_5"
-  top: "batch_normalization_5"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "activation_2"
-  type: "ReLU"
-  bottom: "batch_normalization_2"
-  top: "batch_normalization_2"
-}
-layer {
-  name: "activation_3"
-  type: "ReLU"
-  bottom: "batch_normalization_3"
-  top: "batch_normalization_3"
-}
-layer {
-  name: "activation_4"
-  type: "ReLU"
-  bottom: "batch_normalization_4"
-  top: "batch_normalization_4"
-}
-layer {
-  name: "activation_5"
-  type: "ReLU"
-  bottom: "batch_normalization_5"
-  top: "batch_normalization_5"
-}
-layer {
-  name: "concatenate_1"
-  type: "Concat"
-  bottom: "batch_normalization_2"
-  bottom: "batch_normalization_3"
-  bottom: "batch_normalization_4"
-  bottom: "batch_normalization_5"
-  top: "concatenate_1"
-  concat_param {
-    axis: 1
-  }
-}
-layer {
-  name: "conv_1024_11"
-  type: "Convolution"
-  bottom: "concatenate_1"
-  top: "conv_1024_11"
-  convolution_param {
-    num_output: 1024
-    bias_term: true
-    pad_h: 0
-    pad_w: 0
-    kernel_h: 1
-    kernel_w: 1
-    stride_h: 1
-    stride_w: 1
-  }
-}
-layer {
-  name: "batch_normalization_6"
-  type: "BatchNorm"
-  bottom: "conv_1024_11"
-  top: "batch_normalization_6"
-  batch_norm_param {
-    moving_average_fraction: 0.99
-    eps: 0.001
-  }
-}
-layer {
-  name: "batch_normalization_6_scale"
-  type: "Scale"
-  bottom: "batch_normalization_6"
-  top: "batch_normalization_6"
-  scale_param {
-    bias_term: true
-  }
-}
-layer {
-  name: "activation_6"
-  type: "ReLU"
-  bottom: "batch_normalization_6"
-  top: "batch_normalization_6"
-}
-layer {
-  name: "conv_class_11"
-  type: "Convolution"
-  bottom: "batch_normalization_6"
-  top: "conv_class_11"
-  convolution_param {
-    num_output: 84
-    bias_term: true
-    pad_h: 0
-    pad_w: 0
-    kernel_h: 1
-    kernel_w: 1
-    stride_h: 1
-    stride_w: 1
-  }
-}
-layer {
-  name: "prob"
-  type: "Softmax"
-  bottom: "conv_class_11"
-  top: "prob"
-}
-
diff --git a/Prj-iOS/lpr/Resource/Segmentation.caffemodel b/Prj-iOS/lpr/Resource/Segmentation.caffemodel
deleted file mode 100755
index 208269d..0000000
Binary files a/Prj-iOS/lpr/Resource/Segmentation.caffemodel and /dev/null differ
diff --git a/Prj-iOS/lpr/Resource/Segmentation.prototxt b/Prj-iOS/lpr/Resource/Segmentation.prototxt
deleted file mode 100755
index 45c6829..0000000
--- a/Prj-iOS/lpr/Resource/Segmentation.prototxt
+++ /dev/null
@@ -1,114 +0,0 @@
-input: "data"
-input_dim: 1
-input_dim: 1
-input_dim: 22
-input_dim: 22
-layer {
-  name: "conv2d_12"
-  type: "Convolution"
-  bottom: "data"
-  top: "conv2d_12"
-  convolution_param {
-    num_output: 16
-    bias_term: true
-    pad: 0
-    kernel_size: 3
-    stride: 1
-  }
-}
-layer {
-  name: "activation_18"
-  type: "ReLU"
-  bottom: "conv2d_12"
-  top: "activation_18"
-}
-layer {
-  name: "max_pooling2d_10"
-  type: "Pooling"
-  bottom: "activation_18"
-  top: "max_pooling2d_10"
-  pooling_param {
-    pool: MAX
-    kernel_size: 2
-    stride: 2
-    pad: 0
-  }
-}
-layer {
-  name: "conv2d_13"
-  type: "Convolution"
-  bottom: "max_pooling2d_10"
-  top: "conv2d_13"
-  convolution_param {
-    num_output: 16
-    bias_term: true
-    pad: 0
-    kernel_size: 3
-    stride: 1
-  }
-}
-layer {
-  name: "activation_19"
-  type: "ReLU"
-  bottom: "conv2d_13"
-  top: "activation_19"
-}
-layer {
-  name: "max_pooling2d_11"
-  type: "Pooling"
-  bottom: "activation_19"
-  top: "max_pooling2d_11"
-  pooling_param {
-    pool: MAX
-    kernel_size: 2
-    stride: 2
-    pad: 0
-  }
-}
-layer {
-  name: "flatten_6"
-  type: "Flatten"
-  bottom: "max_pooling2d_11"
-  top: "flatten_6"
-}
-layer {
-  name: "dense_9"
-  type: "InnerProduct"
-  bottom: "flatten_6"
-  top: "dense_9"
-  inner_product_param {
-    num_output: 256
-  }
-}
-layer {
-  name: "dropout_9"
-  type: "Dropout"
-  bottom: "dense_9"
-  top: "dropout_9"
-  dropout_param {
-    dropout_ratio: 0.5
-  }
-}
-layer {
-  name: "activation_20"
-  type: "ReLU"
-  bottom: "dropout_9"
-  top: "activation_20"
-}
-layer {
-  name: "dense_10"
-  type: "InnerProduct"
-  bottom: "activation_20"
-  top: "dense_10"
-  inner_product_param {
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-  }
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-
-
-  layer {
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-  type: "Softmax"
-  bottom: "dense_10"
-  top: "prob"
-}
\ No newline at end of file
diff --git a/Prj-iOS/lpr/Resource/cascade.xml b/Prj-iOS/lpr/Resource/cascade.xml
deleted file mode 100755
index 277775b..0000000
--- a/Prj-iOS/lpr/Resource/cascade.xml
+++ /dev/null
@@ -1,12117 +0,0 @@
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-</opencv_storage>
diff --git a/Prj-iOS/lpr/RootViewController.h b/Prj-iOS/lpr/RootViewController.h
deleted file mode 100755
index 14da8d2..0000000
--- a/Prj-iOS/lpr/RootViewController.h
+++ /dev/null
@@ -1,26 +0,0 @@
-//
-//  RootViewController.h
-//  lpr
-//
-//  Created by baotim on 2018/10/26.
-//  Copyright © 2018 lprSample. All rights reserved.
-//
-
-#ifdef __cplusplus
-#import <opencv2/opencv.hpp>
-#import <opencv2/imgcodecs/ios.h>
-#endif
-
-#ifdef __OBJC__
-#import <UIKit/UIKit.h>
-#import <Foundation/Foundation.h>
-#import <AVFoundation/AVFoundation.h>
-#endif
-
-NS_ASSUME_NONNULL_BEGIN
-
-@interface RootViewController : UIViewController
-
-@end
-
-NS_ASSUME_NONNULL_END
diff --git a/Prj-iOS/lpr/RootViewController.mm b/Prj-iOS/lpr/RootViewController.mm
deleted file mode 100755
index 011dbab..0000000
--- a/Prj-iOS/lpr/RootViewController.mm
+++ /dev/null
@@ -1,197 +0,0 @@
-//
-//  RootViewController.m
-//  lpr
-//
-//  Created by baotim on 2018/10/26.
-//  Copyright © 2018 lprSample. All rights reserved.
-//
-
-#import "RootViewController.h"
-#import <Masonry/Masonry.h>
-#import "Utility.h"
-#import "Pipeline.h"
-#import "CameraViewController.h"
-
-@interface RootViewController () <UIImagePickerControllerDelegate, UINavigationControllerDelegate>
-
-@property (nonatomic, strong) UIButton* albumButton;
-@property (nonatomic, strong) UIButton* cameraButton;
-@property (nonatomic, strong) UIImageView* imageView;
-@property (nonatomic, strong) UILabel* resultLabel;
-
-@end
-
-@implementation RootViewController
-{
-    cv::Mat source_image;
-}
-
-#pragma mark - Lazy Initialize
-- (UIButton *)albumButton
-{
-    if (!_albumButton) {
-        _albumButton = [UIButton buttonWithType:UIButtonTypeCustom];
-        [_albumButton setTitle:@"打开相册" forState:UIControlStateNormal];
-        [_albumButton setBackgroundColor:[UIColor redColor]];
-        [_albumButton addTarget:self action:@selector(openAlbum) forControlEvents:UIControlEventTouchUpInside];
-    }
-    return _albumButton;
-}
-
-- (UIButton *)cameraButton
-{
-    if (!_cameraButton) {
-        _cameraButton = [UIButton buttonWithType:UIButtonTypeCustom];
-        [_cameraButton setBackgroundColor:[UIColor redColor]];
-        [_cameraButton setTitle:@"实时拍照" forState:UIControlStateNormal];
-        [_cameraButton addTarget:self action:@selector(openCamera) forControlEvents:UIControlEventTouchUpInside];
-    }
-    return _cameraButton;
-}
-
-- (UIImageView *)imageView
-{
-    if (!_imageView) {
-        _imageView = [[UIImageView alloc] init];
-        _imageView.contentMode = UIViewContentModeScaleAspectFit;
-    }
-    return _imageView;
-}
-
-- (UILabel *)resultLabel
-{
-    if (!_resultLabel) {
-        _resultLabel = [UILabel new];
-        _resultLabel.textColor = [UIColor redColor];
-        _resultLabel.textAlignment = NSTextAlignmentLeft;
-        _resultLabel.font = [UIFont systemFontOfSize:15];
-    }
-    return _resultLabel;
-}
-
-#pragma mark - Life Cycle
-- (void)viewDidLoad {
-    [super viewDidLoad];
-    // Do any additional setup after loading the view.
-    self.view.backgroundColor = [UIColor whiteColor];
-    self.navigationItem.title = @"车牌识别Demo";
-    
-    [self.view addSubview:self.albumButton];
-    [self.albumButton mas_makeConstraints:^(MASConstraintMaker* make) {
-        make.centerX.equalTo(self.view).offset(-80);
-        make.bottom.equalTo(self.mas_bottomLayoutGuideTop).offset(-20);
-        make.width.mas_equalTo(100);
-        make.height.mas_equalTo(50);
-    }];
-    
-    [self.view addSubview:self.cameraButton];
-    [self.cameraButton mas_makeConstraints:^(MASConstraintMaker* make) {
-        make.centerX.equalTo(self.view).offset(80);
-        make.bottom.equalTo(self.albumButton);
-        make.width.mas_equalTo(100);
-        make.height.mas_equalTo(50);
-    }];
-    
-    [self.view addSubview:self.resultLabel];
-    [self.resultLabel mas_makeConstraints:^(MASConstraintMaker* make) {
-        make.left.right.equalTo(self.view);
-        make.bottom.lessThanOrEqualTo(self.albumButton.mas_top);
-    }];
-    
-    [self.view addSubview:self.imageView];
-    [self.imageView mas_makeConstraints:^(MASConstraintMaker* make) {
-        make.top.left.right.equalTo(self.view);
-        make.bottom.lessThanOrEqualTo(self.resultLabel.mas_top);
-    }];
-}
-
-#pragma mark - Actions
-- (void)openAlbum
-{
-    UIImagePickerController* picker = [[UIImagePickerController alloc] init];
-    picker.delegate = self;
-    
-    if (![UIImagePickerController isSourceTypeAvailable:UIImagePickerControllerSourceTypePhotoLibrary])
-        return;
-    
-    picker.sourceType = UIImagePickerControllerSourceTypePhotoLibrary;
-    
-    [self presentViewController:picker animated:YES completion:nil];
-}
-
-- (void)openCamera
-{
-    CameraViewController* video = [CameraViewController new];
-    video.resultCB = ^(NSString* text, UIImage* image) {
-        self.imageView.image = image;
-        self.resultLabel.text = text;
-    };
-    [self presentViewController:video animated:YES completion:nil];
-}
-
-#pragma mark - Private Methods
-- (NSString *)getPath:(NSString*)fileName
-{
-    NSString *bundlePath = [NSBundle mainBundle].bundlePath;
-    NSString *path = [bundlePath stringByAppendingPathComponent:fileName];
-    return path;
-}
-
-- (void)simpleRecognition:(cv::Mat&)src
-{
-    NSString *path_1 = [self getPath:@"cascade.xml"];
-    NSString *path_2 = [self getPath:@"HorizonalFinemapping.prototxt"];
-    NSString *path_3 = [self getPath:@"HorizonalFinemapping.caffemodel"];
-    NSString *path_4 = [self getPath:@"Segmentation.prototxt"];
-    NSString *path_5 = [self getPath:@"Segmentation.caffemodel"];
-    NSString *path_6 = [self getPath:@"CharacterRecognization.prototxt"];
-    NSString *path_7 = [self getPath:@"CharacterRecognization.caffemodel"];
-    
-    std::string *cpath_1 = new std::string([path_1 UTF8String]);
-    std::string *cpath_2 = new std::string([path_2 UTF8String]);
-    std::string *cpath_3 = new std::string([path_3 UTF8String]);
-    std::string *cpath_4 = new std::string([path_4 UTF8String]);
-    std::string *cpath_5 = new std::string([path_5 UTF8String]);
-    std::string *cpath_6 = new std::string([path_6 UTF8String]);
-    std::string *cpath_7 = new std::string([path_7 UTF8String]);
-    
-    
-    pr::PipelinePR pr2 = pr::PipelinePR(*cpath_1, *cpath_2, *cpath_3, *cpath_4, *cpath_5, *cpath_6, *cpath_7);
-    
-    std::vector<pr::PlateInfo> list_res = pr2.RunPiplineAsImage(src);
-    std::string concat_results = "";
-    for(auto one:list_res) {
-        if(one.confidence>0.7) {
-            concat_results += one.getPlateName()+",";
-        }
-    }
-    
-    NSString *str = [NSString stringWithCString:concat_results.c_str() encoding:NSUTF8StringEncoding];
-    if (str.length > 0) {
-        str = [str substringToIndex:str.length-1];
-        str = [NSString stringWithFormat:@"识别结果: %@",str];
-    } else {
-        str = [NSString stringWithFormat:@"识别结果: 未识别成功"];
-    }
-    
-    [self.resultLabel setText:str];
-}
-
-#pragma mark - UIImagePickerControllerDelegate
-- (void)imagePickerController:(UIImagePickerController*)picker didFinishPickingMediaWithInfo:(NSDictionary *)info
-{
-    [picker dismissViewControllerAnimated:YES completion:nil];
-    
-    UIImage* temp = [info objectForKey:@"UIImagePickerControllerOriginalImage"];
-    UIImage *temp_image = [Utility scaleAndRotateImageBackCamera:temp];
-    source_image = [Utility cvMatFromUIImage:temp_image];
-    [self simpleRecognition:source_image];
-    self.imageView.image = temp;
-}
-
-- (void)imagePickerControllerDidCancel:(UIImagePickerController *)picker
-{
-    [picker dismissViewControllerAnimated:YES completion:nil];
-}
-
-@end
diff --git a/Prj-iOS/lpr/Source/include/CNNRecognizer.h b/Prj-iOS/lpr/Source/include/CNNRecognizer.h
deleted file mode 100755
index ad491a0..0000000
--- a/Prj-iOS/lpr/Source/include/CNNRecognizer.h
+++ /dev/null
@@ -1,24 +0,0 @@
-//
-// Created by 庾金科 on 21/10/2017.
-//
-
-#ifndef SWIFTPR_CNNRECOGNIZER_H
-#define SWIFTPR_CNNRECOGNIZER_H
-
-#include "Recognizer.h"
-namespace pr{
-    class CNNRecognizer: public GeneralRecognizer{
-    public:
-        const int CHAR_INPUT_W = 14;
-        const int CHAR_INPUT_H = 30;
-
-        CNNRecognizer(std::string prototxt,std::string caffemodel);
-        label recognizeCharacter(cv::Mat character);
-    private:
-        cv::dnn::Net net;
-
-    };
-
-}
-
-#endif //SWIFTPR_CNNRECOGNIZER_H
diff --git a/Prj-iOS/lpr/Source/include/FastDeskew.h b/Prj-iOS/lpr/Source/include/FastDeskew.h
deleted file mode 100755
index 08359e5..0000000
--- a/Prj-iOS/lpr/Source/include/FastDeskew.h
+++ /dev/null
@@ -1,18 +0,0 @@
-//
-// Created by 庾金科 on 22/09/2017.
-//
-
-#ifndef SWIFTPR_FASTDESKEW_H
-#define SWIFTPR_FASTDESKEW_H
-
-#include <math.h>
-#include <opencv2/opencv.hpp>
-namespace pr{
-
-    cv::Mat fastdeskew(cv::Mat skewImage,int blockSize);
-//    cv::Mat spatialTransformer(cv::Mat skewImage);
-
-}//namepace pr
-
-
-#endif //SWIFTPR_FASTDESKEW_H
diff --git a/Prj-iOS/lpr/Source/include/FineMapping.h b/Prj-iOS/lpr/Source/include/FineMapping.h
deleted file mode 100755
index 352202e..0000000
--- a/Prj-iOS/lpr/Source/include/FineMapping.h
+++ /dev/null
@@ -1,32 +0,0 @@
-//
-// Created by 庾金科 on 22/09/2017.
-//
-
-#ifndef SWIFTPR_FINEMAPPING_H
-#define SWIFTPR_FINEMAPPING_H
-
-#include <opencv2/opencv.hpp>
-#include <opencv2/dnn.hpp>
-
-#include <string>
-namespace pr{
-    class FineMapping{
-    public:
-        FineMapping();
-
-
-        FineMapping(std::string prototxt,std::string caffemodel);
-        static cv::Mat FineMappingVertical(cv::Mat InputProposal,int sliceNum=15,int upper=0,int lower=-50,int windows_size=17);
-        cv::Mat FineMappingHorizon(cv::Mat FinedVertical,int leftPadding,int rightPadding);
-
-
-    private:
-        cv::dnn::Net net;
-
-    };
-
-
-
-
-}
-#endif //SWIFTPR_FINEMAPPING_H
diff --git a/Prj-iOS/lpr/Source/include/Pipeline.h b/Prj-iOS/lpr/Source/include/Pipeline.h
deleted file mode 100755
index 03d1604..0000000
--- a/Prj-iOS/lpr/Source/include/Pipeline.h
+++ /dev/null
@@ -1,48 +0,0 @@
-//
-// Created by 庾金科 on 22/10/2017.
-//
-
-#ifndef SWIFTPR_PIPLINE_H
-#define SWIFTPR_PIPLINE_H
-
-#include "PlateDetection.h"
-#include "PlateSegmentation.h"
-#include "CNNRecognizer.h"
-#include "PlateInfo.h"
-#include "FastDeskew.h"
-#include "FineMapping.h"
-#include "Recognizer.h"
-
-namespace pr{
-    class PipelinePR{
-        public:
-            GeneralRecognizer *generalRecognizer;
-            PlateDetection *plateDetection;
-            PlateSegmentation *plateSegmentation;
-            FineMapping *fineMapping;
-        
-        
-        
-            PipelinePR(std::string detector_filename,
-                       std::string finemapping_prototxt,std::string finemapping_caffemodel,
-                       std::string segmentation_prototxt,std::string segmentation_caffemodel,
-                       std::string charRecognization_proto,std::string charRecognization_caffemodel
-                       );
-            ~PipelinePR();
-
-
-
-            std::vector<std::string> plateRes;
-            std::vector<PlateInfo> RunPiplineAsImage(cv::Mat plateImage);
-
-
-
-
-
-
-
-    };
-
-
-}
-#endif //SWIFTPR_PIPLINE_H
diff --git a/Prj-iOS/lpr/Source/include/PlateDetection.h b/Prj-iOS/lpr/Source/include/PlateDetection.h
deleted file mode 100755
index f4cf567..0000000
--- a/Prj-iOS/lpr/Source/include/PlateDetection.h
+++ /dev/null
@@ -1,33 +0,0 @@
-//
-// Created by 庾金科 on 20/09/2017.
-//
-
-#ifndef SWIFTPR_PLATEDETECTION_H
-#define SWIFTPR_PLATEDETECTION_H
-
-#include <opencv2/opencv.hpp>
-#include "PlateInfo.h"
-#include <vector>
-namespace pr{
-    class PlateDetection{
-    public:
-        PlateDetection(std::string filename_cascade);
-        PlateDetection();
-        void LoadModel(std::string filename_cascade);
-        void plateDetectionRough(cv::Mat InputImage,std::vector<pr::PlateInfo>  &plateInfos,int min_w=36,int max_w=800);
-//        std::vector<pr::PlateInfo> plateDetectionRough(cv::Mat InputImage,int min_w= 60,int max_h = 400);
-
-
-//        std::vector<pr::PlateInfo> plateDetectionRoughByMultiScaleEdge(cv::Mat InputImage);
-
-
-
-    private:
-        cv::CascadeClassifier cascade;
-
-
-    };
-
-}// namespace pr
-
-#endif //SWIFTPR_PLATEDETECTION_H
diff --git a/Prj-iOS/lpr/Source/include/PlateInfo.h b/Prj-iOS/lpr/Source/include/PlateInfo.h
deleted file mode 100755
index fbf2a8b..0000000
--- a/Prj-iOS/lpr/Source/include/PlateInfo.h
+++ /dev/null
@@ -1,127 +0,0 @@
-//
-// Created by 庾金科 on 20/09/2017.
-//
-
-#ifndef SWIFTPR_PLATEINFO_H
-#define SWIFTPR_PLATEINFO_H
-#include <opencv2/opencv.hpp>
-namespace pr {
-
-    typedef std::vector<cv::Mat> Character;
-
-    enum PlateColor { BLUE, YELLOW, WHITE, GREEN, BLACK,UNKNOWN};
-    enum CharType {CHINESE,LETTER,LETTER_NUMS};
-
-
-    class PlateInfo {
-        public:
-        std::vector<std::pair<CharType,cv::Mat> > plateChars;
-            std::vector<std::pair<CharType,cv::Mat> > plateCoding;
-
-            float confidence = 0;
-
-
-            PlateInfo(const cv::Mat &plateData, std::string plateName, cv::Rect plateRect, PlateColor plateType) {
-                    licensePlate = plateData;
-                    name = plateName;
-                    ROI = plateRect;
-                    Type = plateType;
-                }
-            PlateInfo(const cv::Mat &plateData, cv::Rect plateRect, PlateColor plateType) {
-                licensePlate = plateData;
-                ROI = plateRect;
-                Type = plateType;
-            }
-            PlateInfo(const cv::Mat &plateData, cv::Rect plateRect) {
-                licensePlate = plateData;
-                ROI = plateRect;
-            }
-            PlateInfo() {
-
-            }
-
-            cv::Mat getPlateImage() {
-                return licensePlate;
-            }
-
-            void setPlateImage(cv::Mat plateImage){
-                licensePlate = plateImage;
-            }
-
-            cv::Rect getPlateRect() {
-                return ROI;
-            }
-
-            void setPlateRect(cv::Rect plateRect)   {
-                ROI = plateRect;
-            }
-            cv::String getPlateName() {
-                return name;
-
-            }
-            void setPlateName(cv::String plateName) {
-                name = plateName;
-            }
-            int getPlateType() {
-                return Type;
-            }
-
-            void appendPlateChar(const std::pair<CharType,cv::Mat> &plateChar)
-            {
-                plateChars.push_back(plateChar);
-            }
-
-            void appendPlateCoding(const std::pair<CharType,cv::Mat> &charProb){
-                plateCoding.push_back(charProb);
-            }
-
-    //        cv::Mat getPlateChars(int id) {
-    //            if(id<PlateChars.size())
-    //                return PlateChars[id];
-    //        }
-            std::string decodePlateNormal(std::vector<std::string> mappingTable) {
-                std::string decode;
-                for(auto plate:plateCoding) {
-                    float *prob = (float *)plate.second.data;
-                    if(plate.first == CHINESE) {
-
-                        decode += mappingTable[std::max_element(prob,prob+31) - prob];
-                        confidence+=*std::max_element(prob,prob+31);
-
-
-//                        std::cout<<*std::max_element(prob,prob+31)<<std::endl;
-
-                    }
-
-                    if(plate.first == LETTER) {
-                        decode += mappingTable[std::max_element(prob+41,prob+65)- prob];
-                        confidence+=*std::max_element(prob+41,prob+65);
-                    }
-
-                    if(plate.first == LETTER_NUMS) {
-                        decode += mappingTable[std::max_element(prob+31,prob+65)- prob];
-                        confidence+=*std::max_element(prob+31,prob+65);
-//                        std::cout<<*std::max_element(prob+31,prob+65)<<std::endl;
-
-                    }
-
-                }
-                name = decode;
-
-                confidence/=7;
-
-                return decode;
-            }
-
-
-
-    private:
-        cv::Mat licensePlate;
-        cv::Rect ROI;
-        std::string name;
-        PlateColor Type;
-    };
-}
-
-
-#endif //SWIFTPR_PLATEINFO_H
diff --git a/Prj-iOS/lpr/Source/include/PlateSegmentation.h b/Prj-iOS/lpr/Source/include/PlateSegmentation.h
deleted file mode 100755
index 515c793..0000000
--- a/Prj-iOS/lpr/Source/include/PlateSegmentation.h
+++ /dev/null
@@ -1,39 +0,0 @@
-//
-// Created by 庾金科 on 16/10/2017.
-//
-
-#ifndef SWIFTPR_PLATESEGMENTATION_H
-#define SWIFTPR_PLATESEGMENTATION_H
-
-#include "opencv2/opencv.hpp"
-#include <opencv2/dnn.hpp>
-#include "PlateInfo.h"
-
-namespace pr{
-
-
-    class PlateSegmentation{
-    public:
-        const int PLATE_NORMAL = 6;
-        const int PLATE_NORMAL_GREEN = 7;
-        const int DEFAULT_WIDTH = 20;
-        PlateSegmentation(std::string phototxt,std::string caffemodel);
-        PlateSegmentation(){}
-        void segmentPlatePipline(PlateInfo &plateInfo,int stride,std::vector<cv::Rect> &Char_rects);
-
-        void segmentPlateBySlidingWindows(cv::Mat &plateImage,int windowsWidth,int stride,cv::Mat &respones);
-        void templateMatchFinding(const cv::Mat &respones,int windowsWidth,std::pair<float,std::vector<int> > &candidatePts);
-        void refineRegion(cv::Mat &plateImage,const std::vector<int> &candidatePts,const int padding,std::vector<cv::Rect> &rects);
-        void ExtractRegions(PlateInfo &plateInfo,std::vector<cv::Rect> &rects);
-        cv::Mat classifyResponse(const cv::Mat &cropped);
-    private:
-        cv::dnn::Net net;
-
-
-//        RefineRegion()
-
-    };
-
-}//namespace pr
-
-#endif //SWIFTPR_PLATESEGMENTATION_H
diff --git a/Prj-iOS/lpr/Source/include/Recognizer.h b/Prj-iOS/lpr/Source/include/Recognizer.h
deleted file mode 100755
index 809b717..0000000
--- a/Prj-iOS/lpr/Source/include/Recognizer.h
+++ /dev/null
@@ -1,21 +0,0 @@
-//
-// Created by 庾金科 on 20/10/2017.
-//
-
-
-#ifndef SWIFTPR_RECOGNIZER_H
-#define SWIFTPR_RECOGNIZER_H
-
-#include "PlateInfo.h"
-#include "opencv2/dnn.hpp"
-namespace pr{
-    typedef cv::Mat label;
-    class GeneralRecognizer{
-        public:
-            virtual label recognizeCharacter(cv::Mat character) = 0;
-            void SegmentBasedSequenceRecognition(PlateInfo &plateinfo);
-
-    };
-
-}
-#endif //SWIFTPR_RECOGNIZER_H
diff --git a/Prj-iOS/lpr/Source/include/niBlackThreshold.h b/Prj-iOS/lpr/Source/include/niBlackThreshold.h
deleted file mode 100755
index 5ad7e14..0000000
--- a/Prj-iOS/lpr/Source/include/niBlackThreshold.h
+++ /dev/null
@@ -1,107 +0,0 @@
-//
-// Created by 庾金科 on 26/10/2017.
-//
-
-#ifndef SWIFTPR_NIBLACKTHRESHOLD_H
-#define SWIFTPR_NIBLACKTHRESHOLD_H
-
-
-#include <opencv2/opencv.hpp>
-using namespace cv;
-
-enum LocalBinarizationMethods{
-    BINARIZATION_NIBLACK = 0, //!< Classic Niblack binarization. See @cite Niblack1985 .
-    BINARIZATION_SAUVOLA = 1, //!< Sauvola's technique. See @cite Sauvola1997 .
-    BINARIZATION_WOLF = 2,    //!< Wolf's technique. See @cite Wolf2004 .
-    BINARIZATION_NICK = 3     //!< NICK technique. See @cite Khurshid2009 .
-};
-
-
-void niBlackThreshold( InputArray _src, OutputArray _dst, double maxValue,
-                       int type, int blockSize, double k, int binarizationMethod )
-{
-    // Input grayscale image
-    Mat src = _src.getMat();
-    CV_Assert(src.channels() == 1);
-    CV_Assert(blockSize % 2 == 1 && blockSize > 1);
-    if (binarizationMethod == BINARIZATION_SAUVOLA) {
-        CV_Assert(src.depth() == CV_8U);
-    }
-    type &= THRESH_MASK;
-    // Compute local threshold (T = mean + k * stddev)
-    // using mean and standard deviation in the neighborhood of each pixel
-    // (intermediate calculations are done with floating-point precision)
-    Mat test;
-    Mat thresh;
-    {
-        // note that: Var[X] = E[X^2] - E[X]^2
-        Mat mean, sqmean, variance, stddev, sqrtVarianceMeanSum;
-        double srcMin, stddevMax;
-        boxFilter(src, mean, CV_32F, Size(blockSize, blockSize),
-                  Point(-1,-1), true, BORDER_REPLICATE);
-        sqrBoxFilter(src, sqmean, CV_32F, Size(blockSize, blockSize),
-                     Point(-1,-1), true, BORDER_REPLICATE);
-        variance = sqmean - mean.mul(mean);
-        sqrt(variance, stddev);
-        switch (binarizationMethod)
-        {
-            case BINARIZATION_NIBLACK:
-                thresh = mean + stddev * static_cast<float>(k);
-
-                break;
-            case BINARIZATION_SAUVOLA:
-                thresh = mean.mul(1. + static_cast<float>(k) * (stddev / 128.0 - 1.));
-                break;
-            case BINARIZATION_WOLF:
-                minMaxIdx(src, &srcMin,NULL);
-                minMaxIdx(stddev, NULL, &stddevMax);
-                thresh = mean - static_cast<float>(k) * (mean - srcMin - stddev.mul(mean - srcMin) / stddevMax);
-                break;
-            case BINARIZATION_NICK:
-                sqrt(variance + sqmean, sqrtVarianceMeanSum);
-                thresh = mean + static_cast<float>(k) * sqrtVarianceMeanSum;
-                break;
-            default:
-                CV_Error( CV_StsBadArg, "Unknown binarization method" );
-                break;
-        }
-        thresh.convertTo(thresh, src.depth());
-
-        thresh.convertTo(test, src.depth());
-//
-//        cv::imshow("imagex",test);
-//        cv::waitKey(0);
-
-    }
-    // Prepare output image
-    _dst.create(src.size(), src.type());
-    Mat dst = _dst.getMat();
-    CV_Assert(src.data != dst.data);  // no inplace processing
-    // Apply thresholding: ( pixel > threshold ) ? foreground : background
-    Mat mask;
-    switch (type)
-    {
-        case THRESH_BINARY:      // dst = (src > thresh) ? maxval : 0
-        case THRESH_BINARY_INV:  // dst = (src > thresh) ? 0 : maxval
-            compare(src, thresh, mask, (type == THRESH_BINARY ? CMP_GT : CMP_LE));
-            dst.setTo(0);
-            dst.setTo(maxValue, mask);
-            break;
-        case THRESH_TRUNC:       // dst = (src > thresh) ? thresh : src
-            compare(src, thresh, mask, CMP_GT);
-            src.copyTo(dst);
-            thresh.copyTo(dst, mask);
-            break;
-        case THRESH_TOZERO:      // dst = (src > thresh) ? src : 0
-        case THRESH_TOZERO_INV:  // dst = (src > thresh) ? 0 : src
-            compare(src, thresh, mask, (type == THRESH_TOZERO ? CMP_GT : CMP_LE));
-            dst.setTo(0);
-            src.copyTo(dst, mask);
-            break;
-        default:
-            CV_Error( CV_StsBadArg, "Unknown threshold type" );
-            break;
-    }
-}
-
-#endif //SWIFTPR_NIBLACKTHRESHOLD_H
diff --git a/Prj-iOS/lpr/Source/src/CNNRecognizer.cpp b/Prj-iOS/lpr/Source/src/CNNRecognizer.cpp
deleted file mode 100755
index 909753c..0000000
--- a/Prj-iOS/lpr/Source/src/CNNRecognizer.cpp
+++ /dev/null
@@ -1,19 +0,0 @@
-//
-// Created by 庾金科 on 21/10/2017.
-//
-
-#include "../include/CNNRecognizer.h"
-
-namespace pr{
-    CNNRecognizer::CNNRecognizer(std::string prototxt,std::string caffemodel){
-        net = cv::dnn::readNetFromCaffe(prototxt, caffemodel);
-    }
-
-    label CNNRecognizer::recognizeCharacter(cv::Mat charImage){
-        if(charImage.channels()== 3)
-            cv::cvtColor(charImage,charImage,cv::COLOR_BGR2GRAY);
-        cv::Mat inputBlob = cv::dnn::blobFromImage(charImage, 1/255.0, cv::Size(CHAR_INPUT_W,CHAR_INPUT_H), cv::Scalar(0,0,0),false);
-        net.setInput(inputBlob,"data");
-        return net.forward();
-    }
-}
\ No newline at end of file
diff --git a/Prj-iOS/lpr/Source/src/FastDeskew.cpp b/Prj-iOS/lpr/Source/src/FastDeskew.cpp
deleted file mode 100755
index caad643..0000000
--- a/Prj-iOS/lpr/Source/src/FastDeskew.cpp
+++ /dev/null
@@ -1,133 +0,0 @@
-//
-// Created by 庾金科 on 02/10/2017.
-//
-
-
-
-#include "../include/FastDeskew.h"
-
-namespace pr{
-
-
-    const int  ANGLE_MIN = 30 ;
-    const int ANGLE_MAX = 150 ;
-    const int PLATE_H = 36;
-    const int PLATE_W = 136;
-
-    int angle(float x,float y)
-    {
-        return atan2(x,y)*180/3.1415;
-    }
-
-    std::vector<float> avgfilter(std::vector<float> angle_list,int windowsSize) {
-        std::vector<float> angle_list_filtered(angle_list.size() - windowsSize + 1);
-        for (int i = 0; i < angle_list.size() - windowsSize + 1; i++) {
-            float avg = 0.00f;
-            for (int j = 0; j < windowsSize; j++) {
-                avg += angle_list[i + j];
-            }
-            avg = avg / windowsSize;
-            angle_list_filtered[i] = avg;
-        }
-
-        return angle_list_filtered;
-    }
-
-
-    void drawHist(std::vector<float> seq){
-        cv::Mat image(300,seq.size(),CV_8U);
-        image.setTo(0);
-
-        for(int i = 0;i<seq.size();i++)
-        {
-            float l = *std::max_element(seq.begin(),seq.end());
-
-            int p = int(float(seq[i])/l*300);
-
-            cv::line(image,cv::Point(i,300),cv::Point(i,300-p),cv::Scalar(255,255,255));
-        }
-        //cv::imshow("vis",image);
-    }
-
-    cv::Mat  correctPlateImage(cv::Mat skewPlate,float angle,float maxAngle)
-    {
-
-        cv::Mat dst;
-
-        cv::Size size_o(skewPlate.cols,skewPlate.rows);
-
-
-        int extend_padding = 0;
-//        if(angle<0)
-            extend_padding = static_cast<int>(skewPlate.rows*tan(cv::abs(angle)/180* 3.14) );
-//        else
-//            extend_padding = static_cast<int>(skewPlate.rows/tan(cv::abs(angle)/180* 3.14) );
-
-//        std::cout<<"extend:"<<extend_padding<<std::endl;
-
-        cv::Size size(skewPlate.cols + extend_padding ,skewPlate.rows);
-
-        float interval = abs(sin((angle /180) * 3.14)* skewPlate.rows);
-//        std::cout<<interval<<std::endl;
-
-        cv::Point2f pts1[4] = {cv::Point2f(0,0),cv::Point2f(0,size_o.height),cv::Point2f(size_o.width,0),cv::Point2f(size_o.width,size_o.height)};
-        if(angle>0) {
-            cv::Point2f pts2[4] = {cv::Point2f(interval, 0), cv::Point2f(0, size_o.height),
-                                   cv::Point2f(size_o.width, 0), cv::Point2f(size_o.width - interval, size_o.height)};
-            cv::Mat M  = cv::getPerspectiveTransform(pts1,pts2);
-            cv::warpPerspective(skewPlate,dst,M,size);
-
-
-        }
-        else {
-            cv::Point2f pts2[4] = {cv::Point2f(0, 0), cv::Point2f(interval, size_o.height), cv::Point2f(size_o.width-interval, 0),
-                                   cv::Point2f(size_o.width, size_o.height)};
-            cv::Mat M  = cv::getPerspectiveTransform(pts1,pts2);
-            cv::warpPerspective(skewPlate,dst,M,size,cv::INTER_CUBIC);
-
-        }
-        return  dst;
-    }
-    cv::Mat fastdeskew(cv::Mat skewImage,int blockSize){
-
-
-        const int FILTER_WINDOWS_SIZE = 5;
-        std::vector<float> angle_list(180);
-        memset(angle_list.data(),0,angle_list.size()*sizeof(int));
-
-        cv::Mat bak;
-        skewImage.copyTo(bak);
-        if(skewImage.channels() == 3)
-            cv::cvtColor(skewImage,skewImage,cv::COLOR_RGB2GRAY);
-
-        if(skewImage.channels() == 1)
-        {
-            cv::Mat eigen;
-
-            cv::cornerEigenValsAndVecs(skewImage,eigen,blockSize,5);
-            for( int j = 0; j < skewImage.rows; j+=blockSize )
-            { for( int i = 0; i < skewImage.cols; i+=blockSize )
-                {
-                    float x2 = eigen.at<cv::Vec6f>(j, i)[4];
-                    float y2 = eigen.at<cv::Vec6f>(j, i)[5];
-                    int angle_cell = angle(x2,y2);
-                    angle_list[(angle_cell + 180)%180]+=1.0;
-
-                }
-            }
-        }
-        std::vector<float> filtered = avgfilter(angle_list,5);
-
-        int maxPos = std::max_element(filtered.begin(),filtered.end()) - filtered.begin() + FILTER_WINDOWS_SIZE/2;
-        if(maxPos>ANGLE_MAX)
-            maxPos = (-maxPos+90+180)%180;
-        if(maxPos<ANGLE_MIN)
-            maxPos-=90;
-        maxPos=90-maxPos;
-      cv::Mat deskewed = correctPlateImage(bak, static_cast<float>(maxPos),60.0f);
-        return deskewed;
-    }
-
-
-
-}//namespace pr
diff --git a/Prj-iOS/lpr/Source/src/FineMapping.cpp b/Prj-iOS/lpr/Source/src/FineMapping.cpp
deleted file mode 100755
index 6890eff..0000000
--- a/Prj-iOS/lpr/Source/src/FineMapping.cpp
+++ /dev/null
@@ -1,205 +0,0 @@
-//
-// Created by 庾金科 on 22/09/2017.
-//
-
-#include "FineMapping.h"
-namespace pr{
-
-    const int FINEMAPPING_H = 50;
-    const int FINEMAPPING_W = 120;
-    const int PADDING_UP_DOWN = 30;
-    void drawRect(cv::Mat image,cv::Rect rect)
-    {
-        cv::Point p1(rect.x,rect.y);
-        cv::Point p2(rect.x+rect.width,rect.y+rect.height);
-        cv::rectangle(image,p1,p2,cv::Scalar(0,255,0),1);
-    }
-
-
-    FineMapping::FineMapping(std::string prototxt,std::string caffemodel) {
-         net = cv::dnn::readNetFromCaffe(prototxt, caffemodel);
-
-    }
-
-    cv::Mat FineMapping::FineMappingHorizon(cv::Mat FinedVertical,int leftPadding,int rightPadding)
-    {
-
-//        if(FinedVertical.channels()==1)
-//            cv::cvtColor(FinedVertical,FinedVertical,cv::COLOR_GRAY2BGR);
-        cv::Mat inputBlob = cv::dnn::blobFromImage(FinedVertical, 1/255.0, cv::Size(66,16),
-                                      cv::Scalar(0,0,0),false);
-
-        net.setInput(inputBlob,"data");
-        cv::Mat prob = net.forward();
-        int front = static_cast<int>(prob.at<float>(0,0)*FinedVertical.cols);
-        int back = static_cast<int>(prob.at<float>(0,1)*FinedVertical.cols);
-        front -= leftPadding ;
-        if(front<0) front = 0;
-        back +=rightPadding;
-        if(back>FinedVertical.cols-1) back=FinedVertical.cols - 1;
-        cv::Mat cropped  = FinedVertical.colRange(front,back).clone();
-        return  cropped;
-
-
-    }
-    std::pair<int,int> FitLineRansac(std::vector<cv::Point> pts,int zeroadd = 0 )
-    {
-        std::pair<int,int> res;
-        if(pts.size()>2)
-        {
-            cv::Vec4f line;
-            cv::fitLine(pts,line,cv::DIST_HUBER,0,0.01,0.01);
-            float vx = line[0];
-            float vy = line[1];
-            float x = line[2];
-            float y = line[3];
-            int lefty = static_cast<int>((-x * vy / vx) + y);
-            int righty = static_cast<int>(((136- x) * vy / vx) + y);
-            res.first = lefty+PADDING_UP_DOWN+zeroadd;
-            res.second = righty+PADDING_UP_DOWN+zeroadd;
-            return res;
-        }
-        res.first = zeroadd;
-        res.second = zeroadd;
-        return res;
-    }
-
-    cv::Mat FineMapping::FineMappingVertical(cv::Mat InputProposal,int sliceNum,int upper,int lower,int windows_size){
-
-
-        cv::Mat PreInputProposal;
-        cv::Mat proposal;
-
-        cv::resize(InputProposal,PreInputProposal,cv::Size(FINEMAPPING_W,FINEMAPPING_H));
-        if(InputProposal.channels() == 3)
-            cv::cvtColor(PreInputProposal,proposal,cv::COLOR_BGR2GRAY);
-        else
-            PreInputProposal.copyTo(proposal);
-
-//            proposal = PreInputProposal;
-
-        // this will improve some sen
-        cv::Mat kernal = cv::getStructuringElement(cv::MORPH_ELLIPSE,cv::Size(1,3));
-//        cv::erode(proposal,proposal,kernal);
-
-
-        float diff = static_cast<float>(upper-lower);
-        diff/=static_cast<float>(sliceNum-1);
-        cv::Mat binary_adaptive;
-        std::vector<cv::Point> line_upper;
-        std::vector<cv::Point> line_lower;
-        int contours_nums=0;
-
-        for(int i = 0 ; i < sliceNum ; i++)
-        {
-            std::vector<std::vector<cv::Point> > contours;
-            float k =lower + i*diff;
-            cv::adaptiveThreshold(proposal,binary_adaptive,255,cv::ADAPTIVE_THRESH_MEAN_C,cv::THRESH_BINARY,windows_size,k);
-            cv::Mat draw;
-            binary_adaptive.copyTo(draw);
-            cv::findContours(binary_adaptive,contours,cv::RETR_EXTERNAL,cv::CHAIN_APPROX_SIMPLE);
-            for(auto contour: contours)
-            {
-                cv::Rect bdbox =cv::boundingRect(contour);
-                float lwRatio = bdbox.height/static_cast<float>(bdbox.width);
-                int  bdboxAera = bdbox.width*bdbox.height;
-                if ((   lwRatio>0.7&&bdbox.width*bdbox.height>100 && bdboxAera<300)
-                    || (lwRatio>3.0 && bdboxAera<100 && bdboxAera>10))
-                {
-
-                    cv::Point p1(bdbox.x, bdbox.y);
-                    cv::Point p2(bdbox.x + bdbox.width, bdbox.y + bdbox.height);
-                    line_upper.push_back(p1);
-                    line_lower.push_back(p2);
-                    contours_nums+=1;
-                }
-            }
-        }
-
-        std:: cout<<"contours_nums "<<contours_nums<<std::endl;
-
-        if(contours_nums<41)
-        {
-            cv::bitwise_not(InputProposal,InputProposal);
-            cv::Mat kernal = cv::getStructuringElement(cv::MORPH_ELLIPSE,cv::Size(1,5));
-            cv::Mat bak;
-            cv::resize(InputProposal,bak,cv::Size(FINEMAPPING_W,FINEMAPPING_H));
-            cv::erode(bak,bak,kernal);
-            if(InputProposal.channels() == 3)
-                cv::cvtColor(bak,proposal,cv::COLOR_BGR2GRAY);
-            else
-                proposal = bak;
-            int contours_nums=0;
-
-            for(int i = 0 ; i < sliceNum ; i++)
-            {
-                std::vector<std::vector<cv::Point> > contours;
-                float k =lower + i*diff;
-                cv::adaptiveThreshold(proposal,binary_adaptive,255,cv::ADAPTIVE_THRESH_MEAN_C,cv::THRESH_BINARY,windows_size,k);
-//                cv::imshow("image",binary_adaptive);
-//            cv::waitKey(0);
-                cv::Mat draw;
-                binary_adaptive.copyTo(draw);
-                cv::findContours(binary_adaptive,contours,cv::RETR_EXTERNAL,cv::CHAIN_APPROX_SIMPLE);
-                for(auto contour: contours)
-                {
-                    cv::Rect bdbox =cv::boundingRect(contour);
-                    float lwRatio = bdbox.height/static_cast<float>(bdbox.width);
-                    int  bdboxAera = bdbox.width*bdbox.height;
-                    if ((   lwRatio>0.7&&bdbox.width*bdbox.height>120 && bdboxAera<300)
-                        || (lwRatio>3.0 && bdboxAera<100 && bdboxAera>10))
-                    {
-
-                        cv::Point p1(bdbox.x, bdbox.y);
-                        cv::Point p2(bdbox.x + bdbox.width, bdbox.y + bdbox.height);
-                        line_upper.push_back(p1);
-                        line_lower.push_back(p2);
-                        contours_nums+=1;
-                    }
-                }
-            }
-//            std:: cout<<"contours_nums "<<contours_nums<<std::endl;
-        }
-
-            cv::Mat rgb;
-            cv::copyMakeBorder(PreInputProposal, rgb, 30, 30, 0, 0, cv::BORDER_REPLICATE);
-//        cv::imshow("rgb",rgb);
-//        cv::waitKey(0);
-//
-
-
-            std::pair<int, int> A;
-            std::pair<int, int> B;
-            A = FitLineRansac(line_upper, -2);
-            B = FitLineRansac(line_lower, 2);
-            int leftyB = A.first;
-            int rightyB = A.second;
-            int leftyA = B.first;
-            int rightyA = B.second;
-            int cols = rgb.cols;
-            int rows = rgb.rows;
-//        pts_map1  = np.float32([[cols - 1, rightyA], [0, leftyA],[cols - 1, rightyB], [0, leftyB]])
-//        pts_map2 = np.float32([[136,36],[0,36],[136,0],[0,0]])
-//        mat = cv2.getPerspectiveTransform(pts_map1,pts_map2)
-//        image = cv2.warpPerspective(rgb,mat,(136,36),flags=cv2.INTER_CUBIC)
-            std::vector<cv::Point2f> corners(4);
-            corners[0] = cv::Point2f(cols - 1, rightyA);
-            corners[1] = cv::Point2f(0, leftyA);
-            corners[2] = cv::Point2f(cols - 1, rightyB);
-            corners[3] = cv::Point2f(0, leftyB);
-            std::vector<cv::Point2f> corners_trans(4);
-            corners_trans[0] = cv::Point2f(136, 36);
-            corners_trans[1] = cv::Point2f(0, 36);
-            corners_trans[2] = cv::Point2f(136, 0);
-            corners_trans[3] = cv::Point2f(0, 0);
-            cv::Mat transform = cv::getPerspectiveTransform(corners, corners_trans);
-            cv::Mat quad = cv::Mat::zeros(36, 136, CV_8UC3);
-            cv::warpPerspective(rgb, quad, transform, quad.size());
-        return quad;
-
-    }
-
-
-}
-
-
diff --git a/Prj-iOS/lpr/Source/src/Pipeline.cpp b/Prj-iOS/lpr/Source/src/Pipeline.cpp
deleted file mode 100755
index 9ab9d91..0000000
--- a/Prj-iOS/lpr/Source/src/Pipeline.cpp
+++ /dev/null
@@ -1,71 +0,0 @@
-//
-// Created by 庾金科 on 23/10/2017.
-//
-
-#include "../include/Pipeline.h"
-
-
-namespace pr {
-    
-    std::string str[]={"京","沪","津","渝","冀","晋","蒙","辽","吉","黑","苏","浙","皖","闽","赣","鲁","豫","鄂","湘","粤","桂","琼","川","贵","云","藏","陕","甘","青","宁","新","0","1","2","3","4","5","6","7","8","9","A","B","C","D","E","F","G","H","J","K","L","M","N","P","Q","R","S","T","U","V","W","X","Y","Z"};
-    std::vector<std::string> chars_code(str, str+65);
-
-    
-//    std::vector<std::string> chars_code {"京","沪","津","渝","冀","晋","蒙","辽","吉","黑","苏","浙","皖","闽","赣","鲁","豫","鄂","湘","粤","桂","琼","川","贵","云","藏","陕","甘","青","宁","新","0","1","2","3","4","5","6","7","8","9","A","B","C","D","E","F","G","H","J","K","L","M","N","P","Q","R","S","T","U","V","W","X","Y","Z"};
-
-    PipelinePR::PipelinePR(std::string detector_filename,
-                           std::string finemapping_prototxt, std::string finemapping_caffemodel,
-                           std::string segmentation_prototxt, std::string segmentation_caffemodel,
-                           std::string charRecognization_proto, std::string charRecognization_caffemodel) {
-        plateDetection = new PlateDetection(detector_filename);
-        fineMapping = new FineMapping(finemapping_prototxt, finemapping_caffemodel);
-        plateSegmentation = new PlateSegmentation(segmentation_prototxt, segmentation_caffemodel);
-        generalRecognizer = new CNNRecognizer(charRecognization_proto, charRecognization_caffemodel);
-    }
-
-    PipelinePR::~PipelinePR() {
-
-        delete plateDetection;
-        delete fineMapping;
-        delete plateSegmentation;
-        delete generalRecognizer;
-
-    }
-
-    std::vector<PlateInfo> PipelinePR:: RunPiplineAsImage(cv::Mat plateImage) {
-        std::vector<PlateInfo> results;
-        std::vector<pr::PlateInfo> plates;
-        plateDetection->plateDetectionRough(plateImage,plates);
-
-        for (pr::PlateInfo plateinfo:plates) {
-
-            cv::Mat image_finemapping = plateinfo.getPlateImage();
-            image_finemapping = fineMapping->FineMappingVertical(image_finemapping);
-            image_finemapping = pr::fastdeskew(image_finemapping, 5);
-            image_finemapping = fineMapping->FineMappingHorizon(image_finemapping, 2, 5);
-            cv::resize(image_finemapping, image_finemapping, cv::Size(136, 36));
-            plateinfo.setPlateImage(image_finemapping);
-            std::vector<cv::Rect> rects;
-            plateSegmentation->segmentPlatePipline(plateinfo, 1, rects);
-            plateSegmentation->ExtractRegions(plateinfo, rects);
-            cv::copyMakeBorder(image_finemapping, image_finemapping, 0, 0, 0, 20, cv::BORDER_REPLICATE);
-
-            plateinfo.setPlateImage(image_finemapping);
-            generalRecognizer->SegmentBasedSequenceRecognition(plateinfo);
-            plateinfo.decodePlateNormal(chars_code);
-            results.push_back(plateinfo);
-            std::cout << plateinfo.getPlateName() << std::endl;
-
-
-        }
-
-//        for (auto str:results) {
-//            std::cout << str << std::endl;
-//        }
-        return results;
-
-    }//namespace pr
-
-
-
-}
diff --git a/Prj-iOS/lpr/Source/src/PlateDetection.cpp b/Prj-iOS/lpr/Source/src/PlateDetection.cpp
deleted file mode 100755
index b207190..0000000
--- a/Prj-iOS/lpr/Source/src/PlateDetection.cpp
+++ /dev/null
@@ -1,61 +0,0 @@
-//
-// Created by 庾金科 on 20/09/2017.
-//
-#include "../include/PlateDetection.h"
-
-#include "util.h"
-
-namespace pr{
-
-
-    PlateDetection::PlateDetection(std::string filename_cascade){
-        cascade.load(filename_cascade);
-
-    };
-
-
-    void PlateDetection::plateDetectionRough(cv::Mat InputImage,std::vector<pr::PlateInfo>  &plateInfos,int min_w,int max_w){
-
-        cv::Mat processImage;
-
-         cv::cvtColor(InputImage,processImage,cv::COLOR_BGR2GRAY);
-
-
-        std::vector<cv::Rect> platesRegions;
-//        std::vector<PlateInfo> plates;
-        cv::Size minSize(min_w,min_w/4);
-        cv::Size maxSize(max_w,max_w/4);
-//        cv::imshow("input",InputImage);
-//                cv::waitKey(0);
-        cascade.detectMultiScale( processImage, platesRegions,
-                                  1.1, 3, cv::CASCADE_SCALE_IMAGE,minSize,maxSize);
-        for(auto plate:platesRegions)
-        {
-            // extend rects
-//            x -= w * 0.14
-//            w += w * 0.28
-//            y -= h * 0.6
-//            h += h * 1.1;
-            int zeroadd_w  = static_cast<int>(plate.width*0.28);
-            int zeroadd_h = static_cast<int>(plate.height*1.2);
-            int zeroadd_x = static_cast<int>(plate.width*0.14);
-            int zeroadd_y = static_cast<int>(plate.height*0.6);
-            plate.x-=zeroadd_x;
-            plate.y-=zeroadd_y;
-            plate.height += zeroadd_h;
-            plate.width += zeroadd_w;
-                cv::Mat plateImage = util::cropFromImage(InputImage,plate);
-            PlateInfo plateInfo(plateImage,plate);
-            plateInfos.push_back(plateInfo);
-
-        }
-    }
-//    std::vector<pr::PlateInfo> PlateDetection::plateDetectionRough(cv::Mat InputImage,cv::Rect roi,int min_w,int max_w){
-//        cv::Mat roi_region = util::cropFromImage(InputImage,roi);
-//        return plateDetectionRough(roi_region,min_w,max_w);
-//    }
-
-
-
-
-}//namespace pr
diff --git a/Prj-iOS/lpr/Source/src/PlateSegmentation.cpp b/Prj-iOS/lpr/Source/src/PlateSegmentation.cpp
deleted file mode 100755
index 3904df9..0000000
--- a/Prj-iOS/lpr/Source/src/PlateSegmentation.cpp
+++ /dev/null
@@ -1,402 +0,0 @@
-//
-// Created by 庾金科 on 16/10/2017.
-//
-
-#include "../include/PlateSegmentation.h"
-#include "../include/niBlackThreshold.h"
-
-
-//#define DEBUG
-namespace pr{
-
-    PlateSegmentation::PlateSegmentation(std::string prototxt,std::string caffemodel) {
-        net = cv::dnn::readNetFromCaffe(prototxt, caffemodel);
-    }
-    cv::Mat PlateSegmentation::classifyResponse(const cv::Mat &cropped){
-        cv::Mat inputBlob = cv::dnn::blobFromImage(cropped, 1/255.0, cv::Size(22,22), cv::Scalar(0,0,0),false);
-        net.setInput(inputBlob,"data");
-        return net.forward();
-    }
-
-    void drawHist(float* seq,int size,const char* name){
-        cv::Mat image(300,size,CV_8U);
-        image.setTo(0);
-        float* start =seq;
-        float* end = seq+size;
-        float l = *std::max_element(start,end);
-        for(int i = 0;i<size;i++)
-        {
-            int p = int(float(seq[i])/l*300);
-            cv::line(image,cv::Point(i,300),cv::Point(i,300-p),cv::Scalar(255,255,255));
-        }
-        cv::resize(image,image,cv::Size(600,100));
-        //cv::imshow(name,image);
-    }
-
-    inline void computeSafeMargin(int &val,const int &rows){
-        val = std::min(val,rows);
-        val = std::max(val,0);
-    }
-
-    cv::Rect boxFromCenter(const cv::Point center,int left,int right,int top,int bottom,cv::Size bdSize)
-    {
-        cv::Point p1(center.x -  left ,center.y - top);
-        cv::Point p2( center.x + right, center.y + bottom);
-        p1.x = std::max(0,p1.x);
-        p1.y = std::max(0,p1.y);
-        p2.x = std::min(p2.x,bdSize.width-1);
-        p2.y = std::min(p2.y,bdSize.height-1);
-        cv::Rect rect(p1,p2);
-        return rect;
-    }
-
-    cv::Rect boxPadding(cv::Rect rect,int left,int right,int top,int bottom,cv::Size bdSize)
-    {
-
-        cv::Point center(rect.x+(rect.width>>1),rect.y + (rect.height>>1));
-        int rebuildLeft  = (rect.width>>1 )+ left;
-        int rebuildRight = (rect.width>>1 )+ right;
-        int rebuildTop = (rect.height>>1 )+ top;
-        int rebuildBottom = (rect.height>>1 )+ bottom;
-        return boxFromCenter(center,rebuildLeft,rebuildRight,rebuildTop,rebuildBottom,bdSize);
-
-    }
-
-
-
-    void PlateSegmentation:: refineRegion(cv::Mat &plateImage,const std::vector<int> &candidatePts,const int padding,std::vector<cv::Rect> &rects){
-        int w = candidatePts[5] - candidatePts[4];
-        int cols = plateImage.cols;
-        int rows = plateImage.rows;
-        for(int i = 0 ; i < candidatePts.size()  ; i++)
-        {
-            int left = 0;
-            int right = 0 ;
-
-            if(i == 0 ){
-                left= candidatePts[i];
-                right = left+w+padding;
-                }
-            else {
-                left = candidatePts[i] - padding;
-                right = left + w + padding * 2;
-            }
-
-            computeSafeMargin(right,cols);
-            computeSafeMargin(left,cols);
-            cv::Rect roi(left,0,right - left,rows-1);
-            cv::Mat roiImage;
-            plateImage(roi).copyTo(roiImage);
-
-            if (i>=1)
-            {
-
-                cv::Mat roi_thres;
-//                cv::threshold(roiImage,roi_thres,0,255,cv::THRESH_OTSU|cv::THRESH_BINARY);
-
-                niBlackThreshold(roiImage,roi_thres,255,cv::THRESH_BINARY,15,0.3,BINARIZATION_NIBLACK);
-
-                std::vector<std::vector<cv::Point> > contours;
-                cv::findContours(roi_thres,contours,cv::RETR_LIST,cv::CHAIN_APPROX_SIMPLE);
-                cv::Point boxCenter(roiImage.cols>>1,roiImage.rows>>1);
-
-                cv::Rect final_bdbox;
-                cv::Point final_center;
-                int final_dist = INT_MAX;
-
-
-                for(auto contour:contours)
-                {
-                    cv::Rect bdbox = cv::boundingRect(contour);
-                    cv::Point center(bdbox.x+(bdbox.width>>1),bdbox.y + (bdbox.height>>1));
-                    int dist = (center.x - boxCenter.x)*(center.x - boxCenter.x);
-                    if(dist<final_dist and  bdbox.height > rows>>1)
-                    {   final_dist =dist;
-                        final_center = center;
-                        final_bdbox = bdbox;
-                    }
-                }
-
-                //rebuild box
-                if(final_bdbox.height/ static_cast<float>(final_bdbox.width) > 3.5 && final_bdbox.width*final_bdbox.height<10)
-                    final_bdbox = boxFromCenter(final_center,8,8,(rows>>1)-3 , (rows>>1) - 2,roiImage.size());
-                else {
-                    if(i == candidatePts.size()-1)
-                        final_bdbox = boxPadding(final_bdbox, padding/2, padding, padding/2, padding/2, roiImage.size());
-                    else
-                        final_bdbox = boxPadding(final_bdbox, padding, padding, padding, padding, roiImage.size());
-
-
-//                    std::cout<<final_bdbox<<std::endl;
-//                    std::cout<<roiImage.size()<<std::endl;
-#ifdef DEBUG
-                    //cv::imshow("char_thres",roi_thres);
-
-                    //cv::imshow("char",roiImage(final_bdbox));
-                    //cv::waitKey(0);
-#endif
-
-
-                }
-
-
-                final_bdbox.x += left;
-
-                rects.push_back(final_bdbox);
-//
-
-            }
-            else
-            {
-                rects.push_back(roi);
-            }
-
-//            else
-//            {
-//
-//            }
-
-//            cv::GaussianBlur(roiImage,roiImage,cv::Size(7,7),3);
-//
-//            cv::imshow("image",roiImage);
-//            cv::waitKey(0);
-
-
-        }
-
-
-
-    }
-    void avgfilter(float *angle_list,int size,int windowsSize) {
-        float *filterd = new float[size];
-        for(int i = 0 ; i < size ; i++) filterd [i] = angle_list[i];
-//        memcpy(filterd,angle_list,size);
-
-        cv::Mat kernal_gaussian = cv::getGaussianKernel(windowsSize,3,CV_32F);
-        float *kernal = (float*)kernal_gaussian.data;
-//        kernal+=windowsSize;
-        int r = windowsSize/2;
-
-
-
-
-        for (int i = 0; i < size; i++) {
-            float avg = 0.00f;
-            for (int j = 0; j < windowsSize; j++) {
-                if(i+j-r>0&&i+j+r<size-1)
-                    avg += filterd[i + j-r]*kernal[j];
-            }
-//            avg = avg / windowsSize;
-            angle_list[i] = avg;
-
-        }
-
-        delete filterd;
-    }
-
-    void PlateSegmentation::templateMatchFinding(const cv::Mat &respones,int windowsWidth,std::pair<float,std::vector<int> > &candidatePts){
-        int rows = respones.rows;
-        int cols = respones.cols;
-
-
-
-        float *data = (float*)respones.data;
-        float *engNum_prob = data;
-        float *false_prob = data+cols;
-        float *ch_prob = data+cols*2;
-
-        avgfilter(engNum_prob,cols,5);
-        avgfilter(false_prob,cols,5);
-//        avgfilter(ch_prob,cols,5);
-        std::vector<int> candidate_pts(7);
-#ifdef DEBUG
-        drawHist(engNum_prob,cols,"engNum_prob");
-        drawHist(false_prob,cols,"false_prob");
-        drawHist(ch_prob,cols,"ch_prob");
-                //cv::waitKey(0);
-#endif
-
-
-
-
-        int cp_list[7];
-        float loss_selected = -1;
-
-        for(int start = 0 ; start < 20 ; start+=2)
-            for(int  width = windowsWidth-5; width < windowsWidth+5 ; width++ ){
-                for(int interval = windowsWidth/2; interval < windowsWidth; interval++)
-                {
-                    int cp1_ch  = start;
-                    int cp2_p0 = cp1_ch+ width;
-                    int cp3_p1 = cp2_p0+ width + interval;
-                    int cp4_p2 = cp3_p1 + width;
-                    int cp5_p3 = cp4_p2 + width+1;
-                    int cp6_p4 = cp5_p3 + width+2;
-                    int cp7_p5= cp6_p4+ width+2;
-
-                    int md1 = (cp1_ch+cp2_p0)>>1;
-                    int md2 = (cp2_p0+cp3_p1)>>1;
-                    int md3 = (cp3_p1+cp4_p2)>>1;
-                    int md4 = (cp4_p2+cp5_p3)>>1;
-                    int md5 = (cp5_p3+cp6_p4)>>1;
-                    int md6 = (cp6_p4+cp7_p5)>>1;
-
-
-
-
-                    if(cp7_p5>=cols)
-                        continue;
-                    float loss = ch_prob[cp1_ch]+
-                       engNum_prob[cp2_p0] +engNum_prob[cp3_p1]+engNum_prob[cp4_p2]+engNum_prob[cp5_p3]+engNum_prob[cp6_p4] +engNum_prob[cp7_p5]
-                    + (false_prob[md2]+false_prob[md3]+false_prob[md4]+false_prob[md5]+false_prob[md5] + false_prob[md6]);
-//                    float loss = ch_prob[cp1_ch]*3 -(false_prob[cp3_p1]+false_prob[cp4_p2]+false_prob[cp5_p3]+false_prob[cp6_p4]+false_prob[cp7_p5]);
-
-
-
-
-                    if(loss>loss_selected)
-                    {
-                        loss_selected = loss;
-                        cp_list[0]= cp1_ch;
-                        cp_list[1]= cp2_p0;
-                        cp_list[2]= cp3_p1;
-                        cp_list[3]= cp4_p2;
-                        cp_list[4]= cp5_p3;
-                        cp_list[5]= cp6_p4;
-                        cp_list[6]= cp7_p5;
-                    }
-                }
-            }
-        candidate_pts[0] = cp_list[0];
-        candidate_pts[1] = cp_list[1];
-        candidate_pts[2] = cp_list[2];
-        candidate_pts[3] = cp_list[3];
-        candidate_pts[4] = cp_list[4];
-        candidate_pts[5] = cp_list[5];
-        candidate_pts[6] = cp_list[6];
-
-        candidatePts.first = loss_selected;
-        candidatePts.second = candidate_pts;
-
-    };
-
-
-    void PlateSegmentation::segmentPlateBySlidingWindows(cv::Mat &plateImage,int windowsWidth,int stride,cv::Mat &respones){
-
-
-        cv::resize(plateImage,plateImage,cv::Size(136,36));
-
-        cv::Mat plateImageGray;
-        cv::cvtColor(plateImage,plateImageGray,cv::COLOR_BGR2GRAY);
-
-        int height = plateImage.rows - 1;
-        int width = plateImage.cols - 1;
-
-        for(int i = 0 ; i < plateImage.cols - windowsWidth +1 ; i +=stride)
-        {
-            cv::Rect roi(i,0,windowsWidth,height);
-            cv::Mat roiImage = plateImageGray(roi);
-            cv::Mat response = classifyResponse(roiImage);
-            respones.push_back(response);
-        }
-
-
-
-
-        respones =  respones.t();
-//        std::pair<float,std::vector<int>> images ;
-//
-//
-//        std::cout<<images.first<<" ";
-//        for(int i = 0 ; i < images.second.size() ; i++)
-//        {
-//            std::cout<<images.second[i]<<" ";
-////            cv::line(plateImageGray,cv::Point(images.second[i],0),cv::Point(images.second[i],36),cv::Scalar(255,255,255),1); //DEBUG
-//        }
-
-//        int w = images.second[5] - images.second[4];
-
-//        cv::line(plateImageGray,cv::Point(images.second[5]+w,0),cv::Point(images.second[5]+w,36),cv::Scalar(255,255,255),1); //DEBUG
-//        cv::line(plateImageGray,cv::Point(images.second[5]+2*w,0),cv::Point(images.second[5]+2*w,36),cv::Scalar(255,255,255),1); //DEBUG
-
-
-//        RefineRegion(plateImageGray,images.second,5);
-
-//        std::cout<<w<<std::endl;
-
-//        std::cout<<<<std::endl;
-
-//        cv::resize(plateImageGray,plateImageGray,cv::Size(600,100));
-
-
-
-    }
-
-//    void filterGaussian(cv::Mat &respones,float sigma){
-//
-//    }
-
-
-    void PlateSegmentation::segmentPlatePipline(PlateInfo &plateInfo,int stride,std::vector<cv::Rect> &Char_rects){
-        cv::Mat plateImage = plateInfo.getPlateImage(); // get src image .
-        cv::Mat plateImageGray;
-        cv::cvtColor(plateImage,plateImageGray,cv::COLOR_BGR2GRAY);
-        //do binarzation
-        //
-        std::pair<float,std::vector<int> > sections ; // segment points variables .
-
-        cv::Mat respones; //three response of every sub region from origin image .
-        segmentPlateBySlidingWindows(plateImage,DEFAULT_WIDTH,1,respones);
-        templateMatchFinding(respones,DEFAULT_WIDTH/stride,sections);
-
-//        std::cout<<sections<<std::endl;
-
-        refineRegion(plateImageGray,sections.second,5,Char_rects);
-#ifdef DEBUG
-        for(int i = 0 ; i < sections.second.size() ; i++)
-        {
-            std::cout<<sections.second[i]<<" ";
-            cv::line(plateImageGray,cv::Point(sections.second[i],0),cv::Point(sections.second[i],36),cv::Scalar(255,255,255),1); //DEBUG
-        }
-        //cv::imshow("plate",plateImageGray);
-        //cv::waitKey(0);
-#endif
-//        cv::waitKey(0);
-
-    }
-
-    void PlateSegmentation::ExtractRegions(PlateInfo &plateInfo,std::vector<cv::Rect> &rects){
-        cv::Mat plateImage = plateInfo.getPlateImage();
-        for(int i = 0 ; i < rects.size() ; i++){
-            cv::Mat charImage;
-            plateImage(rects[i]).copyTo(charImage);
-            if(charImage.channels())
-                cv::cvtColor(charImage,charImage,cv::COLOR_BGR2GRAY);
-//            cv::imshow("image",charImage);
-//            cv::waitKey(0);
-            cv::equalizeHist(charImage,charImage);
-//
-
-//
-
-
-            std::pair<CharType,cv::Mat> char_instance;
-            if(i == 0 ){
-
-                char_instance.first = CHINESE;
-
-
-            } else if(i == 1){
-                char_instance.first = LETTER;
-            }
-            else{
-                char_instance.first = LETTER_NUMS;
-            }
-            char_instance.second = charImage;
-            plateInfo.appendPlateChar(char_instance);
-
-        }
-
-    }
-
-}//namespace pr
diff --git a/Prj-iOS/lpr/Source/src/Recognizer.cpp b/Prj-iOS/lpr/Source/src/Recognizer.cpp
deleted file mode 100755
index 0cad4e6..0000000
--- a/Prj-iOS/lpr/Source/src/Recognizer.cpp
+++ /dev/null
@@ -1,26 +0,0 @@
-//
-// Created by 庾金科 on 22/10/2017.
-//
-
-#include "../include/Recognizer.h"
-
-namespace pr{
-    void GeneralRecognizer::SegmentBasedSequenceRecognition(PlateInfo &plateinfo){
-
-
-        for(auto char_instance:plateinfo.plateChars)
-        {
-
-
-            std::pair<CharType,cv::Mat> res;
-            cv::Mat code_table= recognizeCharacter(char_instance.second);
-            res.first = char_instance.first;
-            code_table.copyTo(res.second);
-            plateinfo.appendPlateCoding(res);
-
-        }
-
-
-
-    }
-}
\ No newline at end of file
diff --git a/Prj-iOS/lpr/Source/src/util.h b/Prj-iOS/lpr/Source/src/util.h
deleted file mode 100755
index 76a452f..0000000
--- a/Prj-iOS/lpr/Source/src/util.h
+++ /dev/null
@@ -1,79 +0,0 @@
-//
-// Created by 庾金科 on 04/04/2017.
-//
-
-#include <opencv2/opencv.hpp>
-
-namespace util{
-
-    template <class T> void swap ( T& a, T& b )
-    {
-        T c(a); a=b; b=c;
-    }
-
-    template <class T> T min(T& a,T& b )
-    {
-        return a>b?b:a;
-
-    }
-
-    cv::Mat cropFromImage(const cv::Mat &image,cv::Rect rect){
-        int w = image.cols-1;
-        int h = image.rows-1;
-        rect.x = std::max(rect.x,0);
-        rect.y = std::max(rect.y,0);
-        rect.height = std::min(rect.height,h-rect.y);
-        rect.width = std::min(rect.width,w-rect.x);
-        cv::Mat temp(rect.size(), image.type());
-        cv::Mat cropped;
-        temp = image(rect);
-        temp.copyTo(cropped);
-        return cropped;
-
-    }
-
-    cv::Mat cropBox2dFromImage(const cv::Mat &image,cv::RotatedRect rect)
-    {
-        cv::Mat M, rotated, cropped;
-        float angle = rect.angle;
-        cv::Size rect_size(rect.size.width,rect.size.height);
-        if (rect.angle < -45.) {
-            angle += 90.0;
-            swap(rect_size.width, rect_size.height);
-        }
-        M = cv::getRotationMatrix2D(rect.center, angle, 1.0);
-        cv::warpAffine(image, rotated, M, image.size(), cv::INTER_CUBIC);
-        cv::getRectSubPix(rotated, rect_size, rect.center, cropped);
-        return cropped;
-    }
-
-    cv::Mat calcHist(const cv::Mat &image)
-    {
-        cv::Mat hsv;
-        std::vector<cv::Mat> hsv_planes;
-        cv::cvtColor(image,hsv,cv::COLOR_BGR2HSV);
-        cv::split(hsv,hsv_planes);
-        cv::Mat hist;
-        int histSize = 256;
-        float range[] = {0,255};
-        const float* histRange = {range};
-
-        cv::calcHist( &hsv_planes[0], 1, 0, cv::Mat(), hist, 1, &histSize, &histRange,true, true);
-        return hist;
-
-    }
-    float computeSimilir(const cv::Mat &A,const cv::Mat &B)
-    {
-
-        cv::Mat histA,histB;
-        histA = calcHist(A);
-        histB = calcHist(B);
-        return cv::compareHist(histA,histB,CV_COMP_CORREL);
-
-    }
-
-
-
-
-
-}//namespace util
diff --git a/Prj-iOS/lpr/Utility.h b/Prj-iOS/lpr/Utility.h
deleted file mode 100755
index f2a870d..0000000
--- a/Prj-iOS/lpr/Utility.h
+++ /dev/null
@@ -1,30 +0,0 @@
-//
-//  Utility.h
-//  lpr
-//
-//  Created by baotim on 2018/10/26.
-//  Copyright © 2018 lprSample. All rights reserved.
-//
-#ifdef __cplusplus
-#import <opencv2/opencv.hpp>
-#endif
-#ifdef __OBJC__
-#import <UIKit/UIKit.h>
-#import <Foundation/Foundation.h>
-#endif
-using namespace cv;
-
-NS_ASSUME_NONNULL_BEGIN
-
-@interface Utility : NSObject
-
-+ (cv::Mat)cvMatFromUIImage:(UIImage *)image;
-+ (UIImage *)UIImageFromCVMat:(cv::Mat)image;
-+ (UIImage *)scaleAndRotateImageFrontCamera:(UIImage *)image;
-+ (UIImage *)scaleAndRotateImageBackCamera:(UIImage *)image;
-+ (UIImage *)imageWithMat:(const cv::Mat&) image andImageOrientation: (UIImageOrientation) orientation;
-+ (UIImage *)imageWithMat:(const cv::Mat&) image andDeviceOrientation: (UIDeviceOrientation) orientation;
-
-@end
-
-NS_ASSUME_NONNULL_END
diff --git a/Prj-iOS/lpr/Utility.mm b/Prj-iOS/lpr/Utility.mm
deleted file mode 100755
index d78785c..0000000
--- a/Prj-iOS/lpr/Utility.mm
+++ /dev/null
@@ -1,320 +0,0 @@
-//
-//  Utility.m
-//  lpr
-//
-//  Created by baotim on 2018/10/26.
-//  Copyright © 2018 lprSample. All rights reserved.
-//
-
-#import "Utility.h"
-
-@implementation Utility
-
-+ (UIImage *)UIImageFromCVMat:(cv::Mat)cvMat
-{
-    NSData *data = [NSData dataWithBytes:cvMat.data length:cvMat.elemSize()*cvMat.total()];
-    CGColorSpaceRef colorSpace;
-    
-    if (cvMat.elemSize() == 1) {
-        colorSpace = CGColorSpaceCreateDeviceGray();
-    } else {
-        colorSpace = CGColorSpaceCreateDeviceRGB();
-    }
-    
-    CGDataProviderRef provider = CGDataProviderCreateWithCFData((__bridge CFDataRef)data);
-    
-    // Creating CGImage from cv::Mat
-    CGImageRef imageRef = CGImageCreate(cvMat.cols,                                 //width
-                                        cvMat.rows,                                 //height
-                                        8,                                          //bits per component
-                                        8 * cvMat.elemSize(),                       //bits per pixel
-                                        cvMat.step[0],                            //bytesPerRow
-                                        colorSpace,                                 //colorspace
-                                        kCGImageAlphaNone|kCGBitmapByteOrderDefault,// bitmap info
-                                        provider,                                   //CGDataProviderRef
-                                        NULL,                                       //decode
-                                        false,                                      //should interpolate
-                                        kCGRenderingIntentDefault                   //intent
-                                        );
-    
-    // Getting UIImage from CGImage
-    UIImage *finalImage = [UIImage imageWithCGImage:imageRef scale:1.0 orientation:UIImageOrientationUp];
-    CGImageRelease(imageRef);
-    CGDataProviderRelease(provider);
-    CGColorSpaceRelease(colorSpace);
-    
-    return finalImage;
-}
-
-//缩放调整图片
-+ (UIImage *)scaleAndRotateImageBackCamera:(UIImage *)image
-{
-    static int kMaxResolution = 480;
-    CGImageRef imgRef = image.CGImage;
-    CGFloat width = CGImageGetWidth(imgRef);
-    CGFloat height = CGImageGetHeight(imgRef);
-    
-    CGAffineTransform transform = CGAffineTransformIdentity;
-    CGRect bounds = CGRectMake(0, 0, width, height);
-    if (width > kMaxResolution || height > kMaxResolution) {
-        CGFloat ratio = width/height;
-        if (ratio > 1) {
-            bounds.size.width = kMaxResolution;
-            bounds.size.height = bounds.size.width / ratio;
-        } else {
-            bounds.size.height = kMaxResolution;
-            bounds.size.width = bounds.size.height * ratio;
-        }
-    }
-    CGFloat scaleRatio = bounds.size.width / width;
-    CGSize imageSize = CGSizeMake(CGImageGetWidth(imgRef), CGImageGetHeight(imgRef));
-    CGFloat boundHeight;
-    UIImageOrientation orient = image.imageOrientation;
-    switch(orient) {
-        case UIImageOrientationUp:
-            transform = CGAffineTransformIdentity;
-            break;
-        case UIImageOrientationUpMirrored:
-            transform = CGAffineTransformMakeTranslation(imageSize.width, 0.0);
-            transform = CGAffineTransformScale(transform, -1.0, 1.0);
-            break;
-        case UIImageOrientationDown:
-            transform = CGAffineTransformMakeTranslation(imageSize.width, imageSize.height);
-            transform = CGAffineTransformRotate(transform, M_PI);
-            break;
-        case UIImageOrientationDownMirrored:
-            transform = CGAffineTransformMakeTranslation(0.0, imageSize.height);
-            transform = CGAffineTransformScale(transform, 1.0, -1.0);
-            break;
-        case UIImageOrientationLeftMirrored:
-            boundHeight = bounds.size.height;
-            bounds.size.height = bounds.size.width;
-            bounds.size.width = boundHeight;
-            transform = CGAffineTransformMakeTranslation(imageSize.height, imageSize.width);
-            transform = CGAffineTransformScale(transform, -1.0, 1.0);
-            transform = CGAffineTransformRotate(transform, 3.0 * M_PI / 2.0);
-            break;
-        case UIImageOrientationLeft:
-            boundHeight = bounds.size.height;
-            bounds.size.height = bounds.size.width;
-            bounds.size.width = boundHeight;
-            transform = CGAffineTransformMakeTranslation(0.0, imageSize.width);
-            transform = CGAffineTransformRotate(transform, 3.0 * M_PI / 2.0);
-            break;
-        case UIImageOrientationRightMirrored:
-            boundHeight = bounds.size.height;
-            bounds.size.height = bounds.size.width;
-            bounds.size.width = boundHeight;
-            transform = CGAffineTransformMakeScale(-1.0, 1.0);
-            transform = CGAffineTransformRotate(transform, M_PI / 2.0);
-            break;
-        case UIImageOrientationRight:
-            boundHeight = bounds.size.height;
-            bounds.size.height = bounds.size.width;
-            bounds.size.width = boundHeight;
-            transform = CGAffineTransformMakeTranslation(imageSize.height, 0.0);
-            transform = CGAffineTransformRotate(transform, M_PI / 2.0);
-            break;
-        default:
-            [NSException raise:NSInternalInconsistencyException format:@"Invalid image orientation"];
-    }
-    
-    UIGraphicsBeginImageContext(bounds.size);
-    CGContextRef context = UIGraphicsGetCurrentContext();
-    if (orient == UIImageOrientationRight || orient == UIImageOrientationLeft) {
-        CGContextScaleCTM(context, -scaleRatio, scaleRatio);
-        CGContextTranslateCTM(context, -height, 0);
-    } else {
-        CGContextScaleCTM(context, scaleRatio, -scaleRatio);
-        CGContextTranslateCTM(context, 0, -height);
-    }
-    CGContextConcatCTM(context, transform);
-    CGContextDrawImage(UIGraphicsGetCurrentContext(), CGRectMake(0, 0, width, height), imgRef);
-    UIImage *returnImage = UIGraphicsGetImageFromCurrentImageContext();
-    UIGraphicsEndImageContext();
-    NSLog(@"resize w%f,H%f",returnImage.size.width,returnImage.size.height);
-    return returnImage;
-}
-
-+ (UIImage*)imageWithMat:(const cv::Mat&) image andDeviceOrientation: (UIDeviceOrientation)orientation
-{
-    UIImageOrientation imgOrientation = UIImageOrientationUp;
-    
-    switch (orientation)
-    {
-        case UIDeviceOrientationLandscapeLeft:
-            imgOrientation =UIImageOrientationLeftMirrored; break;
-            
-        case UIDeviceOrientationLandscapeRight:
-            imgOrientation = UIImageOrientationDown; break;
-            
-        case UIDeviceOrientationPortraitUpsideDown:
-            imgOrientation = UIImageOrientationRightMirrored; break;
-        case UIDeviceOrientationFaceUp:
-            imgOrientation = UIImageOrientationRightMirrored; break;
-            
-        default:
-        case UIDeviceOrientationPortrait:
-            imgOrientation = UIImageOrientationRight; break;
-    };
-    
-    return [Utility imageWithMat:image andImageOrientation:imgOrientation];
-}
-
-+ (UIImage*)imageWithMat:(const cv::Mat&)image andImageOrientation:(UIImageOrientation)orientation;
-{
-    cv::Mat rgbaView;
-    
-    if (image.channels() == 3)
-    {
-        cv::cvtColor(image, rgbaView, COLOR_BGR2BGRA);
-    }
-    else if (image.channels() == 4)
-    {
-        cv::cvtColor(image, rgbaView, COLOR_BGR2BGRA);
-    }
-    else if (image.channels() == 1)
-    {
-        cv::cvtColor(image, rgbaView, COLOR_GRAY2RGBA);
-    }
-    
-    NSData *data = [NSData dataWithBytes:rgbaView.data length:rgbaView.elemSize() * rgbaView.total()];
-    
-    CGColorSpaceRef colorSpace = CGColorSpaceCreateDeviceRGB();
-    
-    CGDataProviderRef provider = CGDataProviderCreateWithCFData((__bridge CFDataRef)data);
-    
-    CGBitmapInfo bmInfo = kCGImageAlphaPremultipliedLast | kCGBitmapByteOrder32Big;
-    
-    // Creating CGImage from cv::Mat
-    CGImageRef imageRef = CGImageCreate(rgbaView.cols,                                 //width
-                                        rgbaView.rows,                                 //height
-                                        8,                                          //bits per component
-                                        8 * rgbaView.elemSize(),                       //bits per pixel
-                                        rgbaView.step.p[0],                            //bytesPerRow
-                                        colorSpace,                                 //colorspace
-                                        bmInfo,// bitmap info
-                                        provider,                                   //CGDataProviderRef
-                                        NULL,                                       //decode
-                                        false,                                      //should interpolate
-                                        kCGRenderingIntentDefault                   //intent
-                                        );
-    
-    // Getting UIImage from CGImage
-    UIImage *finalImage = [UIImage imageWithCGImage:imageRef scale:1 orientation:orientation];
-    CGImageRelease(imageRef);
-    CGDataProviderRelease(provider);
-    CGColorSpaceRelease(colorSpace);
-    
-    return finalImage;
-}
-
-+ (cv::Mat)cvMatFromUIImage:(UIImage *)image
-{
-    CGColorSpaceRef colorSpace = CGImageGetColorSpace(image.CGImage);
-    CGFloat cols = image.size.width;
-    CGFloat rows = image.size.height;
-    
-    cv::Mat cvMat(rows, cols, CV_8UC4); // 8 bits per component, 4 channels
-    
-    CGContextRef contextRef = CGBitmapContextCreate(cvMat.data,                 // Pointer to  data
-                                                    cols,                       // Width of bitmap
-                                                    rows,                       // Height of bitmap
-                                                    8,                          // Bits per component
-                                                    cvMat.step[0],              // Bytes per row
-                                                    colorSpace,                 // Colorspace
-                                                    kCGImageAlphaNoneSkipLast |
-                                                    kCGBitmapByteOrderDefault); // Bitmap info flags
-    
-    CGContextDrawImage(contextRef, CGRectMake(0, 0, cols, rows), image.CGImage);
-    CGContextRelease(contextRef);
-    CGColorSpaceRelease(colorSpace);
-    cv::Mat cvMat3(rows, cols, CV_8UC3); // 8 bits per component, 4 channels
-    cv::cvtColor(cvMat, cvMat3,COLOR_RGBA2RGB);
-    
-    return cvMat3;
-}
-
-+ (UIImage *)scaleAndRotateImageFrontCamera:(UIImage *)image
-{
-    static int kMaxResolution = 640;
-    CGImageRef imgRef = image.CGImage;
-    CGFloat width = CGImageGetWidth(imgRef);
-    CGFloat height = CGImageGetHeight(imgRef);
-    CGAffineTransform transform = CGAffineTransformIdentity;
-    CGRect bounds = CGRectMake( 0, 0, width, height);
-    if (width > kMaxResolution || height > kMaxResolution) {
-        CGFloat ratio = width/height;
-        if (ratio > 1) {
-            bounds.size.width = kMaxResolution;
-            bounds.size.height = bounds.size.width / ratio;
-        } else {
-            bounds.size.height = kMaxResolution;
-            bounds.size.width = bounds.size.height * ratio;
-        }
-    }
-    
-    CGFloat scaleRatio = bounds.size.width / width;
-    CGSize imageSize = CGSizeMake(CGImageGetWidth(imgRef), CGImageGetHeight(imgRef));
-    CGFloat boundHeight;
-    UIImageOrientation orient = image.imageOrientation;
-    switch(orient) {
-        case UIImageOrientationUp:
-            transform = CGAffineTransformIdentity;
-            break;
-        case UIImageOrientationUpMirrored:
-            transform = CGAffineTransformMakeTranslation(imageSize.width, 0.0);
-            transform = CGAffineTransformScale(transform, -1.0, 1.0);
-            break;
-        case UIImageOrientationDown:
-            transform = CGAffineTransformMakeTranslation(imageSize.width, imageSize.height);
-            transform = CGAffineTransformRotate(transform, M_PI);
-            break;
-        case UIImageOrientationDownMirrored:
-            transform = CGAffineTransformMakeTranslation(0.0, imageSize.height);
-            transform = CGAffineTransformScale(transform, 1.0, -1.0);
-            break;
-        case UIImageOrientationLeftMirrored:
-            boundHeight = bounds.size.height;
-            bounds.size.height = bounds.size.width;
-            bounds.size.width = boundHeight;
-            transform = CGAffineTransformMakeTranslation(imageSize.height, imageSize.width);
-            transform = CGAffineTransformScale(transform, -1.0, 1.0);
-            transform = CGAffineTransformRotate(transform, 3.0 * M_PI / 2.0);
-            break;
-        case UIImageOrientationLeft:
-            boundHeight = bounds.size.height;
-            bounds.size.height = bounds.size.width;
-            bounds.size.width = boundHeight;
-            transform = CGAffineTransformMakeTranslation(0.0, imageSize.width);
-            transform = CGAffineTransformRotate(transform, 3.0 * M_PI / 2.0);
-            break;
-        case UIImageOrientationRight:
-        case UIImageOrientationRightMirrored:
-            boundHeight = bounds.size.height;
-            bounds.size.height = bounds.size.width;
-            bounds.size.width = boundHeight;
-            transform = CGAffineTransformMakeScale(-1.0, 1.0);
-            transform = CGAffineTransformRotate(transform, M_PI / 2.0);
-            break;
-        default:
-            [NSException raise:NSInternalInconsistencyException format:@"Invalid image orientation"];
-    }
-    UIGraphicsBeginImageContext( bounds.size );
-    CGContextRef context = UIGraphicsGetCurrentContext();
-    if ( orient == UIImageOrientationRight || orient == UIImageOrientationLeft ) {
-        CGContextScaleCTM(context, -scaleRatio, scaleRatio);
-        CGContextTranslateCTM(context, -height, 0);
-    }
-    else {
-        CGContextScaleCTM(context, scaleRatio, -scaleRatio);
-        CGContextTranslateCTM(context, 0, -height);
-    }
-    CGContextConcatCTM( context, transform );
-    CGContextDrawImage( UIGraphicsGetCurrentContext(), CGRectMake(0, 0, width, height), imgRef );
-    UIImage *returnImage = UIGraphicsGetImageFromCurrentImageContext();
-    UIGraphicsEndImageContext();
-    return returnImage;
-}
-
-@end
diff --git a/Prj-iOS/lpr/lpr.xcodeproj/project.pbxproj b/Prj-iOS/lpr/lpr.xcodeproj/project.pbxproj
deleted file mode 100755
index 4af42aa..0000000
--- a/Prj-iOS/lpr/lpr.xcodeproj/project.pbxproj
+++ /dev/null
@@ -1,550 +0,0 @@
-// !$*UTF8*$!
-{
-	archiveVersion = 1;
-	classes = {
-	};
-	objectVersion = 50;
-	objects = {
-
-/* Begin PBXBuildFile section */
-		343B620D21997FC800D03830 /* SegmentationFreeRecognizer.cpp in Sources */ = {isa = PBXBuildFile; fileRef = 343B620C21997FC700D03830 /* SegmentationFreeRecognizer.cpp */; };
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-		343B6211219980FE00D03830 /* SegmenationFree-Inception.prototxt in Resources */ = {isa = PBXBuildFile; fileRef = 343B620F219980FE00D03830 /* SegmenationFree-Inception.prototxt */; };
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-		34ECFDD12182DD9300B162D4 /* AppDelegate.m in Sources */ = {isa = PBXBuildFile; fileRef = 34ECFDD02182DD9300B162D4 /* AppDelegate.m */; };
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-		34ECFDDF2182DD9400B162D4 /* main.m in Sources */ = {isa = PBXBuildFile; fileRef = 34ECFDDE2182DD9400B162D4 /* main.m */; };
-		34ECFE152182DEF500B162D4 /* PlateSegmentation.cpp in Sources */ = {isa = PBXBuildFile; fileRef = 34ECFE0D2182DEF500B162D4 /* PlateSegmentation.cpp */; };
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-				CLANG_WARN_INFINITE_RECURSION = YES;
-				CLANG_WARN_INT_CONVERSION = YES;
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-				GCC_C_LANGUAGE_STANDARD = gnu11;
-				GCC_DYNAMIC_NO_PIC = NO;
-				GCC_NO_COMMON_BLOCKS = YES;
-				GCC_OPTIMIZATION_LEVEL = 0;
-				GCC_PREPROCESSOR_DEFINITIONS = (
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-				GCC_WARN_64_TO_32_BIT_CONVERSION = YES;
-				GCC_WARN_ABOUT_RETURN_TYPE = YES_ERROR;
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-				GCC_WARN_UNUSED_FUNCTION = YES;
-				GCC_WARN_UNUSED_VARIABLE = YES;
-				HEADER_SEARCH_PATHS = (
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-				IPHONEOS_DEPLOYMENT_TARGET = 10.1;
-				MTL_ENABLE_DEBUG_INFO = INCLUDE_SOURCE;
-				MTL_FAST_MATH = YES;
-				ONLY_ACTIVE_ARCH = YES;
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-		};
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-				CLANG_WARN_INT_CONVERSION = YES;
-				CLANG_WARN_NON_LITERAL_NULL_CONVERSION = YES;
-				CLANG_WARN_OBJC_IMPLICIT_RETAIN_SELF = YES;
-				CLANG_WARN_OBJC_LITERAL_CONVERSION = YES;
-				CLANG_WARN_OBJC_ROOT_CLASS = YES_ERROR;
-				CLANG_WARN_RANGE_LOOP_ANALYSIS = YES;
-				CLANG_WARN_STRICT_PROTOTYPES = YES;
-				CLANG_WARN_SUSPICIOUS_MOVE = YES;
-				CLANG_WARN_UNGUARDED_AVAILABILITY = YES_AGGRESSIVE;
-				CLANG_WARN_UNREACHABLE_CODE = YES;
-				CLANG_WARN__DUPLICATE_METHOD_MATCH = YES;
-				CODE_SIGN_IDENTITY = "iPhone Developer";
-				COPY_PHASE_STRIP = NO;
-				DEBUG_INFORMATION_FORMAT = "dwarf-with-dsym";
-				ENABLE_NS_ASSERTIONS = NO;
-				ENABLE_STRICT_OBJC_MSGSEND = YES;
-				GCC_C_LANGUAGE_STANDARD = gnu11;
-				GCC_NO_COMMON_BLOCKS = YES;
-				GCC_WARN_64_TO_32_BIT_CONVERSION = YES;
-				GCC_WARN_ABOUT_RETURN_TYPE = YES_ERROR;
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-				GCC_WARN_UNINITIALIZED_AUTOS = YES_AGGRESSIVE;
-				GCC_WARN_UNUSED_FUNCTION = YES;
-				GCC_WARN_UNUSED_VARIABLE = YES;
-				HEADER_SEARCH_PATHS = (
-					"\"$(SRCROOT)/lpr/Source/incllude\"",
-					"\"$(SRCROOT)\"",
-				);
-				IPHONEOS_DEPLOYMENT_TARGET = 10.1;
-				MTL_ENABLE_DEBUG_INFO = NO;
-				MTL_FAST_MATH = YES;
-				SDKROOT = iphoneos;
-				VALIDATE_PRODUCT = YES;
-			};
-			name = Release;
-		};
-		34ECFDE32182DD9400B162D4 /* Debug */ = {
-			isa = XCBuildConfiguration;
-			baseConfigurationReference = E169020B9373AF28E9C472DD /* Pods-lpr.debug.xcconfig */;
-			buildSettings = {
-				ASSETCATALOG_COMPILER_APPICON_NAME = AppIcon;
-				CLANG_CXX_LANGUAGE_STANDARD = "compiler-default";
-				CLANG_CXX_LIBRARY = "compiler-default";
-				CODE_SIGN_IDENTITY = "iPhone Developer";
-				CODE_SIGN_STYLE = Automatic;
-				DEVELOPMENT_TEAM = YZGMTHK294;
-				ENABLE_BITCODE = YES;
-				GCC_C_LANGUAGE_STANDARD = gnu11;
-				HEADER_SEARCH_PATHS = (
-					"$(inherited)",
-					"\"${PODS_ROOT}/Headers/Public\"",
-					"\"${PODS_ROOT}/Headers/Public/OpenCV\"",
-				);
-				INFOPLIST_FILE = lpr/Info.plist;
-				LD_RUNPATH_SEARCH_PATHS = (
-					"$(inherited)",
-					"@executable_path/Frameworks",
-				);
-				PRODUCT_BUNDLE_IDENTIFIER = com.TongxingPay.IDCardRecognizeDemo;
-				PRODUCT_NAME = "$(TARGET_NAME)";
-				PROVISIONING_PROFILE_SPECIFIER = "";
-				TARGETED_DEVICE_FAMILY = "1,2";
-			};
-			name = Debug;
-		};
-		34ECFDE42182DD9400B162D4 /* Release */ = {
-			isa = XCBuildConfiguration;
-			baseConfigurationReference = 66BAD4CEFABD6705C5100968 /* Pods-lpr.release.xcconfig */;
-			buildSettings = {
-				ASSETCATALOG_COMPILER_APPICON_NAME = AppIcon;
-				CLANG_CXX_LANGUAGE_STANDARD = "compiler-default";
-				CLANG_CXX_LIBRARY = "compiler-default";
-				CODE_SIGN_IDENTITY = "iPhone Developer";
-				CODE_SIGN_STYLE = Automatic;
-				DEVELOPMENT_TEAM = YZGMTHK294;
-				ENABLE_BITCODE = YES;
-				GCC_C_LANGUAGE_STANDARD = gnu11;
-				HEADER_SEARCH_PATHS = (
-					"$(inherited)",
-					"\"${PODS_ROOT}/Headers/Public\"",
-					"\"${PODS_ROOT}/Headers/Public/OpenCV\"",
-				);
-				INFOPLIST_FILE = lpr/Info.plist;
-				LD_RUNPATH_SEARCH_PATHS = (
-					"$(inherited)",
-					"@executable_path/Frameworks",
-				);
-				PRODUCT_BUNDLE_IDENTIFIER = com.TongxingPay.IDCardRecognizeDemo;
-				PRODUCT_NAME = "$(TARGET_NAME)";
-				PROVISIONING_PROFILE_SPECIFIER = "";
-				TARGETED_DEVICE_FAMILY = "1,2";
-			};
-			name = Release;
-		};
-/* End XCBuildConfiguration section */
-
-/* Begin XCConfigurationList section */
-		34ECFDC72182DD9300B162D4 /* Build configuration list for PBXProject "lpr" */ = {
-			isa = XCConfigurationList;
-			buildConfigurations = (
-				34ECFDE02182DD9400B162D4 /* Debug */,
-				34ECFDE12182DD9400B162D4 /* Release */,
-			);
-			defaultConfigurationIsVisible = 0;
-			defaultConfigurationName = Release;
-		};
-		34ECFDE22182DD9400B162D4 /* Build configuration list for PBXNativeTarget "lpr" */ = {
-			isa = XCConfigurationList;
-			buildConfigurations = (
-				34ECFDE32182DD9400B162D4 /* Debug */,
-				34ECFDE42182DD9400B162D4 /* Release */,
-			);
-			defaultConfigurationIsVisible = 0;
-			defaultConfigurationName = Release;
-		};
-/* End XCConfigurationList section */
-	};
-	rootObject = 34ECFDC42182DD9300B162D4 /* Project object */;
-}
diff --git a/Prj-iOS/lpr/lpr.xcodeproj/project.xcworkspace/contents.xcworkspacedata b/Prj-iOS/lpr/lpr.xcodeproj/project.xcworkspace/contents.xcworkspacedata
deleted file mode 100755
index 39e1b8b..0000000
--- a/Prj-iOS/lpr/lpr.xcodeproj/project.xcworkspace/contents.xcworkspacedata
+++ /dev/null
@@ -1,7 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<Workspace
-   version = "1.0">
-   <FileRef
-      location = "self:lpr.xcodeproj">
-   </FileRef>
-</Workspace>
diff --git a/Prj-iOS/lpr/lpr.xcodeproj/project.xcworkspace/xcshareddata/IDEWorkspaceChecks.plist b/Prj-iOS/lpr/lpr.xcodeproj/project.xcworkspace/xcshareddata/IDEWorkspaceChecks.plist
deleted file mode 100755
index 18d9810..0000000
--- a/Prj-iOS/lpr/lpr.xcodeproj/project.xcworkspace/xcshareddata/IDEWorkspaceChecks.plist
+++ /dev/null
@@ -1,8 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
-<plist version="1.0">
-<dict>
-	<key>IDEDidComputeMac32BitWarning</key>
-	<true/>
-</dict>
-</plist>
diff --git a/Prj-iOS/lpr/lpr.xcodeproj/project.xcworkspace/xcuserdata/tbao.xcuserdatad/UserInterfaceState.xcuserstate b/Prj-iOS/lpr/lpr.xcodeproj/project.xcworkspace/xcuserdata/tbao.xcuserdatad/UserInterfaceState.xcuserstate
deleted file mode 100755
index 7314580..0000000
Binary files a/Prj-iOS/lpr/lpr.xcodeproj/project.xcworkspace/xcuserdata/tbao.xcuserdatad/UserInterfaceState.xcuserstate and /dev/null differ
diff --git a/Prj-iOS/lpr/lpr.xcodeproj/xcuserdata/tbao.xcuserdatad/xcschemes/xcschememanagement.plist b/Prj-iOS/lpr/lpr.xcodeproj/xcuserdata/tbao.xcuserdatad/xcschemes/xcschememanagement.plist
deleted file mode 100755
index 008c546..0000000
--- a/Prj-iOS/lpr/lpr.xcodeproj/xcuserdata/tbao.xcuserdatad/xcschemes/xcschememanagement.plist
+++ /dev/null
@@ -1,19 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
-<plist version="1.0">
-<dict>
-	<key>SchemeUserState</key>
-	<dict>
-		<key>lpr.xcscheme</key>
-		<dict>
-			<key>orderHint</key>
-			<integer>2</integer>
-		</dict>
-		<key>lpr.xcscheme_^#shared#^_</key>
-		<dict>
-			<key>orderHint</key>
-			<integer>2</integer>
-		</dict>
-	</dict>
-</dict>
-</plist>
diff --git a/Prj-iOS/lpr/lpr.xcworkspace/contents.xcworkspacedata b/Prj-iOS/lpr/lpr.xcworkspace/contents.xcworkspacedata
deleted file mode 100755
index d677399..0000000
--- a/Prj-iOS/lpr/lpr.xcworkspace/contents.xcworkspacedata
+++ /dev/null
@@ -1,10 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<Workspace
-   version = "1.0">
-   <FileRef
-      location = "group:lpr.xcodeproj">
-   </FileRef>
-   <FileRef
-      location = "group:Pods/Pods.xcodeproj">
-   </FileRef>
-</Workspace>
diff --git a/Prj-iOS/lpr/lpr.xcworkspace/xcshareddata/IDEWorkspaceChecks.plist b/Prj-iOS/lpr/lpr.xcworkspace/xcshareddata/IDEWorkspaceChecks.plist
deleted file mode 100755
index 18d9810..0000000
--- a/Prj-iOS/lpr/lpr.xcworkspace/xcshareddata/IDEWorkspaceChecks.plist
+++ /dev/null
@@ -1,8 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
-<plist version="1.0">
-<dict>
-	<key>IDEDidComputeMac32BitWarning</key>
-	<true/>
-</dict>
-</plist>
diff --git a/Prj-iOS/lpr/lpr.xcworkspace/xcuserdata/tbao.xcuserdatad/UserInterfaceState.xcuserstate b/Prj-iOS/lpr/lpr.xcworkspace/xcuserdata/tbao.xcuserdatad/UserInterfaceState.xcuserstate
deleted file mode 100755
index a999c2c..0000000
Binary files a/Prj-iOS/lpr/lpr.xcworkspace/xcuserdata/tbao.xcuserdatad/UserInterfaceState.xcuserstate and /dev/null differ
diff --git a/Prj-iOS/lpr/lpr.xcworkspace/xcuserdata/tbao.xcuserdatad/xcdebugger/Breakpoints_v2.xcbkptlist b/Prj-iOS/lpr/lpr.xcworkspace/xcuserdata/tbao.xcuserdatad/xcdebugger/Breakpoints_v2.xcbkptlist
deleted file mode 100755
index 37996a6..0000000
--- a/Prj-iOS/lpr/lpr.xcworkspace/xcuserdata/tbao.xcuserdatad/xcdebugger/Breakpoints_v2.xcbkptlist
+++ /dev/null
@@ -1,37 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<Bucket
-   type = "0"
-   version = "2.0">
-   <Breakpoints>
-      <BreakpointProxy
-         BreakpointExtensionID = "Xcode.Breakpoint.ExceptionBreakpoint">
-         <BreakpointContent
-            shouldBeEnabled = "Yes"
-            ignoreCount = "0"
-            continueAfterRunningActions = "No"
-            scope = "0"
-            stopOnStyle = "0">
-         </BreakpointContent>
-      </BreakpointProxy>
-      <BreakpointProxy
-         BreakpointExtensionID = "Xcode.Breakpoint.OpenGLErrorBreakpoint">
-         <BreakpointContent
-            shouldBeEnabled = "Yes"
-            ignoreCount = "0"
-            continueAfterRunningActions = "No"
-            breakpointStackSelectionBehavior = "1"
-            symbolName = "__GPUTOOLS_HAS_DETECTED_AN_OPENGL_ERROR__"
-            moduleName = "libglInterpose.dylib">
-            <Actions>
-               <BreakpointActionProxy
-                  ActionExtensionID = "Xcode.BreakpointAction.OpenGLError">
-                  <ActionContent>
-                  </ActionContent>
-               </BreakpointActionProxy>
-            </Actions>
-            <Locations>
-            </Locations>
-         </BreakpointContent>
-      </BreakpointProxy>
-   </Breakpoints>
-</Bucket>
diff --git a/Prj-iOS/lpr/lpr/AppDelegate.h b/Prj-iOS/lpr/lpr/AppDelegate.h
deleted file mode 100755
index 8ef26f5..0000000
--- a/Prj-iOS/lpr/lpr/AppDelegate.h
+++ /dev/null
@@ -1,17 +0,0 @@
-//
-//  AppDelegate.h
-//  lpr
-//
-//  Created by baotim on 2018/10/26.
-//  Copyright © 2018 lprSample. All rights reserved.
-//
-
-#import <UIKit/UIKit.h>
-
-@interface AppDelegate : UIResponder <UIApplicationDelegate>
-
-@property (strong, nonatomic) UIWindow *window;
-
-
-@end
-
diff --git a/Prj-iOS/lpr/lpr/AppDelegate.m b/Prj-iOS/lpr/lpr/AppDelegate.m
deleted file mode 100755
index e022066..0000000
--- a/Prj-iOS/lpr/lpr/AppDelegate.m
+++ /dev/null
@@ -1,55 +0,0 @@
-//
-//  AppDelegate.m
-//  lpr
-//
-//  Created by baotim on 2018/10/26.
-//  Copyright © 2018 lprSample. All rights reserved.
-//
-
-#import "AppDelegate.h"
-#import "RootViewController.h"
-
-@interface AppDelegate ()
-
-@end
-
-@implementation AppDelegate
-
-
-- (BOOL)application:(UIApplication *)application didFinishLaunchingWithOptions:(NSDictionary *)launchOptions {
-    // Override point for customization after application launch.
-    self.window = [[UIWindow alloc] initWithFrame:[[UIScreen mainScreen] bounds]];
-    self.window.rootViewController = [[UINavigationController alloc] initWithRootViewController:[RootViewController new]];
-    [self.window makeKeyAndVisible];
-    return YES;
-}
-
-
-- (void)applicationWillResignActive:(UIApplication *)application {
-    // Sent when the application is about to move from active to inactive state. This can occur for certain types of temporary interruptions (such as an incoming phone call or SMS message) or when the user quits the application and it begins the transition to the background state.
-    // Use this method to pause ongoing tasks, disable timers, and invalidate graphics rendering callbacks. Games should use this method to pause the game.
-}
-
-
-- (void)applicationDidEnterBackground:(UIApplication *)application {
-    // Use this method to release shared resources, save user data, invalidate timers, and store enough application state information to restore your application to its current state in case it is terminated later.
-    // If your application supports background execution, this method is called instead of applicationWillTerminate: when the user quits.
-}
-
-
-- (void)applicationWillEnterForeground:(UIApplication *)application {
-    // Called as part of the transition from the background to the active state; here you can undo many of the changes made on entering the background.
-}
-
-
-- (void)applicationDidBecomeActive:(UIApplication *)application {
-    // Restart any tasks that were paused (or not yet started) while the application was inactive. If the application was previously in the background, optionally refresh the user interface.
-}
-
-
-- (void)applicationWillTerminate:(UIApplication *)application {
-    // Called when the application is about to terminate. Save data if appropriate. See also applicationDidEnterBackground:.
-}
-
-
-@end
diff --git a/Prj-iOS/lpr/lpr/Assets.xcassets/AppIcon.appiconset/Contents.json b/Prj-iOS/lpr/lpr/Assets.xcassets/AppIcon.appiconset/Contents.json
deleted file mode 100755
index d8db8d6..0000000
--- a/Prj-iOS/lpr/lpr/Assets.xcassets/AppIcon.appiconset/Contents.json
+++ /dev/null
@@ -1,98 +0,0 @@
-{
-  "images" : [
-    {
-      "idiom" : "iphone",
-      "size" : "20x20",
-      "scale" : "2x"
-    },
-    {
-      "idiom" : "iphone",
-      "size" : "20x20",
-      "scale" : "3x"
-    },
-    {
-      "idiom" : "iphone",
-      "size" : "29x29",
-      "scale" : "2x"
-    },
-    {
-      "idiom" : "iphone",
-      "size" : "29x29",
-      "scale" : "3x"
-    },
-    {
-      "idiom" : "iphone",
-      "size" : "40x40",
-      "scale" : "2x"
-    },
-    {
-      "idiom" : "iphone",
-      "size" : "40x40",
-      "scale" : "3x"
-    },
-    {
-      "idiom" : "iphone",
-      "size" : "60x60",
-      "scale" : "2x"
-    },
-    {
-      "idiom" : "iphone",
-      "size" : "60x60",
-      "scale" : "3x"
-    },
-    {
-      "idiom" : "ipad",
-      "size" : "20x20",
-      "scale" : "1x"
-    },
-    {
-      "idiom" : "ipad",
-      "size" : "20x20",
-      "scale" : "2x"
-    },
-    {
-      "idiom" : "ipad",
-      "size" : "29x29",
-      "scale" : "1x"
-    },
-    {
-      "idiom" : "ipad",
-      "size" : "29x29",
-      "scale" : "2x"
-    },
-    {
-      "idiom" : "ipad",
-      "size" : "40x40",
-      "scale" : "1x"
-    },
-    {
-      "idiom" : "ipad",
-      "size" : "40x40",
-      "scale" : "2x"
-    },
-    {
-      "idiom" : "ipad",
-      "size" : "76x76",
-      "scale" : "1x"
-    },
-    {
-      "idiom" : "ipad",
-      "size" : "76x76",
-      "scale" : "2x"
-    },
-    {
-      "idiom" : "ipad",
-      "size" : "83.5x83.5",
-      "scale" : "2x"
-    },
-    {
-      "idiom" : "ios-marketing",
-      "size" : "1024x1024",
-      "scale" : "1x"
-    }
-  ],
-  "info" : {
-    "version" : 1,
-    "author" : "xcode"
-  }
-}
\ No newline at end of file
diff --git a/Prj-iOS/lpr/lpr/Assets.xcassets/Contents.json b/Prj-iOS/lpr/lpr/Assets.xcassets/Contents.json
deleted file mode 100755
index da4a164..0000000
--- a/Prj-iOS/lpr/lpr/Assets.xcassets/Contents.json
+++ /dev/null
@@ -1,6 +0,0 @@
-{
-  "info" : {
-    "version" : 1,
-    "author" : "xcode"
-  }
-}
\ No newline at end of file
diff --git a/Prj-iOS/lpr/lpr/Assets.xcassets/back_camera_btn.imageset/Contents.json b/Prj-iOS/lpr/lpr/Assets.xcassets/back_camera_btn.imageset/Contents.json
deleted file mode 100755
index 8ca20de..0000000
--- a/Prj-iOS/lpr/lpr/Assets.xcassets/back_camera_btn.imageset/Contents.json
+++ /dev/null
@@ -1,22 +0,0 @@
-{
-  "images" : [
-    {
-      "idiom" : "universal",
-      "scale" : "1x"
-    },
-    {
-      "idiom" : "universal",
-      "filename" : "back_camera_btn@2x.png",
-      "scale" : "2x"
-    },
-    {
-      "idiom" : "universal",
-      "filename" : "back_camera_btn@3x.png",
-      "scale" : "3x"
-    }
-  ],
-  "info" : {
-    "version" : 1,
-    "author" : "xcode"
-  }
-}
\ No newline at end of file
diff --git a/Prj-iOS/lpr/lpr/Assets.xcassets/back_camera_btn.imageset/back_camera_btn@2x.png b/Prj-iOS/lpr/lpr/Assets.xcassets/back_camera_btn.imageset/back_camera_btn@2x.png
deleted file mode 100755
index 41e7387..0000000
Binary files a/Prj-iOS/lpr/lpr/Assets.xcassets/back_camera_btn.imageset/back_camera_btn@2x.png and /dev/null differ
diff --git a/Prj-iOS/lpr/lpr/Assets.xcassets/back_camera_btn.imageset/back_camera_btn@3x.png b/Prj-iOS/lpr/lpr/Assets.xcassets/back_camera_btn.imageset/back_camera_btn@3x.png
deleted file mode 100755
index be4379d..0000000
Binary files a/Prj-iOS/lpr/lpr/Assets.xcassets/back_camera_btn.imageset/back_camera_btn@3x.png and /dev/null differ
diff --git a/Prj-iOS/lpr/lpr/Assets.xcassets/flash_camera_btn.imageset/Contents.json b/Prj-iOS/lpr/lpr/Assets.xcassets/flash_camera_btn.imageset/Contents.json
deleted file mode 100755
index fc0efcf..0000000
--- a/Prj-iOS/lpr/lpr/Assets.xcassets/flash_camera_btn.imageset/Contents.json
+++ /dev/null
@@ -1,22 +0,0 @@
-{
-  "images" : [
-    {
-      "idiom" : "universal",
-      "scale" : "1x"
-    },
-    {
-      "idiom" : "universal",
-      "filename" : "flash_camera_btn@2x.png",
-      "scale" : "2x"
-    },
-    {
-      "idiom" : "universal",
-      "filename" : "flash_camera_btn@3x.png",
-      "scale" : "3x"
-    }
-  ],
-  "info" : {
-    "version" : 1,
-    "author" : "xcode"
-  }
-}
\ No newline at end of file
diff --git a/Prj-iOS/lpr/lpr/Assets.xcassets/flash_camera_btn.imageset/flash_camera_btn@2x.png b/Prj-iOS/lpr/lpr/Assets.xcassets/flash_camera_btn.imageset/flash_camera_btn@2x.png
deleted file mode 100755
index 91027cd..0000000
Binary files a/Prj-iOS/lpr/lpr/Assets.xcassets/flash_camera_btn.imageset/flash_camera_btn@2x.png and /dev/null differ
diff --git a/Prj-iOS/lpr/lpr/Assets.xcassets/flash_camera_btn.imageset/flash_camera_btn@3x.png b/Prj-iOS/lpr/lpr/Assets.xcassets/flash_camera_btn.imageset/flash_camera_btn@3x.png
deleted file mode 100755
index e751b60..0000000
Binary files a/Prj-iOS/lpr/lpr/Assets.xcassets/flash_camera_btn.imageset/flash_camera_btn@3x.png and /dev/null differ
diff --git a/Prj-iOS/lpr/lpr/Assets.xcassets/locker_btn_def.imageset/Contents.json b/Prj-iOS/lpr/lpr/Assets.xcassets/locker_btn_def.imageset/Contents.json
deleted file mode 100755
index 7f06e8f..0000000
--- a/Prj-iOS/lpr/lpr/Assets.xcassets/locker_btn_def.imageset/Contents.json
+++ /dev/null
@@ -1,22 +0,0 @@
-{
-  "images" : [
-    {
-      "idiom" : "universal",
-      "scale" : "1x"
-    },
-    {
-      "idiom" : "universal",
-      "filename" : "locker_btn_def@2x.png",
-      "scale" : "2x"
-    },
-    {
-      "idiom" : "universal",
-      "filename" : "locker_btn_def@3x.png",
-      "scale" : "3x"
-    }
-  ],
-  "info" : {
-    "version" : 1,
-    "author" : "xcode"
-  }
-}
\ No newline at end of file
diff --git a/Prj-iOS/lpr/lpr/Assets.xcassets/locker_btn_def.imageset/locker_btn_def@2x.png b/Prj-iOS/lpr/lpr/Assets.xcassets/locker_btn_def.imageset/locker_btn_def@2x.png
deleted file mode 100755
index daeadf0..0000000
Binary files a/Prj-iOS/lpr/lpr/Assets.xcassets/locker_btn_def.imageset/locker_btn_def@2x.png and /dev/null differ
diff --git a/Prj-iOS/lpr/lpr/Assets.xcassets/locker_btn_def.imageset/locker_btn_def@3x.png b/Prj-iOS/lpr/lpr/Assets.xcassets/locker_btn_def.imageset/locker_btn_def@3x.png
deleted file mode 100755
index 2e05dae..0000000
Binary files a/Prj-iOS/lpr/lpr/Assets.xcassets/locker_btn_def.imageset/locker_btn_def@3x.png and /dev/null differ
diff --git a/Prj-iOS/lpr/lpr/Assets.xcassets/take_pic_btn.imageset/Contents.json b/Prj-iOS/lpr/lpr/Assets.xcassets/take_pic_btn.imageset/Contents.json
deleted file mode 100755
index 5fccab2..0000000
--- a/Prj-iOS/lpr/lpr/Assets.xcassets/take_pic_btn.imageset/Contents.json
+++ /dev/null
@@ -1,22 +0,0 @@
-{
-  "images" : [
-    {
-      "idiom" : "universal",
-      "scale" : "1x"
-    },
-    {
-      "idiom" : "universal",
-      "filename" : "take_pic_btn@2x.png",
-      "scale" : "2x"
-    },
-    {
-      "idiom" : "universal",
-      "filename" : "take_pic_btn@3x.png",
-      "scale" : "3x"
-    }
-  ],
-  "info" : {
-    "version" : 1,
-    "author" : "xcode"
-  }
-}
\ No newline at end of file
diff --git a/Prj-iOS/lpr/lpr/Assets.xcassets/take_pic_btn.imageset/take_pic_btn@2x.png b/Prj-iOS/lpr/lpr/Assets.xcassets/take_pic_btn.imageset/take_pic_btn@2x.png
deleted file mode 100755
index fb47d20..0000000
Binary files a/Prj-iOS/lpr/lpr/Assets.xcassets/take_pic_btn.imageset/take_pic_btn@2x.png and /dev/null differ
diff --git a/Prj-iOS/lpr/lpr/Assets.xcassets/take_pic_btn.imageset/take_pic_btn@3x.png b/Prj-iOS/lpr/lpr/Assets.xcassets/take_pic_btn.imageset/take_pic_btn@3x.png
deleted file mode 100755
index db0393b..0000000
Binary files a/Prj-iOS/lpr/lpr/Assets.xcassets/take_pic_btn.imageset/take_pic_btn@3x.png and /dev/null differ
diff --git a/Prj-iOS/lpr/lpr/Base.lproj/LaunchScreen.storyboard b/Prj-iOS/lpr/lpr/Base.lproj/LaunchScreen.storyboard
deleted file mode 100755
index bfa3612..0000000
--- a/Prj-iOS/lpr/lpr/Base.lproj/LaunchScreen.storyboard
+++ /dev/null
@@ -1,25 +0,0 @@
-<?xml version="1.0" encoding="UTF-8" standalone="no"?>
-<document type="com.apple.InterfaceBuilder3.CocoaTouch.Storyboard.XIB" version="3.0" toolsVersion="13122.16" targetRuntime="iOS.CocoaTouch" propertyAccessControl="none" useAutolayout="YES" launchScreen="YES" useTraitCollections="YES" useSafeAreas="YES" colorMatched="YES" initialViewController="01J-lp-oVM">
-    <dependencies>
-        <plugIn identifier="com.apple.InterfaceBuilder.IBCocoaTouchPlugin" version="13104.12"/>
-        <capability name="Safe area layout guides" minToolsVersion="9.0"/>
-        <capability name="documents saved in the Xcode 8 format" minToolsVersion="8.0"/>
-    </dependencies>
-    <scenes>
-        <!--View Controller-->
-        <scene sceneID="EHf-IW-A2E">
-            <objects>
-                <viewController id="01J-lp-oVM" sceneMemberID="viewController">
-                    <view key="view" contentMode="scaleToFill" id="Ze5-6b-2t3">
-                        <rect key="frame" x="0.0" y="0.0" width="375" height="667"/>
-                        <autoresizingMask key="autoresizingMask" widthSizable="YES" heightSizable="YES"/>
-                        <color key="backgroundColor" red="1" green="1" blue="1" alpha="1" colorSpace="custom" customColorSpace="sRGB"/>
-                        <viewLayoutGuide key="safeArea" id="6Tk-OE-BBY"/>
-                    </view>
-                </viewController>
-                <placeholder placeholderIdentifier="IBFirstResponder" id="iYj-Kq-Ea1" userLabel="First Responder" sceneMemberID="firstResponder"/>
-            </objects>
-            <point key="canvasLocation" x="53" y="375"/>
-        </scene>
-    </scenes>
-</document>
diff --git a/Prj-iOS/lpr/lpr/CameraViewController.h b/Prj-iOS/lpr/lpr/CameraViewController.h
deleted file mode 100755
index 8331b68..0000000
--- a/Prj-iOS/lpr/lpr/CameraViewController.h
+++ /dev/null
@@ -1,32 +0,0 @@
-//
-//  CameraViewController.h
-//  lpr
-//
-//  Created by baotim on 2018/10/26.
-//  Copyright © 2018 lprSample. All rights reserved.
-//
-
-#ifdef __cplusplus
-#import <opencv2/opencv.hpp>
-#import <opencv2/imgcodecs/ios.h>
-#endif
-
-#ifdef __OBJC__
-#import <UIKit/UIKit.h>
-#import <Foundation/Foundation.h>
-#import <AVFoundation/AVFoundation.h>
-#import <AudioToolbox/AudioToolbox.h>
-#import <CoreMedia/CoreMedia.h>
-#endif
-
-NS_ASSUME_NONNULL_BEGIN
-
-typedef void(^ResultCallBack)(NSString *reuslt, UIImage *image);
-
-@interface CameraViewController : UIViewController
-
-@property(nonatomic, copy) ResultCallBack resultCB;
-
-@end
-
-NS_ASSUME_NONNULL_END
diff --git a/Prj-iOS/lpr/lpr/CameraViewController.mm b/Prj-iOS/lpr/lpr/CameraViewController.mm
deleted file mode 100755
index ec285bf..0000000
--- a/Prj-iOS/lpr/lpr/CameraViewController.mm
+++ /dev/null
@@ -1,517 +0,0 @@
-//
-//  CameraViewController.m
-//  lpr
-//
-//  Created by baotim on 2018/10/26.
-//  Copyright © 2018 lprSample. All rights reserved.
-//
-
-#import "CameraViewController.h"
-#import "Utility.h"
-#import "Pipeline.h"
-
-//屏幕的宽、高
-#define kScreenWidth  [UIScreen mainScreen].bounds.size.width
-#define kScreenHeight [UIScreen mainScreen].bounds.size.height
-
-@interface CameraViewController () <AVCaptureVideoDataOutputSampleBufferDelegate>
-{
-    AVCaptureSession *_session;
-    AVCaptureDeviceInput *_captureInput;
-    AVCaptureStillImageOutput *_captureOutput;
-    AVCaptureVideoPreviewLayer *_preview;
-    AVCaptureDevice *_device;
-
-    NSTimer *_timer; //定时器
-    BOOL _on; //闪光灯状态
-    BOOL _capture;//导航栏动画是否完成
-    BOOL _isFoucePixel;//是否相位对焦
-    CGRect _imgRect;//拍照裁剪
-    int _count;//每几帧识别
-    CGFloat _isLensChanged;//镜头位置
-    
-    /*相位聚焦下镜头位置 镜头晃动 值不停的改变 */
-    CGFloat _isIOS8AndFoucePixelLensPosition;
-    
-    /*
-     控制识别速度，最小值为1！数值越大识别越慢。
-     相机初始化时，设置默认值为1（不要改动），判断设备若为相位对焦时，设置此值为2（可以修改，最小为1，越大越慢）
-     此值的功能是为了减小相位对焦下，因识别速度过快
-     此值在相机初始化中设置，在相机代理中使用，用户若无特殊需求不用修改。
-     */
-    int _MaxFR;
-    
-    cv::Mat source_image;
-}
-
-@property (assign, nonatomic) BOOL adjustingFocus;
-@property (nonatomic, retain) CALayer *customLayer;
-@property (nonatomic,assign) BOOL isProcessingImage;
-
-@property (nonatomic, strong) UIImage* image;
-
-@end
-
-@implementation CameraViewController
-
-- (void)viewDidLoad {
-    [super viewDidLoad];
-    // Do any additional setup after loading the view.
-    self.view.backgroundColor = [UIColor clearColor];
-    
-    //初始化相机
-    [self initialize];
-    
-    //创建相机界面控件
-    [self createCameraView];
-}
-
-- (void)viewWillAppear:(BOOL)animated {
-    [super viewWillAppear:animated];
-    
-    _capture = NO;
-    [self performSelector:@selector(changeCapture) withObject:nil afterDelay:0.4];
-    //不支持相位对焦情况下(iPhone6以后的手机支持相位对焦) 设置定时器 开启连续对焦
-    if (!_isFoucePixel) {
-        _timer = [NSTimer scheduledTimerWithTimeInterval:1.3 target:self selector:@selector(fouceMode) userInfo:nil repeats:YES];
-    }
-    
-    AVCaptureDevice*camDevice = [AVCaptureDevice defaultDeviceWithMediaType:AVMediaTypeVideo];
-    int flags = NSKeyValueObservingOptionNew;
-    //注册通知
-    [camDevice addObserver:self forKeyPath:@"adjustingFocus" options:flags context:nil];
-    if (_isFoucePixel) {
-        [camDevice addObserver:self forKeyPath:@"lensPosition" options:flags context:nil];
-    }
-    [_session startRunning];
-}
-
-- (void)viewWillDisappear:(BOOL)animated {
-    [super viewWillDisappear:animated];
-    self.navigationController.navigationBarHidden = NO;
-}
-
-- (void)viewDidDisappear:(BOOL)animated {
-    [super viewDidDisappear:animated];
-    
-    if (!_isFoucePixel) {
-        [_timer invalidate];
-        _timer = nil;
-    }
-    AVCaptureDevice*camDevice =[AVCaptureDevice defaultDeviceWithMediaType:AVMediaTypeVideo];
-    [camDevice removeObserver:self forKeyPath:@"adjustingFocus"];
-    if (_isFoucePixel) {
-        [camDevice removeObserver:self forKeyPath:@"lensPosition"];
-    }
-    [_session stopRunning];
-    
-    _capture = NO;
-}
-
-- (void)changeCapture {
-    _capture = YES;
-}
-
-#pragma mark - Private Methods
-//初始化相机
-- (void)initialize {
-    //判断摄像头授权
-    AVAuthorizationStatus authStatus = [AVCaptureDevice authorizationStatusForMediaType:AVMediaTypeVideo];
-    if(authStatus == AVAuthorizationStatusRestricted || authStatus == AVAuthorizationStatusDenied){
-        self.view.backgroundColor = [UIColor blackColor];
-//        UIAlertView * alt = [[UIAlertView alloc] initWithTitle:@"未获得授权使用摄像头" message:@"请在'设置-隐私-相机'打开" delegate:self cancelButtonTitle:nil otherButtonTitles:@"OK", nil];
-//        [alt show];
-        UIAlertController * alert = [UIAlertController
-                                     alertControllerWithTitle:@"未获得授权使用摄像头"
-                                     message:@"请在'设置-隐私-相机'打开"
-                                     preferredStyle:UIAlertControllerStyleAlert];
-        
-        
-        __weak typeof(self) weakSelf = self;
-        UIAlertAction* yesButton = [UIAlertAction
-                                    actionWithTitle:@"OK"
-                                    style:UIAlertActionStyleDefault
-                                    handler:^(UIAlertAction * action) {
-                                        //Handle your yes please button action here
-                                        [weakSelf.navigationController popViewControllerAnimated:YES];
-                                    }];
-        
-        
-        [alert addAction:yesButton];
-        [self presentViewController:alert animated:YES completion:nil];
-        return;
-    }
-    
-    _MaxFR = 1;
-    //1.创建会话层
-    _session = [[AVCaptureSession alloc] init];
-    [_session setSessionPreset:AVCaptureSessionPreset1920x1080];
-    
-    //2.创建、配置输入设备
-    NSArray *devices = [AVCaptureDevice devicesWithMediaType:AVMediaTypeVideo];
-    for (AVCaptureDevice *device in devices){
-        if (device.position == AVCaptureDevicePositionBack){
-            _device = device;
-            _captureInput = [AVCaptureDeviceInput deviceInputWithDevice:device error:nil];
-        }
-    }
-    [_session addInput:_captureInput];
-    
-    //2.创建视频流输出
-    AVCaptureVideoDataOutput *captureOutput = [[AVCaptureVideoDataOutput alloc] init];
-    captureOutput.alwaysDiscardsLateVideoFrames = YES;
-    dispatch_queue_t queue;
-    queue = dispatch_queue_create("cameraQueue", NULL);
-    [captureOutput setSampleBufferDelegate:self queue:queue];
-    NSString* key = (NSString*)kCVPixelBufferPixelFormatTypeKey;
-    NSNumber* value = [NSNumber numberWithUnsignedInt:kCVPixelFormatType_32BGRA];
-    NSDictionary* videoSettings = [NSDictionary dictionaryWithObject:value forKey:key];
-    [captureOutput setVideoSettings:videoSettings];
-    [_session addOutput:captureOutput];
-    
-    //3.创建、配置静态拍照输出
-    _captureOutput = [[AVCaptureStillImageOutput alloc] init];
-    NSDictionary *outputSettings = [[NSDictionary alloc] initWithObjectsAndKeys:AVVideoCodecJPEG,AVVideoCodecKey,nil];
-    [_captureOutput setOutputSettings:outputSettings];
-    [_session addOutput:_captureOutput];
-    
-    //4.预览图层
-    _preview = [AVCaptureVideoPreviewLayer layerWithSession: _session];
-    _preview.frame = CGRectMake(0, 0, self.view.frame.size.width, self.view.frame.size.height);
-    _preview.videoGravity = AVLayerVideoGravityResizeAspectFill;
-    [self.view.layer addSublayer:_preview];
-    
-    //判断是否相位对焦
-    if ([[[UIDevice currentDevice] systemVersion] floatValue] >= 8.0) {
-        AVCaptureDeviceFormat *deviceFormat = _device.activeFormat;
-        if (deviceFormat.autoFocusSystem == AVCaptureAutoFocusSystemPhaseDetection){
-            _isFoucePixel = YES;
-            _MaxFR = 2;
-        }
-    }
-}
-
-- (void)createCameraView
-{
-    //设置覆盖层
-    CAShapeLayer *maskWithHole = [CAShapeLayer layer];
-    // Both frames are defined in the same coordinate system
-    CGRect biggerRect = self.view.bounds;
-    CGFloat offset = 1.0f;
-    if ([[UIScreen mainScreen] scale] >= 2) {
-        offset = 0.5;
-    }
-    CGRect smallFrame = CGRectMake(45, 100, 300, 500);
-    CGRect smallerRect = CGRectInset(smallFrame, -offset, -offset) ;
-    UIBezierPath *maskPath = [UIBezierPath bezierPath];
-    [maskPath moveToPoint:CGPointMake(CGRectGetMinX(biggerRect), CGRectGetMinY(biggerRect))];
-    [maskPath addLineToPoint:CGPointMake(CGRectGetMinX(biggerRect), CGRectGetMaxY(biggerRect))];
-    [maskPath addLineToPoint:CGPointMake(CGRectGetMaxX(biggerRect), CGRectGetMaxY(biggerRect))];
-    [maskPath addLineToPoint:CGPointMake(CGRectGetMaxX(biggerRect), CGRectGetMinY(biggerRect))];
-    [maskPath addLineToPoint:CGPointMake(CGRectGetMinX(biggerRect), CGRectGetMinY(biggerRect))];
-    [maskPath moveToPoint:CGPointMake(CGRectGetMinX(smallerRect), CGRectGetMinY(smallerRect))];
-    [maskPath addLineToPoint:CGPointMake(CGRectGetMinX(smallerRect), CGRectGetMaxY(smallerRect))];
-    [maskPath addLineToPoint:CGPointMake(CGRectGetMaxX(smallerRect), CGRectGetMaxY(smallerRect))];
-    [maskPath addLineToPoint:CGPointMake(CGRectGetMaxX(smallerRect), CGRectGetMinY(smallerRect))];
-    [maskPath addLineToPoint:CGPointMake(CGRectGetMinX(smallerRect), CGRectGetMinY(smallerRect))];
-    [maskWithHole setPath:[maskPath CGPath]];
-    [maskWithHole setFillRule:kCAFillRuleEvenOdd];
-    [maskWithHole setFillColor:[[UIColor colorWithWhite:0 alpha:0.35] CGColor]];
-    [self.view.layer addSublayer:maskWithHole];
-    [self.view.layer setMasksToBounds:YES];
-    
-    /* 相机按钮 适配了iPhone和ipad 不同需求自行修改界面*/
-    //返回、闪光灯按钮
-    CGFloat backWidth = 35;
-    if (kScreenHeight>=1024) {
-        backWidth = 50;
-    }
-    CGFloat s = 80;
-    CGFloat s1 = 0;
-    if (kScreenHeight==480) {
-        s = 60;
-        s1 = 10;
-    }
-    UIButton *backBtn = [[UIButton alloc]initWithFrame:CGRectMake(kScreenWidth/16,kScreenWidth/16-s1, backWidth, backWidth)];
-    [backBtn addTarget:self action:@selector(backAction) forControlEvents:UIControlEventTouchUpInside];
-    [backBtn setImage:[UIImage imageNamed:@"back_camera_btn"] forState:UIControlStateNormal];
-    backBtn.titleLabel.textAlignment = NSTextAlignmentLeft;
-    [self.view addSubview:backBtn];
-    
-    UIButton *flashBtn = [[UIButton alloc]initWithFrame:CGRectMake(kScreenWidth-kScreenWidth/16-backWidth,kScreenWidth/16-s1, backWidth, backWidth)];
-    [flashBtn setImage:[UIImage imageNamed:@"flash_camera_btn"] forState:UIControlStateNormal];
-    [flashBtn addTarget:self action:@selector(modeBtn) forControlEvents:UIControlEventTouchUpInside];
-    [self.view addSubview:flashBtn];
-    
-    //拍照视图 上拉按钮 拍照按钮
-    UIButton *upBtn = [[UIButton alloc]initWithFrame:CGRectMake(kScreenWidth/2-60, kScreenHeight-20, 120, 20)];
-    upBtn.tag = 1001;
-    [upBtn addTarget:self action:@selector(upBtn:) forControlEvents:UIControlEventTouchUpInside];
-    [upBtn setImage:[UIImage imageNamed:@"locker_btn_def"] forState:UIControlStateNormal];
-    [self.view addSubview:upBtn];
-    
-    UIButton *photoBtn = [[UIButton alloc]initWithFrame:CGRectMake(kScreenWidth/2-30,kScreenHeight-s,60, 60)];
-    photoBtn.tag = 1000;
-    photoBtn.hidden = YES;
-    [photoBtn setImage:[UIImage imageNamed:@"take_pic_btn"] forState:UIControlStateNormal];
-    [photoBtn addTarget:self action:@selector(photoBtn) forControlEvents:UIControlEventTouchUpInside];
-    [photoBtn setTitleColor:[UIColor grayColor] forState:UIControlStateHighlighted];
-    [self.view addSubview:photoBtn];
-    [self.view bringSubviewToFront:photoBtn];
-}
-
-//隐藏状态栏
-- (UIStatusBarStyle)preferredStatusBarStyle{
-    return UIStatusBarStyleDefault;
-}
-- (BOOL)prefersStatusBarHidden{
-    return YES;
-}
-
-//对焦
-- (void)fouceMode
-{
-    NSError *error;
-    AVCaptureDevice *device = [self cameraWithPosition:AVCaptureDevicePositionBack];
-    if ([device isFocusModeSupported:AVCaptureFocusModeAutoFocus])
-    {
-        if ([device lockForConfiguration:&error]) {
-            CGPoint cameraPoint = [_preview captureDevicePointOfInterestForPoint:self.view.center];
-            [device setFocusPointOfInterest:cameraPoint];
-            [device setFocusMode:AVCaptureFocusModeAutoFocus];
-            [device unlockForConfiguration];
-        } else {
-            //NSLog(@"Error: %@", error);
-        }
-    }
-}
-
-- (AVCaptureDevice *)cameraWithPosition:(AVCaptureDevicePosition)position
-{
-    NSArray *devices = [AVCaptureDevice devicesWithMediaType:AVMediaTypeVideo];
-    for (AVCaptureDevice *device in devices){
-        if (device.position == position){
-            return device;
-        }
-    }
-    return nil;
-}
-
-- (NSString *)getPath:(NSString*)fileName
-{
-    NSString *bundlePath = [NSBundle mainBundle].bundlePath;
-    NSString *path = [bundlePath stringByAppendingPathComponent:fileName];
-    return path;
-}
-
-- (NSString *)simpleRecognition:(cv::Mat&)src
-{
-    NSString *path_1 = [self getPath:@"cascade.xml"];
-    NSString *path_2 = [self getPath:@"HorizonalFinemapping.prototxt"];
-    NSString *path_3 = [self getPath:@"HorizonalFinemapping.caffemodel"];
-    NSString *path_4 = [self getPath:@"Segmentation.prototxt"];
-    NSString *path_5 = [self getPath:@"Segmentation.caffemodel"];
-    NSString *path_6 = [self getPath:@"CharacterRecognization.prototxt"];
-    NSString *path_7 = [self getPath:@"CharacterRecognization.caffemodel"];
-    NSString *path_8 = [self getPath:@"SegmenationFree-Inception.prototxt"];
-    NSString *path_9 = [self getPath:@"SegmenationFree-Inception.caffemodel"];
-    
-    std::string *cpath_1 = new std::string([path_1 UTF8String]);
-    std::string *cpath_2 = new std::string([path_2 UTF8String]);
-    std::string *cpath_3 = new std::string([path_3 UTF8String]);
-    std::string *cpath_4 = new std::string([path_4 UTF8String]);
-    std::string *cpath_5 = new std::string([path_5 UTF8String]);
-    std::string *cpath_6 = new std::string([path_6 UTF8String]);
-    std::string *cpath_7 = new std::string([path_7 UTF8String]);
-    std::string *cpath_8 = new std::string([path_8 UTF8String]);
-    std::string *cpath_9 = new std::string([path_9 UTF8String]);
-    
-    
-    pr::PipelinePR pr2 = pr::PipelinePR(*cpath_1, *cpath_2, *cpath_3, *cpath_4, *cpath_5, *cpath_6, *cpath_7, *cpath_8, *cpath_9);
-    
-    std::vector<pr::PlateInfo> list_res = pr2.RunPiplineAsImage(src, pr::SEGMENTATION_FREE_METHOD);
-    std::string concat_results = "";
-    for(auto one:list_res) {
-        if(one.confidence>0.7) {
-            concat_results += one.getPlateName()+",";
-        }
-    }
-    
-    NSString *str = [NSString stringWithCString:concat_results.c_str() encoding:NSUTF8StringEncoding];
-    if (str.length > 0) {
-        str = [str substringToIndex:str.length-1];
-        str = [NSString stringWithFormat:@"%@",str];
-    } else {
-        str = [NSString stringWithFormat:@"未识别成功"];
-    }
-    NSLog(@"===> 识别结果 = %@", str);
-    
-    return str;
-}
-
-#pragma mark - Actions
-//返回按钮按钮点击事件
-- (void)backAction
-{
-    [self dismissViewControllerAnimated:YES completion:nil];
-}
-
-//闪光灯按钮点击事件
-- (void)modeBtn
-{
-    if (![_device hasTorch]) {
-        //NSLog(@"no torch");
-    } else {
-        [_device lockForConfiguration:nil];
-        if (!_on) {
-            [_device setTorchMode: AVCaptureTorchModeOn];
-            _on = YES;
-        }else{
-            [_device setTorchMode: AVCaptureTorchModeOff];
-            _on = NO;
-        }
-        [_device unlockForConfiguration];
-    }
-}
-
-//上拉按钮点击事件
-- (void)upBtn:(UIButton *)upBtn
-{
-    UIButton *photoBtn = (UIButton *)[self.view viewWithTag:1000];
-    photoBtn.hidden = NO;
-    upBtn.hidden = YES;
-}
-
-//拍照按钮点击事件
-- (void)photoBtn
-{
-    self.isProcessingImage = YES;
-    //get connection
-    AVCaptureConnection *videoConnection = nil;
-    for (AVCaptureConnection *connection in _captureOutput.connections) {
-        for (AVCaptureInputPort *port in [connection inputPorts]) {
-            if ([[port mediaType] isEqual:AVMediaTypeVideo] ) {
-                videoConnection = connection;
-                break;
-            }
-        }
-        if (videoConnection) { break; }
-    }
-    
-    //get UIImage
-    __weak typeof(self) weakSelf = self;
-    [_captureOutput captureStillImageAsynchronouslyFromConnection:videoConnection completionHandler:
-     ^(CMSampleBufferRef imageSampleBuffer, NSError *error) {
-         if (imageSampleBuffer != NULL) {
-             //停止取景
-             [_session stopRunning];
-
-             NSData *imageData = [AVCaptureStillImageOutput jpegStillImageNSDataRepresentation:imageSampleBuffer];
-             UIImage *tempImage = [[UIImage alloc] initWithData:imageData];
-
-             UIImage *temp_image = [Utility scaleAndRotateImageBackCamera:tempImage];
-             source_image = [Utility cvMatFromUIImage:temp_image];
-             NSString* text = [weakSelf simpleRecognition:source_image];
-
-             NSMutableDictionary* resultDict = [NSMutableDictionary new];
-             resultDict[@"image"] = temp_image;
-             resultDict[@"text"] = text;
-             [self performSelectorOnMainThread:@selector(readyToGetImage:) withObject:resultDict waitUntilDone:NO];
-             weakSelf.isProcessingImage = NO;
-         }
-     }];
-}
-
-//从摄像头缓冲区获取图像
-#pragma mark - AVCaptureSession delegate
-- (void)captureOutput:(AVCaptureOutput *)captureOutput
-didOutputSampleBuffer:(CMSampleBufferRef)sampleBuffer
-       fromConnection:(AVCaptureConnection *)connection
-{
-    CVImageBufferRef imageBuffer = CMSampleBufferGetImageBuffer(sampleBuffer);
-    CVPixelBufferLockBaseAddress(imageBuffer,0);
-    uint8_t *baseAddress = (uint8_t *)CVPixelBufferGetBaseAddress(imageBuffer);
-    size_t bytesPerRow = CVPixelBufferGetBytesPerRow(imageBuffer);
-    size_t width = CVPixelBufferGetWidth(imageBuffer);
-    size_t height = CVPixelBufferGetHeight(imageBuffer);
-    
-    /*We unlock the  image buffer*/
-    CVPixelBufferUnlockBaseAddress(imageBuffer,0);
-    
-    /*Create a CGImageRef from the CVImageBufferRef*/
-    CGColorSpaceRef colorSpace = CGColorSpaceCreateDeviceRGB();
-    CGContextRef newContext = CGBitmapContextCreate(baseAddress, width, height, 8, bytesPerRow, colorSpace, kCGBitmapByteOrder32Little | kCGImageAlphaPremultipliedFirst);
-    CGImageRef newImage = CGBitmapContextCreateImage(newContext);
-    
-    /*We release some components*/
-    CGContextRelease(newContext);
-    CGColorSpaceRelease(colorSpace);
-    
-    /*We display the result on the image view (We need to change the orientation of the image so that the video is displayed correctly)*/
-    self.image = [UIImage imageWithCGImage:newImage scale:1.0 orientation:UIImageOrientationUp];
-    /*We relase the CGImageRef*/
-    CGImageRelease(newImage);
-    
-    //检边识别
-    if (_capture == YES) { //导航栏动画完成
-        if (self.isProcessingImage==NO) {  //点击拍照后 不去识别
-            if (!self.adjustingFocus) {  //反差对焦下 非正在对焦状态（相位对焦下self.adjustingFocus此值不会改变）
-                if (_isLensChanged == _isIOS8AndFoucePixelLensPosition) {
-                    _count++;
-                    if (_count >= _MaxFR) {
-                        
-                        //识别
-                        UIImage *temp_image = [Utility scaleAndRotateImageBackCamera:self.image];
-                        source_image = [Utility cvMatFromUIImage:temp_image];
-                        NSString* text = [self simpleRecognition:source_image];
-                        
-                        if (text.length == 7) { //识别成功
-                            _count = 0;
-                            // 停止取景
-                            [_session stopRunning];
-                            //设置震动
-                            AudioServicesPlaySystemSound(kSystemSoundID_Vibrate);
-                            
-                            NSMutableDictionary* resultDict = [NSMutableDictionary new];
-                            resultDict[@"image"] = temp_image;
-                            resultDict[@"text"] = text;
-
-                            [self performSelectorOnMainThread:@selector(readyToGetImage:) withObject:resultDict waitUntilDone:NO];
-                        }
-                    }
-                } else {
-                    _isLensChanged = _isIOS8AndFoucePixelLensPosition;
-                    _count = 0;
-                }
-            }
-        }
-    }
-    CVPixelBufferUnlockBaseAddress(imageBuffer,0);
-}
-
-//找边成功开始拍照
-- (void)readyToGetImage:(NSDictionary *)resultDict
-{
-    [self dismissViewControllerAnimated:NO completion:^{
-    }];
-    
-    if (self.resultCB) {
-        self.resultCB(resultDict[@"text"], resultDict[@"image"]);
-    }
-}
-
-- (void)observeValueForKeyPath:(NSString*)keyPath ofObject:(id)object change:(NSDictionary*)change context:(void*)context {
-    
-    /*反差对焦 监听反差对焦此*/
-    if([keyPath isEqualToString:@"adjustingFocus"]){
-        self.adjustingFocus =[[change objectForKey:NSKeyValueChangeNewKey] isEqualToNumber:[NSNumber numberWithInt:1]];
-    }
-    /*监听相位对焦此*/
-    if([keyPath isEqualToString:@"lensPosition"]){
-        _isIOS8AndFoucePixelLensPosition =[[change objectForKey:NSKeyValueChangeNewKey] floatValue];
-        //NSLog(@"监听_isIOS8AndFoucePixelLensPosition == %f",_isIOS8AndFoucePixelLensPosition);
-    }
-}
-
-
-@end
diff --git a/Prj-iOS/lpr/lpr/Info.plist b/Prj-iOS/lpr/lpr/Info.plist
deleted file mode 100755
index 19d69a9..0000000
--- a/Prj-iOS/lpr/lpr/Info.plist
+++ /dev/null
@@ -1,45 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
-<plist version="1.0">
-<dict>
-	<key>CFBundleDevelopmentRegion</key>
-	<string>$(DEVELOPMENT_LANGUAGE)</string>
-	<key>CFBundleExecutable</key>
-	<string>$(EXECUTABLE_NAME)</string>
-	<key>CFBundleIdentifier</key>
-	<string>$(PRODUCT_BUNDLE_IDENTIFIER)</string>
-	<key>CFBundleInfoDictionaryVersion</key>
-	<string>6.0</string>
-	<key>CFBundleName</key>
-	<string>$(PRODUCT_NAME)</string>
-	<key>CFBundlePackageType</key>
-	<string>APPL</string>
-	<key>CFBundleShortVersionString</key>
-	<string>1.0</string>
-	<key>CFBundleVersion</key>
-	<string>1</string>
-	<key>LSRequiresIPhoneOS</key>
-	<true/>
-	<key>NSCameraUsageDescription</key>
-	<string>欢迎使用相机</string>
-	<key>NSPhotoLibraryUsageDescription</key>
-	<string>欢迎使用相册</string>
-	<key>UILaunchStoryboardName</key>
-	<string>LaunchScreen</string>
-	<key>UIRequiredDeviceCapabilities</key>
-	<array>
-		<string>armv7</string>
-	</array>
-	<key>UISupportedInterfaceOrientations</key>
-	<array>
-		<string>UIInterfaceOrientationPortrait</string>
-	</array>
-	<key>UISupportedInterfaceOrientations~ipad</key>
-	<array>
-		<string>UIInterfaceOrientationPortrait</string>
-		<string>UIInterfaceOrientationPortraitUpsideDown</string>
-		<string>UIInterfaceOrientationLandscapeLeft</string>
-		<string>UIInterfaceOrientationLandscapeRight</string>
-	</array>
-</dict>
-</plist>
diff --git a/Prj-iOS/lpr/lpr/RootViewController.h b/Prj-iOS/lpr/lpr/RootViewController.h
deleted file mode 100755
index 14da8d2..0000000
--- a/Prj-iOS/lpr/lpr/RootViewController.h
+++ /dev/null
@@ -1,26 +0,0 @@
-//
-//  RootViewController.h
-//  lpr
-//
-//  Created by baotim on 2018/10/26.
-//  Copyright © 2018 lprSample. All rights reserved.
-//
-
-#ifdef __cplusplus
-#import <opencv2/opencv.hpp>
-#import <opencv2/imgcodecs/ios.h>
-#endif
-
-#ifdef __OBJC__
-#import <UIKit/UIKit.h>
-#import <Foundation/Foundation.h>
-#import <AVFoundation/AVFoundation.h>
-#endif
-
-NS_ASSUME_NONNULL_BEGIN
-
-@interface RootViewController : UIViewController
-
-@end
-
-NS_ASSUME_NONNULL_END
diff --git a/Prj-iOS/lpr/lpr/RootViewController.mm b/Prj-iOS/lpr/lpr/RootViewController.mm
deleted file mode 100755
index df13584..0000000
--- a/Prj-iOS/lpr/lpr/RootViewController.mm
+++ /dev/null
@@ -1,201 +0,0 @@
-//
-//  RootViewController.m
-//  lpr
-//
-//  Created by baotim on 2018/10/26.
-//  Copyright © 2018 lprSample. All rights reserved.
-//
-
-#import "RootViewController.h"
-#import <Masonry/Masonry.h>
-#import "CameraViewController.h"
-#import "Utility.h"
-#import "Pipeline.h"
-
-@interface RootViewController () <UIImagePickerControllerDelegate, UINavigationControllerDelegate>
-
-@property (nonatomic, strong) UIButton* albumButton;
-@property (nonatomic, strong) UIButton* cameraButton;
-@property (nonatomic, strong) UIImageView* imageView;
-@property (nonatomic, strong) UILabel* resultLabel;
-
-@end
-
-@implementation RootViewController
-{
-    cv::Mat source_image;
-}
-
-#pragma mark - Lazy Initialize
-- (UIButton *)albumButton
-{
-    if (!_albumButton) {
-        _albumButton = [UIButton buttonWithType:UIButtonTypeCustom];
-        [_albumButton setTitle:@"打开相册" forState:UIControlStateNormal];
-        [_albumButton setBackgroundColor:[UIColor redColor]];
-        [_albumButton addTarget:self action:@selector(openAlbum) forControlEvents:UIControlEventTouchUpInside];
-    }
-    return _albumButton;
-}
-
-- (UIButton *)cameraButton
-{
-    if (!_cameraButton) {
-        _cameraButton = [UIButton buttonWithType:UIButtonTypeCustom];
-        [_cameraButton setBackgroundColor:[UIColor redColor]];
-        [_cameraButton setTitle:@"实时拍照" forState:UIControlStateNormal];
-        [_cameraButton addTarget:self action:@selector(openCamera) forControlEvents:UIControlEventTouchUpInside];
-    }
-    return _cameraButton;
-}
-
-- (UIImageView *)imageView
-{
-    if (!_imageView) {
-        _imageView = [[UIImageView alloc] init];
-        _imageView.contentMode = UIViewContentModeScaleAspectFit;
-    }
-    return _imageView;
-}
-
-- (UILabel *)resultLabel
-{
-    if (!_resultLabel) {
-        _resultLabel = [UILabel new];
-        _resultLabel.textColor = [UIColor redColor];
-        _resultLabel.textAlignment = NSTextAlignmentLeft;
-        _resultLabel.font = [UIFont systemFontOfSize:15];
-    }
-    return _resultLabel;
-}
-
-#pragma mark - Life Cycle
-- (void)viewDidLoad {
-    [super viewDidLoad];
-    // Do any additional setup after loading the view.
-    self.view.backgroundColor = [UIColor whiteColor];
-    self.navigationItem.title = @"车牌识别Demo";
-    
-    [self.view addSubview:self.albumButton];
-    [self.albumButton mas_makeConstraints:^(MASConstraintMaker* make) {
-        make.centerX.equalTo(self.view).offset(-80);
-        make.bottom.equalTo(self.mas_bottomLayoutGuideTop).offset(-20);
-        make.width.mas_equalTo(100);
-        make.height.mas_equalTo(50);
-    }];
-    
-    [self.view addSubview:self.cameraButton];
-    [self.cameraButton mas_makeConstraints:^(MASConstraintMaker* make) {
-        make.centerX.equalTo(self.view).offset(80);
-        make.bottom.equalTo(self.albumButton);
-        make.width.mas_equalTo(100);
-        make.height.mas_equalTo(50);
-    }];
-    
-    [self.view addSubview:self.resultLabel];
-    [self.resultLabel mas_makeConstraints:^(MASConstraintMaker* make) {
-        make.left.right.equalTo(self.view);
-        make.bottom.lessThanOrEqualTo(self.albumButton.mas_top);
-    }];
-    
-    [self.view addSubview:self.imageView];
-    [self.imageView mas_makeConstraints:^(MASConstraintMaker* make) {
-        make.top.left.right.equalTo(self.view);
-        make.bottom.lessThanOrEqualTo(self.resultLabel.mas_top);
-    }];
-}
-
-#pragma mark - Actions
-- (void)openAlbum
-{
-    UIImagePickerController* picker = [[UIImagePickerController alloc] init];
-    picker.delegate = self;
-    
-    if (![UIImagePickerController isSourceTypeAvailable:UIImagePickerControllerSourceTypePhotoLibrary])
-        return;
-    
-    picker.sourceType = UIImagePickerControllerSourceTypePhotoLibrary;
-    
-    [self presentViewController:picker animated:YES completion:nil];
-}
-
-- (void)openCamera
-{
-    CameraViewController* video = [CameraViewController new];
-    video.resultCB = ^(NSString* text, UIImage* image) {
-        self.imageView.image = image;
-        self.resultLabel.text = text;
-    };
-    [self presentViewController:video animated:YES completion:nil];
-}
-
-#pragma mark - Private Methods
-- (NSString *)getPathFromBundle:(NSString*)fileName
-{
-    NSString *bundlePath = [NSBundle mainBundle].bundlePath;
-    NSString *path = [bundlePath stringByAppendingPathComponent:fileName];
-    return path;
-}
-
-- (void)freeRecognition:(cv::Mat&)src
-{
-    NSString *path_1 = [self getPathFromBundle:@"cascade.xml"];
-    NSString *path_2 = [self getPathFromBundle:@"HorizonalFinemapping.prototxt"];
-    NSString *path_3 = [self getPathFromBundle:@"HorizonalFinemapping.caffemodel"];
-    NSString *path_4 = [self getPathFromBundle:@"Segmentation.prototxt"];
-    NSString *path_5 = [self getPathFromBundle:@"Segmentation.caffemodel"];
-    NSString *path_6 = [self getPathFromBundle:@"CharacterRecognization.prototxt"];
-    NSString *path_7 = [self getPathFromBundle:@"CharacterRecognization.caffemodel"];
-    NSString *path_8 = [self getPathFromBundle:@"SegmenationFree-Inception.prototxt"];
-    NSString *path_9 = [self getPathFromBundle:@"SegmenationFree-Inception.caffemodel"];
-    
-    std::string *cpath_1 = new std::string([path_1 UTF8String]);
-    std::string *cpath_2 = new std::string([path_2 UTF8String]);
-    std::string *cpath_3 = new std::string([path_3 UTF8String]);
-    std::string *cpath_4 = new std::string([path_4 UTF8String]);
-    std::string *cpath_5 = new std::string([path_5 UTF8String]);
-    std::string *cpath_6 = new std::string([path_6 UTF8String]);
-    std::string *cpath_7 = new std::string([path_7 UTF8String]);
-    std::string *cpath_8 = new std::string([path_8 UTF8String]);
-    std::string *cpath_9 = new std::string([path_9 UTF8String]);
-    
-    
-    pr::PipelinePR pr2 = pr::PipelinePR(*cpath_1, *cpath_2, *cpath_3, *cpath_4, *cpath_5, *cpath_6, *cpath_7, *cpath_8, *cpath_9);
-    
-    std::vector<pr::PlateInfo> list_res = pr2.RunPiplineAsImage(src, pr::SEGMENTATION_FREE_METHOD);
-    std::string concat_results = "";
-    for(auto one:list_res) {
-        if(one.confidence>0.7) {
-            concat_results += one.getPlateName()+",";
-        }
-    }
-    
-    NSString *str = [NSString stringWithCString:concat_results.c_str() encoding:NSUTF8StringEncoding];
-    if (str.length > 0) {
-        str = [str substringToIndex:str.length-1];
-        str = [NSString stringWithFormat:@"识别结果: %@",str];
-    } else {
-        str = [NSString stringWithFormat:@"识别结果: 未识别成功"];
-    }
-    
-    [self.resultLabel setText:str];
-}
-
-#pragma mark - UIImagePickerControllerDelegate
-- (void)imagePickerController:(UIImagePickerController*)picker didFinishPickingMediaWithInfo:(NSDictionary *)info
-{
-    [picker dismissViewControllerAnimated:YES completion:nil];
-    
-    UIImage* temp = [info objectForKey:@"UIImagePickerControllerOriginalImage"];
-    UIImage *temp_image = [Utility scaleAndRotateImageBackCamera:temp];
-    source_image = [Utility cvMatFromUIImage:temp_image];
-    [self freeRecognition:source_image];
-    self.imageView.image = temp;
-}
-
-- (void)imagePickerControllerDidCancel:(UIImagePickerController *)picker
-{
-    [picker dismissViewControllerAnimated:YES completion:nil];
-}
-
-@end
diff --git a/Prj-iOS/lpr/lpr/Source/include/CNNRecognizer.h b/Prj-iOS/lpr/lpr/Source/include/CNNRecognizer.h
deleted file mode 100755
index a577733..0000000
--- a/Prj-iOS/lpr/lpr/Source/include/CNNRecognizer.h
+++ /dev/null
@@ -1,24 +0,0 @@
-//
-// Created by Jack Yu on 21/10/2017.
-//
-
-#ifndef SWIFTPR_CNNRECOGNIZER_H
-#define SWIFTPR_CNNRECOGNIZER_H
-
-#include "Recognizer.h"
-namespace pr{
-    class CNNRecognizer: public GeneralRecognizer{
-    public:
-        const int CHAR_INPUT_W = 14;
-        const int CHAR_INPUT_H = 30;
-
-        CNNRecognizer(std::string prototxt,std::string caffemodel);
-        label recognizeCharacter(cv::Mat character);
-    private:
-        cv::dnn::Net net;
-
-    };
-
-}
-
-#endif //SWIFTPR_CNNRECOGNIZER_H
diff --git a/Prj-iOS/lpr/lpr/Source/include/FastDeskew.h b/Prj-iOS/lpr/lpr/Source/include/FastDeskew.h
deleted file mode 100755
index 456f08d..0000000
--- a/Prj-iOS/lpr/lpr/Source/include/FastDeskew.h
+++ /dev/null
@@ -1,18 +0,0 @@
-//
-// Created by 庾金科 on 22/09/2017.
-//
-
-#ifndef SWIFTPR_FASTDESKEW_H
-#define SWIFTPR_FASTDESKEW_H
-
-#include <opencv2/opencv.hpp>
-#include <math.h>
-namespace pr{
-
-    cv::Mat fastdeskew(cv::Mat skewImage,int blockSize);
-//    cv::Mat spatialTransformer(cv::Mat skewImage);
-
-}//namepace pr
-
-
-#endif //SWIFTPR_FASTDESKEW_H
diff --git a/Prj-iOS/lpr/lpr/Source/include/FineMapping.h b/Prj-iOS/lpr/lpr/Source/include/FineMapping.h
deleted file mode 100755
index 352202e..0000000
--- a/Prj-iOS/lpr/lpr/Source/include/FineMapping.h
+++ /dev/null
@@ -1,32 +0,0 @@
-//
-// Created by 庾金科 on 22/09/2017.
-//
-
-#ifndef SWIFTPR_FINEMAPPING_H
-#define SWIFTPR_FINEMAPPING_H
-
-#include <opencv2/opencv.hpp>
-#include <opencv2/dnn.hpp>
-
-#include <string>
-namespace pr{
-    class FineMapping{
-    public:
-        FineMapping();
-
-
-        FineMapping(std::string prototxt,std::string caffemodel);
-        static cv::Mat FineMappingVertical(cv::Mat InputProposal,int sliceNum=15,int upper=0,int lower=-50,int windows_size=17);
-        cv::Mat FineMappingHorizon(cv::Mat FinedVertical,int leftPadding,int rightPadding);
-
-
-    private:
-        cv::dnn::Net net;
-
-    };
-
-
-
-
-}
-#endif //SWIFTPR_FINEMAPPING_H
diff --git a/Prj-iOS/lpr/lpr/Source/include/Pipeline.h b/Prj-iOS/lpr/lpr/Source/include/Pipeline.h
deleted file mode 100755
index 144cc80..0000000
--- a/Prj-iOS/lpr/lpr/Source/include/Pipeline.h
+++ /dev/null
@@ -1,45 +0,0 @@
-//
-// Created by 庾金科 on 22/10/2017.
-//
-
-#ifndef SWIFTPR_PIPLINE_H
-#define SWIFTPR_PIPLINE_H
-
-#include "PlateDetection.h"
-#include "PlateSegmentation.h"
-#include "CNNRecognizer.h"
-#include "PlateInfo.h"
-#include "FastDeskew.h"
-#include "FineMapping.h"
-#include "Recognizer.h"
-#include "SegmentationFreeRecognizer.h"
-
-namespace pr{
-
-//    std::string str_code[]={"京", "沪", "津", "渝", "冀", "晋", "蒙", "辽", "吉", "黑", "苏", "浙", "皖", "闽", "赣", "鲁", "豫", "鄂", "湘", "粤", "桂", "琼", "川", "贵", "云", "藏", "陕", "甘", "青", "宁", "新", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "A", "B", "C", "D", "E", "F", "G", "H", "J", "K", "L", "M", "N", "P", "Q", "R", "S", "T", "U", "V", "W", "X", "Y", "Z","港","学","使","警","澳","挂","军","北","南","广","沈","兰","成","济","海","民","航","空"};
-//    const std::vector<std::string> CH_PLATE_CODE(str_code, str_code+83);
-
-    const int SEGMENTATION_FREE_METHOD = 0;
-    const int SEGMENTATION_BASED_METHOD = 1;
-
-    class PipelinePR {
-        public:
-            GeneralRecognizer *generalRecognizer;
-            PlateDetection *plateDetection;
-            PlateSegmentation *plateSegmentation;
-            FineMapping *fineMapping;
-            SegmentationFreeRecognizer *segmentationFreeRecognizer;
-
-            PipelinePR(std::string detector_filename,
-                       std::string finemapping_prototxt,std::string finemapping_caffemodel,
-                       std::string segmentation_prototxt,std::string segmentation_caffemodel,
-                       std::string charRecognization_proto,std::string charRecognization_caffemodel,
-                       std::string segmentationfree_proto,std::string segmentationfree_caffemodel
-                       );
-            ~PipelinePR();
-
-            std::vector<std::string> plateRes;
-            std::vector<PlateInfo> RunPiplineAsImage(cv::Mat plateImage,int method);
-    };
-}
-#endif //SWIFTPR_PIPLINE_H
diff --git a/Prj-iOS/lpr/lpr/Source/include/PlateDetection.h b/Prj-iOS/lpr/lpr/Source/include/PlateDetection.h
deleted file mode 100755
index f4cf567..0000000
--- a/Prj-iOS/lpr/lpr/Source/include/PlateDetection.h
+++ /dev/null
@@ -1,33 +0,0 @@
-//
-// Created by 庾金科 on 20/09/2017.
-//
-
-#ifndef SWIFTPR_PLATEDETECTION_H
-#define SWIFTPR_PLATEDETECTION_H
-
-#include <opencv2/opencv.hpp>
-#include "PlateInfo.h"
-#include <vector>
-namespace pr{
-    class PlateDetection{
-    public:
-        PlateDetection(std::string filename_cascade);
-        PlateDetection();
-        void LoadModel(std::string filename_cascade);
-        void plateDetectionRough(cv::Mat InputImage,std::vector<pr::PlateInfo>  &plateInfos,int min_w=36,int max_w=800);
-//        std::vector<pr::PlateInfo> plateDetectionRough(cv::Mat InputImage,int min_w= 60,int max_h = 400);
-
-
-//        std::vector<pr::PlateInfo> plateDetectionRoughByMultiScaleEdge(cv::Mat InputImage);
-
-
-
-    private:
-        cv::CascadeClassifier cascade;
-
-
-    };
-
-}// namespace pr
-
-#endif //SWIFTPR_PLATEDETECTION_H
diff --git a/Prj-iOS/lpr/lpr/Source/include/PlateInfo.h b/Prj-iOS/lpr/lpr/Source/include/PlateInfo.h
deleted file mode 100755
index f1ea716..0000000
--- a/Prj-iOS/lpr/lpr/Source/include/PlateInfo.h
+++ /dev/null
@@ -1,126 +0,0 @@
-//
-// Created by 庾金科 on 20/09/2017.
-//
-
-#ifndef SWIFTPR_PLATEINFO_H
-#define SWIFTPR_PLATEINFO_H
-#include <opencv2/opencv.hpp>
-namespace pr {
-
-    typedef std::vector<cv::Mat> Character;
-
-    enum PlateColor { BLUE, YELLOW, WHITE, GREEN, BLACK,UNKNOWN};
-    enum CharType {CHINESE,LETTER,LETTER_NUMS,INVALID};
-
-
-    class PlateInfo {
-        public:
-            std::vector<std::pair<CharType,cv::Mat> > plateChars;
-            std::vector<std::pair<CharType,cv::Mat> > plateCoding;
-            float confidence = 0;
-            PlateInfo(const cv::Mat &plateData, std::string plateName, cv::Rect plateRect, PlateColor plateType) {
-                    licensePlate = plateData;
-                    name = plateName;
-                    ROI = plateRect;
-                    Type = plateType;
-                }
-            PlateInfo(const cv::Mat &plateData, cv::Rect plateRect, PlateColor plateType) {
-                licensePlate = plateData;
-                ROI = plateRect;
-                Type = plateType;
-            }
-            PlateInfo(const cv::Mat &plateData, cv::Rect plateRect) {
-                licensePlate = plateData;
-                ROI = plateRect;
-            }
-            PlateInfo() {
-
-            }
-
-            cv::Mat getPlateImage() {
-                return licensePlate;
-            }
-
-            void setPlateImage(cv::Mat plateImage){
-                licensePlate = plateImage;
-            }
-
-            cv::Rect getPlateRect() {
-                return ROI;
-            }
-
-            void setPlateRect(cv::Rect plateRect)   {
-                ROI = plateRect;
-            }
-            cv::String getPlateName() {
-                return name;
-
-            }
-            void setPlateName(cv::String plateName) {
-                name = plateName;
-            }
-            int getPlateType() {
-                return Type;
-            }
-
-            void appendPlateChar(const std::pair<CharType,cv::Mat> &plateChar)
-            {
-                plateChars.push_back(plateChar);
-            }
-
-            void appendPlateCoding(const std::pair<CharType,cv::Mat> &charProb){
-                plateCoding.push_back(charProb);
-            }
-
-    //        cv::Mat getPlateChars(int id) {
-    //            if(id<PlateChars.size())
-    //                return PlateChars[id];
-    //        }
-            std::string decodePlateNormal(std::vector<std::string> mappingTable) {
-                std::string decode;
-                for(auto plate:plateCoding) {
-                    float *prob = (float *)plate.second.data;
-                    if(plate.first == CHINESE) {
-
-                        decode += mappingTable[std::max_element(prob,prob+31) - prob];
-                        confidence+=*std::max_element(prob,prob+31);
-
-
-//                        std::cout<<*std::max_element(prob,prob+31)<<std::endl;
-
-                    }
-
-                    else if(plate.first == LETTER) {
-                        decode += mappingTable[std::max_element(prob+41,prob+65)- prob];
-                        confidence+=*std::max_element(prob+41,prob+65);
-                    }
-
-                    else if(plate.first == LETTER_NUMS) {
-                        decode += mappingTable[std::max_element(prob+31,prob+65)- prob];
-                        confidence+=*std::max_element(prob+31,prob+65);
-//                        std::cout<<*std::max_element(prob+31,prob+65)<<std::endl;
-
-                    }
-                    else if(plate.first == INVALID)
-                    {
-                        decode+='*';
-                    }
-
-                }
-                name = decode;
-
-                confidence/=7;
-
-                return decode;
-            }
-
-    private:
-        cv::Mat licensePlate;
-        cv::Rect ROI;
-        std::string name ;
-        PlateColor Type;
-    };
-}
-
-
-#endif //SWIFTPR_PLATEINFO_H
diff --git a/Prj-iOS/lpr/lpr/Source/include/PlateSegmentation.h b/Prj-iOS/lpr/lpr/Source/include/PlateSegmentation.h
deleted file mode 100755
index 37a06d4..0000000
--- a/Prj-iOS/lpr/lpr/Source/include/PlateSegmentation.h
+++ /dev/null
@@ -1,35 +0,0 @@
-#ifndef SWIFTPR_PLATESEGMENTATION_H
-#define SWIFTPR_PLATESEGMENTATION_H
-
-#include "opencv2/opencv.hpp"
-#include <opencv2/dnn.hpp>
-#include "PlateInfo.h"
-
-namespace pr{
-
-
-    class PlateSegmentation{
-    public:
-        const int PLATE_NORMAL = 6;
-        const int PLATE_NORMAL_GREEN = 7;
-        const int DEFAULT_WIDTH = 20;
-        PlateSegmentation(std::string phototxt,std::string caffemodel);
-        PlateSegmentation(){}
-        void segmentPlatePipline(PlateInfo &plateInfo,int stride,std::vector<cv::Rect> &Char_rects);
-
-        void segmentPlateBySlidingWindows(cv::Mat &plateImage,int windowsWidth,int stride,cv::Mat &respones);
-        void templateMatchFinding(const cv::Mat &respones,int windowsWidth,std::pair<float,std::vector<int> > &candidatePts);
-        void refineRegion(cv::Mat &plateImage,const std::vector<int> &candidatePts,const int padding,std::vector<cv::Rect> &rects);
-        void ExtractRegions(PlateInfo &plateInfo,std::vector<cv::Rect> &rects);
-        cv::Mat classifyResponse(const cv::Mat &cropped);
-    private:
-        cv::dnn::Net net;
-
-
-//        RefineRegion()
-
-    };
-
-}//namespace pr
-
-#endif //SWIFTPR_PLATESEGMENTATION_H
diff --git a/Prj-iOS/lpr/lpr/Source/include/Recognizer.h b/Prj-iOS/lpr/lpr/Source/include/Recognizer.h
deleted file mode 100755
index 04a565f..0000000
--- a/Prj-iOS/lpr/lpr/Source/include/Recognizer.h
+++ /dev/null
@@ -1,23 +0,0 @@
-//
-// Created by 庾金科 on 20/10/2017.
-//
-
-
-#ifndef SWIFTPR_RECOGNIZER_H
-#define SWIFTPR_RECOGNIZER_H
-
-#include <opencv2/dnn.hpp>
-#include "PlateInfo.h"
-namespace pr{
-    typedef cv::Mat label;
-    class GeneralRecognizer{
-        public:
-            virtual label recognizeCharacter(cv::Mat character) = 0;
-//            virtual cv::Mat SegmentationFreeForSinglePlate(cv::Mat plate) = 0;
-            void SegmentBasedSequenceRecognition(PlateInfo &plateinfo);
-            void SegmentationFreeSequenceRecognition(PlateInfo &plateInfo);
-
-    };
-
-}
-#endif //SWIFTPR_RECOGNIZER_H
diff --git a/Prj-iOS/lpr/lpr/Source/include/SegmentationFreeRecognizer.h b/Prj-iOS/lpr/lpr/Source/include/SegmentationFreeRecognizer.h
deleted file mode 100755
index 583899e..0000000
--- a/Prj-iOS/lpr/lpr/Source/include/SegmentationFreeRecognizer.h
+++ /dev/null
@@ -1,28 +0,0 @@
-//
-// Created by 庾金科 on 28/11/2017.
-//
-
-#ifndef SWIFTPR_SEGMENTATIONFREERECOGNIZER_H
-#define SWIFTPR_SEGMENTATIONFREERECOGNIZER_H
-
-#include "Recognizer.h"
-namespace pr{
-
-
-    class SegmentationFreeRecognizer{
-    public:
-        const int CHAR_INPUT_W = 14;
-        const int CHAR_INPUT_H = 30;
-        const int CHAR_LEN = 84;
-
-        SegmentationFreeRecognizer(std::string prototxt,std::string caffemodel);
-        std::pair<std::string,float> SegmentationFreeForSinglePlate(cv::Mat plate,std::vector<std::string> mapping_table);
-
-
-    private:
-        cv::dnn::Net net;
-
-    };
-
-}
-#endif //SWIFTPR_SEGMENTATIONFREERECOGNIZER_H
diff --git a/Prj-iOS/lpr/lpr/Source/include/niBlackThreshold.h b/Prj-iOS/lpr/lpr/Source/include/niBlackThreshold.h
deleted file mode 100755
index 5ad7e14..0000000
--- a/Prj-iOS/lpr/lpr/Source/include/niBlackThreshold.h
+++ /dev/null
@@ -1,107 +0,0 @@
-//
-// Created by 庾金科 on 26/10/2017.
-//
-
-#ifndef SWIFTPR_NIBLACKTHRESHOLD_H
-#define SWIFTPR_NIBLACKTHRESHOLD_H
-
-
-#include <opencv2/opencv.hpp>
-using namespace cv;
-
-enum LocalBinarizationMethods{
-    BINARIZATION_NIBLACK = 0, //!< Classic Niblack binarization. See @cite Niblack1985 .
-    BINARIZATION_SAUVOLA = 1, //!< Sauvola's technique. See @cite Sauvola1997 .
-    BINARIZATION_WOLF = 2,    //!< Wolf's technique. See @cite Wolf2004 .
-    BINARIZATION_NICK = 3     //!< NICK technique. See @cite Khurshid2009 .
-};
-
-
-void niBlackThreshold( InputArray _src, OutputArray _dst, double maxValue,
-                       int type, int blockSize, double k, int binarizationMethod )
-{
-    // Input grayscale image
-    Mat src = _src.getMat();
-    CV_Assert(src.channels() == 1);
-    CV_Assert(blockSize % 2 == 1 && blockSize > 1);
-    if (binarizationMethod == BINARIZATION_SAUVOLA) {
-        CV_Assert(src.depth() == CV_8U);
-    }
-    type &= THRESH_MASK;
-    // Compute local threshold (T = mean + k * stddev)
-    // using mean and standard deviation in the neighborhood of each pixel
-    // (intermediate calculations are done with floating-point precision)
-    Mat test;
-    Mat thresh;
-    {
-        // note that: Var[X] = E[X^2] - E[X]^2
-        Mat mean, sqmean, variance, stddev, sqrtVarianceMeanSum;
-        double srcMin, stddevMax;
-        boxFilter(src, mean, CV_32F, Size(blockSize, blockSize),
-                  Point(-1,-1), true, BORDER_REPLICATE);
-        sqrBoxFilter(src, sqmean, CV_32F, Size(blockSize, blockSize),
-                     Point(-1,-1), true, BORDER_REPLICATE);
-        variance = sqmean - mean.mul(mean);
-        sqrt(variance, stddev);
-        switch (binarizationMethod)
-        {
-            case BINARIZATION_NIBLACK:
-                thresh = mean + stddev * static_cast<float>(k);
-
-                break;
-            case BINARIZATION_SAUVOLA:
-                thresh = mean.mul(1. + static_cast<float>(k) * (stddev / 128.0 - 1.));
-                break;
-            case BINARIZATION_WOLF:
-                minMaxIdx(src, &srcMin,NULL);
-                minMaxIdx(stddev, NULL, &stddevMax);
-                thresh = mean - static_cast<float>(k) * (mean - srcMin - stddev.mul(mean - srcMin) / stddevMax);
-                break;
-            case BINARIZATION_NICK:
-                sqrt(variance + sqmean, sqrtVarianceMeanSum);
-                thresh = mean + static_cast<float>(k) * sqrtVarianceMeanSum;
-                break;
-            default:
-                CV_Error( CV_StsBadArg, "Unknown binarization method" );
-                break;
-        }
-        thresh.convertTo(thresh, src.depth());
-
-        thresh.convertTo(test, src.depth());
-//
-//        cv::imshow("imagex",test);
-//        cv::waitKey(0);
-
-    }
-    // Prepare output image
-    _dst.create(src.size(), src.type());
-    Mat dst = _dst.getMat();
-    CV_Assert(src.data != dst.data);  // no inplace processing
-    // Apply thresholding: ( pixel > threshold ) ? foreground : background
-    Mat mask;
-    switch (type)
-    {
-        case THRESH_BINARY:      // dst = (src > thresh) ? maxval : 0
-        case THRESH_BINARY_INV:  // dst = (src > thresh) ? 0 : maxval
-            compare(src, thresh, mask, (type == THRESH_BINARY ? CMP_GT : CMP_LE));
-            dst.setTo(0);
-            dst.setTo(maxValue, mask);
-            break;
-        case THRESH_TRUNC:       // dst = (src > thresh) ? thresh : src
-            compare(src, thresh, mask, CMP_GT);
-            src.copyTo(dst);
-            thresh.copyTo(dst, mask);
-            break;
-        case THRESH_TOZERO:      // dst = (src > thresh) ? src : 0
-        case THRESH_TOZERO_INV:  // dst = (src > thresh) ? 0 : src
-            compare(src, thresh, mask, (type == THRESH_TOZERO ? CMP_GT : CMP_LE));
-            dst.setTo(0);
-            src.copyTo(dst, mask);
-            break;
-        default:
-            CV_Error( CV_StsBadArg, "Unknown threshold type" );
-            break;
-    }
-}
-
-#endif //SWIFTPR_NIBLACKTHRESHOLD_H
diff --git a/Prj-iOS/lpr/lpr/Source/src/CNNRecognizer.cpp b/Prj-iOS/lpr/lpr/Source/src/CNNRecognizer.cpp
deleted file mode 100755
index 1003e67..0000000
--- a/Prj-iOS/lpr/lpr/Source/src/CNNRecognizer.cpp
+++ /dev/null
@@ -1,19 +0,0 @@
-//
-// Created by Jack Yu on 21/10/2017.
-//
-
-#include "CNNRecognizer.h"
-
-namespace pr{
-    CNNRecognizer::CNNRecognizer(std::string prototxt,std::string caffemodel){
-        net = cv::dnn::readNetFromCaffe(prototxt, caffemodel);
-    }
-
-    label CNNRecognizer::recognizeCharacter(cv::Mat charImage){
-        if(charImage.channels()== 3)
-            cv::cvtColor(charImage,charImage,cv::COLOR_BGR2GRAY);
-        cv::Mat inputBlob = cv::dnn::blobFromImage(charImage, 1/255.0, cv::Size(CHAR_INPUT_W,CHAR_INPUT_H), cv::Scalar(0,0,0),false);
-        net.setInput(inputBlob,"data");
-        return net.forward();
-    }
-}
diff --git a/Prj-iOS/lpr/lpr/Source/src/FastDeskew.cpp b/Prj-iOS/lpr/lpr/Source/src/FastDeskew.cpp
deleted file mode 100755
index 5da3644..0000000
--- a/Prj-iOS/lpr/lpr/Source/src/FastDeskew.cpp
+++ /dev/null
@@ -1,108 +0,0 @@
-//
-// Created by Jack Yu on 02/10/2017.
-//
-
-
-
-#include "FastDeskew.h"
-
-namespace pr{
-    const int ANGLE_MIN = 30 ;
-    const int ANGLE_MAX = 150 ;
-    const int PLATE_H = 36;
-    const int PLATE_W = 136;
-    
-    int angle(float x,float y)
-    {
-        return atan2(x,y)*180/3.1415;
-    }
-
-    std::vector<float> avgfilter(std::vector<float> angle_list,int windowsSize) {
-        std::vector<float> angle_list_filtered(angle_list.size() - windowsSize + 1);
-        for (int i = 0; i < angle_list.size() - windowsSize + 1; i++) {
-            float avg = 0.00f;
-            for (int j = 0; j < windowsSize; j++) {
-                avg += angle_list[i + j];
-            }
-            avg = avg / windowsSize;
-            angle_list_filtered[i] = avg;
-        }
-
-        return angle_list_filtered;
-    }
-
-
-    void drawHist(std::vector<float> seq){
-        cv::Mat image(300,seq.size(),CV_8U);
-        image.setTo(0);
-
-        for(int i = 0;i<seq.size();i++)
-        {
-            float l = *std::max_element(seq.begin(),seq.end());
-
-            int p = int(float(seq[i])/l*300);
-
-            cv::line(image,cv::Point(i,300),cv::Point(i,300-p),cv::Scalar(255,255,255));
-        }
-        cv::imshow("vis",image);
-    }
-
-    cv::Mat  correctPlateImage(cv::Mat skewPlate,float angle,float maxAngle)
-    {
-        cv::Mat dst;
-        cv::Size size_o(skewPlate.cols,skewPlate.rows);
-        int extend_padding = 0;
-        extend_padding = static_cast<int>(skewPlate.rows*tan(cv::abs(angle)/180* 3.14) );
-        cv::Size size(skewPlate.cols + extend_padding ,skewPlate.rows);
-        float interval = abs(sin((angle /180) * 3.14)* skewPlate.rows);
-        cv::Point2f pts1[4] = {cv::Point2f(0,0),cv::Point2f(0,size_o.height),cv::Point2f(size_o.width,0),cv::Point2f(size_o.width,size_o.height)};
-        if(angle>0) {
-            cv::Point2f pts2[4] = {cv::Point2f(interval, 0), cv::Point2f(0, size_o.height),
-                                   cv::Point2f(size_o.width, 0), cv::Point2f(size_o.width - interval, size_o.height)};
-            cv::Mat M  = cv::getPerspectiveTransform(pts1,pts2);
-            cv::warpPerspective(skewPlate,dst,M,size);
-        }
-        else {
-            cv::Point2f pts2[4] = {cv::Point2f(0, 0), cv::Point2f(interval, size_o.height), cv::Point2f(size_o.width-interval, 0),
-                                   cv::Point2f(size_o.width, size_o.height)};
-            cv::Mat M  = cv::getPerspectiveTransform(pts1,pts2);
-            cv::warpPerspective(skewPlate,dst,M,size,cv::INTER_CUBIC);
-        }
-        return  dst;
-    }
-    cv::Mat fastdeskew(cv::Mat skewImage,int blockSize){
-        const int FILTER_WINDOWS_SIZE = 5;
-        std::vector<float> angle_list(180);
-        memset(angle_list.data(),0,angle_list.size()*sizeof(int));
-        cv::Mat bak;
-        skewImage.copyTo(bak);
-        if(skewImage.channels() == 3)
-            cv::cvtColor(skewImage,skewImage,cv::COLOR_RGB2GRAY);
-        if(skewImage.channels() == 1)
-        {
-            cv::Mat eigen;
-            cv::cornerEigenValsAndVecs(skewImage,eigen,blockSize,5);
-            for( int j = 0; j < skewImage.rows; j+=blockSize )
-            { for( int i = 0; i < skewImage.cols; i+=blockSize )
-                {
-                    float x2 = eigen.at<cv::Vec6f>(j, i)[4];
-                    float y2 = eigen.at<cv::Vec6f>(j, i)[5];
-                    int angle_cell = angle(x2,y2);
-                    angle_list[(angle_cell + 180)%180]+=1.0;
-                }
-            }
-        }
-        std::vector<float> filtered = avgfilter(angle_list,5);
-        int maxPos = std::max_element(filtered.begin(),filtered.end()) - filtered.begin() + FILTER_WINDOWS_SIZE/2;
-        if(maxPos>ANGLE_MAX)
-            maxPos = (-maxPos+90+180)%180;
-        if(maxPos<ANGLE_MIN)
-            maxPos-=90;
-        maxPos=90-maxPos;
-      cv::Mat deskewed = correctPlateImage(bak, static_cast<float>(maxPos),60.0f);
-        return deskewed;
-    }
-
-
-
-}//namespace pr
diff --git a/Prj-iOS/lpr/lpr/Source/src/FineMapping.cpp b/Prj-iOS/lpr/lpr/Source/src/FineMapping.cpp
deleted file mode 100755
index 2032b15..0000000
--- a/Prj-iOS/lpr/lpr/Source/src/FineMapping.cpp
+++ /dev/null
@@ -1,170 +0,0 @@
-#include "FineMapping.h"
-namespace pr{
-
-    const int FINEMAPPING_H = 60 ;
-    const int FINEMAPPING_W = 140;
-    const int PADDING_UP_DOWN = 30;
-    void drawRect(cv::Mat image,cv::Rect rect)
-    {
-        cv::Point p1(rect.x,rect.y);
-        cv::Point p2(rect.x+rect.width,rect.y+rect.height);
-        cv::rectangle(image,p1,p2,cv::Scalar(0,255,0),1);
-    }
-
-
-    FineMapping::FineMapping(std::string prototxt,std::string caffemodel) {
-         net = cv::dnn::readNetFromCaffe(prototxt, caffemodel);
-
-    }
-
-    cv::Mat FineMapping::FineMappingHorizon(cv::Mat FinedVertical,int leftPadding,int rightPadding)
-    {
-
-//        if(FinedVertical.channels()==1)
-//            cv::cvtColor(FinedVertical,FinedVertical,cv::COLOR_GRAY2BGR);
-        cv::Mat inputBlob = cv::dnn::blobFromImage(FinedVertical, 1/255.0, cv::Size(66,16),
-                                      cv::Scalar(0,0,0),false);
-
-        net.setInput(inputBlob,"data");
-        cv::Mat prob = net.forward();
-        int front = static_cast<int>(prob.at<float>(0,0)*FinedVertical.cols);
-        int back = static_cast<int>(prob.at<float>(0,1)*FinedVertical.cols);
-        front -= leftPadding ;
-        if(front<0) front = 0;
-        back +=rightPadding;
-        if(back>FinedVertical.cols-1) back=FinedVertical.cols - 1;
-        cv::Mat cropped  = FinedVertical.colRange(front,back).clone();
-        return  cropped;
-
-
-    }
-    std::pair<int,int> FitLineRansac(std::vector<cv::Point> pts,int zeroadd = 0 )
-    {
-        std::pair<int,int> res;
-        if(pts.size()>2)
-        {
-            cv::Vec4f line;
-            cv::fitLine(pts,line,cv::DIST_HUBER,0,0.01,0.01);
-            float vx = line[0];
-            float vy = line[1];
-            float x = line[2];
-            float y = line[3];
-            int lefty = static_cast<int>((-x * vy / vx) + y);
-            int righty = static_cast<int>(((136- x) * vy / vx) + y);
-            res.first = lefty+PADDING_UP_DOWN+zeroadd;
-            res.second = righty+PADDING_UP_DOWN+zeroadd;
-            return res;
-        }
-        res.first = zeroadd;
-        res.second = zeroadd;
-        return res;
-    }
-
-    cv::Mat FineMapping::FineMappingVertical(cv::Mat InputProposal,int sliceNum,int upper,int lower,int windows_size){
-        cv::Mat PreInputProposal;
-        cv::Mat proposal;
-        cv::resize(InputProposal,PreInputProposal,cv::Size(FINEMAPPING_W,FINEMAPPING_H));
-        if(InputProposal.channels() == 3)
-            cv::cvtColor(PreInputProposal,proposal,cv::COLOR_BGR2GRAY);
-        else
-            PreInputProposal.copyTo(proposal);
-        // this will improve some sen
-        cv::Mat kernal = cv::getStructuringElement(cv::MORPH_ELLIPSE,cv::Size(1,3));
-        float diff = static_cast<float>(upper-lower);
-        diff/=static_cast<float>(sliceNum-1);
-        cv::Mat binary_adaptive;
-        std::vector<cv::Point> line_upper;
-        std::vector<cv::Point> line_lower;
-        int contours_nums=0;
-        for(int i = 0 ; i < sliceNum ; i++)
-        {
-            std::vector<std::vector<cv::Point> > contours;
-            float k =lower + i*diff;
-            cv::adaptiveThreshold(proposal,binary_adaptive,255,cv::ADAPTIVE_THRESH_MEAN_C,cv::THRESH_BINARY,windows_size,k);
-            cv::Mat draw;
-            binary_adaptive.copyTo(draw);
-            cv::findContours(binary_adaptive,contours,cv::RETR_EXTERNAL,cv::CHAIN_APPROX_SIMPLE);
-            for(auto contour: contours)
-            {
-                cv::Rect bdbox =cv::boundingRect(contour);
-                float lwRatio = bdbox.height/static_cast<float>(bdbox.width);
-                int  bdboxAera = bdbox.width*bdbox.height;
-                if ((   lwRatio>0.7&&bdbox.width*bdbox.height>100 && bdboxAera<300)
-                    || (lwRatio>3.0 && bdboxAera<100 && bdboxAera>10))
-                {
-                    cv::Point p1(bdbox.x, bdbox.y);
-                    cv::Point p2(bdbox.x + bdbox.width, bdbox.y + bdbox.height);
-                    line_upper.push_back(p1);
-                    line_lower.push_back(p2);
-                    contours_nums+=1;
-                }
-            }
-        }
-        if(contours_nums<41)
-        {
-            cv::bitwise_not(InputProposal,InputProposal);
-            cv::Mat kernal = cv::getStructuringElement(cv::MORPH_ELLIPSE,cv::Size(1,5));
-            cv::Mat bak;
-            cv::resize(InputProposal,bak,cv::Size(FINEMAPPING_W,FINEMAPPING_H));
-            cv::erode(bak,bak,kernal);
-            if(InputProposal.channels() == 3)
-                cv::cvtColor(bak,proposal,cv::COLOR_BGR2GRAY);
-            else
-                proposal = bak;
-            int contours_nums=0;
-            for(int i = 0 ; i < sliceNum ; i++)
-            {
-                std::vector<std::vector<cv::Point> > contours;
-                float k =lower + i*diff;
-                cv::adaptiveThreshold(proposal,binary_adaptive,255,cv::ADAPTIVE_THRESH_MEAN_C,cv::THRESH_BINARY,windows_size,k);
-                cv::Mat draw;
-                binary_adaptive.copyTo(draw);
-                cv::findContours(binary_adaptive,contours,cv::RETR_EXTERNAL,cv::CHAIN_APPROX_SIMPLE);
-                for(auto contour: contours)
-                {
-                    cv::Rect bdbox =cv::boundingRect(contour);
-                    float lwRatio = bdbox.height/static_cast<float>(bdbox.width);
-                    int  bdboxAera = bdbox.width*bdbox.height;
-                    if ((   lwRatio>0.7&&bdbox.width*bdbox.height>120 && bdboxAera<300)
-                        || (lwRatio>3.0 && bdboxAera<100 && bdboxAera>10))
-                    {
-
-                        cv::Point p1(bdbox.x, bdbox.y);
-                        cv::Point p2(bdbox.x + bdbox.width, bdbox.y + bdbox.height);
-                        line_upper.push_back(p1);
-                        line_lower.push_back(p2);
-                        contours_nums+=1;
-                    }
-                }
-            }
-        }
-            cv::Mat rgb;
-            cv::copyMakeBorder(PreInputProposal, rgb, PADDING_UP_DOWN, PADDING_UP_DOWN, 0, 0, cv::BORDER_REPLICATE);
-            std::pair<int, int> A;
-            std::pair<int, int> B;
-            A = FitLineRansac(line_upper, -1);
-            B = FitLineRansac(line_lower, 1);
-            int leftyB = A.first;
-            int rightyB = A.second;
-            int leftyA = B.first;
-            int rightyA = B.second;
-            int cols = rgb.cols;
-            int rows = rgb.rows;
-            std::vector<cv::Point2f> corners(4);
-            corners[0] = cv::Point2f(cols - 1, rightyA);
-            corners[1] = cv::Point2f(0, leftyA);
-            corners[2] = cv::Point2f(cols - 1, rightyB);
-            corners[3] = cv::Point2f(0, leftyB);
-            std::vector<cv::Point2f> corners_trans(4);
-            corners_trans[0] = cv::Point2f(136, 36);
-            corners_trans[1] = cv::Point2f(0, 36);
-            corners_trans[2] = cv::Point2f(136, 0);
-            corners_trans[3] = cv::Point2f(0, 0);
-            cv::Mat transform = cv::getPerspectiveTransform(corners, corners_trans);
-            cv::Mat quad = cv::Mat::zeros(36, 136, CV_8UC3);
-            cv::warpPerspective(rgb, quad, transform, quad.size());
-        return quad;
-    }
-}
-
-
diff --git a/Prj-iOS/lpr/lpr/Source/src/Pipeline.cpp b/Prj-iOS/lpr/lpr/Source/src/Pipeline.cpp
deleted file mode 100755
index 561bb68..0000000
--- a/Prj-iOS/lpr/lpr/Source/src/Pipeline.cpp
+++ /dev/null
@@ -1,103 +0,0 @@
-//
-// Created by 庾金科 on 23/10/2017.
-//
-
-#include "Pipeline.h"
-
-
-namespace pr {
-
-    std::string str_code[]={"京", "沪", "津", "渝", "冀", "晋", "蒙", "辽", "吉", "黑", "苏", "浙", "皖", "闽", "赣", "鲁", "豫", "鄂", "湘", "粤", "桂", "琼", "川", "贵", "云", "藏", "陕", "甘", "青", "宁", "新", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "A", "B", "C", "D", "E", "F", "G", "H", "J", "K", "L", "M", "N", "P", "Q", "R", "S", "T", "U", "V", "W", "X", "Y", "Z","港","学","使","警","澳","挂","军","北","南","广","沈","兰","成","济","海","民","航","空"};
-    const std::vector<std::string> CH_PLATE_CODE(str_code, str_code+83);
-
-
-    const int HorizontalPadding = 4;
-    PipelinePR::PipelinePR(std::string detector_filename,
-                           std::string finemapping_prototxt, std::string finemapping_caffemodel,
-                           std::string segmentation_prototxt, std::string segmentation_caffemodel,
-                           std::string charRecognization_proto, std::string charRecognization_caffemodel,
-                           std::string segmentationfree_proto,std::string segmentationfree_caffemodel) {
-        plateDetection = new PlateDetection(detector_filename);
-        fineMapping = new FineMapping(finemapping_prototxt, finemapping_caffemodel);
-        plateSegmentation = new PlateSegmentation(segmentation_prototxt, segmentation_caffemodel);
-        generalRecognizer = new CNNRecognizer(charRecognization_proto, charRecognization_caffemodel);
-        segmentationFreeRecognizer =  new SegmentationFreeRecognizer(segmentationfree_proto,segmentationfree_caffemodel);
-
-    }
-
-    PipelinePR::~PipelinePR() {
-
-        delete plateDetection;
-        delete fineMapping;
-        delete plateSegmentation;
-        delete (CNNRecognizer *)generalRecognizer;
-        delete segmentationFreeRecognizer;
-
-
-    }
-
-    std::vector<PlateInfo> PipelinePR:: RunPiplineAsImage(cv::Mat plateImage,int method) {
-        std::vector<PlateInfo> results;
-        std::vector<pr::PlateInfo> plates;
-        plateDetection->plateDetectionRough(plateImage,plates,36,700);
-
-        for (pr::PlateInfo plateinfo:plates) {
-
-            cv::Mat image_finemapping = plateinfo.getPlateImage();
-            image_finemapping = fineMapping->FineMappingVertical(image_finemapping);
-            image_finemapping = pr::fastdeskew(image_finemapping, 5);
-
-
-
-            //Segmentation-based
-
-            if(method==SEGMENTATION_BASED_METHOD)
-            {
-                image_finemapping = fineMapping->FineMappingHorizon(image_finemapping, 2, HorizontalPadding);
-                cv::resize(image_finemapping, image_finemapping, cv::Size(136+HorizontalPadding, 36));
-//            cv::imshow("image_finemapping",image_finemapping);
-//            cv::waitKey(0);
-                plateinfo.setPlateImage(image_finemapping);
-                std::vector<cv::Rect> rects;
-
-                plateSegmentation->segmentPlatePipline(plateinfo, 1, rects);
-                plateSegmentation->ExtractRegions(plateinfo, rects);
-                cv::copyMakeBorder(image_finemapping, image_finemapping, 0, 0, 0, 20, cv::BORDER_REPLICATE);
-                plateinfo.setPlateImage(image_finemapping);
-                generalRecognizer->SegmentBasedSequenceRecognition(plateinfo);
-                plateinfo.decodePlateNormal(pr::CH_PLATE_CODE);
-
-            }
-                //Segmentation-free
-            else if(method==SEGMENTATION_FREE_METHOD)
-            {
-
-                image_finemapping = fineMapping->FineMappingHorizon(image_finemapping, 4, HorizontalPadding+3);
-
-                cv::resize(image_finemapping, image_finemapping, cv::Size(136+HorizontalPadding, 36));
-//                cv::imwrite("./test.png",image_finemapping);
-//                cv::imshow("image_finemapping",image_finemapping);
-//                cv::waitKey(0);
-                plateinfo.setPlateImage(image_finemapping);
-//                std::vector<cv::Rect> rects;
-
-                std::pair<std::string,float> res = segmentationFreeRecognizer->SegmentationFreeForSinglePlate(plateinfo.getPlateImage(),pr::CH_PLATE_CODE);
-                plateinfo.confidence = res.second;
-                plateinfo.setPlateName(res.first);
-            }
-
-
-
-            results.push_back(plateinfo);
-        }
-
-//        for (auto str:results) {
-//            std::cout << str << std::endl;
-//        }
-        return results;
-
-    }//namespace pr
-
-
-
-}
diff --git a/Prj-iOS/lpr/lpr/Source/src/PlateDetection.cpp b/Prj-iOS/lpr/lpr/Source/src/PlateDetection.cpp
deleted file mode 100755
index cb3eeef..0000000
--- a/Prj-iOS/lpr/lpr/Source/src/PlateDetection.cpp
+++ /dev/null
@@ -1,32 +0,0 @@
-#include "PlateDetection.h"
-#include "util.h"
-namespace pr{
-    PlateDetection::PlateDetection(std::string filename_cascade){
-        cascade.load(filename_cascade);
-
-    };
-    void PlateDetection::plateDetectionRough(cv::Mat InputImage,std::vector<pr::PlateInfo>  &plateInfos,int min_w,int max_w){
-        cv::Mat processImage;
-        cv::cvtColor(InputImage,processImage,cv::COLOR_BGR2GRAY);
-        std::vector<cv::Rect> platesRegions;
-        cv::Size minSize(min_w,min_w/4);
-        cv::Size maxSize(max_w,max_w/4);
-        cascade.detectMultiScale( processImage, platesRegions,
-                                  1.1, 3, cv::CASCADE_SCALE_IMAGE,minSize,maxSize);
-        for(auto plate:platesRegions)
-        {
-            int zeroadd_w  = static_cast<int>(plate.width*0.30);
-            int zeroadd_h = static_cast<int>(plate.height*2);
-            int zeroadd_x = static_cast<int>(plate.width*0.15);
-            int zeroadd_y = static_cast<int>(plate.height*1);
-            plate.x-=zeroadd_x;
-            plate.y-=zeroadd_y;
-            plate.height += zeroadd_h;
-            plate.width += zeroadd_w;
-                cv::Mat plateImage = util::cropFromImage(InputImage,plate);
-            PlateInfo plateInfo(plateImage,plate);
-            plateInfos.push_back(plateInfo);
-
-        }
-    }
-}//namespace pr
diff --git a/Prj-iOS/lpr/lpr/Source/src/PlateSegmentation.cpp b/Prj-iOS/lpr/lpr/Source/src/PlateSegmentation.cpp
deleted file mode 100755
index d35c0de..0000000
--- a/Prj-iOS/lpr/lpr/Source/src/PlateSegmentation.cpp
+++ /dev/null
@@ -1,404 +0,0 @@
-//
-// Created by Jack  Yu on 16/10/2017.
-//
-
-#include "PlateSegmentation.h"
-#include "niBlackThreshold.h"
-
-
-//#define DEBUG
-namespace pr{
-
-    PlateSegmentation::PlateSegmentation(std::string prototxt,std::string caffemodel) {
-        net = cv::dnn::readNetFromCaffe(prototxt, caffemodel);
-    }
-    cv::Mat PlateSegmentation::classifyResponse(const cv::Mat &cropped){
-        cv::Mat inputBlob = cv::dnn::blobFromImage(cropped, 1/255.0, cv::Size(22,22), cv::Scalar(0,0,0),false);
-        net.setInput(inputBlob,"data");
-        return net.forward();
-    }
-
-    void drawHist(float* seq,int size,const char* name){
-        cv::Mat image(300,size,CV_8U);
-        image.setTo(0);
-        float* start =seq;
-        float* end = seq+size;
-        float l = *std::max_element(start,end);
-        for(int i = 0;i<size;i++)
-        {
-            int p = int(float(seq[i])/l*300);
-            cv::line(image,cv::Point(i,300),cv::Point(i,300-p),cv::Scalar(255,255,255));
-        }
-        cv::resize(image,image,cv::Size(600,100));
-        cv::imshow(name,image);
-    }
-
-    inline void computeSafeMargin(int &val,const int &rows){
-        val = std::min(val,rows);
-        val = std::max(val,0);
-    }
-
-    cv::Rect boxFromCenter(const cv::Point center,int left,int right,int top,int bottom,cv::Size bdSize)
-    {
-        cv::Point p1(center.x -  left ,center.y - top);
-        cv::Point p2( center.x + right, center.y + bottom);
-        p1.x = std::max(0,p1.x);
-        p1.y = std::max(0,p1.y);
-        p2.x = std::min(p2.x,bdSize.width-1);
-        p2.y = std::min(p2.y,bdSize.height-1);
-        cv::Rect rect(p1,p2);
-        return rect;
-    }
-
-    cv::Rect boxPadding(cv::Rect rect,int left,int right,int top,int bottom,cv::Size bdSize)
-    {
-
-        cv::Point center(rect.x+(rect.width>>1),rect.y + (rect.height>>1));
-        int rebuildLeft  = (rect.width>>1 )+ left;
-        int rebuildRight = (rect.width>>1 )+ right;
-        int rebuildTop = (rect.height>>1 )+ top;
-        int rebuildBottom = (rect.height>>1 )+ bottom;
-        return boxFromCenter(center,rebuildLeft,rebuildRight,rebuildTop,rebuildBottom,bdSize);
-
-    }
-
-
-
-    void PlateSegmentation:: refineRegion(cv::Mat &plateImage,const std::vector<int> &candidatePts,const int padding,std::vector<cv::Rect> &rects){
-        int w = candidatePts[5] - candidatePts[4];
-        int cols = plateImage.cols;
-        int rows = plateImage.rows;
-        for(int i = 0 ; i < candidatePts.size()  ; i++)
-        {
-            int left = 0;
-            int right = 0 ;
-
-            if(i == 0 ){
-                left= candidatePts[i];
-                right = left+w+padding;
-                }
-            else {
-                left = candidatePts[i] - padding;
-                right = left + w + padding * 2;
-            }
-
-            computeSafeMargin(right,cols);
-            computeSafeMargin(left,cols);
-            cv::Rect roi(left,0,right - left,rows-1);
-            cv::Mat roiImage;
-            plateImage(roi).copyTo(roiImage);
-
-            if (i>=1)
-            {
-
-                cv::Mat roi_thres;
-//                cv::threshold(roiImage,roi_thres,0,255,cv::THRESH_OTSU|cv::THRESH_BINARY);
-
-                niBlackThreshold(roiImage,roi_thres,255,cv::THRESH_BINARY,15,0.27,BINARIZATION_NIBLACK);
-
-                std::vector<std::vector<cv::Point> > contours;
-                cv::findContours(roi_thres,contours,cv::RETR_LIST,cv::CHAIN_APPROX_SIMPLE);
-                cv::Point boxCenter(roiImage.cols>>1,roiImage.rows>>1);
-
-                cv::Rect final_bdbox;
-                cv::Point final_center;
-                int final_dist = INT_MAX;
-
-
-                for(auto contour:contours)
-                {
-                    cv::Rect bdbox = cv::boundingRect(contour);
-                    cv::Point center(bdbox.x+(bdbox.width>>1),bdbox.y + (bdbox.height>>1));
-                    int dist = (center.x - boxCenter.x)*(center.x - boxCenter.x);
-                    if(dist<final_dist and  bdbox.height > rows>>1)
-                    {   final_dist =dist;
-                        final_center = center;
-                        final_bdbox = bdbox;
-                    }
-                }
-
-                //rebuild box
-                if(final_bdbox.height/ static_cast<float>(final_bdbox.width) > 3.5 && final_bdbox.width*final_bdbox.height<10)
-                    final_bdbox = boxFromCenter(final_center,8,8,(rows>>1)-3 , (rows>>1) - 2,roiImage.size());
-                else {
-                    if(i == candidatePts.size()-1)
-                        final_bdbox = boxPadding(final_bdbox, padding/2, padding, padding/2, padding/2, roiImage.size());
-                    else
-                        final_bdbox = boxPadding(final_bdbox, padding, padding, padding, padding, roiImage.size());
-
-
-//                    std::cout<<final_bdbox<<std::endl;
-//                    std::cout<<roiImage.size()<<std::endl;
-//#ifdef DEBUG
-//                    cv::imshow("char_thres",roi_thres);
-//
-//                    cv::imshow("char",roiImage(final_bdbox));
-//                    cv::waitKey(0);
-//#endif
-
-
-                }
-
-
-                final_bdbox.x += left;
-
-                rects.push_back(final_bdbox);
-//
-
-            }
-            else
-            {
-                rects.push_back(roi);
-            }
-
-//            else
-//            {
-//
-//            }
-
-//            cv::GaussianBlur(roiImage,roiImage,cv::Size(7,7),3);
-//
-//            cv::imshow("image",roiImage);
-//            cv::waitKey(0);
-
-
-        }
-
-
-
-    }
-    void avgfilter(float *angle_list,int size,int windowsSize) {
-        float *filterd = new float[size];
-        for(int i = 0 ; i < size ; i++) filterd [i] = angle_list[i];
-//        memcpy(filterd,angle_list,size);
-
-        cv::Mat kernal_gaussian = cv::getGaussianKernel(windowsSize,3,CV_32F);
-        float *kernal = (float*)kernal_gaussian.data;
-//        kernal+=windowsSize;
-        int r = windowsSize/2;
-
-
-
-
-        for (int i = 0; i < size; i++) {
-            float avg = 0.00f;
-            for (int j = 0; j < windowsSize; j++) {
-                if(i+j-r>0&&i+j+r<size-1)
-                    avg += filterd[i + j-r]*kernal[j];
-            }
-//            avg = avg / windowsSize;
-            angle_list[i] = avg;
-
-        }
-
-        delete [] filterd;
-    }
-
-    void PlateSegmentation::templateMatchFinding(const cv::Mat &respones,int windowsWidth,std::pair<float,std::vector<int> > &candidatePts){
-        int rows = respones.rows;
-        int cols = respones.cols;
-
-
-
-        float *data = (float*)respones.data;
-        float *engNum_prob = data;
-        float *false_prob = data+cols;
-        float *ch_prob = data+cols*2;
-
-        avgfilter(engNum_prob,cols,5);
-        avgfilter(false_prob,cols,5);
-//        avgfilter(ch_prob,cols,5);
-        std::vector<int> candidate_pts(7);
-//#ifdef DEBUG
-//        drawHist(engNum_prob,cols,"engNum_prob");
-//        drawHist(false_prob,cols,"false_prob");
-//        drawHist(ch_prob,cols,"ch_prob");
-//                cv::waitKey(0);
-//#endif
-
-
-
-
-        int cp_list[7];
-        float loss_selected = -10;
-
-        for(int start = 0 ; start < 20 ; start+=2)
-            for(int  width = windowsWidth-5; width < windowsWidth+5 ; width++ ){
-                for(int interval = windowsWidth/2; interval < windowsWidth; interval++)
-                {
-                    int cp1_ch  = start;
-                    int cp2_p0 = cp1_ch+ width;
-                    int cp3_p1 = cp2_p0+ width + interval;
-                    int cp4_p2 = cp3_p1 + width;
-                    int cp5_p3 = cp4_p2 + width+1;
-                    int cp6_p4 = cp5_p3 + width+2;
-                    int cp7_p5= cp6_p4+ width+2;
-
-                    int md1 = (cp1_ch+cp2_p0)>>1;
-                    int md2 = (cp2_p0+cp3_p1)>>1;
-                    int md3 = (cp3_p1+cp4_p2)>>1;
-                    int md4 = (cp4_p2+cp5_p3)>>1;
-                    int md5 = (cp5_p3+cp6_p4)>>1;
-                    int md6 = (cp6_p4+cp7_p5)>>1;
-
-
-
-
-                    if(cp7_p5>=cols)
-                        continue;
-//                    float loss = ch_prob[cp1_ch]+
-//                       engNum_prob[cp2_p0] +engNum_prob[cp3_p1]+engNum_prob[cp4_p2]+engNum_prob[cp5_p3]+engNum_prob[cp6_p4] +engNum_prob[cp7_p5]
-//                    + (false_prob[md2]+false_prob[md3]+false_prob[md4]+false_prob[md5]+false_prob[md5] + false_prob[md6]);
-                    float loss = ch_prob[cp1_ch]*3 -(false_prob[cp3_p1]+false_prob[cp4_p2]+false_prob[cp5_p3]+false_prob[cp6_p4]+false_prob[cp7_p5]);
-
-                    if(loss>loss_selected)
-                    {
-                        loss_selected = loss;
-                        cp_list[0]= cp1_ch;
-                        cp_list[1]= cp2_p0;
-                        cp_list[2]= cp3_p1;
-                        cp_list[3]= cp4_p2;
-                        cp_list[4]= cp5_p3;
-                        cp_list[5]= cp6_p4;
-                        cp_list[6]= cp7_p5;
-                    }
-                }
-            }
-        candidate_pts[0] = cp_list[0];
-        candidate_pts[1] = cp_list[1];
-        candidate_pts[2] = cp_list[2];
-        candidate_pts[3] = cp_list[3];
-        candidate_pts[4] = cp_list[4];
-        candidate_pts[5] = cp_list[5];
-        candidate_pts[6] = cp_list[6];
-
-        candidatePts.first = loss_selected;
-        candidatePts.second = candidate_pts;
-
-    };
-
-
-    void PlateSegmentation::segmentPlateBySlidingWindows(cv::Mat &plateImage,int windowsWidth,int stride,cv::Mat &respones){
-
-
-//        cv::resize(plateImage,plateImage,cv::Size(136,36));
-
-        cv::Mat plateImageGray;
-        cv::cvtColor(plateImage,plateImageGray,cv::COLOR_BGR2GRAY);
-        int padding  =  plateImage.cols-136 ;
-//        int padding  =  0 ;
-        int height = plateImage.rows - 1;
-        int width = plateImage.cols - 1 - padding;
-        for(int i = 0 ; i < width - windowsWidth +1 ; i +=stride)
-        {
-            cv::Rect roi(i,0,windowsWidth,height);
-            cv::Mat roiImage = plateImageGray(roi);
-            cv::Mat response = classifyResponse(roiImage);
-            respones.push_back(response);
-        }
-
-
-
-
-        respones =  respones.t();
-//        std::pair<float,std::vector<int>> images ;
-//
-//
-//        std::cout<<images.first<<" ";
-//        for(int i = 0 ; i < images.second.size() ; i++)
-//        {
-//            std::cout<<images.second[i]<<" ";
-////            cv::line(plateImageGray,cv::Point(images.second[i],0),cv::Point(images.second[i],36),cv::Scalar(255,255,255),1); //DEBUG
-//        }
-
-//        int w = images.second[5] - images.second[4];
-
-//        cv::line(plateImageGray,cv::Point(images.second[5]+w,0),cv::Point(images.second[5]+w,36),cv::Scalar(255,255,255),1); //DEBUG
-//        cv::line(plateImageGray,cv::Point(images.second[5]+2*w,0),cv::Point(images.second[5]+2*w,36),cv::Scalar(255,255,255),1); //DEBUG
-
-
-//        RefineRegion(plateImageGray,images.second,5);
-
-//        std::cout<<w<<std::endl;
-
-//        std::cout<<<<std::endl;
-
-//        cv::resize(plateImageGray,plateImageGray,cv::Size(600,100));
-
-
-
-    }
-
-//    void filterGaussian(cv::Mat &respones,float sigma){
-//
-//    }
-
-
-    void PlateSegmentation::segmentPlatePipline(PlateInfo &plateInfo,int stride,std::vector<cv::Rect> &Char_rects){
-        cv::Mat plateImage = plateInfo.getPlateImage(); // get src image .
-        cv::Mat plateImageGray;
-        cv::cvtColor(plateImage,plateImageGray,cv::COLOR_BGR2GRAY);
-        //do binarzation
-        //
-        std::pair<float,std::vector<int> > sections ; // segment points variables .
-
-        cv::Mat respones; //three response of every sub region from origin image .
-        segmentPlateBySlidingWindows(plateImage,DEFAULT_WIDTH,1,respones);
-        templateMatchFinding(respones,DEFAULT_WIDTH/stride,sections);
-        for(int i = 0; i < sections.second.size() ; i++)
-        {
-            sections.second[i]*=stride;
-
-        }
-
-//        std::cout<<sections<<std::endl;
-
-        refineRegion(plateImageGray,sections.second,5,Char_rects);
-//#ifdef DEBUG
-//        for(int i = 0 ; i < sections.second.size() ; i++)
-//        {
-//            std::cout<<sections.second[i]<<" ";
-//            cv::line(plateImageGray,cv::Point(sections.second[i],0),cv::Point(sections.second[i],36),cv::Scalar(255,255,255),1); //DEBUG
-//        }
-//        cv::imshow("plate",plateImageGray);
-//        cv::waitKey(0);
-//#endif
-//        cv::waitKey(0);
-
-    }
-
-    void PlateSegmentation::ExtractRegions(PlateInfo &plateInfo,std::vector<cv::Rect> &rects){
-        cv::Mat plateImage = plateInfo.getPlateImage();
-        for(int i = 0 ; i < rects.size() ; i++){
-            cv::Mat charImage;
-            plateImage(rects[i]).copyTo(charImage);
-            if(charImage.channels())
-                cv::cvtColor(charImage,charImage,cv::COLOR_BGR2GRAY);
-//            cv::imshow("image",charImage);
-//            cv::waitKey(0);
-            cv::equalizeHist(charImage,charImage);
-//
-
-//
-
-
-            std::pair<CharType,cv::Mat> char_instance;
-            if(i == 0 ){
-
-                char_instance.first = CHINESE;
-
-
-            } else if(i == 1){
-                char_instance.first = LETTER;
-            }
-            else{
-                char_instance.first = LETTER_NUMS;
-            }
-            char_instance.second = charImage;
-            plateInfo.appendPlateChar(char_instance);
-
-        }
-
-    }
-
-}//namespace pr
diff --git a/Prj-iOS/lpr/lpr/Source/src/Recognizer.cpp b/Prj-iOS/lpr/lpr/Source/src/Recognizer.cpp
deleted file mode 100755
index 3bfafef..0000000
--- a/Prj-iOS/lpr/lpr/Source/src/Recognizer.cpp
+++ /dev/null
@@ -1,23 +0,0 @@
-//
-// Created by Jack Yu on 22/10/2017.
-//
-
-#include "Recognizer.h"
-
-namespace pr{
-    void GeneralRecognizer::SegmentBasedSequenceRecognition(PlateInfo &plateinfo){
-        for(auto char_instance:plateinfo.plateChars)
-        {
-            std::pair<CharType,cv::Mat> res;
-            if(char_instance.second.rows*char_instance.second.cols>40) {
-                label code_table = recognizeCharacter(char_instance.second);
-                res.first = char_instance.first;
-                code_table.copyTo(res.second);
-                plateinfo.appendPlateCoding(res);
-            } else{
-                res.first = INVALID;
-                plateinfo.appendPlateCoding(res);
-            }
-        }
-    }
-}
diff --git a/Prj-iOS/lpr/lpr/Source/src/SegmentationFreeRecognizer.cpp b/Prj-iOS/lpr/lpr/Source/src/SegmentationFreeRecognizer.cpp
deleted file mode 100755
index 2e6706d..0000000
--- a/Prj-iOS/lpr/lpr/Source/src/SegmentationFreeRecognizer.cpp
+++ /dev/null
@@ -1,89 +0,0 @@
-//
-// Created by Jack Yu on 28/11/2017.
-//
-#include "SegmentationFreeRecognizer.h"
-
-namespace pr {
-    SegmentationFreeRecognizer::SegmentationFreeRecognizer(std::string prototxt, std::string caffemodel) {
-        net = cv::dnn::readNetFromCaffe(prototxt, caffemodel);
-    }
-    inline int judgeCharRange(int id)
-    {return id<31 || id>63;
-    }
-    std::pair<std::string,float> decodeResults(cv::Mat code_table,std::vector<std::string> mapping_table,float thres)
-    {
-        cv::MatSize mtsize = code_table.size;
-        int sequencelength = mtsize[2];
-        int labellength = mtsize[1];
-        cv::transpose(code_table.reshape(1,1).reshape(1,labellength),code_table);
-        std::string name = "";
-        std::vector<int> seq(sequencelength);
-        std::vector<std::pair<int,float> > seq_decode_res;
-        for(int i = 0 ; i < sequencelength;  i++) {
-            float *fstart = ((float *) (code_table.data) + i * labellength );
-            int id = std::max_element(fstart,fstart+labellength) - fstart;
-            seq[i] =id;
-        }
-
-        float sum_confidence = 0;
-        int plate_lenghth  = 0 ;
-        for(int i = 0 ; i< sequencelength ; i++)
-        {
-            if(seq[i]!=labellength-1 && (i==0 || seq[i]!=seq[i-1]))
-            {
-                float *fstart = ((float *) (code_table.data) + i * labellength );
-                float confidence = *(fstart+seq[i]);
-                std::pair<int,float> pair_(seq[i],confidence);
-                seq_decode_res.push_back(pair_);
-            }
-        }
-        int  i = 0;
-        if (seq_decode_res.size()>1 && judgeCharRange(seq_decode_res[0].first) && judgeCharRange(seq_decode_res[1].first))
-        {
-            i=2;
-            int c = seq_decode_res[0].second<seq_decode_res[1].second;
-            name+=mapping_table[seq_decode_res[c].first];
-            sum_confidence+=seq_decode_res[c].second;
-            plate_lenghth++;
-        }
-
-        for(; i < seq_decode_res.size();i++)
-        {
-            name+=mapping_table[seq_decode_res[i].first];
-            sum_confidence +=seq_decode_res[i].second;
-            plate_lenghth++;
-        }
-        std::pair<std::string,float> res;
-        res.second = sum_confidence/plate_lenghth;
-        res.first = name;
-        return res;
-
-    }
-    std::string decodeResults(cv::Mat code_table,std::vector<std::string> mapping_table)
-    {
-        cv::MatSize mtsize = code_table.size;
-        int sequencelength = mtsize[2];
-        int labellength = mtsize[1];
-        cv::transpose(code_table.reshape(1,1).reshape(1,labellength),code_table);
-        std::string name = "";
-        std::vector<int> seq(sequencelength);
-        for(int i = 0 ; i < sequencelength;  i++) {
-            float *fstart = ((float *) (code_table.data) + i * labellength );
-            int id = std::max_element(fstart,fstart+labellength) - fstart;
-            seq[i] =id;
-        }
-        for(int i = 0 ; i< sequencelength ; i++)
-        {
-            if(seq[i]!=labellength-1 && (i==0 || seq[i]!=seq[i-1]))
-                name+=mapping_table[seq[i]];
-        }
-        return name;
-    }
-    std::pair<std::string,float> SegmentationFreeRecognizer::SegmentationFreeForSinglePlate(cv::Mat Image,std::vector<std::string> mapping_table) {
-        cv::transpose(Image,Image);
-        cv::Mat inputBlob = cv::dnn::blobFromImage(Image, 1 / 255.0, cv::Size(40,160));
-        net.setInput(inputBlob, "data");
-        cv::Mat char_prob_mat = net.forward();
-        return decodeResults(char_prob_mat,mapping_table,0.00);
-    }
-}
diff --git a/Prj-iOS/lpr/lpr/Source/src/util.h b/Prj-iOS/lpr/lpr/Source/src/util.h
deleted file mode 100755
index 3f9f52c..0000000
--- a/Prj-iOS/lpr/lpr/Source/src/util.h
+++ /dev/null
@@ -1,67 +0,0 @@
-//
-// Created by Jack Yu on 04/04/2017.
-//
-
-#include <opencv2/opencv.hpp>
-namespace util{
-    template <class T> void swap ( T& a, T& b )
-    {
-        T c(a); a=b; b=c;
-    }
-    template <class T> T min(T& a,T& b )
-    {
-        return a>b?b:a;
-    }
-
-    cv::Mat cropFromImage(const cv::Mat &image,cv::Rect rect){
-        int w = image.cols-1;
-        int h = image.rows-1;
-        rect.x = std::max(rect.x,0);
-        rect.y = std::max(rect.y,0);
-        rect.height = std::min(rect.height,h-rect.y);
-        rect.width = std::min(rect.width,w-rect.x);
-        cv::Mat temp(rect.size(), image.type());
-        cv::Mat cropped;
-        temp = image(rect);
-        temp.copyTo(cropped);
-        return cropped;
-
-    }
-
-    cv::Mat cropBox2dFromImage(const cv::Mat &image,cv::RotatedRect rect)
-    {
-        cv::Mat M, rotated, cropped;
-        float angle = rect.angle;
-        cv::Size rect_size(rect.size.width,rect.size.height);
-        if (rect.angle < -45.) {
-            angle += 90.0;
-            swap(rect_size.width, rect_size.height);
-        }
-        M = cv::getRotationMatrix2D(rect.center, angle, 1.0);
-        cv::warpAffine(image, rotated, M, image.size(), cv::INTER_CUBIC);
-        cv::getRectSubPix(rotated, rect_size, rect.center, cropped);
-        return cropped;
-    }
-
-    cv::Mat calcHist(const cv::Mat &image)
-    {
-        cv::Mat hsv;
-        std::vector<cv::Mat> hsv_planes;
-        cv::cvtColor(image,hsv,cv::COLOR_BGR2HSV);
-        cv::split(hsv,hsv_planes);
-        cv::Mat hist;
-        int histSize = 256;
-        float range[] = {0,255};
-        const float* histRange = {range};
-        cv::calcHist( &hsv_planes[0], 1, 0, cv::Mat(), hist, 1, &histSize, &histRange,true, true);
-        return hist;
-    }
-    
-    float computeSimilir(const cv::Mat &A,const cv::Mat &B)
-    {
-        cv::Mat histA,histB;
-        histA = calcHist(A);
-        histB = calcHist(B);
-        return cv::compareHist(histA,histB,CV_COMP_CORREL);
-    }
-}//namespace util
diff --git a/Prj-iOS/lpr/lpr/Utility.h b/Prj-iOS/lpr/lpr/Utility.h
deleted file mode 100755
index f2a870d..0000000
--- a/Prj-iOS/lpr/lpr/Utility.h
+++ /dev/null
@@ -1,30 +0,0 @@
-//
-//  Utility.h
-//  lpr
-//
-//  Created by baotim on 2018/10/26.
-//  Copyright © 2018 lprSample. All rights reserved.
-//
-#ifdef __cplusplus
-#import <opencv2/opencv.hpp>
-#endif
-#ifdef __OBJC__
-#import <UIKit/UIKit.h>
-#import <Foundation/Foundation.h>
-#endif
-using namespace cv;
-
-NS_ASSUME_NONNULL_BEGIN
-
-@interface Utility : NSObject
-
-+ (cv::Mat)cvMatFromUIImage:(UIImage *)image;
-+ (UIImage *)UIImageFromCVMat:(cv::Mat)image;
-+ (UIImage *)scaleAndRotateImageFrontCamera:(UIImage *)image;
-+ (UIImage *)scaleAndRotateImageBackCamera:(UIImage *)image;
-+ (UIImage *)imageWithMat:(const cv::Mat&) image andImageOrientation: (UIImageOrientation) orientation;
-+ (UIImage *)imageWithMat:(const cv::Mat&) image andDeviceOrientation: (UIDeviceOrientation) orientation;
-
-@end
-
-NS_ASSUME_NONNULL_END
diff --git a/Prj-iOS/lpr/lpr/Utility.mm b/Prj-iOS/lpr/lpr/Utility.mm
deleted file mode 100755
index d78785c..0000000
--- a/Prj-iOS/lpr/lpr/Utility.mm
+++ /dev/null
@@ -1,320 +0,0 @@
-//
-//  Utility.m
-//  lpr
-//
-//  Created by baotim on 2018/10/26.
-//  Copyright © 2018 lprSample. All rights reserved.
-//
-
-#import "Utility.h"
-
-@implementation Utility
-
-+ (UIImage *)UIImageFromCVMat:(cv::Mat)cvMat
-{
-    NSData *data = [NSData dataWithBytes:cvMat.data length:cvMat.elemSize()*cvMat.total()];
-    CGColorSpaceRef colorSpace;
-    
-    if (cvMat.elemSize() == 1) {
-        colorSpace = CGColorSpaceCreateDeviceGray();
-    } else {
-        colorSpace = CGColorSpaceCreateDeviceRGB();
-    }
-    
-    CGDataProviderRef provider = CGDataProviderCreateWithCFData((__bridge CFDataRef)data);
-    
-    // Creating CGImage from cv::Mat
-    CGImageRef imageRef = CGImageCreate(cvMat.cols,                                 //width
-                                        cvMat.rows,                                 //height
-                                        8,                                          //bits per component
-                                        8 * cvMat.elemSize(),                       //bits per pixel
-                                        cvMat.step[0],                            //bytesPerRow
-                                        colorSpace,                                 //colorspace
-                                        kCGImageAlphaNone|kCGBitmapByteOrderDefault,// bitmap info
-                                        provider,                                   //CGDataProviderRef
-                                        NULL,                                       //decode
-                                        false,                                      //should interpolate
-                                        kCGRenderingIntentDefault                   //intent
-                                        );
-    
-    // Getting UIImage from CGImage
-    UIImage *finalImage = [UIImage imageWithCGImage:imageRef scale:1.0 orientation:UIImageOrientationUp];
-    CGImageRelease(imageRef);
-    CGDataProviderRelease(provider);
-    CGColorSpaceRelease(colorSpace);
-    
-    return finalImage;
-}
-
-//缩放调整图片
-+ (UIImage *)scaleAndRotateImageBackCamera:(UIImage *)image
-{
-    static int kMaxResolution = 480;
-    CGImageRef imgRef = image.CGImage;
-    CGFloat width = CGImageGetWidth(imgRef);
-    CGFloat height = CGImageGetHeight(imgRef);
-    
-    CGAffineTransform transform = CGAffineTransformIdentity;
-    CGRect bounds = CGRectMake(0, 0, width, height);
-    if (width > kMaxResolution || height > kMaxResolution) {
-        CGFloat ratio = width/height;
-        if (ratio > 1) {
-            bounds.size.width = kMaxResolution;
-            bounds.size.height = bounds.size.width / ratio;
-        } else {
-            bounds.size.height = kMaxResolution;
-            bounds.size.width = bounds.size.height * ratio;
-        }
-    }
-    CGFloat scaleRatio = bounds.size.width / width;
-    CGSize imageSize = CGSizeMake(CGImageGetWidth(imgRef), CGImageGetHeight(imgRef));
-    CGFloat boundHeight;
-    UIImageOrientation orient = image.imageOrientation;
-    switch(orient) {
-        case UIImageOrientationUp:
-            transform = CGAffineTransformIdentity;
-            break;
-        case UIImageOrientationUpMirrored:
-            transform = CGAffineTransformMakeTranslation(imageSize.width, 0.0);
-            transform = CGAffineTransformScale(transform, -1.0, 1.0);
-            break;
-        case UIImageOrientationDown:
-            transform = CGAffineTransformMakeTranslation(imageSize.width, imageSize.height);
-            transform = CGAffineTransformRotate(transform, M_PI);
-            break;
-        case UIImageOrientationDownMirrored:
-            transform = CGAffineTransformMakeTranslation(0.0, imageSize.height);
-            transform = CGAffineTransformScale(transform, 1.0, -1.0);
-            break;
-        case UIImageOrientationLeftMirrored:
-            boundHeight = bounds.size.height;
-            bounds.size.height = bounds.size.width;
-            bounds.size.width = boundHeight;
-            transform = CGAffineTransformMakeTranslation(imageSize.height, imageSize.width);
-            transform = CGAffineTransformScale(transform, -1.0, 1.0);
-            transform = CGAffineTransformRotate(transform, 3.0 * M_PI / 2.0);
-            break;
-        case UIImageOrientationLeft:
-            boundHeight = bounds.size.height;
-            bounds.size.height = bounds.size.width;
-            bounds.size.width = boundHeight;
-            transform = CGAffineTransformMakeTranslation(0.0, imageSize.width);
-            transform = CGAffineTransformRotate(transform, 3.0 * M_PI / 2.0);
-            break;
-        case UIImageOrientationRightMirrored:
-            boundHeight = bounds.size.height;
-            bounds.size.height = bounds.size.width;
-            bounds.size.width = boundHeight;
-            transform = CGAffineTransformMakeScale(-1.0, 1.0);
-            transform = CGAffineTransformRotate(transform, M_PI / 2.0);
-            break;
-        case UIImageOrientationRight:
-            boundHeight = bounds.size.height;
-            bounds.size.height = bounds.size.width;
-            bounds.size.width = boundHeight;
-            transform = CGAffineTransformMakeTranslation(imageSize.height, 0.0);
-            transform = CGAffineTransformRotate(transform, M_PI / 2.0);
-            break;
-        default:
-            [NSException raise:NSInternalInconsistencyException format:@"Invalid image orientation"];
-    }
-    
-    UIGraphicsBeginImageContext(bounds.size);
-    CGContextRef context = UIGraphicsGetCurrentContext();
-    if (orient == UIImageOrientationRight || orient == UIImageOrientationLeft) {
-        CGContextScaleCTM(context, -scaleRatio, scaleRatio);
-        CGContextTranslateCTM(context, -height, 0);
-    } else {
-        CGContextScaleCTM(context, scaleRatio, -scaleRatio);
-        CGContextTranslateCTM(context, 0, -height);
-    }
-    CGContextConcatCTM(context, transform);
-    CGContextDrawImage(UIGraphicsGetCurrentContext(), CGRectMake(0, 0, width, height), imgRef);
-    UIImage *returnImage = UIGraphicsGetImageFromCurrentImageContext();
-    UIGraphicsEndImageContext();
-    NSLog(@"resize w%f,H%f",returnImage.size.width,returnImage.size.height);
-    return returnImage;
-}
-
-+ (UIImage*)imageWithMat:(const cv::Mat&) image andDeviceOrientation: (UIDeviceOrientation)orientation
-{
-    UIImageOrientation imgOrientation = UIImageOrientationUp;
-    
-    switch (orientation)
-    {
-        case UIDeviceOrientationLandscapeLeft:
-            imgOrientation =UIImageOrientationLeftMirrored; break;
-            
-        case UIDeviceOrientationLandscapeRight:
-            imgOrientation = UIImageOrientationDown; break;
-            
-        case UIDeviceOrientationPortraitUpsideDown:
-            imgOrientation = UIImageOrientationRightMirrored; break;
-        case UIDeviceOrientationFaceUp:
-            imgOrientation = UIImageOrientationRightMirrored; break;
-            
-        default:
-        case UIDeviceOrientationPortrait:
-            imgOrientation = UIImageOrientationRight; break;
-    };
-    
-    return [Utility imageWithMat:image andImageOrientation:imgOrientation];
-}
-
-+ (UIImage*)imageWithMat:(const cv::Mat&)image andImageOrientation:(UIImageOrientation)orientation;
-{
-    cv::Mat rgbaView;
-    
-    if (image.channels() == 3)
-    {
-        cv::cvtColor(image, rgbaView, COLOR_BGR2BGRA);
-    }
-    else if (image.channels() == 4)
-    {
-        cv::cvtColor(image, rgbaView, COLOR_BGR2BGRA);
-    }
-    else if (image.channels() == 1)
-    {
-        cv::cvtColor(image, rgbaView, COLOR_GRAY2RGBA);
-    }
-    
-    NSData *data = [NSData dataWithBytes:rgbaView.data length:rgbaView.elemSize() * rgbaView.total()];
-    
-    CGColorSpaceRef colorSpace = CGColorSpaceCreateDeviceRGB();
-    
-    CGDataProviderRef provider = CGDataProviderCreateWithCFData((__bridge CFDataRef)data);
-    
-    CGBitmapInfo bmInfo = kCGImageAlphaPremultipliedLast | kCGBitmapByteOrder32Big;
-    
-    // Creating CGImage from cv::Mat
-    CGImageRef imageRef = CGImageCreate(rgbaView.cols,                                 //width
-                                        rgbaView.rows,                                 //height
-                                        8,                                          //bits per component
-                                        8 * rgbaView.elemSize(),                       //bits per pixel
-                                        rgbaView.step.p[0],                            //bytesPerRow
-                                        colorSpace,                                 //colorspace
-                                        bmInfo,// bitmap info
-                                        provider,                                   //CGDataProviderRef
-                                        NULL,                                       //decode
-                                        false,                                      //should interpolate
-                                        kCGRenderingIntentDefault                   //intent
-                                        );
-    
-    // Getting UIImage from CGImage
-    UIImage *finalImage = [UIImage imageWithCGImage:imageRef scale:1 orientation:orientation];
-    CGImageRelease(imageRef);
-    CGDataProviderRelease(provider);
-    CGColorSpaceRelease(colorSpace);
-    
-    return finalImage;
-}
-
-+ (cv::Mat)cvMatFromUIImage:(UIImage *)image
-{
-    CGColorSpaceRef colorSpace = CGImageGetColorSpace(image.CGImage);
-    CGFloat cols = image.size.width;
-    CGFloat rows = image.size.height;
-    
-    cv::Mat cvMat(rows, cols, CV_8UC4); // 8 bits per component, 4 channels
-    
-    CGContextRef contextRef = CGBitmapContextCreate(cvMat.data,                 // Pointer to  data
-                                                    cols,                       // Width of bitmap
-                                                    rows,                       // Height of bitmap
-                                                    8,                          // Bits per component
-                                                    cvMat.step[0],              // Bytes per row
-                                                    colorSpace,                 // Colorspace
-                                                    kCGImageAlphaNoneSkipLast |
-                                                    kCGBitmapByteOrderDefault); // Bitmap info flags
-    
-    CGContextDrawImage(contextRef, CGRectMake(0, 0, cols, rows), image.CGImage);
-    CGContextRelease(contextRef);
-    CGColorSpaceRelease(colorSpace);
-    cv::Mat cvMat3(rows, cols, CV_8UC3); // 8 bits per component, 4 channels
-    cv::cvtColor(cvMat, cvMat3,COLOR_RGBA2RGB);
-    
-    return cvMat3;
-}
-
-+ (UIImage *)scaleAndRotateImageFrontCamera:(UIImage *)image
-{
-    static int kMaxResolution = 640;
-    CGImageRef imgRef = image.CGImage;
-    CGFloat width = CGImageGetWidth(imgRef);
-    CGFloat height = CGImageGetHeight(imgRef);
-    CGAffineTransform transform = CGAffineTransformIdentity;
-    CGRect bounds = CGRectMake( 0, 0, width, height);
-    if (width > kMaxResolution || height > kMaxResolution) {
-        CGFloat ratio = width/height;
-        if (ratio > 1) {
-            bounds.size.width = kMaxResolution;
-            bounds.size.height = bounds.size.width / ratio;
-        } else {
-            bounds.size.height = kMaxResolution;
-            bounds.size.width = bounds.size.height * ratio;
-        }
-    }
-    
-    CGFloat scaleRatio = bounds.size.width / width;
-    CGSize imageSize = CGSizeMake(CGImageGetWidth(imgRef), CGImageGetHeight(imgRef));
-    CGFloat boundHeight;
-    UIImageOrientation orient = image.imageOrientation;
-    switch(orient) {
-        case UIImageOrientationUp:
-            transform = CGAffineTransformIdentity;
-            break;
-        case UIImageOrientationUpMirrored:
-            transform = CGAffineTransformMakeTranslation(imageSize.width, 0.0);
-            transform = CGAffineTransformScale(transform, -1.0, 1.0);
-            break;
-        case UIImageOrientationDown:
-            transform = CGAffineTransformMakeTranslation(imageSize.width, imageSize.height);
-            transform = CGAffineTransformRotate(transform, M_PI);
-            break;
-        case UIImageOrientationDownMirrored:
-            transform = CGAffineTransformMakeTranslation(0.0, imageSize.height);
-            transform = CGAffineTransformScale(transform, 1.0, -1.0);
-            break;
-        case UIImageOrientationLeftMirrored:
-            boundHeight = bounds.size.height;
-            bounds.size.height = bounds.size.width;
-            bounds.size.width = boundHeight;
-            transform = CGAffineTransformMakeTranslation(imageSize.height, imageSize.width);
-            transform = CGAffineTransformScale(transform, -1.0, 1.0);
-            transform = CGAffineTransformRotate(transform, 3.0 * M_PI / 2.0);
-            break;
-        case UIImageOrientationLeft:
-            boundHeight = bounds.size.height;
-            bounds.size.height = bounds.size.width;
-            bounds.size.width = boundHeight;
-            transform = CGAffineTransformMakeTranslation(0.0, imageSize.width);
-            transform = CGAffineTransformRotate(transform, 3.0 * M_PI / 2.0);
-            break;
-        case UIImageOrientationRight:
-        case UIImageOrientationRightMirrored:
-            boundHeight = bounds.size.height;
-            bounds.size.height = bounds.size.width;
-            bounds.size.width = boundHeight;
-            transform = CGAffineTransformMakeScale(-1.0, 1.0);
-            transform = CGAffineTransformRotate(transform, M_PI / 2.0);
-            break;
-        default:
-            [NSException raise:NSInternalInconsistencyException format:@"Invalid image orientation"];
-    }
-    UIGraphicsBeginImageContext( bounds.size );
-    CGContextRef context = UIGraphicsGetCurrentContext();
-    if ( orient == UIImageOrientationRight || orient == UIImageOrientationLeft ) {
-        CGContextScaleCTM(context, -scaleRatio, scaleRatio);
-        CGContextTranslateCTM(context, -height, 0);
-    }
-    else {
-        CGContextScaleCTM(context, scaleRatio, -scaleRatio);
-        CGContextTranslateCTM(context, 0, -height);
-    }
-    CGContextConcatCTM( context, transform );
-    CGContextDrawImage( UIGraphicsGetCurrentContext(), CGRectMake(0, 0, width, height), imgRef );
-    UIImage *returnImage = UIGraphicsGetImageFromCurrentImageContext();
-    UIGraphicsEndImageContext();
-    return returnImage;
-}
-
-@end
diff --git a/Prj-iOS/lpr/lpr/main.m b/Prj-iOS/lpr/lpr/main.m
deleted file mode 100755
index 5e5e2ce..0000000
--- a/Prj-iOS/lpr/lpr/main.m
+++ /dev/null
@@ -1,16 +0,0 @@
-//
-//  main.m
-//  lpr
-//
-//  Created by baotim on 2018/10/26.
-//  Copyright © 2018 lprSample. All rights reserved.
-//
-
-#import <UIKit/UIKit.h>
-#import "AppDelegate.h"
-
-int main(int argc, char * argv[]) {
-    @autoreleasepool {
-        return UIApplicationMain(argc, argv, nil, NSStringFromClass([AppDelegate class]));
-    }
-}
diff --git a/Prj-iOS/lpr/main.m b/Prj-iOS/lpr/main.m
deleted file mode 100755
index 5e5e2ce..0000000
--- a/Prj-iOS/lpr/main.m
+++ /dev/null
@@ -1,16 +0,0 @@
-//
-//  main.m
-//  lpr
-//
-//  Created by baotim on 2018/10/26.
-//  Copyright © 2018 lprSample. All rights reserved.
-//
-
-#import <UIKit/UIKit.h>
-#import "AppDelegate.h"
-
-int main(int argc, char * argv[]) {
-    @autoreleasepool {
-        return UIApplicationMain(argc, argv, nil, NSStringFromClass([AppDelegate class]));
-    }
-}
diff --git a/README.md b/README.md
index 390c47f..e0ff79f 100644
--- a/README.md
+++ b/README.md
@@ -1,35 +1,60 @@
-![logo_t](./demo_images/logo.png)
+![logo_t](./images/logo.png)
 
-## HyperLPR   高性能开源中文车牌识别框架
+## HyperLPR3   高性能开源中文车牌识别框架
 
-#### [![1](https://badge.fury.io/py/hyperlpr.svg "title")](https://pypi.org/project/hyperlpr/)[![1](https://img.shields.io/pypi/pyversions/hyperlpr.svg "title")](https://pypi.org/project/hyperlpr/)
+#### [![1](https://badge.fury.io/py/hyperlpr3.svg "title")](https://pypi.org/project/hyperlpr3/)[![1](https://img.shields.io/pypi/pyversions/hyperlpr3.svg "title")](https://pypi.org/manage/project/hyperlpr3/releases/)
 
 ### 一键安装
 
-`python -m pip install hyperlpr`
+`python -m pip install hyperlpr3`
+
+###### 支持python3, 支持Windows  Mac Linux 树莓派等。
 
-###### 支持python3,支持Windows  Mac Linux 树莓派等。
 
 ###### 720p cpu real-time (st on MBP r15 2.2GHz haswell).
 
+#### 快速体验
+
+```bash
+# 使用命令行测试 - 图像url地址
+lpr3 sample -src https://tunm.oss-cn-hangzhou.aliyuncs.com/hyperlpr3/test_folder/plate_test.png
+
+# 使用命令行测试 - 本地图像路径
+lpr3 sample -src images/test_img.jpg -det high
+```
+
 #### 快速上手
 
 ```python
-#导入包
-from hyperlpr import *
-#导入OpenCV库
+# 导入opencv库
 import cv2
-#读入图片
-image = cv2.imread("demo.jpg")
-#识别结果
-print(HyperLPR_plate_recognition(image))
+# 导入依赖包
+import hyperlpr3 as lpr3
+
+# 实例化识别对象
+catcher = lpr3.LicensePlateCatcher()
+# 读取图片
+image = cv2.imread("images/test_img.jpg")
+# 识别结果
+print(catcher(image))
+
+``` 
+#### 启动WebApi服务
+
+```bash
+# 启动服务
+lpr3 rest --port 8715 --host 0.0.0.0
 ```
+启动后可打开SwaggerUI的路径：[http://localhost:8715/api/v1/docs](http://localhost:8715/api/v1/docs) 查看和测试在线识别API服务:
+
+![swagger_ui](./images/swagger-ui.png)
+
 
 #### Q&A
 
 Q：Android识别率没有所传demo apk的识别率高？
 
-A：请使用[Prj-Linux](https://github.com/zeusees/HyperLPR/tree/master/Prj-Linux/lpr/model)下的模型，android默认包里的配置是相对较早的模型
+A：请自行编译或从release中下载安卓动态库放置于Prj-Android中进行测试。
 
 Q：车牌的训练数据来源？
 
@@ -37,7 +62,7 @@ A：由于用于训练车牌数据涉及到法律隐私等问题，本项目无
 
 Q：训练代码的提供？
 
-A：相关资源中有提供训练代码
+A：相关资源中有提供老版的训练代码，HyperLPR3的训练方法会陆续整理并给出。
 
 Q：关于项目的来源？
 
@@ -46,134 +71,166 @@ A：此项目来源于作者早期的研究和调试代码，代码缺少一定
 
 #### 相关资源
 
-- [Android配置教程](https://www.jianshu.com/p/94784c3bf2c1)
-- [python配置教程](https://www.jianshu.com/p/7ab673abeaae)
-- [Linux下C++配置教程](https://blog.csdn.net/lu_linux/article/details/88707421)
-- [ROS包配置教程](./Prj-ROS/README.md)
-- [带UI界面的工程](https://pan.baidu.com/s/1cNWpK6)(感谢群内小伙伴的工作)。
-- [端到端(多标签分类)训练代码](https://github.com/LCorleone/hyperlpr-train_e2e)(感谢群内小伙伴的工作)。
-- [端到端(CTC)训练代码](https://github.com/armaab/hyperlpr-train)(感谢群内小伙伴工作)。
-
-### 更新
-
-- [增加了ROS包](
-https://github.com/BSSNBSSN/HyperLPR-ROS) (2022.09.17)
-- 更新了Android实现，增加实时扫描接口 (2019.07.24)
-- 更新Windows版本的Visual Studio 2015 工程至端到端模型（2019.07.03）
-- 更新基于端到端的IOS车牌识别工程。(2018.11.13)
-- 可通过pip一键安装、更新的新的识别模型、倾斜车牌校正算法、定位算法。(2018.08.11)
-- 提交新的端到端识别模型，进一步提高识别准确率(2018.08.03)
-- [增加PHP车牌识别工程@coleflowers](https://github.com/zeusees/HyperLPR/tree/master/Prj-PHP) (2018.06.20)
-- 添加了HyperLPR Lite 仅仅需160 行代码即可实现车牌识别(2018.3.12)
-- 感谢 sundyCoder [Android 字符分割版本](https://github.com/sundyCoder/hyperlpr4Android) 
-- 增加字符分割[训练代码和字符分割介绍](https://github.com/zeusees/HyperLPR-Training)(2018.1.)
+- [HyperLPRv1版](https://github.com/szad670401/HyperLPR/tree/v1)
 
+- [HyperLPRv2版](https://github.com/szad670401/HyperLPR/tree/v2)
 
 ### TODO
 
+- 支持快速部署WebApi服务
 - 支持多种车牌以及双层
 - 支持大角度车牌
 - 轻量级识别模型
 
+
 ### 特性
 
 - 速度快 720p,单核 Intel 2.2G CPU (MaBook Pro 2015)平均识别时间低于100ms
 - 基于端到端的车牌识别无需进行字符分割
 - 识别率高,卡口场景准确率在95%-97%左右
-- 轻量,总代码量不超1k行
-
-### 模型资源说明
-
-- cascade.xml  检测模型 - 目前效果最好的cascade检测模型
-- cascade_lbp.xml  召回率效果较好，但其错检太多
-- char_chi_sim.h5 Keras模型-可识别34类数字和大写英文字  使用14W样本训练 
-- char_rec.h5 Keras模型-可识别34类数字和大写英文字  使用7W样本训练 
-- ocr_plate_all_w_rnn_2.h5 基于CNN的序列模型
-- ocr_plate_all_gru.h5 基于GRU的序列模型从OCR模型修改，效果目前最好但速度较慢，需要20ms。
-- plate_type.h5 用于车牌颜色判断的模型
-- model12.h5 左右边界回归模型
+- 支持跨平台编译和快速部署
 
 ### 注意事项:
 
-- Win工程中若需要使用静态库，需单独编译
 - 本项目的C++实现和Python实现无任何关联，都为单独实现
-- 在编译C++工程的时候必须要使用OpenCV 3.3以上版本 (DNN 库)，否则无法编译 
-- 安卓工程编译ndk尽量采用14b版本
+- 在编译C++工程的时候必须要使用OpenCV 4.0和MNN 2.0以上版本，否则无法编译 
+- 安卓工程编译ndk尽量采用21版本
 
 ### Python 依赖
-
-- Keras (>2.0.0)
-- Theano(>0.9) or Tensorflow(>1.1.x)
-- Numpy (>1.10)
-- Scipy (0.19.1)
-- OpenCV(>3.0)
-- Scikit-image (0.13.0)
-- PIL
+- opencv-python (>3.3)
+- onnxruntime (>1.8.1)
+- fastapi (0.92.0)
+- uvicorn (0.20.0)
+- loguru (0.6.0)
+- python-multipart
+- tqdm
+- requests 
 
 ### CPP 依赖
 
-- Opencv 3.4 以上版本
+- Opencv 4.0 以上版本
+- MNN 2.0 以上版本
+
+### C/C++编译依赖库
 
-### Linux/Mac 编译
+编译C/C++工程需要使用第三方依赖库，将库下载后解压，并将其通过拷贝或软链接放入根目录(与CMakeLists.txt同级)即可，依赖的库下载地址：[百度网盘](https://pan.baidu.com/s/1hLzumPbrrnuI__CYv_ognA) code: kdae
 
-- 仅需要的依赖OpenCV 3.4 (需要DNN框架)
+### Linux/Mac动态链接库编译
+
+- 需要将依赖库放置或链接在项目根目录下(与CMakeLists.txt同级)
 
 ```bash
-cd Prj-Linux
-mkdir build 
-cd build
-cmake ../
-sudo make -j 
+# 执行编译脚本
+sh command/build_release_linux_share.sh
+
 ```
+编译后的相关物料放置于根目录下**build/linux/install/hyperlpr3**中，其中包含：
+- include 头文件
+- lib 动态库路径
+- resource 包含测试图片与模型等静态资源
+
+按需取走需要的文件即可
+
+### Linux/Mac编译Demo
 
-### CPP demo
-
-```cpp
-#include "../include/Pipeline.h"
-int main(){
-    pr::PipelinePR prc("model/cascade.xml",
-                      "model/HorizonalFinemapping.prototxt","model/HorizonalFinemapping.caffemodel",
-                      "model/Segmentation.prototxt","model/Segmentation.caffemodel",
-                      "model/CharacterRecognization.prototxt","model/CharacterRecognization.caffemodel",
-                       "model/SegmenationFree-Inception.prototxt","model/SegmenationFree-Inception.caffemodel"
-                    );
-  //定义模型文件
-
-    cv::Mat image = cv::imread("test.png");
-    std::vector<pr::PlateInfo> res = prc.RunPiplineAsImage(image,pr::SEGMENTATION_FREE_METHOD);
-  //使用端到端模型模型进行识别 识别结果将会保存在res里面
- 
-    for(auto st:res) {
-        if(st.confidence>0.75) {
-            std::cout << st.getPlateName() << " " << st.confidence << std::endl;
-          //输出识别结果 、识别置信度
-            cv::Rect region = st.getPlateRect();
-          //获取车牌位置
- cv::rectangle(image,cv::Point(region.x,region.y),cv::Point(region.x+region.width,region.y+region.height),cv::Scalar(255,255,0),2);
-          //画出车牌位置
-          
-        }
+- 需要完成上一步的编译动作，并保证编译成功且编译完成后的物料放置于根目录下的**build/linux/install/hyperlpr3**路径中
+- 需要从根目录中进入到子工程**Prj-Linux**文件夹中进行操作
+```bash
+# 进入到子工程demo
+cd Prj-Linux
+# 创建build文件夹并进入
+mkdir build & cd build
+# 开始编译
+cmake .. & make -j
+```
+编译完成后生成可执行程序**PlateRecDemo**，执行运行测试
+```bash
+# 传入模型文件夹路径和需要预测的图像执行程序
+./PlateRecDemo ../hyperlpr3/resource/models/r2_mobile ../hyperlpr3/resource/images/test_img.jpg
+```
+### Linux/Mac快速使用SDK代码示例
+```C
+// 读取图像
+cv::Mat image = cv::imread(image_path);
+// 创建ImageData
+HLPR_ImageData data = {0};
+data.data = image.ptr<uint8_t>(0);      // 设置图像数据流
+data.width = image.cols;                   // 设置图像宽
+data.height = image.rows;                  // 设置图像高
+data.format = STREAM_BGR;                  // 设置当前图像编码格式
+data.rotation = CAMERA_ROTATION_0;         // 设置当前图像转角
+// 创建数据Buffer
+P_HLPR_DataBuffer buffer = HLPR_CreateDataBuffer(&data);
+
+// 配置车牌识别参数
+HLPR_ContextConfiguration configuration = {0};
+configuration.models_path = model_path;         // 模型文件夹路径
+configuration.max_num = 5;                      // 最大识别车牌数量
+configuration.det_level = DETECT_LEVEL_LOW;     // 检测器等级
+configuration.use_half = false;
+configuration.nms_threshold = 0.5f;             // 非极大值抑制置信度阈值
+configuration.rec_confidence_threshold = 0.5f;  // 车牌号文本阈值
+configuration.box_conf_threshold = 0.30f;       // 检测器阈值
+configuration.threads = 1;
+// 实例化车牌识别算法Context
+P_HLPR_Context ctx = HLPR_CreateContext(&configuration);
+// 查询实例化状态
+HREESULT ret = HLPR_ContextQueryStatus(ctx);
+if (ret != HResultCode::Ok) {
+    printf("create error.\n");
+    return -1;
+}
+HLPR_PlateResultList results = {0};
+// 执行车牌识别算法
+HLPR_ContextUpdateStream(ctx, buffer, &results);
+
+for (int i = 0; i < results.plate_size; ++i) {
+	// 解析识别后的数据
+    std::string type;
+    if (results.plates[i].type == HLPR_PlateType::PLATE_TYPE_UNKNOWN) {
+        type = "未知";
+    } else {
+        type = TYPES[results.plates[i].type];
     }
 
-    cv::imshow("image",image);
-    cv::waitKey(0);
-    return 0 ;
+    printf("<%d> %s, %s, %f\n", i + 1, type.c_str(),
+           results.plates[i].code, results.plates[i].text_confidence);
 }
+
+// 销毁Buffer
+HLPR_ReleaseDataBuffer(buffer);
+// 销毁Context
+HLPR_ReleaseContext(ctx);
 ```
 
+### Android编译动态链接库
+- 需要完成上一步的编译动作，并保证编译成功且编译完成后的物料放置于根目录下的**build/linux/install/hyperlpr3**路径中
+```bash
+# 执行编译脚本
+sh command/build_release_android_share.sh
+```
+编译完成后android的动态库会放置于**build/release_android/**，其中包含：
+- arm64-v8a 64位的动态库
+- armeabi-v7a 32位的动态库
+
+完成Android的动态库编译后，将**arm64-v8a**和**armeabi-v7a**文件夹放置于子项目路径**Prj-Android/hyperlpr3/libs**中，再编译android项目即可使用。**Prj-Android**项目中已内置hyperlpr3的SDK与使用demo。
+
 ###  
 
 ### 可识别和待支持的车牌的类型
 
+#### 支持
 - [x] 单行蓝牌
 - [x] 单行黄牌
 - [x] 新能源车牌
+- [x] 教练车牌
+#### 有限支持
 - [x] 白色警用车牌
 - [x] 使馆/港澳车牌
-- [x] 教练车牌
-- [ ] 武警车牌
-- [ ] 民航车牌
 - [x] 双层黄牌
+- [x] 武警车牌
+#### 待支持
+- [ ] 民航车牌
 - [ ] 双层武警
 - [ ] 双层军牌
 - [ ] 双层农用车牌
@@ -181,36 +238,12 @@ int main(){
 
 ###### Note:由于训练的时候样本存在一些不均衡的问题,一些特殊车牌存在一定识别率低下的问题，如(使馆/港澳车牌)，会在后续的版本进行改进。
 
-### 测试样例
-
-![image](./demo_images/demo1.png)
 
-![image](./demo_images/demo2.jpg)
-
-#### Android示例
-
-![android](./demo_images/android.png)
-
-### 识别测试APP
+### 识别测试APP（老版）
 
 - 体验 Android APP：[http://demo.zeusee.com/HyperLPR](http://demo.zeusee.com/HyperLPR) (根据图片尺寸调整程序中的尺度，提高准确率)
 
 #### 获取帮助
 
-- HyperAI讨论QQ群: 529385694
-
-### 作者和贡献者信息：
-
-##### 作者昵称不分前后
+- HyperAI讨论QQ群: 529385694，加前请备注HyperLPR交流
 
-- Jack Yu 作者(jack-yu-business@foxmail.com / https://github.com/szad670401)
-- 肖潇 (xiaoxiao@xidian.edu.cn)
-- AlanNewImage v2版win工程、python双层完善 (https://github.com/AlanNewImage)
-- lsy17096535 整理(https://github.com/lsy17096535)
-- xiaojun123456 IOS贡献(https://github.com/xiaojun123456)
-- sundyCoder Android第三方贡献(https://github.com/sundyCoder)
-- coleflowers php贡献(@coleflowers)
-- Free&Easy 资源贡献 
-- 海豚嘎嘎 LBP cascade检测器训练
-- Windows工程端到端模型 (https://github.com/SalamanderEyes)
-- Android实时扫描实现 (https://github.com/lxhAndSmh)
diff --git a/WebAPI.py b/WebAPI.py
deleted file mode 100644
index a127e2c..0000000
--- a/WebAPI.py
+++ /dev/null
@@ -1,58 +0,0 @@
-#coding=utf-8
-from flask import Flask, render_template, request
-from werkzeug.utils import secure_filename
-
-import cv2
-import numpy as np
-
-#导入opencv
-
-from hyperlpr_py3 import pipline
-#导入车牌识别库
-
-
-app = Flask(__name__)
-#设置App name
-
-
-def recognize(filename):
-    image = cv2.imread(filename)
-    #通过文件名读入一张图片 放到 image中
-    return pipline.RecognizePlateJson(image)
-    #识别一张图片并返回json结果
-
-#识别函数
-
-import base64
-
-
-def recognizeBase64(base64_code):
-    file_bytes = np.asarray(bytearray(base64.b64decode(base64_code)),dtype=np.uint8)
-    image_data_ndarray = cv2.imdecode(file_bytes,1)
-    return pipline.RecognizePlateJson(image_data_ndarray)
-
-
-import time
-
-@app.route('/uploader', methods=['GET', 'POST'])#设置请求路由
-def upload_file():
-    if request.method == 'POST':
-        #如果请求方法是POST
-        f = request.files['file']
-        f.save("./images_rec/"+secure_filename(f.filename))
-        #保存请求上来的文件
-        t0 = time.time()
-        res = recognize("./images_rec/"+secure_filename(f.filename))
-        print("识别时间",time.time() - t0)
-        return res
-        #返回识别结果
-
-        # return 'file uploaded successfully'
-    return render_template('upload.html')
-
-
-if __name__ == '__main__':
-    #入口函数
-    app.run("0.0.0.0", port=8000, threaded=False, debug=False)
-    #运行app 指定IP 指定端口
-
diff --git a/command/build_arm7.sh b/command/build_arm7.sh
new file mode 100644
index 0000000..ddfbd2f
--- /dev/null
+++ b/command/build_arm7.sh
@@ -0,0 +1,11 @@
+mkdir build/arm32
+cd build/arm32
+# export cross_compile_toolchain=/home/s4129/software/gcc-arm-8.3-2019.03-x86_64-arm-linux-gnueabihf
+ cmake -DCMAKE_SYSTEM_NAME=Linux \
+	-DCMAKE_SYSTEM_VERSION=1 \
+	-DCMAKE_SYSTEM_PROCESSOR=armv7 \
+	-DCMAKE_C_COMPILER=$cross_compile_toolchain/bin/arm-linux-gnueabihf-gcc \
+  -DCMAKE_CXX_COMPILER=$cross_compile_toolchain/bin/arm-linux-gnueabihf-g++ \
+	-DBUILD_LINUX_ARM7=ON ../../
+
+make -j4
diff --git a/command/build_release_android_share.sh b/command/build_release_android_share.sh
new file mode 100644
index 0000000..c58efc7
--- /dev/null
+++ b/command/build_release_android_share.sh
@@ -0,0 +1,47 @@
+#!/bin/bash
+
+set -ex
+RELEASE_HOME=$(cd $(dirname $0)/..; pwd)
+
+BUILD_3RDPARTY_PATH="${RELEASE_HOME}/3rdparty_hyper_inspire_op/"
+OPENCV_DIR=${BUILD_3RDPARTY_PATH}/opencv-4.5.1/opencv-4.5.1-android-sdk/sdk/native/jni
+
+BUILD_DIR=${RELEASE_HOME}/build/release_android
+
+[[ -d ${BUILD_DIR} ]] && rm -r ${BUILD_DIR}
+
+build() {
+    arch=$1
+    NDK_API_LEVEL=$2
+    mkdir -p ${BUILD_DIR}/${arch}
+    pushd ${BUILD_DIR}/${arch}
+    cmake ${RELEASE_HOME} \
+        -G "Unix Makefiles" \
+        -DCMAKE_BUILD_TYPE=Release \
+        -DCMAKE_TOOLCHAIN_FILE=${ANDROID_NDK}/build/cmake/android.toolchain.cmake \
+        -DANDROID_TOOLCHAIN=clang \
+        -DANDROID_ABI=${arch} \
+        -DANDROID_NATIVE_API_LEVEL=${NDK_API_LEVEL} \
+        -DANDROID_STL=c++_static \
+        -DBUILD_CUDA=OFF \
+        -DBUILD_SAMPLES=OFF \
+        -DBUILD_TEST=OFF \
+        -DOpenCV_DIR=${OPENCV_DIR} \
+        -DMNN_LIBS=${BUILD_3RDPARTY_PATH}/MNN-2.2.0/android-static/${arch} \
+        -DMNN_INCLUDE_DIRS=${BUILD_3RDPARTY_PATH}/MNN-2.2.0/android-static/include
+#        -DNCNN_DIR=${RELEASE_HOME}/3rdparty/ncnn/android/${arch} \
+    make -j$(nproc) hyperlpr3
+    ls ${BUILD_DIR}/${arch}| grep -v so| xargs rm -r
+    #make -j$(nproc) track_tool
+    popd
+}
+
+
+build arm64-v8a 21
+build armeabi-v7a 21
+
+date -R > ${BUILD_DIR}/release_note.txt
+cd ${BUILD_DIR}
+find . -type f |xargs md5sum >>release_note.txt
+cd -
+
diff --git a/command/build_release_linux_share.sh b/command/build_release_linux_share.sh
new file mode 100644
index 0000000..1b16df8
--- /dev/null
+++ b/command/build_release_linux_share.sh
@@ -0,0 +1,9 @@
+
+BUILD_DIR=build/linux
+mkdir -p ${BUILD_DIR}
+cd ${BUILD_DIR}
+
+cmake -DBUILD_SHARE=ON -DBUILD_SAMPLES=OFF -DBUILD_TEST=OFF ../..
+make -j4
+make install
+
diff --git a/cpp/c_api/hyper_lpr_sdk.cc b/cpp/c_api/hyper_lpr_sdk.cc
new file mode 100644
index 0000000..f8663b2
--- /dev/null
+++ b/cpp/c_api/hyper_lpr_sdk.cc
@@ -0,0 +1,147 @@
+//
+// Created by tunm on 2023/1/25.
+//
+
+#include "hyper_lpr_sdk_internal.h"
+#include "hyper_lpr_sdk.h"
+#include "context_module/all.h"
+
+
+P_HLPR_DataBuffer HLPR_CreateDataBufferEmpty() {
+    return new HLPR_DataBuffer();
+}
+
+HREESULT HLPR_DataBufferSetData(
+        P_HLPR_DataBuffer buffer,               // [in] CameraStream handle - 相机流组件的句柄指针
+        const uint8_t *data,                    // [in] Raw data stream - 原始的数据流
+        int width,                              // [in] Image width - 图像宽度
+        int height                              // [in] Image height - 图像高度
+) {
+    buffer->impl.SetDataBuffer(data, height, width);
+
+    return hyper::hRetOk;
+}
+
+HREESULT HLPR_DataBufferSetRotationMode(
+        P_HLPR_DataBuffer buffer,              // [in] CameraStream handle - 相机流组件的句柄指针
+        HLPR_Rotation mode                     // [in] CameraRotation mode - 相机流组件旋转模式
+) {
+    if (mode == 1) {
+        buffer->impl.SetRotationMode(hyper::ROTATION_90);
+    } else if (mode == 2) {
+        buffer->impl.SetRotationMode(hyper::ROTATION_180);
+    } else if (mode == 3) {
+        buffer->impl.SetRotationMode(hyper::ROTATION_270);
+    } else {
+        buffer->impl.SetRotationMode(hyper::ROTATION_0);
+    }
+
+    return hyper::hRetOk;
+}
+
+HREESULT HLPR_DataBufferSetStreamFormat(
+        P_HLPR_DataBuffer buffer,            // [in] CameraStream handle - 相机流组件的句柄指针
+        HLPR_ImageFormat mode                       // [in] CameraRotation data format - 相机流组件数据格式
+) {
+    // STREAM_RGB = 0,
+    // STREAM_BGR = 1,
+    // STREAM_RGBA = 2,
+    // STREAM_BGRA = 3,
+    // STREAM_YUV_NV12 = 4,
+    // STREAM_YUV_NV21 = 5,
+    if (mode == 0) {
+        buffer->impl.SetDataFormat(hyper::RGB);
+    } else if (mode == 1) {
+        buffer->impl.SetDataFormat(hyper::BGR);
+    } else if (mode == 2) {
+        buffer->impl.SetDataFormat(hyper::RGBA);
+    } else if (mode == 3) {
+        buffer->impl.SetDataFormat(hyper::BGRA);
+    } else if (mode == 4) {
+        buffer->impl.SetDataFormat(hyper::NV12);
+    } else if (mode == 5) {
+        buffer->impl.SetDataFormat(hyper::NV21);
+    }
+
+    return hyper::hRetOk;
+}
+
+P_HLPR_DataBuffer HLPR_CreateDataBuffer(P_HLPR_ImageData data) {
+    auto buffer = new HLPR_DataBuffer();
+    if (data->rotation == 1) {
+        buffer->impl.SetRotationMode(hyper::ROTATION_90);
+    } else if (data->rotation == 2) {
+        buffer->impl.SetRotationMode(hyper::ROTATION_180);
+    } else if (data->rotation == 3) {
+        buffer->impl.SetRotationMode(hyper::ROTATION_270);
+    } else {
+        buffer->impl.SetRotationMode(hyper::ROTATION_0);
+    }
+    if (data->format == 0) {
+        buffer->impl.SetDataFormat(hyper::RGB);
+    } else if (data->format == 1) {
+        buffer->impl.SetDataFormat(hyper::BGR);
+    } else if (data->format == 2) {
+        buffer->impl.SetDataFormat(hyper::RGBA);
+    } else if (data->format == 3) {
+        buffer->impl.SetDataFormat(hyper::BGRA);
+    } else if (data->format == 4) {
+        buffer->impl.SetDataFormat(hyper::NV12);
+    } else if (data->format == 5) {
+        buffer->impl.SetDataFormat(hyper::NV21);
+    }
+    buffer->impl.SetDataBuffer(data->data, data->height, data->width);
+
+    return buffer;
+}
+
+HREESULT HLPR_ReleaseDataBuffer(P_HLPR_DataBuffer buffer) {
+    delete buffer;
+
+    return hyper::hRetOk;
+}
+
+
+P_HLPR_Context HLPR_CreateContext(P_HLPR_ContextConfiguration configuration) {
+    auto ctx = new HLPR_Context();
+    ctx->impl.Initialize(
+            configuration->models_path,
+            configuration->max_num,
+            hyper::DetectLevel(configuration->det_level),
+            configuration->threads,
+            configuration->use_half,
+            configuration->box_conf_threshold,
+            configuration->nms_threshold,
+            configuration->rec_confidence_threshold);
+
+    return ctx;
+}
+
+
+HREESULT HLPR_ContextUpdateStream(P_HLPR_Context ctx, P_HLPR_DataBuffer buffer, P_HLPR_PlateResultList results) {
+    ctx->impl(buffer->impl);
+    auto &list = ctx->impl.getMObjectResults();
+    results->plate_size = list.size();
+    results->plates = (P_HLPR_PlateResult) list.data();
+
+    return hyper::hRetOk;
+}
+
+HREESULT HLPR_ContextQueryStatus(P_HLPR_Context ctx) {
+    return ctx->impl.getMInitStatus();
+}
+
+
+HREESULT HLPR_ReleaseContext(P_HLPR_Context ctx) {
+    delete ctx;
+
+    return hyper::hRetOk;
+}
+
+
+//HREESULT HLPR_DataBufferTest(P_HLPR_DataBuffer buffer, const char *save_path) {
+//    cv::Mat image = buffer->impl.GetScaledImage(1.0f, true);
+//    cv::imwrite(save_path, image);
+//
+//    return 0;
+//}
\ No newline at end of file
diff --git a/cpp/c_api/hyper_lpr_sdk.h b/cpp/c_api/hyper_lpr_sdk.h
new file mode 100644
index 0000000..661d8e5
--- /dev/null
+++ b/cpp/c_api/hyper_lpr_sdk.h
@@ -0,0 +1,255 @@
+//
+// Created by tunm on 2023/1/25.
+//
+
+#ifndef ZEPHYRLPR_HYPER_LPR_SDK_H
+#define ZEPHYRLPR_HYPER_LPR_SDK_H
+
+#include <stdint.h>
+
+#if defined(_WIN32)
+#ifdef HYPER_BUILD_SHARED_LIB
+#define HYPER_CAPI_EXPORT __declspec(dllexport)
+#else
+#define HYPER_CAPI_EXPORT
+#endif
+#else
+#define HYPER_CAPI_EXPORT __attribute__((visibility("default")))
+#endif // _WIN32
+
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+/**
+ * API Result - API调用结果
+ * */
+typedef int HREESULT;
+
+typedef enum HResultCode{
+    Ok = 0,
+    Err = -1,
+} HResultCode;
+
+/**
+ * camera stream format - 支持的相机流格式
+ * Contains several common camera stream formats on the market -
+ * 包含了几款市面上常见的相机流格式
+ */
+typedef enum HLPR_ImageFormat {
+    STREAM_RGB = 0,             ///< Image in RGB format - RGB排列格式的图像
+    STREAM_BGR = 1,             ///< Image in BGR format (Opencv Mat default) - BGR排列格式的图像(OpenCV的Mat默认)
+    STREAM_RGBA = 2,            ///< Image in RGB with alpha channel format - 带alpha通道的RGB排列格式的图像
+    STREAM_BGRA = 3,            ///< Image in BGR with alpha channel format - 带alpha通道的BGR排列格式的图像
+    STREAM_YUV_NV12 = 4,        ///< Image in YUV NV12 format - YUV NV12排列的图像格式
+    STREAM_YUV_NV21 = 5,        ///< Image in YUV NV21 format - YUV NV21排列的图像格式
+} HLPR_ImageFormat;
+
+
+/**
+ * Camera picture corner mode - 相机画面转角模式
+ * To cope with the rotation of some devices, four image rotation modes are provided here -
+ * 为应对某些设备的画面自带旋转，这里提供四种图像旋转模式
+ */
+typedef enum HLPR_Rotation {
+    CAMERA_ROTATION_0 = 0,      ///< 0 degree - 0
+    CAMERA_ROTATION_90 = 1,     ///< 90 degree - 90
+    CAMERA_ROTATION_180 = 2,    ///< 180 degree - 180
+    CAMERA_ROTATION_270 = 3,    ///< 270 degree - 270
+} HLPR_Rotation;
+
+/**
+ * Image Buffer Data struct - 图像数据流结构
+ * */
+typedef struct HLPR_ImageData {
+    uint8_t *data;                      ///< Image data stream - 图像数据流
+    int width;                          ///< Width of the image - 宽
+    int height;                         ///< Height of the image - 高
+    HLPR_ImageFormat format;            ///< Format of the image - 传入需要解析数据流格式
+    HLPR_Rotation rotation;             ///< The rotation Angle of the image - 图像的画面旋转角角度
+} HLPR_ImageData, *P_HLPR_ImageData;
+
+
+/**
+ * Plate layers - 车牌层数
+ * */
+typedef enum HLPR_PlateLayers {
+    PLATE_LAYERS_MONO = 0,       ///< 单层车牌
+    PLATE_LAYERS_DOUBLE,     ///< 双层车牌
+} HLPR_Layers;
+
+
+/**
+ * Detector Level - 检测器等级
+ * */
+typedef enum HLPR_DetectLevel {
+    DETECT_LEVEL_LOW = 0,       ///< 高开销检测模式 (推荐)
+    DETECT_LEVEL_HIGH,          ///< 低开销检测模式
+} HLPR_DetectLevel;
+
+/**
+ * PlateType Type - 车牌类型(中国)
+ * */
+typedef enum HLPR_PlateType {
+    PLATE_TYPE_UNKNOWN = -1,                         ///< 未知车牌
+    PLATE_TYPE_BLUE = 0,                             ///< 蓝牌
+    PLATE_TYPE_YELLOW_SINGLE = 1,                    ///< 黄牌单层
+    PLATE_TYPE_WHILE_SINGLE = 2,                     ///< 白牌单层
+    PLATE_TYPE_GREEN = 3,                            ///< 绿牌新能源
+    PLATE_TYPE_BLACK_HK_MACAO = 4,                   ///< 黑牌港澳
+    PLATE_TYPE_HK_SINGLE = 5,                        ///< 香港单层
+    PLATE_TYPE_HK_DOUBLE = 6,                        ///< 香港双层
+    PLATE_TYPE_MACAO_SINGLE = 7,                     ///< 澳门单层
+    PLATE_TYPE_MACAO_DOUBLE = 8,                     ///< 澳门双层
+    PLATE_TYPE_YELLOW_DOUBLE = 9,                    ///< 黄牌双层
+} HLPR_PlateType;
+
+/**
+ * Plate Result - 车牌检测结果
+ * */
+typedef struct HLPR_PlateResult {
+    float x1;                                       ///< 左上角点x坐标
+    float y1;                                       ///< 左上角点y坐标
+    float x2;                                       ///< 右下角点x坐标
+    float y2;                                       ///< 右下角点y坐标
+    HLPR_PlateType type;                           ///< 车牌类型
+    float text_confidence;                           ///< 置信度
+    char code[128];                                ///< 车牌号码字符串
+} HLPR_PlateResult, *P_HLPR_PlateResult;
+
+/**
+ * Plate Result List - 车牌检测结果列表
+ * */
+typedef struct HLPR_PlateResultList {
+    unsigned long plate_size;                       ///< 车牌数量
+    P_HLPR_PlateResult plates;                      ///< 检测车牌结果列表
+} HLPR_PlateResultList, *P_HLPR_PlateResultList;
+
+/**
+ * HyperLPR Context Instantiating parameters - Context的实例化参数对象
+ * */
+typedef struct HLPR_ContextConfiguration {
+    char *models_path;                              ///< 模型文件地址
+    int max_num;                                    ///< 识别最大数量
+    int threads;                                    ///< 线程数 (推荐1)
+    bool use_half;                                  ///< 是否使用半精度推理模式
+    float box_conf_threshold;                        ///< 检测框阈值
+    float nms_threshold;                             ///< 非极大值抑制阈值
+    float rec_confidence_threshold;                   ///< 识别置信度阈值
+    HLPR_DetectLevel det_level;                     ///< 检测器等级(推荐low)
+} HLPR_ContextConfiguration, *P_HLPR_ContextConfiguration;
+
+/**
+ * Data Buffer - 数据缓冲流
+ * */
+typedef struct HLPR_DataBuffer HLPR_DataBuffer, *P_HLPR_DataBuffer;
+
+/**
+ * The runtime object after HyperLPR is instantiated - 实例化运行时的Context对象
+ * */
+typedef struct HLPR_Context HLPR_Context, *P_HLPR_Context;
+
+/************************************************************************
+* Carry parameters to create a data buffer stream instantiation object.
+* 携带创建数据缓冲流实例化对象.
+* [out] return: Model instant handle - 返回实例化后的指针句柄
+************************************************************************/
+HYPER_CAPI_EXPORT extern P_HLPR_DataBuffer HLPR_CreateDataBuffer(
+        P_HLPR_ImageData data       // [in] Image Buffer Data struct - 图像数据流结构
+);
+
+/************************************************************************
+* Create a data buffer stream instantiation object.
+* 创建数据缓冲流实例化对象.
+* [out] return: Model instant handle - 返回实例化后的指针句柄
+************************************************************************/
+HYPER_CAPI_EXPORT extern P_HLPR_DataBuffer HLPR_CreateDataBufferEmpty();
+
+/************************************************************************
+* Set the DataBuffer rotation mode.
+* 设置DataBuffer旋转模式.
+* [out] Result Code - 返回结果码
+************************************************************************/
+HYPER_CAPI_EXPORT extern HREESULT HLPR_DataBufferSetData(
+        P_HLPR_DataBuffer buffer,               // [in] DataBuffer handle - 相机流组件的句柄指针
+        const uint8_t *data,                    // [in] Raw data stream - 原始的数据流
+        int width,                              // [in] Image width - 图像宽度
+        int height                              // [in] Image height - 图像高度
+);
+
+/************************************************************************
+* Set the DataBuffer data format.
+* 设置DataBuffer数据格式.
+* [out] Result Code - 返回结果码
+************************************************************************/
+HYPER_CAPI_EXPORT extern HREESULT HLPR_DataBufferSetRotationMode(
+        P_HLPR_DataBuffer buffer,              // [in] DataBuffer handle - 数据流组件的句柄指针
+        HLPR_Rotation mode                     // [in] DataBuffer mode - 数据流组件旋转模式
+);
+
+/************************************************************************
+* Set the DataBuffer rotation mode.
+* 设置DataBuffer旋转模式.
+* [out] Result Code - 返回结果码
+************************************************************************/
+HYPER_CAPI_EXPORT extern HREESULT HLPR_DataBufferSetStreamFormat(
+        P_HLPR_DataBuffer buffer,            // [in] DataBuffer handle - 数据流组件的句柄指针
+        HLPR_ImageFormat mode                // [in] DataBuffer data format - 数据流组件数据格式
+);
+
+/************************************************************************
+* Releases the DataBuffer object that has been instantiated.
+* 释放已经被实例化后的模型对象.
+* [out] Result Code - 返回结果码
+************************************************************************/
+HYPER_CAPI_EXPORT extern HREESULT HLPR_ReleaseDataBuffer(
+        P_HLPR_DataBuffer buffer             // [in] DataBuffer handle - 相机流组件的句柄指针
+);
+
+/************************************************************************
+* Create a data Context instantiation object.
+* 创建Context实例化对象.
+* [out] Result Code - 返回结果码
+************************************************************************/
+HYPER_CAPI_EXPORT extern P_HLPR_Context HLPR_CreateContext(
+        P_HLPR_ContextConfiguration configuration       // [in] Context configuration - 配置表
+);
+
+/************************************************************************
+* Query the Context instantiation state.
+* 查询实例化后的状态.
+* [out] Result Code - 返回结果码
+************************************************************************/
+HYPER_CAPI_EXPORT extern HREESULT HLPR_ContextQueryStatus(
+        P_HLPR_Context ctx                      // [in] Context handle - Context的指针句柄
+);
+
+/************************************************************************
+* Update Data Buffer Stream.
+* 喂入数据流并更新进行车牌识别.
+* [out] Result Code - 返回结果码
+************************************************************************/
+HYPER_CAPI_EXPORT extern HREESULT HLPR_ContextUpdateStream(
+        P_HLPR_Context ctx,                     // [in] Context handle - Context的指针句柄
+        P_HLPR_DataBuffer buffer,               // [in] DataBuffer handle - 数据流组件的句柄指针
+        P_HLPR_PlateResultList results          // [out] Results List - 返回结果的列表
+);
+
+/************************************************************************
+* Release Context.
+* 释放Context的实例化对象.
+* [out] Result Code - 返回结果码
+************************************************************************/
+HYPER_CAPI_EXPORT extern HREESULT HLPR_ReleaseContext(
+        P_HLPR_Context ctx                     // [in] Context handle - Context的指针句柄
+);
+
+//HYPER_CAPI_EXPORT extern HREESULT HLPR_DataBufferTest(P_HLPR_DataBuffer buffer, const char *save_path);
+
+#ifdef __cplusplus
+}
+#endif
+
+
+#endif //ZEPHYRLPR_HYPER_LPR_SDK_H
diff --git a/cpp/c_api/hyper_lpr_sdk_internal.h b/cpp/c_api/hyper_lpr_sdk_internal.h
new file mode 100644
index 0000000..3712e2e
--- /dev/null
+++ b/cpp/c_api/hyper_lpr_sdk_internal.h
@@ -0,0 +1,19 @@
+//
+// Created by tunm on 2023/1/25.
+//
+
+#ifndef ZEPHYRLPR_HYPER_LPR_SDK_INTERNAL_H
+#define ZEPHYRLPR_HYPER_LPR_SDK_INTERNAL_H
+
+#include "hyper_lpr_sdk.h"
+#include "context_module/all.h"
+
+typedef struct HLPR_Context {
+    hyper::HyperLPRContext impl;
+} HyperLPR_Context;
+
+typedef struct HLPR_DataBuffer {
+    hyper::CameraBuffer impl;
+} HLPR_DataBuffer;
+
+#endif //ZEPHYRLPR_HYPER_LPR_SDK_INTERNAL_H
diff --git a/cpp/platform/jni/android/hyperlpr_native.cpp b/cpp/platform/jni/android/hyperlpr_native.cpp
new file mode 100644
index 0000000..8fb3c09
--- /dev/null
+++ b/cpp/platform/jni/android/hyperlpr_native.cpp
@@ -0,0 +1,162 @@
+//
+// Created by tunm on 2023/1/27.
+//
+
+#include <jni.h>
+#include <string>
+#include <android/log.h>
+#include <stdlib.h>
+#include <android/bitmap.h>
+//#include <solexcv/log.h>
+#include <opencv2/opencv.hpp>
+#include "hyper_lpr_sdk.h"
+#include "jni/common/jni_utils.h"
+#include "log.h"
+
+using namespace hyper;
+
+extern "C" {
+
+static double cost_ = 0.0;
+
+JNIEXPORT long JNICALL HYPERLPR_API(core_HyperLPRCore_CreateRecognizerContext) (
+        JNIEnv *env,
+        jobject thiz,
+        jobject parameterObj) {
+    // get object class
+    jclass parameterCls = env->GetObjectClass(parameterObj);
+    // get object method id
+    jmethodID getModelPathMethodId = env->GetMethodID(parameterCls, "getModelPath", "()Ljava/lang/String;");
+    jmethodID getThreadsMethodId = env->GetMethodID(parameterCls, "getThreads", "()I");
+    jmethodID isUseHalfMethodId = env->GetMethodID(parameterCls, "isUseHalf", "()Z");
+    jmethodID getBoxConfThresholdMethodId = env->GetMethodID(parameterCls, "getBoxConfThreshold", "()F");
+    jmethodID getNmsThresholdMethodId = env->GetMethodID(parameterCls, "getNmsThreshold", "()F");
+    jmethodID getRecConfidenceThresholdMethodId = env->GetMethodID(parameterCls, "getRecConfidenceThreshold", "()F");
+    jmethodID getDetLevelMethodId = env->GetMethodID(parameterCls, "getDetLevel", "()I");
+    jmethodID getMaxNumMethodId = env->GetMethodID(parameterCls, "getMaxNum", "()I");
+    // get model path
+    jstring modelPath = (jstring ) env->CallObjectMethod(parameterObj, getModelPathMethodId, NULL);
+    std::string path = jstring2str(env, modelPath);
+    // get number of threads
+    jint threads = env->CallIntMethod(parameterObj, getThreadsMethodId, NULL);
+    // get is use half
+    jboolean useHalf = env->CallBooleanMethod(parameterObj, isUseHalfMethodId, NULL);
+    // get box conf threshold
+    jfloat boxConfThreshold = env->CallFloatMethod(parameterObj, getBoxConfThresholdMethodId, NULL);
+    // get nms threshold
+    jfloat nmsThreshold = env->CallFloatMethod(parameterObj, getNmsThresholdMethodId, NULL);
+    // get rec confidence threshold
+    jfloat textThreshold = env->CallFloatMethod(parameterObj, getRecConfidenceThresholdMethodId, NULL);
+    // get detect level
+    jint detLevel = env->CallIntMethod(parameterObj, getDetLevelMethodId, NULL);
+    // get max num level
+    jint maxNum = env->CallIntMethod(parameterObj, getMaxNumMethodId, NULL);
+
+
+    LOGD("input path: %s", path.c_str());
+    LOGD("threads: %d", threads);
+    LOGD("use half: %d", useHalf);
+    LOGD("det conf threshold: %f", boxConfThreshold);
+    LOGD("nms threshold: %f", nmsThreshold);
+    LOGD("text threshold: %f", textThreshold);
+    LOGD("det level: %d", detLevel);
+
+    // create context
+    HLPR_ContextConfiguration configuration = {0};
+    configuration.models_path = const_cast<char*>(path.c_str());
+    configuration.det_level = HLPR_DetectLevel(detLevel);
+    configuration.use_half = useHalf;
+    configuration.nms_threshold = nmsThreshold;
+    configuration.rec_confidence_threshold = textThreshold;
+    configuration.box_conf_threshold = boxConfThreshold;
+    configuration.threads = threads;
+    configuration.max_num = maxNum;
+
+    P_HLPR_Context ctx = HLPR_CreateContext(&configuration);
+
+    env->DeleteLocalRef(parameterCls);
+
+    return (long )ctx;
+}
+
+JNIEXPORT int JNICALL HYPERLPR_API(core_HyperLPRCore_ReleaseRecognizerContext) (
+        JNIEnv *env,
+        jobject thiz,
+        jlong handle) {
+    P_HLPR_Context ctx = (P_HLPR_Context ) handle;
+
+    return HLPR_ReleaseContext(ctx);
+
+}
+
+
+JNIEXPORT jobjectArray JNICALL HYPERLPR_API(core_HyperLPRCore_PlateRecognitionFromBuffer) (
+        JNIEnv *env,
+        jobject thiz,
+        jlong handle,
+        jbyteArray buf,
+        jint height,
+        jint width,
+        jint rotation,
+        jint format) {
+    P_HLPR_Context ctx = (P_HLPR_Context ) handle;
+
+    uint8_t *pBuf = (uint8_t *) env->GetByteArrayElements(buf, 0);
+    // create ImageData
+    HLPR_ImageData data = {0};
+    data.data = pBuf;
+    data.width = width;
+    data.height = height;
+    data.format = HLPR_ImageFormat(format);
+    data.rotation = HLPR_Rotation(rotation);
+    // create DataBuffer
+    P_HLPR_DataBuffer buffer = HLPR_CreateDataBuffer(&data);
+
+    // exec plate recognition
+    HLPR_PlateResultList results = {0};
+    cost_ = (double)cv::getTickCount();
+    HLPR_ContextUpdateStream(ctx, buffer, &results);
+    cost_ = ((double)cv::getTickCount() - cost_) / cv::getTickFrequency();
+    LOGD("cost: %f", cost_);
+
+    jobjectArray jPlateArray = nullptr;
+    jclass jPlateCls = env->FindClass("com/hyperai/hyperlpr3/bean/Plate");
+    // get object method id
+    jmethodID plateClsInitId = env->GetMethodID(jPlateCls, "<init>", "()V");
+    jmethodID setX1MethodId = env->GetMethodID(jPlateCls, "setX1", "(F)V");
+    jmethodID setY1MethodId = env->GetMethodID(jPlateCls, "setY1", "(F)V");
+    jmethodID setX2MethodId = env->GetMethodID(jPlateCls, "setX2", "(F)V");
+    jmethodID setY2MethodId = env->GetMethodID(jPlateCls, "setY2", "(F)V");
+//    jmethodID setLayersMethodId = env->GetMethodID(jPlateCls, "setLayers", "(I)V");
+    jmethodID setTypeMethodId = env->GetMethodID(jPlateCls, "setType", "(I)V");
+    jmethodID setConfidenceMethodId = env->GetMethodID(jPlateCls, "setConfidence", "(F)V");
+    jmethodID setCodeMethodId = env->GetMethodID(jPlateCls, "setCode", "(Ljava/lang/String;)V");
+
+    int total = results.plate_size;
+    jPlateArray = env->NewObjectArray(total, jPlateCls, 0);
+    for (int i = 0; i < total; ++i) {
+        auto &plate = results.plates[i];
+        // set in location
+        jobject jPlate = env->NewObject(jPlateCls, plateClsInitId);
+        env->CallVoidMethod(jPlate, setX1MethodId, plate.x1);
+        env->CallVoidMethod(jPlate, setY1MethodId, plate.y1);
+        env->CallVoidMethod(jPlate, setX2MethodId, plate.x2);
+        env->CallVoidMethod(jPlate, setY2MethodId, plate.y2);
+        // set in types
+//        env->CallVoidMethod(jPlate, setLayersMethodId, plate.layers);
+        env->CallVoidMethod(jPlate, setTypeMethodId, plate.type);
+        // set in text_confidence
+        env->CallVoidMethod(jPlate, setConfidenceMethodId, plate.text_confidence);
+        // set in plate code
+//        env->CallObjectMethod(jPlate, setCodeMethodId, stringTojstring(env, plate.code));
+        env->CallObjectMethod(jPlate, setCodeMethodId, env->NewStringUTF(plate.code));
+
+        env->SetObjectArrayElement(jPlateArray, i, jPlate);
+        env->DeleteLocalRef(jPlate);
+    }
+    env->DeleteLocalRef(jPlateCls);
+
+    return jPlateArray;
+}
+
+} // extern
\ No newline at end of file
diff --git a/cpp/platform/jni/common/jni_utils.h b/cpp/platform/jni/common/jni_utils.h
new file mode 100644
index 0000000..18fdc2c
--- /dev/null
+++ b/cpp/platform/jni/common/jni_utils.h
@@ -0,0 +1,63 @@
+//
+// Created by tunm on 2023/1/26.
+//
+
+#ifndef ZEPHYRLPR_JNI_UTILS_H
+#define ZEPHYRLPR_JNI_UTILS_H
+
+namespace hyper {
+
+#define HYPERLPR_API(sig) Java_com_hyperai_hyperlpr3_##sig
+
+inline jstring stringTojstring(JNIEnv *env, const char *pat) {
+
+    jclass strClass = (env)->FindClass("java/lang/String");
+    jmethodID ctorID = (env)->GetMethodID(strClass, "<init>", "([BLjava/lang/String;)V");
+    jbyteArray bytes = (env)->NewByteArray(strlen(pat));
+    (env)->SetByteArrayRegion(bytes, 0, strlen(pat), (jbyte *) pat);
+    jstring encoding = (env)->NewStringUTF("GBK");
+    return (jstring)(env)->NewObject(strClass, ctorID, bytes, encoding);
+
+}
+
+
+inline jstring JNIString_getString(JNIEnv *env, const char *c_str) {
+
+        //C 返回 java 字符串
+        jclass str_cls = (env)->FindClass("java/lang/String");
+        jmethodID jmid = (env)->GetMethodID(str_cls, "<init>", "([BLjava/lang/String;)V");
+
+        //jstring -> jbyteArray
+        jbyteArray bytes = (env)->NewByteArray(strlen(c_str));
+        // 将Char * 赋值到 bytes
+        (env)->SetByteArrayRegion(bytes, 0, strlen(c_str), (jbyte *) c_str);
+        jstring charsetName = (env)->NewStringUTF("GB2312");
+
+        return (jstring)(env)->NewObject(str_cls, jmid, bytes, charsetName);
+
+}
+
+
+inline std::string jstring2str(JNIEnv *env, jstring jstr) {
+    char *rtn = NULL;
+    jclass clsstring = env->FindClass("java/lang/String");
+    jstring strencode = env->NewStringUTF("GB2312");
+    jmethodID mid =
+            env->GetMethodID(clsstring, "getBytes", "(Ljava/lang/String;)[B");
+    jbyteArray barr = (jbyteArray) env->CallObjectMethod(jstr, mid, strencode);
+    jsize alen = env->GetArrayLength(barr);
+    jbyte *ba = env->GetByteArrayElements(barr, JNI_FALSE);
+    if (alen > 0) {
+        rtn = (char *) malloc(alen + 1);
+        memcpy(rtn, ba, alen);
+        rtn[alen] = 0;
+    }
+    env->ReleaseByteArrayElements(barr, ba, 0);
+    std::string stemp(rtn);
+    free(rtn);
+    return stemp;
+}
+
+} // namespace
+
+#endif //ZEPHYRLPR_JNI_UTILS_H
diff --git a/cpp/platform/jni/java/README.md b/cpp/platform/jni/java/README.md
new file mode 100644
index 0000000..e69de29
diff --git a/cpp/samples/sample_capi.cpp b/cpp/samples/sample_capi.cpp
new file mode 100644
index 0000000..e806d08
--- /dev/null
+++ b/cpp/samples/sample_capi.cpp
@@ -0,0 +1,75 @@
+//
+// Created by tunm on 2023/1/26.
+//
+
+#include <iostream>
+#include "hyper_lpr_sdk.h"
+#include "opencv2/opencv.hpp"
+
+static const std::vector<std::string> TYPES = {"蓝牌", "黄牌单层", "白牌单层", "绿牌新能源", "黑牌港澳", "香港单层", "香港双层", "澳门单层", "澳门双层", "黄牌双层"};
+
+int main(int argc, char **argv) {
+    char *model_path = argv[1];
+    char *image_path = argv[2];
+    cv::Mat image = cv::imread(image_path);
+    // create ImageData
+    HLPR_ImageData data = {0};
+    data.data = image.ptr<uint8_t>(0);
+    data.width = image.cols;
+    data.height = image.rows;
+    data.format = STREAM_BGR;
+    data.rotation = CAMERA_ROTATION_0;
+    // create DataBuffer
+    P_HLPR_DataBuffer buffer = HLPR_CreateDataBuffer(&data);
+
+    // create context
+    HLPR_ContextConfiguration configuration = {0};
+    configuration.models_path = model_path;
+    configuration.max_num = 5;
+    configuration.det_level = DETECT_LEVEL_LOW;
+    configuration.use_half = false;
+    configuration.nms_threshold = 0.5f;
+    configuration.rec_confidence_threshold = 0.5f;
+    configuration.box_conf_threshold = 0.30f;
+    configuration.threads = 1;
+    P_HLPR_Context ctx = HLPR_CreateContext(&configuration);
+    HREESULT ret = HLPR_ContextQueryStatus(ctx);
+    if (ret != HResultCode::Ok) {
+        printf("create error.\n");
+        return -1;
+    }
+    // exec plate recognition
+    HLPR_PlateResultList results = {0};
+    double time;
+    time = (double)cv::getTickCount();
+    HLPR_ContextUpdateStream(ctx, buffer, &results);
+    time = ((double)cv::getTickCount() - time) / cv::getTickFrequency();
+    printf("cost: %f\n", time);
+
+
+    for (int i = 0; i < results.plate_size; ++i) {
+        std::string type;
+        if (results.plates[i].type == HLPR_PlateType::PLATE_TYPE_UNKNOWN) {
+            type = "未知";
+        } else {
+            type = TYPES[results.plates[i].type];
+        }
+
+        cv::rectangle(image, cv::Point2f(results.plates[i].x1, results.plates[i].y1), cv::Point2f(results.plates[i].x2, results.plates[i].y2),
+                      cv::Scalar(100, 100, 200), 3);
+
+        printf("<%d> %s, %s, %f\n", i + 1, type.c_str(),
+                  results.plates[i].code, results.plates[i].text_confidence);
+    }
+
+//    cv::imwrite("out.jpg", image);
+    cv::imshow("out", image);
+    cv::waitKey(0);
+
+    // release buffer
+    HLPR_ReleaseDataBuffer(buffer);
+    // release context
+    HLPR_ReleaseContext(ctx);
+
+    return 0;
+}
\ No newline at end of file
diff --git a/cpp/samples/sample_context.cpp b/cpp/samples/sample_context.cpp
new file mode 100644
index 0000000..2e4baca
--- /dev/null
+++ b/cpp/samples/sample_context.cpp
@@ -0,0 +1,47 @@
+//
+// Created by tunm on 2023/1/22.
+//
+#include <iostream>
+#include "opencv2/opencv.hpp"
+#include "context_module/all.h"
+#include "buffer_module/all.h"
+
+using namespace hyper;
+
+static const std::vector<std::string> TYPES = {"蓝牌", "黄牌单层", "白牌单层", "绿牌新能源", "黑牌港澳", "香港单层", "香港双层", "澳门单层", "澳门双层", "黄牌双层"};
+
+int main(int argc, char **argv) {
+    char *model_path = argv[1];
+    char *image_path = argv[2];
+    cv::Mat image = cv::imread(image_path);
+
+    HyperLPRContext context;
+    auto ret = context.Initialize(model_path, 5, DetectLevel::DETECT_LEVEL_LOW);
+    if (ret != hRetOk) {
+        LOGE("Load error.");
+        return -1;
+    }
+    CameraBuffer buffer;
+    buffer.SetDataBuffer(image.data, image.rows, image.cols);
+    buffer.SetDataFormat(BGR);
+    buffer.SetRotationMode(ROTATION_0);
+    double time;
+    time = (double)cv::getTickCount();
+    context(buffer);
+    time = ((double)cv::getTickCount() - time) / cv::getTickFrequency();
+    LOGD("pipeline cost: %f", time);
+    auto &objs = context.getMObjectResults();
+    for (auto &obj: objs) {
+        cv::rectangle(image,
+                      cv::Point2f(obj.x1, obj.y1),
+                      cv::Point2f(obj.x2, obj.y2),
+                      cv::Scalar(0, 0, 200),
+                      2);
+        LOGD("[%s]%s", TYPES[obj.type].c_str(), obj.code);
+        LOGD("文本均值置信度: %f", obj.text_confidence);
+    }
+
+    cv::imshow("w", image);
+    cv::waitKey(0);
+    return 0;
+}
\ No newline at end of file
diff --git a/cpp/samples/sample_split_model.cpp b/cpp/samples/sample_split_model.cpp
new file mode 100644
index 0000000..5d657a8
--- /dev/null
+++ b/cpp/samples/sample_split_model.cpp
@@ -0,0 +1,113 @@
+//
+// Created by Tunm-Air13 on 2023/2/8.
+//
+
+#include <iostream>
+#include "opencv2/opencv.hpp"
+//#include "loader_module/all.h"
+#include "nn_implementation_module/all.h"
+#include "configuration.h"
+
+using namespace hyper;
+
+int main(int argc, char **argv) {
+    char *model_path = argv[1];
+    char *image_path = argv[2];
+
+    int input_size = 320;
+    std::string backbone_path = std::string(model_path) + "/" + hyper::DETECT_LOW_BACKBONE_FILENAME;
+    std::string head_path = std::string(model_path) + "/" + hyper::DETECT_LOW_HEAD_FILENAME;
+
+//    std::string backbone_path = std::string(model_path) + "/" + hyper::DETECT_HIGH_BACKBONE_FILENAME;
+//    std::string head_path = std::string(model_path) + "/" + hyper::DETECT_HIGH_HEAD_FILENAME;
+//    int input_size = 640;
+
+    cv::Mat image = cv::imread(image_path);
+
+//    DetBackbone backbone;
+//    backbone.Initialize(backbone_model);
+//
+//    DetHeader header;
+//    header.Initialize(header_model);
+//
+//
+//    backbone.Inference(image);
+//
+//    header.Inference(backbone.getMOutputTensorInfoList()[0].GetDataAsFloat(),
+//                     backbone.getMOutputTensorInfoList()[1].GetDataAsFloat(),
+//                     backbone.getMOutputTensorInfoList()[1].GetDataAsFloat());
+
+
+//    for (int i = 0; i < 20; ++i) {
+//        std::cout << header.getMOutputTensorInfoList()[0].GetDataAsFloat()[i] << std::endl;
+//    }
+
+
+//    float *h = header.getMOutputTensorInfoList()[0].GetDataAsFloat();
+//    for (int i = 0; i < 6300 * 15; ++i) {
+////        std::cout << backbone.m_output_feature_map_40p_.get()[i] << std::endl;
+//        FILE *fp = NULL;
+//        fp = fopen("head.txt", "a");
+//        fprintf(fp, "%f\n", h[i]);
+//        fclose(fp);
+//    }
+//
+
+
+//    float *p40 = backbone.getMOutputTensorInfoList()[0].GetDataAsFloat();
+//    for (int i = 0; i < 45 * 40 *40; ++i) {
+////        std::cout << backbone.m_output_feature_map_40p_.get()[i] << std::endl;
+//        FILE *fp = NULL;
+//        fp = fopen("40.txt", "a");
+//        fprintf(fp, "%f\n", p40[i]);
+//        fclose(fp);
+//    }
+//
+//    std::cout << std::endl;
+//    float *p20 = backbone.getMOutputTensorInfoList()[1].GetDataAsFloat();
+//    for (int i = 0; i < 45 * 20 * 20; ++i) {
+////        std::cout << backbone.m_output_feature_map_20p_.get()[i] << std::endl;
+//        FILE *fp = NULL;
+//        fp = fopen("20.txt", "a");
+//        fprintf(fp, "%f\n", p20[i]);
+//        fclose(fp);
+//    }
+//    std::cout << std::endl;
+//    float *p10 = backbone.getMOutputTensorInfoList()[2].GetDataAsFloat();
+//    for (int i = 0; i < 45 * 10 * 10; ++i) {
+////        std::cout << backbone.m_output_feature_map_10p_.get()[i] << std::endl;
+//        FILE *fp = NULL;
+//        fp = fopen("10.txt", "a");
+//        fprintf(fp, "%f\n", p10[i]);
+//        fclose(fp);
+//    }
+
+    DetArch arch;
+    arch.Initialize(backbone_path, head_path, input_size);
+    double time;
+    time = (double)cv::getTickCount();
+    arch.Detection(image, true);
+    time = ((double)cv::getTickCount() - time) / cv::getTickFrequency();
+
+    auto &results = arch.m_results_;
+
+    for (auto &plate : results) {
+        std::cout << plate.x1 << ", " << plate.y1 << std::endl;
+        cv::rectangle(image, cv::Point2f(plate.x1, plate.y1), cv::Point2f(plate.x2, plate.y2),
+                      cv::Scalar(100, 100, 200), 1);
+        for (int i = 0; i < 4; ++i) {
+            cv::line(image, cv::Point2f(plate.kps[i * 2 + 0], plate.kps[i * 2 + 1]),
+                     cv::Point2f(plate.kps[i * 2 + 0], plate.kps[i * 2 + 1]), cv::Scalar(100, 220, 20), 1);
+        }
+
+    }
+#ifdef BUILD_LINUX_ARM7
+    cv::imwrite("out.jpg", image);
+#else
+    cv::imshow("w", image);
+    cv::waitKey(0);
+#endif
+
+    return 0;
+
+}
\ No newline at end of file
diff --git a/cpp/src/basic_types.h b/cpp/src/basic_types.h
new file mode 100755
index 0000000..cfd3259
--- /dev/null
+++ b/cpp/src/basic_types.h
@@ -0,0 +1,17 @@
+#pragma once
+#ifndef ZEPHYRLPR_BASIC_TYPES_H
+#define ZEPHYRLPR_BASIC_TYPES_H
+#include "opencv2/opencv.hpp"
+#include "log.h"
+
+namespace hyper {
+
+typedef std::vector<size_t> IndexList;
+
+typedef cv::Size InputSize;
+
+
+}
+
+
+#endif // ZEPHYRLPR_BASIC_TYPES_H
diff --git a/cpp/src/buffer_module/all.h b/cpp/src/buffer_module/all.h
new file mode 100644
index 0000000..8c86fc7
--- /dev/null
+++ b/cpp/src/buffer_module/all.h
@@ -0,0 +1,10 @@
+//
+// Created by tunm on 2023/1/25.
+//
+
+#ifndef ZEPHYRLPR_CAMERA_BUFFER_ALL_H
+#define ZEPHYRLPR_CAMERA_BUFFER_ALL_H
+
+#include "camera_buffer.h"
+
+#endif //ZEPHYRLPR_CAMERA_BUFFER_ALL_H
diff --git a/cpp/src/buffer_module/camera_buffer.cpp b/cpp/src/buffer_module/camera_buffer.cpp
new file mode 100644
index 0000000..8439a22
--- /dev/null
+++ b/cpp/src/buffer_module/camera_buffer.cpp
@@ -0,0 +1,236 @@
+//
+// Created by Tunm-Air13 on 2022/4/24.
+//
+
+#include "camera_buffer.h"
+
+namespace hyper {
+
+CameraBuffer::CameraBuffer() {
+    config_.sourceFormat = MNN::CV::YUV_NV21;
+    config_.destFormat = MNN::CV::BGR;
+    config_.filterType = MNN::CV::NEAREST;
+    config_.wrap = MNN::CV::ZERO;
+    rotation_mode_ = ROTATION_0;
+}
+
+void CameraBuffer::SetDataBuffer(const uint8_t *data_buffer, int height, int width) {
+    this->buffer_ = data_buffer;
+    this->height_ = height;
+    this->width_ = width;
+}
+
+void CameraBuffer::SetRotationMode(ROTATION_MODE mode) {
+    rotation_mode_ = mode;
+}
+
+void CameraBuffer::SetDataFormat(DATA_FORMAT data_format) {
+    if (data_format == NV21) {
+        config_.sourceFormat = MNN::CV::YUV_NV21;
+    }
+    if (data_format == NV12) {
+        config_.sourceFormat = MNN::CV::YUV_NV12;
+    }
+    if (data_format == RGBA) {
+        config_.sourceFormat = MNN::CV::RGBA;
+    }
+    if (data_format == RGB) {
+        config_.sourceFormat = MNN::CV::RGB;
+    }
+    if (data_format == BGR) {
+        config_.sourceFormat = MNN::CV::BGR;
+    }
+    if (data_format == BGRA) {
+        config_.sourceFormat = MNN::CV::BGRA;
+    }
+    if (data_format == YCrCb) {
+        config_.sourceFormat = MNN::CV::YCrCb;
+    }
+}
+
+cv::Mat CameraBuffer::GetAffineRGBImage(const cv::Mat &affine_matrix, const int width_out, const int height_out) const {
+    int sw = width_;
+    int sh = height_;
+    int rot_sw = sw;
+    int rot_sh = sh;
+    MNN::CV::Matrix tr;
+    assert(affine_matrix.rows == 2);
+    assert(affine_matrix.cols == 3);
+    assert(affine_matrix.type() == CV_64F);
+    cv::Mat trans_matrix;
+    affine_matrix.convertTo(trans_matrix, CV_32F);
+    std::vector<float> tr_cv({1, 0, 0, 0, 1, 0, 0, 0, 1});
+    memcpy(tr_cv.data(), trans_matrix.data, sizeof(float) * 6);
+    tr.set9(tr_cv.data());
+    MNN::CV::Matrix tr_inv;
+    tr.invert(&tr_inv);
+    std::shared_ptr<MNN::CV::ImageProcess> process(
+            MNN::CV::ImageProcess::create(config_));
+    process->setMatrix(tr_inv);
+    cv::Mat img_out(height_out, width_out, CV_8UC3);
+    std::shared_ptr<MNN::Tensor> tensor(MNN::Tensor::create<uint8_t>(
+            std::vector<int>{1, height_out, width_out, 3}, img_out.data));
+    process->convert(buffer_, sw, sh, 0, tensor.get());
+    return img_out;
+}
+
+cv::Mat CameraBuffer::GetScaledImage(const float scale, bool with_rotation) {
+    int sw = width_;
+    int sh = height_;
+    int rot_sw = sw;
+    int rot_sh = sh;
+    // MNN::CV::Matrix tr;
+    std::shared_ptr<MNN::CV::ImageProcess> process(
+            MNN::CV::ImageProcess::create(config_));
+    if (rotation_mode_ == ROTATION_270 && with_rotation) {
+        float srcPoints[] = {
+                0.0f,
+                0.0f,
+                0.0f,
+                (float)(height_ - 1),
+                (float)(width_ - 1),
+                0.0f,
+                (float)(width_ - 1),
+                (float)(height_ - 1),
+        };
+        float dstPoints[] = {(float)(height_ * scale - 1),
+                             0.0f,
+                             0.0f,
+                             0.0f,
+                             (float)(height_ * scale - 1),
+                             (float)(width_ * scale - 1),
+                             0.0f,
+                             (float)(width_ * scale - 1)};
+
+        tr_.setPolyToPoly((MNN::CV::Point *)dstPoints,
+                          (MNN::CV::Point *)srcPoints, 4);
+        process->setMatrix(tr_);
+        int scaled_height = static_cast<int>(width_ * scale);
+        int scaled_width = static_cast<int>(height_ * scale);
+        cv::Mat img_out(scaled_height, scaled_width, CV_8UC3);
+        std::shared_ptr<MNN::Tensor> tensor(MNN::Tensor::create<uint8_t>(
+                std::vector<int>{1, scaled_height, scaled_width, 3}, img_out.data));
+        process->convert(buffer_, sw, sh, 0, tensor.get());
+        return img_out;
+    } else if (rotation_mode_ == ROTATION_90 && with_rotation) {
+        float srcPoints[] = {
+                0.0f,
+                0.0f,
+                0.0f,
+                (float)(height_ - 1),
+                (float)(width_ - 1),
+                0.0f,
+                (float)(width_ - 1),
+                (float)(height_ - 1),
+        };
+        float dstPoints[] = {
+                0.0f,
+                (float)(width_ * scale - 1),
+                (float)(height_ * scale - 1),
+                (float)(width_ * scale - 1),
+                0.0f,
+                0.0f,
+                (float)(height_ * scale - 1),
+                0.0f,
+        };
+        tr_.setPolyToPoly((MNN::CV::Point *)dstPoints,
+                          (MNN::CV::Point *)srcPoints, 4);
+        process->setMatrix(tr_);
+        int scaled_height = static_cast<int>(width_ * scale);
+        int scaled_width = static_cast<int>(height_ * scale);
+        cv::Mat img_out(scaled_height, scaled_width, CV_8UC3);
+        std::shared_ptr<MNN::Tensor> tensor(MNN::Tensor::create<uint8_t>(
+                std::vector<int>{1, scaled_height, scaled_width, 3}, img_out.data));
+        process->convert(buffer_, sw, sh, 0, tensor.get());
+        return img_out;
+    } else if (rotation_mode_ == ROTATION_180 && with_rotation) {
+        float srcPoints[] = {
+                0.0f,
+                0.0f,
+                0.0f,
+                (float)(height_ - 1),
+                (float)(width_ - 1),
+                0.0f,
+                (float)(width_ - 1),
+                (float)(height_ - 1),
+        };
+        float dstPoints[] = {
+                (float)(width_ * scale - 1),
+                (float)(height_ * scale - 1),
+                (float)(width_ * scale - 1),
+                0.0f,
+                0.0f,
+                (float)(height_ * scale - 1),
+                0.0f,
+                0.0f,
+        };
+        tr_.setPolyToPoly((MNN::CV::Point *)dstPoints,
+                          (MNN::CV::Point *)srcPoints, 4);
+        process->setMatrix(tr_);
+        int scaled_height = static_cast<int>(height_ * scale);
+        int scaled_width = static_cast<int>(width_ * scale);
+        cv::Mat img_out(scaled_height, scaled_width, CV_8UC3);
+        std::shared_ptr<MNN::Tensor> tensor(MNN::Tensor::create<uint8_t>(
+                std::vector<int>{1, scaled_height, scaled_width, 3}, img_out.data));
+        process->convert(buffer_, sw, sh, 0, tensor.get());
+        return img_out;
+    } else {
+        float srcPoints[] = {
+                0.0f,
+                0.0f,
+                0.0f,
+                (float)(height_ - 1),
+                (float)(width_ - 1),
+                0.0f,
+                (float)(width_ - 1),
+                (float)(height_ - 1),
+        };
+        float dstPoints[] = {
+                0.0f,
+                0.0f,
+                0.0f,
+                (float)(height_ * scale - 1),
+                (float)(width_ * scale - 1),
+                0.0f,
+                (float)(width_ * scale - 1),
+                (float)(height_ * scale - 1),
+        };
+        tr_.setPolyToPoly((MNN::CV::Point *)dstPoints,
+                          (MNN::CV::Point *)srcPoints, 4);
+        process->setMatrix(tr_);
+        int scaled_height = static_cast<int>(height_ * scale);
+        int scaled_width = static_cast<int>(width_ * scale);
+        cv::Mat img_out(scaled_height, scaled_width, CV_8UC3);
+        std::shared_ptr<MNN::Tensor> tensor(MNN::Tensor::create<uint8_t>(
+                std::vector<int>{1, scaled_height, scaled_width, 3}, img_out.data));
+        process->convert(buffer_, sw, sh, 0, tensor.get());
+        return img_out;
+    }
+}
+
+cv::Mat CameraBuffer::GetAffineMatrix() const {
+    cv::Mat affine_matrix(3, 3, CV_32F);
+    tr_.get9((float *)affine_matrix.data);
+    cv::Mat affine = affine_matrix.rowRange(0, 2);
+    cv::Mat affine_64;
+    affine.convertTo(affine_64, CV_64F);
+    assert(affine_64.rows == 2);
+    assert(affine_64.cols == 3);
+    assert(affine_64.type() == CV_64F);
+    return affine_64;
+}
+
+int CameraBuffer::GetHeight() const {
+    return height_;
+}
+
+int CameraBuffer::GetWidth() const {
+    return width_;
+}
+
+
+int CameraBuffer::GetRotationMode() const{
+    return rotation_mode_;
+}
+
+}
\ No newline at end of file
diff --git a/cpp/src/buffer_module/camera_buffer.h b/cpp/src/buffer_module/camera_buffer.h
new file mode 100644
index 0000000..b57aaa8
--- /dev/null
+++ b/cpp/src/buffer_module/camera_buffer.h
@@ -0,0 +1,68 @@
+//
+// Created by Tunm-Air13 on 2022/4/24.
+//
+
+#ifndef SOLEXCV_CAMERA_BUFFER_H
+#define SOLEXCV_CAMERA_BUFFER_H
+
+#include <opencv2/opencv.hpp>
+#include <MNN/ImageProcess.hpp>
+#include <memory>
+
+namespace hyper {
+
+enum ROTATION_MODE {
+    ROTATION_0 = 0,
+    ROTATION_90 = 1,
+    ROTATION_180 = 2,
+    ROTATION_270 = 3
+};
+
+enum DATA_FORMAT {
+    NV21 = 0, NV12 = 1, RGBA = 2, RGB = 3, BGR = 4, BGRA = 5, YCrCb = 6
+};
+
+class CameraBuffer {
+public:
+
+    CameraBuffer();
+
+
+    void SetDataBuffer(const uint8_t *data_buffer, int height, int width);
+
+
+    void SetRotationMode(ROTATION_MODE mode);
+
+
+    void SetDataFormat(DATA_FORMAT data_format);
+
+
+    cv::Mat GetAffineRGBImage(const cv::Mat &affine_matrix, const int width_out,
+                              const int height_out) const;
+
+
+    cv::Mat GetScaledImage(const float scale, bool with_rotation);
+
+
+    cv::Mat GetAffineMatrix() const;
+
+    int GetHeight() const;
+
+    int GetWidth() const;
+
+    int GetRotationMode() const;
+
+private:
+    const uint8_t *buffer_;
+    int buffer_size_;
+    std::vector<float> rotation_matrix;
+    int height_;
+    int width_;
+    MNN::CV::Matrix tr_;
+    ROTATION_MODE rotation_mode_;
+    MNN::CV::ImageProcess::Config config_;
+    std::shared_ptr <MNN::CV::ImageProcess> process_;
+};
+
+}
+#endif //SOLEXCV_CAMERA_BUFFER_H
diff --git a/cpp/src/configuration.h b/cpp/src/configuration.h
new file mode 100644
index 0000000..799be21
--- /dev/null
+++ b/cpp/src/configuration.h
@@ -0,0 +1,29 @@
+//
+// Created by Tunm-Air13 on 2023/2/21.
+//
+#pragma once
+#ifndef ZEPHYRLPR_CONFIGURATION_H
+#define ZEPHYRLPR_CONFIGURATION_H
+
+#include "basic_types.h"
+#include <iostream>
+#include "log.h"
+
+namespace hyper {
+
+const std::string DETECT_LOW_BACKBONE_FILENAME = "b320_backbone_h.mnn";
+const std::string DETECT_LOW_HEAD_FILENAME = "b320_header_h.mnn";
+const std::string DETECT_HIGH_BACKBONE_FILENAME = "b640x_backbone_h.mnn";
+const std::string DETECT_HIGH_HEAD_FILENAME = "b640x_head_h.mnn";
+const std::string CLS_MODEL_FILENAME = "litemodel_cls_96xh.mnn";
+const std::string REC_MODEL_FILENAME = "rpv3_mdict_160_r3.mnn";
+
+const InputSize REC_INPUT_SIZE = {160, 48};
+const InputSize CLS_INPUT_SIZE = {96, 96};
+
+const int REC_MAX_CHAR_NUM = 20;
+const int REC_CHAR_CLASS_NUM = 78;
+
+}
+
+#endif //ZEPHYRLPR_CONFIGURATION_H
diff --git a/cpp/src/context_module/all.h b/cpp/src/context_module/all.h
new file mode 100644
index 0000000..a99a820
--- /dev/null
+++ b/cpp/src/context_module/all.h
@@ -0,0 +1,11 @@
+//
+// Created by tunm on 2023/1/25.
+//
+
+#ifndef ZEPHYRLPR_HYPER_LPR_CONTEXT_ALL_H
+#define ZEPHYRLPR_HYPER_LPR_CONTEXT_ALL_H
+
+#include "hyper_lpr_context.h"
+#include "hyper_lpr_common.h"
+
+#endif //ZEPHYRLPR_HYPER_LPR_CONTEXT_ALL_H
diff --git a/cpp/src/context_module/hyper_lpr_common.h b/cpp/src/context_module/hyper_lpr_common.h
new file mode 100644
index 0000000..9e42145
--- /dev/null
+++ b/cpp/src/context_module/hyper_lpr_common.h
@@ -0,0 +1,54 @@
+//
+// Created by tunm on 2023/1/25.
+//
+#pragma once
+#ifndef ZEPHYRLPR_HYPER_LPR_COMMON_H
+#define ZEPHYRLPR_HYPER_LPR_COMMON_H
+
+#include "nn_implementation_module/all.h"
+
+namespace hyper {
+
+enum PlateType {
+    UNKNOWN = -1,                         ///< 未知车牌
+    BLUE = 0,                             ///< 蓝牌
+    YELLOW_SINGLE = 1,                    ///< 黄牌单层
+    WHILE_SINGLE = 2,                     ///< 白牌单层
+    GREEN = 3,                            ///< 绿牌新能源
+    BLACK_HK_MACAO = 4,                   ///< 黑牌港澳
+    HK_SINGLE = 5,                        ///< 香港单层
+    HK_DOUBLE = 6,                        ///< 香港双层
+    MACAO_SINGLE = 7,                     ///< 澳门单层
+    MACAO_DOUBLE = 8,                     ///< 澳门双层
+    YELLOW_DOUBLE = 9,                    ///< 黄牌双层
+};
+
+enum DetectLevel {
+    DETECT_LEVEL_LOW = 0,
+    DETECT_LEVEL_HIGH,
+};
+
+typedef struct PlateObject {
+    PlateLocation location{};
+    TextLine line{};
+    PlateColor type{};
+
+} PlateObject;
+
+typedef struct PlateResult {
+    float x1;
+    float y1;
+    float x2;
+    float y2;
+    PlateType type;
+    float text_confidence;
+    char code[128];
+} PlateResult;
+
+typedef std::vector<PlateObject> PlateObjectList;
+
+typedef std::vector<PlateResult> PlateResultList;
+
+}
+
+#endif //ZEPHYRLPR_HYPER_LPR_COMMON_H
diff --git a/cpp/src/context_module/hyper_lpr_context.cpp b/cpp/src/context_module/hyper_lpr_context.cpp
new file mode 100644
index 0000000..2808aca
--- /dev/null
+++ b/cpp/src/context_module/hyper_lpr_context.cpp
@@ -0,0 +1,223 @@
+//
+// Created by tunm on 2023/1/25.
+//
+#pragma once
+
+#include <string>
+#include "hyper_lpr_context.h"
+#include "configuration.h"
+#include "utils.h"
+#include "log.h"
+
+namespace hyper {
+
+HyperLPRContext::HyperLPRContext() = default;
+
+void HyperLPRContext::operator()(CameraBuffer &buffer) {
+    cv::Mat process_image;
+    process_image = buffer.GetScaledImage(1.0f, true);
+    m_plate_detector_->Detection(process_image, true, 1.0f);
+    PlateResultList().swap(m_object_results_);
+    auto &detect_results = m_plate_detector_->m_results_;
+//    cv::imwrite("/storage/emulated/0/Android/data/com.hyperai.hyperlpr_sdk_demo/files/bug.jpg", process_image);
+
+//    std::sort(detect_results.begin(), detect_results.end(),
+//              [](PlateLocation a, PlateLocation b) { return xyxyArea(a.x1, a.y1, a.x2, a.y2) > xyxyArea(b.x1, b.y1, b.x2, b.y2); });
+    //
+    auto iteration_num = detect_results.size() > m_rec_max_num_ ? m_rec_max_num_: detect_results.size();
+    for (size_t i = 0; i < iteration_num; ++i) {
+        auto &loc = detect_results[i];
+        PlateResult obj;
+        obj.x1 = loc.x1;
+        obj.y1 = loc.y1;
+        obj.x2 = loc.x2;
+        obj.y2 = loc.y2;
+        cv::Mat align_image;
+        getRotateCropAndAlignPad(process_image, align_image, loc.kps);
+//        std::cout << align_image_pad.size << std::endl;
+//        cv::imshow("align_image_pad", align_image_pad);
+//        cv::imshow("align_image", align_image);
+//        cv::waitKey(0);
+
+
+        TextLine text_line;
+        if (loc.layers == LayersNum::DOUBLE) {
+            int line = (int )((float )align_image.rows * 0.4f);
+            int bottom_h = align_image.rows - line;
+            cv::Rect_<int> top_rect(0, 0, align_image.cols, line);
+            cv::Rect_<int> bottom_rect(0, line, align_image.cols, bottom_h);
+            cv::Mat top_crop = align_image(top_rect);
+            cv::Mat bottom_crop = align_image(bottom_rect);
+
+            std::vector<cv::Mat> candidate = {top_crop, bottom_crop};
+            text_line.code = "";
+            text_line.average_score = 0.0f;
+            for (int j = 0; j < candidate.size(); ++j) {
+                cv::Mat &align = candidate[j];
+                cv::Mat align_pad;
+                float wh_ratio = (float) align.cols / align.rows;
+                imagePadding(align, align_pad, wh_ratio, m_plate_recognition_->getMInputImageSize());
+                TextLine candidate_text;
+//                cv::imshow("align_pad", align_pad);
+//                cv::waitKey(0);
+//                if (j == 0)
+//                    cv::imwrite("a.jpg", align_pad);
+                m_plate_recognition_->Inference(align_pad, candidate_text);
+                text_line.code += candidate_text.code;
+                text_line.average_score += candidate_text.average_score;
+            }
+            text_line.average_score /= candidate.size();
+//            cv::imshow("top_crop", top_crop);
+//            cv::imshow("bottom_crop", bottom_crop);
+//            cv::waitKey(0);
+        } else {
+            cv::Mat align_image_pad;
+            float wh_ratio = (float) align_image.cols / align_image.rows;
+            imagePadding(align_image, align_image_pad, wh_ratio, m_plate_recognition_->getMInputImageSize());
+            m_plate_recognition_->Inference(align_image_pad, text_line);
+        }
+        cv::resize(align_image, align_image, m_plate_classification_->getMInputImageSize());
+//        SLOG_CRITICAL("cfg: {}", text_line.average_score);
+//        SLOG_CRITICAL("code: {}", text_line.code);
+        if (text_line.average_score < m_plate_recognition_->getMConfidenceThreshold()) {
+            continue;
+        }
+//        cv::imshow("resize", align_image_pad);
+//        cv::imshow("align_image", align_image);
+//        cv::waitKey(0);
+//        obj.color_classify = PlateColor::GREEN;
+//        obj.layers = loc.layers;
+//        LOGD("size %d", text_line.code.size());
+        if (text_line.code.size() >= 7) {
+            obj.text_confidence = text_line.average_score;
+            auto type = PreGetPlateType(text_line.code);
+            if (type == PlateType::UNKNOWN) {
+                m_plate_classification_->Inference(align_image);
+                auto color_type = m_plate_classification_->getMOutputColor();
+//                obj.color_classify = color_type;
+//                obj.layers = loc.layers;
+                if (color_type == PlateColor::YELLOW) {
+                    // 黄牌
+                    if (loc.layers == LayersNum::DOUBLE) {
+                        // 双层黄牌
+                        type = PlateType::YELLOW_DOUBLE;
+                    } else {
+                        // 单层黄牌
+                        type = PlateType::YELLOW_SINGLE;
+                    }
+                } else if (color_type == PlateColor::BLUE) {
+                    type = PlateType::BLUE;
+                } else if (color_type == PlateColor::GREEN) {
+                    type = PlateType::GREEN;
+                }
+            }
+            obj.type = type;
+
+            strcpy(obj.code, text_line.code.c_str());
+//        obj.code = text_line.code;
+
+//        cv::imshow("align_image_pad", align_image_pad);
+//        cv::waitKey(0);
+            m_object_results_.push_back(obj);
+        }
+    }
+}
+
+int32_t HyperLPRContext::Initialize(const std::string& models_folder_path, int max_num, DetectLevel detect_level,
+                                 int threads, bool use_half, float box_conf_threshold, float nms_threshold, float rec_confidence_threshold) {
+    int32_t ret;
+    std::string det_backbone_model = models_folder_path + "/" + hyper::DETECT_LOW_BACKBONE_FILENAME;
+    if (!exists(det_backbone_model)) {
+        LOGE("file: %s does not exist.", hyper::DETECT_LOW_BACKBONE_FILENAME.c_str());
+        return hRetErr;
+    }
+    std::string det_header_model = models_folder_path + "/" + hyper::DETECT_LOW_HEAD_FILENAME;
+    if (!exists(det_header_model)) {
+        LOGE("file: %s does not exist.", hyper::DETECT_LOW_HEAD_FILENAME.c_str());
+        return hRetErr;
+    }
+    if (detect_level == DETECT_LEVEL_HIGH) {
+        det_backbone_model = models_folder_path + "/" + hyper::DETECT_HIGH_BACKBONE_FILENAME;
+        if (!exists(det_backbone_model)) {
+            LOGE("file: %s does not exist.", hyper::DETECT_HIGH_BACKBONE_FILENAME.c_str());
+            return hRetErr;
+        }
+        det_header_model = models_folder_path + "/" + hyper::DETECT_HIGH_HEAD_FILENAME;
+        if (!exists(det_header_model)) {
+            LOGE("file: %s does not exist.", hyper::DETECT_HIGH_HEAD_FILENAME.c_str());
+            return hRetErr;
+        }
+        m_pre_image_size_ = 640;
+    }
+    m_plate_detector_ = std::make_shared<DetArch>();
+    ret = m_plate_detector_->Initialize(det_backbone_model, det_header_model, m_pre_image_size_, threads, box_conf_threshold, nms_threshold,
+                                  use_half);
+    if (ret != hRetOk) {
+        LOGE("Detect model loading errors.");
+        return hRetErr;
+    }
+    // init classification
+    std::string classification_model = models_folder_path + "/" + hyper::CLS_MODEL_FILENAME;
+    if (!exists(classification_model)) {
+        LOGE("file: %s does not exist.", hyper::CLS_MODEL_FILENAME.c_str());
+        return hRetErr;
+    }
+    m_plate_classification_ = std::make_shared<ClassificationEngine>();
+    ret = m_plate_classification_->Initialize(classification_model, CLS_INPUT_SIZE, threads, use_half);
+    if (ret != hRetOk) {
+        LOGE("Cls model loading errors.");
+        return hRetErr;
+    }
+    // init recognition
+    std::string recognition_model = models_folder_path + "/" + hyper::REC_MODEL_FILENAME;
+    if (!exists(recognition_model)) {
+        LOGE("file: %s does not exist.", hyper::REC_MODEL_FILENAME.c_str());
+        return hRetErr;
+    }
+    m_plate_recognition_ = std::make_shared<RecognitionEngine>();
+    ret = m_plate_recognition_->Initialize(recognition_model, REC_INPUT_SIZE, threads, rec_confidence_threshold,
+                                     use_half);
+    if (ret != hRetOk) {
+        LOGE("Rec model loading errors.");
+        return hRetErr;
+    }
+    m_rec_max_num_ = std::max(1, max_num);      // Can't be less than 1
+
+    m_init_status_ = hRetOk;
+
+    return hRetOk;
+}
+
+PlateResultList &HyperLPRContext::getMObjectResults() {
+    return m_object_results_;
+}
+
+PlateType HyperLPRContext::PreGetPlateType(std::string& code) {
+    PlateType type = PlateType::UNKNOWN;
+    if (code[0] == 'W' && code[1] == 'J'){
+        type = PlateType::WHILE_SINGLE;
+    } else if (code.size() == 10) {
+        type = PlateType::GREEN;
+    } else if (code.find("学") != -1) {
+        type = PlateType::BLUE;
+    } else if (code.find("港") != -1) {
+        type = PlateType::BLACK_HK_MACAO;
+    } else if (code.find("澳") != -1) {
+        type = PlateType::BLACK_HK_MACAO;
+    } else if (code.find("警") != -1) {
+        type = PlateType::WHILE_SINGLE;
+    } else if (code.find("粤Z") != -1) {
+        type = PlateType::BLACK_HK_MACAO;
+    }
+
+    return type;
+}
+
+int32_t HyperLPRContext::getMInitStatus() const {
+    return m_init_status_;
+}
+
+
+}
+
+
diff --git a/cpp/src/context_module/hyper_lpr_context.h b/cpp/src/context_module/hyper_lpr_context.h
new file mode 100644
index 0000000..55eef1a
--- /dev/null
+++ b/cpp/src/context_module/hyper_lpr_context.h
@@ -0,0 +1,76 @@
+//
+// Created by tunm on 2023/1/25.
+//
+
+#ifndef ZEPHYRLPR_HYPER_LPR_CONTEXT_H
+#define ZEPHYRLPR_HYPER_LPR_CONTEXT_H
+
+#include <iostream>
+#include <opencv2/opencv.hpp>
+#include "nn_implementation_module/all.h"
+#include "buffer_module/all.h"
+#include "hyper_lpr_common.h"
+
+namespace hyper {
+
+enum {
+    hRetOk = InferenceHelper::kRetOk,           ///< 成功
+    hRetErr = InferenceHelper::kRetErr,         ///< 失败
+};
+
+
+class HyperLPRContext {
+public:
+
+    HyperLPRContext(const HyperLPRContext &) = delete;
+
+    HyperLPRContext &operator=(const HyperLPRContext &) = delete;
+
+    explicit HyperLPRContext();
+
+    void operator()(CameraBuffer &buffer);
+
+    /**
+     * 手动初始化并实例化内部模型对象
+     * @param models_folder_path 存放模型文件夹的路径地址
+     * @param max_num 最大识别车牌数量
+     * @param detect_level 检测器等级 low速度快，high速度慢检出率略高于low
+     * @param threads 推理线程数量 (暂时无效)
+     * @param use_half 是否开启半精度推理 (暂时无效)
+     * @param box_conf_threshold 检测框置信度阈值
+     * @param nms_threshold 非极大值抑制阈值
+     * @param rec_confidence_threshold 车牌字符识别置信度阈值
+     * @return 初始化状态
+     */
+    int32_t Initialize(const std::string& models_folder_path, int max_num = 1, DetectLevel detect_level = DETECT_LEVEL_LOW, int threads = 1, bool use_half = false, float box_conf_threshold = 0.3f,
+                     float nms_threshold = 0.5f,
+                    float rec_confidence_threshold = 0.75f);
+
+    PlateResultList &getMObjectResults();
+
+    static PlateType PreGetPlateType(std::string& code);
+
+    int32_t getMInitStatus() const;
+
+private:
+    std::shared_ptr<DetArch> m_plate_detector_;
+
+    std::shared_ptr<ClassificationEngine> m_plate_classification_;
+
+    std::shared_ptr<RecognitionEngine> m_plate_recognition_;
+
+    int m_pre_image_size_ = 320;
+
+    PlateResultList m_object_results_;
+
+    int m_rec_max_num_ = 1;
+
+    int32_t m_init_status_ = hRetErr;
+
+};
+
+} // namespace
+
+
+
+#endif //ZEPHYRLPR_HYPER_LPR_CONTEXT_H
diff --git a/cpp/src/doc.h b/cpp/src/doc.h
new file mode 100644
index 0000000..f2aed06
--- /dev/null
+++ b/cpp/src/doc.h
@@ -0,0 +1,8 @@
+//
+// Created by Tunm-Air13 on 2022/12/29.
+//
+
+#ifndef ZEPHYRLPR_DOC_H
+#define ZEPHYRLPR_DOC_H
+
+#endif //ZEPHYRLPR_DOC_H
diff --git a/cpp/src/inference_helper_module/inference_helper.cpp b/cpp/src/inference_helper_module/inference_helper.cpp
new file mode 100644
index 0000000..9512692
--- /dev/null
+++ b/cpp/src/inference_helper_module/inference_helper.cpp
@@ -0,0 +1,430 @@
+/* Copyright 2021 iwatake2222
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+/*** Include ***/
+/* for general */
+#include <cstdint>
+#include <cstdlib>
+#include <cmath>
+#include <cstring>
+#include <string>
+#include <vector>
+#include <array>
+#include <algorithm>
+#include <chrono>
+
+/* for My modules */
+#include "inference_helper_log.h"
+#include "inference_helper.h"
+
+#ifdef INFERENCE_HELPER_ENABLE_OPENCV
+#include "inference_helper_opencv.h"
+#endif
+#if defined(INFERENCE_HELPER_ENABLE_TFLITE) || defined(INFERENCE_HELPER_ENABLE_TFLITE_DELEGATE_XNNPACK) || defined(INFERENCE_HELPER_ENABLE_TFLITE_DELEGATE_GPU) || defined(INFERENCE_HELPER_ENABLE_TFLITE_DELEGATE_EDGETPU)
+#include "inference_helper_tensorflow_lite.h"
+#endif
+#ifdef INFERENCE_HELPER_ENABLE_TENSORRT
+#include "inference_helper_tensorrt.h"
+#endif
+#ifdef INFERENCE_HELPER_ENABLE_NCNN
+#include "inference_helper_ncnn.h"
+#endif
+#ifdef INFERENCE_HELPER_ENABLE_MNN
+#include "inference_helper_mnn.h"
+#endif
+#ifdef INFERENCE_HELPER_ENABLE_SNPE
+#include "inference_helper_snpe.h"
+#endif
+#ifdef INFERENCE_HELPER_ENABLE_ARMNN
+#include "inference_helper_armnn.h"
+#endif
+#if defined(INFERENCE_HELPER_ENABLE_NNABLA) || defined(INFERENCE_HELPER_ENABLE_NNABLA_CUDA)
+#include "inference_helper_nnabla.h"
+#endif
+#if defined(INFERENCE_HELPER_ENABLE_ONNX_RUNTIME) || defined(INFERENCE_HELPER_ENABLE_ONNX_RUNTIME_CUDA)
+#include "inference_helper_onnx_runtime.h"
+#endif
+#if defined(INFERENCE_HELPER_ENABLE_LIBTORCH) || defined(INFERENCE_HELPER_ENABLE_LIBTORCH_CUDA)
+#include "inference_helper_libtorch.h"
+#endif
+#if defined(INFERENCE_HELPER_ENABLE_TENSORFLOW) || defined(INFERENCE_HELPER_ENABLE_TENSORFLOW_GPU)
+#include "inference_helper_tensorflow.h"
+#endif
+#ifdef INFERENCE_HELPER_ENABLE_SAMPLE
+#include "inference_helper_sample.h"
+#endif
+#ifdef INFERENCE_HELPER_ENABLE_RKNN
+#include "inference_helper_rknn.h"
+#endif
+
+/*** Macro ***/
+#define TAG "InferenceHelper"
+#define PRINT(...)   INFERENCE_HELPER_LOG_PRINT(TAG, __VA_ARGS__)
+#define PRINT_E(...) INFERENCE_HELPER_LOG_PRINT_E(TAG, __VA_ARGS__)
+
+
+InferenceHelper* InferenceHelper::Create(const InferenceHelper::HelperType helper_type)
+{
+    InferenceHelper* p = nullptr;
+    switch (helper_type) {
+#ifdef INFERENCE_HELPER_ENABLE_OPENCV
+    case kOpencv:
+    case kOpencvGpu:
+        PRINT("Use OpenCV \n");
+        p = new InferenceHelperOpenCV();
+        break;
+#endif
+#ifdef INFERENCE_HELPER_ENABLE_TFLITE
+    case kTensorflowLite:
+        PRINT("Use TensorflowLite\n");
+        p = new InferenceHelperTensorflowLite();
+        break;
+#endif
+#ifdef INFERENCE_HELPER_ENABLE_TFLITE_DELEGATE_XNNPACK
+    case kTensorflowLiteXnnpack:
+        PRINT("Use TensorflowLite XNNPACK Delegate\n");
+        p = new InferenceHelperTensorflowLite();
+        break;
+#endif
+#ifdef INFERENCE_HELPER_ENABLE_TFLITE_DELEGATE_GPU
+    case kTensorflowLiteGpu:
+        PRINT("Use TensorflowLite GPU Delegate\n");
+        p = new InferenceHelperTensorflowLite();
+        break;
+#endif
+#ifdef INFERENCE_HELPER_ENABLE_TFLITE_DELEGATE_EDGETPU
+    case kTensorflowLiteEdgetpu:
+        PRINT("Use TensorflowLite EdgeTPU Delegate\n");
+        p = new InferenceHelperTensorflowLite();
+        break;
+#endif
+#ifdef INFERENCE_HELPER_ENABLE_TFLITE_DELEGATE_NNAPI
+    case kTensorflowLiteNnapi:
+        PRINT("Use TensorflowLite NNAPI Delegate\n");
+        p = new InferenceHelperTensorflowLite();
+        break;
+#endif
+#ifdef INFERENCE_HELPER_ENABLE_TENSORRT
+    case kTensorrt:
+        PRINT("Use TensorRT \n");
+        p = new InferenceHelperTensorRt();
+        break;
+#endif
+#ifdef INFERENCE_HELPER_ENABLE_NCNN
+    case kNcnn:
+    case kNcnnVulkan:
+        PRINT("Use NCNN\n");
+        p = new InferenceHelperNcnn();
+        break;
+#endif
+#ifdef INFERENCE_HELPER_ENABLE_MNN
+    case kMnn:
+        PRINT("Use MNN\n");
+        p = new InferenceHelperMnn();
+        break;
+#endif
+#ifdef INFERENCE_HELPER_ENABLE_SNPE
+    case kSnpe:
+        PRINT("Use SNPE\n");
+        p = new InferenceHelperSnpe();
+        break;
+#endif
+#ifdef INFERENCE_HELPER_ENABLE_ARMNN
+    case kArmnn:
+        PRINT("Use ARMNN\n");
+        p = new InferenceHelperArmnn();
+        break;
+#endif
+#ifdef INFERENCE_HELPER_ENABLE_NNABLA
+    case kNnabla:
+        PRINT("Use NNabla\n");
+        p = new InferenceHelperNnabla();
+        break;
+#endif
+#ifdef INFERENCE_HELPER_ENABLE_NNABLA_CUDA
+    case kNnablaCuda:
+        PRINT("Use NNabla_CUDA\n");
+        p = new InferenceHelperNnabla();
+        break;
+#endif
+#ifdef INFERENCE_HELPER_ENABLE_ONNX_RUNTIME
+    case kOnnxRuntime:
+        PRINT("Use ONNX Runtime\n");
+        p = new InferenceHelperOnnxRuntime();
+        break;
+#endif
+#ifdef INFERENCE_HELPER_ENABLE_ONNX_RUNTIME_CUDA
+    case kOnnxRuntimeCuda:
+        PRINT("Use ONNX Runtime_CUDA\n");
+        p = new InferenceHelperOnnxRuntime();
+        break;
+#endif
+#ifdef INFERENCE_HELPER_ENABLE_LIBTORCH
+    case kLibtorch:
+        PRINT("Use LibTorch\n");
+        p = new InferenceHelperLibtorch();
+        break;
+#endif
+#ifdef INFERENCE_HELPER_ENABLE_LIBTORCH_CUDA
+    case kLibtorchCuda:
+        PRINT("Use LibTorch CUDA\n");
+        p = new InferenceHelperLibtorch();
+        break;
+#endif
+#ifdef INFERENCE_HELPER_ENABLE_TENSORFLOW
+    case kTensorflow:
+        PRINT("Use TensorFlow\n");
+        p = new InferenceHelperTensorflow();
+        break;
+#endif
+#ifdef INFERENCE_HELPER_ENABLE_TENSORFLOW_GPU
+    case kTensorflowGpu:
+        PRINT("Use TensorFlow GPU\n");
+        p = new InferenceHelperTensorflow();
+        break;
+#endif
+#ifdef INFERENCE_HELPER_ENABLE_SAMPLE
+    case kSample:
+        PRINT("Do not use this. this is just a reference code\n");
+        p = new InferenceHelperSample();
+        break;
+#endif
+#ifdef INFERENCE_HELPER_ENABLE_RKNN
+    case kRknn:
+        PRINT("Use Rknn\n")
+        p = new InferenceHelperRKNN();
+        break;
+
+#endif
+    default:
+        PRINT_E("Unsupported inference helper type (%d)\n", helper_type);
+        break;
+    }
+    if (p == nullptr) {
+        PRINT_E("Failed to create inference helper\n");
+    } else {
+        p->helper_type_ = helper_type;
+    }
+    return p;
+}
+
+#ifdef INFERENCE_HELPER_ENABLE_PRE_PROCESS_BY_OPENCV
+#include <opencv2/opencv.hpp>
+void InferenceHelper::PreProcessByOpenCV(const InputTensorInfo& input_tensor_info, bool is_nchw, cv::Mat& img_blob)
+{
+    /* Generate mat from original data */
+    cv::Mat img_src = cv::Mat(cv::Size(input_tensor_info.image_info.width, input_tensor_info.image_info.height), (input_tensor_info.image_info.channel == 3) ? CV_8UC3 : CV_8UC1, input_tensor_info.data);
+
+    /* Crop image */
+    if (input_tensor_info.image_info.width == input_tensor_info.image_info.crop_width && input_tensor_info.image_info.height == input_tensor_info.image_info.crop_height) {
+        /* do nothing */
+    } else {
+        img_src = img_src(cv::Rect(input_tensor_info.image_info.crop_x, input_tensor_info.image_info.crop_y, input_tensor_info.image_info.crop_width, input_tensor_info.image_info.crop_height));
+    }
+
+    /* Resize image */
+    if (input_tensor_info.image_info.crop_width == input_tensor_info.GetWidth() && input_tensor_info.image_info.crop_height == input_tensor_info.GetHeight()) {
+        /* do nothing */
+    } else {
+        cv::resize(img_src, img_src, cv::Size(input_tensor_info.GetWidth(), input_tensor_info.GetHeight()));
+    }
+
+    /* Convert color type */
+    if (input_tensor_info.image_info.channel == input_tensor_info.GetChannel()) {
+        if (input_tensor_info.image_info.channel == 3 && input_tensor_info.image_info.swap_color) {
+            cv::cvtColor(img_src, img_src, cv::COLOR_BGR2RGB);
+        }
+    } else if (input_tensor_info.image_info.channel == 3 && input_tensor_info.GetChannel() == 1) {
+        cv::cvtColor(img_src, img_src, (input_tensor_info.image_info.is_bgr) ? cv::COLOR_BGR2GRAY : cv::COLOR_RGB2GRAY);
+    } else if (input_tensor_info.image_info.channel == 1 && input_tensor_info.GetChannel() == 3) {
+        cv::cvtColor(img_src, img_src, cv::COLOR_GRAY2BGR);
+    }
+
+    if (input_tensor_info.tensor_type == TensorInfo::kTensorTypeFp32) {
+        /* Normalize image */
+        if (input_tensor_info.GetChannel() == 3) {
+#if 1
+            img_src.convertTo(img_src, CV_32FC3);
+            cv::subtract(img_src, cv::Scalar(cv::Vec<float, 3>(input_tensor_info.normalize.mean)), img_src);
+            cv::multiply(img_src, cv::Scalar(cv::Vec<float, 3>(input_tensor_info.normalize.norm)), img_src);
+#else
+            img_src.convertTo(img_src, CV_32FC3, 1.0 / 255);
+            cv::subtract(img_src, cv::Scalar(cv::Vec<float, 3>(input_tensor_info.normalize.mean)), img_src);
+            cv::divide(img_src, cv::Scalar(cv::Vec<float, 3>(input_tensor_info.normalize.norm)), img_src);
+#endif
+        } else {
+#if 1
+            img_src.convertTo(img_src, CV_32FC1);
+            cv::subtract(img_src, cv::Scalar(cv::Vec<float, 1>(input_tensor_info.normalize.mean)), img_src);
+            cv::multiply(img_src, cv::Scalar(cv::Vec<float, 1>(input_tensor_info.normalize.norm)), img_src);
+#else
+            img_src.convertTo(img_src, CV_32FC1, 1.0 / 255);
+            cv::subtract(img_src, cv::Scalar(cv::Vec<float, 1>(input_tensor_info.normalize.mean)), img_src);
+            cv::divide(img_src, cv::Scalar(cv::Vec<float, 1>(input_tensor_info.normalize.norm)), img_src);
+#endif
+        }
+    } else {
+        /* do nothing */
+    }
+
+    if (is_nchw) {
+        /* Convert to 4-dimensional Mat in NCHW */
+        img_src = cv::dnn::blobFromImage(img_src);
+    }
+
+    img_blob = img_src;
+    //memcpy(blobData, img_src.data, img_src.cols * img_src.rows * img_src.channels());
+
+}
+
+#else 
+/* For the environment where OpenCV is not supported */
+void InferenceHelper::PreProcessByOpenCV(const InputTensorInfo& input_tensor_info, bool is_nchw, cv::Mat& img_blob)
+{
+    PRINT_E("[PreProcessByOpenCV] Unsupported function called\n");
+    exit(-1);
+}
+#endif
+
+
+
+void InferenceHelper::ConvertNormalizeParameters(InputTensorInfo& tensor_info)
+{
+    if (tensor_info.data_type != InputTensorInfo::kDataTypeImage) return;
+
+#if 0
+    /* Convert to speeden up normalization:  ((src / 255) - mean) / norm  = src * 1 / (255 * norm) - (mean / norm) */
+    for (int32_t i = 0; i < 3; i++) {
+        tensor_info.normalize.mean[i] /= tensor_info.normalize.norm[i];
+        tensor_info.normalize.norm[i] *= 255.0f;
+        tensor_info.normalize.norm[i] = 1.0f / tensor_info.normalize.norm[i];
+    }
+#endif
+#if 1
+    /* Convert to speeden up normalization:  ((src / 255) - mean) / norm = (src  - (mean * 255))  * (1 / (255 * norm)) */
+    for (int32_t i = 0; i < 3; i++) {
+        tensor_info.normalize.mean[i] *= 255.0f;
+        tensor_info.normalize.norm[i] *= 255.0f;
+        tensor_info.normalize.norm[i] = 1.0f / tensor_info.normalize.norm[i];
+    }
+#endif
+}
+
+
+void InferenceHelper::PreProcessImage(int32_t num_thread, const InputTensorInfo& input_tensor_info, float* dst)
+{
+    const int32_t img_width = input_tensor_info.GetWidth();
+    const int32_t img_height = input_tensor_info.GetHeight();
+    const int32_t img_channel = input_tensor_info.GetChannel();
+    uint8_t* src = (uint8_t*)(input_tensor_info.data);
+    if (input_tensor_info.is_nchw == true) {
+        /* convert NHWC to NCHW */
+#pragma omp parallel for num_threads(num_thread)
+        for (int32_t c = 0; c < img_channel; c++) {
+            for (int32_t i = 0; i < img_width * img_height; i++) {
+                dst[c * img_width * img_height + i] = (src[i * img_channel + c] - input_tensor_info.normalize.mean[c]) * input_tensor_info.normalize.norm[c];
+            }
+        }
+    } else {
+        /* convert NHWC to NHWC */
+#pragma omp parallel for num_threads(num_thread)
+        for (int32_t i = 0; i < img_width * img_height; i++) {
+            for (int32_t c = 0; c < img_channel; c++) {
+#if 1
+                dst[i * img_channel + c] = (src[i * img_channel + c] - input_tensor_info.normalize.mean[c]) * input_tensor_info.normalize.norm[c];
+#else
+                dst[i * img_channel + c] = (src[i * img_channel + c] / 255.0f - input_tensor_info.normalize.mean[c]) / input_tensor_info.normalize.norm[c];
+#endif
+            }
+        }
+    }
+}
+
+void InferenceHelper::PreProcessImage(int32_t num_thread, const InputTensorInfo& input_tensor_info, uint8_t* dst)
+{
+    const int32_t img_width = input_tensor_info.GetWidth();
+    const int32_t img_height = input_tensor_info.GetHeight();
+    const int32_t img_channel = input_tensor_info.GetChannel();
+    uint8_t* src = (uint8_t*)(input_tensor_info.data);
+    if (input_tensor_info.is_nchw == true) {
+        /* convert NHWC to NCHW */
+#pragma omp parallel for num_threads(num_thread)
+        for (int32_t c = 0; c < img_channel; c++) {
+            for (int32_t i = 0; i < img_width * img_height; i++) {
+                dst[c * img_width * img_height + i] = src[i * img_channel + c];
+            }
+        }
+    } else {
+        /* convert NHWC to NHWC */
+        std::copy(src, src + input_tensor_info.GetElementNum(), dst);
+    }
+}
+
+void InferenceHelper::PreProcessImage(int32_t num_thread, const InputTensorInfo& input_tensor_info, int8_t* dst)
+{
+    const int32_t img_width = input_tensor_info.GetWidth();
+    const int32_t img_height = input_tensor_info.GetHeight();
+    const int32_t img_channel = input_tensor_info.GetChannel();
+    uint8_t* src = (uint8_t*)(input_tensor_info.data);
+    if (input_tensor_info.is_nchw == true) {
+        /* convert NHWC to NCHW */
+#pragma omp parallel for num_threads(num_thread)
+        for (int32_t c = 0; c < img_channel; c++) {
+            for (int32_t i = 0; i < img_width * img_height; i++) {
+                dst[c * img_width * img_height + i] = src[i * img_channel + c] - 128;
+            }
+        }
+    } else {
+#pragma omp parallel for num_threads(num_thread)
+        for (int32_t i = 0; i < img_width * img_height; i++) {
+            for (int32_t c = 0; c < img_channel; c++) {
+                dst[i * img_channel + c] = src[i * img_channel + c] - 128;
+            }
+        }
+    }
+}
+
+template<typename T>
+void InferenceHelper::PreProcessBlob(int32_t num_thread, const InputTensorInfo& input_tensor_info, T* dst)
+{
+    const int32_t img_width = input_tensor_info.GetWidth();
+    const int32_t img_height = input_tensor_info.GetHeight();
+    const int32_t img_channel = input_tensor_info.GetChannel();
+    T* src = static_cast<T*>(input_tensor_info.data);
+    if ((input_tensor_info.data_type == InputTensorInfo::kDataTypeBlobNchw && input_tensor_info.is_nchw) || (input_tensor_info.data_type == InputTensorInfo::kDataTypeBlobNhwc && !input_tensor_info.is_nchw)) {
+        std::copy(src, src + input_tensor_info.GetElementNum(), dst);
+    } else if (input_tensor_info.data_type == InputTensorInfo::kDataTypeBlobNchw) {
+        /* NCHW -> NHWC */
+#pragma omp parallel for num_threads(num_thread)
+        for (int32_t i = 0; i < img_width * img_height; i++) {
+            for (int32_t c = 0; c < img_channel; c++) {
+                dst[i * img_channel + c] = src[c * (img_width * img_height) + i];
+            }
+        }
+    } else if (input_tensor_info.data_type == InputTensorInfo::kDataTypeBlobNhwc) {
+        /* NHWC -> NCHW */
+#pragma omp parallel for num_threads(num_thread)
+        for (int32_t i = 0; i < img_width * img_height; i++) {
+            for (int32_t c = 0; c < img_channel; c++) {
+                dst[c * (img_width * img_height) + i] = src[i * img_channel + c];
+            }
+        }
+    }
+}
+
+template void InferenceHelper::PreProcessBlob<float>(int32_t num_thread, const InputTensorInfo& input_tensor_info, float* dst);
+template void InferenceHelper::PreProcessBlob<int32_t>(int32_t num_thread, const InputTensorInfo& input_tensor_info, int32_t* dst);
+template void InferenceHelper::PreProcessBlob<int64_t>(int32_t num_thread, const InputTensorInfo& input_tensor_info, int64_t* dst);
+template void InferenceHelper::PreProcessBlob<uint8_t>(int32_t num_thread, const InputTensorInfo& input_tensor_info, uint8_t* dst);
+template void InferenceHelper::PreProcessBlob<int8_t>(int32_t num_thread, const InputTensorInfo& input_tensor_info, int8_t* dst);
diff --git a/cpp/src/inference_helper_module/inference_helper.h b/cpp/src/inference_helper_module/inference_helper.h
new file mode 100644
index 0000000..d5f84a2
--- /dev/null
+++ b/cpp/src/inference_helper_module/inference_helper.h
@@ -0,0 +1,280 @@
+/* Copyright 2021 iwatake2222
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+#ifndef INFERENCE_HELPER_
+#define INFERENCE_HELPER_
+
+/* for general */
+#include <cstdint>
+#include <cmath>
+#include <string>
+#include <vector>
+#include <array>
+#include <memory>
+
+class TensorInfo {
+public:
+    enum {
+        kTensorTypeNone,
+        kTensorTypeUint8,
+        kTensorTypeInt8,
+        kTensorTypeFp32,
+        kTensorTypeInt32,
+        kTensorTypeInt64,
+    };
+
+public:
+    TensorInfo()
+        : name("")
+        , id(-1)
+        , tensor_type(kTensorTypeNone)
+        , is_nchw(true)
+    {}
+    ~TensorInfo() {}
+
+    int32_t GetElementNum() const
+    {
+        int32_t element_num = 1;
+        for (const auto& dim : tensor_dims) {
+            element_num *= dim;
+        }
+        return element_num;
+    }
+
+    int32_t GetBatch() const
+    {
+        if (tensor_dims.size() <= 0) return -1;
+        return tensor_dims[0];
+    }
+    
+    int32_t GetChannel() const
+    {
+        if (is_nchw) {
+            if (tensor_dims.size() <= 1) return -1;
+            return tensor_dims[1];
+        } else {
+            if (tensor_dims.size() <= 3) return -1;
+            return tensor_dims[3];
+        }
+    }
+
+    int32_t GetHeight() const
+    {
+        if (is_nchw) {
+            if (tensor_dims.size() <= 2) return -1;
+            return tensor_dims[2];
+        } else {
+            if (tensor_dims.size() <= 1) return -1;
+            return tensor_dims[1];
+        }
+    }
+
+    int32_t GetWidth() const
+    {
+        if (is_nchw) {
+            if (tensor_dims.size() <= 3) return -1;
+            return tensor_dims[3];
+        } else {
+            if (tensor_dims.size() <= 2) return -1;
+            return tensor_dims[2];
+        }
+    }
+
+public:
+    std::string          name;           // [In] Set the name_ of tensor
+    int32_t              id;             // [Out] Do not modify (Used in InferenceHelper)
+    int32_t              tensor_type;    // [In] The type of tensor (e.g. kTensorTypeFp32)
+    std::vector<int32_t> tensor_dims;    // InputTensorInfo:   [In] The dimentions of tensor. (If empty at initialize, the size is updated from model info.)
+                                         // OutputTensorInfo: [Out] The dimentions of tensor is set from model information
+    bool                 is_nchw;        // [IN] NCHW or NHWC
+};
+
+class InputTensorInfo : public TensorInfo {
+public:
+    enum {
+        kDataTypeImage,
+        kDataTypeBlobNhwc,  // data_ which already finished preprocess(color conversion, resize, normalize_, etc.)
+        kDataTypeBlobNchw,
+    };
+
+public:
+    InputTensorInfo()
+        : data(nullptr)
+        , data_type(kDataTypeImage)
+        , image_info({ -1, -1, -1, -1, -1, -1, -1, true, false })
+        , normalize({ 0.0f, 0.0f, 0.0f, 1.0f, 1.0f, 1.0f })
+    {}
+
+    InputTensorInfo(std::string name_, int32_t tensor_type_, bool is_nchw_ = true)
+        : InputTensorInfo()
+    {
+        name = name_;
+        tensor_type = tensor_type_;
+        is_nchw = is_nchw_;
+    }
+
+    ~InputTensorInfo() {}
+
+public:
+    void*   data;      // [In] Set the pointer to image/blob
+    int32_t data_type; // [In] Set the type of data_ (e.g. kDataTypeImage)
+
+    struct {
+        int32_t width;
+        int32_t height;
+        int32_t channel;
+        int32_t crop_x;
+        int32_t crop_y;
+        int32_t crop_width;
+        int32_t crop_height;
+        bool    is_bgr;        // used when channel == 3 (true: BGR, false: RGB)
+        bool    swap_color;
+    } image_info;              // [In] used when data_type_ == kDataTypeImage
+
+    struct {
+        float mean[3];
+        float norm[3];
+    } normalize;              // [In] used when data_type_ == kDataTypeImage
+};
+
+
+class OutputTensorInfo : public TensorInfo {
+public:
+    OutputTensorInfo()
+        : data(nullptr)
+        , quant({ 1.0f, 0 })
+        , data_fp32_(nullptr)
+    {}
+
+    OutputTensorInfo(std::string name_, int32_t tensor_type_, bool is_nchw_ = true)
+        : OutputTensorInfo()
+    {
+        name = name_;
+        tensor_type = tensor_type_;
+        is_nchw = is_nchw_;
+    }
+    
+    ~OutputTensorInfo() {
+        if (data_fp32_ != nullptr) {
+            delete[] data_fp32_;
+        }
+    }
+
+    float* GetDataAsFloat() {       /* Returned pointer should be with const, but returning pointer without const is convenient to create cv::Mat */
+        if (tensor_type == kTensorTypeUint8 || tensor_type == kTensorTypeInt8) {
+            if (data_fp32_ == nullptr) {
+                data_fp32_ = new float[GetElementNum()];
+            }
+            if (tensor_type == kTensorTypeUint8) {
+#pragma omp parallel
+                for (int32_t i = 0; i < GetElementNum(); i++) {
+                    const uint8_t* val_uint8 = static_cast<const uint8_t*>(data);
+                    float val_float = (val_uint8[i] - quant.zero_point) * quant.scale;
+                    data_fp32_[i] = val_float;
+                }
+            } else {
+#pragma omp parallel
+                for (int32_t i = 0; i < GetElementNum(); i++) {
+                    const int8_t* val_int8 = static_cast<const int8_t*>(data);
+                    float val_float = (val_int8[i] - quant.zero_point) * quant.scale;
+                    data_fp32_[i] = val_float;
+                }
+            }
+            return data_fp32_;
+        } else if (tensor_type == kTensorTypeFp32) {
+            return static_cast<float*>(data);
+        } else {
+            return nullptr;
+        }
+    }
+
+public:
+    void* data;     // [Out] Pointer to the output data_
+    struct {
+        float   scale;
+        int32_t zero_point;
+    } quant;        // [Out] Parameters for dequantization (convert uint8 to float)
+
+private:
+    float* data_fp32_;
+};
+
+
+namespace cv {
+    class Mat;
+};
+
+class InferenceHelper {
+public:
+    enum {
+        kRetOk = 0,
+        kRetErr = -1,
+    };
+
+    typedef enum {
+        kOpencv,
+        kOpencvGpu,
+        kTensorflowLite,
+        kTensorflowLiteXnnpack,
+        kTensorflowLiteGpu,
+        kTensorflowLiteEdgetpu,
+        kTensorflowLiteNnapi,
+        kTensorrt,
+        kNcnn,
+        kNcnnVulkan,
+        kMnn,
+        kSnpe,
+        kArmnn,
+        kNnabla,
+        kNnablaCuda,
+        kOnnxRuntime,
+        kOnnxRuntimeCuda,
+        kLibtorch,
+        kLibtorchCuda,
+        kTensorflow,
+        kTensorflowGpu,
+        kSample,
+        kRknn,
+    } HelperType;
+
+public:
+    static InferenceHelper* Create(const HelperType helper_type);
+    static void PreProcessByOpenCV(const InputTensorInfo& input_tensor_info, bool is_nchw, cv::Mat& img_blob);   // use this if the selected inference engine doesn't support pre-process
+
+public:
+    virtual ~InferenceHelper() {}
+    virtual int32_t SetNumThreads(const int32_t num_threads) = 0;
+    virtual int32_t SetCustomOps(const std::vector<std::pair<const char*, const void*>>& custom_ops) = 0;
+    virtual int32_t Initialize(const std::string& model_filename, std::vector<InputTensorInfo>& input_tensor_info_list, std::vector<OutputTensorInfo>& output_tensor_info_list) = 0;
+    virtual int32_t Initialize(char* model_buffer, int model_size, std::vector<InputTensorInfo>& input_tensor_info_list, std::vector<OutputTensorInfo>& output_tensor_info_list) = 0;
+    virtual int32_t Finalize(void) = 0;
+    virtual int32_t PreProcess(const std::vector<InputTensorInfo>& input_tensor_info_list) = 0;
+    virtual int32_t Process(std::vector<OutputTensorInfo>& output_tensor_info_list) = 0;
+    virtual int32_t ParameterInitialization(std::vector<InputTensorInfo>& input_tensor_info_list, std::vector<OutputTensorInfo>& output_tensor_info_list) = 0;
+
+protected:
+    void ConvertNormalizeParameters(InputTensorInfo& tensor_info);
+
+    void PreProcessImage(int32_t num_thread, const InputTensorInfo& input_tensor_info, float* dst);
+    void PreProcessImage(int32_t num_thread, const InputTensorInfo& input_tensor_info, uint8_t* dst);
+    void PreProcessImage(int32_t num_thread, const InputTensorInfo& input_tensor_info, int8_t* dst);
+
+    template<typename T>
+    void PreProcessBlob(int32_t num_thread, const InputTensorInfo& input_tensor_info, T *dst);
+
+protected:
+    HelperType helper_type_;
+};
+
+#endif
diff --git a/cpp/src/inference_helper_module/inference_helper_log.h b/cpp/src/inference_helper_module/inference_helper_log.h
new file mode 100644
index 0000000..19847dc
--- /dev/null
+++ b/cpp/src/inference_helper_module/inference_helper_log.h
@@ -0,0 +1,45 @@
+/* Copyright 2021 iwatake2222
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+#ifndef INFERENCE_HELPER_LOG_
+#define INFERENCE_HELPER_LOG_
+
+/* for general */
+#include <cstdint>
+#include <cmath>
+#include <string>
+#include <vector>
+#include <array>
+
+
+#if defined(ANDROID) || defined(__ANDROID__)
+#define CV_COLOR_IS_RGB
+#include <android/log.h>
+#define INFERENCE_HELPER_LOG_NDK_TAG "HyperLPR3-Native-Inference"
+#define INFERENCE_HELPER_LOG_PRINT_(...) __android_log_print(ANDROID_LOG_INFO, INFERENCE_HELPER_LOG_NDK_TAG, __VA_ARGS__)
+#else
+#define INFERENCE_HELPER_LOG_PRINT_(...) printf(__VA_ARGS__)
+#endif
+
+#define INFERENCE_HELPER_LOG_PRINT(INFERENCE_HELPER_LOG_PRINT_TAG, ...) do { \
+    INFERENCE_HELPER_LOG_PRINT_("[" INFERENCE_HELPER_LOG_PRINT_TAG "][%d] ", __LINE__); \
+    INFERENCE_HELPER_LOG_PRINT_(__VA_ARGS__); \
+} while(0);
+
+#define INFERENCE_HELPER_LOG_PRINT_E(INFERENCE_HELPER_LOG_PRINT_TAG, ...) do { \
+    INFERENCE_HELPER_LOG_PRINT_("[ERR: " INFERENCE_HELPER_LOG_PRINT_TAG "][%d] ", __LINE__); \
+    INFERENCE_HELPER_LOG_PRINT_(__VA_ARGS__); \
+} while(0);
+
+#endif
diff --git a/cpp/src/inference_helper_module/inference_helper_mnn.cpp b/cpp/src/inference_helper_module/inference_helper_mnn.cpp
new file mode 100644
index 0000000..cbc8317
--- /dev/null
+++ b/cpp/src/inference_helper_module/inference_helper_mnn.cpp
@@ -0,0 +1,317 @@
+/* Copyright 2021 iwatake2222
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+/*** Include ***/
+/* for general */
+#include <cstdint>
+#include <cstdlib>
+#include <cmath>
+#include <cstring>
+#include <string>
+#include <vector>
+#include <array>
+#include <algorithm>
+#include <chrono>
+
+/* for MNN */
+#include <MNN/ImageProcess.hpp>
+#include <MNN/Interpreter.hpp>
+#include <MNN/AutoTime.hpp>
+
+/* for My modules */
+#include "inference_helper_log.h"
+#include "inference_helper_mnn.h"
+
+/*** Macro ***/
+#define TAG "InferenceHelperMnn"
+#define PRINT(...)   INFERENCE_HELPER_LOG_PRINT(TAG, __VA_ARGS__)
+#define PRINT_E(...) INFERENCE_HELPER_LOG_PRINT_E(TAG, __VA_ARGS__)
+
+/*** Function ***/
+InferenceHelperMnn::InferenceHelperMnn()
+{
+    num_threads_ = 1;
+}
+
+InferenceHelperMnn::~InferenceHelperMnn()
+{
+}
+
+int32_t InferenceHelperMnn::SetNumThreads(const int32_t num_threads)
+{
+    num_threads_ = num_threads;
+    return kRetOk;
+}
+
+int32_t InferenceHelperMnn::SetCustomOps(const std::vector<std::pair<const char*, const void*>>& custom_ops)
+{
+    PRINT("[WARNING] This method is not supported\n");
+    return kRetOk;
+}
+
+int32_t InferenceHelperMnn::ParameterInitialization(std::vector<InputTensorInfo>& input_tensor_info_list, std::vector<OutputTensorInfo>& output_tensor_info_list) {
+    /* Check tensor info fits the info from model */
+    for (auto& input_tensor_info : input_tensor_info_list) {
+        auto input_tensor = net_->getSessionInput(session_, input_tensor_info.name.c_str());
+        if (input_tensor == nullptr) {
+            PRINT_E("Invalid input name (%s)\n", input_tensor_info.name.c_str());
+            return kRetErr;
+        }
+        if ((input_tensor->getType().code == halide_type_float) && (input_tensor_info.tensor_type == TensorInfo::kTensorTypeFp32)) {
+            /* OK */
+        } else if ((input_tensor->getType().code == halide_type_uint) && (input_tensor_info.tensor_type == TensorInfo::kTensorTypeUint8)) {
+            /* OK */
+        } else {
+            PRINT_E("Incorrect input tensor type (%d, %d)\n", input_tensor->getType().code, input_tensor_info.tensor_type);
+            return kRetErr;
+        }
+        if ((input_tensor->channel() != -1) && (input_tensor->height() != -1) && (input_tensor->width() != -1)) {
+            if (input_tensor_info.GetChannel() != -1) {
+                if ((input_tensor->channel() == input_tensor_info.GetChannel()) && (input_tensor->height() == input_tensor_info.GetHeight()) && (input_tensor->width() == input_tensor_info.GetWidth())) {
+                    /* OK */
+                } else {
+                    PRINT_E("W: %d != %d\n", input_tensor->width() , input_tensor_info.GetWidth());
+                    PRINT_E("H: %d != %d\n", input_tensor->height() , input_tensor_info.GetHeight());
+                    PRINT_E("C: %d != %d\n", input_tensor->channel() , input_tensor_info.GetChannel());
+                    PRINT_E("Incorrect input tensor size\n");
+                    return kRetErr;
+                }
+            } else {
+                PRINT("Input tensor size is set from the model\n");
+                input_tensor_info.tensor_dims.clear();
+                for (int32_t dim = 0; dim < input_tensor->dimensions(); dim++) {
+                    input_tensor_info.tensor_dims.push_back(input_tensor->length(dim));
+                }
+            }
+        } else {
+            if (input_tensor_info.GetChannel() != -1) {
+                PRINT("Input tensor size is resized\n");
+                /* In case the input size  is not fixed */
+                net_->resizeTensor(input_tensor, { 1, input_tensor_info.GetChannel(), input_tensor_info.GetHeight(), input_tensor_info.GetWidth() });
+                net_->resizeSession(session_);
+            } else {
+                PRINT_E("Model input size is not set\n");
+                return kRetErr;
+            }
+        }
+    }
+    for (const auto& output_tensor_info : output_tensor_info_list) {
+        auto output_tensor = net_->getSessionOutput(session_, output_tensor_info.name.c_str());
+        if (output_tensor == nullptr) {
+            PRINT_E("Invalid output name (%s)\n", output_tensor_info.name.c_str());
+            return kRetErr;
+        }
+        /* Output size is set when run inference later */
+    }
+
+    /* Convert normalize parameter to speed up */
+    for (auto& input_tensor_info : input_tensor_info_list) {
+        ConvertNormalizeParameters(input_tensor_info);
+    }
+
+
+    /* Check if tensor info is set */
+    for (const auto& input_tensor_info : input_tensor_info_list) {
+        for (const auto& dim : input_tensor_info.tensor_dims) {
+            if (dim <= 0) {
+                PRINT_E("Invalid tensor size\n");
+                return kRetErr;
+            }
+        }
+    }
+    //for (const auto& output_tensor_info : output_tensor_info_list) {
+    //    for (const auto& dim : output_tensor_info.tensor_dims) {
+    //        if (dim <= 0) {
+    //            PRINT_E("Invalid tensor size\n");
+    //            return kRetErr;
+    //        }
+    //    }
+    //}
+
+    return kRetOk;
+}
+
+int32_t InferenceHelperMnn::Initialize(char* model_buffer, int model_size, std::vector<InputTensorInfo>& input_tensor_info_list, std::vector<OutputTensorInfo>& output_tensor_info_list) {
+    /*** Create network ***/
+    net_.reset(MNN::Interpreter::createFromBuffer(model_buffer, model_size));
+    if (!net_) {
+        PRINT_E("Failed to load model model buffer\n");
+        return kRetErr;
+    }
+
+    MNN::ScheduleConfig scheduleConfig;
+    scheduleConfig.type = MNN_FORWARD_CPU;
+    scheduleConfig.numThread = num_threads_;    // it seems, setting 1 has better performance on Android
+    // MNN::BackendConfig bnconfig;
+    // bnconfig.power = MNN::BackendConfig::Power_High;
+    // bnconfig.precision = MNN::BackendConfig::Precision_Low;
+    // scheduleConfig.backendConfig = &bnconfig;
+    session_ = net_->createSession(scheduleConfig);
+    if (!session_) {
+        PRINT_E("Failed to create session\n");
+        return kRetErr;
+    }
+
+    return ParameterInitialization(input_tensor_info_list, output_tensor_info_list);
+}
+
+int32_t InferenceHelperMnn::Initialize(const std::string& model_filename, std::vector<InputTensorInfo>& input_tensor_info_list, std::vector<OutputTensorInfo>& output_tensor_info_list)
+{
+    /*** Create network ***/
+    net_.reset(MNN::Interpreter::createFromFile(model_filename.c_str()));
+    if (!net_) {
+        PRINT_E("Failed to load model file (%s)\n", model_filename.c_str());
+        return kRetErr;
+    }
+
+    MNN::ScheduleConfig scheduleConfig;
+    scheduleConfig.type = MNN_FORWARD_AUTO;
+    scheduleConfig.numThread = num_threads_;    // it seems, setting 1 has better performance on Android
+    // MNN::BackendConfig bnconfig;
+    // bnconfig.power = MNN::BackendConfig::Power_High;
+    // bnconfig.precision = MNN::BackendConfig::Precision_Low;
+    // scheduleConfig.backendConfig = &bnconfig;
+    session_ = net_->createSession(scheduleConfig);
+    if (!session_) {
+        PRINT_E("Failed to create session\n");
+        return kRetErr;
+    }
+
+    return ParameterInitialization(input_tensor_info_list, output_tensor_info_list);
+
+};
+
+
+int32_t InferenceHelperMnn::Finalize(void)
+{
+    net_->releaseSession(session_);
+    net_->releaseModel();
+    net_.reset();
+    out_mat_list_.clear();
+    return kRetOk;
+}
+
+int32_t InferenceHelperMnn::PreProcess(const std::vector<InputTensorInfo>& input_tensor_info_list)
+{
+    for (const auto& input_tensor_info : input_tensor_info_list) {
+        auto input_tensor = net_->getSessionInput(session_, input_tensor_info.name.c_str());
+        if (input_tensor == nullptr) {
+            PRINT_E("Invalid input name (%s)\n", input_tensor_info.name.c_str());
+            return kRetErr;
+        }
+        if (input_tensor_info.data_type == InputTensorInfo::kDataTypeImage) {
+            /* Crop */
+            if ((input_tensor_info.image_info.width != input_tensor_info.image_info.crop_width) || (input_tensor_info.image_info.height != input_tensor_info.image_info.crop_height)) {
+                PRINT_E("Crop is not supported\n");
+                return kRetErr;
+            }
+
+            MNN::CV::ImageProcess::Config image_processconfig;
+            /* Convert color type */
+            if ((input_tensor_info.image_info.channel == 3) && (input_tensor_info.GetChannel() == 3)) {
+                image_processconfig.sourceFormat = (input_tensor_info.image_info.is_bgr) ? MNN::CV::BGR : MNN::CV::RGB;
+                if (input_tensor_info.image_info.swap_color) {
+                    image_processconfig.destFormat = (input_tensor_info.image_info.is_bgr) ? MNN::CV::RGB : MNN::CV::BGR;
+                } else {
+                    image_processconfig.destFormat = (input_tensor_info.image_info.is_bgr) ? MNN::CV::BGR : MNN::CV::RGB;
+                }
+            } else if ((input_tensor_info.image_info.channel == 1) && (input_tensor_info.GetChannel() == 1)) {
+                image_processconfig.sourceFormat = MNN::CV::GRAY;
+                image_processconfig.destFormat = MNN::CV::GRAY;
+            } else if ((input_tensor_info.image_info.channel == 3) && (input_tensor_info.GetChannel() == 1)) {
+                image_processconfig.sourceFormat = (input_tensor_info.image_info.is_bgr) ? MNN::CV::BGR : MNN::CV::RGB;
+                image_processconfig.destFormat = MNN::CV::GRAY;
+            } else if ((input_tensor_info.image_info.channel == 1) && (input_tensor_info.GetChannel() == 3)) {
+                image_processconfig.sourceFormat = MNN::CV::GRAY;
+                image_processconfig.destFormat = MNN::CV::BGR;
+            } else {
+                PRINT_E("Unsupported color conversion (%d, %d)\n", input_tensor_info.image_info.channel, input_tensor_info.GetChannel());
+                return kRetErr;
+            }
+
+            /* Normalize image */
+            std::memcpy(image_processconfig.mean, input_tensor_info.normalize.mean, sizeof(image_processconfig.mean));
+            std::memcpy(image_processconfig.normal, input_tensor_info.normalize.norm, sizeof(image_processconfig.normal));
+            
+            /* Resize image */
+            image_processconfig.filterType = MNN::CV::BILINEAR;
+            MNN::CV::Matrix trans;
+            trans.setScale(static_cast<float>(input_tensor_info.image_info.crop_width) / input_tensor_info.GetWidth(), static_cast<float>(input_tensor_info.image_info.crop_height) / input_tensor_info.GetHeight());
+
+            /* Do pre-process */
+            std::shared_ptr<MNN::CV::ImageProcess> pretreat(MNN::CV::ImageProcess::create(image_processconfig));
+            pretreat->setMatrix(trans);
+            pretreat->convert(static_cast<uint8_t*>(input_tensor_info.data), input_tensor_info.image_info.crop_width, input_tensor_info.image_info.crop_height, 0, input_tensor);
+        } else if ( (input_tensor_info.data_type == InputTensorInfo::kDataTypeBlobNhwc) || (input_tensor_info.data_type == InputTensorInfo::kDataTypeBlobNchw) ) {
+            std::unique_ptr<MNN::Tensor> tensor;
+            if (input_tensor_info.data_type == InputTensorInfo::kDataTypeBlobNhwc) {
+                tensor.reset(new MNN::Tensor(input_tensor, MNN::Tensor::TENSORFLOW));
+            } else {
+                tensor.reset(new MNN::Tensor(input_tensor, MNN::Tensor::CAFFE));
+            }
+            if (tensor->getType().code == halide_type_float) {
+                for (int32_t i = 0; i < input_tensor_info.GetWidth() * input_tensor_info.GetHeight() * input_tensor_info.GetChannel(); i++) {
+                    tensor->host<float>()[i] = static_cast<float*>(input_tensor_info.data)[i];
+                }
+            } else {
+                for (int32_t i = 0; i < input_tensor_info.GetWidth() * input_tensor_info.GetHeight() * input_tensor_info.GetChannel(); i++) {
+                    tensor->host<uint8_t>()[i] = static_cast<uint8_t*>(input_tensor_info.data)[i];
+                }
+            }
+            input_tensor->copyFromHostTensor(tensor.get());
+        } else {
+            PRINT_E("Unsupported data type (%d)\n", input_tensor_info.data_type);
+            return kRetErr;
+        }
+    }
+    return kRetOk;
+}
+
+int32_t InferenceHelperMnn::Process(std::vector<OutputTensorInfo>& output_tensor_info_list)
+{
+    net_->runSession(session_);
+
+    out_mat_list_.clear();
+    for (auto& output_tensor_info : output_tensor_info_list) {
+        auto output_tensor = net_->getSessionOutput(session_, output_tensor_info.name.c_str());
+        if (output_tensor == nullptr) {
+            PRINT_E("Invalid output name (%s)\n", output_tensor_info.name.c_str());
+            return kRetErr;
+        }
+
+        auto dimType = output_tensor->getDimensionType();
+        std::unique_ptr<MNN::Tensor> outputUser(new MNN::Tensor(output_tensor, dimType));
+        output_tensor->copyToHostTensor(outputUser.get());
+        auto type = outputUser->getType();
+        if (type.code == halide_type_float) {
+            output_tensor_info.tensor_type = TensorInfo::kTensorTypeFp32;
+            output_tensor_info.data = outputUser->host<float>();
+        } else if (type.code == halide_type_uint && type.bytes() == 1) {
+            output_tensor_info.tensor_type = TensorInfo::kTensorTypeUint8;
+            output_tensor_info.data = outputUser->host<uint8_t>();
+        } else {
+            PRINT_E("Unexpected data type\n");
+            return kRetErr;
+        }
+        
+        output_tensor_info.tensor_dims.clear();
+        for (int32_t dim = 0; dim < outputUser->dimensions(); dim++) {
+            output_tensor_info.tensor_dims.push_back(outputUser->length(dim));
+        }
+
+        out_mat_list_.push_back(std::move(outputUser));	// store data in member variable so that data keep exist
+    }
+
+    return kRetOk;
+}
diff --git a/cpp/src/inference_helper_module/inference_helper_mnn.h b/cpp/src/inference_helper_module/inference_helper_mnn.h
new file mode 100644
index 0000000..391b760
--- /dev/null
+++ b/cpp/src/inference_helper_module/inference_helper_mnn.h
@@ -0,0 +1,53 @@
+/* Copyright 2021 iwatake2222
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+#ifndef INFERENCE_HELPER_MNN_
+#define INFERENCE_HELPER_MNN_
+
+/* for general */
+#include <cstdint>
+#include <cmath>
+#include <string>
+#include <vector>
+#include <array>
+#include <memory>
+
+/* for MNN */
+#include <MNN/ImageProcess.hpp>
+#include <MNN/Interpreter.hpp>
+#include <MNN/AutoTime.hpp>
+
+/* for My modules */
+#include "inference_helper.h"
+
+class InferenceHelperMnn : public InferenceHelper {
+public:
+    InferenceHelperMnn();
+    ~InferenceHelperMnn() override;
+    int32_t SetNumThreads(const int32_t num_threads) override;
+    int32_t SetCustomOps(const std::vector<std::pair<const char*, const void*>>& custom_ops) override;
+    int32_t Initialize(const std::string& model_filename, std::vector<InputTensorInfo>& input_tensor_info_list, std::vector<OutputTensorInfo>& output_tensor_info_list) override;
+    int32_t Initialize(char* model_buffer, int model_size, std::vector<InputTensorInfo>& input_tensor_info_list, std::vector<OutputTensorInfo>& output_tensor_info_list) override;
+    int32_t Finalize(void) override;
+    int32_t PreProcess(const std::vector<InputTensorInfo>& input_tensor_info_list) override;
+    int32_t Process(std::vector<OutputTensorInfo>& output_tensor_info_list) override;
+    int32_t ParameterInitialization(std::vector<InputTensorInfo>& input_tensor_info_list, std::vector<OutputTensorInfo>& output_tensor_info_list) override;
+private:
+    std::unique_ptr<MNN::Interpreter> net_;
+    MNN::Session* session_;
+    std::vector<std::unique_ptr<MNN::Tensor>> out_mat_list_;
+    int32_t num_threads_;
+};
+
+#endif
diff --git a/cpp/src/log.h b/cpp/src/log.h
new file mode 100755
index 0000000..6d8df28
--- /dev/null
+++ b/cpp/src/log.h
@@ -0,0 +1,50 @@
+#ifndef _LOG_UTILS_H_
+#define _LOG_UTILS_H_
+
+#include <stdio.h>
+#include <string.h>
+
+//#define DEBUG
+
+#define __FILENAME__ (strrchr(__FILE__, '/') + 1)
+
+//#ifdef DEBUG
+
+#ifdef ANDROID
+
+#include <android/log.h>
+#define TAG "HyperLPR3-Native" // 这个是自定义的LOG的标识
+#define LOGD(...) __android_log_print(ANDROID_LOG_DEBUG,TAG ,__VA_ARGS__) // 定义LOGD类型
+#define LOGI(...) __android_log_print(ANDROID_LOG_INFO,TAG ,__VA_ARGS__) // 定义LOGI类型
+#define LOGW(...) __android_log_print(ANDROID_LOG_WARN,TAG ,__VA_ARGS__) // 定义LOGW类型
+#define LOGE(...) __android_log_print(ANDROID_LOG_ERROR,TAG ,__VA_ARGS__) // 定义LOGE类型
+#define LOGF(...) __android_log_print(ANDROID_LOG_FATAL,TAG ,__VA_ARGS__) // 定义LOGF类型
+
+#else
+
+// color setting
+#define ANSI_COLOR_RED            "\x1b[31m"
+#define ANSI_COLOR_GREEN        "\x1b[32m"
+#define ANSI_COLOR_YELLOW        "\x1b[33m"
+#define ANSI_COLOR_BLUE            "\x1b[34m"
+#define ANSI_COLOR_MAGENTA        "\x1b[35m"
+#define ANSI_COLOR_CYAN            "\x1b[36m"
+#define ANSI_COLOR_WHITE        "\x1b[37m"
+#define ANSI_COLOR_RESET        "\x1b[0m"
+
+#define LOGD(format, ...)                                                      \
+  printf("[%s][%s][%d]: " format "\n", __FILENAME__, __FUNCTION__, __LINE__,   \
+         ##__VA_ARGS__)
+
+#define LOGE(format, ...)                                                      \
+  printf("%s[%s][%s][%d]: " format "\n%s", ANSI_COLOR_RED, __FILENAME__, __FUNCTION__, __LINE__,   \
+         ##__VA_ARGS__, ANSI_COLOR_RESET)
+
+
+#endif //ANDROID
+
+//#else
+//#define LOGD(format, ...)
+//#endif
+
+#endif // _LOG_UTILS_H_
diff --git a/cpp/src/nn_implementation_module/all.h b/cpp/src/nn_implementation_module/all.h
new file mode 100644
index 0000000..8e7dd69
--- /dev/null
+++ b/cpp/src/nn_implementation_module/all.h
@@ -0,0 +1,12 @@
+//
+// Created by tunm on 2023/1/25.
+//
+
+#ifndef ZEPHYRLPR_NN_IMPLEMENTATION_ALL_H
+#define ZEPHYRLPR_NN_IMPLEMENTATION_ALL_H
+
+#include "recognition/all.h"
+#include "detect/all.h"
+#include "classification/all.h"
+
+#endif //ZEPHYRLPR_NN_IMPLEMENTATION_ALL_H
diff --git a/cpp/src/nn_implementation_module/classification/all.h b/cpp/src/nn_implementation_module/classification/all.h
new file mode 100644
index 0000000..066f141
--- /dev/null
+++ b/cpp/src/nn_implementation_module/classification/all.h
@@ -0,0 +1,12 @@
+//
+// Created by tunm on 2023/1/22.
+//
+#pragma once
+#ifndef ZEPHYRLPR_CLASSIFICATION_ALL_H
+#define ZEPHYRLPR_CLASSIFICATION_ALL_H
+
+#include "plate_classification.h"
+#include "plate_cls_common.h"
+#include "classification_engine.h"
+
+#endif //ZEPHYRLPR_CLASSIFICATION_ALL_H
diff --git a/cpp/src/nn_implementation_module/classification/classification_engine.cpp b/cpp/src/nn_implementation_module/classification/classification_engine.cpp
new file mode 100644
index 0000000..6e588e1
--- /dev/null
+++ b/cpp/src/nn_implementation_module/classification/classification_engine.cpp
@@ -0,0 +1,107 @@
+//
+// Created by Tunm-Air13 on 2023/2/9.
+//
+
+#include "classification_engine.h"
+#include "utils.h"
+
+
+namespace hyper {
+
+
+ClassificationEngine::ClassificationEngine() {
+
+}
+
+ClassificationEngine::~ClassificationEngine() {
+
+}
+
+int32_t ClassificationEngine::Initialize(const std::string& model_filename, cv::Size input_size, int threads, bool use_half) {
+    m_input_image_size_ = std::move(input_size);
+
+    /* Set output tensor info */
+    m_output_tensor_info_list_.clear();
+    m_output_tensor_info_list_.push_back(OutputTensorInfo("output", TensorInfo::kTensorTypeFp32));
+
+
+    m_nn_infer_.reset(InferenceHelper::Create(InferenceHelper::kMnn));
+
+    if (m_nn_infer_->SetNumThreads(threads) != InferenceHelper::kRetOk) {
+        m_nn_infer_.reset();
+        return InferenceHelper::kRetErr;
+    }
+
+    if (m_nn_infer_->Initialize(model_filename, m_input_tensor_info_list_, m_output_tensor_info_list_) != InferenceHelper::kRetOk) {
+        m_nn_infer_.reset();
+        return InferenceHelper::kRetErr;
+    }
+
+    m_input_tensor_info_list_.clear();
+
+    InputTensorInfo input_tensor_info("data", TensorInfo::kTensorTypeFp32, true);
+
+    input_tensor_info.tensor_dims =  { 1, 3, m_input_image_size_.height, m_input_image_size_.width };
+    input_tensor_info.data_type = InputTensorInfo::kDataTypeImage;
+
+    input_tensor_info.normalize.mean[0] = 0.0f;
+    input_tensor_info.normalize.mean[1] = 0.0f;
+    input_tensor_info.normalize.mean[2] = 0.0f;
+    input_tensor_info.normalize.norm[0] = 0.003921568627f;
+    input_tensor_info.normalize.norm[1] = 0.003921568627f;
+    input_tensor_info.normalize.norm[2] = 0.003921568627f;
+
+    m_input_tensor_info_list_.push_back(input_tensor_info);
+
+    return InferenceHelper::kRetOk;
+}
+
+int32_t ClassificationEngine::Inference(const cv::Mat &bgr_pad) {
+    InputTensorInfo& input_tensor_info = m_input_tensor_info_list_[0];
+
+    input_tensor_info.data = bgr_pad.data;
+    input_tensor_info.data_type = InputTensorInfo::kDataTypeImage;
+    input_tensor_info.image_info.width = bgr_pad.cols;
+    input_tensor_info.image_info.height = bgr_pad.rows;
+    input_tensor_info.image_info.channel = bgr_pad.channels();
+    input_tensor_info.image_info.crop_x = 0;
+    input_tensor_info.image_info.crop_y = 0;
+    input_tensor_info.image_info.crop_width = bgr_pad.cols;
+    input_tensor_info.image_info.crop_height = bgr_pad.rows;
+    input_tensor_info.image_info.is_bgr = true;
+    input_tensor_info.image_info.swap_color = false;
+
+    if (m_nn_infer_->PreProcess(m_input_tensor_info_list_) != InferenceHelper::kRetOk) {
+        return InferenceHelper::kRetErr;
+    }
+    if (m_nn_infer_->Process(m_output_tensor_info_list_) != InferenceHelper::kRetOk) {
+        return InferenceHelper::kRetErr;
+    }
+
+    std::vector<float> output_score_raw_list(m_output_tensor_info_list_[0].GetDataAsFloat(),
+                                             m_output_tensor_info_list_[0].GetDataAsFloat() +
+                                             m_output_tensor_info_list_[0].GetElementNum());
+
+
+    auto index = argmax(output_score_raw_list.begin(), output_score_raw_list.end());
+    m_output_max_confidence_ = output_score_raw_list[index];
+    m_output_color_ = PlateColor(index);
+
+    return InferenceHelper::kRetOk;
+}
+
+
+PlateColor ClassificationEngine::getMOutputColor() const {
+    return m_output_color_;
+}
+
+const cv::Size &ClassificationEngine::getMInputImageSize() const {
+    return m_input_image_size_;
+}
+
+float ClassificationEngine::getMOutputMaxConfidence() const {
+    return m_output_max_confidence_;
+}
+
+
+} // namespace
\ No newline at end of file
diff --git a/cpp/src/nn_implementation_module/classification/classification_engine.h b/cpp/src/nn_implementation_module/classification/classification_engine.h
new file mode 100644
index 0000000..97eee81
--- /dev/null
+++ b/cpp/src/nn_implementation_module/classification/classification_engine.h
@@ -0,0 +1,54 @@
+//
+// Created by Tunm-Air13 on 2023/2/9.
+//
+#pragma once
+#ifndef ZEPHYRLPR_CLASSIFICATION_ENGINE_H
+#define ZEPHYRLPR_CLASSIFICATION_ENGINE_H
+
+#include "inference_helper_module/inference_helper.h"
+#include "configuration.h"
+#include "plate_cls_common.h"
+#include "basic_types.h"
+#include "opencv2/opencv.hpp"
+
+namespace hyper {
+
+class ClassificationEngine {
+public:
+    ClassificationEngine(const ClassificationEngine &) = delete;
+
+    ClassificationEngine &operator=(const ClassificationEngine &) = delete;
+
+    explicit ClassificationEngine();
+
+    ~ClassificationEngine();
+
+    int32_t Initialize(const std::string& model_filename, cv::Size input_size = CLS_INPUT_SIZE, int threads = 1, bool use_half = false);
+
+    int32_t Inference(const cv::Mat &bgr_pad);
+
+    PlateColor getMOutputColor() const;
+
+    const cv::Size &getMInputImageSize() const;
+
+    float getMOutputMaxConfidence() const;
+
+private:
+
+    cv::Size m_input_image_size_{};                            // 输入图像宽高
+
+    std::unique_ptr<InferenceHelper> m_nn_infer_;               // 推理模块
+
+    std::vector<InputTensorInfo> m_input_tensor_info_list_;
+
+    std::vector<OutputTensorInfo> m_output_tensor_info_list_;
+
+    PlateColor m_output_color_;
+
+    float m_output_max_confidence_;
+
+};
+
+} // namespace
+
+#endif //ZEPHYRLPR_CLASSIFICATION_ENGINE_H
diff --git a/cpp/src/nn_implementation_module/classification/plate_classification.cpp b/cpp/src/nn_implementation_module/classification/plate_classification.cpp
new file mode 100644
index 0000000..a9469c0
--- /dev/null
+++ b/cpp/src/nn_implementation_module/classification/plate_classification.cpp
@@ -0,0 +1,34 @@
+//
+// Created by tunm on 2023/1/22.
+//
+
+#include "plate_classification.h"
+#include "utils.h"
+
+namespace hyper {
+
+PlateClassification::PlateClassification() = default;
+
+int PlateClassification::Initialize(const std::string& model_filename, cv::Size input_size, int threads, bool use_half) {
+    m_input_image_size_ = std::move(input_size);
+    float mean[] = {0.0f, 0.0f, 0.0f};
+    float normal[] = {0.003921568627f, 0.003921568627f, 0.003921568627f};
+    m_nn_adapter_ = std::make_shared<MNNAdapterInference>(model_filename, threads, mean, normal, use_half);
+    m_nn_adapter_->Initialization("data", "output", m_input_image_size_.width, m_input_image_size_.height);
+    return 0;
+}
+
+PlateColor PlateClassification::Inference(const cv::Mat &bgr_pad) {
+    std::vector<float> output = m_nn_adapter_->Invoking(bgr_pad);
+//    for (int i = 0; i < output.size(); ++i) {
+//        std::cout << output[i] << std::endl;
+//    }
+    auto index = argmax(output.begin(), output.end());
+    return PlateColor(index);
+}
+
+const cv::Size &PlateClassification::getMInputImageSize() const {
+    return m_input_image_size_;
+}
+
+} // namespace
\ No newline at end of file
diff --git a/cpp/src/nn_implementation_module/classification/plate_classification.h b/cpp/src/nn_implementation_module/classification/plate_classification.h
new file mode 100644
index 0000000..5d8bea7
--- /dev/null
+++ b/cpp/src/nn_implementation_module/classification/plate_classification.h
@@ -0,0 +1,39 @@
+//
+// Created by tunm on 2023/1/22.
+//
+#pragma once
+#ifndef ZEPHYRLPR_PLATE_CLASSIFICATION_H
+#define ZEPHYRLPR_PLATE_CLASSIFICATION_H
+
+#include "nn_module/mnn_adapter.h"
+#include "basic_types.h"
+#include "plate_cls_common.h"
+
+namespace hyper {
+
+class PlateClassification {
+public:
+
+    PlateClassification(const PlateClassification &) = delete;
+
+    PlateClassification &operator=(const PlateClassification &) = delete;
+
+    explicit PlateClassification();
+
+    int Initialize(const std::string& model_filename, cv::Size input_size = cv::Size(96, 96), int threads = 1, bool use_half = false);
+
+    PlateColor Inference(const cv::Mat &bgr_pad);
+
+    const cv::Size &getMInputImageSize() const;
+
+private:
+
+    cv::Size m_input_image_size_{};
+
+    std::shared_ptr<MNNAdapterInference> m_nn_adapter_;
+};
+
+} // namespace
+
+
+#endif //ZEPHYRLPR_PLATE_CLASSIFICATION_H
diff --git a/cpp/src/nn_implementation_module/classification/plate_cls_common.h b/cpp/src/nn_implementation_module/classification/plate_cls_common.h
new file mode 100644
index 0000000..49a657a
--- /dev/null
+++ b/cpp/src/nn_implementation_module/classification/plate_cls_common.h
@@ -0,0 +1,14 @@
+//
+// Created by tunm on 2023/1/22.
+//
+
+#ifndef ZEPHYRLPR_PLATE_CLS_COMMON_H
+#define ZEPHYRLPR_PLATE_CLS_COMMON_H
+
+typedef enum {
+    BLUE,       ///< 蓝牌
+    GREEN,      ///< 绿牌
+    YELLOW,     ///< 黄牌
+} PlateColor;
+
+#endif //ZEPHYRLPR_PLATE_CLS_COMMON_H
diff --git a/cpp/src/nn_implementation_module/detect/all.h b/cpp/src/nn_implementation_module/detect/all.h
new file mode 100644
index 0000000..4548127
--- /dev/null
+++ b/cpp/src/nn_implementation_module/detect/all.h
@@ -0,0 +1,16 @@
+//
+// Created by tunm on 2022/12/29.
+//
+#pragma once
+#ifndef ZEPHYRLPR_DETECT_ALL_H
+#define ZEPHYRLPR_DETECT_ALL_H
+
+#include "plate_det_common.h"
+#include "plate_detector.h"
+#include "det_backbone.h"
+#include "det_header.h"
+#include "det_arch.h"
+
+
+
+#endif //ZEPHYRLPR_DETECT_ALL_H
diff --git a/cpp/src/nn_implementation_module/detect/det_arch.cpp b/cpp/src/nn_implementation_module/detect/det_arch.cpp
new file mode 100644
index 0000000..decbae1
--- /dev/null
+++ b/cpp/src/nn_implementation_module/detect/det_arch.cpp
@@ -0,0 +1,143 @@
+//
+// Created by tunm on 2023/2/8.
+//
+
+#include "det_arch.h"
+#include "log.h"
+
+namespace hyper {
+
+
+DetArch::DetArch() = default;
+
+
+int32_t DetArch::Initialize(const std::string& backbone_path, const std::string& head_path, int input_size, int threads,
+                            float box_conf_threshold, float nms_threshold, bool use_half) {
+    int32_t ret;
+    m_input_size_ = input_size;
+    m_box_conf_threshold_ = box_conf_threshold;
+    m_nms_threshold_ = nms_threshold;
+    m_backbone_net_ = std::make_shared<DetBackbone>();
+    m_header_net_ = std::make_shared<DetHeader>();
+    ret = m_backbone_net_->Initialize(backbone_path, cv::Size(input_size, input_size), use_half);
+    if (ret != InferenceHelper::kRetOk) {
+        LOGE("Backbone model error.");
+        return InferenceHelper::kRetErr;
+    }
+    ret = m_header_net_->Initialize(head_path, input_size, use_half);
+    if (ret != InferenceHelper::kRetOk) {
+        LOGE("Head model error.");
+        return InferenceHelper::kRetErr;
+    }
+
+    if (input_size == 320) {
+        m_grid_num_ = 6300;
+    } else if (input_size == 640) {
+        m_grid_num_ = 25200;
+    } else {
+        return InferenceHelper::kRetErr;
+    }
+
+    return InferenceHelper::kRetOk;
+}
+
+template<class T>
+void DetArch::Decode(T features, std::vector<PlateLocation> &outputs, float scale, float conf_threshold,
+                     float nms_threshold) {
+
+    int item_num = 15;
+    for (int index = 0; index < m_grid_num_; ++index) {
+        auto det_score = features[index * item_num + 4];
+        if (det_score > conf_threshold) {
+//            std::cout << det_score << std::endl;
+            PlateLocation plate;
+            plate.det_confidence = det_score;
+            auto class_score_1 = features[index * item_num + 13] * det_score;
+            auto class_score_2 = features[index * item_num + 14] * det_score;
+            LayersNum type = class_score_1 > class_score_2 ? MONO : DOUBLE;
+            plate.layers = type;
+            auto x = features[index * item_num + 0];
+            auto y = features[index * item_num + 1];
+            auto w = features[index * item_num + 2];
+            auto h = features[index * item_num + 3];
+            std::vector<float> xyxy = xywh2xyxy(x, y, w, h);
+            plate.x1 = xyxy[0];
+            plate.y1 = xyxy[1];
+            plate.x2 = xyxy[2];
+            plate.y2 = xyxy[3];
+            for (int i = 0; i < 8; ++i) {
+                plate.kps[i] = features[index * item_num + i + 5];
+            }
+            outputs.push_back(plate);
+        }
+
+    }
+    nms(outputs, nms_threshold);
+
+    for (auto &plate : outputs) {
+        plate.x1 = plate.x1 / scale;
+        plate.y1 = plate.y1 / scale;
+        plate.x2 = plate.x2 / scale;
+        plate.y2 = plate.y2 / scale;
+        for (int i = 0; i < 4; ++i) {
+            plate.kps[i * 2 + 0] = plate.kps[i * 2 + 0] / scale;
+            plate.kps[i * 2 + 1] = plate.kps[i * 2 + 1] / scale;
+        }
+    }
+
+
+}
+
+void DetArch::Detection(const cv::Mat &bgr, bool is_resize, float scale) {
+    cv::Mat pad;
+    if (is_resize) {
+        int ori_w = bgr.cols;
+        int ori_h = bgr.rows;
+
+        int w, h;
+        if (ori_w > ori_h) {
+            scale = (float) m_input_size_ / ori_w;
+            w = m_input_size_;
+            h = ori_h * scale;
+        } else {
+            scale = (float) m_input_size_ / ori_h;
+            h = m_input_size_;
+            w = ori_w * scale;
+        }
+        int wpad = m_input_size_ - w;
+        int hpad = m_input_size_ - h;
+        cv::Mat resized_img;
+        cv::resize(bgr, resized_img, cv::Size(w, h));
+        cv::copyMakeBorder(resized_img, pad, 0, hpad, 0, wpad, cv::BORDER_CONSTANT, cv::Scalar(127.5, 127.5, 127.5));
+//        cv::imwrite("i.jpg", pad);
+//        cv::imshow("pad_border", pad);
+//        cv::waitKey(0);
+    } else {
+        pad = bgr;
+    }
+    double time;
+
+    time = (double)cv::getTickCount();
+    m_backbone_net_->Inference(pad);
+    time = ((double)cv::getTickCount() - time) / cv::getTickFrequency();
+
+    time = (double)cv::getTickCount();
+    m_header_net_->Inference(m_backbone_net_->getMOutputTensorInfoList()[0].GetDataAsFloat(),
+                             m_backbone_net_->getMOutputTensorInfoList()[1].GetDataAsFloat(),
+                             m_backbone_net_->getMOutputTensorInfoList()[2].GetDataAsFloat());
+    time = ((double)cv::getTickCount() - time) / cv::getTickFrequency();
+
+//    std::vector<float> tensor(m_header_net_->getMOutputTensorInfoList()[0].GetDataAsFloat(),
+//                              m_header_net_->getMOutputTensorInfoList()[0].GetDataAsFloat() +
+//                              m_header_net_->getMOutputTensorInfoList()[0].GetElementNum());
+
+
+
+    std::vector<PlateLocation>().swap(m_results_);
+    Decode(m_header_net_->getMOutputTensorInfoList()[0].GetDataAsFloat(), m_results_, scale, m_box_conf_threshold_, m_nms_threshold_);
+
+
+}
+
+
+} // namespace
\ No newline at end of file
diff --git a/cpp/src/nn_implementation_module/detect/det_arch.h b/cpp/src/nn_implementation_module/detect/det_arch.h
new file mode 100644
index 0000000..23a1fdc
--- /dev/null
+++ b/cpp/src/nn_implementation_module/detect/det_arch.h
@@ -0,0 +1,54 @@
+//
+// Created by tunm on 2023/2/8.
+//
+#pragma once
+#ifndef ZEPHYRLPR_DET_ARCH_H
+#define ZEPHYRLPR_DET_ARCH_H
+
+#include "det_backbone.h"
+#include "det_header.h"
+#include "plate_det_common.h"
+
+namespace hyper {
+
+class DetArch {
+public:
+    DetArch(const DetArch &) = delete;
+
+    DetArch &operator=(const DetArch &) = delete;
+
+    explicit DetArch();
+
+    int32_t Initialize(const std::string& backbone_path, const std::string& head_path, int input_size = 320, int threads=1, float box_conf_threshold = 0.3f, float nms_threshold = 0.6f, bool use_half = false);
+
+    void Detection(const cv::Mat& bgr, bool is_resize = false, float scale = 1.0f);
+
+public:
+    std::vector<PlateLocation> m_results_;                      // 存放检测结果
+
+private:
+    template<class T>
+    void Decode(T features, std::vector<PlateLocation> &outputs, float scale,
+                float conf_threshold = 0.5, float nms_threshold = 0.5);
+
+private:
+    int m_input_size_{};
+
+    float m_box_conf_threshold_{};
+
+    float m_nms_threshold_{};
+
+    std::shared_ptr<DetBackbone> m_backbone_net_;
+
+    std::shared_ptr<DetHeader> m_header_net_;
+
+    int m_grid_num_ = 6300;
+
+};
+
+
+} // namespace
+
+
+
+#endif //ZEPHYRLPR_DET_ARCH_H
diff --git a/cpp/src/nn_implementation_module/detect/det_backbone.cpp b/cpp/src/nn_implementation_module/detect/det_backbone.cpp
new file mode 100644
index 0000000..38ea2b7
--- /dev/null
+++ b/cpp/src/nn_implementation_module/detect/det_backbone.cpp
@@ -0,0 +1,96 @@
+//
+// Created by Tunm-Air13 on 2023/2/8.
+//
+
+#include <iostream>
+#include "det_backbone.h"
+#include "log.h"
+#include <utility>
+
+namespace hyper {
+
+DetBackbone::~DetBackbone() {
+    if (!m_nn_infer_) {
+        LOGD("[DetBackbone]Inference helper is not created\n");
+        return;
+    }
+    m_nn_infer_->Finalize();
+    m_nn_infer_.reset();
+}
+
+int32_t DetBackbone::Initialize(const std::string& model_filename, cv::Size input_size, bool use_half) {
+    m_input_image_size_ = std::move(input_size);
+
+
+    m_nn_infer_.reset(InferenceHelper::Create(InferenceHelper::kMnn));
+
+    if (m_nn_infer_->SetNumThreads(1) != InferenceHelper::kRetOk) {
+        m_nn_infer_.reset();
+        return InferenceHelper::kRetErr;
+    }
+
+    /* Set output tensor info */
+    m_output_tensor_info_list_.clear();
+
+    m_output_tensor_info_list_ = {OutputTensorInfo("948", TensorInfo::kTensorTypeFp32),
+                                  OutputTensorInfo("1061", TensorInfo::kTensorTypeFp32),
+                                  OutputTensorInfo("1174", TensorInfo::kTensorTypeFp32)};
+    if (m_nn_infer_->Initialize(model_filename, m_input_tensor_info_list_, m_output_tensor_info_list_) != InferenceHelper::kRetOk) {
+        m_nn_infer_.reset();
+        return InferenceHelper::kRetErr;
+    }
+
+    m_input_tensor_info_list_.clear();
+
+    InputTensorInfo input_tensor_info("input", TensorInfo::kTensorTypeFp32, true);
+
+    input_tensor_info.tensor_dims =  { 1, 3, m_input_image_size_.height, m_input_image_size_.width };
+    input_tensor_info.data_type = InputTensorInfo::kDataTypeImage;
+
+    input_tensor_info.normalize.mean[0] = 0.0f;
+    input_tensor_info.normalize.mean[1] = 0.0f;
+    input_tensor_info.normalize.mean[2] = 0.0f;
+    input_tensor_info.normalize.norm[0] = 0.003921568627f;
+    input_tensor_info.normalize.norm[1] = 0.003921568627f;
+    input_tensor_info.normalize.norm[2] = 0.003921568627f;
+    m_input_tensor_info_list_.push_back(input_tensor_info);
+
+    return InferenceHelper::kRetOk;
+}
+
+int32_t DetBackbone::Inference(const cv::Mat &bgr_pad) {
+    InputTensorInfo& input_tensor_info = m_input_tensor_info_list_[0];
+    input_tensor_info.data = bgr_pad.data;
+    input_tensor_info.data_type = InputTensorInfo::kDataTypeImage;
+    input_tensor_info.image_info.width = bgr_pad.cols;
+    input_tensor_info.image_info.height = bgr_pad.rows;
+    input_tensor_info.image_info.channel = bgr_pad.channels();
+    input_tensor_info.image_info.crop_x = 0;
+    input_tensor_info.image_info.crop_y = 0;
+    input_tensor_info.image_info.crop_width = bgr_pad.cols;
+    input_tensor_info.image_info.crop_height = bgr_pad.rows;
+    input_tensor_info.image_info.is_bgr = true;
+    input_tensor_info.image_info.swap_color = false;
+
+    if (m_nn_infer_->PreProcess(m_input_tensor_info_list_) != InferenceHelper::kRetOk) {
+        return InferenceHelper::kRetErr;
+    }
+    if (m_nn_infer_->Process(m_output_tensor_info_list_) != InferenceHelper::kRetOk) {
+        return InferenceHelper::kRetErr;
+    }
+
+
+    return InferenceHelper::kRetOk;
+}
+
+
+DetBackbone::DetBackbone() {
+
+}
+
+std::vector<OutputTensorInfo> &DetBackbone::getMOutputTensorInfoList() {
+    return m_output_tensor_info_list_;
+}
+
+
+} // namespace;
\ No newline at end of file
diff --git a/cpp/src/nn_implementation_module/detect/det_backbone.h b/cpp/src/nn_implementation_module/detect/det_backbone.h
new file mode 100644
index 0000000..bd3ad8d
--- /dev/null
+++ b/cpp/src/nn_implementation_module/detect/det_backbone.h
@@ -0,0 +1,43 @@
+//
+// Created by Tunm-Air13 on 2023/2/8.
+//
+#pragma once
+#ifndef ZEPHYRLPR_DET_BACKBONE_H
+#define ZEPHYRLPR_DET_BACKBONE_H
+
+#include "inference_helper_module/inference_helper.h"
+#include "plate_det_common.h"
+#include "opencv2/opencv.hpp"
+
+namespace hyper {
+
+class DetBackbone {
+public:
+
+    DetBackbone();
+
+    ~DetBackbone();
+
+    int32_t Initialize(const std::string& model_filename, cv::Size input_size = cv::Size(320, 320), bool use_half = false);
+
+    int32_t Inference(const cv::Mat &bgr_pad);
+
+public:
+
+    std::vector<OutputTensorInfo> &getMOutputTensorInfoList();
+
+private:
+
+    cv::Size m_input_image_size_{};                             // 输入图像宽高
+
+    std::shared_ptr<InferenceHelper> m_nn_infer_;               // 推理模块
+
+    std::vector<InputTensorInfo> m_input_tensor_info_list_;
+
+    std::vector<OutputTensorInfo> m_output_tensor_info_list_;
+
+};
+
+} // namespace;
+
+#endif //ZEPHYRLPR_DET_BACKBONE_H
diff --git a/cpp/src/nn_implementation_module/detect/det_header.cpp b/cpp/src/nn_implementation_module/detect/det_header.cpp
new file mode 100644
index 0000000..7550ed3
--- /dev/null
+++ b/cpp/src/nn_implementation_module/detect/det_header.cpp
@@ -0,0 +1,100 @@
+//
+// Created by Tunm-Air13 on 2023/2/8.
+//
+
+#include "det_header.h"
+#include "log.h"
+
+namespace hyper {
+
+#define INPUTS_NUM 3
+#define OUTPUT "output"
+
+DetHeader::DetHeader() = default;
+
+DetHeader::~DetHeader() {
+    if (!m_nn_infer_) {
+        LOGD("Inference helper is not created\n");
+        return;
+    }
+    m_nn_infer_->Finalize();
+    m_nn_infer_.reset();
+}
+
+int32_t DetHeader::Initialize(const std::string& model_filename, int input_size, bool use_half) {
+
+    m_nn_infer_.reset(InferenceHelper::Create(InferenceHelper::kMnn));
+    if (m_nn_infer_->SetNumThreads(1) != InferenceHelper::kRetOk) {
+        m_nn_infer_.reset();
+        return InferenceHelper::kRetErr;
+    }
+    if (input_size == 320) {
+        m_header_list_ = {
+            {"948", { 1, 45, 40, 40 }},
+            {"1061", { 1, 45, 20, 20 }},
+            {"1174", { 1, 45, 10, 10 }},};
+    } else {
+        m_header_list_ = {
+            {"948", { 1, 45, 80, 80 }},
+            {"1061", { 1, 45, 40, 40 }},
+            {"1174", { 1, 45, 20, 20 }},};
+    }
+    m_output_tensor_info_list_.clear();
+    m_output_tensor_info_list_.emplace_back(OUTPUT, TensorInfo::kTensorTypeFp32);
+
+    if (m_nn_infer_->Initialize(model_filename, m_input_tensor_info_list_, m_output_tensor_info_list_) != InferenceHelper::kRetOk) {
+        m_nn_infer_.reset();
+        return InferenceHelper::kRetErr;
+    }
+
+    m_input_tensor_info_list_.clear();
+    /* Set input tensor info */
+    for (int i = 0; i < INPUTS_NUM; ++i) {
+        auto &head = m_header_list_[i];
+        InputTensorInfo input_tensor_info(head.input_name, TensorInfo::kTensorTypeFp32, false);
+        input_tensor_info.tensor_dims =  head.dims;
+        input_tensor_info.data_type = InputTensorInfo::kTensorTypeFp32;
+        input_tensor_info.normalize.mean[0] = 0.0f;
+        input_tensor_info.normalize.mean[1] = 0.0f;
+        input_tensor_info.normalize.mean[2] = 0.0f;
+        input_tensor_info.normalize.norm[0] = 1.0f;
+        input_tensor_info.normalize.norm[1] = 1.0f;
+        input_tensor_info.normalize.norm[2] = 1.0f;
+        m_input_tensor_info_list_.push_back(input_tensor_info);
+    }
+
+
+
+    return InferenceHelper::kRetOk;
+}
+
+int32_t DetHeader::Inference(float* ptr3, float* ptr2, float* ptr1) {
+
+    m_input_tensor_info_list_[0].data_type = InputTensorInfo::kDataTypeBlobNchw;
+    m_input_tensor_info_list_[0].data = ptr3;
+
+    m_input_tensor_info_list_[1].data_type = InputTensorInfo::kDataTypeBlobNchw;
+    m_input_tensor_info_list_[1].data = ptr2;
+
+    m_input_tensor_info_list_[2].data_type = InputTensorInfo::kDataTypeBlobNchw;
+    m_input_tensor_info_list_[2].data = ptr1;
+
+    if (m_nn_infer_->PreProcess(m_input_tensor_info_list_) != InferenceHelper::kRetOk) {
+        return InferenceHelper::kRetErr;
+    }
+    if (m_nn_infer_->Process(m_output_tensor_info_list_) != InferenceHelper::kRetOk) {
+        return InferenceHelper::kRetErr;
+    }
+
+    return InferenceHelper::kRetOk;
+}
+
+
+std::vector<OutputTensorInfo> &DetHeader::getMOutputTensorInfoList() {
+    return m_output_tensor_info_list_;
+}
+
+    void DetHeader::setMOutputTensorInfoList(const std::vector<OutputTensorInfo> &mOutputTensorInfoList) {
+        m_output_tensor_info_list_ = mOutputTensorInfoList;
+    }
+} // namespace
\ No newline at end of file
diff --git a/cpp/src/nn_implementation_module/detect/det_header.h b/cpp/src/nn_implementation_module/detect/det_header.h
new file mode 100644
index 0000000..de55c98
--- /dev/null
+++ b/cpp/src/nn_implementation_module/detect/det_header.h
@@ -0,0 +1,53 @@
+//
+// Created by Tunm-Air13 on 2023/2/8.
+//
+
+#ifndef ZEPHYRLPR_DET_HEADER_H
+#define ZEPHYRLPR_DET_HEADER_H
+
+#include "inference_helper_module/inference_helper.h"
+#include "plate_det_common.h"
+#include "opencv2/opencv.hpp"
+
+namespace hyper {
+
+typedef struct {
+    std::string input_name;
+    std::vector<int> dims;
+} Header;
+
+typedef std::vector<Header> HeaderList;
+
+class DetHeader {
+public:
+    DetHeader(const DetHeader &) = delete;
+
+    DetHeader &operator=(const DetHeader &) = delete;
+
+    explicit DetHeader();
+
+    ~DetHeader();
+
+    int32_t Initialize(const std::string& model_filename, int input_size = 320, bool use_half = false);
+
+    int32_t Inference(float* ptr3, float* ptr2, float* ptr1);
+
+    std::vector<OutputTensorInfo> &getMOutputTensorInfoList();
+
+    void setMOutputTensorInfoList(const std::vector<OutputTensorInfo> &mOutputTensorInfoList);
+
+private:
+
+    HeaderList m_header_list_;
+
+    std::unique_ptr<InferenceHelper> m_nn_infer_;               // 推理模块
+
+    std::vector<InputTensorInfo> m_input_tensor_info_list_;
+
+    std::vector<OutputTensorInfo> m_output_tensor_info_list_;
+
+};
+
+} // namespace
+
+#endif //ZEPHYRLPR_DET_HEADER_H
diff --git a/cpp/src/nn_implementation_module/detect/plate_det_common.h b/cpp/src/nn_implementation_module/detect/plate_det_common.h
new file mode 100644
index 0000000..b37c224
--- /dev/null
+++ b/cpp/src/nn_implementation_module/detect/plate_det_common.h
@@ -0,0 +1,67 @@
+//
+// Created by tunm on 2022/12/29.
+//
+#pragma once
+#ifndef ZEPHYRLPR_PLATE_DET_COMMON_H
+#define ZEPHYRLPR_PLATE_DET_COMMON_H
+
+#include <algorithm>
+
+namespace hyper {
+
+enum LayersNum {
+    MONO,       ///< 单层车牌
+    DOUBLE,     ///< 双层车牌
+};
+
+typedef struct PlateLocation {
+    float x1;                      ///< 左上角点x坐标
+    float y1;                      ///< 左上角点y坐标
+    float x2;                      ///< 右下角点x坐标
+    float y2;                      ///< 右下角点y坐标
+    float det_confidence;        ///< 目标检测的置信度
+    float kps[8];                ///< 四个角的角点信息
+    LayersNum layers;         ///< 车牌层级类型
+};
+
+
+inline void nms(std::vector<PlateLocation> &input_plates, float nms_threshold) {
+    std::sort(input_plates.begin(), input_plates.end(),
+              [](PlateLocation a, PlateLocation b) { return a.det_confidence > b.det_confidence; });
+    std::vector<float> area(input_plates.size());
+    for (int i = 0; i < int(input_plates.size()); ++i) {
+        area[i] =
+                (input_plates.at(i).x2 - input_plates.at(i).x1 + 1) *
+                (input_plates.at(i).y2 - input_plates.at(i).y1 + 1);
+    }
+    for (int i = 0; i < int(input_plates.size()); ++i) {
+        for (int j = i + 1; j < int(input_plates.size());) {
+            float xx1 = (std::max)(input_plates[i].x1, input_plates[j].x1);
+            float yy1 = (std::max)(input_plates[i].y1, input_plates[j].y1);
+            float xx2 = (std::min)(input_plates[i].x2, input_plates[j].x2);
+            float yy2 = (std::min)(input_plates[i].y2, input_plates[j].y2);
+            float w = (std::max)(float(0), xx2 - xx1 + 1);
+            float h = (std::max)(float(0), yy2 - yy1 + 1);
+            float inter = w * h;
+            float ovr = inter / (area[i] + area[j] - inter);
+            if (ovr >= nms_threshold) {
+                input_plates.erase(input_plates.begin() + j);
+                area.erase(area.begin() + j);
+            } else {
+                j++;
+            }
+        }
+    }
+}
+
+inline std::vector<float> xywh2xyxy(float x, float y, float w, float h) {
+    float x1 = x - w / 2;
+    float y1 = y - h / 2;
+    float x2 = x + w / 2;
+    float y2 = y + h / 2;
+    return {x1, y1, x2, y2};
+}
+
+}
+
+#endif //ZEPHYRLPR_PLATE_DET_COMMON_H
diff --git a/cpp/src/nn_implementation_module/detect/plate_detector.cpp b/cpp/src/nn_implementation_module/detect/plate_detector.cpp
new file mode 100644
index 0000000..4bcd4fb
--- /dev/null
+++ b/cpp/src/nn_implementation_module/detect/plate_detector.cpp
@@ -0,0 +1,107 @@
+//
+// Created by tunm on 2022/12/29.
+//
+
+#include "plate_detector.h"
+
+namespace hyper {
+
+PlateDetector::PlateDetector() {
+
+}
+
+int PlateDetector::Initialize(const char *model_path,
+                              int input_size,
+                              int threads,
+                              float box_conf_threshold,
+                              float nms_threshold,
+                              bool use_half){
+    m_input_size_ = input_size;
+    m_box_conf_threshold_ = box_conf_threshold;
+    m_nms_threshold_ = nms_threshold;
+    float mean[] = {0.0f, 0.0f, 0.0f};
+    float normal[] = {0.003921568627f, 0.003921568627f, 0.003921568627f};
+    m_nn_adapter_ = std::make_shared<MNNAdapterInference>(model_path, threads, mean, normal, use_half);
+    m_nn_adapter_->Initialization("input", "output", m_input_size_, m_input_size_);
+
+    return 0;
+}
+
+void PlateDetector::Decode(const std::vector<float> &tensor, std::vector<PlateLocation> &outputs, float scale,
+                           float conf_threshold, float nms_threshold) {
+    int grid_num = 6300;
+    int item_num = 15;
+    for (int index = 0; index < grid_num; ++index) {
+        auto det_score = tensor[index * item_num + 4];
+        if (det_score > conf_threshold) {
+            PlateLocation plate;
+            plate.det_confidence = det_score;
+            auto class_score_1 = tensor[index * item_num + 13] * det_score;
+            auto class_score_2 = tensor[index * item_num + 14] * det_score;
+            LayersNum type = class_score_1 > class_score_2 ? MONO : DOUBLE;
+            plate.layers = type;
+            auto x = tensor[index * item_num + 0];
+            auto y = tensor[index * item_num + 1];
+            auto w = tensor[index * item_num + 2];
+            auto h = tensor[index * item_num + 3];
+            std::vector<float> xyxy = xywh2xyxy(x, y, w, h);
+            plate.x1 = xyxy[0];
+            plate.y1 = xyxy[1];
+            plate.x2 = xyxy[2];
+            plate.y2 = xyxy[3];
+            for (int i = 0; i < 8; ++i) {
+                plate.kps[i] = tensor[index * item_num + i + 5];
+            }
+            outputs.push_back(plate);
+        }
+
+    }
+    nms(outputs, nms_threshold);
+
+    for (auto &plate : outputs) {
+        plate.x1 = plate.x1 / scale;
+        plate.y1 = plate.y1 / scale;
+        plate.x2 = plate.x2 / scale;
+        plate.y2 = plate.y2 / scale;
+        for (int i = 0; i < 4; ++i) {
+            plate.kps[i * 2 + 0] = plate.kps[i * 2 + 0] / scale;
+            plate.kps[i * 2 + 1] = plate.kps[i * 2 + 1] / scale;
+        }
+    }
+}
+
+void PlateDetector::Detection(const cv::Mat &bgr, bool is_resize, float scale) {
+    cv::Mat pad;
+    if (is_resize) {
+        int ori_w = bgr.cols;
+        int ori_h = bgr.rows;
+
+        int w, h;
+        if (ori_w > ori_h) {
+            scale = (float) m_input_size_ / ori_w;
+            w = m_input_size_;
+            h = ori_h * scale;
+        } else {
+            scale = (float) m_input_size_ / ori_h;
+            h = m_input_size_;
+            w = ori_w * scale;
+        }
+        int wpad = m_input_size_ - w;
+        int hpad = m_input_size_ - h;
+        cv::Mat resized_img;
+        cv::resize(bgr, resized_img, cv::Size(w, h));
+        cv::copyMakeBorder(resized_img, pad, 0, hpad, 0, wpad, cv::BORDER_CONSTANT, 0.0f);
+    } else {
+        pad = bgr;
+    }
+//    cv::imshow("a", pad);
+    auto output = m_nn_adapter_->Invoking(pad);
+    std::vector<PlateLocation>().swap(m_results_);
+    Decode(output, m_results_, scale, m_box_conf_threshold_, m_nms_threshold_);
+
+
+}
+
+
+
+}
\ No newline at end of file
diff --git a/cpp/src/nn_implementation_module/detect/plate_detector.h b/cpp/src/nn_implementation_module/detect/plate_detector.h
new file mode 100644
index 0000000..6f92012
--- /dev/null
+++ b/cpp/src/nn_implementation_module/detect/plate_detector.h
@@ -0,0 +1,46 @@
+//
+// Created by tunm on 2022/12/29.
+//
+#pragma once
+#ifndef ZEPHYRLPR_PLATE_DETECTOR_H
+#define ZEPHYRLPR_PLATE_DETECTOR_H
+#include "nn_module/mnn_adapter.h"
+#include "plate_det_common.h"
+
+namespace hyper {
+
+class PlateDetector {
+public:
+
+    PlateDetector(const PlateDetector &) = delete;
+
+    PlateDetector &operator=(const PlateDetector &) = delete;
+
+    explicit PlateDetector();
+
+    int Initialize(const char *model_path, int input_size = 320, int threads=1, float box_conf_threshold = 0.3, float nms_threshold = 0.6, bool use_half = false);
+
+    void Detection(const cv::Mat& bgr, bool is_resize = false, float scale = 1.0f);
+
+private:
+    void Decode(const std::vector<float> &tensor, std::vector<PlateLocation> &outputs, float scale,
+                float conf_threshold = 0.5, float nms_threshold = 0.5);
+
+public:
+    std::vector<PlateLocation> m_results_;
+
+private:
+
+    int m_input_size_{};
+
+    float m_box_conf_threshold_{};
+
+    float m_nms_threshold_{};
+
+    std::shared_ptr<MNNAdapterInference> m_nn_adapter_;
+
+};
+
+}
+
+#endif //ZEPHYRLPR_PLATE_DETECTOR_H
diff --git a/cpp/src/nn_implementation_module/recognition/all.h b/cpp/src/nn_implementation_module/recognition/all.h
new file mode 100644
index 0000000..d328e3b
--- /dev/null
+++ b/cpp/src/nn_implementation_module/recognition/all.h
@@ -0,0 +1,12 @@
+//
+// Created by Tunm-Air13 on 2022/12/30.
+//
+
+#ifndef ZEPHYRLPR_RECOGNITION_ALL_H
+#define ZEPHYRLPR_RECOGNITION_ALL_H
+
+#include "plate_recognition.h"
+#include "recognition_commom.h"
+#include "recognition_engine.h"
+
+#endif //ZEPHYRLPR_RECOGNITION_ALL_H
diff --git a/cpp/src/nn_implementation_module/recognition/plate_recognition.cpp b/cpp/src/nn_implementation_module/recognition/plate_recognition.cpp
new file mode 100644
index 0000000..19a1812
--- /dev/null
+++ b/cpp/src/nn_implementation_module/recognition/plate_recognition.cpp
@@ -0,0 +1,82 @@
+//
+// Created by Tunm-Air13 on 2022/12/30.
+//
+
+#include "plate_recognition.h"
+#include <utility>
+#include "utils.h"
+#include "plate_recognition_tokenize.h"
+
+namespace hyper {
+
+PlateRecognition::PlateRecognition() = default;
+
+int PlateRecognition::Initialize(const std::string& model_filename, cv::Size input_size, int threads, float confidence_threshold,
+                                 bool use_half) {
+    m_input_image_size_ = std::move(input_size);
+    m_confidence_threshold_ = confidence_threshold;
+    float mean[] = {127.5f, 127.5f, 127.5f};
+    float normal[] = {1.0 / 127.5f, 1.0 / 127.5f, 1.0 / 127.5f};
+    m_nn_adapter_ = std::make_shared<MNNAdapterInference>(model_filename, threads, mean, normal, use_half);
+    m_nn_adapter_->Initialization("data", "output", m_input_image_size_.width, m_input_image_size_.height);
+
+    return 0;
+}
+
+TextLine PlateRecognition::Inference(const cv::Mat &bgr_pad) {
+    std::vector<float> output = m_nn_adapter_->Invoking(bgr_pad);
+//    SLOG_INFO("out: {}", output.size());
+    auto line = decode(output);
+    return line;
+}
+
+TextLine PlateRecognition::decode(const std::vector<float>& tensor) {
+    int text_total_num = 24;
+    int classify_num = 75;
+    IndexList index_list;
+    std::vector<float> max_list;
+    for (int index = 0; index < text_total_num; ++index) {
+        std::vector<float> mapping;
+        mapping.reserve(classify_num);
+        for (int i = 0; i < classify_num; ++i) {
+            mapping.push_back(tensor[index * classify_num + i]);
+        }
+        auto max_index = argmax(mapping.begin(), mapping.end());
+        float max_value = mapping[max_index];
+        index_list.push_back(max_index);
+        max_list.push_back(max_value);
+    }
+
+    TextLine line;
+    line.code = "";
+    float total_ = 0.0f;
+    for (int i = 0; i < index_list.size(); ++i) {
+        auto &idx = index_list[i];
+        IndexList::const_iterator result = std::find(m_ignored_tokens_.begin(), m_ignored_tokens_.end(), idx);
+        if (result == m_ignored_tokens_.end()) {
+            if ((i > 0) && index_list[i - 1] == idx) {
+                continue;   // remove_duplicate
+            }
+//            std::cout << "b " << idx << std::endl;
+            line.char_index.push_back(idx);
+            line.char_scores.push_back(max_list[i]);
+            line.code += TOKENIZE[idx];
+//            SLOG_INFO("{}", TOKENIZE[idx]);
+            total_ += max_list[i];
+        }
+    }
+    line.average_score = total_ / line.char_scores.size();
+
+    return line;
+}
+
+const cv::Size &PlateRecognition::getMInputImageSize() const {
+    return m_input_image_size_;
+}
+
+float PlateRecognition::getMConfidenceThreshold() const {
+    return m_confidence_threshold_;
+}
+
+
+}
\ No newline at end of file
diff --git a/cpp/src/nn_implementation_module/recognition/plate_recognition.h b/cpp/src/nn_implementation_module/recognition/plate_recognition.h
new file mode 100644
index 0000000..c50e4b1
--- /dev/null
+++ b/cpp/src/nn_implementation_module/recognition/plate_recognition.h
@@ -0,0 +1,51 @@
+//
+// Created by Tunm-Air13 on 2022/12/30.
+//
+#pragma once
+#ifndef ZEPHYRLPR_PLATE_RECOGNITION_H
+#define ZEPHYRLPR_PLATE_RECOGNITION_H
+
+#include "nn_module/mnn_adapter.h"
+#include "configuration.h"
+#include "recognition_commom.h"
+#include "basic_types.h"
+
+namespace hyper {
+
+class PlateRecognition {
+public:
+
+    PlateRecognition(const PlateRecognition &) = delete;
+
+    PlateRecognition &operator=(const PlateRecognition &) = delete;
+
+    explicit PlateRecognition();
+
+    int Initialize(const std::string& model_filename, cv::Size input_size = REC_INPUT_SIZE, int threads = 1,
+                   float confidence_threshold = 0.5, bool use_half = false);
+
+    TextLine Inference(const cv::Mat &bgr_pad);
+
+    const cv::Size &getMInputImageSize() const;
+
+    float getMConfidenceThreshold() const;
+
+private:
+
+    TextLine decode(const std::vector<float>& tensor);
+
+private:
+
+    cv::Size m_input_image_size_{};                            // 输入图像宽高
+
+    float m_confidence_threshold_{};                             // 置信度阈值
+
+    std::shared_ptr<MNNAdapterInference> m_nn_adapter_;        // 推理模块
+
+    IndexList m_ignored_tokens_ = {0, };                       // 需要被忽略的索引
+
+};
+
+}
+
+#endif //ZEPHYRLPR_PLATE_RECOGNITION_H
diff --git a/cpp/src/nn_implementation_module/recognition/plate_recognition_tokenize.h b/cpp/src/nn_implementation_module/recognition/plate_recognition_tokenize.h
new file mode 100644
index 0000000..8202d43
--- /dev/null
+++ b/cpp/src/nn_implementation_module/recognition/plate_recognition_tokenize.h
@@ -0,0 +1,17 @@
+//
+// Created by tunm on 2023/1/25.
+//
+#pragma once
+
+#ifndef ZEPHYRLPR_PLATE_RECOGNITION_TOKENIZE_H
+#define ZEPHYRLPR_PLATE_RECOGNITION_TOKENIZE_H
+
+#include <iostream>
+
+static const std::vector<std::string> TOKENIZE = {
+        "blank", "'", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "A", "B", "C", "D", "E", "F", "G", "H", "J",
+        "K", "L", "M", "N", "O", "P", "Q", "R", "S", "T", "U", "V", "W", "X", "Y", "Z", "云", "京", "冀", "吉", "学", "宁",
+        "川", "挂", "新", "晋", "桂", "民", "沪", "津", "浙", "渝", "港", "湘", "琼", "甘", "皖", "粤", "航", "苏", "蒙", "藏", "警", "豫",
+        "贵", "赣", "辽", "鄂", "闽", "陕", "青", "鲁", "黑", "领", "使", "澳"};
+
+#endif //ZEPHYRLPR_PLATE_RECOGNITION_TOKENIZE_H
diff --git a/cpp/src/nn_implementation_module/recognition/recognition_commom.h b/cpp/src/nn_implementation_module/recognition/recognition_commom.h
new file mode 100644
index 0000000..3fc4224
--- /dev/null
+++ b/cpp/src/nn_implementation_module/recognition/recognition_commom.h
@@ -0,0 +1,23 @@
+//
+// Created by Tunm-Air13 on 2022/12/30.
+//
+
+#ifndef ZEPHYRLPR_RECOGNITION_COMMOM_H
+#define ZEPHYRLPR_RECOGNITION_COMMOM_H
+
+#include "basic_types.h"
+
+namespace hyper {
+
+typedef struct TextLine {
+    IndexList char_index;                             ///< 索引列表
+    std::vector<float> char_scores;                   ///< 置信度列表
+    float average_score;                              ///< 平均置信度
+    std::string code;                                 ///< 车牌字符串
+};
+
+typedef std::vector<TextLine> TextLines;             ///< 多行文本表达
+
+}
+
+#endif //ZEPHYRLPR_RECOGNITION_COMMOM_H
diff --git a/cpp/src/nn_implementation_module/recognition/recognition_engine.cpp b/cpp/src/nn_implementation_module/recognition/recognition_engine.cpp
new file mode 100644
index 0000000..6c31bcb
--- /dev/null
+++ b/cpp/src/nn_implementation_module/recognition/recognition_engine.cpp
@@ -0,0 +1,151 @@
+//
+// Created by tunm on 2023/2/6.
+//
+
+#include "recognition_engine.h"
+#include <utility>
+#include "utils.h"
+#include "plate_recognition_tokenize.h"
+
+#define INPUT_NAME "data"
+
+namespace hyper {
+
+
+RecognitionEngine::RecognitionEngine() = default;
+
+
+int32_t RecognitionEngine::Initialize(const std::string& model_filename, cv::Size input_size, int threads, float confidence_threshold,
+                                      bool use_half) {
+
+    m_input_image_size_ = input_size;
+    m_confidence_threshold_ = confidence_threshold;
+
+
+    /* Set output tensor info */
+    m_output_tensor_info_list_.clear();
+    m_output_tensor_info_list_.push_back(OutputTensorInfo("output", TensorInfo::kTensorTypeFp32));
+
+
+    m_nn_infer_.reset(InferenceHelper::Create(InferenceHelper::kMnn));
+
+    if (m_nn_infer_->SetNumThreads(threads) != InferenceHelper::kRetOk) {
+        m_nn_infer_.reset();
+        return InferenceHelper::kRetErr;
+    }
+
+    if (m_nn_infer_->Initialize(model_filename, m_input_tensor_info_list_, m_output_tensor_info_list_) != InferenceHelper::kRetOk) {
+        m_nn_infer_.reset();
+        return InferenceHelper::kRetErr;
+    }
+
+    m_input_tensor_info_list_.clear();
+
+    InputTensorInfo input_tensor_info(INPUT_NAME, TensorInfo::kTensorTypeFp32, true);
+
+    input_tensor_info.tensor_dims =  { 1, 3, m_input_image_size_.height, m_input_image_size_.width };
+    input_tensor_info.data_type = InputTensorInfo::kDataTypeImage;
+
+    input_tensor_info.normalize.mean[0] = 127.5f;
+    input_tensor_info.normalize.mean[1] = 127.5f;
+    input_tensor_info.normalize.mean[2] = 127.5f;
+    input_tensor_info.normalize.norm[0] = 0.007843137255f;
+    input_tensor_info.normalize.norm[1] = 0.007843137255f;
+    input_tensor_info.normalize.norm[2] = 0.007843137255f;
+
+    m_input_tensor_info_list_.push_back(input_tensor_info);
+
+    return InferenceHelper::kRetOk;
+}
+
+int32_t RecognitionEngine::Inference(const cv::Mat &bgr_pad, TextLine &line) {
+    InputTensorInfo& input_tensor_info = m_input_tensor_info_list_[0];
+
+    input_tensor_info.data = bgr_pad.data;
+    input_tensor_info.data_type = InputTensorInfo::kDataTypeImage;
+    input_tensor_info.image_info.width = bgr_pad.cols;
+    input_tensor_info.image_info.height = bgr_pad.rows;
+    input_tensor_info.image_info.channel = bgr_pad.channels();
+    input_tensor_info.image_info.crop_x = 0;
+    input_tensor_info.image_info.crop_y = 0;
+    input_tensor_info.image_info.crop_width = bgr_pad.cols;
+    input_tensor_info.image_info.crop_height = bgr_pad.rows;
+    input_tensor_info.image_info.is_bgr = true;
+    input_tensor_info.image_info.swap_color = false;
+
+    if (m_nn_infer_->PreProcess(m_input_tensor_info_list_) != InferenceHelper::kRetOk) {
+        return InferenceHelper::kRetErr;
+    }
+    if (m_nn_infer_->Process(m_output_tensor_info_list_) != InferenceHelper::kRetOk) {
+        return InferenceHelper::kRetErr;
+    }
+
+    std::vector<float> output_score_raw_list(m_output_tensor_info_list_[0].GetDataAsFloat(),
+                                             m_output_tensor_info_list_[0].GetDataAsFloat() +
+                                             m_output_tensor_info_list_[0].GetElementNum());
+
+    decode(output_score_raw_list, line);
+
+
+    return InferenceHelper::kRetOk;
+}
+
+const cv::Size &RecognitionEngine::getMInputImageSize() const {
+    return m_input_image_size_;
+}
+
+
+float RecognitionEngine::getMConfidenceThreshold() const {
+    return m_confidence_threshold_;
+}
+
+
+int32_t RecognitionEngine::decode(const std::vector<float> &tensor, TextLine &line) {
+    int text_total_num = REC_MAX_CHAR_NUM;
+    int classify_num = REC_CHAR_CLASS_NUM;
+    IndexList index_list;
+    std::vector<float> max_list;
+    for (int index = 0; index < text_total_num; ++index) {
+        std::vector<float> mapping;
+        mapping.reserve(classify_num);
+        for (int i = 0; i < classify_num; ++i) {
+            mapping.push_back(tensor[index * classify_num + i]);
+        }
+        auto max_index = argmax(mapping.begin(), mapping.end());
+        float max_value = mapping[max_index];
+        index_list.push_back(max_index);
+        max_list.push_back(max_value);
+    }
+
+    line.code = "";
+    float total_ = 0.0f;
+    for (int i = 0; i < index_list.size(); ++i) {
+        auto &idx = index_list[i];
+        IndexList::const_iterator result = std::find(m_ignored_tokens_.begin(), m_ignored_tokens_.end(), idx);
+        if (result == m_ignored_tokens_.end()) {
+            if ((i > 0) && index_list[i - 1] == idx) {
+                continue;   // remove_duplicate
+            }
+//            std::cout << "b " << idx << std::endl;
+            line.char_index.push_back(idx);
+            line.char_scores.push_back(max_list[i]);
+            line.code += TOKENIZE[idx];
+//            SLOG_INFO("{}", TOKENIZE[idx]);
+            total_ += max_list[i];
+        }
+    }
+    line.average_score = total_ / line.char_scores.size();
+
+    return InferenceHelper::kRetOk;
+}
+
+RecognitionEngine::~RecognitionEngine() {
+    if (!m_nn_infer_) {
+        LOGD("Inference helper is not created\n");
+        return;
+    }
+    m_nn_infer_->Finalize();
+    m_nn_infer_.reset();
+}
+
+} // namespace
\ No newline at end of file
diff --git a/cpp/src/nn_implementation_module/recognition/recognition_engine.h b/cpp/src/nn_implementation_module/recognition/recognition_engine.h
new file mode 100644
index 0000000..ccab82c
--- /dev/null
+++ b/cpp/src/nn_implementation_module/recognition/recognition_engine.h
@@ -0,0 +1,58 @@
+//
+// Created by tunm on 2023/2/6.
+//
+#pragma once
+#ifndef ZEPHYRLPR_RECOGNITION_ENGINE_H
+#define ZEPHYRLPR_RECOGNITION_ENGINE_H
+
+#include "inference_helper_module/inference_helper.h"
+#include "configuration.h"
+#include "recognition_commom.h"
+#include "basic_types.h"
+#include "opencv2/opencv.hpp"
+
+namespace hyper {
+
+class RecognitionEngine {
+public:
+
+    RecognitionEngine(const RecognitionEngine &) = delete;
+
+    RecognitionEngine &operator=(const RecognitionEngine &) = delete;
+
+    explicit RecognitionEngine();
+
+    ~RecognitionEngine();
+
+    int32_t Initialize(const std::string& model_filename, cv::Size input_size = REC_INPUT_SIZE, int threads = 1,
+                       float confidence_threshold = 0.5, bool use_half = false);
+
+    int32_t Inference(const cv::Mat &bgr_pad, TextLine &line);
+
+    const cv::Size &getMInputImageSize() const;
+
+    float getMConfidenceThreshold() const;
+
+private:
+
+    int32_t decode(const std::vector<float>& tensor, TextLine &line);
+
+private:
+    cv::Size m_input_image_size_{};                             // 输入图像宽高
+
+    float m_confidence_threshold_{};                            // 置信度阈值
+
+    std::unique_ptr<InferenceHelper> m_nn_infer_;               // 推理模块
+
+    IndexList m_ignored_tokens_ = {0, };                        // 需要被忽略的索引
+
+    std::vector<InputTensorInfo> m_input_tensor_info_list_;
+
+    std::vector<OutputTensorInfo> m_output_tensor_info_list_;
+
+
+};
+
+} // namespace
+
+#endif //ZEPHYRLPR_RECOGNITION_ENGINE_H
diff --git a/cpp/src/nn_module/all.h b/cpp/src/nn_module/all.h
new file mode 100644
index 0000000..d8e40fd
--- /dev/null
+++ b/cpp/src/nn_module/all.h
@@ -0,0 +1,10 @@
+//
+// Created by tunm on 2023/1/25.
+//
+
+#ifndef ZEPHYRLPR_MNN_ADAPTER_ALL_H
+#define ZEPHYRLPR_MNN_ADAPTER_ALL_H
+
+#include "mnn_adapter.h"
+
+#endif //ZEPHYRLPR_MNN_ADAPTER_ALL_H
diff --git a/cpp/src/nn_module/mnn_adapter.cpp b/cpp/src/nn_module/mnn_adapter.cpp
new file mode 100644
index 0000000..43ff1b3
--- /dev/null
+++ b/cpp/src/nn_module/mnn_adapter.cpp
@@ -0,0 +1,107 @@
+//
+// Created by tunm on 2022/4/24.
+//
+
+#include "mnn_adapter.h"
+
+namespace hyper {
+
+
+MNNAdapterInference::MNNAdapterInference(const std::string &model, int thread, const float *mean,
+                                         const float *normal, bool is_use_half,
+                                         bool use_model_bin, bool is_use_cuda) {
+
+    if (is_use_cuda) {
+        backend_ = MNN_FORWARD_CUDA;
+    } else {
+        backend_ = MNN_FORWARD_CPU;
+    }
+    if (use_model_bin) {
+        raw_model_ = std::shared_ptr<MNN::Interpreter>(
+                MNN::Interpreter::createFromBuffer(model.c_str(), model.size()));
+    } else {
+        raw_model_ = std::shared_ptr<MNN::Interpreter>(
+                MNN::Interpreter::createFromFile(model.c_str()));
+    }
+
+    _config.type = backend_;
+    //_config.layers = MNN_FORWARD_CUDA;
+    _config.numThread = 2;
+    //_config.numThread = thread;
+    MNN::BackendConfig backendConfig;
+    if (is_use_half) {
+        backendConfig.precision = MNN::BackendConfig::Precision_Low;
+    } else {
+        backendConfig.precision = MNN::BackendConfig::Precision_High;
+    }
+    backendConfig.power = MNN::BackendConfig::Power_High;
+    _config.backendConfig = &backendConfig;
+    for (int i = 0; i < 3; i++) {
+        this->mean[i] = mean[i];
+        this->normal[i] = normal[i];
+    }
+}
+
+
+MNNAdapterInference::~MNNAdapterInference() {
+    raw_model_->releaseModel();
+    raw_model_->releaseSession(sess);
+}
+
+void
+MNNAdapterInference::Initialization(const std::string &input, const std::string &output, int width, int height) {
+    sess = raw_model_->createSession(_config);
+    tensor_shape_.resize(4);
+    tensor_shape_ = {1, 3, height, width};
+    input_ = raw_model_->getSessionInput(sess, input.c_str());
+    output_ = raw_model_->getSessionOutput(sess, output.c_str());
+    width_ = width;
+    height_ = height;
+}
+
+std::vector<float> MNNAdapterInference::Invoking(const cv::Mat &mat) {
+    assert(mat.rows == height_);
+    assert(mat.cols == width_);
+    MNN::CV::ImageProcess::Config config;
+    config.destFormat = image_format_dst_;
+    config.sourceFormat = image_format_src_;
+    for (int i = 0; i < 3; i++) {
+        config.mean[i] = mean[i];
+        config.normal[i] = normal[i];
+    }
+
+
+    std::unique_ptr<MNN::CV::ImageProcess> process(
+            MNN::CV::ImageProcess::create(config));
+    process->convert(mat.data, mat.cols, mat.rows, (int) mat.step1(), input_);
+    raw_model_->runSession(sess);
+    auto dimType = input_->getDimensionType();
+
+    if (output_->getType().code != halide_type_float) {
+        dimType = MNN::Tensor::TENSORFLOW;
+    }
+    std::shared_ptr<MNN::Tensor> outputUser(new MNN::Tensor(output_, dimType));
+    output_->copyToHostTensor(outputUser.get());
+    auto type = outputUser->getType();
+    auto size = outputUser->elementSize();
+    std::vector<float> tempValues(size);
+    if (type.code == halide_type_float) {
+        auto values = outputUser->host<float>();
+        for (int i = 0; i < size; ++i) {
+            tempValues[i] = values[i];
+        }
+    }
+    return tempValues;
+}
+
+void MNNAdapterInference::setImageFormatSrc(MNN::CV::ImageFormat imageFormatSrc) {
+    image_format_src_ = imageFormatSrc;
+}
+
+void MNNAdapterInference::setImageFormatDst(MNN::CV::ImageFormat imageFormatDst) {
+    image_format_dst_ = imageFormatDst;
+}
+
+
+}
+
diff --git a/cpp/src/nn_module/mnn_adapter.h b/cpp/src/nn_module/mnn_adapter.h
new file mode 100644
index 0000000..fb9b8c7
--- /dev/null
+++ b/cpp/src/nn_module/mnn_adapter.h
@@ -0,0 +1,54 @@
+//
+// Created by tunm on 2022/4/24.
+//
+
+#ifndef ZEPHYRLPR_MNN_ADAPTER_H
+#define ZEPHYRLPR_MNN_ADAPTER_H
+
+#include <opencv2/opencv.hpp>
+#include <MNN/ImageProcess.hpp>
+#include <MNN/Interpreter.hpp>
+#include <MNN/MNNDefine.h>
+#include <MNN/Tensor.hpp>
+#include <MNN/MNNForwardType.h>
+
+namespace hyper {
+
+class MNNAdapterInference {
+public:
+
+    MNNAdapterInference(const std::string &model, int thread, const float mean[], const float normal[], bool is_use_half = false,
+                        bool use_model_bin = false, bool is_use_cuda = false);
+
+    ~MNNAdapterInference();
+
+
+    void Initialization(const std::string &input, const std::string &output, int width, int height);
+
+
+    std::vector<float> Invoking(const cv::Mat &mat);
+
+    void setImageFormatSrc(MNN::CV::ImageFormat imageFormatSrc);
+
+    void setImageFormatDst(MNN::CV::ImageFormat imageFormatDst);
+
+private:
+    float mean[3]{};
+    float normal[3]{};
+    std::shared_ptr<MNN::Interpreter> raw_model_;
+    MNN::Tensor *input_{};
+    MNN::Tensor *output_{};
+    MNN::Session *sess{};
+    std::vector<int> tensor_shape_;
+    MNN::ScheduleConfig _config;
+    MNNForwardType backend_;
+    int width_{};
+    int height_{};
+
+    MNN::CV::ImageFormat image_format_src_{MNN::CV::ImageFormat::BGR};
+    MNN::CV::ImageFormat image_format_dst_{MNN::CV::ImageFormat::BGR};
+
+};
+
+}
+#endif //ZEPHYRLPR_MNN_ADAPTER_H
diff --git a/cpp/src/utils.h b/cpp/src/utils.h
new file mode 100644
index 0000000..1aa2656
--- /dev/null
+++ b/cpp/src/utils.h
@@ -0,0 +1,148 @@
+//
+// Created by Tunm-Air13 on 2022/12/30.
+//
+#pragma once
+#ifndef ZEPHYRLPR_UTILS_H
+#define ZEPHYRLPR_UTILS_H
+
+#include <unistd.h>
+
+inline bool exists(const std::string &file_path) {
+    return (access(file_path.c_str(), F_OK) != -1);
+}
+
+template<class ForwardIterator>
+inline size_t argmin(ForwardIterator first, ForwardIterator last) {
+    return std::distance(first, std::min_element(first, last));
+}
+
+template<class ForwardIterator>
+inline size_t argmax(ForwardIterator first, ForwardIterator last) {
+    return std::distance(first, std::max_element(first, last));
+}
+
+inline double L2NormFor2Points(const cv::Point2f &p1, const cv::Point2f &p2) {
+    auto r = pow((p1.x - p2.x), 2) + pow((p1.y - p2.y), 2);
+
+    return sqrt(r);
+}
+
+inline cv::Mat getRotateCropAndAlignMatrix(int img_crop_width, int img_crop_height,
+                                           float *points2d, int point_num = 4) {
+    assert(point_num == 4);
+    float dst_array[] = {0, 0,
+                         (float) img_crop_width, 0,
+                         (float) img_crop_width, (float) img_crop_height,
+                         0, (float) img_crop_height};
+    cv::Mat src(4, 2, CV_32F, points2d);
+    cv::Mat dst(4, 2, CV_32F);
+    dst.data = (uchar *) dst_array;
+
+    cv::Mat transform_matrix = cv::getPerspectiveTransform(src, dst);
+
+    return transform_matrix;
+}
+
+inline void getRotateCropAndAlignPad(const cv::Mat &image, cv::Mat &out, float *points2d, int point_num = 4) {
+    assert(point_num == 4);
+    std::vector<cv::Point2f> vertex;
+    for (int i = 0; i < point_num; ++i) {
+        auto point = cv::Point2f(points2d[i * 2 + 0], points2d[i * 2 + 1]);
+        vertex.push_back(point);
+    }
+    int img_crop_width = (int) std::max(L2NormFor2Points(vertex[0], vertex[1]), L2NormFor2Points(vertex[2], vertex[3]));
+    int img_crop_height = (int) std::max(L2NormFor2Points(vertex[0], vertex[3]),
+                                         L2NormFor2Points(vertex[1], vertex[2]));
+
+    cv::Mat transform_matrix = getRotateCropAndAlignMatrix(img_crop_width, img_crop_height, points2d, point_num);
+    cv::warpPerspective(image, out, transform_matrix, cv::Size(img_crop_width, img_crop_height), cv::INTER_CUBIC,
+                        cv::BORDER_REPLICATE);
+
+    if ((float) img_crop_height / (float) img_crop_width >= 1.5) {
+        cv::rotate(out, out, cv::ROTATE_90_CLOCKWISE);
+    }
+
+}
+
+inline void
+imagePadding(const cv::Mat &image, cv::Mat &out, cv::Size target_size) {
+    int ori_w = image.cols;
+    int ori_h = image.rows;
+
+    int w, h;
+    float scale;
+    if (ori_w > ori_h) {
+        scale = (float) target_size.width / ori_w;
+        w = target_size.width;
+        h = ori_h * scale;
+    } else {
+        scale = (float) target_size.height / ori_h;
+        h = target_size.height;
+        w = ori_w * scale;
+    }
+    int wpad = std::max(target_size.width - w, 0);
+    int hpad = std::max(target_size.height - h, 0);
+    cv::Mat resized_img;
+    cv::resize(image, resized_img, cv::Size(w, h));
+    cv::copyMakeBorder(resized_img, out, 0, hpad, 0, wpad, cv::BORDER_CONSTANT, cv::Scalar(127.5f, 127.5f, 127.5f));
+}
+
+inline void
+imagePadding(const cv::Mat &image, cv::Mat &out, float max_wh_ratio, cv::Size target_size,
+             int limited_max_width = 160,
+             int limited_min_width = 48) {
+//    cv::imshow("w", image);
+//    cv::waitKey(0);
+//    std::cout << image.size << std::endl;
+    int target_h = target_size.height;
+    int target_w = target_size.width;
+    max_wh_ratio = std::max(max_wh_ratio, (float) target_w / (float) target_h);
+    target_w = (int) (max_wh_ratio * target_h);
+    target_w = std::max(std::min(target_w, limited_max_width), limited_min_width);
+    auto image_h = image.rows;
+    auto image_w = image.cols;
+    auto ratio = (float) image_w / image_h;
+    int ratio_img_h = ceil(target_h * ratio);
+    ratio_img_h = std::max(ratio_img_h, limited_min_width);
+    int resized_w;
+    if (ratio_img_h > target_w) {
+        resized_w = target_w;
+    } else {
+        resized_w = int(ratio_img_h);
+    }
+    cv::Mat resized_image;
+    cv::resize(image, resized_image, cv::Size(resized_w, target_h));
+
+
+    int wpad = std::max(target_size.width - resized_w, 0);
+    int hpad = std::max(target_size.height - target_h, 0);
+//    std::cout << resized_w << "," << target_h << std::endl;
+    cv::copyMakeBorder(resized_image, out, 0, hpad, 0, wpad, cv::BORDER_CONSTANT, cv::Scalar(127.5f, 127.5f, 127.5f));
+
+
+}
+
+template<class T>
+inline float xyxyArea(T x1, T y1, T x2, T y2) {
+    auto w = x2 - x1;
+    auto h = y2 - y1;
+
+    return (float) (w * h);
+}
+
+template<class T>
+inline float boundBoxOverlap(const cv::Rect_<T> &box1, const cv::Rect_<T> &box2) {
+    if (box1.x > box2.x + box2.width) { return 0.0; }
+    if (box1.y > box2.y + box2.height) { return 0.0; }
+    if (box1.x + box1.width < box2.x) { return 0.0; }
+    if (box1.y + box1.height < box2.y) { return 0.0; }
+    T colInt = std::min(box1.x + box1.width, box2.x + box2.width) - std::max(box1.x, box2.x);
+    T rowInt = std::min(box1.y + box1.height, box2.y + box2.height) - std::max(box1.y, box2.y);
+    T intersection = colInt * rowInt;
+    T area1 = box1.width * box1.height;
+    T area2 = box2.width * box2.height;
+
+    return intersection / (area1 + area2 - intersection);
+}
+
+#endif //ZEPHYRLPR_UTILS_H
diff --git a/cpp/test/nn_module/test_classification.cpp b/cpp/test/nn_module/test_classification.cpp
new file mode 100644
index 0000000..0cc23f7
--- /dev/null
+++ b/cpp/test/nn_module/test_classification.cpp
@@ -0,0 +1,54 @@
+//
+// Created by tunm on 2023/2/11.
+//
+
+
+#include "basic_types.h"
+#include "../test_settings.h"
+#include "opencv2/opencv.hpp"
+#include "nn_implementation_module/classification/all.h"
+#include "basic_types.h"
+#include "utils.h"
+
+using namespace hyper;
+
+TEST_CASE("test_Classification", "[nn_cls]") {
+    PRINT_SPLIT_LINE
+    LOGD("[UnitTest]->Classification Model");
+
+    std::string model_path = GET_DATA("models/r2_mobile/litemodel_cls_96xh.mnn");
+
+    std::vector<std::string> predict_images_list = {
+            GET_DATA("images/align/1.jpg"),
+            GET_DATA("images/align/3.jpg"),
+            GET_DATA("images/align/5.jpg"),
+    };
+    std::vector<PlateColor> predict_results_cls = {
+            PlateColor::BLUE, PlateColor::YELLOW, PlateColor::GREEN,
+    };
+    std::vector<float> predict_results_confidence = {
+            0.9999293f, 0.8975975f, 0.9997952f
+    };
+
+    CHECK(predict_results_confidence.size() == predict_results_cls.size());
+    CHECK(predict_results_confidence.size() == predict_images_list.size());
+
+    ClassificationEngine clsEngine;
+    auto ret = clsEngine.Initialize(model_path, cv::Size_<int>(96, 96));
+    CHECK(ret == InferenceHelper::kRetOk);
+
+    SECTION("test_ClassificationModelPredict") {
+        for (int i = 0; i < predict_images_list.size(); ++i) {
+            cv::Mat img = cv::imread(predict_images_list[i]);
+            CHECK(!img.empty());
+            CHECK(img.cols == 96);
+            CHECK(img.rows == 96);
+            ret = clsEngine.Inference(img);
+            CHECK(ret == InferenceHelper::kRetOk);
+            CHECK(PlateColor(clsEngine.getMOutputColor()) == predict_results_cls[i]);
+            CHECK(clsEngine.getMOutputMaxConfidence() == Approx(predict_results_confidence[i]).epsilon(0.001));
+        }
+    }
+
+
+}
\ No newline at end of file
diff --git a/cpp/test/nn_module/test_detection.cpp b/cpp/test/nn_module/test_detection.cpp
new file mode 100644
index 0000000..0f04ce0
--- /dev/null
+++ b/cpp/test/nn_module/test_detection.cpp
@@ -0,0 +1,46 @@
+//
+// Created by tunm on 2023/2/11.
+//
+#include "basic_types.h"
+#include "../test_settings.h"
+#include "opencv2/opencv.hpp"
+#include "nn_implementation_module/detect/all.h"
+#include "basic_types.h"
+#include "utils.h"
+
+using namespace hyper;
+
+TEST_CASE("test_Detection", "[nn_detect]") {
+    PRINT_SPLIT_LINE
+    LOGD("[UnitTest]->Detect Model");
+
+    std::string b_model_path = GET_DATA("models/r2_mobile/b320_backbone_h.mnn");
+    std::string h_model_path = GET_DATA("models/r2_mobile/b320_header_h.mnn");
+
+    cv::Mat test_image_1 = cv::imread(GET_DATA("images/pre.jpg"));
+    CHECK(test_image_1.cols == 320);
+    CHECK(test_image_1.rows == 320);
+
+    SECTION("test_SplitDetectionSplitModel") {
+        LOGD("Detect Model SplitModel");
+        DetArch det;
+        auto ret = det.Initialize(b_model_path, h_model_path, 320, 1);
+        CHECK(ret == InferenceHelper::kRetOk);
+
+        det.Detection(test_image_1);
+        auto &result = det.m_results_;
+        CHECK(result.size() == 1);
+
+        auto &box = result[0];
+        cv::Rect2f proposal_box(cv::Point(153, 205), cv::Point(183, 215));
+        cv::Rect2f detected_box(cv::Point(box.x1, box.y1), cv::Point(box.x2, box.y2));
+        auto iou = boundBoxOverlap(proposal_box, detected_box);
+        CHECK(iou > 0.85);
+    }
+
+#if ENABLE_BENCHMARK_TEST
+    SECTION("test_DetectionBenchmark") {
+        LOGD("[UnitTest]->Detection Benchmark");
+    }
+#endif
+}
diff --git a/cpp/test/nn_module/test_recognition.cpp b/cpp/test/nn_module/test_recognition.cpp
new file mode 100644
index 0000000..ce30e9a
--- /dev/null
+++ b/cpp/test/nn_module/test_recognition.cpp
@@ -0,0 +1,51 @@
+//
+// Created by tunm on 2023/2/11.
+//
+#include "basic_types.h"
+#include "../test_settings.h"
+#include "opencv2/opencv.hpp"
+#include "nn_implementation_module/recognition/all.h"
+#include "basic_types.h"
+#include "utils.h"
+
+using namespace hyper;
+
+TEST_CASE("test_Recognition", "[nn_rec]") {
+    PRINT_SPLIT_LINE
+    LOGD("[UnitTest]->Recognition Model");
+
+    std::string model_path = GET_DATA("models/r2_mobile/rpv3_mdict_160h.mnn");
+
+    std::vector<std::string> predict_images_list = {
+            GET_DATA("images/rec_crop/_0_津B6H920.jpg"),
+            GET_DATA("images/rec_crop/_1_皖KD01833.jpg"),
+            GET_DATA("images/rec_crop/_6_蒙B023H6.jpg"),
+            GET_DATA("images/rec_crop/_8_冀D5L690.jpg"),
+    };
+
+    std::vector<std::string> predict_results_code = {
+            "津B6H920", "皖KD01833", "蒙B023H6", "冀D5L690",
+    };
+
+    RecognitionEngine recEngine;
+    auto ret = recEngine.Initialize(model_path);
+    CHECK(ret == InferenceHelper::kRetOk);
+
+    SECTION("test_SplitDetectionSplitModel") {
+        LOGD("Rec Model RPV3");
+        for (int i = 0; i < predict_images_list.size(); ++i) {
+            cv::Mat img = cv::imread(predict_images_list[i]);
+            CHECK(!img.empty());
+            float wh_ratio = (float) img.cols / img.rows;
+            cv::Mat align_image_pad;
+            imagePadding(img, align_image_pad, wh_ratio, recEngine.getMInputImageSize());
+            TextLine line;
+            ret = recEngine.Inference(align_image_pad, line);
+            LOGD("%s -> %s", predict_results_code[i].c_str(), line.code.c_str());
+            CHECK(ret == InferenceHelper::kRetOk);
+            CHECK(strcmp(predict_results_code[i].c_str(), line.code.c_str()) == 0);
+        }
+
+    }
+
+}
\ No newline at end of file
diff --git a/cpp/test/test_main.cpp b/cpp/test/test_main.cpp
new file mode 100644
index 0000000..35bb92a
--- /dev/null
+++ b/cpp/test/test_main.cpp
@@ -0,0 +1,10 @@
+//
+// Created by tunm on 2023/2/11.
+//
+#define CATCH_CONFIG_RUNNER
+#include "test_settings.h"
+
+int main(int argc, char* argv[]) {
+
+    return Catch::Session().run(argc, argv);;
+}
\ No newline at end of file
diff --git a/cpp/test/test_settings.cpp b/cpp/test/test_settings.cpp
new file mode 100644
index 0000000..4bf62ce
--- /dev/null
+++ b/cpp/test/test_settings.cpp
@@ -0,0 +1,21 @@
+//
+// Created by tunm on 2023/2/11.
+//
+
+#include "test_settings.h"
+
+std::string getTestDataDir() {
+    return "./resource/";
+}
+
+std::string getTestData(const std::string& name) {
+    return getTestDataDir() + "/" + name;
+}
+
+std::string getTestSaveDir() {
+    return "./resource/save";
+}
+
+std::string getTestSaveData(const std::string& name) {
+    return getTestSaveDir() + "/" + name;
+}
\ No newline at end of file
diff --git a/cpp/test/test_settings.h b/cpp/test/test_settings.h
new file mode 100644
index 0000000..1a58956
--- /dev/null
+++ b/cpp/test/test_settings.h
@@ -0,0 +1,41 @@
+//
+// Created by tunm on 2023/2/11.
+//
+#pragma once
+#ifndef ZEPHYRLPR_TEST_SETTINGS_H
+#define ZEPHYRLPR_TEST_SETTINGS_H
+#include <catch2/catch.hpp>
+#include <iostream>
+
+using namespace Catch::Detail;
+
+#define ENABLE_BENCHMARK_TEST 0  // 是否开启性能测试相关用例执行，默认不开启
+
+#define TEST_MSG(...) SPDLOG_LOGGER_CALL(spdlog::get("TEST"), spdlog::level::trace, __VA_ARGS__)
+#define GET_DIR getTestDataDir()
+#define GET_DATA(filename) getTestData(filename)
+
+#define GET_TMP_DIR getTestSaveDir()
+#define GET_TMP_DATA(filename) getTestSaveData(filename)
+
+std::string getTestDataDir();
+
+std::string getTestData(const std::string &name);
+
+std::string getTestSaveDir();
+
+std::string getTestSaveData(const std::string &name);
+
+struct test_case_line {
+    ~test_case_line() {
+        std::cout
+                << "==============================================================================="
+                << std::endl;
+    }
+
+
+#define PRINT_SPLIT_LINE test_case_line split_line_x;
+
+};
+
+#endif //ZEPHYRLPR_TEST_SETTINGS_H
diff --git a/demo.py b/demo.py
deleted file mode 100644
index 6804b1f..0000000
--- a/demo.py
+++ /dev/null
@@ -1,59 +0,0 @@
-import sys
-reload(sys)
-sys.setdefaultencoding("utf-8")
-
-
-
-
-import time
-
-def SpeedTest(image_path):
-    grr = cv2.imread(image_path)
-    model = pr.LPR("model/cascade.xml", "model/model12.h5", "model/ocr_plate_all_gru.h5")
-    model.SimpleRecognizePlateByE2E(grr)
-    t0 = time.time()
-    for x in range(20):
-        model.SimpleRecognizePlateByE2E(grr)
-    t = (time.time() - t0)/20.0
-    print "Image size :" + str(grr.shape[1])+"x"+str(grr.shape[0]) +  " need " + str(round(t*1000,2))+"ms"
-
-    
-
-from PIL import ImageFont
-from PIL import Image
-from PIL import ImageDraw
-fontC = ImageFont.truetype("./Font/platech.ttf", 14, 0)
-
-def drawRectBox(image,rect,addText):
-    cv2.rectangle(image, (int(rect[0]), int(rect[1])), (int(rect[0] + rect[2]), int(rect[1] + rect[3])), (0,0, 255), 2,cv2.LINE_AA)
-    cv2.rectangle(image, (int(rect[0]-1), int(rect[1])-16), (int(rect[0] + 115), int(rect[1])), (0, 0, 255), -1,
-                  cv2.LINE_AA)
-    img = Image.fromarray(image)
-    draw = ImageDraw.Draw(img)
-    draw.text((int(rect[0]+1), int(rect[1]-16)), addText.decode("utf-8"), (255, 255, 255), font=fontC)
-    imagex = np.array(img)
-    return imagex
-
-
-
-
-
-import HyperLPRLite as pr
-import cv2
-import numpy as np
-grr = cv2.imread("images_rec/2.jpg")
-model = pr.LPR("model/cascade.xml","model/model12.h5","model/ocr_plate_all_gru.h5")
-for pstr,confidence,rect in model.SimpleRecognizePlateByE2E(grr):
-        if confidence>0.7:
-            image = drawRectBox(grr, rect, pstr+" "+str(round(confidence,3)))
-            print "plate_str:"
-            print pstr
-            print "plate_confidence"
-            print confidence
-            
-cv2.imshow("image",image)
-cv2.waitKey(0)
-
-
-
-#SpeedTest("images_rec/2.jpg")
diff --git a/demo_images/android.png b/demo_images/android.png
deleted file mode 100644
index 89a4a09..0000000
Binary files a/demo_images/android.png and /dev/null differ
diff --git a/demo_images/demo1.png b/demo_images/demo1.png
deleted file mode 100644
index 6fa672c..0000000
Binary files a/demo_images/demo1.png and /dev/null differ
diff --git a/demo_images/demo2.jpg b/demo_images/demo2.jpg
deleted file mode 100644
index 144cb3f..0000000
Binary files a/demo_images/demo2.jpg and /dev/null differ
diff --git a/hyperlpr/cache.py b/hyperlpr/cache.py
deleted file mode 100644
index 378d0b2..0000000
--- a/hyperlpr/cache.py
+++ /dev/null
@@ -1,11 +0,0 @@
-import cv2
-import os
-import hashlib
-
-def verticalMappingToFolder(image):
-    name = hashlib.md5(image.data).hexdigest()[:8]
-    print name
-
-    cv2.imwrite("./cache/finemapping/"+name+".png",image)
-
-
diff --git a/hyperlpr/colourDetection.py b/hyperlpr/colourDetection.py
deleted file mode 100644
index 5a64d56..0000000
--- a/hyperlpr/colourDetection.py
+++ /dev/null
@@ -1,103 +0,0 @@
-# -- coding: UTF-8
-import cv2
-import matplotlib.pyplot as plt
-from sklearn.cluster import KMeans
-import os
-
-boundaries = [
-    ([100,80,0],[240,220,110]), # yellow
-    ([0,40,50],[110,180,250]), # blue
-    ([0,60,0],[60,160,70]), # green
-]
-color_attr = ["黄牌","蓝牌",'绿牌','白牌','黑牌']
-
-threhold_green = 13
-threhold_blue = 13
-threhold_yellow1 = 50
-threhold_yellow2 = 70
-
-# plt.figure()
-# plt.axis("off")
-# plt.imshow(image)
-# plt.show()
-
-import numpy as np
-def centroid_histogram(clt):
-    numLabels = np.arange(0, len(np.unique(clt.labels_)) + 1)
-    (hist, _) = np.histogram(clt.labels_, bins=numLabels)
-
-    # normalize the histogram, such that it sums to one
-    hist = hist.astype("float")
-    hist /= hist.sum()
-
-    # return the histogram
-    return hist
-
-
-def plot_colors(hist, centroids):
-    bar = np.zeros((50, 300, 3), dtype="uint8")
-    startX = 0
-
-    for (percent, color) in zip(hist, centroids):
-
-        endX = startX + (percent * 300)
-        cv2.rectangle(bar, (int(startX), 0), (int(endX), 50),
-                      color.astype("uint8").tolist(), -1)
-        startX = endX
-
-    # return the bar chart
-    return bar
-
-def search_boundaries(color):
-    for i,color_bound in enumerate(boundaries):
-        if np.all(color >= color_bound[0]) and np.all(color <= color_bound[1]):
-            return i
-    return -1
-
-def judge_color(color):
-    r = color[0]
-    g = color[1]
-    b = color[2]
-    if g - r >= threhold_green and g - b >= threhold_green:
-        return 2
-    if b - r >= threhold_blue and b - g >= threhold_blue:
-        return 1
-    if r- b > threhold_yellow2 and g - b > threhold_yellow2:
-        return 0
-    if r > 200 and b > 200 and g > 200:
-        return 3
-    if r < 50 and b < 50 and g < 50:
-        return 4
-    return -1
-
-def judge_plate_color(img):
-    image = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-    image = image.reshape((image.shape[0] * image.shape[1], 3))
-    clt = KMeans(n_clusters=2)
-    clt.fit(image)
-
-    hist = centroid_histogram(clt)
-    index = np.argmax(hist)
-    #print clt.cluster_centers_[index]
-    #color_index = search_boundaries(clt.cluster_centers_[index])
-    color_index = judge_color(clt.cluster_centers_[index])
-    if color_index == -1:
-        if index == 0:
-            secound_index = 1
-        else:
-            secound_index = 0
-        color_index = judge_color(clt.cluster_centers_[secound_index])
-
-    if color_index == -1:
-        print clt.cluster_centers_
-        bar = plot_colors(hist, clt.cluster_centers_)
-        # show our color bart
-        plt.figure()
-        plt.axis("off")
-        plt.imshow(bar)
-        plt.show()
-
-    if color_index != -1:
-        return color_attr[color_index],clt.cluster_centers_[index]
-    else:
-        return None,clt.cluster_centers_[index]
\ No newline at end of file
diff --git a/hyperlpr/config.py b/hyperlpr/config.py
deleted file mode 100644
index 40f2099..0000000
--- a/hyperlpr/config.py
+++ /dev/null
@@ -1,6 +0,0 @@
-import json
-
-
-
-with open("/Users/universe/ProgramUniverse/zeusees/HyperLPR/config.json") as f:
-    configuration = json.load(f)
diff --git a/hyperlpr/deskew.py b/hyperlpr/deskew.py
deleted file mode 100644
index 78a4871..0000000
--- a/hyperlpr/deskew.py
+++ /dev/null
@@ -1,111 +0,0 @@
-#coding=utf-8
-import numpy as np
-import cv2
-import time;
-from matplotlib import pyplot as plt
-import math
-
-from scipy.ndimage import filters
-#
-# def strokeFiter():
-#     pass;
-
-def angle(x,y):
-    return int(math.atan2(float(y),float(x))*180.0/3.1415);
-
-def h_rot(src, angle, scale=1.):
-    w = src.shape[1]
-    h = src.shape[0]
-    rangle = np.deg2rad(angle)
-    nw = (abs(np.sin(rangle)*h) + abs(np.cos(rangle)*w))*scale
-    nh = (abs(np.cos(rangle)*h) + abs(np.sin(rangle)*w))*scale
-
-    rot_mat = cv2.getRotationMatrix2D((nw*0.5, nh*0.5), angle, scale)
-
-    rot_move = np.dot(rot_mat, np.array([(nw-w)*0.5, (nh-h)*0.5,0]))
-
-    rot_mat[0,2] += rot_move[0]
-    rot_mat[1,2] += rot_move[1]
-    return cv2.warpAffine(src, rot_mat, (int(math.ceil(nw)), int(math.ceil(nh))), flags=cv2.INTER_LANCZOS4)
-    pass
-
-
-def v_rot(img,angel,shape,max_angel):
-
-    size_o = [shape[1],shape[0]]
-
-    size = (shape[1]+ int(shape[0]*np.cos((float(max_angel )/180) * 3.14)),shape[0])
-
-
-    interval = abs( int( np.sin((float(angel) /180) * 3.14)* shape[0]));
-
-    pts1 = np.float32([[0,0]         ,[0,size_o[1]],[size_o[0],0],[size_o[0],size_o[1]]])
-    if(angel>0):
-
-        pts2 = np.float32([[interval,0],[0,size[1]  ],[size[0],0  ],[size[0]-interval,size_o[1]]])
-    else:
-        pts2 = np.float32([[0,0],[interval,size[1]  ],[size[0]-interval,0  ],[size[0],size_o[1]]])
-
-    M  = cv2.getPerspectiveTransform(pts1,pts2);
-    dst = cv2.warpPerspective(img,M,size);
-    return dst,M;
-
-def skew_detection(image_gray):
-    h, w = image_gray.shape[:2]
-    eigen = cv2.cornerEigenValsAndVecs(image_gray,12, 5)
-    angle_sur = np.zeros(180,np.uint);
-    eigen = eigen.reshape(h, w, 3, 2)
-    flow = eigen[:,:,2]
-    vis = image_gray.copy()
-    vis[:] = (192 + np.uint32(vis)) / 2
-    d = 12
-    points =  np.dstack( np.mgrid[d/2:w:d, d/2:h:d] ).reshape(-1, 2)
-    for x, y in points:
-       vx, vy = np.int32(flow[y, x]*d)
-       # cv2.line(rgb, (x-vx, y-vy), (x+vx, y+vy), (0, 355, 0), 1, cv2.LINE_AA)
-       ang = angle(vx,vy);
-       angle_sur[(ang+180)%180] +=1;
-    # torr_bin = 30
-    angle_sur = angle_sur.astype(np.float)
-    angle_sur = (angle_sur-angle_sur.min())/(angle_sur.max()-angle_sur.min())
-    angle_sur = filters.gaussian_filter1d(angle_sur,5)
-    skew_v_val =  angle_sur[20:180-20].max();
-    skew_v = angle_sur[30:180-30].argmax() + 30;
-    skew_h_A = angle_sur[0:30].max()
-    skew_h_B = angle_sur[150:180].max()
-    skew_h = 0;
-    if (skew_h_A > skew_v_val*0.3 or skew_h_B > skew_v_val*0.3):
-        if skew_h_A>=skew_h_B:
-            skew_h = angle_sur[0:20].argmax()
-        else:
-            skew_h = - angle_sur[160:180].argmax()
-    return skew_h,skew_v
-
-
-
-
-def fastDeskew(image):
-    image_gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
-    skew_h,skew_v = skew_detection(image_gray)
-
-    print "校正角度 h ",skew_h,"v",skew_v
-
-    deskew,M = v_rot(image,int((90-skew_v)*1.5),image.shape,60)
-    return deskew,M
-
-
-
-if __name__ == '__main__':
-    fn = 'test_data/test4.png'
-
-    img = cv2.imread(fn)
-    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-    skew_h,skew_v = skew_detection(img,gray)
-    img = v_rot(img,(90-skew_v ),img.shape,60)
-    # img = h_rot(img,skew_h)
-    # if img.shape[0]>img.shape[1]:
-    #     img = h_rot(img, -90)
-
-    plt.show()
-
-    cv2.waitKey()
diff --git a/hyperlpr/detect.py b/hyperlpr/detect.py
deleted file mode 100644
index 90865e7..0000000
--- a/hyperlpr/detect.py
+++ /dev/null
@@ -1,76 +0,0 @@
-
-import cv2
-import numpy as np
-
-
-
-watch_cascade = cv2.CascadeClassifier('./model/cascade.xml')
-
-
-def computeSafeRegion(shape,bounding_rect):
-    top = bounding_rect[1] # y
-    bottom  = bounding_rect[1] + bounding_rect[3] # y +  h
-    left = bounding_rect[0] # x
-    right =   bounding_rect[0] + bounding_rect[2] # x +  w
-
-    min_top = 0
-    max_bottom = shape[0]
-    min_left = 0
-    max_right = shape[1]
-
-    # print "computeSateRegion input shape",shape
-    if top < min_top:
-        top = min_top
-        # print "tap top 0"
-    if left < min_left:
-        left = min_left
-        # print "tap left 0"
-
-    if bottom > max_bottom:
-        bottom = max_bottom
-        #print "tap max_bottom max"
-    if right > max_right:
-        right = max_right
-        #print "tap max_right max"
-
-    # print "corr",left,top,right,bottom
-    return [left,top,right-left,bottom-top]
-
-
-def cropped_from_image(image,rect):
-    x, y, w, h = computeSafeRegion(image.shape,rect)
-    return image[y:y+h,x:x+w]
-
-
-def detectPlateRough(image_gray,resize_h = 720,en_scale =1.08 ,top_bottom_padding_rate = 0.05):
-    print image_gray.shape
-
-    if top_bottom_padding_rate>0.2:
-        print "error:top_bottom_padding_rate > 0.2:",top_bottom_padding_rate
-        exit(1)
-
-    height = image_gray.shape[0]
-    padding =    int(height*top_bottom_padding_rate)
-    scale = image_gray.shape[1]/float(image_gray.shape[0])
-
-    image = cv2.resize(image_gray, (int(scale*resize_h), resize_h))
-
-    image_color_cropped = image[padding:resize_h-padding,0:image_gray.shape[1]]
-
-    image_gray = cv2.cvtColor(image_color_cropped,cv2.COLOR_RGB2GRAY)
-
-    watches = watch_cascade.detectMultiScale(image_gray, en_scale, 2, minSize=(36, 9),maxSize=(36*40, 9*40))
-
-    cropped_images = []
-    for (x, y, w, h) in watches:
-        cropped_origin = cropped_from_image(image_color_cropped, (int(x), int(y), int(w), int(h)))
-        x -= w * 0.14
-        w += w * 0.28
-        y -= h * 0.6
-        h += h * 1.1;
-
-        cropped = cropped_from_image(image_color_cropped, (int(x), int(y), int(w), int(h)))
-
-
-        cropped_images.append([cropped,[x, y+padding, w, h],cropped_origin])
-    return cropped_images
diff --git a/hyperlpr/e2e.py b/hyperlpr/e2e.py
deleted file mode 100755
index 6333ff4..0000000
--- a/hyperlpr/e2e.py
+++ /dev/null
@@ -1,63 +0,0 @@
-#coding=utf-8
-from keras import backend as K
-from keras.models import load_model
-from keras.layers import *
-import numpy as np
-import random
-import string
-
-import cv2
-import e2emodel as model
-chars = [u"京", u"沪", u"津", u"渝", u"冀", u"晋", u"蒙", u"辽", u"吉", u"黑", u"苏", u"浙", u"皖", u"闽", u"赣", u"鲁", u"豫", u"鄂", u"湘", u"粤", u"桂",
-             u"琼", u"川", u"贵", u"云", u"藏", u"陕", u"甘", u"青", u"宁", u"新", u"0", u"1", u"2", u"3", u"4", u"5", u"6", u"7", u"8", u"9", u"A",
-             u"B", u"C", u"D", u"E", u"F", u"G", u"H", u"J", u"K", u"L", u"M", u"N", u"P", u"Q", u"R", u"S", u"T", u"U", u"V", u"W", u"X",
-             u"Y", u"Z",u"港",u"学",u"使",u"警",u"澳",u"挂",u"军",u"北",u"南",u"广",u"沈",u"兰",u"成",u"济",u"海",u"民",u"航",u"空"
-             ];
-pred_model = model.construct_model("./model/ocr_plate_all_w_rnn_2.h5",)
-import time
-
-
-
-def fastdecode(y_pred):
-    results = ""
-    confidence = 0.0
-    table_pred = y_pred.reshape(-1, len(chars)+1)
-
-    res = table_pred.argmax(axis=1)
-
-    for i,one in enumerate(res):
-        if one<len(chars) and (i==0 or (one!=res[i-1])):
-            results+= chars[one]
-            confidence+=table_pred[i][one]
-    confidence/= len(results)
-    return results,confidence
-
-def recognizeOne(src):
-    # x_tempx= cv2.imread(src)
-    x_tempx = src
-    # x_tempx = cv2.bitwise_not(x_tempx)
-    x_temp = cv2.resize(x_tempx,( 160,40))
-    x_temp = x_temp.transpose(1, 0, 2)
-    t0 = time.time()
-    y_pred = pred_model.predict(np.array([x_temp]))
-    y_pred = y_pred[:,2:,:]
-    # plt.imshow(y_pred.reshape(16,66))
-    # plt.show()
-
-    #
-    # cv2.imshow("x_temp",x_tempx)
-    # cv2.waitKey(0)
-    return fastdecode(y_pred)
-#
-#
-# import os
-#
-# path = "/Users/yujinke/PycharmProjects/HyperLPR_Python_web/cache/finemapping"
-# for filename in os.listdir(path):
-#     if filename.endswith(".png") or filename.endswith(".jpg") or filename.endswith(".bmp"):
-#         x = os.path.join(path,filename)
-#         recognizeOne(x)
-#         # print time.time() - t0
-#
-#         # cv2.imshow("x",x)
-#         # cv2.waitKey()
diff --git a/hyperlpr/e2emodel.py b/hyperlpr/e2emodel.py
deleted file mode 100644
index 97356c6..0000000
--- a/hyperlpr/e2emodel.py
+++ /dev/null
@@ -1,34 +0,0 @@
-
-from keras import backend as K
-from keras.models import *
-from keras.layers import *
-import e2e
-
-
-def ctc_lambda_func(args):
-    y_pred, labels, input_length, label_length = args
-    y_pred = y_pred[:, 2:, :]
-    return K.ctc_batch_cost(labels, y_pred, input_length, label_length)
-
-
-def construct_model(model_path):
-    input_tensor = Input((None, 40, 3))
-    x = input_tensor
-    base_conv = 32
-
-    for i in range(3):
-        x = Conv2D(base_conv * (2 ** (i)), (3, 3),padding="same")(x)
-        x = BatchNormalization()(x)
-        x = Activation('relu')(x)
-        x = MaxPooling2D(pool_size=(2, 2))(x)
-    x = Conv2D(256, (5, 5))(x)
-    x = BatchNormalization()(x)
-    x = Activation('relu')(x)
-    x = Conv2D(1024, (1, 1))(x)
-    x = BatchNormalization()(x)
-    x = Activation('relu')(x)
-    x = Conv2D(len(e2e.chars)+1, (1, 1))(x)
-    x = Activation('softmax')(x)
-    base_model = Model(inputs=input_tensor, outputs=x)
-    base_model.load_weights(model_path)
-    return base_model
diff --git a/hyperlpr/finemapping.py b/hyperlpr/finemapping.py
deleted file mode 100644
index bf3b454..0000000
--- a/hyperlpr/finemapping.py
+++ /dev/null
@@ -1,130 +0,0 @@
-#coding=utf-8
-import cv2
-import numpy as np
-
-
-import niblack_thresholding as nt
-
-import deskew
-
-def fitLine_ransac(pts,zero_add = 0 ):
-    if len(pts)>=2:
-        [vx, vy, x, y] = cv2.fitLine(pts, cv2.DIST_HUBER, 0, 0.01, 0.01)
-        lefty = int((-x * vy / vx) + y)
-        righty = int(((136- x) * vy / vx) + y)
-        return lefty+30+zero_add,righty+30+zero_add
-    return 0,0
-
-
-
-#精定位算法
-def findContoursAndDrawBoundingBox(image_rgb):
-
-
-    line_upper  = [];
-    line_lower = [];
-
-    line_experiment = []
-    grouped_rects = []
-    gray_image = cv2.cvtColor(image_rgb,cv2.COLOR_BGR2GRAY)
-
-    # for k in np.linspace(-1.5, -0.2,10):
-    for k in np.linspace(-50, 0, 15):
-
-        # thresh_niblack = threshold_niblack(gray_image, window_size=21, k=k)
-        # binary_niblack = gray_image > thresh_niblack
-        # binary_niblack = binary_niblack.astype(np.uint8) * 255
-
-        binary_niblack = cv2.adaptiveThreshold(gray_image,255,cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY,17,k)
-        # cv2.imshow("image1",binary_niblack)
-        # cv2.waitKey(0)
-        imagex, contours, hierarchy = cv2.findContours(binary_niblack.copy(),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
-        for contour in contours:
-            bdbox = cv2.boundingRect(contour)
-            if (bdbox[3]/float(bdbox[2])>0.7 and bdbox[3]*bdbox[2]>100 and bdbox[3]*bdbox[2]<1200) or (bdbox[3]/float(bdbox[2])>3 and bdbox[3]*bdbox[2]<100):
-                # cv2.rectangle(rgb,(bdbox[0],bdbox[1]),(bdbox[0]+bdbox[2],bdbox[1]+bdbox[3]),(255,0,0),1)
-                line_upper.append([bdbox[0],bdbox[1]])
-                line_lower.append([bdbox[0]+bdbox[2],bdbox[1]+bdbox[3]])
-
-                line_experiment.append([bdbox[0],bdbox[1]])
-                line_experiment.append([bdbox[0]+bdbox[2],bdbox[1]+bdbox[3]])
-                # grouped_rects.append(bdbox)
-
-    rgb = cv2.copyMakeBorder(image_rgb,30,30,0,0,cv2.BORDER_REPLICATE)
-    leftyA, rightyA = fitLine_ransac(np.array(line_lower),3)
-    rows,cols = rgb.shape[:2]
-
-    # rgb = cv2.line(rgb, (cols - 1, rightyA), (0, leftyA), (0, 0, 255), 1,cv2.LINE_AA)
-
-    leftyB, rightyB = fitLine_ransac(np.array(line_upper),-3)
-
-    rows,cols = rgb.shape[:2]
-
-    # rgb = cv2.line(rgb, (cols - 1, rightyB), (0, leftyB), (0,255, 0), 1,cv2.LINE_AA)
-    pts_map1  = np.float32([[cols - 1, rightyA], [0, leftyA],[cols - 1, rightyB], [0, leftyB]])
-    pts_map2 = np.float32([[136,36],[0,36],[136,0],[0,0]])
-    mat = cv2.getPerspectiveTransform(pts_map1,pts_map2)
-    image = cv2.warpPerspective(rgb,mat,(136,36),flags=cv2.INTER_CUBIC)
-    image,M = deskew.fastDeskew(image)
-
-    return image
-
-
-
-#多级
-def findContoursAndDrawBoundingBox2(image_rgb):
-
-
-    line_upper  = [];
-    line_lower = [];
-
-    line_experiment = []
-
-    grouped_rects = []
-
-    gray_image = cv2.cvtColor(image_rgb,cv2.COLOR_BGR2GRAY)
-
-    for k in np.linspace(-1.6, -0.2,10):
-    # for k in np.linspace(-15, 0, 15):
-    # #
-    #     thresh_niblack = threshold_niblack(gray_image, window_size=21, k=k)
-    #     binary_niblack = gray_image > thresh_niblack
-    #     binary_niblack = binary_niblack.astype(np.uint8) * 255
-
-        binary_niblack = nt.niBlackThreshold(gray_image,19,k)
-        # cv2.imshow("binary_niblack_opencv",binary_niblack_)
-        # cv2.imshow("binary_niblack_skimage", binary_niblack)
-
-        # cv2.waitKey(0)
-        imagex, contours, hierarchy = cv2.findContours(binary_niblack.copy(),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
-
-        for contour in contours:
-            bdbox = cv2.boundingRect(contour)
-            if (bdbox[3]/float(bdbox[2])>0.7 and bdbox[3]*bdbox[2]>100 and bdbox[3]*bdbox[2]<1000) or (bdbox[3]/float(bdbox[2])>3 and bdbox[3]*bdbox[2]<100):
-                # cv2.rectangle(rgb,(bdbox[0],bdbox[1]),(bdbox[0]+bdbox[2],bdbox[1]+bdbox[3]),(255,0,0),1)
-                line_upper.append([bdbox[0],bdbox[1]])
-                line_lower.append([bdbox[0]+bdbox[2],bdbox[1]+bdbox[3]])
-
-                line_experiment.append([bdbox[0],bdbox[1]])
-                line_experiment.append([bdbox[0]+bdbox[2],bdbox[1]+bdbox[3]])
-                # grouped_rects.append(bdbox)
-
-    rgb = cv2.copyMakeBorder(image_rgb,30,30,0,0,cv2.BORDER_REPLICATE)
-    leftyA, rightyA = fitLine_ransac(np.array(line_lower),2)
-    rows,cols = rgb.shape[:2]
-
-    # rgb = cv2.line(rgb, (cols - 1, rightyA), (0, leftyA), (0, 0, 255), 1,cv2.LINE_AA)
-
-    leftyB, rightyB = fitLine_ransac(np.array(line_upper),-4)
-
-    rows,cols = rgb.shape[:2]
-
-    # rgb = cv2.line(rgb, (cols - 1, rightyB), (0, leftyB), (0,255, 0), 1,cv2.LINE_AA)
-    pts_map1  = np.float32([[cols - 1, rightyA], [0, leftyA],[cols - 1, rightyB], [0, leftyB]])
-    pts_map2 = np.float32([[136,36],[0,36],[136,0],[0,0]])
-    mat = cv2.getPerspectiveTransform(pts_map1,pts_map2)
-    image = cv2.warpPerspective(rgb,mat,(136,36),flags=cv2.INTER_CUBIC)
-    image,M= deskew.fastDeskew(image)
-
-
-    return image
diff --git a/hyperlpr/finemapping_vertical.py b/hyperlpr/finemapping_vertical.py
deleted file mode 100644
index 1d6eeb6..0000000
--- a/hyperlpr/finemapping_vertical.py
+++ /dev/null
@@ -1,92 +0,0 @@
-#coding=utf-8
-from keras.layers import Conv2D, Input,MaxPool2D, Reshape,Activation,Flatten, Dense
-from keras.models import Model, Sequential
-from keras.layers.advanced_activations import PReLU
-from keras.optimizers import adam
-import numpy as np
-
-import cv2
-
-def getModel():
-    input = Input(shape=[16, 66, 3])  # change this shape to [None,None,3] to enable arbitraty shape input
-    x = Conv2D(10, (3, 3), strides=1, padding='valid', name='conv1')(input)
-    x = Activation("relu", name='relu1')(x)
-    x = MaxPool2D(pool_size=2)(x)
-    x = Conv2D(16, (3, 3), strides=1, padding='valid', name='conv2')(x)
-    x = Activation("relu", name='relu2')(x)
-    x = Conv2D(32, (3, 3), strides=1, padding='valid', name='conv3')(x)
-    x = Activation("relu", name='relu3')(x)
-    x = Flatten()(x)
-    output = Dense(2,name = "dense")(x)
-    output = Activation("relu", name='relu4')(output)
-    model = Model([input], [output])
-    return model
-
-
-
-model = getModel()
-model.load_weights("./model/model12.h5")
-
-
-def getmodel():
-    return model
-
-def gettest_model():
-    input = Input(shape=[16, 66, 3])  # change this shape to [None,None,3] to enable arbitraty shape input
-    A = Conv2D(10, (3, 3), strides=1, padding='valid', name='conv1')(input)
-    B = Activation("relu", name='relu1')(A)
-    C = MaxPool2D(pool_size=2)(B)
-    x = Conv2D(16, (3, 3), strides=1, padding='valid', name='conv2')(C)
-    x = Activation("relu", name='relu2')(x)
-    x = Conv2D(32, (3, 3), strides=1, padding='valid', name='conv3')(x)
-    K = Activation("relu", name='relu3')(x)
-
-
-    x = Flatten()(K)
-    dense = Dense(2,name = "dense")(x)
-    output = Activation("relu", name='relu4')(dense)
-    x = Model([input], [output])
-    x.load_weights("./model/model12.h5")
-    ok = Model([input], [dense])
-
-    for layer in ok.layers:
-        print layer
-
-    return ok
-
-
-
-
-def finemappingVertical(image):
-    resized = cv2.resize(image,(66,16))
-    resized = resized.astype(np.float)/255
-    res= model.predict(np.array([resized]))[0]
-    print "keras_predict",res
-    res  =res*image.shape[1]
-    res = res.astype(np.int)
-    H,T = res
-    H-=3
-    #3 79.86
-    #4 79.3
-    #5 79.5
-    #6 78.3
-
-
-    #T
-    #T+1 80.9
-    #T+2 81.75
-    #T+3 81.75
-
-
-
-    if H<0:
-        H=0
-    T+=2;
-
-    if T>= image.shape[1]-1:
-        T= image.shape[1]-1
-
-    image = image[0:35,H:T+2]
-
-    image = cv2.resize(image, (int(136), int(36)))
-    return image
\ No newline at end of file
diff --git a/hyperlpr/niblack_thresholding.py b/hyperlpr/niblack_thresholding.py
deleted file mode 100644
index 652bae0..0000000
--- a/hyperlpr/niblack_thresholding.py
+++ /dev/null
@@ -1,18 +0,0 @@
-import cv2
-import numpy as np
-
-
-
-def niBlackThreshold(  src,  blockSize,  k,  binarizationMethod= 0 ):
-    mean = cv2.boxFilter(src,cv2.CV_32F,(blockSize, blockSize),borderType=cv2.BORDER_REPLICATE)
-    sqmean = cv2.sqrBoxFilter(src, cv2.CV_32F, (blockSize, blockSize), borderType = cv2.BORDER_REPLICATE)
-    variance = sqmean - (mean*mean)
-    stddev  = np.sqrt(variance)
-    thresh = mean + stddev * float(-k)
-    thresh = thresh.astype(src.dtype)
-    k = (src>thresh)*255
-    k = k.astype(np.uint8)
-    return k
-
-
-# cv2.imshow()
\ No newline at end of file
diff --git a/hyperlpr/pipline.py b/hyperlpr/pipline.py
deleted file mode 100644
index 165fee2..0000000
--- a/hyperlpr/pipline.py
+++ /dev/null
@@ -1,264 +0,0 @@
-#coding=utf-8
-import detect
-import  finemapping  as  fm
-
-import segmentation
-import cv2
-
-import time
-import numpy as np
-
-from PIL import ImageFont
-from PIL import Image
-from PIL import ImageDraw
-import json
-
-import sys
-import typeDistinguish as td
-
-
-reload(sys)
-sys.setdefaultencoding("utf-8")
-
-fontC = ImageFont.truetype("./Font/platech.ttf", 14, 0);
-
-import e2e
-#寻找车牌左右边界
-
-def find_edge(image):
-    sum_i = image.sum(axis=0)
-    sum_i =  sum_i.astype(np.float)
-    sum_i/=image.shape[0]*255
-    # print sum_i
-
-    start= 0 ;
-    end = image.shape[1]-1
-
-    for i,one in enumerate(sum_i):
-        if one>0.4:
-            start = i;
-            if start-3<0:
-                start = 0
-            else:
-                start -=3
-
-            break;
-    for i,one in enumerate(sum_i[::-1]):
-
-        if one>0.4:
-            end = end - i;
-            if end+4>image.shape[1]-1:
-                end = image.shape[1]-1
-            else:
-                end+=4
-            break
-    return start,end
-
-
-#垂直边缘检测
-
-def verticalEdgeDetection(image):
-    image_sobel = cv2.Sobel(image.copy(),cv2.CV_8U,1,0)
-    # image = auto_canny(image_sobel)
-
-    # img_sobel, CV_8U, 1, 0, 3, 1, 0, BORDER_DEFAULT
-    # canny_image  = auto_canny(image)
-    flag,thres = cv2.threshold(image_sobel,0,255,cv2.THRESH_OTSU|cv2.THRESH_BINARY)
-    print flag
-    flag,thres = cv2.threshold(image_sobel,int(flag*0.7),255,cv2.THRESH_BINARY)
-    # thres = simpleThres(image_sobel)
-    kernal = np.ones(shape=(3,15))
-    thres = cv2.morphologyEx(thres,cv2.MORPH_CLOSE,kernal)
-    return thres
-
-#确定粗略的左右边界
-def horizontalSegmentation(image):
-
-    thres = verticalEdgeDetection(image)
-    # thres = thres*image
-    head,tail = find_edge(thres)
-    # print head,tail
-    # cv2.imshow("edge",thres)
-    tail = tail+5
-    if tail>135:
-        tail = 135
-    image = image[0:35,head:tail]
-    image = cv2.resize(image, (int(136), int(36)))
-    return image
-
-
-
-#打上boundingbox和标签
-def drawRectBox(image,rect,addText):
-    cv2.rectangle(image, (int(rect[0]), int(rect[1])), (int(rect[0] + rect[2]), int(rect[1] + rect[3])), (0,0, 255), 2,cv2.LINE_AA)
-    cv2.rectangle(image, (int(rect[0]-1), int(rect[1])-16), (int(rect[0] + 115), int(rect[1])), (0, 0, 255), -1,
-                  cv2.LINE_AA)
-
-    img = Image.fromarray(image)
-    draw = ImageDraw.Draw(img)
-    draw.text((int(rect[0]+1), int(rect[1]-16)), addText.decode("utf-8"), (255, 255, 255), font=fontC)
-    imagex = np.array(img)
-
-    return imagex
-
-
-
-
-import cache
-import finemapping_vertical as fv
-
-
-def RecognizePlateJson(image):
-
-    images = detect.detectPlateRough(image,image.shape[0],top_bottom_padding_rate=0.1)
-
-    jsons = []
-
-    for j,plate in enumerate(images):
-
-
-        plate,rect,origin_plate =plate
-        res, confidence = e2e.recognizeOne(origin_plate)
-        print "res",res
-
-        cv2.imwrite("./"+str(j)+"_rough.jpg",plate)
-
-        # print "车牌类型:",ptype
-        # plate = cv2.cvtColor(plate, cv2.COLOR_RGB2GRAY)
-        plate  =cv2.resize(plate,(136,int(36*2.5)))
-        t1 = time.time()
-
-
-        ptype = td.SimplePredict(plate)
-        if ptype>0 and ptype<4:
-            plate = cv2.bitwise_not(plate)
-        # demo = verticalEdgeDetection(plate)
-
-        image_rgb = fm.findContoursAndDrawBoundingBox(plate)
-        image_rgb = fv.finemappingVertical(image_rgb)
-        cache.verticalMappingToFolder(image_rgb)
-        # print time.time() - t1,"校正"
-        print "e2e:",e2e.recognizeOne(image_rgb)[0]
-        image_gray = cv2.cvtColor(image_rgb,cv2.COLOR_BGR2GRAY)
-
-
-        cv2.imwrite("./"+str(j)+".jpg",image_gray)
-        # image_gray = horizontalSegmentation(image_gray)
-
-
-        t2 = time.time()
-        res, confidence = e2e.recognizeOne(image_rgb)
-        res_json = {}
-        if confidence  > 0.6:
-            res_json["Name"] = res
-            res_json["Type"] = td.plateType[ptype]
-            res_json["Confidence"] = confidence;
-            res_json["x"] = int(rect[0])
-            res_json["y"] = int(rect[1])
-            res_json["w"] = int(rect[2])
-            res_json["h"] = int(rect[3])
-            jsons.append(res_json)
-    print json.dumps(jsons,ensure_ascii=False,encoding="gb2312")
-
-    return json.dumps(jsons,ensure_ascii=False,encoding="gb2312")
-
-
-
-
-def SimpleRecognizePlateByE2E(image):
-    t0 = time.time()
-    images = detect.detectPlateRough(image,image.shape[0],top_bottom_padding_rate=0.1)
-    res_set = []
-    for j,plate in enumerate(images):
-        plate, rect, origin_plate  =plate
-        # plate = cv2.cvtColor(plate, cv2.COLOR_RGB2GRAY)
-        plate  =cv2.resize(plate,(136,36*2))
-        res,confidence = e2e.recognizeOne(origin_plate)
-        print "res",res
-
-        t1 = time.time()
-        ptype = td.SimplePredict(plate)
-        if ptype>0 and ptype<5:
-            # pass
-            plate = cv2.bitwise_not(plate)
-        image_rgb = fm.findContoursAndDrawBoundingBox(plate)
-        image_rgb = fv.finemappingVertical(image_rgb)
-        image_rgb = fv.finemappingVertical(image_rgb)
-        cache.verticalMappingToFolder(image_rgb)
-        #cv2.imwrite("./"+str(j)+".jpg",image_rgb)
-        res,confidence = e2e.recognizeOne(image_rgb)
-        print res,confidence
-        res_set.append([[],res,confidence])
-
-        if confidence>0.7:
-            image = drawRectBox(image, rect, res+" "+str(round(confidence,3)))
-    return image,res_set
-
-
-
-
-
-
-
-def SimpleRecognizePlate(image):
-    t0 = time.time()
-    images = detect.detectPlateRough(image,image.shape[0],top_bottom_padding_rate=0.1)
-    res_set = []
-    for j,plate in enumerate(images):
-        plate, rect, origin_plate  =plate
-        # plate = cv2.cvtColor(plate, cv2.COLOR_RGB2GRAY)
-        plate  =cv2.resize(plate,(136,36*2))
-        t1 = time.time()
-
-        ptype = td.SimplePredict(plate)
-        if ptype>0 and ptype<5:
-            plate = cv2.bitwise_not(plate)
-
-        image_rgb = fm.findContoursAndDrawBoundingBox(plate)
-
-        image_rgb = fv.finemappingVertical(image_rgb)
-        cache.verticalMappingToFolder(image_rgb)
-        print "e2e:", e2e.recognizeOne(image_rgb)
-        image_gray = cv2.cvtColor(image_rgb,cv2.COLOR_RGB2GRAY)
-
-        # image_gray = horizontalSegmentation(image_gray)
-        cv2.imshow("image_gray",image_gray)
-        # cv2.waitKey()
-
-        cv2.imwrite("./"+str(j)+".jpg",image_gray)
-        # cv2.imshow("image",image_gray)
-        # cv2.waitKey(0)
-        print "校正",time.time() - t1,"s"
-        # cv2.imshow("image,",image_gray)
-        # cv2.waitKey(0)
-        t2 = time.time()
-        val = segmentation.slidingWindowsEval(image_gray)
-        # print val
-        print "分割和识别",time.time() - t2,"s"
-        if len(val)==3:
-            blocks, res, confidence = val
-            if confidence/7>0.7:
-                image =  drawRectBox(image,rect,res)
-                res_set.append(res)
-                for i,block in enumerate(blocks):
-
-                    block_ = cv2.resize(block,(25,25))
-                    block_ = cv2.cvtColor(block_,cv2.COLOR_GRAY2BGR)
-                    image[j * 25:(j * 25) + 25, i * 25:(i * 25) + 25] = block_
-                    if image[j*25:(j*25)+25,i*25:(i*25)+25].shape == block_.shape:
-                        pass
-
-
-            if confidence>0:
-                print "车牌:",res,"置信度:",confidence/7
-            else:
-                pass
-
-                # print "不确定的车牌:", res, "置信度:", confidence
-
-    print time.time() - t0,"s"
-    return image,res_set
-
-
-
-
diff --git a/hyperlpr/recognizer.py b/hyperlpr/recognizer.py
deleted file mode 100644
index c60b7a4..0000000
--- a/hyperlpr/recognizer.py
+++ /dev/null
@@ -1,154 +0,0 @@
-#coding=utf-8
-from keras.models import Sequential
-from keras.layers import Dense, Dropout, Activation, Flatten
-from keras.layers import Conv2D,MaxPool2D
-from keras.optimizers import SGD
-from keras import backend as K
-
-K.image_data_format()
-
-
-import cv2
-import numpy as np
-
-
-
-index = {u"京": 0, u"沪": 1, u"津": 2, u"渝": 3, u"冀": 4, u"晋": 5, u"蒙": 6, u"辽": 7, u"吉": 8, u"黑": 9, u"苏": 10, u"浙": 11, u"皖": 12,
-         u"闽": 13, u"赣": 14, u"鲁": 15, u"豫": 16, u"鄂": 17, u"湘": 18, u"粤": 19, u"桂": 20, u"琼": 21, u"川": 22, u"贵": 23, u"云": 24,
-         u"藏": 25, u"陕": 26, u"甘": 27, u"青": 28, u"宁": 29, u"新": 30, u"0": 31, u"1": 32, u"2": 33, u"3": 34, u"4": 35, u"5": 36,
-         u"6": 37, u"7": 38, u"8": 39, u"9": 40, u"A": 41, u"B": 42, u"C": 43, u"D": 44, u"E": 45, u"F": 46, u"G": 47, u"H": 48,
-         u"J": 49, u"K": 50, u"L": 51, u"M": 52, u"N": 53, u"P": 54, u"Q": 55, u"R": 56, u"S": 57, u"T": 58, u"U": 59, u"V": 60,
-         u"W": 61, u"X": 62, u"Y": 63, u"Z": 64,u"港":65,u"学":66 ,u"O":67 ,u"使":68,u"警":69,u"澳":70,u"挂":71};
-
-chars = ["京", "沪", "津", "渝", "冀", "晋", "蒙", "辽", "吉", "黑", "苏", "浙", "皖", "闽", "赣", "鲁", "豫", "鄂", "湘", "粤", "桂",
-             "琼", "川", "贵", "云", "藏", "陕", "甘", "青", "宁", "新", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "A",
-             "B", "C", "D", "E", "F", "G", "H", "J", "K", "L", "M", "N", "P",
-         "Q", "R", "S", "T", "U", "V", "W", "X",
-             "Y", "Z","港","学","O","使","警","澳","挂" ];
-
-
-
-def Getmodel_tensorflow(nb_classes):
-    # nb_classes = len(charset)
-
-    img_rows, img_cols = 23, 23
-    # number of convolutional filters to use
-    nb_filters = 32
-    # size of pooling area for max pooling
-    nb_pool = 2
-    # convolution kernel size
-    nb_conv = 3
-
-    # x = np.load('x.npy')
-    
-    # y = np_utils.to_categorical(range(3062)*45*5*2, nb_classes)
-    # weight = ((type_class - np.arange(type_class)) / type_class + 1) ** 3
-    # weight = dict(zip(range(3063), weight / weight.mean()))  # 调整权重，高频字优先
-
-    model = Sequential()
-    model.add(Conv2D(32, (5, 5),input_shape=(img_rows, img_cols,1)))
-    model.add(Activation('relu'))
-    model.add(MaxPool2D(pool_size=(nb_pool, nb_pool)))
-    model.add(Dropout(0.25))
-    model.add(Conv2D(32, (3, 3)))
-    model.add(Activation('relu'))
-    model.add(MaxPool2D(pool_size=(nb_pool, nb_pool)))
-    model.add(Dropout(0.25))
-    model.add(Conv2D(512, (3, 3)))
-    # model.add(Activation('relu'))
-    # model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool)))
-    # model.add(Dropout(0.25))
-    model.add(Flatten())
-    model.add(Dense(512))
-    model.add(Activation('relu'))
-    model.add(Dropout(0.5))
-    model.add(Dense(nb_classes))
-    model.add(Activation('softmax'))
-    model.compile(loss='categorical_crossentropy',
-                  optimizer='adam',
-                  metrics=['accuracy'])
-    return model
-
-
-
-
-def Getmodel_ch(nb_classes):
-    # nb_classes = len(charset)
-
-    img_rows, img_cols = 23, 23
-    # number of convolutional filters to use
-    nb_filters = 32
-    # size of pooling area for max pooling
-    nb_pool = 2
-    # convolution kernel size
-    nb_conv = 3
-
-    # x = np.load('x.npy')
-    # y = np_utils.to_categorical(range(3062)*45*5*2, nb_classes)
-    # weight = ((type_class - np.arange(type_class)) / type_class + 1) ** 3
-    # weight = dict(zip(range(3063), weight / weight.mean()))  # 调整权重，高频字优先
-
-    model = Sequential()
-    model.add(Conv2D(32, (5, 5),input_shape=(img_rows, img_cols,1)))
-    model.add(Activation('relu'))
-    model.add(MaxPool2D(pool_size=(nb_pool, nb_pool)))
-    model.add(Dropout(0.25))
-    model.add(Conv2D(32, (3, 3)))
-    model.add(Activation('relu'))
-    model.add(MaxPool2D(pool_size=(nb_pool, nb_pool)))
-    model.add(Dropout(0.25))
-    model.add(Conv2D(512, (3, 3)))
-    # model.add(Activation('relu'))
-    # model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool)))
-    # model.add(Dropout(0.25))
-    model.add(Flatten())
-    model.add(Dense(756))
-    model.add(Activation('relu'))
-    model.add(Dropout(0.5))
-    model.add(Dense(nb_classes))
-    model.add(Activation('softmax'))
-    model.compile(loss='categorical_crossentropy',
-                  optimizer='adam',
-                  metrics=['accuracy'])
-    return model
-
-
-
-model  = Getmodel_tensorflow(65)
-#构建网络
-
-model_ch = Getmodel_ch(31)
-
-model_ch.load_weights("./model/char_chi_sim.h5")
-# model_ch.save_weights("./model/char_chi_sim.h5")
-model.load_weights("./model/char_rec.h5")
-# model.save("./model/char_rec.h5")
-
-
-def SimplePredict(image,pos):
-    image = cv2.resize(image, (23, 23))
-    image = cv2.equalizeHist(image)
-    image = image.astype(np.float) / 255
-    image -= image.mean()
-    image = np.expand_dims(image, 3)
-    if pos!=0:
-        res = np.array(model.predict(np.array([image]))[0])
-    else:
-        res = np.array(model_ch.predict(np.array([image]))[0])
-
-    zero_add = 0 ;
-
-    if pos==0:
-        res = res[:31]
-    elif pos==1:
-        res = res[31+10:65]
-        zero_add = 31+10
-    else:
-        res = res[31:]
-        zero_add = 31
-
-    max_id = res.argmax()
-
-
-    return res.max(),chars[max_id+zero_add],max_id+zero_add
-
diff --git a/hyperlpr/segmentation.py b/hyperlpr/segmentation.py
deleted file mode 100644
index 371a529..0000000
--- a/hyperlpr/segmentation.py
+++ /dev/null
@@ -1,320 +0,0 @@
-#coding=utf-8
-import cv2
-import numpy as np
-
-# from matplotlib import pyplot as plt
-import scipy.ndimage.filters as f
-import scipy
-
-import time
-import scipy.signal as l
-
-
-
-
-
-from keras.models import Sequential
-from keras.layers import Dense, Dropout, Activation, Flatten
-from keras.layers import Conv2D, MaxPool2D
-from keras.optimizers import SGD
-from keras import backend as K
-
-K.image_data_format()
-
-
-def Getmodel_tensorflow(nb_classes):
-    # nb_classes = len(charset)
-    img_rows, img_cols = 23, 23
-    # number of convolutional filters to use
-    nb_filters = 16
-    # size of pooling area for max pooling
-    nb_pool = 2
-    # convolution kernel size
-    nb_conv = 3
-    # x = np.load('x.npy')
-    # y = np_utils.to_categorical(range(3062)*45*5*2, nb_classes)
-    # weight = ((type_class - np.arange(type_class)) / type_class + 1) ** 3
-    # weight = dict(zip(range(3063), weight / weight.mean()))  # 调整权重，高频字优先
-
-    model = Sequential()
-    model.add(Conv2D(nb_filters, (nb_conv, nb_conv),input_shape=(img_rows, img_cols,1)))
-    model.add(Activation('relu'))
-    model.add(MaxPool2D(pool_size=(nb_pool, nb_pool)))
-    model.add(Conv2D(nb_filters, (nb_conv, nb_conv)))
-    model.add(Activation('relu'))
-    model.add(MaxPool2D(pool_size=(nb_pool, nb_pool)))
-    model.add(Flatten())
-    model.add(Dense(256))
-    model.add(Dropout(0.5))
-
-    model.add(Activation('relu'))
-    model.add(Dense(nb_classes))
-    model.add(Activation('softmax'))
-    model.compile(loss='categorical_crossentropy',
-                  optimizer='sgd',
-                  metrics=['accuracy'])
-    return model
-
-
-
-def Getmodel_tensorflow_light(nb_classes):
-    # nb_classes = len(charset)
-    img_rows, img_cols = 23, 23
-    # number of convolutional filters to use
-    nb_filters = 8
-    # size of pooling area for max pooling
-    nb_pool = 2
-    # convolution kernel size
-    nb_conv = 3
-    # x = np.load('x.npy')
-    # y = np_utils.to_categorical(range(3062)*45*5*2, nb_classes)
-    # weight = ((type_class - np.arange(type_class)) / type_class + 1) ** 3
-    # weight = dict(zip(range(3063), weight / weight.mean()))  # 调整权重，高频字优先
-
-    model = Sequential()
-    model.add(Conv2D(nb_filters, (nb_conv, nb_conv),input_shape=(img_rows, img_cols, 1)))
-    model.add(Activation('relu'))
-    model.add(MaxPool2D(pool_size=(nb_pool, nb_pool)))
-    model.add(Conv2D(nb_filters, (nb_conv * 2, nb_conv * 2)))
-    model.add(Activation('relu'))
-    model.add(MaxPool2D(pool_size=(nb_pool, nb_pool)))
-    model.add(Flatten())
-    model.add(Dense(32))
-    # model.add(Dropout(0.25))
-
-    model.add(Activation('relu'))
-    model.add(Dense(nb_classes))
-    model.add(Activation('softmax'))
-    model.compile(loss='categorical_crossentropy',
-                  optimizer='adam',
-                  metrics=['accuracy'])
-    return model
-
-
-
-
-model  = Getmodel_tensorflow_light(3)
-model2  = Getmodel_tensorflow(3)
-
-import os
-model.load_weights("./model/char_judgement1.h5")
-# model.save("./model/char_judgement1.h5")
-model2.load_weights("./model/char_judgement.h5")
-# model2.save("./model/char_judgement.h5")
-
-
-model = model2
-def get_median(data):
-   data = sorted(data)
-   size = len(data)
-   # print size
-
-   if size % 2 == 0: # 判断列表长度为偶数
-    median = (data[size//2]+data[size//2-1])/2
-    data[0] = median
-   if size % 2 == 1: # 判断列表长度为奇数
-    median = data[(size-1)//2]
-    data[0] = median
-   return data[0]
-import time
-
-def searchOptimalCuttingPoint(rgb,res_map,start,width_boundingbox,interval_range):
-    t0  = time.time()
-    #
-    # for x in xrange(10):
-    #     res_map = np.vstack((res_map,res_map[-1]))
-    length = res_map.shape[0]
-    refine_s = -2;
-
-    if width_boundingbox>20:
-        refine_s = -9
-    score_list = []
-    interval_big = int(width_boundingbox * 0.3)  #
-    p = 0
-    for zero_add in xrange(start,start+50,3):
-        # for interval_small in xrange(-0,width_boundingbox/2):
-            for i in xrange(-8,int(width_boundingbox/1)-8):
-                for refine in xrange(refine_s,width_boundingbox/2+3):
-                    p1 = zero_add# this point is province
-                    p2 = p1 + width_boundingbox +refine #
-                    p3 = p2 + width_boundingbox + interval_big+i+1
-                    p4 = p3 + width_boundingbox +refine
-                    p5 = p4 + width_boundingbox +refine
-                    p6 = p5 + width_boundingbox +refine
-                    p7 = p6 + width_boundingbox +refine
-                    if p7>=length:
-                        continue
-                    score = res_map[p1][2]*3 -(res_map[p3][1]+res_map[p4][1]+res_map[p5][1]+res_map[p6][1]+res_map[p7][1])+7
-                    # print score
-                    score_list.append([score,[p1,p2,p3,p4,p5,p6,p7]])
-                    p+=1
-    print p
-
-    score_list = sorted(score_list , key=lambda x:x[0])
-    # for one in score_list[-1][1]:
-    #     cv2.line(debug,(one,0),(one,36),(255,0,0),1)
-    # #
-    # cv2.imshow("one",debug)
-    # cv2.waitKey(0)
-    #
-    print "寻找最佳点",time.time()-t0
-    return score_list[-1]
-
-
-import sys
-
-sys.path.append('../')
-import recognizer as cRP
-import niblack_thresholding as nt
-
-def refineCrop(sections,width=16):
-    new_sections = []
-    for section in sections:
-        # cv2.imshow("section¡",section)
-
-        # cv2.blur(section,(3,3),3)
-
-        sec_center = np.array([section.shape[1]/2,section.shape[0]/2])
-        binary_niblack = nt.niBlackThreshold(section,17,-0.255)
-        imagex, contours, hierarchy  = cv2.findContours(binary_niblack,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
-        boxs = []
-        for contour in contours:
-            x,y,w,h = cv2.boundingRect(contour)
-
-            ratio = w/float(h)
-            if ratio<1 and h>36*0.4 and y<16\
-                    :
-                box = [x,y,w,h]
-
-                boxs.append([box,np.array([x+w/2,y+h/2])])
-                # cv2.rectangle(section,(x,y),(x+w,y+h),255,1)
-
-
-
-
-        # print boxs
-
-        dis_ = np.array([ ((one[1]-sec_center)**2).sum() for one in boxs])
-        if len(dis_)==0:
-            kernal = [0, 0, section.shape[1], section.shape[0]]
-        else:
-            kernal = boxs[dis_.argmin()][0]
-
-        center_c  = (kernal[0]+kernal[2]/2,kernal[1]+kernal[3]/2)
-        w_2 = int(width/2)
-        h_2 = kernal[3]/2
-
-        if center_c[0] - w_2< 0:
-            w_2 = center_c[0]
-        new_box = [center_c[0] - w_2,kernal[1],width,kernal[3]]
-        # print new_box[2]/float(new_box[3])
-        if new_box[2]/float(new_box[3])>0.5:
-            # print "异常"
-            h = int((new_box[2]/0.35 )/2)
-            if h>35:
-                h = 35
-            new_box[1] = center_c[1]- h
-            if new_box[1]<0:
-                new_box[1] = 1
-
-            new_box[3] = h*2
-
-
-
-
-
-
-
-
-        section  = section[new_box[1]:new_box[1]+new_box[3],new_box[0]:new_box[0]+new_box[2]]
-        # cv2.imshow("section",section)
-        # cv2.waitKey(0)
-        new_sections.append(section)
-        # print new_box
-
-
-    return new_sections
-
-
-
-
-
-def slidingWindowsEval(image):
-    windows_size = 16;
-    stride = 1
-    height= image.shape[0]
-    t0 = time.time()
-    data_sets = []
-
-    for i in range(0,image.shape[1]-windows_size+1,stride):
-        data = image[0:height,i:i+windows_size]
-        data = cv2.resize(data,(23,23))
-        # cv2.imshow("image",data)
-        data = cv2.equalizeHist(data)
-        data = data.astype(np.float)/255
-        data=  np.expand_dims(data,3)
-        data_sets.append(data)
-
-    res = model2.predict(np.array(data_sets))
-    print "分割",time.time() - t0
-
-    pin = res
-    p = 1 -  (res.T)[1]
-    p = f.gaussian_filter1d(np.array(p,dtype=np.float),3)
-    lmin = l.argrelmax(np.array(p),order = 3)[0]
-    interval = []
-    for i in xrange(len(lmin)-1):
-        interval.append(lmin[i+1]-lmin[i])
-
-    if(len(interval)>3):
-        mid  = get_median(interval)
-    else:
-        return []
-    pin = np.array(pin)
-    res =  searchOptimalCuttingPoint(image,pin,0,mid,3)
-
-    cutting_pts = res[1]
-    last =  cutting_pts[-1] + mid
-    if last < image.shape[1]:
-        cutting_pts.append(last)
-    else:
-        cutting_pts.append(image.shape[1]-1)
-    name = ""
-    confidence =0.00
-    seg_block = []
-    for x in xrange(1,len(cutting_pts)):
-        if x != len(cutting_pts)-1 and x!=1:
-            section = image[0:36,cutting_pts[x-1]-2:cutting_pts[x]+2]
-        elif  x==1:
-            c_head = cutting_pts[x - 1]- 2
-            if c_head<0:
-                c_head=0
-            c_tail = cutting_pts[x] + 2
-            section = image[0:36, c_head:c_tail]
-        elif x==len(cutting_pts)-1:
-            end = cutting_pts[x]
-            diff = image.shape[1]-end
-            c_head = cutting_pts[x - 1]
-            c_tail = cutting_pts[x]
-            if diff<7 :
-                section = image[0:36, c_head-5:c_tail+5]
-            else:
-                diff-=1
-                section = image[0:36, c_head - diff:c_tail + diff]
-        elif  x==2:
-            section = image[0:36, cutting_pts[x - 1] - 3:cutting_pts[x-1]+ mid]
-        else:
-            section = image[0:36,cutting_pts[x-1]:cutting_pts[x]]
-        seg_block.append(section)
-    refined = refineCrop(seg_block,mid-1)
-
-    t0 = time.time()
-    for i,one in enumerate(refined):
-        res_pre = cRP.SimplePredict(one, i )
-        # cv2.imshow(str(i),one)
-        # cv2.waitKey(0)
-        confidence+=res_pre[0]
-        name+= res_pre[1]
-    print "字符识别",time.time() - t0
-
-    return refined,name,confidence
diff --git a/hyperlpr/typeDistinguish.py b/hyperlpr/typeDistinguish.py
deleted file mode 100644
index 4100e73..0000000
--- a/hyperlpr/typeDistinguish.py
+++ /dev/null
@@ -1,56 +0,0 @@
-#coding=utf-8
-from keras.models import Sequential
-from keras.layers import Dense, Dropout, Activation, Flatten
-from keras.layers import Conv2D, MaxPool2D
-from keras.optimizers import SGD
-from keras import backend as K
-
-K.image_data_format()
-
-
-import cv2
-import numpy as np
-
-
-plateType  = [u"蓝牌",u"单层黄牌",u"新能源车牌",u"白色",u"黑色-港澳"]
-def Getmodel_tensorflow(nb_classes):
-    # nb_classes = len(charset)
-
-    img_rows, img_cols = 9, 34
-    # number of convolutional filters to use
-    nb_filters = 32
-    # size of pooling area for max pooling
-    nb_pool = 2
-    # convolution kernel size
-    nb_conv = 3
-
-    # x = np.load('x.npy')
-    # y = np_utils.to_categorical(range(3062)*45*5*2, nb_classes)
-    # weight = ((type_class - np.arange(type_class)) / type_class + 1) ** 3
-    # weight = dict(zip(range(3063), weight / weight.mean()))  # 调整权重，高频字优先
-
-    model = Sequential()
-    model.add(Conv2D(16, (5, 5),input_shape=(img_rows, img_cols,3)))
-    model.add(Activation('relu'))
-    model.add(MaxPool2D(pool_size=(nb_pool, nb_pool)))
-    model.add(Flatten())
-    model.add(Dense(64))
-    model.add(Activation('relu'))
-    model.add(Dropout(0.5))
-    model.add(Dense(nb_classes))
-    model.add(Activation('softmax'))
-    model.compile(loss='categorical_crossentropy',
-                  optimizer='adam',
-                  metrics=['accuracy'])
-    return model
-
-model = Getmodel_tensorflow(5)
-model.load_weights("./model/plate_type.h5")
-model.save("./model/plate_type.h5")
-def SimplePredict(image):
-    image = cv2.resize(image, (34, 9))
-    image = image.astype(np.float) / 255
-    res = np.array(model.predict(np.array([image]))[0])
-    return res.argmax()
-
-
diff --git a/hyperlpr_py3/cache.py b/hyperlpr_py3/cache.py
deleted file mode 100644
index 80a2953..0000000
--- a/hyperlpr_py3/cache.py
+++ /dev/null
@@ -1,11 +0,0 @@
-import cv2
-import os
-import hashlib
-
-def verticalMappingToFolder(image):
-    name = hashlib.md5(image.data).hexdigest()[:8]
-    print(name)
-
-    cv2.imwrite("./cache/finemapping/"+name+".png",image)
-
-
diff --git a/hyperlpr_py3/colourDetection.py b/hyperlpr_py3/colourDetection.py
deleted file mode 100644
index 78c5854..0000000
--- a/hyperlpr_py3/colourDetection.py
+++ /dev/null
@@ -1,103 +0,0 @@
-# -- coding: UTF-8
-import cv2
-import matplotlib.pyplot as plt
-from sklearn.cluster import KMeans
-import os
-
-boundaries = [
-    ([100,80,0],[240,220,110]), # yellow
-    ([0,40,50],[110,180,250]), # blue
-    ([0,60,0],[60,160,70]), # green
-]
-color_attr = ["黄牌","蓝牌",'绿牌','白牌','黑牌']
-
-threhold_green = 13
-threhold_blue = 13
-threhold_yellow1 = 50
-threhold_yellow2 = 70
-
-# plt.figure()
-# plt.axis("off")
-# plt.imshow(image)
-# plt.show()
-
-import numpy as np
-def centroid_histogram(clt):
-    numLabels = np.arange(0, len(np.unique(clt.labels_)) + 1)
-    (hist, _) = np.histogram(clt.labels_, bins=numLabels)
-
-    # normalize the histogram, such that it sums to one
-    hist = hist.astype("float")
-    hist /= hist.sum()
-
-    # return the histogram
-    return hist
-
-
-def plot_colors(hist, centroids):
-    bar = np.zeros((50, 300, 3), dtype="uint8")
-    startX = 0
-
-    for (percent, color) in zip(hist, centroids):
-
-        endX = startX + (percent * 300)
-        cv2.rectangle(bar, (int(startX), 0), (int(endX), 50),
-                      color.astype("uint8").tolist(), -1)
-        startX = endX
-
-    # return the bar chart
-    return bar
-
-def search_boundaries(color):
-    for i,color_bound in enumerate(boundaries):
-        if np.all(color >= color_bound[0]) and np.all(color <= color_bound[1]):
-            return i
-    return -1
-
-def judge_color(color):
-    r = color[0]
-    g = color[1]
-    b = color[2]
-    if g - r >= threhold_green and g - b >= threhold_green:
-        return 2
-    if b - r >= threhold_blue and b - g >= threhold_blue:
-        return 1
-    if r- b > threhold_yellow2 and g - b > threhold_yellow2:
-        return 0
-    if r > 200 and b > 200 and g > 200:
-        return 3
-    if r < 50 and b < 50 and g < 50:
-        return 4
-    return -1
-
-def judge_plate_color(img):
-    image = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-    image = image.reshape((image.shape[0] * image.shape[1], 3))
-    clt = KMeans(n_clusters=2)
-    clt.fit(image)
-
-    hist = centroid_histogram(clt)
-    index = np.argmax(hist)
-    #print clt.cluster_centers_[index]
-    #color_index = search_boundaries(clt.cluster_centers_[index])
-    color_index = judge_color(clt.cluster_centers_[index])
-    if color_index == -1:
-        if index == 0:
-            secound_index = 1
-        else:
-            secound_index = 0
-        color_index = judge_color(clt.cluster_centers_[secound_index])
-
-    if color_index == -1:
-        print(clt.cluster_centers_)
-        bar = plot_colors(hist, clt.cluster_centers_)
-        # show our color bart
-        plt.figure()
-        plt.axis("off")
-        plt.imshow(bar)
-        plt.show()
-
-    if color_index != -1:
-        return color_attr[color_index],clt.cluster_centers_[index]
-    else:
-        return None,clt.cluster_centers_[index]
\ No newline at end of file
diff --git a/hyperlpr_py3/config.py b/hyperlpr_py3/config.py
deleted file mode 100644
index 40f2099..0000000
--- a/hyperlpr_py3/config.py
+++ /dev/null
@@ -1,6 +0,0 @@
-import json
-
-
-
-with open("/Users/universe/ProgramUniverse/zeusees/HyperLPR/config.json") as f:
-    configuration = json.load(f)
diff --git a/hyperlpr_py3/deskew.py b/hyperlpr_py3/deskew.py
deleted file mode 100644
index 1a5ea42..0000000
--- a/hyperlpr_py3/deskew.py
+++ /dev/null
@@ -1,100 +0,0 @@
-#coding=utf-8
-import numpy as np
-import cv2
-import time
-from matplotlib import pyplot as plt
-import math
-
-from scipy.ndimage import filters
-#
-# def strokeFiter():
-#     pass;
-
-def angle(x,y):
-    return int(math.atan2(float(y),float(x))*180.0/3.1415)
-
-
-def h_rot(src, angle, scale=1.0):
-    w = src.shape[1]
-    h = src.shape[0]
-    rangle = np.deg2rad(angle)
-    nw = (abs(np.sin(rangle)*h) + abs(np.cos(rangle)*w))*scale
-    nh = (abs(np.cos(rangle)*h) + abs(np.sin(rangle)*w))*scale
-    rot_mat = cv2.getRotationMatrix2D((nw*0.5, nh*0.5), angle, scale)
-    rot_move = np.dot(rot_mat, np.array([(nw-w)*0.5, (nh-h)*0.5,0]))
-    rot_mat[0,2] += rot_move[0]
-    rot_mat[1,2] += rot_move[1]
-    return cv2.warpAffine(src, rot_mat, (int(math.ceil(nw)), int(math.ceil(nh))), flags=cv2.INTER_LANCZOS4)
-    pass
-
-
-def v_rot(img, angel, shape, max_angel):
-    size_o = [shape[1],shape[0]]
-    size = (shape[1]+ int(shape[0]*np.cos((float(max_angel )/180) * 3.14)),shape[0])
-    interval = abs( int( np.sin((float(angel) /180) * 3.14)* shape[0]))
-    pts1 = np.float32([[0,0],[0,size_o[1]],[size_o[0],0],[size_o[0],size_o[1]]])
-    if(angel>0):
-        pts2 = np.float32([[interval,0],[0,size[1]  ],[size[0],0  ],[size[0]-interval,size_o[1]]])
-    else:
-        pts2 = np.float32([[0,0],[interval,size[1]  ],[size[0]-interval,0  ],[size[0],size_o[1]]])
-
-    M  = cv2.getPerspectiveTransform(pts1,pts2)
-    dst = cv2.warpPerspective(img,M,size)
-    return dst,M
-
-
-def skew_detection(image_gray):
-    h, w = image_gray.shape[:2]
-    eigen = cv2.cornerEigenValsAndVecs(image_gray,12, 5)
-    angle_sur = np.zeros(180,np.uint)
-    eigen = eigen.reshape(h, w, 3, 2)
-    flow = eigen[:,:,2]
-    vis = image_gray.copy()
-    vis[:] = (192 + np.uint32(vis)) / 2
-    d = 12
-    points =  np.dstack( np.mgrid[d/2:w:d, d/2:h:d] ).reshape(-1, 2)
-    for x, y in points:
-        vx, vy = np.int32(flow[int(y), int(x)]*d)
-        # cv2.line(rgb, (x-vx, y-vy), (x+vx, y+vy), (0, 355, 0), 1, cv2.LINE_AA)
-        ang = angle(vx,vy)
-        angle_sur[(ang+180)%180] +=1
-
-    # torr_bin = 30
-    angle_sur = angle_sur.astype(np.float)
-    angle_sur = (angle_sur-angle_sur.min())/(angle_sur.max()-angle_sur.min())
-    angle_sur = filters.gaussian_filter1d(angle_sur,5)
-    skew_v_val =  angle_sur[20:180-20].max()
-    skew_v = angle_sur[30:180-30].argmax() + 30
-    skew_h_A = angle_sur[0:30].max()
-    skew_h_B = angle_sur[150:180].max()
-    skew_h = 0
-    if (skew_h_A > skew_v_val*0.3 or skew_h_B > skew_v_val*0.3):
-        if skew_h_A>=skew_h_B:
-            skew_h = angle_sur[0:20].argmax()
-        else:
-            skew_h = - angle_sur[160:180].argmax()
-    return skew_h,skew_v
-
-
-def fastDeskew(image):
-    image_gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
-    skew_h,skew_v = skew_detection(image_gray)
-    print("校正角度 h ",skew_h,"v",skew_v)
-    deskew,M = v_rot(image,int((90-skew_v)*1.5),image.shape,60)
-    return deskew,M
-
-
-
-if __name__ == '__main__':
-    fn = './dataset/0.jpg'
-
-    img = cv2.imread(fn)
-    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-    skew_h,skew_v = skew_detection(gray)
-    img = v_rot(img,(90-skew_v ),img.shape,60)
-    # img = h_rot(img,skew_h)
-    # if img.shape[0]>img.shape[1]:
-    #     img = h_rot(img, -90)
-
-    plt.show()
-    cv2.waitKey()
diff --git a/hyperlpr_py3/detect.py b/hyperlpr_py3/detect.py
deleted file mode 100644
index c4bc4ad..0000000
--- a/hyperlpr_py3/detect.py
+++ /dev/null
@@ -1,76 +0,0 @@
-
-import cv2
-import numpy as np
-
-
-
-watch_cascade = cv2.CascadeClassifier('./model/cascade.xml')
-
-
-def computeSafeRegion(shape,bounding_rect):
-    top = bounding_rect[1] # y
-    bottom  = bounding_rect[1] + bounding_rect[3] # y +  h
-    left = bounding_rect[0] # x
-    right =   bounding_rect[0] + bounding_rect[2] # x +  w
-
-    min_top = 0
-    max_bottom = shape[0]
-    min_left = 0
-    max_right = shape[1]
-
-    # print "computeSateRegion input shape",shape
-    if top < min_top:
-        top = min_top
-        # print "tap top 0"
-    if left < min_left:
-        left = min_left
-        # print "tap left 0"
-
-    if bottom > max_bottom:
-        bottom = max_bottom
-        #print "tap max_bottom max"
-    if right > max_right:
-        right = max_right
-        #print "tap max_right max"
-
-    # print "corr",left,top,right,bottom
-    return [left,top,right-left,bottom-top]
-
-
-def cropped_from_image(image,rect):
-    x, y, w, h = computeSafeRegion(image.shape,rect)
-    return image[y:y+h,x:x+w]
-
-
-def detectPlateRough(image_gray,resize_h = 720,en_scale =1.08 ,top_bottom_padding_rate = 0.05):
-    print(image_gray.shape)
-
-    if top_bottom_padding_rate>0.2:
-        print("error:top_bottom_padding_rate > 0.2:",top_bottom_padding_rate)
-        exit(1)
-
-    height = image_gray.shape[0]
-    padding =    int(height*top_bottom_padding_rate)
-    scale = image_gray.shape[1]/float(image_gray.shape[0])
-
-    image = cv2.resize(image_gray, (int(scale*resize_h), resize_h))
-
-    image_color_cropped = image[padding:resize_h-padding,0:image_gray.shape[1]]
-
-    image_gray = cv2.cvtColor(image_color_cropped,cv2.COLOR_RGB2GRAY)
-
-    watches = watch_cascade.detectMultiScale(image_gray, en_scale, 2, minSize=(36, 9),maxSize=(36*40, 9*40))
-
-    cropped_images = []
-    for (x, y, w, h) in watches:
-        cropped_origin = cropped_from_image(image_color_cropped, (int(x), int(y), int(w), int(h)))
-        x -= w * 0.14
-        w += w * 0.28
-        y -= h * 0.6
-        h += h * 1.1;
-
-        cropped = cropped_from_image(image_color_cropped, (int(x), int(y), int(w), int(h)))
-
-
-        cropped_images.append([cropped,[x, y+padding, w, h],cropped_origin])
-    return cropped_images
diff --git a/hyperlpr_py3/e2e.py b/hyperlpr_py3/e2e.py
deleted file mode 100644
index 2748826..0000000
--- a/hyperlpr_py3/e2e.py
+++ /dev/null
@@ -1,63 +0,0 @@
-#coding=utf-8
-from keras import backend as K
-from keras.models import load_model
-from keras.layers import *
-import numpy as np
-import random
-import string
-
-import cv2
-from . import e2emodel as model
-chars = ["京", "沪", "津", "渝", "冀", "晋", "蒙", "辽", "吉", "黑", "苏", "浙", "皖", "闽", "赣", "鲁", "豫", "鄂", "湘", "粤", "桂",
-             "琼", "川", "贵", "云", "藏", "陕", "甘", "青", "宁", "新", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "A",
-             "B", "C", "D", "E", "F", "G", "H", "J", "K", "L", "M", "N", "P", "Q", "R", "S", "T", "U", "V", "W", "X",
-             "Y", "Z","港","学","使","警","澳","挂","军","北","南","广","沈","兰","成","济","海","民","航","空"
-             ];
-pred_model = model.construct_model("./model/ocr_plate_all_w_rnn_2.h5")
-import time
-
-
-
-def fastdecode(y_pred):
-    results = ""
-    confidence = 0.0
-    table_pred = y_pred.reshape(-1, len(chars)+1)
-
-    res = table_pred.argmax(axis=1)
-
-    for i,one in enumerate(res):
-        if one<len(chars) and (i==0 or (one!=res[i-1])):
-            results+= chars[one]
-            confidence+=table_pred[i][one]
-    confidence/= len(results)
-    return results,confidence
-
-def recognizeOne(src):
-    # x_tempx= cv2.imread(src)
-    x_tempx = src
-    # x_tempx = cv2.bitwise_not(x_tempx)
-    x_temp = cv2.resize(x_tempx,( 160,40))
-    x_temp = x_temp.transpose(1, 0, 2)
-    t0 = time.time()
-    y_pred = pred_model.predict(np.array([x_temp]))
-    y_pred = y_pred[:,2:,:]
-    # plt.imshow(y_pred.reshape(16,66))
-    # plt.show()
-
-    #
-    # cv2.imshow("x_temp",x_tempx)
-    # cv2.waitKey(0)
-    return fastdecode(y_pred)
-#
-#
-# import os
-#
-# path = "/Users/yujinke/PycharmProjects/HyperLPR_Python_web/cache/finemapping"
-# for filename in os.listdir(path):
-#     if filename.endswith(".png") or filename.endswith(".jpg") or filename.endswith(".bmp"):
-#         x = os.path.join(path,filename)
-#         recognizeOne(x)
-#         # print time.time() - t0
-#
-#         # cv2.imshow("x",x)
-#         # cv2.waitKey()
diff --git a/hyperlpr_py3/e2emodel.py b/hyperlpr_py3/e2emodel.py
deleted file mode 100644
index c9f529b..0000000
--- a/hyperlpr_py3/e2emodel.py
+++ /dev/null
@@ -1,34 +0,0 @@
-
-from keras import backend as K
-from keras.models import *
-from keras.layers import *
-from . import e2e
-
-
-def ctc_lambda_func(args):
-    y_pred, labels, input_length, label_length = args
-    y_pred = y_pred[:, 2:, :]
-    return K.ctc_batch_cost(labels, y_pred, input_length, label_length)
-
-
-def construct_model(model_path):
-    input_tensor = Input((None, 40, 3))
-    x = input_tensor
-    base_conv = 32
-
-    for i in range(3):
-        x = Conv2D(base_conv * (2 ** (i)), (3, 3),padding="same")(x)
-        x = BatchNormalization()(x)
-        x = Activation('relu')(x)
-        x = MaxPooling2D(pool_size=(2, 2))(x)
-    x = Conv2D(256, (5, 5))(x)
-    x = BatchNormalization()(x)
-    x = Activation('relu')(x)
-    x = Conv2D(1024, (1, 1))(x)
-    x = BatchNormalization()(x)
-    x = Activation('relu')(x)
-    x = Conv2D(len(e2e.chars)+1, (1, 1))(x)
-    x = Activation('softmax')(x)
-    base_model = Model(inputs=input_tensor, outputs=x)
-    base_model.load_weights(model_path)
-    return base_model
diff --git a/hyperlpr_py3/finemapping.py b/hyperlpr_py3/finemapping.py
deleted file mode 100644
index d6ff5a2..0000000
--- a/hyperlpr_py3/finemapping.py
+++ /dev/null
@@ -1,130 +0,0 @@
-#coding=utf-8
-import cv2
-import numpy as np
-
-
-from . import niblack_thresholding as nt
-
-from . import deskew
-
-def fitLine_ransac(pts,zero_add = 0 ):
-    if len(pts)>=2:
-        [vx, vy, x, y] = cv2.fitLine(pts, cv2.DIST_HUBER, 0, 0.01, 0.01)
-        lefty = int((-x * vy / vx) + y)
-        righty = int(((136- x) * vy / vx) + y)
-        return lefty+30+zero_add,righty+30+zero_add
-    return 0,0
-
-
-
-#精定位算法
-def findContoursAndDrawBoundingBox(image_rgb):
-
-
-    line_upper  = [];
-    line_lower = [];
-
-    line_experiment = []
-    grouped_rects = []
-    gray_image = cv2.cvtColor(image_rgb,cv2.COLOR_BGR2GRAY)
-
-    # for k in np.linspace(-1.5, -0.2,10):
-    for k in np.linspace(-50, 0, 15):
-
-        # thresh_niblack = threshold_niblack(gray_image, window_size=21, k=k)
-        # binary_niblack = gray_image > thresh_niblack
-        # binary_niblack = binary_niblack.astype(np.uint8) * 255
-
-        binary_niblack = cv2.adaptiveThreshold(gray_image,255,cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY,17,k)
-        # cv2.imshow("image1",binary_niblack)
-        # cv2.waitKey(0)
-        imagex, contours, hierarchy = cv2.findContours(binary_niblack.copy(),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
-        for contour in contours:
-            bdbox = cv2.boundingRect(contour)
-            if (bdbox[3]/float(bdbox[2])>0.7 and bdbox[3]*bdbox[2]>100 and bdbox[3]*bdbox[2]<1200) or (bdbox[3]/float(bdbox[2])>3 and bdbox[3]*bdbox[2]<100):
-                # cv2.rectangle(rgb,(bdbox[0],bdbox[1]),(bdbox[0]+bdbox[2],bdbox[1]+bdbox[3]),(255,0,0),1)
-                line_upper.append([bdbox[0],bdbox[1]])
-                line_lower.append([bdbox[0]+bdbox[2],bdbox[1]+bdbox[3]])
-
-                line_experiment.append([bdbox[0],bdbox[1]])
-                line_experiment.append([bdbox[0]+bdbox[2],bdbox[1]+bdbox[3]])
-                # grouped_rects.append(bdbox)
-
-    rgb = cv2.copyMakeBorder(image_rgb,30,30,0,0,cv2.BORDER_REPLICATE)
-    leftyA, rightyA = fitLine_ransac(np.array(line_lower),3)
-    rows,cols = rgb.shape[:2]
-
-    # rgb = cv2.line(rgb, (cols - 1, rightyA), (0, leftyA), (0, 0, 255), 1,cv2.LINE_AA)
-
-    leftyB, rightyB = fitLine_ransac(np.array(line_upper),-3)
-
-    rows,cols = rgb.shape[:2]
-
-    # rgb = cv2.line(rgb, (cols - 1, rightyB), (0, leftyB), (0,255, 0), 1,cv2.LINE_AA)
-    pts_map1  = np.float32([[cols - 1, rightyA], [0, leftyA],[cols - 1, rightyB], [0, leftyB]])
-    pts_map2 = np.float32([[136,36],[0,36],[136,0],[0,0]])
-    mat = cv2.getPerspectiveTransform(pts_map1,pts_map2)
-    image = cv2.warpPerspective(rgb,mat,(136,36),flags=cv2.INTER_CUBIC)
-    image,M = deskew.fastDeskew(image)
-
-    return image
-
-
-
-#多级
-def findContoursAndDrawBoundingBox2(image_rgb):
-
-
-    line_upper  = [];
-    line_lower = [];
-
-    line_experiment = []
-
-    grouped_rects = []
-
-    gray_image = cv2.cvtColor(image_rgb,cv2.COLOR_BGR2GRAY)
-
-    for k in np.linspace(-1.6, -0.2,10):
-    # for k in np.linspace(-15, 0, 15):
-    # #
-    #     thresh_niblack = threshold_niblack(gray_image, window_size=21, k=k)
-    #     binary_niblack = gray_image > thresh_niblack
-    #     binary_niblack = binary_niblack.astype(np.uint8) * 255
-
-        binary_niblack = nt.niBlackThreshold(gray_image,19,k)
-        # cv2.imshow("binary_niblack_opencv",binary_niblack_)
-        # cv2.imshow("binary_niblack_skimage", binary_niblack)
-
-        # cv2.waitKey(0)
-        imagex, contours, hierarchy = cv2.findContours(binary_niblack.copy(),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
-
-        for contour in contours:
-            bdbox = cv2.boundingRect(contour)
-            if (bdbox[3]/float(bdbox[2])>0.7 and bdbox[3]*bdbox[2]>100 and bdbox[3]*bdbox[2]<1000) or (bdbox[3]/float(bdbox[2])>3 and bdbox[3]*bdbox[2]<100):
-                # cv2.rectangle(rgb,(bdbox[0],bdbox[1]),(bdbox[0]+bdbox[2],bdbox[1]+bdbox[3]),(255,0,0),1)
-                line_upper.append([bdbox[0],bdbox[1]])
-                line_lower.append([bdbox[0]+bdbox[2],bdbox[1]+bdbox[3]])
-
-                line_experiment.append([bdbox[0],bdbox[1]])
-                line_experiment.append([bdbox[0]+bdbox[2],bdbox[1]+bdbox[3]])
-                # grouped_rects.append(bdbox)
-
-    rgb = cv2.copyMakeBorder(image_rgb,30,30,0,0,cv2.BORDER_REPLICATE)
-    leftyA, rightyA = fitLine_ransac(np.array(line_lower),2)
-    rows,cols = rgb.shape[:2]
-
-    # rgb = cv2.line(rgb, (cols - 1, rightyA), (0, leftyA), (0, 0, 255), 1,cv2.LINE_AA)
-
-    leftyB, rightyB = fitLine_ransac(np.array(line_upper),-4)
-
-    rows,cols = rgb.shape[:2]
-
-    # rgb = cv2.line(rgb, (cols - 1, rightyB), (0, leftyB), (0,255, 0), 1,cv2.LINE_AA)
-    pts_map1  = np.float32([[cols - 1, rightyA], [0, leftyA],[cols - 1, rightyB], [0, leftyB]])
-    pts_map2 = np.float32([[136,36],[0,36],[136,0],[0,0]])
-    mat = cv2.getPerspectiveTransform(pts_map1,pts_map2)
-    image = cv2.warpPerspective(rgb,mat,(136,36),flags=cv2.INTER_CUBIC)
-    image,M= deskew.fastDeskew(image)
-
-
-    return image
diff --git a/hyperlpr_py3/finemapping_vertical.py b/hyperlpr_py3/finemapping_vertical.py
deleted file mode 100644
index 83cbcf4..0000000
--- a/hyperlpr_py3/finemapping_vertical.py
+++ /dev/null
@@ -1,92 +0,0 @@
-#coding=utf-8
-from keras.layers import Conv2D, Input,MaxPool2D, Reshape,Activation,Flatten, Dense
-from keras.models import Model, Sequential
-from keras.layers.advanced_activations import PReLU
-from keras.optimizers import adam
-import numpy as np
-
-import cv2
-
-def getModel():
-    input = Input(shape=[16, 66, 3])  # change this shape to [None,None,3] to enable arbitraty shape input
-    x = Conv2D(10, (3, 3), strides=1, padding='valid', name='conv1')(input)
-    x = Activation("relu", name='relu1')(x)
-    x = MaxPool2D(pool_size=2)(x)
-    x = Conv2D(16, (3, 3), strides=1, padding='valid', name='conv2')(x)
-    x = Activation("relu", name='relu2')(x)
-    x = Conv2D(32, (3, 3), strides=1, padding='valid', name='conv3')(x)
-    x = Activation("relu", name='relu3')(x)
-    x = Flatten()(x)
-    output = Dense(2,name = "dense")(x)
-    output = Activation("relu", name='relu4')(output)
-    model = Model([input], [output])
-    return model
-
-
-
-model = getModel()
-model.load_weights("./model/model12.h5")
-
-
-def getmodel():
-    return model
-
-def gettest_model():
-    input = Input(shape=[16, 66, 3])  # change this shape to [None,None,3] to enable arbitraty shape input
-    A = Conv2D(10, (3, 3), strides=1, padding='valid', name='conv1')(input)
-    B = Activation("relu", name='relu1')(A)
-    C = MaxPool2D(pool_size=2)(B)
-    x = Conv2D(16, (3, 3), strides=1, padding='valid', name='conv2')(C)
-    x = Activation("relu", name='relu2')(x)
-    x = Conv2D(32, (3, 3), strides=1, padding='valid', name='conv3')(x)
-    K = Activation("relu", name='relu3')(x)
-
-
-    x = Flatten()(K)
-    dense = Dense(2,name = "dense")(x)
-    output = Activation("relu", name='relu4')(dense)
-    x = Model([input], [output])
-    x.load_weights("./model/model12.h5")
-    ok = Model([input], [dense])
-
-    for layer in ok.layers:
-        print(layer)
-
-    return ok
-
-
-
-
-def finemappingVertical(image):
-    resized = cv2.resize(image,(66,16))
-    resized = resized.astype(np.float)/255
-    res= model.predict(np.array([resized]))[0]
-    print("keras_predict",res)
-    res  =res*image.shape[1]
-    res = res.astype(np.int)
-    H,T = res
-    H-=3
-    #3 79.86
-    #4 79.3
-    #5 79.5
-    #6 78.3
-
-
-    #T
-    #T+1 80.9
-    #T+2 81.75
-    #T+3 81.75
-
-
-
-    if H<0:
-        H=0
-    T+=2;
-
-    if T>= image.shape[1]-1:
-        T= image.shape[1]-1
-
-    image = image[0:35,H:T+2]
-
-    image = cv2.resize(image, (int(136), int(36)))
-    return image
\ No newline at end of file
diff --git a/hyperlpr_py3/niblack_thresholding.py b/hyperlpr_py3/niblack_thresholding.py
deleted file mode 100644
index 652bae0..0000000
--- a/hyperlpr_py3/niblack_thresholding.py
+++ /dev/null
@@ -1,18 +0,0 @@
-import cv2
-import numpy as np
-
-
-
-def niBlackThreshold(  src,  blockSize,  k,  binarizationMethod= 0 ):
-    mean = cv2.boxFilter(src,cv2.CV_32F,(blockSize, blockSize),borderType=cv2.BORDER_REPLICATE)
-    sqmean = cv2.sqrBoxFilter(src, cv2.CV_32F, (blockSize, blockSize), borderType = cv2.BORDER_REPLICATE)
-    variance = sqmean - (mean*mean)
-    stddev  = np.sqrt(variance)
-    thresh = mean + stddev * float(-k)
-    thresh = thresh.astype(src.dtype)
-    k = (src>thresh)*255
-    k = k.astype(np.uint8)
-    return k
-
-
-# cv2.imshow()
\ No newline at end of file
diff --git a/hyperlpr_py3/pipline.py b/hyperlpr_py3/pipline.py
deleted file mode 100644
index 1940eab..0000000
--- a/hyperlpr_py3/pipline.py
+++ /dev/null
@@ -1,246 +0,0 @@
-#coding=utf-8
-from . import detect
-from . import  finemapping  as  fm
-
-from . import segmentation
-import cv2
-
-import time
-import numpy as np
-
-from PIL import ImageFont
-from PIL import Image
-from PIL import ImageDraw
-import json
-
-import sys
-from . import typeDistinguish as td
-import imp
-
-
-imp.reload(sys)
-fontC = ImageFont.truetype("./Font/platech.ttf", 14, 0);
-
-from . import e2e
-#寻找车牌左右边界
-
-def find_edge(image):
-    sum_i = image.sum(axis=0)
-    sum_i =  sum_i.astype(np.float)
-    sum_i/=image.shape[0]*255
-    # print sum_i
-
-    start= 0 ;
-    end = image.shape[1]-1
-
-    for i,one in enumerate(sum_i):
-        if one>0.4:
-            start = i;
-            if start-3<0:
-                start = 0
-            else:
-                start -=3
-
-            break;
-    for i,one in enumerate(sum_i[::-1]):
-
-        if one>0.4:
-            end = end - i;
-            if end+4>image.shape[1]-1:
-                end = image.shape[1]-1
-            else:
-                end+=4
-            break
-    return start,end
-
-
-#垂直边缘检测
-def verticalEdgeDetection(image):
-    image_sobel = cv2.Sobel(image.copy(),cv2.CV_8U,1,0)
-    # image = auto_canny(image_sobel)
-
-    # img_sobel, CV_8U, 1, 0, 3, 1, 0, BORDER_DEFAULT
-    # canny_image  = auto_canny(image)
-    flag,thres = cv2.threshold(image_sobel,0,255,cv2.THRESH_OTSU|cv2.THRESH_BINARY)
-    print(flag)
-    flag,thres = cv2.threshold(image_sobel,int(flag*0.7),255,cv2.THRESH_BINARY)
-    # thres = simpleThres(image_sobel)
-    kernal = np.ones(shape=(3,15))
-    thres = cv2.morphologyEx(thres,cv2.MORPH_CLOSE,kernal)
-    return thres
-
-
-#确定粗略的左右边界
-def horizontalSegmentation(image):
-
-    thres = verticalEdgeDetection(image)
-    # thres = thres*image
-    head,tail = find_edge(thres)
-    # print head,tail
-    # cv2.imshow("edge",thres)
-    tail = tail+5
-    if tail>135:
-        tail = 135
-    image = image[0:35,head:tail]
-    image = cv2.resize(image, (int(136), int(36)))
-    return image
-
-
-#打上boundingbox和标签
-def drawRectBox(image,rect,addText):
-    cv2.rectangle(image, (int(rect[0]), int(rect[1])), (int(rect[0] + rect[2]), int(rect[1] + rect[3])), (0,0, 255), 2, cv2.LINE_AA)
-    cv2.rectangle(image, (int(rect[0]-1), int(rect[1])-16), (int(rect[0] + 115), int(rect[1])), (0, 0, 255), -1, cv2.LINE_AA)
-
-    img = Image.fromarray(image)
-    draw = ImageDraw.Draw(img)
-    #draw.text((int(rect[0]+1), int(rect[1]-16)), addText.decode("utf-8"), (255, 255, 255), font=fontC)
-    draw.text((int(rect[0]+1), int(rect[1]-16)), addText, (255, 255, 255), font=fontC)
-    imagex = np.array(img)
-
-    return imagex
-
-
-from . import cache
-from . import finemapping_vertical as fv
-
-def RecognizePlateJson(image):
-    images = detect.detectPlateRough(image,image.shape[0],top_bottom_padding_rate=0.1)
-    jsons = []
-    for j,plate in enumerate(images):
-        plate,rect,origin_plate =plate
-        res, confidence = e2e.recognizeOne(origin_plate)
-        print("res",res)
-
-        cv2.imwrite("./"+str(j)+"_rough.jpg",plate)
-
-        # print "车牌类型:",ptype
-        # plate = cv2.cvtColor(plate, cv2.COLOR_RGB2GRAY)
-        plate  =cv2.resize(plate,(136,int(36*2.5)))
-        t1 = time.time()
-
-        ptype = td.SimplePredict(plate)
-        if ptype>0 and ptype<4:
-            plate = cv2.bitwise_not(plate)
-        # demo = verticalEdgeDetection(plate)
-
-        image_rgb = fm.findContoursAndDrawBoundingBox(plate)
-        image_rgb = fv.finemappingVertical(image_rgb)
-        cache.verticalMappingToFolder(image_rgb)
-        # print time.time() - t1,"校正"
-        print("e2e:",e2e.recognizeOne(image_rgb)[0])
-        image_gray = cv2.cvtColor(image_rgb,cv2.COLOR_BGR2GRAY)
-
-        cv2.imwrite("./"+str(j)+".jpg",image_gray)
-        # image_gray = horizontalSegmentation(image_gray)
-
-        t2 = time.time()
-        res, confidence = e2e.recognizeOne(image_rgb)
-        res_json = {}
-        if confidence  > 0.6:
-            res_json["Name"] = res
-            res_json["Type"] = td.plateType[ptype]
-            res_json["Confidence"] = confidence;
-            res_json["x"] = int(rect[0])
-            res_json["y"] = int(rect[1])
-            res_json["w"] = int(rect[2])
-            res_json["h"] = int(rect[3])
-            jsons.append(res_json)
-    print(json.dumps(jsons,ensure_ascii=False))
-
-    return json.dumps(jsons,ensure_ascii=False)
-
-
-
-
-def SimpleRecognizePlateByE2E(image):
-    t0 = time.time()
-    images = detect.detectPlateRough(image,image.shape[0],top_bottom_padding_rate=0.1)
-    res_set = []
-    for j,plate in enumerate(images):
-        plate, rect, origin_plate  =plate
-        # plate = cv2.cvtColor(plate, cv2.COLOR_RGB2GRAY)
-        plate  =cv2.resize(plate,(136,36*2))
-        res,confidence = e2e.recognizeOne(origin_plate)
-        print("res",res)
-
-        t1 = time.time()
-        ptype = td.SimplePredict(plate)
-        if ptype>0 and ptype<5:
-            # pass
-            plate = cv2.bitwise_not(plate)
-        image_rgb = fm.findContoursAndDrawBoundingBox(plate)
-        image_rgb = fv.finemappingVertical(image_rgb)
-        image_rgb = fv.finemappingVertical(image_rgb)
-        cache.verticalMappingToFolder(image_rgb)
-        #cv2.imwrite("./"+str(j)+".jpg",image_rgb)
-        res,confidence = e2e.recognizeOne(image_rgb)
-        print(res,confidence)
-        res_set.append([[],res,confidence])
-
-        if confidence>0.7:
-            image = drawRectBox(image, rect, res+" "+str(round(confidence,3)))
-    return image,res_set
-
-
-def SimpleRecognizePlate(image):
-    t0 = time.time()
-    images = detect.detectPlateRough(image,image.shape[0],top_bottom_padding_rate=0.1)
-    res_set = []
-    for j,plate in enumerate(images):
-        plate, rect, origin_plate  =plate
-        # plate = cv2.cvtColor(plate, cv2.COLOR_RGB2GRAY)
-        plate  =cv2.resize(plate,(136,36*2))
-        t1 = time.time()
-
-        ptype = td.SimplePredict(plate)
-        if ptype>0 and ptype<5:
-            plate = cv2.bitwise_not(plate)
-
-        image_rgb = fm.findContoursAndDrawBoundingBox(plate)
-
-        image_rgb = fv.finemappingVertical(image_rgb)
-        cache.verticalMappingToFolder(image_rgb)
-        print("e2e:", e2e.recognizeOne(image_rgb))
-        image_gray = cv2.cvtColor(image_rgb,cv2.COLOR_RGB2GRAY)
-
-        # image_gray = horizontalSegmentation(image_gray)
-        cv2.imshow("image_gray",image_gray)
-        # cv2.waitKey()
-
-        cv2.imwrite("./"+str(j)+".jpg",image_gray)
-        # cv2.imshow("image",image_gray)
-        # cv2.waitKey(0)
-        print("校正",time.time() - t1,"s")
-        # cv2.imshow("image,",image_gray)
-        # cv2.waitKey(0)
-        t2 = time.time()
-        val = segmentation.slidingWindowsEval(image_gray)
-        # print val
-        print("分割和识别",time.time() - t2,"s")
-        if len(val)==3:
-            blocks, res, confidence = val
-            if confidence/7>0.7:
-                image =  drawRectBox(image,rect,res)
-                res_set.append(res)
-                for i,block in enumerate(blocks):
-
-                    block_ = cv2.resize(block,(25,25))
-                    block_ = cv2.cvtColor(block_,cv2.COLOR_GRAY2BGR)
-                    image[j * 25:(j * 25) + 25, i * 25:(i * 25) + 25] = block_
-                    if image[j*25:(j*25)+25,i*25:(i*25)+25].shape == block_.shape:
-                        pass
-
-
-            if confidence>0:
-                print("车牌:",res,"置信度:",confidence/7)
-            else:
-                pass
-
-                # print "不确定的车牌:", res, "置信度:", confidence
-
-    print(time.time() - t0,"s")
-    return image,res_set
-
-
-
-
diff --git a/hyperlpr_py3/recognizer.py b/hyperlpr_py3/recognizer.py
deleted file mode 100644
index cd84276..0000000
--- a/hyperlpr_py3/recognizer.py
+++ /dev/null
@@ -1,154 +0,0 @@
-#coding=utf-8
-from keras.models import Sequential
-from keras.layers import Dense, Dropout, Activation, Flatten
-from keras.layers import Conv2D,MaxPool2D
-from keras.optimizers import SGD
-from keras import backend as K
-
-K.image_data_format()
-
-
-import cv2
-import numpy as np
-
-
-
-index = {"京": 0, "沪": 1, "津": 2, "渝": 3, "冀": 4, "晋": 5, "蒙": 6, "辽": 7, "吉": 8, "黑": 9, "苏": 10, "浙": 11, "皖": 12,
-         "闽": 13, "赣": 14, "鲁": 15, "豫": 16, "鄂": 17, "湘": 18, "粤": 19, "桂": 20, "琼": 21, "川": 22, "贵": 23, "云": 24,
-         "藏": 25, "陕": 26, "甘": 27, "青": 28, "宁": 29, "新": 30, "0": 31, "1": 32, "2": 33, "3": 34, "4": 35, "5": 36,
-         "6": 37, "7": 38, "8": 39, "9": 40, "A": 41, "B": 42, "C": 43, "D": 44, "E": 45, "F": 46, "G": 47, "H": 48,
-         "J": 49, "K": 50, "L": 51, "M": 52, "N": 53, "P": 54, "Q": 55, "R": 56, "S": 57, "T": 58, "U": 59, "V": 60,
-         "W": 61, "X": 62, "Y": 63, "Z": 64,"港":65,"学":66 ,"O":67 ,"使":68,"警":69,"澳":70,"挂":71};
-
-chars = ["京", "沪", "津", "渝", "冀", "晋", "蒙", "辽", "吉", "黑", "苏", "浙", "皖", "闽", "赣", "鲁", "豫", "鄂", "湘", "粤", "桂",
-             "琼", "川", "贵", "云", "藏", "陕", "甘", "青", "宁", "新", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "A",
-             "B", "C", "D", "E", "F", "G", "H", "J", "K", "L", "M", "N", "P",
-         "Q", "R", "S", "T", "U", "V", "W", "X",
-             "Y", "Z","港","学","O","使","警","澳","挂" ];
-
-
-
-def Getmodel_tensorflow(nb_classes):
-    # nb_classes = len(charset)
-
-    img_rows, img_cols = 23, 23
-    # number of convolutional filters to use
-    nb_filters = 32
-    # size of pooling area for max pooling
-    nb_pool = 2
-    # convolution kernel size
-    nb_conv = 3
-
-    # x = np.load('x.npy')
-    
-    # y = np_utils.to_categorical(range(3062)*45*5*2, nb_classes)
-    # weight = ((type_class - np.arange(type_class)) / type_class + 1) ** 3
-    # weight = dict(zip(range(3063), weight / weight.mean()))  # 调整权重，高频字优先
-
-    model = Sequential()
-    model.add(Conv2D(32, (5, 5),input_shape=(img_rows, img_cols,1)))
-    model.add(Activation('relu'))
-    model.add(MaxPool2D(pool_size=(nb_pool, nb_pool)))
-    model.add(Dropout(0.25))
-    model.add(Conv2D(32, (3, 3)))
-    model.add(Activation('relu'))
-    model.add(MaxPool2D(pool_size=(nb_pool, nb_pool)))
-    model.add(Dropout(0.25))
-    model.add(Conv2D(512, (3, 3)))
-    # model.add(Activation('relu'))
-    # model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool)))
-    # model.add(Dropout(0.25))
-    model.add(Flatten())
-    model.add(Dense(512))
-    model.add(Activation('relu'))
-    model.add(Dropout(0.5))
-    model.add(Dense(nb_classes))
-    model.add(Activation('softmax'))
-    model.compile(loss='categorical_crossentropy',
-                  optimizer='adam',
-                  metrics=['accuracy'])
-    return model
-
-
-
-
-def Getmodel_ch(nb_classes):
-    # nb_classes = len(charset)
-
-    img_rows, img_cols = 23, 23
-    # number of convolutional filters to use
-    nb_filters = 32
-    # size of pooling area for max pooling
-    nb_pool = 2
-    # convolution kernel size
-    nb_conv = 3
-
-    # x = np.load('x.npy')
-    # y = np_utils.to_categorical(range(3062)*45*5*2, nb_classes)
-    # weight = ((type_class - np.arange(type_class)) / type_class + 1) ** 3
-    # weight = dict(zip(range(3063), weight / weight.mean()))  # 调整权重，高频字优先
-
-    model = Sequential()
-    model.add(Conv2D(32, (5, 5),input_shape=(img_rows, img_cols,1)))
-    model.add(Activation('relu'))
-    model.add(MaxPool2D(pool_size=(nb_pool, nb_pool)))
-    model.add(Dropout(0.25))
-    model.add(Conv2D(32, (3, 3)))
-    model.add(Activation('relu'))
-    model.add(MaxPool2D(pool_size=(nb_pool, nb_pool)))
-    model.add(Dropout(0.25))
-    model.add(Conv2D(512, (3, 3)))
-    # model.add(Activation('relu'))
-    # model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool)))
-    # model.add(Dropout(0.25))
-    model.add(Flatten())
-    model.add(Dense(756))
-    model.add(Activation('relu'))
-    model.add(Dropout(0.5))
-    model.add(Dense(nb_classes))
-    model.add(Activation('softmax'))
-    model.compile(loss='categorical_crossentropy',
-                  optimizer='adam',
-                  metrics=['accuracy'])
-    return model
-
-
-
-model  = Getmodel_tensorflow(65)
-#构建网络
-
-model_ch = Getmodel_ch(31)
-
-model_ch.load_weights("./model/char_chi_sim.h5")
-# model_ch.save_weights("./model/char_chi_sim.h5")
-model.load_weights("./model/char_rec.h5")
-# model.save("./model/char_rec.h5")
-
-
-def SimplePredict(image,pos):
-    image = cv2.resize(image, (23, 23))
-    image = cv2.equalizeHist(image)
-    image = image.astype(np.float) / 255
-    image -= image.mean()
-    image = np.expand_dims(image, 3)
-    if pos!=0:
-        res = np.array(model.predict(np.array([image]))[0])
-    else:
-        res = np.array(model_ch.predict(np.array([image]))[0])
-
-    zero_add = 0 ;
-
-    if pos==0:
-        res = res[:31]
-    elif pos==1:
-        res = res[31+10:65]
-        zero_add = 31+10
-    else:
-        res = res[31:]
-        zero_add = 31
-
-    max_id = res.argmax()
-
-
-    return res.max(),chars[max_id+zero_add],max_id+zero_add
-
diff --git a/hyperlpr_py3/requirements.txt b/hyperlpr_py3/requirements.txt
deleted file mode 100644
index 48fdfe2..0000000
--- a/hyperlpr_py3/requirements.txt
+++ /dev/null
@@ -1,53 +0,0 @@
-absl-py==0.9.0
-alembic==1.4.2
-astor==0.8.1
-click==7.1.1
-cycler==0.10.0
-decorator==4.4.2
-Flask==1.0
-Flask-Migrate==2.5.3
-Flask-MySQLdb==0.2.0
-Flask-Script==2.0.6
-Flask-Session==0.3.1
-Flask-SQLAlchemy==2.4.1
-gast==0.3.3
-grpcio==1.27.2
-h5py==2.10.0
-imageio==2.8.0
-itsdangerous==1.1.0
-Jinja2==2.11.1
-joblib==0.14.1
-Keras==2.1.1
-Keras-Applications==1.0.8
-Keras-Preprocessing==1.1.0
-kiwisolver==1.1.0
-Mako==1.1.2
-Markdown==3.2.1
-MarkupSafe==1.1.1
-matplotlib==3.2.1
-mock==4.0.2
-mysqlclient==1.4.6
-networkx==2.4
-numpy==1.17.4
-opencv-contrib-python==3.4.2.16
-opencv-python==3.4.2.16
-Pillow==7.0.0
-protobuf==3.11.3
-pyparsing==2.4.6
-python-dateutil==2.8.1
-python-editor==1.0.4
-PyWavelets==1.1.1
-PyYAML==5.3.1
-redis==3.4.1
-scikit-image==0.16.2
-scikit-learn==0.22.2.post1
-scipy==1.4.1
-six==1.14.0
-sklearn==0.0
-SQLAlchemy==1.3.15
-tensorboard==1.13.1
-tensorflow==1.15.2
-tensorflow-estimator==1.13.0
-termcolor==1.1.0
-Theano==1.0.4
-Werkzeug==1.0.0
diff --git a/hyperlpr_py3/segmentation.py b/hyperlpr_py3/segmentation.py
deleted file mode 100644
index 686e626..0000000
--- a/hyperlpr_py3/segmentation.py
+++ /dev/null
@@ -1,307 +0,0 @@
-#coding=utf-8
-import cv2
-import numpy as np
-
-# from matplotlib import pyplot as plt
-import scipy.ndimage.filters as f
-import scipy
-
-import time
-import scipy.signal as l
-
-
-
-
-
-from keras.models import Sequential
-from keras.layers import Dense, Dropout, Activation, Flatten
-from keras.layers import Conv2D, MaxPool2D
-from keras.optimizers import SGD
-from keras import backend as K
-
-K.image_data_format()
-
-
-def Getmodel_tensorflow(nb_classes):
-    # nb_classes = len(charset)
-    img_rows, img_cols = 23, 23
-    # number of convolutional filters to use
-    nb_filters = 16
-    # size of pooling area for max pooling
-    nb_pool = 2
-    # convolution kernel size
-    nb_conv = 3
-    # x = np.load('x.npy')
-    # y = np_utils.to_categorical(range(3062)*45*5*2, nb_classes)
-    # weight = ((type_class - np.arange(type_class)) / type_class + 1) ** 3
-    # weight = dict(zip(range(3063), weight / weight.mean()))  # 调整权重，高频字优先
-
-    model = Sequential()
-    model.add(Conv2D(nb_filters, (nb_conv, nb_conv),input_shape=(img_rows, img_cols,1)))
-    model.add(Activation('relu'))
-    model.add(MaxPool2D(pool_size=(nb_pool, nb_pool)))
-    model.add(Conv2D(nb_filters, (nb_conv, nb_conv)))
-    model.add(Activation('relu'))
-    model.add(MaxPool2D(pool_size=(nb_pool, nb_pool)))
-    model.add(Flatten())
-    model.add(Dense(256))
-    model.add(Dropout(0.5))
-
-    model.add(Activation('relu'))
-    model.add(Dense(nb_classes))
-    model.add(Activation('softmax'))
-    model.compile(loss='categorical_crossentropy',
-                  optimizer='sgd',
-                  metrics=['accuracy'])
-    return model
-
-
-
-def Getmodel_tensorflow_light(nb_classes):
-    # nb_classes = len(charset)
-    img_rows, img_cols = 23, 23
-    # number of convolutional filters to use
-    nb_filters = 8
-    # size of pooling area for max pooling
-    nb_pool = 2
-    # convolution kernel size
-    nb_conv = 3
-    # x = np.load('x.npy')
-    # y = np_utils.to_categorical(range(3062)*45*5*2, nb_classes)
-    # weight = ((type_class - np.arange(type_class)) / type_class + 1) ** 3
-    # weight = dict(zip(range(3063), weight / weight.mean()))  # 调整权重，高频字优先
-
-    model = Sequential()
-    model.add(Conv2D(nb_filters, (nb_conv, nb_conv),input_shape=(img_rows, img_cols, 1)))
-    model.add(Activation('relu'))
-    model.add(MaxPool2D(pool_size=(nb_pool, nb_pool)))
-    model.add(Conv2D(nb_filters, (nb_conv * 2, nb_conv * 2)))
-    model.add(Activation('relu'))
-    model.add(MaxPool2D(pool_size=(nb_pool, nb_pool)))
-    model.add(Flatten())
-    model.add(Dense(32))
-    # model.add(Dropout(0.25))
-
-    model.add(Activation('relu'))
-    model.add(Dense(nb_classes))
-    model.add(Activation('softmax'))
-    model.compile(loss='categorical_crossentropy',
-                  optimizer='adam',
-                  metrics=['accuracy'])
-    return model
-
-
-
-
-model  = Getmodel_tensorflow_light(3)
-model2  = Getmodel_tensorflow(3)
-
-import os
-model.load_weights("./model/char_judgement1.h5")
-# model.save("./model/char_judgement1.h5")
-model2.load_weights("./model/char_judgement.h5")
-# model2.save("./model/char_judgement.h5")
-
-
-model = model2
-def get_median(data):
-   data = sorted(data)
-   size = len(data)
-   # print size
-
-   if size % 2 == 0: # 判断列表长度为偶数
-    median = (data[size//2]+data[size//2-1])//2
-    data[0] = median
-   if size % 2 == 1: # 判断列表长度为奇数
-    median = data[(size-1)//2]
-    data[0] = median
-   return data[0]
-import time
-
-def searchOptimalCuttingPoint(rgb,res_map,start,width_boundingbox,interval_range):
-    t0  = time.time()
-    #
-    # for x in xrange(10):
-    #     res_map = np.vstack((res_map,res_map[-1]))
-    length = res_map.shape[0]
-    refine_s = -2;
-
-    if width_boundingbox>20:
-        refine_s = -9
-    score_list = []
-    interval_big = int(width_boundingbox * 0.3)  #
-    p = 0
-    for zero_add in range(start,start+50,3):
-        # for interval_small in xrange(-0,width_boundingbox/2):
-        for i in range(-8,int(width_boundingbox/1)-8):
-            for refine in range(refine_s, int(width_boundingbox/2+3)):
-                p1 = zero_add# this point is province
-                p2 = p1 + width_boundingbox +refine #
-                p3 = p2 + width_boundingbox + interval_big+i+1
-                p4 = p3 + width_boundingbox +refine
-                p5 = p4 + width_boundingbox +refine
-                p6 = p5 + width_boundingbox +refine
-                p7 = p6 + width_boundingbox +refine
-                if p7>=length:
-                    continue
-                score = res_map[p1][2]*3 -(res_map[p3][1]+res_map[p4][1]+res_map[p5][1]+res_map[p6][1]+res_map[p7][1])+7
-                # print score
-                score_list.append([score,[p1,p2,p3,p4,p5,p6,p7]])
-                p+=1
-    print(p)
-
-    score_list = sorted(score_list , key=lambda x:x[0])
-    # for one in score_list[-1][1]:
-    #     cv2.line(debug,(one,0),(one,36),(255,0,0),1)
-    # #
-    # cv2.imshow("one",debug)
-    # cv2.waitKey(0)
-    #
-    print("寻找最佳点",time.time()-t0)
-    return score_list[-1]
-
-
-import sys
-
-sys.path.append('../')
-from . import recognizer as cRP
-from . import niblack_thresholding as nt
-
-def refineCrop(sections,width=16):
-    new_sections = []
-    for section in sections:
-        # cv2.imshow("section¡",section)
-
-        # cv2.blur(section,(3,3),3)
-
-        sec_center = np.array([section.shape[1]/2,section.shape[0]/2])
-        binary_niblack = nt.niBlackThreshold(section,17,-0.255)
-        imagex, contours, hierarchy  = cv2.findContours(binary_niblack,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
-        boxs = []
-        for contour in contours:
-            x,y,w,h = cv2.boundingRect(contour)
-
-            ratio = w/float(h)
-            if ratio<1 and h>36*0.4 and y<16\
-                    :
-                box = [x,y,w,h]
-
-                boxs.append([box,np.array([x+w/2,y+h/2])])
-                # cv2.rectangle(section,(x,y),(x+w,y+h),255,1)
-
-
-
-
-        # print boxs
-
-        dis_ = np.array([ ((one[1]-sec_center)**2).sum() for one in boxs])
-        if len(dis_)==0:
-            kernal = [0, 0, section.shape[1], section.shape[0]]
-        else:
-            kernal = boxs[dis_.argmin()][0]
-
-        center_c  = (kernal[0]+kernal[2]/2,kernal[1]+kernal[3]/2)
-        w_2 = int(width/2)
-        h_2 = kernal[3]/2
-
-        if center_c[0] - w_2< 0:
-            w_2 = center_c[0]
-        new_box = [center_c[0] - w_2,kernal[1],width,kernal[3]]
-        # print new_box[2]/float(new_box[3])
-        if new_box[2]/float(new_box[3])>0.5:
-            # print "异常"
-            h = int((new_box[2]/0.35 )/2)
-            if h>35:
-                h = 35
-            new_box[1] = center_c[1]- h
-            if new_box[1]<0:
-                new_box[1] = 1
-            new_box[3] = h*2
-        
-        section  = section[int(new_box[1]):int(new_box[1]+new_box[3]), int(new_box[0]):int(new_box[0]+new_box[2])]
-        # cv2.imshow("section",section)
-        # cv2.waitKey(0)
-        new_sections.append(section)
-        # print new_box
-    return new_sections
-
-
-def slidingWindowsEval(image):
-    windows_size = 16;
-    stride = 1
-    height= image.shape[0]
-    t0 = time.time()
-    data_sets = []
-
-    for i in range(0,image.shape[1]-windows_size+1,stride):
-        data = image[0:height,i:i+windows_size]
-        data = cv2.resize(data,(23,23))
-        # cv2.imshow("image",data)
-        data = cv2.equalizeHist(data)
-        data = data.astype(np.float)/255
-        data=  np.expand_dims(data,3)
-        data_sets.append(data)
-
-    res = model2.predict(np.array(data_sets))
-    print("分割",time.time() - t0)
-
-    pin = res
-    p = 1 -  (res.T)[1]
-    p = f.gaussian_filter1d(np.array(p,dtype=np.float),3)
-    lmin = l.argrelmax(np.array(p),order = 3)[0]
-    interval = []
-    for i in range(len(lmin)-1):
-        interval.append(lmin[i+1]-lmin[i])
-
-    if(len(interval)>3):
-        mid  = get_median(interval)
-    else:
-        return []
-    pin = np.array(pin)
-    res =  searchOptimalCuttingPoint(image,pin,0,mid,3)
-
-    cutting_pts = res[1]
-    last =  cutting_pts[-1] + mid
-    if last < image.shape[1]:
-        cutting_pts.append(last)
-    else:
-        cutting_pts.append(image.shape[1]-1)
-    name = ""
-    confidence =0.00
-    seg_block = []
-    for x in range(1,len(cutting_pts)):
-        if x != len(cutting_pts)-1 and x!=1:
-            section = image[0:36,cutting_pts[x-1]-2:cutting_pts[x]+2]
-        elif  x==1:
-            c_head = cutting_pts[x - 1]- 2
-            if c_head<0:
-                c_head=0
-            c_tail = cutting_pts[x] + 2
-            section = image[0:36, c_head:c_tail]
-        elif x==len(cutting_pts)-1:
-            end = cutting_pts[x]
-            diff = image.shape[1]-end
-            c_head = cutting_pts[x - 1]
-            c_tail = cutting_pts[x]
-            if diff<7 :
-                section = image[0:36, c_head-5:c_tail+5]
-            else:
-                diff-=1
-                section = image[0:36, c_head - diff:c_tail + diff]
-        elif  x==2:
-            section = image[0:36, cutting_pts[x - 1] - 3:cutting_pts[x-1]+ mid]
-        else:
-            section = image[0:36,cutting_pts[x-1]:cutting_pts[x]]
-        seg_block.append(section)
-    refined = refineCrop(seg_block,mid-1)
-
-    t0 = time.time()
-    for i,one in enumerate(refined):
-        res_pre = cRP.SimplePredict(one, i )
-        # cv2.imshow(str(i),one)
-        # cv2.waitKey(0)
-        confidence+=res_pre[0]
-        name+= res_pre[1]
-    print("字符识别",time.time() - t0)
-
-    return refined,name,confidence
diff --git a/hyperlpr_py3/typeDistinguish.py b/hyperlpr_py3/typeDistinguish.py
deleted file mode 100644
index a71f539..0000000
--- a/hyperlpr_py3/typeDistinguish.py
+++ /dev/null
@@ -1,56 +0,0 @@
-#coding=utf-8
-from keras.models import Sequential
-from keras.layers import Dense, Dropout, Activation, Flatten
-from keras.layers import Conv2D, MaxPool2D
-from keras.optimizers import SGD
-from keras import backend as K
-
-K.image_data_format()
-
-
-import cv2
-import numpy as np
-
-
-plateType  = ["蓝牌","单层黄牌","新能源车牌","白色","黑色-港澳"]
-def Getmodel_tensorflow(nb_classes):
-    # nb_classes = len(charset)
-
-    img_rows, img_cols = 9, 34
-    # number of convolutional filters to use
-    nb_filters = 32
-    # size of pooling area for max pooling
-    nb_pool = 2
-    # convolution kernel size
-    nb_conv = 3
-
-    # x = np.load('x.npy')
-    # y = np_utils.to_categorical(range(3062)*45*5*2, nb_classes)
-    # weight = ((type_class - np.arange(type_class)) / type_class + 1) ** 3
-    # weight = dict(zip(range(3063), weight / weight.mean()))  # 调整权重，高频字优先
-
-    model = Sequential()
-    model.add(Conv2D(16, (5, 5),input_shape=(img_rows, img_cols,3)))
-    model.add(Activation('relu'))
-    model.add(MaxPool2D(pool_size=(nb_pool, nb_pool)))
-    model.add(Flatten())
-    model.add(Dense(64))
-    model.add(Activation('relu'))
-    model.add(Dropout(0.5))
-    model.add(Dense(nb_classes))
-    model.add(Activation('softmax'))
-    model.compile(loss='categorical_crossentropy',
-                  optimizer='adam',
-                  metrics=['accuracy'])
-    return model
-
-model = Getmodel_tensorflow(5)
-model.load_weights("./model/plate_type.h5")
-model.save("./model/plate_type.h5")
-def SimplePredict(image):
-    image = cv2.resize(image, (34, 9))
-    image = image.astype(np.float) / 255
-    res = np.array(model.predict(np.array([image]))[0])
-    return res.argmax()
-
-
diff --git a/demo_images/logo.png b/images/logo.png
similarity index 100%
rename from demo_images/logo.png
rename to images/logo.png
diff --git a/images/sw.png b/images/sw.png
new file mode 100644
index 0000000..bd3b896
Binary files /dev/null and b/images/sw.png differ
diff --git a/images/swagger-ui.png b/images/swagger-ui.png
new file mode 100644
index 0000000..4953596
Binary files /dev/null and b/images/swagger-ui.png differ
diff --git a/images_rec/1.jpg b/images_rec/1.jpg
deleted file mode 100644
index 5d1b156..0000000
Binary files a/images_rec/1.jpg and /dev/null differ
diff --git a/images_rec/2.jpg b/images_rec/2.jpg
deleted file mode 100644
index 49e9b86..0000000
Binary files a/images_rec/2.jpg and /dev/null differ
diff --git a/model/cascade.xml b/model/cascade.xml
deleted file mode 100644
index 277775b..0000000
--- a/model/cascade.xml
+++ /dev/null
@@ -1,12117 +0,0 @@
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-<opencv_storage>
-<cascade>
-  <stageType>BOOST</stageType>
-  <featureType>HAAR</featureType>
-  <height>13</height>
-  <width>51</width>
-  <stageParams>
-    <boostType>GAB</boostType>
-    <minHitRate>9.9900001287460327e-001</minHitRate>
-    <maxFalseAlarm>5.0000000000000000e-001</maxFalseAlarm>
-    <weightTrimRate>9.4999999999999996e-001</weightTrimRate>
-    <maxDepth>1</maxDepth>
-    <maxWeakCount>100</maxWeakCount></stageParams>
-  <featureParams>
-    <maxCatCount>0</maxCatCount>
-    <featSize>1</featSize>
-    <mode>ALL</mode></featureParams>
-  <stageNum>20</stageNum>
-  <stages>
-    <!-- stage 0 -->
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-      <maxWeakCount>8</maxWeakCount>
-      <stageThreshold>-1.9158077239990234e+000</stageThreshold>
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index 1117b89..0000000
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-    <_>
-      <rect>
-        48 0 4 3</rect></_>
-    <_>
-      <rect>
-        52 2 2 4</rect></_>
-    <_>
-      <rect>
-        56 3 2 4</rect></_>
-    <_>
-      <rect>
-        61 2 1 3</rect></_>
-    <_>
-      <rect>
-        61 2 1 4</rect></_>
-    <_>
-      <rect>
-        61 8 1 2</rect></_></features></cascade>
-</opencv_storage>
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-matplotlib==2.2.4
-opencv_contrib_python==3.4.3.18
-numpy==1.16.6
-Keras==2.3.1
-Werkzeug==0.14.1
-scipy==1.2.2
-Flask==1.1.2
-Pillow==7.1.2
-PyQt5==5.15.0
-scikit_learn==0.23.1
-wxpy==0.3.9.8
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diff --git a/templates/upload.html b/templates/upload.html
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index a04fcf3..0000000
--- a/templates/upload.html
+++ /dev/null
@@ -1,12 +0,0 @@
-<html>
-   <body>
-   <h1>车牌识别</h1>
-
-      <form action = "uploader" method = "POST"
-         enctype = "multipart/form-data">
-         <input type = "file" name = "file" />
-         <input type = "submit"/>
-      </form>
-
-   </body>
-</html>
\ No newline at end of file
diff --git a/wxpy_uploader.py b/wxpy_uploader.py
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index ee54ade..0000000
--- a/wxpy_uploader.py
+++ /dev/null
@@ -1,28 +0,0 @@
-#coding=utf-8
-
-from wxpy import *
-import numpy
-import cv2
-import time
-import os
-from hyperlpr import pipline
-
-def recognize(filename):
-    image = cv2.imread(filename)
-    return pipline.RecognizePlateJson(image)
-
-bot = Bot(console_qr=True, cache_path=True)
-
-@bot.register(Friend,PICTURE)
-def pr_msg(msg):
-    image_name = msg.file_name
-    friend = msg.chat
-    print msg.chat
-    print '接收图片'
-    # face(image_name)
-    msg.get_file('' + msg.file_name)
-    json_text = recognize(image_name)
-    msg.reply(json_text)
-    msg.reply_image("0.jpg")
-    os.remove(image_name)
-embed()